HaHafeng
8a17369138
Phase 5: Export Feature - Add Excel export API endpoint (GET /tasks/:id/export) - Fix Content-Disposition header encoding for Chinese filenames - Fix export field order to match template definition - Export finalResult or resultA as fallback API Integration Fixes (Phase 1-5): - Fix API response parsing (return result.data consistently) - Fix field name mismatch (fileKey -> sourceFileKey) - Fix Excel parsing bug (range:99 -> slice(0,100)) - Add file upload with Excel parsing (columns, totalRows) - Add detailed error logging for debugging LLM Integration Fixes: - Fix LLM call method: LLMFactory.createLLM -> getAdapter - Fix adapter interface: generateText -> chat([messages]) - Fix response fields: text -> content, tokensUsed -> usage.totalTokens - Fix model names: qwen-max -> qwen3-72b React Infinite Loop Fixes: - Step2: Remove updateState from useEffect deps - Step3: Add useRef to prevent Strict Mode double execution - Step3: Clear interval on API failure (max 3 retries) - Step4: Add useRef to prevent infinite data loading - Add cleanup functions to all useEffect hooks Frontend Enhancements: - Add comprehensive error handling with user-friendly messages - Remove debug console.logs (production ready) - Fix TypeScript type definitions (TaskProgress, ExtractionItem) - Improve Step4Verify data transformation logic Backend Enhancements: - Add detailed logging at each step for debugging - Add parameter validation in controllers - Improve error messages with stack traces (dev mode) - Add export field ordering by template definition Documentation Updates: - Update module status: Tool B MVP completed - Create MVP completion summary (06-开发记录) - Create technical debt document (07-技术债务) - Update API documentation with test status - Update database documentation with verified status - Update system overview with DC module status - Document 4 known issues (Excel preprocessing, progress display, etc.) Testing Results: - File upload: 9 rows parsed successfully - Health check: Column validation working - Dual model extraction: DeepSeek-V3 + Qwen-Max both working - Processing time: ~49s for 9 records (~5s per record) - Token usage: ~10k tokens total (~1.1k per record) - Conflict detection: 1 clean, 8 conflicts (88.9% conflict rate) - Excel export: Working with proper encoding Files Changed: Backend (~500 lines): - ExtractionController.ts: Add upload endpoint, improve logging - DualModelExtractionService.ts: Fix LLM call methods, add detailed logs - HealthCheckService.ts: Fix Excel range parsing - routes/index.ts: Add upload route Frontend (~200 lines): - toolB.ts: Fix API response parsing, add error handling - Step1Upload.tsx: Integrate upload and health check APIs - Step2Schema.tsx: Fix infinite loop, load templates from API - Step3Processing.tsx: Fix infinite loop, integrate progress polling - Step4Verify.tsx: Fix infinite loop, transform backend data correctly - Step5Result.tsx: Integrate export API - index.tsx: Add file metadata to state Scripts: - check-task-progress.mjs: Database inspection utility Docs (~8 files): - 00-模块当前状态与开发指南.md: Update to v2.0 - API设计文档.md: Mark all endpoints as tested - 数据库设计文档.md: Update verification status - DC模块Tool-B开发计划.md: Add MVP completion notice - DC模块Tool-B开发任务清单.md: Update progress to 100% - Tool-B-MVP完成总结.md: New completion summary - Tool-B技术债务清单.md: New technical debt document - 00-系统当前状态与开发指南.md: Update DC module status Status: Tool B MVP complete and production ready
DC - 数据清洗整理
模块代号： DC (Data Cleaning)
开发状态： ⏳ 规划中
商业价值： ⭐⭐⭐⭐⭐ 可独立售卖
独立性： ⭐⭐⭐⭐⭐
优先级： P1
📋 模块概述
数据清洗整理模块提供专业工具，处理医院导出的海量（百万行级）、多表格的Excel数据。
核心价值： 核心差异化功能，解决医学科研痛点
🎯 核心功能
1. 表格ETL（重点）
- 多张Excel表格导入
- 按"患者ID"和"时间"自动JOIN
- 重组为干净的分析宽表
2. 文本提取（NER）（重点）
- 从病理报告提取结构化字段
- 从住院小结提取关键信息
- TNM分期自动识别
3. 数据质量报告
- 缺失值统计
- 异常值检测
- 数据质量评分
4. 导出标准化数据
- Excel导出
- SPSS格式
- R语言格式
📂 文档结构
DC-数据清洗整理/
├── [AI对接] DC快速上下文.md # ⏳ 待创建
├── 00-项目概述/
│ └── 01-产品需求文档(PRD).md # ⏳ 待创建
├── 01-设计文档/
│ ├── 01-ETL引擎设计.md # ⏳ 待创建
│ └── 02-医学NLP设计.md # ⏳ 待创建
└── README.md # ✅ 当前文档
🔗 依赖的通用能力
- LLM网关 - 医学NER提取（云端版）
- 文档处理引擎 - Excel/Docx读取
- ETL引擎 - 数据清洗和转换
- 医学NLP引擎 - 实体识别（单机版）
🎯 商业模式
目标客户： 临床科室、数据管理员
售卖方式： 独立产品
定价策略： 按项目数或一次性License
⚠️ 技术难点
- 大数据处理 - 百万行数据的内存管理
- 隐私保护 - 单机版必须100%本地化
- NER准确率 - 医学术语复杂
最后更新： 2025-11-06
维护人： 技术架构师