feat(dc/tool-c): Add missing value imputation feature with 6 methods and MICE

Major features:
1. Missing value imputation (6 simple methods + MICE):
   - Mean/Median/Mode/Constant imputation
   - Forward fill (ffill) and Backward fill (bfill) for time series
   - MICE multivariate imputation (in progress, shape issue to fix)

2. Auto precision detection:
   - Automatically match decimal places of original data
   - Prevent false precision (e.g. 13.57 instead of 13.566716417910449)

3. Categorical variable detection:
   - Auto-detect and skip categorical columns in MICE
   - Show warnings for unsuitable columns
   - Suggest mode imputation for categorical data

4. UI improvements:
   - Rename button: "Delete Missing" to "Missing Value Handling"
   - Remove standalone "Dedup" and "MICE" buttons
   - 3-tab dialog: Delete / Fill / Advanced Fill
   - Display column statistics and recommended methods
   - Extended warning messages (8 seconds for skipped columns)

5. Bug fixes:
   - Fix sessionService.updateSessionData -> saveProcessedData
   - Fix OperationResult interface (add message and stats)
   - Fix Toolbar button labels and removal

Modified files:
Python: operations/fillna.py (new, 556 lines), main.py (3 new endpoints)
Backend: QuickActionService.ts, QuickActionController.ts, routes/index.ts
Frontend: MissingValueDialog.tsx (new, 437 lines), Toolbar.tsx, index.tsx
Tests: test_fillna_operations.py (774 lines), test scripts and docs
Docs: 5 documentation files updated

Known issues:
- MICE imputation has DataFrame shape mismatch issue (under debugging)
- Workaround: Use 6 simple imputation methods first

Status: Development complete, MICE debugging in progress
Lines added: ~2000 lines across 3 tiers

tests/QUICKSTART_快速开始.md

@@ -0,0 +1,98 @@
+# 🚀 快速开始 - 1分钟运行测试
+
+## Windows用户
+
+### 方法1：双击运行（最简单）
+1. 双击 `run_tests.bat`
+2. 等待测试完成
+
+### 方法2：命令行
+```cmd
+cd AIclinicalresearch\tests
+run_tests.bat
+```
+
+---
+
+## Linux/Mac用户
+
+```bash
+cd AIclinicalresearch/tests
+chmod +x run_tests.sh
+./run_tests.sh
+```
+
+---
+
+## ⚠️ 前提条件
+
+**必须先启动Python服务！**
+
+```bash
+# 打开新终端
+cd AIclinicalresearch/extraction_service
+python main.py
+```
+
+看到这行表示启动成功：
+```
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://0.0.0.0:8001
+```
+
+---
+
+## 📊 预期结果
+
+✅ **全部通过**：
+```
+总测试数: 18
+✅ 通过: 18
+❌ 失败: 0
+通过率: 100.0%
+
+🎉 所有测试通过！
+```
+
+⚠️ **部分失败**：
+- 查看红色错误信息
+- 检查失败的具体测试
+- 查看Python服务日志
+
+---
+
+## 🎯 测试内容
+
+- ✅ 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）
+- ✅ MICE多重插补（单列、多列）
+- ✅ 边界情况（100%缺失、0%缺失、特殊字符）
+- ✅ 各种数据类型（数值、分类、混合）
+- ✅ 性能测试（1000行数据）
+
+---
+
+## 💡 提示
+
+- **第一次运行**会自动安装依赖（pandas, numpy, requests）
+- **测试时间**约 45-60 秒
+- **测试数据**自动生成，无需手动准备
+- **颜色输出**：绿色=通过，红色=失败，黄色=警告
+
+---
+
+## 🆘 遇到问题？
+
+### 问题1：无法连接到服务
+**解决**：确保Python服务在运行（`python main.py`）
+
+### 问题2：依赖安装失败
+**解决**：手动安装 `pip install pandas numpy requests`
+
+### 问题3：测试失败
+**解决**：查看错误信息，检查代码逻辑
+
+---
+
+**准备好了吗？启动服务，运行测试！** 🚀
+
+