HaHafeng
decff0bb1f
Summary: - Successfully deployed complete system to Aliyun SAE (2025-12-25) - All services running: Python microservice + Node.js backend + Frontend Nginx + CLB - Public access available at http://8.140.53.236/ Major Achievements: 1. Python microservice deployed (v1.0, internal IP: 172.17.173.66:8000) 2. Node.js backend deployed (v1.3, internal IP: 172.17.173.73:3001) - Fixed 4 critical issues: bash path, config directory, pino-pretty, ES Module 3. Frontend Nginx deployed (v1.0, internal IP: 172.17.173.72:80) 4. CLB load balancer configured (public IP: 8.140.53.236) New Documentation (9 docs): - 11-Node.js backend SAE deployment config checklist (21 env vars) - 12-Node.js backend SAE deployment operation manual - 13-Node.js backend image fix record (config directory) - 14-Node.js backend pino-pretty fix - 15-Node.js backend deployment success summary - 16-Frontend Nginx deployment success summary - 17-Complete deployment practical manual 2025 edition (1800 lines) - 18-Deployment documentation usage guide - 19-Daily update quick operation manual (670 lines) Key Fixes: - Environment variable name correction: EXTRACTION_SERVICE_URL (not PYTHON_SERVICE_URL) - Dockerfile fix: added COPY config ./config - Logger configuration: conditional pino-pretty for dev only - Health check fix: ES Module compatibility (require -> import) Updated Files: - System status document updated with full deployment info - Deployment progress overview updated with latest IPs - All 3 Docker services' Dockerfiles and configs refined Verification: - All health checks passed - Tool C 7 features working correctly - Literature screening module functional - Response time < 1 second BREAKING CHANGE: Node.js backend internal IP changed from 172.17.173.71 to 172.17.173.73 Closes #deployment-milestone
118 lines
1.8 KiB
Markdown
118 lines
1.8 KiB
Markdown
# 🚀 快速开始 - 1分钟运行测试
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## Windows用户
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### 方法1：双击运行（最简单）
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1. 双击 `run_tests.bat`
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2. 等待测试完成
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### 方法2：命令行
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```cmd
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cd AIclinicalresearch\tests
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run_tests.bat
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```
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---
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## Linux/Mac用户
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```bash
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cd AIclinicalresearch/tests
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chmod +x run_tests.sh
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./run_tests.sh
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```
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---
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## ⚠️ 前提条件
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**必须先启动Python服务！**
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```bash
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# 打开新终端
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cd AIclinicalresearch/extraction_service
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python main.py
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```
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看到这行表示启动成功：
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```
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INFO: Application startup complete.
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INFO: Uvicorn running on http://0.0.0.0:8001
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```
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---
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## 📊 预期结果
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✅ **全部通过**：
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```
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总测试数: 18
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✅ 通过: 18
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❌ 失败: 0
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通过率: 100.0%
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🎉 所有测试通过！
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```
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⚠️ **部分失败**：
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- 查看红色错误信息
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- 检查失败的具体测试
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- 查看Python服务日志
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---
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## 🎯 测试内容
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- ✅ 6种简单填补方法（均值、中位数、众数、固定值、前向填充、后向填充）
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- ✅ MICE多重插补（单列、多列）
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- ✅ 边界情况（100%缺失、0%缺失、特殊字符）
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- ✅ 各种数据类型（数值、分类、混合）
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- ✅ 性能测试（1000行数据）
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---
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## 💡 提示
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- **第一次运行**会自动安装依赖（pandas, numpy, requests）
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- **测试时间**约 45-60 秒
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- **测试数据**自动生成，无需手动准备
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- **颜色输出**：绿色=通过，红色=失败，黄色=警告
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---
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## 🆘 遇到问题？
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### 问题1：无法连接到服务
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**解决**：确保Python服务在运行（`python main.py`）
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### 问题2：依赖安装失败
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**解决**：手动安装 `pip install pandas numpy requests`
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### 问题3：测试失败
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**解决**：查看错误信息，检查代码逻辑
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---
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**准备好了吗？启动服务，运行测试！** 🚀
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