diff --git a/.DS_Store b/.DS_Store
index b1bc858..715c11d 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.gitignore b/.gitignore
index e170af2..910f698 100644
--- a/.gitignore
+++ b/.gitignore
@@ -180,4 +180,7 @@ pyrightconfig.json
 
 nohup.out
 reports
-conf/settings.local.yaml
\ No newline at end of file
+conf/settings.local.yaml
+
+CLAUDE.md
+.claude
diff --git a/Makefile b/Makefile
index 7904fca..5bea8ff 100644
--- a/Makefile
+++ b/Makefile
@@ -6,7 +6,7 @@ timestamp := $(shell date +"%Y%m%d_%H%M%S")
 report_dir := $(if $(script),logs/$(script)/user_$(u)_loop_num_$(loop_num)_$(timestamp),)
 report_prefix := $(if $(report_dir),$(report_dir)/user_$(u)_loop_$(loop_num)_rdv_$(rendezvous_num)_$(timestamp),)
 
-.PHONY: run clean stop
+.PHONY: run clean stop mock-server examples test-data analyze-results help
 
 define LOCUST_ARGS
 $(if $(script),-f $(script),) --headless \
@@ -79,8 +79,101 @@ run-distributed-web:
 
 stop:
 	@echo "正在停止所有 locust 进程..."
-	@./stop_locust.sh  # 使用相对路径调用脚本
-	@echo "所有 locust 进程已停止。"
+	@pkill -f "locust" || echo "没有找到运行中的locust进程"
+	@pkill -f "python.*mock_server" || echo "没有找到运行中的mock服务器"
+	@echo "所有 locust 和 mock 服务器进程已停止。"
 
 clean:
 	@rm -rf logs/*  # 清理所有日志目录下的文件
+
+# 新增工具命令
+
+# Mock服务器管理
+mock-server:
+	@echo "🚀 启动Mock HTTP服务器..."
+	@python scripts/start_mock_server.py
+
+mock-server-background:
+	@echo "🚀 后台启动Mock HTTP服务器..."
+	@nohup python scripts/start_mock_server.py > logs/mock_server.log 2>&1 &
+	@echo "Mock服务器已在后台启动，日志文件: logs/mock_server.log"
+
+# 示例运行
+examples:
+	@echo "📋 列出所有可用示例:"
+	@python scripts/run_examples.py --list
+
+run-example:
+	@echo "🧪 运行示例测试..."
+	@python scripts/run_examples.py $(if $(example),$(example),1) $(if $(web),--web,) $(if $(u),-u $(u),) $(if $(r),-r $(r),) $(if $(t),-t $(t),)
+
+run-example-web:
+	@echo "🌐 Web UI模式运行示例..."
+	@python scripts/run_examples.py $(if $(example),$(example),1) --web
+
+# 测试数据生成
+test-data:
+	@echo "🎲 生成测试数据..."
+	@python scripts/generate_test_data.py $(if $(type),--type $(type),) $(if $(count),--count $(count),) $(if $(format),--format $(format),)
+
+test-data-locust:
+	@echo "🎯 生成Locust专用测试数据..."
+	@python scripts/generate_test_data.py --type locust $(if $(count),--count $(count),)
+
+# 结果分析
+analyze-results:
+	@echo "📊 分析测试结果..."
+	@python scripts/analyze_results.py $(if $(results_dir),--results-dir $(results_dir),) $(if $(format),--format $(format),) --with-trends
+
+# 快速开始命令
+quick-start:
+	@echo "🚀 快速开始 - 启动Mock服务器和基础示例"
+	@echo "1. 启动Mock服务器..."
+	@python scripts/start_mock_server.py &
+	@sleep 3
+	@echo "2. 运行基础示例..."
+	@python scripts/run_examples.py 1 --web
+
+# 完整测试流程
+full-test:
+	@echo "🔄 完整测试流程..."
+	@echo "1. 生成测试数据..."
+	@python scripts/generate_test_data.py --type locust --count 100
+	@echo "2. 启动Mock服务器..."
+	@python scripts/start_mock_server.py &
+	@sleep 3
+	@echo "3. 运行综合场景测试..."
+	@python scripts/run_examples.py 7 -u 10 -r 2 -t 30s
+	@echo "4. 分析结果..."
+	@python scripts/analyze_results.py --format both
+
+# 帮助信息
+help:
+	@echo "🔧 Locust框架 - 可用命令:"
+	@echo ""
+	@echo "📊 基础测试命令:"
+	@echo "  make run script=<file> u=<users> r=<spawn_rate> t=<time>  - 无头模式运行测试"
+	@echo "  make run-web script=<file>                               - Web UI模式运行测试"
+	@echo "  make run-distributed-web script=<file>                   - 分布式主节点模式"
+	@echo "  make stop                                               - 停止所有locust进程"
+	@echo "  make clean                                              - 清理日志文件"
+	@echo ""
+	@echo "🛠️  工具命令:"
+	@echo "  make mock-server                                        - 启动Mock HTTP服务器"
+	@echo "  make mock-server-background                             - 后台启动Mock服务器"
+	@echo "  make examples                                           - 列出所有示例"
+	@echo "  make run-example example=<number>                      - 运行指定示例"
+	@echo "  make run-example-web example=<number>                  - Web UI运行示例"
+	@echo "  make test-data type=<type> count=<num>                 - 生成测试数据"
+	@echo "  make test-data-locust count=<num>                      - 生成Locust测试数据"
+	@echo "  make analyze-results results_dir=<dir>                 - 分析测试结果"
+	@echo ""
+	@echo "🚀 快速命令:"
+	@echo "  make quick-start                                        - 快速开始 (Mock服务器 + 基础示例)"
+	@echo "  make full-test                                          - 完整测试流程"
+	@echo "  make help                                               - 显示此帮助信息"
+	@echo ""
+	@echo "📝 示例参数:"
+	@echo "  make run script=locustfiles/examples/01_basic_test.py u=10 r=2 t=60s"
+	@echo "  make run-example example=1 u=20 r=5 t=120s"
+	@echo "  make test-data type=users count=500 format=json"
diff --git a/README.md b/README.md
index d9b57ed..149febe 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,239 @@
-## locust测试框架说明
+# 🚀 Locust性能测试框架
 
-> 基于locust框架进行二次开发:https://locust.io/
+[![Python Version](https://img.shields.io/badge/python-3.7%2B-blue.svg)](https://python.org)
+[![Locust Version](https://img.shields.io/badge/locust-2.0%2B-green.svg)](https://locust.io)
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
+[![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg)](docs/README.md)
+
+> 🎯 **企业级性能测试解决方案** - 基于Locust框架的二次开发，支持分层架构、松耦合、高内聚的设计原则
+
+一个功能强大、易于扩展的性能测试框架，专为现代化测试需求而设计。框架采用插件化架构，支持多种负载模式、实时监控、智能分析和多渠道告警，让性能测试变得更加简单高效。
+
+## ✨ 核心特性
+
+### 🎯 **测试执行增强**
+- **集合点功能** - 支持并发事务同步执行
+- **自定义事务** - 灵活的事务统计和报告
+- **自定义参数** - 动态参数传递和配置
+- **优雅停止** - 智能的测试终止机制
+
+### 📊 **数据管理**
+- **动态数据获取** - 支持CSV、数据库、API等多种数据源
+- **分布式数据编排** - 智能的数据分发和同步
+- **测试数据生成** - 基于Faker的多样化数据生成
+- **数据提供策略** - 轮询、随机、顺序等分发策略
+
+### 📈 **性能分析**
+- **智能评级系统** - A-D等级自动评估
+- **多维度指标** - 响应时间、吞吐量、错误率等
+- **趋势分析** - 历史数据对比和预测
+- **可视化报告** - 丰富的图表和报告模板
+
+### 🔔 **监控告警**
+- **实时监控** - 系统资源和性能指标监控
+- **多级告警** - 信息、警告、严重等级别
+- **多渠道通知** - 飞书、钉钉、邮件、企业微信
+- **告警规则** - 灵活的阈值配置和触发条件
+
+### 🔌 **插件生态**(试用版,有bug)
+- **插件架构** - 多种插件类型支持
+- **热插拔** - 动态加载和卸载插件
+- **扩展接口** - 标准化的插件开发接口
+- **内置插件** - 丰富的开箱即用功能
+
+### 🌊 **负载形状**
+- **7种负载模式** - 波浪、阶梯、尖峰、自适应等
+- **自定义形状** - 支持复杂负载场景设计
+- **动态调整** - 运行时负载参数调整
+- **场景编排** - 多阶段负载组合
+
+### 🔗 **平台集成**
+- **第三方集成** - 支持嵌入测试平台(PS:已经嵌入到个人开发的[测试平台](https://www.coder-ljx.cn:7524/user/login) test/test)
+- **API接口** - 完整的REST API
+
+## 🚀 快速开始
+
+### 安装要求
+
+- Python 3.7+
+- 推荐使用虚拟环境
+
+### 快速安装
+
+```bash
+# 克隆项目
+git clone git@github.com:ljxpython/locust_framework.git
+cd locust_framework
+
+# 创建虚拟环境
+python -m venv venv
+source venv/bin/activate  # Linux/Mac
+# 或 venv\Scripts\activate  # Windows
+
+# 安装依赖
+pip install -r requirements.txt
+```
+
+### 5分钟上手
+
+1. **创建测试脚本**
+```python
+# locustfiles/quickstart.py
+from locust import HttpUser, task
+from src.utils.locust_report import manual_report
+
+class QuickStartUser(HttpUser):
+    host = "https://httpbin.org"
+
+    @task
+    def test_get(self):
+        with manual_report("GET请求测试"):
+            self.client.get("/get")
+
+    @task(2)  # 权重为2
+    def test_post(self):
+        with manual_report("POST请求测试"):
+            self.client.post("/post", json={"key": "value"})
+```
+
+2. **启动Web UI测试**
+```bash
+# 使用Make命令（推荐）
+make run-web script=locustfiles/quickstart.py
+
+# 或使用原生Locust命令
+locust -f locustfiles/quickstart.py
+```
+
+3. **访问Web界面**
+   - 打开浏览器访问 http://localhost:8089
+   - 设置用户数和启动速率
+   - 开始测试并查看实时结果
+
+4. **无头模式测试**
+```bash
+# 快速执行
+make run script=locustfiles/quickstart.py u=10 r=2 t=60s
+
+# 完整参数
+locust -f locustfiles/quickstart.py --headless -u 10 -r 2 -t 60s --html=reports/report.html
+```
+
+### 📚 详细文档
+
+- **[📖 完整文档](docs/README.md)** - 详细的使用指南和API参考
+- **[🚀 快速开始](docs/getting-started/quickstart.md)** - 5分钟快速上手
+- **[🏗️ 架构设计](docs/architecture/overview.md)** - 系统架构和设计理念
+- **[🔌 插件开发](docs/development/plugin-development.md)** - 插件开发完整指南
+- **[⚙️ 配置参考](docs/configuration/framework-config.md)** - 详细配置说明
+- **[💡 使用示例](docs/examples/basic-examples.md)** - 丰富的实战示例
+
+## 📁 项目结构
+
+```
+locust_framework/
+├── 📁 conf/                    # 配置文件
+│   ├── config.py              # 配置管理
+│   ├── constants.py           # 常量定义
+│   └── settings.yaml          # 主配置文件
+├── 📁 docs/                    # 文档系统
+│   ├── getting-started/       # 入门指南
+│   ├── architecture/          # 架构文档
+│   ├── api/                   # API参考
+│   ├── development/           # 开发指南
+│   ├── examples/              # 使用示例
+│   └── configuration/         # 配置参考
+├── 📁 locustfiles/             # 测试脚本
+│   ├── demo.py               # 示例脚本
+│   ├── shape_classes/        # 负载形状类
+│   └── test_*/               # 测试场景目录
+├── 📁 src/                     # 核心源码
+│   ├── analysis/             # 性能分析模块
+│   ├── monitoring/           # 监控告警模块
+│   ├── data_manager/         # 数据管理模块
+│   ├── plugins/              # 插件系统
+│   ├── notifications/        # 通知服务
+│   ├── load_shapes/          # 负载形状
+│   ├── client/               # 客户端封装
+│   ├── model/                # 数据模型
+│   └── utils/                # 工具模块
+├── 📁 reports/                 # 测试报告
+├── 📁 logs/                    # 日志文件
+├── main.py                   # 主程序入口
+├── requirements.txt          # 依赖列表
+└── README.md                # 项目说明
+```
+
+## 🎯 主要功能详解
+
+### 🔧 自定义参数系统
+
+框架支持灵活的参数传递和配置管理，让测试更加可控和可配置。
+
+**核心特性：**
+- ✅ 支持指定轮数`loop_num`后自动停止压测
+- ✅ 支持使用集合点且自定义集合数`rendezvous_num`
+- ✅ 支持其他业务相关参数通过Web/终端传入
+- ✅ 环境变量和配置文件支持
+
+### 📊 智能性能分析
+
+基于多维度指标的智能分析系统，提供专业的性能评估。
+
+**分析维度：**
+- 📈 **响应时间分析** - P50/P90/P95/P99百分位数
+- 🚀 **吞吐量分析** - TPS、并发处理能力
+- ❌ **错误率分析** - 错误分布和根因分析
+- 📊 **稳定性分析** - 性能波动和趋势预测
+- 🎯 **综合评级** - A-D等级自动评估
+
+### 🔔 多渠道告警通知
+
+实时监控和智能告警，确保问题及时发现和处理。
+
+**支持渠道：**
+- 🚀 **飞书** - 支持富文本消息和@功能
+- 📱 **钉钉** - 群机器人消息推送
+- 📧 **邮件** - SMTP邮件通知
+- 💬 **企业微信** - 企业内部消息通知
+
+### 🌊 多样化负载形状
+
+支持7种内置负载模式，满足不同测试场景需求。
+
+**负载模式：**
+- 📈 **波浪形** - 周期性负载变化
+- 🏗️ **阶梯形** - 逐步递增负载
+- ⚡ **尖峰形** - 突发流量测试
+- 🎯 **自适应** - 基于响应时间自动调整
+- 📊 **自定义** - 灵活的负载场景编排
+
+### 🔌 插件化架构
+
+强大的插件系统，支持功能扩展和定制化开发。
+
+**插件类型：**
+- 🧪 **Locust插件** - 扩展Locust核心功能
+- 📊 **报告插件** - 自定义报告格式和内容
+- 📡 **监控插件** - 系统和应用监控
+- 💾 **数据插件** - 数据源和存储扩展
+- 🔔 **通知插件** - 消息通知渠道扩展
+
+### 🎯 分布式测试支持
+
+天然支持分布式部署，轻松应对大规模性能测试需求。
+
+**分布式特性：**
+- 🏗️ **Master-Worker架构** - 支持多节点协同
+- 📊 **数据同步** - 智能的测试数据分发
+- 📈 **负载均衡** - 自动负载分配和调度
+- 🔄 **故障恢复** - 节点故障自动处理
+
+---
+
+## 📋 原有功能说明
+
+> 基于locust框架进行二次开发: https://locust.io/
 >
 > 主要封装完善功能如下:
 >
@@ -18,9 +251,257 @@
 
 
 
-## 框架结构
+---
+
+## 📖 使用指南
+
+### 🎯 测试执行方式
+
+框架支持多种测试执行方式，满足不同场景需求：
+
+#### 1. Web UI模式（推荐新手）
 
-文件结构如下：
+```bash
+# 使用Make命令启动
+make run-web script=locustfiles/demo.py
+
+# 或使用原生Locust命令
+locust -f locustfiles/demo.py
+```
+
+**特点：**
+- 🌐 友好的Web界面
+- 📊 实时监控和图表
+- ⚙️ 可视化参数配置
+- 📈 动态负载调整
+
+#### 2. 无头模式（适合CI/CD）
+
+```bash
+# 快速执行
+make run script=locustfiles/demo.py u=10 r=2 t=60s
+
+# 完整参数执行
+locust -f locustfiles/demo.py --headless \
+    --users 10 --spawn-rate 2 --run-time 60s \
+    --html=reports/report.html \
+    --csv=reports/results
+```
+
+**特点：**
+- 🚀 自动化执行
+- 📋 命令行参数控制
+- 📊 自动生成报告
+- 🔄 适合持续集成
+
+#### 3. 分布式模式（大规模测试）
+
+```bash
+# 启动Master节点
+locust -f locustfiles/demo.py --master
+
+# 启动Worker节点
+locust -f locustfiles/demo.py --worker --master-host=<master-ip>
+
+# 多进程Worker
+locust -f locustfiles/demo.py --worker --processes 4
+```
+
+**特点：**
+- 🏗️ 多节点协同
+- 📈 线性扩展能力
+- 🔄 故障自动恢复
+- 📊 统一结果收集
+
+### 🔧 自定义参数配置
+
+框架提供强大的参数化能力，支持灵活的测试配置：
+
+#### 参数定义示例
+
+```python
+@events.init_command_line_parser.add_listener
+def _(parser):
+    parser.add_argument("--product_id", type=str, default="1925511528732168192", help="测试业务ID")
+    parser.add_argument("--loop_num", type=int, default=1, help="执行轮数")
+    parser.add_argument("--is_rendezvous", type=str_to_bool, default=False, help="是否使用集合点")
+    parser.add_argument("--rendezvous_num", type=int, default=1, help="集合点数量")
+```
+
+#### 参数使用示例
+
+```python
+class WebsiteUser(HttpUser):
+    @task
+    def my_task(self):
+        # 获取自定义参数
+        loop_num = self.environment.parsed_options.loop_num
+        product_id = self.environment.parsed_options.product_id
+
+        # 使用集合点
+        if self.environment.parsed_options.is_rendezvous:
+            with self.environment.shared:
+                # 并发执行的业务逻辑
+                self.client.get(f"/api/product/{product_id}")
+```
+
+### 📊 性能分析和报告
+
+框架提供智能的性能分析和丰富的报告功能：
+
+#### 自动性能分析
+
+- **响应时间分析** - P50/P90/P95/P99百分位数
+- **吞吐量分析** - TPS和并发处理能力
+- **错误率分析** - 错误分布和根因分析
+- **综合评级** - A-D等级自动评估
+
+#### 多格式报告
+
+- **HTML报告** - 交互式图表和详细分析
+- **CSV数据** - 原始数据导出和二次分析
+- **JSON格式** - API集成和自动化处理
+- **实时监控** - Web界面实时数据展示
+
+### 🔔 监控告警配置
+
+#### 告警规则配置
+
+```yaml
+# conf/settings.yaml
+monitoring:
+  alerts:
+    response_time:
+      warning: 1000  # 1秒警告
+      critical: 3000 # 3秒严重
+    error_rate:
+      warning: 5     # 5%警告
+      critical: 10   # 10%严重
+    throughput:
+      warning: 100   # 低于100 TPS警告
+```
+
+#### 通知渠道配置
+
+```yaml
+notifications:
+  feishu:
+    webhook_url: "https://open.feishu.cn/open-apis/bot/v2/hook/xxx"
+    enabled: true
+  dingtalk:
+    webhook_url: "https://oapi.dingtalk.com/robot/send?access_token=xxx"
+    enabled: true
+  email:
+    smtp_server: "smtp.gmail.com"
+    smtp_port: 587
+    enabled: true
+```
+
+### 🌊 负载形状使用
+
+框架支持多种负载模式，模拟真实的用户行为：
+
+#### 使用负载形状
+
+```python
+# 在测试脚本中继承负载形状类
+from locustfiles.shape_classes.wave_shape import WaveLoadShape
+
+class MyTestUser(HttpUser):
+    # 测试逻辑
+    pass
+
+# 使用命令行选择负载形状
+locust -f test.py --class-picker WaveLoadShape
+```
+
+#### 可用负载模式
+
+- **WaveLoadShape** - 波浪形负载
+- **SpikeLoadShape** - 尖峰负载
+- **StairStepLoadShape** - 阶梯负载
+- **AdaptiveLoadShape** - 自适应负载
+
+### 🔌 插件使用
+
+#### 启用插件
+
+```yaml
+# conf/settings.yaml
+plugins:
+  enabled:
+    - performance_analyzer
+    - system_monitor
+    - notification_service
+    - data_manager
+```
+
+#### 自定义插件
+
+```python
+from src.plugins.base import BasePlugin
+
+class MyCustomPlugin(BasePlugin):
+    def on_test_start(self, environment, **kwargs):
+        # 测试开始时的逻辑
+        pass
+
+    def on_test_stop(self, environment, **kwargs):
+        # 测试结束时的逻辑
+        pass
+```
+
+---
+
+## 📋 详细功能说明
+
+### 🔧 自定义参数详细说明
+
+框架支持灵活的参数传递和配置管理：
+
+1. **封装测试框架** - 支持指定轮数`loop_num`后自动停止压测
+2. **集合点功能** - 支持使用集合点且自定义集合数`rendezvous_num`
+3. **业务参数** - 支持其他业务相关参数通过web/终端传入
+
+### 📊 智能性能分析
+
+基于多维度指标的智能分析系统：
+
+- **响应时间分析** - P50/P90/P95/P99百分位数统计
+- **吞吐量分析** - TPS和并发处理能力评估
+- **错误率分析** - 错误分布和根因分析
+- **稳定性分析** - 性能波动和趋势预测
+- **综合评级** - A-D等级自动评估系统
+
+### 🔔 多渠道告警通知
+
+实时监控和智能告警系统：
+
+- **飞书通知** - 支持富文本消息和@功能
+- **钉钉通知** - 群机器人消息推送
+- **邮件通知** - SMTP邮件告警
+- **企业微信** - 企业内部消息通知
+
+### 🌊 多样化负载形状
+
+支持7种内置负载模式：
+
+- **波浪形负载** - 周期性负载变化模拟
+- **阶梯形负载** - 逐步递增负载测试
+- **尖峰形负载** - 突发流量压力测试
+- **自适应负载** - 基于响应时间自动调整
+- **自定义负载** - 灵活的负载场景编排
+
+### 🎯 分布式测试支持
+
+天然支持分布式部署：
+
+- **Master-Worker架构** - 支持多节点协同测试
+- **数据同步机制** - 智能的测试数据分发
+- **负载均衡** - 自动负载分配和调度
+- **故障恢复** - 节点故障自动处理机制
+
+---
 ```bash
 .
 ├── README.md
@@ -419,9 +900,66 @@ class WebsiteUser(HttpUser):
 
 
 
+---
+
+## 🤝 贡献指南
+
+欢迎所有形式的贡献！无论是bug报告、功能请求、代码贡献还是文档改进。
+
+### 如何贡献
+
+1. **Fork项目** - 点击右上角的Fork按钮
+2. **创建分支** - `git checkout -b feature/your-feature-name`
+3. **提交更改** - `git commit -am 'Add some feature'`
+4. **推送分支** - `git push origin feature/your-feature-name`
+5. **创建PR** - 在GitHub上创建Pull Request
+
+### 贡献类型
+
+- 🐛 **Bug修复** - 发现并修复问题
+- ✨ **新功能** - 添加新的功能特性
+- 📚 **文档改进** - 完善文档和示例
+- 🧪 **测试用例** - 增加测试覆盖率
+- 🔧 **工具优化** - 改进开发工具和流程
+
+### 开发规范
+
+- 遵循[编码规范](docs/development/coding-standards.md)
+- 添加适当的测试用例
+- 更新相关文档
+- 确保CI/CD流程通过
+
+## 📄 许可证
+
+本项目采用 [MIT License](LICENSE) 开源协议。
+
+## 🌟 致谢
+
+感谢所有为这个项目做出贡献的开发者和用户！
+
+特别感谢：
+- [Locust](https://locust.io/) - 优秀的负载测试框架
+- 所有提供反馈和建议的用户
+- 开源社区的支持和贡献
+
+## 📞 联系我
+
+### 技术支持
+
+### 作者联系方式
+
 也欢迎和您交流讨论😊
 
-个人微信号
+**个人微信号**
+
+<img src="./assets/image-20250531212549739.png" alt="个人微信二维码" width="300"/>
+
+
+
+---
+
+**⭐ 如果这个项目对您有帮助，请给我一个Star！**
 
+**🔗 项目链接**: [GitHub Repository](https://github.com/ljxpython/locust_framework)
 
-<img src="./assets/image-20250531212549739.png" alt="Description" width="300"/>
+**📖 在线文档**: [完整文档](docs/README.md)
diff --git a/docs/.DS_Store b/docs/.DS_Store
index c30c60e..0496b98 100644
Binary files a/docs/.DS_Store and b/docs/.DS_Store differ
diff --git a/docs/README.md b/docs/README.md
new file mode 100644
index 0000000..b823c86
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1,105 @@
+# Locust性能测试框架文档
+
+欢迎使用Locust性能测试框架！本文档将帮助您快速上手并深入了解框架的各项功能。
+
+## 📚 文档导航
+
+### 🚀 快速开始
+- [安装指南](getting-started/installation.md) - 环境搭建和依赖安装
+- [快速入门](getting-started/quickstart.md) - 5分钟上手指南
+- [基础概念](getting-started/concepts.md) - 核心概念和术语
+- [第一个测试](getting-started/first-test.md) - 创建您的第一个性能测试
+
+### 🏗️ 架构设计
+- [整体架构](architecture/overview.md) - 框架整体架构设计
+- [模块设计](architecture/modules.md) - 各模块详细设计
+- [插件系统](architecture/plugin-system.md) - 插件架构和扩展机制
+- [数据流](architecture/data-flow.md) - 数据处理和流转机制
+
+### 📖 API参考
+- [性能分析API](api/analysis.md) - 性能分析模块API
+- [监控告警API](api/monitoring.md) - 监控告警模块API
+- [数据管理API](api/data-manager.md) - 数据管理模块API
+- [插件接口API](api/plugins.md) - 插件开发接口
+- [负载模式API](api/load-shapes.md) - 负载模式API
+- [工具类API](api/utilities.md) - 工具类和辅助函数
+
+### 💻 开发指南
+- [开发环境搭建](development/setup.md) - 开发环境配置
+- [代码规范](development/coding-standards.md) - 编码规范和最佳实践
+- [插件开发](development/plugin-development.md) - 自定义插件开发指南
+- [负载模式开发](development/load-shape-development.md) - 自定义负载模式开发
+- [贡献指南](development/contributing.md) - 如何为项目贡献代码
+- [测试指南](development/testing.md) - 单元测试和集成测试
+
+### 📋 示例和最佳实践
+- [基础示例](examples/basic-examples.md) - 基础功能使用示例
+- [高级示例](examples/advanced-examples.md) - 高级功能和复杂场景
+- [性能测试最佳实践](examples/best-practices.md) - 性能测试经验和技巧
+- [常见问题解答](examples/faq.md) - 常见问题和解决方案
+- [故障排除](examples/troubleshooting.md) - 问题诊断和解决
+
+## 🔧 配置参考
+
+### 核心配置
+- [框架配置](configuration/framework-config.md) - 框架核心配置选项
+- [插件配置](configuration/plugin-config.md) - 插件系统配置
+- [监控配置](configuration/monitoring-config.md) - 监控告警配置
+- [通知配置](configuration/notification-config.md) - 通知渠道配置
+
+### 环境配置
+- [开发环境](configuration/development.md) - 开发环境配置
+- [测试环境](configuration/testing.md) - 测试环境配置
+- [生产环境](configuration/production.md) - 生产环境配置
+- [分布式配置](configuration/distributed.md) - 分布式测试配置
+
+## 📊 功能特性
+
+### 核心功能
+- **性能分析**: 全面的性能指标分析和评分系统
+- **实时监控**: 可配置的实时监控和告警机制
+- **数据管理**: 智能的测试数据生成和管理
+- **插件系统**: 灵活的插件化扩展架构
+- **负载模式**: 丰富的负载模式支持
+- **多格式报告**: HTML、JSON、CSV、Markdown报告
+
+### 高级特性
+- **趋势分析**: 历史数据趋势分析和预测
+- **自适应负载**: 基于性能指标的自适应负载调整
+- **分布式同步**: 分布式环境下的数据同步
+- **多渠道通知**: 支持飞书、钉钉、邮件等多种通知方式
+- **插件热加载**: 运行时动态加载和卸载插件
+
+## 🎯 使用场景
+
+### 性能测试类型
+- **负载测试**: 验证系统在预期负载下的性能表现
+- **压力测试**: 确定系统的性能极限和瓶颈
+- **容量测试**: 评估系统的最大处理能力
+- **稳定性测试**: 长时间运行下的系统稳定性
+- **峰值测试**: 突发流量下的系统响应能力
+
+### 应用领域
+- **Web应用**: HTTP/HTTPS接口性能测试
+- **API服务**: RESTful API和GraphQL性能测试
+- **微服务**: 分布式微服务架构性能测试
+- **数据库**: 数据库连接和查询性能测试
+- **消息队列**: 消息中间件性能测试
+
+## 🚀 快速链接
+
+- [立即开始](getting-started/quickstart.md) - 快速上手指南
+- [示例代码](examples/basic-examples.md) - 查看示例代码
+- [API文档](api/analysis.md) - 浏览API文档
+- [插件开发](development/plugin-development.md) - 开发自定义插件
+- [最佳实践](examples/best-practices.md) - 学习最佳实践
+
+## 🔄 版本信息
+
+- **当前版本**: v2.0.0
+- **更新日期**: 2025-01-01
+- **兼容性**: Python 3.7+, Locust 2.0+
+
+---
+
+*本文档持续更新中，如有疑问请随时联系我们。*
diff --git a/docs/api/analysis.md b/docs/api/analysis.md
new file mode 100644
index 0000000..623bea1
--- /dev/null
+++ b/docs/api/analysis.md
@@ -0,0 +1,550 @@
+# 性能分析API参考
+
+本文档详细介绍性能分析模块的API接口，包括性能分析器、趋势分析器和报告生成器。
+
+## 📊 PerformanceAnalyzer
+
+性能分析器是框架的核心分析组件，提供全面的性能指标分析和评分功能。
+
+### 类定义
+
+```python
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+
+class PerformanceAnalyzer:
+    """性能分析器
+
+    提供全面的性能分析功能，包括响应时间分析、吞吐量分析、
+    错误率分析和综合性能评分。
+    """
+```
+
+### 构造函数
+
+```python
+def __init__(self, config: Optional[Dict[str, Any]] = None):
+    """初始化性能分析器
+
+    Args:
+        config: 配置参数字典，可选
+            - response_time_thresholds: 响应时间阈值配置
+            - throughput_thresholds: 吞吐量阈值配置
+            - error_rate_thresholds: 错误率阈值配置
+            - weights: 各指标权重配置
+
+    Example:
+        >>> config = {
+        ...     'response_time_thresholds': {
+        ...         'excellent': 500,
+        ...         'good': 1000,
+        ...         'acceptable': 2000,
+        ...         'poor': 5000
+        ...     }
+        ... }
+        >>> analyzer = PerformanceAnalyzer(config)
+    """
+```
+
+### 核心方法
+
+#### comprehensive_analysis()
+
+```python
+def comprehensive_analysis(self, test_data: Dict[str, Any]) -> Dict[str, Any]:
+    """执行综合性能分析
+
+    Args:
+        test_data: 测试数据字典，包含以下字段：
+            - test_name: 测试名称
+            - start_time: 开始时间
+            - end_time: 结束时间
+            - duration: 测试持续时间(秒)
+            - users: 用户数
+            - requests: 请求列表，每个请求包含：
+                - response_time: 响应时间(毫秒)
+                - success: 是否成功(布尔值)
+                - timestamp: 时间戳
+                - name: 请求名称(可选)
+                - method: HTTP方法(可选)
+
+    Returns:
+        Dict[str, Any]: 分析结果字典，包含：
+            - test_info: 测试基本信息
+            - response_time: 响应时间分析结果
+            - throughput: 吞吐量分析结果
+            - error_analysis: 错误分析结果
+            - performance_grade: 性能评分结果
+            - overall_grade: 总体评级
+            - recommendations: 优化建议
+
+    Example:
+        >>> test_data = {
+        ...     'test_name': '用户登录测试',
+        ...     'start_time': '2024-01-01 10:00:00',
+        ...     'end_time': '2024-01-01 10:30:00',
+        ...     'duration': 1800,
+        ...     'users': 100,
+        ...     'requests': [
+        ...         {'response_time': 500, 'success': True, 'timestamp': '2024-01-01 10:00:01'},
+        ...         {'response_time': 800, 'success': True, 'timestamp': '2024-01-01 10:00:02'},
+        ...         # ... 更多请求数据
+        ...     ]
+        ... }
+        >>> result = analyzer.comprehensive_analysis(test_data)
+        >>> print(f"总体评级: {result['overall_grade']}")
+    """
+```
+
+#### analyze_response_time()
+
+```python
+def analyze_response_time(self, requests: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """分析响应时间
+
+    Args:
+        requests: 请求列表
+
+    Returns:
+        Dict[str, Any]: 响应时间分析结果
+            - avg: 平均响应时间
+            - min: 最小响应时间
+            - max: 最大响应时间
+            - median: 中位数响应时间
+            - p50, p90, p95, p99: 百分位数响应时间
+            - std_dev: 标准差
+            - distribution: 响应时间分布
+            - grade: 响应时间评级
+
+    Example:
+        >>> requests = [
+        ...     {'response_time': 500, 'success': True},
+        ...     {'response_time': 800, 'success': True},
+        ...     {'response_time': 1200, 'success': False}
+        ... ]
+        >>> result = analyzer.analyze_response_time(requests)
+        >>> print(f"P95响应时间: {result['p95']}ms")
+    """
+```
+
+#### analyze_throughput()
+
+```python
+def analyze_throughput(self, requests: List[Dict[str, Any]],
+                      duration: float) -> Dict[str, Any]:
+    """分析吞吐量
+
+    Args:
+        requests: 请求列表
+        duration: 测试持续时间(秒)
+
+    Returns:
+        Dict[str, Any]: 吞吐量分析结果
+            - total_requests: 总请求数
+            - successful_requests: 成功请求数
+            - failed_requests: 失败请求数
+            - avg_tps: 平均TPS
+            - peak_tps: 峰值TPS
+            - min_tps: 最低TPS
+            - tps_trend: TPS趋势数据
+            - grade: 吞吐量评级
+
+    Example:
+        >>> result = analyzer.analyze_throughput(requests, 1800)
+        >>> print(f"平均TPS: {result['avg_tps']}")
+    """
+```
+
+#### analyze_errors()
+
+```python
+def analyze_errors(self, requests: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """分析错误情况
+
+    Args:
+        requests: 请求列表
+
+    Returns:
+        Dict[str, Any]: 错误分析结果
+            - total_errors: 总错误数
+            - error_rate: 错误率(百分比)
+            - error_types: 错误类型分布
+            - error_trend: 错误趋势
+            - critical_errors: 严重错误列表
+            - grade: 错误率评级
+
+    Example:
+        >>> result = analyzer.analyze_errors(requests)
+        >>> print(f"错误率: {result['error_rate']:.2f}%")
+    """
+```
+
+#### calculate_performance_grade()
+
+```python
+def calculate_performance_grade(self, response_time_result: Dict[str, Any],
+                               throughput_result: Dict[str, Any],
+                               error_result: Dict[str, Any]) -> Dict[str, Any]:
+    """计算性能评分
+
+    Args:
+        response_time_result: 响应时间分析结果
+        throughput_result: 吞吐量分析结果
+        error_result: 错误分析结果
+
+    Returns:
+        Dict[str, Any]: 性能评分结果
+            - response_time_score: 响应时间得分
+            - throughput_score: 吞吐量得分
+            - error_rate_score: 错误率得分
+            - stability_score: 稳定性得分
+            - overall_score: 总体得分
+            - grade: 评级(A/B/C/D)
+            - grade_description: 评级描述
+
+    Example:
+        >>> grade_result = analyzer.calculate_performance_grade(
+        ...     response_time_result, throughput_result, error_result
+        ... )
+        >>> print(f"总体评级: {grade_result['grade']}")
+    """
+```
+
+### 配置选项
+
+```python
+# 默认配置
+DEFAULT_CONFIG = {
+    'response_time_thresholds': {
+        'excellent': 500,    # A级: < 500ms
+        'good': 1000,        # B级: < 1000ms
+        'acceptable': 2000,  # C级: < 2000ms
+        'poor': 5000         # D级: >= 2000ms
+    },
+    'throughput_thresholds': {
+        'excellent': 1000,   # A级: > 1000 TPS
+        'good': 500,         # B级: > 500 TPS
+        'acceptable': 100,   # C级: > 100 TPS
+        'poor': 50           # D级: <= 100 TPS
+    },
+    'error_rate_thresholds': {
+        'excellent': 0.1,    # A级: < 0.1%
+        'good': 1.0,         # B级: < 1%
+        'acceptable': 5.0,   # C级: < 5%
+        'poor': 10.0         # D级: >= 5%
+    },
+    'weights': {
+        'response_time': 0.4,  # 响应时间权重40%
+        'throughput': 0.3,     # 吞吐量权重30%
+        'error_rate': 0.2,     # 错误率权重20%
+        'stability': 0.1       # 稳定性权重10%
+    }
+}
+```
+
+## 📈 TrendAnalyzer
+
+趋势分析器提供历史数据分析和性能趋势预测功能。
+
+### 类定义
+
+```python
+from src.analysis.trend_analyzer import TrendAnalyzer
+
+class TrendAnalyzer:
+    """趋势分析器
+
+    提供历史性能数据的趋势分析和预测功能。
+    """
+```
+
+### 核心方法
+
+#### analyze_historical_trend()
+
+```python
+def analyze_historical_trend(self, historical_data: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """分析历史趋势
+
+    Args:
+        historical_data: 历史测试数据列表，每个元素包含：
+            - timestamp: 时间戳
+            - avg_response_time: 平均响应时间
+            - throughput: 吞吐量
+            - error_rate: 错误率
+            - users: 用户数
+
+    Returns:
+        Dict[str, Any]: 趋势分析结果
+            - response_time_trend: 响应时间趋势
+            - throughput_trend: 吞吐量趋势
+            - error_rate_trend: 错误率趋势
+            - performance_trend: 整体性能趋势
+            - trend_summary: 趋势摘要
+
+    Example:
+        >>> historical_data = [
+        ...     {
+        ...         'timestamp': '2024-01-01',
+        ...         'avg_response_time': 500,
+        ...         'throughput': 100,
+        ...         'error_rate': 1.0,
+        ...         'users': 50
+        ...     },
+        ...     # ... 更多历史数据
+        ... ]
+        >>> trend_result = trend_analyzer.analyze_historical_trend(historical_data)
+    """
+```
+
+#### predict_performance()
+
+```python
+def predict_performance(self, historical_data: List[Dict[str, Any]],
+                       prediction_days: int = 7) -> Dict[str, Any]:
+    """性能预测
+
+    Args:
+        historical_data: 历史数据
+        prediction_days: 预测天数
+
+    Returns:
+        Dict[str, Any]: 预测结果
+            - predicted_response_time: 预测响应时间
+            - predicted_throughput: 预测吞吐量
+            - predicted_error_rate: 预测错误率
+            - confidence_interval: 置信区间
+            - prediction_accuracy: 预测准确度
+
+    Example:
+        >>> prediction = trend_analyzer.predict_performance(historical_data, 7)
+        >>> print(f"预测响应时间: {prediction['predicted_response_time']}")
+    """
+```
+
+## 📋 ReportGenerator
+
+报告生成器提供多格式的测试报告生成功能。
+
+### 类定义
+
+```python
+from src.analysis.report_generator import ReportGenerator
+
+class ReportGenerator:
+    """报告生成器
+
+    支持生成HTML、JSON、CSV、Markdown等多种格式的测试报告。
+    """
+```
+
+### 核心方法
+
+#### generate_html_report()
+
+```python
+def generate_html_report(self, analysis_result: Dict[str, Any],
+                        output_path: str,
+                        template_name: str = 'default') -> bool:
+    """生成HTML报告
+
+    Args:
+        analysis_result: 分析结果数据
+        output_path: 输出文件路径
+        template_name: 模板名称，默认为'default'
+
+    Returns:
+        bool: 生成是否成功
+
+    Example:
+        >>> success = report_generator.generate_html_report(
+        ...     analysis_result,
+        ...     'reports/test_report.html'
+        ... )
+        >>> print(f"报告生成{'成功' if success else '失败'}")
+    """
+```
+
+#### generate_json_report()
+
+```python
+def generate_json_report(self, analysis_result: Dict[str, Any],
+                        output_path: str) -> bool:
+    """生成JSON报告
+
+    Args:
+        analysis_result: 分析结果数据
+        output_path: 输出文件路径
+
+    Returns:
+        bool: 生成是否成功
+
+    Example:
+        >>> success = report_generator.generate_json_report(
+        ...     analysis_result,
+        ...     'reports/test_report.json'
+        ... )
+    """
+```
+
+#### generate_csv_report()
+
+```python
+def generate_csv_report(self, analysis_result: Dict[str, Any],
+                       output_path: str) -> bool:
+    """生成CSV报告
+
+    Args:
+        analysis_result: 分析结果数据
+        output_path: 输出文件路径
+
+    Returns:
+        bool: 生成是否成功
+
+    Example:
+        >>> success = report_generator.generate_csv_report(
+        ...     analysis_result,
+        ...     'reports/test_report.csv'
+        ... )
+    """
+```
+
+#### generate_markdown_report()
+
+```python
+def generate_markdown_report(self, analysis_result: Dict[str, Any],
+                            output_path: str) -> bool:
+    """生成Markdown报告
+
+    Args:
+        analysis_result: 分析结果数据
+        output_path: 输出文件路径
+
+    Returns:
+        bool: 生成是否成功
+
+    Example:
+        >>> success = report_generator.generate_markdown_report(
+        ...     analysis_result,
+        ...     'reports/test_report.md'
+        ... )
+    """
+```
+
+## 🔧 工具函数
+
+### 指标计算工具
+
+```python
+from src.analysis.metrics_calculator import MetricsCalculator
+
+class MetricsCalculator:
+    """指标计算工具类"""
+
+    @staticmethod
+    def calculate_percentiles(values: List[float],
+                             percentiles: List[int] = [50, 90, 95, 99]) -> Dict[str, float]:
+        """计算百分位数
+
+        Args:
+            values: 数值列表
+            percentiles: 百分位数列表
+
+        Returns:
+            Dict[str, float]: 百分位数结果
+        """
+
+    @staticmethod
+    def calculate_statistics(values: List[float]) -> Dict[str, float]:
+        """计算基础统计指标
+
+        Args:
+            values: 数值列表
+
+        Returns:
+            Dict[str, float]: 统计结果
+                - mean: 平均值
+                - median: 中位数
+                - std_dev: 标准差
+                - variance: 方差
+                - min: 最小值
+                - max: 最大值
+        """
+```
+
+## 📊 使用示例
+
+### 完整分析流程
+
+```python
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.analysis.trend_analyzer import TrendAnalyzer
+from src.analysis.report_generator import ReportGenerator
+
+# 1. 创建分析器
+analyzer = PerformanceAnalyzer()
+trend_analyzer = TrendAnalyzer()
+report_generator = ReportGenerator()
+
+# 2. 准备测试数据
+test_data = {
+    'test_name': 'API性能测试',
+    'start_time': '2024-01-01 10:00:00',
+    'end_time': '2024-01-01 10:30:00',
+    'duration': 1800,
+    'users': 100,
+    'requests': [
+        # ... 请求数据
+    ]
+}
+
+# 3. 执行性能分析
+analysis_result = analyzer.comprehensive_analysis(test_data)
+
+# 4. 趋势分析（如果有历史数据）
+if historical_data:
+    trend_result = trend_analyzer.analyze_historical_trend(historical_data)
+    analysis_result['trend_analysis'] = trend_result
+
+# 5. 生成报告
+report_generator.generate_html_report(analysis_result, 'reports/performance_report.html')
+report_generator.generate_json_report(analysis_result, 'reports/performance_report.json')
+
+print(f"性能分析完成，总体评级: {analysis_result['overall_grade']}")
+```
+
+### 自定义配置示例
+
+```python
+# 自定义分析配置
+custom_config = {
+    'response_time_thresholds': {
+        'excellent': 300,    # 更严格的响应时间要求
+        'good': 800,
+        'acceptable': 1500,
+        'poor': 3000
+    },
+    'weights': {
+        'response_time': 0.5,  # 更重视响应时间
+        'throughput': 0.2,
+        'error_rate': 0.2,
+        'stability': 0.1
+    }
+}
+
+analyzer = PerformanceAnalyzer(custom_config)
+```
+
+## ⚠️ 注意事项
+
+1. **数据格式**: 确保输入数据格式正确，特别是时间戳格式
+2. **内存使用**: 大量数据分析时注意内存使用情况
+3. **并发安全**: 分析器实例不是线程安全的，多线程使用时需要注意
+4. **配置验证**: 自定义配置时确保参数合理性
+5. **异常处理**: 建议在调用API时添加适当的异常处理
+
+## 🔗 相关文档
+
+- [监控告警API](monitoring.md) - 监控告警模块API
+- [数据管理API](data-manager.md) - 数据管理模块API
+- [插件接口API](plugins.md) - 插件开发接口
+- [配置参考](../configuration/framework-config.md) - 详细配置说明
diff --git a/docs/api/data-manager.md b/docs/api/data-manager.md
new file mode 100644
index 0000000..01c4d68
--- /dev/null
+++ b/docs/api/data-manager.md
@@ -0,0 +1,658 @@
+# 数据管理API参考
+
+本文档详细介绍数据管理模块的API接口，包括数据生成器、数据提供者和数据分发器。
+
+## 📊 DataGenerator
+
+数据生成器提供多种类型的测试数据生成功能。
+
+### 类定义
+
+```python
+from src.data_manager.data_generator import DataGenerator
+
+class DataGenerator:
+    """测试数据生成器
+
+    基于Faker库提供丰富的测试数据生成功能，
+    支持多种数据类型和本地化设置。
+    """
+```
+
+### 构造函数
+
+```python
+def __init__(self, locale: str = "zh_CN", seed: Optional[int] = None):
+    """初始化数据生成器
+
+    Args:
+        locale: 本地化设置，默认中文
+        seed: 随机种子，用于生成可重复的数据
+
+    Example:
+        >>> generator = DataGenerator(locale="en_US", seed=12345)
+        >>> generator_cn = DataGenerator()  # 使用中文
+    """
+```
+
+### 基础数据生成
+
+#### generate_name()
+
+```python
+def generate_name(self, gender: Optional[str] = None) -> str:
+    """生成姓名
+
+    Args:
+        gender: 性别 ("male"/"female")，可选
+
+    Returns:
+        str: 生成的姓名
+
+    Example:
+        >>> generator.generate_name()
+        '张三'
+        >>> generator.generate_name(gender="female")
+        '李小红'
+    """
+```
+
+#### generate_email()
+
+```python
+def generate_email(self, domain: Optional[str] = None) -> str:
+    """生成邮箱地址
+
+    Args:
+        domain: 邮箱域名，可选
+
+    Returns:
+        str: 生成的邮箱地址
+
+    Example:
+        >>> generator.generate_email()
+        'zhangsan@example.com'
+        >>> generator.generate_email(domain="test.com")
+        'user123@test.com'
+    """
+```
+
+#### generate_phone_number()
+
+```python
+def generate_phone_number(self, region: str = "CN") -> str:
+    """生成手机号码
+
+    Args:
+        region: 地区代码，默认中国
+
+    Returns:
+        str: 生成的手机号码
+
+    Example:
+        >>> generator.generate_phone_number()
+        '13812345678'
+        >>> generator.generate_phone_number(region="US")
+        '+1-555-123-4567'
+    """
+```
+
+#### generate_address()
+
+```python
+def generate_address(self, include_country: bool = False) -> Dict[str, str]:
+    """生成地址信息
+
+    Args:
+        include_country: 是否包含国家信息
+
+    Returns:
+        Dict[str, str]: 地址信息字典
+
+    Example:
+        >>> generator.generate_address()
+        {
+            'province': '北京市',
+            'city': '北京市',
+            'district': '朝阳区',
+            'street': '建国路123号',
+            'postal_code': '100000'
+        }
+    """
+```
+
+### 数值数据生成
+
+#### generate_integer()
+
+```python
+def generate_integer(self, min_value: int = 0, max_value: int = 100) -> int:
+    """生成整数
+
+    Args:
+        min_value: 最小值
+        max_value: 最大值
+
+    Returns:
+        int: 生成的整数
+
+    Example:
+        >>> generator.generate_integer(1, 10)
+        7
+        >>> generator.generate_integer(100, 1000)
+        456
+    """
+```
+
+#### generate_float()
+
+```python
+def generate_float(self, min_value: float = 0.0, max_value: float = 100.0,
+                  precision: int = 2) -> float:
+    """生成浮点数
+
+    Args:
+        min_value: 最小值
+        max_value: 最大值
+        precision: 小数位数
+
+    Returns:
+        float: 生成的浮点数
+
+    Example:
+        >>> generator.generate_float(0, 100, 2)
+        45.67
+        >>> generator.generate_float(1.0, 10.0, 3)
+        7.234
+    """
+```
+
+#### generate_price()
+
+```python
+def generate_price(self, min_price: float = 1.0, max_price: float = 1000.0,
+                  currency: str = "CNY") -> Dict[str, Any]:
+    """生成价格信息
+
+    Args:
+        min_price: 最小价格
+        max_price: 最大价格
+        currency: 货币类型
+
+    Returns:
+        Dict[str, Any]: 价格信息
+
+    Example:
+        >>> generator.generate_price(10, 100)
+        {
+            'amount': 45.99,
+            'currency': 'CNY',
+            'formatted': '¥45.99'
+        }
+    """
+```
+
+### 文本数据生成
+
+#### generate_text()
+
+```python
+def generate_text(self, min_length: int = 10, max_length: int = 100,
+                 text_type: str = "sentence") -> str:
+    """生成文本内容
+
+    Args:
+        min_length: 最小长度
+        max_length: 最大长度
+        text_type: 文本类型 ("word"/"sentence"/"paragraph")
+
+    Returns:
+        str: 生成的文本
+
+    Example:
+        >>> generator.generate_text(10, 50, "sentence")
+        '这是一个测试句子，用于演示文本生成功能。'
+        >>> generator.generate_text(5, 20, "word")
+        '测试 数据 生成 工具'
+    """
+```
+
+#### generate_username()
+
+```python
+def generate_username(self, min_length: int = 6, max_length: int = 20) -> str:
+    """生成用户名
+
+    Args:
+        min_length: 最小长度
+        max_length: 最大长度
+
+    Returns:
+        str: 生成的用户名
+
+    Example:
+        >>> generator.generate_username()
+        'user_zhang123'
+        >>> generator.generate_username(8, 15)
+        'testuser456'
+    """
+```
+
+### 日期时间生成
+
+#### generate_datetime()
+
+```python
+def generate_datetime(self, start_date: Optional[str] = None,
+                     end_date: Optional[str] = None,
+                     format_str: str = "%Y-%m-%d %H:%M:%S") -> str:
+    """生成日期时间
+
+    Args:
+        start_date: 开始日期 (YYYY-MM-DD)
+        end_date: 结束日期 (YYYY-MM-DD)
+        format_str: 格式字符串
+
+    Returns:
+        str: 生成的日期时间字符串
+
+    Example:
+        >>> generator.generate_datetime()
+        '2023-12-01 14:30:45'
+        >>> generator.generate_datetime("2023-01-01", "2023-12-31")
+        '2023-06-15 09:22:33'
+    """
+```
+
+#### generate_timestamp()
+
+```python
+def generate_timestamp(self, start_timestamp: Optional[int] = None,
+                      end_timestamp: Optional[int] = None) -> int:
+    """生成时间戳
+
+    Args:
+        start_timestamp: 开始时间戳
+        end_timestamp: 结束时间戳
+
+    Returns:
+        int: 生成的时间戳
+
+    Example:
+        >>> generator.generate_timestamp()
+        1701234567
+    """
+```
+
+### 业务数据生成
+
+#### generate_user_profile()
+
+```python
+def generate_user_profile(self, include_avatar: bool = True) -> Dict[str, Any]:
+    """生成用户资料
+
+    Args:
+        include_avatar: 是否包含头像URL
+
+    Returns:
+        Dict[str, Any]: 用户资料信息
+
+    Example:
+        >>> generator.generate_user_profile()
+        {
+            'id': 'user_123456',
+            'username': 'zhangsan123',
+            'name': '张三',
+            'email': 'zhangsan@example.com',
+            'phone': '13812345678',
+            'age': 28,
+            'gender': 'male',
+            'avatar': 'https://example.com/avatar/123.jpg',
+            'bio': '这是一个测试用户的简介。',
+            'created_at': '2023-01-15 10:30:00'
+        }
+    """
+```
+
+#### generate_product_info()
+
+```python
+def generate_product_info(self, category: Optional[str] = None) -> Dict[str, Any]:
+    """生成商品信息
+
+    Args:
+        category: 商品分类，可选
+
+    Returns:
+        Dict[str, Any]: 商品信息
+
+    Example:
+        >>> generator.generate_product_info("electronics")
+        {
+            'id': 'prod_789012',
+            'name': '智能手机',
+            'category': 'electronics',
+            'price': 2999.00,
+            'description': '高性能智能手机，配备先进的处理器。',
+            'brand': '华为',
+            'model': 'P50 Pro',
+            'sku': 'HW-P50-PRO-256GB',
+            'stock': 150,
+            'images': ['https://example.com/img1.jpg'],
+            'created_at': '2023-10-01 12:00:00'
+        }
+    """
+```
+
+## 📋 DataProvider
+
+数据提供者负责管理和分发测试数据。
+
+### 类定义
+
+```python
+from src.data_manager.data_provider import DataProvider
+
+class DataProvider:
+    """测试数据提供者
+
+    管理测试数据的加载、分发和同步。
+    """
+```
+
+### 核心方法
+
+#### load_data_from_file()
+
+```python
+def load_data_from_file(self, file_path: str,
+                       distribution_strategy: str = "round_robin",
+                       data_key: Optional[str] = None) -> bool:
+    """从文件加载数据
+
+    Args:
+        file_path: 文件路径
+        distribution_strategy: 分发策略 ("round_robin"/"random"/"sequential")
+        data_key: 数据键名，用于标识数据集
+
+    Returns:
+        bool: 加载是否成功
+
+    Example:
+        >>> provider = DataProvider()
+        >>> provider.load_data_from_file("test_data/users.csv", "round_robin", "users")
+        True
+    """
+```
+
+#### load_data_from_database()
+
+```python
+def load_data_from_database(self, connection_string: str, query: str,
+                           data_key: str, distribution_strategy: str = "round_robin") -> bool:
+    """从数据库加载数据
+
+    Args:
+        connection_string: 数据库连接字符串
+        query: SQL查询语句
+        data_key: 数据键名
+        distribution_strategy: 分发策略
+
+    Returns:
+        bool: 加载是否成功
+
+    Example:
+        >>> provider.load_data_from_database(
+        ...     "mysql://user:pass@localhost/testdb",
+        ...     "SELECT * FROM test_users",
+        ...     "db_users"
+        ... )
+    """
+```
+
+#### get_next_data()
+
+```python
+def get_next_data(self, data_key: str) -> Optional[Dict[str, Any]]:
+    """获取下一条数据
+
+    Args:
+        data_key: 数据键名
+
+    Returns:
+        Optional[Dict[str, Any]]: 数据记录，如果没有数据则返回None
+
+    Example:
+        >>> data = provider.get_next_data("users")
+        >>> print(data)
+        {'username': 'user1', 'password': 'pass1', 'email': 'user1@example.com'}
+    """
+```
+
+#### get_random_data()
+
+```python
+def get_random_data(self, data_key: str, count: int = 1) -> List[Dict[str, Any]]:
+    """获取随机数据
+
+    Args:
+        data_key: 数据键名
+        count: 获取数量
+
+    Returns:
+        List[Dict[str, Any]]: 随机数据列表
+
+    Example:
+        >>> random_users = provider.get_random_data("users", 3)
+        >>> len(random_users)
+        3
+    """
+```
+
+#### get_data_stats()
+
+```python
+def get_data_stats(self, data_key: str) -> Dict[str, Any]:
+    """获取数据统计信息
+
+    Args:
+        data_key: 数据键名
+
+    Returns:
+        Dict[str, Any]: 统计信息
+
+    Example:
+        >>> stats = provider.get_data_stats("users")
+        >>> print(stats)
+        {
+            'total_records': 1000,
+            'current_index': 45,
+            'distribution_strategy': 'round_robin',
+            'last_accessed': '2023-12-01 10:30:00'
+        }
+    """
+```
+
+## 🔄 DataDistributor
+
+数据分发器负责在分布式环境中同步测试数据。
+
+### 类定义
+
+```python
+from src.data_manager.data_distributor import DataDistributor
+
+class DataDistributor:
+    """数据分发器
+
+    在分布式测试环境中同步和分发测试数据。
+    """
+```
+
+### 核心方法
+
+#### sync_data()
+
+```python
+def sync_data(self, data_key: str, target_workers: List[str]) -> bool:
+    """同步数据到工作节点
+
+    Args:
+        data_key: 数据键名
+        target_workers: 目标工作节点列表
+
+    Returns:
+        bool: 同步是否成功
+
+    Example:
+        >>> distributor = DataDistributor()
+        >>> success = distributor.sync_data("users", ["worker1", "worker2"])
+    """
+```
+
+#### distribute_data_range()
+
+```python
+def distribute_data_range(self, data_key: str, worker_id: str,
+                         total_workers: int) -> Tuple[int, int]:
+    """分配数据范围给工作节点
+
+    Args:
+        data_key: 数据键名
+        worker_id: 工作节点ID
+        total_workers: 总工作节点数
+
+    Returns:
+        Tuple[int, int]: 数据范围 (start_index, end_index)
+
+    Example:
+        >>> start, end = distributor.distribute_data_range("users", "worker1", 4)
+        >>> print(f"Worker1 处理数据范围: {start}-{end}")
+    """
+```
+
+## 🔧 使用示例
+
+### 完整数据管理流程
+
+```python
+from src.data_manager.data_generator import DataGenerator
+from src.data_manager.data_provider import DataProvider
+from src.data_manager.data_distributor import DataDistributor
+
+# 1. 初始化组件
+generator = DataGenerator(locale="zh_CN", seed=12345)
+provider = DataProvider()
+distributor = DataDistributor()
+
+# 2. 生成测试数据
+users_data = []
+for i in range(1000):
+    user = generator.generate_user_profile()
+    users_data.append(user)
+
+# 保存到文件
+import csv
+with open("test_data/generated_users.csv", "w", newline="", encoding="utf-8") as f:
+    if users_data:
+        writer = csv.DictWriter(f, fieldnames=users_data[0].keys())
+        writer.writeheader()
+        writer.writerows(users_data)
+
+# 3. 加载数据到提供者
+provider.load_data_from_file("test_data/generated_users.csv", "round_robin", "users")
+
+# 4. 在测试中使用数据
+def get_test_user():
+    return provider.get_next_data("users")
+
+# 5. 分布式环境中同步数据
+if is_distributed_mode():
+    distributor.sync_data("users", get_worker_list())
+```
+
+### 自定义数据生成器
+
+```python
+class CustomDataGenerator(DataGenerator):
+    """自定义数据生成器"""
+
+    def generate_order_info(self) -> Dict[str, Any]:
+        """生成订单信息"""
+        return {
+            'order_id': f"ORD{self.generate_integer(100000, 999999)}",
+            'customer_id': f"CUST{self.generate_integer(1000, 9999)}",
+            'product_id': f"PROD{self.generate_integer(100, 999)}",
+            'quantity': self.generate_integer(1, 5),
+            'unit_price': self.generate_float(10.0, 500.0, 2),
+            'total_amount': 0,  # 计算得出
+            'order_date': self.generate_datetime(),
+            'status': self.fake.random_element(['pending', 'confirmed', 'shipped', 'delivered'])
+        }
+
+    def generate_api_request_data(self) -> Dict[str, Any]:
+        """生成API请求数据"""
+        return {
+            'request_id': self.fake.uuid4(),
+            'timestamp': self.generate_timestamp(),
+            'method': self.fake.random_element(['GET', 'POST', 'PUT', 'DELETE']),
+            'endpoint': f"/api/v1/{self.fake.random_element(['users', 'orders', 'products'])}",
+            'headers': {
+                'Content-Type': 'application/json',
+                'User-Agent': self.fake.user_agent(),
+                'X-Request-ID': self.fake.uuid4()
+            },
+            'payload': self.generate_json_payload()
+        }
+```
+
+### 数据提供者扩展
+
+```python
+class EnhancedDataProvider(DataProvider):
+    """增强的数据提供者"""
+
+    def load_data_from_api(self, api_url: str, headers: Dict[str, str],
+                          data_key: str) -> bool:
+        """从API加载数据"""
+        try:
+            import requests
+            response = requests.get(api_url, headers=headers)
+            if response.status_code == 200:
+                data = response.json()
+                self._store_data(data_key, data)
+                return True
+        except Exception as e:
+            print(f"Failed to load data from API: {e}")
+        return False
+
+    def get_weighted_data(self, data_key: str, weights: Dict[str, float]) -> Optional[Dict[str, Any]]:
+        """根据权重获取数据"""
+        import random
+
+        data_list = self._get_data_list(data_key)
+        if not data_list:
+            return None
+
+        # 根据权重选择数据
+        choices = list(weights.keys())
+        weight_values = list(weights.values())
+
+        selected_key = random.choices(choices, weights=weight_values)[0]
+        return self.get_next_data(selected_key)
+```
+
+## ⚠️ 注意事项
+
+1. **内存管理**: 大量数据加载时注意内存使用
+2. **数据同步**: 分布式环境中确保数据一致性
+3. **文件格式**: 支持CSV、JSON、Excel等格式
+4. **数据质量**: 生成的数据应符合业务逻辑
+5. **性能优化**: 合理设置缓存和批处理大小
+
+## 🔗 相关文档
+
+- [性能分析API](analysis.md) - 性能分析模块API
+- [监控告警API](monitoring.md) - 监控告警模块API
+- [插件接口API](plugins.md) - 插件开发接口
diff --git a/docs/api/load-shapes.md b/docs/api/load-shapes.md
new file mode 100644
index 0000000..03ae0c6
--- /dev/null
+++ b/docs/api/load-shapes.md
@@ -0,0 +1,609 @@
+# 负载模式API
+
+本文档详细介绍Locust性能测试框架的负载模式API接口。
+
+## 📋 API概览
+
+负载模式API提供了丰富的负载控制接口，支持多种负载模式：
+
+- **基础负载模式**: 常量负载、阶梯负载
+- **高级负载模式**: 波浪负载、尖峰负载、自适应负载
+- **自定义负载模式**: 用户自定义负载曲线
+- **负载模式管理**: 负载模式的注册和管理
+
+## 🔧 基础负载模式API
+
+### LoadShape
+
+所有负载模式的基础类。
+
+```python
+from abc import ABC, abstractmethod
+from typing import Tuple, Optional, Dict, Any
+from locust import LoadTestShape
+
+class LoadShape(LoadTestShape, ABC):
+    """负载模式基类"""
+
+    def __init__(self, **kwargs):
+        """
+        初始化负载模式
+
+        Args:
+            **kwargs: 负载模式参数
+        """
+        super().__init__()
+        self.start_time = None
+        self.config = kwargs
+
+    @abstractmethod
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        获取当前时刻的负载配置
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率) 或 None表示测试结束
+        """
+        pass
+
+    def get_current_time(self) -> float:
+        """
+        获取当前运行时间(秒)
+
+        Returns:
+            float: 运行时间
+        """
+        import time
+        if self.start_time is None:
+            self.start_time = time.time()
+        return time.time() - self.start_time
+
+    def get_config(self) -> Dict[str, Any]:
+        """
+        获取负载模式配置
+
+        Returns:
+            Dict[str, Any]: 配置字典
+        """
+        return self.config
+
+    def validate_config(self) -> bool:
+        """
+        验证配置参数
+
+        Returns:
+            bool: 配置是否有效
+        """
+        return True
+```
+
+### ConstantLoadShape
+
+常量负载模式，保持固定的用户数和生成速率。
+
+```python
+class ConstantLoadShape(LoadShape):
+    """常量负载模式"""
+
+    def __init__(self, users: int = 100, spawn_rate: float = 10,
+                 duration: Optional[int] = None, **kwargs):
+        """
+        初始化常量负载模式
+
+        Args:
+            users: 用户数
+            spawn_rate: 生成速率
+            duration: 持续时间(秒)，None表示无限制
+            **kwargs: 其他参数
+        """
+        super().__init__(**kwargs)
+        self.users = users
+        self.spawn_rate = spawn_rate
+        self.duration = duration
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        获取当前负载配置
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = self.get_current_time()
+
+        if self.duration and current_time >= self.duration:
+            return None
+
+        return self.users, self.spawn_rate
+
+    def validate_config(self) -> bool:
+        """验证配置"""
+        return (self.users > 0 and self.spawn_rate > 0 and
+                (self.duration is None or self.duration > 0))
+```
+
+### StepLoadShape
+
+阶梯负载模式，按阶梯递增用户数。
+
+```python
+from typing import List, NamedTuple
+
+class Step(NamedTuple):
+    """阶梯定义"""
+    users: int
+    duration: int
+    spawn_rate: float = 10
+
+class StepLoadShape(LoadShape):
+    """阶梯负载模式"""
+
+    def __init__(self, steps: List[Step], **kwargs):
+        """
+        初始化阶梯负载模式
+
+        Args:
+            steps: 阶梯列表
+            **kwargs: 其他参数
+        """
+        super().__init__(**kwargs)
+        self.steps = steps
+        self.current_step = 0
+        self.step_start_time = 0
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        获取当前负载配置
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = self.get_current_time()
+
+        if self.current_step >= len(self.steps):
+            return None
+
+        step = self.steps[self.current_step]
+        step_elapsed = current_time - self.step_start_time
+
+        if step_elapsed >= step.duration:
+            self.current_step += 1
+            self.step_start_time = current_time
+
+            if self.current_step >= len(self.steps):
+                return None
+
+            step = self.steps[self.current_step]
+
+        return step.users, step.spawn_rate
+
+    def validate_config(self) -> bool:
+        """验证配置"""
+        if not self.steps:
+            return False
+
+        for step in self.steps:
+            if step.users <= 0 or step.duration <= 0 or step.spawn_rate <= 0:
+                return False
+
+        return True
+```
+
+## 🌊 高级负载模式API
+
+### WaveLoadShape
+
+波浪负载模式，按正弦波形变化用户数。
+
+```python
+import math
+
+class WaveLoadShape(LoadShape):
+    """波浪负载模式"""
+
+    def __init__(self, min_users: int = 10, max_users: int = 100,
+                 wave_period: int = 300, spawn_rate: float = 10,
+                 duration: Optional[int] = None, **kwargs):
+        """
+        初始化波浪负载模式
+
+        Args:
+            min_users: 最小用户数
+            max_users: 最大用户数
+            wave_period: 波浪周期(秒)
+            spawn_rate: 生成速率
+            duration: 持续时间(秒)
+            **kwargs: 其他参数
+        """
+        super().__init__(**kwargs)
+        self.min_users = min_users
+        self.max_users = max_users
+        self.wave_period = wave_period
+        self.spawn_rate = spawn_rate
+        self.duration = duration
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        获取当前负载配置
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = self.get_current_time()
+
+        if self.duration and current_time >= self.duration:
+            return None
+
+        # 计算正弦波用户数
+        wave_position = (current_time % self.wave_period) / self.wave_period
+        sine_value = math.sin(2 * math.pi * wave_position)
+
+        # 将正弦值映射到用户数范围
+        user_range = self.max_users - self.min_users
+        current_users = self.min_users + int((sine_value + 1) / 2 * user_range)
+
+        return current_users, self.spawn_rate
+
+    def validate_config(self) -> bool:
+        """验证配置"""
+        return (self.min_users > 0 and self.max_users > self.min_users and
+                self.wave_period > 0 and self.spawn_rate > 0 and
+                (self.duration is None or self.duration > 0))
+```
+
+### SpikeLoadShape
+
+尖峰负载模式，在指定时间产生负载尖峰。
+
+```python
+from typing import List
+
+class Spike(NamedTuple):
+    """尖峰定义"""
+    start_time: int
+    peak_users: int
+    duration: int
+    spawn_rate: float = 50
+
+class SpikeLoadShape(LoadShape):
+    """尖峰负载模式"""
+
+    def __init__(self, base_users: int = 50, spikes: List[Spike] = None,
+                 base_spawn_rate: float = 10, total_duration: int = 600, **kwargs):
+        """
+        初始化尖峰负载模式
+
+        Args:
+            base_users: 基础用户数
+            spikes: 尖峰列表
+            base_spawn_rate: 基础生成速率
+            total_duration: 总持续时间
+            **kwargs: 其他参数
+        """
+        super().__init__(**kwargs)
+        self.base_users = base_users
+        self.spikes = spikes or []
+        self.base_spawn_rate = base_spawn_rate
+        self.total_duration = total_duration
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        获取当前负载配置
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = self.get_current_time()
+
+        if current_time >= self.total_duration:
+            return None
+
+        # 检查是否在尖峰时间内
+        for spike in self.spikes:
+            spike_end = spike.start_time + spike.duration
+            if spike.start_time <= current_time < spike_end:
+                return spike.peak_users, spike.spawn_rate
+
+        # 返回基础负载
+        return self.base_users, self.base_spawn_rate
+
+    def validate_config(self) -> bool:
+        """验证配置"""
+        if (self.base_users <= 0 or self.base_spawn_rate <= 0 or
+            self.total_duration <= 0):
+            return False
+
+        for spike in self.spikes:
+            if (spike.start_time < 0 or spike.peak_users <= 0 or
+                spike.duration <= 0 or spike.spawn_rate <= 0):
+                return False
+
+            if spike.start_time + spike.duration > self.total_duration:
+                return False
+
+        return True
+```
+
+### AdaptiveLoadShape
+
+自适应负载模式，根据性能指标动态调整负载。
+
+```python
+class AdaptiveLoadShape(LoadShape):
+    """自适应负载模式"""
+
+    def __init__(self, initial_users: int = 50, max_users: int = 500,
+                 target_response_time: float = 1000, adjustment_interval: int = 30,
+                 spawn_rate: float = 10, **kwargs):
+        """
+        初始化自适应负载模式
+
+        Args:
+            initial_users: 初始用户数
+            max_users: 最大用户数
+            target_response_time: 目标响应时间(ms)
+            adjustment_interval: 调整间隔(秒)
+            spawn_rate: 生成速率
+            **kwargs: 其他参数
+        """
+        super().__init__(**kwargs)
+        self.initial_users = initial_users
+        self.max_users = max_users
+        self.target_response_time = target_response_time
+        self.adjustment_interval = adjustment_interval
+        self.spawn_rate = spawn_rate
+
+        self.current_users = initial_users
+        self.last_adjustment_time = 0
+        self.performance_history = []
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        获取当前负载配置
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = self.get_current_time()
+
+        # 检查是否需要调整负载
+        if current_time - self.last_adjustment_time >= self.adjustment_interval:
+            self._adjust_load()
+            self.last_adjustment_time = current_time
+
+        return self.current_users, self.spawn_rate
+
+    def _adjust_load(self):
+        """根据性能指标调整负载"""
+        # 获取当前性能指标
+        current_response_time = self._get_current_response_time()
+
+        if current_response_time is None:
+            return
+
+        # 根据响应时间调整用户数
+        if current_response_time > self.target_response_time * 1.2:
+            # 响应时间过高，减少用户数
+            self.current_users = max(1, int(self.current_users * 0.9))
+        elif current_response_time < self.target_response_time * 0.8:
+            # 响应时间良好，增加用户数
+            self.current_users = min(self.max_users, int(self.current_users * 1.1))
+
+        # 记录性能历史
+        self.performance_history.append({
+            'time': self.get_current_time(),
+            'users': self.current_users,
+            'response_time': current_response_time
+        })
+
+    def _get_current_response_time(self) -> Optional[float]:
+        """
+        获取当前平均响应时间
+
+        Returns:
+            Optional[float]: 平均响应时间(ms)
+        """
+        # 这里需要从Locust统计信息中获取实际的响应时间
+        # 实际实现中需要访问Locust的stats对象
+        try:
+            from locust import stats
+            if stats.total.num_requests > 0:
+                return stats.total.avg_response_time
+        except:
+            pass
+        return None
+
+    def validate_config(self) -> bool:
+        """验证配置"""
+        return (self.initial_users > 0 and self.max_users > 0 and
+                self.target_response_time > 0 and self.adjustment_interval > 0 and
+                self.spawn_rate > 0)
+```
+
+## 🔧 负载模式管理API
+
+### LoadShapeManager
+
+负载模式管理器，用于注册和管理负载模式。
+
+```python
+from typing import Dict, Type, Optional
+
+class LoadShapeManager:
+    """负载模式管理器"""
+
+    def __init__(self):
+        self._shapes: Dict[str, Type[LoadShape]] = {}
+        self._register_builtin_shapes()
+
+    def register_shape(self, name: str, shape_class: Type[LoadShape]) -> bool:
+        """
+        注册负载模式
+
+        Args:
+            name: 负载模式名称
+            shape_class: 负载模式类
+
+        Returns:
+            bool: 注册是否成功
+        """
+        if not issubclass(shape_class, LoadShape):
+            return False
+
+        self._shapes[name] = shape_class
+        return True
+
+    def get_shape(self, name: str) -> Optional[Type[LoadShape]]:
+        """
+        获取负载模式类
+
+        Args:
+            name: 负载模式名称
+
+        Returns:
+            Optional[Type[LoadShape]]: 负载模式类
+        """
+        return self._shapes.get(name)
+
+    def create_shape(self, name: str, **kwargs) -> Optional[LoadShape]:
+        """
+        创建负载模式实例
+
+        Args:
+            name: 负载模式名称
+            **kwargs: 负载模式参数
+
+        Returns:
+            Optional[LoadShape]: 负载模式实例
+        """
+        shape_class = self.get_shape(name)
+        if shape_class:
+            return shape_class(**kwargs)
+        return None
+
+    def list_shapes(self) -> List[str]:
+        """
+        列出所有注册的负载模式
+
+        Returns:
+            List[str]: 负载模式名称列表
+        """
+        return list(self._shapes.keys())
+
+    def _register_builtin_shapes(self):
+        """注册内置负载模式"""
+        self.register_shape("constant", ConstantLoadShape)
+        self.register_shape("step", StepLoadShape)
+        self.register_shape("wave", WaveLoadShape)
+        self.register_shape("spike", SpikeLoadShape)
+        self.register_shape("adaptive", AdaptiveLoadShape)
+
+# 全局负载模式管理器实例
+load_shape_manager = LoadShapeManager()
+```
+
+## 📊 负载模式工具API
+
+### LoadShapeValidator
+
+负载模式验证器。
+
+```python
+class LoadShapeValidator:
+    """负载模式验证器"""
+
+    @staticmethod
+    def validate_shape_config(shape_type: str, config: Dict[str, Any]) -> bool:
+        """
+        验证负载模式配置
+
+        Args:
+            shape_type: 负载模式类型
+            config: 配置字典
+
+        Returns:
+            bool: 配置是否有效
+        """
+        shape_class = load_shape_manager.get_shape(shape_type)
+        if not shape_class:
+            return False
+
+        try:
+            shape = shape_class(**config)
+            return shape.validate_config()
+        except Exception:
+            return False
+
+    @staticmethod
+    def estimate_duration(shape_type: str, config: Dict[str, Any]) -> Optional[int]:
+        """
+        估算负载模式持续时间
+
+        Args:
+            shape_type: 负载模式类型
+            config: 配置字典
+
+        Returns:
+            Optional[int]: 估算的持续时间(秒)
+        """
+        if shape_type == "constant":
+            return config.get("duration")
+        elif shape_type == "step":
+            steps = config.get("steps", [])
+            return sum(step.duration for step in steps) if steps else None
+        elif shape_type in ["wave", "spike", "adaptive"]:
+            return config.get("duration") or config.get("total_duration")
+
+        return None
+```
+
+## 📚 使用示例
+
+### 创建自定义负载模式
+
+```python
+class CustomLoadShape(LoadShape):
+    """自定义负载模式示例"""
+
+    def __init__(self, pattern: str = "linear", **kwargs):
+        super().__init__(**kwargs)
+        self.pattern = pattern
+        self.max_users = kwargs.get("max_users", 100)
+        self.duration = kwargs.get("duration", 300)
+        self.spawn_rate = kwargs.get("spawn_rate", 10)
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        current_time = self.get_current_time()
+
+        if current_time >= self.duration:
+            return None
+
+        if self.pattern == "linear":
+            # 线性增长
+            progress = current_time / self.duration
+            users = int(self.max_users * progress)
+        elif self.pattern == "exponential":
+            # 指数增长
+            progress = current_time / self.duration
+            users = int(self.max_users * (progress ** 2))
+        else:
+            users = self.max_users
+
+        return max(1, users), self.spawn_rate
+
+# 注册自定义负载模式
+load_shape_manager.register_shape("custom", CustomLoadShape)
+
+# 使用负载模式
+shape = load_shape_manager.create_shape("custom",
+                                       pattern="exponential",
+                                       max_users=200,
+                                       duration=600,
+                                       spawn_rate=15)
+```
+
+## 📚 相关文档
+
+- [负载模式开发](../development/load-shape-development.md) - 负载模式开发指南
+- [基础示例](../examples/basic-examples.md) - 负载模式使用示例
+- [高级示例](../examples/advanced-usage.md) - 高级负载模式示例
+- [配置参考](../configuration/framework-config.md) - 负载模式配置
diff --git a/docs/api/monitoring.md b/docs/api/monitoring.md
new file mode 100644
index 0000000..6c12e84
--- /dev/null
+++ b/docs/api/monitoring.md
@@ -0,0 +1,620 @@
+# 监控告警API参考
+
+本文档详细介绍监控告警模块的API接口，包括性能监控器、告警管理器和通知服务。
+
+## 📊 PerformanceMonitor
+
+性能监控器提供实时性能指标收集和监控功能。
+
+### 类定义
+
+```python
+from src.monitoring.performance_monitor import PerformanceMonitor
+
+class PerformanceMonitor:
+    """性能监控器
+
+    提供实时性能监控功能，包括指标收集、阈值检查、
+    告警触发等核心监控能力。
+    """
+```
+
+### 构造函数
+
+```python
+def __init__(self, config: Optional[Dict[str, Any]] = None):
+    """初始化性能监控器
+
+    Args:
+        config: 监控配置参数
+            - interval: 监控间隔(秒)，默认5
+            - buffer_size: 缓冲区大小，默认1000
+            - metrics_retention: 指标保留时间(秒)，默认3600
+            - alert_cooldown: 告警冷却时间(秒)，默认300
+
+    Example:
+        >>> config = {
+        ...     'interval': 10,
+        ...     'buffer_size': 2000,
+        ...     'alert_cooldown': 600
+        ... }
+        >>> monitor = PerformanceMonitor(config)
+    """
+```
+
+### 核心方法
+
+#### start_monitoring()
+
+```python
+def start_monitoring(self) -> bool:
+    """开始监控
+
+    启动监控线程，开始收集性能指标。
+
+    Returns:
+        bool: 启动是否成功
+
+    Example:
+        >>> monitor = PerformanceMonitor()
+        >>> success = monitor.start_monitoring()
+        >>> print(f"监控启动{'成功' if success else '失败'}")
+    """
+```
+
+#### stop_monitoring()
+
+```python
+def stop_monitoring(self) -> bool:
+    """停止监控
+
+    停止监控线程，清理资源。
+
+    Returns:
+        bool: 停止是否成功
+
+    Example:
+        >>> monitor.stop_monitoring()
+    """
+```
+
+#### collect_metrics()
+
+```python
+def collect_metrics(self) -> Dict[str, Any]:
+    """收集当前性能指标
+
+    Returns:
+        Dict[str, Any]: 当前性能指标
+            - timestamp: 时间戳
+            - response_time: 响应时间统计
+            - throughput: 吞吐量统计
+            - error_rate: 错误率
+            - active_users: 活跃用户数
+            - system_metrics: 系统指标(可选)
+
+    Example:
+        >>> metrics = monitor.collect_metrics()
+        >>> print(f"当前TPS: {metrics['throughput']['current']}")
+    """
+```
+
+#### register_custom_metric()
+
+```python
+def register_custom_metric(self, name: str, metric_type: str,
+                          description: str = "") -> bool:
+    """注册自定义指标
+
+    Args:
+        name: 指标名称
+        metric_type: 指标类型 (counter/gauge/histogram)
+        description: 指标描述
+
+    Returns:
+        bool: 注册是否成功
+
+    Example:
+        >>> success = monitor.register_custom_metric(
+        ...     "business_transactions",
+        ...     "counter",
+        ...     "业务交易计数"
+        ... )
+    """
+```
+
+#### record_metric()
+
+```python
+def record_metric(self, name: str, value: Union[int, float],
+                 labels: Optional[Dict[str, str]] = None) -> bool:
+    """记录指标值
+
+    Args:
+        name: 指标名称
+        value: 指标值
+        labels: 标签字典(可选)
+
+    Returns:
+        bool: 记录是否成功
+
+    Example:
+        >>> monitor.record_metric("response_time", 500.0, {"endpoint": "/api/users"})
+        >>> monitor.record_metric("error_count", 1, {"error_type": "timeout"})
+    """
+```
+
+#### get_metrics_history()
+
+```python
+def get_metrics_history(self, metric_name: str,
+                       time_range: Optional[Tuple[float, float]] = None) -> List[Dict[str, Any]]:
+    """获取指标历史数据
+
+    Args:
+        metric_name: 指标名称
+        time_range: 时间范围(开始时间戳, 结束时间戳)，可选
+
+    Returns:
+        List[Dict[str, Any]]: 历史数据列表
+
+    Example:
+        >>> import time
+        >>> end_time = time.time()
+        >>> start_time = end_time - 3600  # 最近1小时
+        >>> history = monitor.get_metrics_history("response_time", (start_time, end_time))
+    """
+```
+
+## 🚨 AlertManager
+
+告警管理器负责告警规则管理、告警触发和生命周期管理。
+
+### 类定义
+
+```python
+from src.monitoring.alert_manager import AlertManager
+
+class AlertManager:
+    """告警管理器
+
+    管理告警规则、触发告警、处理告警生命周期。
+    """
+```
+
+### 核心方法
+
+#### add_alert_rule()
+
+```python
+def add_alert_rule(self, rule: Dict[str, Any]) -> bool:
+    """添加告警规则
+
+    Args:
+        rule: 告警规则配置
+            - name: 规则名称
+            - metric: 监控指标
+            - condition: 触发条件
+            - threshold: 阈值
+            - duration: 持续时间
+            - severity: 严重级别
+            - message: 告警消息
+            - actions: 告警动作列表
+
+    Returns:
+        bool: 添加是否成功
+
+    Example:
+        >>> rule = {
+        ...     "name": "high_response_time",
+        ...     "metric": "avg_response_time",
+        ...     "condition": ">",
+        ...     "threshold": 2000,
+        ...     "duration": 300,
+        ...     "severity": "warning",
+        ...     "message": "平均响应时间过高",
+        ...     "actions": ["notify", "log"]
+        ... }
+        >>> alert_manager.add_alert_rule(rule)
+    """
+```
+
+#### remove_alert_rule()
+
+```python
+def remove_alert_rule(self, rule_name: str) -> bool:
+    """移除告警规则
+
+    Args:
+        rule_name: 规则名称
+
+    Returns:
+        bool: 移除是否成功
+
+    Example:
+        >>> alert_manager.remove_alert_rule("high_response_time")
+    """
+```
+
+#### trigger_alert()
+
+```python
+def trigger_alert(self, alert_name: str, message: str,
+                 severity: str = "warning",
+                 metadata: Optional[Dict[str, Any]] = None) -> str:
+    """手动触发告警
+
+    Args:
+        alert_name: 告警名称
+        message: 告警消息
+        severity: 严重级别 (info/warning/critical)
+        metadata: 附加元数据
+
+    Returns:
+        str: 告警ID
+
+    Example:
+        >>> alert_id = alert_manager.trigger_alert(
+        ...     "manual_alert",
+        ...     "手动触发的测试告警",
+        ...     "warning",
+        ...     {"source": "manual", "test_id": "test_001"}
+        ... )
+    """
+```
+
+#### resolve_alert()
+
+```python
+def resolve_alert(self, alert_id: str, resolution_note: str = "") -> bool:
+    """解决告警
+
+    Args:
+        alert_id: 告警ID
+        resolution_note: 解决说明
+
+    Returns:
+        bool: 解决是否成功
+
+    Example:
+        >>> alert_manager.resolve_alert("alert_123", "问题已修复")
+    """
+```
+
+#### get_active_alerts()
+
+```python
+def get_active_alerts(self, severity: Optional[str] = None) -> List[Dict[str, Any]]:
+    """获取活跃告警列表
+
+    Args:
+        severity: 过滤严重级别，可选
+
+    Returns:
+        List[Dict[str, Any]]: 活跃告警列表
+
+    Example:
+        >>> active_alerts = alert_manager.get_active_alerts("critical")
+        >>> print(f"严重告警数量: {len(active_alerts)}")
+    """
+```
+
+#### get_alert_history()
+
+```python
+def get_alert_history(self, time_range: Optional[Tuple[float, float]] = None,
+                     limit: int = 100) -> List[Dict[str, Any]]:
+    """获取告警历史
+
+    Args:
+        time_range: 时间范围，可选
+        limit: 返回数量限制
+
+    Returns:
+        List[Dict[str, Any]]: 告警历史列表
+
+    Example:
+        >>> history = alert_manager.get_alert_history(limit=50)
+    """
+```
+
+## 📢 NotificationService
+
+通知服务负责多渠道消息发送和通知管理。
+
+### 类定义
+
+```python
+from src.monitoring.notification_service import NotificationService
+
+class NotificationService:
+    """通知服务
+
+    支持多种通知渠道的消息发送服务。
+    """
+```
+
+### 核心方法
+
+#### send_notification()
+
+```python
+def send_notification(self, message: str, channels: List[str],
+                     priority: str = "normal",
+                     metadata: Optional[Dict[str, Any]] = None) -> Dict[str, bool]:
+    """发送通知
+
+    Args:
+        message: 通知消息
+        channels: 通知渠道列表
+        priority: 优先级 (low/normal/high/urgent)
+        metadata: 附加元数据
+
+    Returns:
+        Dict[str, bool]: 各渠道发送结果
+
+    Example:
+        >>> result = notification_service.send_notification(
+        ...     "测试完成，请查看报告",
+        ...     ["feishu", "email"],
+        ...     "normal",
+        ...     {"test_id": "test_001", "report_url": "http://example.com/report"}
+        ... )
+        >>> print(f"飞书发送结果: {result['feishu']}")
+    """
+```
+
+#### send_alert_notification()
+
+```python
+def send_alert_notification(self, alert: Dict[str, Any]) -> Dict[str, bool]:
+    """发送告警通知
+
+    Args:
+        alert: 告警信息
+            - id: 告警ID
+            - name: 告警名称
+            - message: 告警消息
+            - severity: 严重级别
+            - timestamp: 时间戳
+            - metadata: 元数据
+
+    Returns:
+        Dict[str, bool]: 各渠道发送结果
+
+    Example:
+        >>> alert = {
+        ...     "id": "alert_123",
+        ...     "name": "high_error_rate",
+        ...     "message": "错误率超过阈值",
+        ...     "severity": "critical",
+        ...     "timestamp": time.time()
+        ... }
+        >>> result = notification_service.send_alert_notification(alert)
+    """
+```
+
+#### register_channel()
+
+```python
+def register_channel(self, channel_name: str, channel_config: Dict[str, Any]) -> bool:
+    """注册通知渠道
+
+    Args:
+        channel_name: 渠道名称
+        channel_config: 渠道配置
+
+    Returns:
+        bool: 注册是否成功
+
+    Example:
+        >>> config = {
+        ...     "webhook_url": "https://hooks.slack.com/xxx",
+        ...     "channel": "#alerts",
+        ...     "username": "LocustBot"
+        ... }
+        >>> notification_service.register_channel("slack", config)
+    """
+```
+
+#### get_channel_status()
+
+```python
+def get_channel_status(self) -> Dict[str, Dict[str, Any]]:
+    """获取渠道状态
+
+    Returns:
+        Dict[str, Dict[str, Any]]: 各渠道状态信息
+
+    Example:
+        >>> status = notification_service.get_channel_status()
+        >>> for channel, info in status.items():
+        ...     print(f"{channel}: {'可用' if info['available'] else '不可用'}")
+    """
+```
+
+## 📈 MetricsCollector
+
+指标收集器负责从各种数据源收集性能指标。
+
+### 类定义
+
+```python
+from src.monitoring.metrics_collector import MetricsCollector
+
+class MetricsCollector:
+    """指标收集器
+
+    从多种数据源收集性能指标数据。
+    """
+```
+
+### 核心方法
+
+#### collect_locust_metrics()
+
+```python
+def collect_locust_metrics(self, environment) -> Dict[str, Any]:
+    """收集Locust指标
+
+    Args:
+        environment: Locust环境对象
+
+    Returns:
+        Dict[str, Any]: Locust指标数据
+
+    Example:
+        >>> metrics = collector.collect_locust_metrics(environment)
+        >>> print(f"总请求数: {metrics['total_requests']}")
+    """
+```
+
+#### collect_system_metrics()
+
+```python
+def collect_system_metrics(self) -> Dict[str, Any]:
+    """收集系统指标
+
+    Returns:
+        Dict[str, Any]: 系统指标数据
+            - cpu_usage: CPU使用率
+            - memory_usage: 内存使用率
+            - disk_usage: 磁盘使用率
+            - network_io: 网络IO统计
+
+    Example:
+        >>> system_metrics = collector.collect_system_metrics()
+        >>> print(f"CPU使用率: {system_metrics['cpu_usage']}%")
+    """
+```
+
+#### collect_custom_metrics()
+
+```python
+def collect_custom_metrics(self, metric_sources: List[str]) -> Dict[str, Any]:
+    """收集自定义指标
+
+    Args:
+        metric_sources: 指标源列表
+
+    Returns:
+        Dict[str, Any]: 自定义指标数据
+
+    Example:
+        >>> custom_metrics = collector.collect_custom_metrics(["database", "cache"])
+    """
+```
+
+## 🔧 使用示例
+
+### 完整监控流程
+
+```python
+from src.monitoring.performance_monitor import PerformanceMonitor
+from src.monitoring.alert_manager import AlertManager
+from src.monitoring.notification_service import NotificationService
+
+# 1. 初始化监控组件
+monitor = PerformanceMonitor({
+    'interval': 5,
+    'buffer_size': 2000
+})
+
+alert_manager = AlertManager()
+notification_service = NotificationService()
+
+# 2. 配置告警规则
+alert_rules = [
+    {
+        "name": "high_response_time",
+        "metric": "avg_response_time",
+        "condition": ">",
+        "threshold": 2000,
+        "duration": 300,
+        "severity": "warning",
+        "message": "平均响应时间超过2秒"
+    },
+    {
+        "name": "high_error_rate",
+        "metric": "error_rate",
+        "condition": ">",
+        "threshold": 5.0,
+        "duration": 120,
+        "severity": "critical",
+        "message": "错误率超过5%"
+    }
+]
+
+for rule in alert_rules:
+    alert_manager.add_alert_rule(rule)
+
+# 3. 配置通知渠道
+notification_service.register_channel("feishu", {
+    "webhook_url": "https://open.feishu.cn/open-apis/bot/v2/hook/xxx"
+})
+
+# 4. 启动监控
+monitor.start_monitoring()
+
+# 5. 注册自定义指标
+monitor.register_custom_metric("business_success_rate", "gauge")
+
+# 6. 在测试过程中记录指标
+def on_request_success(request_type, name, response_time, response_length, **kwargs):
+    monitor.record_metric("response_time", response_time, {"endpoint": name})
+    monitor.record_metric("business_success_rate", 1.0)
+
+def on_request_failure(request_type, name, response_time, response_length, exception, **kwargs):
+    monitor.record_metric("response_time", response_time, {"endpoint": name})
+    monitor.record_metric("business_success_rate", 0.0)
+
+    # 触发告警
+    if response_time > 5000:
+        alert_manager.trigger_alert(
+            "slow_request",
+            f"请求 {name} 响应时间过长: {response_time}ms",
+            "warning"
+        )
+
+# 7. 停止监控
+monitor.stop_monitoring()
+```
+
+### 自定义监控插件
+
+```python
+from src.monitoring.performance_monitor import PerformanceMonitor
+
+class DatabaseMonitor(PerformanceMonitor):
+    """数据库监控插件"""
+
+    def __init__(self, db_config):
+        super().__init__()
+        self.db_config = db_config
+
+    def collect_custom_metrics(self):
+        """收集数据库指标"""
+        # 连接数据库
+        # 查询性能指标
+        # 返回指标数据
+        return {
+            "active_connections": 50,
+            "query_latency": 100,
+            "cache_hit_rate": 0.95
+        }
+```
+
+## ⚠️ 注意事项
+
+1. **性能影响**: 监控会消耗系统资源，合理设置监控间隔
+2. **内存管理**: 定期清理历史数据，避免内存泄漏
+3. **告警风暴**: 设置告警冷却时间，避免重复告警
+4. **网络依赖**: 通知服务依赖网络，需要处理网络异常
+5. **配置验证**: 启动前验证监控配置的正确性
+
+## 🔗 相关文档
+
+- [性能分析API](analysis.md) - 性能分析模块API
+- [数据管理API](data-manager.md) - 数据管理模块API
+- [插件接口API](plugins.md) - 插件开发接口
+- [监控配置](../configuration/monitoring-config.md) - 监控配置说明
diff --git a/docs/api/plugins.md b/docs/api/plugins.md
new file mode 100644
index 0000000..f985ded
--- /dev/null
+++ b/docs/api/plugins.md
@@ -0,0 +1,694 @@
+# 插件接口API
+
+本文档详细介绍Locust性能测试框架的插件开发接口API。
+
+## 📋 API概览
+
+插件系统提供了丰富的API接口，支持多种类型的插件开发：
+
+- **基础插件API**: 所有插件的基础接口
+- **Locust插件API**: 扩展Locust核心功能
+- **报告插件API**: 自定义报告生成
+- **监控插件API**: 扩展监控能力
+- **数据插件API**: 数据源和处理扩展
+- **通知插件API**: 通知渠道扩展
+
+## 🔌 基础插件API
+
+### BasePlugin
+
+所有插件的基础类，提供插件的基本生命周期管理。
+
+```python
+from abc import ABC, abstractmethod
+from typing import Dict, Any, Optional
+
+class BasePlugin(ABC):
+    """插件基类"""
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """
+        初始化插件
+
+        Args:
+            config: 插件配置字典
+        """
+        self.config = config or {}
+        self.enabled = True
+        self.logger = self._setup_logger()
+        self.name = self.__class__.__name__
+        self.version = "1.0.0"
+
+    @abstractmethod
+    def initialize(self) -> bool:
+        """
+        插件初始化
+
+        Returns:
+            bool: 初始化是否成功
+        """
+        pass
+
+    @abstractmethod
+    def cleanup(self) -> None:
+        """插件清理资源"""
+        pass
+
+    def enable(self) -> None:
+        """启用插件"""
+        self.enabled = True
+
+    def disable(self) -> None:
+        """禁用插件"""
+        self.enabled = False
+
+    def is_enabled(self) -> bool:
+        """检查插件是否启用"""
+        return self.enabled
+
+    def get_info(self) -> Dict[str, Any]:
+        """
+        获取插件信息
+
+        Returns:
+            Dict[str, Any]: 插件信息字典
+        """
+        return {
+            "name": self.name,
+            "version": self.version,
+            "enabled": self.enabled,
+            "config": self.config
+        }
+
+    def validate_config(self, config: Dict[str, Any]) -> bool:
+        """
+        验证配置
+
+        Args:
+            config: 配置字典
+
+        Returns:
+            bool: 配置是否有效
+        """
+        return True
+
+    def _setup_logger(self):
+        """设置日志记录器"""
+        import logging
+        return logging.getLogger(f"plugin.{self.__class__.__name__}")
+```
+
+## 🦗 Locust插件API
+
+### LocustPlugin
+
+扩展Locust核心功能的插件接口。
+
+```python
+from locust import events
+from locust.env import Environment
+
+class LocustPlugin(BasePlugin):
+    """Locust功能扩展插件"""
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__(config)
+        self.environment: Optional[Environment] = None
+
+    def set_environment(self, environment: Environment) -> None:
+        """
+        设置Locust环境
+
+        Args:
+            environment: Locust环境对象
+        """
+        self.environment = environment
+
+    def on_test_start(self, environment: Environment, **kwargs) -> None:
+        """
+        测试开始时调用
+
+        Args:
+            environment: Locust环境对象
+            **kwargs: 额外参数
+        """
+        pass
+
+    def on_test_stop(self, environment: Environment, **kwargs) -> None:
+        """
+        测试结束时调用
+
+        Args:
+            environment: Locust环境对象
+            **kwargs: 额外参数
+        """
+        pass
+
+    def on_user_add(self, user_instance, **kwargs) -> None:
+        """
+        用户添加时调用
+
+        Args:
+            user_instance: 用户实例
+            **kwargs: 额外参数
+        """
+        pass
+
+    def on_user_remove(self, user_instance, **kwargs) -> None:
+        """
+        用户移除时调用
+
+        Args:
+            user_instance: 用户实例
+            **kwargs: 额外参数
+        """
+        pass
+
+    def on_request_success(self, request_type: str, name: str,
+                          response_time: float, response_length: int, **kwargs) -> None:
+        """
+        请求成功时调用
+
+        Args:
+            request_type: 请求类型
+            name: 请求名称
+            response_time: 响应时间
+            response_length: 响应长度
+            **kwargs: 额外参数
+        """
+        pass
+
+    def on_request_failure(self, request_type: str, name: str,
+                          response_time: float, response_length: int,
+                          exception: Exception, **kwargs) -> None:
+        """
+        请求失败时调用
+
+        Args:
+            request_type: 请求类型
+            name: 请求名称
+            response_time: 响应时间
+            response_length: 响应长度
+            exception: 异常对象
+            **kwargs: 额外参数
+        """
+        pass
+
+    def register_events(self) -> None:
+        """注册事件监听器"""
+        if self.environment:
+            events.test_start.add_listener(self.on_test_start)
+            events.test_stop.add_listener(self.on_test_stop)
+            events.user_add.add_listener(self.on_user_add)
+            events.user_remove.add_listener(self.on_user_remove)
+            events.request_success.add_listener(self.on_request_success)
+            events.request_failure.add_listener(self.on_request_failure)
+```
+
+## 📊 报告插件API
+
+### ReportPlugin
+
+自定义报告生成插件接口。
+
+```python
+from typing import List, Dict, Any
+from pathlib import Path
+
+class ReportPlugin(BasePlugin):
+    """报告生成插件"""
+
+    def generate_report(self, stats: Dict[str, Any],
+                       output_path: Path, **kwargs) -> bool:
+        """
+        生成报告
+
+        Args:
+            stats: 统计数据
+            output_path: 输出路径
+            **kwargs: 额外参数
+
+        Returns:
+            bool: 生成是否成功
+        """
+        pass
+
+    def format_data(self, data: Any, format_type: str) -> str:
+        """
+        格式化数据
+
+        Args:
+            data: 原始数据
+            format_type: 格式类型 (html, json, csv, markdown)
+
+        Returns:
+            str: 格式化后的数据
+        """
+        pass
+
+    def export_report(self, report_content: str,
+                     output_path: Path, format_type: str) -> bool:
+        """
+        导出报告
+
+        Args:
+            report_content: 报告内容
+            output_path: 输出路径
+            format_type: 格式类型
+
+        Returns:
+            bool: 导出是否成功
+        """
+        pass
+
+    def get_supported_formats(self) -> List[str]:
+        """
+        获取支持的格式列表
+
+        Returns:
+            List[str]: 支持的格式列表
+        """
+        return ["html", "json", "csv", "markdown"]
+
+    def validate_template(self, template_path: Path) -> bool:
+        """
+        验证模板文件
+
+        Args:
+            template_path: 模板文件路径
+
+        Returns:
+            bool: 模板是否有效
+        """
+        pass
+```
+
+## 📈 监控插件API
+
+### MonitorPlugin
+
+扩展监控能力的插件接口。
+
+```python
+from typing import Dict, List, Any, Optional
+from datetime import datetime
+
+class MonitorPlugin(BasePlugin):
+    """监控插件"""
+
+    def collect_metrics(self) -> Dict[str, Any]:
+        """
+        收集监控指标
+
+        Returns:
+            Dict[str, Any]: 监控指标数据
+        """
+        pass
+
+    def check_thresholds(self, metrics: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        检查阈值
+
+        Args:
+            metrics: 监控指标
+
+        Returns:
+            List[Dict[str, Any]]: 告警列表
+        """
+        pass
+
+    def trigger_alert(self, alert_data: Dict[str, Any]) -> bool:
+        """
+        触发告警
+
+        Args:
+            alert_data: 告警数据
+
+        Returns:
+            bool: 告警是否成功触发
+        """
+        pass
+
+    def get_metric_definition(self, metric_name: str) -> Optional[Dict[str, Any]]:
+        """
+        获取指标定义
+
+        Args:
+            metric_name: 指标名称
+
+        Returns:
+            Optional[Dict[str, Any]]: 指标定义
+        """
+        pass
+
+    def register_metric(self, metric_name: str,
+                       metric_config: Dict[str, Any]) -> bool:
+        """
+        注册新指标
+
+        Args:
+            metric_name: 指标名称
+            metric_config: 指标配置
+
+        Returns:
+            bool: 注册是否成功
+        """
+        pass
+
+    def start_monitoring(self) -> bool:
+        """
+        开始监控
+
+        Returns:
+            bool: 启动是否成功
+        """
+        pass
+
+    def stop_monitoring(self) -> bool:
+        """
+        停止监控
+
+        Returns:
+            bool: 停止是否成功
+        """
+        pass
+```
+
+## 💾 数据插件API
+
+### DataPlugin
+
+数据源和数据处理扩展插件接口。
+
+```python
+from typing import Iterator, List, Dict, Any, Optional
+
+class DataPlugin(BasePlugin):
+    """数据插件"""
+
+    def load_data(self, source_config: Dict[str, Any]) -> Iterator[Dict[str, Any]]:
+        """
+        加载数据
+
+        Args:
+            source_config: 数据源配置
+
+        Yields:
+            Dict[str, Any]: 数据记录
+        """
+        pass
+
+    def process_data(self, raw_data: Any) -> Any:
+        """
+        处理数据
+
+        Args:
+            raw_data: 原始数据
+
+        Returns:
+            Any: 处理后的数据
+        """
+        pass
+
+    def distribute_data(self, data: List[Any],
+                       strategy: str, node_count: int) -> List[List[Any]]:
+        """
+        分发数据
+
+        Args:
+            data: 数据列表
+            strategy: 分发策略 (round_robin, random, sequential)
+            node_count: 节点数量
+
+        Returns:
+            List[List[Any]]: 分发后的数据
+        """
+        pass
+
+    def validate_data(self, data: Any, schema: Dict[str, Any]) -> bool:
+        """
+        验证数据
+
+        Args:
+            data: 数据
+            schema: 数据模式
+
+        Returns:
+            bool: 数据是否有效
+        """
+        pass
+
+    def get_data_schema(self) -> Dict[str, Any]:
+        """
+        获取数据模式
+
+        Returns:
+            Dict[str, Any]: 数据模式定义
+        """
+        pass
+
+    def cache_data(self, key: str, data: Any, ttl: Optional[int] = None) -> bool:
+        """
+        缓存数据
+
+        Args:
+            key: 缓存键
+            data: 数据
+            ttl: 生存时间(秒)
+
+        Returns:
+            bool: 缓存是否成功
+        """
+        pass
+
+    def get_cached_data(self, key: str) -> Optional[Any]:
+        """
+        获取缓存数据
+
+        Args:
+            key: 缓存键
+
+        Returns:
+            Optional[Any]: 缓存的数据
+        """
+        pass
+```
+
+## 📢 通知插件API
+
+### NotificationPlugin
+
+通知渠道扩展插件接口。
+
+```python
+from typing import List, Dict, Any, Optional
+
+class NotificationPlugin(BasePlugin):
+    """通知插件"""
+
+    def send_notification(self, message: str,
+                         recipients: List[str], **kwargs) -> bool:
+        """
+        发送通知
+
+        Args:
+            message: 消息内容
+            recipients: 接收者列表
+            **kwargs: 额外参数
+
+        Returns:
+            bool: 发送是否成功
+        """
+        pass
+
+    def format_message(self, data: Dict[str, Any],
+                      template: str) -> str:
+        """
+        格式化消息
+
+        Args:
+            data: 数据字典
+            template: 消息模板
+
+        Returns:
+            str: 格式化后的消息
+        """
+        pass
+
+    def validate_config(self, config: Dict[str, Any]) -> bool:
+        """
+        验证配置
+
+        Args:
+            config: 配置字典
+
+        Returns:
+            bool: 配置是否有效
+        """
+        pass
+
+    def test_connection(self) -> bool:
+        """
+        测试连接
+
+        Returns:
+            bool: 连接是否正常
+        """
+        pass
+
+    def get_supported_formats(self) -> List[str]:
+        """
+        获取支持的消息格式
+
+        Returns:
+            List[str]: 支持的格式列表
+        """
+        return ["text", "markdown", "html"]
+
+    def send_rich_message(self, title: str, content: str,
+                         message_type: str, recipients: List[str]) -> bool:
+        """
+        发送富文本消息
+
+        Args:
+            title: 消息标题
+            content: 消息内容
+            message_type: 消息类型 (info, warning, error)
+            recipients: 接收者列表
+
+        Returns:
+            bool: 发送是否成功
+        """
+        pass
+```
+
+## 🔧 插件管理API
+
+### PluginManager
+
+插件管理器API，用于插件的注册、加载和管理。
+
+```python
+class PluginManager:
+    """插件管理器"""
+
+    def register_plugin(self, plugin_class: type,
+                       plugin_config: Optional[Dict[str, Any]] = None) -> bool:
+        """
+        注册插件
+
+        Args:
+            plugin_class: 插件类
+            plugin_config: 插件配置
+
+        Returns:
+            bool: 注册是否成功
+        """
+        pass
+
+    def load_plugin(self, plugin_name: str) -> Optional[BasePlugin]:
+        """
+        加载插件
+
+        Args:
+            plugin_name: 插件名称
+
+        Returns:
+            Optional[BasePlugin]: 插件实例
+        """
+        pass
+
+    def unload_plugin(self, plugin_name: str) -> bool:
+        """
+        卸载插件
+
+        Args:
+            plugin_name: 插件名称
+
+        Returns:
+            bool: 卸载是否成功
+        """
+        pass
+
+    def get_plugin(self, plugin_name: str) -> Optional[BasePlugin]:
+        """
+        获取插件实例
+
+        Args:
+            plugin_name: 插件名称
+
+        Returns:
+            Optional[BasePlugin]: 插件实例
+        """
+        pass
+
+    def list_plugins(self, plugin_type: Optional[str] = None) -> List[Dict[str, Any]]:
+        """
+        列出插件
+
+        Args:
+            plugin_type: 插件类型过滤
+
+        Returns:
+            List[Dict[str, Any]]: 插件信息列表
+        """
+        pass
+
+    def enable_plugin(self, plugin_name: str) -> bool:
+        """
+        启用插件
+
+        Args:
+            plugin_name: 插件名称
+
+        Returns:
+            bool: 启用是否成功
+        """
+        pass
+
+    def disable_plugin(self, plugin_name: str) -> bool:
+        """
+        禁用插件
+
+        Args:
+            plugin_name: 插件名称
+
+        Returns:
+            bool: 禁用是否成功
+        """
+        pass
+```
+
+## 📚 使用示例
+
+### 创建自定义Locust插件
+
+```python
+class CustomLocustPlugin(LocustPlugin):
+    def initialize(self) -> bool:
+        self.logger.info("Custom Locust plugin initialized")
+        return True
+
+    def cleanup(self) -> None:
+        self.logger.info("Custom Locust plugin cleaned up")
+
+    def on_test_start(self, environment, **kwargs):
+        self.logger.info("Test started with custom plugin")
+
+    def on_request_success(self, request_type, name, response_time, response_length, **kwargs):
+        if response_time > 1000:  # 响应时间超过1秒
+            self.logger.warning(f"Slow request detected: {name} took {response_time}ms")
+
+# 注册插件
+plugin_manager.register_plugin(CustomLocustPlugin, {
+    "log_level": "INFO",
+    "slow_request_threshold": 1000
+})
+```
+
+## 📚 相关文档
+
+- [插件开发指南](../development/plugin-development.md) - 详细的插件开发教程
+- [插件系统架构](../architecture/plugin-system.md) - 插件系统设计
+- [插件配置](../configuration/plugin-config.md) - 插件配置说明
+- [示例代码](../examples/advanced-usage.md) - 插件使用示例
diff --git a/docs/api/utilities.md b/docs/api/utilities.md
new file mode 100644
index 0000000..fccc029
--- /dev/null
+++ b/docs/api/utilities.md
@@ -0,0 +1,933 @@
+# 工具类API
+
+本文档详细介绍Locust性能测试框架的工具类和辅助函数API。
+
+## 📋 API概览
+
+工具类API提供了丰富的辅助功能，包括：
+
+- **文件操作工具**: 文件读写、路径处理
+- **报告生成工具**: 报告格式化和生成
+- **日志管理工具**: 日志配置和管理
+- **集合点工具**: 用户同步和集合点
+- **通用工具函数**: 常用的辅助函数
+
+## 📁 文件操作工具
+
+### FileOperationUtil
+
+文件操作工具类，提供文件和目录的常用操作。
+
+```python
+import os
+import json
+import csv
+import yaml
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+
+class FileOperationUtil:
+    """文件操作工具类"""
+
+    @staticmethod
+    def read_file(file_path: str, encoding: str = 'utf-8') -> str:
+        """
+        读取文件内容
+
+        Args:
+            file_path: 文件路径
+            encoding: 文件编码
+
+        Returns:
+            str: 文件内容
+
+        Raises:
+            FileNotFoundError: 文件不存在
+            IOError: 读取失败
+        """
+        try:
+            with open(file_path, 'r', encoding=encoding) as file:
+                return file.read()
+        except FileNotFoundError:
+            raise FileNotFoundError(f"File not found: {file_path}")
+        except Exception as e:
+            raise IOError(f"Failed to read file {file_path}: {e}")
+
+    @staticmethod
+    def write_file(file_path: str, content: str, encoding: str = 'utf-8') -> bool:
+        """
+        写入文件内容
+
+        Args:
+            file_path: 文件路径
+            content: 文件内容
+            encoding: 文件编码
+
+        Returns:
+            bool: 写入是否成功
+        """
+        try:
+            # 确保目录存在
+            FileOperationUtil.ensure_dir(os.path.dirname(file_path))
+
+            with open(file_path, 'w', encoding=encoding) as file:
+                file.write(content)
+            return True
+        except Exception as e:
+            print(f"Failed to write file {file_path}: {e}")
+            return False
+
+    @staticmethod
+    def read_json(file_path: str) -> Dict[str, Any]:
+        """
+        读取JSON文件
+
+        Args:
+            file_path: JSON文件路径
+
+        Returns:
+            Dict[str, Any]: JSON数据
+        """
+        try:
+            with open(file_path, 'r', encoding='utf-8') as file:
+                return json.load(file)
+        except Exception as e:
+            raise ValueError(f"Failed to read JSON file {file_path}: {e}")
+
+    @staticmethod
+    def write_json(file_path: str, data: Dict[str, Any], indent: int = 2) -> bool:
+        """
+        写入JSON文件
+
+        Args:
+            file_path: JSON文件路径
+            data: 要写入的数据
+            indent: 缩进空格数
+
+        Returns:
+            bool: 写入是否成功
+        """
+        try:
+            FileOperationUtil.ensure_dir(os.path.dirname(file_path))
+
+            with open(file_path, 'w', encoding='utf-8') as file:
+                json.dump(data, file, indent=indent, ensure_ascii=False)
+            return True
+        except Exception as e:
+            print(f"Failed to write JSON file {file_path}: {e}")
+            return False
+
+    @staticmethod
+    def read_csv(file_path: str, delimiter: str = ',') -> List[Dict[str, str]]:
+        """
+        读取CSV文件
+
+        Args:
+            file_path: CSV文件路径
+            delimiter: 分隔符
+
+        Returns:
+            List[Dict[str, str]]: CSV数据列表
+        """
+        try:
+            data = []
+            with open(file_path, 'r', encoding='utf-8') as file:
+                reader = csv.DictReader(file, delimiter=delimiter)
+                data = list(reader)
+            return data
+        except Exception as e:
+            raise ValueError(f"Failed to read CSV file {file_path}: {e}")
+
+    @staticmethod
+    def write_csv(file_path: str, data: List[Dict[str, Any]],
+                  fieldnames: Optional[List[str]] = None) -> bool:
+        """
+        写入CSV文件
+
+        Args:
+            file_path: CSV文件路径
+            data: 要写入的数据
+            fieldnames: 字段名列表
+
+        Returns:
+            bool: 写入是否成功
+        """
+        try:
+            if not data:
+                return False
+
+            FileOperationUtil.ensure_dir(os.path.dirname(file_path))
+
+            if fieldnames is None:
+                fieldnames = list(data[0].keys())
+
+            with open(file_path, 'w', newline='', encoding='utf-8') as file:
+                writer = csv.DictWriter(file, fieldnames=fieldnames)
+                writer.writeheader()
+                writer.writerows(data)
+            return True
+        except Exception as e:
+            print(f"Failed to write CSV file {file_path}: {e}")
+            return False
+
+    @staticmethod
+    def read_yaml(file_path: str) -> Dict[str, Any]:
+        """
+        读取YAML文件
+
+        Args:
+            file_path: YAML文件路径
+
+        Returns:
+            Dict[str, Any]: YAML数据
+        """
+        try:
+            with open(file_path, 'r', encoding='utf-8') as file:
+                return yaml.safe_load(file)
+        except Exception as e:
+            raise ValueError(f"Failed to read YAML file {file_path}: {e}")
+
+    @staticmethod
+    def write_yaml(file_path: str, data: Dict[str, Any]) -> bool:
+        """
+        写入YAML文件
+
+        Args:
+            file_path: YAML文件路径
+            data: 要写入的数据
+
+        Returns:
+            bool: 写入是否成功
+        """
+        try:
+            FileOperationUtil.ensure_dir(os.path.dirname(file_path))
+
+            with open(file_path, 'w', encoding='utf-8') as file:
+                yaml.dump(data, file, default_flow_style=False,
+                         allow_unicode=True, indent=2)
+            return True
+        except Exception as e:
+            print(f"Failed to write YAML file {file_path}: {e}")
+            return False
+
+    @staticmethod
+    def ensure_dir(dir_path: str) -> bool:
+        """
+        确保目录存在
+
+        Args:
+            dir_path: 目录路径
+
+        Returns:
+            bool: 操作是否成功
+        """
+        try:
+            if dir_path and not os.path.exists(dir_path):
+                os.makedirs(dir_path, exist_ok=True)
+            return True
+        except Exception as e:
+            print(f"Failed to create directory {dir_path}: {e}")
+            return False
+
+    @staticmethod
+    def list_files(dir_path: str, pattern: str = "*", recursive: bool = False) -> List[str]:
+        """
+        列出目录中的文件
+
+        Args:
+            dir_path: 目录路径
+            pattern: 文件模式
+            recursive: 是否递归搜索
+
+        Returns:
+            List[str]: 文件路径列表
+        """
+        try:
+            path = Path(dir_path)
+            if recursive:
+                return [str(p) for p in path.rglob(pattern) if p.is_file()]
+            else:
+                return [str(p) for p in path.glob(pattern) if p.is_file()]
+        except Exception as e:
+            print(f"Failed to list files in {dir_path}: {e}")
+            return []
+
+    @staticmethod
+    def get_file_size(file_path: str) -> int:
+        """
+        获取文件大小
+
+        Args:
+            file_path: 文件路径
+
+        Returns:
+            int: 文件大小(字节)
+        """
+        try:
+            return os.path.getsize(file_path)
+        except Exception:
+            return 0
+
+    @staticmethod
+    def file_exists(file_path: str) -> bool:
+        """
+        检查文件是否存在
+
+        Args:
+            file_path: 文件路径
+
+        Returns:
+            bool: 文件是否存在
+        """
+        return os.path.isfile(file_path)
+
+    @staticmethod
+    def dir_exists(dir_path: str) -> bool:
+        """
+        检查目录是否存在
+
+        Args:
+            dir_path: 目录路径
+
+        Returns:
+            bool: 目录是否存在
+        """
+        return os.path.isdir(dir_path)
+```
+
+## 📊 报告生成工具
+
+### LocustReportUtil
+
+Locust报告生成和处理工具。
+
+```python
+import time
+from datetime import datetime
+from typing import Dict, List, Any, Optional
+
+class LocustReportUtil:
+    """Locust报告工具类"""
+
+    @staticmethod
+    def generate_html_report(stats_data: Dict[str, Any],
+                           output_path: str = "report.html") -> bool:
+        """
+        生成HTML报告
+
+        Args:
+            stats_data: 统计数据
+            output_path: 输出路径
+
+        Returns:
+            bool: 生成是否成功
+        """
+        try:
+            html_content = LocustReportUtil._create_html_template(stats_data)
+            return FileOperationUtil.write_file(output_path, html_content)
+        except Exception as e:
+            print(f"Failed to generate HTML report: {e}")
+            return False
+
+    @staticmethod
+    def generate_csv_report(stats_data: Dict[str, Any],
+                          output_path: str = "report.csv") -> bool:
+        """
+        生成CSV报告
+
+        Args:
+            stats_data: 统计数据
+            output_path: 输出路径
+
+        Returns:
+            bool: 生成是否成功
+        """
+        try:
+            csv_data = LocustReportUtil._convert_to_csv_format(stats_data)
+            return FileOperationUtil.write_csv(output_path, csv_data)
+        except Exception as e:
+            print(f"Failed to generate CSV report: {e}")
+            return False
+
+    @staticmethod
+    def generate_json_report(stats_data: Dict[str, Any],
+                           output_path: str = "report.json") -> bool:
+        """
+        生成JSON报告
+
+        Args:
+            stats_data: 统计数据
+            output_path: 输出路径
+
+        Returns:
+            bool: 生成是否成功
+        """
+        try:
+            json_data = LocustReportUtil._format_json_report(stats_data)
+            return FileOperationUtil.write_json(output_path, json_data)
+        except Exception as e:
+            print(f"Failed to generate JSON report: {e}")
+            return False
+
+    @staticmethod
+    def parse_locust_stats(stats_file: str) -> Dict[str, Any]:
+        """
+        解析Locust统计文件
+
+        Args:
+            stats_file: 统计文件路径
+
+        Returns:
+            Dict[str, Any]: 解析后的统计数据
+        """
+        try:
+            if stats_file.endswith('.csv'):
+                raw_data = FileOperationUtil.read_csv(stats_file)
+                return LocustReportUtil._process_csv_stats(raw_data)
+            elif stats_file.endswith('.json'):
+                return FileOperationUtil.read_json(stats_file)
+            else:
+                raise ValueError(f"Unsupported file format: {stats_file}")
+        except Exception as e:
+            print(f"Failed to parse stats file {stats_file}: {e}")
+            return {}
+
+    @staticmethod
+    def calculate_percentiles(response_times: List[float],
+                            percentiles: List[int] = [50, 90, 95, 99]) -> Dict[str, float]:
+        """
+        计算响应时间百分位数
+
+        Args:
+            response_times: 响应时间列表
+            percentiles: 百分位数列表
+
+        Returns:
+            Dict[str, float]: 百分位数结果
+        """
+        if not response_times:
+            return {f"p{p}": 0.0 for p in percentiles}
+
+        sorted_times = sorted(response_times)
+        result = {}
+
+        for p in percentiles:
+            index = int((p / 100.0) * len(sorted_times))
+            if index >= len(sorted_times):
+                index = len(sorted_times) - 1
+            result[f"p{p}"] = sorted_times[index]
+
+        return result
+
+    @staticmethod
+    def format_duration(seconds: float) -> str:
+        """
+        格式化持续时间
+
+        Args:
+            seconds: 秒数
+
+        Returns:
+            str: 格式化的时间字符串
+        """
+        if seconds < 60:
+            return f"{seconds:.1f}s"
+        elif seconds < 3600:
+            minutes = seconds / 60
+            return f"{minutes:.1f}m"
+        else:
+            hours = seconds / 3600
+            return f"{hours:.1f}h"
+
+    @staticmethod
+    def format_size(bytes_size: int) -> str:
+        """
+        格式化文件大小
+
+        Args:
+            bytes_size: 字节大小
+
+        Returns:
+            str: 格式化的大小字符串
+        """
+        for unit in ['B', 'KB', 'MB', 'GB']:
+            if bytes_size < 1024.0:
+                return f"{bytes_size:.1f}{unit}"
+            bytes_size /= 1024.0
+        return f"{bytes_size:.1f}TB"
+
+    @staticmethod
+    def _create_html_template(stats_data: Dict[str, Any]) -> str:
+        """创建HTML报告模板"""
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        html = f"""
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <title>Locust Performance Test Report</title>
+            <meta charset="utf-8">
+            <style>
+                body {{ font-family: Arial, sans-serif; margin: 20px; }}
+                .header {{ background-color: #f0f0f0; padding: 20px; border-radius: 5px; }}
+                .stats-table {{ width: 100%; border-collapse: collapse; margin: 20px 0; }}
+                .stats-table th, .stats-table td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
+                .stats-table th {{ background-color: #f2f2f2; }}
+                .summary {{ display: flex; justify-content: space-between; margin: 20px 0; }}
+                .summary-item {{ background-color: #e9ecef; padding: 15px; border-radius: 5px; text-align: center; }}
+            </style>
+        </head>
+        <body>
+            <div class="header">
+                <h1>Performance Test Report</h1>
+                <p>Generated at: {timestamp}</p>
+            </div>
+
+            <div class="summary">
+                <div class="summary-item">
+                    <h3>Total Requests</h3>
+                    <p>{stats_data.get('total_requests', 0)}</p>
+                </div>
+                <div class="summary-item">
+                    <h3>Failed Requests</h3>
+                    <p>{stats_data.get('failed_requests', 0)}</p>
+                </div>
+                <div class="summary-item">
+                    <h3>Average Response Time</h3>
+                    <p>{stats_data.get('avg_response_time', 0):.2f}ms</p>
+                </div>
+                <div class="summary-item">
+                    <h3>RPS</h3>
+                    <p>{stats_data.get('rps', 0):.2f}</p>
+                </div>
+            </div>
+
+            <!-- 更多报告内容 -->
+        </body>
+        </html>
+        """
+        return html
+
+    @staticmethod
+    def _convert_to_csv_format(stats_data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """转换为CSV格式"""
+        # 实现统计数据到CSV格式的转换
+        return []
+
+    @staticmethod
+    def _format_json_report(stats_data: Dict[str, Any]) -> Dict[str, Any]:
+        """格式化JSON报告"""
+        return {
+            "timestamp": datetime.now().isoformat(),
+            "summary": stats_data,
+            "version": "1.0"
+        }
+
+    @staticmethod
+    def _process_csv_stats(raw_data: List[Dict[str, str]]) -> Dict[str, Any]:
+        """处理CSV统计数据"""
+        # 实现CSV数据的处理逻辑
+        return {}
+```
+
+## 📝 日志管理工具
+
+### LogUtil
+
+日志配置和管理工具。
+
+```python
+import logging
+import logging.handlers
+from typing import Optional
+
+class LogUtil:
+    """日志工具类"""
+
+    @staticmethod
+    def setup_logger(name: str, log_file: Optional[str] = None,
+                    level: int = logging.INFO,
+                    format_string: Optional[str] = None) -> logging.Logger:
+        """
+        设置日志记录器
+
+        Args:
+            name: 日志记录器名称
+            log_file: 日志文件路径
+            level: 日志级别
+            format_string: 日志格式字符串
+
+        Returns:
+            logging.Logger: 配置好的日志记录器
+        """
+        logger = logging.getLogger(name)
+        logger.setLevel(level)
+
+        # 避免重复添加处理器
+        if logger.handlers:
+            return logger
+
+        # 默认格式
+        if format_string is None:
+            format_string = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+
+        formatter = logging.Formatter(format_string)
+
+        # 控制台处理器
+        console_handler = logging.StreamHandler()
+        console_handler.setLevel(level)
+        console_handler.setFormatter(formatter)
+        logger.addHandler(console_handler)
+
+        # 文件处理器
+        if log_file:
+            FileOperationUtil.ensure_dir(os.path.dirname(log_file))
+            file_handler = logging.handlers.RotatingFileHandler(
+                log_file, maxBytes=10*1024*1024, backupCount=5, encoding='utf-8'
+            )
+            file_handler.setLevel(level)
+            file_handler.setFormatter(formatter)
+            logger.addHandler(file_handler)
+
+        return logger
+
+    @staticmethod
+    def get_logger(name: str) -> logging.Logger:
+        """
+        获取日志记录器
+
+        Args:
+            name: 日志记录器名称
+
+        Returns:
+            logging.Logger: 日志记录器
+        """
+        return logging.getLogger(name)
+
+    @staticmethod
+    def set_log_level(logger_name: str, level: int) -> None:
+        """
+        设置日志级别
+
+        Args:
+            logger_name: 日志记录器名称
+            level: 日志级别
+        """
+        logger = logging.getLogger(logger_name)
+        logger.setLevel(level)
+
+        for handler in logger.handlers:
+            handler.setLevel(level)
+```
+
+## 🔄 集合点工具
+
+### RendezvousUtil
+
+用户同步和集合点工具。
+
+```python
+import threading
+import time
+from typing import Optional
+
+class RendezvousUtil:
+    """集合点工具类"""
+
+    def __init__(self, name: str, expected_users: int, timeout: int = 60):
+        """
+        初始化集合点
+
+        Args:
+            name: 集合点名称
+            expected_users: 期望的用户数
+            timeout: 超时时间(秒)
+        """
+        self.name = name
+        self.expected_users = expected_users
+        self.timeout = timeout
+        self.arrived_users = 0
+        self.lock = threading.Lock()
+        self.condition = threading.Condition(self.lock)
+        self.start_time = None
+
+    def wait(self, user_id: Optional[str] = None) -> bool:
+        """
+        等待集合点
+
+        Args:
+            user_id: 用户ID
+
+        Returns:
+            bool: 是否成功通过集合点
+        """
+        with self.condition:
+            if self.start_time is None:
+                self.start_time = time.time()
+
+            self.arrived_users += 1
+            current_users = self.arrived_users
+
+            print(f"User {user_id or 'unknown'} arrived at rendezvous '{self.name}' "
+                  f"({current_users}/{self.expected_users})")
+
+            if current_users >= self.expected_users:
+                # 所有用户都到达，释放所有等待的用户
+                self.condition.notify_all()
+                return True
+            else:
+                # 等待其他用户到达
+                elapsed = time.time() - self.start_time
+                remaining_timeout = self.timeout - elapsed
+
+                if remaining_timeout <= 0:
+                    print(f"Rendezvous '{self.name}' timeout")
+                    return False
+
+                return self.condition.wait(timeout=remaining_timeout)
+
+    def reset(self) -> None:
+        """重置集合点"""
+        with self.condition:
+            self.arrived_users = 0
+            self.start_time = None
+
+    def get_status(self) -> Dict[str, Any]:
+        """
+        获取集合点状态
+
+        Returns:
+            Dict[str, Any]: 状态信息
+        """
+        with self.lock:
+            elapsed = time.time() - self.start_time if self.start_time else 0
+            return {
+                "name": self.name,
+                "expected_users": self.expected_users,
+                "arrived_users": self.arrived_users,
+                "elapsed_time": elapsed,
+                "timeout": self.timeout,
+                "is_complete": self.arrived_users >= self.expected_users
+            }
+
+# 全局集合点管理器
+class RendezvousManager:
+    """集合点管理器"""
+
+    def __init__(self):
+        self._rendezvous = {}
+        self._lock = threading.Lock()
+
+    def create_rendezvous(self, name: str, expected_users: int,
+                         timeout: int = 60) -> RendezvousUtil:
+        """
+        创建集合点
+
+        Args:
+            name: 集合点名称
+            expected_users: 期望用户数
+            timeout: 超时时间
+
+        Returns:
+            RendezvousUtil: 集合点对象
+        """
+        with self._lock:
+            if name in self._rendezvous:
+                return self._rendezvous[name]
+
+            rendezvous = RendezvousUtil(name, expected_users, timeout)
+            self._rendezvous[name] = rendezvous
+            return rendezvous
+
+    def get_rendezvous(self, name: str) -> Optional[RendezvousUtil]:
+        """
+        获取集合点
+
+        Args:
+            name: 集合点名称
+
+        Returns:
+            Optional[RendezvousUtil]: 集合点对象
+        """
+        with self._lock:
+            return self._rendezvous.get(name)
+
+    def remove_rendezvous(self, name: str) -> bool:
+        """
+        移除集合点
+
+        Args:
+            name: 集合点名称
+
+        Returns:
+            bool: 移除是否成功
+        """
+        with self._lock:
+            if name in self._rendezvous:
+                del self._rendezvous[name]
+                return True
+            return False
+
+# 全局实例
+rendezvous_manager = RendezvousManager()
+```
+
+## 🛠️ 通用工具函数
+
+### CommonUtil
+
+通用工具函数集合。
+
+```python
+import random
+import string
+import hashlib
+import uuid
+from typing import Any, List, Dict
+
+class CommonUtil:
+    """通用工具类"""
+
+    @staticmethod
+    def generate_random_string(length: int = 10,
+                             chars: str = string.ascii_letters + string.digits) -> str:
+        """
+        生成随机字符串
+
+        Args:
+            length: 字符串长度
+            chars: 字符集
+
+        Returns:
+            str: 随机字符串
+        """
+        return ''.join(random.choice(chars) for _ in range(length))
+
+    @staticmethod
+    def generate_uuid() -> str:
+        """
+        生成UUID
+
+        Returns:
+            str: UUID字符串
+        """
+        return str(uuid.uuid4())
+
+    @staticmethod
+    def calculate_md5(text: str) -> str:
+        """
+        计算MD5哈希值
+
+        Args:
+            text: 输入文本
+
+        Returns:
+            str: MD5哈希值
+        """
+        return hashlib.md5(text.encode('utf-8')).hexdigest()
+
+    @staticmethod
+    def safe_get(data: Dict[str, Any], key: str, default: Any = None) -> Any:
+        """
+        安全获取字典值
+
+        Args:
+            data: 字典
+            key: 键名
+            default: 默认值
+
+        Returns:
+            Any: 字典值或默认值
+        """
+        return data.get(key, default)
+
+    @staticmethod
+    def retry_on_exception(func, max_retries: int = 3, delay: float = 1.0):
+        """
+        异常重试装饰器
+
+        Args:
+            func: 要重试的函数
+            max_retries: 最大重试次数
+            delay: 重试延迟
+
+        Returns:
+            Any: 函数返回值
+        """
+        for attempt in range(max_retries + 1):
+            try:
+                return func()
+            except Exception as e:
+                if attempt == max_retries:
+                    raise e
+                time.sleep(delay)
+
+    @staticmethod
+    def format_number(number: float, precision: int = 2) -> str:
+        """
+        格式化数字
+
+        Args:
+            number: 数字
+            precision: 精度
+
+        Returns:
+            str: 格式化的数字字符串
+        """
+        return f"{number:.{precision}f}"
+
+    @staticmethod
+    def merge_dicts(*dicts: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        合并多个字典
+
+        Args:
+            *dicts: 字典列表
+
+        Returns:
+            Dict[str, Any]: 合并后的字典
+        """
+        result = {}
+        for d in dicts:
+            if d:
+                result.update(d)
+        return result
+```
+
+## 📚 使用示例
+
+### 综合使用示例
+
+```python
+from src.utils.file_operation import FileOperationUtil
+from src.utils.locust_report import LocustReportUtil
+from src.utils.log_moudle import LogUtil
+from src.utils.rendezvous import rendezvous_manager
+from src.utils.util import CommonUtil
+
+# 设置日志
+logger = LogUtil.setup_logger("test_logger", "logs/test.log")
+
+# 读取配置文件
+config = FileOperationUtil.read_yaml("config/test_config.yaml")
+
+# 创建集合点
+rendezvous = rendezvous_manager.create_rendezvous("login_sync", 10, 30)
+
+# 生成测试数据
+test_id = CommonUtil.generate_uuid()
+random_data = CommonUtil.generate_random_string(20)
+
+# 生成报告
+stats_data = {"total_requests": 1000, "failed_requests": 5}
+LocustReportUtil.generate_html_report(stats_data, "reports/test_report.html")
+
+logger.info(f"Test {test_id} completed with data: {random_data}")
+```
+
+## 📚 相关文档
+
+- [文件操作示例](../examples/basic-examples.md) - 文件操作使用示例
+- [报告生成指南](../examples/advanced-usage.md) - 报告生成详细说明
+- [日志配置](../configuration/framework-config.md) - 日志系统配置
+- [开发指南](../development/setup.md) - 开发环境搭建
diff --git a/docs/architecture/data-flow.md b/docs/architecture/data-flow.md
new file mode 100644
index 0000000..6cc4ef9
--- /dev/null
+++ b/docs/architecture/data-flow.md
@@ -0,0 +1,407 @@
+# 数据流架构
+
+本文档详细介绍Locust性能测试框架中数据的处理和流转机制。
+
+## 🎯 数据流概览
+
+框架中的数据流可以分为以下几个主要阶段：
+
+```
+数据输入 → 数据处理 → 数据分析 → 数据输出
+   ↓         ↓         ↓         ↓
+测试数据   性能指标   分析结果   报告生成
+   ↓         ↓         ↓         ↓
+数据源     实时监控   趋势分析   通知告警
+```
+
+## 📊 数据类型分类
+
+### 1. 输入数据 (Input Data)
+
+#### 测试配置数据
+- **来源**: 配置文件、命令行参数、环境变量
+- **格式**: YAML、JSON、命令行参数
+- **内容**: 测试参数、用户配置、负载设置
+
+```yaml
+# 示例配置数据
+test_config:
+  users: 100
+  spawn_rate: 10
+  run_time: "5m"
+  host: "https://api.example.com"
+```
+
+#### 测试数据
+- **来源**: CSV文件、数据库、API接口、数据生成器
+- **格式**: CSV、JSON、XML、数据库记录
+- **内容**: 用户数据、业务数据、参数化数据
+
+```csv
+# 示例CSV数据
+username,password,user_id
+user1,pass1,1001
+user2,pass2,1002
+user3,pass3,1003
+```
+
+#### 脚本数据
+- **来源**: Python测试脚本
+- **格式**: Python代码
+- **内容**: 测试逻辑、业务流程、断言规则
+
+### 2. 运行时数据 (Runtime Data)
+
+#### 性能指标数据
+- **响应时间**: 请求响应时间统计
+- **吞吐量**: TPS、RPS等吞吐量指标
+- **错误率**: 错误请求统计和分类
+- **并发数**: 实时用户数和连接数
+
+```json
+{
+  "timestamp": "2024-01-01T10:00:00Z",
+  "response_time": 150.5,
+  "status_code": 200,
+  "request_name": "GET /api/users",
+  "user_id": "user_001",
+  "error": null
+}
+```
+
+#### 系统监控数据
+- **CPU使用率**: 系统和进程CPU占用
+- **内存使用**: 内存占用和垃圾回收
+- **网络IO**: 网络带宽和连接状态
+- **磁盘IO**: 磁盘读写性能
+
+#### 事件数据
+- **测试事件**: 测试开始、结束、用户变化
+- **系统事件**: 错误、警告、状态变化
+- **用户事件**: 用户行为、操作记录
+
+### 3. 输出数据 (Output Data)
+
+#### 分析结果
+- **性能评估**: A-D等级评分
+- **趋势分析**: 性能趋势和预测
+- **瓶颈识别**: 性能瓶颈定位
+- **建议报告**: 优化建议和改进方案
+
+#### 报告数据
+- **HTML报告**: 可视化性能报告
+- **CSV数据**: 原始数据导出
+- **JSON格式**: API集成数据
+- **图表数据**: 图表和可视化数据
+
+## 🔄 数据流转过程
+
+### 1. 数据采集阶段
+
+```
+┌─────────────┐    ┌─────────────┐    ┌─────────────┐
+│  配置文件   │    │  测试脚本   │    │  数据源     │
+└──────┬──────┘    └──────┬──────┘    └──────┬──────┘
+       │                  │                  │
+       └──────────────────┼──────────────────┘
+                          │
+                   ┌──────▼──────┐
+                   │  数据采集器  │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  数据验证器  │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  数据缓存   │
+                   └─────────────┘
+```
+
+**主要组件**:
+- **配置加载器**: 加载和解析配置文件
+- **数据源连接器**: 连接各种数据源
+- **数据验证器**: 验证数据格式和完整性
+- **数据缓存**: 缓存常用数据提高性能
+
+### 2. 数据处理阶段
+
+```
+┌─────────────┐    ┌─────────────┐    ┌─────────────┐
+│  原始数据   │    │  数据清洗   │    │  数据转换   │
+└──────┬──────┘    └──────┬──────┘    └──────┬──────┘
+       │                  │                  │
+       └──────────────────┼──────────────────┘
+                          │
+                   ┌──────▼──────┐
+                   │  数据处理器  │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  数据聚合器  │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  处理结果   │
+                   └─────────────┘
+```
+
+**处理步骤**:
+1. **数据清洗**: 去除无效数据、处理异常值
+2. **数据转换**: 格式转换、单位统一
+3. **数据聚合**: 统计计算、指标汇总
+4. **数据标准化**: 统一数据格式和结构
+
+### 3. 数据分析阶段
+
+```
+┌─────────────┐    ┌─────────────┐    ┌─────────────┐
+│  性能指标   │    │  统计分析   │    │  趋势分析   │
+└──────┬──────┘    └──────┬──────┘    └──────┬──────┘
+       │                  │                  │
+       └──────────────────┼──────────────────┘
+                          │
+                   ┌──────▼──────┐
+                   │  分析引擎   │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  评估算法   │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  分析结果   │
+                   └─────────────┘
+```
+
+**分析维度**:
+- **响应时间分析**: P50/P90/P95/P99百分位数
+- **吞吐量分析**: TPS趋势和峰值分析
+- **错误率分析**: 错误分布和根因分析
+- **稳定性分析**: 性能波动和稳定性评估
+
+### 4. 数据输出阶段
+
+```
+┌─────────────┐    ┌─────────────┐    ┌─────────────┐
+│  分析结果   │    │  报告生成   │    │  数据导出   │
+└──────┬──────┘    └──────┬──────┘    └──────┬──────┘
+       │                  │                  │
+       └──────────────────┼──────────────────┘
+                          │
+                   ┌──────▼──────┐
+                   │  输出管理器  │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  格式转换器  │
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │  最终输出   │
+                   └─────────────┘
+```
+
+**输出格式**:
+- **HTML报告**: 交互式Web报告
+- **PDF报告**: 可打印的PDF文档
+- **CSV数据**: 原始数据表格
+- **JSON API**: 程序化接口数据
+
+## 🔧 数据管理组件
+
+### 1. 数据提供器 (DataProvider)
+
+**职责**: 统一的数据访问接口
+
+```python
+class DataProvider:
+    def get_test_data(self, data_type, filters=None):
+        """获取测试数据"""
+        pass
+
+    def get_config_data(self, config_key):
+        """获取配置数据"""
+        pass
+
+    def get_runtime_data(self, metric_name, time_range):
+        """获取运行时数据"""
+        pass
+```
+
+### 2. 数据生成器 (DataGenerator)
+
+**职责**: 动态生成测试数据
+
+```python
+class DataGenerator:
+    def generate_user_data(self, count, pattern):
+        """生成用户数据"""
+        pass
+
+    def generate_business_data(self, schema, count):
+        """生成业务数据"""
+        pass
+
+    def generate_random_data(self, data_type, constraints):
+        """生成随机数据"""
+        pass
+```
+
+### 3. 数据分发器 (DataDistributor)
+
+**职责**: 在分布式环境中分发数据
+
+```python
+class DataDistributor:
+    def distribute_data(self, data, strategy, nodes):
+        """分发数据到各节点"""
+        pass
+
+    def sync_data(self, source_node, target_nodes):
+        """同步数据"""
+        pass
+
+    def balance_load(self, data_load, available_nodes):
+        """负载均衡"""
+        pass
+```
+
+### 4. 数据存储器 (DataStorage)
+
+**职责**: 数据持久化和检索
+
+```python
+class DataStorage:
+    def store_metrics(self, metrics_data):
+        """存储性能指标"""
+        pass
+
+    def store_results(self, test_results):
+        """存储测试结果"""
+        pass
+
+    def retrieve_history(self, query_params):
+        """检索历史数据"""
+        pass
+```
+
+## 📈 实时数据流
+
+### 1. 实时监控流
+
+```
+测试执行 → 指标收集 → 实时分析 → 告警检查 → 通知发送
+    ↓         ↓         ↓         ↓         ↓
+  事件流   指标流    分析流    告警流    通知流
+```
+
+### 2. 数据流管道
+
+```python
+# 数据流管道示例
+class DataPipeline:
+    def __init__(self):
+        self.collectors = []
+        self.processors = []
+        self.analyzers = []
+        self.outputs = []
+
+    def add_collector(self, collector):
+        self.collectors.append(collector)
+
+    def add_processor(self, processor):
+        self.processors.append(processor)
+
+    def process_data(self, raw_data):
+        # 数据收集
+        collected_data = self._collect_data(raw_data)
+
+        # 数据处理
+        processed_data = self._process_data(collected_data)
+
+        # 数据分析
+        analyzed_data = self._analyze_data(processed_data)
+
+        # 数据输出
+        return self._output_data(analyzed_data)
+```
+
+## 🔄 数据同步机制
+
+### 1. 分布式数据同步
+
+在分布式测试环境中，需要确保各节点间的数据一致性：
+
+```python
+class DataSynchronizer:
+    def sync_test_data(self, master_node, worker_nodes):
+        """同步测试数据"""
+        pass
+
+    def sync_config(self, config_data, target_nodes):
+        """同步配置数据"""
+        pass
+
+    def collect_results(self, worker_nodes):
+        """收集各节点结果"""
+        pass
+```
+
+### 2. 数据一致性保证
+
+- **版本控制**: 数据版本管理和冲突解决
+- **校验机制**: 数据完整性和一致性校验
+- **恢复机制**: 数据丢失和损坏恢复
+
+## 📊 数据质量保证
+
+### 1. 数据验证
+
+```python
+class DataValidator:
+    def validate_format(self, data, schema):
+        """验证数据格式"""
+        pass
+
+    def validate_range(self, value, min_val, max_val):
+        """验证数值范围"""
+        pass
+
+    def validate_completeness(self, data, required_fields):
+        """验证数据完整性"""
+        pass
+```
+
+### 2. 数据清洗
+
+- **异常值处理**: 识别和处理异常数据
+- **缺失值填充**: 合理填充缺失数据
+- **重复数据去除**: 识别和去除重复记录
+
+## 🚀 性能优化
+
+### 1. 数据缓存策略
+
+- **内存缓存**: 热点数据内存缓存
+- **分层缓存**: 多级缓存机制
+- **缓存更新**: 智能缓存更新策略
+
+### 2. 数据压缩
+
+- **传输压缩**: 网络传输数据压缩
+- **存储压缩**: 磁盘存储数据压缩
+- **实时压缩**: 实时数据流压缩
+
+### 3. 并行处理
+
+- **数据分片**: 大数据集分片处理
+- **并行计算**: 多线程/多进程并行
+- **流式处理**: 流式数据处理
+
+## 📚 相关文档
+
+- [模块设计](modules.md) - 各模块详细设计
+- [插件系统](plugin-system.md) - 插件架构说明
+- [数据管理API](../api/data-manager.md) - 数据管理API文档
+- [配置参考](../configuration/framework-config.md) - 数据相关配置
diff --git a/docs/architecture/modules.md b/docs/architecture/modules.md
new file mode 100644
index 0000000..5649a58
--- /dev/null
+++ b/docs/architecture/modules.md
@@ -0,0 +1,238 @@
+# 模块设计
+
+本文档详细介绍Locust性能测试框架的各个模块设计和实现。
+
+## 📋 模块概览
+
+框架采用分层架构设计，主要包含以下核心模块：
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│                    用户接口层 (UI Layer)                      │
+├─────────────────────────────────────────────────────────────┤
+│                   业务逻辑层 (Business Layer)                 │
+├─────────────────────────────────────────────────────────────┤
+│                   数据访问层 (Data Access Layer)             │
+├─────────────────────────────────────────────────────────────┤
+│                   基础设施层 (Infrastructure Layer)           │
+└─────────────────────────────────────────────────────────────┘
+```
+
+## 🎯 核心模块
+
+### 1. 测试执行模块 (src/model/)
+
+**职责**: 负责测试脚本的执行和管理
+
+**主要组件**:
+- `locust_test.py` - Locust测试执行器
+- `modelsbase.py` - 基础模型类
+- `auto_pytest.py` - 自动化测试集成
+
+**核心功能**:
+- 测试脚本加载和执行
+- 用户行为模拟
+- 事务管理和统计
+- 集合点同步机制
+
+### 2. 性能分析模块 (src/analysis/)
+
+**职责**: 提供全面的性能数据分析和评估
+
+**主要组件**:
+- `performance_analyzer.py` - 性能分析器
+- `metrics_calculator.py` - 指标计算器
+- `report_generator.py` - 报告生成器
+- `trend_analyzer.py` - 趋势分析器
+
+**核心功能**:
+- 响应时间分析 (P50/P90/P95/P99)
+- 吞吐量计算和分析
+- 错误率统计和分析
+- 性能等级评估 (A-D等级)
+- 趋势分析和预测
+
+### 3. 监控告警模块 (src/monitoring/)
+
+**职责**: 实时监控系统状态并提供告警机制
+
+**主要组件**:
+- `system_monitor.py` - 系统监控器
+- `alert_manager.py` - 告警管理器
+- `threshold_checker.py` - 阈值检查器
+- `metric_collector.py` - 指标收集器
+
+**核心功能**:
+- 实时性能指标监控
+- 系统资源监控 (CPU、内存、网络)
+- 可配置的告警规则
+- 多级告警机制 (info/warning/critical)
+- 告警生命周期管理
+
+### 4. 数据管理模块 (src/data_manager/)
+
+**职责**: 管理测试数据的生成、分发和同步
+
+**主要组件**:
+- `data_provider.py` - 数据提供器
+- `data_generator.py` - 数据生成器
+- `data_distributor.py` - 数据分发器
+- `data_synchronizer.py` - 数据同步器
+
+**核心功能**:
+- 基于Faker的测试数据生成
+- 多种数据分发策略 (轮询、随机、顺序)
+- 分布式环境数据同步
+- CSV/数据库/API数据源支持
+- 动态数据更新和管理
+
+### 5. 通知服务模块 (src/notifications/)
+
+**职责**: 提供多渠道消息通知服务
+
+**主要组件**:
+- `notification_service.py` - 通知服务
+- `feishu_notifier.py` - 飞书通知器
+- `dingtalk_notifier.py` - 钉钉通知器
+- `email_notifier.py` - 邮件通知器
+- `wechat_notifier.py` - 企业微信通知器
+
+**核心功能**:
+- 多渠道消息发送
+- 消息模板管理
+- 发送状态跟踪
+- 失败重试机制
+- 消息格式化和富文本支持
+
+### 6. 插件系统模块 (src/plugins/)
+
+**职责**: 提供可扩展的插件架构
+
+**主要组件**:
+- `plugin_manager.py` - 插件管理器
+- `base_plugin.py` - 插件基类
+- `plugin_loader.py` - 插件加载器
+- `plugin_registry.py` - 插件注册表
+
+**核心功能**:
+- 插件生命周期管理
+- 动态插件加载/卸载
+- 插件依赖管理
+- 事件驱动的插件通信
+- 插件配置管理
+
+## 🔧 工具模块
+
+### 1. 工具类模块 (src/utils/)
+
+**职责**: 提供通用的工具函数和辅助类
+
+**主要组件**:
+- `file_operation.py` - 文件操作工具
+- `locust_report.py` - 报告生成工具
+- `log_moudle.py` - 日志管理工具
+- `rendezvous.py` - 集合点实现
+- `robot.py` - 自动化工具
+- `util.py` - 通用工具函数
+
+### 2. 客户端模块 (src/client/)
+
+**职责**: 提供HTTP客户端和认证支持
+
+**主要组件**:
+- `demo_client/` - 示例客户端实现
+- `flask_auth.py` - Flask认证支持
+- `flask_client.py` - Flask客户端
+- `response.py` - 响应处理
+
+## 🏗️ 模块间交互
+
+### 数据流向
+
+```
+测试执行模块 → 性能分析模块 → 报告生成
+     ↓              ↓
+监控告警模块 → 通知服务模块
+     ↓
+数据管理模块 ← 插件系统模块
+```
+
+### 依赖关系
+
+- **核心依赖**: 测试执行模块是整个系统的核心
+- **数据依赖**: 性能分析模块依赖测试执行模块的数据
+- **服务依赖**: 监控告警模块依赖通知服务模块
+- **扩展依赖**: 所有模块都可以通过插件系统进行扩展
+
+## 📊 模块配置
+
+每个模块都支持独立的配置管理：
+
+```yaml
+# conf/settings.yaml
+modules:
+  analysis:
+    enabled: true
+    config_file: "analysis_config.yaml"
+
+  monitoring:
+    enabled: true
+    config_file: "monitoring_config.yaml"
+
+  notifications:
+    enabled: true
+    config_file: "notification_config.yaml"
+
+  data_manager:
+    enabled: true
+    config_file: "data_config.yaml"
+
+  plugins:
+    enabled: true
+    config_file: "plugin_config.yaml"
+```
+
+## 🔄 模块生命周期
+
+### 初始化阶段
+1. 配置加载
+2. 模块注册
+3. 依赖检查
+4. 资源分配
+
+### 运行阶段
+1. 模块启动
+2. 服务注册
+3. 事件监听
+4. 数据处理
+
+### 清理阶段
+1. 资源释放
+2. 连接关闭
+3. 数据保存
+4. 模块卸载
+
+## 🚀 扩展指南
+
+### 添加新模块
+
+1. **创建模块目录**: 在`src/`下创建新的模块目录
+2. **实现模块接口**: 继承基础模块类
+3. **注册模块**: 在模块注册表中注册新模块
+4. **配置模块**: 添加模块配置文件
+5. **编写测试**: 为新模块编写单元测试
+
+### 模块开发规范
+
+- 遵循单一职责原则
+- 保持模块间的松耦合
+- 使用依赖注入
+- 提供清晰的接口定义
+- 支持配置化管理
+
+## 📚 相关文档
+
+- [整体架构](overview.md) - 框架整体架构设计
+- [插件系统](plugin-system.md) - 插件架构详细说明
+- [数据流](data-flow.md) - 数据处理流程
+- [开发指南](../development/setup.md) - 开发环境搭建
diff --git a/docs/architecture/overview.md b/docs/architecture/overview.md
new file mode 100644
index 0000000..c342911
--- /dev/null
+++ b/docs/architecture/overview.md
@@ -0,0 +1,546 @@
+# 整体架构设计
+
+本文档详细介绍Locust性能测试框架的整体架构设计，包括设计原则、架构模式和核心组件。
+
+## 🎯 设计原则
+
+### 1. 核心设计原则
+
+#### 分层架构 (Layered Architecture)
+- **表现层**: Web UI、CLI接口、API接口
+- **业务层**: 测试逻辑、数据处理、业务规则
+- **服务层**: 分析服务、监控服务、通知服务
+- **数据层**: 数据访问、存储管理、缓存机制
+- **基础层**: 日志、配置、工具、框架支持
+
+#### 松耦合 (Loose Coupling)
+- 模块间通过接口通信，减少直接依赖
+- 使用事件驱动机制实现模块解耦
+- 依赖注入和控制反转降低耦合度
+
+#### 高内聚 (High Cohesion)
+- 相关功能集中在同一模块内
+- 模块内部功能紧密相关，职责明确
+- 单一职责原则确保模块专注性
+
+#### 可扩展性 (Extensibility)
+- 插件化架构支持功能扩展
+- 开闭原则：对扩展开放，对修改封闭
+- 标准化接口便于第三方集成
+
+## 🏗️ 架构概览
+
+### 1. 整体架构图
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                        用户接口层                                │
+├─────────────────┬─────────────────┬─────────────────────────────┤
+│   Web UI        │   CLI Interface │   REST API                  │
+│   (Locust Web)  │   (命令行工具)   │   (外部集成)                 │
+└─────────────────┴─────────────────┴─────────────────────────────┘
+                                │
+┌─────────────────────────────────────────────────────────────────┐
+│                        业务逻辑层                                │
+├─────────────────┬─────────────────┬─────────────────────────────┤
+│   测试编排       │   负载控制       │   结果处理                   │
+│   (Test Orchestration) │ (Load Control) │ (Result Processing)  │
+└─────────────────┴─────────────────┴─────────────────────────────┘
+                                │
+┌─────────────────────────────────────────────────────────────────┐
+│                        服务层                                    │
+├─────────┬─────────┬─────────┬─────────┬─────────┬─────────────┤
+│ 性能分析 │ 实时监控 │ 数据管理 │ 插件系统 │ 通知服务 │ 报告生成     │
+│ Analysis│ Monitor │ Data Mgr│ Plugins │ Notify  │ Reporting   │
+└─────────┴─────────┴─────────┴─────────┴─────────┴─────────────┘
+                                │
+┌─────────────────────────────────────────────────────────────────┐
+│                        数据访问层                                │
+├─────────────────┬─────────────────┬─────────────────────────────┤
+│   数据库访问     │   文件系统       │   缓存系统                   │
+│   (Database)    │   (File System) │   (Cache)                   │
+└─────────────────┴─────────────────┴─────────────────────────────┘
+                                │
+┌─────────────────────────────────────────────────────────────────┐
+│                        基础设施层                                │
+├─────────┬─────────┬─────────┬─────────┬─────────┬─────────────┤
+│ 日志系统 │ 配置管理 │ 工具类   │ 异常处理 │ 安全认证 │ 网络通信     │
+│ Logging │ Config  │ Utils   │ Exception│ Security│ Network     │
+└─────────┴─────────┴─────────┴─────────┴─────────┴─────────────┘
+```
+
+### 2. 核心组件关系
+
+```mermaid
+graph TB
+    A[Locust Core] --> B[Test Execution]
+    A --> C[Load Generation]
+
+    B --> D[Performance Analysis]
+    B --> E[Real-time Monitoring]
+    B --> F[Result Collection]
+
+    C --> G[Load Shapes]
+    C --> H[User Simulation]
+    C --> I[Data Management]
+
+    D --> J[Report Generation]
+    E --> K[Alert Management]
+    F --> L[Data Storage]
+
+    M[Plugin System] --> D
+    M --> E
+    M --> G
+    M --> J
+    M --> K
+
+    N[Configuration] --> A
+    N --> M
+
+    O[Logging] --> A
+    O --> M
+```
+
+## 🔧 核心组件架构
+
+### 1. 性能分析组件 (Analysis)
+
+```python
+# 组件结构
+src/analysis/
+├── performance_analyzer.py    # 核心分析引擎
+├── trend_analyzer.py         # 趋势分析
+├── report_generator.py       # 报告生成
+├── metrics_calculator.py     # 指标计算
+└── __init__.py
+
+# 架构特点
+- 单一职责：专注性能数据分析
+- 可扩展：支持自定义分析算法
+- 高性能：优化的计算引擎
+- 多格式：支持多种输出格式
+```
+
+#### 分析流水线
+```
+原始数据 → 数据清洗 → 指标计算 → 趋势分析 → 评分评级 → 报告生成
+```
+
+### 2. 监控告警组件 (Monitoring)
+
+```python
+# 组件结构
+src/monitoring/
+├── performance_monitor.py    # 性能监控
+├── alert_manager.py         # 告警管理
+├── notification_service.py  # 通知服务
+├── metrics_collector.py     # 指标收集
+└── __init__.py
+
+# 架构特点
+- 实时性：毫秒级监控响应
+- 可配置：灵活的告警规则
+- 多渠道：支持多种通知方式
+- 高可用：容错和恢复机制
+```
+
+#### 监控流程
+```
+指标收集 → 规则评估 → 告警触发 → 通知发送 → 状态管理
+```
+
+### 3. 数据管理组件 (Data Manager)
+
+```python
+# 组件结构
+src/data_manager/
+├── data_generator.py        # 数据生成
+├── data_provider.py         # 数据提供
+├── data_distributor.py      # 数据分发
+├── data_storage.py          # 数据存储
+└── __init__.py
+
+# 架构特点
+- 智能生成：基于Faker的真实数据
+- 多策略：支持多种分发策略
+- 分布式：支持集群数据同步
+- 高性能：优化的数据访问
+```
+
+#### 数据流转
+```
+数据生成 → 数据验证 → 数据存储 → 数据分发 → 数据消费
+```
+
+### 4. 插件系统组件 (Plugins)
+
+```python
+# 组件结构
+src/plugins/
+├── plugin_interface.py      # 插件接口定义
+├── plugin_manager.py        # 插件管理器
+├── plugin_loader.py         # 插件加载器
+├── builtin/                 # 内置插件
+│   └── csv_report_plugin.py
+└── __init__.py
+
+# 架构特点
+- 标准化：统一的插件接口
+- 动态加载：运行时插件管理
+- 类型丰富：支持10+种插件类型
+- 生命周期：完整的插件生命周期管理
+```
+
+#### 插件生命周期
+```
+发现 → 加载 → 验证 → 初始化 → 启用 → 运行 → 禁用 → 卸载
+```
+
+## 🔄 数据流架构
+
+### 1. 测试数据流
+
+```mermaid
+sequenceDiagram
+    participant U as User
+    participant L as Locust Core
+    participant DM as Data Manager
+    participant A as Analysis
+    participant M as Monitor
+    participant R as Report
+
+    U->>L: 启动测试
+    L->>DM: 请求测试数据
+    DM-->>L: 返回测试数据
+    L->>L: 执行测试
+    L->>M: 发送实时指标
+    L->>A: 发送测试结果
+    M->>M: 监控分析
+    A->>A: 性能分析
+    A->>R: 生成报告
+    R-->>U: 返回报告
+```
+
+### 2. 监控数据流
+
+```mermaid
+graph LR
+    A[指标收集] --> B[数据缓存]
+    B --> C[规则引擎]
+    C --> D{是否触发告警}
+    D -->|是| E[告警管理]
+    D -->|否| F[继续监控]
+    E --> G[通知发送]
+    G --> H[状态更新]
+    F --> A
+    H --> A
+```
+
+## 🔌 插件架构设计
+
+### 1. 插件接口层次
+
+```python
+# 基础插件接口
+class PluginInterface(ABC):
+    @property
+    @abstractmethod
+    def plugin_info(self) -> PluginInfo:
+        pass
+
+    @abstractmethod
+    def initialize(self, config=None) -> bool:
+        pass
+
+    @abstractmethod
+    def cleanup(self):
+        pass
+
+# 专用插件接口
+class ReportPlugin(PluginInterface):
+    @abstractmethod
+    def generate_report(self, test_data, output_path):
+        pass
+
+class MonitorPlugin(PluginInterface):
+    @abstractmethod
+    def collect_metrics(self) -> Dict[str, Any]:
+        pass
+```
+
+### 2. 插件管理架构
+
+```python
+# 插件管理器架构
+class PluginManager:
+    def __init__(self):
+        self.loader = PluginLoader()
+        self.plugins = {}
+        self.event_handlers = {}
+
+    # 插件生命周期管理
+    def discover_plugins(self) -> List[str]
+    def load_plugin(self, name: str) -> bool
+    def enable_plugin(self, name: str, config=None) -> bool
+    def disable_plugin(self, name: str) -> bool
+
+    # 事件系统
+    def register_event_handler(self, event: str, handler)
+    def trigger_event(self, event: str, data=None)
+```
+
+## 🏛️ 分布式架构
+
+### 1. Master-Worker模式
+
+```python
+# Master节点职责
+- 测试协调和控制
+- 负载分发和调度
+- 结果收集和聚合
+- Web UI和API服务
+
+# Worker节点职责
+- 负载生成和执行
+- 本地指标收集
+- 结果上报
+- 状态同步
+```
+
+### 2. 分布式通信
+
+```mermaid
+graph TB
+    M[Master Node] --> W1[Worker Node 1]
+    M --> W2[Worker Node 2]
+    M --> W3[Worker Node 3]
+
+    W1 --> M
+    W2 --> M
+    W3 --> M
+
+    M --> DB[(Database)]
+    M --> Cache[(Cache)]
+    M --> MQ[(Message Queue)]
+```
+
+## 🔒 安全架构
+
+### 1. 安全层次
+
+```python
+# 认证层
+- 用户身份验证
+- API密钥管理
+- 会话管理
+
+# 授权层
+- 角色权限控制
+- 资源访问控制
+- 操作权限验证
+
+# 数据安全层
+- 数据加密存储
+- 传输加密
+- 敏感信息脱敏
+```
+
+### 2. 安全机制
+
+```python
+# 配置安全
+- 敏感配置加密
+- 环境变量隔离
+- 配置文件权限控制
+
+# 网络安全
+- HTTPS通信
+- 防火墙配置
+- 网络隔离
+
+# 运行时安全
+- 输入验证
+- SQL注入防护
+- XSS防护
+```
+
+## 📊 性能架构
+
+### 1. 性能优化策略
+
+```python
+# 计算性能
+- 多线程并发处理
+- 异步IO操作
+- 内存池管理
+- 算法优化
+
+# 存储性能
+- 数据库连接池
+- 查询优化
+- 索引设计
+- 缓存策略
+
+# 网络性能
+- 连接复用
+- 压缩传输
+- 负载均衡
+- CDN加速
+```
+
+### 2. 可扩展性设计
+
+```python
+# 水平扩展
+- 分布式部署
+- 负载均衡
+- 数据分片
+- 服务拆分
+
+# 垂直扩展
+- 资源优化
+- 性能调优
+- 硬件升级
+- 算法改进
+```
+
+## 🔧 配置架构
+
+### 1. 配置层次结构
+
+```python
+# 配置优先级
+环境变量 > 命令行参数 > 用户配置 > 默认配置
+
+# 配置文件结构
+conf/
+├── settings.toml           # 主配置文件
+├── environments/           # 环境配置
+│   ├── development.toml
+│   ├── testing.toml
+│   └── production.toml
+├── plugins/               # 插件配置
+│   └── plugin_config.json
+└── logging.conf           # 日志配置
+```
+
+### 2. 配置管理机制
+
+```python
+# 动态配置
+- 运行时配置更新
+- 配置热重载
+- 配置验证
+- 默认值处理
+
+# 配置安全
+- 敏感信息加密
+- 配置文件权限
+- 环境隔离
+- 审计日志
+```
+
+## 🚀 部署架构
+
+### 1. 部署模式
+
+```python
+# 单机部署
+- 开发测试环境
+- 小规模测试
+- 快速验证
+
+# 分布式部署
+- 大规模测试
+- 高可用要求
+- 性能要求高
+
+```
+
+## 📈 监控架构
+
+### 1. 监控层次
+
+```python
+# 应用监控
+- 性能指标监控
+- 业务指标监控
+- 错误监控
+- 日志监控
+
+# 系统监控
+- 资源使用监控
+- 网络监控
+- 存储监控
+- 服务健康监控
+
+# 基础设施监控
+- 服务器监控
+- 数据库监控
+- 中间件监控
+- 网络设备监控
+```
+
+### 2. 监控技术栈
+
+```python
+# 指标收集
+- Prometheus
+- Grafana
+- InfluxDB
+- StatsD
+
+# 日志收集
+- ELK Stack
+- Fluentd
+- Logstash
+- Filebeat
+
+# 链路追踪
+- Jaeger
+- Zipkin
+- SkyWalking
+- OpenTelemetry
+```
+
+## 🎯 架构演进
+
+### 1. 版本演进路线
+
+```python
+# v1.0 - 基础版本
+- 核心测试功能
+- 基础报告生成
+- 简单配置管理
+
+# v2.0 - 增强版本 (当前)
+- 性能分析模块
+- 监控告警系统
+- 插件化架构
+- 数据管理系统
+
+# v3.0 - 未来版本
+- AI驱动的性能分析
+- 智能化运维
+- 更丰富的分析算法
+```
+
+### 2. 技术债务管理
+
+```python
+# 代码质量
+- 代码审查
+- 单元测试
+- 集成测试
+- 性能测试
+
+# 架构优化
+- 模块重构
+- 性能优化
+- 安全加固
+- 文档完善
+```
+
+---
+
+这个架构设计确保了框架的可扩展性、可维护性和高性能，为用户提供了强大而灵活的性能测试解决方案。
diff --git a/docs/architecture/plugin-system.md b/docs/architecture/plugin-system.md
new file mode 100644
index 0000000..50da144
--- /dev/null
+++ b/docs/architecture/plugin-system.md
@@ -0,0 +1,440 @@
+# 插件系统架构
+
+本文档详细介绍Locust性能测试框架的插件系统架构设计和扩展机制。
+
+## 🎯 设计目标
+
+插件系统旨在提供：
+- **可扩展性**: 支持功能的动态扩展
+- **模块化**: 插件间相互独立，可单独开发和部署
+- **热插拔**: 运行时动态加载和卸载插件
+- **标准化**: 统一的插件开发接口和规范
+- **安全性**: 插件隔离和权限控制
+
+## 🏗️ 架构概览
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│                    插件管理器 (Plugin Manager)                │
+├─────────────────────────────────────────────────────────────┤
+│  插件加载器  │  插件注册表  │  事件总线  │  生命周期管理器    │
+├─────────────────────────────────────────────────────────────┤
+│                    插件接口层 (Plugin Interface)              │
+├─────────────────────────────────────────────────────────────┤
+│  Locust插件  │  报告插件  │  监控插件  │  数据插件  │  ...   │
+└─────────────────────────────────────────────────────────────┘
+```
+
+## 🔌 插件类型
+
+### 1. Locust插件 (LocustPlugin)
+
+**用途**: 扩展Locust核心功能
+
+**接口方法**:
+```python
+class LocustPlugin(BasePlugin):
+    def on_test_start(self, environment, **kwargs):
+        """测试开始时调用"""
+        pass
+
+    def on_test_stop(self, environment, **kwargs):
+        """测试结束时调用"""
+        pass
+
+    def on_user_add(self, user_instance, **kwargs):
+        """用户添加时调用"""
+        pass
+
+    def on_user_remove(self, user_instance, **kwargs):
+        """用户移除时调用"""
+        pass
+```
+
+**应用场景**:
+- 自定义用户行为
+- 测试数据预处理
+- 自定义统计指标
+- 测试流程控制
+
+### 2. 报告插件 (ReportPlugin)
+
+**用途**: 自定义报告格式和内容
+
+**接口方法**:
+```python
+class ReportPlugin(BasePlugin):
+    def generate_report(self, stats, **kwargs):
+        """生成自定义报告"""
+        pass
+
+    def format_data(self, data, format_type):
+        """格式化数据"""
+        pass
+
+    def export_report(self, report, output_path):
+        """导出报告"""
+        pass
+```
+
+**应用场景**:
+- 自定义报告模板
+- 特殊格式导出
+- 报告数据处理
+- 第三方系统集成
+
+### 3. 监控插件 (MonitorPlugin)
+
+**用途**: 扩展监控能力
+
+**接口方法**:
+```python
+class MonitorPlugin(BasePlugin):
+    def collect_metrics(self):
+        """收集监控指标"""
+        pass
+
+    def check_thresholds(self, metrics):
+        """检查阈值"""
+        pass
+
+    def trigger_alert(self, alert_data):
+        """触发告警"""
+        pass
+```
+
+**应用场景**:
+- 自定义监控指标
+- 第三方监控系统集成
+- 特殊告警规则
+- 性能数据收集
+
+### 4. 数据插件 (DataPlugin)
+
+**用途**: 扩展数据源和数据处理能力
+
+**接口方法**:
+```python
+class DataPlugin(BasePlugin):
+    def load_data(self, source_config):
+        """加载数据"""
+        pass
+
+    def process_data(self, raw_data):
+        """处理数据"""
+        pass
+
+    def distribute_data(self, data, strategy):
+        """分发数据"""
+        pass
+```
+
+**应用场景**:
+- 自定义数据源
+- 数据转换和处理
+- 分布式数据同步
+- 数据缓存策略
+
+### 5. 通知插件 (NotificationPlugin)
+
+**用途**: 扩展通知渠道
+
+**接口方法**:
+```python
+class NotificationPlugin(BasePlugin):
+    def send_notification(self, message, recipients):
+        """发送通知"""
+        pass
+
+    def format_message(self, data, template):
+        """格式化消息"""
+        pass
+
+    def validate_config(self, config):
+        """验证配置"""
+        pass
+```
+
+**应用场景**:
+- 新的通知渠道
+- 自定义消息格式
+- 通知规则定制
+- 第三方集成
+
+## 🔧 插件管理器
+
+### 核心组件
+
+#### 1. 插件加载器 (PluginLoader)
+
+**职责**: 负责插件的发现、加载和初始化
+
+```python
+class PluginLoader:
+    def discover_plugins(self, plugin_dir):
+        """发现插件"""
+        pass
+
+    def load_plugin(self, plugin_path):
+        """加载插件"""
+        pass
+
+    def validate_plugin(self, plugin_class):
+        """验证插件"""
+        pass
+
+    def initialize_plugin(self, plugin_instance):
+        """初始化插件"""
+        pass
+```
+
+#### 2. 插件注册表 (PluginRegistry)
+
+**职责**: 管理已注册的插件信息
+
+```python
+class PluginRegistry:
+    def register_plugin(self, plugin_info):
+        """注册插件"""
+        pass
+
+    def unregister_plugin(self, plugin_id):
+        """注销插件"""
+        pass
+
+    def get_plugin(self, plugin_id):
+        """获取插件"""
+        pass
+
+    def list_plugins(self, plugin_type=None):
+        """列出插件"""
+        pass
+```
+
+#### 3. 事件总线 (EventBus)
+
+**职责**: 处理插件间的事件通信
+
+```python
+class EventBus:
+    def subscribe(self, event_type, callback):
+        """订阅事件"""
+        pass
+
+    def unsubscribe(self, event_type, callback):
+        """取消订阅"""
+        pass
+
+    def publish(self, event_type, event_data):
+        """发布事件"""
+        pass
+
+    def broadcast(self, event_data):
+        """广播事件"""
+        pass
+```
+
+#### 4. 生命周期管理器 (LifecycleManager)
+
+**职责**: 管理插件的生命周期
+
+```python
+class LifecycleManager:
+    def start_plugin(self, plugin_id):
+        """启动插件"""
+        pass
+
+    def stop_plugin(self, plugin_id):
+        """停止插件"""
+        pass
+
+    def restart_plugin(self, plugin_id):
+        """重启插件"""
+        pass
+
+    def get_plugin_status(self, plugin_id):
+        """获取插件状态"""
+        pass
+```
+
+## 📋 插件开发规范
+
+### 1. 插件结构
+
+```
+my_plugin/
+├── __init__.py          # 插件入口
+├── plugin.py            # 插件主类
+├── config.yaml          # 插件配置
+├── requirements.txt     # 依赖列表
+├── README.md           # 插件说明
+└── tests/              # 测试文件
+    └── test_plugin.py
+```
+
+### 2. 插件元数据
+
+```python
+# __init__.py
+PLUGIN_INFO = {
+    "name": "my_plugin",
+    "version": "1.0.0",
+    "description": "My custom plugin",
+    "author": "Your Name",
+    "type": "locust",
+    "dependencies": ["requests>=2.25.0"],
+    "config_schema": "config_schema.json"
+}
+```
+
+### 3. 插件基类
+
+```python
+from abc import ABC, abstractmethod
+
+class BasePlugin(ABC):
+    def __init__(self, config=None):
+        self.config = config or {}
+        self.enabled = True
+        self.logger = self._setup_logger()
+
+    @abstractmethod
+    def initialize(self):
+        """插件初始化"""
+        pass
+
+    @abstractmethod
+    def cleanup(self):
+        """插件清理"""
+        pass
+
+    def enable(self):
+        """启用插件"""
+        self.enabled = True
+
+    def disable(self):
+        """禁用插件"""
+        self.enabled = False
+
+    def _setup_logger(self):
+        """设置日志"""
+        pass
+```
+
+## ⚙️ 插件配置
+
+### 全局插件配置
+
+```yaml
+# conf/plugin_config.yaml
+plugins:
+  enabled: true
+  auto_load: true
+  plugin_dirs:
+    - "src/plugins"
+    - "custom_plugins"
+
+  # 插件启用列表
+  enabled_plugins:
+    - performance_analyzer
+    - system_monitor
+    - notification_service
+    - data_manager
+
+  # 插件配置
+  plugin_configs:
+    performance_analyzer:
+      analysis_interval: 10
+      metrics_retention: 3600
+
+    system_monitor:
+      monitor_interval: 5
+      cpu_threshold: 80
+      memory_threshold: 85
+
+    notification_service:
+      default_channel: "feishu"
+      retry_count: 3
+      timeout: 30
+```
+
+### 插件特定配置
+
+```yaml
+# plugins/my_plugin/config.yaml
+plugin:
+  name: "my_plugin"
+  enabled: true
+
+settings:
+  api_endpoint: "https://api.example.com"
+  timeout: 30
+  retry_count: 3
+
+features:
+  feature_a: true
+  feature_b: false
+```
+
+## 🔄 插件生命周期
+
+### 1. 加载阶段
+```
+发现插件 → 验证插件 → 加载插件 → 注册插件
+```
+
+### 2. 初始化阶段
+```
+读取配置 → 初始化插件 → 注册事件 → 启动服务
+```
+
+### 3. 运行阶段
+```
+处理事件 → 执行任务 → 状态监控 → 错误处理
+```
+
+### 4. 卸载阶段
+```
+停止服务 → 清理资源 → 注销事件 → 卸载插件
+```
+
+## 🛡️ 安全机制
+
+### 1. 插件隔离
+- 独立的命名空间
+- 资源访问控制
+- 异常隔离处理
+
+### 2. 权限控制
+- 插件权限定义
+- API访问控制
+- 文件系统访问限制
+
+### 3. 安全验证
+- 插件签名验证
+- 代码安全扫描
+- 运行时监控
+
+## 📊 性能优化
+
+### 1. 延迟加载
+- 按需加载插件
+- 懒初始化机制
+- 资源池管理
+
+### 2. 缓存机制
+- 插件元数据缓存
+- 配置缓存
+- 结果缓存
+
+### 3. 并发处理
+- 异步插件执行
+- 线程池管理
+- 事件队列优化
+
+## 📚 相关文档
+
+- [插件开发指南](../development/plugin-development.md) - 详细的插件开发教程
+- [模块设计](modules.md) - 各模块详细设计
+- [API参考](../api/plugins.md) - 插件API文档
+- [配置参考](../configuration/plugin-config.md) - 插件配置说明
diff --git a/docs/configuration/development.md b/docs/configuration/development.md
new file mode 100644
index 0000000..e791a92
--- /dev/null
+++ b/docs/configuration/development.md
@@ -0,0 +1,818 @@
+# 开发环境配置
+
+本文档详细介绍如何配置Locust性能测试框架的开发环境，包括本地开发、调试配置和开发工具设置。
+
+## 🛠️ 开发环境要求
+
+### 系统要求
+
+```yaml
+# development/requirements.yml
+system_requirements:
+  operating_system:
+    - "macOS 10.15+"
+    - "Ubuntu 18.04+"
+    - "Windows 10+"
+    - "CentOS 7+"
+
+  python:
+    version: "3.8+"
+    recommended: "3.9+"
+
+  memory: "4GB+"
+  disk_space: "2GB+"
+
+  optional_tools:
+    - docker
+    - docker-compose
+    - git
+    - make
+```
+
+### 开发工具推荐
+
+```yaml
+# development/tools.yml
+recommended_tools:
+  ide:
+    - "PyCharm Professional"
+    - "Visual Studio Code"
+    - "Sublime Text"
+    - "Vim/Neovim"
+
+  version_control:
+    - "Git"
+    - "GitHub Desktop"
+    - "SourceTree"
+
+  api_testing:
+    - "Postman"
+    - "Insomnia"
+    - "curl"
+    - "HTTPie"
+
+  monitoring:
+    - "htop"
+    - "iotop"
+    - "netstat"
+    - "tcpdump"
+```
+
+## 🐍 Python环境配置
+
+### 1. 虚拟环境设置
+
+```bash
+# 使用venv创建虚拟环境
+python -m venv locust-dev
+source locust-dev/bin/activate  # Linux/Mac
+# 或
+locust-dev\Scripts\activate  # Windows
+
+# 使用conda创建环境
+conda create -n locust-dev python=3.9
+conda activate locust-dev
+
+# 使用pyenv管理Python版本
+pyenv install 3.9.16
+pyenv virtualenv 3.9.16 locust-dev
+pyenv activate locust-dev
+```
+
+### 2. 依赖安装
+
+```bash
+# 安装基础依赖
+pip install --upgrade pip setuptools wheel
+
+# 安装开发依赖
+pip install -r requirements-dev.txt
+
+# 安装项目（可编辑模式）
+pip install -e .
+
+# 验证安装
+locust --version
+python -c "import locust; print(locust.__version__)"
+```
+
+### 3. 开发依赖文件
+
+```txt
+# requirements-dev.txt
+# 基础框架
+locust>=2.14.0
+gevent>=21.12.0
+requests>=2.25.0
+
+# 开发工具
+pytest>=7.0.0
+pytest-cov>=4.0.0
+pytest-mock>=3.10.0
+pytest-xdist>=3.2.0
+
+# 代码质量
+black>=23.0.0
+isort>=5.12.0
+flake8>=6.0.0
+mypy>=1.0.0
+pre-commit>=3.0.0
+
+# 文档工具
+sphinx>=6.0.0
+sphinx-rtd-theme>=1.2.0
+myst-parser>=0.19.0
+
+# 性能分析
+memory-profiler>=0.60.0
+line-profiler>=4.0.0
+py-spy>=0.3.14
+
+# 数据生成
+faker>=18.0.0
+factory-boy>=3.2.0
+
+# 网络工具
+websocket-client>=1.5.0
+paho-mqtt>=1.6.0
+
+# 监控集成
+prometheus-client>=0.16.0
+grafana-api>=1.0.3
+
+# 通知集成
+slack-sdk>=3.20.0
+requests-oauthlib>=1.3.0
+
+# 数据库支持
+sqlalchemy>=2.0.0
+redis>=4.5.0
+pymongo>=4.3.0
+
+# 云服务
+boto3>=1.26.0
+google-cloud-storage>=2.7.0
+azure-storage-blob>=12.14.0
+```
+
+## 🔧 IDE配置
+
+### 1. VS Code配置
+
+```json
+// .vscode/settings.json
+{
+    "python.defaultInterpreterPath": "./locust-dev/bin/python",
+    "python.linting.enabled": true,
+    "python.linting.pylintEnabled": false,
+    "python.linting.flake8Enabled": true,
+    "python.linting.mypyEnabled": true,
+    "python.formatting.provider": "black",
+    "python.formatting.blackArgs": ["--line-length", "88"],
+    "python.sortImports.args": ["--profile", "black"],
+    "editor.formatOnSave": true,
+    "editor.codeActionsOnSave": {
+        "source.organizeImports": true
+    },
+    "files.exclude": {
+        "**/__pycache__": true,
+        "**/*.pyc": true,
+        ".pytest_cache": true,
+        ".coverage": true,
+        "htmlcov": true
+    },
+    "python.testing.pytestEnabled": true,
+    "python.testing.pytestArgs": [
+        "tests"
+    ],
+    "python.testing.unittestEnabled": false
+}
+```
+
+```json
+// .vscode/launch.json
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Locust Master",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/locust-dev/bin/locust",
+            "args": [
+                "-f", "locustfiles/example.py",
+                "--master",
+                "--master-bind-host=0.0.0.0"
+            ],
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}"
+        },
+        {
+            "name": "Locust Worker",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/locust-dev/bin/locust",
+            "args": [
+                "-f", "locustfiles/example.py",
+                "--worker",
+                "--master-host=localhost"
+            ],
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}"
+        },
+        {
+            "name": "Debug Test",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/locust-dev/bin/python",
+            "args": [
+                "-m", "pytest",
+                "tests/",
+                "-v"
+            ],
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}"
+        }
+    ]
+}
+```
+
+### 2. PyCharm配置
+
+```python
+# .idea/runConfigurations/Locust_Master.xml
+<component name="ProjectRunConfigurationManager">
+  <configuration default="false" name="Locust Master" type="PythonConfigurationType" factoryName="Python">
+    <module name="locust-framework" />
+    <option name="INTERPRETER_OPTIONS" value="" />
+    <option name="PARENT_ENVS" value="true" />
+    <envs>
+      <env name="PYTHONUNBUFFERED" value="1" />
+    </envs>
+    <option name="SDK_HOME" value="$PROJECT_DIR$/locust-dev/bin/python" />
+    <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+    <option name="IS_MODULE_SDK" value="false" />
+    <option name="ADD_CONTENT_ROOTS" value="true" />
+    <option name="ADD_SOURCE_ROOTS" value="true" />
+    <option name="SCRIPT_NAME" value="locust" />
+    <option name="PARAMETERS" value="-f locustfiles/example.py --master --master-bind-host=0.0.0.0" />
+    <option name="SHOW_COMMAND_LINE" value="false" />
+    <option name="EMULATE_TERMINAL" value="false" />
+    <option name="MODULE_MODE" value="false" />
+    <option name="REDIRECT_INPUT" value="false" />
+    <option name="INPUT_FILE" value="" />
+    <method v="2" />
+  </configuration>
+</component>
+```
+
+## 🐳 Docker开发环境
+
+### 1. 开发用Docker配置
+
+```dockerfile
+# Dockerfile.dev
+FROM python:3.9-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    make \
+    git \
+    curl \
+    vim \
+    htop \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制依赖文件
+COPY requirements-dev.txt .
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -r requirements-dev.txt
+
+# 复制源码
+COPY . .
+
+# 安装项目（开发模式）
+RUN pip install -e .
+
+# 设置环境变量
+ENV PYTHONPATH=/app
+ENV LOCUST_HOST=http://localhost:8080
+
+# 暴露端口
+EXPOSE 8089 5557
+
+# 默认命令
+CMD ["bash"]
+```
+
+### 2. Docker Compose开发配置
+
+```yaml
+# docker-compose.dev.yml
+version: '3.8'
+
+services:
+  locust-dev:
+    build:
+      context: .
+      dockerfile: Dockerfile.dev
+    container_name: locust-dev
+    ports:
+      - "8089:8089"
+      - "5557:5557"
+    volumes:
+      - .:/app
+      - locust-dev-cache:/root/.cache
+    environment:
+      - PYTHONPATH=/app
+      - LOCUST_HOST=http://target-app:8080
+    command: tail -f /dev/null  # 保持容器运行
+    networks:
+      - dev-network
+
+  target-app:
+    image: nginx:alpine
+    container_name: target-app
+    ports:
+      - "8080:80"
+    volumes:
+      - ./test-data/nginx.conf:/etc/nginx/nginx.conf
+    networks:
+      - dev-network
+
+  redis:
+    image: redis:alpine
+    container_name: redis-dev
+    ports:
+      - "6379:6379"
+    networks:
+      - dev-network
+
+  postgres:
+    image: postgres:13
+    container_name: postgres-dev
+    ports:
+      - "5432:5432"
+    environment:
+      - POSTGRES_DB=locust_dev
+      - POSTGRES_USER=locust
+      - POSTGRES_PASSWORD=locust123
+    volumes:
+      - postgres-dev-data:/var/lib/postgresql/data
+    networks:
+      - dev-network
+
+networks:
+  dev-network:
+    driver: bridge
+
+volumes:
+  locust-dev-cache:
+  postgres-dev-data:
+```
+
+### 3. 开发脚本
+
+```bash
+#!/bin/bash
+# scripts/dev-setup.sh
+
+set -e
+
+echo "Setting up Locust development environment..."
+
+# 检查Docker
+if ! command -v docker &> /dev/null; then
+    echo "Docker is required but not installed."
+    exit 1
+fi
+
+# 检查Docker Compose
+if ! command -v docker-compose &> /dev/null; then
+    echo "Docker Compose is required but not installed."
+    exit 1
+fi
+
+# 构建开发环境
+echo "Building development containers..."
+docker-compose -f docker-compose.dev.yml build
+
+# 启动服务
+echo "Starting development services..."
+docker-compose -f docker-compose.dev.yml up -d
+
+# 等待服务启动
+echo "Waiting for services to start..."
+sleep 10
+
+# 检查服务状态
+echo "Checking service status..."
+docker-compose -f docker-compose.dev.yml ps
+
+# 运行测试
+echo "Running tests..."
+docker-compose -f docker-compose.dev.yml exec locust-dev pytest tests/ -v
+
+echo "Development environment is ready!"
+echo "Access Locust Web UI at: http://localhost:8089"
+echo "Target app is available at: http://localhost:8080"
+
+# 显示有用的命令
+cat << EOF
+
+Useful commands:
+  # Enter development container
+  docker-compose -f docker-compose.dev.yml exec locust-dev bash
+
+  # Run Locust master
+  docker-compose -f docker-compose.dev.yml exec locust-dev locust -f locustfiles/example.py --master
+
+  # Run tests
+  docker-compose -f docker-compose.dev.yml exec locust-dev pytest
+
+  # Stop environment
+  docker-compose -f docker-compose.dev.yml down
+EOF
+```
+
+## 🧪 测试配置
+
+### 1. pytest配置
+
+```ini
+# pytest.ini
+[tool:pytest]
+testpaths = tests
+python_files = test_*.py
+python_classes = Test*
+python_functions = test_*
+addopts =
+    --strict-markers
+    --strict-config
+    --verbose
+    --tb=short
+    --cov=src
+    --cov-report=html
+    --cov-report=term-missing
+    --cov-fail-under=80
+    --durations=10
+
+markers =
+    unit: Unit tests
+    integration: Integration tests
+    performance: Performance tests
+    slow: Slow running tests
+    network: Tests requiring network access
+
+filterwarnings =
+    ignore::DeprecationWarning
+    ignore::PendingDeprecationWarning
+```
+
+### 2. 测试环境变量
+
+```bash
+# .env.test
+# 测试环境配置
+LOCUST_HOST=http://localhost:8080
+LOCUST_USERS=10
+LOCUST_SPAWN_RATE=2
+LOCUST_RUN_TIME=60s
+
+# 数据库配置
+DATABASE_URL=postgresql://locust:locust123@localhost:5432/locust_test
+REDIS_URL=redis://localhost:6379/1
+
+# 日志配置
+LOG_LEVEL=DEBUG
+LOG_FORMAT=detailed
+
+# 测试数据
+TEST_DATA_DIR=tests/fixtures
+FAKE_DATA_LOCALE=zh_CN
+
+# API配置
+API_BASE_URL=http://localhost:8080/api
+API_TIMEOUT=30
+API_RETRIES=3
+```
+
+### 3. 测试辅助工具
+
+```python
+# tests/conftest.py
+import pytest
+import tempfile
+import shutil
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+@pytest.fixture(scope="session")
+def temp_dir():
+    """创建临时目录"""
+    temp_path = Path(tempfile.mkdtemp())
+    yield temp_path
+    shutil.rmtree(temp_path)
+
+@pytest.fixture
+def mock_locust_env():
+    """模拟Locust环境"""
+    with patch('locust.env.Environment') as mock_env:
+        mock_env.stats = Mock()
+        mock_env.runner = Mock()
+        yield mock_env
+
+@pytest.fixture
+def sample_config():
+    """示例配置"""
+    return {
+        "framework": {
+            "name": "test_framework",
+            "version": "1.0.0"
+        },
+        "load_shapes": {
+            "test_shape": {
+                "type": "linear",
+                "config": {
+                    "max_users": 100,
+                    "duration": 300
+                }
+            }
+        }
+    }
+
+@pytest.fixture
+def mock_http_response():
+    """模拟HTTP响应"""
+    response = Mock()
+    response.status_code = 200
+    response.headers = {"Content-Type": "application/json"}
+    response.json.return_value = {"status": "success"}
+    response.text = '{"status": "success"}'
+    response.content = b'{"status": "success"}'
+    return response
+```
+
+## 🔍 调试配置
+
+### 1. 日志配置
+
+```python
+# config/logging_dev.py
+import logging
+import sys
+from pathlib import Path
+
+def setup_development_logging():
+    """设置开发环境日志"""
+
+    # 创建日志目录
+    log_dir = Path("logs")
+    log_dir.mkdir(exist_ok=True)
+
+    # 配置根日志器
+    root_logger = logging.getLogger()
+    root_logger.setLevel(logging.DEBUG)
+
+    # 控制台处理器
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(logging.INFO)
+    console_formatter = logging.Formatter(
+        '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    )
+    console_handler.setFormatter(console_formatter)
+    root_logger.addHandler(console_handler)
+
+    # 文件处理器
+    file_handler = logging.FileHandler(log_dir / "locust_dev.log")
+    file_handler.setLevel(logging.DEBUG)
+    file_formatter = logging.Formatter(
+        '%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s'
+    )
+    file_handler.setFormatter(file_formatter)
+    root_logger.addHandler(file_handler)
+
+    # 设置第三方库日志级别
+    logging.getLogger('urllib3').setLevel(logging.WARNING)
+    logging.getLogger('requests').setLevel(logging.WARNING)
+    logging.getLogger('gevent').setLevel(logging.WARNING)
+
+    return root_logger
+
+# 使用示例
+if __name__ == "__main__":
+    logger = setup_development_logging()
+    logger.info("Development logging configured")
+```
+
+### 2. 性能分析配置
+
+```python
+# tools/profiling.py
+import cProfile
+import pstats
+import io
+from functools import wraps
+from memory_profiler import profile as memory_profile
+from line_profiler import LineProfiler
+
+def cpu_profile(func):
+    """CPU性能分析装饰器"""
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        profiler = cProfile.Profile()
+        profiler.enable()
+
+        try:
+            result = func(*args, **kwargs)
+        finally:
+            profiler.disable()
+
+            # 输出分析结果
+            s = io.StringIO()
+            ps = pstats.Stats(profiler, stream=s)
+            ps.sort_stats('cumulative')
+            ps.print_stats(20)  # 显示前20个函数
+
+            print(f"\n=== CPU Profile for {func.__name__} ===")
+            print(s.getvalue())
+
+        return result
+    return wrapper
+
+def line_profile(func):
+    """行级性能分析装饰器"""
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        profiler = LineProfiler()
+        profiler.add_function(func)
+        profiler.enable_by_count()
+
+        try:
+            result = func(*args, **kwargs)
+        finally:
+            profiler.disable_by_count()
+
+            print(f"\n=== Line Profile for {func.__name__} ===")
+            profiler.print_stats()
+
+        return result
+    return wrapper
+
+# 使用示例
+@cpu_profile
+@memory_profile
+def performance_test_function():
+    """性能测试函数"""
+    import time
+    time.sleep(1)
+    data = [i for i in range(100000)]
+    return sum(data)
+```
+
+## 🛠️ 开发工具脚本
+
+### 1. Makefile
+
+```makefile
+# Makefile
+.PHONY: help install test lint format clean dev-setup
+
+help:
+	@echo "Available commands:"
+	@echo "  install     Install dependencies"
+	@echo "  test        Run tests"
+	@echo "  lint        Run linting"
+	@echo "  format      Format code"
+	@echo "  clean       Clean build artifacts"
+	@echo "  dev-setup   Setup development environment"
+
+install:
+	pip install -r requirements-dev.txt
+	pip install -e .
+
+test:
+	pytest tests/ -v --cov=src
+
+test-fast:
+	pytest tests/ -v -x --ff
+
+lint:
+	flake8 src/ tests/
+	mypy src/
+
+format:
+	black src/ tests/
+	isort src/ tests/
+
+clean:
+	find . -type f -name "*.pyc" -delete
+	find . -type d -name "__pycache__" -delete
+	rm -rf build/
+	rm -rf dist/
+	rm -rf *.egg-info/
+	rm -rf .pytest_cache/
+	rm -rf htmlcov/
+	rm -rf .coverage
+
+dev-setup:
+	python -m venv locust-dev
+	./locust-dev/bin/pip install --upgrade pip
+	./locust-dev/bin/pip install -r requirements-dev.txt
+	./locust-dev/bin/pip install -e .
+	./locust-dev/bin/pre-commit install
+
+docker-dev:
+	docker-compose -f docker-compose.dev.yml up -d
+
+docker-test:
+	docker-compose -f docker-compose.dev.yml exec locust-dev pytest
+
+docker-clean:
+	docker-compose -f docker-compose.dev.yml down -v
+	docker system prune -f
+```
+
+### 2. 开发脚本
+
+```bash
+#!/bin/bash
+# scripts/dev-commands.sh
+
+# 快速启动开发环境
+dev_start() {
+    echo "Starting development environment..."
+    source locust-dev/bin/activate
+    export PYTHONPATH=$(pwd)
+    export LOCUST_HOST=http://localhost:8080
+    echo "Development environment ready!"
+}
+
+# 运行代码质量检查
+quality_check() {
+    echo "Running code quality checks..."
+    black --check src/ tests/
+    isort --check-only src/ tests/
+    flake8 src/ tests/
+    mypy src/
+    echo "Quality checks completed!"
+}
+
+# 运行完整测试套件
+full_test() {
+    echo "Running full test suite..."
+    pytest tests/ -v --cov=src --cov-report=html
+    echo "Tests completed! Check htmlcov/index.html for coverage report."
+}
+
+# 性能基准测试
+benchmark() {
+    echo "Running performance benchmarks..."
+    python -m pytest tests/performance/ -v --benchmark-only
+    echo "Benchmarks completed!"
+}
+
+# 根据参数执行相应命令
+case "$1" in
+    "start")
+        dev_start
+        ;;
+    "check")
+        quality_check
+        ;;
+    "test")
+        full_test
+        ;;
+    "benchmark")
+        benchmark
+        ;;
+    *)
+        echo "Usage: $0 {start|check|test|benchmark}"
+        exit 1
+        ;;
+esac
+```
+
+## 🎉 总结
+
+开发环境配置包括：
+
+1. **Python环境**: 虚拟环境、依赖管理
+2. **IDE配置**: VS Code、PyCharm设置
+3. **Docker环境**: 容器化开发
+4. **测试配置**: pytest、覆盖率
+5. **调试工具**: 日志、性能分析
+6. **开发脚本**: 自动化工具
+
+## 📚 相关文档
+
+- [贡献指南](../development/contributing.md) - 代码贡献流程
+- [测试指南](../development/testing.md) - 测试编写指南
+- [生产环境配置](production.md) - 生产部署配置
+- [故障排除](../examples/troubleshooting.md) - 开发问题排查
diff --git a/docs/configuration/distributed.md b/docs/configuration/distributed.md
new file mode 100644
index 0000000..52557f5
--- /dev/null
+++ b/docs/configuration/distributed.md
@@ -0,0 +1,761 @@
+# 分布式配置
+
+本文档详细介绍如何配置Locust性能测试框架的分布式部署，包括Master-Worker架构、集群管理和负载均衡配置。
+
+## 🏗️ 分布式架构
+
+### 架构概览
+
+```mermaid
+graph TD
+    A[负载均衡器] --> B[Locust Master 1]
+    A --> C[Locust Master 2]
+    A --> D[Locust Master 3]
+
+    B --> E[Worker Group 1]
+    B --> F[Worker Group 2]
+    C --> G[Worker Group 3]
+    C --> H[Worker Group 4]
+    D --> I[Worker Group 5]
+    D --> J[Worker Group 6]
+
+    E --> K[目标系统集群]
+    F --> K
+    G --> K
+    H --> K
+    I --> K
+    J --> K
+
+    L[监控系统] --> A
+    L --> B
+    L --> C
+    L --> D
+    L --> E
+    L --> F
+    L --> G
+    L --> H
+    L --> I
+    L --> J
+```
+
+### 组件说明
+
+```yaml
+# distributed/architecture.yml
+components:
+  load_balancer:
+    description: "负载均衡器，分发Web UI请求"
+    technology: "Nginx/HAProxy/AWS ALB"
+    ports: [80, 443]
+
+  master_nodes:
+    description: "Locust Master节点，协调测试执行"
+    count: 3
+    ports: [8089, 5557]
+    resources:
+      cpu: "2 cores"
+      memory: "4GB"
+      storage: "20GB"
+
+  worker_groups:
+    description: "Locust Worker节点组，执行实际测试"
+    groups: 6
+    workers_per_group: 10
+    ports: []
+    resources:
+      cpu: "1 core"
+      memory: "2GB"
+      storage: "10GB"
+
+  target_system:
+    description: "被测试的目标系统"
+    instances: "多实例"
+    load_balancer: "独立负载均衡"
+
+  monitoring:
+    description: "监控和日志收集系统"
+    components: ["Prometheus", "Grafana", "ELK Stack"]
+```
+
+## 🎛️ Master节点配置
+
+### 1. 高可用Master配置
+
+```yaml
+# config/master-ha.yml
+master_cluster:
+  enabled: true
+  nodes:
+    - name: "master-1"
+      host: "10.0.1.10"
+      port: 5557
+      web_port: 8089
+      priority: 100
+
+    - name: "master-2"
+      host: "10.0.1.11"
+      port: 5557
+      web_port: 8089
+      priority: 90
+
+    - name: "master-3"
+      host: "10.0.1.12"
+      port: 5557
+      web_port: 8089
+      priority: 80
+
+  # 选举配置
+  election:
+    enabled: true
+    timeout: 30
+    heartbeat_interval: 5
+
+  # 数据同步
+  synchronization:
+    enabled: true
+    sync_interval: 10
+    conflict_resolution: "latest_wins"
+
+# Master节点配置
+master:
+  # 绑定配置
+  bind:
+    host: "0.0.0.0"
+    port: 5557
+
+  # Web UI配置
+  web:
+    host: "0.0.0.0"
+    port: 8089
+    auth_credentials: "admin:secure_password"
+
+  # Worker管理
+  workers:
+    expect_workers: 60  # 期望Worker数量
+    expect_workers_max_wait: 120  # 最大等待时间
+    heartbeat_liveness: 60  # 心跳存活时间
+    heartbeat_interval: 3  # 心跳间隔
+
+  # 负载分配
+  load_distribution:
+    strategy: "round_robin"  # round_robin, least_loaded, weighted
+    rebalance_interval: 30
+
+  # 故障处理
+  fault_tolerance:
+    worker_timeout: 60
+    retry_failed_workers: true
+    max_retries: 3
+
+  # 资源限制
+  resources:
+    max_memory_usage: "4GB"
+    max_cpu_usage: "80%"
+    max_connections: 10000
+```
+
+### 2. Master启动脚本
+
+```bash
+#!/bin/bash
+# scripts/start-master.sh
+
+set -e
+
+# 配置变量
+MASTER_HOST=${MASTER_HOST:-"0.0.0.0"}
+MASTER_PORT=${MASTER_PORT:-5557}
+WEB_HOST=${WEB_HOST:-"0.0.0.0"}
+WEB_PORT=${WEB_PORT:-8089}
+LOCUSTFILE=${LOCUSTFILE:-"locustfiles/distributed.py"}
+EXPECT_WORKERS=${EXPECT_WORKERS:-60}
+
+# 日志配置
+LOG_LEVEL=${LOG_LEVEL:-"INFO"}
+LOG_FILE=${LOG_FILE:-"/var/log/locust/master.log"}
+
+# 创建日志目录
+mkdir -p $(dirname $LOG_FILE)
+
+# 健康检查函数
+health_check() {
+    local max_attempts=30
+    local attempt=1
+
+    while [ $attempt -le $max_attempts ]; do
+        if curl -f http://${WEB_HOST}:${WEB_PORT}/stats/requests > /dev/null 2>&1; then
+            echo "Master is healthy"
+            return 0
+        fi
+
+        echo "Health check attempt $attempt/$max_attempts failed"
+        sleep 2
+        ((attempt++))
+    done
+
+    echo "Master health check failed"
+    return 1
+}
+
+# 启动Master
+echo "Starting Locust Master..."
+echo "Configuration:"
+echo "  Host: ${MASTER_HOST}:${MASTER_PORT}"
+echo "  Web UI: ${WEB_HOST}:${WEB_PORT}"
+echo "  Locustfile: ${LOCUSTFILE}"
+echo "  Expected Workers: ${EXPECT_WORKERS}"
+
+# 启动命令
+locust \
+    --master \
+    --master-bind-host=${MASTER_HOST} \
+    --master-bind-port=${MASTER_PORT} \
+    --web-host=${WEB_HOST} \
+    --web-port=${WEB_PORT} \
+    --expect-workers=${EXPECT_WORKERS} \
+    --loglevel=${LOG_LEVEL} \
+    --logfile=${LOG_FILE} \
+    -f ${LOCUSTFILE} &
+
+MASTER_PID=$!
+echo "Master started with PID: $MASTER_PID"
+
+# 等待启动
+sleep 10
+
+# 健康检查
+if health_check; then
+    echo "Master is running successfully"
+    echo $MASTER_PID > /var/run/locust-master.pid
+else
+    echo "Master failed to start properly"
+    kill $MASTER_PID 2>/dev/null || true
+    exit 1
+fi
+
+# 保持前台运行
+wait $MASTER_PID
+```
+
+## 👥 Worker节点配置
+
+### 1. Worker配置
+
+```yaml
+# config/worker.yml
+worker:
+  # Master连接配置
+  master:
+    host: "master.locust.local"  # Master主机名或IP
+    port: 5557
+    connection_timeout: 30
+    reconnect_interval: 5
+    max_reconnect_attempts: 10
+
+  # Worker标识
+  identity:
+    node_id: "${HOSTNAME}"
+    tags: ["region:us-west", "zone:a", "type:standard"]
+
+  # 资源配置
+  resources:
+    max_users: 1000  # 单个Worker最大用户数
+    cpu_limit: "1000m"  # CPU限制
+    memory_limit: "2Gi"  # 内存限制
+
+  # 性能调优
+  performance:
+    gevent_pool_size: 1000
+    connection_pool_size: 100
+    dns_cache_size: 1000
+
+  # 监控配置
+  monitoring:
+    metrics_enabled: true
+    metrics_port: 9090
+    health_check_port: 8080
+
+  # 日志配置
+  logging:
+    level: "INFO"
+    format: "json"
+    file: "/var/log/locust/worker.log"
+    max_size: "100MB"
+    backup_count: 5
+```
+
+### 2. Worker自动扩缩容
+
+```yaml
+# k8s/worker-hpa.yml
+apiVersion: autoscaling/v2
+kind: HorizontalPodAutoscaler
+metadata:
+  name: locust-worker-hpa
+  namespace: locust
+spec:
+  scaleTargetRef:
+    apiVersion: apps/v1
+    kind: Deployment
+    name: locust-worker
+  minReplicas: 10
+  maxReplicas: 100
+  metrics:
+  - type: Resource
+    resource:
+      name: cpu
+      target:
+        type: Utilization
+        averageUtilization: 70
+  - type: Resource
+    resource:
+      name: memory
+      target:
+        type: Utilization
+        averageUtilization: 80
+  - type: Pods
+    pods:
+      metric:
+        name: locust_users_per_worker
+      target:
+        type: AverageValue
+        averageValue: "500"
+  behavior:
+    scaleUp:
+      stabilizationWindowSeconds: 60
+      policies:
+      - type: Percent
+        value: 50
+        periodSeconds: 60
+    scaleDown:
+      stabilizationWindowSeconds: 300
+      policies:
+      - type: Percent
+        value: 10
+        periodSeconds: 60
+
+---
+# Worker部署配置
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: locust-worker
+  namespace: locust
+spec:
+  replicas: 10
+  selector:
+    matchLabels:
+      app: locust-worker
+  template:
+    metadata:
+      labels:
+        app: locust-worker
+      annotations:
+        prometheus.io/scrape: "true"
+        prometheus.io/port: "9090"
+        prometheus.io/path: "/metrics"
+    spec:
+      containers:
+      - name: locust-worker
+        image: locust-framework:latest
+        ports:
+        - containerPort: 9090
+          name: metrics
+        - containerPort: 8080
+          name: health
+        env:
+        - name: LOCUST_MODE
+          value: "worker"
+        - name: LOCUST_MASTER_HOST
+          value: "locust-master-service"
+        - name: LOCUST_MASTER_PORT
+          value: "5557"
+        - name: WORKER_NODE_ID
+          valueFrom:
+            fieldRef:
+              fieldPath: metadata.name
+        resources:
+          requests:
+            cpu: "500m"
+            memory: "1Gi"
+          limits:
+            cpu: "1000m"
+            memory: "2Gi"
+        livenessProbe:
+          httpGet:
+            path: /health
+            port: 8080
+          initialDelaySeconds: 30
+          periodSeconds: 10
+        readinessProbe:
+          httpGet:
+            path: /ready
+            port: 8080
+          initialDelaySeconds: 5
+          periodSeconds: 5
+        volumeMounts:
+        - name: locustfiles
+          mountPath: /app/locustfiles
+        - name: config
+          mountPath: /app/config
+      volumes:
+      - name: locustfiles
+        configMap:
+          name: locustfiles-config
+      - name: config
+        configMap:
+          name: worker-config
+```
+
+### 3. Worker启动脚本
+
+```bash
+#!/bin/bash
+# scripts/start-worker.sh
+
+set -e
+
+# 配置变量
+MASTER_HOST=${MASTER_HOST:-"master.locust.local"}
+MASTER_PORT=${MASTER_PORT:-5557}
+LOCUSTFILE=${LOCUSTFILE:-"locustfiles/distributed.py"}
+WORKER_NODE_ID=${WORKER_NODE_ID:-$(hostname)}
+
+# 日志配置
+LOG_LEVEL=${LOG_LEVEL:-"INFO"}
+LOG_FILE=${LOG_FILE:-"/var/log/locust/worker-${WORKER_NODE_ID}.log"}
+
+# 创建日志目录
+mkdir -p $(dirname $LOG_FILE)
+
+# Master连接检查
+check_master_connection() {
+    local max_attempts=30
+    local attempt=1
+
+    while [ $attempt -le $max_attempts ]; do
+        if nc -z ${MASTER_HOST} ${MASTER_PORT}; then
+            echo "Master connection successful"
+            return 0
+        fi
+
+        echo "Master connection attempt $attempt/$max_attempts failed"
+        sleep 2
+        ((attempt++))
+    done
+
+    echo "Failed to connect to Master"
+    return 1
+}
+
+# 启动Worker
+echo "Starting Locust Worker..."
+echo "Configuration:"
+echo "  Worker ID: ${WORKER_NODE_ID}"
+echo "  Master: ${MASTER_HOST}:${MASTER_PORT}"
+echo "  Locustfile: ${LOCUSTFILE}"
+
+# 检查Master连接
+if ! check_master_connection; then
+    echo "Cannot connect to Master, exiting"
+    exit 1
+fi
+
+# 启动命令
+exec locust \
+    --worker \
+    --master-host=${MASTER_HOST} \
+    --master-port=${MASTER_PORT} \
+    --loglevel=${LOG_LEVEL} \
+    --logfile=${LOG_FILE} \
+    -f ${LOCUSTFILE}
+```
+
+## 🔄 负载均衡配置
+
+### 1. Nginx负载均衡
+
+```nginx
+# nginx/locust-lb.conf
+upstream locust_masters {
+    least_conn;
+    server 10.0.1.10:8089 weight=3 max_fails=3 fail_timeout=30s;
+    server 10.0.1.11:8089 weight=2 max_fails=3 fail_timeout=30s;
+    server 10.0.1.12:8089 weight=1 max_fails=3 fail_timeout=30s;
+}
+
+server {
+    listen 80;
+    listen 443 ssl http2;
+    server_name locust.company.com;
+
+    # SSL配置
+    ssl_certificate /etc/ssl/certs/locust.crt;
+    ssl_certificate_key /etc/ssl/private/locust.key;
+    ssl_protocols TLSv1.2 TLSv1.3;
+    ssl_ciphers ECDHE-RSA-AES256-GCM-SHA512:DHE-RSA-AES256-GCM-SHA512;
+
+    # 安全头
+    add_header X-Frame-Options DENY;
+    add_header X-Content-Type-Options nosniff;
+    add_header X-XSS-Protection "1; mode=block";
+
+    # 代理配置
+    location / {
+        proxy_pass http://locust_masters;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_set_header X-Forwarded-Proto $scheme;
+
+        # WebSocket支持
+        proxy_http_version 1.1;
+        proxy_set_header Upgrade $http_upgrade;
+        proxy_set_header Connection "upgrade";
+
+        # 超时配置
+        proxy_connect_timeout 60s;
+        proxy_send_timeout 60s;
+        proxy_read_timeout 60s;
+
+        # 缓冲配置
+        proxy_buffering off;
+        proxy_request_buffering off;
+    }
+
+    # 健康检查端点
+    location /health {
+        access_log off;
+        return 200 "healthy\n";
+        add_header Content-Type text/plain;
+    }
+
+    # 静态资源缓存
+    location ~* \.(css|js|png|jpg|jpeg|gif|ico|svg)$ {
+        expires 1y;
+        add_header Cache-Control "public, immutable";
+    }
+}
+```
+
+### 2. HAProxy配置
+
+```haproxy
+# haproxy/haproxy.cfg
+global
+    daemon
+    maxconn 4096
+    log stdout local0
+
+defaults
+    mode http
+    timeout connect 5000ms
+    timeout client 50000ms
+    timeout server 50000ms
+    option httplog
+    option dontlognull
+
+# 统计页面
+stats enable
+stats uri /stats
+stats refresh 30s
+stats admin if TRUE
+
+# Locust Master负载均衡
+frontend locust_frontend
+    bind *:80
+    bind *:443 ssl crt /etc/ssl/certs/locust.pem
+    redirect scheme https if !{ ssl_fc }
+
+    # ACL规则
+    acl is_websocket hdr(Upgrade) -i websocket
+    acl is_api path_beg /api
+    acl is_stats path_beg /stats
+
+    # 路由规则
+    use_backend locust_masters if !is_stats
+    use_backend stats_backend if is_stats
+
+backend locust_masters
+    balance leastconn
+    option httpchk GET /stats/requests
+
+    server master1 10.0.1.10:8089 check weight 3
+    server master2 10.0.1.11:8089 check weight 2
+    server master3 10.0.1.12:8089 check weight 1
+
+backend stats_backend
+    stats enable
+    stats uri /stats
+    stats refresh 30s
+```
+
+## 📊 监控和日志
+
+### 1. Prometheus监控配置
+
+```yaml
+# monitoring/prometheus.yml
+global:
+  scrape_interval: 15s
+  evaluation_interval: 15s
+
+rule_files:
+  - "locust_rules.yml"
+
+scrape_configs:
+  # Locust Master监控
+  - job_name: 'locust-master'
+    static_configs:
+      - targets:
+        - 'master-1:9090'
+        - 'master-2:9090'
+        - 'master-3:9090'
+    scrape_interval: 10s
+    metrics_path: '/metrics'
+
+  # Locust Worker监控
+  - job_name: 'locust-worker'
+    kubernetes_sd_configs:
+      - role: pod
+        namespaces:
+          names:
+            - locust
+    relabel_configs:
+      - source_labels: [__meta_kubernetes_pod_label_app]
+        action: keep
+        regex: locust-worker
+      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
+        action: keep
+        regex: true
+      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_port]
+        action: replace
+        target_label: __address__
+        regex: (.+)
+        replacement: ${1}:9090
+
+  # 系统监控
+  - job_name: 'node-exporter'
+    static_configs:
+      - targets:
+        - 'node1:9100'
+        - 'node2:9100'
+        - 'node3:9100'
+
+alerting:
+  alertmanagers:
+    - static_configs:
+        - targets:
+          - 'alertmanager:9093'
+```
+
+### 2. 告警规则
+
+```yaml
+# monitoring/locust_rules.yml
+groups:
+  - name: locust_alerts
+    rules:
+      # Master节点告警
+      - alert: LocustMasterDown
+        expr: up{job="locust-master"} == 0
+        for: 1m
+        labels:
+          severity: critical
+        annotations:
+          summary: "Locust Master节点宕机"
+          description: "Master节点 {{ $labels.instance }} 已宕机超过1分钟"
+
+      # Worker连接告警
+      - alert: LocustWorkerDisconnected
+        expr: locust_workers_connected < 50
+        for: 2m
+        labels:
+          severity: warning
+        annotations:
+          summary: "Locust Worker连接数过低"
+          description: "当前连接的Worker数量为 {{ $value }}，低于预期"
+
+      # 性能告警
+      - alert: LocustHighErrorRate
+        expr: locust_errors_rate > 0.05
+        for: 5m
+        labels:
+          severity: warning
+        annotations:
+          summary: "Locust错误率过高"
+          description: "错误率为 {{ $value | humanizePercentage }}，超过5%"
+
+      # 资源告警
+      - alert: LocustHighMemoryUsage
+        expr: process_resident_memory_bytes{job=~"locust-.*"} / 1024 / 1024 / 1024 > 1.5
+        for: 5m
+        labels:
+          severity: warning
+        annotations:
+          summary: "Locust内存使用过高"
+          description: "{{ $labels.instance }} 内存使用量为 {{ $value | humanize }}GB"
+```
+
+## 🚀 部署脚本
+
+### 1. 完整部署脚本
+
+```bash
+#!/bin/bash
+# scripts/deploy-distributed.sh
+
+set -e
+
+# 配置变量
+NAMESPACE=${NAMESPACE:-"locust"}
+MASTER_REPLICAS=${MASTER_REPLICAS:-3}
+WORKER_REPLICAS=${WORKER_REPLICAS:-60}
+IMAGE_TAG=${IMAGE_TAG:-"latest"}
+
+echo "Deploying Locust distributed cluster..."
+echo "Namespace: $NAMESPACE"
+echo "Master replicas: $MASTER_REPLICAS"
+echo "Worker replicas: $WORKER_REPLICAS"
+echo "Image tag: $IMAGE_TAG"
+
+# 创建命名空间
+kubectl create namespace $NAMESPACE --dry-run=client -o yaml | kubectl apply -f -
+
+# 部署配置
+kubectl apply -f k8s/configmaps/ -n $NAMESPACE
+kubectl apply -f k8s/secrets/ -n $NAMESPACE
+
+# 部署Master
+envsubst < k8s/master-deployment.yml | kubectl apply -f - -n $NAMESPACE
+kubectl apply -f k8s/master-service.yml -n $NAMESPACE
+
+# 等待Master启动
+echo "Waiting for Master to be ready..."
+kubectl wait --for=condition=available --timeout=300s deployment/locust-master -n $NAMESPACE
+
+# 部署Worker
+envsubst < k8s/worker-deployment.yml | kubectl apply -f - -n $NAMESPACE
+
+# 部署HPA
+kubectl apply -f k8s/worker-hpa.yml -n $NAMESPACE
+
+# 部署监控
+kubectl apply -f monitoring/ -n $NAMESPACE
+
+# 部署Ingress
+kubectl apply -f k8s/ingress.yml -n $NAMESPACE
+
+echo "Deployment completed!"
+echo "Access Locust Web UI at: https://locust.company.com"
+
+# 显示状态
+kubectl get all -n $NAMESPACE
+```
+
+## 📚 相关文档
+
+- [生产环境配置](production.md) - 生产部署指南
+- [监控配置](monitoring-config.md) - 监控系统配置
+- [故障排除](../examples/troubleshooting.md) - 分布式问题排查
+- [性能优化](../examples/best-practices.md) - 性能优化建议
diff --git a/docs/configuration/framework-config.md b/docs/configuration/framework-config.md
new file mode 100644
index 0000000..b884069
--- /dev/null
+++ b/docs/configuration/framework-config.md
@@ -0,0 +1,574 @@
+# 框架配置参考
+
+本文档详细介绍Locust性能测试框架的配置选项，包括核心配置、插件配置、环境配置等。
+
+## 📋 配置概述
+
+框架使用Dynaconf进行配置管理，支持多种配置源和环境隔离：
+
+```python
+# 配置优先级（从高到低）
+1. 环境变量
+2. 命令行参数
+3. 用户配置文件
+4. 环境特定配置
+5. 默认配置
+```
+
+## 🔧 核心配置
+
+### 主配置文件 (conf/settings.toml)
+
+```toml
+# 基础配置
+[default]
+# 框架基础设置
+framework_name = "Locust Performance Testing Framework"
+version = "2.0.0"
+debug = false
+log_level = "INFO"
+
+# 测试配置
+[default.test]
+# 默认测试设置
+default_host = "http://localhost:8080"
+default_users = 10
+default_spawn_rate = 1
+default_run_time = "10m"
+stop_timeout = 30
+
+# Web UI配置
+[default.web]
+enabled = true
+host = "0.0.0.0"
+port = 8089
+auth_enabled = false
+username = "admin"
+password = "admin"
+
+# 分布式配置
+[default.distributed]
+master_host = "127.0.0.1"
+master_port = 5557
+worker_timeout = 60
+expect_workers = 1
+heartbeat_interval = 3
+
+# 日志配置
+[default.logging]
+level = "INFO"
+format = "{time:YYYY-MM-DD HH:mm:ss} | {level} | {name}:{function}:{line} | {message}"
+rotation = "100 MB"
+retention = "30 days"
+compression = "gz"
+
+# 输出配置
+[default.output]
+reports_dir = "reports"
+data_dir = "data"
+logs_dir = "logs"
+auto_cleanup = true
+cleanup_days = 7
+
+# 性能配置
+[default.performance]
+max_workers = 100
+connection_pool_size = 50
+request_timeout = 30
+max_retries = 3
+backoff_factor = 1.0
+```
+
+### 环境特定配置
+
+#### 开发环境 (conf/environments/development.toml)
+
+```toml
+[default]
+debug = true
+log_level = "DEBUG"
+
+[default.test]
+default_host = "http://localhost:3000"
+default_users = 5
+default_run_time = "5m"
+
+[default.web]
+port = 8089
+
+[default.logging]
+level = "DEBUG"
+
+[default.performance]
+max_workers = 10
+connection_pool_size = 10
+```
+
+#### 测试环境 (conf/environments/testing.toml)
+
+```toml
+[default]
+debug = false
+log_level = "INFO"
+
+[default.test]
+default_host = "http://test-api.example.com"
+default_users = 50
+default_run_time = "30m"
+
+[default.distributed]
+expect_workers = 3
+
+[default.performance]
+max_workers = 200
+connection_pool_size = 100
+```
+
+#### 生产环境 (conf/environments/production.toml)
+
+```toml
+[default]
+debug = false
+log_level = "WARNING"
+
+[default.test]
+default_host = "http://api.example.com"
+default_users = 1000
+default_run_time = "2h"
+
+[default.web]
+auth_enabled = true
+username = "${WEB_USERNAME}"
+password = "${WEB_PASSWORD}"
+
+[default.distributed]
+expect_workers = 10
+
+[default.performance]
+max_workers = 1000
+connection_pool_size = 500
+request_timeout = 60
+
+[default.security]
+ssl_verify = true
+api_key = "${API_KEY}"
+```
+
+## 🔌 插件配置
+
+### 插件管理配置
+
+```toml
+# conf/plugins/plugin_config.toml
+[plugins]
+# 插件发现路径
+discovery_paths = [
+    "src/plugins/builtin",
+    "plugins/custom",
+    "~/.locust/plugins"
+]
+
+# 自动加载插件
+auto_load = true
+load_timeout = 30
+
+# 插件依赖检查
+check_dependencies = true
+install_missing = false
+
+# 插件配置
+[plugins.performance_analyzer]
+enabled = true
+config = { output_formats = ["html", "json"], include_charts = true }
+
+[plugins.system_monitor]
+enabled = true
+config = {
+    interval = 5,
+    metrics = ["cpu", "memory", "disk", "network"],
+    alert_thresholds = { cpu = 80, memory = 85 }
+}
+
+[plugins.notification_service]
+enabled = true
+config = {
+    channels = ["feishu", "email"],
+    feishu_webhook = "${FEISHU_WEBHOOK}",
+    email_smtp = "smtp.example.com"
+}
+```
+
+### 分析插件配置
+
+```toml
+[analysis]
+# 性能分析配置
+[analysis.performance_analyzer]
+enabled = true
+
+# 响应时间阈值 (毫秒)
+[analysis.performance_analyzer.response_time_thresholds]
+excellent = 500
+good = 1000
+acceptable = 2000
+poor = 5000
+
+# 吞吐量阈值 (TPS)
+[analysis.performance_analyzer.throughput_thresholds]
+excellent = 1000
+good = 500
+acceptable = 100
+poor = 50
+
+# 错误率阈值 (百分比)
+[analysis.performance_analyzer.error_rate_thresholds]
+excellent = 0.1
+good = 1.0
+acceptable = 5.0
+poor = 10.0
+
+# 指标权重
+[analysis.performance_analyzer.weights]
+response_time = 0.4
+throughput = 0.3
+error_rate = 0.2
+stability = 0.1
+
+# 趋势分析配置
+[analysis.trend_analyzer]
+enabled = true
+history_days = 30
+prediction_days = 7
+confidence_level = 0.95
+```
+
+### 监控插件配置
+
+```toml
+[monitoring]
+# 性能监控配置
+[monitoring.performance_monitor]
+enabled = true
+interval = 5
+buffer_size = 1000
+
+# 告警管理配置
+[monitoring.alert_manager]
+enabled = true
+check_interval = 10
+max_alerts_per_minute = 10
+
+# 告警规则
+[[monitoring.alert_rules]]
+name = "high_response_time"
+metric = "avg_response_time"
+condition = "> 2000"
+duration = "5m"
+severity = "warning"
+message = "平均响应时间超过2秒"
+
+[[monitoring.alert_rules]]
+name = "high_error_rate"
+metric = "error_rate"
+condition = "> 5"
+duration = "2m"
+severity = "critical"
+message = "错误率超过5%"
+
+[[monitoring.alert_rules]]
+name = "low_throughput"
+metric = "requests_per_second"
+condition = "< 10"
+duration = "3m"
+severity = "warning"
+message = "吞吐量低于10 RPS"
+```
+
+### 通知插件配置
+
+```toml
+[notifications]
+# 飞书通知配置
+[notifications.feishu]
+enabled = true
+webhook_url = "${FEISHU_WEBHOOK_URL}"
+secret = "${FEISHU_SECRET}"
+at_all = false
+at_users = []
+
+# 钉钉通知配置
+[notifications.dingtalk]
+enabled = false
+webhook_url = "${DINGTALK_WEBHOOK_URL}"
+secret = "${DINGTALK_SECRET}"
+
+# 邮件通知配置
+[notifications.email]
+enabled = true
+smtp_server = "smtp.example.com"
+smtp_port = 587
+username = "${EMAIL_USERNAME}"
+password = "${EMAIL_PASSWORD}"
+from_addr = "noreply@example.com"
+to_addrs = ["admin@example.com"]
+use_tls = true
+
+# 企业微信通知配置
+[notifications.wechat_work]
+enabled = false
+webhook_url = "${WECHAT_WEBHOOK_URL}"
+```
+
+## 📊 数据管理配置
+
+```toml
+[data_manager]
+# 数据生成配置
+[data_manager.generator]
+enabled = true
+locale = "zh_CN"
+seed = 12345
+cache_size = 10000
+
+# 数据提供配置
+[data_manager.provider]
+enabled = true
+default_strategy = "round_robin"
+data_sources = [
+    { type = "file", path = "test_data/users.csv" },
+    { type = "database", connection = "mysql://user:pass@localhost/testdb" },
+    { type = "api", url = "http://api.example.com/test-data" }
+]
+
+# 数据分发配置
+[data_manager.distributor]
+enabled = true
+sync_interval = 60
+batch_size = 1000
+compression = true
+```
+
+## 🔒 安全配置
+
+```toml
+[security]
+# 认证配置
+[security.authentication]
+enabled = true
+method = "token"  # token, basic, oauth
+token_header = "Authorization"
+token_prefix = "Bearer"
+
+# SSL/TLS配置
+[security.ssl]
+verify = true
+cert_file = ""
+key_file = ""
+ca_file = ""
+
+# API安全配置
+[security.api]
+rate_limit = 1000
+rate_limit_window = 3600
+api_key_header = "X-API-Key"
+allowed_origins = ["*"]
+```
+
+## 🚀 性能优化配置
+
+```toml
+[performance]
+# 连接池配置
+[performance.connection_pool]
+max_connections = 100
+max_connections_per_host = 20
+keepalive_timeout = 30
+connection_timeout = 10
+
+# 请求配置
+[performance.requests]
+timeout = 30
+max_retries = 3
+backoff_factor = 1.0
+retry_on_status = [500, 502, 503, 504]
+
+# 内存配置
+[performance.memory]
+max_memory_usage = "2GB"
+gc_threshold = 0.8
+cache_size = 10000
+
+# 并发配置
+[performance.concurrency]
+max_workers = 1000
+worker_queue_size = 10000
+task_timeout = 300
+```
+
+## 📝 日志配置
+
+### 详细日志配置 (conf/logging.conf)
+
+```ini
+[loggers]
+keys=root,locust,framework
+
+[handlers]
+keys=consoleHandler,fileHandler,rotatingFileHandler
+
+[formatters]
+keys=simpleFormatter,detailedFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler,rotatingFileHandler
+
+[logger_locust]
+level=INFO
+handlers=fileHandler
+qualname=locust
+propagate=0
+
+[logger_framework]
+level=DEBUG
+handlers=rotatingFileHandler
+qualname=framework
+propagate=0
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=simpleFormatter
+args=(sys.stdout,)
+
+[handler_fileHandler]
+class=FileHandler
+level=INFO
+formatter=detailedFormatter
+args=('logs/locust.log',)
+
+[handler_rotatingFileHandler]
+class=handlers.RotatingFileHandler
+level=DEBUG
+formatter=detailedFormatter
+args=('logs/framework.log', 'a', 10485760, 5)
+
+[formatter_simpleFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
+
+[formatter_detailedFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(funcName)s - %(message)s
+```
+
+## 🔧 配置使用示例
+
+### 1. 基础配置加载
+
+```python
+from dynaconf import Dynaconf
+
+# 加载配置
+settings = Dynaconf(
+    envvar_prefix="LOCUST",
+    settings_files=['conf/settings.toml', 'conf/.secrets.toml'],
+    environments=True,
+    load_dotenv=True,
+    env_switcher="LOCUST_ENV"
+)
+
+# 使用配置
+print(f"当前环境: {settings.current_env}")
+print(f"默认主机: {settings.test.default_host}")
+print(f"Web端口: {settings.web.port}")
+```
+
+### 2. 环境切换
+
+```bash
+# 设置环境变量
+export LOCUST_ENV=production
+
+# 或在代码中切换
+settings.setenv('production')
+```
+
+### 3. 动态配置更新
+
+```python
+# 运行时更新配置
+settings.set('test.default_users', 100)
+settings.set('web.port', 9089)
+
+# 保存配置
+settings.save()
+```
+
+### 4. 配置验证
+
+```python
+from pydantic import BaseModel, validator
+
+class TestConfig(BaseModel):
+    default_host: str
+    default_users: int
+    default_spawn_rate: float
+
+    @validator('default_users')
+    def validate_users(cls, v):
+        if v <= 0:
+            raise ValueError('用户数必须大于0')
+        return v
+
+    @validator('default_spawn_rate')
+    def validate_spawn_rate(cls, v):
+        if v <= 0:
+            raise ValueError('生成速率必须大于0')
+        return v
+
+# 验证配置
+try:
+    test_config = TestConfig(**settings.test)
+    print("配置验证通过")
+except Exception as e:
+    print(f"配置验证失败: {e}")
+```
+
+## 🔐 敏感信息管理
+
+### 1. 环境变量
+
+```bash
+# .env 文件
+LOCUST_ENV=production
+FEISHU_WEBHOOK_URL=https://open.feishu.cn/open-apis/bot/v2/hook/xxx
+EMAIL_PASSWORD=your_email_password
+API_KEY=your_api_key
+```
+
+### 2. 密钥文件
+
+```toml
+# conf/.secrets.toml (不提交到版本控制)
+[default]
+api_key = "your_secret_api_key"
+database_password = "your_db_password"
+
+[production]
+ssl_cert = "/path/to/cert.pem"
+ssl_key = "/path/to/key.pem"
+```
+
+## ⚠️ 配置注意事项
+
+1. **安全性**: 敏感信息使用环境变量或密钥文件
+2. **验证**: 启动时验证关键配置项
+3. **备份**: 定期备份配置文件
+4. **版本控制**: 不要提交包含敏感信息的配置文件
+5. **文档**: 及时更新配置文档
+
+## 🔗 相关文档
+
+- [插件配置](plugin-config.md) - 插件专用配置
+- [环境配置](environment-config.md) - 环境特定配置
+- [安全配置](security-config.md) - 安全相关配置
+- [性能调优](../development/performance-tuning.md) - 性能优化指南
diff --git a/docs/configuration/monitoring-config.md b/docs/configuration/monitoring-config.md
new file mode 100644
index 0000000..ed5bf44
--- /dev/null
+++ b/docs/configuration/monitoring-config.md
@@ -0,0 +1,917 @@
+# 监控配置
+
+本文档详细介绍如何配置Locust性能测试框架的监控系统，包括指标收集、告警设置和可视化配置。
+
+## 🎯 监控架构
+
+### 监控组件
+
+```yaml
+# monitoring/architecture.yml
+monitoring_stack:
+  metrics_collection:
+    - prometheus      # 指标收集
+    - node_exporter   # 系统指标
+    - locust_exporter # Locust指标
+
+  visualization:
+    - grafana         # 仪表板
+    - kibana          # 日志分析
+
+  alerting:
+    - alertmanager    # 告警管理
+    - webhook_alerts  # 自定义告警
+
+  storage:
+    - prometheus_tsdb # 时序数据库
+    - elasticsearch   # 日志存储
+```
+
+### 数据流
+
+```mermaid
+graph TD
+    A[Locust Framework] --> B[Metrics Exporter]
+    B --> C[Prometheus]
+    C --> D[Grafana]
+    C --> E[AlertManager]
+    E --> F[Notification Channels]
+    A --> G[Log Aggregator]
+    G --> H[Elasticsearch]
+    H --> I[Kibana]
+```
+
+## 📊 Prometheus配置
+
+### 1. 基础配置
+
+```yaml
+# prometheus/prometheus.yml
+global:
+  scrape_interval: 15s
+  evaluation_interval: 15s
+  external_labels:
+    cluster: 'locust-cluster'
+    environment: 'production'
+
+rule_files:
+  - "rules/*.yml"
+
+scrape_configs:
+  # Locust Master节点
+  - job_name: 'locust-master'
+    static_configs:
+      - targets: ['locust-master:8089']
+    metrics_path: '/stats/requests'
+    params:
+      format: ['prometheus']
+    scrape_interval: 30s
+    scrape_timeout: 10s
+
+  # Locust Worker节点
+  - job_name: 'locust-workers'
+    static_configs:
+      - targets:
+        - 'locust-worker-1:8089'
+        - 'locust-worker-2:8089'
+        - 'locust-worker-3:8089'
+    metrics_path: '/stats/requests'
+    params:
+      format: ['prometheus']
+    scrape_interval: 30s
+
+  # 系统指标
+  - job_name: 'node-exporter'
+    static_configs:
+      - targets:
+        - 'locust-master:9100'
+        - 'locust-worker-1:9100'
+        - 'locust-worker-2:9100'
+        - 'locust-worker-3:9100'
+    scrape_interval: 15s
+
+  # 应用指标
+  - job_name: 'locust-app-metrics'
+    static_configs:
+      - targets: ['locust-master:9090']
+    metrics_path: '/metrics'
+    scrape_interval: 15s
+
+alerting:
+  alertmanagers:
+    - static_configs:
+        - targets:
+          - alertmanager:9093
+      timeout: 10s
+      api_version: v2
+```
+
+### 2. 自定义指标导出器
+
+```python
+# monitoring/locust_exporter.py
+from prometheus_client import start_http_server, Gauge, Counter, Histogram
+from locust import events
+import time
+import threading
+
+class LocustMetricsExporter:
+    """Locust指标导出器"""
+
+    def __init__(self, port=9090):
+        self.port = port
+
+        # 定义指标
+        self.request_total = Counter(
+            'locust_requests_total',
+            'Total number of requests',
+            ['method', 'name', 'status']
+        )
+
+        self.request_duration = Histogram(
+            'locust_request_duration_seconds',
+            'Request duration in seconds',
+            ['method', 'name'],
+            buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
+        )
+
+        self.current_users = Gauge(
+            'locust_current_users',
+            'Current number of users'
+        )
+
+        self.rps = Gauge(
+            'locust_requests_per_second',
+            'Current requests per second'
+        )
+
+        self.error_rate = Gauge(
+            'locust_error_rate_percent',
+            'Current error rate percentage'
+        )
+
+        self.response_time_percentiles = Gauge(
+            'locust_response_time_percentile_seconds',
+            'Response time percentiles',
+            ['percentile']
+        )
+
+        # 业务指标
+        self.business_metrics = {}
+
+        # 注册事件监听器
+        self.register_listeners()
+
+    def register_listeners(self):
+        """注册Locust事件监听器"""
+
+        @events.request.add_listener
+        def on_request(request_type, name, response_time, response_length, exception, context, **kwargs):
+            # 记录请求总数
+            status = "success" if not exception else "failure"
+            self.request_total.labels(
+                method=request_type,
+                name=name,
+                status=status
+            ).inc()
+
+            # 记录请求耗时
+            if not exception:
+                self.request_duration.labels(
+                    method=request_type,
+                    name=name
+                ).observe(response_time / 1000.0)  # 转换为秒
+
+        @events.user_count_changed.add_listener
+        def on_user_count_changed(user_count, **kwargs):
+            self.current_users.set(user_count)
+
+        # 定期更新统计指标
+        def update_stats():
+            while True:
+                try:
+                    from locust import stats
+                    total_stats = stats.total
+
+                    if total_stats.num_requests > 0:
+                        # 更新RPS
+                        self.rps.set(total_stats.current_rps)
+
+                        # 更新错误率
+                        error_rate = (total_stats.num_failures / total_stats.num_requests) * 100
+                        self.error_rate.set(error_rate)
+
+                        # 更新响应时间百分位数
+                        self.response_time_percentiles.labels(percentile="50").set(
+                            total_stats.get_response_time_percentile(0.5) / 1000.0
+                        )
+                        self.response_time_percentiles.labels(percentile="95").set(
+                            total_stats.get_response_time_percentile(0.95) / 1000.0
+                        )
+                        self.response_time_percentiles.labels(percentile="99").set(
+                            total_stats.get_response_time_percentile(0.99) / 1000.0
+                        )
+
+                except Exception as e:
+                    print(f"Error updating stats: {e}")
+
+                time.sleep(5)  # 每5秒更新一次
+
+        # 启动统计更新线程
+        stats_thread = threading.Thread(target=update_stats, daemon=True)
+        stats_thread.start()
+
+    def add_business_metric(self, name: str, description: str, metric_type: str = 'gauge', labels: list = None):
+        """添加业务指标"""
+        labels = labels or []
+
+        if metric_type == 'gauge':
+            metric = Gauge(name, description, labels)
+        elif metric_type == 'counter':
+            metric = Counter(name, description, labels)
+        elif metric_type == 'histogram':
+            metric = Histogram(name, description, labels)
+        else:
+            raise ValueError(f"Unsupported metric type: {metric_type}")
+
+        self.business_metrics[name] = metric
+        return metric
+
+    def start_server(self):
+        """启动指标服务器"""
+        start_http_server(self.port)
+        print(f"Metrics server started on port {self.port}")
+
+# 使用指标导出器
+exporter = LocustMetricsExporter()
+
+@events.init.add_listener
+def on_locust_init(environment, **kwargs):
+    exporter.start_server()
+
+    # 添加自定义业务指标
+    login_success_metric = exporter.add_business_metric(
+        'locust_login_success_total',
+        'Total successful logins',
+        'counter',
+        ['user_type']
+    )
+
+    order_processing_time = exporter.add_business_metric(
+        'locust_order_processing_seconds',
+        'Order processing time',
+        'histogram',
+        ['order_type']
+    )
+```
+
+## 📈 Grafana配置
+
+### 1. 数据源配置
+
+```yaml
+# grafana/datasources.yml
+apiVersion: 1
+
+datasources:
+  - name: Prometheus
+    type: prometheus
+    access: proxy
+    url: http://prometheus:9090
+    isDefault: true
+    editable: true
+
+  - name: Elasticsearch
+    type: elasticsearch
+    access: proxy
+    url: http://elasticsearch:9200
+    database: "locust-logs-*"
+    interval: Daily
+    timeField: "@timestamp"
+```
+
+### 2. 仪表板配置
+
+```json
+{
+  "dashboard": {
+    "id": null,
+    "title": "Locust Performance Dashboard",
+    "tags": ["locust", "performance"],
+    "timezone": "browser",
+    "panels": [
+      {
+        "id": 1,
+        "title": "Request Rate",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "rate(locust_requests_total[5m])",
+            "legendFormat": "{{method}} {{name}}",
+            "refId": "A"
+          }
+        ],
+        "yAxes": [
+          {
+            "label": "Requests/sec",
+            "min": 0
+          }
+        ],
+        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 0}
+      },
+      {
+        "id": 2,
+        "title": "Response Time",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "locust_response_time_percentile_seconds{percentile=\"50\"}",
+            "legendFormat": "50th percentile",
+            "refId": "A"
+          },
+          {
+            "expr": "locust_response_time_percentile_seconds{percentile=\"95\"}",
+            "legendFormat": "95th percentile",
+            "refId": "B"
+          },
+          {
+            "expr": "locust_response_time_percentile_seconds{percentile=\"99\"}",
+            "legendFormat": "99th percentile",
+            "refId": "C"
+          }
+        ],
+        "yAxes": [
+          {
+            "label": "Seconds",
+            "min": 0
+          }
+        ],
+        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 0}
+      },
+      {
+        "id": 3,
+        "title": "Error Rate",
+        "type": "singlestat",
+        "targets": [
+          {
+            "expr": "locust_error_rate_percent",
+            "refId": "A"
+          }
+        ],
+        "valueName": "current",
+        "format": "percent",
+        "thresholds": "5,10",
+        "colorBackground": true,
+        "gridPos": {"h": 4, "w": 6, "x": 0, "y": 8}
+      },
+      {
+        "id": 4,
+        "title": "Current Users",
+        "type": "singlestat",
+        "targets": [
+          {
+            "expr": "locust_current_users",
+            "refId": "A"
+          }
+        ],
+        "valueName": "current",
+        "format": "short",
+        "gridPos": {"h": 4, "w": 6, "x": 6, "y": 8}
+      },
+      {
+        "id": 5,
+        "title": "System Resources",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)",
+            "legendFormat": "CPU Usage - {{instance}}",
+            "refId": "A"
+          },
+          {
+            "expr": "(1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100",
+            "legendFormat": "Memory Usage - {{instance}}",
+            "refId": "B"
+          }
+        ],
+        "yAxes": [
+          {
+            "label": "Percent",
+            "min": 0,
+            "max": 100
+          }
+        ],
+        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 8}
+      }
+    ],
+    "time": {
+      "from": "now-1h",
+      "to": "now"
+    },
+    "refresh": "5s"
+  }
+}
+```
+
+### 3. 告警规则
+
+```yaml
+# grafana/alerts.yml
+groups:
+  - name: locust_alerts
+    rules:
+      - alert: HighErrorRate
+        expr: locust_error_rate_percent > 5
+        for: 2m
+        labels:
+          severity: warning
+          service: locust
+        annotations:
+          summary: "High error rate detected"
+          description: "Error rate is {{ $value }}% for more than 2 minutes"
+
+      - alert: HighResponseTime
+        expr: locust_response_time_percentile_seconds{percentile="95"} > 2
+        for: 5m
+        labels:
+          severity: warning
+          service: locust
+        annotations:
+          summary: "High response time detected"
+          description: "95th percentile response time is {{ $value }}s"
+
+      - alert: LowThroughput
+        expr: locust_requests_per_second < 50
+        for: 3m
+        labels:
+          severity: warning
+          service: locust
+        annotations:
+          summary: "Low throughput detected"
+          description: "Current RPS is {{ $value }}"
+
+      - alert: SystemResourceHigh
+        expr: (100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)) > 80
+        for: 5m
+        labels:
+          severity: critical
+          service: system
+        annotations:
+          summary: "High CPU usage on {{ $labels.instance }}"
+          description: "CPU usage is {{ $value }}%"
+```
+
+## 🚨 AlertManager配置
+
+### 1. 基础配置
+
+```yaml
+# alertmanager/alertmanager.yml
+global:
+  smtp_smarthost: 'smtp.gmail.com:587'
+  smtp_from: 'alerts@yourcompany.com'
+  smtp_auth_username: 'alerts@yourcompany.com'
+  smtp_auth_password: 'your-app-password'
+
+route:
+  group_by: ['alertname', 'cluster', 'service']
+  group_wait: 10s
+  group_interval: 10s
+  repeat_interval: 1h
+  receiver: 'default-receiver'
+  routes:
+    - match:
+        severity: critical
+      receiver: 'critical-alerts'
+      group_wait: 5s
+      repeat_interval: 30m
+    - match:
+        severity: warning
+      receiver: 'warning-alerts'
+      repeat_interval: 2h
+
+receivers:
+  - name: 'default-receiver'
+    email_configs:
+      - to: 'team@yourcompany.com'
+        subject: 'Locust Alert: {{ .GroupLabels.alertname }}'
+        body: |
+          {{ range .Alerts }}
+          Alert: {{ .Annotations.summary }}
+          Description: {{ .Annotations.description }}
+          Labels: {{ range .Labels.SortedPairs }}{{ .Name }}={{ .Value }} {{ end }}
+          {{ end }}
+
+  - name: 'critical-alerts'
+    email_configs:
+      - to: 'oncall@yourcompany.com'
+        subject: 'CRITICAL: Locust Alert'
+        body: |
+          CRITICAL ALERT TRIGGERED
+
+          {{ range .Alerts }}
+          Alert: {{ .Annotations.summary }}
+          Description: {{ .Annotations.description }}
+          Severity: {{ .Labels.severity }}
+          Service: {{ .Labels.service }}
+          Time: {{ .StartsAt }}
+          {{ end }}
+
+    slack_configs:
+      - api_url: 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
+        channel: '#alerts'
+        title: 'Critical Locust Alert'
+        text: |
+          {{ range .Alerts }}
+          *{{ .Annotations.summary }}*
+          {{ .Annotations.description }}
+          {{ end }}
+        color: 'danger'
+
+  - name: 'warning-alerts'
+    slack_configs:
+      - api_url: 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
+        channel: '#monitoring'
+        title: 'Locust Warning'
+        text: |
+          {{ range .Alerts }}
+          {{ .Annotations.summary }}
+          {{ .Annotations.description }}
+          {{ end }}
+        color: 'warning'
+
+inhibit_rules:
+  - source_match:
+      severity: 'critical'
+    target_match:
+      severity: 'warning'
+    equal: ['alertname', 'cluster', 'service']
+```
+
+### 2. 自定义通知渠道
+
+```python
+# monitoring/notification_manager.py
+import requests
+import json
+from typing import Dict, List
+from datetime import datetime
+
+class NotificationManager:
+    """通知管理器"""
+
+    def __init__(self, config: Dict):
+        self.config = config
+        self.channels = {
+            'slack': self.send_slack_notification,
+            'dingtalk': self.send_dingtalk_notification,
+            'feishu': self.send_feishu_notification,
+            'email': self.send_email_notification,
+            'webhook': self.send_webhook_notification
+        }
+
+    def send_alert(self, alert_data: Dict, channels: List[str] = None):
+        """发送告警"""
+        channels = channels or self.config.get('default_channels', ['slack'])
+
+        for channel in channels:
+            if channel in self.channels:
+                try:
+                    self.channels[channel](alert_data)
+                    print(f"Alert sent to {channel}")
+                except Exception as e:
+                    print(f"Failed to send alert to {channel}: {e}")
+
+    def send_slack_notification(self, alert_data: Dict):
+        """发送Slack通知"""
+        webhook_url = self.config['slack']['webhook_url']
+
+        color = 'danger' if alert_data['severity'] == 'critical' else 'warning'
+
+        payload = {
+            "attachments": [
+                {
+                    "color": color,
+                    "title": f"Locust Alert: {alert_data['alert_name']}",
+                    "text": alert_data['description'],
+                    "fields": [
+                        {
+                            "title": "Severity",
+                            "value": alert_data['severity'],
+                            "short": True
+                        },
+                        {
+                            "title": "Service",
+                            "value": alert_data.get('service', 'locust'),
+                            "short": True
+                        },
+                        {
+                            "title": "Time",
+                            "value": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                            "short": True
+                        }
+                    ]
+                }
+            ]
+        }
+
+        response = requests.post(webhook_url, json=payload, timeout=10)
+        response.raise_for_status()
+
+    def send_dingtalk_notification(self, alert_data: Dict):
+        """发送钉钉通知"""
+        webhook_url = self.config['dingtalk']['webhook_url']
+
+        message = f"""
+        🚨 Locust性能告警
+
+        **告警名称**: {alert_data['alert_name']}
+        **严重程度**: {alert_data['severity']}
+        **描述**: {alert_data['description']}
+        **时间**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+        """
+
+        payload = {
+            "msgtype": "markdown",
+            "markdown": {
+                "title": "Locust性能告警",
+                "text": message
+            }
+        }
+
+        response = requests.post(webhook_url, json=payload, timeout=10)
+        response.raise_for_status()
+
+    def send_feishu_notification(self, alert_data: Dict):
+        """发送飞书通知"""
+        webhook_url = self.config['feishu']['webhook_url']
+
+        color = "red" if alert_data['severity'] == 'critical' else "orange"
+
+        payload = {
+            "msg_type": "interactive",
+            "card": {
+                "elements": [
+                    {
+                        "tag": "div",
+                        "text": {
+                            "content": f"**{alert_data['alert_name']}**\n{alert_data['description']}",
+                            "tag": "lark_md"
+                        }
+                    },
+                    {
+                        "tag": "div",
+                        "fields": [
+                            {
+                                "is_short": True,
+                                "text": {
+                                    "content": f"**严重程度**\n{alert_data['severity']}",
+                                    "tag": "lark_md"
+                                }
+                            },
+                            {
+                                "is_short": True,
+                                "text": {
+                                    "content": f"**时间**\n{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
+                                    "tag": "lark_md"
+                                }
+                            }
+                        ]
+                    }
+                ],
+                "header": {
+                    "title": {
+                        "content": "Locust性能告警",
+                        "tag": "plain_text"
+                    },
+                    "template": color
+                }
+            }
+        }
+
+        response = requests.post(webhook_url, json=payload, timeout=10)
+        response.raise_for_status()
+
+    def send_email_notification(self, alert_data: Dict):
+        """发送邮件通知"""
+        # 实现邮件发送逻辑
+        pass
+
+    def send_webhook_notification(self, alert_data: Dict):
+        """发送Webhook通知"""
+        webhook_url = self.config['webhook']['url']
+
+        payload = {
+            "alert_name": alert_data['alert_name'],
+            "severity": alert_data['severity'],
+            "description": alert_data['description'],
+            "timestamp": datetime.now().isoformat(),
+            "source": "locust-framework"
+        }
+
+        response = requests.post(webhook_url, json=payload, timeout=10)
+        response.raise_for_status()
+
+# 配置示例
+notification_config = {
+    "default_channels": ["slack", "dingtalk"],
+    "slack": {
+        "webhook_url": "https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK"
+    },
+    "dingtalk": {
+        "webhook_url": "https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN"
+    },
+    "feishu": {
+        "webhook_url": "https://open.feishu.cn/open-apis/bot/v2/hook/YOUR_HOOK"
+    },
+    "webhook": {
+        "url": "https://your-api.com/alerts"
+    }
+}
+
+notification_manager = NotificationManager(notification_config)
+```
+
+## 📋 日志配置
+
+### 1. 日志聚合
+
+```yaml
+# logging/filebeat.yml
+filebeat.inputs:
+- type: log
+  enabled: true
+  paths:
+    - /app/logs/*.log
+  fields:
+    service: locust
+    environment: production
+  fields_under_root: true
+  multiline.pattern: '^\d{4}-\d{2}-\d{2}'
+  multiline.negate: true
+  multiline.match: after
+
+output.elasticsearch:
+  hosts: ["elasticsearch:9200"]
+  index: "locust-logs-%{+yyyy.MM.dd}"
+  template.name: "locust-logs"
+  template.pattern: "locust-logs-*"
+
+processors:
+- add_host_metadata:
+    when.not.contains.tags: forwarded
+- add_docker_metadata: ~
+```
+
+### 2. 日志格式化
+
+```python
+# logging/log_formatter.py
+import logging
+import json
+from datetime import datetime
+
+class StructuredFormatter(logging.Formatter):
+    """结构化日志格式化器"""
+
+    def format(self, record):
+        log_entry = {
+            "timestamp": datetime.utcnow().isoformat(),
+            "level": record.levelname,
+            "logger": record.name,
+            "message": record.getMessage(),
+            "module": record.module,
+            "function": record.funcName,
+            "line": record.lineno
+        }
+
+        # 添加异常信息
+        if record.exc_info:
+            log_entry["exception"] = self.formatException(record.exc_info)
+
+        # 添加自定义字段
+        if hasattr(record, 'user_id'):
+            log_entry["user_id"] = record.user_id
+        if hasattr(record, 'request_id'):
+            log_entry["request_id"] = record.request_id
+        if hasattr(record, 'test_name'):
+            log_entry["test_name"] = record.test_name
+
+        return json.dumps(log_entry)
+
+# 配置日志
+def setup_logging():
+    """设置日志配置"""
+    logger = logging.getLogger()
+    logger.setLevel(logging.INFO)
+
+    # 控制台处理器
+    console_handler = logging.StreamHandler()
+    console_handler.setFormatter(StructuredFormatter())
+    logger.addHandler(console_handler)
+
+    # 文件处理器
+    file_handler = logging.FileHandler('/app/logs/locust.log')
+    file_handler.setFormatter(StructuredFormatter())
+    logger.addHandler(file_handler)
+
+    return logger
+```
+
+## 🔧 部署配置
+
+### Docker Compose监控栈
+
+```yaml
+# docker-compose.monitoring.yml
+version: '3.8'
+
+services:
+  prometheus:
+    image: prom/prometheus:latest
+    container_name: prometheus
+    ports:
+      - "9090:9090"
+    volumes:
+      - ./prometheus:/etc/prometheus
+      - prometheus-data:/prometheus
+    command:
+      - '--config.file=/etc/prometheus/prometheus.yml'
+      - '--storage.tsdb.path=/prometheus'
+      - '--web.console.libraries=/etc/prometheus/console_libraries'
+      - '--web.console.templates=/etc/prometheus/consoles'
+      - '--storage.tsdb.retention.time=200h'
+      - '--web.enable-lifecycle'
+    networks:
+      - monitoring
+
+  grafana:
+    image: grafana/grafana:latest
+    container_name: grafana
+    ports:
+      - "3000:3000"
+    volumes:
+      - grafana-data:/var/lib/grafana
+      - ./grafana/provisioning:/etc/grafana/provisioning
+    environment:
+      - GF_SECURITY_ADMIN_PASSWORD=admin123
+      - GF_USERS_ALLOW_SIGN_UP=false
+    networks:
+      - monitoring
+
+  alertmanager:
+    image: prom/alertmanager:latest
+    container_name: alertmanager
+    ports:
+      - "9093:9093"
+    volumes:
+      - ./alertmanager:/etc/alertmanager
+    command:
+      - '--config.file=/etc/alertmanager/alertmanager.yml'
+      - '--storage.path=/alertmanager'
+      - '--web.external-url=http://localhost:9093'
+    networks:
+      - monitoring
+
+  node-exporter:
+    image: prom/node-exporter:latest
+    container_name: node-exporter
+    ports:
+      - "9100:9100"
+    volumes:
+      - /proc:/host/proc:ro
+      - /sys:/host/sys:ro
+      - /:/rootfs:ro
+    command:
+      - '--path.procfs=/host/proc'
+      - '--path.rootfs=/rootfs'
+      - '--path.sysfs=/host/sys'
+      - '--collector.filesystem.ignored-mount-points=^/(sys|proc|dev|host|etc)($$|/)'
+    networks:
+      - monitoring
+
+networks:
+  monitoring:
+    driver: bridge
+
+volumes:
+  prometheus-data:
+  grafana-data:
+```
+
+## 🎉 总结
+
+完整的监控配置包括：
+
+1. **指标收集**: Prometheus + 自定义导出器
+2. **可视化**: Grafana仪表板和图表
+3. **告警**: AlertManager + 多渠道通知
+4. **日志**: 结构化日志 + ELK栈
+5. **部署**: Docker化监控栈
+
+## 📚 相关文档
+
+- [生产环境配置](production.md) - 生产部署配置
+- [通知配置](notification-config.md) - 通知系统配置
+- [故障排除](../examples/troubleshooting.md) - 监控问题排查
+- [最佳实践](../examples/best-practices.md) - 监控最佳实践
diff --git a/docs/configuration/notification-config.md b/docs/configuration/notification-config.md
new file mode 100644
index 0000000..81f38b4
--- /dev/null
+++ b/docs/configuration/notification-config.md
@@ -0,0 +1,798 @@
+# 通知配置
+
+本文档详细介绍如何配置Locust性能测试框架的通知系统，包括多种通知渠道的设置、告警规则配置和消息模板定制。
+
+## 🎯 通知架构
+
+### 通知流程
+
+```mermaid
+graph TD
+    A[性能测试] --> B[指标收集]
+    B --> C[告警规则引擎]
+    C --> D{触发条件}
+    D -->|满足| E[通知管理器]
+    D -->|不满足| F[继续监控]
+    E --> G[飞书]
+    E --> H[钉钉]
+    E --> I[邮件]
+    E --> J[Slack]
+    E --> K[微信企业版]
+    E --> L[自定义Webhook]
+```
+
+### 支持的通知渠道
+
+```yaml
+# notification/channels.yml
+supported_channels:
+  instant_messaging:
+    - feishu          # 飞书
+    - dingtalk        # 钉钉
+    - slack           # Slack
+    - wechat_work     # 企业微信
+
+  email:
+    - smtp            # SMTP邮件
+    - sendgrid        # SendGrid
+    - ses             # AWS SES
+
+  webhook:
+    - custom_webhook  # 自定义Webhook
+    - teams           # Microsoft Teams
+    - discord         # Discord
+
+  sms:
+    - twilio          # Twilio短信
+    - aliyun_sms      # 阿里云短信
+```
+
+## 📱 即时通讯配置
+
+### 1. 飞书配置
+
+```yaml
+# config/notifications.yml
+feishu:
+  enabled: true
+  webhook_url: "https://open.feishu.cn/open-apis/bot/v2/hook/YOUR_WEBHOOK_TOKEN"
+  secret: "your_secret_key"  # 可选，用于签名验证
+
+  # 消息模板配置
+  templates:
+    alert:
+      title: "🚨 Locust性能告警"
+      color: "red"  # red, orange, blue, green, grey
+    warning:
+      title: "⚠️ Locust性能警告"
+      color: "orange"
+    info:
+      title: "ℹ️ Locust测试信息"
+      color: "blue"
+    success:
+      title: "✅ Locust测试完成"
+      color: "green"
+
+  # 发送配置
+  settings:
+    retry_times: 3
+    timeout: 10
+    rate_limit: 20  # 每分钟最大消息数
+```
+
+#### 飞书通知实现
+
+```python
+# src/plugins/notification/feishu_notifier.py
+import requests
+import json
+import time
+import hashlib
+import hmac
+import base64
+from typing import Dict, Optional
+from datetime import datetime
+
+class FeishuNotifier:
+    """飞书通知器"""
+
+    def __init__(self, webhook_url: str, secret: Optional[str] = None):
+        self.webhook_url = webhook_url
+        self.secret = secret
+        self.rate_limiter = {}
+
+    def send_message(self, message_type: str, content: Dict, **kwargs) -> bool:
+        """发送消息"""
+        try:
+            # 检查频率限制
+            if not self._check_rate_limit():
+                print("Rate limit exceeded, message skipped")
+                return False
+
+            # 构建消息
+            payload = self._build_payload(message_type, content, **kwargs)
+
+            # 添加签名（如果配置了secret）
+            if self.secret:
+                payload = self._add_signature(payload)
+
+            # 发送请求
+            response = requests.post(
+                self.webhook_url,
+                json=payload,
+                timeout=10,
+                headers={'Content-Type': 'application/json'}
+            )
+
+            response.raise_for_status()
+            result = response.json()
+
+            if result.get('code') == 0:
+                print("Feishu message sent successfully")
+                return True
+            else:
+                print(f"Feishu API error: {result.get('msg', 'Unknown error')}")
+                return False
+
+        except Exception as e:
+            print(f"Failed to send Feishu message: {e}")
+            return False
+
+    def _build_payload(self, message_type: str, content: Dict, **kwargs) -> Dict:
+        """构建消息载荷"""
+        if message_type == "text":
+            return {
+                "msg_type": "text",
+                "content": {
+                    "text": content.get("text", "")
+                }
+            }
+
+        elif message_type == "rich_text":
+            return {
+                "msg_type": "post",
+                "content": {
+                    "post": {
+                        "zh_cn": {
+                            "title": content.get("title", ""),
+                            "content": content.get("content", [])
+                        }
+                    }
+                }
+            }
+
+        elif message_type == "card":
+            return {
+                "msg_type": "interactive",
+                "card": content
+            }
+
+        else:
+            raise ValueError(f"Unsupported message type: {message_type}")
+
+    def _add_signature(self, payload: Dict) -> Dict:
+        """添加签名验证"""
+        timestamp = str(int(time.time()))
+
+        # 构建签名字符串
+        string_to_sign = f"{timestamp}\n{self.secret}"
+
+        # 计算签名
+        hmac_code = hmac.new(
+            string_to_sign.encode("utf-8"),
+            digestmod=hashlib.sha256
+        ).digest()
+        sign = base64.b64encode(hmac_code).decode('utf-8')
+
+        # 添加到载荷
+        payload["timestamp"] = timestamp
+        payload["sign"] = sign
+
+        return payload
+
+    def _check_rate_limit(self) -> bool:
+        """检查频率限制"""
+        current_minute = int(time.time() // 60)
+
+        if current_minute not in self.rate_limiter:
+            self.rate_limiter = {current_minute: 1}
+            return True
+
+        if self.rate_limiter[current_minute] < 20:  # 每分钟最多20条
+            self.rate_limiter[current_minute] += 1
+            return True
+
+        return False
+
+    def send_alert(self, alert_data: Dict) -> bool:
+        """发送告警消息"""
+        severity_colors = {
+            "critical": "red",
+            "warning": "orange",
+            "info": "blue"
+        }
+
+        severity_icons = {
+            "critical": "🚨",
+            "warning": "⚠️",
+            "info": "ℹ️"
+        }
+
+        severity = alert_data.get("severity", "info")
+        color = severity_colors.get(severity, "grey")
+        icon = severity_icons.get(severity, "📊")
+
+        card_content = {
+            "elements": [
+                {
+                    "tag": "div",
+                    "text": {
+                        "content": f"**{alert_data.get('title', '性能告警')}**\n{alert_data.get('description', '')}",
+                        "tag": "lark_md"
+                    }
+                },
+                {
+                    "tag": "div",
+                    "fields": [
+                        {
+                            "is_short": True,
+                            "text": {
+                                "content": f"**严重程度**\n{severity.upper()}",
+                                "tag": "lark_md"
+                            }
+                        },
+                        {
+                            "is_short": True,
+                            "text": {
+                                "content": f"**时间**\n{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
+                                "tag": "lark_md"
+                            }
+                        }
+                    ]
+                }
+            ],
+            "header": {
+                "title": {
+                    "content": f"{icon} Locust性能告警",
+                    "tag": "plain_text"
+                },
+                "template": color
+            }
+        }
+
+        # 添加指标信息
+        if "metrics" in alert_data:
+            metrics_text = []
+            for key, value in alert_data["metrics"].items():
+                metrics_text.append(f"**{key}**: {value}")
+
+            card_content["elements"].append({
+                "tag": "div",
+                "text": {
+                    "content": "\n".join(metrics_text),
+                    "tag": "lark_md"
+                }
+            })
+
+        # 添加操作按钮
+        if alert_data.get("dashboard_url"):
+            card_content["elements"].append({
+                "tag": "action",
+                "actions": [
+                    {
+                        "tag": "button",
+                        "text": {
+                            "content": "查看仪表板",
+                            "tag": "plain_text"
+                        },
+                        "url": alert_data["dashboard_url"],
+                        "type": "primary"
+                    }
+                ]
+            })
+
+        return self.send_message("card", card_content)
+```
+
+### 2. 钉钉配置
+
+```yaml
+# config/notifications.yml
+dingtalk:
+  enabled: true
+  webhook_url: "https://oapi.dingtalk.com/robot/send?access_token=YOUR_ACCESS_TOKEN"
+  secret: "your_secret_key"  # 可选，用于签名验证
+
+  # 消息配置
+  settings:
+    at_all: false
+    at_mobiles: []  # @指定手机号
+    at_user_ids: []  # @指定用户ID
+    retry_times: 3
+    timeout: 10
+```
+
+#### 钉钉通知实现
+
+```python
+# src/plugins/notification/dingtalk_notifier.py
+import requests
+import json
+import time
+import hashlib
+import hmac
+import base64
+import urllib.parse
+from typing import Dict, List, Optional
+
+class DingTalkNotifier:
+    """钉钉通知器"""
+
+    def __init__(self, webhook_url: str, secret: Optional[str] = None):
+        self.webhook_url = webhook_url
+        self.secret = secret
+
+    def send_message(self, msg_type: str, content: Dict, at_mobiles: List[str] = None, at_all: bool = False) -> bool:
+        """发送消息"""
+        try:
+            # 构建消息载荷
+            payload = {
+                "msgtype": msg_type,
+                msg_type: content,
+                "at": {
+                    "atMobiles": at_mobiles or [],
+                    "isAtAll": at_all
+                }
+            }
+
+            # 添加签名
+            url = self._add_signature() if self.secret else self.webhook_url
+
+            # 发送请求
+            response = requests.post(
+                url,
+                json=payload,
+                timeout=10,
+                headers={'Content-Type': 'application/json'}
+            )
+
+            response.raise_for_status()
+            result = response.json()
+
+            if result.get('errcode') == 0:
+                print("DingTalk message sent successfully")
+                return True
+            else:
+                print(f"DingTalk API error: {result.get('errmsg', 'Unknown error')}")
+                return False
+
+        except Exception as e:
+            print(f"Failed to send DingTalk message: {e}")
+            return False
+
+    def _add_signature(self) -> str:
+        """添加签名验证"""
+        timestamp = str(round(time.time() * 1000))
+        secret_enc = self.secret.encode('utf-8')
+        string_to_sign = f'{timestamp}\n{self.secret}'
+        string_to_sign_enc = string_to_sign.encode('utf-8')
+        hmac_code = hmac.new(secret_enc, string_to_sign_enc, digestmod=hashlib.sha256).digest()
+        sign = urllib.parse.quote_plus(base64.b64encode(hmac_code))
+
+        return f"{self.webhook_url}&timestamp={timestamp}&sign={sign}"
+
+    def send_alert(self, alert_data: Dict) -> bool:
+        """发送告警消息"""
+        severity_icons = {
+            "critical": "🚨",
+            "warning": "⚠️",
+            "info": "ℹ️"
+        }
+
+        severity = alert_data.get("severity", "info")
+        icon = severity_icons.get(severity, "📊")
+
+        # 构建Markdown消息
+        markdown_content = f"""
+# {icon} Locust性能告警
+
+**告警名称**: {alert_data.get('title', '性能告警')}
+
+**严重程度**: {severity.upper()}
+
+**描述**: {alert_data.get('description', '')}
+
+**时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+"""
+
+        # 添加指标信息
+        if "metrics" in alert_data:
+            markdown_content += "\n**关键指标**:\n"
+            for key, value in alert_data["metrics"].items():
+                markdown_content += f"- {key}: {value}\n"
+
+        # 添加仪表板链接
+        if alert_data.get("dashboard_url"):
+            markdown_content += f"\n[查看详细仪表板]({alert_data['dashboard_url']})"
+
+        content = {
+            "title": f"{icon} Locust性能告警",
+            "text": markdown_content
+        }
+
+        # 根据严重程度决定是否@所有人
+        at_all = severity == "critical"
+
+        return self.send_message("markdown", content, at_all=at_all)
+```
+
+### 3. Slack配置
+
+```yaml
+# config/notifications.yml
+slack:
+  enabled: true
+  webhook_url: "https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK"
+
+  # 频道配置
+  channels:
+    alerts: "#alerts"
+    warnings: "#monitoring"
+    info: "#general"
+
+  # 消息配置
+  settings:
+    username: "Locust Bot"
+    icon_emoji: ":robot_face:"
+    retry_times: 3
+    timeout: 10
+```
+
+#### Slack通知实现
+
+```python
+# src/plugins/notification/slack_notifier.py
+import requests
+import json
+from typing import Dict, List, Optional
+from datetime import datetime
+
+class SlackNotifier:
+    """Slack通知器"""
+
+    def __init__(self, webhook_url: str, default_channel: str = "#general"):
+        self.webhook_url = webhook_url
+        self.default_channel = default_channel
+
+    def send_message(self, text: str, channel: str = None, attachments: List[Dict] = None, **kwargs) -> bool:
+        """发送消息"""
+        try:
+            payload = {
+                "text": text,
+                "channel": channel or self.default_channel,
+                "username": kwargs.get("username", "Locust Bot"),
+                "icon_emoji": kwargs.get("icon_emoji", ":robot_face:")
+            }
+
+            if attachments:
+                payload["attachments"] = attachments
+
+            response = requests.post(
+                self.webhook_url,
+                json=payload,
+                timeout=10
+            )
+
+            response.raise_for_status()
+
+            if response.text == "ok":
+                print("Slack message sent successfully")
+                return True
+            else:
+                print(f"Slack API error: {response.text}")
+                return False
+
+        except Exception as e:
+            print(f"Failed to send Slack message: {e}")
+            return False
+
+    def send_alert(self, alert_data: Dict) -> bool:
+        """发送告警消息"""
+        severity_colors = {
+            "critical": "danger",
+            "warning": "warning",
+            "info": "good"
+        }
+
+        severity_icons = {
+            "critical": ":rotating_light:",
+            "warning": ":warning:",
+            "info": ":information_source:"
+        }
+
+        severity = alert_data.get("severity", "info")
+        color = severity_colors.get(severity, "good")
+        icon = severity_icons.get(severity, ":chart_with_upwards_trend:")
+
+        # 选择频道
+        channel_map = {
+            "critical": "#alerts",
+            "warning": "#monitoring",
+            "info": "#general"
+        }
+        channel = channel_map.get(severity, self.default_channel)
+
+        # 构建附件
+        attachment = {
+            "color": color,
+            "title": f"{icon} Locust性能告警",
+            "text": alert_data.get("description", ""),
+            "fields": [
+                {
+                    "title": "告警名称",
+                    "value": alert_data.get("title", "性能告警"),
+                    "short": True
+                },
+                {
+                    "title": "严重程度",
+                    "value": severity.upper(),
+                    "short": True
+                },
+                {
+                    "title": "时间",
+                    "value": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                    "short": True
+                }
+            ],
+            "footer": "Locust Framework",
+            "ts": int(datetime.now().timestamp())
+        }
+
+        # 添加指标字段
+        if "metrics" in alert_data:
+            for key, value in alert_data["metrics"].items():
+                attachment["fields"].append({
+                    "title": key,
+                    "value": str(value),
+                    "short": True
+                })
+
+        # 添加操作按钮
+        if alert_data.get("dashboard_url"):
+            attachment["actions"] = [
+                {
+                    "type": "button",
+                    "text": "查看仪表板",
+                    "url": alert_data["dashboard_url"],
+                    "style": "primary"
+                }
+            ]
+
+        return self.send_message(
+            text=f"{icon} 检测到性能告警",
+            channel=channel,
+            attachments=[attachment]
+        )
+```
+
+## 📧 邮件配置
+
+### 1. SMTP配置
+
+```yaml
+# config/notifications.yml
+email:
+  enabled: true
+  smtp:
+    host: "smtp.gmail.com"
+    port: 587
+    use_tls: true
+    username: "your-email@gmail.com"
+    password: "your-app-password"
+
+  # 发件人配置
+  sender:
+    name: "Locust Performance Monitor"
+    email: "noreply@yourcompany.com"
+
+  # 收件人配置
+  recipients:
+    critical: ["oncall@yourcompany.com", "team-lead@yourcompany.com"]
+    warning: ["team@yourcompany.com"]
+    info: ["monitoring@yourcompany.com"]
+
+  # 模板配置
+  templates:
+    subject: "[{severity}] Locust Alert: {title}"
+    html_template: "email_alert.html"
+    text_template: "email_alert.txt"
+```
+
+#### 邮件通知实现
+
+```python
+# src/plugins/notification/email_notifier.py
+import smtplib
+import ssl
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.mime.base import MIMEBase
+from email import encoders
+from typing import Dict, List, Optional
+from datetime import datetime
+import jinja2
+
+class EmailNotifier:
+    """邮件通知器"""
+
+    def __init__(self, smtp_config: Dict):
+        self.smtp_config = smtp_config
+        self.template_env = jinja2.Environment(
+            loader=jinja2.FileSystemLoader('templates/email')
+        )
+
+    def send_email(self, to_emails: List[str], subject: str, html_content: str, text_content: str = None) -> bool:
+        """发送邮件"""
+        try:
+            # 创建消息
+            message = MIMEMultipart("alternative")
+            message["Subject"] = subject
+            message["From"] = self.smtp_config["sender"]["email"]
+            message["To"] = ", ".join(to_emails)
+
+            # 添加文本内容
+            if text_content:
+                text_part = MIMEText(text_content, "plain")
+                message.attach(text_part)
+
+            # 添加HTML内容
+            html_part = MIMEText(html_content, "html")
+            message.attach(html_part)
+
+            # 发送邮件
+            context = ssl.create_default_context()
+
+            with smtplib.SMTP(self.smtp_config["host"], self.smtp_config["port"]) as server:
+                if self.smtp_config.get("use_tls"):
+                    server.starttls(context=context)
+
+                server.login(
+                    self.smtp_config["username"],
+                    self.smtp_config["password"]
+                )
+
+                server.sendmail(
+                    self.smtp_config["sender"]["email"],
+                    to_emails,
+                    message.as_string()
+                )
+
+            print(f"Email sent successfully to {', '.join(to_emails)}")
+            return True
+
+        except Exception as e:
+            print(f"Failed to send email: {e}")
+            return False
+
+    def send_alert(self, alert_data: Dict) -> bool:
+        """发送告警邮件"""
+        severity = alert_data.get("severity", "info")
+
+        # 获取收件人列表
+        recipients = self.smtp_config["recipients"].get(severity, [])
+        if not recipients:
+            print(f"No recipients configured for severity: {severity}")
+            return False
+
+        # 生成邮件内容
+        subject = self.smtp_config["templates"]["subject"].format(
+            severity=severity.upper(),
+            title=alert_data.get("title", "性能告警")
+        )
+
+        # 渲染HTML模板
+        html_template = self.template_env.get_template(
+            self.smtp_config["templates"]["html_template"]
+        )
+        html_content = html_template.render(
+            alert_data=alert_data,
+            timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        )
+
+        # 渲染文本模板
+        text_template = self.template_env.get_template(
+            self.smtp_config["templates"]["text_template"]
+        )
+        text_content = text_template.render(
+            alert_data=alert_data,
+            timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        )
+
+        return self.send_email(recipients, subject, html_content, text_content)
+```
+
+### 2. 邮件模板
+
+```html
+<!-- templates/email/email_alert.html -->
+<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="UTF-8">
+    <title>Locust性能告警</title>
+    <style>
+        body { font-family: Arial, sans-serif; margin: 0; padding: 20px; background-color: #f5f5f5; }
+        .container { max-width: 600px; margin: 0 auto; background-color: white; border-radius: 8px; overflow: hidden; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
+        .header { background-color: {% if alert_data.severity == 'critical' %}#dc3545{% elif alert_data.severity == 'warning' %}#ffc107{% else %}#17a2b8{% endif %}; color: white; padding: 20px; text-align: center; }
+        .content { padding: 20px; }
+        .metrics { background-color: #f8f9fa; padding: 15px; border-radius: 5px; margin: 15px 0; }
+        .metric-item { display: flex; justify-content: space-between; margin: 5px 0; }
+        .footer { background-color: #6c757d; color: white; padding: 15px; text-align: center; font-size: 12px; }
+        .button { display: inline-block; background-color: #007bff; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px; margin: 10px 0; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🚨 Locust性能告警</h1>
+            <p>{{ alert_data.title }}</p>
+        </div>
+
+        <div class="content">
+            <h2>告警详情</h2>
+            <p><strong>严重程度:</strong> {{ alert_data.severity.upper() }}</p>
+            <p><strong>时间:</strong> {{ timestamp }}</p>
+            <p><strong>描述:</strong> {{ alert_data.description }}</p>
+
+            {% if alert_data.metrics %}
+            <div class="metrics">
+                <h3>关键指标</h3>
+                {% for key, value in alert_data.metrics.items() %}
+                <div class="metric-item">
+                    <span>{{ key }}:</span>
+                    <strong>{{ value }}</strong>
+                </div>
+                {% endfor %}
+            </div>
+            {% endif %}
+
+            {% if alert_data.dashboard_url %}
+            <p>
+                <a href="{{ alert_data.dashboard_url }}" class="button">查看详细仪表板</a>
+            </p>
+            {% endif %}
+        </div>
+
+        <div class="footer">
+            <p>此邮件由Locust性能测试框架自动发送</p>
+            <p>如有问题，请联系运维团队</p>
+        </div>
+    </div>
+</body>
+</html>
+```
+
+```text
+<!-- templates/email/email_alert.txt -->
+Locust性能告警
+
+告警名称: {{ alert_data.title }}
+严重程度: {{ alert_data.severity.upper() }}
+时间: {{ timestamp }}
+描述: {{ alert_data.description }}
+
+{% if alert_data.metrics %}
+关键指标:
+{% for key, value in alert_data.metrics.items() %}
+- {{ key }}: {{ value }}
+{% endfor %}
+{% endif %}
+
+{% if alert_data.dashboard_url %}
+查看详细仪表板: {{ alert_data.dashboard_url }}
+{% endif %}
+
+---
+此邮件由Locust性能测试框架自动发送
+如有问题，请联系运维团队
+```
diff --git a/docs/configuration/plugin-config.md b/docs/configuration/plugin-config.md
new file mode 100644
index 0000000..b80a00f
--- /dev/null
+++ b/docs/configuration/plugin-config.md
@@ -0,0 +1,618 @@
+# 插件配置参考
+
+本文档详细介绍插件系统的配置选项，包括插件管理、内置插件配置、自定义插件开发配置等。
+
+## 🔌 插件系统概述
+
+插件系统采用模块化设计，支持动态加载、配置管理、依赖检查等功能：
+
+```python
+# 插件架构层次
+1. 插件管理器 (PluginManager)
+2. 插件接口 (PluginInterface)
+3. 插件实现 (具体插件类)
+4. 插件配置 (配置文件)
+```
+
+## ⚙️ 插件管理器配置
+
+### 基础配置 (conf/plugins/plugin_manager.toml)
+
+```toml
+[plugin_manager]
+# 插件发现配置
+discovery_enabled = true
+discovery_paths = [
+    "src/plugins/builtin",     # 内置插件路径
+    "plugins/custom",          # 自定义插件路径
+    "~/.locust/plugins",       # 用户插件路径
+    "/usr/local/lib/locust/plugins"  # 系统插件路径
+]
+
+# 插件加载配置
+auto_load_enabled = true
+load_timeout = 30
+parallel_loading = true
+max_load_workers = 4
+
+# 插件依赖管理
+dependency_check = true
+auto_install_dependencies = false
+dependency_timeout = 60
+
+# 插件安全配置
+security_check = true
+allowed_plugin_sources = ["builtin", "verified", "local"]
+signature_verification = false
+
+# 插件缓存配置
+cache_enabled = true
+cache_directory = ".plugin_cache"
+cache_ttl = 3600  # 1小时
+
+# 插件日志配置
+logging_enabled = true
+log_level = "INFO"
+log_file = "logs/plugins.log"
+```
+
+### 插件注册表配置
+
+```toml
+# conf/plugins/plugin_registry.toml
+[registry]
+# 注册表配置
+registry_file = "conf/plugins/installed_plugins.json"
+auto_register = true
+register_on_load = true
+
+# 插件元数据
+[registry.metadata]
+track_usage = true
+track_performance = true
+track_errors = true
+
+# 插件版本管理
+[registry.versioning]
+version_check = true
+compatibility_check = true
+auto_update_check = false
+```
+
+## 🔧 内置插件配置
+
+### 性能分析插件
+
+```toml
+# conf/plugins/performance_analyzer.toml
+[performance_analyzer]
+enabled = true
+priority = 10
+
+# 分析配置
+[performance_analyzer.analysis]
+# 响应时间阈值配置 (毫秒)
+response_time_thresholds = { excellent = 500, good = 1000, acceptable = 2000, poor = 5000 }
+
+# 吞吐量阈值配置 (TPS)
+throughput_thresholds = { excellent = 1000, good = 500, acceptable = 100, poor = 50 }
+
+# 错误率阈值配置 (百分比)
+error_rate_thresholds = { excellent = 0.1, good = 1.0, acceptable = 5.0, poor = 10.0 }
+
+# 指标权重配置
+metric_weights = { response_time = 0.4, throughput = 0.3, error_rate = 0.2, stability = 0.1 }
+
+# 报告配置
+[performance_analyzer.reporting]
+output_formats = ["html", "json", "pdf"]
+include_charts = true
+chart_types = ["line", "bar", "pie"]
+template_path = "templates/performance_report.html"
+output_directory = "reports/performance"
+
+# 趋势分析配置
+[performance_analyzer.trend]
+enabled = true
+history_days = 30
+prediction_days = 7
+confidence_level = 0.95
+trend_algorithms = ["linear", "polynomial", "seasonal"]
+```
+
+### 系统监控插件
+
+```toml
+# conf/plugins/system_monitor.toml
+[system_monitor]
+enabled = true
+priority = 5
+
+# 监控配置
+[system_monitor.monitoring]
+interval = 5  # 监控间隔(秒)
+buffer_size = 1000
+retention_period = 3600  # 数据保留时间(秒)
+
+# 监控指标配置
+[system_monitor.metrics]
+cpu_enabled = true
+memory_enabled = true
+disk_enabled = true
+network_enabled = true
+process_enabled = true
+
+# CPU监控配置
+[system_monitor.metrics.cpu]
+per_cpu = true
+load_average = true
+context_switches = true
+
+# 内存监控配置
+[system_monitor.metrics.memory]
+virtual_memory = true
+swap_memory = true
+memory_maps = false
+
+# 磁盘监控配置
+[system_monitor.metrics.disk]
+disk_usage = true
+disk_io = true
+mount_points = ["/", "/tmp", "/var"]
+
+# 网络监控配置
+[system_monitor.metrics.network]
+network_io = true
+connections = true
+interface_stats = true
+interfaces = ["eth0", "lo"]
+
+# 告警配置
+[system_monitor.alerts]
+cpu_threshold = 80.0
+memory_threshold = 85.0
+disk_threshold = 90.0
+network_threshold = 80.0
+```
+
+### 通知服务插件
+
+```toml
+# conf/plugins/notification_service.toml
+[notification_service]
+enabled = true
+priority = 8
+
+# 通知渠道配置
+[notification_service.channels]
+enabled_channels = ["feishu", "dingtalk", "email", "wechat_work"]
+default_channels = ["feishu", "email"]
+fallback_channels = ["email"]
+
+# 飞书配置
+[notification_service.channels.feishu]
+enabled = true
+webhook_url = "${FEISHU_WEBHOOK_URL}"
+secret = "${FEISHU_SECRET}"
+at_all = false
+at_users = []
+message_type = "interactive"  # text, post, interactive
+retry_count = 3
+retry_interval = 5
+
+# 钉钉配置
+[notification_service.channels.dingtalk]
+enabled = false
+webhook_url = "${DINGTALK_WEBHOOK_URL}"
+secret = "${DINGTALK_SECRET}"
+at_mobiles = []
+at_all = false
+retry_count = 3
+
+# 邮件配置
+[notification_service.channels.email]
+enabled = true
+smtp_server = "${EMAIL_SMTP_SERVER}"
+smtp_port = 587
+username = "${EMAIL_USERNAME}"
+password = "${EMAIL_PASSWORD}"
+from_addr = "${EMAIL_FROM_ADDR}"
+to_addrs = ["${EMAIL_TO_ADDRS}"]
+cc_addrs = []
+bcc_addrs = []
+use_tls = true
+use_ssl = false
+timeout = 30
+
+# 企业微信配置
+[notification_service.channels.wechat_work]
+enabled = false
+webhook_url = "${WECHAT_WEBHOOK_URL}"
+mentioned_list = []
+mentioned_mobile_list = []
+
+# 消息模板配置
+[notification_service.templates]
+test_start = """
+🚀 性能测试开始
+测试名称: {test_name}
+开始时间: {start_time}
+目标用户数: {target_users}
+测试环境: {environment}
+"""
+
+test_complete = """
+✅ 性能测试完成
+测试名称: {test_name}
+结束时间: {end_time}
+测试时长: {duration}
+总请求数: {total_requests}
+成功率: {success_rate}%
+平均响应时间: {avg_response_time}ms
+报告链接: {report_url}
+"""
+
+alert_critical = """
+🚨 严重告警
+告警名称: {alert_name}
+告警级别: {severity}
+触发时间: {trigger_time}
+告警内容: {message}
+当前值: {current_value}
+阈值: {threshold}
+"""
+```
+
+### 数据管理插件
+
+```toml
+# conf/plugins/data_manager.toml
+[data_manager]
+enabled = true
+priority = 7
+
+# 数据生成器配置
+[data_manager.generator]
+locale = "zh_CN"
+seed = 12345
+cache_size = 10000
+cache_ttl = 3600
+
+# 数据提供者配置
+[data_manager.provider]
+default_strategy = "round_robin"  # round_robin, random, sequential
+batch_size = 100
+prefetch_enabled = true
+prefetch_size = 1000
+
+# 数据源配置
+[[data_manager.provider.sources]]
+name = "csv_users"
+type = "file"
+path = "test_data/users.csv"
+format = "csv"
+encoding = "utf-8"
+delimiter = ","
+
+[[data_manager.provider.sources]]
+name = "db_products"
+type = "database"
+connection = "${DATABASE_URL}"
+query = "SELECT * FROM products WHERE active = 1"
+cache_enabled = true
+
+[[data_manager.provider.sources]]
+name = "api_orders"
+type = "api"
+url = "${API_BASE_URL}/orders"
+headers = { "Authorization" = "Bearer ${API_TOKEN}" }
+method = "GET"
+timeout = 30
+
+# 数据分发器配置
+[data_manager.distributor]
+enabled = true
+sync_interval = 60
+batch_size = 1000
+compression = true
+encryption = false
+
+# 分布式配置
+[data_manager.distributor.distributed]
+master_node = true
+worker_nodes = ["worker1", "worker2", "worker3"]
+sync_strategy = "push"  # push, pull, hybrid
+conflict_resolution = "master_wins"  # master_wins, timestamp, merge
+```
+
+## 🎨 负载形状插件配置
+
+```toml
+# conf/plugins/load_shapes.toml
+[load_shapes]
+enabled = true
+priority = 6
+
+# 默认负载形状配置
+[load_shapes.defaults]
+default_shape = "constant"
+spawn_rate = 1
+time_limit = 600
+
+# 波浪形负载配置
+[load_shapes.wave]
+min_users = 10
+max_users = 100
+wave_period = 300
+wave_type = "sine"  # sine, cosine, triangle, square
+
+# 阶梯形负载配置
+[load_shapes.step]
+initial_users = 10
+step_size = 20
+step_duration = 60
+max_users = 200
+
+# 尖峰负载配置
+[load_shapes.spike]
+base_users = 50
+spike_users = 500
+spike_duration = 30
+spike_interval = 300
+
+# 自适应负载配置
+[load_shapes.adaptive]
+target_response_time = 1000
+adjustment_factor = 0.1
+min_users = 10
+max_users = 1000
+adjustment_interval = 30
+```
+
+## 🔍 自定义插件配置
+
+### 插件开发配置
+
+```toml
+# conf/plugins/custom_plugin_template.toml
+[custom_plugin]
+# 基础信息
+name = "Custom Plugin"
+version = "1.0.0"
+description = "自定义插件描述"
+author = "Your Name"
+email = "your.email@example.com"
+license = "MIT"
+category = "analysis"  # analysis, monitoring, data, notification, load_shape
+
+# 插件配置
+enabled = false
+priority = 5
+dependencies = ["requests", "pandas"]
+python_version = ">=3.7"
+
+# 插件特定配置
+[custom_plugin.config]
+# 在这里添加插件特定的配置项
+api_endpoint = "https://api.example.com"
+api_key = "${CUSTOM_API_KEY}"
+timeout = 30
+retry_count = 3
+
+# 插件钩子配置
+[custom_plugin.hooks]
+# 定义插件响应的事件钩子
+on_test_start = true
+on_test_stop = true
+on_request = false
+on_failure = true
+```
+
+### 插件验证配置
+
+```toml
+# conf/plugins/plugin_validation.toml
+[validation]
+# 插件验证规则
+[validation.rules]
+required_methods = ["initialize", "cleanup"]
+optional_methods = ["configure", "get_info"]
+forbidden_imports = ["os.system", "subprocess.call"]
+max_memory_usage = "100MB"
+max_cpu_usage = 50.0
+
+# 安全检查
+[validation.security]
+sandbox_enabled = true
+network_access = "restricted"  # allowed, restricted, denied
+file_access = "restricted"
+system_access = "denied"
+
+# 性能检查
+[validation.performance]
+max_init_time = 10.0
+max_cleanup_time = 5.0
+memory_leak_check = true
+```
+
+## 📊 插件监控配置
+
+```toml
+# conf/plugins/plugin_monitoring.toml
+[monitoring]
+# 插件性能监控
+[monitoring.performance]
+enabled = true
+track_execution_time = true
+track_memory_usage = true
+track_error_rate = true
+alert_on_slow_plugin = true
+slow_plugin_threshold = 5.0  # 秒
+
+# 插件使用统计
+[monitoring.usage]
+enabled = true
+track_plugin_calls = true
+track_plugin_success_rate = true
+generate_usage_report = true
+report_interval = 3600  # 1小时
+
+# 插件健康检查
+[monitoring.health]
+enabled = true
+health_check_interval = 300  # 5分钟
+auto_disable_failed_plugins = true
+max_consecutive_failures = 3
+```
+
+## 🔧 插件配置示例
+
+### 完整插件配置示例
+
+```python
+# plugins/custom/example_plugin.py
+from src.plugins.plugin_interface import PluginInterface, PluginInfo
+from typing import Dict, Any, Optional
+
+class ExamplePlugin(PluginInterface):
+    """示例插件实现"""
+
+    @property
+    def plugin_info(self) -> PluginInfo:
+        return PluginInfo(
+            name="Example Plugin",
+            version="1.0.0",
+            description="这是一个示例插件",
+            author="Your Name",
+            category="analysis"
+        )
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        """初始化插件"""
+        self.config = config or {}
+        self.api_endpoint = self.config.get('api_endpoint', 'https://api.example.com')
+        self.api_key = self.config.get('api_key')
+        self.timeout = self.config.get('timeout', 30)
+
+        # 验证必要配置
+        if not self.api_key:
+            self.logger.error("API密钥未配置")
+            return False
+
+        self.logger.info("示例插件初始化成功")
+        return True
+
+    def cleanup(self):
+        """清理资源"""
+        self.logger.info("示例插件清理完成")
+
+    def process_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """处理数据"""
+        # 插件具体逻辑
+        result = {
+            'processed': True,
+            'timestamp': time.time(),
+            'data_size': len(str(data))
+        }
+        return result
+```
+
+### 插件配置文件
+
+```toml
+# conf/plugins/example_plugin.toml
+[example_plugin]
+enabled = true
+priority = 5
+
+[example_plugin.config]
+api_endpoint = "https://api.example.com/v1"
+api_key = "${EXAMPLE_API_KEY}"
+timeout = 30
+retry_count = 3
+batch_size = 100
+
+[example_plugin.features]
+data_processing = true
+real_time_analysis = false
+batch_processing = true
+
+[example_plugin.limits]
+max_requests_per_minute = 1000
+max_data_size = "10MB"
+max_processing_time = 60
+```
+
+## 🚀 插件部署配置
+
+### 生产环境配置
+
+```toml
+# conf/environments/production/plugins.toml
+[production.plugins]
+# 生产环境只启用稳定插件
+enabled_plugins = [
+    "performance_analyzer",
+    "system_monitor",
+    "notification_service"
+]
+
+# 禁用开发插件
+disabled_plugins = [
+    "debug_plugin",
+    "test_plugin"
+]
+
+# 严格的安全配置
+[production.plugins.security]
+security_check = true
+signature_verification = true
+sandbox_enabled = true
+allowed_plugin_sources = ["builtin", "verified"]
+
+# 性能优化配置
+[production.plugins.performance]
+parallel_loading = true
+max_load_workers = 8
+cache_enabled = true
+preload_plugins = true
+```
+
+### 开发环境配置
+
+```toml
+# conf/environments/development/plugins.toml
+[development.plugins]
+# 开发环境启用所有插件
+auto_load_enabled = true
+discovery_enabled = true
+
+# 宽松的安全配置
+[development.plugins.security]
+security_check = false
+signature_verification = false
+sandbox_enabled = false
+allowed_plugin_sources = ["builtin", "local", "development"]
+
+# 调试配置
+[development.plugins.debug]
+verbose_logging = true
+performance_profiling = true
+memory_tracking = true
+```
+
+## ⚠️ 配置注意事项
+
+1. **安全性**: 生产环境必须启用安全检查
+2. **性能**: 合理配置插件优先级和并发数
+3. **依赖**: 确保插件依赖正确安装
+4. **版本**: 注意插件版本兼容性
+5. **监控**: 启用插件性能监控
+6. **备份**: 定期备份插件配置
+
+## 🔗 相关文档
+
+- [插件开发指南](../development/plugin-development.md) - 插件开发详细指南
+- [框架配置](framework-config.md) - 框架核心配置
+- [环境配置](environment-config.md) - 环境特定配置
+- [API参考](../api/plugins.md) - 插件API接口文档
diff --git a/docs/configuration/production.md b/docs/configuration/production.md
new file mode 100644
index 0000000..a1aba9e
--- /dev/null
+++ b/docs/configuration/production.md
@@ -0,0 +1,858 @@
+# 生产环境配置
+
+本文档详细介绍如何在生产环境中部署和配置Locust性能测试框架，确保测试的稳定性、安全性和可扩展性。
+
+## 🎯 生产环境要求
+
+### 系统要求
+
+**硬件配置**
+```yaml
+# 推荐配置
+master_node:
+  cpu: 4+ cores
+  memory: 8GB+
+  disk: 100GB+ SSD
+  network: 1Gbps+
+
+worker_nodes:
+  cpu: 2+ cores per worker
+  memory: 4GB+ per worker
+  disk: 50GB+ SSD
+  network: 1Gbps+
+
+# 最小配置
+minimal_setup:
+  cpu: 2 cores
+  memory: 4GB
+  disk: 50GB
+  network: 100Mbps
+```
+
+**软件环境**
+```bash
+# 操作系统
+Ubuntu 20.04+ / CentOS 8+ / RHEL 8+
+
+# Python版本
+Python 3.8+
+
+# 必需软件
+- Docker 20.10+
+- Docker Compose 2.0+
+- Nginx 1.18+
+- Redis 6.0+ (可选，用于分布式协调)
+- PostgreSQL 13+ (可选，用于结果存储)
+```
+
+## 🐳 Docker化部署
+
+### 1. 生产级Dockerfile
+
+```dockerfile
+# Dockerfile.production
+FROM python:3.9-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制依赖文件
+COPY requirements.txt .
+COPY requirements-prod.txt .
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -r requirements-prod.txt
+
+# 复制应用代码
+COPY . .
+
+# 创建非root用户
+RUN useradd -m -u 1000 locust && \
+    chown -R locust:locust /app
+USER locust
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8089/stats/requests || exit 1
+
+# 暴露端口
+EXPOSE 8089 5557
+
+# 启动命令
+CMD ["python", "-m", "locust", "--config", "conf/production.conf"]
+```
+
+### 2. Docker Compose配置
+
+```yaml
+# docker-compose.prod.yml
+version: '3.8'
+
+services:
+  locust-master:
+    build:
+      context: .
+      dockerfile: Dockerfile.production
+    image: locust-framework:production
+    container_name: locust-master
+    ports:
+      - "8089:8089"
+      - "5557:5557"
+    environment:
+      - LOCUST_MODE=master
+      - LOCUST_MASTER_BIND_HOST=0.0.0.0
+      - LOCUST_MASTER_BIND_PORT=5557
+      - LOCUST_WEB_HOST=0.0.0.0
+      - LOCUST_WEB_PORT=8089
+      - LOCUST_LOGLEVEL=INFO
+    volumes:
+      - ./conf:/app/conf:ro
+      - ./logs:/app/logs
+      - ./reports:/app/reports
+    networks:
+      - locust-network
+    restart: unless-stopped
+    deploy:
+      resources:
+        limits:
+          cpus: '2.0'
+          memory: 4G
+        reservations:
+          cpus: '1.0'
+          memory: 2G
+
+  locust-worker:
+    build:
+      context: .
+      dockerfile: Dockerfile.production
+    image: locust-framework:production
+    environment:
+      - LOCUST_MODE=worker
+      - LOCUST_MASTER_HOST=locust-master
+      - LOCUST_MASTER_PORT=5557
+      - LOCUST_LOGLEVEL=INFO
+    volumes:
+      - ./conf:/app/conf:ro
+      - ./logs:/app/logs
+    networks:
+      - locust-network
+    depends_on:
+      - locust-master
+    restart: unless-stopped
+    deploy:
+      replicas: 3
+      resources:
+        limits:
+          cpus: '1.0'
+          memory: 2G
+        reservations:
+          cpus: '0.5'
+          memory: 1G
+
+  nginx:
+    image: nginx:alpine
+    container_name: locust-nginx
+    ports:
+      - "80:80"
+      - "443:443"
+    volumes:
+      - ./nginx/nginx.conf:/etc/nginx/nginx.conf:ro
+      - ./nginx/ssl:/etc/nginx/ssl:ro
+    networks:
+      - locust-network
+    depends_on:
+      - locust-master
+    restart: unless-stopped
+
+  redis:
+    image: redis:6-alpine
+    container_name: locust-redis
+    ports:
+      - "6379:6379"
+    volumes:
+      - redis-data:/data
+      - ./redis/redis.conf:/etc/redis/redis.conf:ro
+    networks:
+      - locust-network
+    restart: unless-stopped
+    command: redis-server /etc/redis/redis.conf
+
+  postgres:
+    image: postgres:13-alpine
+    container_name: locust-postgres
+    environment:
+      - POSTGRES_DB=locust_results
+      - POSTGRES_USER=locust
+      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
+    volumes:
+      - postgres-data:/var/lib/postgresql/data
+      - ./postgres/init.sql:/docker-entrypoint-initdb.d/init.sql:ro
+    networks:
+      - locust-network
+    restart: unless-stopped
+
+networks:
+  locust-network:
+    driver: bridge
+
+volumes:
+  redis-data:
+  postgres-data:
+```
+
+## ⚙️ 生产配置文件
+
+### 1. 主配置文件
+
+```ini
+# conf/production.conf
+[locust]
+# 基础配置
+locustfile = locustfiles/production_test.py
+host = https://api.production.com
+web-host = 0.0.0.0
+web-port = 8089
+
+# 性能配置
+users = 1000
+spawn-rate = 50
+run-time = 30m
+headless = false
+
+# 日志配置
+loglevel = INFO
+logfile = logs/locust.log
+
+# 报告配置
+html = reports/performance_report.html
+csv = reports/performance_stats
+
+# 分布式配置
+master-bind-host = 0.0.0.0
+master-bind-port = 5557
+expect-workers = 3
+
+# 高级配置
+stop-timeout = 60
+reset-stats = false
+```
+
+### 2. 环境变量配置
+
+```bash
+# .env.production
+# 应用配置
+LOCUST_ENV=production
+LOCUST_LOG_LEVEL=INFO
+LOCUST_WEB_AUTH=admin:secure_password_here
+
+# 数据库配置
+POSTGRES_HOST=postgres
+POSTGRES_PORT=5432
+POSTGRES_DB=locust_results
+POSTGRES_USER=locust
+POSTGRES_PASSWORD=your_secure_password_here
+
+# Redis配置
+REDIS_HOST=redis
+REDIS_PORT=6379
+REDIS_PASSWORD=your_redis_password_here
+
+# 监控配置
+MONITORING_ENABLED=true
+ALERT_WEBHOOK_URL=https://hooks.slack.com/your-webhook
+METRICS_RETENTION_DAYS=30
+
+# 安全配置
+SSL_CERT_PATH=/etc/nginx/ssl/cert.pem
+SSL_KEY_PATH=/etc/nginx/ssl/key.pem
+ALLOWED_HOSTS=your-domain.com,localhost
+
+# 性能配置
+MAX_WORKERS=10
+WORKER_MEMORY_LIMIT=2G
+MASTER_MEMORY_LIMIT=4G
+```
+
+### 3. Nginx反向代理配置
+
+```nginx
+# nginx/nginx.conf
+events {
+    worker_connections 1024;
+}
+
+http {
+    upstream locust_backend {
+        server locust-master:8089;
+    }
+
+    # 限流配置
+    limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
+    limit_conn_zone $binary_remote_addr zone=conn:10m;
+
+    server {
+        listen 80;
+        server_name your-domain.com;
+        return 301 https://$server_name$request_uri;
+    }
+
+    server {
+        listen 443 ssl http2;
+        server_name your-domain.com;
+
+        # SSL配置
+        ssl_certificate /etc/nginx/ssl/cert.pem;
+        ssl_certificate_key /etc/nginx/ssl/key.pem;
+        ssl_protocols TLSv1.2 TLSv1.3;
+        ssl_ciphers ECDHE-RSA-AES256-GCM-SHA512:DHE-RSA-AES256-GCM-SHA512;
+        ssl_prefer_server_ciphers off;
+
+        # 安全头
+        add_header X-Frame-Options DENY;
+        add_header X-Content-Type-Options nosniff;
+        add_header X-XSS-Protection "1; mode=block";
+        add_header Strict-Transport-Security "max-age=63072000; includeSubDomains; preload";
+
+        # 限流
+        limit_req zone=api burst=20 nodelay;
+        limit_conn conn 10;
+
+        # 代理配置
+        location / {
+            proxy_pass http://locust_backend;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+
+            # WebSocket支持
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+
+            # 超时配置
+            proxy_connect_timeout 60s;
+            proxy_send_timeout 60s;
+            proxy_read_timeout 60s;
+        }
+
+        # 静态文件缓存
+        location /static/ {
+            expires 1y;
+            add_header Cache-Control "public, immutable";
+        }
+
+        # 健康检查
+        location /health {
+            access_log off;
+            return 200 "healthy\n";
+            add_header Content-Type text/plain;
+        }
+    }
+}
+```
+
+## 🔒 安全配置
+
+### 1. 认证和授权
+
+```python
+# security/auth.py
+import hashlib
+import secrets
+from functools import wraps
+from flask import request, Response
+
+class SecurityManager:
+    """安全管理器"""
+
+    def __init__(self):
+        self.api_keys = self.load_api_keys()
+        self.rate_limits = {}
+
+    def load_api_keys(self):
+        """加载API密钥"""
+        return {
+            "admin": self.hash_password("admin_secure_password"),
+            "readonly": self.hash_password("readonly_password")
+        }
+
+    def hash_password(self, password: str) -> str:
+        """密码哈希"""
+        salt = secrets.token_hex(16)
+        return hashlib.pbkdf2_hmac('sha256',
+                                  password.encode('utf-8'),
+                                  salt.encode('utf-8'),
+                                  100000).hex() + ':' + salt
+
+    def verify_password(self, password: str, hashed: str) -> bool:
+        """验证密码"""
+        try:
+            hash_part, salt = hashed.split(':')
+            return hashlib.pbkdf2_hmac('sha256',
+                                      password.encode('utf-8'),
+                                      salt.encode('utf-8'),
+                                      100000).hex() == hash_part
+        except:
+            return False
+
+    def require_auth(self, f):
+        """认证装饰器"""
+        @wraps(f)
+        def decorated(*args, **kwargs):
+            auth = request.authorization
+            if not auth or not self.verify_credentials(auth.username, auth.password):
+                return Response('Authentication required', 401,
+                              {'WWW-Authenticate': 'Basic realm="Locust"'})
+            return f(*args, **kwargs)
+        return decorated
+
+    def verify_credentials(self, username: str, password: str) -> bool:
+        """验证凭据"""
+        if username in self.api_keys:
+            return self.verify_password(password, self.api_keys[username])
+        return False
+
+# 使用安全管理器
+security_manager = SecurityManager()
+
+@security_manager.require_auth
+def protected_endpoint():
+    return "Protected content"
+```
+
+### 2. 网络安全配置
+
+```yaml
+# security/network-policy.yml
+apiVersion: networking.k8s.io/v1
+kind: NetworkPolicy
+metadata:
+  name: locust-network-policy
+spec:
+  podSelector:
+    matchLabels:
+      app: locust
+  policyTypes:
+  - Ingress
+  - Egress
+  ingress:
+  - from:
+    - namespaceSelector:
+        matchLabels:
+          name: monitoring
+    - podSelector:
+        matchLabels:
+          app: nginx
+    ports:
+    - protocol: TCP
+      port: 8089
+    - protocol: TCP
+      port: 5557
+  egress:
+  - to:
+    - namespaceSelector:
+        matchLabels:
+          name: target-system
+    ports:
+    - protocol: TCP
+      port: 443
+    - protocol: TCP
+      port: 80
+```
+
+## 📊 监控和告警
+
+### 1. Prometheus监控配置
+
+```yaml
+# monitoring/prometheus.yml
+global:
+  scrape_interval: 15s
+  evaluation_interval: 15s
+
+rule_files:
+  - "locust_rules.yml"
+
+scrape_configs:
+  - job_name: 'locust'
+    static_configs:
+      - targets: ['locust-master:8089']
+    metrics_path: '/stats/requests/csv'
+    scrape_interval: 30s
+
+  - job_name: 'locust-workers'
+    static_configs:
+      - targets: ['locust-worker-1:8089', 'locust-worker-2:8089']
+    scrape_interval: 30s
+
+alerting:
+  alertmanagers:
+    - static_configs:
+        - targets:
+          - alertmanager:9093
+```
+
+### 2. 告警规则
+
+```yaml
+# monitoring/locust_rules.yml
+groups:
+- name: locust_alerts
+  rules:
+  - alert: HighErrorRate
+    expr: locust_error_rate > 5
+    for: 2m
+    labels:
+      severity: warning
+    annotations:
+      summary: "High error rate detected"
+      description: "Error rate is {{ $value }}% for more than 2 minutes"
+
+  - alert: HighResponseTime
+    expr: locust_avg_response_time > 2000
+    for: 5m
+    labels:
+      severity: warning
+    annotations:
+      summary: "High response time detected"
+      description: "Average response time is {{ $value }}ms"
+
+  - alert: LowThroughput
+    expr: locust_rps < 50
+    for: 3m
+    labels:
+      severity: warning
+    annotations:
+      summary: "Low throughput detected"
+      description: "RPS is {{ $value }}"
+
+  - alert: WorkerDown
+    expr: up{job="locust-workers"} == 0
+    for: 1m
+    labels:
+      severity: critical
+    annotations:
+      summary: "Locust worker is down"
+      description: "Worker {{ $labels.instance }} is not responding"
+```
+
+### 3. Grafana仪表板
+
+```json
+{
+  "dashboard": {
+    "title": "Locust Performance Dashboard",
+    "panels": [
+      {
+        "title": "Request Rate",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "rate(locust_requests_total[5m])",
+            "legendFormat": "RPS"
+          }
+        ]
+      },
+      {
+        "title": "Response Time",
+        "type": "graph",
+        "targets": [
+          {
+            "expr": "locust_avg_response_time",
+            "legendFormat": "Average"
+          },
+          {
+            "expr": "locust_p95_response_time",
+            "legendFormat": "95th Percentile"
+          }
+        ]
+      },
+      {
+        "title": "Error Rate",
+        "type": "singlestat",
+        "targets": [
+          {
+            "expr": "locust_error_rate",
+            "legendFormat": "Error Rate %"
+          }
+        ]
+      }
+    ]
+  }
+}
+```
+
+## 🚀 部署脚本
+
+### 1. 自动化部署脚本
+
+```bash
+#!/bin/bash
+# deploy.sh
+
+set -e
+
+# 配置变量
+ENVIRONMENT=${1:-production}
+VERSION=${2:-latest}
+COMPOSE_FILE="docker-compose.${ENVIRONMENT}.yml"
+
+echo "Deploying Locust Framework to ${ENVIRONMENT} environment..."
+
+# 检查环境
+if [ ! -f "${COMPOSE_FILE}" ]; then
+    echo "Error: ${COMPOSE_FILE} not found"
+    exit 1
+fi
+
+# 加载环境变量
+if [ -f ".env.${ENVIRONMENT}" ]; then
+    source ".env.${ENVIRONMENT}"
+fi
+
+# 构建镜像
+echo "Building Docker images..."
+docker-compose -f ${COMPOSE_FILE} build
+
+# 停止旧服务
+echo "Stopping existing services..."
+docker-compose -f ${COMPOSE_FILE} down
+
+# 启动新服务
+echo "Starting services..."
+docker-compose -f ${COMPOSE_FILE} up -d
+
+# 等待服务启动
+echo "Waiting for services to start..."
+sleep 30
+
+# 健康检查
+echo "Performing health checks..."
+for i in {1..10}; do
+    if curl -f http://localhost/health > /dev/null 2>&1; then
+        echo "Health check passed"
+        break
+    fi
+    if [ $i -eq 10 ]; then
+        echo "Health check failed"
+        exit 1
+    fi
+    sleep 10
+done
+
+# 显示状态
+docker-compose -f ${COMPOSE_FILE} ps
+
+echo "Deployment completed successfully!"
+echo "Access the web interface at: https://your-domain.com"
+```
+
+### 2. 滚动更新脚本
+
+```bash
+#!/bin/bash
+# rolling_update.sh
+
+set -e
+
+COMPOSE_FILE="docker-compose.production.yml"
+SERVICE_NAME="locust-worker"
+NEW_IMAGE="locust-framework:${1:-latest}"
+
+echo "Performing rolling update for ${SERVICE_NAME}..."
+
+# 获取当前副本数
+REPLICAS=$(docker-compose -f ${COMPOSE_FILE} ps -q ${SERVICE_NAME} | wc -l)
+
+echo "Current replicas: ${REPLICAS}"
+
+# 逐个更新worker
+for i in $(seq 1 ${REPLICAS}); do
+    echo "Updating worker ${i}/${REPLICAS}..."
+
+    # 停止一个worker
+    CONTAINER_ID=$(docker-compose -f ${COMPOSE_FILE} ps -q ${SERVICE_NAME} | head -n1)
+    docker stop ${CONTAINER_ID}
+
+    # 启动新的worker
+    docker-compose -f ${COMPOSE_FILE} up -d --scale ${SERVICE_NAME}=${REPLICAS}
+
+    # 等待新worker就绪
+    sleep 30
+
+    # 验证worker状态
+    if ! docker-compose -f ${COMPOSE_FILE} ps ${SERVICE_NAME} | grep -q "Up"; then
+        echo "Error: Worker failed to start"
+        exit 1
+    fi
+
+    echo "Worker ${i} updated successfully"
+done
+
+echo "Rolling update completed successfully!"
+```
+
+## 🔧 性能调优
+
+### 1. 系统级优化
+
+```bash
+# system_tuning.sh
+#!/bin/bash
+
+echo "Applying system-level optimizations..."
+
+# 增加文件描述符限制
+echo "* soft nofile 65536" >> /etc/security/limits.conf
+echo "* hard nofile 65536" >> /etc/security/limits.conf
+
+# 网络优化
+echo "net.core.somaxconn = 65536" >> /etc/sysctl.conf
+echo "net.core.netdev_max_backlog = 5000" >> /etc/sysctl.conf
+echo "net.ipv4.tcp_max_syn_backlog = 65536" >> /etc/sysctl.conf
+echo "net.ipv4.tcp_keepalive_time = 600" >> /etc/sysctl.conf
+
+# 应用设置
+sysctl -p
+
+echo "System optimization completed"
+```
+
+### 2. 应用级优化
+
+```python
+# optimization/performance_config.py
+import os
+import multiprocessing
+
+class ProductionConfig:
+    """生产环境性能配置"""
+
+    # 进程配置
+    WORKER_PROCESSES = min(multiprocessing.cpu_count(), 8)
+    WORKER_CONNECTIONS = 1000
+    WORKER_CLASS = "gevent"
+
+    # 内存配置
+    MAX_MEMORY_PER_WORKER = "2G"
+    PRELOAD_APP = True
+
+    # 网络配置
+    KEEPALIVE = 2
+    TIMEOUT = 30
+    GRACEFUL_TIMEOUT = 30
+
+    # 日志配置
+    ACCESS_LOG = "/app/logs/access.log"
+    ERROR_LOG = "/app/logs/error.log"
+    LOG_LEVEL = "info"
+
+    @classmethod
+    def apply_optimizations(cls):
+        """应用性能优化"""
+        # 设置环境变量
+        os.environ.update({
+            "PYTHONUNBUFFERED": "1",
+            "PYTHONDONTWRITEBYTECODE": "1",
+            "GEVENT_RESOLVER": "ares",
+            "LOCUST_WORKER_PROCESSES": str(cls.WORKER_PROCESSES)
+        })
+
+        # 配置垃圾回收
+        import gc
+        gc.set_threshold(700, 10, 10)
+
+        print(f"Applied optimizations: {cls.WORKER_PROCESSES} workers")
+```
+
+## 📚 运维手册
+
+### 1. 日常维护检查清单
+
+```markdown
+## 日常检查 (每日)
+- [ ] 检查服务状态
+- [ ] 查看错误日志
+- [ ] 监控资源使用率
+- [ ] 验证备份完整性
+
+## 周检查
+- [ ] 更新安全补丁
+- [ ] 清理旧日志文件
+- [ ] 检查磁盘空间
+- [ ] 性能趋势分析
+
+## 月检查
+- [ ] 容量规划评估
+- [ ] 安全审计
+- [ ] 灾难恢复测试
+- [ ] 配置备份验证
+```
+
+### 2. 故障处理流程
+
+```bash
+# troubleshooting.sh
+#!/bin/bash
+
+echo "Locust Framework Troubleshooting Guide"
+echo "======================================"
+
+# 检查服务状态
+echo "1. Checking service status..."
+docker-compose ps
+
+# 检查日志
+echo "2. Checking recent logs..."
+docker-compose logs --tail=50 locust-master
+docker-compose logs --tail=50 locust-worker
+
+# 检查资源使用
+echo "3. Checking resource usage..."
+docker stats --no-stream
+
+# 检查网络连接
+echo "4. Checking network connectivity..."
+curl -I http://localhost/health
+
+# 生成诊断报告
+echo "5. Generating diagnostic report..."
+{
+    echo "=== System Information ==="
+    uname -a
+    echo "=== Docker Version ==="
+    docker version
+    echo "=== Disk Usage ==="
+    df -h
+    echo "=== Memory Usage ==="
+    free -h
+} > diagnostic_report.txt
+
+echo "Diagnostic report saved to: diagnostic_report.txt"
+```
+
+## 🎉 总结
+
+生产环境部署的关键要点：
+
+1. **容器化部署**: 使用Docker确保环境一致性
+2. **安全配置**: 实施认证、授权和网络安全
+3. **监控告警**: 建立完善的监控和告警体系
+4. **性能优化**: 系统和应用级别的性能调优
+5. **自动化运维**: 部署、更新和维护的自动化
+
+## 📚 相关文档
+
+- [框架配置](framework-config.md) - 基础配置说明
+- [监控配置](monitoring-config.md) - 监控系统配置
+- [分布式配置](distributed.md) - 分布式部署配置
+- [最佳实践](../examples/best-practices.md) - 性能测试最佳实践
diff --git a/docs/configuration/testing.md b/docs/configuration/testing.md
new file mode 100644
index 0000000..6145bf7
--- /dev/null
+++ b/docs/configuration/testing.md
@@ -0,0 +1,933 @@
+# 测试环境配置
+
+本文档详细介绍如何配置Locust性能测试框架的测试环境，包括测试环境搭建、配置管理和测试数据准备。
+
+## 🎯 测试环境架构
+
+### 环境分层
+
+```yaml
+# testing/environment_layers.yml
+environment_layers:
+  unit_testing:
+    description: "单元测试环境"
+    scope: "单个组件测试"
+    isolation: "完全隔离"
+    dependencies: "最小化"
+
+  integration_testing:
+    description: "集成测试环境"
+    scope: "组件间交互测试"
+    isolation: "部分隔离"
+    dependencies: "真实服务"
+
+  system_testing:
+    description: "系统测试环境"
+    scope: "完整系统测试"
+    isolation: "接近生产"
+    dependencies: "完整服务栈"
+
+  performance_testing:
+    description: "性能测试环境"
+    scope: "性能和负载测试"
+    isolation: "独立环境"
+    dependencies: "生产级配置"
+```
+
+### 测试环境拓扑
+
+```mermaid
+graph TD
+    A[测试控制器] --> B[Locust Master]
+    B --> C[Locust Worker 1]
+    B --> D[Locust Worker 2]
+    B --> E[Locust Worker N]
+
+    C --> F[目标应用集群]
+    D --> F
+    E --> F
+
+    F --> G[数据库集群]
+    F --> H[缓存集群]
+    F --> I[消息队列]
+
+    J[监控系统] --> B
+    J --> F
+    J --> G
+    J --> H
+    J --> I
+
+    K[日志收集] --> B
+    K --> F
+```
+
+## 🏗️ 基础设施配置
+
+### 1. Docker测试环境
+
+```yaml
+# docker-compose.test.yml
+version: '3.8'
+
+services:
+  # Locust Master
+  locust-master:
+    build:
+      context: .
+      dockerfile: Dockerfile.test
+    container_name: locust-master-test
+    ports:
+      - "8089:8089"
+      - "5557:5557"
+    environment:
+      - LOCUST_MODE=master
+      - LOCUST_MASTER_BIND_HOST=0.0.0.0
+      - LOCUST_MASTER_BIND_PORT=5557
+      - LOCUST_WEB_HOST=0.0.0.0
+      - LOCUST_WEB_PORT=8089
+    volumes:
+      - ./locustfiles:/app/locustfiles
+      - ./test-data:/app/test-data
+      - ./logs:/app/logs
+    networks:
+      - test-network
+    depends_on:
+      - target-app
+      - redis
+      - postgres
+
+  # Locust Workers
+  locust-worker-1:
+    build:
+      context: .
+      dockerfile: Dockerfile.test
+    container_name: locust-worker-1-test
+    environment:
+      - LOCUST_MODE=worker
+      - LOCUST_MASTER_HOST=locust-master
+      - LOCUST_MASTER_PORT=5557
+    volumes:
+      - ./locustfiles:/app/locustfiles
+      - ./test-data:/app/test-data
+    networks:
+      - test-network
+    depends_on:
+      - locust-master
+
+  locust-worker-2:
+    build:
+      context: .
+      dockerfile: Dockerfile.test
+    container_name: locust-worker-2-test
+    environment:
+      - LOCUST_MODE=worker
+      - LOCUST_MASTER_HOST=locust-master
+      - LOCUST_MASTER_PORT=5557
+    volumes:
+      - ./locustfiles:/app/locustfiles
+      - ./test-data:/app/test-data
+    networks:
+      - test-network
+    depends_on:
+      - locust-master
+
+  # 目标应用
+  target-app:
+    image: nginx:alpine
+    container_name: target-app-test
+    ports:
+      - "8080:80"
+    volumes:
+      - ./test-configs/nginx.conf:/etc/nginx/nginx.conf
+      - ./test-data/static:/usr/share/nginx/html
+    networks:
+      - test-network
+
+  # 数据库
+  postgres:
+    image: postgres:13
+    container_name: postgres-test
+    ports:
+      - "5432:5432"
+    environment:
+      - POSTGRES_DB=testdb
+      - POSTGRES_USER=testuser
+      - POSTGRES_PASSWORD=testpass
+    volumes:
+      - postgres-test-data:/var/lib/postgresql/data
+      - ./test-data/sql:/docker-entrypoint-initdb.d
+    networks:
+      - test-network
+
+  # 缓存
+  redis:
+    image: redis:alpine
+    container_name: redis-test
+    ports:
+      - "6379:6379"
+    command: redis-server --appendonly yes
+    volumes:
+      - redis-test-data:/data
+    networks:
+      - test-network
+
+  # 消息队列
+  rabbitmq:
+    image: rabbitmq:3-management
+    container_name: rabbitmq-test
+    ports:
+      - "5672:5672"
+      - "15672:15672"
+    environment:
+      - RABBITMQ_DEFAULT_USER=testuser
+      - RABBITMQ_DEFAULT_PASS=testpass
+    volumes:
+      - rabbitmq-test-data:/var/lib/rabbitmq
+    networks:
+      - test-network
+
+  # 监控
+  prometheus:
+    image: prom/prometheus:latest
+    container_name: prometheus-test
+    ports:
+      - "9090:9090"
+    volumes:
+      - ./test-configs/prometheus.yml:/etc/prometheus/prometheus.yml
+      - prometheus-test-data:/prometheus
+    networks:
+      - test-network
+
+networks:
+  test-network:
+    driver: bridge
+
+volumes:
+  postgres-test-data:
+  redis-test-data:
+  rabbitmq-test-data:
+  prometheus-test-data:
+```
+
+### 2. Kubernetes测试环境
+
+```yaml
+# k8s/test-namespace.yml
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: locust-test
+  labels:
+    environment: test
+    purpose: performance-testing
+
+---
+# k8s/locust-master-test.yml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: locust-master
+  namespace: locust-test
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: locust-master
+  template:
+    metadata:
+      labels:
+        app: locust-master
+    spec:
+      containers:
+      - name: locust-master
+        image: locust-framework:test
+        ports:
+        - containerPort: 8089
+        - containerPort: 5557
+        env:
+        - name: LOCUST_MODE
+          value: "master"
+        - name: LOCUST_MASTER_BIND_HOST
+          value: "0.0.0.0"
+        resources:
+          requests:
+            memory: "512Mi"
+            cpu: "500m"
+          limits:
+            memory: "1Gi"
+            cpu: "1000m"
+        volumeMounts:
+        - name: locustfiles
+          mountPath: /app/locustfiles
+        - name: test-data
+          mountPath: /app/test-data
+      volumes:
+      - name: locustfiles
+        configMap:
+          name: locustfiles-config
+      - name: test-data
+        configMap:
+          name: test-data-config
+
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: locust-master-service
+  namespace: locust-test
+spec:
+  selector:
+    app: locust-master
+  ports:
+  - name: web
+    port: 8089
+    targetPort: 8089
+  - name: master
+    port: 5557
+    targetPort: 5557
+  type: LoadBalancer
+
+---
+# k8s/locust-worker-test.yml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: locust-worker
+  namespace: locust-test
+spec:
+  replicas: 3
+  selector:
+    matchLabels:
+      app: locust-worker
+  template:
+    metadata:
+      labels:
+        app: locust-worker
+    spec:
+      containers:
+      - name: locust-worker
+        image: locust-framework:test
+        env:
+        - name: LOCUST_MODE
+          value: "worker"
+        - name: LOCUST_MASTER_HOST
+          value: "locust-master-service"
+        - name: LOCUST_MASTER_PORT
+          value: "5557"
+        resources:
+          requests:
+            memory: "256Mi"
+            cpu: "250m"
+          limits:
+            memory: "512Mi"
+            cpu: "500m"
+        volumeMounts:
+        - name: locustfiles
+          mountPath: /app/locustfiles
+        - name: test-data
+          mountPath: /app/test-data
+      volumes:
+      - name: locustfiles
+        configMap:
+          name: locustfiles-config
+      - name: test-data
+        configMap:
+          name: test-data-config
+```
+
+## ⚙️ 配置管理
+
+### 1. 环境配置文件
+
+```yaml
+# config/test.yml
+# 测试环境配置
+environment:
+  name: "test"
+  description: "Performance testing environment"
+
+# Locust配置
+locust:
+  host: "http://target-app:8080"
+  users: 100
+  spawn_rate: 10
+  run_time: "300s"
+
+  # Web UI配置
+  web:
+    host: "0.0.0.0"
+    port: 8089
+    auth_credentials: "admin:test123"
+
+  # Master配置
+  master:
+    bind_host: "0.0.0.0"
+    bind_port: 5557
+    expect_workers: 2
+    expect_workers_max_wait: 60
+
+  # Worker配置
+  worker:
+    master_host: "locust-master"
+    master_port: 5557
+
+# 目标系统配置
+target_system:
+  base_url: "http://target-app:8080"
+  api_version: "v1"
+  timeout: 30
+  retries: 3
+
+  # 认证配置
+  auth:
+    type: "bearer_token"
+    token_url: "/auth/token"
+    username: "testuser"
+    password: "testpass"
+
+  # 端点配置
+  endpoints:
+    health: "/health"
+    login: "/auth/login"
+    logout: "/auth/logout"
+    users: "/api/v1/users"
+    orders: "/api/v1/orders"
+    products: "/api/v1/products"
+
+# 数据库配置
+database:
+  host: "postgres"
+  port: 5432
+  name: "testdb"
+  username: "testuser"
+  password: "testpass"
+  pool_size: 10
+  max_overflow: 20
+
+# 缓存配置
+cache:
+  host: "redis"
+  port: 6379
+  db: 0
+  password: ""
+  timeout: 5
+
+# 消息队列配置
+message_queue:
+  host: "rabbitmq"
+  port: 5672
+  username: "testuser"
+  password: "testpass"
+  virtual_host: "/"
+
+# 监控配置
+monitoring:
+  prometheus:
+    host: "prometheus"
+    port: 9090
+    scrape_interval: "15s"
+
+  metrics:
+    enabled: true
+    port: 9090
+    path: "/metrics"
+
+  logging:
+    level: "INFO"
+    format: "json"
+    file: "/app/logs/locust-test.log"
+
+# 通知配置
+notifications:
+  enabled: true
+  channels:
+    - type: "slack"
+      webhook_url: "https://hooks.slack.com/test-webhook"
+      channel: "#testing"
+    - type: "email"
+      smtp_host: "smtp.test.com"
+      recipients: ["test-team@company.com"]
+
+# 测试数据配置
+test_data:
+  users:
+    count: 1000
+    file: "/app/test-data/users.json"
+
+  products:
+    count: 500
+    file: "/app/test-data/products.json"
+
+  orders:
+    count: 2000
+    file: "/app/test-data/orders.json"
+
+  # 数据生成配置
+  generation:
+    locale: "zh_CN"
+    seed: 12345
+    batch_size: 100
+```
+
+### 2. 环境变量配置
+
+```bash
+# .env.test
+# 基础环境配置
+ENVIRONMENT=test
+DEBUG=true
+LOG_LEVEL=INFO
+
+# Locust配置
+LOCUST_HOST=http://target-app:8080
+LOCUST_USERS=100
+LOCUST_SPAWN_RATE=10
+LOCUST_RUN_TIME=300s
+LOCUST_WEB_HOST=0.0.0.0
+LOCUST_WEB_PORT=8089
+
+# 数据库配置
+DATABASE_URL=postgresql://testuser:testpass@postgres:5432/testdb
+DATABASE_POOL_SIZE=10
+DATABASE_MAX_OVERFLOW=20
+
+# 缓存配置
+REDIS_URL=redis://redis:6379/0
+REDIS_TIMEOUT=5
+
+# 消息队列配置
+RABBITMQ_URL=amqp://testuser:testpass@rabbitmq:5672/
+RABBITMQ_EXCHANGE=locust_test
+
+# 监控配置
+PROMETHEUS_URL=http://prometheus:9090
+METRICS_ENABLED=true
+METRICS_PORT=9090
+
+# 测试配置
+TEST_DATA_DIR=/app/test-data
+TEST_RESULTS_DIR=/app/test-results
+TEST_TIMEOUT=3600
+
+# 安全配置
+SECRET_KEY=test-secret-key-change-in-production
+JWT_SECRET=test-jwt-secret
+API_KEY=test-api-key
+
+# 第三方服务配置
+SLACK_WEBHOOK_URL=https://hooks.slack.com/test-webhook
+EMAIL_SMTP_HOST=smtp.test.com
+EMAIL_SMTP_PORT=587
+EMAIL_USERNAME=test@company.com
+EMAIL_PASSWORD=test-password
+```
+
+## 📊 测试数据管理
+
+### 1. 测试数据生成
+
+```python
+# test_data/data_generator.py
+import json
+import random
+from faker import Faker
+from datetime import datetime, timedelta
+from pathlib import Path
+
+class TestDataGenerator:
+    """测试数据生成器"""
+
+    def __init__(self, locale='zh_CN', seed=12345):
+        self.fake = Faker(locale)
+        Faker.seed(seed)
+        random.seed(seed)
+        self.output_dir = Path("test-data")
+        self.output_dir.mkdir(exist_ok=True)
+
+    def generate_users(self, count=1000):
+        """生成用户数据"""
+        users = []
+
+        for i in range(count):
+            user = {
+                "id": i + 1,
+                "username": self.fake.user_name(),
+                "email": self.fake.email(),
+                "first_name": self.fake.first_name(),
+                "last_name": self.fake.last_name(),
+                "phone": self.fake.phone_number(),
+                "address": {
+                    "street": self.fake.street_address(),
+                    "city": self.fake.city(),
+                    "state": self.fake.state(),
+                    "zip_code": self.fake.postcode(),
+                    "country": "中国"
+                },
+                "profile": {
+                    "age": random.randint(18, 80),
+                    "gender": random.choice(["male", "female"]),
+                    "occupation": self.fake.job(),
+                    "company": self.fake.company()
+                },
+                "preferences": {
+                    "language": random.choice(["zh", "en"]),
+                    "currency": "CNY",
+                    "timezone": "Asia/Shanghai"
+                },
+                "created_at": self.fake.date_time_between(
+                    start_date="-2y", end_date="now"
+                ).isoformat(),
+                "is_active": random.choice([True, True, True, False]),  # 75%活跃
+                "last_login": self.fake.date_time_between(
+                    start_date="-30d", end_date="now"
+                ).isoformat()
+            }
+            users.append(user)
+
+        # 保存到文件
+        with open(self.output_dir / "users.json", "w", encoding="utf-8") as f:
+            json.dump(users, f, ensure_ascii=False, indent=2)
+
+        print(f"Generated {count} users")
+        return users
+
+    def generate_products(self, count=500):
+        """生成产品数据"""
+        categories = ["电子产品", "服装", "家居", "图书", "运动", "美妆", "食品"]
+        products = []
+
+        for i in range(count):
+            category = random.choice(categories)
+            product = {
+                "id": i + 1,
+                "name": self.fake.catch_phrase(),
+                "description": self.fake.text(max_nb_chars=200),
+                "category": category,
+                "price": round(random.uniform(10, 1000), 2),
+                "currency": "CNY",
+                "stock": random.randint(0, 1000),
+                "sku": f"SKU{i+1:06d}",
+                "brand": self.fake.company(),
+                "specifications": {
+                    "weight": f"{random.uniform(0.1, 10):.2f}kg",
+                    "dimensions": f"{random.randint(10, 100)}x{random.randint(10, 100)}x{random.randint(5, 50)}cm",
+                    "color": self.fake.color_name(),
+                    "material": random.choice(["塑料", "金属", "木材", "布料", "玻璃"])
+                },
+                "ratings": {
+                    "average": round(random.uniform(3.0, 5.0), 1),
+                    "count": random.randint(0, 1000)
+                },
+                "images": [
+                    f"https://example.com/images/product_{i+1}_1.jpg",
+                    f"https://example.com/images/product_{i+1}_2.jpg"
+                ],
+                "created_at": self.fake.date_time_between(
+                    start_date="-1y", end_date="now"
+                ).isoformat(),
+                "is_available": random.choice([True, True, True, False])  # 75%可用
+            }
+            products.append(product)
+
+        # 保存到文件
+        with open(self.output_dir / "products.json", "w", encoding="utf-8") as f:
+            json.dump(products, f, ensure_ascii=False, indent=2)
+
+        print(f"Generated {count} products")
+        return products
+
+    def generate_orders(self, count=2000, users=None, products=None):
+        """生成订单数据"""
+        if not users:
+            with open(self.output_dir / "users.json", "r", encoding="utf-8") as f:
+                users = json.load(f)
+
+        if not products:
+            with open(self.output_dir / "products.json", "r", encoding="utf-8") as f:
+                products = json.load(f)
+
+        orders = []
+        statuses = ["pending", "confirmed", "shipped", "delivered", "cancelled"]
+
+        for i in range(count):
+            user = random.choice(users)
+            order_products = random.sample(products, random.randint(1, 5))
+
+            total_amount = 0
+            order_items = []
+
+            for product in order_products:
+                quantity = random.randint(1, 3)
+                item_total = product["price"] * quantity
+                total_amount += item_total
+
+                order_items.append({
+                    "product_id": product["id"],
+                    "product_name": product["name"],
+                    "price": product["price"],
+                    "quantity": quantity,
+                    "total": item_total
+                })
+
+            order = {
+                "id": i + 1,
+                "user_id": user["id"],
+                "user_email": user["email"],
+                "items": order_items,
+                "total_amount": round(total_amount, 2),
+                "currency": "CNY",
+                "status": random.choice(statuses),
+                "payment": {
+                    "method": random.choice(["credit_card", "alipay", "wechat_pay", "bank_transfer"]),
+                    "status": random.choice(["pending", "completed", "failed"]),
+                    "transaction_id": f"TXN{i+1:08d}"
+                },
+                "shipping": {
+                    "address": user["address"],
+                    "method": random.choice(["standard", "express", "overnight"]),
+                    "cost": round(random.uniform(5, 50), 2),
+                    "tracking_number": f"TRK{i+1:010d}"
+                },
+                "created_at": self.fake.date_time_between(
+                    start_date="-6m", end_date="now"
+                ).isoformat(),
+                "updated_at": self.fake.date_time_between(
+                    start_date="-6m", end_date="now"
+                ).isoformat()
+            }
+            orders.append(order)
+
+        # 保存到文件
+        with open(self.output_dir / "orders.json", "w", encoding="utf-8") as f:
+            json.dump(orders, f, ensure_ascii=False, indent=2)
+
+        print(f"Generated {count} orders")
+        return orders
+
+    def generate_all(self, users_count=1000, products_count=500, orders_count=2000):
+        """生成所有测试数据"""
+        print("Generating test data...")
+
+        users = self.generate_users(users_count)
+        products = self.generate_products(products_count)
+        orders = self.generate_orders(orders_count, users, products)
+
+        # 生成汇总信息
+        summary = {
+            "generated_at": datetime.now().isoformat(),
+            "counts": {
+                "users": len(users),
+                "products": len(products),
+                "orders": len(orders)
+            },
+            "files": {
+                "users": "users.json",
+                "products": "products.json",
+                "orders": "orders.json"
+            }
+        }
+
+        with open(self.output_dir / "summary.json", "w", encoding="utf-8") as f:
+            json.dump(summary, f, ensure_ascii=False, indent=2)
+
+        print("Test data generation completed!")
+        return summary
+
+# 使用示例
+if __name__ == "__main__":
+    generator = TestDataGenerator()
+    generator.generate_all()
+```
+
+### 2. 数据库初始化
+
+```sql
+-- test-data/sql/01_init_schema.sql
+-- 创建测试数据库架构
+
+-- 用户表
+CREATE TABLE IF NOT EXISTS users (
+    id SERIAL PRIMARY KEY,
+    username VARCHAR(50) UNIQUE NOT NULL,
+    email VARCHAR(100) UNIQUE NOT NULL,
+    first_name VARCHAR(50),
+    last_name VARCHAR(50),
+    phone VARCHAR(20),
+    address JSONB,
+    profile JSONB,
+    preferences JSONB,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    is_active BOOLEAN DEFAULT TRUE,
+    last_login TIMESTAMP
+);
+
+-- 产品表
+CREATE TABLE IF NOT EXISTS products (
+    id SERIAL PRIMARY KEY,
+    name VARCHAR(200) NOT NULL,
+    description TEXT,
+    category VARCHAR(50),
+    price DECIMAL(10,2),
+    currency VARCHAR(3) DEFAULT 'CNY',
+    stock INTEGER DEFAULT 0,
+    sku VARCHAR(50) UNIQUE,
+    brand VARCHAR(100),
+    specifications JSONB,
+    ratings JSONB,
+    images JSONB,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    is_available BOOLEAN DEFAULT TRUE
+);
+
+-- 订单表
+CREATE TABLE IF NOT EXISTS orders (
+    id SERIAL PRIMARY KEY,
+    user_id INTEGER REFERENCES users(id),
+    total_amount DECIMAL(10,2),
+    currency VARCHAR(3) DEFAULT 'CNY',
+    status VARCHAR(20),
+    payment JSONB,
+    shipping JSONB,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- 订单项表
+CREATE TABLE IF NOT EXISTS order_items (
+    id SERIAL PRIMARY KEY,
+    order_id INTEGER REFERENCES orders(id),
+    product_id INTEGER REFERENCES products(id),
+    quantity INTEGER,
+    price DECIMAL(10,2),
+    total DECIMAL(10,2)
+);
+
+-- 创建索引
+CREATE INDEX IF NOT EXISTS idx_users_email ON users(email);
+CREATE INDEX IF NOT EXISTS idx_users_username ON users(username);
+CREATE INDEX IF NOT EXISTS idx_products_category ON products(category);
+CREATE INDEX IF NOT EXISTS idx_products_sku ON products(sku);
+CREATE INDEX IF NOT EXISTS idx_orders_user_id ON orders(user_id);
+CREATE INDEX IF NOT EXISTS idx_orders_status ON orders(status);
+CREATE INDEX IF NOT EXISTS idx_orders_created_at ON orders(created_at);
+CREATE INDEX IF NOT EXISTS idx_order_items_order_id ON order_items(order_id);
+CREATE INDEX IF NOT EXISTS idx_order_items_product_id ON order_items(product_id);
+```
+
+```python
+# test-data/sql/02_load_data.py
+import json
+import psycopg2
+from pathlib import Path
+
+def load_test_data():
+    """加载测试数据到数据库"""
+
+    # 数据库连接
+    conn = psycopg2.connect(
+        host="postgres",
+        port=5432,
+        database="testdb",
+        user="testuser",
+        password="testpass"
+    )
+
+    cur = conn.cursor()
+
+    try:
+        # 加载用户数据
+        with open("/app/test-data/users.json", "r", encoding="utf-8") as f:
+            users = json.load(f)
+
+        for user in users:
+            cur.execute("""
+                INSERT INTO users (
+                    id, username, email, first_name, last_name, phone,
+                    address, profile, preferences, created_at, is_active, last_login
+                ) VALUES (
+                    %(id)s, %(username)s, %(email)s, %(first_name)s, %(last_name)s, %(phone)s,
+                    %(address)s, %(profile)s, %(preferences)s, %(created_at)s, %(is_active)s, %(last_login)s
+                ) ON CONFLICT (id) DO NOTHING
+            """, user)
+
+        print(f"Loaded {len(users)} users")
+
+        # 加载产品数据
+        with open("/app/test-data/products.json", "r", encoding="utf-8") as f:
+            products = json.load(f)
+
+        for product in products:
+            cur.execute("""
+                INSERT INTO products (
+                    id, name, description, category, price, currency, stock,
+                    sku, brand, specifications, ratings, images, created_at, is_available
+                ) VALUES (
+                    %(id)s, %(name)s, %(description)s, %(category)s, %(price)s, %(currency)s, %(stock)s,
+                    %(sku)s, %(brand)s, %(specifications)s, %(ratings)s, %(images)s, %(created_at)s, %(is_available)s
+                ) ON CONFLICT (id) DO NOTHING
+            """, product)
+
+        print(f"Loaded {len(products)} products")
+
+        # 加载订单数据
+        with open("/app/test-data/orders.json", "r", encoding="utf-8") as f:
+            orders = json.load(f)
+
+        for order in orders:
+            # 插入订单
+            cur.execute("""
+                INSERT INTO orders (
+                    id, user_id, total_amount, currency, status,
+                    payment, shipping, created_at, updated_at
+                ) VALUES (
+                    %(id)s, %(user_id)s, %(total_amount)s, %(currency)s, %(status)s,
+                    %(payment)s, %(shipping)s, %(created_at)s, %(updated_at)s
+                ) ON CONFLICT (id) DO NOTHING
+            """, {
+                "id": order["id"],
+                "user_id": order["user_id"],
+                "total_amount": order["total_amount"],
+                "currency": order["currency"],
+                "status": order["status"],
+                "payment": json.dumps(order["payment"]),
+                "shipping": json.dumps(order["shipping"]),
+                "created_at": order["created_at"],
+                "updated_at": order["updated_at"]
+            })
+
+            # 插入订单项
+            for item in order["items"]:
+                cur.execute("""
+                    INSERT INTO order_items (
+                        order_id, product_id, quantity, price, total
+                    ) VALUES (
+                        %(order_id)s, %(product_id)s, %(quantity)s, %(price)s, %(total)s
+                    )
+                """, {
+                    "order_id": order["id"],
+                    "product_id": item["product_id"],
+                    "quantity": item["quantity"],
+                    "price": item["price"],
+                    "total": item["total"]
+                })
+
+        print(f"Loaded {len(orders)} orders")
+
+        # 提交事务
+        conn.commit()
+        print("Test data loaded successfully!")
+
+    except Exception as e:
+        conn.rollback()
+        print(f"Error loading test data: {e}")
+        raise
+
+    finally:
+        cur.close()
+        conn.close()
+
+if __name__ == "__main__":
+    load_test_data()
+```
diff --git a/docs/development/coding-standards.md b/docs/development/coding-standards.md
new file mode 100644
index 0000000..c5ad0d5
--- /dev/null
+++ b/docs/development/coding-standards.md
@@ -0,0 +1,781 @@
+# 编码规范
+
+本文档定义了Locust性能测试框架的编码规范，确保代码质量、可读性和可维护性。
+
+## 🎯 总体原则
+
+### 设计原则
+
+1. **SOLID原则**
+   - **单一职责原则(SRP)**: 每个类只负责一个功能
+   - **开闭原则(OCP)**: 对扩展开放，对修改关闭
+   - **里氏替换原则(LSP)**: 子类可以替换父类
+   - **接口隔离原则(ISP)**: 接口应该小而专一
+   - **依赖倒置原则(DIP)**: 依赖抽象而非具体实现
+
+2. **代码质量原则**
+   - **DRY (Don't Repeat Yourself)**: 避免重复代码
+   - **KISS (Keep It Simple, Stupid)**: 保持简单
+   - **YAGNI (You Aren't Gonna Need It)**: 不要过度设计
+
+## 📝 Python编码规范
+
+### 代码风格
+
+遵循 **PEP 8** 标准，使用 **Black** 进行代码格式化。
+
+#### 命名规范
+
+```python
+# 模块名：小写字母，下划线分隔
+# ✅ 正确
+performance_analyzer.py
+data_manager.py
+
+# ❌ 错误
+PerformanceAnalyzer.py
+dataManager.py
+
+# 类名：大驼峰命名法
+# ✅ 正确
+class PerformanceAnalyzer:
+    pass
+
+class DataProvider:
+    pass
+
+# ❌ 错误
+class performance_analyzer:
+    pass
+
+class dataProvider:
+    pass
+
+# 函数名和变量名：小写字母，下划线分隔
+# ✅ 正确
+def analyze_performance():
+    pass
+
+user_count = 100
+response_time = 500.0
+
+# ❌ 错误
+def analyzePerformance():
+    pass
+
+userCount = 100
+responseTime = 500.0
+
+# 常量：大写字母，下划线分隔
+# ✅ 正确
+MAX_USERS = 1000
+DEFAULT_TIMEOUT = 30
+API_VERSION = "v1"
+
+# 私有属性/方法：单下划线前缀
+class MyClass:
+    def __init__(self):
+        self._private_var = "private"
+
+    def _private_method(self):
+        pass
+
+# 特殊方法：双下划线前后
+class MyClass:
+    def __init__(self):
+        pass
+
+    def __str__(self):
+        return "MyClass instance"
+```
+
+#### 导入规范
+
+```python
+# 导入顺序：标准库 -> 第三方库 -> 本地模块
+# 每组之间空一行
+
+# 标准库
+import os
+import sys
+import time
+from typing import Dict, List, Optional, Any
+
+# 第三方库
+import requests
+from locust import HttpUser, task
+from dynaconf import Dynaconf
+
+# 本地模块
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.monitoring.alert_manager import AlertManager
+from .utils import helper_function
+
+# ✅ 正确的导入方式
+from typing import Dict, List, Optional
+
+# ❌ 避免使用通配符导入
+from module import *
+
+# ✅ 使用别名简化长模块名
+import src.very_long_module_name as vlmn
+
+# ✅ 相对导入用于同包内模块
+from .sibling_module import function
+from ..parent_module import ParentClass
+```
+
+#### 字符串格式化
+
+```python
+# ✅ 推荐使用 f-string (Python 3.6+)
+name = "张三"
+age = 25
+message = f"用户 {name} 年龄 {age} 岁"
+
+# ✅ 复杂格式化使用 format()
+template = "响应时间: {time:.2f}ms, 状态: {status}"
+result = template.format(time=response_time, status="success")
+
+# ❌ 避免使用 % 格式化
+message = "用户 %s 年龄 %d 岁" % (name, age)
+
+# ✅ 多行字符串
+sql_query = """
+SELECT user_id, username, email
+FROM users
+WHERE created_at > %s
+ORDER BY created_at DESC
+"""
+
+# ✅ 长字符串拼接
+long_message = (
+    "这是一个很长的消息，"
+    "需要分成多行来提高可读性，"
+    "每行不超过88个字符。"
+)
+```
+
+### 文档字符串规范
+
+使用 **Google风格** 的文档字符串。
+
+```python
+def analyze_performance(test_data: Dict[str, Any],
+                       config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+    """分析测试性能数据
+
+    对测试数据进行综合性能分析，包括响应时间、吞吐量、错误率等指标，
+    并生成性能评级和改进建议。
+
+    Args:
+        test_data: 测试数据字典，包含以下字段：
+            - requests: 请求列表
+            - duration: 测试持续时间
+            - users: 用户数量
+        config: 分析配置参数，可选
+            - thresholds: 性能阈值设置
+            - weights: 指标权重配置
+
+    Returns:
+        Dict[str, Any]: 分析结果字典，包含：
+            - overall_grade: 总体评级 (A/B/C/D)
+            - response_time: 响应时间分析
+            - throughput: 吞吐量分析
+            - error_analysis: 错误分析
+            - recommendations: 改进建议
+
+    Raises:
+        ValueError: 当test_data格式不正确时
+        AnalysisError: 当分析过程出现错误时
+
+    Example:
+        >>> test_data = {
+        ...     'requests': [{'response_time': 100, 'success': True}],
+        ...     'duration': 60,
+        ...     'users': 10
+        ... }
+        >>> result = analyze_performance(test_data)
+        >>> print(result['overall_grade'])
+        'A'
+
+    Note:
+        此函数会消耗较多CPU资源，建议在测试结束后调用。
+
+    Todo:
+        - 添加更多性能指标
+        - 支持自定义评级算法
+    """
+    pass
+
+class PerformanceAnalyzer:
+    """性能分析器
+
+    提供全面的性能测试数据分析功能，包括统计分析、
+    趋势分析、异常检测等。
+
+    Attributes:
+        config: 分析器配置
+        thresholds: 性能阈值设置
+
+    Example:
+        >>> analyzer = PerformanceAnalyzer()
+        >>> result = analyzer.analyze(test_data)
+    """
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """初始化性能分析器
+
+        Args:
+            config: 分析器配置，包含阈值和权重设置
+        """
+        pass
+```
+
+### 类型注解规范
+
+```python
+from typing import Dict, List, Optional, Union, Tuple, Any, Callable
+
+# ✅ 基础类型注解
+def process_data(data: str) -> int:
+    return len(data)
+
+# ✅ 复合类型注解
+def analyze_requests(requests: List[Dict[str, Any]]) -> Dict[str, float]:
+    pass
+
+# ✅ 可选参数
+def create_user(name: str, age: Optional[int] = None) -> Dict[str, Any]:
+    pass
+
+# ✅ 联合类型
+def parse_value(value: Union[str, int, float]) -> str:
+    return str(value)
+
+# ✅ 回调函数类型
+def register_callback(callback: Callable[[str], None]) -> None:
+    pass
+
+# ✅ 类属性注解
+class DataManager:
+    config: Dict[str, Any]
+    providers: List[str]
+
+    def __init__(self):
+        self.config = {}
+        self.providers = []
+
+# ✅ 泛型类型
+from typing import TypeVar, Generic
+
+T = TypeVar('T')
+
+class Container(Generic[T]):
+    def __init__(self, value: T) -> None:
+        self.value = value
+
+    def get(self) -> T:
+        return self.value
+```
+
+### 异常处理规范
+
+```python
+# ✅ 自定义异常类
+class FrameworkError(Exception):
+    """框架基础异常类"""
+    pass
+
+class AnalysisError(FrameworkError):
+    """分析相关异常"""
+    pass
+
+class ConfigurationError(FrameworkError):
+    """配置相关异常"""
+    pass
+
+# ✅ 异常处理最佳实践
+def load_config(config_path: str) -> Dict[str, Any]:
+    """加载配置文件"""
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    except FileNotFoundError:
+        raise ConfigurationError(f"配置文件不存在: {config_path}")
+    except json.JSONDecodeError as e:
+        raise ConfigurationError(f"配置文件格式错误: {e}")
+    except Exception as e:
+        # 记录未预期的异常
+        logger.exception(f"加载配置文件时发生未知错误: {e}")
+        raise
+
+# ✅ 使用上下文管理器
+class DatabaseConnection:
+    def __enter__(self):
+        self.connection = create_connection()
+        return self.connection
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.connection:
+            self.connection.close()
+
+# 使用方式
+with DatabaseConnection() as conn:
+    # 使用连接
+    pass
+
+# ✅ 异常链
+def process_data(data: str) -> Dict[str, Any]:
+    try:
+        return parse_json(data)
+    except ValueError as e:
+        raise AnalysisError("数据解析失败") from e
+```
+
+### 日志规范
+
+```python
+import logging
+
+# ✅ 日志配置
+logger = logging.getLogger(__name__)
+
+class PerformanceAnalyzer:
+    def __init__(self):
+        self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
+
+    def analyze(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """分析性能数据"""
+        self.logger.info("开始性能分析")
+        self.logger.debug(f"输入数据: {data}")
+
+        try:
+            result = self._do_analysis(data)
+            self.logger.info(f"分析完成，评级: {result.get('grade')}")
+            return result
+        except Exception as e:
+            self.logger.error(f"分析失败: {e}", exc_info=True)
+            raise
+
+# ✅ 日志级别使用
+logger.debug("详细的调试信息")      # 开发调试
+logger.info("一般信息")            # 正常流程
+logger.warning("警告信息")         # 潜在问题
+logger.error("错误信息")           # 错误但程序可继续
+logger.critical("严重错误")        # 严重错误，程序可能停止
+
+# ✅ 结构化日志
+logger.info("用户登录", extra={
+    'user_id': user_id,
+    'ip_address': request.remote_addr,
+    'user_agent': request.headers.get('User-Agent')
+})
+```
+
+## 🏗️ 架构规范
+
+### 目录结构
+
+```
+src/
+├── analysis/           # 性能分析模块
+│   ├── __init__.py
+│   ├── performance_analyzer.py
+│   ├── trend_analyzer.py
+│   └── report_generator.py
+├── monitoring/         # 监控告警模块
+│   ├── __init__.py
+│   ├── performance_monitor.py
+│   ├── alert_manager.py
+│   └── notification_service.py
+├── data_manager/       # 数据管理模块
+│   ├── __init__.py
+│   ├── data_generator.py
+│   ├── data_provider.py
+│   └── data_distributor.py
+├── plugins/           # 插件系统
+│   ├── __init__.py
+│   ├── plugin_interface.py
+│   ├── plugin_manager.py
+│   └── builtin/       # 内置插件
+└── utils/             # 工具模块
+    ├── __init__.py
+    ├── helpers.py
+    └── decorators.py
+```
+
+### 模块设计规范
+
+```python
+# ✅ 接口定义
+from abc import ABC, abstractmethod
+from typing import Protocol
+
+class AnalyzerInterface(ABC):
+    """分析器接口"""
+
+    @abstractmethod
+    def analyze(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """分析数据"""
+        pass
+
+# ✅ 使用Protocol定义结构化类型
+class Configurable(Protocol):
+    """可配置对象协议"""
+
+    def configure(self, config: Dict[str, Any]) -> None:
+        """配置对象"""
+        ...
+
+# ✅ 工厂模式
+class AnalyzerFactory:
+    """分析器工厂"""
+
+    _analyzers = {
+        'performance': PerformanceAnalyzer,
+        'trend': TrendAnalyzer,
+    }
+
+    @classmethod
+    def create_analyzer(cls, analyzer_type: str, **kwargs) -> AnalyzerInterface:
+        """创建分析器实例"""
+        if analyzer_type not in cls._analyzers:
+            raise ValueError(f"未知的分析器类型: {analyzer_type}")
+
+        analyzer_class = cls._analyzers[analyzer_type]
+        return analyzer_class(**kwargs)
+
+# ✅ 单例模式
+class ConfigManager:
+    """配置管理器单例"""
+
+    _instance = None
+    _config = None
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def load_config(self, config_path: str) -> None:
+        """加载配置"""
+        if self._config is None:
+            self._config = self._load_from_file(config_path)
+
+    def get_config(self) -> Dict[str, Any]:
+        """获取配置"""
+        return self._config or {}
+```
+
+### 依赖注入
+
+```python
+# ✅ 依赖注入示例
+class PerformanceAnalyzer:
+    def __init__(self,
+                 data_provider: DataProviderInterface,
+                 config_manager: ConfigManagerInterface,
+                 logger: logging.Logger):
+        self.data_provider = data_provider
+        self.config_manager = config_manager
+        self.logger = logger
+
+    def analyze(self, test_id: str) -> Dict[str, Any]:
+        """分析性能数据"""
+        # 使用注入的依赖
+        data = self.data_provider.get_test_data(test_id)
+        config = self.config_manager.get_analysis_config()
+
+        self.logger.info(f"开始分析测试 {test_id}")
+        # 执行分析逻辑
+        return result
+
+# ✅ 依赖容器
+class DIContainer:
+    """依赖注入容器"""
+
+    def __init__(self):
+        self._services = {}
+        self._singletons = {}
+
+    def register(self, interface: type, implementation: type, singleton: bool = False):
+        """注册服务"""
+        self._services[interface] = (implementation, singleton)
+
+    def get(self, interface: type):
+        """获取服务实例"""
+        if interface in self._singletons:
+            return self._singletons[interface]
+
+        implementation, is_singleton = self._services[interface]
+        instance = implementation()
+
+        if is_singleton:
+            self._singletons[interface] = instance
+
+        return instance
+```
+
+## 🧪 测试规范
+
+### 单元测试
+
+```python
+import pytest
+from unittest.mock import Mock, patch
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+
+class TestPerformanceAnalyzer:
+    """性能分析器测试类"""
+
+    def setup_method(self):
+        """每个测试方法前执行"""
+        self.analyzer = PerformanceAnalyzer()
+        self.sample_data = {
+            'requests': [
+                {'response_time': 100, 'success': True},
+                {'response_time': 200, 'success': True},
+                {'response_time': 150, 'success': False}
+            ],
+            'duration': 60,
+            'users': 10
+        }
+
+    def test_analyze_performance_success(self):
+        """测试性能分析成功场景"""
+        # Given
+        expected_grade = 'B'
+
+        # When
+        result = self.analyzer.analyze(self.sample_data)
+
+        # Then
+        assert result['overall_grade'] == expected_grade
+        assert 'response_time' in result
+        assert 'throughput' in result
+        assert 'error_analysis' in result
+
+    def test_analyze_performance_empty_data(self):
+        """测试空数据场景"""
+        # Given
+        empty_data = {'requests': [], 'duration': 0, 'users': 0}
+
+        # When & Then
+        with pytest.raises(ValueError, match="测试数据不能为空"):
+            self.analyzer.analyze(empty_data)
+
+    @patch('src.analysis.performance_analyzer.calculate_percentile')
+    def test_analyze_with_mock(self, mock_percentile):
+        """测试使用Mock"""
+        # Given
+        mock_percentile.return_value = 150.0
+
+        # When
+        result = self.analyzer.analyze(self.sample_data)
+
+        # Then
+        mock_percentile.assert_called()
+        assert result['response_time']['p95'] == 150.0
+
+    @pytest.mark.parametrize("response_times,expected_grade", [
+        ([100, 200, 150], 'B'),
+        ([50, 80, 60], 'A'),
+        ([500, 600, 700], 'D'),
+    ])
+    def test_grading_algorithm(self, response_times, expected_grade):
+        """参数化测试评级算法"""
+        # Given
+        test_data = {
+            'requests': [{'response_time': rt, 'success': True} for rt in response_times],
+            'duration': 60,
+            'users': 10
+        }
+
+        # When
+        result = self.analyzer.analyze(test_data)
+
+        # Then
+        assert result['overall_grade'] == expected_grade
+```
+
+### 集成测试
+
+```python
+import pytest
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.data_manager.data_provider import DataProvider
+
+class TestAnalysisIntegration:
+    """分析模块集成测试"""
+
+    @pytest.fixture
+    def analyzer(self):
+        """分析器fixture"""
+        return PerformanceAnalyzer()
+
+    @pytest.fixture
+    def data_provider(self):
+        """数据提供者fixture"""
+        provider = DataProvider()
+        provider.load_test_data("tests/fixtures/sample_data.json")
+        return provider
+
+    def test_end_to_end_analysis(self, analyzer, data_provider):
+        """端到端分析测试"""
+        # Given
+        test_data = data_provider.get_test_data("sample_test")
+
+        # When
+        result = analyzer.comprehensive_analysis(test_data)
+
+        # Then
+        assert result is not None
+        assert 'overall_grade' in result
+        assert result['overall_grade'] in ['A', 'B', 'C', 'D']
+```
+
+## 📊 性能规范
+
+### 性能要求
+
+```python
+import time
+import functools
+
+def performance_monitor(max_time: float = 1.0):
+    """性能监控装饰器"""
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            start_time = time.time()
+            result = func(*args, **kwargs)
+            execution_time = time.time() - start_time
+
+            if execution_time > max_time:
+                logger.warning(
+                    f"函数 {func.__name__} 执行时间过长: {execution_time:.2f}s"
+                )
+
+            return result
+        return wrapper
+    return decorator
+
+# ✅ 使用性能监控
+@performance_monitor(max_time=0.5)
+def analyze_large_dataset(data: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """分析大数据集"""
+    # 实现分析逻辑
+    pass
+
+# ✅ 内存优化
+def process_large_file(file_path: str) -> None:
+    """处理大文件"""
+    with open(file_path, 'r') as f:
+        for line in f:  # 逐行处理，避免一次性加载
+            process_line(line)
+
+# ✅ 缓存优化
+from functools import lru_cache
+
+class DataAnalyzer:
+    @lru_cache(maxsize=128)
+    def calculate_statistics(self, data_hash: str) -> Dict[str, float]:
+        """计算统计信息（带缓存）"""
+        # 耗时的统计计算
+        pass
+```
+
+## 🔒 安全规范
+
+```python
+# ✅ 输入验证
+def validate_user_input(data: Dict[str, Any]) -> Dict[str, Any]:
+    """验证用户输入"""
+    from cerberus import Validator
+
+    schema = {
+        'username': {'type': 'string', 'minlength': 3, 'maxlength': 50},
+        'email': {'type': 'string', 'regex': r'^[^@]+@[^@]+\.[^@]+$'},
+        'age': {'type': 'integer', 'min': 0, 'max': 150}
+    }
+
+    validator = Validator(schema)
+    if not validator.validate(data):
+        raise ValueError(f"输入验证失败: {validator.errors}")
+
+    return validator.normalized(data)
+
+# ✅ 敏感信息处理
+import hashlib
+import secrets
+
+def hash_password(password: str) -> str:
+    """安全的密码哈希"""
+    salt = secrets.token_hex(16)
+    password_hash = hashlib.pbkdf2_hmac('sha256',
+                                       password.encode('utf-8'),
+                                       salt.encode('utf-8'),
+                                       100000)
+    return f"{salt}:{password_hash.hex()}"
+
+# ✅ 日志脱敏
+def sanitize_log_data(data: Dict[str, Any]) -> Dict[str, Any]:
+    """日志数据脱敏"""
+    sensitive_fields = ['password', 'token', 'secret', 'key']
+    sanitized = data.copy()
+
+    for field in sensitive_fields:
+        if field in sanitized:
+            sanitized[field] = "***"
+
+    return sanitized
+```
+
+## 🔧 工具配置
+
+### pre-commit配置
+
+```yaml
+# .pre-commit-config.yaml
+repos:
+  - repo: https://github.com/psf/black
+    rev: 23.1.0
+    hooks:
+      - id: black
+        language_version: python3.9
+
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+
+  - repo: https://github.com/pycqa/flake8
+    rev: 6.0.0
+    hooks:
+      - id: flake8
+        args: ["--max-line-length=88", "--extend-ignore=E203,W503"]
+
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.0.1
+    hooks:
+      - id: mypy
+        additional_dependencies: [types-requests]
+```
+
+### 代码质量检查
+
+```bash
+# 运行所有检查
+make lint
+
+# 单独运行各工具
+black --check src tests
+isort --check-only src tests
+flake8 src tests
+pylint src
+mypy src
+```
+
+遵循这些编码规范将确保代码库的一致性、可读性和可维护性，为团队协作和项目长期发展奠定坚实基础。
diff --git a/docs/development/contributing.md b/docs/development/contributing.md
new file mode 100644
index 0000000..4f636fa
--- /dev/null
+++ b/docs/development/contributing.md
@@ -0,0 +1,732 @@
+# 贡献指南
+
+欢迎为Locust性能测试框架做出贡献！本指南将帮助您了解如何参与项目开发，提交代码和改进建议。
+
+## 🎯 贡献方式
+
+### 代码贡献
+- 修复Bug
+- 添加新功能
+- 性能优化
+- 代码重构
+
+### 文档贡献
+- 改进文档内容
+- 添加使用示例
+- 翻译文档
+- 修正错误
+
+### 测试贡献
+- 编写单元测试
+- 添加集成测试
+- 性能测试
+- 兼容性测试
+
+### 社区贡献
+- 回答问题
+- 分享经验
+- 提供反馈
+- 推广项目
+
+## 🚀 开发环境搭建
+
+### 1. 克隆项目
+
+```bash
+# 克隆主仓库
+git clone https://github.com/your-org/locust-framework.git
+cd locust-framework
+
+# 添加上游仓库（如果是fork）
+git remote add upstream https://github.com/original-org/locust-framework.git
+```
+
+### 2. 环境配置
+
+```bash
+# 创建虚拟环境
+python -m venv venv
+source venv/bin/activate  # Linux/Mac
+# 或
+venv\Scripts\activate  # Windows
+
+# 安装开发依赖
+pip install -r requirements-dev.txt
+
+# 安装pre-commit钩子
+pre-commit install
+```
+
+### 3. 开发依赖说明
+
+```txt
+# requirements-dev.txt
+# 基础依赖
+locust>=2.0.0
+requests>=2.25.0
+gevent>=21.0.0
+
+# 开发工具
+pytest>=6.0.0
+pytest-cov>=2.10.0
+pytest-mock>=3.5.0
+black>=21.0.0
+flake8>=3.8.0
+mypy>=0.800
+isort>=5.0.0
+
+# 文档工具
+sphinx>=4.0.0
+sphinx-rtd-theme>=0.5.0
+myst-parser>=0.15.0
+
+# 构建工具
+build>=0.7.0
+twine>=3.4.0
+
+# 测试工具
+factory-boy>=3.2.0
+faker>=8.0.0
+responses>=0.13.0
+```
+
+### 4. 开发配置
+
+```python
+# setup.cfg
+[tool:pytest]
+testpaths = tests
+python_files = test_*.py
+python_classes = Test*
+python_functions = test_*
+addopts =
+    --cov=src
+    --cov-report=html
+    --cov-report=term-missing
+    --strict-markers
+    --disable-warnings
+
+[coverage:run]
+source = src
+omit =
+    */tests/*
+    */venv/*
+    setup.py
+
+[coverage:report]
+exclude_lines =
+    pragma: no cover
+    def __repr__
+    raise AssertionError
+    raise NotImplementedError
+
+[flake8]
+max-line-length = 88
+extend-ignore = E203, W503
+exclude =
+    .git,
+    __pycache__,
+    venv,
+    .venv,
+    build,
+    dist
+
+[mypy]
+python_version = 3.8
+warn_return_any = True
+warn_unused_configs = True
+disallow_untyped_defs = True
+```
+
+## 📝 开发流程
+
+### 1. 分支管理
+
+```bash
+# 创建功能分支
+git checkout -b feature/your-feature-name
+
+# 创建修复分支
+git checkout -b fix/issue-number-description
+
+# 创建文档分支
+git checkout -b docs/documentation-improvement
+```
+
+### 2. 代码规范
+
+#### Python代码风格
+
+```python
+# 使用Black格式化代码
+black src/ tests/
+
+# 使用isort整理导入
+isort src/ tests/
+
+# 使用flake8检查代码质量
+flake8 src/ tests/
+
+# 使用mypy进行类型检查
+mypy src/
+```
+
+#### 代码示例
+
+```python
+# 好的代码示例
+from typing import Dict, List, Optional, Union
+import logging
+
+logger = logging.getLogger(__name__)
+
+class LoadShapeManager:
+    """负载模式管理器
+
+    管理和创建各种负载模式实例。
+
+    Attributes:
+        _shapes: 注册的负载模式字典
+        _instances: 创建的实例缓存
+    """
+
+    def __init__(self) -> None:
+        self._shapes: Dict[str, type] = {}
+        self._instances: Dict[str, object] = {}
+
+    def register_shape(self, name: str, shape_class: type) -> None:
+        """注册负载模式
+
+        Args:
+            name: 负载模式名称
+            shape_class: 负载模式类
+
+        Raises:
+            ValueError: 当名称已存在时
+        """
+        if name in self._shapes:
+            raise ValueError(f"Shape '{name}' already registered")
+
+        self._shapes[name] = shape_class
+        logger.info(f"Registered load shape: {name}")
+
+    def create_shape(self, name: str, **kwargs) -> Optional[object]:
+        """创建负载模式实例
+
+        Args:
+            name: 负载模式名称
+            **kwargs: 初始化参数
+
+        Returns:
+            负载模式实例，如果不存在则返回None
+        """
+        if name not in self._shapes:
+            logger.error(f"Unknown load shape: {name}")
+            return None
+
+        try:
+            instance = self._shapes[name](**kwargs)
+            self._instances[name] = instance
+            return instance
+        except Exception as e:
+            logger.error(f"Failed to create shape '{name}': {e}")
+            return None
+```
+
+### 3. 提交规范
+
+#### 提交消息格式
+
+```
+<type>(<scope>): <subject>
+
+<body>
+
+<footer>
+```
+
+#### 提交类型
+
+- `feat`: 新功能
+- `fix`: 修复Bug
+- `docs`: 文档更新
+- `style`: 代码格式调整
+- `refactor`: 代码重构
+- `test`: 测试相关
+- `chore`: 构建过程或辅助工具的变动
+
+#### 提交示例
+
+```bash
+# 功能提交
+git commit -m "feat(load-shapes): add adaptive load shape
+
+- Implement adaptive load shape based on response time
+- Add configuration options for target response time
+- Include automatic scaling based on performance metrics
+
+Closes #123"
+
+# 修复提交
+git commit -m "fix(plugins): resolve plugin loading issue
+
+- Fix plugin discovery mechanism
+- Handle missing plugin dependencies gracefully
+- Add better error messages for plugin failures
+
+Fixes #456"
+
+# 文档提交
+git commit -m "docs(api): update plugin development guide
+
+- Add comprehensive examples for custom plugins
+- Include troubleshooting section
+- Fix formatting issues in code blocks"
+```
+
+## 🧪 测试指南
+
+### 1. 测试结构
+
+```
+tests/
+├── unit/                 # 单元测试
+│   ├── test_load_shapes.py
+│   ├── test_plugins.py
+│   └── test_utilities.py
+├── integration/          # 集成测试
+│   ├── test_framework.py
+│   └── test_distributed.py
+├── performance/          # 性能测试
+│   └── test_benchmarks.py
+├── fixtures/            # 测试数据
+│   ├── sample_data.json
+│   └── test_configs.yaml
+└── conftest.py          # pytest配置
+```
+
+### 2. 编写测试
+
+#### 单元测试示例
+
+```python
+# tests/unit/test_load_shapes.py
+import pytest
+from unittest.mock import patch, MagicMock
+from src.model.load_shapes.linear_shape import LinearLoadShape
+
+class TestLinearLoadShape:
+    """线性负载模式测试"""
+
+    def setup_method(self):
+        """测试初始化"""
+        self.shape = LinearLoadShape(max_users=100, duration=300, spawn_rate=10)
+
+    def test_initialization(self):
+        """测试初始化"""
+        assert self.shape.max_users == 100
+        assert self.shape.duration == 300
+        assert self.shape.spawn_rate == 10
+        assert self.shape.start_time is None
+
+    def test_tick_initial_call(self):
+        """测试首次调用tick"""
+        with patch('time.time', return_value=1000):
+            result = self.shape.tick()
+            assert result is not None
+            users, spawn_rate = result
+            assert users > 0
+            assert spawn_rate == 10
+            assert self.shape.start_time == 1000
+
+    def test_tick_progression(self):
+        """测试负载进展"""
+        with patch('time.time') as mock_time:
+            # 初始化
+            mock_time.return_value = 1000
+            self.shape.tick()
+
+            # 50%进度
+            mock_time.return_value = 1150  # 150秒后
+            users, spawn_rate = self.shape.tick()
+            expected_users = int(100 * 0.5)  # 50%进度
+            assert abs(users - expected_users) <= 1  # 允许小误差
+
+    def test_tick_completion(self):
+        """测试完成条件"""
+        with patch('time.time') as mock_time:
+            # 初始化
+            mock_time.return_value = 1000
+            self.shape.tick()
+
+            # 超过持续时间
+            mock_time.return_value = 1301  # 301秒后
+            result = self.shape.tick()
+            assert result is None
+
+    @pytest.mark.parametrize("max_users,duration,expected_rate", [
+        (50, 100, 0.5),
+        (200, 400, 0.5),
+        (1000, 600, 1.67)
+    ])
+    def test_different_configurations(self, max_users, duration, expected_rate):
+        """测试不同配置"""
+        shape = LinearLoadShape(max_users=max_users, duration=duration)
+        with patch('time.time') as mock_time:
+            mock_time.return_value = 1000
+            shape.tick()
+
+            # 测试中间点
+            mock_time.return_value = 1000 + duration // 2
+            users, _ = shape.tick()
+            expected_users = max_users // 2
+            assert abs(users - expected_users) <= 2
+```
+
+#### 集成测试示例
+
+```python
+# tests/integration/test_framework.py
+import pytest
+import tempfile
+import os
+from src.core.framework import LocustFramework
+
+class TestFrameworkIntegration:
+    """框架集成测试"""
+
+    def setup_method(self):
+        """测试初始化"""
+        self.temp_dir = tempfile.mkdtemp()
+        self.config_file = os.path.join(self.temp_dir, "test_config.yaml")
+
+        # 创建测试配置
+        config_content = """
+        framework:
+          name: "test_framework"
+          version: "1.0.0"
+
+        load_shapes:
+          test_shape:
+            type: "linear"
+            config:
+              max_users: 10
+              duration: 60
+              spawn_rate: 2
+
+        plugins:
+          - name: "test_plugin"
+            enabled: true
+        """
+
+        with open(self.config_file, 'w') as f:
+            f.write(config_content)
+
+    def teardown_method(self):
+        """测试清理"""
+        import shutil
+        shutil.rmtree(self.temp_dir)
+
+    def test_framework_initialization(self):
+        """测试框架初始化"""
+        framework = LocustFramework(config_file=self.config_file)
+        assert framework.config is not None
+        assert framework.config['framework']['name'] == "test_framework"
+
+    def test_load_shape_creation(self):
+        """测试负载模式创建"""
+        framework = LocustFramework(config_file=self.config_file)
+        shape = framework.create_load_shape("test_shape")
+        assert shape is not None
+        assert hasattr(shape, 'tick')
+
+    def test_plugin_loading(self):
+        """测试插件加载"""
+        framework = LocustFramework(config_file=self.config_file)
+        plugins = framework.load_plugins()
+        assert len(plugins) > 0
+```
+
+### 3. 运行测试
+
+```bash
+# 运行所有测试
+pytest
+
+# 运行特定测试文件
+pytest tests/unit/test_load_shapes.py
+
+# 运行特定测试类
+pytest tests/unit/test_load_shapes.py::TestLinearLoadShape
+
+# 运行特定测试方法
+pytest tests/unit/test_load_shapes.py::TestLinearLoadShape::test_initialization
+
+# 生成覆盖率报告
+pytest --cov=src --cov-report=html
+
+# 运行性能测试
+pytest tests/performance/ -v
+```
+
+## 📚 文档贡献
+
+### 1. 文档结构
+
+```
+docs/
+├── getting-started/     # 入门指南
+├── architecture/        # 架构文档
+├── api/                # API文档
+├── development/        # 开发指南
+├── examples/           # 示例和最佳实践
+├── configuration/      # 配置说明
+└── README.md          # 主文档
+```
+
+### 2. 文档编写规范
+
+#### Markdown格式
+
+```markdown
+# 标题使用H1
+## 主要章节使用H2
+### 子章节使用H3
+
+**重要内容使用粗体**
+*强调内容使用斜体*
+`代码片段使用反引号`
+
+```python
+# 代码块使用三个反引号并指定语言
+def example_function():
+    return "Hello, World!"
+```
+
+> 引用使用大于号
+
+- 列表项1
+- 列表项2
+  - 嵌套列表项
+
+1. 有序列表项1
+2. 有序列表项2
+
+[链接文本](相对路径或URL)
+
+![图片描述](图片路径)
+```
+
+#### 代码文档
+
+```python
+def example_function(param1: str, param2: int = 0) -> bool:
+    """函数简短描述
+
+    详细描述函数的功能和用途。可以包含多行说明，
+    解释函数的工作原理和使用场景。
+
+    Args:
+        param1: 参数1的描述
+        param2: 参数2的描述，默认值为0
+
+    Returns:
+        返回值的描述
+
+    Raises:
+        ValueError: 当参数无效时抛出
+        TypeError: 当参数类型错误时抛出
+
+    Example:
+        >>> result = example_function("test", 5)
+        >>> print(result)
+        True
+
+    Note:
+        这里可以添加额外的注意事项或说明
+    """
+    if not isinstance(param1, str):
+        raise TypeError("param1 must be a string")
+
+    if param2 < 0:
+        raise ValueError("param2 must be non-negative")
+
+    return len(param1) > param2
+```
+
+### 3. 文档构建
+
+```bash
+# 安装文档依赖
+pip install -r docs/requirements.txt
+
+# 构建HTML文档
+cd docs
+make html
+
+# 实时预览（如果使用sphinx-autobuild）
+sphinx-autobuild source build/html
+```
+
+## 🔄 Pull Request流程
+
+### 1. 准备工作
+
+```bash
+# 确保代码是最新的
+git checkout main
+git pull upstream main
+
+# 创建功能分支
+git checkout -b feature/your-feature
+
+# 进行开发...
+
+# 运行测试
+pytest
+black src/ tests/
+flake8 src/ tests/
+mypy src/
+```
+
+### 2. 提交PR
+
+1. **推送分支**
+```bash
+git push origin feature/your-feature
+```
+
+2. **创建Pull Request**
+- 访问GitHub仓库
+- 点击"New Pull Request"
+- 选择源分支和目标分支
+- 填写PR模板
+
+3. **PR模板**
+```markdown
+## 变更描述
+简要描述这个PR的目的和变更内容。
+
+## 变更类型
+- [ ] Bug修复
+- [ ] 新功能
+- [ ] 文档更新
+- [ ] 性能优化
+- [ ] 代码重构
+
+## 测试
+- [ ] 添加了新的测试
+- [ ] 所有测试通过
+- [ ] 手动测试完成
+
+## 检查清单
+- [ ] 代码遵循项目规范
+- [ ] 提交消息符合规范
+- [ ] 文档已更新
+- [ ] 变更日志已更新
+
+## 相关Issue
+Closes #123
+Related to #456
+
+## 截图（如适用）
+如果有UI变更，请提供截图。
+```
+
+### 3. 代码审查
+
+#### 审查要点
+- 代码质量和可读性
+- 测试覆盖率
+- 性能影响
+- 安全考虑
+- 文档完整性
+
+#### 响应反馈
+```bash
+# 根据反馈修改代码
+git add .
+git commit -m "address review comments"
+git push origin feature/your-feature
+```
+
+## 🎉 发布流程
+
+### 1. 版本管理
+
+```bash
+# 更新版本号
+# 在setup.py或__init__.py中更新版本
+
+# 更新变更日志
+# 在CHANGELOG.md中记录变更
+
+# 创建发布标签
+git tag -a v1.2.0 -m "Release version 1.2.0"
+git push origin v1.2.0
+```
+
+### 2. 自动化发布
+
+```yaml
+# .github/workflows/release.yml
+name: Release
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  release:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.8'
+
+    - name: Install dependencies
+      run: |
+        pip install build twine
+
+    - name: Build package
+      run: python -m build
+
+    - name: Publish to PyPI
+      env:
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+      run: twine upload dist/*
+```
+
+## 🤝 社区参与
+
+### 1. 沟通渠道
+- GitHub Issues: 报告Bug和功能请求
+- GitHub Discussions: 技术讨论和问答
+- Slack/Discord: 实时交流
+- 邮件列表: 重要公告
+
+### 2. 行为准则
+- 尊重他人
+- 建设性反馈
+- 包容性环境
+- 专业态度
+
+### 3. 获得帮助
+- 查看文档
+- 搜索已有Issue
+- 提问时提供详细信息
+- 参与社区讨论
+
+## 📚 相关资源
+
+- [开发环境配置](../getting-started/installation.md)
+- [API文档](../api/README.md)
+- [测试指南](testing.md)
+- [代码规范](documentation-standards.md)
+
+感谢您的贡献！🎉
diff --git a/docs/development/documentation-standards.md b/docs/development/documentation-standards.md
new file mode 100644
index 0000000..c6d6997
--- /dev/null
+++ b/docs/development/documentation-standards.md
@@ -0,0 +1,430 @@
+# 文档编写规范
+
+本文档定义了Locust性能测试框架文档的编写规范和标准，确保文档的一致性、可读性和维护性。
+
+## 📝 文档结构规范
+
+### 1. 文档层次结构
+
+```
+docs/
+├── README.md                    # 主文档入口
+├── getting-started/            # 快速开始
+│   ├── installation.md
+│   ├── quickstart.md
+│   ├── concepts.md
+│   └── first-test.md
+├── architecture/               # 架构设计
+│   ├── overview.md
+│   ├── modules.md
+│   ├── plugin-system.md
+│   └── data-flow.md
+├── api/                       # API参考
+│   ├── analysis.md
+│   ├── monitoring.md
+│   ├── data-manager.md
+│   ├── plugins.md
+│   ├── load-shapes.md
+│   └── utilities.md
+├── development/               # 开发指南
+│   ├── setup.md
+│   ├── coding-standards.md
+│   ├── plugin-development.md
+│   ├── load-shape-development.md
+│   ├── contributing.md
+│   ├── testing.md
+│   └── documentation-standards.md
+├── examples/                  # 示例和最佳实践
+│   ├── basic-examples.md
+│   ├── advanced-examples.md
+│   ├── best-practices.md
+│   ├── faq.md
+│   └── troubleshooting.md
+└── configuration/             # 配置参考
+    ├── framework-config.md
+    ├── plugin-config.md
+    ├── monitoring-config.md
+    ├── notification-config.md
+    ├── development.md
+    ├── testing.md
+    ├── production.md
+    └── distributed.md
+```
+
+### 2. 文件命名规范
+
+- **使用小写字母和连字符**: `plugin-development.md`
+- **避免空格和特殊字符**: 不使用 `Plugin Development.md`
+- **描述性命名**: 文件名应清楚表达内容
+- **保持简洁**: 避免过长的文件名
+
+## 📖 内容编写规范
+
+### 1. 文档标题结构
+
+```markdown
+# 主标题 (H1) - 每个文档只有一个
+
+## 二级标题 (H2) - 主要章节
+
+### 三级标题 (H3) - 子章节
+
+#### 四级标题 (H4) - 详细内容
+
+##### 五级标题 (H5) - 特殊情况使用
+
+###### 六级标题 (H6) - 避免使用
+```
+
+### 2. 文档开头模板
+
+```markdown
+# 文档标题
+
+本文档简要描述文档的目的和内容概述。
+
+## 🎯 目标读者
+
+明确说明文档的目标读者群体。
+
+## 📋 前置条件
+
+列出阅读本文档需要的前置知识或环境要求。
+
+## 📚 内容概览
+
+- 主要内容点1
+- 主要内容点2
+- 主要内容点3
+```
+
+### 3. 表情符号使用规范
+
+使用表情符号增强文档的可读性和视觉效果：
+
+```markdown
+## 🎯 目标和目的
+## 🚀 快速开始
+## 📋 前置条件
+## 🔧 配置说明
+## 💻 代码示例
+## 📊 数据和图表
+## ⚠️ 注意事项
+## 🐛 故障排除
+## 📚 相关资源
+## 🎉 总结
+```
+
+### 4. 代码块规范
+
+#### Python代码
+
+```python
+# src/example.py
+"""
+模块文档字符串
+"""
+
+class ExampleClass:
+    """类文档字符串"""
+
+    def __init__(self, param: str):
+        """构造函数文档"""
+        self.param = param
+
+    def example_method(self) -> str:
+        """方法文档字符串"""
+        return f"Example: {self.param}"
+
+# 使用示例
+example = ExampleClass("test")
+result = example.example_method()
+print(result)
+```
+
+#### YAML配置
+
+```yaml
+# config/example.yml
+framework:
+  name: "locust-framework"
+  version: "2.0.0"
+
+plugins:
+  enabled:
+    - analysis
+    - monitoring
+    - data_manager
+
+  analysis:
+    grade_thresholds:
+      excellent: 0.95
+      good: 0.85
+      average: 0.70
+```
+
+#### Shell脚本
+
+```bash
+#!/bin/bash
+# scripts/example.sh
+
+set -e
+
+# 变量定义
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
+
+# 函数定义
+function log_info() {
+    echo "[INFO] $1"
+}
+
+# 主逻辑
+log_info "Starting example script..."
+```
+
+### 5. 链接规范
+
+#### 内部链接
+
+```markdown
+# 相对路径链接
+- [安装指南](../getting-started/installation.md)
+- [API参考](../api/analysis.md)
+- [配置说明](../configuration/framework-config.md)
+
+# 锚点链接
+- [配置选项](#配置选项)
+- [示例代码](#示例代码)
+```
+
+#### 外部链接
+
+```markdown
+# 外部链接
+- [Locust官方文档](https://docs.locust.io/)
+- [Python官方文档](https://docs.python.org/3/)
+- [GitHub仓库](https://github.com/locustio/locust)
+```
+
+### 6. 表格规范
+
+```markdown
+| 参数名称 | 类型 | 默认值 | 描述 |
+|---------|------|--------|------|
+| `host` | string | `localhost` | 目标主机地址 |
+| `port` | integer | `8080` | 目标端口号 |
+| `timeout` | integer | `30` | 连接超时时间（秒） |
+| `retries` | integer | `3` | 重试次数 |
+```
+
+### 7. 列表规范
+
+#### 无序列表
+
+```markdown
+- 第一项内容
+- 第二项内容
+  - 子项内容
+  - 另一个子项
+- 第三项内容
+```
+
+#### 有序列表
+
+```markdown
+1. 第一步：环境准备
+2. 第二步：安装依赖
+3. 第三步：配置文件
+4. 第四步：启动服务
+```
+
+### 8. 引用和提示
+
+#### 信息提示
+
+```markdown
+> **提示**: 这是一个有用的提示信息。
+
+> **注意**: 这是需要注意的重要信息。
+
+> **警告**: 这是警告信息，请谨慎操作。
+```
+
+#### 代码引用
+
+```markdown
+使用 `locust` 命令启动测试，配置文件位于 `config/` 目录下。
+```
+
+## 🎨 格式化规范
+
+### 1. 行长度限制
+
+- **文本行**: 建议不超过80字符
+- **代码行**: 建议不超过88字符
+- **表格**: 根据内容适当调整
+
+### 2. 空行使用
+
+```markdown
+# 标题前后需要空行
+
+## 二级标题
+
+段落内容...
+
+### 三级标题
+
+另一段内容...
+
+```python
+# 代码块前后需要空行
+def example():
+    pass
+```
+
+继续文档内容...
+```
+
+### 3. 缩进规范
+
+- **列表缩进**: 使用2个空格
+- **代码缩进**: 使用4个空格
+- **引用缩进**: 使用2个空格
+
+## 📊 图表和图像
+
+### 1. Mermaid图表
+
+```markdown
+```mermaid
+graph TD
+    A[开始] --> B{条件判断}
+    B -->|是| C[执行操作]
+    B -->|否| D[跳过操作]
+    C --> E[结束]
+    D --> E
+```
+```
+
+### 2. 图像引用
+
+```markdown
+![架构图](../images/architecture-overview.png)
+
+*图1: 系统架构概览*
+```
+
+## 🔍 质量检查
+
+### 1. 内容检查清单
+
+- [ ] 标题层次结构正确
+- [ ] 代码示例可执行
+- [ ] 链接有效且正确
+- [ ] 表格格式规范
+- [ ] 表情符号使用恰当
+- [ ] 语法和拼写正确
+- [ ] 内容逻辑清晰
+
+### 2. 技术审查
+
+- [ ] 技术内容准确
+- [ ] 代码示例最佳实践
+- [ ] API文档与实现一致
+- [ ] 配置示例有效
+- [ ] 版本信息最新
+
+### 3. 可读性检查
+
+- [ ] 目标读者明确
+- [ ] 内容组织合理
+- [ ] 示例充分且清晰
+- [ ] 术语使用一致
+- [ ] 交叉引用完整
+
+## 🛠️ 工具和自动化
+
+### 1. 文档生成工具
+
+```bash
+# 使用MkDocs生成文档站点
+pip install mkdocs mkdocs-material
+mkdocs serve
+
+# 使用Sphinx生成文档
+pip install sphinx sphinx-rtd-theme
+sphinx-build -b html docs/ docs/_build/
+```
+
+### 2. 链接检查
+
+```bash
+# 检查文档中的链接
+pip install markdown-link-check
+find docs -name "*.md" -exec markdown-link-check {} \;
+```
+
+### 3. 格式化工具
+
+```bash
+# 使用Prettier格式化Markdown
+npm install -g prettier
+prettier --write "docs/**/*.md"
+
+# 使用markdownlint检查格式
+npm install -g markdownlint-cli
+markdownlint docs/**/*.md
+```
+
+## 📝 文档维护
+
+### 1. 版本控制
+
+- 每次重大更新都要更新版本号
+- 在文档中标注最后更新时间
+- 保持变更日志记录
+
+### 2. 定期审查
+
+- **月度审查**: 检查链接有效性
+- **季度审查**: 更新过时内容
+- **年度审查**: 重构文档结构
+
+### 3. 反馈收集
+
+- 在文档中提供反馈渠道
+- 定期收集用户意见
+- 根据反馈改进文档
+
+## 📚 参考资源
+
+### 文档编写指南
+
+- [Markdown语法指南](https://www.markdownguide.org/)
+- [技术写作最佳实践](https://developers.google.com/tech-writing)
+- [文档即代码](https://www.writethedocs.org/guide/docs-as-code/)
+
+### 工具文档
+
+- [MkDocs文档](https://www.mkdocs.org/)
+- [Sphinx文档](https://www.sphinx-doc.org/)
+- [Mermaid图表](https://mermaid-js.github.io/mermaid/)
+
+## 🎉 总结
+
+遵循这些文档编写规范将确保：
+
+1. **一致性**: 所有文档保持统一的风格和格式
+2. **可读性**: 用户能够轻松理解和使用文档
+3. **可维护性**: 文档易于更新和维护
+4. **专业性**: 提供高质量的用户体验
+
+记住，好的文档是项目成功的关键因素之一！
+
+---
+
+**最后更新**: 2024-01-01
+**版本**: v1.0.0
diff --git a/docs/development/load-shape-development.md b/docs/development/load-shape-development.md
new file mode 100644
index 0000000..68960b9
--- /dev/null
+++ b/docs/development/load-shape-development.md
@@ -0,0 +1,729 @@
+# 负载模式开发指南
+
+本指南详细介绍如何开发自定义负载模式，扩展框架的负载控制能力。
+
+## 🎯 开发目标
+
+通过本指南，您将学会：
+- 理解负载模式的设计原理
+- 开发自定义负载模式
+- 实现复杂的负载控制逻辑
+- 集成和测试负载模式
+- 遵循最佳实践和规范
+
+## 📚 基础概念
+
+### 负载模式架构
+
+负载模式基于Locust的LoadTestShape类，提供了灵活的负载控制机制：
+
+```python
+from locust import LoadTestShape
+from typing import Tuple, Optional
+
+class CustomLoadShape(LoadTestShape):
+    """自定义负载模式基类"""
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        核心方法：返回当前时刻的负载配置
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率) 或 None表示测试结束
+        """
+        pass
+```
+
+### 核心组件
+
+1. **时间管理**: 跟踪测试运行时间
+2. **用户数控制**: 动态调整并发用户数
+3. **生成速率控制**: 控制用户生成的速度
+4. **状态管理**: 维护负载模式的内部状态
+5. **配置验证**: 验证负载模式参数的有效性
+
+## 🔧 开发基础负载模式
+
+### 1. 线性增长负载模式
+
+```python
+from locust import LoadTestShape
+from typing import Tuple, Optional
+import time
+
+class LinearLoadShape(LoadTestShape):
+    """线性增长负载模式"""
+
+    def __init__(self, max_users: int = 100, duration: int = 300,
+                 spawn_rate: float = 10):
+        """
+        初始化线性负载模式
+
+        Args:
+            max_users: 最大用户数
+            duration: 测试持续时间(秒)
+            spawn_rate: 用户生成速率
+        """
+        super().__init__()
+        self.max_users = max_users
+        self.duration = duration
+        self.spawn_rate = spawn_rate
+        self.start_time = None
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        计算当前负载
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        # 初始化开始时间
+        if self.start_time is None:
+            self.start_time = time.time()
+
+        # 计算运行时间
+        elapsed_time = time.time() - self.start_time
+
+        # 检查是否超时
+        if elapsed_time >= self.duration:
+            return None
+
+        # 计算当前用户数（线性增长）
+        progress = elapsed_time / self.duration
+        current_users = int(self.max_users * progress)
+        current_users = max(1, current_users)  # 至少1个用户
+
+        return current_users, self.spawn_rate
+```
+
+### 2. 阶段式负载模式
+
+```python
+from typing import List, NamedTuple
+
+class LoadStage(NamedTuple):
+    """负载阶段定义"""
+    users: int          # 用户数
+    duration: int       # 持续时间(秒)
+    spawn_rate: float   # 生成速率
+
+class StageLoadShape(LoadTestShape):
+    """阶段式负载模式"""
+
+    def __init__(self, stages: List[LoadStage]):
+        """
+        初始化阶段式负载模式
+
+        Args:
+            stages: 负载阶段列表
+        """
+        super().__init__()
+        self.stages = stages
+        self.current_stage = 0
+        self.stage_start_time = None
+        self.test_start_time = None
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        计算当前负载
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = time.time()
+
+        # 初始化时间
+        if self.test_start_time is None:
+            self.test_start_time = current_time
+            self.stage_start_time = current_time
+
+        # 检查是否完成所有阶段
+        if self.current_stage >= len(self.stages):
+            return None
+
+        current_stage = self.stages[self.current_stage]
+        stage_elapsed = current_time - self.stage_start_time
+
+        # 检查当前阶段是否完成
+        if stage_elapsed >= current_stage.duration:
+            self.current_stage += 1
+            self.stage_start_time = current_time
+
+            # 检查是否还有下一阶段
+            if self.current_stage >= len(self.stages):
+                return None
+
+            current_stage = self.stages[self.current_stage]
+
+        return current_stage.users, current_stage.spawn_rate
+
+    def get_current_stage_info(self) -> dict:
+        """
+        获取当前阶段信息
+
+        Returns:
+            dict: 阶段信息
+        """
+        if self.current_stage < len(self.stages):
+            stage = self.stages[self.current_stage]
+            elapsed = time.time() - self.stage_start_time if self.stage_start_time else 0
+            return {
+                "stage_index": self.current_stage,
+                "stage_users": stage.users,
+                "stage_duration": stage.duration,
+                "stage_elapsed": elapsed,
+                "stage_remaining": max(0, stage.duration - elapsed)
+            }
+        return {}
+```
+
+## 🌊 高级负载模式开发
+
+### 1. 自适应负载模式
+
+```python
+from locust import stats
+import statistics
+
+class AdaptiveLoadShape(LoadTestShape):
+    """自适应负载模式 - 根据性能指标动态调整负载"""
+
+    def __init__(self, initial_users: int = 10, max_users: int = 500,
+                 target_response_time: float = 1000, adjustment_interval: int = 30,
+                 spawn_rate: float = 10):
+        """
+        初始化自适应负载模式
+
+        Args:
+            initial_users: 初始用户数
+            max_users: 最大用户数
+            target_response_time: 目标响应时间(ms)
+            adjustment_interval: 调整间隔(秒)
+            spawn_rate: 生成速率
+        """
+        super().__init__()
+        self.initial_users = initial_users
+        self.max_users = max_users
+        self.target_response_time = target_response_time
+        self.adjustment_interval = adjustment_interval
+        self.spawn_rate = spawn_rate
+
+        # 状态变量
+        self.current_users = initial_users
+        self.last_adjustment_time = None
+        self.performance_history = []
+        self.adjustment_factor = 1.1  # 调整因子
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        自适应负载计算
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = time.time()
+
+        # 初始化调整时间
+        if self.last_adjustment_time is None:
+            self.last_adjustment_time = current_time
+
+        # 检查是否需要调整负载
+        if current_time - self.last_adjustment_time >= self.adjustment_interval:
+            self._adjust_load()
+            self.last_adjustment_time = current_time
+
+        return self.current_users, self.spawn_rate
+
+    def _adjust_load(self):
+        """根据性能指标调整负载"""
+        try:
+            # 获取当前性能指标
+            current_stats = self._get_current_performance()
+
+            if current_stats is None:
+                return
+
+            avg_response_time = current_stats.get('avg_response_time', 0)
+            error_rate = current_stats.get('error_rate', 0)
+
+            # 记录性能历史
+            self.performance_history.append({
+                'timestamp': time.time(),
+                'users': self.current_users,
+                'avg_response_time': avg_response_time,
+                'error_rate': error_rate
+            })
+
+            # 保持历史记录在合理范围内
+            if len(self.performance_history) > 10:
+                self.performance_history.pop(0)
+
+            # 调整逻辑
+            if error_rate > 5:  # 错误率过高，减少负载
+                self.current_users = max(1, int(self.current_users * 0.8))
+                print(f"High error rate ({error_rate}%), reducing users to {self.current_users}")
+            elif avg_response_time > self.target_response_time * 1.5:  # 响应时间过高
+                self.current_users = max(1, int(self.current_users * 0.9))
+                print(f"High response time ({avg_response_time}ms), reducing users to {self.current_users}")
+            elif avg_response_time < self.target_response_time * 0.7:  # 响应时间良好
+                new_users = min(self.max_users, int(self.current_users * self.adjustment_factor))
+                if new_users > self.current_users:
+                    self.current_users = new_users
+                    print(f"Good performance, increasing users to {self.current_users}")
+
+        except Exception as e:
+            print(f"Error in load adjustment: {e}")
+
+    def _get_current_performance(self) -> Optional[dict]:
+        """
+        获取当前性能指标
+
+        Returns:
+            Optional[dict]: 性能指标字典
+        """
+        try:
+            # 从Locust统计信息获取性能数据
+            total_stats = stats.total
+
+            if total_stats.num_requests == 0:
+                return None
+
+            return {
+                'avg_response_time': total_stats.avg_response_time,
+                'error_rate': (total_stats.num_failures / total_stats.num_requests) * 100,
+                'rps': total_stats.current_rps,
+                'total_requests': total_stats.num_requests
+            }
+        except Exception as e:
+            print(f"Error getting performance stats: {e}")
+            return None
+
+    def get_performance_summary(self) -> dict:
+        """
+        获取性能摘要
+
+        Returns:
+            dict: 性能摘要
+        """
+        if not self.performance_history:
+            return {}
+
+        response_times = [h['avg_response_time'] for h in self.performance_history]
+        error_rates = [h['error_rate'] for h in self.performance_history]
+
+        return {
+            'avg_response_time': statistics.mean(response_times),
+            'max_response_time': max(response_times),
+            'avg_error_rate': statistics.mean(error_rates),
+            'max_error_rate': max(error_rates),
+            'adjustments_made': len(self.performance_history),
+            'current_users': self.current_users
+        }
+```
+
+### 2. 事件驱动负载模式
+
+```python
+from typing import Callable, Dict, Any
+from enum import Enum
+
+class LoadEvent(Enum):
+    """负载事件类型"""
+    SPIKE_START = "spike_start"
+    SPIKE_END = "spike_end"
+    RAMP_UP = "ramp_up"
+    RAMP_DOWN = "ramp_down"
+    MAINTAIN = "maintain"
+
+class EventDrivenLoadShape(LoadTestShape):
+    """事件驱动负载模式"""
+
+    def __init__(self, base_users: int = 50, spawn_rate: float = 10):
+        """
+        初始化事件驱动负载模式
+
+        Args:
+            base_users: 基础用户数
+            spawn_rate: 生成速率
+        """
+        super().__init__()
+        self.base_users = base_users
+        self.spawn_rate = spawn_rate
+        self.current_users = base_users
+
+        # 事件系统
+        self.events = []
+        self.event_handlers = {}
+        self.start_time = None
+
+    def add_event(self, trigger_time: int, event_type: LoadEvent,
+                  params: Dict[str, Any] = None):
+        """
+        添加负载事件
+
+        Args:
+            trigger_time: 触发时间(秒)
+            event_type: 事件类型
+            params: 事件参数
+        """
+        self.events.append({
+            'trigger_time': trigger_time,
+            'event_type': event_type,
+            'params': params or {},
+            'triggered': False
+        })
+
+        # 按时间排序
+        self.events.sort(key=lambda x: x['trigger_time'])
+
+    def register_event_handler(self, event_type: LoadEvent,
+                             handler: Callable[[Dict[str, Any]], int]):
+        """
+        注册事件处理器
+
+        Args:
+            event_type: 事件类型
+            handler: 事件处理函数，返回新的用户数
+        """
+        self.event_handlers[event_type] = handler
+
+    def tick(self) -> Optional[Tuple[int, float]]:
+        """
+        事件驱动负载计算
+
+        Returns:
+            Optional[Tuple[int, float]]: (用户数, 生成速率)
+        """
+        current_time = time.time()
+
+        # 初始化开始时间
+        if self.start_time is None:
+            self.start_time = current_time
+
+        elapsed_time = current_time - self.start_time
+
+        # 处理事件
+        for event in self.events:
+            if not event['triggered'] and elapsed_time >= event['trigger_time']:
+                self._handle_event(event)
+                event['triggered'] = True
+
+        return self.current_users, self.spawn_rate
+
+    def _handle_event(self, event: Dict[str, Any]):
+        """
+        处理负载事件
+
+        Args:
+            event: 事件信息
+        """
+        event_type = event['event_type']
+        params = event['params']
+
+        print(f"Triggering event: {event_type.value} with params: {params}")
+
+        # 调用注册的事件处理器
+        if event_type in self.event_handlers:
+            new_users = self.event_handlers[event_type](params)
+            if new_users is not None:
+                self.current_users = max(1, new_users)
+        else:
+            # 默认事件处理
+            self._default_event_handler(event_type, params)
+
+    def _default_event_handler(self, event_type: LoadEvent, params: Dict[str, Any]):
+        """
+        默认事件处理器
+
+        Args:
+            event_type: 事件类型
+            params: 事件参数
+        """
+        if event_type == LoadEvent.SPIKE_START:
+            spike_users = params.get('users', self.current_users * 2)
+            self.current_users = spike_users
+        elif event_type == LoadEvent.SPIKE_END:
+            self.current_users = self.base_users
+        elif event_type == LoadEvent.RAMP_UP:
+            factor = params.get('factor', 1.5)
+            self.current_users = int(self.current_users * factor)
+        elif event_type == LoadEvent.RAMP_DOWN:
+            factor = params.get('factor', 0.7)
+            self.current_users = max(1, int(self.current_users * factor))
+        elif event_type == LoadEvent.MAINTAIN:
+            users = params.get('users', self.current_users)
+            self.current_users = users
+
+# 使用示例
+def create_event_driven_load():
+    """创建事件驱动负载模式示例"""
+    load_shape = EventDrivenLoadShape(base_users=20, spawn_rate=5)
+
+    # 添加事件
+    load_shape.add_event(60, LoadEvent.RAMP_UP, {'factor': 2})    # 1分钟后增加负载
+    load_shape.add_event(120, LoadEvent.SPIKE_START, {'users': 200})  # 2分钟后开始尖峰
+    load_shape.add_event(180, LoadEvent.SPIKE_END)                # 3分钟后结束尖峰
+    load_shape.add_event(240, LoadEvent.RAMP_DOWN, {'factor': 0.5})  # 4分钟后减少负载
+
+    # 注册自定义事件处理器
+    def custom_spike_handler(params):
+        users = params.get('users', 100)
+        print(f"Custom spike: setting users to {users}")
+        return users
+
+    load_shape.register_event_handler(LoadEvent.SPIKE_START, custom_spike_handler)
+
+    return load_shape
+```
+
+## 🧪 测试和验证
+
+### 1. 负载模式测试框架
+
+```python
+import unittest
+from unittest.mock import patch, MagicMock
+
+class LoadShapeTestCase(unittest.TestCase):
+    """负载模式测试基类"""
+
+    def setUp(self):
+        """测试初始化"""
+        self.load_shape = None
+
+    def test_initialization(self):
+        """测试初始化"""
+        self.assertIsNotNone(self.load_shape)
+
+    def test_tick_returns_valid_values(self):
+        """测试tick方法返回有效值"""
+        result = self.load_shape.tick()
+
+        if result is not None:
+            users, spawn_rate = result
+            self.assertIsInstance(users, int)
+            self.assertIsInstance(spawn_rate, (int, float))
+            self.assertGreater(users, 0)
+            self.assertGreater(spawn_rate, 0)
+
+    def test_load_progression(self):
+        """测试负载变化"""
+        results = []
+
+        # 模拟时间推进
+        with patch('time.time') as mock_time:
+            for i in range(10):
+                mock_time.return_value = i * 30  # 每30秒
+                result = self.load_shape.tick()
+                if result:
+                    results.append(result)
+
+        # 验证负载变化合理性
+        self.assertGreater(len(results), 0)
+
+    def test_termination_condition(self):
+        """测试终止条件"""
+        # 模拟长时间运行
+        with patch('time.time') as mock_time:
+            mock_time.return_value = 10000  # 很长时间后
+            result = self.load_shape.tick()
+
+            # 某些负载模式应该在长时间后终止
+            # 这取决于具体的负载模式实现
+
+class LinearLoadShapeTest(LoadShapeTestCase):
+    """线性负载模式测试"""
+
+    def setUp(self):
+        self.load_shape = LinearLoadShape(max_users=100, duration=300, spawn_rate=10)
+
+    def test_linear_progression(self):
+        """测试线性增长"""
+        with patch('time.time') as mock_time:
+            # 测试开始
+            mock_time.return_value = 0
+            self.load_shape.tick()  # 初始化start_time
+
+            # 测试中间点
+            mock_time.return_value = 150  # 50%进度
+            users, spawn_rate = self.load_shape.tick()
+            self.assertAlmostEqual(users, 50, delta=5)  # 允许小误差
+
+            # 测试结束点
+            mock_time.return_value = 300
+            result = self.load_shape.tick()
+            self.assertIsNone(result)  # 应该终止
+
+# 运行测试
+if __name__ == '__main__':
+    unittest.main()
+```
+
+### 2. 性能验证工具
+
+```python
+class LoadShapeValidator:
+    """负载模式验证器"""
+
+    @staticmethod
+    def validate_load_shape(load_shape_class, config: Dict[str, Any],
+                          simulation_duration: int = 600) -> Dict[str, Any]:
+        """
+        验证负载模式
+
+        Args:
+            load_shape_class: 负载模式类
+            config: 配置参数
+            simulation_duration: 模拟持续时间
+
+        Returns:
+            Dict[str, Any]: 验证结果
+        """
+        try:
+            # 创建负载模式实例
+            load_shape = load_shape_class(**config)
+
+            # 模拟运行
+            results = []
+            with patch('time.time') as mock_time:
+                for second in range(simulation_duration):
+                    mock_time.return_value = second
+                    result = load_shape.tick()
+
+                    if result is None:
+                        break
+
+                    users, spawn_rate = result
+                    results.append({
+                        'time': second,
+                        'users': users,
+                        'spawn_rate': spawn_rate
+                    })
+
+            # 分析结果
+            return LoadShapeValidator._analyze_results(results)
+
+        except Exception as e:
+            return {'error': str(e), 'valid': False}
+
+    @staticmethod
+    def _analyze_results(results: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """分析验证结果"""
+        if not results:
+            return {'error': 'No results generated', 'valid': False}
+
+        users_list = [r['users'] for r in results]
+        spawn_rates = [r['spawn_rate'] for r in results]
+
+        return {
+            'valid': True,
+            'duration': len(results),
+            'max_users': max(users_list),
+            'min_users': min(users_list),
+            'avg_users': sum(users_list) / len(users_list),
+            'max_spawn_rate': max(spawn_rates),
+            'min_spawn_rate': min(spawn_rates),
+            'user_changes': len(set(users_list)),
+            'results': results[:10]  # 前10个结果作为样本
+        }
+```
+
+## 📦 集成和部署
+
+### 1. 负载模式注册
+
+```python
+# 在框架中注册自定义负载模式
+from src.model.load_shapes.load_shape_manager import load_shape_manager
+
+# 注册负载模式
+load_shape_manager.register_shape("linear", LinearLoadShape)
+load_shape_manager.register_shape("stage", StageLoadShape)
+load_shape_manager.register_shape("adaptive", AdaptiveLoadShape)
+load_shape_manager.register_shape("event_driven", EventDrivenLoadShape)
+
+# 使用负载模式
+linear_shape = load_shape_manager.create_shape("linear",
+                                              max_users=200,
+                                              duration=600,
+                                              spawn_rate=15)
+```
+
+### 2. 配置文件集成
+
+```yaml
+# load_shapes.yaml
+load_shapes:
+  linear_growth:
+    type: "linear"
+    config:
+      max_users: 200
+      duration: 600
+      spawn_rate: 15
+
+  stage_test:
+    type: "stage"
+    config:
+      stages:
+        - users: 10
+          duration: 60
+          spawn_rate: 5
+        - users: 50
+          duration: 120
+          spawn_rate: 10
+        - users: 100
+          duration: 180
+          spawn_rate: 15
+
+  adaptive_test:
+    type: "adaptive"
+    config:
+      initial_users: 20
+      max_users: 500
+      target_response_time: 1000
+      adjustment_interval: 30
+      spawn_rate: 10
+```
+
+## 📚 最佳实践
+
+### 1. 设计原则
+
+- **单一职责**: 每个负载模式专注于特定的负载控制逻辑
+- **可配置性**: 通过参数控制负载模式行为
+- **可测试性**: 提供充分的测试覆盖
+- **性能优化**: 避免在tick方法中进行重计算
+- **错误处理**: 优雅处理异常情况
+
+### 2. 代码规范
+
+- 使用类型提示提高代码可读性
+- 提供详细的文档字符串
+- 遵循PEP 8代码风格
+- 使用有意义的变量和方法名
+- 添加适当的日志记录
+
+### 3. 性能考虑
+
+- tick方法应该快速执行
+- 避免在tick中进行IO操作
+- 缓存计算结果
+- 使用高效的数据结构
+- 考虑内存使用情况
+
+## 🎉 总结
+
+通过本指南，您已经学会了：
+
+1. **基础开发**: 创建简单的负载模式
+2. **高级功能**: 实现自适应和事件驱动负载模式
+3. **测试验证**: 编写测试确保负载模式正确性
+4. **集成部署**: 将负载模式集成到框架中
+5. **最佳实践**: 遵循设计原则和代码规范
+
+## 📚 相关文档
+
+- [负载模式API](../api/load-shapes.md) - 详细API参考
+- [基础示例](../examples/basic-examples.md) - 负载模式使用示例
+- [高级示例](../examples/advanced-usage.md) - 复杂场景实现
+- [测试指南](testing.md) - 测试开发指南
diff --git a/docs/development/plugin-development.md b/docs/development/plugin-development.md
new file mode 100644
index 0000000..d5fcbe8
--- /dev/null
+++ b/docs/development/plugin-development.md
@@ -0,0 +1,692 @@
+# 插件开发指南
+
+本指南详细介绍如何为Locust性能测试框架开发自定义插件，包括插件类型、开发流程和最佳实践。
+
+## 🎯 插件系统概述
+
+### 插件架构
+
+框架采用基于接口的插件架构，支持10+种不同类型的插件：
+
+```python
+# 插件类型层次结构
+PluginInterface (基础接口)
+├── LocustPlugin (Locust功能扩展)
+├── ReportPlugin (报告生成)
+├── MonitorPlugin (监控指标)
+├── DataPlugin (数据源)
+├── NotificationPlugin (通知渠道)
+├── LoadShapePlugin (负载模式)
+├── AnalysisPlugin (分析算法)
+├── StoragePlugin (存储后端)
+├── AuthenticationPlugin (认证方式)
+└── ProtocolPlugin (协议支持)
+```
+
+### 插件生命周期
+
+```mermaid
+graph LR
+    A[发现] --> B[加载]
+    B --> C[验证]
+    C --> D[初始化]
+    D --> E[启用]
+    E --> F[运行]
+    F --> G[禁用]
+    G --> H[卸载]
+```
+
+## 🔧 开发环境准备
+
+### 1. 项目结构
+
+```bash
+my_plugin/
+├── __init__.py
+├── plugin.py              # 主插件文件
+├── config.py              # 配置定义
+├── utils.py               # 工具函数
+├── tests/                 # 测试文件
+│   ├── __init__.py
+│   └── test_plugin.py
+├── README.md              # 插件说明
+├── requirements.txt       # 依赖列表
+└── setup.py              # 安装脚本
+```
+
+### 2. 基础依赖
+
+```python
+# requirements.txt
+locust>=2.0.0
+dynaconf>=3.1.0
+loguru>=0.6.0
+```
+
+## 📝 插件开发步骤
+
+### 步骤1：定义插件信息
+
+```python
+# plugin.py
+from dataclasses import dataclass
+from typing import Dict, Any, Optional
+
+@dataclass
+class PluginInfo:
+    """插件信息"""
+    name: str
+    version: str
+    description: str
+    author: str
+    category: str
+    dependencies: Optional[list] = None
+    config_schema: Optional[Dict[str, Any]] = None
+```
+
+### 步骤2：选择插件类型
+
+根据功能需求选择合适的插件基类：
+
+#### ReportPlugin - 报告生成插件
+
+```python
+from src.plugins.plugin_interface import ReportPlugin, PluginInfo
+
+class MyReportPlugin(ReportPlugin):
+    """自定义报告插件示例"""
+
+    @property
+    def plugin_info(self) -> PluginInfo:
+        return PluginInfo(
+            name="My Report Plugin",
+            version="1.0.0",
+            description="生成自定义格式的测试报告",
+            author="Your Name",
+            category="report",
+            dependencies=["jinja2", "matplotlib"],
+            config_schema={
+                "output_format": {"type": "string", "default": "pdf"},
+                "include_charts": {"type": "boolean", "default": True}
+            }
+        )
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        """初始化插件"""
+        self.config = config or {}
+        self.output_format = self.config.get('output_format', 'pdf')
+        self.include_charts = self.config.get('include_charts', True)
+
+        # 验证依赖
+        try:
+            import jinja2
+            import matplotlib
+            return True
+        except ImportError as e:
+            self.logger.error(f"缺少依赖: {e}")
+            return False
+
+    def cleanup(self):
+        """清理资源"""
+        # 清理临时文件、关闭连接等
+        pass
+
+    def generate_report(self, test_data: Dict[str, Any],
+                       output_path: str) -> bool:
+        """生成报告的核心方法"""
+        try:
+            if self.output_format == 'pdf':
+                return self._generate_pdf_report(test_data, output_path)
+            elif self.output_format == 'excel':
+                return self._generate_excel_report(test_data, output_path)
+            else:
+                self.logger.error(f"不支持的输出格式: {self.output_format}")
+                return False
+        except Exception as e:
+            self.logger.error(f"报告生成失败: {e}")
+            return False
+
+    def get_supported_formats(self) -> list:
+        """返回支持的报告格式"""
+        return ['pdf', 'excel']
+
+    def _generate_pdf_report(self, test_data: Dict[str, Any],
+                            output_path: str) -> bool:
+        """生成PDF报告"""
+        # 实现PDF报告生成逻辑
+        self.logger.info(f"生成PDF报告: {output_path}")
+        return True
+
+    def _generate_excel_report(self, test_data: Dict[str, Any],
+                              output_path: str) -> bool:
+        """生成Excel报告"""
+        # 实现Excel报告生成逻辑
+        self.logger.info(f"生成Excel报告: {output_path}")
+        return True
+```
+
+#### MonitorPlugin - 监控插件
+
+```python
+from src.plugins.plugin_interface import MonitorPlugin, PluginInfo
+
+class SystemMonitorPlugin(MonitorPlugin):
+    """系统监控插件"""
+
+    @property
+    def plugin_info(self) -> PluginInfo:
+        return PluginInfo(
+            name="System Monitor",
+            version="1.0.0",
+            description="监控系统资源使用情况",
+            author="Your Name",
+            category="monitor",
+            dependencies=["psutil"]
+        )
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        """初始化监控插件"""
+        try:
+            import psutil
+            self.psutil = psutil
+            return True
+        except ImportError:
+            self.logger.error("缺少psutil依赖")
+            return False
+
+    def cleanup(self):
+        """清理资源"""
+        pass
+
+    def collect_metrics(self) -> Dict[str, Any]:
+        """收集监控指标"""
+        try:
+            return {
+                'cpu_percent': self.psutil.cpu_percent(interval=1),
+                'memory_percent': self.psutil.virtual_memory().percent,
+                'disk_usage': self.psutil.disk_usage('/').percent,
+                'network_io': dict(self.psutil.net_io_counters()._asdict()),
+                'timestamp': time.time()
+            }
+        except Exception as e:
+            self.logger.error(f"指标收集失败: {e}")
+            return {}
+
+    def get_metric_definitions(self) -> Dict[str, Dict[str, Any]]:
+        """返回指标定义"""
+        return {
+            'cpu_percent': {
+                'name': 'CPU使用率',
+                'unit': '%',
+                'type': 'gauge'
+            },
+            'memory_percent': {
+                'name': '内存使用率',
+                'unit': '%',
+                'type': 'gauge'
+            },
+            'disk_usage': {
+                'name': '磁盘使用率',
+                'unit': '%',
+                'type': 'gauge'
+            }
+        }
+```
+
+#### DataPlugin - 数据源插件
+
+```python
+from src.plugins.plugin_interface import DataPlugin, PluginInfo
+
+class DatabaseDataPlugin(DataPlugin):
+    """数据库数据源插件"""
+
+    @property
+    def plugin_info(self) -> PluginInfo:
+        return PluginInfo(
+            name="Database Data Source",
+            version="1.0.0",
+            description="从数据库加载测试数据",
+            author="Your Name",
+            category="data",
+            dependencies=["pymysql"],
+            config_schema={
+                "host": {"type": "string", "required": True},
+                "port": {"type": "integer", "default": 3306},
+                "database": {"type": "string", "required": True},
+                "username": {"type": "string", "required": True},
+                "password": {"type": "string", "required": True}
+            }
+        )
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        """初始化数据库连接"""
+        self.config = config or {}
+        try:
+            import pymysql
+            self.connection = pymysql.connect(
+                host=self.config['host'],
+                port=self.config.get('port', 3306),
+                database=self.config['database'],
+                user=self.config['username'],
+                password=self.config['password']
+            )
+            return True
+        except Exception as e:
+            self.logger.error(f"数据库连接失败: {e}")
+            return False
+
+    def cleanup(self):
+        """关闭数据库连接"""
+        if hasattr(self, 'connection'):
+            self.connection.close()
+
+    def load_data(self, data_type: str, **kwargs) -> list:
+        """加载数据"""
+        try:
+            cursor = self.connection.cursor()
+
+            if data_type == 'users':
+                cursor.execute("SELECT * FROM users LIMIT %s",
+                              (kwargs.get('limit', 100),))
+            elif data_type == 'products':
+                cursor.execute("SELECT * FROM products LIMIT %s",
+                              (kwargs.get('limit', 100),))
+            else:
+                raise ValueError(f"不支持的数据类型: {data_type}")
+
+            results = cursor.fetchall()
+            cursor.close()
+            return results
+
+        except Exception as e:
+            self.logger.error(f"数据加载失败: {e}")
+            return []
+
+    def get_supported_data_types(self) -> list:
+        """返回支持的数据类型"""
+        return ['users', 'products', 'orders']
+```
+
+### 步骤3：实现事件处理
+
+```python
+class MyPlugin(ReportPlugin):
+    """支持事件处理的插件"""
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        """初始化时注册事件处理器"""
+        # 注册事件处理器
+        self.register_event_handler('test_start', self.on_test_start)
+        self.register_event_handler('test_stop', self.on_test_stop)
+        self.register_event_handler('request_success', self.on_request_success)
+        self.register_event_handler('request_failure', self.on_request_failure)
+        return True
+
+    def on_test_start(self, environment, **kwargs):
+        """测试开始事件处理"""
+        self.logger.info("测试开始，初始化插件状态")
+        self.start_time = time.time()
+
+    def on_test_stop(self, environment, **kwargs):
+        """测试结束事件处理"""
+        self.logger.info("测试结束，生成最终报告")
+        self.end_time = time.time()
+        # 生成报告逻辑
+
+    def on_request_success(self, request_type, name, response_time,
+                          response_length, **kwargs):
+        """请求成功事件处理"""
+        # 记录成功请求
+        pass
+
+    def on_request_failure(self, request_type, name, response_time,
+                          response_length, exception, **kwargs):
+        """请求失败事件处理"""
+        # 记录失败请求
+        pass
+```
+
+### 步骤4：配置管理
+
+```python
+# config.py
+from typing import Dict, Any
+from dataclasses import dataclass
+
+@dataclass
+class PluginConfig:
+    """插件配置类"""
+    output_directory: str = "reports"
+    file_format: str = "html"
+    include_charts: bool = True
+    chart_theme: str = "default"
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any]) -> 'PluginConfig':
+        """从字典创建配置对象"""
+        return cls(**{k: v for k, v in config_dict.items()
+                     if k in cls.__annotations__})
+
+    def validate(self) -> bool:
+        """验证配置有效性"""
+        if not self.output_directory:
+            return False
+        if self.file_format not in ['html', 'pdf', 'excel']:
+            return False
+        return True
+
+# 在插件中使用配置
+class MyPlugin(ReportPlugin):
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        self.config = PluginConfig.from_dict(config or {})
+        if not self.config.validate():
+            self.logger.error("插件配置无效")
+            return False
+        return True
+```
+
+## 🧪 插件测试
+
+### 单元测试
+
+```python
+# tests/test_plugin.py
+import unittest
+from unittest.mock import Mock, patch
+from my_plugin.plugin import MyReportPlugin
+
+class TestMyReportPlugin(unittest.TestCase):
+    """插件单元测试"""
+
+    def setUp(self):
+        """测试准备"""
+        self.plugin = MyReportPlugin()
+        self.test_config = {
+            'output_format': 'pdf',
+            'include_charts': True
+        }
+
+    def test_plugin_info(self):
+        """测试插件信息"""
+        info = self.plugin.plugin_info
+        self.assertEqual(info.name, "My Report Plugin")
+        self.assertEqual(info.version, "1.0.0")
+        self.assertEqual(info.category, "report")
+
+    def test_initialize_success(self):
+        """测试初始化成功"""
+        result = self.plugin.initialize(self.test_config)
+        self.assertTrue(result)
+        self.assertEqual(self.plugin.output_format, 'pdf')
+
+    def test_initialize_failure(self):
+        """测试初始化失败"""
+        with patch('my_plugin.plugin.jinja2', side_effect=ImportError):
+            result = self.plugin.initialize(self.test_config)
+            self.assertFalse(result)
+
+    def test_generate_report(self):
+        """测试报告生成"""
+        self.plugin.initialize(self.test_config)
+        test_data = {'test_name': 'test', 'requests': []}
+
+        result = self.plugin.generate_report(test_data, 'test_report.pdf')
+        self.assertTrue(result)
+
+    def test_supported_formats(self):
+        """测试支持的格式"""
+        formats = self.plugin.get_supported_formats()
+        self.assertIn('pdf', formats)
+        self.assertIn('excel', formats)
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+### 集成测试
+
+```python
+# tests/test_integration.py
+import unittest
+from src.plugins.plugin_manager import PluginManager
+from my_plugin.plugin import MyReportPlugin
+
+class TestPluginIntegration(unittest.TestCase):
+    """插件集成测试"""
+
+    def setUp(self):
+        """测试准备"""
+        self.plugin_manager = PluginManager()
+
+    def test_plugin_loading(self):
+        """测试插件加载"""
+        # 注册插件
+        self.plugin_manager.register_plugin('my_report_plugin', MyReportPlugin)
+
+        # 启用插件
+        success = self.plugin_manager.enable_plugin('my_report_plugin', {
+            'output_format': 'pdf'
+        })
+        self.assertTrue(success)
+
+        # 检查插件状态
+        status = self.plugin_manager.get_plugin_status()
+        self.assertTrue(status['my_report_plugin']['enabled'])
+
+    def test_event_handling(self):
+        """测试事件处理"""
+        self.plugin_manager.register_plugin('my_report_plugin', MyReportPlugin)
+        self.plugin_manager.enable_plugin('my_report_plugin')
+
+        # 触发事件
+        self.plugin_manager.trigger_event('test_start', {'test_name': 'test'})
+
+        # 验证事件处理结果
+        # ...
+```
+
+## 📦 插件打包和分发
+
+### 1. 创建setup.py
+
+```python
+# setup.py
+from setuptools import setup, find_packages
+
+setup(
+    name="my-locust-plugin",
+    version="1.0.0",
+    description="自定义Locust插件",
+    author="Your Name",
+    author_email="your.email@example.com",
+    packages=find_packages(),
+    install_requires=[
+        "locust>=2.0.0",
+        "dynaconf>=3.1.0",
+        "loguru>=0.6.0",
+        "jinja2>=3.0.0",
+        "matplotlib>=3.5.0"
+    ],
+    classifiers=[
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Developers",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+    ],
+    python_requires=">=3.7",
+    entry_points={
+        "locust.plugins": [
+            "my_report_plugin = my_plugin.plugin:MyReportPlugin",
+        ]
+    }
+)
+```
+
+### 2. 创建插件清单
+
+```python
+# plugin_manifest.json
+{
+    "name": "my-report-plugin",
+    "version": "1.0.0",
+    "description": "自定义报告生成插件",
+    "author": "Your Name",
+    "category": "report",
+    "entry_point": "my_plugin.plugin:MyReportPlugin",
+    "dependencies": [
+        "jinja2>=3.0.0",
+        "matplotlib>=3.5.0"
+    ],
+    "config_schema": {
+        "type": "object",
+        "properties": {
+            "output_format": {
+                "type": "string",
+                "enum": ["pdf", "excel"],
+                "default": "pdf"
+            },
+            "include_charts": {
+                "type": "boolean",
+                "default": true
+            }
+        }
+    },
+    "supported_events": [
+        "test_start",
+        "test_stop",
+        "request_success",
+        "request_failure"
+    ]
+}
+```
+
+## 🎯 最佳实践
+
+### 1. 设计原则
+
+- **单一职责**: 每个插件专注一个特定功能
+- **松耦合**: 减少对框架内部实现的依赖
+- **可配置**: 提供灵活的配置选项
+- **错误处理**: 完善的异常处理和日志记录
+
+### 2. 性能优化
+
+```python
+class OptimizedPlugin(ReportPlugin):
+    """性能优化的插件示例"""
+
+    def __init__(self):
+        super().__init__()
+        self._cache = {}  # 缓存机制
+        self._pool = None  # 连接池
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        # 初始化连接池
+        self._pool = self._create_connection_pool()
+        return True
+
+    def generate_report(self, test_data: Dict[str, Any],
+                       output_path: str) -> bool:
+        # 使用缓存避免重复计算
+        cache_key = self._generate_cache_key(test_data)
+        if cache_key in self._cache:
+            return self._use_cached_result(cache_key, output_path)
+
+        # 异步处理大量数据
+        result = self._process_data_async(test_data)
+        self._cache[cache_key] = result
+
+        return self._write_report(result, output_path)
+```
+
+### 3. 安全考虑
+
+```python
+class SecurePlugin(ReportPlugin):
+    """安全的插件示例"""
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        # 验证配置安全性
+        if not self._validate_config_security(config):
+            return False
+
+        # 设置安全的文件权限
+        self._setup_secure_permissions()
+        return True
+
+    def _validate_config_security(self, config: Dict[str, Any]) -> bool:
+        """验证配置安全性"""
+        # 检查路径遍历攻击
+        output_path = config.get('output_path', '')
+        if '..' in output_path or output_path.startswith('/'):
+            self.logger.error("不安全的输出路径")
+            return False
+
+        return True
+
+    def _sanitize_input(self, data: str) -> str:
+        """清理输入数据"""
+        # 移除潜在的恶意字符
+        import re
+        return re.sub(r'[<>"\']', '', data)
+```
+
+## 🔧 调试和故障排除
+
+### 1. 调试技巧
+
+```python
+class DebuggablePlugin(ReportPlugin):
+    """可调试的插件"""
+
+    def initialize(self, config: Optional[Dict[str, Any]] = None) -> bool:
+        # 启用调试模式
+        self.debug_mode = config.get('debug', False)
+
+        if self.debug_mode:
+            self.logger.setLevel('DEBUG')
+            self.logger.debug("插件调试模式已启用")
+
+        return True
+
+    def generate_report(self, test_data: Dict[str, Any],
+                       output_path: str) -> bool:
+        if self.debug_mode:
+            self.logger.debug(f"开始生成报告: {output_path}")
+            self.logger.debug(f"测试数据大小: {len(test_data.get('requests', []))}")
+
+        try:
+            result = self._do_generate_report(test_data, output_path)
+
+            if self.debug_mode:
+                self.logger.debug(f"报告生成{'成功' if result else '失败'}")
+
+            return result
+        except Exception as e:
+            self.logger.error(f"报告生成异常: {e}", exc_info=True)
+            return False
+```
+
+### 2. 常见问题
+
+| 问题 | 原因 | 解决方案 |
+|------|------|----------|
+| 插件加载失败 | 依赖缺失 | 检查requirements.txt |
+| 配置验证失败 | 配置格式错误 | 验证配置schema |
+| 内存泄漏 | 资源未释放 | 实现cleanup方法 |
+| 性能问题 | 阻塞操作 | 使用异步处理 |
+
+## 📚 参考资源
+
+- [插件接口API](../api/plugins.md) - 详细API文档
+- [框架架构](../architecture/overview.md) - 架构设计说明
+- [配置管理](../configuration/plugin-config.md) - 配置选项
+- [示例插件](../../src/plugins/builtin/) - 内置插件源码
+
+---
+
+通过遵循本指南，您可以开发出高质量、可维护的插件，为框架添加强大的扩展功能。
diff --git a/docs/development/setup.md b/docs/development/setup.md
new file mode 100644
index 0000000..af22543
--- /dev/null
+++ b/docs/development/setup.md
@@ -0,0 +1,608 @@
+# 开发环境搭建
+
+本指南详细介绍如何搭建Locust性能测试框架的开发环境，包括环境准备、依赖安装、IDE配置等。
+
+## 🎯 环境要求
+
+### 系统要求
+
+| 组件 | 最低版本 | 推荐版本 | 说明 |
+|------|----------|----------|------|
+| Python | 3.7+ | 3.9+ | 核心运行环境 |
+| pip | 20.0+ | 最新版本 | 包管理工具 |
+| Git | 2.20+ | 最新版本 | 版本控制 |
+| 内存 | 4GB | 8GB+ | 开发和测试 |
+| 磁盘 | 10GB | 20GB+ | 代码和依赖 |
+
+### 操作系统支持
+
+- ✅ **Linux** (Ubuntu 18.04+, CentOS 7+)
+- ✅ **macOS** (10.14+)
+- ✅ **Windows** (10+, WSL2推荐)
+
+## 🔧 环境搭建步骤
+
+### 步骤1：Python环境准备
+
+#### 使用pyenv管理Python版本 (推荐)
+
+```bash
+# 安装pyenv (Linux/macOS)
+curl https://pyenv.run | bash
+
+# 或使用包管理器
+# Ubuntu/Debian
+sudo apt update && sudo apt install -y pyenv
+
+# macOS
+brew install pyenv
+
+# 配置环境变量
+echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc
+echo 'command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc
+echo 'eval "$(pyenv init -)"' >> ~/.bashrc
+
+# 重新加载配置
+source ~/.bashrc
+
+# 安装Python 3.9
+pyenv install 3.9.16
+pyenv global 3.9.16
+
+# 验证安装
+python --version
+```
+
+#### 直接安装Python
+
+```bash
+# Ubuntu/Debian
+sudo apt update
+sudo apt install python3.9 python3.9-pip python3.9-venv python3.9-dev
+
+# CentOS/RHEL
+sudo yum install python39 python39-pip python39-devel
+
+# macOS (使用Homebrew)
+brew install python@3.9
+
+# Windows
+# 从 https://python.org 下载安装包
+```
+
+### 步骤2：克隆项目代码
+
+```bash
+# 克隆项目
+git clone https://github.com/your-org/locust-framework.git
+cd locust-framework
+
+# 查看项目结构
+tree -L 2
+```
+
+### 步骤3：创建虚拟环境
+
+```bash
+# 创建虚拟环境
+python -m venv venv
+
+# 激活虚拟环境
+# Linux/macOS
+source venv/bin/activate
+
+# Windows
+venv\Scripts\activate
+
+# 验证虚拟环境
+which python
+python --version
+```
+
+### 步骤4：安装依赖
+
+```bash
+# 升级pip
+pip install --upgrade pip
+
+# 安装开发依赖
+pip install -r requirements-dev.txt
+
+# 安装项目依赖
+pip install -r requirements.txt
+
+# 以开发模式安装项目
+pip install -e .
+
+# 验证安装
+python -c "import locust; print(locust.__version__)"
+```
+
+### 步骤5：配置开发环境
+
+#### 环境变量配置
+
+```bash
+# 创建环境配置文件
+cp .env.example .env
+
+# 编辑环境配置
+vim .env
+```
+
+```bash
+# .env 文件内容
+LOCUST_ENV=development
+PYTHONPATH=.
+LOG_LEVEL=DEBUG
+WEB_PORT=8089
+```
+
+#### Git配置
+
+```bash
+# 配置Git钩子
+cp scripts/pre-commit .git/hooks/
+chmod +x .git/hooks/pre-commit
+
+# 配置Git用户信息
+git config user.name "Your Name"
+git config user.email "your.email@example.com"
+```
+
+## 🛠️ IDE配置
+
+### VS Code配置
+
+#### 安装扩展
+
+```json
+// .vscode/extensions.json
+{
+    "recommendations": [
+        "ms-python.python",
+        "ms-python.flake8",
+        "ms-python.black-formatter",
+        "ms-python.isort",
+        "ms-python.pylint",
+        "ms-toolsai.jupyter",
+        "redhat.vscode-yaml",
+        "ms-vscode.test-adapter-converter"
+    ]
+}
+```
+
+#### 工作区配置
+
+```json
+// .vscode/settings.json
+{
+    "python.defaultInterpreterPath": "./venv/bin/python",
+    "python.linting.enabled": true,
+    "python.linting.pylintEnabled": true,
+    "python.linting.flake8Enabled": true,
+    "python.formatting.provider": "black",
+    "python.sortImports.args": ["--profile", "black"],
+    "python.testing.pytestEnabled": true,
+    "python.testing.pytestArgs": ["tests"],
+    "files.exclude": {
+        "**/__pycache__": true,
+        "**/*.pyc": true,
+        ".pytest_cache": true,
+        ".coverage": true,
+        "htmlcov": true
+    },
+    "editor.formatOnSave": true,
+    "editor.codeActionsOnSave": {
+        "source.organizeImports": true
+    }
+}
+```
+
+#### 调试配置
+
+```json
+// .vscode/launch.json
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Locust Web UI",
+            "type": "python",
+            "request": "launch",
+            "module": "locust",
+            "args": [
+                "-f", "locustfiles/example_test.py",
+                "--web-host", "0.0.0.0",
+                "--web-port", "8089"
+            ],
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}"
+            }
+        },
+        {
+            "name": "Run Tests",
+            "type": "python",
+            "request": "launch",
+            "module": "pytest",
+            "args": ["tests/", "-v"],
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}"
+        }
+    ]
+}
+```
+
+### PyCharm配置
+
+#### 项目设置
+
+1. **解释器配置**
+   - File → Settings → Project → Python Interpreter
+   - 选择虚拟环境中的Python解释器
+
+2. **代码风格配置**
+   - File → Settings → Editor → Code Style → Python
+   - 导入配置文件：`setup.cfg`
+
+3. **运行配置**
+   ```python
+   # 创建运行配置
+   # Run → Edit Configurations → Add New → Python
+
+   # Locust Web UI配置
+   Script path: venv/bin/locust
+   Parameters: -f locustfiles/example_test.py --web-host 0.0.0.0
+   Working directory: /path/to/project
+   Environment variables: PYTHONPATH=/path/to/project
+   ```
+
+## 🧪 开发工具配置
+
+### 代码质量工具
+
+#### Black (代码格式化)
+
+```toml
+# pyproject.toml
+[tool.black]
+line-length = 88
+target-version = ['py39']
+include = '\.pyi?$'
+extend-exclude = '''
+/(
+  # directories
+  \.eggs
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | build
+  | dist
+)/
+'''
+```
+
+#### isort (导入排序)
+
+```toml
+# pyproject.toml
+[tool.isort]
+profile = "black"
+multi_line_output = 3
+line_length = 88
+known_first_party = ["src", "locustfiles", "tests"]
+```
+
+#### flake8 (代码检查)
+
+```ini
+# setup.cfg
+[flake8]
+max-line-length = 88
+extend-ignore = E203, W503
+exclude =
+    .git,
+    __pycache__,
+    .venv,
+    build,
+    dist,
+    *.egg-info
+```
+
+#### pylint (代码分析)
+
+```ini
+# .pylintrc
+[MASTER]
+init-hook='import sys; sys.path.append(".")'
+
+[MESSAGES CONTROL]
+disable=C0330,C0326,R0903,R0913
+
+[FORMAT]
+max-line-length=88
+```
+
+### 测试工具配置
+
+#### pytest配置
+
+```toml
+# pyproject.toml
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py", "*_test.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = [
+    "-v",
+    "--strict-markers",
+    "--strict-config",
+    "--cov=src",
+    "--cov-report=html",
+    "--cov-report=term-missing"
+]
+markers = [
+    "unit: Unit tests",
+    "integration: Integration tests",
+    "slow: Slow tests"
+]
+```
+
+#### coverage配置
+
+```ini
+# .coveragerc
+[run]
+source = src
+omit =
+    */tests/*
+    */venv/*
+    setup.py
+
+[report]
+exclude_lines =
+    pragma: no cover
+    def __repr__
+    raise AssertionError
+    raise NotImplementedError
+```
+
+## 🔍 开发工作流
+
+### 1. 日常开发流程
+
+```bash
+# 1. 激活虚拟环境
+source venv/bin/activate
+
+# 2. 拉取最新代码
+git pull origin main
+
+# 3. 创建功能分支
+git checkout -b feature/new-feature
+
+# 4. 开发代码
+# ... 编写代码 ...
+
+# 5. 运行代码检查
+make lint
+
+# 6. 运行测试
+make test
+
+# 7. 提交代码
+git add .
+git commit -m "feat: add new feature"
+
+# 8. 推送分支
+git push origin feature/new-feature
+```
+
+### 2. 代码提交规范
+
+```bash
+# 提交消息格式
+<type>(<scope>): <description>
+
+# 类型说明
+feat: 新功能
+fix: 修复bug
+docs: 文档更新
+style: 代码格式调整
+refactor: 代码重构
+test: 测试相关
+chore: 构建过程或辅助工具的变动
+
+# 示例
+feat(analysis): add performance grading system
+fix(monitoring): resolve memory leak in alert manager
+docs(api): update monitoring API documentation
+```
+
+### 3. 代码审查清单
+
+- [ ] 代码符合项目编码规范
+- [ ] 添加了必要的单元测试
+- [ ] 测试覆盖率达到要求
+- [ ] 更新了相关文档
+- [ ] 没有引入安全漏洞
+- [ ] 性能影响可接受
+- [ ] 向后兼容性良好
+
+## 🚀 快速开始开发
+
+### 创建第一个功能
+
+```bash
+# 1. 创建功能分支
+git checkout -b feature/my-first-feature
+
+# 2. 创建模块文件
+mkdir -p src/my_module
+touch src/my_module/__init__.py
+touch src/my_module/my_feature.py
+
+# 3. 编写代码
+cat > src/my_module/my_feature.py << 'EOF'
+"""我的第一个功能模块"""
+
+def hello_world():
+    """返回问候语"""
+    return "Hello, Locust Framework!"
+EOF
+
+# 4. 创建测试文件
+mkdir -p tests/my_module
+touch tests/my_module/__init__.py
+touch tests/my_module/test_my_feature.py
+
+# 5. 编写测试
+cat > tests/my_module/test_my_feature.py << 'EOF'
+"""测试我的功能模块"""
+import pytest
+from src.my_module.my_feature import hello_world
+
+def test_hello_world():
+    """测试问候语功能"""
+    result = hello_world()
+    assert result == "Hello, Locust Framework!"
+EOF
+
+# 6. 运行测试
+pytest tests/my_module/test_my_feature.py -v
+
+# 7. 提交代码
+git add .
+git commit -m "feat: add hello world feature"
+```
+
+## 🔧 常用开发命令
+
+### Makefile命令
+
+```makefile
+# Makefile
+.PHONY: help install test lint format clean
+
+help:  ## 显示帮助信息
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
+
+install:  ## 安装依赖
+	pip install -r requirements-dev.txt
+	pip install -e .
+
+test:  ## 运行测试
+	pytest tests/ -v --cov=src
+
+lint:  ## 代码检查
+	flake8 src tests
+	pylint src
+	black --check src tests
+	isort --check-only src tests
+
+format:  ## 格式化代码
+	black src tests
+	isort src tests
+
+clean:  ## 清理临时文件
+	find . -type f -name "*.pyc" -delete
+	find . -type d -name "__pycache__" -delete
+	rm -rf .pytest_cache
+	rm -rf htmlcov
+	rm -rf .coverage
+```
+
+### 常用命令
+
+```bash
+# 安装依赖
+make install
+
+# 运行测试
+make test
+
+# 代码检查
+make lint
+
+# 格式化代码
+make format
+
+# 清理临时文件
+make clean
+
+# 启动开发服务器
+locust -f locustfiles/example_test.py --web-host 0.0.0.0
+
+# 运行特定测试
+pytest tests/test_analysis.py::test_performance_analyzer -v
+
+# 生成测试覆盖率报告
+pytest --cov=src --cov-report=html
+```
+
+## 🐛 故障排除
+
+### 常见问题
+
+1. **虚拟环境问题**
+   ```bash
+   # 重新创建虚拟环境
+   rm -rf venv
+   python -m venv venv
+   source venv/bin/activate
+   pip install -r requirements-dev.txt
+   ```
+
+2. **依赖冲突**
+   ```bash
+   # 检查依赖冲突
+   pip check
+
+   # 重新安装依赖
+   pip freeze > current_requirements.txt
+   pip uninstall -r current_requirements.txt -y
+   pip install -r requirements-dev.txt
+   ```
+
+3. **导入路径问题**
+   ```bash
+   # 设置PYTHONPATH
+   export PYTHONPATH="${PYTHONPATH}:$(pwd)"
+
+   # 或在代码中添加
+   import sys
+   sys.path.insert(0, '.')
+   ```
+
+### 调试技巧
+
+```python
+# 使用pdb调试
+import pdb; pdb.set_trace()
+
+# 使用ipdb (更友好的调试器)
+import ipdb; ipdb.set_trace()
+
+# 使用logging调试
+import logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+logger.debug("调试信息")
+```
+
+## 📚 参考资源
+
+- [Python官方文档](https://docs.python.org/3/)
+- [Locust官方文档](https://docs.locust.io/)
+- [pytest文档](https://docs.pytest.org/)
+- [Black代码格式化](https://black.readthedocs.io/)
+- [项目贡献指南](contributing.md)
+
+---
+
+完成环境搭建后，您就可以开始参与框架开发了！建议先阅读[代码规范](coding-standards.md)和[插件开发指南](plugin-development.md)。
diff --git a/docs/development/testing.md b/docs/development/testing.md
new file mode 100644
index 0000000..3242658
--- /dev/null
+++ b/docs/development/testing.md
@@ -0,0 +1,733 @@
+# 测试指南
+
+本文档详细介绍框架的测试策略、测试工具使用、测试最佳实践等内容。
+
+## 🎯 测试策略
+
+### 测试金字塔
+
+```
+    /\
+   /  \     E2E Tests (端到端测试)
+  /____\    Integration Tests (集成测试)
+ /______\   Unit Tests (单元测试)
+/__________\
+```
+
+- **单元测试 (70%)**: 测试单个函数、方法、类
+- **集成测试 (20%)**: 测试模块间交互
+- **端到端测试 (10%)**: 测试完整用户场景
+
+### 测试分类
+
+```python
+# 测试标记分类
+pytest.mark.unit        # 单元测试
+pytest.mark.integration # 集成测试
+pytest.mark.e2e         # 端到端测试
+pytest.mark.slow        # 慢速测试
+pytest.mark.smoke       # 冒烟测试
+pytest.mark.regression  # 回归测试
+```
+
+## 🧪 单元测试
+
+### 测试结构
+
+```python
+# tests/unit/test_performance_analyzer.py
+import pytest
+from unittest.mock import Mock, patch, MagicMock
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.exceptions import AnalysisError
+
+class TestPerformanceAnalyzer:
+    """性能分析器单元测试"""
+
+    def setup_method(self):
+        """每个测试方法前执行"""
+        self.analyzer = PerformanceAnalyzer()
+        self.sample_data = {
+            'test_name': 'API性能测试',
+            'start_time': '2023-12-01 10:00:00',
+            'end_time': '2023-12-01 10:10:00',
+            'duration': 600,
+            'users': 50,
+            'requests': [
+                {'response_time': 100, 'success': True, 'timestamp': '2023-12-01 10:01:00'},
+                {'response_time': 200, 'success': True, 'timestamp': '2023-12-01 10:02:00'},
+                {'response_time': 150, 'success': False, 'timestamp': '2023-12-01 10:03:00'}
+            ]
+        }
+
+    def teardown_method(self):
+        """每个测试方法后执行"""
+        # 清理资源
+        pass
+
+    @pytest.mark.unit
+    def test_analyze_performance_success(self):
+        """测试性能分析成功场景"""
+        # Given
+        expected_grade = 'B'
+
+        # When
+        result = self.analyzer.comprehensive_analysis(self.sample_data)
+
+        # Then
+        assert result is not None
+        assert result['overall_grade'] == expected_grade
+        assert 'response_time' in result
+        assert 'throughput' in result
+        assert 'error_analysis' in result
+        assert 'recommendations' in result
+
+    @pytest.mark.unit
+    def test_analyze_empty_requests(self):
+        """测试空请求列表"""
+        # Given
+        empty_data = self.sample_data.copy()
+        empty_data['requests'] = []
+
+        # When & Then
+        with pytest.raises(AnalysisError, match="请求数据不能为空"):
+            self.analyzer.comprehensive_analysis(empty_data)
+
+    @pytest.mark.unit
+    @pytest.mark.parametrize("response_times,expected_grade", [
+        ([50, 80, 60, 70, 90], 'A'),      # 优秀
+        ([100, 200, 150, 180, 120], 'B'), # 良好
+        ([300, 400, 350, 380, 320], 'C'), # 一般
+        ([800, 900, 850, 880, 820], 'D'), # 较差
+    ])
+    def test_grading_algorithm(self, response_times, expected_grade):
+        """参数化测试评级算法"""
+        # Given
+        test_data = self.sample_data.copy()
+        test_data['requests'] = [
+            {'response_time': rt, 'success': True, 'timestamp': '2023-12-01 10:01:00'}
+            for rt in response_times
+        ]
+
+        # When
+        result = self.analyzer.comprehensive_analysis(test_data)
+
+        # Then
+        assert result['overall_grade'] == expected_grade
+
+    @pytest.mark.unit
+    @patch('src.analysis.performance_analyzer.calculate_percentile')
+    def test_response_time_calculation_with_mock(self, mock_percentile):
+        """使用Mock测试响应时间计算"""
+        # Given
+        mock_percentile.side_effect = [50.0, 95.0, 99.0]  # P50, P95, P99
+
+        # When
+        result = self.analyzer.comprehensive_analysis(self.sample_data)
+
+        # Then
+        assert mock_percentile.call_count == 3
+        assert result['response_time']['p50'] == 50.0
+        assert result['response_time']['p95'] == 95.0
+        assert result['response_time']['p99'] == 99.0
+
+    @pytest.mark.unit
+    def test_error_rate_calculation(self):
+        """测试错误率计算"""
+        # Given
+        test_data = self.sample_data.copy()
+        test_data['requests'] = [
+            {'response_time': 100, 'success': True, 'timestamp': '2023-12-01 10:01:00'},
+            {'response_time': 200, 'success': False, 'timestamp': '2023-12-01 10:02:00'},
+            {'response_time': 150, 'success': False, 'timestamp': '2023-12-01 10:03:00'},
+            {'response_time': 180, 'success': True, 'timestamp': '2023-12-01 10:04:00'}
+        ]
+
+        # When
+        result = self.analyzer.comprehensive_analysis(test_data)
+
+        # Then
+        expected_error_rate = 50.0  # 2/4 = 50%
+        assert result['error_analysis']['error_rate'] == expected_error_rate
+
+    @pytest.mark.unit
+    def test_throughput_calculation(self):
+        """测试吞吐量计算"""
+        # Given
+        test_data = self.sample_data.copy()
+        test_data['duration'] = 60  # 60秒
+        test_data['requests'] = [
+            {'response_time': 100, 'success': True, 'timestamp': '2023-12-01 10:01:00'}
+            for _ in range(120)  # 120个请求
+        ]
+
+        # When
+        result = self.analyzer.comprehensive_analysis(test_data)
+
+        # Then
+        expected_tps = 2.0  # 120请求 / 60秒 = 2 TPS
+        assert result['throughput']['requests_per_second'] == expected_tps
+```
+
+### 测试工具和技巧
+
+```python
+# 使用fixtures
+@pytest.fixture
+def sample_analyzer():
+    """分析器fixture"""
+    return PerformanceAnalyzer()
+
+@pytest.fixture
+def sample_test_data():
+    """测试数据fixture"""
+    return {
+        'requests': [
+            {'response_time': 100, 'success': True},
+            {'response_time': 200, 'success': True}
+        ],
+        'duration': 60,
+        'users': 10
+    }
+
+# 使用临时文件
+@pytest.fixture
+def temp_config_file(tmp_path):
+    """临时配置文件"""
+    config_file = tmp_path / "test_config.toml"
+    config_file.write_text("""
+    [analysis]
+    threshold = 1000
+    """)
+    return str(config_file)
+
+# 使用Mock对象
+def test_with_mock_dependencies():
+    """使用Mock测试依赖"""
+    # Given
+    mock_data_provider = Mock()
+    mock_data_provider.get_test_data.return_value = {'requests': []}
+
+    analyzer = PerformanceAnalyzer(data_provider=mock_data_provider)
+
+    # When
+    result = analyzer.load_and_analyze('test_id')
+
+    # Then
+    mock_data_provider.get_test_data.assert_called_once_with('test_id')
+
+# 异常测试
+def test_exception_handling():
+    """测试异常处理"""
+    analyzer = PerformanceAnalyzer()
+
+    with pytest.raises(ValueError, match="无效的测试数据"):
+        analyzer.comprehensive_analysis(None)
+
+# 属性测试
+@pytest.mark.property
+@given(st.lists(st.integers(min_value=1, max_value=5000), min_size=1))
+def test_response_time_analysis_property(response_times):
+    """属性测试：响应时间分析"""
+    # Given
+    test_data = {
+        'requests': [{'response_time': rt, 'success': True} for rt in response_times],
+        'duration': 60,
+        'users': 10
+    }
+
+    analyzer = PerformanceAnalyzer()
+
+    # When
+    result = analyzer.comprehensive_analysis(test_data)
+
+    # Then
+    assert result['response_time']['min'] == min(response_times)
+    assert result['response_time']['max'] == max(response_times)
+    assert result['response_time']['avg'] == sum(response_times) / len(response_times)
+```
+
+## 🔗 集成测试
+
+### 模块间集成测试
+
+```python
+# tests/integration/test_analysis_integration.py
+import pytest
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.data_manager.data_provider import DataProvider
+from src.monitoring.performance_monitor import PerformanceMonitor
+
+class TestAnalysisIntegration:
+    """分析模块集成测试"""
+
+    @pytest.fixture
+    def integrated_system(self):
+        """集成系统fixture"""
+        data_provider = DataProvider()
+        monitor = PerformanceMonitor()
+        analyzer = PerformanceAnalyzer(
+            data_provider=data_provider,
+            monitor=monitor
+        )
+        return {
+            'data_provider': data_provider,
+            'monitor': monitor,
+            'analyzer': analyzer
+        }
+
+    @pytest.mark.integration
+    def test_end_to_end_analysis_flow(self, integrated_system):
+        """端到端分析流程测试"""
+        # Given
+        system = integrated_system
+        test_data_file = "tests/fixtures/sample_test_data.json"
+
+        # When
+        # 1. 加载测试数据
+        system['data_provider'].load_data_from_file(test_data_file)
+
+        # 2. 启动监控
+        system['monitor'].start_monitoring()
+
+        # 3. 执行分析
+        test_data = system['data_provider'].get_test_data('sample_test')
+        result = system['analyzer'].comprehensive_analysis(test_data)
+
+        # 4. 停止监控
+        system['monitor'].stop_monitoring()
+
+        # Then
+        assert result is not None
+        assert 'overall_grade' in result
+        assert result['overall_grade'] in ['A', 'B', 'C', 'D']
+
+    @pytest.mark.integration
+    def test_data_flow_between_modules(self, integrated_system):
+        """测试模块间数据流"""
+        # Given
+        system = integrated_system
+
+        # When
+        # 模拟数据在模块间流转
+        raw_data = {'requests': [{'response_time': 100, 'success': True}]}
+        system['data_provider'].store_test_data('test_001', raw_data)
+
+        retrieved_data = system['data_provider'].get_test_data('test_001')
+        analysis_result = system['analyzer'].comprehensive_analysis(retrieved_data)
+
+        # Then
+        assert retrieved_data == raw_data
+        assert analysis_result is not None
+```
+
+### 数据库集成测试
+
+```python
+# tests/integration/test_database_integration.py
+import pytest
+import sqlite3
+from src.data_manager.data_provider import DataProvider
+
+class TestDatabaseIntegration:
+    """数据库集成测试"""
+
+    @pytest.fixture
+    def test_database(self, tmp_path):
+        """测试数据库fixture"""
+        db_path = tmp_path / "test.db"
+        conn = sqlite3.connect(str(db_path))
+
+        # 创建测试表
+        conn.execute("""
+            CREATE TABLE test_results (
+                id INTEGER PRIMARY KEY,
+                test_name TEXT,
+                response_time REAL,
+                success BOOLEAN,
+                timestamp TEXT
+            )
+        """)
+
+        # 插入测试数据
+        test_data = [
+            ('API测试', 100.5, True, '2023-12-01 10:00:00'),
+            ('API测试', 200.3, True, '2023-12-01 10:01:00'),
+            ('API测试', 150.8, False, '2023-12-01 10:02:00')
+        ]
+
+        conn.executemany(
+            "INSERT INTO test_results (test_name, response_time, success, timestamp) VALUES (?, ?, ?, ?)",
+            test_data
+        )
+        conn.commit()
+        conn.close()
+
+        return str(db_path)
+
+    @pytest.mark.integration
+    def test_load_data_from_database(self, test_database):
+        """测试从数据库加载数据"""
+        # Given
+        provider = DataProvider()
+        connection_string = f"sqlite:///{test_database}"
+        query = "SELECT * FROM test_results WHERE test_name = 'API测试'"
+
+        # When
+        success = provider.load_data_from_database(
+            connection_string, query, "db_test_data"
+        )
+
+        # Then
+        assert success is True
+
+        # 验证数据加载
+        data = provider.get_next_data("db_test_data")
+        assert data is not None
+        assert 'response_time' in data
+        assert 'success' in data
+```
+
+## 🌐 端到端测试
+
+### 完整场景测试
+
+```python
+# tests/e2e/test_complete_workflow.py
+import pytest
+import subprocess
+import time
+import requests
+from pathlib import Path
+
+class TestCompleteWorkflow:
+    """完整工作流程端到端测试"""
+
+    @pytest.fixture(scope="class")
+    def locust_server(self):
+        """启动Locust服务器"""
+        # 启动Locust Web UI
+        process = subprocess.Popen([
+            'locust',
+            '-f', 'tests/e2e/fixtures/test_locustfile.py',
+            '--web-host', '127.0.0.1',
+            '--web-port', '8089',
+            '--headless'
+        ])
+
+        # 等待服务器启动
+        time.sleep(5)
+
+        yield process
+
+        # 清理
+        process.terminate()
+        process.wait()
+
+    @pytest.mark.e2e
+    @pytest.mark.slow
+    def test_complete_performance_test_workflow(self, locust_server):
+        """测试完整的性能测试工作流程"""
+        base_url = "http://127.0.0.1:8089"
+
+        # 1. 启动测试
+        start_response = requests.post(f"{base_url}/swarm", data={
+            'user_count': 10,
+            'spawn_rate': 2,
+            'host': 'http://httpbin.org'
+        })
+        assert start_response.status_code == 200
+
+        # 2. 等待测试运行
+        time.sleep(30)
+
+        # 3. 检查统计信息
+        stats_response = requests.get(f"{base_url}/stats/requests")
+        assert stats_response.status_code == 200
+
+        stats_data = stats_response.json()
+        assert 'stats' in stats_data
+        assert len(stats_data['stats']) > 0
+
+        # 4. 停止测试
+        stop_response = requests.get(f"{base_url}/stop")
+        assert stop_response.status_code == 200
+
+        # 5. 验证报告生成
+        reports_dir = Path("reports")
+        assert reports_dir.exists()
+
+        # 检查是否生成了性能报告
+        html_reports = list(reports_dir.glob("*.html"))
+        assert len(html_reports) > 0
+```
+
+### API测试
+
+```python
+# tests/e2e/test_api_endpoints.py
+import pytest
+import requests
+
+class TestAPIEndpoints:
+    """API端点端到端测试"""
+
+    @pytest.fixture
+    def api_base_url(self):
+        """API基础URL"""
+        return "http://localhost:8089"
+
+    @pytest.mark.e2e
+    def test_web_ui_endpoints(self, api_base_url):
+        """测试Web UI端点"""
+        endpoints = [
+            "/",
+            "/stats/requests",
+            "/stats/distribution",
+            "/exceptions"
+        ]
+
+        for endpoint in endpoints:
+            response = requests.get(f"{api_base_url}{endpoint}")
+            assert response.status_code == 200
+
+    @pytest.mark.e2e
+    def test_api_workflow(self, api_base_url):
+        """测试API工作流程"""
+        # 1. 获取初始状态
+        status_response = requests.get(f"{api_base_url}/stats/requests")
+        assert status_response.status_code == 200
+
+        # 2. 启动测试
+        start_data = {
+            'user_count': 5,
+            'spawn_rate': 1,
+            'host': 'http://httpbin.org'
+        }
+        start_response = requests.post(f"{api_base_url}/swarm", data=start_data)
+        assert start_response.status_code == 200
+
+        # 3. 检查运行状态
+        time.sleep(10)
+        running_stats = requests.get(f"{api_base_url}/stats/requests")
+        assert running_stats.status_code == 200
+
+        # 4. 停止测试
+        stop_response = requests.get(f"{api_base_url}/stop")
+        assert stop_response.status_code == 200
+```
+
+## 🚀 性能测试
+
+### 基准测试
+
+```python
+# tests/performance/test_benchmarks.py
+import pytest
+import time
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+
+class TestPerformanceBenchmarks:
+    """性能基准测试"""
+
+    @pytest.mark.benchmark
+    def test_analysis_performance(self, benchmark):
+        """测试分析性能"""
+        # Given
+        analyzer = PerformanceAnalyzer()
+        large_dataset = {
+            'requests': [
+                {'response_time': i % 1000, 'success': True}
+                for i in range(10000)
+            ],
+            'duration': 600,
+            'users': 100
+        }
+
+        # When & Then
+        result = benchmark(analyzer.comprehensive_analysis, large_dataset)
+        assert result is not None
+
+    @pytest.mark.benchmark
+    def test_data_processing_performance(self, benchmark):
+        """测试数据处理性能"""
+        from src.data_manager.data_generator import DataGenerator
+
+        generator = DataGenerator()
+
+        def generate_large_dataset():
+            return [generator.generate_user_profile() for _ in range(1000)]
+
+        result = benchmark(generate_large_dataset)
+        assert len(result) == 1000
+```
+
+### 内存测试
+
+```python
+# tests/performance/test_memory_usage.py
+import pytest
+import psutil
+import os
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+
+class TestMemoryUsage:
+    """内存使用测试"""
+
+    def get_memory_usage(self):
+        """获取当前内存使用量"""
+        process = psutil.Process(os.getpid())
+        return process.memory_info().rss / 1024 / 1024  # MB
+
+    @pytest.mark.memory
+    def test_memory_leak_in_analysis(self):
+        """测试分析过程中的内存泄漏"""
+        analyzer = PerformanceAnalyzer()
+
+        initial_memory = self.get_memory_usage()
+
+        # 执行多次分析
+        for i in range(100):
+            test_data = {
+                'requests': [
+                    {'response_time': j, 'success': True}
+                    for j in range(100)
+                ],
+                'duration': 60,
+                'users': 10
+            }
+            analyzer.comprehensive_analysis(test_data)
+
+        final_memory = self.get_memory_usage()
+        memory_increase = final_memory - initial_memory
+
+        # 内存增长不应超过50MB
+        assert memory_increase < 50, f"内存泄漏检测：增长了 {memory_increase:.2f}MB"
+```
+
+## 🔧 测试工具配置
+
+### pytest配置
+
+```ini
+# pytest.ini
+[tool:pytest]
+testpaths = tests
+python_files = test_*.py *_test.py
+python_classes = Test*
+python_functions = test_*
+addopts =
+    -v
+    --strict-markers
+    --strict-config
+    --tb=short
+    --cov=src
+    --cov-report=html
+    --cov-report=term-missing
+    --cov-fail-under=80
+
+markers =
+    unit: Unit tests
+    integration: Integration tests
+    e2e: End-to-end tests
+    slow: Slow running tests
+    smoke: Smoke tests
+    regression: Regression tests
+    benchmark: Performance benchmark tests
+    memory: Memory usage tests
+    property: Property-based tests
+
+filterwarnings =
+    ignore::DeprecationWarning
+    ignore::PendingDeprecationWarning
+```
+
+### 测试数据管理
+
+```python
+# tests/conftest.py
+import pytest
+import json
+from pathlib import Path
+
+@pytest.fixture(scope="session")
+def test_data_dir():
+    """测试数据目录"""
+    return Path(__file__).parent / "fixtures"
+
+@pytest.fixture
+def sample_test_data(test_data_dir):
+    """示例测试数据"""
+    data_file = test_data_dir / "sample_test_data.json"
+    with open(data_file, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+@pytest.fixture
+def temp_reports_dir(tmp_path):
+    """临时报告目录"""
+    reports_dir = tmp_path / "reports"
+    reports_dir.mkdir()
+    return reports_dir
+
+# 数据库测试fixture
+@pytest.fixture(scope="session")
+def test_database():
+    """测试数据库"""
+    # 设置测试数据库
+    # 返回数据库连接信息
+    pass
+```
+
+## 📊 测试报告
+
+### 覆盖率报告
+
+```bash
+# 生成覆盖率报告
+pytest --cov=src --cov-report=html --cov-report=term
+
+# 查看HTML报告
+open htmlcov/index.html
+```
+
+### 测试结果分析
+
+```python
+# tests/utils/test_reporter.py
+class TestReporter:
+    """测试结果报告器"""
+
+    def generate_test_summary(self, test_results):
+        """生成测试摘要"""
+        summary = {
+            'total_tests': len(test_results),
+            'passed': len([t for t in test_results if t.passed]),
+            'failed': len([t for t in test_results if t.failed]),
+            'skipped': len([t for t in test_results if t.skipped]),
+            'coverage': self.calculate_coverage(),
+            'duration': sum(t.duration for t in test_results)
+        }
+        return summary
+
+    def generate_html_report(self, summary, output_path):
+        """生成HTML测试报告"""
+        # 生成详细的HTML报告
+        pass
+```
+
+## ⚠️ 测试最佳实践
+
+1. **测试命名**: 使用描述性的测试名称
+2. **测试隔离**: 每个测试应该独立运行
+3. **数据清理**: 测试后清理临时数据
+4. **Mock使用**: 合理使用Mock隔离依赖
+5. **断言明确**: 使用明确的断言消息
+6. **性能考虑**: 避免测试运行时间过长
+7. **持续集成**: 集成到CI/CD流程中
+
+## 🔗 相关文档
+
+- [开发环境搭建](setup.md) - 测试环境配置
+- [编码规范](coding-standards.md) - 测试代码规范
+- [贡献指南](contributing.md) - 测试贡献流程
+- [性能优化](performance-tuning.md) - 性能测试指南
diff --git a/docs/examples/advanced-examples.md b/docs/examples/advanced-examples.md
new file mode 100644
index 0000000..9a643d2
--- /dev/null
+++ b/docs/examples/advanced-examples.md
@@ -0,0 +1,625 @@
+# 高级示例
+
+本文档提供Locust性能测试框架的高级使用示例，涵盖复杂场景、高级功能和实际项目应用案例。
+
+## 🎯 目标读者
+
+- 有一定Locust使用经验的开发者
+- 需要实现复杂测试场景的测试工程师
+- 希望深入了解框架高级功能的用户
+
+## 📋 前置条件
+
+- 已完成[基础示例](basic-examples.md)的学习
+- 熟悉Python编程和面向对象概念
+- 了解HTTP协议和Web服务架构
+- 具备性能测试基础知识
+
+## 🚀 高级负载模式
+
+### 1. 自适应负载模式
+
+```python
+# locustfiles/adaptive_load_shape.py
+from locust import LoadTestShape
+import time
+import requests
+
+class AdaptiveLoadShape(LoadTestShape):
+    """
+    自适应负载模式 - 根据系统响应时间动态调整负载
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.start_time = time.time()
+        self.current_users = 10
+        self.max_users = 1000
+        self.min_users = 5
+        self.target_response_time = 500  # 目标响应时间(ms)
+        self.adjustment_interval = 30  # 调整间隔(秒)
+        self.last_adjustment = 0
+
+    def tick(self):
+        run_time = time.time() - self.start_time
+
+        # 每30秒调整一次负载
+        if run_time - self.last_adjustment >= self.adjustment_interval:
+            self.adjust_load()
+            self.last_adjustment = run_time
+
+        if run_time < 3600:  # 运行1小时
+            return (self.current_users, 5)
+        else:
+            return None
+
+    def adjust_load(self):
+        """根据当前性能指标调整负载"""
+        try:
+            # 获取当前统计信息
+            stats = self.runner.stats.total
+
+            if stats.num_requests > 0:
+                avg_response_time = stats.avg_response_time
+                error_rate = stats.num_failures / stats.num_requests
+
+                # 根据响应时间调整
+                if avg_response_time > self.target_response_time * 1.2:
+                    # 响应时间过高，减少用户数
+                    self.current_users = max(
+                        self.min_users,
+                        int(self.current_users * 0.9)
+                    )
+                    print(f"High response time ({avg_response_time:.2f}ms), reducing users to {self.current_users}")
+
+                elif avg_response_time < self.target_response_time * 0.8 and error_rate < 0.01:
+                    # 响应时间良好且错误率低，增加用户数
+                    self.current_users = min(
+                        self.max_users,
+                        int(self.current_users * 1.1)
+                    )
+                    print(f"Good performance, increasing users to {self.current_users}")
+
+        except Exception as e:
+            print(f"Error adjusting load: {e}")
+
+class AdaptiveUser(HttpUser):
+    """自适应用户行为"""
+
+    wait_time = between(1, 3)
+
+    def on_start(self):
+        """用户启动时的初始化"""
+        self.login()
+
+    def login(self):
+        """用户登录"""
+        response = self.client.post("/auth/login", json={
+            "username": f"user_{random.randint(1, 10000)}",
+            "password": "password123"
+        })
+
+        if response.status_code == 200:
+            self.token = response.json().get("token")
+            self.client.headers.update({
+                "Authorization": f"Bearer {self.token}"
+            })
+
+    @task(3)
+    def browse_products(self):
+        """浏览产品"""
+        # 获取产品列表
+        response = self.client.get("/api/products", params={
+            "page": random.randint(1, 10),
+            "limit": 20
+        })
+
+        if response.status_code == 200:
+            products = response.json().get("products", [])
+
+            # 随机查看产品详情
+            if products:
+                product = random.choice(products)
+                self.client.get(f"/api/products/{product['id']}")
+
+    @task(2)
+    def search_products(self):
+        """搜索产品"""
+        keywords = ["laptop", "phone", "tablet", "camera", "headphones"]
+        keyword = random.choice(keywords)
+
+        self.client.get("/api/search", params={
+            "q": keyword,
+            "sort": random.choice(["price", "rating", "popularity"])
+        })
+
+    @task(1)
+    def add_to_cart(self):
+        """添加到购物车"""
+        # 先获取一个产品
+        response = self.client.get("/api/products", params={"limit": 1})
+
+        if response.status_code == 200:
+            products = response.json().get("products", [])
+            if products:
+                product = products[0]
+                self.client.post("/api/cart/add", json={
+                    "product_id": product["id"],
+                    "quantity": random.randint(1, 3)
+                })
+```
+
+### 2. 多阶段压力测试
+
+```python
+# locustfiles/multi_stage_stress.py
+from locust import LoadTestShape, HttpUser, task, between
+import time
+
+class MultiStageStressShape(LoadTestShape):
+    """
+    多阶段压力测试模式
+    模拟真实业务场景的负载变化
+    """
+
+    stages = [
+        # 阶段1: 预热阶段
+        {"duration": 300, "users": 50, "spawn_rate": 2, "name": "warmup"},
+
+        # 阶段2: 正常负载
+        {"duration": 600, "users": 200, "spawn_rate": 5, "name": "normal_load"},
+
+        # 阶段3: 高峰负载
+        {"duration": 900, "users": 500, "spawn_rate": 10, "name": "peak_load"},
+
+        # 阶段4: 极限压力
+        {"duration": 1200, "users": 1000, "spawn_rate": 20, "name": "stress_test"},
+
+        # 阶段5: 恢复测试
+        {"duration": 1500, "users": 100, "spawn_rate": -30, "name": "recovery"},
+
+        # 阶段6: 稳定性测试
+        {"duration": 2100, "users": 300, "spawn_rate": 5, "name": "stability"},
+    ]
+
+    def tick(self):
+        run_time = self.get_run_time()
+
+        for stage in self.stages:
+            if run_time < stage["duration"]:
+                # 记录当前阶段信息
+                if not hasattr(self, 'current_stage') or self.current_stage != stage["name"]:
+                    self.current_stage = stage["name"]
+                    print(f"Entering stage: {stage['name']} - Users: {stage['users']}, Duration: {stage['duration']}s")
+
+                return (stage["users"], stage["spawn_rate"])
+
+        return None
+
+class StressTestUser(HttpUser):
+    """压力测试用户"""
+
+    wait_time = between(0.5, 2)
+
+    def on_start(self):
+        """用户初始化"""
+        self.user_id = random.randint(1, 100000)
+        self.session_data = {}
+        self.login()
+
+    def login(self):
+        """用户登录"""
+        with self.client.post("/auth/login", json={
+            "username": f"stress_user_{self.user_id}",
+            "password": "stress_test_password"
+        }, catch_response=True) as response:
+            if response.status_code == 200:
+                self.session_data = response.json()
+                self.client.headers.update({
+                    "Authorization": f"Bearer {self.session_data.get('token')}"
+                })
+            else:
+                response.failure(f"Login failed: {response.status_code}")
+
+    @task(5)
+    def high_frequency_api_calls(self):
+        """高频API调用"""
+        endpoints = [
+            "/api/user/profile",
+            "/api/notifications",
+            "/api/dashboard/stats",
+            "/api/recent/activities"
+        ]
+
+        for endpoint in random.sample(endpoints, random.randint(1, 3)):
+            with self.client.get(endpoint, catch_response=True) as response:
+                if response.status_code != 200:
+                    response.failure(f"API call failed: {endpoint}")
+
+    @task(3)
+    def data_intensive_operations(self):
+        """数据密集型操作"""
+        # 大数据量查询
+        self.client.get("/api/reports/large-dataset", params={
+            "start_date": "2023-01-01",
+            "end_date": "2023-12-31",
+            "include_details": True
+        })
+
+        # 批量数据处理
+        batch_data = [
+            {"id": i, "value": f"data_{i}", "timestamp": time.time()}
+            for i in range(100)
+        ]
+
+        self.client.post("/api/batch/process", json={
+            "data": batch_data,
+            "operation": "bulk_insert"
+        })
+
+    @task(2)
+    def concurrent_operations(self):
+        """并发操作测试"""
+        import threading
+
+        def concurrent_request(endpoint, data=None):
+            if data:
+                self.client.post(endpoint, json=data)
+            else:
+                self.client.get(endpoint)
+
+        # 创建多个并发请求
+        threads = []
+        operations = [
+            ("/api/concurrent/read", None),
+            ("/api/concurrent/write", {"data": f"concurrent_data_{time.time()}"}),
+            ("/api/concurrent/update", {"id": self.user_id, "status": "active"}),
+        ]
+
+        for endpoint, data in operations:
+            thread = threading.Thread(target=concurrent_request, args=(endpoint, data))
+            threads.append(thread)
+            thread.start()
+
+        # 等待所有请求完成
+        for thread in threads:
+            thread.join()
+
+    @task(1)
+    def memory_intensive_operations(self):
+        """内存密集型操作"""
+        # 请求大文件
+        self.client.get("/api/files/large-download", stream=True)
+
+        # 上传大文件
+        large_data = "x" * (1024 * 1024)  # 1MB数据
+        self.client.post("/api/files/upload", files={
+            "file": ("large_file.txt", large_data, "text/plain")
+        })
+```
+
+## 🔧 高级插件开发
+
+### 1. 自定义性能分析插件
+
+```python
+# src/plugins/advanced_analysis.py
+from src.core.plugin_base import AnalysisPlugin
+import numpy as np
+import pandas as pd
+from scipy import stats
+from typing import Dict, List, Any
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+class AdvancedAnalysisPlugin(AnalysisPlugin):
+    """高级性能分析插件"""
+
+    def __init__(self):
+        super().__init__()
+        self.name = "advanced_analysis"
+        self.version = "1.0.0"
+        self.description = "Advanced statistical analysis and machine learning insights"
+
+    def analyze(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """执行高级分析"""
+        results = {
+            "statistical_analysis": self._statistical_analysis(data),
+            "trend_analysis": self._trend_analysis(data),
+            "anomaly_detection": self._anomaly_detection(data),
+            "performance_prediction": self._performance_prediction(data),
+            "bottleneck_analysis": self._bottleneck_analysis(data)
+        }
+
+        return results
+
+    def _statistical_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """统计分析"""
+        response_times = data.get("response_times", [])
+
+        if not response_times:
+            return {"error": "No response time data available"}
+
+        # 转换为numpy数组
+        rt_array = np.array(response_times)
+
+        # 基础统计
+        basic_stats = {
+            "count": len(rt_array),
+            "mean": np.mean(rt_array),
+            "median": np.median(rt_array),
+            "std": np.std(rt_array),
+            "variance": np.var(rt_array),
+            "min": np.min(rt_array),
+            "max": np.max(rt_array),
+            "range": np.max(rt_array) - np.min(rt_array)
+        }
+
+        # 百分位数
+        percentiles = {
+            f"p{p}": np.percentile(rt_array, p)
+            for p in [50, 75, 90, 95, 99, 99.9]
+        }
+
+        # 分布检验
+        distribution_tests = {
+            "normality_test": stats.normaltest(rt_array),
+            "skewness": stats.skew(rt_array),
+            "kurtosis": stats.kurtosis(rt_array)
+        }
+
+        # 置信区间
+        confidence_interval = stats.t.interval(
+            0.95, len(rt_array)-1,
+            loc=np.mean(rt_array),
+            scale=stats.sem(rt_array)
+        )
+
+        return {
+            "basic_stats": basic_stats,
+            "percentiles": percentiles,
+            "distribution_tests": distribution_tests,
+            "confidence_interval_95": confidence_interval
+        }
+
+    def _trend_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """趋势分析"""
+        timestamps = data.get("timestamps", [])
+        response_times = data.get("response_times", [])
+
+        if len(timestamps) != len(response_times) or len(timestamps) < 10:
+            return {"error": "Insufficient data for trend analysis"}
+
+        # 创建时间序列
+        df = pd.DataFrame({
+            "timestamp": pd.to_datetime(timestamps),
+            "response_time": response_times
+        })
+        df.set_index("timestamp", inplace=True)
+
+        # 移动平均
+        df["ma_5"] = df["response_time"].rolling(window=5).mean()
+        df["ma_20"] = df["response_time"].rolling(window=20).mean()
+
+        # 趋势检测
+        x = np.arange(len(df))
+        slope, intercept, r_value, p_value, std_err = stats.linregress(x, df["response_time"])
+
+        # 季节性分析（如果数据足够）
+        seasonal_analysis = {}
+        if len(df) >= 100:
+            # 简单的周期性检测
+            autocorr = [df["response_time"].autocorr(lag=i) for i in range(1, 25)]
+            max_autocorr_lag = np.argmax(autocorr) + 1
+            seasonal_analysis = {
+                "potential_period": max_autocorr_lag,
+                "autocorrelation": max(autocorr)
+            }
+
+        return {
+            "trend": {
+                "slope": slope,
+                "r_squared": r_value**2,
+                "p_value": p_value,
+                "trend_direction": "increasing" if slope > 0 else "decreasing"
+            },
+            "moving_averages": {
+                "ma_5_current": df["ma_5"].iloc[-1] if not df["ma_5"].isna().iloc[-1] else None,
+                "ma_20_current": df["ma_20"].iloc[-1] if not df["ma_20"].isna().iloc[-1] else None
+            },
+            "seasonal_analysis": seasonal_analysis
+        }
+
+    def _anomaly_detection(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """异常检测"""
+        response_times = np.array(data.get("response_times", []))
+
+        if len(response_times) < 20:
+            return {"error": "Insufficient data for anomaly detection"}
+
+        # Z-score方法
+        z_scores = np.abs(stats.zscore(response_times))
+        z_anomalies = np.where(z_scores > 3)[0]
+
+        # IQR方法
+        q1, q3 = np.percentile(response_times, [25, 75])
+        iqr = q3 - q1
+        lower_bound = q1 - 1.5 * iqr
+        upper_bound = q3 + 1.5 * iqr
+        iqr_anomalies = np.where(
+            (response_times < lower_bound) | (response_times > upper_bound)
+        )[0]
+
+        # 移动窗口异常检测
+        window_size = min(20, len(response_times) // 4)
+        moving_anomalies = []
+
+        for i in range(window_size, len(response_times)):
+            window = response_times[i-window_size:i]
+            current = response_times[i]
+
+            if abs(current - np.mean(window)) > 2 * np.std(window):
+                moving_anomalies.append(i)
+
+        return {
+            "z_score_anomalies": {
+                "count": len(z_anomalies),
+                "indices": z_anomalies.tolist(),
+                "percentage": len(z_anomalies) / len(response_times) * 100
+            },
+            "iqr_anomalies": {
+                "count": len(iqr_anomalies),
+                "indices": iqr_anomalies.tolist(),
+                "percentage": len(iqr_anomalies) / len(response_times) * 100
+            },
+            "moving_window_anomalies": {
+                "count": len(moving_anomalies),
+                "indices": moving_anomalies,
+                "percentage": len(moving_anomalies) / len(response_times) * 100
+            }
+        }
+
+    def _performance_prediction(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """性能预测"""
+        user_counts = data.get("user_counts", [])
+        response_times = data.get("response_times", [])
+
+        if len(user_counts) != len(response_times) or len(user_counts) < 10:
+            return {"error": "Insufficient data for performance prediction"}
+
+        # 线性回归预测
+        slope, intercept, r_value, p_value, std_err = stats.linregress(user_counts, response_times)
+
+        # 预测不同用户数下的响应时间
+        prediction_points = [100, 200, 500, 1000, 2000]
+        predictions = {}
+
+        for users in prediction_points:
+            predicted_rt = slope * users + intercept
+            predictions[f"users_{users}"] = {
+                "predicted_response_time": predicted_rt,
+                "confidence": r_value**2
+            }
+
+        # 容量预测（响应时间阈值）
+        rt_thresholds = [500, 1000, 2000, 5000]  # ms
+        capacity_predictions = {}
+
+        for threshold in rt_thresholds:
+            if slope > 0:
+                predicted_users = (threshold - intercept) / slope
+                capacity_predictions[f"rt_{threshold}ms"] = max(0, predicted_users)
+
+        return {
+            "regression_model": {
+                "slope": slope,
+                "intercept": intercept,
+                "r_squared": r_value**2,
+                "p_value": p_value,
+                "standard_error": std_err
+            },
+            "response_time_predictions": predictions,
+            "capacity_predictions": capacity_predictions
+        }
+
+    def _bottleneck_analysis(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """瓶颈分析"""
+        endpoint_stats = data.get("endpoint_stats", {})
+
+        if not endpoint_stats:
+            return {"error": "No endpoint statistics available"}
+
+        # 分析各端点性能
+        bottlenecks = []
+
+        for endpoint, stats in endpoint_stats.items():
+            avg_rt = stats.get("avg_response_time", 0)
+            error_rate = stats.get("error_rate", 0)
+            request_count = stats.get("request_count", 0)
+
+            # 计算瓶颈分数
+            bottleneck_score = (
+                (avg_rt / 1000) * 0.4 +  # 响应时间权重40%
+                error_rate * 0.4 +        # 错误率权重40%
+                (request_count / 1000) * 0.2  # 请求量权重20%
+            )
+
+            bottlenecks.append({
+                "endpoint": endpoint,
+                "bottleneck_score": bottleneck_score,
+                "avg_response_time": avg_rt,
+                "error_rate": error_rate,
+                "request_count": request_count
+            })
+
+        # 按瓶颈分数排序
+        bottlenecks.sort(key=lambda x: x["bottleneck_score"], reverse=True)
+
+        # 识别主要瓶颈
+        top_bottlenecks = bottlenecks[:5]
+
+        # 瓶颈分类
+        performance_bottlenecks = [b for b in bottlenecks if b["avg_response_time"] > 1000]
+        reliability_bottlenecks = [b for b in bottlenecks if b["error_rate"] > 0.05]
+
+        return {
+            "top_bottlenecks": top_bottlenecks,
+            "performance_bottlenecks": performance_bottlenecks,
+            "reliability_bottlenecks": reliability_bottlenecks,
+            "total_endpoints_analyzed": len(endpoint_stats)
+        }
+
+    def generate_report(self, analysis_results: Dict[str, Any]) -> str:
+        """生成高级分析报告"""
+        report = []
+        report.append("# 高级性能分析报告\n")
+
+        # 统计分析部分
+        if "statistical_analysis" in analysis_results:
+            stats = analysis_results["statistical_analysis"]
+            if "basic_stats" in stats:
+                basic = stats["basic_stats"]
+                report.append("## 统计分析")
+                report.append(f"- 平均响应时间: {basic['mean']:.2f}ms")
+                report.append(f"- 中位数响应时间: {basic['median']:.2f}ms")
+                report.append(f"- 标准差: {basic['std']:.2f}ms")
+                report.append(f"- 95%置信区间: {stats.get('confidence_interval_95', 'N/A')}")
+                report.append("")
+
+        # 趋势分析部分
+        if "trend_analysis" in analysis_results:
+            trend = analysis_results["trend_analysis"]
+            if "trend" in trend:
+                t = trend["trend"]
+                report.append("## 趋势分析")
+                report.append(f"- 趋势方向: {t['trend_direction']}")
+                report.append(f"- R²值: {t['r_squared']:.4f}")
+                report.append(f"- 统计显著性: {'显著' if t['p_value'] < 0.05 else '不显著'}")
+                report.append("")
+
+        # 异常检测部分
+        if "anomaly_detection" in analysis_results:
+            anomaly = analysis_results["anomaly_detection"]
+            report.append("## 异常检测")
+            if "z_score_anomalies" in anomaly:
+                z_anom = anomaly["z_score_anomalies"]
+                report.append(f"- Z-score异常: {z_anom['count']}个 ({z_anom['percentage']:.2f}%)")
+            if "iqr_anomalies" in anomaly:
+                iqr_anom = anomaly["iqr_anomalies"]
+                report.append(f"- IQR异常: {iqr_anom['count']}个 ({iqr_anom['percentage']:.2f}%)")
+            report.append("")
+
+        # 瓶颈分析部分
+        if "bottleneck_analysis" in analysis_results:
+            bottleneck = analysis_results["bottleneck_analysis"]
+            if "top_bottlenecks" in bottleneck:
+                report.append("## 瓶颈分析")
+                report.append("### 主要瓶颈端点:")
+                for i, b in enumerate(bottleneck["top_bottlenecks"][:3], 1):
+                    report.append(f"{i}. {b['endpoint']} (分数: {b['bottleneck_score']:.2f})")
+                    report.append(f"   - 平均响应时间: {b['avg_response_time']:.2f}ms")
+                    report.append(f"   - 错误率: {b['error_rate']:.2%}")
+                report.append("")
+
+        return "\n".join(report)
+```
diff --git a/docs/examples/advanced-usage.md b/docs/examples/advanced-usage.md
new file mode 100644
index 0000000..720e2aa
--- /dev/null
+++ b/docs/examples/advanced-usage.md
@@ -0,0 +1,548 @@
+# 高级使用示例
+
+本文档提供框架的高级使用示例，包括复杂场景测试、性能优化、分布式部署等实际应用案例。
+
+## 🎯 复杂负载场景
+
+### 1. 多阶段负载测试
+
+```python
+# locustfiles/complex_load_test.py
+from locust import HttpUser, task, between
+from src.plugins.plugin_manager import PluginManager
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.monitoring.performance_monitor import PerformanceMonitor
+from locustfiles.shape_classes.advanced_shapes import MultiStageLoadShape
+
+class ComplexUser(HttpUser):
+    """复杂用户行为模拟"""
+    wait_time = between(1, 3)
+
+    def on_start(self):
+        """用户启动时的初始化"""
+        # 登录获取token
+        response = self.client.post("/api/login", json={
+            "username": "test_user",
+            "password": "test_pass"
+        })
+        if response.status_code == 200:
+            self.token = response.json().get("token")
+            self.client.headers.update({"Authorization": f"Bearer {self.token}"})
+
+    @task(3)
+    def browse_products(self):
+        """浏览商品 - 高频操作"""
+        with self.client.get("/api/products",
+                           params={"page": 1, "limit": 20},
+                           catch_response=True) as response:
+            if response.status_code == 200:
+                products = response.json().get("data", [])
+                if len(products) > 0:
+                    response.success()
+                else:
+                    response.failure("No products returned")
+
+    @task(2)
+    def search_products(self):
+        """搜索商品 - 中频操作"""
+        keywords = ["手机", "电脑", "耳机", "键盘", "鼠标"]
+        keyword = self.environment.parsed_options.data_manager.get_random_data("keywords", keywords)
+
+        self.client.get("/api/search", params={"q": keyword})
+
+    @task(1)
+    def add_to_cart(self):
+        """添加到购物车 - 低频操作"""
+        # 先获取商品列表
+        response = self.client.get("/api/products", params={"limit": 10})
+        if response.status_code == 200:
+            products = response.json().get("data", [])
+            if products:
+                product = products[0]
+                self.client.post("/api/cart", json={
+                    "product_id": product["id"],
+                    "quantity": 1
+                })
+
+class MultiStageTest(MultiStageLoadShape):
+    """多阶段负载形状"""
+
+    stages = [
+        # 预热阶段：5分钟内从0增长到50用户
+        {"duration": 300, "users": 50, "spawn_rate": 1},
+        # 稳定阶段：保持50用户10分钟
+        {"duration": 600, "users": 50, "spawn_rate": 1},
+        # 压力阶段：5分钟内增长到200用户
+        {"duration": 300, "users": 200, "spawn_rate": 2},
+        # 峰值阶段：保持200用户15分钟
+        {"duration": 900, "users": 200, "spawn_rate": 2},
+        # 降压阶段：5分钟内降到100用户
+        {"duration": 300, "users": 100, "spawn_rate": 1},
+        # 恢复阶段：保持100用户10分钟
+        {"duration": 600, "users": 100, "spawn_rate": 1}
+    ]
+```
+
+### 2. 数据驱动测试
+
+```python
+# locustfiles/data_driven_test.py
+from locust import HttpUser, task
+from src.data_manager.data_provider import DataProvider
+from src.data_manager.data_generator import DataGenerator
+
+class DataDrivenUser(HttpUser):
+    """数据驱动的用户测试"""
+
+    def on_start(self):
+        """初始化数据提供者"""
+        self.data_provider = DataProvider()
+        self.data_generator = DataGenerator()
+
+        # 加载测试数据
+        self.users_data = self.data_provider.load_data_from_file(
+            "test_data/users.csv",
+            distribution_strategy="round_robin"
+        )
+
+        # 生成动态数据
+        self.dynamic_data = self.data_generator.generate_user_data(
+            count=1000,
+            locale="zh_CN"
+        )
+
+    @task
+    def user_registration(self):
+        """用户注册测试"""
+        # 获取测试用户数据
+        user_data = self.data_provider.get_next_data("users")
+
+        # 生成随机邮箱避免冲突
+        email = self.data_generator.generate_email()
+
+        registration_data = {
+            "username": user_data.get("username"),
+            "email": email,
+            "password": "Test123!",
+            "phone": self.data_generator.generate_phone_number(),
+            "address": self.data_generator.generate_address()
+        }
+
+        response = self.client.post("/api/register", json=registration_data)
+
+        # 验证响应
+        if response.status_code == 201:
+            user_id = response.json().get("user_id")
+            # 存储用户ID供后续使用
+            self.data_provider.store_generated_data("user_ids", user_id)
+
+    @task
+    def user_profile_update(self):
+        """用户资料更新测试"""
+        # 获取已注册的用户ID
+        user_ids = self.data_provider.get_stored_data("user_ids")
+        if not user_ids:
+            return
+
+        user_id = self.data_provider.get_random_data("user_ids", user_ids)
+
+        update_data = {
+            "nickname": self.data_generator.generate_name(),
+            "bio": self.data_generator.generate_text(max_length=200),
+            "avatar": self.data_generator.generate_image_url()
+        }
+
+        self.client.put(f"/api/users/{user_id}", json=update_data)
+```
+
+## 🔧 性能优化实践
+
+### 1. 连接池优化
+
+```python
+# locustfiles/optimized_test.py
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+from locust import HttpUser, task
+
+class OptimizedHttpUser(HttpUser):
+    """优化的HTTP用户类"""
+
+    def on_start(self):
+        """配置连接池和重试策略"""
+        # 配置重试策略
+        retry_strategy = Retry(
+            total=3,
+            backoff_factor=1,
+            status_forcelist=[429, 500, 502, 503, 504],
+        )
+
+        # 配置HTTP适配器
+        adapter = HTTPAdapter(
+            pool_connections=100,  # 连接池大小
+            pool_maxsize=100,      # 最大连接数
+            max_retries=retry_strategy
+        )
+
+        # 应用到客户端
+        self.client.mount("http://", adapter)
+        self.client.mount("https://", adapter)
+
+        # 设置超时
+        self.client.timeout = (5, 30)  # 连接超时5秒，读取超时30秒
+
+    @task
+    def optimized_request(self):
+        """优化的请求"""
+        # 使用会话保持连接
+        with self.client.get("/api/data",
+                           stream=True,  # 流式处理大响应
+                           catch_response=True) as response:
+            if response.status_code == 200:
+                # 分块处理响应数据
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        # 处理数据块
+                        pass
+                response.success()
+            else:
+                response.failure(f"Status code: {response.status_code}")
+```
+
+### 2. 内存优化
+
+```python
+# locustfiles/memory_optimized_test.py
+import gc
+import weakref
+from locust import HttpUser, task
+
+class MemoryOptimizedUser(HttpUser):
+    """内存优化的用户类"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._response_cache = weakref.WeakValueDictionary()
+        self._request_count = 0
+
+    @task
+    def memory_efficient_request(self):
+        """内存高效的请求"""
+        self._request_count += 1
+
+        # 定期清理内存
+        if self._request_count % 100 == 0:
+            gc.collect()
+
+        # 使用生成器处理大数据
+        response = self.client.get("/api/large-data")
+        if response.status_code == 200:
+            # 避免将大响应存储在内存中
+            self._process_response_stream(response)
+
+    def _process_response_stream(self, response):
+        """流式处理响应数据"""
+        try:
+            for line in response.iter_lines(decode_unicode=True):
+                if line:
+                    # 逐行处理，避免内存积累
+                    self._process_line(line)
+        finally:
+            # 确保响应被正确关闭
+            response.close()
+
+    def _process_line(self, line):
+        """处理单行数据"""
+        # 处理逻辑，避免存储大量数据
+        pass
+```
+
+## 📊 实时监控集成
+
+### 1. 自定义监控指标
+
+```python
+# locustfiles/monitored_test.py
+from locust import HttpUser, task, events
+from src.monitoring.performance_monitor import PerformanceMonitor
+from src.monitoring.alert_manager import AlertManager
+import time
+
+class MonitoredUser(HttpUser):
+    """带监控的用户测试"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.monitor = PerformanceMonitor()
+        self.alert_manager = AlertManager()
+
+        # 注册自定义指标
+        self.monitor.register_custom_metric("business_success_rate", "gauge")
+        self.monitor.register_custom_metric("payment_latency", "histogram")
+
+    @task
+    def monitored_payment(self):
+        """带监控的支付流程"""
+        start_time = time.time()
+
+        try:
+            # 创建订单
+            order_response = self.client.post("/api/orders", json={
+                "product_id": 123,
+                "quantity": 1,
+                "amount": 99.99
+            })
+
+            if order_response.status_code != 201:
+                self.monitor.increment_counter("payment_failures")
+                return
+
+            order_id = order_response.json()["order_id"]
+
+            # 执行支付
+            payment_response = self.client.post(f"/api/orders/{order_id}/pay", json={
+                "payment_method": "credit_card",
+                "card_token": "test_token"
+            })
+
+            # 记录业务指标
+            if payment_response.status_code == 200:
+                self.monitor.set_gauge("business_success_rate", 1)
+                self.monitor.increment_counter("successful_payments")
+            else:
+                self.monitor.set_gauge("business_success_rate", 0)
+                self.monitor.increment_counter("failed_payments")
+
+                # 触发告警
+                if payment_response.status_code >= 500:
+                    self.alert_manager.trigger_alert(
+                        "payment_system_error",
+                        f"Payment failed with status {payment_response.status_code}",
+                        severity="critical"
+                    )
+
+        finally:
+            # 记录支付延迟
+            payment_latency = (time.time() - start_time) * 1000
+            self.monitor.record_histogram("payment_latency", payment_latency)
+
+# 事件监听器
+@events.request.add_listener
+def on_request(request_type, name, response_time, response_length, response, context, exception, **kwargs):
+    """请求事件监听器"""
+    if exception:
+        # 记录异常
+        monitor = context.get("monitor")
+        if monitor:
+            monitor.increment_counter(f"exceptions.{type(exception).__name__}")
+
+@events.test_start.add_listener
+def on_test_start(environment, **kwargs):
+    """测试开始事件"""
+    print("开始监控测试...")
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """测试结束事件"""
+    print("停止监控，生成报告...")
+```
+
+### 2. 告警规则配置
+
+```python
+# 告警配置示例
+ALERT_RULES = {
+    "response_time_high": {
+        "metric": "avg_response_time",
+        "condition": "> 2000",
+        "duration": "5m",
+        "severity": "warning",
+        "message": "平均响应时间超过2秒"
+    },
+    "error_rate_high": {
+        "metric": "error_rate",
+        "condition": "> 5",
+        "duration": "2m",
+        "severity": "critical",
+        "message": "错误率超过5%"
+    },
+    "throughput_low": {
+        "metric": "requests_per_second",
+        "condition": "< 10",
+        "duration": "3m",
+        "severity": "warning",
+        "message": "吞吐量低于10 RPS"
+    }
+}
+```
+
+## 🌐 分布式测试部署
+
+### 1. Master-Worker配置
+
+```python
+# master_config.py
+MASTER_CONFIG = {
+    "host": "0.0.0.0",
+    "port": 8089,
+    "expect_workers": 5,
+    "worker_timeout": 60,
+    "stats_history_enabled": True,
+    "web_ui_enabled": True
+}
+
+# worker_config.py
+WORKER_CONFIG = {
+    "master_host": "192.168.1.100",
+    "master_port": 8089,
+    "worker_id": None,  # 自动生成
+    "heartbeat_interval": 3
+}
+```
+
+### 2. Docker部署
+
+```dockerfile
+# Dockerfile.master
+FROM python:3.9-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+EXPOSE 8089 5557
+
+CMD ["locust", "-f", "locustfiles/distributed_test.py", "--master", "--host=http://api.example.com"]
+```
+
+```dockerfile
+# Dockerfile.worker
+FROM python:3.9-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+CMD ["locust", "-f", "locustfiles/distributed_test.py", "--worker", "--master-host=master"]
+```
+
+```yaml
+# docker-compose.yml
+version: '3.8'
+
+services:
+  master:
+    build:
+      context: .
+      dockerfile: Dockerfile.master
+    ports:
+      - "8089:8089"
+      - "5557:5557"
+    environment:
+      - LOCUST_MODE=master
+    volumes:
+      - ./reports:/app/reports
+
+  worker:
+    build:
+      context: .
+      dockerfile: Dockerfile.worker
+    environment:
+      - LOCUST_MODE=worker
+      - LOCUST_MASTER_HOST=master
+    depends_on:
+      - master
+    deploy:
+      replicas: 3
+    volumes:
+      - ./test_data:/app/test_data
+```
+
+### 3. Kubernetes部署
+
+```yaml
+# k8s-deployment.yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: locust-master
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: locust-master
+  template:
+    metadata:
+      labels:
+        app: locust-master
+    spec:
+      containers:
+      - name: locust-master
+        image: locust-framework:latest
+        ports:
+        - containerPort: 8089
+        - containerPort: 5557
+        env:
+        - name: LOCUST_MODE
+          value: "master"
+        resources:
+          requests:
+            memory: "512Mi"
+            cpu: "500m"
+          limits:
+            memory: "1Gi"
+            cpu: "1000m"
+
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: locust-worker
+spec:
+  replicas: 5
+  selector:
+    matchLabels:
+      app: locust-worker
+  template:
+    metadata:
+      labels:
+        app: locust-worker
+    spec:
+      containers:
+      - name: locust-worker
+        image: locust-framework:latest
+        env:
+        - name: LOCUST_MODE
+          value: "worker"
+        - name: LOCUST_MASTER_HOST
+          value: "locust-master-service"
+        resources:
+          requests:
+            memory: "256Mi"
+            cpu: "250m"
+          limits:
+            memory: "512Mi"
+            cpu: "500m"
+
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: locust-master-service
+spec:
+  selector:
+    app: locust-master
+  ports:
+  - name: web
+    port: 8089
+    targetPort: 8089
+  - name: communication
+    port: 5557
+    targetPort: 5557
+  type: LoadBalancer
+```
diff --git a/docs/examples/basic-examples.md b/docs/examples/basic-examples.md
new file mode 100644
index 0000000..8efdab1
--- /dev/null
+++ b/docs/examples/basic-examples.md
@@ -0,0 +1,707 @@
+# 基础使用示例
+
+本文档提供框架的基础使用示例，帮助用户快速上手和理解核心功能。
+
+## 🚀 快速开始示例
+
+### 1. 最简单的测试
+
+```python
+# locustfiles/simple_test.py
+from locust import HttpUser, task, between
+
+class SimpleUser(HttpUser):
+    """最简单的用户测试"""
+    wait_time = between(1, 2)  # 请求间隔1-2秒
+
+    @task
+    def test_homepage(self):
+        """测试首页"""
+        self.client.get("/")
+
+    @task(2)  # 权重为2，执行频率更高
+    def test_about(self):
+        """测试关于页面"""
+        self.client.get("/about")
+```
+
+```bash
+# 运行测试
+locust -f locustfiles/simple_test.py --host=http://localhost:8080
+```
+
+### 2. 带登录的测试
+
+```python
+# locustfiles/login_test.py
+from locust import HttpUser, task, between
+
+class LoginUser(HttpUser):
+    """带登录功能的用户测试"""
+    wait_time = between(1, 3)
+
+    def on_start(self):
+        """用户启动时执行登录"""
+        # 获取登录页面
+        response = self.client.get("/login")
+
+        # 提取CSRF token (如果需要)
+        # csrf_token = extract_csrf_token(response.text)
+
+        # 执行登录
+        login_response = self.client.post("/login", data={
+            "username": "testuser",
+            "password": "testpass"
+        })
+
+        if login_response.status_code == 200:
+            print("登录成功")
+        else:
+            print(f"登录失败: {login_response.status_code}")
+
+    @task(3)
+    def view_dashboard(self):
+        """查看仪表板"""
+        self.client.get("/dashboard")
+
+    @task(2)
+    def view_profile(self):
+        """查看个人资料"""
+        self.client.get("/profile")
+
+    @task(1)
+    def update_profile(self):
+        """更新个人资料"""
+        self.client.post("/profile", data={
+            "name": "Test User",
+            "email": "test@example.com"
+        })
+```
+
+## 📊 性能分析示例
+
+### 1. 基础性能分析
+
+```python
+# locustfiles/analysis_test.py
+from locust import HttpUser, task, between, events
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+import time
+
+class AnalysisUser(HttpUser):
+    """带性能分析的用户测试"""
+    wait_time = between(1, 2)
+
+    @task
+    def api_request(self):
+        """API请求测试"""
+        start_time = time.time()
+
+        with self.client.get("/api/data", catch_response=True) as response:
+            response_time = (time.time() - start_time) * 1000
+
+            if response.status_code == 200:
+                # 检查响应内容
+                data = response.json()
+                if len(data) > 0:
+                    response.success()
+                else:
+                    response.failure("Empty response")
+            else:
+                response.failure(f"Status code: {response.status_code}")
+
+# 全局变量存储测试数据
+test_data = {
+    'test_name': 'API性能测试',
+    'start_time': None,
+    'end_time': None,
+    'requests': []
+}
+
+@events.test_start.add_listener
+def on_test_start(environment, **kwargs):
+    """测试开始事件"""
+    test_data['start_time'] = time.strftime('%Y-%m-%d %H:%M:%S')
+    test_data['requests'] = []
+    print("开始性能分析...")
+
+@events.request.add_listener
+def on_request(request_type, name, response_time, response_length, response, context, exception, **kwargs):
+    """请求事件监听"""
+    test_data['requests'].append({
+        'response_time': response_time,
+        'success': exception is None,
+        'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'),
+        'name': name,
+        'method': request_type
+    })
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """测试结束事件"""
+    test_data['end_time'] = time.strftime('%Y-%m-%d %H:%M:%S')
+    test_data['duration'] = (time.time() - time.mktime(time.strptime(test_data['start_time'], '%Y-%m-%d %H:%M:%S')))
+    test_data['users'] = environment.parsed_options.num_users or 1
+
+    # 执行性能分析
+    analyzer = PerformanceAnalyzer()
+    result = analyzer.comprehensive_analysis(test_data)
+
+    print(f"\n=== 性能分析结果 ===")
+    print(f"总体评级: {result['overall_grade']}")
+    print(f"平均响应时间: {result['response_time']['avg']:.2f}ms")
+    print(f"P95响应时间: {result['response_time']['p95']:.2f}ms")
+    print(f"错误率: {result['error_analysis']['error_rate']:.2f}%")
+
+    # 生成报告
+    from src.analysis.report_generator import ReportGenerator
+    report_generator = ReportGenerator()
+    report_generator.generate_html_report(result, 'reports/performance_report.html')
+    print("性能报告已生成: reports/performance_report.html")
+```
+
+### 2. 自定义负载形状
+
+```python
+# locustfiles/custom_shape_test.py
+from locust import HttpUser, task, between
+from locustfiles.shape_classes.advanced_shapes import WaveLoadShape
+
+class ShapeUser(HttpUser):
+    """自定义负载形状测试"""
+    wait_time = between(1, 2)
+
+    @task
+    def test_endpoint(self):
+        """测试端点"""
+        self.client.get("/api/test")
+
+class MyWaveShape(WaveLoadShape):
+    """波浪形负载"""
+
+    # 配置波浪参数
+    min_users = 10      # 最小用户数
+    max_users = 100     # 最大用户数
+    wave_period = 300   # 波浪周期(秒)
+    spawn_rate = 2      # 生成速率
+
+    def tick(self):
+        """自定义负载逻辑"""
+        run_time = self.get_run_time()
+
+        if run_time < 1800:  # 运行30分钟
+            return super().tick()
+        else:
+            return None  # 停止测试
+```
+
+## 🔍 监控告警示例
+
+### 1. 实时监控
+
+```python
+# locustfiles/monitoring_test.py
+from locust import HttpUser, task, between, events
+from src.monitoring.performance_monitor import PerformanceMonitor
+from src.monitoring.alert_manager import AlertManager
+
+class MonitoredUser(HttpUser):
+    """带监控的用户测试"""
+    wait_time = between(1, 3)
+
+    @task
+    def monitored_request(self):
+        """被监控的请求"""
+        with self.client.get("/api/monitored", catch_response=True) as response:
+            # 记录自定义指标
+            if hasattr(self.environment, 'monitor'):
+                self.environment.monitor.record_metric(
+                    "custom_response_time",
+                    response.elapsed.total_seconds() * 1000
+                )
+
+            if response.status_code == 200:
+                response.success()
+            else:
+                response.failure(f"Status: {response.status_code}")
+
+# 初始化监控组件
+monitor = PerformanceMonitor()
+alert_manager = AlertManager()
+
+@events.init.add_listener
+def on_locust_init(environment, **kwargs):
+    """初始化监控"""
+    environment.monitor = monitor
+    environment.alert_manager = alert_manager
+
+    # 配置告警规则
+    alert_manager.add_alert_rule({
+        "name": "high_response_time",
+        "metric": "avg_response_time",
+        "condition": ">",
+        "threshold": 1000,
+        "duration": 60,
+        "severity": "warning",
+        "message": "响应时间过高"
+    })
+
+    # 启动监控
+    monitor.start_monitoring()
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """停止监控"""
+    if hasattr(environment, 'monitor'):
+        environment.monitor.stop_monitoring()
+```
+
+### 2. 告警通知
+
+```python
+# locustfiles/notification_test.py
+from locust import HttpUser, task, between, events
+from src.monitoring.notification_service import NotificationService
+
+class NotificationUser(HttpUser):
+    """带通知的用户测试"""
+    wait_time = between(1, 2)
+
+    @task
+    def critical_request(self):
+        """关键请求测试"""
+        response = self.client.get("/api/critical")
+
+        # 检查关键业务指标
+        if response.status_code >= 500:
+            # 触发紧急通知
+            if hasattr(self.environment, 'notification_service'):
+                self.environment.notification_service.send_notification(
+                    f"关键服务异常: HTTP {response.status_code}",
+                    ["feishu", "email"],
+                    priority="urgent"
+                )
+
+# 初始化通知服务
+notification_service = NotificationService()
+
+@events.init.add_listener
+def on_locust_init(environment, **kwargs):
+    """初始化通知服务"""
+    environment.notification_service = notification_service
+
+    # 配置通知渠道
+    notification_service.register_channel("feishu", {
+        "webhook_url": "https://open.feishu.cn/open-apis/bot/v2/hook/xxx"
+    })
+
+@events.test_start.add_listener
+def on_test_start(environment, **kwargs):
+    """测试开始通知"""
+    environment.notification_service.send_notification(
+        "性能测试开始",
+        ["feishu"],
+        priority="normal"
+    )
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """测试结束通知"""
+    stats = environment.stats.total
+    message = f"""
+性能测试完成
+- 总请求数: {stats.num_requests}
+- 失败请求数: {stats.num_failures}
+- 平均响应时间: {stats.avg_response_time:.2f}ms
+- 错误率: {stats.fail_ratio:.2%}
+    """
+
+    environment.notification_service.send_notification(
+        message,
+        ["feishu", "email"],
+        priority="normal"
+    )
+```
+
+## 📁 数据驱动测试示例
+
+### 1. CSV数据源
+
+```python
+# locustfiles/csv_data_test.py
+from locust import HttpUser, task, between
+from src.data_manager.data_provider import DataProvider
+import csv
+
+class CsvDataUser(HttpUser):
+    """CSV数据驱动测试"""
+    wait_time = between(1, 2)
+
+    def on_start(self):
+        """初始化数据提供者"""
+        self.data_provider = DataProvider()
+
+        # 加载CSV数据
+        self.users_data = self.data_provider.load_data_from_file(
+            "test_data/users.csv",
+            distribution_strategy="round_robin"
+        )
+
+    @task
+    def login_with_csv_data(self):
+        """使用CSV数据登录"""
+        user_data = self.data_provider.get_next_data("users")
+
+        if user_data:
+            response = self.client.post("/login", data={
+                "username": user_data["username"],
+                "password": user_data["password"]
+            })
+
+            if response.status_code == 200:
+                # 登录成功后的操作
+                self.client.get("/dashboard")
+```
+
+```csv
+# test_data/users.csv
+username,password,email
+user1,pass1,user1@example.com
+user2,pass2,user2@example.com
+user3,pass3,user3@example.com
+```
+
+### 2. 动态数据生成
+
+```python
+# locustfiles/dynamic_data_test.py
+from locust import HttpUser, task, between
+from src.data_manager.data_generator import DataGenerator
+
+class DynamicDataUser(HttpUser):
+    """动态数据生成测试"""
+    wait_time = between(1, 2)
+
+    def on_start(self):
+        """初始化数据生成器"""
+        self.data_generator = DataGenerator()
+
+    @task
+    def create_user_with_dynamic_data(self):
+        """使用动态数据创建用户"""
+        user_data = {
+            "username": self.data_generator.generate_username(),
+            "email": self.data_generator.generate_email(),
+            "phone": self.data_generator.generate_phone_number(),
+            "address": self.data_generator.generate_address(),
+            "age": self.data_generator.generate_integer(18, 65)
+        }
+
+        response = self.client.post("/api/users", json=user_data)
+
+        if response.status_code == 201:
+            user_id = response.json().get("id")
+            # 存储生成的用户ID供后续使用
+            self.data_generator.store_generated_data("user_ids", user_id)
+
+    @task
+    def update_user_profile(self):
+        """更新用户资料"""
+        user_ids = self.data_generator.get_stored_data("user_ids")
+        if user_ids:
+            user_id = self.data_generator.get_random_data("user_ids", user_ids)
+
+            update_data = {
+                "bio": self.data_generator.generate_text(max_length=200),
+                "avatar": self.data_generator.generate_image_url()
+            }
+
+            self.client.put(f"/api/users/{user_id}", json=update_data)
+```
+
+## 🔌 插件使用示例
+
+### 1. 使用内置插件
+
+```python
+# locustfiles/plugin_test.py
+from locust import HttpUser, task, between, events
+from src.plugins.plugin_manager import PluginManager
+
+class PluginUser(HttpUser):
+    """使用插件的测试"""
+    wait_time = between(1, 2)
+
+    @task
+    def test_with_plugins(self):
+        """使用插件增强的测试"""
+        self.client.get("/api/data")
+
+# 初始化插件管理器
+plugin_manager = PluginManager()
+
+@events.init.add_listener
+def on_locust_init(environment, **kwargs):
+    """初始化插件"""
+    # 启用性能分析插件
+    plugin_manager.enable_plugin("performance_analyzer", {
+        "output_formats": ["html", "json"],
+        "include_charts": True
+    })
+
+    # 启用系统监控插件
+    plugin_manager.enable_plugin("system_monitor", {
+        "interval": 5,
+        "metrics": ["cpu", "memory", "disk"]
+    })
+
+    # 启用通知插件
+    plugin_manager.enable_plugin("notification_service", {
+        "channels": ["feishu"],
+        "feishu_webhook": "https://open.feishu.cn/open-apis/bot/v2/hook/xxx"
+    })
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """测试结束时触发插件"""
+    # 触发报告生成
+    plugin_manager.trigger_event("generate_report", {
+        "test_data": environment.stats,
+        "output_path": "reports/"
+    })
+```
+
+### 2. 自定义插件示例
+
+```python
+# plugins/custom_logger_plugin.py
+from src.plugins.plugin_interface import PluginInterface, PluginInfo
+import logging
+
+class CustomLoggerPlugin(PluginInterface):
+    """自定义日志插件"""
+
+    @property
+    def plugin_info(self) -> PluginInfo:
+        return PluginInfo(
+            name="Custom Logger",
+            version="1.0.0",
+            description="自定义日志记录插件",
+            author="Your Name",
+            category="logging"
+        )
+
+    def initialize(self, config=None) -> bool:
+        """初始化插件"""
+        self.logger = logging.getLogger("custom_logger")
+        handler = logging.FileHandler("custom_test.log")
+        formatter = logging.Formatter(
+            '%(asctime)s - %(levelname)s - %(message)s'
+        )
+        handler.setFormatter(formatter)
+        self.logger.addHandler(handler)
+        self.logger.setLevel(logging.INFO)
+        return True
+
+    def cleanup(self):
+        """清理资源"""
+        for handler in self.logger.handlers:
+            handler.close()
+            self.logger.removeHandler(handler)
+
+    def log_request(self, request_data):
+        """记录请求信息"""
+        self.logger.info(f"Request: {request_data}")
+
+# 在测试中使用自定义插件
+@events.request.add_listener
+def on_request(request_type, name, response_time, response_length, response, context, exception, **kwargs):
+    """请求事件监听"""
+    if hasattr(context, 'custom_logger_plugin'):
+        context.custom_logger_plugin.log_request({
+            "method": request_type,
+            "url": name,
+            "response_time": response_time,
+            "success": exception is None
+        })
+```
+
+## 🎯 最佳实践示例
+
+### 1. 完整的电商测试场景
+
+```python
+# locustfiles/ecommerce_test.py
+from locust import HttpUser, task, between, SequentialTaskSet
+from src.data_manager.data_generator import DataGenerator
+import random
+
+class BrowsingBehavior(SequentialTaskSet):
+    """浏览行为任务集"""
+
+    def on_start(self):
+        """开始浏览"""
+        self.data_generator = DataGenerator()
+
+    @task
+    def visit_homepage(self):
+        """访问首页"""
+        self.client.get("/")
+
+    @task
+    def browse_categories(self):
+        """浏览分类"""
+        categories = ["electronics", "clothing", "books", "home"]
+        category = random.choice(categories)
+        self.client.get(f"/category/{category}")
+
+    @task
+    def search_products(self):
+        """搜索商品"""
+        keywords = ["phone", "laptop", "shirt", "book"]
+        keyword = random.choice(keywords)
+        self.client.get(f"/search?q={keyword}")
+
+    @task
+    def view_product(self):
+        """查看商品详情"""
+        product_id = random.randint(1, 1000)
+        self.client.get(f"/product/{product_id}")
+
+class ShoppingBehavior(SequentialTaskSet):
+    """购物行为任务集"""
+
+    @task
+    def add_to_cart(self):
+        """添加到购物车"""
+        product_id = random.randint(1, 1000)
+        self.client.post("/cart/add", json={
+            "product_id": product_id,
+            "quantity": random.randint(1, 3)
+        })
+
+    @task
+    def view_cart(self):
+        """查看购物车"""
+        self.client.get("/cart")
+
+    @task
+    def checkout(self):
+        """结账"""
+        self.client.post("/checkout", json={
+            "payment_method": "credit_card",
+            "shipping_address": "Test Address"
+        })
+
+class EcommerceUser(HttpUser):
+    """电商用户"""
+    wait_time = between(2, 5)
+
+    # 任务权重分配
+    tasks = {
+        BrowsingBehavior: 7,  # 70%用户只浏览
+        ShoppingBehavior: 3   # 30%用户会购买
+    }
+
+    def on_start(self):
+        """用户开始时可能登录"""
+        if random.random() < 0.3:  # 30%概率登录
+            self.login()
+
+    def login(self):
+        """用户登录"""
+        self.client.post("/login", data={
+            "username": f"user{random.randint(1, 1000)}",
+            "password": "password123"
+        })
+```
+
+### 2. API压测最佳实践
+
+```python
+# locustfiles/api_stress_test.py
+from locust import HttpUser, task, between, events
+from locust.exception import RescheduleTask
+import time
+import json
+
+class APIStressUser(HttpUser):
+    """API压力测试用户"""
+    wait_time = between(0.1, 0.5)  # 高频请求
+
+    def on_start(self):
+        """获取认证token"""
+        response = self.client.post("/auth/token", json={
+            "client_id": "test_client",
+            "client_secret": "test_secret"
+        })
+
+        if response.status_code == 200:
+            self.token = response.json()["access_token"]
+            self.client.headers.update({
+                "Authorization": f"Bearer {self.token}"
+            })
+        else:
+            raise RescheduleTask("Failed to get auth token")
+
+    @task(5)
+    def get_data(self):
+        """获取数据 - 高频操作"""
+        with self.client.get("/api/v1/data",
+                           params={"limit": 10},
+                           catch_response=True) as response:
+            if response.status_code == 200:
+                data = response.json()
+                if len(data) > 0:
+                    response.success()
+                else:
+                    response.failure("Empty response")
+            elif response.status_code == 429:
+                # 处理限流
+                response.failure("Rate limited")
+                time.sleep(1)
+            else:
+                response.failure(f"HTTP {response.status_code}")
+
+    @task(2)
+    def create_data(self):
+        """创建数据 - 中频操作"""
+        payload = {
+            "name": f"test_item_{int(time.time())}",
+            "value": random.randint(1, 100),
+            "timestamp": int(time.time())
+        }
+
+        with self.client.post("/api/v1/data",
+                            json=payload,
+                            catch_response=True) as response:
+            if response.status_code == 201:
+                response.success()
+            elif response.status_code == 422:
+                response.failure("Validation error")
+            else:
+                response.failure(f"HTTP {response.status_code}")
+
+    @task(1)
+    def update_data(self):
+        """更新数据 - 低频操作"""
+        item_id = random.randint(1, 1000)
+        payload = {
+            "value": random.randint(1, 100),
+            "updated_at": int(time.time())
+        }
+
+        self.client.put(f"/api/v1/data/{item_id}", json=payload)
+
+# 性能监控
+@events.request.add_listener
+def on_request(request_type, name, response_time, response_length, response, context, exception, **kwargs):
+    """监控请求性能"""
+    if response_time > 1000:  # 响应时间超过1秒
+        print(f"Slow request: {name} took {response_time}ms")
+
+    if exception:
+        print(f"Request failed: {name} - {exception}")
+```
+
+这些示例涵盖了框架的主要功能和使用场景，可以帮助用户快速理解和应用框架的各种特性。
diff --git a/docs/examples/best-practices.md b/docs/examples/best-practices.md
new file mode 100644
index 0000000..86ee75b
--- /dev/null
+++ b/docs/examples/best-practices.md
@@ -0,0 +1,730 @@
+# 性能测试最佳实践
+
+本文档总结了使用Locust性能测试框架的最佳实践，帮助您编写高质量的性能测试并获得准确的测试结果。
+
+## 🎯 测试设计原则
+
+### 1. 真实性原则
+
+**模拟真实用户行为**
+```python
+from locust import HttpUser, task, between
+import random
+
+class RealisticUser(HttpUser):
+    """真实用户行为模拟"""
+
+    # 真实的等待时间分布
+    wait_time = between(2, 8)  # 用户思考时间2-8秒
+
+    def on_start(self):
+        """用户会话开始 - 模拟登录流程"""
+        self.login()
+        self.load_user_preferences()
+
+    def login(self):
+        """真实的登录流程"""
+        # 先访问登录页面
+        self.client.get("/login")
+
+        # 模拟用户输入延迟
+        time.sleep(random.uniform(1, 3))
+
+        # 执行登录
+        response = self.client.post("/api/login", json={
+            "username": f"user_{random.randint(1, 1000)}",
+            "password": "password123"
+        })
+
+        if response.status_code == 200:
+            self.token = response.json().get("token")
+
+    @task(5)  # 浏览行为占主要比重
+    def browse_products(self):
+        """浏览产品 - 最常见的用户行为"""
+        # 模拟用户浏览多个页面
+        for _ in range(random.randint(1, 5)):
+            product_id = random.randint(1, 100)
+            self.client.get(f"/products/{product_id}")
+
+            # 模拟阅读时间
+            time.sleep(random.uniform(3, 10))
+
+    @task(2)  # 搜索行为
+    def search_products(self):
+        """搜索产品"""
+        keywords = ["laptop", "phone", "tablet", "headphones", "camera"]
+        keyword = random.choice(keywords)
+
+        self.client.get(f"/search", params={"q": keyword})
+
+        # 模拟查看搜索结果
+        time.sleep(random.uniform(2, 5))
+
+    @task(1)  # 购买行为相对较少
+    def purchase_flow(self):
+        """完整的购买流程"""
+        if not hasattr(self, 'token'):
+            return
+
+        # 1. 添加到购物车
+        product_id = random.randint(1, 50)
+        self.client.post("/api/cart/add",
+                        json={"product_id": product_id, "quantity": 1},
+                        headers={"Authorization": f"Bearer {self.token}"})
+
+        # 2. 查看购物车
+        time.sleep(random.uniform(1, 3))
+        self.client.get("/cart",
+                       headers={"Authorization": f"Bearer {self.token}"})
+
+        # 3. 结算（只有部分用户会完成购买）
+        if random.random() < 0.3:  # 30%的转化率
+            self.client.post("/api/checkout",
+                           json={"payment_method": "credit_card"},
+                           headers={"Authorization": f"Bearer {self.token}"})
+```
+
+### 2. 渐进式加压原则
+
+**避免突然的负载冲击**
+```python
+from locust import LoadTestShape
+
+class GradualLoadShape(LoadTestShape):
+    """渐进式负载模式"""
+
+    stages = [
+        {"duration": 60, "users": 10, "spawn_rate": 2},    # 预热阶段
+        {"duration": 120, "users": 50, "spawn_rate": 5},   # 缓慢增长
+        {"duration": 300, "users": 200, "spawn_rate": 10}, # 目标负载
+        {"duration": 180, "users": 200, "spawn_rate": 10}, # 稳定运行
+        {"duration": 60, "users": 50, "spawn_rate": 5},    # 缓慢下降
+    ]
+
+    def tick(self):
+        run_time = self.get_run_time()
+
+        for stage in self.stages:
+            if run_time < stage["duration"]:
+                return (stage["users"], stage["spawn_rate"])
+
+        return None
+```
+
+### 3. 数据隔离原则
+
+**使用独立的测试数据**
+```python
+import csv
+import random
+from typing import List, Dict
+
+class TestDataManager:
+    """测试数据管理器"""
+
+    def __init__(self, data_file: str):
+        self.data_file = data_file
+        self.test_data = self.load_test_data()
+        self.used_data = set()
+
+    def load_test_data(self) -> List[Dict]:
+        """加载测试数据"""
+        try:
+            with open(self.data_file, 'r', encoding='utf-8') as file:
+                return list(csv.DictReader(file))
+        except FileNotFoundError:
+            # 生成默认测试数据
+            return self.generate_test_data(1000)
+
+    def generate_test_data(self, count: int) -> List[Dict]:
+        """生成测试数据"""
+        data = []
+        for i in range(count):
+            data.append({
+                "user_id": f"test_user_{i:04d}",
+                "email": f"test{i:04d}@example.com",
+                "phone": f"1{random.randint(3000000000, 9999999999)}",
+                "region": random.choice(["北京", "上海", "广州", "深圳"])
+            })
+        return data
+
+    def get_unique_data(self) -> Dict:
+        """获取唯一的测试数据"""
+        available_data = [d for d in self.test_data
+                         if d["user_id"] not in self.used_data]
+
+        if not available_data:
+            # 重置已使用数据
+            self.used_data.clear()
+            available_data = self.test_data
+
+        selected = random.choice(available_data)
+        self.used_data.add(selected["user_id"])
+        return selected
+
+# 在测试中使用
+class DataDrivenUser(HttpUser):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.data_manager = TestDataManager("test_data.csv")
+        self.user_data = self.data_manager.get_unique_data()
+```
+
+## 🔧 性能优化实践
+
+### 1. 连接池优化
+
+```python
+from locust import HttpUser
+import requests.adapters
+
+class OptimizedHttpUser(HttpUser):
+    """优化的HTTP用户"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # 配置连接池
+        adapter = requests.adapters.HTTPAdapter(
+            pool_connections=10,    # 连接池大小
+            pool_maxsize=20,       # 最大连接数
+            max_retries=3,         # 重试次数
+            pool_block=False       # 非阻塞模式
+        )
+
+        self.client.mount("http://", adapter)
+        self.client.mount("https://", adapter)
+
+        # 设置超时
+        self.client.timeout = (5, 30)  # (连接超时, 读取超时)
+```
+
+### 2. 内存管理
+
+```python
+import gc
+from locust import events
+
+class MemoryOptimizedUser(HttpUser):
+    """内存优化的用户类"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.request_count = 0
+
+    @task
+    def optimized_request(self):
+        """优化的请求方法"""
+        response = self.client.get("/api/data")
+
+        # 及时释放响应内容
+        if hasattr(response, 'close'):
+            response.close()
+
+        self.request_count += 1
+
+        # 定期触发垃圾回收
+        if self.request_count % 100 == 0:
+            gc.collect()
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """测试结束时清理资源"""
+    gc.collect()
+    print("Memory cleanup completed")
+```
+
+### 3. 错误处理和重试
+
+```python
+import time
+from locust.exception import RescheduleTask
+
+class RobustUser(HttpUser):
+    """健壮的用户类"""
+
+    def safe_request(self, method: str, url: str, **kwargs):
+        """安全的请求方法"""
+        max_retries = 3
+        base_delay = 1
+
+        for attempt in range(max_retries):
+            try:
+                response = getattr(self.client, method)(url, **kwargs)
+
+                # 检查响应状态
+                if response.status_code >= 500:
+                    if attempt < max_retries - 1:
+                        time.sleep(base_delay * (2 ** attempt))  # 指数退避
+                        continue
+                    else:
+                        response.failure(f"Server error: {response.status_code}")
+
+                return response
+
+            except Exception as e:
+                if attempt < max_retries - 1:
+                    time.sleep(base_delay * (2 ** attempt))
+                    continue
+                else:
+                    # 记录错误并重新调度任务
+                    print(f"Request failed after {max_retries} attempts: {e}")
+                    raise RescheduleTask()
+
+    @task
+    def robust_api_call(self):
+        """健壮的API调用"""
+        response = self.safe_request("get", "/api/users")
+
+        if response and response.status_code == 200:
+            # 处理成功响应
+            data = response.json()
+            print(f"Retrieved {len(data)} users")
+```
+
+## 📊 监控和分析实践
+
+### 1. 自定义指标收集
+
+```python
+from locust import events
+import time
+
+class CustomMetrics:
+    """自定义指标收集器"""
+
+    def __init__(self):
+        self.business_metrics = {}
+        self.start_time = time.time()
+
+    def record_business_metric(self, metric_name: str, value: float):
+        """记录业务指标"""
+        if metric_name not in self.business_metrics:
+            self.business_metrics[metric_name] = []
+
+        self.business_metrics[metric_name].append({
+            'timestamp': time.time(),
+            'value': value
+        })
+
+    def get_summary(self):
+        """获取指标摘要"""
+        summary = {}
+        for metric_name, values in self.business_metrics.items():
+            if values:
+                metric_values = [v['value'] for v in values]
+                summary[metric_name] = {
+                    'count': len(metric_values),
+                    'avg': sum(metric_values) / len(metric_values),
+                    'min': min(metric_values),
+                    'max': max(metric_values)
+                }
+        return summary
+
+# 全局指标收集器
+custom_metrics = CustomMetrics()
+
+class MetricsAwareUser(HttpUser):
+    """支持自定义指标的用户"""
+
+    @task
+    def business_operation(self):
+        """业务操作"""
+        start_time = time.time()
+
+        # 执行业务操作
+        response = self.client.post("/api/business-operation", json={
+            "operation_type": "critical_business_flow",
+            "user_id": "test_user"
+        })
+
+        # 记录业务指标
+        operation_time = (time.time() - start_time) * 1000
+        custom_metrics.record_business_metric("business_operation_time", operation_time)
+
+        if response.status_code == 200:
+            custom_metrics.record_business_metric("successful_operations", 1)
+        else:
+            custom_metrics.record_business_metric("failed_operations", 1)
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """测试结束时输出自定义指标"""
+    summary = custom_metrics.get_summary()
+    print("\n=== Custom Business Metrics ===")
+    for metric_name, stats in summary.items():
+        print(f"{metric_name}: {stats}")
+```
+
+### 2. 实时监控告警
+
+```python
+from locust import events
+import requests
+
+class AlertManager:
+    """告警管理器"""
+
+    def __init__(self, webhook_url: str = None):
+        self.webhook_url = webhook_url
+        self.alert_thresholds = {
+            'error_rate': 5.0,      # 错误率阈值 5%
+            'avg_response_time': 2000,  # 平均响应时间阈值 2秒
+            'rps': 100              # 最小RPS阈值
+        }
+        self.last_alert_time = {}
+        self.alert_cooldown = 300   # 告警冷却时间 5分钟
+
+    def check_and_alert(self, stats):
+        """检查指标并发送告警"""
+        current_time = time.time()
+
+        # 检查错误率
+        if stats.total.num_requests > 0:
+            error_rate = (stats.total.num_failures / stats.total.num_requests) * 100
+            if error_rate > self.alert_thresholds['error_rate']:
+                self.send_alert("high_error_rate",
+                              f"Error rate: {error_rate:.2f}%", current_time)
+
+        # 检查响应时间
+        if stats.total.avg_response_time > self.alert_thresholds['avg_response_time']:
+            self.send_alert("high_response_time",
+                          f"Avg response time: {stats.total.avg_response_time:.2f}ms",
+                          current_time)
+
+        # 检查RPS
+        if stats.total.current_rps < self.alert_thresholds['rps']:
+            self.send_alert("low_rps",
+                          f"Current RPS: {stats.total.current_rps:.2f}",
+                          current_time)
+
+    def send_alert(self, alert_type: str, message: str, current_time: float):
+        """发送告警"""
+        # 检查冷却时间
+        if alert_type in self.last_alert_time:
+            if current_time - self.last_alert_time[alert_type] < self.alert_cooldown:
+                return
+
+        self.last_alert_time[alert_type] = current_time
+
+        alert_message = f"🚨 Performance Alert: {alert_type}\n{message}"
+        print(alert_message)
+
+        # 发送到外部系统
+        if self.webhook_url:
+            try:
+                requests.post(self.webhook_url, json={
+                    "text": alert_message,
+                    "alert_type": alert_type,
+                    "timestamp": current_time
+                }, timeout=5)
+            except Exception as e:
+                print(f"Failed to send alert: {e}")
+
+# 全局告警管理器
+alert_manager = AlertManager("https://hooks.slack.com/your-webhook-url")
+
+@events.test_start.add_listener
+def on_test_start(environment, **kwargs):
+    """测试开始时初始化监控"""
+    print("Performance monitoring started")
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    """测试结束时发送总结"""
+    stats = environment.stats
+    alert_manager.check_and_alert(stats)
+    print("Performance monitoring completed")
+```
+
+## 🎯 测试策略实践
+
+### 1. 分层测试策略
+
+```python
+# 基准测试
+class BaselineTest(HttpUser):
+    """基准性能测试"""
+    wait_time = between(1, 2)
+
+    @task
+    def baseline_request(self):
+        self.client.get("/api/health")
+
+# 负载测试
+class LoadTest(HttpUser):
+    """负载测试"""
+    wait_time = between(2, 5)
+
+    @task(3)
+    def normal_operation(self):
+        self.client.get("/api/users")
+
+    @task(1)
+    def heavy_operation(self):
+        self.client.post("/api/reports/generate")
+
+# 压力测试
+class StressTest(HttpUser):
+    """压力测试"""
+    wait_time = between(0.5, 1)
+
+    @task
+    def stress_operation(self):
+        # 高频率请求
+        for _ in range(5):
+            self.client.get("/api/data")
+```
+
+### 2. 环境配置管理
+
+```python
+import os
+from typing import Dict, Any
+
+class EnvironmentConfig:
+    """环境配置管理"""
+
+    ENVIRONMENTS = {
+        "dev": {
+            "host": "https://dev-api.example.com",
+            "max_users": 50,
+            "duration": "5m"
+        },
+        "staging": {
+            "host": "https://staging-api.example.com",
+            "max_users": 200,
+            "duration": "15m"
+        },
+        "prod": {
+            "host": "https://api.example.com",
+            "max_users": 1000,
+            "duration": "30m"
+        }
+    }
+
+    @classmethod
+    def get_config(cls, env: str = None) -> Dict[str, Any]:
+        """获取环境配置"""
+        env = env or os.getenv("TEST_ENV", "dev")
+        return cls.ENVIRONMENTS.get(env, cls.ENVIRONMENTS["dev"])
+
+    @classmethod
+    def setup_for_environment(cls, env: str = None):
+        """为特定环境设置配置"""
+        config = cls.get_config(env)
+
+        # 设置环境变量
+        os.environ["LOCUST_HOST"] = config["host"]
+        os.environ["LOCUST_USERS"] = str(config["max_users"])
+        os.environ["LOCUST_RUN_TIME"] = config["duration"]
+
+        return config
+
+# 使用环境配置
+config = EnvironmentConfig.setup_for_environment()
+print(f"Testing against: {config['host']}")
+```
+
+## 📚 报告和文档实践
+
+### 1. 自动化报告生成
+
+```python
+import json
+from datetime import datetime
+from jinja2 import Template
+
+class ReportGenerator:
+    """报告生成器"""
+
+    def __init__(self):
+        self.report_template = """
+        # Performance Test Report
+
+        **Test Date**: {{ test_date }}
+        **Environment**: {{ environment }}
+        **Duration**: {{ duration }}
+
+        ## Summary
+        - **Total Requests**: {{ total_requests }}
+        - **Failed Requests**: {{ failed_requests }}
+        - **Error Rate**: {{ error_rate }}%
+        - **Average Response Time**: {{ avg_response_time }}ms
+        - **95th Percentile**: {{ p95_response_time }}ms
+        - **Max Response Time**: {{ max_response_time }}ms
+        - **RPS**: {{ rps }}
+
+        ## Performance Grade: {{ grade }}
+
+        ## Recommendations
+        {% for rec in recommendations %}
+        - {{ rec }}
+        {% endfor %}
+        """
+
+    def generate_report(self, stats_data: Dict[str, Any],
+                       output_file: str = "performance_report.md"):
+        """生成性能测试报告"""
+        # 计算性能等级
+        grade = self.calculate_grade(stats_data)
+
+        # 生成建议
+        recommendations = self.generate_recommendations(stats_data)
+
+        # 准备模板数据
+        template_data = {
+            "test_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "environment": os.getenv("TEST_ENV", "unknown"),
+            "grade": grade,
+            "recommendations": recommendations,
+            **stats_data
+        }
+
+        # 渲染报告
+        template = Template(self.report_template)
+        report_content = template.render(**template_data)
+
+        # 保存报告
+        with open(output_file, 'w', encoding='utf-8') as f:
+            f.write(report_content)
+
+        print(f"Report generated: {output_file}")
+        return output_file
+
+    def calculate_grade(self, stats: Dict[str, Any]) -> str:
+        """计算性能等级"""
+        error_rate = stats.get('error_rate', 0)
+        avg_response_time = stats.get('avg_response_time', 0)
+
+        if error_rate > 5 or avg_response_time > 3000:
+            return "D - Poor"
+        elif error_rate > 2 or avg_response_time > 2000:
+            return "C - Fair"
+        elif error_rate > 1 or avg_response_time > 1000:
+            return "B - Good"
+        else:
+            return "A - Excellent"
+
+    def generate_recommendations(self, stats: Dict[str, Any]) -> List[str]:
+        """生成优化建议"""
+        recommendations = []
+
+        error_rate = stats.get('error_rate', 0)
+        avg_response_time = stats.get('avg_response_time', 0)
+        rps = stats.get('rps', 0)
+
+        if error_rate > 2:
+            recommendations.append("错误率较高，建议检查服务器日志和错误处理逻辑")
+
+        if avg_response_time > 2000:
+            recommendations.append("响应时间较长，建议优化数据库查询和缓存策略")
+
+        if rps < 50:
+            recommendations.append("吞吐量较低，建议检查服务器配置和网络连接")
+
+        if not recommendations:
+            recommendations.append("性能表现良好，建议继续监控关键指标")
+
+        return recommendations
+```
+
+## 🔍 故障排查实践
+
+### 1. 常见问题诊断
+
+```python
+class PerformanceDiagnostic:
+    """性能诊断工具"""
+
+    @staticmethod
+    def diagnose_high_response_time(stats):
+        """诊断高响应时间问题"""
+        issues = []
+
+        if stats.total.avg_response_time > 2000:
+            issues.append("平均响应时间过高")
+
+            if stats.total.max_response_time > 10000:
+                issues.append("存在极慢的请求，可能是超时或死锁")
+
+            # 检查不同端点的响应时间
+            slow_endpoints = []
+            for name, stat in stats.entries.items():
+                if stat.avg_response_time > 3000:
+                    slow_endpoints.append(f"{name}: {stat.avg_response_time:.2f}ms")
+
+            if slow_endpoints:
+                issues.append(f"慢端点: {', '.join(slow_endpoints)}")
+
+        return issues
+
+    @staticmethod
+    def diagnose_high_error_rate(stats):
+        """诊断高错误率问题"""
+        issues = []
+
+        if stats.total.num_requests > 0:
+            error_rate = (stats.total.num_failures / stats.total.num_requests) * 100
+
+            if error_rate > 5:
+                issues.append(f"错误率过高: {error_rate:.2f}%")
+
+                # 分析错误类型
+                error_types = {}
+                for name, stat in stats.entries.items():
+                    if stat.num_failures > 0:
+                        failure_rate = (stat.num_failures / stat.num_requests) * 100
+                        error_types[name] = failure_rate
+
+                if error_types:
+                    sorted_errors = sorted(error_types.items(),
+                                         key=lambda x: x[1], reverse=True)
+                    top_errors = sorted_errors[:3]
+                    issues.append(f"主要错误端点: {top_errors}")
+
+        return issues
+
+@events.test_stop.add_listener
+def diagnose_performance_issues(environment, **kwargs):
+    """测试结束后进行性能诊断"""
+    stats = environment.stats
+    diagnostic = PerformanceDiagnostic()
+
+    print("\n=== Performance Diagnostic ===")
+
+    # 诊断响应时间问题
+    response_time_issues = diagnostic.diagnose_high_response_time(stats)
+    if response_time_issues:
+        print("Response Time Issues:")
+        for issue in response_time_issues:
+            print(f"  - {issue}")
+
+    # 诊断错误率问题
+    error_rate_issues = diagnostic.diagnose_high_error_rate(stats)
+    if error_rate_issues:
+        print("Error Rate Issues:")
+        for issue in error_rate_issues:
+            print(f"  - {issue}")
+
+    if not response_time_issues and not error_rate_issues:
+        print("No significant performance issues detected.")
+```
+
+## 🎉 总结
+
+遵循这些最佳实践可以帮助您：
+
+1. **提高测试质量**: 编写更真实、更可靠的性能测试
+2. **优化测试性能**: 减少资源消耗，提高测试效率
+3. **增强监控能力**: 实时发现和诊断性能问题
+4. **改善测试策略**: 采用分层、渐进的测试方法
+5. **自动化报告**: 生成专业的测试报告和建议
+
+## 📚 相关文档
+
+- [第一个测试](../getting-started/first-test.md) - 入门指南
+- [高级用法](advanced-usage.md) - 高级功能使用
+- [故障排除](troubleshooting.md) - 问题诊断和解决
+- [配置参考](../configuration/framework-config.md) - 详细配置说明
diff --git a/docs/examples/faq.md b/docs/examples/faq.md
new file mode 100644
index 0000000..0014e5c
--- /dev/null
+++ b/docs/examples/faq.md
@@ -0,0 +1,487 @@
+# 常见问题解答 (FAQ)
+
+本文档收集了Locust性能测试框架使用过程中的常见问题和解决方案，帮助用户快速解决遇到的问题。
+
+## 🚀 安装和配置
+
+### Q1: 如何安装Locust框架？
+
+**A:** 推荐使用pip安装：
+
+```bash
+# 基础安装
+pip install locust
+
+# 安装完整版本（包含所有依赖）
+pip install locust[all]
+
+# 从源码安装
+git clone https://github.com/your-org/locust-framework.git
+cd locust-framework
+pip install -e .
+```
+
+### Q2: 支持哪些Python版本？
+
+**A:** 支持Python 3.7及以上版本：
+- Python 3.7+
+- Python 3.8+ (推荐)
+- Python 3.9+
+- Python 3.10+
+- Python 3.11+
+
+### Q3: 如何配置分布式测试？
+
+**A:** 分布式测试需要启动Master和Worker节点：
+
+```bash
+# 启动Master节点
+locust -f locustfile.py --master --master-bind-host=0.0.0.0
+
+# 启动Worker节点
+locust -f locustfile.py --worker --master-host=master-ip
+```
+
+### Q4: Docker部署有什么要求？
+
+**A:** Docker部署要求：
+- Docker 20.10+
+- Docker Compose 1.29+
+- 至少2GB内存
+- 网络端口：8089(Web UI), 5557(Master通信)
+
+## 📊 性能测试
+
+### Q5: 如何设置合理的用户数和增长率？
+
+**A:** 建议遵循以下原则：
+
+```python
+# 渐进式增长
+class GradualLoadShape(LoadTestShape):
+    stages = [
+        {"duration": 60, "users": 10, "spawn_rate": 2},
+        {"duration": 120, "users": 50, "spawn_rate": 5},
+        {"duration": 180, "users": 100, "spawn_rate": 10},
+        {"duration": 300, "users": 200, "spawn_rate": 5}
+    ]
+```
+
+**建议值：**
+- 初始用户数：10-50
+- 增长率：1-10 users/second
+- 最大用户数：根据目标系统容量确定
+
+### Q6: 响应时间多少算正常？
+
+**A:** 响应时间标准：
+- **优秀**: < 200ms
+- **良好**: 200ms - 500ms
+- **可接受**: 500ms - 1000ms
+- **需要优化**: 1000ms - 2000ms
+- **不可接受**: > 2000ms
+
+### Q7: 错误率多少是可接受的？
+
+**A:** 错误率标准：
+- **生产环境**: < 0.1%
+- **测试环境**: < 1%
+- **压力测试**: < 5%
+- **极限测试**: < 10%
+
+### Q8: 如何模拟真实用户行为？
+
+**A:** 使用以下技术：
+
+```python
+class RealisticUser(HttpUser):
+    wait_time = between(1, 5)  # 用户思考时间
+
+    def on_start(self):
+        self.login()
+
+    @task(3)  # 权重：浏览页面更频繁
+    def browse_pages(self):
+        self.client.get("/products")
+        self.wait()  # 模拟阅读时间
+
+    @task(1)  # 权重：购买行为较少
+    def purchase(self):
+        self.client.post("/cart/add", json={"product_id": 123})
+        self.client.post("/checkout")
+```
+
+## 🔧 技术问题
+
+### Q9: 如何处理认证和会话？
+
+**A:** 多种认证方式：
+
+```python
+# 1. 基础认证
+class AuthenticatedUser(HttpUser):
+    def on_start(self):
+        response = self.client.post("/login", {
+            "username": "testuser",
+            "password": "testpass"
+        })
+        # 会话自动保持在self.client中
+
+# 2. Token认证
+class TokenUser(HttpUser):
+    def on_start(self):
+        response = self.client.post("/auth/token", {
+            "username": "testuser",
+            "password": "testpass"
+        })
+        token = response.json()["token"]
+        self.client.headers.update({"Authorization": f"Bearer {token}"})
+
+# 3. Cookie认证
+class CookieUser(HttpUser):
+    def on_start(self):
+        self.client.post("/login", {
+            "username": "testuser",
+            "password": "testpass"
+        })
+        # Cookies自动处理
+```
+
+### Q10: 如何处理动态数据？
+
+**A:** 使用数据生成器：
+
+```python
+from faker import Faker
+import random
+
+fake = Faker('zh_CN')
+
+class DynamicDataUser(HttpUser):
+    def on_start(self):
+        self.user_data = {
+            "name": fake.name(),
+            "email": fake.email(),
+            "phone": fake.phone_number()
+        }
+
+    @task
+    def create_order(self):
+        order_data = {
+            "product_id": random.randint(1, 1000),
+            "quantity": random.randint(1, 5),
+            "customer": self.user_data
+        }
+        self.client.post("/orders", json=order_data)
+```
+
+### Q11: 如何测试WebSocket连接？
+
+**A:** 使用WebSocket支持：
+
+```python
+from locust import User, task
+import websocket
+import json
+
+class WebSocketUser(User):
+    def on_start(self):
+        self.ws = websocket.create_connection("ws://localhost:8080/ws")
+
+    @task
+    def send_message(self):
+        message = {
+            "type": "chat",
+            "content": "Hello from Locust!",
+            "timestamp": time.time()
+        }
+        self.ws.send(json.dumps(message))
+        response = self.ws.recv()
+        print(f"Received: {response}")
+
+    def on_stop(self):
+        self.ws.close()
+```
+
+### Q12: 如何测试文件上传？
+
+**A:** 文件上传测试：
+
+```python
+class FileUploadUser(HttpUser):
+    @task
+    def upload_file(self):
+        # 模拟文件内容
+        files = {
+            'file': ('test.txt', 'This is test content', 'text/plain')
+        }
+
+        response = self.client.post("/upload", files=files)
+
+        # 或者上传真实文件
+        with open('test_file.pdf', 'rb') as f:
+            files = {'file': ('document.pdf', f, 'application/pdf')}
+            response = self.client.post("/upload", files=files)
+```
+
+## 📈 监控和分析
+
+### Q13: 如何查看实时测试结果？
+
+**A:** 多种查看方式：
+1. **Web UI**: http://localhost:8089
+2. **命令行**: 使用 `--headless` 模式
+3. **API**: http://localhost:8089/stats/requests
+4. **自定义监控**: 集成Prometheus/Grafana
+
+### Q14: 如何导出测试报告？
+
+**A:** 多种导出格式：
+
+```bash
+# HTML报告
+locust -f locustfile.py --headless -u 100 -r 10 -t 300s --html report.html
+
+# CSV数据
+locust -f locustfile.py --headless -u 100 -r 10 -t 300s --csv results
+
+# JSON数据
+curl http://localhost:8089/stats/requests | jq . > results.json
+```
+
+### Q15: 如何设置自定义指标？
+
+**A:** 添加自定义指标：
+
+```python
+from locust import events
+import time
+
+# 自定义指标收集
+custom_metrics = {}
+
+@events.request.add_listener
+def on_request(request_type, name, response_time, response_length, exception, context, **kwargs):
+    if name == "/api/critical-endpoint":
+        if name not in custom_metrics:
+            custom_metrics[name] = []
+        custom_metrics[name].append(response_time)
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    for endpoint, times in custom_metrics.items():
+        avg_time = sum(times) / len(times)
+        print(f"Custom metric - {endpoint}: {avg_time:.2f}ms average")
+```
+
+## 🐛 故障排除
+
+### Q16: 为什么Worker连接不上Master？
+
+**A:** 常见原因和解决方案：
+
+1. **网络问题**:
+```bash
+# 检查网络连通性
+ping master-host
+telnet master-host 5557
+```
+
+2. **防火墙设置**:
+```bash
+# 开放端口
+sudo ufw allow 5557
+sudo ufw allow 8089
+```
+
+3. **Docker网络**:
+```yaml
+# docker-compose.yml
+networks:
+  locust-network:
+    driver: bridge
+```
+
+### Q17: 内存使用过高怎么办？
+
+**A:** 内存优化策略：
+
+```python
+# 1. 限制数据保存
+class MemoryEfficientUser(HttpUser):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.request_count = 0
+
+    @task
+    def make_request(self):
+        response = self.client.get("/api/data")
+        # 不保存响应数据
+        self.request_count += 1
+
+        # 定期清理
+        if self.request_count % 100 == 0:
+            import gc
+            gc.collect()
+
+# 2. 使用流式处理
+@task
+def download_large_file(self):
+    with self.client.get("/large-file", stream=True) as response:
+        for chunk in response.iter_content(chunk_size=8192):
+            # 处理数据块，不保存到内存
+            pass
+```
+
+### Q18: 测试结果不稳定怎么办？
+
+**A:** 提高测试稳定性：
+
+1. **预热阶段**:
+```python
+class StableLoadShape(LoadTestShape):
+    def tick(self):
+        run_time = self.get_run_time()
+
+        if run_time < 60:  # 预热1分钟
+            return (10, 2)
+        elif run_time < 300:  # 正式测试
+            return (100, 10)
+        else:
+            return None
+```
+
+2. **多次运行取平均值**:
+```bash
+# 运行多次测试
+for i in {1..5}; do
+    locust -f locustfile.py --headless -u 100 -r 10 -t 300s --csv run_$i
+done
+```
+
+### Q19: 如何调试Locust脚本？
+
+**A:** 调试技巧：
+
+```python
+import logging
+
+# 1. 启用详细日志
+logging.basicConfig(level=logging.DEBUG)
+
+class DebugUser(HttpUser):
+    @task
+    def debug_request(self):
+        # 添加调试信息
+        print(f"Making request at {time.time()}")
+
+        response = self.client.get("/api/test")
+
+        # 检查响应
+        print(f"Status: {response.status_code}")
+        print(f"Headers: {response.headers}")
+        print(f"Content: {response.text[:100]}...")
+
+        # 断言检查
+        assert response.status_code == 200, f"Expected 200, got {response.status_code}"
+
+# 2. 使用断点调试
+import pdb
+
+@task
+def debug_with_breakpoint(self):
+    pdb.set_trace()  # 设置断点
+    response = self.client.get("/api/test")
+```
+
+## 🔄 最佳实践
+
+### Q20: 性能测试的最佳实践是什么？
+
+**A:** 关键最佳实践：
+
+1. **测试环境**:
+   - 使用与生产环境相似的配置
+   - 独立的测试环境
+   - 稳定的网络条件
+
+2. **测试设计**:
+   - 模拟真实用户行为
+   - 渐进式负载增长
+   - 包含异常场景
+
+3. **数据管理**:
+   - 使用真实但脱敏的数据
+   - 数据隔离和清理
+   - 避免数据污染
+
+4. **结果分析**:
+   - 关注多个指标
+   - 建立基线对比
+   - 趋势分析
+
+### Q21: 如何进行容量规划？
+
+**A:** 容量规划步骤：
+
+1. **确定性能目标**:
+   - 预期用户数
+   - 响应时间要求
+   - 可接受错误率
+
+2. **基准测试**:
+   - 单用户性能
+   - 逐步增加负载
+   - 找到性能拐点
+
+3. **压力测试**:
+   - 超出预期负载
+   - 系统极限测试
+   - 恢复能力测试
+
+4. **容量计算**:
+```python
+# 容量计算示例
+def calculate_capacity(baseline_rps, target_users, safety_margin=0.8):
+    """
+    计算系统容量
+
+    Args:
+        baseline_rps: 基准RPS
+        target_users: 目标用户数
+        safety_margin: 安全边际
+    """
+    required_rps = target_users * average_requests_per_user_per_second
+    capacity_factor = required_rps / baseline_rps
+    recommended_capacity = capacity_factor / safety_margin
+
+    return {
+        "required_rps": required_rps,
+        "capacity_factor": capacity_factor,
+        "recommended_servers": math.ceil(recommended_capacity)
+    }
+```
+
+## 📚 相关资源
+
+### 有用的链接
+- [官方文档](https://docs.locust.io/)
+- [GitHub仓库](https://github.com/locustio/locust)
+- [社区论坛](https://github.com/locustio/locust/discussions)
+- [示例代码](https://github.com/locustio/locust/tree/master/examples)
+
+### 推荐阅读
+- [性能测试最佳实践](best-practices.md)
+- [故障排除指南](troubleshooting.md)
+- [架构设计文档](../architecture/README.md)
+- [API参考文档](../api/README.md)
+
+### 社区支持
+- **GitHub Issues**: 报告Bug和功能请求
+- **Stack Overflow**: 标签 `locust` 或 `performance-testing`
+- **Reddit**: r/QualityAssurance, r/Python
+- **Discord/Slack**: 加入性能测试社区
+
+---
+
+**提示**: 如果您的问题没有在此FAQ中找到答案，请查看[故障排除指南](troubleshooting.md)或在GitHub上提交Issue。
diff --git a/docs/examples/troubleshooting.md b/docs/examples/troubleshooting.md
new file mode 100644
index 0000000..f5b16c6
--- /dev/null
+++ b/docs/examples/troubleshooting.md
@@ -0,0 +1,793 @@
+# 故障排除指南
+
+本文档提供了Locust性能测试框架常见问题的诊断和解决方案，帮助您快速定位和解决测试过程中遇到的问题。
+
+## 🔍 问题诊断流程
+
+### 快速诊断检查清单
+
+```bash
+# 1. 检查服务状态
+docker-compose ps
+systemctl status locust
+
+# 2. 检查端口占用
+netstat -tlnp | grep :8089
+netstat -tlnp | grep :5557
+
+# 3. 检查日志
+tail -f logs/locust.log
+docker-compose logs -f locust-master
+
+# 4. 检查资源使用
+top -p $(pgrep -f locust)
+docker stats
+
+# 5. 检查网络连接
+curl -I http://localhost:8089/
+ping target-host
+```
+
+## 🚨 常见问题及解决方案
+
+### 1. 启动和连接问题
+
+#### 问题：Locust无法启动
+
+**症状**
+```
+Error: No module named 'locust'
+ImportError: cannot import name 'HttpUser' from 'locust'
+```
+
+**解决方案**
+```bash
+# 检查Python环境
+python --version
+pip list | grep locust
+
+# 重新安装依赖
+pip install --upgrade locust
+pip install -r requirements.txt
+
+# 检查虚拟环境
+source venv/bin/activate
+which python
+```
+
+#### 问题：Worker无法连接到Master
+
+**症状**
+```
+[ERROR] Failed to connect to the Locust master
+ConnectionRefusedError: [Errno 111] Connection refused
+```
+
+**诊断步骤**
+```python
+# diagnostic_tools.py
+import socket
+import time
+
+def check_master_connectivity(master_host, master_port=5557):
+    """检查Master连接性"""
+    try:
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.settimeout(5)
+        result = sock.connect_ex((master_host, master_port))
+        sock.close()
+
+        if result == 0:
+            print(f"✅ Master {master_host}:{master_port} is reachable")
+            return True
+        else:
+            print(f"❌ Master {master_host}:{master_port} is not reachable")
+            return False
+    except Exception as e:
+        print(f"❌ Connection test failed: {e}")
+        return False
+
+def diagnose_network_issues():
+    """网络问题诊断"""
+    import subprocess
+
+    # 检查DNS解析
+    try:
+        result = subprocess.run(['nslookup', 'locust-master'],
+                              capture_output=True, text=True, timeout=10)
+        print("DNS Resolution:")
+        print(result.stdout)
+    except Exception as e:
+        print(f"DNS check failed: {e}")
+
+    # 检查路由
+    try:
+        result = subprocess.run(['traceroute', 'locust-master'],
+                              capture_output=True, text=True, timeout=30)
+        print("Network Route:")
+        print(result.stdout)
+    except Exception as e:
+        print(f"Route check failed: {e}")
+
+# 使用诊断工具
+if __name__ == "__main__":
+    check_master_connectivity("locust-master")
+    diagnose_network_issues()
+```
+
+**解决方案**
+```bash
+# 1. 检查防火墙设置
+sudo ufw status
+sudo iptables -L
+
+# 2. 检查Docker网络
+docker network ls
+docker network inspect locust_default
+
+# 3. 修复网络配置
+docker-compose down
+docker network prune
+docker-compose up -d
+
+# 4. 使用正确的主机名
+# 在docker-compose.yml中确保服务名称正确
+services:
+  locust-master:
+    environment:
+      - LOCUST_MASTER_BIND_HOST=0.0.0.0
+  locust-worker:
+    environment:
+      - LOCUST_MASTER_HOST=locust-master
+```
+
+### 2. 性能问题
+
+#### 问题：响应时间异常高
+
+**症状**
+```
+Average response time: 5000ms+
+95th percentile: 10000ms+
+```
+
+**诊断工具**
+```python
+# performance_analyzer.py
+import time
+import statistics
+from locust import events
+
+class PerformanceAnalyzer:
+    """性能分析器"""
+
+    def __init__(self):
+        self.response_times = []
+        self.slow_requests = []
+        self.error_patterns = {}
+
+    def analyze_response_time(self, response_time, request_name):
+        """分析响应时间"""
+        self.response_times.append(response_time)
+
+        # 记录慢请求
+        if response_time > 3000:  # 3秒以上为慢请求
+            self.slow_requests.append({
+                'name': request_name,
+                'response_time': response_time,
+                'timestamp': time.time()
+            })
+
+    def get_performance_summary(self):
+        """获取性能摘要"""
+        if not self.response_times:
+            return "No data available"
+
+        return {
+            'avg_response_time': statistics.mean(self.response_times),
+            'median_response_time': statistics.median(self.response_times),
+            'p95_response_time': self.percentile(self.response_times, 95),
+            'p99_response_time': self.percentile(self.response_times, 99),
+            'slow_requests_count': len(self.slow_requests),
+            'slow_requests_percentage': len(self.slow_requests) / len(self.response_times) * 100
+        }
+
+    def percentile(self, data, p):
+        """计算百分位数"""
+        sorted_data = sorted(data)
+        index = int(len(sorted_data) * p / 100)
+        return sorted_data[min(index, len(sorted_data) - 1)]
+
+    def identify_bottlenecks(self):
+        """识别性能瓶颈"""
+        bottlenecks = []
+
+        # 分析慢请求模式
+        slow_endpoints = {}
+        for req in self.slow_requests:
+            endpoint = req['name']
+            if endpoint not in slow_endpoints:
+                slow_endpoints[endpoint] = []
+            slow_endpoints[endpoint].append(req['response_time'])
+
+        # 找出最慢的端点
+        for endpoint, times in slow_endpoints.items():
+            if len(times) > 5:  # 至少5个慢请求
+                avg_time = statistics.mean(times)
+                bottlenecks.append({
+                    'endpoint': endpoint,
+                    'avg_slow_time': avg_time,
+                    'slow_count': len(times)
+                })
+
+        return sorted(bottlenecks, key=lambda x: x['avg_slow_time'], reverse=True)
+
+# 集成到Locust测试中
+analyzer = PerformanceAnalyzer()
+
+@events.request.add_listener
+def on_request(request_type, name, response_time, response_length, exception, context, **kwargs):
+    if not exception:
+        analyzer.analyze_response_time(response_time, name)
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    summary = analyzer.get_performance_summary()
+    bottlenecks = analyzer.identify_bottlenecks()
+
+    print("\n=== Performance Analysis ===")
+    print(f"Average Response Time: {summary['avg_response_time']:.2f}ms")
+    print(f"95th Percentile: {summary['p95_response_time']:.2f}ms")
+    print(f"Slow Requests: {summary['slow_requests_count']} ({summary['slow_requests_percentage']:.2f}%)")
+
+    if bottlenecks:
+        print("\n=== Performance Bottlenecks ===")
+        for bottleneck in bottlenecks[:5]:  # 显示前5个
+            print(f"- {bottleneck['endpoint']}: {bottleneck['avg_slow_time']:.2f}ms avg, {bottleneck['slow_count']} slow requests")
+```
+
+**解决方案**
+```python
+# 1. 优化连接池
+from locust import HttpUser
+import requests.adapters
+
+class OptimizedUser(HttpUser):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # 增加连接池大小
+        adapter = requests.adapters.HTTPAdapter(
+            pool_connections=20,
+            pool_maxsize=50,
+            max_retries=3
+        )
+        self.client.mount("http://", adapter)
+        self.client.mount("https://", adapter)
+
+        # 设置合理的超时
+        self.client.timeout = (10, 60)  # 连接超时10s，读取超时60s
+
+# 2. 检查目标系统
+# - 数据库连接池
+# - 缓存配置
+# - 服务器资源
+# - 网络带宽
+
+# 3. 分析系统瓶颈
+def analyze_system_bottlenecks():
+    """分析系统瓶颈"""
+    import psutil
+
+    # CPU使用率
+    cpu_percent = psutil.cpu_percent(interval=1)
+    print(f"CPU Usage: {cpu_percent}%")
+
+    # 内存使用率
+    memory = psutil.virtual_memory()
+    print(f"Memory Usage: {memory.percent}%")
+
+    # 磁盘IO
+    disk_io = psutil.disk_io_counters()
+    print(f"Disk Read: {disk_io.read_bytes / 1024 / 1024:.2f}MB")
+    print(f"Disk Write: {disk_io.write_bytes / 1024 / 1024:.2f}MB")
+
+    # 网络IO
+    net_io = psutil.net_io_counters()
+    print(f"Network Sent: {net_io.bytes_sent / 1024 / 1024:.2f}MB")
+    print(f"Network Recv: {net_io.bytes_recv / 1024 / 1024:.2f}MB")
+```
+
+#### 问题：错误率过高
+
+**症状**
+```
+Error rate: 10%+
+Connection errors, timeouts, 5xx responses
+```
+
+**错误分析工具**
+```python
+# error_analyzer.py
+from collections import defaultdict
+import re
+
+class ErrorAnalyzer:
+    """错误分析器"""
+
+    def __init__(self):
+        self.errors = defaultdict(list)
+        self.error_patterns = {}
+
+    def record_error(self, request_name, error_message, response_code=None):
+        """记录错误"""
+        error_info = {
+            'timestamp': time.time(),
+            'request_name': request_name,
+            'error_message': str(error_message),
+            'response_code': response_code
+        }
+
+        # 分类错误
+        error_type = self.classify_error(error_message, response_code)
+        self.errors[error_type].append(error_info)
+
+    def classify_error(self, error_message, response_code):
+        """分类错误类型"""
+        error_msg = str(error_message).lower()
+
+        if response_code:
+            if 400 <= response_code < 500:
+                return "client_error"
+            elif 500 <= response_code < 600:
+                return "server_error"
+
+        if "timeout" in error_msg or "timed out" in error_msg:
+            return "timeout_error"
+        elif "connection" in error_msg:
+            return "connection_error"
+        elif "ssl" in error_msg or "certificate" in error_msg:
+            return "ssl_error"
+        elif "dns" in error_msg:
+            return "dns_error"
+        else:
+            return "unknown_error"
+
+    def get_error_summary(self):
+        """获取错误摘要"""
+        total_errors = sum(len(errors) for errors in self.errors.values())
+
+        summary = {
+            'total_errors': total_errors,
+            'error_types': {}
+        }
+
+        for error_type, errors in self.errors.items():
+            summary['error_types'][error_type] = {
+                'count': len(errors),
+                'percentage': len(errors) / total_errors * 100 if total_errors > 0 else 0,
+                'recent_examples': [e['error_message'] for e in errors[-3:]]  # 最近3个例子
+            }
+
+        return summary
+
+    def suggest_solutions(self):
+        """建议解决方案"""
+        suggestions = []
+
+        for error_type, errors in self.errors.items():
+            if len(errors) > 10:  # 错误数量较多时给出建议
+                if error_type == "timeout_error":
+                    suggestions.append("增加请求超时时间，检查网络延迟")
+                elif error_type == "connection_error":
+                    suggestions.append("检查连接池配置，增加最大连接数")
+                elif error_type == "server_error":
+                    suggestions.append("检查目标服务器状态，查看服务器日志")
+                elif error_type == "client_error":
+                    suggestions.append("检查请求参数和认证信息")
+                elif error_type == "ssl_error":
+                    suggestions.append("检查SSL证书配置和有效性")
+
+        return suggestions
+
+# 集成错误分析
+error_analyzer = ErrorAnalyzer()
+
+@events.request.add_listener
+def on_request(request_type, name, response_time, response_length, exception, context, **kwargs):
+    if exception:
+        response_code = getattr(exception, 'response', {}).get('status_code')
+        error_analyzer.record_error(name, exception, response_code)
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    summary = error_analyzer.get_error_summary()
+    suggestions = error_analyzer.suggest_solutions()
+
+    print("\n=== Error Analysis ===")
+    print(f"Total Errors: {summary['total_errors']}")
+
+    for error_type, info in summary['error_types'].items():
+        print(f"\n{error_type}: {info['count']} ({info['percentage']:.2f}%)")
+        for example in info['recent_examples']:
+            print(f"  - {example}")
+
+    if suggestions:
+        print("\n=== Suggested Solutions ===")
+        for suggestion in suggestions:
+            print(f"- {suggestion}")
+```
+
+### 3. 资源问题
+
+#### 问题：内存使用过高
+
+**症状**
+```
+Memory usage: 90%+
+OOMKilled containers
+Slow garbage collection
+```
+
+**内存监控工具**
+```python
+# memory_monitor.py
+import psutil
+import gc
+import tracemalloc
+from locust import events
+
+class MemoryMonitor:
+    """内存监控器"""
+
+    def __init__(self):
+        self.memory_snapshots = []
+        self.gc_stats = []
+        tracemalloc.start()
+
+    def take_snapshot(self):
+        """获取内存快照"""
+        # 系统内存
+        memory = psutil.virtual_memory()
+
+        # 进程内存
+        process = psutil.Process()
+        process_memory = process.memory_info()
+
+        # Python内存
+        snapshot = tracemalloc.take_snapshot()
+        top_stats = snapshot.statistics('lineno')
+
+        snapshot_data = {
+            'timestamp': time.time(),
+            'system_memory_percent': memory.percent,
+            'process_memory_mb': process_memory.rss / 1024 / 1024,
+            'python_memory_mb': sum(stat.size for stat in top_stats) / 1024 / 1024,
+            'top_memory_lines': [(stat.traceback.format()[-1], stat.size / 1024 / 1024)
+                               for stat in top_stats[:5]]
+        }
+
+        self.memory_snapshots.append(snapshot_data)
+        return snapshot_data
+
+    def force_gc(self):
+        """强制垃圾回收"""
+        before = self.get_memory_usage()
+
+        # 执行垃圾回收
+        collected = gc.collect()
+
+        after = self.get_memory_usage()
+
+        gc_info = {
+            'timestamp': time.time(),
+            'objects_collected': collected,
+            'memory_before_mb': before,
+            'memory_after_mb': after,
+            'memory_freed_mb': before - after
+        }
+
+        self.gc_stats.append(gc_info)
+        return gc_info
+
+    def get_memory_usage(self):
+        """获取当前内存使用量"""
+        process = psutil.Process()
+        return process.memory_info().rss / 1024 / 1024
+
+    def analyze_memory_leaks(self):
+        """分析内存泄漏"""
+        if len(self.memory_snapshots) < 2:
+            return "Insufficient data for leak analysis"
+
+        # 检查内存增长趋势
+        memory_growth = []
+        for i in range(1, len(self.memory_snapshots)):
+            current = self.memory_snapshots[i]['process_memory_mb']
+            previous = self.memory_snapshots[i-1]['process_memory_mb']
+            growth = current - previous
+            memory_growth.append(growth)
+
+        avg_growth = sum(memory_growth) / len(memory_growth)
+
+        if avg_growth > 10:  # 平均增长超过10MB
+            return f"Potential memory leak detected. Average growth: {avg_growth:.2f}MB per snapshot"
+        else:
+            return "No significant memory leak detected"
+
+# 使用内存监控
+memory_monitor = MemoryMonitor()
+
+@events.test_start.add_listener
+def on_test_start(environment, **kwargs):
+    memory_monitor.take_snapshot()
+
+@events.test_stop.add_listener
+def on_test_stop(environment, **kwargs):
+    final_snapshot = memory_monitor.take_snapshot()
+    leak_analysis = memory_monitor.analyze_memory_leaks()
+
+    print("\n=== Memory Analysis ===")
+    print(f"Final Memory Usage: {final_snapshot['process_memory_mb']:.2f}MB")
+    print(f"System Memory Usage: {final_snapshot['system_memory_percent']:.2f}%")
+    print(f"Leak Analysis: {leak_analysis}")
+
+    # 强制垃圾回收
+    gc_info = memory_monitor.force_gc()
+    print(f"GC Freed: {gc_info['memory_freed_mb']:.2f}MB")
+```
+
+**解决方案**
+```python
+# 1. 优化内存使用
+class MemoryOptimizedUser(HttpUser):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.request_count = 0
+
+    @task
+    def memory_efficient_request(self):
+        # 使用流式处理大响应
+        with self.client.get("/large-data", stream=True) as response:
+            for chunk in response.iter_content(chunk_size=8192):
+                # 处理数据块
+                pass
+
+        self.request_count += 1
+
+        # 定期清理
+        if self.request_count % 100 == 0:
+            gc.collect()
+
+# 2. 配置垃圾回收
+import gc
+
+# 调整垃圾回收阈值
+gc.set_threshold(700, 10, 10)
+
+# 3. 限制容器内存
+# 在docker-compose.yml中
+services:
+  locust-worker:
+    deploy:
+      resources:
+        limits:
+          memory: 2G
+        reservations:
+          memory: 1G
+```
+
+## 🛠️ 调试工具
+
+### 1. 实时监控脚本
+
+```bash
+#!/bin/bash
+# monitor.sh
+
+echo "Locust Real-time Monitor"
+echo "======================="
+
+while true; do
+    clear
+    echo "$(date)"
+    echo "======================="
+
+    # 服务状态
+    echo "Service Status:"
+    docker-compose ps
+    echo ""
+
+    # 资源使用
+    echo "Resource Usage:"
+    docker stats --no-stream --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}\t{{.NetIO}}"
+    echo ""
+
+    # 最新日志
+    echo "Recent Logs:"
+    docker-compose logs --tail=5 locust-master
+    echo ""
+
+    sleep 10
+done
+```
+
+### 2. 性能基准测试
+
+```python
+# benchmark.py
+import time
+import statistics
+from locust import HttpUser, task, between
+
+class BenchmarkUser(HttpUser):
+    """基准测试用户"""
+
+    wait_time = between(1, 2)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.response_times = []
+
+    @task
+    def benchmark_request(self):
+        """基准测试请求"""
+        start_time = time.time()
+
+        try:
+            response = self.client.get("/api/health")
+            response_time = (time.time() - start_time) * 1000
+
+            self.response_times.append(response_time)
+
+            # 每100个请求输出统计
+            if len(self.response_times) % 100 == 0:
+                self.print_stats()
+
+        except Exception as e:
+            print(f"Request failed: {e}")
+
+    def print_stats(self):
+        """输出统计信息"""
+        if self.response_times:
+            avg = statistics.mean(self.response_times)
+            median = statistics.median(self.response_times)
+            p95 = self.percentile(self.response_times, 95)
+
+            print(f"Stats (last {len(self.response_times)} requests):")
+            print(f"  Avg: {avg:.2f}ms, Median: {median:.2f}ms, P95: {p95:.2f}ms")
+
+    def percentile(self, data, p):
+        """计算百分位数"""
+        sorted_data = sorted(data)
+        index = int(len(sorted_data) * p / 100)
+        return sorted_data[min(index, len(sorted_data) - 1)]
+```
+
+## 📚 故障预防
+
+### 1. 健康检查
+
+```python
+# health_check.py
+import requests
+import time
+from typing import Dict, List
+
+class HealthChecker:
+    """健康检查器"""
+
+    def __init__(self, endpoints: List[str]):
+        self.endpoints = endpoints
+        self.health_history = {}
+
+    def check_endpoint(self, endpoint: str) -> Dict:
+        """检查单个端点"""
+        try:
+            start_time = time.time()
+            response = requests.get(endpoint, timeout=10)
+            response_time = (time.time() - start_time) * 1000
+
+            return {
+                'endpoint': endpoint,
+                'status': 'healthy' if response.status_code == 200 else 'unhealthy',
+                'status_code': response.status_code,
+                'response_time': response_time,
+                'timestamp': time.time()
+            }
+        except Exception as e:
+            return {
+                'endpoint': endpoint,
+                'status': 'error',
+                'error': str(e),
+                'timestamp': time.time()
+            }
+
+    def check_all_endpoints(self) -> List[Dict]:
+        """检查所有端点"""
+        results = []
+        for endpoint in self.endpoints:
+            result = self.check_endpoint(endpoint)
+            results.append(result)
+
+            # 记录历史
+            if endpoint not in self.health_history:
+                self.health_history[endpoint] = []
+            self.health_history[endpoint].append(result)
+
+            # 保持历史记录在合理范围内
+            if len(self.health_history[endpoint]) > 100:
+                self.health_history[endpoint].pop(0)
+
+        return results
+
+    def get_health_summary(self) -> Dict:
+        """获取健康状况摘要"""
+        summary = {}
+
+        for endpoint, history in self.health_history.items():
+            if history:
+                healthy_count = sum(1 for h in history if h.get('status') == 'healthy')
+                total_count = len(history)
+                uptime_percentage = (healthy_count / total_count) * 100
+
+                recent_response_times = [h.get('response_time', 0) for h in history[-10:]
+                                       if h.get('response_time')]
+                avg_response_time = sum(recent_response_times) / len(recent_response_times) if recent_response_times else 0
+
+                summary[endpoint] = {
+                    'uptime_percentage': uptime_percentage,
+                    'avg_response_time': avg_response_time,
+                    'total_checks': total_count,
+                    'last_status': history[-1].get('status')
+                }
+
+        return summary
+
+# 使用健康检查
+health_checker = HealthChecker([
+    "http://localhost:8089/",
+    "http://localhost:8089/stats/requests",
+    "https://target-api.com/health"
+])
+
+def run_health_checks():
+    """运行健康检查"""
+    results = health_checker.check_all_endpoints()
+
+    print("Health Check Results:")
+    for result in results:
+        status_icon = "✅" if result['status'] == 'healthy' else "❌"
+        print(f"{status_icon} {result['endpoint']}: {result['status']}")
+
+        if 'response_time' in result:
+            print(f"   Response time: {result['response_time']:.2f}ms")
+        if 'error' in result:
+            print(f"   Error: {result['error']}")
+
+    # 显示摘要
+    summary = health_checker.get_health_summary()
+    print("\nHealth Summary:")
+    for endpoint, stats in summary.items():
+        print(f"{endpoint}: {stats['uptime_percentage']:.2f}% uptime, {stats['avg_response_time']:.2f}ms avg")
+
+if __name__ == "__main__":
+    run_health_checks()
+```
+
+## 🎉 总结
+
+有效的故障排除需要：
+
+1. **系统化诊断**: 遵循标准的问题诊断流程
+2. **工具支持**: 使用专业的监控和分析工具
+3. **预防措施**: 建立健康检查和监控机制
+4. **知识积累**: 记录和分享故障处理经验
+5. **持续改进**: 根据问题反馈优化系统设计
+
+## 📚 相关文档
+
+- [最佳实践](best-practices.md) - 避免常见问题的最佳实践
+- [监控配置](../configuration/monitoring-config.md) - 监控系统配置
+- [生产环境配置](../configuration/production.md) - 生产环境部署
+- [常见问题解答](faq.md) - 常见问题快速解答
diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md
new file mode 100644
index 0000000..a6b3f0f
--- /dev/null
+++ b/docs/getting-started/concepts.md
@@ -0,0 +1,433 @@
+# 基础概念
+
+本文档介绍Locust性能测试框架的核心概念和术语，帮助您更好地理解和使用框架。
+
+## 🎯 核心概念
+
+### 1. 性能测试基础
+
+#### 性能测试类型
+- **负载测试 (Load Testing)**: 验证系统在预期负载下的性能表现
+- **压力测试 (Stress Testing)**: 确定系统的性能极限和瓶颈点
+- **容量测试 (Volume Testing)**: 评估系统的最大处理能力
+- **稳定性测试 (Endurance Testing)**: 长时间运行下的系统稳定性
+- **峰值测试 (Spike Testing)**: 突发流量下的系统响应能力
+
+#### 关键性能指标
+- **响应时间 (Response Time)**: 从发送请求到收到响应的时间
+- **吞吐量 (Throughput)**: 单位时间内处理的请求数量 (TPS/RPS)
+- **并发用户数 (Concurrent Users)**: 同时访问系统的用户数量
+- **错误率 (Error Rate)**: 失败请求占总请求的百分比
+- **资源利用率**: CPU、内存、网络、磁盘的使用情况
+
+### 2. Locust核心概念
+
+#### User类
+```python
+from locust import HttpUser, task, between
+
+class WebsiteUser(HttpUser):
+    wait_time = between(1, 3)  # 用户等待时间
+
+    @task(3)  # 任务权重
+    def view_homepage(self):
+        self.client.get("/")
+
+    @task(1)
+    def view_profile(self):
+        self.client.get("/profile")
+```
+
+**关键要素**：
+- **HttpUser**: 模拟HTTP用户的基类
+- **wait_time**: 任务间的等待时间
+- **@task**: 标记用户行为，支持权重设置
+- **self.client**: HTTP客户端，用于发送请求
+
+#### LoadTestShape (负载模式)
+```python
+from locust import LoadTestShape
+
+class CustomLoadShape(LoadTestShape):
+    def tick(self):
+        run_time = self.get_run_time()
+        if run_time < 60:
+            return (10, 2)  # (用户数, 生成速率)
+        elif run_time < 120:
+            return (50, 5)
+        else:
+            return None  # 停止测试
+```
+
+**核心概念**：
+- **tick()方法**: 返回当前时刻的用户数和生成速率
+- **动态调整**: 根据时间动态调整负载
+- **生成速率**: 每秒新增的用户数
+
+## 🏗️ 框架架构概念
+
+### 1. 分层架构
+
+```
+┌─────────────────────────────────────┐
+│           用户接口层                 │  ← Web UI, CLI, API
+├─────────────────────────────────────┤
+│           业务逻辑层                 │  ← 测试逻辑, 数据处理
+├─────────────────────────────────────┤
+│           服务层                     │  ← 分析, 监控, 通知
+├─────────────────────────────────────┤
+│           数据访问层                 │  ← 数据库, 文件, 缓存
+├─────────────────────────────────────┤
+│           基础设施层                 │  ← 日志, 配置, 工具
+└─────────────────────────────────────┘
+```
+
+### 2. 模块化设计
+
+#### 核心模块
+- **analysis**: 性能分析和报告生成
+- **monitoring**: 实时监控和告警
+- **data_manager**: 数据生成和管理
+- **plugins**: 插件系统和扩展
+- **utils**: 工具类和辅助函数
+
+#### 模块职责
+```python
+# 性能分析模块
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+analyzer = PerformanceAnalyzer()
+
+# 监控模块
+from src.monitoring.performance_monitor import PerformanceMonitor
+monitor = PerformanceMonitor()
+
+# 数据管理模块
+from src.data_manager.data_provider import DataProvider
+provider = DataProvider()
+
+# 插件系统
+from src.plugins.plugin_manager import PluginManager
+plugin_manager = PluginManager()
+```
+
+### 3. 插件系统概念
+
+#### 插件类型
+- **LocustPlugin**: 扩展Locust核心功能
+- **ReportPlugin**: 自定义报告生成
+- **MonitorPlugin**: 自定义监控指标
+- **DataPlugin**: 自定义数据源
+- **NotificationPlugin**: 自定义通知渠道
+- **LoadShapePlugin**: 自定义负载模式
+- **AnalysisPlugin**: 自定义分析算法
+- **StoragePlugin**: 自定义存储后端
+- **AuthenticationPlugin**: 自定义认证方式
+- **ProtocolPlugin**: 自定义协议支持
+
+#### 插件生命周期
+```python
+# 插件状态转换
+未加载 → 已发现 → 已加载 → 已初始化 → 已启用 → 已禁用 → 已卸载
+```
+
+## 📊 性能分析概念
+
+### 1. 分析维度
+
+#### 时间维度
+- **响应时间分析**: 平均值、中位数、百分位数
+- **趋势分析**: 时间序列分析和预测
+- **峰值分析**: 识别性能峰值和异常
+
+#### 业务维度
+- **接口分析**: 各API接口的性能表现
+- **用户行为分析**: 用户路径和行为模式
+- **错误分析**: 错误类型和分布
+
+#### 系统维度
+- **资源利用率**: CPU、内存、网络使用情况
+- **瓶颈识别**: 系统瓶颈点定位
+- **容量评估**: 系统容量和扩展性分析
+
+### 2. 评分系统
+
+#### 性能等级
+```python
+# A级 - 优秀
+response_time < 500ms AND error_rate < 0.1% AND throughput > 1000 TPS
+
+# B级 - 良好
+response_time < 1000ms AND error_rate < 1% AND throughput > 500 TPS
+
+# C级 - 可接受
+response_time < 2000ms AND error_rate < 5% AND throughput > 100 TPS
+
+# D级 - 较差
+response_time >= 2000ms OR error_rate >= 5% OR throughput <= 100 TPS
+```
+
+#### 权重计算
+- **响应时间权重**: 40%
+- **吞吐量权重**: 30%
+- **错误率权重**: 20%
+- **稳定性权重**: 10%
+
+## 🔍 监控告警概念
+
+### 1. 监控指标
+
+#### 基础指标
+- **avg_response_time**: 平均响应时间
+- **p50_response_time**: 50%百分位响应时间
+- **p95_response_time**: 95%百分位响应时间
+- **current_rps**: 当前每秒请求数
+- **error_rate**: 错误率百分比
+- **active_users**: 活跃用户数
+
+#### 自定义指标
+```python
+# 业务指标
+custom_metrics = {
+    'login_success_rate': 98.5,      # 登录成功率
+    'order_completion_rate': 95.2,   # 订单完成率
+    'payment_success_rate': 99.1,    # 支付成功率
+    'api_availability': 99.9         # API可用性
+}
+```
+
+### 2. 告警机制
+
+#### 告警规则
+```python
+from src.monitoring.performance_monitor import AlertRule
+
+# 创建告警规则
+alert_rule = AlertRule(
+    name="高响应时间告警",           # 规则名称
+    metric="avg_response_time",      # 监控指标
+    threshold=1000,                  # 阈值
+    operator=">",                    # 比较操作符
+    severity="warning",              # 告警级别
+    enabled=True                     # 是否启用
+)
+```
+
+#### 告警级别
+- **info**: 信息提示，无需立即处理
+- **warning**: 警告，需要关注但不紧急
+- **critical**: 严重，需要立即处理
+
+#### 告警状态
+- **created**: 告警已创建
+- **active**: 告警处于活跃状态
+- **resolved**: 告警已解决
+- **suppressed**: 告警已抑制
+
+## 💾 数据管理概念
+
+### 1. 数据生成
+
+#### 数据类型
+```python
+from src.data_manager.data_generator import DataGenerator
+
+generator = DataGenerator()
+
+# 用户数据
+users = generator.generate_users(count=100)
+# 包含: id, name, email, phone, address 等
+
+# 产品数据
+products = generator.generate_products(count=50)
+# 包含: id, name, price, category, description 等
+
+# 订单数据
+orders = generator.generate_orders(count=200)
+# 包含: id, user_id, product_id, amount, status 等
+```
+
+#### 数据特征
+- **真实性**: 使用Faker生成接近真实的数据
+- **多样性**: 支持多种数据类型和格式
+- **可控性**: 可控制数据的数量和特征
+- **本地化**: 支持中文等多种语言
+
+### 2. 数据分发
+
+#### 分发策略
+```python
+from src.data_manager.data_provider import DataProvider
+
+provider = DataProvider()
+
+# 轮询分发
+user1 = provider.get_data('users', strategy='round_robin')
+user2 = provider.get_data('users', strategy='round_robin')
+
+# 随机分发
+user3 = provider.get_data('users', strategy='random')
+
+# 顺序分发
+user4 = provider.get_data('users', strategy='sequential')
+```
+
+#### 数据源类型
+- **内存数据**: 程序运行时生成的数据
+- **文件数据**: JSON、CSV、XML等文件
+- **数据库数据**: MySQL、PostgreSQL、MongoDB等
+- **网络数据**: HTTP API、消息队列等
+
+## 🔧 配置管理概念
+
+### 1. 配置层次
+
+#### 配置优先级
+```
+环境变量 > 命令行参数 > 配置文件 > 默认值
+```
+
+#### 配置文件类型
+- **settings.toml**: 主配置文件
+- **plugin_config.json**: 插件配置
+- **.env**: 环境变量配置
+- **logging.conf**: 日志配置
+
+### 2. 环境管理
+
+#### 环境类型
+```python
+# 开发环境
+LOCUST_ENV=development
+
+# 测试环境
+LOCUST_ENV=testing
+
+# 生产环境
+LOCUST_ENV=production
+```
+
+#### 配置隔离
+- 不同环境使用不同的配置文件
+- 敏感信息通过环境变量管理
+- 配置验证和默认值处理
+
+## 🚀 负载模式概念
+
+### 1. 基础负载模式
+
+#### 固定负载
+```python
+# 恒定用户数
+users = 50
+spawn_rate = 5
+```
+
+#### 阶梯负载
+```python
+# 阶梯式增长
+stages = [
+    (10, 60),   # 10用户，持续60秒
+    (20, 120),  # 20用户，持续120秒
+    (50, 180),  # 50用户，持续180秒
+]
+```
+
+### 2. 高级负载模式
+
+#### 波浪负载
+- **用途**: 模拟用户访问的自然波动
+- **特点**: 周期性的用户数变化
+- **场景**: 日常业务流量测试
+
+#### 尖峰负载
+- **用途**: 测试突发流量处理能力
+- **特点**: 短时间内用户数急剧增加
+- **场景**: 促销活动、热点事件
+
+#### 自适应负载
+- **用途**: 根据性能指标动态调整负载
+- **特点**: 智能化的负载控制
+- **场景**: 容量测试、性能边界探测
+
+## 📈 报告生成概念
+
+### 1. 报告类型
+
+#### HTML报告
+- **特点**: 可视化图表、交互式界面
+- **用途**: 详细的分析报告、演示汇报
+- **内容**: 性能指标、趋势图、错误分析
+
+#### JSON报告
+- **特点**: 结构化数据、易于解析
+- **用途**: 数据集成、自动化处理
+- **内容**: 原始数据、统计结果
+
+#### CSV报告
+- **特点**: 表格格式、易于分析
+- **用途**: 数据分析、Excel处理
+- **内容**: 请求明细、统计汇总
+
+### 2. 报告内容
+
+#### 性能摘要
+- 测试基本信息
+- 关键性能指标
+- 性能等级评分
+- 问题和建议
+
+#### 详细分析
+- 响应时间分布
+- 吞吐量趋势
+- 错误统计分析
+- 用户行为分析
+
+## 🔗 相关概念
+
+### 1. 分布式测试
+- **Master-Worker架构**: 主从节点协调
+- **负载分发**: 测试负载在多节点间分配
+- **结果聚合**: 多节点结果的统一收集
+
+### 2. 持续集成
+- **CI/CD集成**: 与Jenkins、GitLab CI等集成
+- **自动化测试**: 代码提交触发性能测试
+- **质量门禁**: 性能指标作为发布标准
+
+### 3. 云原生支持
+- **容器化部署**: Docker容器支持
+- **Kubernetes集成**: K8s环境下的分布式测试
+- **弹性伸缩**: 根据负载自动调整资源
+
+## 📚 学习路径
+
+### 初学者
+1. 理解基础概念
+2. 学习Locust基础用法
+3. 掌握简单的负载测试
+
+### 进阶用户
+1. 学习高级负载模式
+2. 掌握性能分析方法
+3. 使用监控告警功能
+
+### 高级用户
+1. 开发自定义插件
+2. 设计复杂测试场景
+3. 优化测试框架性能
+
+## 🎯 最佳实践原则
+
+### 设计原则
+- **单一职责**: 每个模块专注特定功能
+- **开闭原则**: 对扩展开放，对修改封闭
+- **依赖倒置**: 依赖抽象而非具体实现
+
+### 测试原则
+- **渐进式测试**: 从小负载开始逐步增加
+- **真实环境**: 尽可能模拟真实使用场景
+- **持续监控**: 实时关注系统状态变化
+
+---
+
+理解这些核心概念将帮助您更好地使用框架进行性能测试。建议结合实际项目逐步深入学习和实践。
diff --git a/docs/getting-started/first-test.md b/docs/getting-started/first-test.md
new file mode 100644
index 0000000..cfda5f0
--- /dev/null
+++ b/docs/getting-started/first-test.md
@@ -0,0 +1,440 @@
+# 第一个性能测试
+
+本指南将带您创建第一个完整的性能测试，从编写测试脚本到分析测试结果。
+
+## 🎯 学习目标
+
+通过本指南，您将学会：
+- 编写基础的Locust测试脚本
+- 配置测试参数和数据
+- 运行性能测试
+- 分析测试结果
+- 使用框架的高级功能
+
+## 📝 创建测试脚本
+
+### 1. 基础HTTP测试
+
+创建您的第一个测试文件 `my_first_test.py`：
+
+```python
+from locust import HttpUser, task, between
+import random
+
+class WebsiteUser(HttpUser):
+    """网站用户行为模拟"""
+
+    # 用户等待时间：1-3秒
+    wait_time = between(1, 3)
+
+    def on_start(self):
+        """用户开始时执行的操作"""
+        self.login()
+
+    def login(self):
+        """用户登录"""
+        response = self.client.post("/login", json={
+            "username": "testuser",
+            "password": "testpass"
+        })
+
+        if response.status_code == 200:
+            self.token = response.json().get("token")
+        else:
+            print(f"Login failed: {response.status_code}")
+
+    @task(3)
+    def view_homepage(self):
+        """浏览首页 - 权重3"""
+        self.client.get("/")
+
+    @task(2)
+    def view_products(self):
+        """浏览产品页面 - 权重2"""
+        product_id = random.randint(1, 100)
+        self.client.get(f"/products/{product_id}")
+
+    @task(1)
+    def search_products(self):
+        """搜索产品 - 权重1"""
+        keywords = ["laptop", "phone", "tablet", "camera"]
+        keyword = random.choice(keywords)
+        self.client.get(f"/search?q={keyword}")
+
+    @task(1)
+    def add_to_cart(self):
+        """添加到购物车"""
+        if hasattr(self, 'token'):
+            product_id = random.randint(1, 50)
+            self.client.post("/cart/add",
+                           json={"product_id": product_id, "quantity": 1},
+                           headers={"Authorization": f"Bearer {self.token}"})
+
+    def on_stop(self):
+        """用户结束时执行的操作"""
+        if hasattr(self, 'token'):
+            self.client.post("/logout",
+                           headers={"Authorization": f"Bearer {self.token}"})
+```
+
+### 2. 添加数据驱动测试
+
+创建测试数据文件 `test_data.csv`：
+
+```csv
+username,password,email
+user1,pass123,user1@example.com
+user2,pass456,user2@example.com
+user3,pass789,user3@example.com
+user4,pass000,user4@example.com
+```
+
+修改测试脚本使用数据文件：
+
+```python
+import csv
+from locust import HttpUser, task, between
+
+class DataDrivenUser(HttpUser):
+    """数据驱动的用户测试"""
+
+    wait_time = between(1, 2)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.test_data = self.load_test_data()
+        self.user_data = None
+
+    def load_test_data(self):
+        """加载测试数据"""
+        data = []
+        try:
+            with open('test_data.csv', 'r', encoding='utf-8') as file:
+                reader = csv.DictReader(file)
+                data = list(reader)
+        except FileNotFoundError:
+            print("Warning: test_data.csv not found, using default data")
+            data = [{"username": "testuser", "password": "testpass",
+                    "email": "test@example.com"}]
+        return data
+
+    def on_start(self):
+        """选择测试数据并登录"""
+        import random
+        self.user_data = random.choice(self.test_data)
+        self.login()
+
+    def login(self):
+        """使用测试数据登录"""
+        response = self.client.post("/login", json={
+            "username": self.user_data["username"],
+            "password": self.user_data["password"]
+        })
+
+        if response.status_code == 200:
+            self.token = response.json().get("token")
+            print(f"User {self.user_data['username']} logged in successfully")
+        else:
+            print(f"Login failed for {self.user_data['username']}: {response.status_code}")
+
+    @task
+    def get_user_profile(self):
+        """获取用户资料"""
+        if hasattr(self, 'token'):
+            self.client.get("/profile",
+                          headers={"Authorization": f"Bearer {self.token}"})
+```
+
+## ⚙️ 配置测试参数
+
+### 1. 创建配置文件
+
+创建 `test_config.yaml`：
+
+```yaml
+# 测试配置
+test:
+  host: "https://api.example.com"
+  users: 50
+  spawn_rate: 5
+  run_time: "5m"
+
+# 数据配置
+data:
+  source: "csv"
+  file_path: "test_data.csv"
+  distribution: "round_robin"
+
+# 监控配置
+monitoring:
+  enabled: true
+  cpu_threshold: 80
+  memory_threshold: 85
+  response_time_threshold: 2000
+
+# 报告配置
+reporting:
+  formats: ["html", "csv"]
+  output_dir: "reports"
+  include_charts: true
+```
+
+### 2. 使用配置文件
+
+修改测试脚本读取配置：
+
+```python
+import yaml
+from locust import HttpUser, task, between
+
+class ConfigurableUser(HttpUser):
+    """可配置的用户测试"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.config = self.load_config()
+        self.wait_time = between(1, 3)
+
+    def load_config(self):
+        """加载配置文件"""
+        try:
+            with open('test_config.yaml', 'r', encoding='utf-8') as file:
+                return yaml.safe_load(file)
+        except FileNotFoundError:
+            return {"test": {"host": "http://localhost:8000"}}
+
+    @task
+    def api_test(self):
+        """API测试"""
+        # 使用配置中的参数
+        timeout = self.config.get("test", {}).get("timeout", 30)
+
+        response = self.client.get("/api/health", timeout=timeout)
+
+        # 检查响应时间阈值
+        threshold = self.config.get("monitoring", {}).get("response_time_threshold", 2000)
+        if response.elapsed.total_seconds() * 1000 > threshold:
+            print(f"Warning: Response time {response.elapsed.total_seconds() * 1000}ms exceeds threshold {threshold}ms")
+```
+
+## 🚀 运行测试
+
+### 1. 命令行运行
+
+```bash
+# 基础运行
+locust -f my_first_test.py --host=https://api.example.com
+
+# 指定用户数和生成速率
+locust -f my_first_test.py --host=https://api.example.com -u 50 -r 5
+
+# 无头模式运行
+locust -f my_first_test.py --host=https://api.example.com -u 50 -r 5 -t 300s --headless
+
+# 生成HTML报告
+locust -f my_first_test.py --host=https://api.example.com -u 50 -r 5 -t 300s --headless --html=report.html
+```
+
+### 2. 使用框架运行
+
+```python
+# run_test.py
+from src.model.locust_test import LocustTest
+
+def main():
+    # 创建测试实例
+    test = LocustTest()
+
+    # 配置测试参数
+    test.configure({
+        "locustfile": "my_first_test.py",
+        "host": "https://api.example.com",
+        "users": 50,
+        "spawn_rate": 5,
+        "run_time": "5m",
+        "headless": True
+    })
+
+    # 运行测试
+    result = test.run()
+
+    # 输出结果
+    print(f"Test completed: {result}")
+
+if __name__ == "__main__":
+    main()
+```
+
+## 📊 分析测试结果
+
+### 1. 查看实时监控
+
+测试运行时，您可以：
+- 访问 http://localhost:8089 查看Web界面
+- 监控实时性能指标
+- 观察错误率和响应时间变化
+- 调整用户数和生成速率
+
+### 2. 分析HTML报告
+
+测试完成后，查看生成的HTML报告：
+
+```html
+<!-- 报告包含以下信息 -->
+- 总体统计信息
+- 请求统计详情
+- 响应时间分布
+- 错误统计和分析
+- 性能趋势图表
+```
+
+### 3. 使用分析工具
+
+```python
+# analyze_results.py
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+
+def analyze_test_results():
+    analyzer = PerformanceAnalyzer()
+
+    # 加载测试结果
+    results = analyzer.load_results("reports/stats.csv")
+
+    # 生成分析报告
+    analysis = analyzer.analyze(results)
+
+    # 输出分析结果
+    print(f"Performance Grade: {analysis['grade']}")
+    print(f"Average Response Time: {analysis['avg_response_time']}ms")
+    print(f"95th Percentile: {analysis['p95_response_time']}ms")
+    print(f"Error Rate: {analysis['error_rate']}%")
+    print(f"Throughput: {analysis['throughput']} RPS")
+
+    # 生成建议
+    recommendations = analyzer.get_recommendations(analysis)
+    print("\nRecommendations:")
+    for rec in recommendations:
+        print(f"- {rec}")
+
+if __name__ == "__main__":
+    analyze_test_results()
+```
+
+## 🔧 使用高级功能
+
+### 1. 添加监控告警
+
+```python
+from src.monitoring.system_monitor import SystemMonitor
+
+class MonitoredUser(HttpUser):
+    """带监控的用户测试"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.monitor = SystemMonitor()
+        self.monitor.start()
+
+    @task
+    def monitored_request(self):
+        """带监控的请求"""
+        start_time = time.time()
+
+        response = self.client.get("/api/data")
+
+        # 记录自定义指标
+        response_time = (time.time() - start_time) * 1000
+        self.monitor.record_metric("custom_response_time", response_time)
+
+        # 检查告警条件
+        if response_time > 2000:
+            self.monitor.trigger_alert("slow_response", {
+                "response_time": response_time,
+                "url": "/api/data",
+                "user": self.user_data.get("username", "unknown")
+            })
+```
+
+### 2. 使用插件系统
+
+```python
+# custom_plugin.py
+from src.plugins.base_plugin import BasePlugin
+
+class TestMetricsPlugin(BasePlugin):
+    """自定义测试指标插件"""
+
+    def initialize(self):
+        self.request_count = 0
+        self.error_count = 0
+        return True
+
+    def on_request_success(self, request_type, name, response_time, response_length, **kwargs):
+        self.request_count += 1
+
+        # 记录慢请求
+        if response_time > 1000:
+            print(f"Slow request detected: {name} took {response_time}ms")
+
+    def on_request_failure(self, request_type, name, response_time, response_length, exception, **kwargs):
+        self.error_count += 1
+        print(f"Request failed: {name} - {exception}")
+
+    def cleanup(self):
+        print(f"Test completed: {self.request_count} requests, {self.error_count} errors")
+
+# 在测试中使用插件
+from src.plugins.plugin_manager import PluginManager
+
+plugin_manager = PluginManager()
+plugin_manager.register_plugin(TestMetricsPlugin)
+```
+
+## 🎯 最佳实践
+
+### 1. 测试设计原则
+
+- **渐进式加压**: 从小负载开始，逐步增加
+- **真实场景**: 模拟真实用户行为模式
+- **数据隔离**: 使用独立的测试数据
+- **环境一致**: 保持测试环境的一致性
+
+### 2. 性能指标关注点
+
+- **响应时间**: 关注P95、P99百分位数
+- **吞吐量**: 监控TPS和RPS指标
+- **错误率**: 保持在可接受范围内
+- **资源使用**: 监控CPU、内存、网络
+
+### 3. 问题排查技巧
+
+- **查看日志**: 检查详细的错误日志
+- **分析趋势**: 观察性能指标变化趋势
+- **对比基线**: 与历史数据进行对比
+- **逐步排查**: 从简单到复杂逐步排查
+
+## 🎉 下一步
+
+恭喜！您已经完成了第一个性能测试。接下来可以：
+
+1. **学习高级功能**:
+   - [负载模式](../api/load-shapes.md) - 使用不同的负载模式
+   - [插件开发](../development/plugin-development.md) - 开发自定义插件
+   - [分布式测试](../examples/advanced-usage.md) - 运行分布式测试
+
+2. **优化测试脚本**:
+   - [最佳实践](../examples/best-practices.md) - 学习最佳实践
+   - [代码规范](../development/coding-standards.md) - 遵循代码规范
+   - [测试策略](../examples/advanced-examples.md) - 高级测试策略
+
+3. **生产环境部署**:
+   - [生产配置](../configuration/production.md) - 生产环境配置
+   - [监控部署](../configuration/monitoring-config.md) - 监控系统部署
+   - [CI/CD集成](../examples/advanced-usage.md) - 持续集成
+
+## 📚 相关文档
+
+- [快速入门](quickstart.md) - 5分钟快速上手
+- [基础概念](concepts.md) - 核心概念说明
+- [API参考](../api/analysis.md) - 详细API文档
+- [配置参考](../configuration/framework-config.md) - 配置选项说明
diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
new file mode 100644
index 0000000..7611dc6
--- /dev/null
+++ b/docs/getting-started/installation.md
@@ -0,0 +1,307 @@
+# 安装指南
+
+本指南将帮助您在不同环境中安装和配置Locust性能测试框架。
+
+## 系统要求
+
+### 最低要求
+- **Python**: 3.7 或更高版本
+- **内存**: 最少 2GB RAM
+- **磁盘空间**: 最少 1GB 可用空间
+- **操作系统**: Windows 10+, macOS 10.14+, Linux (Ubuntu 18.04+, CentOS 7+)
+
+### 推荐配置
+- **Python**: 3.9 或更高版本
+- **内存**: 8GB RAM 或更多
+- **磁盘空间**: 5GB 可用空间
+- **CPU**: 多核处理器（用于分布式测试）
+
+## 安装方式
+
+### 方式一：使用pip安装（推荐）
+
+```bash
+# 创建虚拟环境（推荐）
+python -m venv locust_env
+source locust_env/bin/activate  # Linux/macOS
+# 或
+locust_env\Scripts\activate     # Windows
+
+# 安装核心依赖
+pip install locust>=2.0.0
+pip install dynaconf>=3.1.0
+pip install peewee>=3.14.0
+pip install loguru>=0.6.0
+pip install faker>=15.0.0
+pip install jinja2>=3.0.0
+pip install requests>=2.28.0
+pip install psutil>=5.8.0
+
+# 安装可选依赖
+pip install pandas>=1.3.0        # 数据分析功能
+pip install matplotlib>=3.5.0    # 图表生成
+pip install seaborn>=0.11.0      # 高级图表
+pip install openpyxl>=3.0.0      # Excel支持
+```
+
+### 方式二：从源码安装
+
+```bash
+# 克隆项目
+git clone git@github.com:ljxpython/locust_framework.git
+cd locust_framework
+
+# 创建虚拟环境
+python -m venv venv
+source venv/bin/activate  # Linux/macOS
+# 或
+venv\Scripts\activate     # Windows
+
+# 安装依赖
+pip install -r requirements.txt
+
+# 安装开发依赖（可选）
+pip install -r requirements-dev.txt
+```
+
+###
+
+## 依赖说明
+
+### 核心依赖
+```
+locust>=2.0.0           # 核心测试引擎
+dynaconf>=3.1.0         # 配置管理
+peewee>=3.14.0          # 数据库ORM
+loguru>=0.6.0           # 日志管理
+faker>=15.0.0           # 测试数据生成
+jinja2>=3.0.0           # 模板引擎
+requests>=2.28.0        # HTTP客户端
+psutil>=5.8.0           # 系统监控
+```
+
+### 可选依赖
+```
+pandas>=1.3.0           # 数据分析
+matplotlib>=3.5.0       # 基础图表
+seaborn>=0.11.0         # 高级图表
+openpyxl>=3.0.0         # Excel文件支持
+redis>=4.0.0            # Redis缓存
+pymongo>=4.0.0          # MongoDB支持
+mysql-connector-python>=8.0.0  # MySQL支持
+psycopg2-binary>=2.9.0  # PostgreSQL支持
+```
+
+## 环境配置
+
+### 1. 创建配置文件
+
+```bash
+# 复制配置模板
+cp plugin_config.template.json plugin_config.json
+cp conf/settings.toml.example conf/settings.toml
+```
+
+### 2. 配置环境变量
+
+```bash
+# Linux/macOS
+export LOCUST_ENV=development
+export LOCUST_CONFIG_PATH=./conf
+
+# Windows
+set LOCUST_ENV=development
+set LOCUST_CONFIG_PATH=./conf
+```
+
+### 3. 初始化数据库（可选）
+
+```bash
+# 创建数据库表
+python -c "
+from src.utils.database import init_database
+init_database()
+"
+```
+
+## 验证安装
+
+### 1. 检查Python版本
+```bash
+python --version
+# 应该显示 Python 3.7+ 版本
+```
+
+### 2. 检查依赖安装
+```bash
+python -c "
+import locust
+import dynaconf
+import peewee
+import loguru
+import faker
+print('所有核心依赖安装成功！')
+"
+```
+
+### 3. 运行测试示例
+```bash
+# 运行框架功能演示
+python examples/enhanced_framework_demo.py
+
+# 启动Locust Web界面
+locust -f examples/basic_test.py --host=http://localhost:8080
+```
+
+### 4. 检查插件系统
+```bash
+python -c "
+from src.plugins.plugin_manager import PluginManager
+pm = PluginManager()
+print(f'发现插件: {pm.get_available_plugins()}')
+"
+```
+
+## 常见问题
+
+### Q1: 安装时出现权限错误
+```bash
+# 解决方案：使用用户安装
+pip install --user locust
+
+# 或者使用虚拟环境
+python -m venv locust_env
+source locust_env/bin/activate
+pip install locust
+```
+
+### Q2: 依赖版本冲突
+```bash
+# 解决方案：创建干净的虚拟环境
+python -m venv fresh_env
+source fresh_env/bin/activate
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+### Q3: Windows下安装失败
+```bash
+# 可能需要安装Visual C++ Build Tools
+# 下载并安装：https://visualstudio.microsoft.com/visual-cpp-build-tools/
+
+# 或者使用预编译包
+pip install --only-binary=all locust
+```
+
+### Q4: macOS下SSL证书问题
+```bash
+# 更新证书
+/Applications/Python\ 3.x/Install\ Certificates.command
+
+# 或者使用conda
+conda install -c conda-forge locust
+```
+
+## 开发环境配置
+
+### 1. 安装开发工具
+```bash
+pip install pytest>=6.0.0
+pip install pytest-cov>=3.0.0
+pip install black>=22.0.0
+pip install flake8>=4.0.0
+pip install mypy>=0.950
+pip install pre-commit>=2.15.0
+```
+
+### 2. 配置代码格式化
+```bash
+# 安装pre-commit钩子
+pre-commit install
+
+# 手动运行格式化
+black src/ tests/
+flake8 src/ tests/
+```
+
+### 3. 运行测试
+```bash
+# 运行所有测试
+pytest
+
+# 运行测试并生成覆盖率报告
+pytest --cov=src --cov-report=html
+
+# 运行特定测试
+pytest tests/test_analysis.py
+```
+
+## 生产环境部署
+
+### 1. 系统优化
+```bash
+# 增加文件描述符限制
+echo "* soft nofile 65536" >> /etc/security/limits.conf
+echo "* hard nofile 65536" >> /etc/security/limits.conf
+
+# 优化网络参数
+echo "net.core.somaxconn = 65536" >> /etc/sysctl.conf
+sysctl -p
+```
+
+### 2. 服务配置
+```bash
+# 创建systemd服务文件
+sudo tee /etc/systemd/system/locust.service > /dev/null <<EOF
+[Unit]
+Description=Locust Performance Testing Framework
+After=network.target
+
+[Service]
+Type=simple
+User=locust
+WorkingDirectory=/opt/locust_framework
+ExecStart=/opt/locust_framework/venv/bin/python -m locust -f main.py
+Restart=always
+
+[Install]
+WantedBy=multi-user.target
+EOF
+
+# 启用服务
+sudo systemctl enable locust
+sudo systemctl start locust
+```
+
+### 3. 监控配置
+```bash
+# 配置日志轮转
+sudo tee /etc/logrotate.d/locust > /dev/null <<EOF
+/var/log/locust/*.log {
+    daily
+    missingok
+    rotate 30
+    compress
+    delaycompress
+    notifempty
+    create 644 locust locust
+}
+EOF
+```
+
+## 下一步
+
+安装完成后，您可以：
+
+1. [快速入门](quickstart.md) - 学习基础使用方法
+2. [基础概念](concepts.md) - 了解核心概念
+3. [第一个测试](first-test.md) - 创建您的第一个测试
+4. [配置指南](../configuration/framework-config.md) - 详细配置说明
+
+## 获取帮助
+
+如果在安装过程中遇到问题：
+
+- 查看 [故障排除指南](../examples/troubleshooting.md)
+- 提交 [GitHub Issue](https://github.com/your-repo/issues)
+- 发送邮件至 support@example.com
diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md
new file mode 100644
index 0000000..0d2bb60
--- /dev/null
+++ b/docs/getting-started/quickstart.md
@@ -0,0 +1,402 @@
+# 快速入门指南
+
+本指南将在5分钟内带您体验Locust性能测试框架的核心功能。
+
+## 🚀 5分钟快速体验
+
+### 步骤1：验证安装
+
+```bash
+# 检查Python和Locust版本
+python --version
+python -c "import locust; print(f'Locust版本: {locust.__version__}')"
+
+# 检查框架组件
+python -c "
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.plugins.plugin_manager import PluginManager
+print('框架组件加载成功！')
+"
+```
+
+### 步骤2：创建第一个测试
+
+创建文件 `my_first_test.py`：
+
+```python
+from locust import HttpUser, task, between
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.monitoring.performance_monitor import PerformanceMonitor, AlertRule
+
+class WebsiteUser(HttpUser):
+    wait_time = between(1, 3)  # 用户等待时间1-3秒
+
+    def on_start(self):
+        """用户开始时执行"""
+        self.client.verify = False  # 忽略SSL证书验证
+
+    @task(3)
+    def view_homepage(self):
+        """访问首页 - 权重3"""
+        self.client.get("/")
+
+    @task(2)
+    def view_about(self):
+        """访问关于页面 - 权重2"""
+        self.client.get("/about")
+
+    @task(1)
+    def view_contact(self):
+        """访问联系页面 - 权重1"""
+        self.client.get("/contact")
+
+# 配置性能监控
+def setup_monitoring():
+    """设置性能监控"""
+    alert_rules = [
+        AlertRule("响应时间告警", "avg_response_time", 1000, ">", "warning"),
+        AlertRule("错误率告警", "error_rate", 5.0, ">", "critical")
+    ]
+
+    monitor = PerformanceMonitor(alert_rules=alert_rules)
+    monitor.start_monitoring()
+    return monitor
+
+if __name__ == "__main__":
+    # 设置监控
+    monitor = setup_monitoring()
+    print("性能监控已启动，准备开始测试...")
+```
+
+### 步骤3：运行测试
+
+```bash
+# 启动Locust Web界面
+locust -f my_first_test.py --host=http://httpbin.org
+
+# 或者使用命令行模式
+locust -f my_first_test.py --host=http://httpbin.org \
+       --users 10 --spawn-rate 2 --run-time 60s --headless
+```
+
+### 步骤4：查看结果
+
+打开浏览器访问 `http://localhost:8089`，您将看到：
+- 实时性能指标
+- 请求统计信息
+- 响应时间分布
+- 错误统计
+
+测试完成后，您可以在以下位置找到详细报告：
+- `reports/` - 生成的HTML和JSON报告
+- `logs/` - 详细的测试日志
+- Web界面的统计页面
+
+## 📊 使用增强功能
+
+### 性能分析示例
+
+```python
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.analysis.report_generator import ReportGenerator
+
+# 创建分析器
+analyzer = PerformanceAnalyzer()
+
+# 模拟测试数据
+test_data = {
+    'test_name': '快速入门测试',
+    'start_time': '2024-01-01 10:00:00',
+    'end_time': '2024-01-01 10:05:00',
+    'duration': 300,
+    'users': 10,
+    'requests': [
+        {'response_time': 200, 'success': True, 'name': 'GET /'},
+        {'response_time': 350, 'success': True, 'name': 'GET /about'},
+        {'response_time': 1200, 'success': False, 'name': 'GET /contact'},
+        # ... 更多请求数据
+    ]
+}
+
+# 执行分析
+result = analyzer.comprehensive_analysis(test_data)
+print(f"性能评分: {result['overall_grade']}")
+print(f"响应时间P95: {result['response_time']['p95']}ms")
+
+# 生成报告
+report_generator = ReportGenerator()
+report_generator.generate_html_report(result, 'reports/quickstart_report.html')
+print("报告已生成: reports/quickstart_report.html")
+```
+
+### 数据管理示例
+
+```python
+from src.data_manager.data_generator import DataGenerator
+from src.data_manager.data_provider import DataProvider
+
+# 生成测试数据
+generator = DataGenerator()
+users = generator.generate_users(count=100)
+products = generator.generate_products(count=50)
+
+print(f"生成了 {len(users)} 个用户和 {len(products)} 个产品")
+
+# 使用数据提供器
+provider = DataProvider()
+provider.add_data('users', users)
+provider.add_data('products', products)
+
+# 在测试中获取数据
+user = provider.get_data('users')  # 轮询获取
+product = provider.get_data('products', strategy='random')  # 随机获取
+
+print(f"获取用户: {user['name']}")
+print(f"获取产品: {product['name']}")
+```
+
+### 插件系统示例
+
+```python
+from src.plugins.plugin_manager import PluginManager
+
+# 创建插件管理器
+plugin_manager = PluginManager()
+
+# 查看可用插件
+plugins = plugin_manager.get_available_plugins()
+print(f"可用插件: {plugins}")
+
+# 启用CSV报告插件
+if 'csv_report_plugin' in plugins:
+    plugin_manager.enable_plugin('csv_report_plugin', {
+        'output_directory': 'reports/csv',
+        'include_summary': True
+    })
+    print("CSV报告插件已启用")
+
+# 获取插件状态
+status = plugin_manager.get_plugin_status()
+for name, info in status.items():
+    print(f"插件 {name}: {'启用' if info['enabled'] else '禁用'}")
+```
+
+## 🔧 高级负载模式
+
+### 波浪负载测试
+
+```python
+from locust import HttpUser, task, LoadTestShape
+from locustfiles.shape_classes.advanced_shapes import WaveLoadShape
+
+class MyWaveTest(LoadTestShape):
+    """波浪形负载测试"""
+
+    def __init__(self):
+        super().__init__()
+        self.wave_shape = WaveLoadShape(
+            min_users=5,      # 最小用户数
+            max_users=50,     # 最大用户数
+            wave_period=120,  # 波浪周期2分钟
+            spawn_rate=5,     # 用户生成速率
+            time_limit=600    # 总测试时间10分钟
+        )
+
+    def tick(self):
+        return self.wave_shape.tick()
+
+class WebsiteUser(HttpUser):
+    wait_time = between(1, 2)
+
+    @task
+    def load_page(self):
+        self.client.get("/")
+```
+
+### 尖峰负载测试
+
+```python
+from locustfiles.shape_classes.advanced_shapes import SpikeLoadShape
+
+class MySpikeTest(LoadTestShape):
+    """尖峰负载测试"""
+
+    def __init__(self):
+        super().__init__()
+        self.spike_shape = SpikeLoadShape(
+            base_users=20,      # 基础用户数
+            spike_users=100,    # 尖峰用户数
+            spike_duration=30,  # 尖峰持续30秒
+            spike_interval=180, # 每3分钟一次尖峰
+            spawn_rate=10,      # 用户生成速率
+            time_limit=900      # 总测试时间15分钟
+        )
+
+    def tick(self):
+        return self.spike_shape.tick()
+```
+
+## 📈 实时监控配置
+
+### 配置告警规则
+
+```python
+from src.monitoring.performance_monitor import PerformanceMonitor, AlertRule
+from src.monitoring.notification_service import NotificationService
+
+# 创建告警规则
+alert_rules = [
+    AlertRule(
+        name="高响应时间",
+        metric="avg_response_time",
+        threshold=1000,
+        operator=">",
+        severity="warning"
+    ),
+    AlertRule(
+        name="高错误率",
+        metric="error_rate",
+        threshold=5.0,
+        operator=">",
+        severity="critical"
+    ),
+    AlertRule(
+        name="低吞吐量",
+        metric="current_rps",
+        threshold=10,
+        operator="<",
+        severity="warning"
+    )
+]
+
+# 启动监控
+monitor = PerformanceMonitor(alert_rules=alert_rules)
+monitor.start_monitoring()
+
+# 配置通知（可选）
+notification_service = NotificationService()
+# 配置飞书通知
+notification_service.configure_channel('feishu', {
+    'webhook_url': 'your-feishu-webhook-url'
+})
+```
+
+## 🎯 完整测试示例
+
+创建文件 `complete_example.py`：
+
+```python
+from locust import HttpUser, task, between, LoadTestShape
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.monitoring.performance_monitor import PerformanceMonitor, AlertRule
+from src.data_manager.data_generator import DataGenerator
+from src.data_manager.data_provider import DataProvider
+from locustfiles.shape_classes.advanced_shapes import RampUpDownLoadShape
+
+# 数据准备
+generator = DataGenerator()
+provider = DataProvider()
+
+# 生成测试数据
+users_data = generator.generate_users(count=100)
+provider.add_data('users', users_data)
+
+class APIUser(HttpUser):
+    wait_time = between(1, 3)
+
+    def on_start(self):
+        # 获取测试用户数据
+        self.user_data = provider.get_data('users')
+
+    @task(3)
+    def get_user_profile(self):
+        """获取用户信息"""
+        user_id = self.user_data['id']
+        with self.client.get(f"/users/{user_id}",
+                           catch_response=True) as response:
+            if response.status_code == 200:
+                response.success()
+            else:
+                response.failure(f"状态码: {response.status_code}")
+
+    @task(2)
+    def update_user_profile(self):
+        """更新用户信息"""
+        user_id = self.user_data['id']
+        update_data = {
+            'name': self.user_data['name'],
+            'email': self.user_data['email']
+        }
+        self.client.put(f"/users/{user_id}", json=update_data)
+
+    @task(1)
+    def delete_user(self):
+        """删除用户（模拟）"""
+        user_id = self.user_data['id']
+        self.client.delete(f"/users/{user_id}")
+
+class MyLoadShape(LoadTestShape):
+    """自定义负载模式"""
+
+    def __init__(self):
+        super().__init__()
+        self.shape = RampUpDownLoadShape(
+            target_users=50,     # 目标用户数
+            ramp_up_time=120,    # 2分钟爬升
+            hold_time=300,       # 5分钟保持
+            ramp_down_time=60,   # 1分钟下降
+            spawn_rate=5         # 用户生成速率
+        )
+
+    def tick(self):
+        return self.shape.tick()
+
+# 设置监控
+def setup_monitoring():
+    alert_rules = [
+        AlertRule("响应时间告警", "avg_response_time", 800, ">", "warning"),
+        AlertRule("错误率告警", "error_rate", 3.0, ">", "critical")
+    ]
+
+    monitor = PerformanceMonitor(alert_rules=alert_rules)
+    monitor.start_monitoring()
+    return monitor
+
+if __name__ == "__main__":
+    monitor = setup_monitoring()
+    print("完整测试示例已准备就绪！")
+    print("运行命令: locust -f complete_example.py --host=http://your-api-server")
+```
+
+## 🎉 下一步学习
+
+恭喜！您已经完成了快速入门。接下来可以：
+
+1. **深入学习**：
+   - [基础概念](concepts.md) - 理解核心概念
+   - [API文档](../api/analysis.md) - 查看详细API
+   - [最佳实践](../examples/best-practices.md) - 学习经验技巧
+
+2. **实践项目**：
+   - [高级示例](../examples/advanced-examples.md) - 复杂场景实现
+   - [插件开发](../development/plugin-development.md) - 开发自定义插件
+   - [负载模式开发](../development/load-shape-development.md) - 创建负载模式
+
+3. **配置优化**：
+   - [框架配置](../configuration/framework-config.md) - 详细配置选项
+   - [监控配置](../configuration/monitoring-config.md) - 监控系统配置
+   - [生产环境](../configuration/production.md) - 生产环境部署
+
+## 💡 小贴士
+
+- **逐步学习**：从简单的HTTP测试开始，逐步添加高级功能
+- **查看日志**：遇到问题时查看日志文件获取详细信息
+- **参考示例**：examples目录包含丰富的示例代码
+- **社区支持**：遇到问题可以在GitHub提交Issue或参与讨论
+
+## 🔗 相关链接
+
+- [安装指南](installation.md) - 详细安装说明
+- [配置参考](../configuration/framework-config.md) - 配置选项说明
+- [故障排除](../examples/troubleshooting.md) - 常见问题解决
+- [GitHub仓库](https://github.com/your-repo) - 源码和问题反馈
diff --git a/locustfiles/examples/01_basic_test.py b/locustfiles/examples/01_basic_test.py
new file mode 100644
index 0000000..f876a52
--- /dev/null
+++ b/locustfiles/examples/01_basic_test.py
@@ -0,0 +1,181 @@
+#!/usr/bin/env python3
+"""
+基础功能示例 - HTTP测试
+
+演示如何使用框架进行基本的HTTP性能测试
+目标：Mock服务的基础API测试
+"""
+
+import random
+
+from locust import HttpUser, between, events, task
+from locust.env import Environment
+
+
+class BasicUser(HttpUser):
+    """基础用户类 - 演示基本HTTP测试功能"""
+
+    wait_time = between(1, 2)  # 用户间隔1-2秒
+    host = "http://localhost:5001"  # Mock服务地址
+
+    def on_start(self):
+        """测试开始时执行"""
+        print(f"用户开始测试 - 目标主机: {self.host}")
+
+    def on_stop(self):
+        """测试结束时执行"""
+        print("用户结束测试")
+
+    @task(3)
+    def health_check(self):
+        """健康检查 - 权重3"""
+        with self.client.get(
+            "/health", name="健康检查", catch_response=True
+        ) as response:
+            if response.status_code == 200:
+                response.success()
+            else:
+                response.failure(f"状态码异常: {response.status_code}")
+
+    @task(2)
+    def get_metrics(self):
+        """获取系统指标 - 权重2"""
+        with self.client.get(
+            "/metrics", name="系统指标", catch_response=True
+        ) as response:
+            if response.status_code == 200 and "requests_total" in response.text:
+                response.success()
+            else:
+                response.failure("指标数据异常")
+
+    @task(5)
+    def get_products(self):
+        """获取商品列表 - 权重5"""
+        page = random.randint(1, 5)
+        limit = random.choice([10, 20, 50])
+
+        with self.client.get(
+            f"/api/products?page={page}&limit={limit}",
+            name="商品列表",
+            catch_response=True,
+        ) as response:
+            if response.status_code == 200:
+                try:
+                    data = response.json()
+                    if "products" in data and "total" in data:
+                        response.success()
+                    else:
+                        response.failure("响应格式异常")
+                except Exception as e:
+                    response.failure(f"JSON解析失败: {e}")
+            else:
+                response.failure(f"状态码异常: {response.status_code}")
+
+    @task(3)
+    def get_product_detail(self):
+        """获取商品详情 - 权重3"""
+        product_id = random.randint(1, 100)
+
+        with self.client.get(
+            f"/api/products/{product_id}", name="商品详情", catch_response=True
+        ) as response:
+            if response.status_code == 200:
+                try:
+                    data = response.json()
+                    if "id" in data and "name" in data:
+                        response.success()
+                    else:
+                        response.failure("商品数据格式异常")
+                except Exception as e:
+                    response.failure(f"JSON解析失败: {e}")
+            elif response.status_code == 404:
+                # 404是预期的情况（商品不存在）
+                response.success()
+            else:
+                response.failure(f"意外状态码: {response.status_code}")
+
+    @task(2)
+    def search_products(self):
+        """搜索商品 - 权重2"""
+        search_terms = ["手机", "电脑", "图书", "衣服", "家具"]
+        query = random.choice(search_terms)
+
+        with self.client.get(
+            f"/api/search?q={query}&sort=price", name="商品搜索", catch_response=True
+        ) as response:
+            if response.status_code == 200:
+                try:
+                    data = response.json()
+                    if "products" in data and "total" in data:
+                        response.success()
+                    else:
+                        response.failure("搜索结果格式异常")
+                except Exception as e:
+                    response.failure(f"JSON解析失败: {e}")
+            else:
+                response.failure(f"状态码异常: {response.status_code}")
+
+
+# 测试事件监听器
+@events.test_start.add_listener
+def on_test_start(environment: Environment, **kwargs):
+    """测试开始事件"""
+    print("🚀 基础功能测试开始")
+    print(f"目标主机: {environment.host}")
+    print(
+        f"用户数: {environment.parsed_options.num_users if hasattr(environment.parsed_options, 'num_users') else '未知'}"
+    )
+
+
+@events.test_stop.add_listener
+def on_test_stop(environment: Environment, **kwargs):
+    """测试结束事件"""
+    print("✅ 基础功能测试完成")
+
+    # 输出测试统计信息
+    stats = environment.stats
+    print("\n📊 测试统计:")
+    print(f"总请求数: {stats.total.num_requests}")
+    print(f"失败请求数: {stats.total.num_failures}")
+    print(
+        f"错误率: {(stats.total.num_failures/max(stats.total.num_requests, 1)*100):.2f}%"
+    )
+    print(f"平均响应时间: {stats.total.avg_response_time:.2f}ms")
+    print(f"RPS: {stats.total.current_rps:.2f}")
+
+
+# 命令行参数解析器
+@events.init_command_line_parser.add_listener
+def _(parser):
+    """添加自定义命令行参数"""
+    parser.add_argument(
+        "--test-mode",
+        type=str,
+        default="normal",
+        choices=["normal", "stress", "spike"],
+        help="测试模式: normal(正常), stress(压力), spike(峰值)",
+    )
+
+
+if __name__ == "__main__":
+    print("💡 这是一个基础功能示例文件")
+    print("🔧 使用方法:")
+    print("   locust -f locustfiles/examples/01_basic_test.py")
+    print(
+        "   locust -f locustfiles/examples/01_basic_test.py --headless -u 10 -r 2 -t 60s"
+    )
+    print("   locust -f locustfiles/examples/01_basic_test.py --test-mode=stress")
+    print("")
+    print("📋 测试内容:")
+    print("   - 健康检查接口")
+    print("   - 系统指标接口")
+    print("   - 商品列表接口")
+    print("   - 商品详情接口")
+    print("   - 商品搜索接口")
+    print("")
+    print("🎯 学习要点:")
+    print("   - 基础HttpUser使用")
+    print("   - @task装饰器和权重")
+    print("   - catch_response错误处理")
+    print("   - 事件监听器使用")
+    print("   - 自定义命令行参数")
diff --git a/locustfiles/examples/02_custom_params.py b/locustfiles/examples/02_custom_params.py
new file mode 100644
index 0000000..73270b2
--- /dev/null
+++ b/locustfiles/examples/02_custom_params.py
@@ -0,0 +1,261 @@
+#!/usr/bin/env python3
+"""
+自定义参数示例 - 演示自定义参数和集合点功能
+
+展示如何使用框架的自定义参数系统和集合点(Rendezvous)功能
+目标：演示高级参数控制和并发同步
+"""
+
+import time
+
+from locust import HttpUser, between, events, task
+from locust.env import Environment
+
+from src.utils.locust_report import manual_report, measure
+from src.utils.rendezvous import Rendezvous
+
+
+# 自定义参数转换函数
+def str_to_bool(v):
+    """字符串转布尔值"""
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ("yes", "true", "1", "on"):
+        return True
+    elif v.lower() in ("no", "false", "0", "off"):
+        return False
+    else:
+        raise ValueError(f"无法转换布尔值: {v}")
+
+
+class CustomParamUser(HttpUser):
+    """自定义参数用户类"""
+
+    wait_time = between(1, 2)
+    host = "http://localhost:5000"
+
+    def on_start(self):
+        """测试开始 - 获取自定义参数"""
+        self.test_params = {
+            "loop_num": getattr(self.environment.parsed_options, "loop_num", 1),
+            "product_id": getattr(
+                self.environment.parsed_options, "product_id", "test_product"
+            ),
+            "is_rendezvous": getattr(
+                self.environment.parsed_options, "is_rendezvous", False
+            ),
+            "rendezvous_num": getattr(
+                self.environment.parsed_options, "rendezvous_num", 1
+            ),
+        }
+
+        print(f"🔧 用户参数配置: {self.test_params}")
+
+    @task
+    def custom_loop_test(self):
+        """自定义循环测试"""
+        loop_num = self.test_params["loop_num"]
+
+        # 使用自定义事务报告
+        with manual_report("自定义循环测试"):
+            for i in range(loop_num):
+                # 执行API调用
+                response = self.client.get(
+                    f"/api/products/{self.test_params['product_id']}",
+                    name=f"循环测试-{i+1}",
+                )
+
+                # 模拟处理时间
+                time.sleep(0.1)
+
+                if response.status_code != 200:
+                    break
+
+    @task
+    def rendezvous_test(self):
+        """集合点测试"""
+        if not self.test_params["is_rendezvous"]:
+            # 非集合点模式，直接执行
+            self._execute_critical_section()
+            return
+
+        # 集合点模式 - 等待其他用户到达
+        rendezvous_count = self.test_params["rendezvous_num"]
+
+        try:
+            # 使用集合点等待其他用户
+            with self.environment.shared:
+                print(f"🚥 用户已到达集合点，等待其他 {rendezvous_count-1} 个用户...")
+                # 所有用户同时执行关键业务
+                self._execute_critical_section()
+        except AttributeError:
+            # 如果集合点未初始化，则直接执行
+            print("⚠️  集合点未初始化，直接执行业务逻辑")
+            self._execute_critical_section()
+
+    def _execute_critical_section(self):
+        """执行关键业务逻辑"""
+        start_time = time.time()
+
+        try:
+            # 模拟关键业务：创建订单
+            order_data = {
+                "products": [
+                    {"product_id": self.test_params["product_id"], "quantity": 1}
+                ],
+                "total_amount": 99.99,
+            }
+
+            # 先登录获取token
+            login_response = self.client.post(
+                "/auth/login",
+                json={"username": "test_user", "password": "test123"},
+                name="登录",
+            )
+
+            if login_response.status_code == 200:
+                token = login_response.json().get("token")
+                headers = {"Authorization": f"Bearer {token}"}
+
+                # 创建订单
+                order_response = self.client.post(
+                    "/api/orders", json=order_data, headers=headers, name="创建订单"
+                )
+
+                # 手动记录事务时间
+                measure(
+                    "关键业务事务",
+                    start_time,
+                    exception=(
+                        None
+                        if order_response.status_code == 201
+                        else Exception("订单创建失败")
+                    ),
+                )
+
+                print(f"✅ 关键业务执行完成，状态码: {order_response.status_code}")
+            else:
+                measure("关键业务事务", start_time, exception=Exception("登录失败"))
+
+        except Exception as e:
+            measure("关键业务事务", start_time, exception=e)
+            print(f"❌ 关键业务执行失败: {e}")
+
+    @task
+    def parameter_driven_test(self):
+        """参数驱动测试"""
+        # 根据产品ID参数调整测试行为
+        product_id = self.test_params["product_id"]
+
+        if product_id == "test_product":
+            # 测试模式 - 访问固定商品
+            self.client.get(f"/api/products/1", name="测试模式-固定商品")
+        elif product_id.startswith("prod_"):
+            # 生产模式 - 访问指定商品
+            self.client.get(
+                f"/api/products/{product_id.split('_')[1]}", name="生产模式-指定商品"
+            )
+        else:
+            # 动态模式 - 搜索商品
+            self.client.get(f"/api/search?q={product_id}", name="动态模式-搜索商品")
+
+
+# 事件监听器
+@events.test_start.add_listener
+def on_test_start(environment: Environment, **kwargs):
+    """测试开始 - 初始化集合点"""
+    print("🚀 自定义参数测试开始")
+
+    # 检查是否需要初始化集合点
+    is_rendezvous = getattr(environment.parsed_options, "is_rendezvous", False)
+    rendezvous_num = getattr(environment.parsed_options, "rendezvous_num", 1)
+
+    if is_rendezvous and rendezvous_num > 1:
+        # 初始化集合点
+        environment.shared = Rendezvous(rendezvous_num)
+        print(f"🚥 集合点已初始化，等待 {rendezvous_num} 个用户同步")
+
+    # 打印测试参数
+    print("\n📋 测试参数:")
+    print(f"   循环次数: {getattr(environment.parsed_options, 'loop_num', 1)}")
+    print(
+        f"   产品ID: {getattr(environment.parsed_options, 'product_id', 'test_product')}"
+    )
+    print(f"   使用集合点: {is_rendezvous}")
+    if is_rendezvous:
+        print(f"   集合点用户数: {rendezvous_num}")
+
+
+@events.test_stop.add_listener
+def on_test_stop(environment: Environment, **kwargs):
+    """测试结束"""
+    print("✅ 自定义参数测试完成")
+
+    # 输出自定义事务统计
+    print("\n📊 自定义事务统计:")
+    for name, stats in environment.stats.entries.items():
+        if "自定义" in name or "关键业务" in name:
+            print(
+                f"   {name}: 平均响应时间 {stats.avg_response_time:.2f}ms, "
+                f"请求数 {stats.num_requests}, 失败数 {stats.num_failures}"
+            )
+
+
+# 自定义命令行参数
+@events.init_command_line_parser.add_listener
+def _(parser):
+    """添加自定义命令行参数"""
+    parser.add_argument(
+        "--loop_num", type=int, default=1, help="每个测试循环的次数 (默认: 1)"
+    )
+
+    parser.add_argument(
+        "--product_id",
+        type=str,
+        default="test_product",
+        help="测试的产品ID (默认: test_product)",
+    )
+
+    parser.add_argument(
+        "--is_rendezvous",
+        type=str_to_bool,
+        default=False,
+        help="是否使用集合点同步 (默认: false)",
+    )
+
+    parser.add_argument(
+        "--rendezvous_num", type=int, default=1, help="集合点等待的用户数量 (默认: 1)"
+    )
+
+
+if __name__ == "__main__":
+    print("💡 这是一个自定义参数和集合点示例")
+    print("🔧 使用方法:")
+    print("   # 基础用法")
+    print("   locust -f locustfiles/examples/02_custom_params.py")
+    print("")
+    print("   # 自定义循环次数")
+    print("   locust -f locustfiles/examples/02_custom_params.py --loop_num=5")
+    print("")
+    print("   # 指定产品ID")
+    print("   locust -f locustfiles/examples/02_custom_params.py --product_id=prod_123")
+    print("")
+    print("   # 使用集合点(需要多用户)")
+    print(
+        "   locust -f locustfiles/examples/02_custom_params.py --headless -u 5 -r 5 \\"
+    )
+    print("         --is_rendezvous=true --rendezvous_num=5 -t 30s")
+    print("")
+    print("📋 功能特性:")
+    print("   - 自定义命令行参数")
+    print("   - 循环控制逻辑")
+    print("   - 集合点同步机制")
+    print("   - 自定义事务报告")
+    print("   - 参数驱动测试")
+    print("")
+    print("🎯 学习要点:")
+    print("   - init_command_line_parser事件")
+    print("   - environment.parsed_options使用")
+    print("   - Rendezvous集合点实现")
+    print("   - manual_report自定义事务")
+    print("   - 测试逻辑的参数化")
diff --git a/locustfiles/examples/03_data_driven.py b/locustfiles/examples/03_data_driven.py
new file mode 100644
index 0000000..00be083
--- /dev/null
+++ b/locustfiles/examples/03_data_driven.py
@@ -0,0 +1,320 @@
+#!/usr/bin/env python3
+"""
+数据驱动测试示例
+
+演示如何使用框架的数据管理功能进行数据驱动测试
+目标：展示数据生成器和数据提供者的使用
+"""
+
+import json
+import random
+
+from locust import HttpUser, between, events, task
+from locust.env import Environment
+
+from src.data_manager.data_generator import DataGenerator
+from src.data_manager.data_provider import DataProvider
+
+
+class DataDrivenUser(HttpUser):
+    """数据驱动用户类"""
+
+    wait_time = between(1, 2)
+    host = "http://localhost:5000"
+
+    def on_start(self):
+        """测试开始 - 初始化数据"""
+        # 获取共享的数据提供者
+        self.data_provider = getattr(self.environment, "data_provider", None)
+
+        if self.data_provider is None:
+            print("⚠️  数据提供者未初始化")
+            return
+
+        print("✅ 数据驱动用户启动成功")
+
+    @task(3)
+    def test_with_generated_users(self):
+        """使用生成用户数据进行测试"""
+        if not self.data_provider:
+            return
+
+        # 获取下一个用户数据
+        user_data = self.data_provider.get_next_data("users")
+        if not user_data:
+            print("⚠️  用户数据已用完")
+            return
+
+        # 使用用户数据进行登录测试
+        login_data = {
+            "username": user_data["username"],
+            "password": "test123",  # 统一测试密码
+        }
+
+        with self.client.post(
+            "/auth/login",
+            json=login_data,
+            name="数据驱动-用户登录",
+            catch_response=True,
+        ) as response:
+            if response.status_code == 200:
+                try:
+                    token_data = response.json()
+                    if "token" in token_data:
+                        response.success()
+
+                        # 使用token获取用户信息
+                        headers = {"Authorization": f'Bearer {token_data["token"]}'}
+                        self.client.get(
+                            "/auth/profile",
+                            headers=headers,
+                            name="数据驱动-获取用户信息",
+                        )
+                    else:
+                        response.failure("登录响应缺少token")
+                except Exception as e:
+                    response.failure(f"JSON解析失败: {e}")
+            else:
+                response.failure(f"登录失败: {response.status_code}")
+
+    @task(4)
+    def test_with_generated_products(self):
+        """使用生成商品数据进行测试"""
+        if not self.data_provider:
+            return
+
+        # 获取下一个商品数据
+        product_data = self.data_provider.get_next_data("products")
+        if not product_data:
+            print("⚠️  商品数据已用完")
+            return
+
+        # 使用商品数据进行搜索测试
+        search_query = product_data["name"].split()[0]  # 取商品名称的第一个词
+
+        with self.client.get(
+            f"/api/search?q={search_query}",
+            name="数据驱动-商品搜索",
+            catch_response=True,
+        ) as response:
+            if response.status_code == 200:
+                try:
+                    search_result = response.json()
+                    if "products" in search_result:
+                        response.success()
+                        print(
+                            f"🔍 搜索 '{search_query}' 找到 {search_result.get('total', 0)} 个商品"
+                        )
+                    else:
+                        response.failure("搜索结果格式异常")
+                except Exception as e:
+                    response.failure(f"JSON解析失败: {e}")
+            else:
+                response.failure(f"搜索失败: {response.status_code}")
+
+    @task(2)
+    def test_with_random_data(self):
+        """使用随机数据进行测试"""
+        if not self.data_provider:
+            return
+
+        # 获取随机用户数据
+        random_users = self.data_provider.get_random_data("users", count=2)
+        if not random_users:
+            return
+
+        user = random_users[0]
+
+        # 使用随机数据添加商品到购物车
+        cart_data = {
+            "product_id": str(random.randint(1, 100)),
+            "quantity": random.randint(1, 3),
+        }
+
+        with self.client.post(
+            "/api/cart/add",
+            json=cart_data,
+            name="数据驱动-添加购物车",
+            catch_response=True,
+        ) as response:
+            if response.status_code == 200:
+                response.success()
+                print(
+                    f"🛒 用户 {user['name']} 添加商品 {cart_data['product_id']} 到购物车"
+                )
+            else:
+                response.failure(f"添加购物车失败: {response.status_code}")
+
+    @task(1)
+    def test_data_statistics(self):
+        """测试数据统计功能"""
+        if not self.data_provider:
+            return
+
+        # 获取数据统计信息
+        user_stats = self.data_provider.get_data_stats("users")
+        product_stats = self.data_provider.get_data_stats("products")
+
+        print(
+            f"📊 数据统计 - 用户: {user_stats.get('total_records', 0)}, "
+            f"商品: {product_stats.get('total_records', 0)}"
+        )
+
+        # 简单的健康检查
+        self.client.get("/health", name="数据统计-健康检查")
+
+
+# 事件监听器
+@events.test_start.add_listener
+def on_test_start(environment: Environment, **kwargs):
+    """测试开始 - 初始化数据"""
+    print("🚀 数据驱动测试开始")
+    print("🔄 正在生成测试数据...")
+
+    # 初始化数据生成器
+    generator = DataGenerator(locale="zh_CN", seed=12345)  # 使用固定种子确保可重复性
+
+    # 生成用户数据
+    print("👥 生成用户数据...")
+    users_data = []
+    for i in range(100):  # 生成100个用户
+        user = generator.generate_user_profile(include_avatar=True)
+        users_data.append(user)
+
+    # 生成商品数据
+    print("📦 生成商品数据...")
+    products_data = []
+    categories = ["electronics", "clothing", "books", "home", "sports"]
+    for category in categories:
+        for i in range(20):  # 每个类别20个商品
+            product = generator.generate_product_info(category=category)
+            products_data.append(product)
+
+    # 初始化数据提供者
+    data_provider = DataProvider()
+
+    # 将生成的数据加载到数据提供者
+    # 这里我们手动加载数据，实际使用中可以从CSV文件加载
+    data_provider._load_data_from_list("users", users_data, "round_robin")
+    data_provider._load_data_from_list("products", products_data, "random")
+
+    # 将数据提供者附加到环境
+    environment.data_provider = data_provider
+
+    print("✅ 测试数据生成完成")
+    print(f"   用户数据: {len(users_data)} 条")
+    print(f"   商品数据: {len(products_data)} 条")
+
+    # 打印一些示例数据
+    print("\n📋 数据示例:")
+    if users_data:
+        sample_user = users_data[0]
+        print(f"   用户: {sample_user['name']} ({sample_user['email']})")
+    if products_data:
+        sample_product = products_data[0]
+        print(f"   商品: {sample_product['name']} - {sample_product['category']}")
+
+
+@events.test_stop.add_listener
+def on_test_stop(environment: Environment, **kwargs):
+    """测试结束"""
+    print("✅ 数据驱动测试完成")
+
+    # 输出数据使用统计
+    data_provider = getattr(environment, "data_provider", None)
+    if data_provider:
+        print("\n📊 数据使用统计:")
+        for data_key in ["users", "products"]:
+            stats = data_provider.get_data_stats(data_key)
+            if stats:
+                print(
+                    f"   {data_key}: 总记录 {stats.get('total_records', 0)}, "
+                    f"当前索引 {stats.get('current_index', 0)}"
+                )
+
+
+# 为DataProvider添加缺失的方法
+class DataProvider:
+    """数据提供者 - 简化实现"""
+
+    def __init__(self):
+        self.data_sets = {}
+        self.current_indices = {}
+        self.distribution_strategies = {}
+
+    def _load_data_from_list(
+        self, data_key: str, data: list, strategy: str = "sequential"
+    ):
+        """从列表加载数据"""
+        self.data_sets[data_key] = data
+        self.current_indices[data_key] = 0
+        self.distribution_strategies[data_key] = strategy
+        print(f"✅ 加载数据集 '{data_key}': {len(data)} 条记录, 策略: {strategy}")
+
+    def get_next_data(self, data_key: str):
+        """获取下一条数据"""
+        if data_key not in self.data_sets:
+            return None
+
+        data_list = self.data_sets[data_key]
+        if not data_list:
+            return None
+
+        strategy = self.distribution_strategies.get(data_key, "sequential")
+
+        if strategy == "round_robin" or strategy == "sequential":
+            index = self.current_indices[data_key]
+            data = data_list[index % len(data_list)]
+            self.current_indices[data_key] = index + 1
+            return data
+        elif strategy == "random":
+            return random.choice(data_list)
+
+        return data_list[0]
+
+    def get_random_data(self, data_key: str, count: int = 1):
+        """获取随机数据"""
+        if data_key not in self.data_sets:
+            return []
+
+        data_list = self.data_sets[data_key]
+        if not data_list:
+            return []
+
+        return random.sample(data_list, min(count, len(data_list)))
+
+    def get_data_stats(self, data_key: str):
+        """获取数据统计信息"""
+        if data_key not in self.data_sets:
+            return {}
+
+        return {
+            "total_records": len(self.data_sets[data_key]),
+            "current_index": self.current_indices.get(data_key, 0),
+            "distribution_strategy": self.distribution_strategies.get(
+                data_key, "sequential"
+            ),
+            "last_accessed": "now",
+        }
+
+
+if __name__ == "__main__":
+    print("💡 这是一个数据驱动测试示例")
+    print("🔧 使用方法:")
+    print("   locust -f locustfiles/examples/03_data_driven.py")
+    print(
+        "   locust -f locustfiles/examples/03_data_driven.py --headless -u 5 -r 2 -t 60s"
+    )
+    print("")
+    print("📋 测试内容:")
+    print("   - 使用生成的用户数据进行登录测试")
+    print("   - 使用生成的商品数据进行搜索测试")
+    print("   - 随机数据的购物车测试")
+    print("   - 数据统计功能验证")
+    print("")
+    print("🎯 学习要点:")
+    print("   - DataGenerator数据生成")
+    print("   - DataProvider数据管理")
+    print("   - 数据分发策略(round_robin/random)")
+    print("   - 测试开始时的数据初始化")
+    print("   - 数据驱动的测试逻辑")
diff --git a/locustfiles/examples/04_performance_analysis.py b/locustfiles/examples/04_performance_analysis.py
new file mode 100644
index 0000000..d1ff551
--- /dev/null
+++ b/locustfiles/examples/04_performance_analysis.py
@@ -0,0 +1,350 @@
+#!/usr/bin/env python3
+"""
+性能分析示例
+
+演示如何集成框架的性能分析功能
+目标：展示性能分析器、趋势分析和报告生成
+"""
+
+import json
+import random
+import time
+from datetime import datetime
+
+from locust import HttpUser, between, events, task
+from locust.env import Environment
+
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+from src.analysis.report_generator import ReportGenerator
+from src.analysis.trend_analyzer import TrendAnalyzer
+
+
+class PerformanceAnalysisUser(HttpUser):
+    """性能分析用户类"""
+
+    wait_time = between(1, 2)
+    host = "http://localhost:5002"
+
+    @task(4)
+    def api_performance_test(self):
+        """API性能测试"""
+        # 测试商品列表API的性能
+        start_time = time.time()
+
+        with self.client.get(
+            "/api/products?page=1&limit=20",
+            name="商品列表性能测试",
+            catch_response=True,
+        ) as response:
+            response_time = (time.time() - start_time) * 1000
+
+            if response.status_code == 200:
+                # 记录性能数据到环境共享存储
+                self.environment.performance_data.append(
+                    {
+                        "response_time": response_time,
+                        "success": True,
+                        "timestamp": datetime.now().isoformat(),
+                        "name": "商品列表API",
+                        "method": "GET",
+                    }
+                )
+                response.success()
+            else:
+                self.environment.performance_data.append(
+                    {
+                        "response_time": response_time,
+                        "success": False,
+                        "timestamp": datetime.now().isoformat(),
+                        "name": "商品列表API",
+                        "method": "GET",
+                    }
+                )
+                response.failure(f"状态码异常: {response.status_code}")
+
+    @task(3)
+    def search_performance_test(self):
+        """搜索性能测试"""
+        import random
+
+        search_terms = ["手机", "电脑", "图书", "衣服", "家具"]
+        query = random.choice(search_terms)
+
+        start_time = time.time()
+
+        with self.client.get(
+            f"/api/search?q={query}&sort=price",
+            name="搜索性能测试",
+            catch_response=True,
+        ) as response:
+            response_time = (time.time() - start_time) * 1000
+
+            success = response.status_code == 200
+            self.environment.performance_data.append(
+                {
+                    "response_time": response_time,
+                    "success": success,
+                    "timestamp": datetime.now().isoformat(),
+                    "name": "商品搜索API",
+                    "method": "GET",
+                }
+            )
+
+            if success:
+                response.success()
+            else:
+                response.failure(f"搜索失败: {response.status_code}")
+
+    @task(2)
+    def auth_performance_test(self):
+        """认证性能测试"""
+        login_data = {
+            "username": f"test_user_{random.randint(1, 100)}",
+            "password": "test123",
+        }
+
+        start_time = time.time()
+
+        with self.client.post(
+            "/auth/login", json=login_data, name="登录性能测试", catch_response=True
+        ) as response:
+            response_time = (time.time() - start_time) * 1000
+
+            success = response.status_code == 200
+            self.environment.performance_data.append(
+                {
+                    "response_time": response_time,
+                    "success": success,
+                    "timestamp": datetime.now().isoformat(),
+                    "name": "用户登录API",
+                    "method": "POST",
+                }
+            )
+
+            if success:
+                response.success()
+            else:
+                response.failure(f"登录失败: {response.status_code}")
+
+    @task(1)
+    def slow_operation_test(self):
+        """慢操作测试 - 用于演示性能问题检测"""
+        # 模拟一个可能较慢的操作
+        start_time = time.time()
+
+        # 获取商品详情（可能触发慢查询）
+        product_id = random.randint(80, 100)  # 使用较大的ID，可能触发未找到情况
+
+        with self.client.get(
+            f"/api/products/{product_id}", name="慢操作测试", catch_response=True
+        ) as response:
+            response_time = (time.time() - start_time) * 1000
+
+            # 强制增加一些处理时间来模拟慢操作
+            time.sleep(0.05)  # 50ms额外延迟
+
+            success = response.status_code in [200, 404]  # 404也算正常
+            self.environment.performance_data.append(
+                {
+                    "response_time": response_time + 50,  # 包含额外延迟
+                    "success": success,
+                    "timestamp": datetime.now().isoformat(),
+                    "name": "商品详情API(慢)",
+                    "method": "GET",
+                }
+            )
+
+            if success:
+                response.success()
+            else:
+                response.failure(f"意外错误: {response.status_code}")
+
+
+@events.test_start.add_listener
+def on_test_start(environment: Environment, **kwargs):
+    """测试开始 - 初始化性能分析"""
+    print("🚀 性能分析测试开始")
+
+    # 初始化性能数据收集
+    environment.performance_data = []
+
+    # 初始化分析器
+    environment.performance_analyzer = PerformanceAnalyzer()
+    environment.trend_analyzer = TrendAnalyzer()
+    environment.report_generator = ReportGenerator()
+
+    # 设置性能阈值
+    environment.performance_analyzer.set_thresholds(
+        response_time_p95=800,  # 95%响应时间阈值调整为800ms
+        response_time_p99=1500,  # 99%响应时间阈值调整为1500ms
+        error_rate=0.03,  # 错误率阈值3%
+        throughput_min=15,  # 最小吞吐量15 TPS
+    )
+
+    # 记录开始时间
+    environment.test_start_time = datetime.now()
+
+    print("📊 性能分析器已初始化")
+    print(
+        f"   响应时间阈值: P95={environment.performance_analyzer.thresholds['response_time_p95']}ms"
+    )
+    print(
+        f"   错误率阈值: {environment.performance_analyzer.thresholds['error_rate']*100}%"
+    )
+    print(
+        f"   吞吐量阈值: {environment.performance_analyzer.thresholds['throughput_min']} TPS"
+    )
+
+
+@events.test_stop.add_listener
+def on_test_stop(environment: Environment, **kwargs):
+    """测试结束 - 执行性能分析"""
+    print("✅ 性能测试完成，开始分析...")
+
+    if not hasattr(environment, "performance_data") or not environment.performance_data:
+        print("⚠️  没有收集到性能数据")
+        return
+
+    try:
+        # 准备测试数据
+        test_data = {
+            "test_name": "性能分析测试",
+            "start_time": environment.test_start_time.isoformat(),
+            "end_time": datetime.now().isoformat(),
+            "duration": int(
+                (datetime.now() - environment.test_start_time).total_seconds()
+            ),
+            "users": getattr(environment.parsed_options, "num_users", 1),
+            "requests": environment.performance_data,
+        }
+
+        print(f"📊 开始分析 {len(environment.performance_data)} 条请求数据...")
+
+        # 执行综合性能分析
+        analysis_result = environment.performance_analyzer.comprehensive_analysis(
+            test_data
+        )
+
+        # 输出分析结果摘要
+        print("\n🔍 性能分析结果:")
+        print(f"   综合评级: {analysis_result['overall_grade']}")
+
+        if analysis_result.get("response_time"):
+            rt = analysis_result["response_time"]
+            print(
+                f"   响应时间: 平均{rt.get('mean', 0):.1f}ms, P95={rt.get('p95', 0):.1f}ms, 评级={rt.get('performance_grade', 'N/A')}"
+            )
+
+        if analysis_result.get("throughput"):
+            tp = analysis_result["throughput"]
+            print(
+                f"   吞吐量: 平均{tp.get('avg_tps', 0):.1f} TPS, 评级={tp.get('performance_grade', 'N/A')}"
+            )
+
+        if analysis_result.get("error_analysis"):
+            er = analysis_result["error_analysis"]
+            print(
+                f"   错误率: {er.get('error_percentage', 0):.2f}%, 评级={er.get('performance_grade', 'N/A')}"
+            )
+
+        # 输出优化建议
+        if analysis_result.get("recommendations"):
+            print("\n💡 优化建议:")
+            for i, recommendation in enumerate(analysis_result["recommendations"], 1):
+                print(f"   {i}. {recommendation}")
+
+        # 生成报告
+        print("\n📋 正在生成性能报告...")
+
+        # 生成JSON报告
+        json_report_path = environment.report_generator.generate_json_report(
+            analysis_result,
+            output_filename=f"performance_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
+        )
+        print(f"   JSON报告: {json_report_path}")
+
+        # 生成HTML报告 (暂时跳过，模板有问题)
+        print("   HTML报告生成跳过 (模板修复中...)")
+
+        # 生成CSV摘要
+        csv_report_path = environment.report_generator.generate_csv_summary(
+            analysis_result,
+            output_filename=f"performance_summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+        )
+        print(f"   CSV摘要: {csv_report_path}")
+
+        # 添加到趋势分析（为未来的趋势分析做准备）
+        environment.trend_analyzer.add_historical_data(analysis_result)
+
+        print(f"\n🎯 性能分析完成! 总体评级: {analysis_result['overall_grade']}")
+
+        # 如果性能不佳，给出警告
+        if analysis_result["overall_grade"] in ["C", "D"]:
+            print("⚠️  检测到性能问题，请检查分析报告中的优化建议")
+
+    except Exception as e:
+        print(f"❌ 性能分析过程中出现错误: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+# 定期执行中间分析
+# 注意：使用request事件代替用户生命周期事件进行中间分析
+@events.request.add_listener
+def on_request(
+    request_type,
+    name,
+    response_time,
+    response_length,
+    response,
+    context,
+    exception,
+    start_time,
+    url,
+    **kwargs,
+):
+    """请求完成时的事件 - 用于中间分析"""
+    global request_count
+    if not hasattr(on_request, "counter"):
+        on_request.counter = 0
+
+    on_request.counter += 1
+
+    # 每100个请求执行一次中间分析
+    if on_request.counter % 100 == 0:
+        print(f"📊 已完成 {on_request.counter} 个请求，执行中间分析...")
+        if exception:
+            print(f"❌ 检测到错误: {exception}")
+
+
+# 用于跟踪请求计数
+request_count = 0
+
+
+if __name__ == "__main__":
+    print("💡 这是一个性能分析集成示例")
+    print("🔧 使用方法:")
+    print("   locust -f locustfiles/examples/04_performance_analysis.py")
+    print(
+        "   locust -f locustfiles/examples/04_performance_analysis.py --headless -u 10 -r 2 -t 120s"
+    )
+    print("")
+    print("📋 测试内容:")
+    print("   - API性能测试和数据收集")
+    print("   - 实时性能数据分析")
+    print("   - 综合性能评分")
+    print("   - 多格式报告生成")
+    print("   - 性能优化建议")
+    print("")
+    print("🎯 学习要点:")
+    print("   - PerformanceAnalyzer集成")
+    print("   - 实时性能数据收集")
+    print("   - comprehensive_analysis API")
+    print("   - 自动报告生成")
+    print("   - 性能阈值配置")
+    print("")
+    print("📊 生成的报告:")
+    print("   - reports/performance_analysis_*.json")
+    print("   - reports/performance_analysis_*.html")
+    print("   - reports/performance_summary_*.csv")
diff --git a/locustfiles/examples/05_monitoring_alerts.py b/locustfiles/examples/05_monitoring_alerts.py
new file mode 100644
index 0000000..677eaca
--- /dev/null
+++ b/locustfiles/examples/05_monitoring_alerts.py
@@ -0,0 +1,356 @@
+#!/usr/bin/env python3
+"""
+监控告警示例
+
+演示如何使用框架的监控告警功能
+目标：展示实时监控、告警规则和通知系统
+"""
+
+import random
+import time
+
+from locust import HttpUser, between, events, task
+from locust.env import Environment
+
+from src.monitoring.alert_manager import AlertManager
+from src.monitoring.performance_monitor import AlertRule, PerformanceMonitor
+
+
+class MonitoringUser(HttpUser):
+    """监控告警用户类"""
+
+    wait_time = between(1, 2)
+    host = "http://localhost:5003"
+
+    @task(4)
+    def normal_request(self):
+        """正常请求 - 模拟正常业务流量"""
+        with self.client.get(
+            "/api/products?page=1&limit=10", name="正常请求", catch_response=True
+        ) as response:
+
+            # 记录监控指标
+            if hasattr(self.environment, "performance_monitor"):
+                response_time = response.elapsed.total_seconds() * 1000
+                self.environment.performance_monitor.add_metric(
+                    "response_time",
+                    response_time,
+                    tags={"endpoint": "/api/products", "method": "GET"},
+                )
+
+                # 记录成功/失败
+                success_rate = 1.0 if response.status_code == 200 else 0.0
+                self.environment.performance_monitor.add_metric(
+                    "success_rate", success_rate, tags={"endpoint": "/api/products"}
+                )
+
+            if response.status_code == 200:
+                response.success()
+            else:
+                response.failure(f"请求失败: {response.status_code}")
+
+    @task(2)
+    def slow_request(self):
+        """慢请求 - 可能触发响应时间告警"""
+        # 随机产生慢请求
+        if random.random() < 0.3:  # 30%概率产生慢请求
+            time.sleep(random.uniform(1.0, 2.0))  # 额外延迟1-2秒
+
+        start_time = time.time()
+
+        with self.client.get(
+            f"/api/products/{random.randint(1, 100)}",
+            name="慢请求测试",
+            catch_response=True,
+        ) as response:
+
+            response_time = (time.time() - start_time) * 1000
+
+            # 记录监控指标
+            if hasattr(self.environment, "performance_monitor"):
+                self.environment.performance_monitor.add_metric(
+                    "response_time",
+                    response_time,
+                    tags={"endpoint": "/api/products/{id}", "method": "GET"},
+                )
+
+                # 如果响应时间超过1秒，记录为慢请求
+                if response_time > 1000:
+                    self.environment.performance_monitor.add_metric(
+                        "slow_request_count", 1, tags={"endpoint": "/api/products/{id}"}
+                    )
+
+            if response.status_code in [200, 404]:
+                response.success()
+            else:
+                response.failure(f"请求异常: {response.status_code}")
+
+    @task(1)
+    def error_request(self):
+        """错误请求 - 可能触发错误率告警"""
+        # 随机产生错误请求
+        if random.random() < 0.2:  # 20%概率访问不存在的端点
+            endpoint = "/api/nonexistent"
+            expected_error = True
+        else:
+            endpoint = "/api/products"
+            expected_error = False
+
+        with self.client.get(
+            endpoint, name="错误测试", catch_response=True
+        ) as response:
+
+            # 记录错误率指标
+            if hasattr(self.environment, "performance_monitor"):
+                error_rate = 1.0 if response.status_code >= 400 else 0.0
+                self.environment.performance_monitor.add_metric(
+                    "error_rate",
+                    error_rate,
+                    tags={
+                        "endpoint": endpoint,
+                        "status_code": str(response.status_code),
+                    },
+                )
+
+                # 记录总请求数
+                self.environment.performance_monitor.add_metric(
+                    "total_requests", 1, tags={"endpoint": endpoint}
+                )
+
+            if expected_error and response.status_code >= 400:
+                response.success()  # 预期的错误
+            elif not expected_error and response.status_code == 200:
+                response.success()  # 正常响应
+            else:
+                response.failure(f"意外的响应: {response.status_code}")
+
+    @task(1)
+    def high_load_request(self):
+        """高负载请求 - 测试系统负载监控"""
+        # 模拟CPU密集型操作
+        if random.random() < 0.1:  # 10%概率产生高负载
+            # 模拟高CPU使用率
+            start = time.time()
+            while time.time() - start < 0.1:  # 100ms的CPU密集计算
+                _ = sum(range(1000))
+
+        with self.client.get("/api/search?q=test", name="高负载测试") as response:
+
+            # 记录负载相关指标
+            if hasattr(self.environment, "performance_monitor"):
+                # 模拟CPU使用率指标
+                cpu_usage = random.uniform(20, 90)  # 随机CPU使用率
+                self.environment.performance_monitor.add_metric(
+                    "cpu_usage", cpu_usage, tags={"host": "test-server"}
+                )
+
+                # 模拟内存使用率
+                memory_usage = random.uniform(30, 85)
+                self.environment.performance_monitor.add_metric(
+                    "memory_usage", memory_usage, tags={"host": "test-server"}
+                )
+
+
+def alert_callback(alert_info):
+    """告警回调函数"""
+    severity_emoji = {"info": "ℹ️", "warning": "⚠️", "critical": "🚨"}
+
+    severity = alert_info.get("tags", {}).get("severity", "warning")
+    emoji = severity_emoji.get(severity, "⚠️")
+
+    print(f"{emoji} 告警触发: {alert_info['rule_name']}")
+    print(f"   指标: {alert_info['metric_name']}")
+    print(f"   当前值: {alert_info['current_value']:.2f}")
+    print(f"   阈值: {alert_info['threshold']}")
+    print(f"   时间: {alert_info['timestamp']}")
+
+
+@events.test_start.add_listener
+def on_test_start(environment: Environment, **kwargs):
+    """测试开始 - 初始化监控系统"""
+    print("🚀 监控告警测试开始")
+
+    # 初始化性能监控器
+    environment.performance_monitor = PerformanceMonitor(check_interval=5)
+
+    # 添加告警回调
+    environment.performance_monitor.add_alert_callback(alert_callback)
+
+    # 创建告警规则
+    alert_rules = [
+        # 响应时间告警
+        AlertRule(
+            name="响应时间过高",
+            metric_name="response_time",
+            condition="gt",
+            threshold=1000,  # 1秒
+            duration=10,  # 持续10秒
+            callback=lambda alert: print(
+                f"🐌 检测到慢响应: {alert['current_value']:.0f}ms"
+            ),
+        ),
+        # 错误率告警
+        AlertRule(
+            name="错误率过高",
+            metric_name="error_rate",
+            condition="gt",
+            threshold=0.15,  # 15%
+            duration=15,  # 持续15秒
+            callback=lambda alert: print(
+                f"💥 检测到高错误率: {alert['current_value']*100:.1f}%"
+            ),
+        ),
+        # CPU使用率告警
+        AlertRule(
+            name="CPU使用率过高",
+            metric_name="cpu_usage",
+            condition="gt",
+            threshold=80,  # 80%
+            duration=20,  # 持续20秒
+            callback=lambda alert: print(
+                f"🔥 CPU使用率过高: {alert['current_value']:.1f}%"
+            ),
+        ),
+        # 内存使用率告警
+        AlertRule(
+            name="内存使用率过高",
+            metric_name="memory_usage",
+            condition="gt",
+            threshold=80,  # 80%
+            duration=20,  # 持续20秒
+            callback=lambda alert: print(
+                f"💾 内存使用率过高: {alert['current_value']:.1f}%"
+            ),
+        ),
+        # 慢请求数量告警
+        AlertRule(
+            name="慢请求数量过多",
+            metric_name="slow_request_count",
+            condition="gt",
+            threshold=3,  # 超过3个慢请求
+            duration=30,  # 持续30秒
+            callback=lambda alert: print(
+                f"🚫 慢请求过多: {alert['current_value']:.0f}个"
+            ),
+        ),
+    ]
+
+    # 添加告警规则
+    for rule in alert_rules:
+        environment.performance_monitor.add_alert_rule(rule)
+
+    # 启动监控
+    environment.performance_monitor.start_monitoring()
+
+    print("📊 性能监控系统已启动")
+    print(f"   监控间隔: {environment.performance_monitor.check_interval}秒")
+    print(f"   告警规则数: {len(alert_rules)}")
+
+    # 显示监控阈值
+    print("\n🎯 监控阈值:")
+    print(f"   响应时间: > 1000ms")
+    print(f"   错误率: > 15%")
+    print(f"   CPU使用率: > 80%")
+    print(f"   内存使用率: > 80%")
+    print(f"   慢请求数: > 3个")
+
+
+@events.test_stop.add_listener
+def on_test_stop(environment: Environment, **kwargs):
+    """测试结束 - 输出监控统计"""
+    print("✅ 监控告警测试完成")
+
+    if hasattr(environment, "performance_monitor"):
+        # 停止监控
+        environment.performance_monitor.stop_monitoring()
+
+        # 获取监控统计
+        print("\n📊 监控统计摘要:")
+
+        metrics_summary = environment.performance_monitor.get_all_metrics_summary()
+
+        for metric_name, stats in metrics_summary.items():
+            if stats:  # 确保有数据
+                print(f"   {metric_name}:")
+                print(f"     数量: {stats.get('count', 0)}")
+                print(f"     平均值: {stats.get('avg', 0):.2f}")
+                print(f"     最大值: {stats.get('max', 0):.2f}")
+                print(f"     最小值: {stats.get('min', 0):.2f}")
+
+        # 导出监控数据
+        try:
+            exported_data = environment.performance_monitor.export_metrics(
+                duration_hours=1
+            )
+            if exported_data:
+                # 保存到文件
+                import json
+                from datetime import datetime
+
+                filename = (
+                    f"monitoring_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+                )
+                with open(f"reports/{filename}", "w", encoding="utf-8") as f:
+                    json.dump(exported_data, f, indent=2, ensure_ascii=False)
+
+                print(f"\n💾 监控数据已导出: reports/{filename}")
+                print(f"   记录数量: {len(exported_data)}")
+
+        except Exception as e:
+            print(f"⚠️  导出监控数据失败: {e}")
+
+
+# 中间状态报告
+@events.request.add_listener
+def on_request_monitoring(
+    request_type,
+    name,
+    response_time,
+    response_length,
+    response,
+    context,
+    exception,
+    **kwargs,
+):
+    """请求完成时更新监控状态"""
+    # 静态计数器
+    if not hasattr(on_request_monitoring, "counter"):
+        on_request_monitoring.counter = 0
+    on_request_monitoring.counter += 1
+
+    # 每50个请求输出一次监控状态
+    if on_request_monitoring.counter % 50 == 0:
+        print(f"📊 监控状态更新 - 已完成 {on_request_monitoring.counter} 个请求")
+        if exception:
+            print(f"🚨 检测到错误: {exception} - 可能触发告警")
+        else:
+            print(f"✅ 响应时间: {response_time:.2f}ms")
+
+
+if __name__ == "__main__":
+    print("💡 这是一个监控告警集成示例")
+    print("🔧 使用方法:")
+    print("   locust -f locustfiles/examples/05_monitoring_alerts.py")
+    print(
+        "   locust -f locustfiles/examples/05_monitoring_alerts.py --headless -u 20 -r 5 -t 180s"
+    )
+    print("")
+    print("📋 测试内容:")
+    print("   - 实时性能指标监控")
+    print("   - 多种告警规则触发")
+    print("   - 告警回调函数执行")
+    print("   - 监控数据导出")
+    print("")
+    print("🎯 学习要点:")
+    print("   - PerformanceMonitor集成")
+    print("   - AlertRule配置和使用")
+    print("   - 实时指标收集")
+    print("   - 告警回调处理")
+    print("   - 监控数据导出")
+    print("")
+    print("📊 监控指标:")
+    print("   - response_time (响应时间)")
+    print("   - error_rate (错误率)")
+    print("   - cpu_usage (CPU使用率)")
+    print("   - memory_usage (内存使用率)")
+    print("   - slow_request_count (慢请求数)")
diff --git a/locustfiles/examples/06_load_shapes.py b/locustfiles/examples/06_load_shapes.py
new file mode 100644
index 0000000..3e38951
--- /dev/null
+++ b/locustfiles/examples/06_load_shapes.py
@@ -0,0 +1,322 @@
+#!/usr/bin/env python3
+"""
+负载模式示例
+
+演示框架支持的各种负载模式
+目标：展示不同负载曲线对系统性能的影响
+"""
+
+import math
+import time
+
+from locust import HttpUser, LoadTestShape, between, events, task
+from locust.env import Environment
+
+# ==================== 负载模式定义 ====================
+# 注意：同时只能有一个LoadTestShape类处于激活状态
+# 要使用不同的负载模式，请注释掉其他形状，只保留一个取消注释
+
+
+# 1. 阶梯负载模式 (默认激活)
+class StepLoadShape(LoadTestShape):
+    """阶梯负载模式"""
+
+    stages = [
+        {"duration": 30, "users": 5, "spawn_rate": 2},  # 阶段1: 30秒内到达5用户
+        {"duration": 60, "users": 10, "spawn_rate": 2},  # 阶段2: 60秒内到达10用户
+        {"duration": 90, "users": 20, "spawn_rate": 5},  # 阶段3: 90秒内到达20用户
+        {"duration": 120, "users": 15, "spawn_rate": 3},  # 阶段4: 120秒内降到15用户
+        {"duration": 150, "users": 5, "spawn_rate": 5},  # 阶段5: 150秒内降到5用户
+    ]
+
+    def tick(self):
+        run_time = self.get_run_time()
+
+        for stage in self.stages:
+            if run_time < stage["duration"]:
+                return stage["users"], stage["spawn_rate"]
+
+        return None  # 测试结束
+
+
+# 2. 波浪负载模式 (取消注释以使用)
+## class WaveLoadShape(LoadTestShape):
+##     """波浪负载模式"""
+#
+#    def __init__(self):
+#        self.min_users = 5
+#        self.max_users = 25
+#        self.wave_period = 60  # 60秒一个周期
+#        self.spawn_rate = 3
+#        self.total_duration = 180  # 总共3分钟
+#
+#    def tick(self):
+#        run_time = self.get_run_time()
+#
+#        if run_time >= self.total_duration:
+#            return None
+#
+#        # 计算正弦波用户数
+#        wave_position = (run_time % self.wave_period) / self.wave_period
+#        sine_value = math.sin(2 * math.pi * wave_position)
+#
+#        # 将正弦值映射到用户数范围
+#        user_range = self.max_users - self.min_users
+#        current_users = self.min_users + int((sine_value + 1) / 2 * user_range)
+#
+#        return current_users, self.spawn_rate
+# """
+#
+## 3. 尖峰负载模式 (取消注释以使用)
+# """
+# class SpikeLoadShape(LoadTestShape):
+#    """尖峰负载模式"""
+#
+#    def __init__(self):
+#        self.base_users = 8
+#        self.spike_users = 30
+#        self.spawn_rate = 10
+#        self.spike_start = 60   # 60秒时开始尖峰
+#        self.spike_duration = 30  # 尖峰持续30秒
+#        self.total_duration = 180
+#
+#    def tick(self):
+#        run_time = self.get_run_time()
+#
+#        if run_time >= self.total_duration:
+#            return None
+#
+#        # 检查是否在尖峰期间
+#        spike_end = self.spike_start + self.spike_duration
+#        if self.spike_start <= run_time < spike_end:
+#            return self.spike_users, self.spawn_rate
+#        else:
+#            return self.base_users, self.spawn_rate
+# """
+#
+## 4. 自定义负载模式 (取消注释以使用)
+# """
+# class CustomLoadShape(LoadTestShape):
+#    """自定义负载模式 - 演示复杂场景"""
+#
+#    def tick(self):
+#        run_time = self.get_run_time()
+#
+#        # 阶段1: 预热 (0-30秒)
+#        if run_time < 30:
+#            return 3, 1
+#
+#        # 阶段2: 正常负载 (30-90秒)
+#        elif run_time < 90:
+#            return 10, 2
+#
+#        # 阶段3: 压力测试 (90-120秒)
+#        elif run_time < 120:
+#            return 25, 5
+#
+#        # 阶段4: 峰值负载 (120-140秒)
+#        elif run_time < 140:
+#            return 40, 10
+#
+#        # 阶段5: 恢复 (140-180秒)
+#        elif run_time < 180:
+#            return 8, 3
+#
+#        # 测试结束
+#        return None
+# """
+#
+# ==================== 用户行为定义 ====================
+
+
+class LoadShapeUser(HttpUser):
+    """负载模式测试用户"""
+
+    wait_time = between(1, 2)
+    host = "http://localhost:5004"
+
+    def on_start(self):
+        """用户启动时记录"""
+        current_users = getattr(self.environment.runner, "user_count", 0)
+        print(f"🏃 用户启动 (当前总用户数: {current_users})")
+
+    def on_stop(self):
+        """用户停止时记录"""
+        current_users = getattr(self.environment.runner, "user_count", 0)
+        print(f"🛑 用户停止 (当前总用户数: {current_users})")
+
+    @task(3)
+    def lightweight_request(self):
+        """轻量级请求"""
+        with self.client.get("/health", name="健康检查") as response:
+            pass
+
+    @task(2)
+    def medium_request(self):
+        """中等负载请求"""
+        with self.client.get(
+            "/api/products?page=1&limit=10", name="商品列表"
+        ) as response:
+            pass
+
+    @task(1)
+    def heavy_request(self):
+        """重负载请求"""
+        # 模拟较重的操作
+        time.sleep(0.1)  # 100ms额外处理时间
+        with self.client.get(
+            "/api/search?q=test&sort=price", name="商品搜索"
+        ) as response:
+            pass
+
+
+# ==================== 事件监听器 ====================
+
+
+@events.test_start.add_listener
+def on_test_start(environment: Environment, **kwargs):
+    """测试开始"""
+    print("🚀 负载模式测试开始")
+
+    # 检测负载模式类型
+    shape_class = (
+        environment.parsed_options.shape_class
+        if hasattr(environment.parsed_options, "shape_class")
+        else None
+    )
+
+    if shape_class:
+        shape_name = shape_class.__name__
+        print(f"📊 负载模式: {shape_name}")
+
+        # 根据负载模式输出说明
+        if shape_name == "StepLoadShape":
+            print("   阶梯式增加负载，测试系统在不同负载级别下的表现")
+        elif shape_name == "WaveLoadShape":
+            print("   波浪式负载变化，测试系统对动态负载的适应性")
+        elif shape_name == "SpikeLoadShape":
+            print("   突发尖峰负载，测试系统的峰值处理能力")
+        elif shape_name == "CustomLoadShape":
+            print("   自定义复杂负载场景，全面测试系统性能")
+    else:
+        print("📊 使用默认负载模式")
+
+    # 初始化负载统计
+    environment.load_stats = {
+        "start_time": time.time(),
+        "user_changes": [],
+        "max_users": 0,
+        "total_user_seconds": 0,
+    }
+
+
+# 使用request事件替代用户生命周期事件来跟踪负载变化
+@events.request.add_listener
+def on_load_tracking(
+    request_type,
+    name,
+    response_time,
+    response_length,
+    response,
+    context,
+    exception,
+    **kwargs,
+):
+    """通过请求事件跟踪负载模式效果"""
+    if not hasattr(on_load_tracking, "last_report_time"):
+        on_load_tracking.last_report_time = time.time()
+        on_load_tracking.request_count = 0
+
+    on_load_tracking.request_count += 1
+    current_time = time.time()
+
+    # 每10秒报告一次负载状态
+    if current_time - on_load_tracking.last_report_time >= 10:
+        print(f"📈 负载状态报告 - 已完成 {on_load_tracking.request_count} 个请求")
+        on_load_tracking.last_report_time = current_time
+
+
+@events.test_stop.add_listener
+def on_test_stop(environment: Environment, **kwargs):
+    """测试结束 - 分析负载模式效果"""
+    print("✅ 负载模式测试完成")
+
+    if hasattr(environment, "load_stats"):
+        load_stats = environment.load_stats
+        total_time = time.time() - load_stats["start_time"]
+
+        print(f"\n📊 负载统计摘要:")
+        print(f"   测试总时长: {total_time:.1f}秒")
+        print(f"   最大并发用户: {load_stats['max_users']}")
+        print(f"   用户变化次数: {len(load_stats['user_changes'])}")
+
+        # 分析负载变化模式
+        if load_stats["user_changes"]:
+            print(f"\n📈 负载变化时间线:")
+            for i, change in enumerate(
+                load_stats["user_changes"][:10]
+            ):  # 显示前10次变化
+                action_emoji = "📈" if change["action"] == "add" else "📉"
+                print(
+                    f"   {action_emoji} {change['time']:6.1f}s -> {change['users']:2d} 用户"
+                )
+
+            if len(load_stats["user_changes"]) > 10:
+                print(f"   ... 还有 {len(load_stats['user_changes']) - 10} 次变化")
+
+        # 输出性能统计
+        stats = environment.stats
+        if stats.total.num_requests > 0:
+            print(f"\n📊 性能表现:")
+            print(f"   总请求数: {stats.total.num_requests}")
+            print(f"   平均响应时间: {stats.total.avg_response_time:.1f}ms")
+            print(
+                f"   错误率: {(stats.total.num_failures/stats.total.num_requests*100):.1f}%"
+            )
+            print(f"   峰值RPS: {stats.total.max_response_time:.1f}")
+
+            # 根据负载模式给出分析建议
+            error_rate = stats.total.num_failures / stats.total.num_requests * 100
+            avg_response_time = stats.total.avg_response_time
+
+            print(f"\n💡 负载模式分析:")
+            if error_rate > 5:
+                print(f"   ⚠️  高错误率 ({error_rate:.1f}%) - 系统在高负载下出现问题")
+            elif avg_response_time > 1000:
+                print(
+                    f"   ⚠️  高响应时间 ({avg_response_time:.1f}ms) - 系统性能在负载压力下下降"
+                )
+            else:
+                print(f"   ✅ 系统在该负载模式下表现良好")
+
+            if load_stats["max_users"] > 0:
+                requests_per_user = stats.total.num_requests / load_stats["max_users"]
+                print(f"   📊 平均每用户请求数: {requests_per_user:.1f}")
+
+
+if __name__ == "__main__":
+    print("💡 这是一个负载模式示例")
+    print("🔧 使用方法:")
+    print("   # 当前活跃的负载模式 (默认: StepLoadShape)")
+    print("   locust -f locustfiles/examples/06_load_shapes.py --headless")
+    print("")
+    print("   # 要使用不同的负载模式:")
+    print("   1. 编辑文件，注释掉当前激活的LoadTestShape类")
+    print("   2. 取消注释想要使用的LoadTestShape类")
+    print("   3. 重新运行测试")
+    print("")
+    print("   # Web UI模式查看实时负载变化")
+    print("   locust -f locustfiles/examples/06_load_shapes.py")
+    print("")
+    print("📋 负载模式特点:")
+    print("   - StepLoadShape: 阶梯式负载增长，适合容量规划")
+    print("   - WaveLoadShape: 正弦波动负载，模拟真实流量波动")
+    print("   - SpikeLoadShape: 突发流量测试，验证系统弹性")
+    print("   - CustomLoadShape: 复杂多阶段测试，全面性能评估")
+    print("")
+    print("🎯 学习要点:")
+    print("   - LoadTestShape基类使用")
+    print("   - tick()方法实现负载控制")
+    print("   - 用户生命周期事件监听")
+    print("   - 负载统计和分析")
+    print("   - 不同负载模式的适用场景")
diff --git a/locustfiles/examples/07_ecommerce_scenario.py b/locustfiles/examples/07_ecommerce_scenario.py
new file mode 100644
index 0000000..d3ec1d2
--- /dev/null
+++ b/locustfiles/examples/07_ecommerce_scenario.py
@@ -0,0 +1,527 @@
+#!/usr/bin/env python3
+"""
+电商综合场景示例
+
+演示完整的电商业务流程性能测试
+目标：整合所有框架功能，模拟真实电商场景
+"""
+
+import json
+import random
+import time
+from datetime import datetime
+
+from locust import HttpUser, LoadTestShape, events, task
+from locust.env import Environment
+
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+
+# 框架模块导入
+from src.data_manager.data_generator import DataGenerator
+from src.monitoring.performance_monitor import AlertRule, PerformanceMonitor
+from src.utils.locust_report import manual_report, measure
+from src.utils.rendezvous import Rendezvous
+
+
+class EcommerceLoadShape(LoadTestShape):
+    """电商负载模式 - 模拟真实电商流量"""
+
+    def tick(self):
+        run_time = self.get_run_time()
+
+        # 预热期 (0-60s): 少量用户
+        if run_time < 60:
+            return 3, 1
+
+        # 正常浏览期 (60-180s): 中等用户数
+        elif run_time < 180:
+            return 15, 2
+
+        # 促销高峰期 (180-240s): 大量用户涌入
+        elif run_time < 240:
+            return 35, 5
+
+        # 下单高峰期 (240-300s): 最高负载
+        elif run_time < 300:
+            return 50, 8
+
+        # 恢复期 (300-360s): 负载下降
+        elif run_time < 360:
+            return 20, 5
+
+        # 收尾期 (360-420s): 少量用户
+        elif run_time < 420:
+            return 8, 2
+
+        return None  # 测试结束
+
+
+class EcommerceBuyer(HttpUser):
+    """电商买家用户 - 完整购物流程"""
+
+    wait_time_seconds = random.uniform(1, 3)
+    host = "http://localhost:5005"
+    weight = 7  # 占70%用户
+
+    def on_start(self):
+        """用户会话开始"""
+        # 从数据提供者获取用户数据
+        self.user_data = getattr(
+            self.environment, "user_generator", DataGenerator()
+        ).generate_user_profile()
+        self.session_data = {
+            "cart_items": [],
+            "token": None,
+            "orders": [],
+            "session_start": time.time(),
+        }
+
+        print(f"🛍️  买家 {self.user_data['name']} 开始购物")
+
+    def on_stop(self):
+        """用户会话结束"""
+        session_duration = time.time() - self.session_data["session_start"]
+        print(
+            f"🏁 买家 {self.user_data['name']} 结束购物 (耗时: {session_duration:.1f}s, "
+            f"购物车: {len(self.session_data['cart_items'])}件, "
+            f"下单: {len(self.session_data['orders'])}笔)"
+        )
+
+    @task(8)
+    def browse_products(self):
+        """浏览商品 - 最高频操作"""
+        page = random.randint(1, 10)
+        limit = random.choice([10, 20, 50])
+        category = random.choice(["", "electronics", "clothing", "books"])
+
+        params = f"page={page}&limit={limit}"
+        if category:
+            params += f"&category={category}"
+
+        with manual_report("商品浏览"):
+            with self.client.get(
+                f"/api/products?{params}", name="浏览商品列表", catch_response=True
+            ) as response:
+                if response.status_code == 200:
+                    try:
+                        data = response.json()
+                        product_count = len(data.get("products", []))
+                        response.success()
+
+                        # 模拟用户阅读商品信息的时间
+                        reading_time = product_count * 0.1  # 每个商品0.1秒
+                        time.sleep(min(reading_time, 2))  # 最多2秒
+
+                    except Exception as e:
+                        response.failure(f"解析商品数据失败: {e}")
+                else:
+                    response.failure(f"获取商品列表失败: {response.status_code}")
+
+    @task(5)
+    def search_products(self):
+        """搜索商品"""
+        search_keywords = [
+            "手机",
+            "笔记本",
+            "衣服",
+            "图书",
+            "家具",
+            "运动",
+            "美食",
+            "化妆品",
+        ]
+        keyword = random.choice(search_keywords)
+        sort = random.choice(["price", "rating", "name"])
+
+        with manual_report("商品搜索"):
+            with self.client.get(
+                f"/api/search?q={keyword}&sort={sort}",
+                name="搜索商品",
+                catch_response=True,
+            ) as response:
+                if response.status_code == 200:
+                    try:
+                        data = response.json()
+                        total = data.get("total", 0)
+                        response.success()
+                        print(f"🔍 搜索 '{keyword}' 找到 {total} 个商品")
+                    except Exception as e:
+                        response.failure(f"解析搜索结果失败: {e}")
+                else:
+                    response.failure(f"搜索失败: {response.status_code}")
+
+    @task(6)
+    def view_product_detail(self):
+        """查看商品详情"""
+        product_id = random.randint(1, 100)
+
+        with manual_report("商品详情"):
+            with self.client.get(
+                f"/api/products/{product_id}", name="商品详情", catch_response=True
+            ) as response:
+                if response.status_code == 200:
+                    try:
+                        product = response.json()
+                        response.success()
+
+                        # 有一定概率加入购物车
+                        if random.random() < 0.3:  # 30%概率
+                            self._add_to_cart(
+                                product_id, product.get("name", f"商品{product_id}")
+                            )
+
+                        # 模拟查看详情的时间
+                        time.sleep(random.uniform(0.5, 2))
+
+                    except Exception as e:
+                        response.failure(f"解析商品详情失败: {e}")
+                elif response.status_code == 404:
+                    response.success()  # 商品不存在是正常情况
+                else:
+                    response.failure(f"获取商品详情失败: {response.status_code}")
+
+    @task(2)
+    def login_and_get_profile(self):
+        """登录并获取用户信息"""
+        if self.session_data["token"]:
+            # 已登录，获取用户信息
+            headers = {"Authorization": f'Bearer {self.session_data["token"]}'}
+            with self.client.get(
+                "/auth/profile", headers=headers, name="获取用户信息"
+            ) as response:
+                pass
+            return
+
+        # 执行登录
+        login_data = {"username": self.user_data["username"], "password": "test123"}
+
+        with manual_report("用户登录"):
+            with self.client.post(
+                "/auth/login", json=login_data, name="用户登录", catch_response=True
+            ) as response:
+                if response.status_code == 200:
+                    try:
+                        data = response.json()
+                        self.session_data["token"] = data.get("token")
+                        response.success()
+                        print(f"✅ 用户 {self.user_data['name']} 登录成功")
+                    except Exception as e:
+                        response.failure(f"登录响应解析失败: {e}")
+                else:
+                    response.failure(f"登录失败: {response.status_code}")
+
+    @task(1)
+    def checkout_process(self):
+        """结账流程 - 需要登录和购物车商品"""
+        if not self.session_data["token"]:
+            return  # 未登录
+
+        if len(self.session_data["cart_items"]) < 1:
+            return  # 购物车为空
+
+        # 模拟结账过程
+        with manual_report("订单结账"):
+            order_data = {
+                "products": self.session_data["cart_items"],
+                "total_amount": sum(
+                    item["price"] * item["quantity"]
+                    for item in self.session_data["cart_items"]
+                ),
+            }
+
+            headers = {"Authorization": f'Bearer {self.session_data["token"]}'}
+
+            with self.client.post(
+                "/api/orders",
+                json=order_data,
+                headers=headers,
+                name="创建订单",
+                catch_response=True,
+            ) as response:
+                if response.status_code == 201:
+                    try:
+                        order = response.json()
+                        self.session_data["orders"].append(order)
+                        self.session_data["cart_items"] = []  # 清空购物车
+                        response.success()
+                        print(
+                            f"💰 订单创建成功: {order.get('id', 'unknown')}, "
+                            f"金额: ¥{order_data['total_amount']:.2f}"
+                        )
+                    except Exception as e:
+                        response.failure(f"订单响应解析失败: {e}")
+                else:
+                    response.failure(f"创建订单失败: {response.status_code}")
+
+    def _add_to_cart(self, product_id: int, product_name: str):
+        """添加商品到购物车"""
+        cart_item = {
+            "product_id": str(product_id),
+            "quantity": random.randint(1, 3),
+            "price": random.uniform(10, 500),
+            "name": product_name,
+        }
+
+        with self.client.post(
+            "/api/cart/add", json=cart_item, name="添加购物车"
+        ) as response:
+            if response.status_code == 200:
+                self.session_data["cart_items"].append(cart_item)
+                print(f"🛒 添加到购物车: {product_name} x{cart_item['quantity']}")
+
+
+class EcommerceBrowser(HttpUser):
+    """电商浏览者 - 只浏览不购买"""
+
+    wait_time_seconds = random.uniform(0.5, 2)
+    host = "http://localhost:5005"
+    weight = 3  # 占30%用户
+
+    @task(10)
+    def casual_browsing(self):
+        """随机浏览"""
+        # 随机选择浏览行为
+        actions = [
+            lambda: self.client.get(
+                "/api/products?page=1&limit=20", name="浏览首页商品"
+            ),
+            lambda: self.client.get(
+                f"/api/products/{random.randint(1, 50)}", name="随机查看商品"
+            ),
+            lambda: self.client.get(f"/api/search?q=test", name="随机搜索"),
+            lambda: self.client.get("/health", name="健康检查"),
+        ]
+
+        action = random.choice(actions)
+        action()
+
+        # 模拟浏览时间
+        time.sleep(random.uniform(0.2, 1))
+
+
+# ==================== 事件监听器 ====================
+
+
+@events.test_start.add_listener
+def on_test_start(environment: Environment, **kwargs):
+    """测试开始 - 初始化完整的测试环境"""
+    print("🚀 电商综合场景测试开始")
+    print("📋 初始化测试环境...")
+
+    # 1. 初始化数据生成器
+    environment.user_generator = DataGenerator(locale="zh_CN", seed=12345)
+    print("✅ 数据生成器初始化完成")
+
+    # 2. 初始化性能分析器
+    environment.performance_analyzer = PerformanceAnalyzer()
+    environment.performance_analyzer.set_thresholds(
+        response_time_p95=1200,  # 电商场景允许稍高的响应时间
+        error_rate=0.02,  # 2%错误率
+        throughput_min=20,  # 最小20 TPS
+    )
+    environment.performance_data = []
+    print("✅ 性能分析器初始化完成")
+
+    # 3. 初始化监控系统
+    environment.performance_monitor = PerformanceMonitor(check_interval=10)
+
+    # 添加电商特定的监控规则
+    ecommerce_alert_rules = [
+        AlertRule(
+            name="购物车响应时间过长",
+            metric_name="cart_response_time",
+            condition="gt",
+            threshold=1500,
+            duration=20,
+            callback=lambda alert: print(
+                f"🛒⚠️  购物车性能告警: {alert['current_value']:.0f}ms"
+            ),
+        ),
+        AlertRule(
+            name="订单创建失败率过高",
+            metric_name="order_error_rate",
+            condition="gt",
+            threshold=0.05,  # 5%
+            duration=30,
+            callback=lambda alert: print(
+                f"💰🚨 订单创建告警: {alert['current_value']*100:.1f}%"
+            ),
+        ),
+    ]
+
+    for rule in ecommerce_alert_rules:
+        environment.performance_monitor.add_alert_rule(rule)
+
+    environment.performance_monitor.start_monitoring()
+    print("✅ 监控告警系统启动完成")
+
+    # 4. 初始化集合点(用于模拟抢购场景)
+    expected_users = getattr(environment.parsed_options, "num_users", 10)
+    if expected_users >= 20:  # 用户数足够时启用集合点
+        environment.flash_sale_rendezvous = Rendezvous(min(expected_users // 2, 25))
+        print("✅ 抢购集合点初始化完成")
+
+    # 5. 记录测试开始时间
+    environment.test_start_time = datetime.now()
+    environment.business_metrics = {
+        "total_orders": 0,
+        "total_revenue": 0.0,
+        "cart_additions": 0,
+        "user_sessions": 0,
+    }
+
+    print(f"🎯 电商测试场景配置:")
+    print(f"   目标用户数: {expected_users}")
+    print(f"   响应时间阈值: P95 < 1200ms")
+    print(f"   错误率阈值: < 2%")
+    print(f"   吞吐量阈值: > 20 TPS")
+    print(
+        f"   集合点: {'启用' if hasattr(environment, 'flash_sale_rendezvous') else '禁用'}"
+    )
+
+
+@events.test_stop.add_listener
+def on_test_stop(environment: Environment, **kwargs):
+    """测试结束 - 综合分析和报告生成"""
+    print("✅ 电商综合场景测试完成")
+
+    # 停止监控系统
+    if hasattr(environment, "performance_monitor"):
+        environment.performance_monitor.stop_monitoring()
+
+    # 执行业务指标分析
+    print("\n📊 业务指标统计:")
+    if hasattr(environment, "business_metrics"):
+        metrics = environment.business_metrics
+        print(f"   总订单数: {metrics['total_orders']}")
+        print(f"   总营收: ¥{metrics['total_revenue']:.2f}")
+        print(f"   购物车添加: {metrics['cart_additions']}")
+        print(f"   用户会话: {metrics['user_sessions']}")
+
+    # 执行性能分析
+    if hasattr(environment, "performance_data") and environment.performance_data:
+        print("\n🔍 执行性能分析...")
+
+        test_data = {
+            "test_name": "电商综合场景测试",
+            "start_time": environment.test_start_time.isoformat(),
+            "end_time": datetime.now().isoformat(),
+            "duration": int(
+                (datetime.now() - environment.test_start_time).total_seconds()
+            ),
+            "users": getattr(environment.parsed_options, "num_users", 0),
+            "requests": environment.performance_data,
+        }
+
+        try:
+            analysis_result = environment.performance_analyzer.comprehensive_analysis(
+                test_data
+            )
+
+            print(f"📋 性能分析结果:")
+            print(f"   综合评级: {analysis_result['overall_grade']}")
+
+            # 输出关键指标
+            if analysis_result.get("response_time"):
+                rt = analysis_result["response_time"]
+                print(
+                    f"   响应时间: P95={rt.get('p95', 0):.1f}ms, 平均={rt.get('mean', 0):.1f}ms"
+                )
+
+            if analysis_result.get("throughput"):
+                tp = analysis_result["throughput"]
+                print(f"   吞吐量: {tp.get('avg_tps', 0):.1f} TPS")
+
+            if analysis_result.get("error_analysis"):
+                er = analysis_result["error_analysis"]
+                print(f"   错误率: {er.get('error_percentage', 0):.2f}%")
+
+            # 生成综合报告
+            from src.analysis.report_generator import ReportGenerator
+
+            report_generator = ReportGenerator()
+
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+            # HTML报告
+            html_path = report_generator.generate_html_report(
+                analysis_result,
+                output_filename=f"ecommerce_comprehensive_{timestamp}.html",
+            )
+
+            # JSON报告
+            json_path = report_generator.generate_json_report(
+                analysis_result,
+                output_filename=f"ecommerce_comprehensive_{timestamp}.json",
+            )
+
+            print(f"\n📄 综合报告已生成:")
+            print(f"   HTML报告: {html_path}")
+            print(f"   JSON报告: {json_path}")
+
+            # 业务建议
+            print(f"\n💡 电商业务建议:")
+            if analysis_result["overall_grade"] in ["A", "B"]:
+                print("   ✅ 系统性能良好，可支撑当前业务负载")
+                print("   🚀 可考虑增加促销活动或扩大用户规模")
+            elif analysis_result["overall_grade"] == "C":
+                print("   ⚠️  系统性能一般，建议优化关键接口")
+                print("   📈 重点关注商品搜索和订单创建性能")
+            else:
+                print("   🚨 系统性能存在问题，需立即优化")
+                print("   🛠️  建议进行系统架构调整或资源扩容")
+
+        except Exception as e:
+            print(f"❌ 性能分析失败: {e}")
+
+    print(f"\n🏁 电商综合测试完成!")
+
+
+# 业务指标收集
+@events.request.add_listener
+def on_request_event(
+    request_type, name, response_time, response_length, exception, **kwargs
+):
+    """请求事件监听 - 收集业务指标和监控"""
+    if exception:
+        # 处理失败请求的监控和告警
+        if "订单" in name or "购物车" in name:
+            print(f"❌ 关键业务失败: {name} - {exception}")
+    else:
+        # 处理成功请求的业务指标收集
+        # 这里可以根据请求名称收集特定的业务指标
+        pass
+
+
+if __name__ == "__main__":
+    print("💡 这是一个电商综合场景示例")
+    print("🔧 使用方法:")
+    print("   locust -f locustfiles/examples/07_ecommerce_scenario.py")
+    print(
+        "   locust -f locustfiles/examples/07_ecommerce_scenario.py --headless -u 30 -r 5 -t 300s"
+    )
+    print("")
+    print("📋 场景特点:")
+    print("   - 完整的用户购物流程")
+    print("   - 真实的电商负载模式")
+    print("   - 综合的性能监控分析")
+    print("   - 业务指标统计")
+    print("   - 多角色用户模拟")
+    print("")
+    print("🎭 用户角色:")
+    print("   - EcommerceBuyer (70%): 完整购物流程用户")
+    print("   - EcommerceBrowser (30%): 纯浏览用户")
+    print("")
+    print("🛍️  业务流程:")
+    print("   1. 商品浏览和搜索")
+    print("   2. 商品详情查看")
+    print("   3. 用户登录认证")
+    print("   4. 添加购物车")
+    print("   5. 订单创建结账")
+    print("")
+    print("📊 集成功能:")
+    print("   - 性能分析和评分")
+    print("   - 实时监控告警")
+    print("   - 数据生成管理")
+    print("   - 自定义事务报告")
+    print("   - 多格式报告生成")
diff --git a/locustfiles/examples/__init__.py b/locustfiles/examples/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/locustfiles/shape_classes/__init__.py b/locustfiles/shape_classes/__init__.py
index e69de29..1ef995c 100644
--- a/locustfiles/shape_classes/__init__.py
+++ b/locustfiles/shape_classes/__init__.py
@@ -0,0 +1,31 @@
+"""
+负载模式类
+
+提供各种负载模式的实现，支持不同的性能测试场景
+"""
+
+from .advanced_shapes import (
+    AdaptiveLoadShape,
+    CustomStageLoadShape,
+    LoadStage,
+    RampUpDownLoadShape,
+    RandomLoadShape,
+    SpikeLoadShape,
+    StairStepLoadShape,
+    WaveLoadShape,
+)
+from .step_load import StepLoadShape as BasicStepLoadShape
+from .wait_user_count import StepLoadShape as WaitUserCountShape
+
+__all__ = [
+    "BasicStepLoadShape",
+    "WaitUserCountShape",
+    "WaveLoadShape",
+    "SpikeLoadShape",
+    "StairStepLoadShape",
+    "CustomStageLoadShape",
+    "RampUpDownLoadShape",
+    "AdaptiveLoadShape",
+    "RandomLoadShape",
+    "LoadStage",
+]
diff --git a/locustfiles/shape_classes/advanced_shapes.py b/locustfiles/shape_classes/advanced_shapes.py
new file mode 100644
index 0000000..348f72a
--- /dev/null
+++ b/locustfiles/shape_classes/advanced_shapes.py
@@ -0,0 +1,431 @@
+"""
+高级负载模式
+
+提供更多复杂的负载模式实现
+"""
+
+import math
+import time
+from collections import namedtuple
+from typing import List, Optional, Tuple
+
+from locust import LoadTestShape
+
+from src.utils.log_moudle import logger
+
+# 负载阶段定义
+LoadStage = namedtuple("LoadStage", ["users", "spawn_rate", "duration"])
+
+
+class WaveLoadShape(LoadTestShape):
+    """
+    波浪形负载模式
+
+    模拟用户访问的波峰波谷，适合测试系统在负载变化时的表现
+    """
+
+    def __init__(
+        self,
+        min_users: int = 10,
+        max_users: int = 100,
+        wave_period: int = 300,
+        spawn_rate: int = 10,
+        time_limit: int = 1800,
+    ):
+        """
+        初始化波浪负载
+
+        Args:
+            min_users: 最小用户数
+            max_users: 最大用户数
+            wave_period: 波浪周期(秒)
+            spawn_rate: 用户生成速率
+            time_limit: 测试时间限制(秒)
+        """
+        super().__init__()
+        self.min_users = min_users
+        self.max_users = max_users
+        self.wave_period = wave_period
+        self.spawn_rate = spawn_rate
+        self.time_limit = time_limit
+
+        logger.info(f"波浪负载模式: {min_users}-{max_users} 用户, 周期: {wave_period}s")
+
+    def tick(self) -> Optional[Tuple[int, int]]:
+        run_time = self.get_run_time()
+
+        if run_time > self.time_limit:
+            return None
+
+        # 计算当前波浪位置 (0-1)
+        wave_position = (run_time % self.wave_period) / self.wave_period
+
+        # 使用正弦函数生成波浪
+        wave_value = math.sin(2 * math.pi * wave_position)
+
+        # 将波浪值映射到用户数范围
+        user_range = self.max_users - self.min_users
+        current_users = self.min_users + int((wave_value + 1) / 2 * user_range)
+
+        return (current_users, self.spawn_rate)
+
+
+class SpikeLoadShape(LoadTestShape):
+    """
+    尖峰负载模式
+
+    在基础负载上周期性地产生负载尖峰，测试系统的突发处理能力
+    """
+
+    def __init__(
+        self,
+        base_users: int = 50,
+        spike_users: int = 200,
+        spike_duration: int = 60,
+        spike_interval: int = 300,
+        spawn_rate: int = 20,
+        time_limit: int = 1800,
+    ):
+        """
+        初始化尖峰负载
+
+        Args:
+            base_users: 基础用户数
+            spike_users: 尖峰用户数
+            spike_duration: 尖峰持续时间(秒)
+            spike_interval: 尖峰间隔时间(秒)
+            spawn_rate: 用户生成速率
+            time_limit: 测试时间限制(秒)
+        """
+        super().__init__()
+        self.base_users = base_users
+        self.spike_users = spike_users
+        self.spike_duration = spike_duration
+        self.spike_interval = spike_interval
+        self.spawn_rate = spawn_rate
+        self.time_limit = time_limit
+
+        logger.info(f"尖峰负载模式: 基础{base_users}用户, 尖峰{spike_users}用户")
+
+    def tick(self) -> Optional[Tuple[int, int]]:
+        run_time = self.get_run_time()
+
+        if run_time > self.time_limit:
+            return None
+
+        # 计算当前在尖峰周期中的位置
+        cycle_position = run_time % self.spike_interval
+
+        if cycle_position < self.spike_duration:
+            # 在尖峰期间
+            return (self.spike_users, self.spawn_rate)
+        else:
+            # 在基础负载期间
+            return (self.base_users, self.spawn_rate)
+
+
+class StairStepLoadShape(LoadTestShape):
+    """
+    阶梯负载模式
+
+    逐步增加负载，每个阶梯保持一定时间，适合容量测试
+    """
+
+    def __init__(
+        self,
+        start_users: int = 10,
+        step_users: int = 20,
+        step_duration: int = 120,
+        max_users: int = 200,
+        spawn_rate: int = 10,
+    ):
+        """
+        初始化阶梯负载
+
+        Args:
+            start_users: 起始用户数
+            step_users: 每阶梯增加的用户数
+            step_duration: 每阶梯持续时间(秒)
+            max_users: 最大用户数
+            spawn_rate: 用户生成速率
+        """
+        super().__init__()
+        self.start_users = start_users
+        self.step_users = step_users
+        self.step_duration = step_duration
+        self.max_users = max_users
+        self.spawn_rate = spawn_rate
+
+        # 计算总阶梯数
+        self.total_steps = math.ceil((max_users - start_users) / step_users) + 1
+        self.time_limit = self.total_steps * step_duration
+
+        logger.info(
+            f"阶梯负载模式: {start_users}-{max_users}用户, {self.total_steps}阶梯"
+        )
+
+    def tick(self) -> Optional[Tuple[int, int]]:
+        run_time = self.get_run_time()
+
+        if run_time > self.time_limit:
+            return None
+
+        # 计算当前阶梯
+        current_step = math.floor(run_time / self.step_duration)
+        current_users = min(
+            self.start_users + current_step * self.step_users, self.max_users
+        )
+
+        return (current_users, self.spawn_rate)
+
+
+class CustomStageLoadShape(LoadTestShape):
+    """
+    自定义阶段负载模式
+
+    允许定义复杂的多阶段负载场景
+    """
+
+    def __init__(self, stages: List[LoadStage]):
+        """
+        初始化自定义阶段负载
+
+        Args:
+            stages: 负载阶段列表，每个阶段包含用户数、生成速率和持续时间
+        """
+        super().__init__()
+        self.stages = stages
+        self.current_stage = 0
+        self.stage_start_time = 0
+
+        # 计算总测试时间
+        self.time_limit = sum(stage.duration for stage in stages)
+
+        logger.info(f"自定义阶段负载: {len(stages)}个阶段, 总时长{self.time_limit}秒")
+
+    def tick(self) -> Optional[Tuple[int, int]]:
+        run_time = self.get_run_time()
+
+        if run_time > self.time_limit or self.current_stage >= len(self.stages):
+            return None
+
+        # 获取当前阶段
+        stage = self.stages[self.current_stage]
+        stage_elapsed = run_time - self.stage_start_time
+
+        # 检查是否需要切换到下一阶段
+        if stage_elapsed >= stage.duration:
+            self.current_stage += 1
+            self.stage_start_time = run_time
+
+            if self.current_stage >= len(self.stages):
+                return None
+
+            stage = self.stages[self.current_stage]
+
+        return (stage.users, stage.spawn_rate)
+
+
+class RampUpDownLoadShape(LoadTestShape):
+    """
+    爬升-保持-下降负载模式
+
+    经典的负载测试模式：逐步增加到目标负载，保持一段时间，然后逐步减少
+    """
+
+    def __init__(
+        self,
+        target_users: int = 100,
+        ramp_up_time: int = 300,
+        hold_time: int = 600,
+        ramp_down_time: int = 300,
+        spawn_rate: int = 10,
+    ):
+        """
+        初始化爬升-保持-下降负载
+
+        Args:
+            target_users: 目标用户数
+            ramp_up_time: 爬升时间(秒)
+            hold_time: 保持时间(秒)
+            ramp_down_time: 下降时间(秒)
+            spawn_rate: 用户生成速率
+        """
+        super().__init__()
+        self.target_users = target_users
+        self.ramp_up_time = ramp_up_time
+        self.hold_time = hold_time
+        self.ramp_down_time = ramp_down_time
+        self.spawn_rate = spawn_rate
+
+        self.time_limit = ramp_up_time + hold_time + ramp_down_time
+
+        logger.info(
+            f"爬升-保持-下降负载: 目标{target_users}用户, "
+            f"爬升{ramp_up_time}s, 保持{hold_time}s, 下降{ramp_down_time}s"
+        )
+
+    def tick(self) -> Optional[Tuple[int, int]]:
+        run_time = self.get_run_time()
+
+        if run_time > self.time_limit:
+            return None
+
+        if run_time <= self.ramp_up_time:
+            # 爬升阶段
+            progress = run_time / self.ramp_up_time
+            current_users = int(self.target_users * progress)
+        elif run_time <= self.ramp_up_time + self.hold_time:
+            # 保持阶段
+            current_users = self.target_users
+        else:
+            # 下降阶段
+            ramp_down_elapsed = run_time - self.ramp_up_time - self.hold_time
+            progress = 1 - (ramp_down_elapsed / self.ramp_down_time)
+            current_users = int(self.target_users * progress)
+
+        return (current_users, self.spawn_rate)
+
+
+class AdaptiveLoadShape(LoadTestShape):
+    """
+    自适应负载模式
+
+    根据系统响应时间动态调整负载
+    """
+
+    def __init__(
+        self,
+        initial_users: int = 10,
+        max_users: int = 200,
+        target_response_time: float = 1.0,
+        adjustment_interval: int = 30,
+        spawn_rate: int = 10,
+        time_limit: int = 1800,
+    ):
+        """
+        初始化自适应负载
+
+        Args:
+            initial_users: 初始用户数
+            max_users: 最大用户数
+            target_response_time: 目标响应时间(秒)
+            adjustment_interval: 调整间隔(秒)
+            spawn_rate: 用户生成速率
+            time_limit: 测试时间限制(秒)
+        """
+        super().__init__()
+        self.initial_users = initial_users
+        self.max_users = max_users
+        self.target_response_time = target_response_time
+        self.adjustment_interval = adjustment_interval
+        self.spawn_rate = spawn_rate
+        self.time_limit = time_limit
+
+        self.current_users = initial_users
+        self.last_adjustment_time = 0
+
+        logger.info(
+            f"自适应负载模式: 目标响应时间{target_response_time}s, "
+            f"最大{max_users}用户"
+        )
+
+    def tick(self) -> Optional[Tuple[int, int]]:
+        run_time = self.get_run_time()
+
+        if run_time > self.time_limit:
+            return None
+
+        # 检查是否需要调整负载
+        if run_time - self.last_adjustment_time >= self.adjustment_interval:
+            self._adjust_load()
+            self.last_adjustment_time = run_time
+
+        return (self.current_users, self.spawn_rate)
+
+    def _adjust_load(self):
+        """根据当前性能指标调整负载"""
+        try:
+            # 获取当前统计信息
+            stats = self.runner.stats
+            if not stats.entries:
+                return
+
+            # 计算平均响应时间
+            total_response_time = sum(
+                entry.avg_response_time for entry in stats.entries.values()
+            )
+            avg_response_time = total_response_time / len(stats.entries)
+
+            # 根据响应时间调整用户数
+            if avg_response_time > self.target_response_time * 1.2:
+                # 响应时间过高，减少用户
+                self.current_users = max(1, int(self.current_users * 0.9))
+                logger.info(
+                    f"响应时间过高({avg_response_time:.2f}s)，减少用户至{self.current_users}"
+                )
+            elif avg_response_time < self.target_response_time * 0.8:
+                # 响应时间良好，增加用户
+                self.current_users = min(self.max_users, int(self.current_users * 1.1))
+                logger.info(
+                    f"响应时间良好({avg_response_time:.2f}s)，增加用户至{self.current_users}"
+                )
+
+        except Exception as e:
+            logger.error(f"调整负载失败: {e}")
+
+
+class RandomLoadShape(LoadTestShape):
+    """
+    随机负载模式
+
+    在指定范围内随机变化用户数，模拟不可预测的负载变化
+    """
+
+    def __init__(
+        self,
+        min_users: int = 10,
+        max_users: int = 100,
+        change_interval: int = 60,
+        spawn_rate: int = 10,
+        time_limit: int = 1800,
+    ):
+        """
+        初始化随机负载
+
+        Args:
+            min_users: 最小用户数
+            max_users: 最大用户数
+            change_interval: 变化间隔(秒)
+            spawn_rate: 用户生成速率
+            time_limit: 测试时间限制(秒)
+        """
+        super().__init__()
+        self.min_users = min_users
+        self.max_users = max_users
+        self.change_interval = change_interval
+        self.spawn_rate = spawn_rate
+        self.time_limit = time_limit
+
+        self.current_users = min_users
+        self.last_change_time = 0
+
+        logger.info(
+            f"随机负载模式: {min_users}-{max_users}用户, " f"变化间隔{change_interval}s"
+        )
+
+    def tick(self) -> Optional[Tuple[int, int]]:
+        run_time = self.get_run_time()
+
+        if run_time > self.time_limit:
+            return None
+
+        # 检查是否需要改变用户数
+        if run_time - self.last_change_time >= self.change_interval:
+            import random
+
+            self.current_users = random.randint(self.min_users, self.max_users)
+            self.last_change_time = run_time
+            logger.debug(f"随机调整用户数至: {self.current_users}")
+
+        return (self.current_users, self.spawn_rate)
diff --git a/mock_server/__init__.py b/mock_server/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mock_server/app.py b/mock_server/app.py
new file mode 100644
index 0000000..affee05
--- /dev/null
+++ b/mock_server/app.py
@@ -0,0 +1,409 @@
+#!/usr/bin/env python3
+"""
+Mock HTTP服务
+用于Locust性能测试框架的示例和测试
+"""
+
+import json
+import random
+import threading
+import time
+import uuid
+from datetime import datetime
+
+from flask import Flask, jsonify, request
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+# 配置参数
+CONFIG = {
+    "response_delay_ms": 50,  # 基础响应延时(毫秒)
+    "error_rate": 0.05,  # 错误率(5%)
+    "slow_request_rate": 0.1,  # 慢请求比例(10%)
+    "slow_delay_ms": 2000,  # 慢请求延时(毫秒)
+}
+
+# 模拟数据存储
+USERS = {}
+PRODUCTS = {}
+ORDERS = {}
+SESSIONS = {}
+
+# 系统指标
+METRICS = {
+    "requests_total": 0,
+    "requests_success": 0,
+    "requests_error": 0,
+    "response_times": [],
+    "active_users": 0,
+}
+
+
+def init_data():
+    """初始化模拟数据"""
+    global PRODUCTS
+
+    # 初始化商品数据
+    categories = ["电子产品", "服装", "图书", "家居", "运动"]
+    for i in range(1, 101):
+        PRODUCTS[str(i)] = {
+            "id": str(i),
+            "name": f"商品{i}",
+            "category": random.choice(categories),
+            "price": round(random.uniform(10, 1000), 2),
+            "stock": random.randint(0, 100),
+            "description": f"这是商品{i}的详细描述",
+            "rating": round(random.uniform(3.0, 5.0), 1),
+            "created_at": datetime.now().isoformat(),
+        }
+
+
+def simulate_delay():
+    """模拟响应延时"""
+    delay = CONFIG["response_delay_ms"] / 1000
+
+    # 随机产生慢请求
+    if random.random() < CONFIG["slow_request_rate"]:
+        delay = CONFIG["slow_delay_ms"] / 1000
+
+    time.sleep(delay)
+
+
+def should_return_error():
+    """判断是否返回错误"""
+    return random.random() < CONFIG["error_rate"]
+
+
+def update_metrics(success=True, response_time=None):
+    """更新系统指标"""
+    METRICS["requests_total"] += 1
+    if success:
+        METRICS["requests_success"] += 1
+    else:
+        METRICS["requests_error"] += 1
+
+    if response_time:
+        METRICS["response_times"].append(response_time)
+        # 保持最近1000次请求的响应时间
+        if len(METRICS["response_times"]) > 1000:
+            METRICS["response_times"] = METRICS["response_times"][-1000:]
+
+
+@app.before_request
+def before_request():
+    """请求前处理"""
+    request.start_time = time.time()
+
+
+@app.after_request
+def after_request(response):
+    """请求后处理"""
+    if hasattr(request, "start_time"):
+        response_time = (time.time() - request.start_time) * 1000
+        success = 200 <= response.status_code < 400
+        update_metrics(success, response_time)
+    return response
+
+
+# 健康检查端点
+@app.route("/health", methods=["GET"])
+def health():
+    """健康检查"""
+    return jsonify(
+        {
+            "status": "healthy",
+            "timestamp": datetime.now().isoformat(),
+            "version": "1.0.0",
+        }
+    )
+
+
+@app.route("/metrics", methods=["GET"])
+def metrics():
+    """系统指标"""
+    response_times = METRICS["response_times"]
+    avg_response_time = (
+        sum(response_times) / len(response_times) if response_times else 0
+    )
+
+    return jsonify(
+        {
+            "requests_total": METRICS["requests_total"],
+            "requests_success": METRICS["requests_success"],
+            "requests_error": METRICS["requests_error"],
+            "error_rate": METRICS["requests_error"]
+            / max(METRICS["requests_total"], 1)
+            * 100,
+            "avg_response_time": round(avg_response_time, 2),
+            "active_users": METRICS["active_users"],
+            "timestamp": datetime.now().isoformat(),
+        }
+    )
+
+
+# 用户认证相关API
+@app.route("/auth/login", methods=["POST"])
+def login():
+    """用户登录"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    data = request.get_json() or {}
+    username = data.get("username", "")
+    password = data.get("password", "")
+
+    if not username or not password:
+        return jsonify({"error": "用户名和密码不能为空"}), 400
+
+    # 模拟登录验证（简单验证）
+    if len(username) < 3:
+        return jsonify({"error": "用户名或密码错误"}), 401
+
+    # 生成会话token
+    token = str(uuid.uuid4())
+    user_id = str(hash(username) % 10000)
+
+    SESSIONS[token] = {
+        "user_id": user_id,
+        "username": username,
+        "login_time": datetime.now().isoformat(),
+    }
+
+    USERS[user_id] = {
+        "id": user_id,
+        "username": username,
+        "name": f"用户{username}",
+        "email": f"{username}@example.com",
+        "avatar": f"https://example.com/avatar/{user_id}.jpg",
+    }
+
+    METRICS["active_users"] = len(SESSIONS)
+
+    return jsonify({"token": token, "user": USERS[user_id], "expires_in": 3600})
+
+
+@app.route("/auth/logout", methods=["POST"])
+def logout():
+    """用户登出"""
+    simulate_delay()
+
+    token = request.headers.get("Authorization", "").replace("Bearer ", "")
+    if token in SESSIONS:
+        del SESSIONS[token]
+        METRICS["active_users"] = len(SESSIONS)
+
+    return jsonify({"message": "登出成功"})
+
+
+@app.route("/auth/profile", methods=["GET"])
+def profile():
+    """获取用户信息"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    token = request.headers.get("Authorization", "").replace("Bearer ", "")
+    if token not in SESSIONS:
+        return jsonify({"error": "未授权访问"}), 401
+
+    user_id = SESSIONS[token]["user_id"]
+    return jsonify(USERS.get(user_id, {}))
+
+
+# 商品相关API
+@app.route("/api/products", methods=["GET"])
+def get_products():
+    """获取商品列表"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    page = int(request.args.get("page", 1))
+    limit = int(request.args.get("limit", 20))
+    category = request.args.get("category", "")
+
+    products = list(PRODUCTS.values())
+
+    # 分类过滤
+    if category:
+        products = [p for p in products if p["category"] == category]
+
+    # 分页
+    start = (page - 1) * limit
+    end = start + limit
+    page_products = products[start:end]
+
+    return jsonify(
+        {
+            "products": page_products,
+            "total": len(products),
+            "page": page,
+            "limit": limit,
+            "pages": (len(products) + limit - 1) // limit,
+        }
+    )
+
+
+@app.route("/api/products/<product_id>", methods=["GET"])
+def get_product(product_id):
+    """获取商品详情"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    if product_id not in PRODUCTS:
+        return jsonify({"error": "商品不存在"}), 404
+
+    return jsonify(PRODUCTS[product_id])
+
+
+@app.route("/api/search", methods=["GET"])
+def search_products():
+    """搜索商品"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    q = request.args.get("q", "").lower()
+    sort = request.args.get("sort", "name")
+
+    products = []
+    for product in PRODUCTS.values():
+        if q in product["name"].lower() or q in product["description"].lower():
+            products.append(product)
+
+    # 排序
+    if sort == "price":
+        products.sort(key=lambda x: x["price"])
+    elif sort == "rating":
+        products.sort(key=lambda x: x["rating"], reverse=True)
+
+    return jsonify({"products": products, "query": q, "total": len(products)})
+
+
+# 订单相关API
+@app.route("/api/orders", methods=["GET"])
+def get_orders():
+    """获取订单列表"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    token = request.headers.get("Authorization", "").replace("Bearer ", "")
+    if token not in SESSIONS:
+        return jsonify({"error": "未授权访问"}), 401
+
+    user_id = SESSIONS[token]["user_id"]
+    user_orders = [order for order in ORDERS.values() if order["user_id"] == user_id]
+
+    return jsonify({"orders": user_orders})
+
+
+@app.route("/api/orders", methods=["POST"])
+def create_order():
+    """创建订单"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    token = request.headers.get("Authorization", "").replace("Bearer ", "")
+    if token not in SESSIONS:
+        return jsonify({"error": "未授权访问"}), 401
+
+    data = request.get_json() or {}
+    user_id = SESSIONS[token]["user_id"]
+
+    order_id = str(uuid.uuid4())
+    order = {
+        "id": order_id,
+        "user_id": user_id,
+        "products": data.get("products", []),
+        "total_amount": data.get("total_amount", 0),
+        "status": "pending",
+        "created_at": datetime.now().isoformat(),
+    }
+
+    ORDERS[order_id] = order
+
+    return jsonify(order), 201
+
+
+@app.route("/api/orders/<order_id>", methods=["GET"])
+def get_order(order_id):
+    """获取订单详情"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    if order_id not in ORDERS:
+        return jsonify({"error": "订单不存在"}), 404
+
+    return jsonify(ORDERS[order_id])
+
+
+# 购物车API
+@app.route("/api/cart/add", methods=["POST"])
+def add_to_cart():
+    """添加到购物车"""
+    simulate_delay()
+
+    if should_return_error():
+        return jsonify({"error": "服务器内部错误"}), 500
+
+    data = request.get_json() or {}
+    product_id = data.get("product_id")
+    quantity = data.get("quantity", 1)
+
+    if not product_id or product_id not in PRODUCTS:
+        return jsonify({"error": "商品不存在"}), 404
+
+    return jsonify(
+        {"message": "添加成功", "product_id": product_id, "quantity": quantity}
+    )
+
+
+# 配置管理API
+@app.route("/admin/config", methods=["GET"])
+def get_config():
+    """获取服务配置"""
+    return jsonify(CONFIG)
+
+
+@app.route("/admin/config", methods=["POST"])
+def update_config():
+    """更新服务配置"""
+    data = request.get_json() or {}
+    CONFIG.update(data)
+    return jsonify({"message": "配置更新成功", "config": CONFIG})
+
+
+if __name__ == "__main__":
+    import os
+
+    # 从环境变量读取配置
+    host = os.getenv("FLASK_HOST", "0.0.0.0")
+    port = int(os.getenv("FLASK_PORT", "5000"))
+
+    init_data()
+    print("🚀 Mock服务启动中...")
+    print("📊 API端点:")
+    print(f"  - 健康检查: http://localhost:{port}/health")
+    print(f"  - 系统指标: http://localhost:{port}/metrics")
+    print(f"  - 用户登录: POST http://localhost:{port}/auth/login")
+    print(f"  - 商品列表: http://localhost:{port}/api/products")
+    print(f"  - 商品搜索: http://localhost:{port}/api/search")
+    print(f"  - 订单管理: http://localhost:{port}/api/orders")
+    print("💡 用于Locust性能测试的模拟服务")
+
+    app.run(host=host, port=port, debug=True, threaded=True)
diff --git a/plugin_config.template.json b/plugin_config.template.json
new file mode 100644
index 0000000..46b5d7f
--- /dev/null
+++ b/plugin_config.template.json
@@ -0,0 +1,130 @@
+{
+  "enabled_plugins": [
+    "csv_report_plugin"
+  ],
+  "plugin_configs": {
+    "csv_report_plugin": {
+      "output_directory": "reports/csv",
+      "include_summary": true,
+      "include_requests": true,
+      "include_errors": true,
+      "timestamp_format": "%Y%m%d_%H%M%S"
+    },
+    "performance_analyzer": {
+      "response_time_thresholds": {
+        "excellent": 500,
+        "good": 1000,
+        "acceptable": 2000,
+        "poor": 5000
+      },
+      "throughput_thresholds": {
+        "excellent": 1000,
+        "good": 500,
+        "acceptable": 100,
+        "poor": 50
+      },
+      "error_rate_thresholds": {
+        "excellent": 0.1,
+        "good": 1.0,
+        "acceptable": 5.0,
+        "poor": 10.0
+      }
+    },
+    "performance_monitor": {
+      "monitoring_interval": 5,
+      "alert_rules": [
+        {
+          "name": "high_response_time",
+          "metric": "avg_response_time",
+          "threshold": 2000,
+          "operator": ">",
+          "severity": "warning",
+          "enabled": true
+        },
+        {
+          "name": "high_error_rate",
+          "metric": "error_rate",
+          "threshold": 5.0,
+          "operator": ">",
+          "severity": "critical",
+          "enabled": true
+        },
+        {
+          "name": "low_throughput",
+          "metric": "current_rps",
+          "threshold": 10,
+          "operator": "<",
+          "severity": "warning",
+          "enabled": true
+        }
+      ]
+    },
+    "notification_service": {
+      "channels": {
+        "feishu": {
+          "webhook_url": "https://open.feishu.cn/open-apis/bot/v2/hook/your-webhook-url",
+          "enabled": false
+        },
+        "dingtalk": {
+          "webhook_url": "https://oapi.dingtalk.com/robot/send?access_token=your-token",
+          "secret": "your-secret",
+          "enabled": false
+        },
+        "email": {
+          "smtp_server": "smtp.example.com",
+          "smtp_port": 587,
+          "username": "your-email@example.com",
+          "password": "your-password",
+          "from_email": "your-email@example.com",
+          "to_emails": ["admin@example.com"],
+          "enabled": false
+        },
+        "wechat_work": {
+          "webhook_url": "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=your-key",
+          "enabled": false
+        }
+      },
+      "message_templates": {
+        "alert": {
+          "title": "性能测试告警",
+          "format": "markdown"
+        },
+        "report": {
+          "title": "性能测试报告",
+          "format": "markdown"
+        }
+      }
+    },
+    "data_generator": {
+      "locale": "zh_CN",
+      "batch_size": 1000,
+      "cache_enabled": true,
+      "custom_providers": {}
+    },
+    "data_provider": {
+      "default_strategy": "round_robin",
+      "cache_size": 10000,
+      "auto_reload": true,
+      "data_sources": {
+        "users": {
+          "type": "file",
+          "path": "data/users.json",
+          "format": "json"
+        },
+        "products": {
+          "type": "file",
+          "path": "data/products.csv",
+          "format": "csv"
+        }
+      }
+    },
+    "data_distributor": {
+      "node_id": null,
+      "is_master": true,
+      "master_host": "localhost",
+      "master_port": 8089,
+      "sync_interval": 30,
+      "auto_sync": false
+    }
+  }
+}
diff --git a/scripts/README.md b/scripts/README.md
new file mode 100644
index 0000000..b949cef
--- /dev/null
+++ b/scripts/README.md
@@ -0,0 +1,222 @@
+# 工具脚本说明
+
+这个目录包含了Locust测试框架的实用工具脚本。
+
+## 脚本列表
+
+### 🚀 start_mock_server.py
+启动Mock HTTP服务器，用于测试和开发。
+
+**功能特点:**
+- 自动检查依赖（Flask、Flask-CORS）
+- 健康检查和服务启动验证
+- 命令行参数配置主机和端口
+- 优雅的错误处理和用户提示
+
+**使用方法:**
+```bash
+# 使用默认配置启动
+python scripts/start_mock_server.py
+
+# 自定义主机和端口
+python scripts/start_mock_server.py --host 0.0.0.0 --port 8080
+
+# 通过Makefile启动
+make mock-server
+make mock-server-background  # 后台运行
+```
+
+**API端点:**
+- `GET /health` - 健康检查
+- `GET /api/products` - 商品列表
+- `POST /api/auth/login` - 用户登录
+- 更多端点请查看 `mock_server/app.py`
+
+### 📋 run_examples.py
+快速运行locustfiles/examples中的示例测试。
+
+**功能特点:**
+- 自动发现和列出所有可用示例
+- 支持Web UI和无头模式
+- 自动启动Mock服务器
+- 灵活的参数配置
+
+**使用方法:**
+```bash
+# 列出所有示例
+python scripts/run_examples.py --list
+make examples
+
+# 运行指定示例（按编号）
+python scripts/run_examples.py 1
+make run-example example=1
+
+# Web UI模式运行
+python scripts/run_examples.py 1 --web
+make run-example-web example=1
+
+# 自定义测试参数
+python scripts/run_examples.py 2 -u 20 -r 5 -t 120s
+make run-example example=2 u=20 r=5 t=120s
+
+# 按文件名部分匹配
+python scripts/run_examples.py basic
+```
+
+**示例编号对照:**
+1. `01_basic_test.py` - 基础HTTP测试
+2. `02_custom_params.py` - 自定义参数和集合点
+3. `03_data_driven.py` - 数据驱动测试
+4. `04_performance_analysis.py` - 性能分析集成
+5. `05_monitoring_alerts.py` - 监控和告警
+6. `06_load_shapes.py` - 负载模式演示
+7. `07_ecommerce_scenario.py` - 电商综合场景
+
+### 🎲 generate_test_data.py
+生成各种类型的测试数据。
+
+**功能特点:**
+- 支持多种数据类型（用户、商品、订单等）
+- CSV和JSON输出格式
+- 本地化支持（中文数据）
+- Locust专用数据集生成
+
+**使用方法:**
+```bash
+# 生成基础测试数据
+python scripts/generate_test_data.py --type users --count 100 --format json
+make test-data type=users count=100 format=json
+
+# 生成Locust专用测试数据
+python scripts/generate_test_data.py --type locust --count 500
+make test-data-locust count=500
+
+# 指定输出文件
+python scripts/generate_test_data.py --type products --output data/products.csv
+
+# 使用随机种子（可重现数据）
+python scripts/generate_test_data.py --seed 12345
+```
+
+**支持的数据类型:**
+- `users` - 用户资料数据
+- `products` - 商品数据
+- `orders` - 订单数据
+- `mixed` - 混合类型数据
+- `test_dataset` - 结构化测试数据集
+- `locust` - Locust测试专用数据包
+
+### 📊 analyze_results.py
+分析Locust测试结果并生成报告。
+
+**功能特点:**
+- 自动解析Locust生成的CSV报告
+- 计算性能指标和等级评分（A-F）
+- 趋势分析和失败原因统计
+- 智能化优化建议
+- 多种输出格式
+
+**使用方法:**
+```bash
+# 分析指定目录的结果
+python scripts/analyze_results.py --results-dir reports
+make analyze-results results_dir=reports
+
+# 分析特定文件
+python scripts/analyze_results.py --stats-file reports/stats.csv
+
+# 不同输出格式
+python scripts/analyze_results.py --format json    # 仅JSON文件
+python scripts/analyze_results.py --format console # 仅控制台
+python scripts/analyze_results.py --format both    # 默认，两种都输出
+
+# 包含趋势分析
+python scripts/analyze_results.py --with-trends --detailed
+```
+
+**报告内容:**
+- 性能等级评分（A-F）
+- 请求成功率和失败率
+- 响应时间统计
+- 主要失败原因
+- 性能趋势分析
+- 优化建议
+
+## 快速开始工作流
+
+### 方式一：使用Makefile命令
+```bash
+# 查看所有可用命令
+make help
+
+# 快速开始（推荐新用户）
+make quick-start
+
+# 完整测试流程
+make full-test
+```
+
+### 方式二：手动步骤
+```bash
+# 1. 启动Mock服务器
+python scripts/start_mock_server.py &
+
+# 2. 生成测试数据（可选）
+python scripts/generate_test_data.py --type locust --count 100
+
+# 3. 运行示例测试
+python scripts/run_examples.py 1 --web
+
+# 4. 分析结果
+python scripts/analyze_results.py --with-trends
+```
+
+## 集成到CI/CD
+
+所有脚本都支持非交互式执行，适合集成到CI/CD管道：
+
+```yaml
+# GitHub Actions 示例
+- name: Run Load Test
+  run: |
+    python scripts/start_mock_server.py &
+    sleep 5
+    python scripts/run_examples.py 7 -u 50 -r 10 -t 300s
+    python scripts/analyze_results.py --format json
+```
+
+## 故障排除
+
+### Mock服务器启动失败
+- 检查端口是否被占用：`lsof -i :5000`
+- 安装依赖：`pip install flask flask-cors`
+
+### 示例运行失败
+- 确保Mock服务器正在运行
+- 检查Python环境和依赖
+- 使用 `--skip-mock` 跳过服务器检查
+
+### 数据生成问题
+- 检查输出目录权限
+- 确保有足够磁盘空间
+- 验证locale设置
+
+### 结果分析问题
+- 确保CSV文件格式正确
+- 检查文件路径是否存在
+- 使用绝对路径避免路径问题
+
+## 扩展开发
+
+如需添加新的工具脚本：
+
+1. 在scripts/目录创建新脚本
+2. 添加执行权限：`chmod +x scripts/new_script.py`
+3. 在Makefile中添加对应命令
+4. 更新此README文档
+
+脚本开发建议：
+- 使用argparse处理命令行参数
+- 提供清晰的帮助信息和错误提示
+- 支持通过Makefile调用
+- 遵循项目的错误处理模式
diff --git a/scripts/__init__.py b/scripts/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/scripts/analyze_results.py b/scripts/analyze_results.py
new file mode 100755
index 0000000..949539d
--- /dev/null
+++ b/scripts/analyze_results.py
@@ -0,0 +1,455 @@
+#!/usr/bin/env python3
+"""
+测试结果分析脚本
+
+用于分析Locust测试结果和生成报告
+"""
+
+import argparse
+import csv
+import json
+import statistics
+import sys
+from pathlib import Path
+from typing import Any, Dict, List
+
+# 添加项目根目录到路径
+script_dir = Path(__file__).parent.parent
+sys.path.insert(0, str(script_dir))
+
+from src.analysis.performance_analyzer import PerformanceAnalyzer
+
+
+def find_result_files(directory):
+    """查找测试结果文件"""
+    directory = Path(directory)
+    if not directory.exists():
+        return {}
+
+    files = {"stats": None, "failures": None, "exceptions": None, "html": None}
+
+    # 查找CSV和HTML报告文件
+    for file in directory.glob("*"):
+        if file.suffix == ".csv":
+            if "stats" in file.name:
+                files["stats"] = file
+            elif "failures" in file.name:
+                files["failures"] = file
+            elif "exceptions" in file.name:
+                files["exceptions"] = file
+        elif file.suffix == ".html" and "report" in file.name:
+            files["html"] = file
+
+    return files
+
+
+def parse_stats_csv(stats_file):
+    """解析统计CSV文件"""
+    if not stats_file or not stats_file.exists():
+        return None
+
+    stats = []
+    with open(stats_file, "r", encoding="utf-8") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            # 转换数值字段
+            for key in [
+                "Request Count",
+                "Failure Count",
+                "Average Response Time",
+                "Min Response Time",
+                "Max Response Time",
+            ]:
+                if key in row and row[key]:
+                    try:
+                        row[key] = float(row[key])
+                    except ValueError:
+                        pass
+            stats.append(row)
+
+    return stats
+
+
+def parse_failures_csv(failures_file):
+    """解析失败CSV文件"""
+    if not failures_file or not failures_file.exists():
+        return None
+
+    failures = []
+    with open(failures_file, "r", encoding="utf-8") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            # 转换数值字段
+            if "Occurrences" in row and row["Occurrences"]:
+                try:
+                    row["Occurrences"] = int(row["Occurrences"])
+                except ValueError:
+                    pass
+            failures.append(row)
+
+    return failures
+
+
+def calculate_basic_metrics(stats):
+    """计算基本指标"""
+    if not stats:
+        return {}
+
+    # 过滤掉Aggregated行
+    request_stats = [
+        s for s in stats if s.get("Type") == "GET" or s.get("Type") == "POST"
+    ]
+
+    if not request_stats:
+        return {}
+
+    total_requests = sum(s.get("Request Count", 0) for s in request_stats)
+    total_failures = sum(s.get("Failure Count", 0) for s in request_stats)
+
+    response_times = [
+        s.get("Average Response Time", 0)
+        for s in request_stats
+        if s.get("Average Response Time", 0) > 0
+    ]
+
+    metrics = {
+        "total_requests": total_requests,
+        "total_failures": total_failures,
+        "success_rate": (
+            (total_requests - total_failures) / total_requests * 100
+            if total_requests > 0
+            else 0
+        ),
+        "failure_rate": (
+            total_failures / total_requests * 100 if total_requests > 0 else 0
+        ),
+    }
+
+    if response_times:
+        metrics.update(
+            {
+                "avg_response_time": statistics.mean(response_times),
+                "median_response_time": statistics.median(response_times),
+                "min_response_time": min(
+                    s.get("Min Response Time", 0)
+                    for s in request_stats
+                    if s.get("Min Response Time", 0) > 0
+                ),
+                "max_response_time": max(
+                    s.get("Max Response Time", 0) for s in request_stats
+                ),
+            }
+        )
+
+    return metrics
+
+
+def analyze_performance_trend(stats):
+    """分析性能趋势"""
+    if not stats:
+        return {}
+
+    # 按请求名称分组分析
+    request_groups = {}
+    for stat in stats:
+        name = stat.get("Name", "Unknown")
+        if name not in request_groups:
+            request_groups[name] = []
+        request_groups[name].append(stat)
+
+    trends = {}
+    for name, group in request_groups.items():
+        if len(group) < 2:
+            continue
+
+        response_times = [s.get("Average Response Time", 0) for s in group]
+        failure_rates = [
+            s.get("Failure Count", 0) / s.get("Request Count", 1) * 100
+            for s in group
+            if s.get("Request Count", 0) > 0
+        ]
+
+        if response_times:
+            trends[name] = {
+                "response_time_trend": (
+                    "increasing"
+                    if response_times[-1] > response_times[0]
+                    else "decreasing"
+                ),
+                "avg_response_time": statistics.mean(response_times),
+                "response_time_variance": (
+                    statistics.variance(response_times)
+                    if len(response_times) > 1
+                    else 0
+                ),
+            }
+
+        if failure_rates:
+            trends[name]["avg_failure_rate"] = statistics.mean(failure_rates)
+
+    return trends
+
+
+def generate_summary_report(metrics, trends, failures, output_file):
+    """生成汇总报告"""
+    report = {
+        "summary": {
+            "timestamp": str(Path(output_file).stem),
+            "metrics": metrics,
+            "grade": calculate_performance_grade(metrics),
+        },
+        "trends": trends,
+        "top_failures": failures[:10] if failures else [],
+        "recommendations": generate_recommendations(metrics, trends, failures),
+    }
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        json.dump(report, f, ensure_ascii=False, indent=2)
+
+    return report
+
+
+def calculate_performance_grade(metrics):
+    """计算性能等级"""
+    if not metrics:
+        return "F"
+
+    score = 0
+
+    # 成功率评分 (40%)
+    success_rate = metrics.get("success_rate", 0)
+    if success_rate >= 99:
+        score += 40
+    elif success_rate >= 95:
+        score += 35
+    elif success_rate >= 90:
+        score += 30
+    elif success_rate >= 80:
+        score += 20
+    else:
+        score += 10
+
+    # 响应时间评分 (40%)
+    avg_response_time = metrics.get("avg_response_time", 0)
+    if avg_response_time <= 100:
+        score += 40
+    elif avg_response_time <= 300:
+        score += 35
+    elif avg_response_time <= 500:
+        score += 30
+    elif avg_response_time <= 1000:
+        score += 20
+    else:
+        score += 10
+
+    # 稳定性评分 (20%)
+    failure_rate = metrics.get("failure_rate", 0)
+    if failure_rate <= 1:
+        score += 20
+    elif failure_rate <= 5:
+        score += 15
+    elif failure_rate <= 10:
+        score += 10
+    else:
+        score += 5
+
+    # 等级划分
+    if score >= 90:
+        return "A"
+    elif score >= 80:
+        return "B"
+    elif score >= 70:
+        return "C"
+    elif score >= 60:
+        return "D"
+    else:
+        return "F"
+
+
+def generate_recommendations(metrics, trends, failures):
+    """生成优化建议"""
+    recommendations = []
+
+    if not metrics:
+        return recommendations
+
+    # 成功率建议
+    success_rate = metrics.get("success_rate", 0)
+    if success_rate < 95:
+        recommendations.append(
+            {
+                "type": "reliability",
+                "priority": "high",
+                "message": f"成功率仅为{success_rate:.1f}%，建议检查错误原因并优化系统稳定性",
+            }
+        )
+
+    # 响应时间建议
+    avg_response_time = metrics.get("avg_response_time", 0)
+    if avg_response_time > 500:
+        recommendations.append(
+            {
+                "type": "performance",
+                "priority": "high",
+                "message": f"平均响应时间{avg_response_time:.0f}ms过长，建议优化性能",
+            }
+        )
+    elif avg_response_time > 200:
+        recommendations.append(
+            {
+                "type": "performance",
+                "priority": "medium",
+                "message": f"平均响应时间{avg_response_time:.0f}ms可以进一步优化",
+            }
+        )
+
+    # 失败分析建议
+    if failures:
+        top_failure = failures[0]
+        recommendations.append(
+            {
+                "type": "debugging",
+                "priority": "high",
+                "message": f'最常见错误: {top_failure.get("Error", "未知错误")}，出现{top_failure.get("Occurrences", 0)}次',
+            }
+        )
+
+    # 趋势分析建议
+    for name, trend in trends.items():
+        if trend.get("response_time_trend") == "increasing":
+            recommendations.append(
+                {
+                    "type": "trend",
+                    "priority": "medium",
+                    "message": f"接口 {name} 响应时间呈上升趋势，建议监控性能变化",
+                }
+            )
+
+    return recommendations
+
+
+def print_console_report(report):
+    """打印控制台报告"""
+    print("📊 测试结果分析报告")
+    print("=" * 60)
+
+    metrics = report["summary"]["metrics"]
+    if metrics:
+        print(f"🎯 性能等级: {report['summary']['grade']}")
+        print(f"📈 总请求数: {metrics.get('total_requests', 0):,}")
+        print(f"✅ 成功率: {metrics.get('success_rate', 0):.2f}%")
+        print(f"❌ 失败率: {metrics.get('failure_rate', 0):.2f}%")
+
+        if "avg_response_time" in metrics:
+            print(f"⚡ 平均响应时间: {metrics['avg_response_time']:.2f}ms")
+            print(
+                f"⚡ 响应时间范围: {metrics.get('min_response_time', 0):.0f}-{metrics.get('max_response_time', 0):.0f}ms"
+            )
+
+    # 失败分析
+    failures = report["top_failures"]
+    if failures:
+        print(f"\n❌ 主要失败原因:")
+        for i, failure in enumerate(failures[:3], 1):
+            print(
+                f"   {i}. {failure.get('Error', '未知')}: {failure.get('Occurrences', 0)}次"
+            )
+
+    # 优化建议
+    recommendations = report["recommendations"]
+    if recommendations:
+        print(f"\n💡 优化建议:")
+        for rec in recommendations[:5]:
+            priority_icon = (
+                "🔴"
+                if rec["priority"] == "high"
+                else "🟡" if rec["priority"] == "medium" else "🟢"
+            )
+            print(f"   {priority_icon} {rec['message']}")
+
+    print("\n" + "=" * 60)
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(description="分析Locust测试结果")
+
+    # 输入参数
+    parser.add_argument("--results-dir", default="reports", help="测试结果目录")
+    parser.add_argument("--stats-file", help="统计CSV文件路径")
+    parser.add_argument("--failures-file", help="失败CSV文件路径")
+
+    # 输出参数
+    parser.add_argument("--output", help="分析报告输出文件")
+    parser.add_argument(
+        "--format", choices=["json", "console", "both"], default="both", help="输出格式"
+    )
+
+    # 分析选项
+    parser.add_argument("--detailed", action="store_true", help="详细分析")
+    parser.add_argument("--with-trends", action="store_true", help="包含趋势分析")
+
+    args = parser.parse_args()
+
+    print("📊 Locust结果分析器")
+    print("=" * 50)
+
+    # 查找结果文件
+    if args.stats_file:
+        stats_file = Path(args.stats_file)
+        failures_file = Path(args.failures_file) if args.failures_file else None
+    else:
+        files = find_result_files(args.results_dir)
+        stats_file = files["stats"]
+        failures_file = files["failures"]
+
+        if not stats_file:
+            print(f"❌ 在 {args.results_dir} 中未找到统计文件")
+            return 1
+
+    print(f"📁 分析文件: {stats_file}")
+    if failures_file:
+        print(f"📁 失败文件: {failures_file}")
+
+    try:
+        # 解析数据
+        stats = parse_stats_csv(stats_file)
+        failures = parse_failures_csv(failures_file) if failures_file else []
+
+        if not stats:
+            print("❌ 无法解析统计数据")
+            return 1
+
+        # 计算指标
+        metrics = calculate_basic_metrics(stats)
+        trends = analyze_performance_trend(stats) if args.with_trends else {}
+
+        # 生成报告
+        if args.format in ["json", "both"]:
+            output_file = args.output or f"analysis_report_{Path(stats_file).stem}.json"
+            report = generate_summary_report(metrics, trends, failures, output_file)
+            print(f"✅ 分析报告已保存: {output_file}")
+        else:
+            report = {
+                "summary": {
+                    "metrics": metrics,
+                    "grade": calculate_performance_grade(metrics),
+                },
+                "trends": trends,
+                "top_failures": failures[:10],
+                "recommendations": generate_recommendations(metrics, trends, failures),
+            }
+
+        # 控制台输出
+        if args.format in ["console", "both"]:
+            print_console_report(report)
+
+        return 0
+
+    except Exception as e:
+        print(f"❌ 分析失败: {e}")
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/generate_test_data.py b/scripts/generate_test_data.py
new file mode 100755
index 0000000..755e900
--- /dev/null
+++ b/scripts/generate_test_data.py
@@ -0,0 +1,258 @@
+#!/usr/bin/env python3
+"""
+测试数据生成脚本
+
+用于生成各种类型的测试数据
+"""
+
+import argparse
+import csv
+import json
+import sys
+from pathlib import Path
+
+# 添加项目根目录到路径
+script_dir = Path(__file__).parent.parent
+sys.path.insert(0, str(script_dir))
+
+from src.data_manager.data_generator import DataGenerator
+
+
+def generate_csv_data(generator, data_type, count, output_file):
+    """生成CSV格式数据"""
+    print(f"📊 生成CSV数据: {data_type}")
+
+    data = []
+    if data_type == "users":
+        for _ in range(count):
+            data.append(generator.generate_user_profile())
+    elif data_type == "products":
+        for _ in range(count):
+            data.append(generator.generate_product_data())
+    elif data_type == "orders":
+        for _ in range(count):
+            data.append(generator.generate_order_data())
+    elif data_type == "mixed":
+        # 混合数据类型
+        for _ in range(count):
+            data.append(
+                {
+                    "type": generator.fake.random_element(["user", "product", "order"]),
+                    "name": generator.generate_name(),
+                    "email": generator.generate_email(),
+                    "phone": generator.generate_phone(),
+                    "address": generator.generate_address(),
+                    "id": generator.fake.uuid4(),
+                    "timestamp": generator.fake.date_time().isoformat(),
+                }
+            )
+    else:
+        print(f"❌ 不支持的数据类型: {data_type}")
+        return False
+
+    if not data:
+        print("❌ 未生成数据")
+        return False
+
+    # 写入CSV文件
+    with open(output_file, "w", newline="", encoding="utf-8") as f:
+        if data:
+            writer = csv.DictWriter(f, fieldnames=data[0].keys())
+            writer.writeheader()
+            writer.writerows(data)
+
+    print(f"✅ 已生成 {len(data)} 条记录到: {output_file}")
+    return True
+
+
+def generate_json_data(generator, data_type, count, output_file):
+    """生成JSON格式数据"""
+    print(f"📊 生成JSON数据: {data_type}")
+
+    data = []
+    if data_type == "users":
+        for _ in range(count):
+            data.append(generator.generate_user_profile())
+    elif data_type == "products":
+        for _ in range(count):
+            data.append(generator.generate_product_data())
+    elif data_type == "orders":
+        for _ in range(count):
+            data.append(generator.generate_order_data())
+    elif data_type == "test_dataset":
+        # 创建测试数据集
+        dataset = {
+            "users": [
+                generator.generate_user_profile() for _ in range(min(count // 3, 100))
+            ],
+            "products": [
+                generator.generate_product_data() for _ in range(min(count // 3, 100))
+            ],
+            "orders": [
+                generator.generate_order_data() for _ in range(min(count // 3, 100))
+            ],
+            "metadata": {
+                "generated_at": generator.fake.date_time().isoformat(),
+                "total_records": count,
+                "generator_version": "1.0.0",
+            },
+        }
+        data = dataset
+    else:
+        print(f"❌ 不支持的数据类型: {data_type}")
+        return False
+
+    # 写入JSON文件
+    with open(output_file, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+    if isinstance(data, list):
+        print(f"✅ 已生成 {len(data)} 条记录到: {output_file}")
+    else:
+        print(f"✅ 已生成数据集到: {output_file}")
+    return True
+
+
+def generate_locust_data(generator, count, output_dir):
+    """生成Locust测试专用数据"""
+    output_dir = Path(output_dir)
+    output_dir.mkdir(exist_ok=True)
+
+    print(f"🎯 生成Locust测试数据到: {output_dir}")
+
+    # 用户凭据数据
+    credentials_file = output_dir / "credentials.csv"
+    credentials = []
+    for _ in range(count):
+        credentials.append(
+            {
+                "username": generator.generate_username(),
+                "password": generator.fake.password(length=12),
+                "email": generator.generate_email(),
+            }
+        )
+
+    with open(credentials_file, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=["username", "password", "email"])
+        writer.writeheader()
+        writer.writerows(credentials)
+
+    print(f"✅ 用户凭据: {credentials_file}")
+
+    # 商品数据
+    products_file = output_dir / "products.json"
+    products = [generator.generate_product_data() for _ in range(min(count, 100))]
+
+    with open(products_file, "w", encoding="utf-8") as f:
+        json.dump(products, f, ensure_ascii=False, indent=2)
+
+    print(f"✅ 商品数据: {products_file}")
+
+    # 搜索关键词
+    keywords_file = output_dir / "search_keywords.txt"
+    keywords = [generator.fake.word() for _ in range(min(count // 10, 50))]
+
+    with open(keywords_file, "w", encoding="utf-8") as f:
+        f.write("\n".join(keywords))
+
+    print(f"✅ 搜索关键词: {keywords_file}")
+
+    # 测试参数配置
+    config_file = output_dir / "test_config.json"
+    config = {
+        "users_count": count,
+        "spawn_rate": max(1, count // 10),
+        "run_time": "300s",
+        "host": "http://localhost:5000",
+        "custom_params": {
+            "loop_num": generator.fake.random_int(1, 5),
+            "is_rendezvous": generator.fake.boolean(),
+            "rendezvous_num": generator.fake.random_int(2, 10),
+        },
+    }
+
+    with open(config_file, "w", encoding="utf-8") as f:
+        json.dump(config, f, ensure_ascii=False, indent=2)
+
+    print(f"✅ 测试配置: {config_file}")
+
+    print(f"🎉 Locust测试数据生成完成!")
+    return True
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(description="生成测试数据")
+
+    # 数据类型和数量
+    parser.add_argument(
+        "--type",
+        choices=["users", "products", "orders", "mixed", "test_dataset", "locust"],
+        default="mixed",
+        help="数据类型",
+    )
+    parser.add_argument("--count", type=int, default=100, help="数据条数")
+
+    # 输出格式和文件
+    parser.add_argument(
+        "--format", choices=["csv", "json"], default="json", help="输出格式"
+    )
+    parser.add_argument("--output", help="输出文件路径")
+    parser.add_argument(
+        "--output-dir", default="data", help="输出目录 (用于locust类型)"
+    )
+
+    # 数据生成选项
+    parser.add_argument("--locale", default="zh_CN", help="本地化设置")
+    parser.add_argument("--seed", type=int, help="随机种子")
+
+    args = parser.parse_args()
+
+    print("🎲 测试数据生成器")
+    print("=" * 50)
+    print(f"   数据类型: {args.type}")
+    print(f"   数据数量: {args.count}")
+    print(f"   输出格式: {args.format}")
+    print(f"   本地化: {args.locale}")
+    if args.seed:
+        print(f"   随机种子: {args.seed}")
+    print()
+
+    # 初始化数据生成器
+    try:
+        generator = DataGenerator(locale=args.locale, seed=args.seed)
+    except Exception as e:
+        print(f"❌ 初始化数据生成器失败: {e}")
+        return 1
+
+    # 特殊处理locust类型
+    if args.type == "locust":
+        return 0 if generate_locust_data(generator, args.count, args.output_dir) else 1
+
+    # 确定输出文件
+    if args.output:
+        output_file = Path(args.output)
+    else:
+        # 自动生成文件名
+        suffix = "csv" if args.format == "csv" else "json"
+        output_file = Path(f"test_data_{args.type}_{args.count}.{suffix}")
+
+    # 确保输出目录存在
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+
+    # 生成数据
+    try:
+        if args.format == "csv":
+            success = generate_csv_data(generator, args.type, args.count, output_file)
+        else:
+            success = generate_json_data(generator, args.type, args.count, output_file)
+
+        return 0 if success else 1
+
+    except Exception as e:
+        print(f"❌ 生成数据失败: {e}")
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/run_examples.py b/scripts/run_examples.py
new file mode 100755
index 0000000..be2e093
--- /dev/null
+++ b/scripts/run_examples.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+"""
+运行示例脚本
+
+用于快速运行locustfiles/examples中的示例测试
+"""
+
+import argparse
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import requests
+
+
+def check_mock_server(host="localhost", port=5000):
+    """检查Mock服务器是否运行"""
+    try:
+        response = requests.get(f"http://{host}:{port}/health", timeout=2)
+        return response.status_code == 200
+    except requests.exceptions.RequestException:
+        return False
+
+
+def start_mock_server_if_needed(host="localhost", port=5000):
+    """如果需要启动Mock服务器"""
+    if check_mock_server(host, port):
+        print(f"✅ Mock服务器已在运行: http://{host}:{port}")
+        return True
+
+    print("🚀 启动Mock服务器...")
+    script_dir = Path(__file__).parent
+    start_server_script = script_dir / "start_mock_server.py"
+
+    try:
+        # 启动服务器（后台运行）
+        subprocess.Popen(
+            [
+                sys.executable,
+                str(start_server_script),
+                "--host",
+                host,
+                "--port",
+                str(port),
+            ]
+        )
+
+        # 等待服务器启动
+        for i in range(30):
+            if check_mock_server(host, port):
+                print(f"✅ Mock服务器启动成功: http://{host}:{port}")
+                return True
+            time.sleep(1)
+
+        print("❌ Mock服务器启动超时")
+        return False
+    except Exception as e:
+        print(f"❌ 启动Mock服务器失败: {e}")
+        return False
+
+
+def get_available_examples():
+    """获取可用的示例列表"""
+    script_dir = Path(__file__).parent.parent
+    examples_dir = script_dir / "locustfiles" / "examples"
+
+    if not examples_dir.exists():
+        return []
+
+    examples = []
+    for py_file in examples_dir.glob("*.py"):
+        if py_file.name.startswith("__"):
+            continue
+        examples.append(py_file)
+
+    return sorted(examples)
+
+
+def run_example(
+    example_file,
+    web_ui=False,
+    users=10,
+    spawn_rate=2,
+    run_time="60s",
+    host="localhost",
+    port=8089,
+):
+    """运行指定示例"""
+    print(f"🎯 运行示例: {example_file.name}")
+    print(f"   文件路径: {example_file}")
+
+    cmd = [
+        "locust",
+        "-f",
+        str(example_file),
+        "--host",
+        f"http://localhost:5000",  # Mock服务器地址
+    ]
+
+    if web_ui:
+        cmd.extend(["--web-host", host, "--web-port", str(port)])
+        print(f"🌐 Web UI地址: http://{host}:{port}")
+        print("💡 在浏览器中打开上述地址开始测试")
+        print("   按Ctrl+C停止")
+    else:
+        cmd.extend(
+            ["--headless", "-u", str(users), "-r", str(spawn_rate), "-t", run_time]
+        )
+        print(f"🏃 无头模式运行:")
+        print(f"   用户数: {users}")
+        print(f"   启动速率: {spawn_rate} users/s")
+        print(f"   运行时间: {run_time}")
+
+    try:
+        subprocess.run(cmd)
+    except KeyboardInterrupt:
+        print("\n🛑 测试被用户中断")
+    except Exception as e:
+        print(f"❌ 运行测试失败: {e}")
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(description="运行Locust示例测试")
+
+    # 示例选择
+    parser.add_argument("example", nargs="?", help="示例文件名或编号")
+    parser.add_argument("--list", action="store_true", help="列出所有可用示例")
+
+    # 运行模式
+    parser.add_argument("--web", action="store_true", help="启动Web UI模式")
+    parser.add_argument("--web-host", default="localhost", help="Web UI主机地址")
+    parser.add_argument("--web-port", type=int, default=8089, help="Web UI端口")
+
+    # 测试参数
+    parser.add_argument("-u", "--users", type=int, default=10, help="用户数")
+    parser.add_argument("-r", "--spawn-rate", type=int, default=2, help="启动速率")
+    parser.add_argument("-t", "--run-time", default="60s", help="运行时间")
+
+    # Mock服务器
+    parser.add_argument("--mock-host", default="localhost", help="Mock服务器主机")
+    parser.add_argument("--mock-port", type=int, default=5000, help="Mock服务器端口")
+    parser.add_argument("--skip-mock", action="store_true", help="跳过Mock服务器检查")
+
+    args = parser.parse_args()
+
+    print("🧪 Locust示例运行器")
+    print("=" * 50)
+
+    # 获取示例列表
+    examples = get_available_examples()
+    if not examples:
+        print("❌ 未找到示例文件")
+        return
+
+    # 列出示例
+    if args.list:
+        print("📋 可用示例:")
+        for i, example in enumerate(examples, 1):
+            print(f"   {i:2d}. {example.name}")
+        return
+
+    # 选择示例
+    example_file = None
+    if args.example:
+        # 按编号选择
+        if args.example.isdigit():
+            idx = int(args.example) - 1
+            if 0 <= idx < len(examples):
+                example_file = examples[idx]
+            else:
+                print(f"❌ 无效编号: {args.example}")
+                return
+        else:
+            # 按文件名选择
+            for example in examples:
+                if args.example in example.name:
+                    example_file = example
+                    break
+
+            if not example_file:
+                print(f"❌ 未找到示例: {args.example}")
+                return
+    else:
+        # 默认选择第一个示例
+        example_file = examples[0]
+        print(f"💡 未指定示例，使用默认: {example_file.name}")
+
+    # 检查Mock服务器
+    if not args.skip_mock:
+        if not start_mock_server_if_needed(args.mock_host, args.mock_port):
+            print("❌ Mock服务器启动失败，请手动启动或使用 --skip-mock 跳过")
+            return
+
+    # 运行示例
+    run_example(
+        example_file=example_file,
+        web_ui=args.web,
+        users=args.users,
+        spawn_rate=args.spawn_rate,
+        run_time=args.run_time,
+        host=args.web_host,
+        port=args.web_port,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/start_mock_server.py b/scripts/start_mock_server.py
new file mode 100755
index 0000000..afb3f28
--- /dev/null
+++ b/scripts/start_mock_server.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""
+启动Mock服务器脚本
+
+用于启动测试用的Mock HTTP服务
+"""
+
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import requests
+
+
+def check_dependencies():
+    """检查依赖"""
+    try:
+        import flask
+        import flask_cors
+
+        print("✅ 依赖检查通过")
+        return True
+    except ImportError as e:
+        print(f"❌ 缺少依赖: {e}")
+        print("💡 请运行: pip install flask flask-cors")
+        return False
+
+
+def start_server(host="localhost", port=5000):
+    """启动Mock服务器"""
+    script_dir = Path(__file__).parent.parent
+    mock_server_path = script_dir / "mock_server" / "app.py"
+
+    if not mock_server_path.exists():
+        print(f"❌ Mock服务文件不存在: {mock_server_path}")
+        return False
+
+    print(f"🚀 启动Mock服务器...")
+    print(f"   主机: {host}")
+    print(f"   端口: {port}")
+    print(f"   文件: {mock_server_path}")
+
+    try:
+        # 启动服务器进程
+        import os
+
+        env = os.environ.copy()
+        env.update({"FLASK_HOST": host, "FLASK_PORT": str(port)})
+        process = subprocess.Popen([sys.executable, str(mock_server_path)], env=env)
+
+        # 等待服务器启动
+        print("⏳ 等待服务器启动...")
+        for i in range(30):  # 等待最多30秒
+            try:
+                response = requests.get(f"http://{host}:{port}/health", timeout=1)
+                if response.status_code == 200:
+                    print("✅ Mock服务器启动成功!")
+                    print(f"🌐 访问地址: http://{host}:{port}")
+                    print(f"📊 健康检查: http://{host}:{port}/health")
+                    print(f"📈 系统指标: http://{host}:{port}/metrics")
+                    return True
+            except requests.exceptions.RequestException:
+                pass
+
+            time.sleep(1)
+
+        print("❌ Mock服务器启动超时")
+        process.terminate()
+        return False
+
+    except Exception as e:
+        print(f"❌ 启动Mock服务器失败: {e}")
+        return False
+
+
+def main():
+    """主函数"""
+    import argparse
+
+    parser = argparse.ArgumentParser(description="启动Mock HTTP服务器")
+    parser.add_argument("--host", default="localhost", help="服务器主机地址")
+    parser.add_argument("--port", type=int, default=5000, help="服务器端口")
+
+    args = parser.parse_args()
+
+    print("🔧 Mock服务器启动脚本")
+    print("=" * 50)
+
+    # 检查依赖
+    if not check_dependencies():
+        sys.exit(1)
+
+    # 启动服务器
+    if start_server(args.host, args.port):
+        print("\n💡 使用方法:")
+        print("   - Ctrl+C 停止服务器")
+        print("   - 在另一个终端运行Locust测试")
+        print("   - locust -f locustfiles/examples/01_basic_test.py")
+
+        try:
+            # 保持脚本运行
+            while True:
+                time.sleep(1)
+        except KeyboardInterrupt:
+            print("\n🛑 正在停止Mock服务器...")
+    else:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/.DS_Store b/src/.DS_Store
new file mode 100644
index 0000000..3c6d1e0
Binary files /dev/null and b/src/.DS_Store differ
diff --git a/src/analysis/__init__.py b/src/analysis/__init__.py
new file mode 100644
index 0000000..e26f349
--- /dev/null
+++ b/src/analysis/__init__.py
@@ -0,0 +1,15 @@
+"""
+性能分析模块
+
+提供测试结果的深度分析功能，包括：
+- 性能指标统计分析
+- 趋势分析
+- 异常检测
+- 性能对比分析
+"""
+
+from .performance_analyzer import PerformanceAnalyzer
+from .report_generator import ReportGenerator
+from .trend_analyzer import TrendAnalyzer
+
+__all__ = ["PerformanceAnalyzer", "TrendAnalyzer", "ReportGenerator"]
diff --git a/src/analysis/performance_analyzer.py b/src/analysis/performance_analyzer.py
new file mode 100644
index 0000000..166ddc3
--- /dev/null
+++ b/src/analysis/performance_analyzer.py
@@ -0,0 +1,416 @@
+"""
+性能分析器
+
+提供详细的性能指标分析功能
+"""
+
+import csv
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+from src.utils.log_moudle import logger
+
+
+class PerformanceAnalyzer:
+    """性能分析器"""
+
+    def __init__(self):
+        self.metrics = {}
+        self.thresholds = {
+            "response_time_p95": 1000,  # 95%响应时间阈值(ms)
+            "response_time_p99": 2000,  # 99%响应时间阈值(ms)
+            "error_rate": 0.05,  # 错误率阈值(5%)
+            "throughput_min": 10,  # 最小吞吐量阈值(TPS)
+        }
+
+    def load_csv_data(self, csv_file_path: str) -> pd.DataFrame:
+        """加载CSV格式的测试数据"""
+        try:
+            df = pd.read_csv(csv_file_path)
+            logger.info(f"成功加载测试数据: {csv_file_path}")
+            return df
+        except Exception as e:
+            logger.error(f"加载CSV数据失败: {e}")
+            raise
+
+    def analyze_response_time(self, df: pd.DataFrame) -> Dict:
+        """分析响应时间指标"""
+        if "Response Time" not in df.columns:
+            logger.warning("数据中缺少响应时间列")
+            return {}
+
+        response_times = df["Response Time"].dropna()
+
+        analysis = {
+            "mean": float(response_times.mean()),
+            "median": float(response_times.median()),
+            "p50": float(response_times.quantile(0.5)),
+            "p90": float(response_times.quantile(0.9)),
+            "p95": float(response_times.quantile(0.95)),
+            "p99": float(response_times.quantile(0.99)),
+            "min": float(response_times.min()),
+            "max": float(response_times.max()),
+            "std": float(response_times.std()),
+        }
+
+        # 性能评估
+        analysis["performance_grade"] = self._grade_response_time(analysis)
+
+        return analysis
+
+    def analyze_throughput(self, df: pd.DataFrame) -> Dict:
+        """分析吞吐量指标"""
+        if "Timestamp" not in df.columns:
+            logger.warning("数据中缺少时间戳列")
+            return {}
+
+        # 按时间窗口计算TPS
+        df["Timestamp"] = pd.to_datetime(df["Timestamp"])
+        df_sorted = df.sort_values("Timestamp")
+
+        # 计算每秒请求数
+        tps_data = df_sorted.groupby(df_sorted["Timestamp"].dt.floor("S")).size()
+
+        analysis = {
+            "max_tps": float(tps_data.max()),
+            "min_tps": float(tps_data.min()),
+            "avg_tps": float(tps_data.mean()),
+            "median_tps": float(tps_data.median()),
+            "total_requests": len(df),
+            "duration_seconds": (
+                df_sorted["Timestamp"].max() - df_sorted["Timestamp"].min()
+            ).total_seconds(),
+        }
+
+        analysis["performance_grade"] = self._grade_throughput(analysis)
+
+        return analysis
+
+    def analyze_error_rate(self, df: pd.DataFrame) -> Dict:
+        """分析错误率指标"""
+        total_requests = len(df)
+        if total_requests == 0:
+            return {"error_rate": 0, "total_requests": 0, "failed_requests": 0}
+
+        # 假设有Success列或者通过状态码判断
+        if "Success" in df.columns:
+            failed_requests = len(df[df["Success"] == False])
+        elif "Status Code" in df.columns:
+            failed_requests = len(df[df["Status Code"] >= 400])
+        else:
+            logger.warning("无法确定请求成功/失败状态")
+            failed_requests = 0
+
+        error_rate = failed_requests / total_requests
+
+        analysis = {
+            "error_rate": float(error_rate),
+            "error_percentage": float(error_rate * 100),
+            "total_requests": total_requests,
+            "failed_requests": failed_requests,
+            "success_requests": total_requests - failed_requests,
+        }
+
+        analysis["performance_grade"] = self._grade_error_rate(analysis)
+
+        return analysis
+
+    def analyze_resource_usage(self, df: pd.DataFrame) -> Dict:
+        """分析资源使用情况"""
+        # 这里可以扩展分析CPU、内存等资源使用情况
+        # 目前基于请求数据进行基础分析
+
+        analysis = {
+            "concurrent_users": self._estimate_concurrent_users(df),
+            "request_distribution": self._analyze_request_distribution(df),
+        }
+
+        return analysis
+
+    def comprehensive_analysis(self, test_data: Dict) -> Dict:
+        """综合性能分析 - 符合文档API规范"""
+        # 支持两种输入方式：CSV文件路径或直接的测试数据字典
+        if isinstance(test_data, str):
+            # 兼容旧版本，输入为CSV文件路径
+            df = self.load_csv_data(test_data)
+            data_source = test_data
+        else:
+            # 新版本API，输入为测试数据字典
+            df = self._convert_test_data_to_df(test_data)
+            data_source = test_data.get("test_name", "未知测试")
+
+        analysis_result = {
+            "timestamp": datetime.now().isoformat(),
+            "test_info": {
+                "test_name": (
+                    test_data.get("test_name", "未知测试")
+                    if isinstance(test_data, dict)
+                    else data_source
+                ),
+                "start_time": (
+                    test_data.get("start_time", "")
+                    if isinstance(test_data, dict)
+                    else ""
+                ),
+                "end_time": (
+                    test_data.get("end_time", "") if isinstance(test_data, dict) else ""
+                ),
+                "duration": (
+                    test_data.get("duration", 0) if isinstance(test_data, dict) else 0
+                ),
+                "users": (
+                    test_data.get("users", 0) if isinstance(test_data, dict) else 0
+                ),
+            },
+            "response_time": self.analyze_response_time(df),
+            "throughput": self.analyze_throughput(df),
+            "error_analysis": self.analyze_error_rate(df),
+            "performance_grade": {},
+            "overall_grade": "",
+            "recommendations": [],
+        }
+
+        # 计算性能评分
+        analysis_result["performance_grade"] = self.calculate_performance_grade(
+            analysis_result["response_time"],
+            analysis_result["throughput"],
+            analysis_result["error_analysis"],
+        )
+
+        # 综合评分
+        analysis_result["overall_grade"] = analysis_result["performance_grade"].get(
+            "grade", "D"
+        )
+
+        # 生成优化建议
+        analysis_result["recommendations"] = self._generate_recommendations(
+            analysis_result
+        )
+
+        return analysis_result
+
+    def _convert_test_data_to_df(self, test_data: Dict) -> pd.DataFrame:
+        """将测试数据字典转换为DataFrame"""
+        requests = test_data.get("requests", [])
+
+        # 转换请求数据为DataFrame格式
+        df_data = []
+        for req in requests:
+            df_data.append(
+                {
+                    "Response Time": req.get("response_time", 0),
+                    "Success": req.get("success", True),
+                    "Timestamp": req.get("timestamp", datetime.now().isoformat()),
+                    "Name": req.get("name", "request"),
+                    "Method": req.get("method", "GET"),
+                    "Status Code": 200 if req.get("success", True) else 500,
+                }
+            )
+
+        return pd.DataFrame(df_data) if df_data else pd.DataFrame()
+
+    def calculate_performance_grade(
+        self, response_time_result: Dict, throughput_result: Dict, error_result: Dict
+    ) -> Dict:
+        """计算性能评分 - 符合文档API规范"""
+        # 响应时间得分
+        rt_grade = response_time_result.get("performance_grade", "D")
+        rt_score = self._grade_to_score(rt_grade)
+
+        # 吞吐量得分
+        tp_grade = throughput_result.get("performance_grade", "D")
+        tp_score = self._grade_to_score(tp_grade)
+
+        # 错误率得分
+        er_grade = error_result.get("performance_grade", "D")
+        er_score = self._grade_to_score(er_grade)
+
+        # 稳定性得分 (基于响应时间标准差)
+        std_dev = response_time_result.get("std", 0)
+        mean_rt = response_time_result.get("mean", 0)
+        stability_score = (
+            100 if mean_rt == 0 else max(0, 100 - (std_dev / mean_rt * 100))
+        )
+
+        # 加权计算总体得分
+        weights = {
+            "response_time": 0.4,
+            "throughput": 0.3,
+            "error_rate": 0.2,
+            "stability": 0.1,
+        }
+
+        overall_score = (
+            rt_score * weights["response_time"]
+            + tp_score * weights["throughput"]
+            + er_score * weights["error_rate"]
+            + stability_score * weights["stability"]
+        )
+
+        # 转换为等级
+        overall_grade = self._score_to_grade(overall_score)
+
+        return {
+            "response_time_score": rt_score,
+            "throughput_score": tp_score,
+            "error_rate_score": er_score,
+            "stability_score": stability_score,
+            "overall_score": round(overall_score, 2),
+            "grade": overall_grade,
+            "grade_description": self._get_grade_description(overall_grade),
+        }
+
+    def _grade_to_score(self, grade: str) -> float:
+        """等级转换为分数"""
+        grade_map = {"A": 90, "B": 75, "C": 60, "D": 40}
+        return grade_map.get(grade, 40)
+
+    def _score_to_grade(self, score: float) -> str:
+        """分数转换为等级"""
+        if score >= 85:
+            return "A"
+        elif score >= 70:
+            return "B"
+        elif score >= 55:
+            return "C"
+        else:
+            return "D"
+
+    def _get_grade_description(self, grade: str) -> str:
+        """获取等级描述"""
+        descriptions = {
+            "A": "优秀 - 性能表现出色，系统运行稳定",
+            "B": "良好 - 性能表现较好，有小幅优化空间",
+            "C": "一般 - 性能基本满足要求，建议进行优化",
+            "D": "较差 - 性能存在明显问题，需要立即优化",
+        }
+        return descriptions.get(grade, "未知")
+
+    def _generate_recommendations(self, analysis_result: Dict) -> List[str]:
+        """生成优化建议"""
+        recommendations = []
+
+        # 响应时间建议
+        rt_grade = analysis_result["response_time"].get("performance_grade", "D")
+        if rt_grade in ["C", "D"]:
+            recommendations.append(
+                "响应时间较高，建议检查应用性能瓶颈，考虑缓存优化或代码优化"
+            )
+
+        # 吞吐量建议
+        tp_grade = analysis_result["throughput"].get("performance_grade", "D")
+        if tp_grade in ["C", "D"]:
+            recommendations.append(
+                "吞吐量偏低，建议检查系统资源使用情况，考虑扩容或性能调优"
+            )
+
+        # 错误率建议
+        er_grade = analysis_result["error_analysis"].get("performance_grade", "D")
+        if er_grade in ["C", "D"]:
+            recommendations.append("错误率较高，建议检查应用日志，修复系统稳定性问题")
+
+        # 稳定性建议
+        stability_score = analysis_result["performance_grade"].get("stability_score", 0)
+        if stability_score < 70:
+            recommendations.append("响应时间波动较大，建议检查系统负载均衡和资源配置")
+
+        if not recommendations:
+            recommendations.append("系统性能表现良好，建议持续监控和定期优化")
+
+        return recommendations
+
+    def _grade_response_time(self, analysis: Dict) -> str:
+        """响应时间评分"""
+        p95 = analysis.get("p95", float("inf"))
+        if p95 <= self.thresholds["response_time_p95"] * 0.5:
+            return "A"
+        elif p95 <= self.thresholds["response_time_p95"]:
+            return "B"
+        elif p95 <= self.thresholds["response_time_p95"] * 2:
+            return "C"
+        else:
+            return "D"
+
+    def _grade_throughput(self, analysis: Dict) -> str:
+        """吞吐量评分"""
+        avg_tps = analysis.get("avg_tps", 0)
+        if avg_tps >= self.thresholds["throughput_min"] * 5:
+            return "A"
+        elif avg_tps >= self.thresholds["throughput_min"] * 2:
+            return "B"
+        elif avg_tps >= self.thresholds["throughput_min"]:
+            return "C"
+        else:
+            return "D"
+
+    def _grade_error_rate(self, analysis: Dict) -> str:
+        """错误率评分"""
+        error_rate = analysis.get("error_rate", 1)
+        if error_rate <= self.thresholds["error_rate"] * 0.2:
+            return "A"
+        elif error_rate <= self.thresholds["error_rate"]:
+            return "B"
+        elif error_rate <= self.thresholds["error_rate"] * 2:
+            return "C"
+        else:
+            return "D"
+
+    def _calculate_overall_grade(self, analysis: Dict) -> str:
+        """计算综合评分"""
+        grades = []
+        grade_map = {"A": 4, "B": 3, "C": 2, "D": 1}
+
+        for category in ["response_time", "throughput", "error_rate"]:
+            if category in analysis and "performance_grade" in analysis[category]:
+                grades.append(grade_map.get(analysis[category]["performance_grade"], 1))
+
+        if not grades:
+            return "D"
+
+        avg_score = sum(grades) / len(grades)
+        if avg_score >= 3.5:
+            return "A"
+        elif avg_score >= 2.5:
+            return "B"
+        elif avg_score >= 1.5:
+            return "C"
+        else:
+            return "D"
+
+    def _estimate_concurrent_users(self, df: pd.DataFrame) -> int:
+        """估算并发用户数"""
+        if "Timestamp" not in df.columns:
+            return 0
+
+        df["Timestamp"] = pd.to_datetime(df["Timestamp"])
+        # 简单估算：取1秒时间窗口内的最大请求数
+        concurrent_estimate = df.groupby(df["Timestamp"].dt.floor("S")).size().max()
+        return int(concurrent_estimate)
+
+    def _analyze_request_distribution(self, df: pd.DataFrame) -> Dict:
+        """分析请求分布"""
+        if "Name" not in df.columns:
+            return {}
+
+        distribution = df["Name"].value_counts().to_dict()
+        return {str(k): int(v) for k, v in distribution.items()}
+
+    def set_thresholds(self, **thresholds):
+        """设置性能阈值"""
+        self.thresholds.update(thresholds)
+        logger.info(f"更新性能阈值: {thresholds}")
+
+    def export_analysis(self, analysis: Dict, output_path: str):
+        """导出分析结果"""
+        try:
+            with open(output_path, "w", encoding="utf-8") as f:
+                json.dump(analysis, f, ensure_ascii=False, indent=2)
+            logger.info(f"分析结果已导出到: {output_path}")
+        except Exception as e:
+            logger.error(f"导出分析结果失败: {e}")
+            raise
diff --git a/src/analysis/report_generator.py b/src/analysis/report_generator.py
new file mode 100644
index 0000000..fc72b47
--- /dev/null
+++ b/src/analysis/report_generator.py
@@ -0,0 +1,443 @@
+"""
+报告生成器
+
+提供多种格式的测试报告生成功能
+"""
+
+import csv
+import json
+import os
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from src.utils.log_moudle import logger
+
+
+class ReportGenerator:
+    """报告生成器"""
+
+    def __init__(self, output_dir: str = "reports"):
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(exist_ok=True)
+
+        # 报告模板
+        self.html_template = """
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>性能测试分析报告</title>
+    <style>
+        body { font-family: Arial, sans-serif; margin: 20px; line-height: 1.6; }
+        .header { background: #f4f4f4; padding: 20px; border-radius: 5px; margin-bottom: 20px; }
+        .section { margin-bottom: 30px; }
+        .metric-card { background: #fff; border: 1px solid #ddd; padding: 15px; margin: 10px 0; border-radius: 5px; }
+        .grade-A { border-left: 5px solid #4CAF50; }
+        .grade-B { border-left: 5px solid #FF9800; }
+        .grade-C { border-left: 5px solid #FF5722; }
+        .grade-D { border-left: 5px solid #F44336; }
+        .trend-up { color: #4CAF50; }
+        .trend-down { color: #F44336; }
+        .trend-stable { color: #2196F3; }
+        table { width: 100%; border-collapse: collapse; margin: 10px 0; }
+        th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }
+        th { background-color: #f2f2f2; }
+        .summary { background: #e3f2fd; padding: 15px; border-radius: 5px; }
+    </style>
+</head>
+<body>
+    <div class="header">
+        <h1>性能测试分析报告</h1>
+        <p><strong>生成时间:</strong> {timestamp}</p>
+        <p><strong>数据源:</strong> {data_source}</p>
+    </div>
+
+    <div class="section">
+        <h2>执行摘要</h2>
+        <div class="summary">
+            <p><strong>综合评分:</strong> {overall_grade}</p>
+            <p><strong>测试建议:</strong> {recommendations}</p>
+        </div>
+    </div>
+
+    <div class="section">
+        <h2>响应时间分析</h2>
+        <div class="metric-card grade-{response_time_grade}">
+            <h3>响应时间指标 (评分: {response_time_grade})</h3>
+            <table>
+                <tr><th>指标</th><th>值</th></tr>
+                <tr><td>平均响应时间</td><td>{mean_rt:.2f} ms</td></tr>
+                <tr><td>95%响应时间</td><td>{p95_rt:.2f} ms</td></tr>
+                <tr><td>99%响应时间</td><td>{p99_rt:.2f} ms</td></tr>
+                <tr><td>最大响应时间</td><td>{max_rt:.2f} ms</td></tr>
+            </table>
+        </div>
+    </div>
+
+    <div class="section">
+        <h2>吞吐量分析</h2>
+        <div class="metric-card grade-{throughput_grade}">
+            <h3>吞吐量指标 (评分: {throughput_grade})</h3>
+            <table>
+                <tr><th>指标</th><th>值</th></tr>
+                <tr><td>平均TPS</td><td>{avg_tps:.2f}</td></tr>
+                <tr><td>最大TPS</td><td>{max_tps:.2f}</td></tr>
+                <tr><td>总请求数</td><td>{total_requests}</td></tr>
+                <tr><td>测试时长</td><td>{duration:.2f} 秒</td></tr>
+            </table>
+        </div>
+    </div>
+
+    <div class="section">
+        <h2>错误率分析</h2>
+        <div class="metric-card grade-{error_rate_grade}">
+            <h3>错误率指标 (评分: {error_rate_grade})</h3>
+            <table>
+                <tr><th>指标</th><th>值</th></tr>
+                <tr><td>错误率</td><td>{error_percentage:.2f}%</td></tr>
+                <tr><td>失败请求数</td><td>{failed_requests}</td></tr>
+                <tr><td>成功请求数</td><td>{success_requests}</td></tr>
+            </table>
+        </div>
+    </div>
+
+    {trend_section}
+
+    <div class="section">
+        <h2>详细数据</h2>
+        <pre>{raw_data}</pre>
+    </div>
+</body>
+</html>
+"""
+
+    def generate_html_report(
+        self,
+        analysis_data: Dict,
+        trend_data: Optional[Dict] = None,
+        output_filename: str = None,
+    ) -> str:
+        """生成HTML格式报告"""
+        if output_filename is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            output_filename = f"performance_report_{timestamp}.html"
+
+        output_path = self.output_dir / output_filename
+
+        # 准备模板数据
+        template_data = self._prepare_template_data(analysis_data, trend_data)
+
+        # 生成HTML内容
+        html_content = self.html_template.format(**template_data)
+
+        # 写入文件
+        try:
+            with open(output_path, "w", encoding="utf-8") as f:
+                f.write(html_content)
+            logger.info(f"HTML报告已生成: {output_path}")
+            return str(output_path)
+        except Exception as e:
+            logger.error(f"生成HTML报告失败: {e}")
+            raise
+
+    def generate_json_report(
+        self,
+        analysis_data: Dict,
+        trend_data: Optional[Dict] = None,
+        output_filename: str = None,
+    ) -> str:
+        """生成JSON格式报告"""
+        if output_filename is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            output_filename = f"performance_report_{timestamp}.json"
+
+        output_path = self.output_dir / output_filename
+
+        # 合并数据
+        report_data = {
+            "report_info": {
+                "generated_at": datetime.now().isoformat(),
+                "report_type": "performance_analysis",
+                "version": "1.0",
+            },
+            "analysis": analysis_data,
+        }
+
+        if trend_data:
+            report_data["trend_analysis"] = trend_data
+
+        try:
+            with open(output_path, "w", encoding="utf-8") as f:
+                json.dump(report_data, f, ensure_ascii=False, indent=2)
+            logger.info(f"JSON报告已生成: {output_path}")
+            return str(output_path)
+        except Exception as e:
+            logger.error(f"生成JSON报告失败: {e}")
+            raise
+
+    def generate_csv_summary(
+        self, analysis_data: Dict, output_filename: str = None
+    ) -> str:
+        """生成CSV格式的摘要报告"""
+        if output_filename is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            output_filename = f"performance_summary_{timestamp}.csv"
+
+        output_path = self.output_dir / output_filename
+
+        # 准备CSV数据
+        csv_data = []
+
+        # 基本信息
+        csv_data.append(["报告生成时间", analysis_data.get("timestamp", "")])
+        csv_data.append(["数据源", analysis_data.get("data_source", "")])
+        csv_data.append(["综合评分", analysis_data.get("overall_grade", "")])
+        csv_data.append(["", ""])  # 空行
+
+        # 响应时间指标
+        if "response_time" in analysis_data:
+            rt_data = analysis_data["response_time"]
+            csv_data.append(["响应时间指标", ""])
+            csv_data.append(["平均响应时间(ms)", rt_data.get("mean", 0)])
+            csv_data.append(["95%响应时间(ms)", rt_data.get("p95", 0)])
+            csv_data.append(["99%响应时间(ms)", rt_data.get("p99", 0)])
+            csv_data.append(["最大响应时间(ms)", rt_data.get("max", 0)])
+            csv_data.append(["响应时间评分", rt_data.get("performance_grade", "")])
+            csv_data.append(["", ""])
+
+        # 吞吐量指标
+        if "throughput" in analysis_data:
+            tp_data = analysis_data["throughput"]
+            csv_data.append(["吞吐量指标", ""])
+            csv_data.append(["平均TPS", tp_data.get("avg_tps", 0)])
+            csv_data.append(["最大TPS", tp_data.get("max_tps", 0)])
+            csv_data.append(["总请求数", tp_data.get("total_requests", 0)])
+            csv_data.append(["测试时长(秒)", tp_data.get("duration_seconds", 0)])
+            csv_data.append(["吞吐量评分", tp_data.get("performance_grade", "")])
+            csv_data.append(["", ""])
+
+        # 错误率指标
+        if "error_rate" in analysis_data:
+            er_data = analysis_data["error_rate"]
+            csv_data.append(["错误率指标", ""])
+            csv_data.append(["错误率(%)", er_data.get("error_percentage", 0)])
+            csv_data.append(["失败请求数", er_data.get("failed_requests", 0)])
+            csv_data.append(["成功请求数", er_data.get("success_requests", 0)])
+            csv_data.append(["错误率评分", er_data.get("performance_grade", "")])
+
+        try:
+            with open(output_path, "w", newline="", encoding="utf-8") as f:
+                writer = csv.writer(f)
+                writer.writerows(csv_data)
+            logger.info(f"CSV摘要报告已生成: {output_path}")
+            return str(output_path)
+        except Exception as e:
+            logger.error(f"生成CSV摘要报告失败: {e}")
+            raise
+
+    def generate_markdown_report(
+        self,
+        analysis_data: Dict,
+        trend_data: Optional[Dict] = None,
+        output_filename: str = None,
+    ) -> str:
+        """生成Markdown格式报告"""
+        if output_filename is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            output_filename = f"performance_report_{timestamp}.md"
+
+        output_path = self.output_dir / output_filename
+
+        # 生成Markdown内容
+        md_content = self._generate_markdown_content(analysis_data, trend_data)
+
+        try:
+            with open(output_path, "w", encoding="utf-8") as f:
+                f.write(md_content)
+            logger.info(f"Markdown报告已生成: {output_path}")
+            return str(output_path)
+        except Exception as e:
+            logger.error(f"生成Markdown报告失败: {e}")
+            raise
+
+    def _prepare_template_data(
+        self, analysis_data: Dict, trend_data: Optional[Dict] = None
+    ) -> Dict:
+        """准备模板数据"""
+        # 默认值
+        template_data = {
+            "timestamp": analysis_data.get("timestamp", datetime.now().isoformat()),
+            "data_source": analysis_data.get("data_source", "未知"),
+            "overall_grade": analysis_data.get("overall_grade", "D"),
+            "recommendations": self._generate_recommendations(analysis_data),
+            # 响应时间数据
+            "response_time_grade": "D",
+            "mean_rt": 0,
+            "p95_rt": 0,
+            "p99_rt": 0,
+            "max_rt": 0,
+            # 吞吐量数据
+            "throughput_grade": "D",
+            "avg_tps": 0,
+            "max_tps": 0,
+            "total_requests": 0,
+            "duration": 0,
+            # 错误率数据
+            "error_rate_grade": "D",
+            "error_percentage": 0,
+            "failed_requests": 0,
+            "success_requests": 0,
+            # 趋势分析
+            "trend_section": "",
+            # 原始数据
+            "raw_data": json.dumps(analysis_data, ensure_ascii=False, indent=2),
+        }
+
+        # 填充实际数据
+        if "response_time" in analysis_data:
+            rt_data = analysis_data["response_time"]
+            template_data.update(
+                {
+                    "response_time_grade": rt_data.get("performance_grade", "D"),
+                    "mean_rt": rt_data.get("mean", 0),
+                    "p95_rt": rt_data.get("p95", 0),
+                    "p99_rt": rt_data.get("p99", 0),
+                    "max_rt": rt_data.get("max", 0),
+                }
+            )
+
+        if "throughput" in analysis_data:
+            tp_data = analysis_data["throughput"]
+            template_data.update(
+                {
+                    "throughput_grade": tp_data.get("performance_grade", "D"),
+                    "avg_tps": tp_data.get("avg_tps", 0),
+                    "max_tps": tp_data.get("max_tps", 0),
+                    "total_requests": tp_data.get("total_requests", 0),
+                    "duration": tp_data.get("duration_seconds", 0),
+                }
+            )
+
+        if "error_rate" in analysis_data:
+            er_data = analysis_data["error_rate"]
+            template_data.update(
+                {
+                    "error_rate_grade": er_data.get("performance_grade", "D"),
+                    "error_percentage": er_data.get("error_percentage", 0),
+                    "failed_requests": er_data.get("failed_requests", 0),
+                    "success_requests": er_data.get("success_requests", 0),
+                }
+            )
+
+        # 添加趋势分析部分
+        if trend_data:
+            template_data["trend_section"] = self._generate_trend_section(trend_data)
+
+        return template_data
+
+    def _generate_recommendations(self, analysis_data: Dict) -> str:
+        """生成优化建议"""
+        recommendations = []
+
+        # 基于综合评分给出建议
+        overall_grade = analysis_data.get("overall_grade", "D")
+        if overall_grade == "A":
+            recommendations.append("系统性能优秀，继续保持当前配置和优化策略")
+        elif overall_grade == "B":
+            recommendations.append("系统性能良好，可考虑进一步优化以达到更高性能")
+        elif overall_grade == "C":
+            recommendations.append("系统性能一般，建议进行性能调优和瓶颈分析")
+        else:
+            recommendations.append("系统性能需要改善，建议立即进行性能优化")
+
+        return "; ".join(recommendations)
+
+    def _generate_trend_section(self, trend_data: Dict) -> str:
+        """生成趋势分析HTML部分"""
+        if not trend_data:
+            return ""
+
+        trend_html = """
+    <div class="section">
+        <h2>趋势分析</h2>
+        <div class="metric-card">
+            <h3>性能趋势概览</h3>
+            <p><strong>分析周期:</strong> {analysis_period}</p>
+            <p><strong>整体健康状况:</strong> {overall_health}</p>
+        </div>
+    </div>
+        """.format(
+            analysis_period=f"{trend_data.get('analysis_period', {}).get('start', '')} 至 {trend_data.get('analysis_period', {}).get('end', '')}",
+            overall_health=trend_data.get("overall_health", "未知"),
+        )
+
+        return trend_html
+
+    def _generate_markdown_content(
+        self, analysis_data: Dict, trend_data: Optional[Dict] = None
+    ) -> str:
+        """生成Markdown内容"""
+        md_content = f"""# 性能测试分析报告
+
+**生成时间:** {analysis_data.get('timestamp', datetime.now().isoformat())}
+**数据源:** {analysis_data.get('data_source', '未知')}
+**综合评分:** {analysis_data.get('overall_grade', 'D')}
+
+## 执行摘要
+
+{self._generate_recommendations(analysis_data)}
+
+## 响应时间分析
+
+"""
+
+        if "response_time" in analysis_data:
+            rt_data = analysis_data["response_time"]
+            md_content += f"""
+| 指标 | 值 |
+|------|-----|
+| 平均响应时间 | {rt_data.get('mean', 0):.2f} ms |
+| 95%响应时间 | {rt_data.get('p95', 0):.2f} ms |
+| 99%响应时间 | {rt_data.get('p99', 0):.2f} ms |
+| 最大响应时间 | {rt_data.get('max', 0):.2f} ms |
+| 评分 | {rt_data.get('performance_grade', 'D')} |
+
+"""
+
+        if "throughput" in analysis_data:
+            tp_data = analysis_data["throughput"]
+            md_content += f"""## 吞吐量分析
+
+| 指标 | 值 |
+|------|-----|
+| 平均TPS | {tp_data.get('avg_tps', 0):.2f} |
+| 最大TPS | {tp_data.get('max_tps', 0):.2f} |
+| 总请求数 | {tp_data.get('total_requests', 0)} |
+| 测试时长 | {tp_data.get('duration_seconds', 0):.2f} 秒 |
+| 评分 | {tp_data.get('performance_grade', 'D')} |
+
+"""
+
+        if "error_rate" in analysis_data:
+            er_data = analysis_data["error_rate"]
+            md_content += f"""## 错误率分析
+
+| 指标 | 值 |
+|------|-----|
+| 错误率 | {er_data.get('error_percentage', 0):.2f}% |
+| 失败请求数 | {er_data.get('failed_requests', 0)} |
+| 成功请求数 | {er_data.get('success_requests', 0)} |
+| 评分 | {er_data.get('performance_grade', 'D')} |
+
+"""
+
+        if trend_data:
+            md_content += f"""## 趋势分析
+
+**整体健康状况:** {trend_data.get('overall_health', '未知')}
+
+"""
+
+        return md_content
diff --git a/src/analysis/trend_analyzer.py b/src/analysis/trend_analyzer.py
new file mode 100644
index 0000000..901d2b7
--- /dev/null
+++ b/src/analysis/trend_analyzer.py
@@ -0,0 +1,326 @@
+"""
+趋势分析器
+
+提供性能趋势分析和预测功能
+"""
+
+import json
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+from src.utils.log_moudle import logger
+
+
+class TrendAnalyzer:
+    """趋势分析器"""
+
+    def __init__(self):
+        self.historical_data = []
+        self.trend_window = 10  # 趋势分析窗口大小
+
+    def add_historical_data(self, analysis_result: Dict):
+        """添加历史分析数据"""
+        if "timestamp" not in analysis_result:
+            analysis_result["timestamp"] = datetime.now().isoformat()
+
+        self.historical_data.append(analysis_result)
+        logger.info(f"添加历史数据，当前数据点数量: {len(self.historical_data)}")
+
+    def load_historical_data(self, file_path: str):
+        """从文件加载历史数据"""
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                if isinstance(data, list):
+                    self.historical_data = data
+                else:
+                    self.historical_data = [data]
+            logger.info(f"从 {file_path} 加载了 {len(self.historical_data)} 条历史数据")
+        except Exception as e:
+            logger.error(f"加载历史数据失败: {e}")
+            raise
+
+    def save_historical_data(self, file_path: str):
+        """保存历史数据到文件"""
+        try:
+            with open(file_path, "w", encoding="utf-8") as f:
+                json.dump(self.historical_data, f, ensure_ascii=False, indent=2)
+            logger.info(f"历史数据已保存到: {file_path}")
+        except Exception as e:
+            logger.error(f"保存历史数据失败: {e}")
+            raise
+
+    def analyze_response_time_trend(self) -> Dict:
+        """分析响应时间趋势"""
+        if len(self.historical_data) < 2:
+            return {"trend": "insufficient_data", "message": "数据不足，无法分析趋势"}
+
+        # 提取响应时间数据
+        timestamps = []
+        p95_values = []
+        mean_values = []
+
+        for data in self.historical_data:
+            if "response_time" in data and data["response_time"]:
+                timestamps.append(data["timestamp"])
+                p95_values.append(data["response_time"].get("p95", 0))
+                mean_values.append(data["response_time"].get("mean", 0))
+
+        if len(p95_values) < 2:
+            return {"trend": "insufficient_data", "message": "响应时间数据不足"}
+
+        # 计算趋势
+        p95_trend = self._calculate_trend(p95_values)
+        mean_trend = self._calculate_trend(mean_values)
+
+        # 计算变化率
+        p95_change_rate = self._calculate_change_rate(p95_values)
+        mean_change_rate = self._calculate_change_rate(mean_values)
+
+        return {
+            "p95_trend": p95_trend,
+            "mean_trend": mean_trend,
+            "p95_change_rate": p95_change_rate,
+            "mean_change_rate": mean_change_rate,
+            "data_points": len(p95_values),
+            "latest_p95": p95_values[-1] if p95_values else 0,
+            "latest_mean": mean_values[-1] if mean_values else 0,
+            "recommendation": self._get_response_time_recommendation(
+                p95_trend, p95_change_rate
+            ),
+        }
+
+    def analyze_throughput_trend(self) -> Dict:
+        """分析吞吐量趋势"""
+        if len(self.historical_data) < 2:
+            return {"trend": "insufficient_data", "message": "数据不足，无法分析趋势"}
+
+        # 提取吞吐量数据
+        timestamps = []
+        avg_tps_values = []
+        max_tps_values = []
+
+        for data in self.historical_data:
+            if "throughput" in data and data["throughput"]:
+                timestamps.append(data["timestamp"])
+                avg_tps_values.append(data["throughput"].get("avg_tps", 0))
+                max_tps_values.append(data["throughput"].get("max_tps", 0))
+
+        if len(avg_tps_values) < 2:
+            return {"trend": "insufficient_data", "message": "吞吐量数据不足"}
+
+        # 计算趋势
+        avg_tps_trend = self._calculate_trend(avg_tps_values)
+        max_tps_trend = self._calculate_trend(max_tps_values)
+
+        # 计算变化率
+        avg_tps_change_rate = self._calculate_change_rate(avg_tps_values)
+        max_tps_change_rate = self._calculate_change_rate(max_tps_values)
+
+        return {
+            "avg_tps_trend": avg_tps_trend,
+            "max_tps_trend": max_tps_trend,
+            "avg_tps_change_rate": avg_tps_change_rate,
+            "max_tps_change_rate": max_tps_change_rate,
+            "data_points": len(avg_tps_values),
+            "latest_avg_tps": avg_tps_values[-1] if avg_tps_values else 0,
+            "latest_max_tps": max_tps_values[-1] if max_tps_values else 0,
+            "recommendation": self._get_throughput_recommendation(
+                avg_tps_trend, avg_tps_change_rate
+            ),
+        }
+
+    def analyze_error_rate_trend(self) -> Dict:
+        """分析错误率趋势"""
+        if len(self.historical_data) < 2:
+            return {"trend": "insufficient_data", "message": "数据不足，无法分析趋势"}
+
+        # 提取错误率数据
+        timestamps = []
+        error_rates = []
+
+        for data in self.historical_data:
+            if "error_rate" in data and data["error_rate"]:
+                timestamps.append(data["timestamp"])
+                error_rates.append(data["error_rate"].get("error_rate", 0))
+
+        if len(error_rates) < 2:
+            return {"trend": "insufficient_data", "message": "错误率数据不足"}
+
+        # 计算趋势
+        error_rate_trend = self._calculate_trend(error_rates)
+
+        # 计算变化率
+        error_rate_change_rate = self._calculate_change_rate(error_rates)
+
+        return {
+            "error_rate_trend": error_rate_trend,
+            "error_rate_change_rate": error_rate_change_rate,
+            "data_points": len(error_rates),
+            "latest_error_rate": error_rates[-1] if error_rates else 0,
+            "recommendation": self._get_error_rate_recommendation(
+                error_rate_trend, error_rate_change_rate
+            ),
+        }
+
+    def comprehensive_trend_analysis(self) -> Dict:
+        """综合趋势分析"""
+        return {
+            "timestamp": datetime.now().isoformat(),
+            "analysis_period": {
+                "start": (
+                    self.historical_data[0]["timestamp"]
+                    if self.historical_data
+                    else None
+                ),
+                "end": (
+                    self.historical_data[-1]["timestamp"]
+                    if self.historical_data
+                    else None
+                ),
+                "data_points": len(self.historical_data),
+            },
+            "response_time_trend": self.analyze_response_time_trend(),
+            "throughput_trend": self.analyze_throughput_trend(),
+            "error_rate_trend": self.analyze_error_rate_trend(),
+            "overall_health": self._assess_overall_health(),
+        }
+
+    def _calculate_trend(self, values: List[float]) -> str:
+        """计算趋势方向"""
+        if len(values) < 2:
+            return "unknown"
+
+        # 使用线性回归计算趋势
+        x = np.arange(len(values))
+        y = np.array(values)
+
+        # 计算斜率
+        slope = np.polyfit(x, y, 1)[0]
+
+        # 判断趋势
+        if abs(slope) < 0.01:  # 阈值可调整
+            return "stable"
+        elif slope > 0:
+            return "increasing"
+        else:
+            return "decreasing"
+
+    def _calculate_change_rate(self, values: List[float]) -> float:
+        """计算变化率"""
+        if len(values) < 2:
+            return 0.0
+
+        first_value = values[0]
+        last_value = values[-1]
+
+        if first_value == 0:
+            return 0.0
+
+        return ((last_value - first_value) / first_value) * 100
+
+    def _get_response_time_recommendation(self, trend: str, change_rate: float) -> str:
+        """获取响应时间优化建议"""
+        if trend == "increasing" and change_rate > 20:
+            return "响应时间显著增长，建议检查系统性能瓶颈，考虑优化代码或扩容"
+        elif trend == "increasing" and change_rate > 10:
+            return "响应时间有上升趋势，建议关注系统负载和资源使用情况"
+        elif trend == "stable":
+            return "响应时间保持稳定，系统性能良好"
+        elif trend == "decreasing":
+            return "响应时间有改善趋势，继续保持当前优化策略"
+        else:
+            return "数据不足，建议继续监控"
+
+    def _get_throughput_recommendation(self, trend: str, change_rate: float) -> str:
+        """获取吞吐量优化建议"""
+        if trend == "decreasing" and change_rate < -20:
+            return "吞吐量显著下降，建议检查系统瓶颈，可能需要性能调优或扩容"
+        elif trend == "decreasing" and change_rate < -10:
+            return "吞吐量有下降趋势，建议关注系统资源使用情况"
+        elif trend == "stable":
+            return "吞吐量保持稳定，系统处理能力良好"
+        elif trend == "increasing":
+            return "吞吐量有提升趋势，系统性能优化效果良好"
+        else:
+            return "数据不足，建议继续监控"
+
+    def _get_error_rate_recommendation(self, trend: str, change_rate: float) -> str:
+        """获取错误率优化建议"""
+        if trend == "increasing" and change_rate > 50:
+            return "错误率显著上升，建议立即检查系统稳定性和错误日志"
+        elif trend == "increasing" and change_rate > 20:
+            return "错误率有上升趋势，建议关注系统异常情况"
+        elif trend == "stable" and change_rate < 5:
+            return "错误率保持稳定且较低，系统稳定性良好"
+        elif trend == "decreasing":
+            return "错误率有下降趋势，系统稳定性在改善"
+        else:
+            return "数据不足，建议继续监控"
+
+    def _assess_overall_health(self) -> str:
+        """评估系统整体健康状况"""
+        if len(self.historical_data) < 2:
+            return "数据不足"
+
+        response_trend = self.analyze_response_time_trend()
+        throughput_trend = self.analyze_throughput_trend()
+        error_trend = self.analyze_error_rate_trend()
+
+        # 简单的健康评估逻辑
+        health_score = 0
+
+        # 响应时间评估
+        if response_trend.get("p95_trend") == "decreasing":
+            health_score += 2
+        elif response_trend.get("p95_trend") == "stable":
+            health_score += 1
+
+        # 吞吐量评估
+        if throughput_trend.get("avg_tps_trend") == "increasing":
+            health_score += 2
+        elif throughput_trend.get("avg_tps_trend") == "stable":
+            health_score += 1
+
+        # 错误率评估
+        if error_trend.get("error_rate_trend") == "decreasing":
+            health_score += 2
+        elif error_trend.get("error_rate_trend") == "stable":
+            health_score += 1
+
+        if health_score >= 5:
+            return "优秀"
+        elif health_score >= 3:
+            return "良好"
+        elif health_score >= 1:
+            return "一般"
+        else:
+            return "需要关注"
+
+    def predict_future_performance(self, days_ahead: int = 7) -> Dict:
+        """预测未来性能趋势（简单线性预测）"""
+        if len(self.historical_data) < 3:
+            return {
+                "prediction": "insufficient_data",
+                "message": "数据不足，无法进行预测",
+            }
+
+        # 提取最近的数据进行预测
+        recent_data = self.historical_data[-min(10, len(self.historical_data)) :]
+
+        # 简单的线性预测逻辑
+        prediction = {
+            "prediction_date": (
+                datetime.now() + timedelta(days=days_ahead)
+            ).isoformat(),
+            "confidence": "low" if len(recent_data) < 5 else "medium",
+            "warning": [],
+        }
+
+        # 这里可以实现更复杂的预测算法
+        # 目前只提供基础的趋势延续预测
+
+        return prediction
diff --git a/src/client/.DS_Store b/src/client/.DS_Store
new file mode 100644
index 0000000..0280487
Binary files /dev/null and b/src/client/.DS_Store differ
diff --git a/src/data_manager/__init__.py b/src/data_manager/__init__.py
new file mode 100644
index 0000000..c757642
--- /dev/null
+++ b/src/data_manager/__init__.py
@@ -0,0 +1,11 @@
+"""
+测试数据管理模块
+
+提供测试数据的生成、管理、分发和存储功能
+"""
+
+from .data_distributor import DataDistributor
+from .data_generator import DataGenerator
+from .data_provider import DataProvider
+
+__all__ = ["DataGenerator", "DataProvider", "DataDistributor"]
diff --git a/src/data_manager/data_distributor.py b/src/data_manager/data_distributor.py
new file mode 100644
index 0000000..6d04067
--- /dev/null
+++ b/src/data_manager/data_distributor.py
@@ -0,0 +1,384 @@
+"""
+测试数据分发器
+
+在分布式测试环境中分发和同步测试数据
+"""
+
+import hashlib
+import json
+import pickle
+import socket
+import threading
+import time
+from collections import deque
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional
+
+from src.utils.log_moudle import logger
+
+
+class DataDistributor:
+    """测试数据分发器"""
+
+    def __init__(self, node_id: str = None, is_master: bool = True):
+        self.node_id = node_id or self._generate_node_id()
+        self.is_master = is_master
+        self.data_cache = {}  # data_key -> data
+        self.data_versions = {}  # data_key -> version
+        self.subscribers = {}  # data_key -> [callback_functions]
+        self.distribution_log = deque(maxlen=1000)  # 分发日志
+        self.lock = threading.Lock()
+
+        # 网络配置
+        self.master_host = "localhost"
+        self.master_port = 8089
+        self.sync_interval = 30  # 同步间隔(秒)
+
+        # 统计信息
+        self.stats = {
+            "data_distributed": 0,
+            "data_received": 0,
+            "sync_count": 0,
+            "last_sync_time": None,
+        }
+
+        # 同步线程
+        self.sync_thread = None
+        self.running = False
+
+    def start(self):
+        """启动分发器"""
+        if self.running:
+            logger.warning("数据分发器已在运行")
+            return
+
+        self.running = True
+
+        if not self.is_master:
+            # 从节点启动同步线程
+            self.sync_thread = threading.Thread(target=self._sync_loop, daemon=True)
+            self.sync_thread.start()
+            logger.info(f"数据分发器已启动 (从节点: {self.node_id})")
+        else:
+            logger.info(f"数据分发器已启动 (主节点: {self.node_id})")
+
+    def stop(self):
+        """停止分发器"""
+        self.running = False
+        if self.sync_thread:
+            self.sync_thread.join(timeout=5)
+        logger.info("数据分发器已停止")
+
+    def distribute_data(self, data_key: str, data: Any, version: str = None) -> bool:
+        """分发数据"""
+        try:
+            with self.lock:
+                # 生成版本号
+                if version is None:
+                    version = self._generate_version()
+
+                # 存储数据
+                self.data_cache[data_key] = data
+                self.data_versions[data_key] = version
+
+                # 记录分发日志
+                log_entry = {
+                    "timestamp": datetime.now().isoformat(),
+                    "action": "distribute",
+                    "data_key": data_key,
+                    "version": version,
+                    "node_id": self.node_id,
+                    "data_size": len(str(data)),
+                }
+                self.distribution_log.append(log_entry)
+
+                # 更新统计
+                self.stats["data_distributed"] += 1
+
+                # 通知订阅者
+                self._notify_subscribers(data_key, data, version)
+
+                logger.info(f"分发数据: {data_key} (版本: {version})")
+                return True
+
+        except Exception as e:
+            logger.error(f"分发数据失败 {data_key}: {e}")
+            return False
+
+    def get_data(self, data_key: str) -> Optional[Any]:
+        """获取数据"""
+        with self.lock:
+            return self.data_cache.get(data_key)
+
+    def get_data_version(self, data_key: str) -> Optional[str]:
+        """获取数据版本"""
+        with self.lock:
+            return self.data_versions.get(data_key)
+
+    def subscribe_data(self, data_key: str, callback: Callable[[str, Any, str], None]):
+        """订阅数据更新"""
+        with self.lock:
+            if data_key not in self.subscribers:
+                self.subscribers[data_key] = []
+            self.subscribers[data_key].append(callback)
+            logger.info(f"订阅数据更新: {data_key}")
+
+    def unsubscribe_data(self, data_key: str, callback: Callable):
+        """取消订阅数据更新"""
+        with self.lock:
+            if data_key in self.subscribers and callback in self.subscribers[data_key]:
+                self.subscribers[data_key].remove(callback)
+                logger.info(f"取消订阅数据更新: {data_key}")
+
+    def sync_with_master(self) -> bool:
+        """与主节点同步数据"""
+        if self.is_master:
+            logger.warning("主节点无需同步")
+            return True
+
+        try:
+            # 获取主节点数据清单
+            master_manifest = self._get_master_manifest()
+            if not master_manifest:
+                return False
+
+            # 比较版本并同步
+            sync_count = 0
+            for data_key, master_version in master_manifest.items():
+                local_version = self.data_versions.get(data_key)
+
+                if local_version != master_version:
+                    # 需要同步
+                    data = self._fetch_data_from_master(data_key)
+                    if data is not None:
+                        with self.lock:
+                            self.data_cache[data_key] = data
+                            self.data_versions[data_key] = master_version
+                            self._notify_subscribers(data_key, data, master_version)
+                        sync_count += 1
+                        logger.debug(f"同步数据: {data_key} (版本: {master_version})")
+
+            # 更新统计
+            self.stats["sync_count"] += 1
+            self.stats["last_sync_time"] = datetime.now().isoformat()
+
+            if sync_count > 0:
+                logger.info(f"同步完成: {sync_count} 个数据项")
+
+            return True
+
+        except Exception as e:
+            logger.error(f"同步数据失败: {e}")
+            return False
+
+    def export_data(self, output_path: str, data_keys: List[str] = None) -> bool:
+        """导出数据"""
+        try:
+            export_data = {}
+
+            with self.lock:
+                keys_to_export = data_keys or list(self.data_cache.keys())
+
+                for data_key in keys_to_export:
+                    if data_key in self.data_cache:
+                        export_data[data_key] = {
+                            "data": self.data_cache[data_key],
+                            "version": self.data_versions[data_key],
+                            "exported_at": datetime.now().isoformat(),
+                            "node_id": self.node_id,
+                        }
+
+            # 保存到文件
+            output_file = Path(output_path)
+            output_file.parent.mkdir(parents=True, exist_ok=True)
+
+            if output_file.suffix.lower() == ".json":
+                with open(output_file, "w", encoding="utf-8") as f:
+                    json.dump(export_data, f, ensure_ascii=False, indent=2)
+            else:
+                # 使用pickle保存
+                with open(output_file, "wb") as f:
+                    pickle.dump(export_data, f)
+
+            logger.info(f"导出数据: {len(export_data)} 项 -> {output_file}")
+            return True
+
+        except Exception as e:
+            logger.error(f"导出数据失败: {e}")
+            return False
+
+    def import_data(self, input_path: str, overwrite: bool = False) -> bool:
+        """导入数据"""
+        try:
+            input_file = Path(input_path)
+            if not input_file.exists():
+                logger.error(f"导入文件不存在: {input_path}")
+                return False
+
+            # 加载数据
+            if input_file.suffix.lower() == ".json":
+                with open(input_file, "r", encoding="utf-8") as f:
+                    import_data = json.load(f)
+            else:
+                # 使用pickle加载
+                with open(input_file, "rb") as f:
+                    import_data = pickle.load(f)
+
+            # 导入数据
+            imported_count = 0
+            with self.lock:
+                for data_key, data_info in import_data.items():
+                    if not overwrite and data_key in self.data_cache:
+                        logger.warning(f"数据已存在，跳过: {data_key}")
+                        continue
+
+                    self.data_cache[data_key] = data_info["data"]
+                    self.data_versions[data_key] = data_info["version"]
+                    imported_count += 1
+
+                    # 通知订阅者
+                    self._notify_subscribers(
+                        data_key, data_info["data"], data_info["version"]
+                    )
+
+            logger.info(f"导入数据: {imported_count} 项")
+            return True
+
+        except Exception as e:
+            logger.error(f"导入数据失败: {e}")
+            return False
+
+    def get_distribution_stats(self) -> Dict[str, Any]:
+        """获取分发统计信息"""
+        with self.lock:
+            stats = self.stats.copy()
+            stats.update(
+                {
+                    "node_id": self.node_id,
+                    "is_master": self.is_master,
+                    "cached_data_count": len(self.data_cache),
+                    "subscribers_count": sum(
+                        len(subs) for subs in self.subscribers.values()
+                    ),
+                    "running": self.running,
+                }
+            )
+            return stats
+
+    def get_data_manifest(self) -> Dict[str, str]:
+        """获取数据清单"""
+        with self.lock:
+            return self.data_versions.copy()
+
+    def clear_data(self, data_key: str = None):
+        """清理数据"""
+        with self.lock:
+            if data_key:
+                if data_key in self.data_cache:
+                    del self.data_cache[data_key]
+                    del self.data_versions[data_key]
+                    logger.info(f"清理数据: {data_key}")
+            else:
+                self.data_cache.clear()
+                self.data_versions.clear()
+                logger.info("清理所有数据")
+
+    def _generate_node_id(self) -> str:
+        """生成节点ID"""
+        hostname = socket.gethostname()
+        timestamp = str(int(time.time()))
+        return hashlib.md5(f"{hostname}_{timestamp}".encode()).hexdigest()[:8]
+
+    def _generate_version(self) -> str:
+        """生成版本号"""
+        return str(int(time.time() * 1000))  # 毫秒时间戳
+
+    def _notify_subscribers(self, data_key: str, data: Any, version: str):
+        """通知订阅者"""
+        if data_key in self.subscribers:
+            for callback in self.subscribers[data_key]:
+                try:
+                    callback(data_key, data, version)
+                except Exception as e:
+                    logger.error(f"通知订阅者失败: {e}")
+
+    def _sync_loop(self):
+        """同步循环"""
+        while self.running:
+            try:
+                self.sync_with_master()
+                time.sleep(self.sync_interval)
+            except Exception as e:
+                logger.error(f"同步循环异常: {e}")
+                time.sleep(self.sync_interval)
+
+    def _get_master_manifest(self) -> Optional[Dict[str, str]]:
+        """获取主节点数据清单"""
+        # 这里应该实现与主节点的网络通信
+        # 为了简化，这里返回空字典
+        try:
+            # TODO: 实现HTTP/TCP通信获取主节点清单
+            logger.debug("获取主节点数据清单")
+            return {}
+        except Exception as e:
+            logger.error(f"获取主节点清单失败: {e}")
+            return None
+
+    def _fetch_data_from_master(self, data_key: str) -> Optional[Any]:
+        """从主节点获取数据"""
+        # 这里应该实现与主节点的网络通信
+        # 为了简化，这里返回None
+        try:
+            # TODO: 实现HTTP/TCP通信获取数据
+            logger.debug(f"从主节点获取数据: {data_key}")
+            return None
+        except Exception as e:
+            logger.error(f"从主节点获取数据失败 {data_key}: {e}")
+            return None
+
+    def create_data_snapshot(self, snapshot_name: str) -> bool:
+        """创建数据快照"""
+        try:
+            snapshot_data = {
+                "snapshot_name": snapshot_name,
+                "created_at": datetime.now().isoformat(),
+                "node_id": self.node_id,
+                "data_cache": self.data_cache.copy(),
+                "data_versions": self.data_versions.copy(),
+            }
+
+            snapshot_file = Path(f"snapshots/{snapshot_name}.snapshot")
+            snapshot_file.parent.mkdir(parents=True, exist_ok=True)
+
+            with open(snapshot_file, "wb") as f:
+                pickle.dump(snapshot_data, f)
+
+            logger.info(f"创建数据快照: {snapshot_name}")
+            return True
+
+        except Exception as e:
+            logger.error(f"创建数据快照失败: {e}")
+            return False
+
+    def restore_data_snapshot(self, snapshot_name: str) -> bool:
+        """恢复数据快照"""
+        try:
+            snapshot_file = Path(f"snapshots/{snapshot_name}.snapshot")
+            if not snapshot_file.exists():
+                logger.error(f"快照文件不存在: {snapshot_name}")
+                return False
+
+            with open(snapshot_file, "rb") as f:
+                snapshot_data = pickle.load(f)
+
+            with self.lock:
+                self.data_cache = snapshot_data["data_cache"]
+                self.data_versions = snapshot_data["data_versions"]
+
+            logger.info(f"恢复数据快照: {snapshot_name}")
+            return True
+
+        except Exception as e:
+            logger.error(f"恢复数据快照失败: {e}")
+            return False
diff --git a/src/data_manager/data_generator.py b/src/data_manager/data_generator.py
new file mode 100644
index 0000000..85f91a3
--- /dev/null
+++ b/src/data_manager/data_generator.py
@@ -0,0 +1,554 @@
+"""
+测试数据生成器
+
+提供各种类型测试数据的生成功能
+"""
+
+import csv
+import json
+import random
+import string
+import uuid
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional
+
+from faker import Faker
+
+from src.utils.log_moudle import logger
+
+
+class DataGenerator:
+    """测试数据生成器"""
+
+    def __init__(self, locale: str = "zh_CN", seed: Optional[int] = None):
+        self.faker = Faker(locale)
+        if seed:
+            self.faker.seed_instance(seed)
+        self.locale = locale
+        self.generators = {}
+        self._register_default_generators()
+
+    # API方法 - 符合文档规范
+    def generate_name(self, gender: Optional[str] = None) -> str:
+        """生成姓名"""
+        if gender == "male":
+            return self.faker.name_male()
+        elif gender == "female":
+            return self.faker.name_female()
+        return self.faker.name()
+
+    def generate_email(self, domain: Optional[str] = None) -> str:
+        """生成邮箱地址"""
+        if domain:
+            username = self.faker.user_name()
+            return f"{username}@{domain}"
+        return self.faker.email()
+
+    def generate_phone_number(self, region: str = "CN") -> str:
+        """生成手机号码"""
+        if region == "CN":
+            return self.faker.phone_number()
+        elif region == "US":
+            return self.faker.numerify("+1-###-###-####")
+        return self.faker.phone_number()
+
+    def generate_address(self, include_country: bool = False) -> Dict[str, str]:
+        """生成地址信息"""
+        address = {
+            "province": self.faker.province(),
+            "city": self.faker.city(),
+            "district": self.faker.district(),
+            "street": self.faker.street_address(),
+            "postal_code": self.faker.postcode(),
+        }
+        if include_country:
+            address["country"] = self.faker.country()
+        return address
+
+    def generate_integer(self, min_value: int = 0, max_value: int = 100) -> int:
+        """生成整数"""
+        return random.randint(min_value, max_value)
+
+    def generate_float(
+        self, min_value: float = 0.0, max_value: float = 100.0, precision: int = 2
+    ) -> float:
+        """生成浮点数"""
+        value = random.uniform(min_value, max_value)
+        return round(value, precision)
+
+    def generate_price(
+        self, min_price: float = 1.0, max_price: float = 1000.0, currency: str = "CNY"
+    ) -> Dict[str, Any]:
+        """生成价格信息"""
+        amount = round(random.uniform(min_price, max_price), 2)
+        currency_symbols = {"CNY": "¥", "USD": "$", "EUR": "€", "JPY": "¥"}
+        symbol = currency_symbols.get(currency, currency)
+
+        return {
+            "amount": amount,
+            "currency": currency,
+            "formatted": f"{symbol}{amount}",
+        }
+
+    def generate_text(
+        self, min_length: int = 10, max_length: int = 100, text_type: str = "sentence"
+    ) -> str:
+        """生成文本内容"""
+        if text_type == "word":
+            words_count = max(1, min_length // 5)  # 假设平均每个词5个字符
+            return " ".join(self.faker.words(nb=words_count))
+        elif text_type == "paragraph":
+            return self.faker.paragraph(nb_sentences=3)
+        else:  # sentence
+            return self.faker.sentence(nb_words=random.randint(5, 15))
+
+    def generate_username(self, min_length: int = 6, max_length: int = 20) -> str:
+        """生成用户名"""
+        base_username = self.faker.user_name()
+        if len(base_username) < min_length:
+            base_username += str(random.randint(100, 999))
+        if len(base_username) > max_length:
+            base_username = base_username[:max_length]
+        return base_username
+
+    def generate_datetime(
+        self,
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None,
+        format_str: str = "%Y-%m-%d %H:%M:%S",
+    ) -> str:
+        """生成日期时间"""
+        start = (
+            datetime.strptime(start_date, "%Y-%m-%d")
+            if start_date
+            else datetime.now() - timedelta(days=365)
+        )
+        end = datetime.strptime(end_date, "%Y-%m-%d") if end_date else datetime.now()
+
+        random_date = self.faker.date_time_between(start_date=start, end_date=end)
+        return random_date.strftime(format_str)
+
+    def generate_timestamp(
+        self, start_timestamp: Optional[int] = None, end_timestamp: Optional[int] = None
+    ) -> int:
+        """生成时间戳"""
+        now = int(datetime.now().timestamp())
+        start = start_timestamp or (now - 86400 * 365)  # 1年前
+        end = end_timestamp or now
+
+        return random.randint(start, end)
+
+    def generate_user_profile(self, include_avatar: bool = True) -> Dict[str, Any]:
+        """生成用户资料"""
+        user_id = f"user_{random.randint(100000, 999999)}"
+        profile = {
+            "id": user_id,
+            "username": self.generate_username(),
+            "name": self.generate_name(),
+            "email": self.generate_email(),
+            "phone": self.generate_phone_number(),
+            "age": self.generate_integer(18, 65),
+            "gender": random.choice(["male", "female"]),
+            "bio": self.generate_text(20, 100, "sentence"),
+            "created_at": self.generate_datetime(),
+        }
+
+        if include_avatar:
+            profile["avatar"] = f"https://example.com/avatar/{user_id}.jpg"
+
+        return profile
+
+    def generate_product_info(self, category: Optional[str] = None) -> Dict[str, Any]:
+        """生成商品信息"""
+        categories = (
+            ["electronics", "clothing", "books", "home", "sports"]
+            if not category
+            else [category]
+        )
+        selected_category = random.choice(categories)
+
+        product_id = f"prod_{random.randint(100000, 999999)}"
+
+        return {
+            "id": product_id,
+            "name": self.faker.catch_phrase(),
+            "category": selected_category,
+            "price": self.generate_float(10.0, 2000.0, 2),
+            "description": self.generate_text(50, 200, "sentence"),
+            "brand": self.faker.company(),
+            "model": f"{self.faker.word().upper()}-{random.randint(100, 999)}",
+            "sku": f"{self.faker.lexify('??').upper()}-{product_id[-6:]}",
+            "stock": self.generate_integer(0, 200),
+            "images": [
+                f"https://example.com/img/{product_id}_{i}.jpg" for i in range(1, 4)
+            ],
+            "created_at": self.generate_datetime(),
+        }
+
+    def register_generator(self, name: str, generator_func: Callable):
+        """注册自定义数据生成器"""
+        self.generators[name] = generator_func
+        logger.info(f"注册数据生成器: {name}")
+
+    def generate_user_data(self, count: int = 100) -> List[Dict]:
+        """生成用户数据"""
+        users = []
+        for _ in range(count):
+            user = {
+                "user_id": str(uuid.uuid4()),
+                "username": self.faker.user_name(),
+                "email": self.faker.email(),
+                "phone": self.faker.phone_number(),
+                "name": self.faker.name(),
+                "age": random.randint(18, 65),
+                "gender": random.choice(["male", "female"]),
+                "address": self.faker.address(),
+                "company": self.faker.company(),
+                "job": self.faker.job(),
+                "created_at": self.faker.date_time_between(
+                    start_date="-2y", end_date="now"
+                ).isoformat(),
+                "is_active": random.choice([True, False]),
+                "balance": round(random.uniform(0, 10000), 2),
+            }
+            users.append(user)
+
+        logger.info(f"生成了 {count} 条用户数据")
+        return users
+
+    def generate_product_data(self, count: int = 50) -> List[Dict]:
+        """生成商品数据"""
+        categories = [
+            "电子产品",
+            "服装",
+            "食品",
+            "图书",
+            "家居",
+            "运动",
+            "美妆",
+            "汽车",
+        ]
+        products = []
+
+        for _ in range(count):
+            product = {
+                "product_id": str(uuid.uuid4()),
+                "name": self.faker.catch_phrase(),
+                "description": self.faker.text(max_nb_chars=200),
+                "category": random.choice(categories),
+                "price": round(random.uniform(10, 1000), 2),
+                "stock": random.randint(0, 1000),
+                "brand": self.faker.company(),
+                "rating": round(random.uniform(1, 5), 1),
+                "reviews_count": random.randint(0, 1000),
+                "created_at": self.faker.date_time_between(
+                    start_date="-1y", end_date="now"
+                ).isoformat(),
+                "is_available": random.choice([True, False]),
+                "weight": round(random.uniform(0.1, 10), 2),
+                "dimensions": {
+                    "length": round(random.uniform(1, 50), 1),
+                    "width": round(random.uniform(1, 50), 1),
+                    "height": round(random.uniform(1, 50), 1),
+                },
+            }
+            products.append(product)
+
+        logger.info(f"生成了 {count} 条商品数据")
+        return products
+
+    def generate_order_data(
+        self,
+        count: int = 200,
+        user_ids: List[str] = None,
+        product_ids: List[str] = None,
+    ) -> List[Dict]:
+        """生成订单数据"""
+        if not user_ids:
+            user_ids = [str(uuid.uuid4()) for _ in range(20)]
+        if not product_ids:
+            product_ids = [str(uuid.uuid4()) for _ in range(50)]
+
+        orders = []
+        statuses = ["pending", "processing", "shipped", "delivered", "cancelled"]
+
+        for _ in range(count):
+            order_items = []
+            item_count = random.randint(1, 5)
+
+            for _ in range(item_count):
+                order_items.append(
+                    {
+                        "product_id": random.choice(product_ids),
+                        "quantity": random.randint(1, 10),
+                        "unit_price": round(random.uniform(10, 500), 2),
+                    }
+                )
+
+            total_amount = sum(
+                item["quantity"] * item["unit_price"] for item in order_items
+            )
+
+            order = {
+                "order_id": str(uuid.uuid4()),
+                "user_id": random.choice(user_ids),
+                "items": order_items,
+                "total_amount": round(total_amount, 2),
+                "status": random.choice(statuses),
+                "payment_method": random.choice(
+                    ["credit_card", "debit_card", "paypal", "alipay", "wechat_pay"]
+                ),
+                "shipping_address": self.faker.address(),
+                "created_at": self.faker.date_time_between(
+                    start_date="-6m", end_date="now"
+                ).isoformat(),
+                "updated_at": self.faker.date_time_between(
+                    start_date="-6m", end_date="now"
+                ).isoformat(),
+                "notes": (
+                    self.faker.text(max_nb_chars=100) if random.random() < 0.3 else ""
+                ),
+            }
+            orders.append(order)
+
+        logger.info(f"生成了 {count} 条订单数据")
+        return orders
+
+    def generate_api_test_data(self, count: int = 100) -> List[Dict]:
+        """生成API测试数据"""
+        methods = ["GET", "POST", "PUT", "DELETE"]
+        status_codes = [200, 201, 400, 401, 403, 404, 500]
+
+        api_data = []
+        for _ in range(count):
+            data = {
+                "request_id": str(uuid.uuid4()),
+                "method": random.choice(methods),
+                "url": f"https://api.example.com/{self.faker.uri_path()}",
+                "headers": {
+                    "Content-Type": "application/json",
+                    "User-Agent": self.faker.user_agent(),
+                    "Authorization": f"Bearer {self.faker.sha256()[:32]}",
+                },
+                "params": {
+                    "page": random.randint(1, 100),
+                    "limit": random.choice([10, 20, 50, 100]),
+                    "sort": random.choice(["asc", "desc"]),
+                },
+                "body": {
+                    "data": self.faker.json(),
+                    "timestamp": datetime.now().isoformat(),
+                },
+                "expected_status": random.choice(status_codes),
+                "timeout": random.randint(5, 30),
+            }
+            api_data.append(data)
+
+        logger.info(f"生成了 {count} 条API测试数据")
+        return api_data
+
+    def generate_performance_test_data(self, count: int = 1000) -> List[Dict]:
+        """生成性能测试数据"""
+        scenarios = ["login", "browse", "search", "purchase", "logout"]
+
+        perf_data = []
+        for _ in range(count):
+            data = {
+                "session_id": str(uuid.uuid4()),
+                "user_id": str(uuid.uuid4()),
+                "scenario": random.choice(scenarios),
+                "start_time": self.faker.date_time_between(
+                    start_date="-1d", end_date="now"
+                ).isoformat(),
+                "response_time": round(random.uniform(100, 5000), 2),  # ms
+                "success": random.choice([True, False]),
+                "error_message": (
+                    self.faker.sentence() if random.random() < 0.1 else None
+                ),
+                "request_size": random.randint(100, 10000),  # bytes
+                "response_size": random.randint(500, 50000),  # bytes
+                "concurrent_users": random.randint(1, 100),
+                "server_load": round(random.uniform(0, 100), 2),  # %
+            }
+            perf_data.append(data)
+
+        logger.info(f"生成了 {count} 条性能测试数据")
+        return perf_data
+
+    def generate_custom_data(
+        self, generator_name: str, count: int = 100, **kwargs
+    ) -> List[Dict]:
+        """使用自定义生成器生成数据"""
+        if generator_name not in self.generators:
+            raise ValueError(f"未找到数据生成器: {generator_name}")
+
+        generator_func = self.generators[generator_name]
+        data = []
+
+        for _ in range(count):
+            item = generator_func(self.faker, **kwargs)
+            data.append(item)
+
+        logger.info(f"使用 {generator_name} 生成了 {count} 条数据")
+        return data
+
+    def generate_random_string(self, length: int = 10, chars: str = None) -> str:
+        """生成随机字符串"""
+        if chars is None:
+            chars = string.ascii_letters + string.digits
+        return "".join(random.choices(chars, k=length))
+
+    def generate_random_number(self, min_val: int = 0, max_val: int = 100) -> int:
+        """生成随机数字"""
+        return random.randint(min_val, max_val)
+
+    def generate_random_email(self, domain: str = None) -> str:
+        """生成随机邮箱"""
+        if domain:
+            username = self.generate_random_string(8)
+            return f"{username}@{domain}"
+        return self.faker.email()
+
+    def generate_random_phone(self, country_code: str = "+86") -> str:
+        """生成随机手机号"""
+        if country_code == "+86":
+            # 中国手机号格式
+            prefixes = [
+                "130",
+                "131",
+                "132",
+                "133",
+                "134",
+                "135",
+                "136",
+                "137",
+                "138",
+                "139",
+                "150",
+                "151",
+                "152",
+                "153",
+                "155",
+                "156",
+                "157",
+                "158",
+                "159",
+                "180",
+                "181",
+                "182",
+                "183",
+                "184",
+                "185",
+                "186",
+                "187",
+                "188",
+                "189",
+            ]
+            prefix = random.choice(prefixes)
+            suffix = "".join([str(random.randint(0, 9)) for _ in range(8)])
+            return f"{country_code}{prefix}{suffix}"
+        else:
+            return self.faker.phone_number()
+
+    def save_to_csv(self, data: List[Dict], filename: str, output_dir: str = "data"):
+        """保存数据到CSV文件"""
+        output_path = Path(output_dir)
+        output_path.mkdir(exist_ok=True)
+
+        file_path = output_path / filename
+
+        if not data:
+            logger.warning("没有数据可保存")
+            return
+
+        try:
+            with open(file_path, "w", newline="", encoding="utf-8") as csvfile:
+                fieldnames = data[0].keys()
+                writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+                writer.writeheader()
+
+                for row in data:
+                    # 处理嵌套字典和列表
+                    processed_row = {}
+                    for key, value in row.items():
+                        if isinstance(value, (dict, list)):
+                            processed_row[key] = json.dumps(value, ensure_ascii=False)
+                        else:
+                            processed_row[key] = value
+                    writer.writerow(processed_row)
+
+            logger.info(f"数据已保存到: {file_path}")
+
+        except Exception as e:
+            logger.error(f"保存CSV文件失败: {e}")
+            raise
+
+    def save_to_json(self, data: List[Dict], filename: str, output_dir: str = "data"):
+        """保存数据到JSON文件"""
+        output_path = Path(output_dir)
+        output_path.mkdir(exist_ok=True)
+
+        file_path = output_path / filename
+
+        try:
+            with open(file_path, "w", encoding="utf-8") as jsonfile:
+                json.dump(data, jsonfile, ensure_ascii=False, indent=2)
+
+            logger.info(f"数据已保存到: {file_path}")
+
+        except Exception as e:
+            logger.error(f"保存JSON文件失败: {e}")
+            raise
+
+    def _register_default_generators(self):
+        """注册默认数据生成器"""
+
+        def simple_user_generator(faker, **kwargs):
+            return {
+                "id": str(uuid.uuid4()),
+                "name": faker.name(),
+                "email": faker.email(),
+            }
+
+        def simple_product_generator(faker, **kwargs):
+            return {
+                "id": str(uuid.uuid4()),
+                "name": faker.catch_phrase(),
+                "price": round(random.uniform(10, 1000), 2),
+            }
+
+        self.register_generator("simple_user", simple_user_generator)
+        self.register_generator("simple_product", simple_product_generator)
+
+    def batch_generate(self, generators_config: List[Dict]) -> Dict[str, List[Dict]]:
+        """批量生成多种类型的数据"""
+        results = {}
+
+        for config in generators_config:
+            data_type = config.get("type")
+            count = config.get("count", 100)
+            params = config.get("params", {})
+
+            if data_type == "user":
+                results["users"] = self.generate_user_data(count)
+            elif data_type == "product":
+                results["products"] = self.generate_product_data(count)
+            elif data_type == "order":
+                user_ids = params.get("user_ids")
+                product_ids = params.get("product_ids")
+                results["orders"] = self.generate_order_data(
+                    count, user_ids, product_ids
+                )
+            elif data_type == "api":
+                results["api_data"] = self.generate_api_test_data(count)
+            elif data_type == "performance":
+                results["performance_data"] = self.generate_performance_test_data(count)
+            elif data_type in self.generators:
+                results[data_type] = self.generate_custom_data(
+                    data_type, count, **params
+                )
+            else:
+                logger.warning(f"未知的数据类型: {data_type}")
+
+        return results
diff --git a/src/data_manager/data_provider.py b/src/data_manager/data_provider.py
new file mode 100644
index 0000000..02376af
--- /dev/null
+++ b/src/data_manager/data_provider.py
@@ -0,0 +1,429 @@
+"""
+测试数据提供器
+
+管理和提供测试数据，支持多种数据源和分发策略
+"""
+
+import csv
+import json
+import random
+import sqlite3
+import threading
+from abc import ABC, abstractmethod
+from collections import deque
+from pathlib import Path
+from typing import Any, Dict, Iterator, List, Optional, Union
+
+from src.utils.log_moudle import logger
+
+
+class DataSource(ABC):
+    """数据源抽象基类"""
+
+    @abstractmethod
+    def load_data(self) -> List[Dict]:
+        """加载数据"""
+        pass
+
+    @abstractmethod
+    def get_data_count(self) -> int:
+        """获取数据总数"""
+        pass
+
+
+class FileDataSource(DataSource):
+    """文件数据源"""
+
+    def __init__(self, file_path: str):
+        self.file_path = Path(file_path)
+        self._data = None
+        self._load_data()
+
+    def _load_data(self):
+        """加载文件数据"""
+        if not self.file_path.exists():
+            raise FileNotFoundError(f"数据文件不存在: {self.file_path}")
+
+        try:
+            if self.file_path.suffix.lower() == ".json":
+                with open(self.file_path, "r", encoding="utf-8") as f:
+                    self._data = json.load(f)
+            elif self.file_path.suffix.lower() == ".csv":
+                self._data = []
+                with open(self.file_path, "r", encoding="utf-8") as f:
+                    reader = csv.DictReader(f)
+                    for row in reader:
+                        # 尝试解析JSON字段
+                        processed_row = {}
+                        for key, value in row.items():
+                            try:
+                                processed_row[key] = json.loads(value)
+                            except (json.JSONDecodeError, TypeError):
+                                processed_row[key] = value
+                        self._data.append(processed_row)
+            else:
+                raise ValueError(f"不支持的文件格式: {self.file_path.suffix}")
+
+            logger.info(f"从文件加载了 {len(self._data)} 条数据: {self.file_path}")
+
+        except Exception as e:
+            logger.error(f"加载数据文件失败: {e}")
+            raise
+
+    def load_data(self) -> List[Dict]:
+        """获取数据"""
+        return self._data.copy() if self._data else []
+
+    def get_data_count(self) -> int:
+        """获取数据总数"""
+        return len(self._data) if self._data else 0
+
+
+class DatabaseDataSource(DataSource):
+    """数据库数据源"""
+
+    def __init__(self, db_path: str, table_name: str, query: str = None):
+        self.db_path = db_path
+        self.table_name = table_name
+        self.query = query or f"SELECT * FROM {table_name}"
+        self._data = None
+        self._load_data()
+
+    def _load_data(self):
+        """从数据库加载数据"""
+        try:
+            conn = sqlite3.connect(self.db_path)
+            conn.row_factory = sqlite3.Row  # 使结果可以按列名访问
+            cursor = conn.cursor()
+
+            cursor.execute(self.query)
+            rows = cursor.fetchall()
+
+            self._data = [dict(row) for row in rows]
+
+            conn.close()
+            logger.info(f"从数据库加载了 {len(self._data)} 条数据")
+
+        except Exception as e:
+            logger.error(f"从数据库加载数据失败: {e}")
+            raise
+
+    def load_data(self) -> List[Dict]:
+        """获取数据"""
+        return self._data.copy() if self._data else []
+
+    def get_data_count(self) -> int:
+        """获取数据总数"""
+        return len(self._data) if self._data else 0
+
+
+class MemoryDataSource(DataSource):
+    """内存数据源"""
+
+    def __init__(self, data: List[Dict]):
+        self._data = data.copy() if data else []
+
+    def load_data(self) -> List[Dict]:
+        """获取数据"""
+        return self._data.copy()
+
+    def get_data_count(self) -> int:
+        """获取数据总数"""
+        return len(self._data)
+
+    def add_data(self, data: Union[Dict, List[Dict]]):
+        """添加数据"""
+        if isinstance(data, dict):
+            self._data.append(data)
+        elif isinstance(data, list):
+            self._data.extend(data)
+        else:
+            raise ValueError("数据必须是字典或字典列表")
+
+    def clear_data(self):
+        """清空数据"""
+        self._data.clear()
+
+
+class DataProvider:
+    """测试数据提供器"""
+
+    def __init__(self, name: str):
+        self.name = name
+        self.data_sources = {}  # source_name -> DataSource
+        self.current_data = {}  # source_name -> current_data
+        self.data_iterators = {}  # source_name -> iterator
+        self.distribution_strategy = "round_robin"  # 分发策略
+        self.lock = threading.Lock()
+
+        # 统计信息
+        self.access_count = {}  # source_name -> count
+        self.last_access_time = {}  # source_name -> timestamp
+
+    def add_data_source(self, source_name: str, data_source: DataSource):
+        """添加数据源"""
+        with self.lock:
+            self.data_sources[source_name] = data_source
+            self.current_data[source_name] = data_source.load_data()
+            self.access_count[source_name] = 0
+
+            # 初始化迭代器
+            self._init_iterator(source_name)
+
+            logger.info(
+                f"添加数据源: {source_name}, 数据量: {data_source.get_data_count()}"
+            )
+
+    def remove_data_source(self, source_name: str):
+        """移除数据源"""
+        with self.lock:
+            if source_name in self.data_sources:
+                del self.data_sources[source_name]
+                del self.current_data[source_name]
+                del self.data_iterators[source_name]
+                del self.access_count[source_name]
+                if source_name in self.last_access_time:
+                    del self.last_access_time[source_name]
+
+                logger.info(f"移除数据源: {source_name}")
+
+    def get_data(self, source_name: str, count: int = 1) -> List[Dict]:
+        """获取指定数量的数据"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        with self.lock:
+            data = []
+            iterator = self.data_iterators[source_name]
+
+            for _ in range(count):
+                try:
+                    item = next(iterator)
+                    data.append(item)
+                except StopIteration:
+                    # 重新初始化迭代器
+                    self._init_iterator(source_name)
+                    iterator = self.data_iterators[source_name]
+                    try:
+                        item = next(iterator)
+                        data.append(item)
+                    except StopIteration:
+                        # 数据源为空
+                        break
+
+            # 更新统计信息
+            self.access_count[source_name] += len(data)
+            self.last_access_time[source_name] = threading.current_thread().ident
+
+            return data
+
+    def get_random_data(self, source_name: str, count: int = 1) -> List[Dict]:
+        """随机获取数据"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        with self.lock:
+            source_data = self.current_data[source_name]
+            if not source_data:
+                return []
+
+            # 随机选择数据
+            if count >= len(source_data):
+                selected_data = source_data.copy()
+            else:
+                selected_data = random.sample(source_data, count)
+
+            # 更新统计信息
+            self.access_count[source_name] += len(selected_data)
+
+            return selected_data
+
+    def get_all_data(self, source_name: str) -> List[Dict]:
+        """获取所有数据"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        return self.current_data[source_name].copy()
+
+    def reload_data_source(self, source_name: str):
+        """重新加载数据源"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        with self.lock:
+            data_source = self.data_sources[source_name]
+            self.current_data[source_name] = data_source.load_data()
+            self._init_iterator(source_name)
+
+            logger.info(f"重新加载数据源: {source_name}")
+
+    def set_distribution_strategy(self, strategy: str):
+        """设置分发策略"""
+        valid_strategies = ["round_robin", "random", "sequential"]
+        if strategy not in valid_strategies:
+            raise ValueError(
+                f"无效的分发策略: {strategy}, 支持的策略: {valid_strategies}"
+            )
+
+        self.distribution_strategy = strategy
+
+        # 重新初始化所有迭代器
+        with self.lock:
+            for source_name in self.data_sources:
+                self._init_iterator(source_name)
+
+        logger.info(f"设置分发策略: {strategy}")
+
+    def _init_iterator(self, source_name: str):
+        """初始化数据迭代器"""
+        data = self.current_data[source_name]
+
+        if self.distribution_strategy == "round_robin":
+            # 循环迭代器
+            def round_robin_iterator():
+                while True:
+                    for item in data:
+                        yield item
+
+            self.data_iterators[source_name] = round_robin_iterator()
+
+        elif self.distribution_strategy == "random":
+            # 随机迭代器
+            def random_iterator():
+                while True:
+                    if data:
+                        yield random.choice(data)
+
+            self.data_iterators[source_name] = random_iterator()
+
+        elif self.distribution_strategy == "sequential":
+            # 顺序迭代器（一次性）
+            self.data_iterators[source_name] = iter(data)
+
+    def get_data_statistics(self) -> Dict[str, Dict]:
+        """获取数据统计信息"""
+        stats = {}
+
+        for source_name in self.data_sources:
+            stats[source_name] = {
+                "total_count": len(self.current_data[source_name]),
+                "access_count": self.access_count.get(source_name, 0),
+                "last_access_thread": self.last_access_time.get(source_name),
+                "data_source_type": type(self.data_sources[source_name]).__name__,
+            }
+
+        return stats
+
+    def filter_data(self, source_name: str, filter_func: callable) -> List[Dict]:
+        """过滤数据"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        source_data = self.current_data[source_name]
+        filtered_data = [item for item in source_data if filter_func(item)]
+
+        logger.info(
+            f"过滤数据源 {source_name}: {len(source_data)} -> {len(filtered_data)}"
+        )
+        return filtered_data
+
+    def search_data(self, source_name: str, search_criteria: Dict) -> List[Dict]:
+        """搜索数据"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        source_data = self.current_data[source_name]
+        results = []
+
+        for item in source_data:
+            match = True
+            for key, value in search_criteria.items():
+                if key not in item or item[key] != value:
+                    match = False
+                    break
+            if match:
+                results.append(item)
+
+        logger.info(f"搜索数据源 {source_name}: 找到 {len(results)} 条匹配记录")
+        return results
+
+    def create_data_pool(self, pool_name: str, source_configs: List[Dict]):
+        """创建数据池"""
+        pool_data = []
+
+        for config in source_configs:
+            source_name = config["source_name"]
+            count = config.get("count", 10)
+            strategy = config.get("strategy", "random")
+
+            if source_name not in self.data_sources:
+                logger.warning(f"数据源不存在: {source_name}")
+                continue
+
+            if strategy == "random":
+                data = self.get_random_data(source_name, count)
+            else:
+                data = self.get_data(source_name, count)
+
+            pool_data.extend(data)
+
+        # 创建内存数据源
+        pool_source = MemoryDataSource(pool_data)
+        self.add_data_source(pool_name, pool_source)
+
+        logger.info(f"创建数据池 {pool_name}: {len(pool_data)} 条数据")
+        return pool_data
+
+    def export_data(self, source_name: str, output_path: str, format: str = "json"):
+        """导出数据"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        data = self.current_data[source_name]
+        output_file = Path(output_path)
+
+        try:
+            if format.lower() == "json":
+                with open(output_file, "w", encoding="utf-8") as f:
+                    json.dump(data, f, ensure_ascii=False, indent=2)
+            elif format.lower() == "csv":
+                if not data:
+                    logger.warning("没有数据可导出")
+                    return
+
+                with open(output_file, "w", newline="", encoding="utf-8") as f:
+                    fieldnames = data[0].keys()
+                    writer = csv.DictWriter(f, fieldnames=fieldnames)
+                    writer.writeheader()
+
+                    for row in data:
+                        # 处理嵌套对象
+                        processed_row = {}
+                        for key, value in row.items():
+                            if isinstance(value, (dict, list)):
+                                processed_row[key] = json.dumps(
+                                    value, ensure_ascii=False
+                                )
+                            else:
+                                processed_row[key] = value
+                        writer.writerow(processed_row)
+            else:
+                raise ValueError(f"不支持的导出格式: {format}")
+
+            logger.info(f"数据已导出到: {output_file}")
+
+        except Exception as e:
+            logger.error(f"导出数据失败: {e}")
+            raise
+
+    def get_data_sample(self, source_name: str, sample_size: int = 5) -> List[Dict]:
+        """获取数据样本"""
+        if source_name not in self.data_sources:
+            raise ValueError(f"数据源不存在: {source_name}")
+
+        source_data = self.current_data[source_name]
+        if not source_data:
+            return []
+
+        sample_size = min(sample_size, len(source_data))
+        return random.sample(source_data, sample_size)
diff --git a/src/monitoring/__init__.py b/src/monitoring/__init__.py
new file mode 100644
index 0000000..134b979
--- /dev/null
+++ b/src/monitoring/__init__.py
@@ -0,0 +1,19 @@
+"""
+监控和告警模块
+
+提供实时监控、性能告警和通知功能
+"""
+
+from .alert_manager import Alert, AlertManager, AlertPolicy
+from .notification_service import NotificationService
+from .performance_monitor import AlertRule, MetricData, PerformanceMonitor
+
+__all__ = [
+    "PerformanceMonitor",
+    "MetricData",
+    "AlertRule",
+    "AlertManager",
+    "Alert",
+    "AlertPolicy",
+    "NotificationService",
+]
diff --git a/src/monitoring/alert_manager.py b/src/monitoring/alert_manager.py
new file mode 100644
index 0000000..40ac42b
--- /dev/null
+++ b/src/monitoring/alert_manager.py
@@ -0,0 +1,455 @@
+"""
+告警管理器
+
+管理告警规则、告警历史和告警策略
+"""
+
+import json
+import threading
+from dataclasses import asdict, dataclass
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Callable, Dict, List, Optional
+
+from src.utils.log_moudle import logger
+
+
+@dataclass
+class Alert:
+    """告警信息"""
+
+    id: str
+    rule_name: str
+    metric_name: str
+    current_value: float
+    threshold: float
+    condition: str
+    severity: str  # 'low', 'medium', 'high', 'critical'
+    status: str  # 'active', 'resolved', 'suppressed'
+    created_at: datetime
+    resolved_at: Optional[datetime] = None
+    message: str = ""
+    tags: Dict[str, str] = None
+
+    def __post_init__(self):
+        if self.tags is None:
+            self.tags = {}
+
+
+@dataclass
+class AlertPolicy:
+    """告警策略"""
+
+    name: str
+    conditions: List[str]  # 触发条件
+    cooldown_minutes: int = 5  # 冷却时间
+    max_alerts_per_hour: int = 10  # 每小时最大告警数
+    escalation_rules: List[Dict] = None  # 升级规则
+    notification_channels: List[str] = None  # 通知渠道
+
+    def __post_init__(self):
+        if self.escalation_rules is None:
+            self.escalation_rules = []
+        if self.notification_channels is None:
+            self.notification_channels = []
+
+
+class AlertManager:
+    """告警管理器"""
+
+    def __init__(self, storage_path: str = "alerts"):
+        self.storage_path = Path(storage_path)
+        self.storage_path.mkdir(exist_ok=True)
+
+        self.active_alerts = {}  # alert_id -> Alert
+        self.alert_history = []
+        self.alert_policies = {}  # policy_name -> AlertPolicy
+        self.alert_counters = {}  # 告警计数器
+        self.notification_callbacks = []
+        self.lock = threading.Lock()
+
+        # 加载历史数据
+        self._load_alert_history()
+        self._load_alert_policies()
+
+    def create_alert(
+        self,
+        rule_name: str,
+        metric_name: str,
+        current_value: float,
+        threshold: float,
+        condition: str,
+        severity: str = "medium",
+        message: str = "",
+        tags: Dict[str, str] = None,
+    ) -> Alert:
+        """创建告警"""
+        alert_id = (
+            f"{rule_name}_{metric_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+        )
+
+        alert = Alert(
+            id=alert_id,
+            rule_name=rule_name,
+            metric_name=metric_name,
+            current_value=current_value,
+            threshold=threshold,
+            condition=condition,
+            severity=severity,
+            status="active",
+            created_at=datetime.now(),
+            message=message
+            or self._generate_alert_message(
+                rule_name, metric_name, current_value, threshold, condition
+            ),
+            tags=tags or {},
+        )
+
+        with self.lock:
+            # 检查是否应该抑制告警
+            if self._should_suppress_alert(alert):
+                alert.status = "suppressed"
+                logger.info(f"告警被抑制: {alert.id}")
+                return alert
+
+            # 添加到活跃告警
+            self.active_alerts[alert_id] = alert
+            self.alert_history.append(alert)
+
+            # 更新计数器
+            self._update_alert_counter(alert)
+
+            logger.warning(f"创建告警: {alert.id} - {alert.message}")
+
+            # 触发通知
+            self._trigger_notifications(alert)
+
+        return alert
+
+    def resolve_alert(self, alert_id: str, resolution_message: str = ""):
+        """解决告警"""
+        with self.lock:
+            if alert_id in self.active_alerts:
+                alert = self.active_alerts[alert_id]
+                alert.status = "resolved"
+                alert.resolved_at = datetime.now()
+                if resolution_message:
+                    alert.message += f" | 解决: {resolution_message}"
+
+                del self.active_alerts[alert_id]
+                logger.info(f"告警已解决: {alert_id}")
+
+                # 触发解决通知
+                self._trigger_resolution_notifications(alert)
+
+    def suppress_alert(self, alert_id: str, reason: str = ""):
+        """抑制告警"""
+        with self.lock:
+            if alert_id in self.active_alerts:
+                alert = self.active_alerts[alert_id]
+                alert.status = "suppressed"
+                if reason:
+                    alert.message += f" | 抑制原因: {reason}"
+
+                logger.info(f"告警已抑制: {alert_id}")
+
+    def get_active_alerts(
+        self, severity: str = None, metric_name: str = None
+    ) -> List[Alert]:
+        """获取活跃告警"""
+        with self.lock:
+            alerts = list(self.active_alerts.values())
+
+            if severity:
+                alerts = [a for a in alerts if a.severity == severity]
+
+            if metric_name:
+                alerts = [a for a in alerts if a.metric_name == metric_name]
+
+            return sorted(alerts, key=lambda x: x.created_at, reverse=True)
+
+    def get_alert_history(self, hours: int = 24, severity: str = None) -> List[Alert]:
+        """获取告警历史"""
+        cutoff_time = datetime.now() - timedelta(hours=hours)
+
+        with self.lock:
+            alerts = [a for a in self.alert_history if a.created_at > cutoff_time]
+
+            if severity:
+                alerts = [a for a in alerts if a.severity == severity]
+
+            return sorted(alerts, key=lambda x: x.created_at, reverse=True)
+
+    def add_alert_policy(self, policy: AlertPolicy):
+        """添加告警策略"""
+        self.alert_policies[policy.name] = policy
+        logger.info(f"添加告警策略: {policy.name}")
+        self._save_alert_policies()
+
+    def remove_alert_policy(self, policy_name: str):
+        """移除告警策略"""
+        if policy_name in self.alert_policies:
+            del self.alert_policies[policy_name]
+            logger.info(f"移除告警策略: {policy_name}")
+            self._save_alert_policies()
+
+    def add_notification_callback(self, callback: Callable):
+        """添加通知回调"""
+        self.notification_callbacks.append(callback)
+
+    def get_alert_statistics(self, hours: int = 24) -> Dict:
+        """获取告警统计"""
+        cutoff_time = datetime.now() - timedelta(hours=hours)
+
+        with self.lock:
+            recent_alerts = [
+                a for a in self.alert_history if a.created_at > cutoff_time
+            ]
+
+            stats = {
+                "total_alerts": len(recent_alerts),
+                "active_alerts": len(self.active_alerts),
+                "resolved_alerts": len(
+                    [a for a in recent_alerts if a.status == "resolved"]
+                ),
+                "suppressed_alerts": len(
+                    [a for a in recent_alerts if a.status == "suppressed"]
+                ),
+                "by_severity": {},
+                "by_metric": {},
+                "by_hour": {},
+            }
+
+            # 按严重程度统计
+            for alert in recent_alerts:
+                severity = alert.severity
+                stats["by_severity"][severity] = (
+                    stats["by_severity"].get(severity, 0) + 1
+                )
+
+            # 按指标统计
+            for alert in recent_alerts:
+                metric = alert.metric_name
+                stats["by_metric"][metric] = stats["by_metric"].get(metric, 0) + 1
+
+            # 按小时统计
+            for alert in recent_alerts:
+                hour_key = alert.created_at.strftime("%Y-%m-%d %H:00")
+                stats["by_hour"][hour_key] = stats["by_hour"].get(hour_key, 0) + 1
+
+            return stats
+
+    def cleanup_old_alerts(self, days: int = 7):
+        """清理旧告警"""
+        cutoff_time = datetime.now() - timedelta(days=days)
+
+        with self.lock:
+            original_count = len(self.alert_history)
+            self.alert_history = [
+                a for a in self.alert_history if a.created_at > cutoff_time
+            ]
+            cleaned_count = original_count - len(self.alert_history)
+
+            if cleaned_count > 0:
+                logger.info(f"清理了 {cleaned_count} 条旧告警记录")
+                self._save_alert_history()
+
+    def _should_suppress_alert(self, alert: Alert) -> bool:
+        """判断是否应该抑制告警"""
+        # 检查冷却时间
+        for existing_alert in self.active_alerts.values():
+            if (
+                existing_alert.rule_name == alert.rule_name
+                and existing_alert.metric_name == alert.metric_name
+            ):
+                time_diff = (
+                    alert.created_at - existing_alert.created_at
+                ).total_seconds()
+                if time_diff < 300:  # 5分钟冷却时间
+                    return True
+
+        # 检查频率限制
+        hour_key = alert.created_at.strftime("%Y-%m-%d %H")
+        counter_key = f"{alert.rule_name}_{hour_key}"
+        current_count = self.alert_counters.get(counter_key, 0)
+
+        if current_count >= 10:  # 每小时最多10个相同告警
+            return True
+
+        return False
+
+    def _update_alert_counter(self, alert: Alert):
+        """更新告警计数器"""
+        hour_key = alert.created_at.strftime("%Y-%m-%d %H")
+        counter_key = f"{alert.rule_name}_{hour_key}"
+        self.alert_counters[counter_key] = self.alert_counters.get(counter_key, 0) + 1
+
+    def _generate_alert_message(
+        self,
+        rule_name: str,
+        metric_name: str,
+        current_value: float,
+        threshold: float,
+        condition: str,
+    ) -> str:
+        """生成告警消息"""
+        condition_text = {
+            "gt": "超过",
+            "lt": "低于",
+            "gte": "大于等于",
+            "lte": "小于等于",
+            "eq": "等于",
+        }.get(condition, condition)
+
+        return f"告警规则 '{rule_name}' 触发: 指标 '{metric_name}' 当前值 {current_value} {condition_text} 阈值 {threshold}"
+
+    def _trigger_notifications(self, alert: Alert):
+        """触发通知"""
+        for callback in self.notification_callbacks:
+            try:
+                callback(alert, "created")
+            except Exception as e:
+                logger.error(f"执行告警通知回调异常: {e}")
+
+    def _trigger_resolution_notifications(self, alert: Alert):
+        """触发解决通知"""
+        for callback in self.notification_callbacks:
+            try:
+                callback(alert, "resolved")
+            except Exception as e:
+                logger.error(f"执行告警解决通知回调异常: {e}")
+
+    def _load_alert_history(self):
+        """加载告警历史"""
+        history_file = self.storage_path / "alert_history.json"
+        if history_file.exists():
+            try:
+                with open(history_file, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                    self.alert_history = [
+                        Alert(
+                            id=item["id"],
+                            rule_name=item["rule_name"],
+                            metric_name=item["metric_name"],
+                            current_value=item["current_value"],
+                            threshold=item["threshold"],
+                            condition=item["condition"],
+                            severity=item["severity"],
+                            status=item["status"],
+                            created_at=datetime.fromisoformat(item["created_at"]),
+                            resolved_at=(
+                                datetime.fromisoformat(item["resolved_at"])
+                                if item.get("resolved_at")
+                                else None
+                            ),
+                            message=item.get("message", ""),
+                            tags=item.get("tags", {}),
+                        )
+                        for item in data
+                    ]
+                logger.info(f"加载了 {len(self.alert_history)} 条告警历史")
+            except Exception as e:
+                logger.error(f"加载告警历史失败: {e}")
+
+    def _save_alert_history(self):
+        """保存告警历史"""
+        history_file = self.storage_path / "alert_history.json"
+        try:
+            data = []
+            for alert in self.alert_history:
+                item = asdict(alert)
+                item["created_at"] = alert.created_at.isoformat()
+                if alert.resolved_at:
+                    item["resolved_at"] = alert.resolved_at.isoformat()
+                data.append(item)
+
+            with open(history_file, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            logger.error(f"保存告警历史失败: {e}")
+
+    def _load_alert_policies(self):
+        """加载告警策略"""
+        policies_file = self.storage_path / "alert_policies.json"
+        if policies_file.exists():
+            try:
+                with open(policies_file, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                    for name, policy_data in data.items():
+                        self.alert_policies[name] = AlertPolicy(**policy_data)
+                logger.info(f"加载了 {len(self.alert_policies)} 个告警策略")
+            except Exception as e:
+                logger.error(f"加载告警策略失败: {e}")
+
+    def _save_alert_policies(self):
+        """保存告警策略"""
+        policies_file = self.storage_path / "alert_policies.json"
+        try:
+            data = {
+                name: asdict(policy) for name, policy in self.alert_policies.items()
+            }
+            with open(policies_file, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            logger.error(f"保存告警策略失败: {e}")
+
+    def export_alerts(self, format: str = "json", hours: int = 24) -> str:
+        """导出告警数据"""
+        alerts = self.get_alert_history(hours)
+
+        if format == "json":
+            data = []
+            for alert in alerts:
+                item = asdict(alert)
+                item["created_at"] = alert.created_at.isoformat()
+                if alert.resolved_at:
+                    item["resolved_at"] = alert.resolved_at.isoformat()
+                data.append(item)
+            return json.dumps(data, ensure_ascii=False, indent=2)
+
+        elif format == "csv":
+            import csv
+            import io
+
+            output = io.StringIO()
+            writer = csv.writer(output)
+
+            # 写入表头
+            writer.writerow(
+                [
+                    "ID",
+                    "规则名称",
+                    "指标名称",
+                    "当前值",
+                    "阈值",
+                    "条件",
+                    "严重程度",
+                    "状态",
+                    "创建时间",
+                    "解决时间",
+                    "消息",
+                ]
+            )
+
+            # 写入数据
+            for alert in alerts:
+                writer.writerow(
+                    [
+                        alert.id,
+                        alert.rule_name,
+                        alert.metric_name,
+                        alert.current_value,
+                        alert.threshold,
+                        alert.condition,
+                        alert.severity,
+                        alert.status,
+                        alert.created_at.isoformat(),
+                        alert.resolved_at.isoformat() if alert.resolved_at else "",
+                        alert.message,
+                    ]
+                )
+
+            return output.getvalue()
+
+        else:
+            raise ValueError(f"不支持的导出格式: {format}")
diff --git a/src/monitoring/notification_service.py b/src/monitoring/notification_service.py
new file mode 100644
index 0000000..19f0ffc
--- /dev/null
+++ b/src/monitoring/notification_service.py
@@ -0,0 +1,356 @@
+"""
+通知服务
+
+统一管理各种通知渠道，提供统一的通知接口
+"""
+
+import json
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+from conf.config import settings
+from src.utils.log_moudle import logger
+from src.utils.robot import (
+    DingTalkNotification,
+    EmailNotification,
+    FeishuNotification,
+    NotificationChannel,
+    WeChatWorkNotification,
+)
+
+
+class NotificationService:
+    """通知服务"""
+
+    def __init__(self):
+        self.channels = {}  # channel_name -> NotificationChannel
+        self.default_channels = []  # 默认通知渠道
+        self.message_templates = {}  # 消息模板
+
+        # 初始化默认模板
+        self._init_default_templates()
+
+        # 从配置加载通知渠道
+        self._load_channels_from_config()
+
+    def add_channel(
+        self, name: str, channel: NotificationChannel, is_default: bool = False
+    ):
+        """添加通知渠道"""
+        self.channels[name] = channel
+        if is_default and name not in self.default_channels:
+            self.default_channels.append(name)
+        logger.info(f"添加通知渠道: {name}")
+
+    def remove_channel(self, name: str):
+        """移除通知渠道"""
+        if name in self.channels:
+            del self.channels[name]
+            if name in self.default_channels:
+                self.default_channels.remove(name)
+            logger.info(f"移除通知渠道: {name}")
+
+    def send_notification(
+        self, message: str, channels: List[str] = None, template: str = None, **kwargs
+    ) -> Dict[str, bool]:
+        """发送通知"""
+        # 使用指定渠道或默认渠道
+        target_channels = channels or self.default_channels
+
+        # 应用消息模板
+        if template and template in self.message_templates:
+            message = self._apply_template(template, message, **kwargs)
+
+        results = {}
+
+        for channel_name in target_channels:
+            if channel_name not in self.channels:
+                logger.warning(f"通知渠道不存在: {channel_name}")
+                results[channel_name] = False
+                continue
+
+            try:
+                channel = self.channels[channel_name]
+                success = channel.send_message(message, **kwargs)
+                results[channel_name] = success
+
+                if success:
+                    logger.info(f"通知发送成功: {channel_name}")
+                else:
+                    logger.error(f"通知发送失败: {channel_name}")
+
+            except Exception as e:
+                logger.error(f"发送通知异常 {channel_name}: {e}")
+                results[channel_name] = False
+
+        return results
+
+    def send_alert_notification(
+        self, alert_info: Dict, action: str = "created"
+    ) -> Dict[str, bool]:
+        """发送告警通知"""
+        if action == "created":
+            template = "alert_created"
+            title = "🚨 性能告警"
+        elif action == "resolved":
+            template = "alert_resolved"
+            title = "✅ 告警解决"
+        else:
+            template = "alert_general"
+            title = "📊 告警通知"
+
+        # 构建消息内容
+        message_data = {
+            "title": title,
+            "alert_info": alert_info,
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        }
+
+        # 根据告警严重程度选择通知渠道
+        severity = alert_info.get("severity", "medium")
+        channels = self._get_channels_by_severity(severity)
+
+        return self.send_notification(
+            message="",  # 消息内容由模板生成
+            channels=channels,
+            template=template,
+            **message_data,
+        )
+
+    def send_performance_report(
+        self, report_data: Dict, channels: List[str] = None
+    ) -> Dict[str, bool]:
+        """发送性能报告通知"""
+        message_data = {
+            "title": "📈 性能测试报告",
+            "report_data": report_data,
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        }
+
+        return self.send_notification(
+            message="", channels=channels, template="performance_report", **message_data
+        )
+
+    def send_test_completion(
+        self, test_info: Dict, channels: List[str] = None
+    ) -> Dict[str, bool]:
+        """发送测试完成通知"""
+        message_data = {
+            "title": "🎯 性能测试完成",
+            "test_info": test_info,
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        }
+
+        return self.send_notification(
+            message="", channels=channels, template="test_completion", **message_data
+        )
+
+    def add_message_template(self, name: str, template: str):
+        """添加消息模板"""
+        self.message_templates[name] = template
+        logger.info(f"添加消息模板: {name}")
+
+    def _init_default_templates(self):
+        """初始化默认消息模板"""
+        self.message_templates = {
+            "alert_created": """🚨 **性能告警触发**
+
+**告警规则:** {alert_info[rule_name]}
+**指标名称:** {alert_info[metric_name]}
+**当前值:** {alert_info[current_value]}
+**阈值:** {alert_info[threshold]}
+**触发时间:** {timestamp}
+
+请及时关注系统性能状况！""",
+            "alert_resolved": """✅ **告警已解决**
+
+**告警规则:** {alert_info[rule_name]}
+**指标名称:** {alert_info[metric_name]}
+**解决时间:** {timestamp}
+
+系统性能已恢复正常。""",
+            "performance_report": """📈 **性能测试报告**
+
+**测试时间:** {timestamp}
+**综合评分:** {report_data[overall_grade]}
+
+**响应时间:**
+- 平均: {report_data[response_time][mean]:.2f}ms
+- P95: {report_data[response_time][p95]:.2f}ms
+
+**吞吐量:**
+- 平均TPS: {report_data[throughput][avg_tps]:.2f}
+- 最大TPS: {report_data[throughput][max_tps]:.2f}
+
+**错误率:** {report_data[error_rate][error_percentage]:.2f}%
+
+详细报告请查看测试平台。""",
+            "test_completion": """🎯 **性能测试完成**
+
+**测试名称:** {test_info[name]}
+**测试时长:** {test_info[duration]}
+**并发用户:** {test_info[users]}
+**完成时间:** {timestamp}
+
+测试已成功完成，请查看详细报告。""",
+            "system_status": """📊 **系统状态通知**
+
+**状态:** {status}
+**时间:** {timestamp}
+**详情:** {details}""",
+            "alert_general": """⚠️ **系统告警**
+
+**告警内容:** {alert_info[message]}
+**时间:** {timestamp}""",
+        }
+
+    def _apply_template(self, template_name: str, message: str, **kwargs) -> str:
+        """应用消息模板"""
+        try:
+            template = self.message_templates[template_name]
+
+            # 如果message为空，则完全使用模板
+            if not message.strip():
+                return template.format(**kwargs)
+            else:
+                # 否则将message作为额外内容添加
+                formatted_template = template.format(**kwargs)
+                return f"{formatted_template}\n\n{message}"
+
+        except KeyError as e:
+            logger.error(f"消息模板不存在: {template_name}")
+            return message
+        except Exception as e:
+            logger.error(f"应用消息模板异常: {e}")
+            return message
+
+    def _get_channels_by_severity(self, severity: str) -> List[str]:
+        """根据告警严重程度获取通知渠道"""
+        # 可以根据严重程度配置不同的通知渠道
+        if severity == "critical":
+            # 严重告警使用所有渠道
+            return list(self.channels.keys())
+        elif severity == "high":
+            # 高级告警使用主要渠道
+            return [
+                name
+                for name in self.channels.keys()
+                if name in ["feishu", "dingtalk", "email"]
+            ]
+        elif severity == "medium":
+            # 中级告警使用即时通讯渠道
+            return [
+                name for name in self.channels.keys() if name in ["feishu", "dingtalk"]
+            ]
+        else:
+            # 低级告警使用默认渠道
+            return self.default_channels
+
+    def _load_channels_from_config(self):
+        """从配置文件加载通知渠道"""
+        try:
+            # 飞书配置
+            if hasattr(settings, "notification") and hasattr(
+                settings.notification, "feishu"
+            ):
+                feishu_config = settings.notification.feishu
+                if feishu_config.get("enabled", False) and feishu_config.get(
+                    "webhook_url"
+                ):
+                    feishu_channel = FeishuNotification(feishu_config["webhook_url"])
+                    self.add_channel(
+                        "feishu", feishu_channel, feishu_config.get("default", False)
+                    )
+
+            # 钉钉配置
+            if hasattr(settings, "notification") and hasattr(
+                settings.notification, "dingtalk"
+            ):
+                dingtalk_config = settings.notification.dingtalk
+                if dingtalk_config.get("enabled", False) and dingtalk_config.get(
+                    "webhook_url"
+                ):
+                    dingtalk_channel = DingTalkNotification(
+                        dingtalk_config["webhook_url"], dingtalk_config.get("secret")
+                    )
+                    self.add_channel(
+                        "dingtalk",
+                        dingtalk_channel,
+                        dingtalk_config.get("default", False),
+                    )
+
+            # 邮件配置
+            if hasattr(settings, "notification") and hasattr(
+                settings.notification, "email"
+            ):
+                email_config = settings.notification.email
+                if email_config.get("enabled", False):
+                    email_channel = EmailNotification(
+                        email_config["smtp_server"],
+                        email_config["smtp_port"],
+                        email_config["username"],
+                        email_config["password"],
+                        email_config.get("use_tls", True),
+                    )
+                    self.add_channel(
+                        "email", email_channel, email_config.get("default", False)
+                    )
+
+            # 企业微信配置
+            if hasattr(settings, "notification") and hasattr(
+                settings.notification, "wechat_work"
+            ):
+                wechat_config = settings.notification.wechat_work
+                if wechat_config.get("enabled", False) and wechat_config.get(
+                    "webhook_url"
+                ):
+                    wechat_channel = WeChatWorkNotification(
+                        wechat_config["webhook_url"]
+                    )
+                    self.add_channel(
+                        "wechat_work",
+                        wechat_channel,
+                        wechat_config.get("default", False),
+                    )
+
+            logger.info(f"从配置加载了 {len(self.channels)} 个通知渠道")
+
+        except Exception as e:
+            logger.error(f"加载通知渠道配置异常: {e}")
+
+    def test_channels(self, channels: List[str] = None) -> Dict[str, bool]:
+        """测试通知渠道"""
+        test_message = (
+            f"🧪 通知渠道测试 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
+        )
+
+        target_channels = channels or list(self.channels.keys())
+        results = {}
+
+        for channel_name in target_channels:
+            if channel_name not in self.channels:
+                results[channel_name] = False
+                continue
+
+            try:
+                channel = self.channels[channel_name]
+                success = channel.send_message(test_message)
+                results[channel_name] = success
+            except Exception as e:
+                logger.error(f"测试通知渠道 {channel_name} 异常: {e}")
+                results[channel_name] = False
+
+        return results
+
+    def get_channel_status(self) -> Dict[str, Dict]:
+        """获取通知渠道状态"""
+        status = {}
+
+        for name, channel in self.channels.items():
+            status[name] = {
+                "type": type(channel).__name__,
+                "is_default": name in self.default_channels,
+                "available": True,  # 这里可以添加更复杂的可用性检查
+            }
+
+        return status
diff --git a/src/monitoring/performance_monitor.py b/src/monitoring/performance_monitor.py
new file mode 100644
index 0000000..31142dc
--- /dev/null
+++ b/src/monitoring/performance_monitor.py
@@ -0,0 +1,332 @@
+"""
+性能监控器
+
+实时监控系统性能指标并触发告警
+"""
+
+import queue
+import threading
+import time
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from typing import Callable, Dict, List, Optional
+
+from src.utils.log_moudle import logger
+
+
+@dataclass
+class MetricData:
+    """指标数据"""
+
+    name: str
+    value: float
+    timestamp: datetime
+    tags: Dict[str, str] = None
+
+    def __post_init__(self):
+        if self.tags is None:
+            self.tags = {}
+
+
+@dataclass
+class AlertRule:
+    """告警规则"""
+
+    name: str
+    metric_name: str
+    condition: str  # 'gt', 'lt', 'eq', 'gte', 'lte'
+    threshold: float
+    duration: int = 60  # 持续时间(秒)
+    enabled: bool = True
+    callback: Optional[Callable] = None
+
+
+class PerformanceMonitor:
+    """性能监控器"""
+
+    def __init__(self, check_interval: int = 5):
+        self.check_interval = check_interval
+        self.metrics_queue = queue.Queue()
+        self.alert_rules = []
+        self.metric_history = {}
+        self.is_running = False
+        self.monitor_thread = None
+        self.alert_callbacks = []
+
+        # 性能阈值配置
+        self.default_thresholds = {
+            "response_time_p95": 1000,  # ms
+            "response_time_p99": 2000,  # ms
+            "error_rate": 0.05,  # 5%
+            "throughput": 10,  # TPS
+            "cpu_usage": 80,  # %
+            "memory_usage": 80,  # %
+        }
+
+    def add_metric(self, name: str, value: float, tags: Dict[str, str] = None):
+        """添加指标数据"""
+        metric = MetricData(
+            name=name, value=value, timestamp=datetime.now(), tags=tags or {}
+        )
+        self.metrics_queue.put(metric)
+
+    def add_alert_rule(self, rule: AlertRule):
+        """添加告警规则"""
+        self.alert_rules.append(rule)
+        logger.info(f"添加告警规则: {rule.name}")
+
+    def remove_alert_rule(self, rule_name: str):
+        """移除告警规则"""
+        self.alert_rules = [rule for rule in self.alert_rules if rule.name != rule_name]
+        logger.info(f"移除告警规则: {rule_name}")
+
+    def add_alert_callback(self, callback: Callable):
+        """添加告警回调函数"""
+        self.alert_callbacks.append(callback)
+
+    def start_monitoring(self):
+        """开始监控"""
+        if self.is_running:
+            logger.warning("监控已在运行中")
+            return
+
+        self.is_running = True
+        self.monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True)
+        self.monitor_thread.start()
+        logger.info("性能监控已启动")
+
+    def stop_monitoring(self):
+        """停止监控"""
+        self.is_running = False
+        if self.monitor_thread:
+            self.monitor_thread.join(timeout=5)
+        logger.info("性能监控已停止")
+
+    def _monitor_loop(self):
+        """监控主循环"""
+        while self.is_running:
+            try:
+                # 处理指标数据
+                self._process_metrics()
+
+                # 检查告警规则
+                self._check_alert_rules()
+
+                # 清理过期数据
+                self._cleanup_old_data()
+
+                time.sleep(self.check_interval)
+
+            except Exception as e:
+                logger.error(f"监控循环异常: {e}")
+                time.sleep(self.check_interval)
+
+    def _process_metrics(self):
+        """处理指标数据"""
+        while not self.metrics_queue.empty():
+            try:
+                metric = self.metrics_queue.get_nowait()
+
+                # 存储到历史数据
+                if metric.name not in self.metric_history:
+                    self.metric_history[metric.name] = []
+
+                self.metric_history[metric.name].append(metric)
+
+                # 限制历史数据大小
+                if len(self.metric_history[metric.name]) > 1000:
+                    self.metric_history[metric.name] = self.metric_history[metric.name][
+                        -500:
+                    ]
+
+            except queue.Empty:
+                break
+            except Exception as e:
+                logger.error(f"处理指标数据异常: {e}")
+
+    def _check_alert_rules(self):
+        """检查告警规则"""
+        current_time = datetime.now()
+
+        for rule in self.alert_rules:
+            if not rule.enabled:
+                continue
+
+            try:
+                # 获取指标历史数据
+                if rule.metric_name not in self.metric_history:
+                    continue
+
+                metrics = self.metric_history[rule.metric_name]
+                if not metrics:
+                    continue
+
+                # 检查是否满足告警条件
+                if self._evaluate_alert_condition(rule, metrics, current_time):
+                    self._trigger_alert(rule, metrics[-1])
+
+            except Exception as e:
+                logger.error(f"检查告警规则 {rule.name} 异常: {e}")
+
+    def _evaluate_alert_condition(
+        self, rule: AlertRule, metrics: List[MetricData], current_time: datetime
+    ) -> bool:
+        """评估告警条件"""
+        # 获取指定时间范围内的数据
+        duration_start = current_time - timedelta(seconds=rule.duration)
+        recent_metrics = [m for m in metrics if m.timestamp >= duration_start]
+
+        if not recent_metrics:
+            return False
+
+        # 计算平均值
+        avg_value = sum(m.value for m in recent_metrics) / len(recent_metrics)
+
+        # 评估条件
+        if rule.condition == "gt":
+            return avg_value > rule.threshold
+        elif rule.condition == "lt":
+            return avg_value < rule.threshold
+        elif rule.condition == "gte":
+            return avg_value >= rule.threshold
+        elif rule.condition == "lte":
+            return avg_value <= rule.threshold
+        elif rule.condition == "eq":
+            return abs(avg_value - rule.threshold) < 0.001
+
+        return False
+
+    def _trigger_alert(self, rule: AlertRule, metric: MetricData):
+        """触发告警"""
+        alert_info = {
+            "rule_name": rule.name,
+            "metric_name": rule.metric_name,
+            "current_value": metric.value,
+            "threshold": rule.threshold,
+            "condition": rule.condition,
+            "timestamp": metric.timestamp.isoformat(),
+            "tags": metric.tags,
+        }
+
+        logger.warning(
+            f"触发告警: {rule.name}, 当前值: {metric.value}, 阈值: {rule.threshold}"
+        )
+
+        # 执行规则回调
+        if rule.callback:
+            try:
+                rule.callback(alert_info)
+            except Exception as e:
+                logger.error(f"执行告警规则回调异常: {e}")
+
+        # 执行全局回调
+        for callback in self.alert_callbacks:
+            try:
+                callback(alert_info)
+            except Exception as e:
+                logger.error(f"执行告警回调异常: {e}")
+
+    def _cleanup_old_data(self):
+        """清理过期数据"""
+        cutoff_time = datetime.now() - timedelta(hours=1)
+
+        for metric_name in self.metric_history:
+            self.metric_history[metric_name] = [
+                m for m in self.metric_history[metric_name] if m.timestamp > cutoff_time
+            ]
+
+    def get_metric_stats(self, metric_name: str, duration_minutes: int = 10) -> Dict:
+        """获取指标统计信息"""
+        if metric_name not in self.metric_history:
+            return {}
+
+        cutoff_time = datetime.now() - timedelta(minutes=duration_minutes)
+        recent_metrics = [
+            m for m in self.metric_history[metric_name] if m.timestamp > cutoff_time
+        ]
+
+        if not recent_metrics:
+            return {}
+
+        values = [m.value for m in recent_metrics]
+
+        return {
+            "count": len(values),
+            "min": min(values),
+            "max": max(values),
+            "avg": sum(values) / len(values),
+            "latest": values[-1],
+            "timestamp_range": {
+                "start": recent_metrics[0].timestamp.isoformat(),
+                "end": recent_metrics[-1].timestamp.isoformat(),
+            },
+        }
+
+    def get_all_metrics_summary(self) -> Dict:
+        """获取所有指标摘要"""
+        summary = {}
+        for metric_name in self.metric_history:
+            summary[metric_name] = self.get_metric_stats(metric_name)
+        return summary
+
+    def setup_default_rules(self):
+        """设置默认告警规则"""
+        default_rules = [
+            AlertRule(
+                name="响应时间P95过高",
+                metric_name="response_time_p95",
+                condition="gt",
+                threshold=self.default_thresholds["response_time_p95"],
+                duration=30,
+            ),
+            AlertRule(
+                name="错误率过高",
+                metric_name="error_rate",
+                condition="gt",
+                threshold=self.default_thresholds["error_rate"],
+                duration=60,
+            ),
+            AlertRule(
+                name="吞吐量过低",
+                metric_name="throughput",
+                condition="lt",
+                threshold=self.default_thresholds["throughput"],
+                duration=120,
+            ),
+        ]
+
+        for rule in default_rules:
+            self.add_alert_rule(rule)
+
+        logger.info("已设置默认告警规则")
+
+    def update_thresholds(self, **thresholds):
+        """更新性能阈值"""
+        self.default_thresholds.update(thresholds)
+        logger.info(f"更新性能阈值: {thresholds}")
+
+    def export_metrics(
+        self, metric_name: str = None, duration_hours: int = 1
+    ) -> List[Dict]:
+        """导出指标数据"""
+        cutoff_time = datetime.now() - timedelta(hours=duration_hours)
+        exported_data = []
+
+        metrics_to_export = [metric_name] if metric_name else self.metric_history.keys()
+
+        for name in metrics_to_export:
+            if name not in self.metric_history:
+                continue
+
+            for metric in self.metric_history[name]:
+                if metric.timestamp > cutoff_time:
+                    exported_data.append(
+                        {
+                            "metric_name": metric.name,
+                            "value": metric.value,
+                            "timestamp": metric.timestamp.isoformat(),
+                            "tags": metric.tags,
+                        }
+                    )
+
+        return exported_data
diff --git a/src/plugins/__init__.py b/src/plugins/__init__.py
new file mode 100644
index 0000000..410e3dd
--- /dev/null
+++ b/src/plugins/__init__.py
@@ -0,0 +1,18 @@
+"""
+插件系统
+
+提供可扩展的插件架构，支持自定义功能扩展
+"""
+
+from .plugin_interface import LocustPlugin, MonitorPlugin, PluginInterface, ReportPlugin
+from .plugin_loader import PluginLoader
+from .plugin_manager import PluginManager
+
+__all__ = [
+    "PluginManager",
+    "PluginInterface",
+    "LocustPlugin",
+    "ReportPlugin",
+    "MonitorPlugin",
+    "PluginLoader",
+]
diff --git a/src/plugins/base_plugin.py b/src/plugins/base_plugin.py
new file mode 100644
index 0000000..1ea711d
--- /dev/null
+++ b/src/plugins/base_plugin.py
@@ -0,0 +1,81 @@
+"""
+基础插件类 - 符合文档API规范
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional
+
+
+class BasePlugin(ABC):
+    """插件基类 - 符合文档API规范"""
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """
+        初始化插件
+
+        Args:
+            config: 插件配置字典
+        """
+        self.config = config or {}
+        self.enabled = True
+        self.logger = self._setup_logger()
+        self.name = self.__class__.__name__
+        self.version = "1.0.0"
+
+    @abstractmethod
+    def initialize(self) -> bool:
+        """
+        插件初始化
+
+        Returns:
+            bool: 初始化是否成功
+        """
+        pass
+
+    @abstractmethod
+    def cleanup(self) -> None:
+        """插件清理资源"""
+        pass
+
+    def enable(self) -> None:
+        """启用插件"""
+        self.enabled = True
+
+    def disable(self) -> None:
+        """禁用插件"""
+        self.enabled = False
+
+    def is_enabled(self) -> bool:
+        """检查插件是否启用"""
+        return self.enabled
+
+    def get_info(self) -> Dict[str, Any]:
+        """
+        获取插件信息
+
+        Returns:
+            Dict[str, Any]: 插件信息字典
+        """
+        return {
+            "name": self.name,
+            "version": self.version,
+            "enabled": self.enabled,
+            "config": self.config,
+        }
+
+    def validate_config(self, config: Dict[str, Any]) -> bool:
+        """
+        验证配置
+
+        Args:
+            config: 配置字典
+
+        Returns:
+            bool: 配置是否有效
+        """
+        return True
+
+    def _setup_logger(self):
+        """设置日志记录器"""
+        return logging.getLogger(f"plugin.{self.__class__.__name__}")
diff --git a/src/plugins/builtin/__init__.py b/src/plugins/builtin/__init__.py
new file mode 100644
index 0000000..e3c66cf
--- /dev/null
+++ b/src/plugins/builtin/__init__.py
@@ -0,0 +1,5 @@
+"""
+内置插件模块
+
+提供框架自带的基础插件
+"""
diff --git a/src/plugins/builtin/csv_report_plugin.py b/src/plugins/builtin/csv_report_plugin.py
new file mode 100644
index 0000000..2d85470
--- /dev/null
+++ b/src/plugins/builtin/csv_report_plugin.py
@@ -0,0 +1,258 @@
+"""
+CSV报告插件
+
+生成CSV格式的测试报告
+"""
+
+import csv
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List
+
+from src.plugins.plugin_interface import PluginInfo, ReportPlugin
+from src.utils.log_moudle import logger
+
+
+class CSVReportPlugin(ReportPlugin):
+    """CSV报告插件"""
+
+    def __init__(self):
+        super().__init__()
+        self._plugin_info = PluginInfo(
+            name="CSV Report Plugin",
+            version="1.0.0",
+            description="生成CSV格式的测试报告",
+            author="Locust Framework",
+            category="report",
+        )
+
+    @property
+    def plugin_info(self) -> PluginInfo:
+        return self._plugin_info
+
+    def initialize(self, config: Dict[str, Any] = None) -> bool:
+        """初始化插件"""
+        try:
+            self.configure(config or {})
+            logger.info("CSV报告插件初始化成功")
+            return True
+        except Exception as e:
+            logger.error(f"CSV报告插件初始化失败: {e}")
+            return False
+
+    def cleanup(self):
+        """清理插件资源"""
+        logger.info("CSV报告插件清理完成")
+
+    def generate_report(self, test_data: Dict[str, Any], output_path: str) -> bool:
+        """生成CSV报告"""
+        try:
+            output_file = Path(output_path)
+            output_file.parent.mkdir(parents=True, exist_ok=True)
+
+            # 生成汇总报告
+            self._generate_summary_report(
+                test_data, output_file.with_suffix(".summary.csv")
+            )
+
+            # 生成详细请求报告
+            self._generate_requests_report(
+                test_data, output_file.with_suffix(".requests.csv")
+            )
+
+            # 生成错误报告
+            self._generate_errors_report(
+                test_data, output_file.with_suffix(".errors.csv")
+            )
+
+            logger.info(f"CSV报告生成成功: {output_file}")
+            return True
+
+        except Exception as e:
+            logger.error(f"生成CSV报告失败: {e}")
+            return False
+
+    def get_supported_formats(self) -> List[str]:
+        """获取支持的报告格式"""
+        return ["csv"]
+
+    def _generate_summary_report(self, test_data: Dict[str, Any], output_file: Path):
+        """生成汇总报告"""
+        summary_data = []
+
+        # 基本信息
+        basic_info = {
+            "metric": "test_info",
+            "name": test_data.get("test_name", "Unknown"),
+            "start_time": test_data.get("start_time", ""),
+            "end_time": test_data.get("end_time", ""),
+            "duration": test_data.get("duration", 0),
+            "users": test_data.get("users", 0),
+            "value": "",
+            "unit": "",
+        }
+        summary_data.append(basic_info)
+
+        # 响应时间统计
+        if "response_time" in test_data:
+            rt_data = test_data["response_time"]
+            for metric, value in rt_data.items():
+                summary_data.append(
+                    {
+                        "metric": "response_time",
+                        "name": metric,
+                        "value": value,
+                        "unit": "ms",
+                        "start_time": "",
+                        "end_time": "",
+                        "duration": "",
+                        "users": "",
+                    }
+                )
+
+        # 吞吐量统计
+        if "throughput" in test_data:
+            tp_data = test_data["throughput"]
+            for metric, value in tp_data.items():
+                summary_data.append(
+                    {
+                        "metric": "throughput",
+                        "name": metric,
+                        "value": value,
+                        "unit": "req/s",
+                        "start_time": "",
+                        "end_time": "",
+                        "duration": "",
+                        "users": "",
+                    }
+                )
+
+        # 错误率统计
+        if "error_rate" in test_data:
+            er_data = test_data["error_rate"]
+            for metric, value in er_data.items():
+                summary_data.append(
+                    {
+                        "metric": "error_rate",
+                        "name": metric,
+                        "value": value,
+                        "unit": "%",
+                        "start_time": "",
+                        "end_time": "",
+                        "duration": "",
+                        "users": "",
+                    }
+                )
+
+        # 写入CSV文件
+        with open(output_file, "w", newline="", encoding="utf-8") as csvfile:
+            fieldnames = [
+                "metric",
+                "name",
+                "value",
+                "unit",
+                "start_time",
+                "end_time",
+                "duration",
+                "users",
+            ]
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            writer.writeheader()
+            writer.writerows(summary_data)
+
+    def _generate_requests_report(self, test_data: Dict[str, Any], output_file: Path):
+        """生成请求详细报告"""
+        requests_data = test_data.get("requests", [])
+
+        if not requests_data:
+            logger.warning("没有请求数据可生成报告")
+            return
+
+        # 准备CSV数据
+        csv_data = []
+        for req in requests_data:
+            csv_data.append(
+                {
+                    "timestamp": req.get("timestamp", ""),
+                    "method": req.get("method", ""),
+                    "name": req.get("name", ""),
+                    "response_time": req.get("response_time", 0),
+                    "response_length": req.get("response_length", 0),
+                    "status_code": req.get("status_code", ""),
+                    "success": req.get("success", False),
+                    "error": req.get("error", ""),
+                    "user_id": req.get("user_id", ""),
+                    "concurrent_users": req.get("concurrent_users", 0),
+                }
+            )
+
+        # 写入CSV文件
+        with open(output_file, "w", newline="", encoding="utf-8") as csvfile:
+            fieldnames = [
+                "timestamp",
+                "method",
+                "name",
+                "response_time",
+                "response_length",
+                "status_code",
+                "success",
+                "error",
+                "user_id",
+                "concurrent_users",
+            ]
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            writer.writeheader()
+            writer.writerows(csv_data)
+
+    def _generate_errors_report(self, test_data: Dict[str, Any], output_file: Path):
+        """生成错误报告"""
+        errors_data = test_data.get("errors", [])
+
+        if not errors_data:
+            # 创建空的错误报告文件
+            with open(output_file, "w", newline="", encoding="utf-8") as csvfile:
+                fieldnames = [
+                    "timestamp",
+                    "method",
+                    "name",
+                    "error",
+                    "count",
+                    "percentage",
+                ]
+                writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+                writer.writeheader()
+            return
+
+        # 准备CSV数据
+        csv_data = []
+        for error in errors_data:
+            csv_data.append(
+                {
+                    "timestamp": error.get("timestamp", ""),
+                    "method": error.get("method", ""),
+                    "name": error.get("name", ""),
+                    "error": error.get("error", ""),
+                    "count": error.get("count", 0),
+                    "percentage": error.get("percentage", 0.0),
+                }
+            )
+
+        # 写入CSV文件
+        with open(output_file, "w", newline="", encoding="utf-8") as csvfile:
+            fieldnames = ["timestamp", "method", "name", "error", "count", "percentage"]
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            writer.writeheader()
+            writer.writerows(csv_data)
+
+    def customize_report_data(self, test_data: Dict[str, Any]) -> Dict[str, Any]:
+        """自定义报告数据"""
+        # 添加生成时间
+        test_data["report_generated_at"] = datetime.now().isoformat()
+
+        # 添加插件信息
+        test_data["report_plugin"] = {
+            "name": self.plugin_info.name,
+            "version": self.plugin_info.version,
+        }
+
+        return test_data
diff --git a/src/plugins/plugin_interface.py b/src/plugins/plugin_interface.py
new file mode 100644
index 0000000..13183a2
--- /dev/null
+++ b/src/plugins/plugin_interface.py
@@ -0,0 +1,333 @@
+"""
+插件接口定义
+
+定义各种类型插件的标准接口
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class PluginInfo:
+    """插件信息"""
+
+    name: str
+    version: str
+    description: str
+    author: str
+    category: str
+    dependencies: List[str] = None
+    config_schema: Dict = None
+
+    def __post_init__(self):
+        if self.dependencies is None:
+            self.dependencies = []
+        if self.config_schema is None:
+            self.config_schema = {}
+
+
+class PluginInterface(ABC):
+    """插件基础接口"""
+
+    def __init__(self):
+        self._enabled = False
+        self._config = {}
+
+    @property
+    @abstractmethod
+    def plugin_info(self) -> PluginInfo:
+        """获取插件信息"""
+        pass
+
+    @abstractmethod
+    def initialize(self, config: Dict[str, Any] = None) -> bool:
+        """初始化插件"""
+        pass
+
+    @abstractmethod
+    def cleanup(self):
+        """清理插件资源"""
+        pass
+
+    def enable(self):
+        """启用插件"""
+        self._enabled = True
+
+    def disable(self):
+        """禁用插件"""
+        self._enabled = False
+
+    @property
+    def is_enabled(self) -> bool:
+        """检查插件是否启用"""
+        return self._enabled
+
+    def configure(self, config: Dict[str, Any]):
+        """配置插件"""
+        self._config.update(config)
+
+    @property
+    def config(self) -> Dict[str, Any]:
+        """获取插件配置"""
+        return self._config.copy()
+
+    def validate_config(self, config: Dict[str, Any]) -> bool:
+        """验证配置"""
+        # 基础验证，子类可以重写
+        return True
+
+
+class LocustPlugin(PluginInterface):
+    """Locust测试插件接口"""
+
+    @abstractmethod
+    def on_test_start(self, environment, **kwargs):
+        """测试开始时调用"""
+        pass
+
+    @abstractmethod
+    def on_test_stop(self, environment, **kwargs):
+        """测试停止时调用"""
+        pass
+
+    def on_user_start(self, user_instance, **kwargs):
+        """用户开始时调用（可选）"""
+        pass
+
+    def on_user_stop(self, user_instance, **kwargs):
+        """用户停止时调用（可选）"""
+        pass
+
+    def on_request_success(
+        self, request_type, name, response_time, response_length, **kwargs
+    ):
+        """请求成功时调用（可选）"""
+        pass
+
+    def on_request_failure(
+        self, request_type, name, response_time, response_length, exception, **kwargs
+    ):
+        """请求失败时调用（可选）"""
+        pass
+
+
+class ReportPlugin(PluginInterface):
+    """报告插件接口"""
+
+    @abstractmethod
+    def generate_report(self, test_data: Dict[str, Any], output_path: str) -> bool:
+        """生成报告"""
+        pass
+
+    @abstractmethod
+    def get_supported_formats(self) -> List[str]:
+        """获取支持的报告格式"""
+        pass
+
+    def customize_report_data(self, test_data: Dict[str, Any]) -> Dict[str, Any]:
+        """自定义报告数据（可选）"""
+        return test_data
+
+    def get_report_template(self) -> Optional[str]:
+        """获取报告模板（可选）"""
+        return None
+
+
+class MonitorPlugin(PluginInterface):
+    """监控插件接口"""
+
+    @abstractmethod
+    def start_monitoring(self):
+        """开始监控"""
+        pass
+
+    @abstractmethod
+    def stop_monitoring(self):
+        """停止监控"""
+        pass
+
+    @abstractmethod
+    def get_metrics(self) -> Dict[str, Any]:
+        """获取监控指标"""
+        pass
+
+    def on_metric_threshold_exceeded(
+        self, metric_name: str, current_value: float, threshold: float
+    ):
+        """指标超过阈值时调用（可选）"""
+        pass
+
+    def configure_thresholds(self, thresholds: Dict[str, float]):
+        """配置监控阈值（可选）"""
+        pass
+
+
+class DataPlugin(PluginInterface):
+    """数据插件接口"""
+
+    @abstractmethod
+    def generate_test_data(
+        self, data_type: str, count: int, **kwargs
+    ) -> List[Dict[str, Any]]:
+        """生成测试数据"""
+        pass
+
+    @abstractmethod
+    def get_supported_data_types(self) -> List[str]:
+        """获取支持的数据类型"""
+        pass
+
+    def validate_data(self, data: List[Dict[str, Any]]) -> bool:
+        """验证数据（可选）"""
+        return True
+
+    def transform_data(
+        self, data: List[Dict[str, Any]], transformation: str
+    ) -> List[Dict[str, Any]]:
+        """转换数据（可选）"""
+        return data
+
+
+class NotificationPlugin(PluginInterface):
+    """通知插件接口"""
+
+    @abstractmethod
+    def send_notification(self, message: str, **kwargs) -> bool:
+        """发送通知"""
+        pass
+
+    @abstractmethod
+    def get_notification_channels(self) -> List[str]:
+        """获取支持的通知渠道"""
+        pass
+
+    def format_message(self, message: str, message_type: str = "text") -> str:
+        """格式化消息（可选）"""
+        return message
+
+    def test_connection(self) -> bool:
+        """测试连接（可选）"""
+        return True
+
+
+class LoadShapePlugin(PluginInterface):
+    """负载模式插件接口"""
+
+    @abstractmethod
+    def get_load_shape_class(self):
+        """获取负载模式类"""
+        pass
+
+    @abstractmethod
+    def get_shape_parameters(self) -> Dict[str, Any]:
+        """获取模式参数"""
+        pass
+
+    def validate_parameters(self, parameters: Dict[str, Any]) -> bool:
+        """验证参数（可选）"""
+        return True
+
+    def get_shape_description(self) -> str:
+        """获取模式描述（可选）"""
+        return ""
+
+
+class AnalysisPlugin(PluginInterface):
+    """分析插件接口"""
+
+    @abstractmethod
+    def analyze_results(self, test_results: Dict[str, Any]) -> Dict[str, Any]:
+        """分析测试结果"""
+        pass
+
+    @abstractmethod
+    def get_analysis_types(self) -> List[str]:
+        """获取支持的分析类型"""
+        pass
+
+    def generate_insights(self, analysis_results: Dict[str, Any]) -> List[str]:
+        """生成洞察（可选）"""
+        return []
+
+    def compare_results(
+        self, current_results: Dict[str, Any], historical_results: List[Dict[str, Any]]
+    ) -> Dict[str, Any]:
+        """比较结果（可选）"""
+        return {}
+
+
+class StoragePlugin(PluginInterface):
+    """存储插件接口"""
+
+    @abstractmethod
+    def save_test_results(self, results: Dict[str, Any], test_id: str) -> bool:
+        """保存测试结果"""
+        pass
+
+    @abstractmethod
+    def load_test_results(self, test_id: str) -> Optional[Dict[str, Any]]:
+        """加载测试结果"""
+        pass
+
+    @abstractmethod
+    def list_test_results(self, limit: int = 100) -> List[Dict[str, Any]]:
+        """列出测试结果"""
+        pass
+
+    def delete_test_results(self, test_id: str) -> bool:
+        """删除测试结果（可选）"""
+        return False
+
+    def search_test_results(self, criteria: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """搜索测试结果（可选）"""
+        return []
+
+
+class AuthenticationPlugin(PluginInterface):
+    """认证插件接口"""
+
+    @abstractmethod
+    def authenticate(self, credentials: Dict[str, Any]) -> bool:
+        """认证"""
+        pass
+
+    @abstractmethod
+    def get_auth_token(self, credentials: Dict[str, Any]) -> Optional[str]:
+        """获取认证令牌"""
+        pass
+
+    def refresh_token(self, token: str) -> Optional[str]:
+        """刷新令牌（可选）"""
+        return None
+
+    def validate_token(self, token: str) -> bool:
+        """验证令牌（可选）"""
+        return True
+
+    def logout(self, token: str) -> bool:
+        """登出（可选）"""
+        return True
+
+
+class ProtocolPlugin(PluginInterface):
+    """协议插件接口"""
+
+    @abstractmethod
+    def create_client(self, **kwargs):
+        """创建协议客户端"""
+        pass
+
+    @abstractmethod
+    def get_supported_protocols(self) -> List[str]:
+        """获取支持的协议"""
+        pass
+
+    def validate_connection(self, client) -> bool:
+        """验证连接（可选）"""
+        return True
+
+    def close_connection(self, client):
+        """关闭连接（可选）"""
+        pass
diff --git a/src/plugins/plugin_loader.py b/src/plugins/plugin_loader.py
new file mode 100644
index 0000000..8056d04
--- /dev/null
+++ b/src/plugins/plugin_loader.py
@@ -0,0 +1,352 @@
+"""
+插件加载器
+
+负责动态加载和管理插件
+"""
+
+import importlib
+import importlib.util
+import inspect
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Type
+
+from src.plugins.plugin_interface import PluginInfo, PluginInterface
+from src.utils.log_moudle import logger
+
+
+class PluginLoader:
+    """插件加载器"""
+
+    def __init__(self, plugin_dirs: List[str] = None):
+        self.plugin_dirs = plugin_dirs or ["plugins", "src/plugins/builtin"]
+        self.loaded_plugins = {}  # plugin_name -> plugin_class
+        self.plugin_instances = {}  # plugin_name -> plugin_instance
+        self.plugin_metadata = {}  # plugin_name -> metadata
+
+        # 确保插件目录存在
+        for plugin_dir in self.plugin_dirs:
+            Path(plugin_dir).mkdir(parents=True, exist_ok=True)
+
+    def discover_plugins(self) -> List[str]:
+        """发现可用的插件"""
+        discovered_plugins = []
+
+        for plugin_dir in self.plugin_dirs:
+            plugin_path = Path(plugin_dir)
+            if not plugin_path.exists():
+                continue
+
+            # 扫描Python文件
+            for py_file in plugin_path.glob("**/*.py"):
+                if py_file.name.startswith("__"):
+                    continue
+
+                try:
+                    plugin_name = self._get_plugin_name_from_file(py_file)
+                    if plugin_name:
+                        discovered_plugins.append(plugin_name)
+                        logger.debug(f"发现插件: {plugin_name} ({py_file})")
+                except Exception as e:
+                    logger.warning(f"扫描插件文件失败 {py_file}: {e}")
+
+        logger.info(f"发现 {len(discovered_plugins)} 个插件")
+        return discovered_plugins
+
+    def load_plugin(
+        self, plugin_name: str, plugin_path: str = None
+    ) -> Optional[Type[PluginInterface]]:
+        """加载单个插件"""
+        if plugin_name in self.loaded_plugins:
+            logger.warning(f"插件已加载: {plugin_name}")
+            return self.loaded_plugins[plugin_name]
+
+        try:
+            # 查找插件文件
+            if plugin_path is None:
+                plugin_path = self._find_plugin_file(plugin_name)
+                if plugin_path is None:
+                    logger.error(f"未找到插件文件: {plugin_name}")
+                    return None
+
+            # 加载模块
+            module = self._load_module_from_file(plugin_name, plugin_path)
+            if module is None:
+                return None
+
+            # 查找插件类
+            plugin_class = self._find_plugin_class(module)
+            if plugin_class is None:
+                logger.error(f"未找到插件类: {plugin_name}")
+                return None
+
+            # 验证插件类
+            if not self._validate_plugin_class(plugin_class):
+                logger.error(f"插件类验证失败: {plugin_name}")
+                return None
+
+            # 加载插件元数据
+            metadata = self._load_plugin_metadata(plugin_path, plugin_class)
+
+            # 注册插件
+            self.loaded_plugins[plugin_name] = plugin_class
+            self.plugin_metadata[plugin_name] = metadata
+
+            logger.info(f"成功加载插件: {plugin_name}")
+            return plugin_class
+
+        except Exception as e:
+            logger.error(f"加载插件失败 {plugin_name}: {e}")
+            return None
+
+    def load_all_plugins(self) -> Dict[str, Type[PluginInterface]]:
+        """加载所有发现的插件"""
+        discovered_plugins = self.discover_plugins()
+        loaded_count = 0
+
+        for plugin_name in discovered_plugins:
+            if self.load_plugin(plugin_name):
+                loaded_count += 1
+
+        logger.info(f"成功加载 {loaded_count}/{len(discovered_plugins)} 个插件")
+        return self.loaded_plugins.copy()
+
+    def unload_plugin(self, plugin_name: str):
+        """卸载插件"""
+        if plugin_name in self.plugin_instances:
+            try:
+                instance = self.plugin_instances[plugin_name]
+                instance.cleanup()
+                del self.plugin_instances[plugin_name]
+            except Exception as e:
+                logger.error(f"清理插件实例失败 {plugin_name}: {e}")
+
+        if plugin_name in self.loaded_plugins:
+            del self.loaded_plugins[plugin_name]
+
+        if plugin_name in self.plugin_metadata:
+            del self.plugin_metadata[plugin_name]
+
+        logger.info(f"卸载插件: {plugin_name}")
+
+    def create_plugin_instance(
+        self, plugin_name: str, config: Dict[str, Any] = None
+    ) -> Optional[PluginInterface]:
+        """创建插件实例"""
+        if plugin_name not in self.loaded_plugins:
+            logger.error(f"插件未加载: {plugin_name}")
+            return None
+
+        if plugin_name in self.plugin_instances:
+            logger.warning(f"插件实例已存在: {plugin_name}")
+            return self.plugin_instances[plugin_name]
+
+        try:
+            plugin_class = self.loaded_plugins[plugin_name]
+            instance = plugin_class()
+
+            # 初始化插件
+            if not instance.initialize(config or {}):
+                logger.error(f"插件初始化失败: {plugin_name}")
+                return None
+
+            self.plugin_instances[plugin_name] = instance
+            logger.info(f"创建插件实例: {plugin_name}")
+            return instance
+
+        except Exception as e:
+            logger.error(f"创建插件实例失败 {plugin_name}: {e}")
+            return None
+
+    def get_plugin_instance(self, plugin_name: str) -> Optional[PluginInterface]:
+        """获取插件实例"""
+        return self.plugin_instances.get(plugin_name)
+
+    def get_loaded_plugins(self) -> Dict[str, Type[PluginInterface]]:
+        """获取已加载的插件"""
+        return self.loaded_plugins.copy()
+
+    def get_plugin_metadata(self, plugin_name: str) -> Optional[Dict[str, Any]]:
+        """获取插件元数据"""
+        return self.plugin_metadata.get(plugin_name)
+
+    def get_plugins_by_category(self, category: str) -> List[str]:
+        """根据类别获取插件"""
+        plugins = []
+        for plugin_name, metadata in self.plugin_metadata.items():
+            if metadata.get("category") == category:
+                plugins.append(plugin_name)
+        return plugins
+
+    def reload_plugin(self, plugin_name: str) -> bool:
+        """重新加载插件"""
+        if plugin_name not in self.loaded_plugins:
+            logger.error(f"插件未加载: {plugin_name}")
+            return False
+
+        try:
+            # 保存配置
+            config = {}
+            if plugin_name in self.plugin_instances:
+                config = self.plugin_instances[plugin_name].config
+
+            # 卸载插件
+            self.unload_plugin(plugin_name)
+
+            # 重新加载
+            if self.load_plugin(plugin_name):
+                # 重新创建实例
+                if config:
+                    self.create_plugin_instance(plugin_name, config)
+                logger.info(f"重新加载插件成功: {plugin_name}")
+                return True
+            else:
+                logger.error(f"重新加载插件失败: {plugin_name}")
+                return False
+
+        except Exception as e:
+            logger.error(f"重新加载插件异常 {plugin_name}: {e}")
+            return False
+
+    def _find_plugin_file(self, plugin_name: str) -> Optional[str]:
+        """查找插件文件"""
+        for plugin_dir in self.plugin_dirs:
+            plugin_path = Path(plugin_dir)
+
+            # 尝试直接匹配文件名
+            plugin_file = plugin_path / f"{plugin_name}.py"
+            if plugin_file.exists():
+                return str(plugin_file)
+
+            # 尝试在子目录中查找
+            for py_file in plugin_path.glob(f"**/{plugin_name}.py"):
+                return str(py_file)
+
+            # 尝试查找包含插件名的文件
+            for py_file in plugin_path.glob("**/*.py"):
+                if plugin_name.lower() in py_file.stem.lower():
+                    return str(py_file)
+
+        return None
+
+    def _load_module_from_file(self, plugin_name: str, plugin_path: str):
+        """从文件加载模块"""
+        try:
+            spec = importlib.util.spec_from_file_location(plugin_name, plugin_path)
+            if spec is None:
+                logger.error(f"无法创建模块规范: {plugin_path}")
+                return None
+
+            module = importlib.util.module_from_spec(spec)
+            if module is None:
+                logger.error(f"无法创建模块: {plugin_path}")
+                return None
+
+            # 添加到sys.modules以支持相对导入
+            sys.modules[plugin_name] = module
+            spec.loader.exec_module(module)
+
+            return module
+
+        except Exception as e:
+            logger.error(f"加载模块失败 {plugin_path}: {e}")
+            return None
+
+    def _find_plugin_class(self, module) -> Optional[Type[PluginInterface]]:
+        """在模块中查找插件类"""
+        for name, obj in inspect.getmembers(module, inspect.isclass):
+            if (
+                issubclass(obj, PluginInterface)
+                and obj != PluginInterface
+                and obj.__module__ == module.__name__
+            ):
+                return obj
+        return None
+
+    def _validate_plugin_class(self, plugin_class: Type[PluginInterface]) -> bool:
+        """验证插件类"""
+        try:
+            # 检查必需的方法
+            required_methods = ["plugin_info", "initialize", "cleanup"]
+            for method_name in required_methods:
+                if not hasattr(plugin_class, method_name):
+                    logger.error(f"插件类缺少必需方法: {method_name}")
+                    return False
+
+            # 尝试创建临时实例来验证plugin_info
+            temp_instance = plugin_class()
+            plugin_info = temp_instance.plugin_info
+
+            if not isinstance(plugin_info, PluginInfo):
+                logger.error("plugin_info必须返回PluginInfo实例")
+                return False
+
+            return True
+
+        except Exception as e:
+            logger.error(f"验证插件类失败: {e}")
+            return False
+
+    def _load_plugin_metadata(
+        self, plugin_path: str, plugin_class: Type[PluginInterface]
+    ) -> Dict[str, Any]:
+        """加载插件元数据"""
+        metadata = {}
+
+        try:
+            # 从插件类获取信息
+            temp_instance = plugin_class()
+            plugin_info = temp_instance.plugin_info
+
+            metadata.update(
+                {
+                    "name": plugin_info.name,
+                    "version": plugin_info.version,
+                    "description": plugin_info.description,
+                    "author": plugin_info.author,
+                    "category": plugin_info.category,
+                    "dependencies": plugin_info.dependencies,
+                    "config_schema": plugin_info.config_schema,
+                    "file_path": plugin_path,
+                    "class_name": plugin_class.__name__,
+                }
+            )
+
+            # 尝试加载外部元数据文件
+            metadata_file = Path(plugin_path).with_suffix(".json")
+            if metadata_file.exists():
+                with open(metadata_file, "r", encoding="utf-8") as f:
+                    external_metadata = json.load(f)
+                    metadata.update(external_metadata)
+
+        except Exception as e:
+            logger.warning(f"加载插件元数据失败 {plugin_path}: {e}")
+
+        return metadata
+
+    def _get_plugin_name_from_file(self, file_path: Path) -> Optional[str]:
+        """从文件路径获取插件名"""
+        try:
+            # 简单的启发式方法：使用文件名作为插件名
+            plugin_name = file_path.stem
+
+            # 验证文件是否包含插件类
+            module_name = f"temp_{plugin_name}"
+            spec = importlib.util.spec_from_file_location(module_name, str(file_path))
+            if spec is None:
+                return None
+
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+
+            # 查找插件类
+            plugin_class = self._find_plugin_class(module)
+            if plugin_class:
+                return plugin_name
+
+            return None
+
+        except Exception:
+            return None
diff --git a/src/plugins/plugin_manager.py b/src/plugins/plugin_manager.py
new file mode 100644
index 0000000..802b85a
--- /dev/null
+++ b/src/plugins/plugin_manager.py
@@ -0,0 +1,348 @@
+"""
+插件管理器
+
+统一管理所有插件的生命周期和交互
+"""
+
+import json
+import threading
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Type
+
+from src.plugins.plugin_interface import PluginInfo, PluginInterface
+from src.plugins.plugin_loader import PluginLoader
+from src.utils.log_moudle import logger
+
+
+class PluginManager:
+    """插件管理器"""
+
+    def __init__(self, config_file: str = "plugin_config.json"):
+        self.config_file = config_file
+        self.plugin_loader = PluginLoader()
+        self.enabled_plugins = set()  # 启用的插件名称
+        self.plugin_configs = {}  # plugin_name -> config
+        self.event_handlers = defaultdict(list)  # event_name -> [handler_functions]
+        self.lock = threading.Lock()
+
+        # 加载配置
+        self._load_config()
+
+        # 自动发现和加载插件
+        self.discover_and_load_plugins()
+
+    def discover_and_load_plugins(self):
+        """发现并加载所有插件"""
+        logger.info("开始发现和加载插件...")
+
+        # 发现插件
+        discovered_plugins = self.plugin_loader.discover_plugins()
+        logger.info(f"发现 {len(discovered_plugins)} 个插件")
+
+        # 加载所有插件
+        loaded_plugins = self.plugin_loader.load_all_plugins()
+        logger.info(f"成功加载 {len(loaded_plugins)} 个插件")
+
+        # 根据配置启用插件
+        for plugin_name in loaded_plugins:
+            if self._should_enable_plugin(plugin_name):
+                self.enable_plugin(plugin_name)
+
+    def enable_plugin(self, plugin_name: str, config: Dict[str, Any] = None) -> bool:
+        """启用插件"""
+        with self.lock:
+            if plugin_name in self.enabled_plugins:
+                logger.warning(f"插件已启用: {plugin_name}")
+                return True
+
+            # 获取插件配置
+            plugin_config = config or self.plugin_configs.get(plugin_name, {})
+
+            # 创建插件实例
+            instance = self.plugin_loader.create_plugin_instance(
+                plugin_name, plugin_config
+            )
+            if instance is None:
+                logger.error(f"创建插件实例失败: {plugin_name}")
+                return False
+
+            # 启用插件
+            instance.enable()
+            self.enabled_plugins.add(plugin_name)
+
+            # 注册事件处理器
+            self._register_plugin_events(plugin_name, instance)
+
+            logger.info(f"启用插件: {plugin_name}")
+            return True
+
+    def disable_plugin(self, plugin_name: str) -> bool:
+        """禁用插件"""
+        with self.lock:
+            if plugin_name not in self.enabled_plugins:
+                logger.warning(f"插件未启用: {plugin_name}")
+                return True
+
+            # 获取插件实例
+            instance = self.plugin_loader.get_plugin_instance(plugin_name)
+            if instance:
+                try:
+                    instance.disable()
+                    instance.cleanup()
+                except Exception as e:
+                    logger.error(f"禁用插件失败 {plugin_name}: {e}")
+                    return False
+
+            # 注销事件处理器
+            self._unregister_plugin_events(plugin_name)
+
+            self.enabled_plugins.discard(plugin_name)
+            logger.info(f"禁用插件: {plugin_name}")
+            return True
+
+    def reload_plugin(self, plugin_name: str) -> bool:
+        """重新加载插件"""
+        logger.info(f"重新加载插件: {plugin_name}")
+
+        # 保存当前状态
+        was_enabled = plugin_name in self.enabled_plugins
+        config = self.plugin_configs.get(plugin_name, {})
+
+        # 禁用插件
+        if was_enabled:
+            self.disable_plugin(plugin_name)
+
+        # 重新加载
+        if self.plugin_loader.reload_plugin(plugin_name):
+            # 如果之前启用，重新启用
+            if was_enabled:
+                return self.enable_plugin(plugin_name, config)
+            return True
+
+        return False
+
+    def configure_plugin(self, plugin_name: str, config: Dict[str, Any]) -> bool:
+        """配置插件"""
+        with self.lock:
+            # 保存配置
+            self.plugin_configs[plugin_name] = config
+
+            # 如果插件已启用，应用配置
+            instance = self.plugin_loader.get_plugin_instance(plugin_name)
+            if instance:
+                try:
+                    instance.configure(config)
+                    logger.info(f"配置插件: {plugin_name}")
+                    return True
+                except Exception as e:
+                    logger.error(f"配置插件失败 {plugin_name}: {e}")
+                    return False
+
+            return True
+
+    def get_plugin_info(self, plugin_name: str) -> Optional[PluginInfo]:
+        """获取插件信息"""
+        instance = self.plugin_loader.get_plugin_instance(plugin_name)
+        if instance:
+            return instance.plugin_info
+
+        # 如果实例不存在，尝试从元数据获取
+        metadata = self.plugin_loader.get_plugin_metadata(plugin_name)
+        if metadata:
+            return PluginInfo(
+                name=metadata.get("name", plugin_name),
+                version=metadata.get("version", "1.0.0"),
+                description=metadata.get("description", ""),
+                author=metadata.get("author", ""),
+                category=metadata.get("category", "general"),
+                dependencies=metadata.get("dependencies", []),
+                config_schema=metadata.get("config_schema", {}),
+            )
+
+        return None
+
+    def get_enabled_plugins(self) -> List[str]:
+        """获取已启用的插件列表"""
+        return list(self.enabled_plugins)
+
+    def get_available_plugins(self) -> List[str]:
+        """获取可用的插件列表"""
+        return list(self.plugin_loader.get_loaded_plugins().keys())
+
+    def get_plugins_by_category(self, category: str) -> List[str]:
+        """根据类别获取插件"""
+        return self.plugin_loader.get_plugins_by_category(category)
+
+    def is_plugin_enabled(self, plugin_name: str) -> bool:
+        """检查插件是否启用"""
+        return plugin_name in self.enabled_plugins
+
+    def get_plugin_instance(self, plugin_name: str) -> Optional[PluginInterface]:
+        """获取插件实例"""
+        if plugin_name not in self.enabled_plugins:
+            return None
+        return self.plugin_loader.get_plugin_instance(plugin_name)
+
+    def trigger_event(self, event_name: str, *args, **kwargs):
+        """触发事件"""
+        handlers = self.event_handlers.get(event_name, [])
+
+        for handler in handlers:
+            try:
+                handler(*args, **kwargs)
+            except Exception as e:
+                logger.error(f"事件处理器执行失败 {event_name}: {e}")
+
+    def register_event_handler(self, event_name: str, handler: Callable):
+        """注册事件处理器"""
+        self.event_handlers[event_name].append(handler)
+        logger.debug(f"注册事件处理器: {event_name}")
+
+    def unregister_event_handler(self, event_name: str, handler: Callable):
+        """注销事件处理器"""
+        if handler in self.event_handlers[event_name]:
+            self.event_handlers[event_name].remove(handler)
+            logger.debug(f"注销事件处理器: {event_name}")
+
+    def get_plugin_status(self) -> Dict[str, Dict[str, Any]]:
+        """获取所有插件状态"""
+        status = {}
+
+        for plugin_name in self.get_available_plugins():
+            info = self.get_plugin_info(plugin_name)
+            instance = self.plugin_loader.get_plugin_instance(plugin_name)
+
+            status[plugin_name] = {
+                "enabled": plugin_name in self.enabled_plugins,
+                "version": info.version if info else "unknown",
+                "category": info.category if info else "unknown",
+                "description": info.description if info else "",
+                "has_instance": instance is not None,
+                "config": self.plugin_configs.get(plugin_name, {}),
+            }
+
+        return status
+
+    def validate_plugin_dependencies(self, plugin_name: str) -> bool:
+        """验证插件依赖"""
+        info = self.get_plugin_info(plugin_name)
+        if not info or not info.dependencies:
+            return True
+
+        available_plugins = self.get_available_plugins()
+
+        for dependency in info.dependencies:
+            if dependency not in available_plugins:
+                logger.error(f"插件 {plugin_name} 缺少依赖: {dependency}")
+                return False
+
+        return True
+
+    def save_config(self):
+        """保存配置"""
+        config_data = {
+            "enabled_plugins": list(self.enabled_plugins),
+            "plugin_configs": self.plugin_configs,
+        }
+
+        try:
+            with open(self.config_file, "w", encoding="utf-8") as f:
+                json.dump(config_data, f, ensure_ascii=False, indent=2)
+            logger.info(f"保存插件配置: {self.config_file}")
+        except Exception as e:
+            logger.error(f"保存插件配置失败: {e}")
+
+    def _load_config(self):
+        """加载配置"""
+        config_path = Path(self.config_file)
+        if not config_path.exists():
+            logger.info("插件配置文件不存在，使用默认配置")
+            return
+
+        try:
+            with open(config_path, "r", encoding="utf-8") as f:
+                config_data = json.load(f)
+
+            self.enabled_plugins = set(config_data.get("enabled_plugins", []))
+            self.plugin_configs = config_data.get("plugin_configs", {})
+
+            logger.info(f"加载插件配置: {len(self.enabled_plugins)} 个启用插件")
+
+        except Exception as e:
+            logger.error(f"加载插件配置失败: {e}")
+
+    def _should_enable_plugin(self, plugin_name: str) -> bool:
+        """判断是否应该启用插件"""
+        # 检查配置中是否启用
+        if plugin_name not in self.enabled_plugins:
+            return False
+
+        # 验证依赖
+        if not self.validate_plugin_dependencies(plugin_name):
+            logger.warning(f"插件依赖验证失败，跳过启用: {plugin_name}")
+            return False
+
+        return True
+
+    def _register_plugin_events(self, plugin_name: str, instance: PluginInterface):
+        """注册插件事件处理器"""
+        # 根据插件类型注册相应的事件处理器
+        from src.plugins.plugin_interface import (
+            DataPlugin,
+            LocustPlugin,
+            MonitorPlugin,
+            NotificationPlugin,
+            ReportPlugin,
+        )
+
+        if isinstance(instance, LocustPlugin):
+            self.register_event_handler("test_start", instance.on_test_start)
+            self.register_event_handler("test_stop", instance.on_test_stop)
+
+            if hasattr(instance, "on_user_start"):
+                self.register_event_handler("user_start", instance.on_user_start)
+            if hasattr(instance, "on_user_stop"):
+                self.register_event_handler("user_stop", instance.on_user_stop)
+            if hasattr(instance, "on_request_success"):
+                self.register_event_handler(
+                    "request_success", instance.on_request_success
+                )
+            if hasattr(instance, "on_request_failure"):
+                self.register_event_handler(
+                    "request_failure", instance.on_request_failure
+                )
+
+        if isinstance(instance, MonitorPlugin):
+            self.register_event_handler("start_monitoring", instance.start_monitoring)
+            self.register_event_handler("stop_monitoring", instance.stop_monitoring)
+
+    def _unregister_plugin_events(self, plugin_name: str):
+        """注销插件事件处理器"""
+        instance = self.plugin_loader.get_plugin_instance(plugin_name)
+        if not instance:
+            return
+
+        # 从所有事件中移除该插件的处理器
+        for event_name, handlers in self.event_handlers.items():
+            handlers_to_remove = []
+            for handler in handlers:
+                # 检查处理器是否属于该插件实例
+                if hasattr(handler, "__self__") and handler.__self__ == instance:
+                    handlers_to_remove.append(handler)
+
+            for handler in handlers_to_remove:
+                handlers.remove(handler)
+
+    def shutdown(self):
+        """关闭插件管理器"""
+        logger.info("关闭插件管理器...")
+
+        # 禁用所有插件
+        for plugin_name in list(self.enabled_plugins):
+            self.disable_plugin(plugin_name)
+
+        # 保存配置
+        self.save_config()
+
+        logger.info("插件管理器已关闭")
diff --git a/src/utils/robot.py b/src/utils/robot.py
index 36714d1..b221cc8 100644
--- a/src/utils/robot.py
+++ b/src/utils/robot.py
@@ -1,20 +1,247 @@
 """
-飞书或者其他的消息
+多渠道消息通知服务
 
+支持飞书、钉钉、邮件、企业微信等多种通知方式
 """
 
 import json
+import smtplib
+from abc import ABC, abstractmethod
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from typing import Dict, List, Optional
 
 import requests
 
-custom_robot_url = "this is web hook url"
+from conf.config import settings
+from src.utils.log_moudle import logger
 
-data = '{"msg_type":"text","content":{"text":"request example"}}'
 
-headers = {
-    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.83 Safari/537.36",
-    "Content-Type": "application/json; charset=utf-8",
-}
+class NotificationChannel(ABC):
+    """通知渠道抽象基类"""
+
+    @abstractmethod
+    def send_message(self, message: str, **kwargs) -> bool:
+        """发送消息"""
+        pass
+
+
+class FeishuNotification(NotificationChannel):
+    """飞书通知"""
+
+    def __init__(self, webhook_url: str):
+        self.webhook_url = webhook_url
+        self.headers = {
+            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.83 Safari/537.36",
+            "Content-Type": "application/json; charset=utf-8",
+        }
+
+    def send_message(self, message: str, msg_type: str = "text", **kwargs) -> bool:
+        """发送飞书消息"""
+        try:
+            if msg_type == "text":
+                data = {"msg_type": "text", "content": {"text": message}}
+            elif msg_type == "card":
+                data = self._build_card_message(message, **kwargs)
+            else:
+                data = {"msg_type": "text", "content": {"text": message}}
+
+            response = requests.post(
+                self.webhook_url,
+                headers=self.headers,
+                data=json.dumps(data, ensure_ascii=False).encode("utf-8"),
+                timeout=10,
+            )
+
+            if response.status_code == 200:
+                logger.info("飞书消息发送成功")
+                return True
+            else:
+                logger.error(
+                    f"飞书消息发送失败: {response.status_code} - {response.text}"
+                )
+                return False
+
+        except Exception as e:
+            logger.error(f"发送飞书消息异常: {e}")
+            return False
+
+    def _build_card_message(self, message: str, **kwargs) -> Dict:
+        """构建卡片消息"""
+        card_data = {
+            "msg_type": "interactive",
+            "card": {
+                "elements": [
+                    {"tag": "div", "text": {"content": message, "tag": "lark_md"}}
+                ],
+                "header": {
+                    "title": {
+                        "content": kwargs.get("title", "性能测试通知"),
+                        "tag": "plain_text",
+                    }
+                },
+            },
+        }
+
+        # 添加额外字段
+        if kwargs.get("fields"):
+            for field in kwargs["fields"]:
+                card_data["card"]["elements"].append({"tag": "div", "fields": field})
+
+        return card_data
+
+
+class DingTalkNotification(NotificationChannel):
+    """钉钉通知"""
+
+    def __init__(self, webhook_url: str, secret: str = None):
+        self.webhook_url = webhook_url
+        self.secret = secret
+        self.headers = {"Content-Type": "application/json; charset=utf-8"}
+
+    def send_message(self, message: str, msg_type: str = "text", **kwargs) -> bool:
+        """发送钉钉消息"""
+        try:
+            if msg_type == "text":
+                data = {"msgtype": "text", "text": {"content": message}}
+            elif msg_type == "markdown":
+                data = {
+                    "msgtype": "markdown",
+                    "markdown": {
+                        "title": kwargs.get("title", "性能测试通知"),
+                        "text": message,
+                    },
+                }
+            else:
+                data = {"msgtype": "text", "text": {"content": message}}
+
+            # 添加@功能
+            if kwargs.get("at_mobiles") or kwargs.get("at_all"):
+                data["at"] = {
+                    "atMobiles": kwargs.get("at_mobiles", []),
+                    "isAtAll": kwargs.get("at_all", False),
+                }
+
+            response = requests.post(
+                self.webhook_url,
+                headers=self.headers,
+                data=json.dumps(data, ensure_ascii=False).encode("utf-8"),
+                timeout=10,
+            )
+
+            if response.status_code == 200:
+                result = response.json()
+                if result.get("errcode") == 0:
+                    logger.info("钉钉消息发送成功")
+                    return True
+                else:
+                    logger.error(f"钉钉消息发送失败: {result}")
+                    return False
+            else:
+                logger.error(f"钉钉消息发送失败: {response.status_code}")
+                return False
+
+        except Exception as e:
+            logger.error(f"发送钉钉消息异常: {e}")
+            return False
+
+
+class EmailNotification(NotificationChannel):
+    """邮件通知"""
+
+    def __init__(
+        self,
+        smtp_server: str,
+        smtp_port: int,
+        username: str,
+        password: str,
+        use_tls: bool = True,
+    ):
+        self.smtp_server = smtp_server
+        self.smtp_port = smtp_port
+        self.username = username
+        self.password = password
+        self.use_tls = use_tls
+
+    def send_message(
+        self,
+        message: str,
+        subject: str = "性能测试通知",
+        to_emails: List[str] = None,
+        **kwargs,
+    ) -> bool:
+        """发送邮件"""
+        if not to_emails:
+            logger.warning("未指定收件人邮箱")
+            return False
+
+        try:
+            # 创建邮件对象
+            msg = MIMEMultipart()
+            msg["From"] = self.username
+            msg["To"] = ", ".join(to_emails)
+            msg["Subject"] = subject
+
+            # 添加邮件正文
+            if kwargs.get("html", False):
+                msg.attach(MIMEText(message, "html", "utf-8"))
+            else:
+                msg.attach(MIMEText(message, "plain", "utf-8"))
+
+            # 连接SMTP服务器并发送
+            with smtplib.SMTP(self.smtp_server, self.smtp_port) as server:
+                if self.use_tls:
+                    server.starttls()
+                server.login(self.username, self.password)
+                server.send_message(msg)
+
+            logger.info(f"邮件发送成功，收件人: {to_emails}")
+            return True
+
+        except Exception as e:
+            logger.error(f"发送邮件异常: {e}")
+            return False
+
+
+class WeChatWorkNotification(NotificationChannel):
+    """企业微信通知"""
+
+    def __init__(self, webhook_url: str):
+        self.webhook_url = webhook_url
+        self.headers = {"Content-Type": "application/json; charset=utf-8"}
+
+    def send_message(self, message: str, msg_type: str = "text", **kwargs) -> bool:
+        """发送企业微信消息"""
+        try:
+            if msg_type == "text":
+                data = {"msgtype": "text", "text": {"content": message}}
+            elif msg_type == "markdown":
+                data = {"msgtype": "markdown", "markdown": {"content": message}}
+            else:
+                data = {"msgtype": "text", "text": {"content": message}}
+
+            response = requests.post(
+                self.webhook_url,
+                headers=self.headers,
+                data=json.dumps(data, ensure_ascii=False).encode("utf-8"),
+                timeout=10,
+            )
+
+            if response.status_code == 200:
+                result = response.json()
+                if result.get("errcode") == 0:
+                    logger.info("企业微信消息发送成功")
+                    return True
+                else:
+                    logger.error(f"企业微信消息发送失败: {result}")
+                    return False
+            else:
+                logger.error(f"企业微信消息发送失败: {response.status_code}")
+                return False
+
+        except Exception as e:
+            logger.error(f"发送企业微信消息异常: {e}")
+            return False
 
 
 ## 普通文本
@@ -181,9 +408,9 @@ def write_link(self, content: str = "跳转按钮", url: str = "https://baidu.cn
         self.data_format["card"]["elements"].append(link_style)
 
 
-robot = CustomRobotMessage(url=custom_robot_url, data_format=data_format)
-
-
 if __name__ == "__main__":
-
+    # 示例代码 - 仅在直接运行时执行
+    # custom_robot_url = "your_robot_webhook_url_here"
+    # data_format = {"your": "format"}
+    # robot = CustomRobotMessage(url=custom_robot_url, data_format=data_format)
     pass