feat(dc): Complete Tool B MVP with full API integration and bug fixes

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

backend/scripts/check-task-progress.mjs

@@ -0,0 +1,101 @@
+/**
+ * 检查DC模块任务进度
+ * 用于诊断LLM是否正常工作
+ */
+
+import { PrismaClient } from '@prisma/client';
+
+const prisma = new PrismaClient();
+
+async function checkTaskProgress() {
+  try {
+    console.log('📊 检查DC模块任务进度...\n');
+    
+    // 1. 获取最新的任务
+    const latestTasks = await prisma.dCExtractionTask.findMany({
+      orderBy: { createdAt: 'desc' },
+      take: 3,
+      select: {
+        id: true,
+        projectName: true,
+        status: true,
+        totalCount: true,
+        processedCount: true,
+        cleanCount: true,
+        conflictCount: true,
+        failedCount: true,
+        totalTokens: true,
+        createdAt: true,
+        startedAt: true,
+        completedAt: true,
+        error: true
+      }
+    });
+    
+    console.log('=== 最近3个任务 ===');
+    latestTasks.forEach((task, index) => {
+      console.log(`\n${index + 1}. 任务: ${task.projectName}`);
+      console.log(`   ID: ${task.id}`);
+      console.log(`   状态: ${task.status}`);
+      console.log(`   进度: ${task.processedCount}/${task.totalCount} (${task.totalCount > 0 ? Math.round(task.processedCount / task.totalCount * 100) : 0}%)`);
+      console.log(`   结果: 一致=${task.cleanCount}, 冲突=${task.conflictCount}, 失败=${task.failedCount}`);
+      console.log(`   Tokens: ${task.totalTokens || 0}`);
+      console.log(`   创建时间: ${task.createdAt.toLocaleString('zh-CN')}`);
+      console.log(`   开始时间: ${task.startedAt ? task.startedAt.toLocaleString('zh-CN') : '未开始'}`);
+      console.log(`   完成时间: ${task.completedAt ? task.completedAt.toLocaleString('zh-CN') : '未完成'}`);
+      if (task.error) {
+        console.log(`   ❌ 错误: ${task.error}`);
+      }
+    });
+    
+    // 2. 如果有任务，检查第一个任务的items详情
+    if (latestTasks.length > 0) {
+      const taskId = latestTasks[0].id;
+      console.log(`\n\n=== 最新任务的Item详情 (${taskId}) ===`);
+      
+      const items = await prisma.dCExtractionItem.findMany({
+        where: { taskId },
+        orderBy: { rowIndex: 'asc' },
+        take: 3,  // 只显示前3条
+        select: {
+          id: true,
+          rowIndex: true,
+          originalText: true,
+          status: true,
+          resultA: true,
+          resultB: true,
+          finalResult: true,
+          tokensA: true,
+          tokensB: true,
+          conflictFields: true,
+          error: true
+        }
+      });
+      
+      console.log(`\n总共 ${items.length} 条记录（显示前3条）:\n`);
+      
+      items.forEach(item => {
+        console.log(`行 ${item.rowIndex}:`);
+        console.log(`  原文: ${item.originalText.substring(0, 60)}...`);
+        console.log(`  状态: ${item.status}`);
+        console.log(`  DeepSeek结果: ${item.resultA ? JSON.stringify(item.resultA).substring(0, 100) + '...' : '未提取'}`);
+        console.log(`  Qwen结果: ${item.resultB ? JSON.stringify(item.resultB).substring(0, 100) + '...' : '未提取'}`);
+        console.log(`  🎯 最终结果(finalResult): ${item.finalResult ? JSON.stringify(item.finalResult) : 'null'}`);
+        console.log(`  Tokens: DeepSeek=${item.tokensA || 0}, Qwen=${item.tokensB || 0}`);
+        console.log(`  冲突字段: ${item.conflictFields.length > 0 ? item.conflictFields.join(', ') : '无'}`);
+        if (item.error) {
+          console.log(`  ❌ 错误: ${item.error}`);
+        }
+        console.log('');
+      });
+    }
+    
+  } catch (error) {
+    console.error('❌ 检查失败:', error);
+  } finally {
+    await prisma.$disconnect();
+  }
+}
+
+checkTaskProgress();
+

backend/src/modules/dc/tool-b/controllers/ExtractionController.ts

@@ -27,6 +27,70 @@ import { prisma } from '../../../../config/database.js';
 import * as xlsx from 'xlsx';
 
 export class ExtractionController {
+  /**
+   * 文件上传
+   * POST /upload
+   */
+  async uploadFile(request: FastifyRequest, reply: FastifyReply) {
+    try {
+      const data = await request.file();
+      
+      if (!data) {
+        return reply.code(400).send({
+          success: false,
+          error: 'No file uploaded'
+        });
+      }
+
+      const userId = (request as any).userId || 'default-user';
+      const buffer = await data.toBuffer();
+      const originalFilename = data.filename;
+      const timestamp = Date.now();
+      const fileKey = `dc/tool-b/${userId}/${timestamp}_${originalFilename}`;
+      
+      logger.info('[API] File upload request', { 
+        filename: originalFilename, 
+        size: buffer.length,
+        userId 
+      });
+      
+      // 解析Excel文件获取列名和行数
+      const workbook = xlsx.read(buffer, { type: 'buffer' });
+      const sheetName = workbook.SheetNames[0];
+      const worksheet = workbook.Sheets[sheetName];
+      const jsonData = xlsx.utils.sheet_to_json<Record<string, any>>(worksheet);
+      
+      // 获取列名（从第一行数据的keys）
+      const columns = jsonData.length > 0 ? Object.keys(jsonData[0]) : [];
+      const totalRows = jsonData.length;
+      
+      logger.info('[API] Excel parsed', { columns, totalRows });
+      
+      // 上传到storage
+      const url = await storage.upload(fileKey, buffer);
+      
+      logger.info('[API] File uploaded successfully', { fileKey, url });
+      
+      return reply.code(200).send({
+        success: true,
+        data: {
+          fileKey,
+          url,
+          filename: originalFilename,
+          size: buffer.length,
+          totalRows,
+          columns
+        }
+      });
+    } catch (error) {
+      logger.error('[API] File upload failed', { error });
+      return reply.code(500).send({
+        success: false,
+        error: String(error)
+      });
+    }
+  }
+
   /**
    * 健康检查
    * POST /health-check
@@ -43,18 +107,36 @@ export class ExtractionController {
       
       logger.info('[API] Health check request', { fileKey, columnName, userId });
       
+      // 参数验证
+      if (!fileKey || !columnName) {
+        logger.error('[API] Missing required parameters', { fileKey, columnName });
+        return reply.code(400).send({
+          success: false,
+          error: 'Missing required parameters: fileKey or columnName'
+        });
+      }
+      
       const result = await healthCheckService.check(fileKey, columnName, userId);
       
+      logger.info('[API] Health check success', { status: result.status });
+      
       return reply.code(200).send({
         success: true,
         data: result
       });
       
-    } catch (error) {
-      logger.error('[API] Health check failed', { error });
+    } catch (error: any) {
+      logger.error('[API] Health check failed', { 
+        error: error.message,
+        stack: error.stack,
+        fileKey: request.body?.fileKey,
+        columnName: request.body?.columnName
+      });
+      
       return reply.code(500).send({
         success: false,
-        error: String(error)
+        error: error.message || String(error),
+        details: process.env.NODE_ENV === 'development' ? error.stack : undefined
       });
     }
   }
@@ -99,6 +181,8 @@ export class ExtractionController {
     }
   }>, reply: FastifyReply) {
     try {
+      logger.info('[API] ===== CREATE TASK START =====');
+      
       const {
         projectName,
         sourceFileKey,
@@ -113,34 +197,48 @@ export class ExtractionController {
       logger.info('[API] Create task request', {
         userId,
         projectName,
+        sourceFileKey,
+        textColumn,
         diseaseType,
         reportType
       });
       
       // 1. 获取模板
+      logger.info('[API] Step 1: Getting template', { diseaseType, reportType });
       const template = await templateService.getTemplate(diseaseType, reportType);
       if (!template) {
+        logger.error('[API] Template not found', { diseaseType, reportType });
         return reply.code(404).send({
           success: false,
           error: `Template not found: ${diseaseType}/${reportType}`
         });
       }
+      logger.info('[API] Template found', { templateId: template.id });
       
       // 2. 读取Excel文件，创建items
+      logger.info('[API] Step 2: Downloading Excel file', { sourceFileKey });
       const fileBuffer = await storage.download(sourceFileKey);
       if (!fileBuffer) {
+        logger.error('[API] File not found in storage', { sourceFileKey });
         return reply.code(404).send({
           success: false,
           error: `File not found: ${sourceFileKey}`
         });
       }
+      logger.info('[API] File downloaded', { size: fileBuffer.length });
       
+      logger.info('[API] Step 3: Parsing Excel file');
       const workbook = xlsx.read(fileBuffer, { type: 'buffer' });
       const sheetName = workbook.SheetNames[0];
       const worksheet = workbook.Sheets[sheetName];
       const data = xlsx.utils.sheet_to_json<Record<string, any>>(worksheet);
+      logger.info('[API] Excel parsed', { rowCount: data.length });
       
       if (!data[0].hasOwnProperty(textColumn)) {
+        logger.error('[API] Column not found', { 
+          textColumn, 
+          availableColumns: Object.keys(data[0]) 
+        });
         return reply.code(400).send({
           success: false,
           error: `Column '${textColumn}' not found in Excel`
@@ -148,6 +246,7 @@ export class ExtractionController {
       }
       
       // 3. 创建任务
+      logger.info('[API] Step 4: Creating task in database');
       const task = await prisma.dCExtractionTask.create({
         data: {
           userId,
@@ -156,15 +255,17 @@ export class ExtractionController {
           textColumn,
           diseaseType,
           reportType,
-          targetFields: template.fields,
+          targetFields: template.fields as any, // Prisma Json类型
           modelA,
           modelB,
           totalCount: data.length,
           status: 'pending'
         }
       });
+      logger.info('[API] Task created in database', { taskId: task.id });
       
       // 4. 创建items
+      logger.info('[API] Step 5: Creating extraction items', { count: data.length });
       const itemsData = data.map((row, index) => ({
         taskId: task.id,
         rowIndex: index + 1,
@@ -174,13 +275,24 @@ export class ExtractionController {
       await prisma.dCExtractionItem.createMany({
         data: itemsData
       });
+      logger.info('[API] Items created', { count: itemsData.length });
       
       // 5. 启动异步任务
       // TODO: 使用jobQueue.add()
       // 暂时直接调用
-      dualModelExtractionService.batchExtract(task.id).catch(err => {
-        logger.error('[API] Batch extraction failed', { error: err, taskId: task.id });
-      });
+      logger.info('[API] Starting batch extraction (async)', { taskId: task.id });
+      
+      dualModelExtractionService.batchExtract(task.id)
+        .then(() => {
+          logger.info('[API] Batch extraction completed successfully', { taskId: task.id });
+        })
+        .catch(err => {
+          logger.error('[API] Batch extraction failed', { 
+            error: err.message,
+            stack: err.stack,
+            taskId: task.id 
+          });
+        });
       
       logger.info('[API] Task created', { taskId: task.id, itemCount: data.length });
       
@@ -380,6 +492,93 @@ export class ExtractionController {
       });
     }
   }
+
+  /**
+   * 导出结果
+   * GET /tasks/:taskId/export
+   */
+  async exportResults(request: FastifyRequest<{
+    Params: { taskId: string };
+  }>, reply: FastifyReply) {
+    try {
+      const { taskId } = request.params;
+      
+      logger.info('[API] Export results request', { taskId });
+      
+      // 获取任务和所有items
+      const task = await prisma.dCExtractionTask.findUnique({
+        where: { id: taskId },
+        include: {
+          items: {
+            orderBy: { rowIndex: 'asc' }
+          }
+        }
+      });
+      
+      if (!task) {
+        return reply.code(404).send({
+          success: false,
+          error: 'Task not found'
+        });
+      }
+      
+      // 创建Excel工作簿
+      const workbook = xlsx.utils.book_new();
+      
+      // 🔑 获取字段顺序（从targetFields）
+      const targetFields = task.targetFields as { name: string; desc: string }[];
+      const fieldNames = targetFields.map(f => f.name);
+      
+      // 构建数据行，按模板字段顺序
+      const rows = task.items.map(item => {
+        // 优先使用finalResult，如果为空则使用resultA
+        const finalResult = item.finalResult as Record<string, string> | null;
+        const resultA = item.resultA as Record<string, string> | null;
+        const extractedData = finalResult || resultA || {};
+        
+        // 🔑 按字段顺序构建行对象
+        const row: Record<string, any> = {
+          '行号': item.rowIndex,
+          '原文': item.originalText,
+          '状态': item.status === 'resolved' ? '已解决' : item.status === 'clean' ? '一致' : '待裁决'
+        };
+        
+        // 按模板定义的顺序添加字段
+        fieldNames.forEach(fieldName => {
+          row[fieldName] = extractedData[fieldName] || '未提及';
+        });
+        
+        return row;
+      });
+      
+      // 创建工作表
+      const worksheet = xlsx.utils.json_to_sheet(rows);
+      xlsx.utils.book_append_sheet(workbook, worksheet, '提取结果');
+      
+      // 生成Excel Buffer
+      const excelBuffer = xlsx.write(workbook, { type: 'buffer', bookType: 'xlsx' });
+      
+      logger.info('[API] Export results success', { taskId, rowCount: rows.length });
+      
+      // 返回文件
+      // 🔑 对文件名进行URL编码以支持中文
+      const filename = `${task.projectName}_结果.xlsx`;
+      const encodedFilename = encodeURIComponent(filename);
+      
+      return reply
+        .code(200)
+        .header('Content-Type', 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')
+        .header('Content-Disposition', `attachment; filename*=UTF-8''${encodedFilename}`)
+        .send(excelBuffer);
+      
+    } catch (error) {
+      logger.error('[API] Export results failed', { error });
+      return reply.code(500).send({
+        success: false,
+        error: String(error)
+      });
+    }
+  }
 }
 
 // 导出单例

backend/src/modules/dc/tool-b/routes/index.ts

@@ -11,6 +11,11 @@ import { logger } from '../../../../common/logging/index.js';
 export async function registerToolBRoutes(fastify: FastifyInstance) {
   logger.info('[Routes] Registering DC Tool-B routes');
   
+  // 文件上传
+  fastify.post('/upload', {
+    handler: extractionController.uploadFile.bind(extractionController)
+  });
+  
   // 健康检查
   fastify.post('/health-check', {
     schema: {
@@ -109,6 +114,20 @@ export async function registerToolBRoutes(fastify: FastifyInstance) {
     handler: extractionController.resolveConflict.bind(extractionController)
   });
   
+  // 导出结果
+  fastify.get('/tasks/:taskId/export', {
+    schema: {
+      params: {
+        type: 'object',
+        required: ['taskId'],
+        properties: {
+          taskId: { type: 'string' }
+        }
+      }
+    },
+    handler: extractionController.exportResults.bind(extractionController)
+  });
+  
   logger.info('[Routes] DC Tool-B routes registered successfully');
 }
 

backend/src/modules/dc/tool-b/services/DualModelExtractionService.ts

@@ -142,34 +142,56 @@ ${text}
     fields: { name: string; desc: string }[]
   ): Promise<ExtractionOutput> {
     try {
-      // 使用LLMFactory获取LLM客户端
-      const modelName = modelType === 'deepseek' ? 'deepseek-v3' : 'qwen-max';
-      const llm = LLMFactory.createLLM(modelName);
+      // 🔑 使用LLMFactory获取适配器（正确的方法）
+      const modelName = modelType === 'deepseek' ? 'deepseek-v3' : 'qwen3-72b';
       
-      logger.info(`[${modelType.toUpperCase()}] Calling model`, { modelName });
+      logger.info(`[${modelType.toUpperCase()}] Getting adapter`, { modelName });
+      const adapter = LLMFactory.getAdapter(modelName as any);
+      logger.info(`[${modelType.toUpperCase()}] Adapter created successfully`);
       
-      // 调用LLM
-      const response = await llm.generateText(prompt, {
+      logger.info(`[${modelType.toUpperCase()}] Calling model with prompt`, { 
+        modelName,
+        promptLength: prompt.length,
+        promptPreview: prompt.substring(0, 100) + '...'
+      });
+      
+      // 🔑 调用LLM（使用chat方法，符合ILLMAdapter接口）
+      const startTime = Date.now();
+      const response = await adapter.chat([
+        { role: 'user', content: prompt }
+      ], {
         temperature: 0, // 最大确定性
         maxTokens: 1000
       });
+      const elapsedTime = Date.now() - startTime;
       
-      logger.info(`[${modelType.toUpperCase()}] Model responded`, {
+      logger.info(`[${modelType.toUpperCase()}] Model responded successfully`, {
         modelName,
-        tokensUsed: response.tokensUsed
+        tokensUsed: response.usage?.totalTokens,
+        elapsedMs: elapsedTime,
+        contentLength: response.content.length,
+        contentPreview: response.content.substring(0, 200)
       });
       
       // 解析JSON（3层容错）
-      const result = this.parseJSON(response.text, fields);
+      logger.info(`[${modelType.toUpperCase()}] Parsing JSON response`);
+      const result = this.parseJSON(response.content, fields);
+      logger.info(`[${modelType.toUpperCase()}] JSON parsed successfully`, { 
+        fieldCount: Object.keys(result).length 
+      });
       
       return {
         result,
-        tokensUsed: response.tokensUsed || 0,
-        rawOutput: response.text
+        tokensUsed: response.usage?.totalTokens || 0,
+        rawOutput: response.content
       };
       
-    } catch (error) {
-      logger.error(`[${modelType.toUpperCase()}] Model call failed`, { error, modelType });
+    } catch (error: any) {
+      logger.error(`[${modelType.toUpperCase()}] Model call failed`, { 
+        error: error.message,
+        stack: error.stack,
+        modelType 
+      });
       throw error;
     }
   }
@@ -246,18 +268,27 @@ ${text}
    */
   async batchExtract(taskId: string): Promise<void> {
     try {
-      logger.info('[Batch] Starting batch extraction', { taskId });
+      logger.info('[Batch] ===== Starting batch extraction =====', { taskId });
       
       // 1. 获取任务
+      logger.info('[Batch] Step 1: Fetching task from database', { taskId });
       const task = await prisma.dCExtractionTask.findUnique({
         where: { id: taskId },
         include: { items: true }
       });
       
       if (!task) {
+        logger.error('[Batch] Task not found in database', { taskId });
         throw new Error(`Task not found: ${taskId}`);
       }
       
+      logger.info('[Batch] Task fetched successfully', { 
+        taskId, 
+        itemCount: task.items.length,
+        diseaseType: task.diseaseType,
+        reportType: task.reportType
+      });
+      
       // 2. 更新任务状态
       await prisma.dCExtractionTask.update({
         where: { id: taskId },
@@ -309,12 +340,12 @@ ${text}
           await prisma.dCExtractionItem.update({
             where: { id: item.id },
             data: {
-              resultA: resultA.result,
-              resultB: resultB.result,
+              resultA: resultA.result as any,
+              resultB: resultB.result as any,
               tokensA: resultA.tokensUsed,
               tokensB: resultB.tokensUsed,
               status: hasConflict ? 'conflict' : 'clean',
-              finalResult: hasConflict ? null : resultA.result // 一致时自动采纳
+              finalResult: (hasConflict ? null : resultA.result) as any // 一致时自动采纳
             }
           });
           

backend/src/modules/dc/tool-b/services/HealthCheckService.ts

@@ -51,22 +51,73 @@ export class HealthCheckService {
       }
       
       // 2. 从Storage读取Excel文件
-      const fileBuffer = await storage.download(fileKey);
-      if (!fileBuffer) {
-        throw new Error(`File not found: ${fileKey}`);
+      logger.info('[HealthCheck] Downloading file from storage', { fileKey });
+      let fileBuffer: Buffer;
+      
+      try {
+        fileBuffer = await storage.download(fileKey);
+        if (!fileBuffer) {
+          throw new Error(`File not found in storage: ${fileKey}`);
+        }
+        logger.info('[HealthCheck] File downloaded successfully', { 
+          fileKey, 
+          size: fileBuffer.length 
+        });
+      } catch (storageError: any) {
+        logger.error('[HealthCheck] Storage download failed', { 
+          fileKey, 
+          error: storageError.message,
+          stack: storageError.stack
+        });
+        throw new Error(`Failed to download file from storage: ${storageError.message}`);
       }
       
-      // 3. 解析Excel（仅前100行）
-      const workbook = xlsx.read(fileBuffer, { type: 'buffer' });
-      const sheetName = workbook.SheetNames[0];
-      const worksheet = workbook.Sheets[sheetName];
-      const data = xlsx.utils.sheet_to_json<Record<string, any>>(worksheet, { range: 99 }); // 前100行
+      // 3. 解析Excel（取前100行用于采样）
+      logger.info('[HealthCheck] Parsing Excel file');
+      let workbook: xlsx.WorkBook;
+      let data: Record<string, any>[];
       
-      logger.info('[HealthCheck] Excel parsed', { totalRows: data.length });
+      try {
+        workbook = xlsx.read(fileBuffer, { type: 'buffer' });
+        
+        if (!workbook.SheetNames || workbook.SheetNames.length === 0) {
+          throw new Error('Excel文件中没有工作表');
+        }
+        
+        const sheetName = workbook.SheetNames[0];
+        const worksheet = workbook.Sheets[sheetName];
+        
+        // 读取所有数据
+        const allData = xlsx.utils.sheet_to_json<Record<string, any>>(worksheet);
+        
+        // 取前100行作为采样（如果不足100行则取全部）
+        data = allData.slice(0, 100);
+        
+        logger.info('[HealthCheck] Excel parsed successfully', { 
+          sheetName, 
+          totalRows: allData.length,
+          sampleRows: data.length
+        });
+      } catch (xlsxError: any) {
+        logger.error('[HealthCheck] Excel parsing failed', { 
+          error: xlsxError.message,
+          stack: xlsxError.stack
+        });
+        throw new Error(`Excel解析失败: ${xlsxError.message}`);
+      }
       
       // 4. 检查列是否存在
-      if (data.length === 0 || !data[0].hasOwnProperty(columnName)) {
-        throw new Error(`Column '${columnName}' not found in Excel`);
+      if (data.length === 0) {
+        throw new Error('Excel文件无有效数据');
+      }
+      
+      const availableColumns = Object.keys(data[0]);
+      logger.info('[HealthCheck] Available columns', { availableColumns });
+      
+      if (!data[0].hasOwnProperty(columnName)) {
+        throw new Error(
+          `列 "${columnName}" 不存在。可用列：${availableColumns.join(', ')}`
+        );
       }
       
       // 5. 计算统计指标
@@ -97,8 +148,14 @@ export class HealthCheckService {
       
       return result;
       
-    } catch (error) {
-      logger.error('[HealthCheck] Check failed', { error, fileKey, columnName });
+    } catch (error: any) {
+      logger.error('[HealthCheck] Check failed', { 
+        error: error.message, 
+        stack: error.stack,
+        fileKey, 
+        columnName,
+        userId
+      });
       throw error;
     }
   }