修改所有bug

Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyClassUserController.java

@@ -275,7 +275,6 @@ public class StudyClassUserController extends BaseController
         StudentImportData sample = new StudentImportData();
         sample.setUserId("001");
         sample.setUserName("张三");
-        sample.setPrisonName("XX监狱");
         sample.setPrisonArea("一监区");
         sample.setClassName("一班");
         sample.setSex("男");

Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyVoiceEvaluationController.java

@@ -515,6 +515,20 @@ public class StudyVoiceEvaluationController extends BaseController
 
             Map<String, Object> evaluationResult = voiceService.evaluateVoice(fileName, content, language, format);
 
+            // ✅ 从resultDetail中提取recognizedText
+            String recognizedText = "";
+            String resultDetail = (String) evaluationResult.get("resultDetail");
+            if (resultDetail != null && resultDetail.contains("recognizedText")) {
+                try {
+                    com.fasterxml.jackson.databind.ObjectMapper mapper = new com.fasterxml.jackson.databind.ObjectMapper();
+                    Map<String, Object> detailMap = mapper.readValue(resultDetail, Map.class);
+                    recognizedText = (String) detailMap.getOrDefault("recognizedText", "");
+                    logger.info("✅ 提取识别文本: {}", recognizedText);
+                } catch (Exception e) {
+                    logger.warn("解析resultDetail失败", e);
+                }
+            }
+
             // 保存评测记录
             StudyVoiceEvaluation voiceEvaluation = new StudyVoiceEvaluation();
             voiceEvaluation.setStudentId(studentId);
@@ -537,6 +551,8 @@ public class StudyVoiceEvaluationController extends BaseController
             AjaxResult ajax = AjaxResult.success("评测完成");
             ajax.put("evaluation", voiceEvaluation);
             ajax.put("audioUrl", audioUrl);
+            // ✅ 添加recognizedText到返回数据
+            ajax.put("recognizedText", recognizedText);
             return ajax;
         }
         catch (Exception e)

Study-Vue-redis/ry-study-admin/src/main/resources/application-druid.yml

@@ -8,7 +8,7 @@ spring:
             master:
                 url: jdbc:mysql://127.0.0.1:3306/study?useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&useSSL=false&serverTimezone=GMT%2B8&allowPublicKeyRetrieval=true
                 username: root
-                password: root
+                password: 123456
           # 从库数据源
             slave:
                 # 从数据源开关/默认关闭

Study-Vue-redis/ry-study-common/src/main/java/com/ddnai/common/core/domain/entity/StudentImportData.java

@@ -20,12 +20,11 @@ public class StudentImportData
     @Excel(name = "罪犯姓名", sort = 2, prompt = "必填项，如：张三")
     private String userName;
 
-    /** 监狱（可选） */
+    /** 监狱（不导出到模板，仅保留字段用于兼容旧数据导入） */
-    @Excel(name = "监狱", sort = 3, prompt = "可选，如：第一监狱")
     private String prisonName;
 
     /** 监区 - 支持多种列名匹配 */
-    @Excel(name = "监区", sort = 4, prompt = "必填项，如：第三监区")
+    @Excel(name = "监区", sort = 3, prompt = "必填项，如：第三监区")
     private String prisonArea;
 
     /** 班级名称（用于按名称匹配班级，可选） */

Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/domain/study/StudyVoiceEvaluation.java

@@ -48,22 +48,27 @@ public class StudyVoiceEvaluation extends BaseEntity
 
     /** 评分（总分，0-100） */
     @Excel(name = "总分", cellType = ColumnType.NUMERIC)
+    @com.fasterxml.jackson.databind.annotation.JsonSerialize(using = com.fasterxml.jackson.databind.ser.std.ToStringSerializer.class)
     private BigDecimal score;
 
     /** 准确度（0-100） */
     @Excel(name = "准确度", cellType = ColumnType.NUMERIC)
+    @com.fasterxml.jackson.databind.annotation.JsonSerialize(using = com.fasterxml.jackson.databind.ser.std.ToStringSerializer.class)
     private BigDecimal accuracy;
 
     /** 流畅度（0-100） */
     @Excel(name = "流畅度", cellType = ColumnType.NUMERIC)
+    @com.fasterxml.jackson.databind.annotation.JsonSerialize(using = com.fasterxml.jackson.databind.ser.std.ToStringSerializer.class)
     private BigDecimal fluency;
 
     /** 完整度（0-100） */
     @Excel(name = "完整度", cellType = ColumnType.NUMERIC)
+    @com.fasterxml.jackson.databind.annotation.JsonSerialize(using = com.fasterxml.jackson.databind.ser.std.ToStringSerializer.class)
     private BigDecimal completeness;
 
     /** 发音得分（0-100） */
     @Excel(name = "发音得分", cellType = ColumnType.NUMERIC)
+    @com.fasterxml.jackson.databind.annotation.JsonSerialize(using = com.fasterxml.jackson.databind.ser.std.ToStringSerializer.class)
     private BigDecimal pronunciation;
 
     /** 评测详情（JSON格式，包含详细评测结果） */

Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/service/voice/DeepSeekService.java

@@ -123,13 +123,27 @@ public class DeepSeekService {
         return parseEvaluationResponse(response, recognizedText, standardText);
     }
     
+    /**
+     * 清理文本：去除标点符号和空格，只保留文字
+     */
+    private String cleanText(String text) {
+        if (text == null) return "";
+        // 去除所有标点符号(\p{P})、符号(\p{S})和空格(\s)，只保留中文、英文、数字
+        return text.replaceAll("[\\p{P}\\p{S}\\s]+", "").toLowerCase();
+    }
+    
     /**
      * 构建评测提示词
      */
     private String buildEvaluationPrompt(String recognizedText, String standardText) {
+        // ✅ 清理标点符号，只评测文字内容
+        String cleanRecognized = cleanText(recognizedText);
+        String cleanStandard = cleanText(standardText);
+        
         return "你是一位专业的语音评测专家。请对以下语音识别结果进行评测:\n\n" +
-            "[标准文本] " + standardText + "\n" +
+            "[标准文本] " + cleanStandard + "\n" +
-            "[识别文本] " + recognizedText + "\n\n" +
+            "[识别文本] " + cleanRecognized + "\n\n" +
+            "注意：已去除标点符号，只评测文字内容。\n\n" +
             "请从以下维度进行评分(每项0-100分):\n" +
             "1. 准确度(accuracy): 文本是否与标准一致\n" +
             "2. 完整度(completeness): 是否完整表达了标准内容\n" +
@@ -218,8 +232,9 @@ public class DeepSeekService {
      * 计算文本相似度
      */
     private double calculateSimilarity(String text1, String text2) {
-        String clean1 = text1.replaceAll("\\s+", "").toLowerCase();
+        // ✅ 使用 cleanText 方法，自动去除标点符号和空格
-        String clean2 = text2.replaceAll("\\s+", "").toLowerCase();
+        String clean1 = cleanText(text1);
+        String clean2 = cleanText(text2);
         
         int maxLen = Math.max(clean1.length(), clean2.length());
         if (maxLen == 0) return 1.0;

Study-Vue-redis/ry-study-ui/src/views/study/voiceEvaluation/index.vue

@@ -261,7 +261,7 @@
     </el-dialog>
 
     <!-- 查看对话框 -->
-    <el-dialog title="语音评测详情" :visible.sync="viewOpen" width="800px" append-to-body>
+    <el-dialog title="语音评测详情" :visible.sync="viewOpen" width="800px" append-to-body :close-on-click-modal="false" class="view-dialog">
       <el-descriptions :column="2" border>
         <el-descriptions-item label="评测ID">{{ form.id }}</el-descriptions-item>
         <el-descriptions-item label="学员姓名">{{ form.studentName }}</el-descriptions-item>
@@ -271,19 +271,20 @@
           <el-tag v-if="form.isSubmitted === 1" type="success" size="small">已提交</el-tag>
           <el-tag v-else type="info" size="small">未提交</el-tag>
         </el-descriptions-item>
-        <el-descriptions-item label="提交时间" v-if="form.submitTime">
+        <el-descriptions-item label="提交时间">
-          {{ parseTime(form.submitTime, '{y}-{m}-{d} {h}:{i}:{s}') }}
+          <span v-if="form.submitTime">{{ parseTime(form.submitTime, '{y}-{m}-{d} {h}:{i}:{s}') }}</span>
+          <span v-else style="color: #909399;">未提交</span>
         </el-descriptions-item>
         <el-descriptions-item label="评测内容" :span="2">
           <div style="max-height: 100px; overflow-y: auto;">{{ form.content }}</div>
         </el-descriptions-item>
-        <el-descriptions-item label="总分">
+        <el-descriptions-item label="总分" :span="2">
-          <span style="color: #409eff; font-weight: bold; font-size: 18px;">{{ form.score || 0 }}分</span>
+          <span style="color: #409eff; font-weight: bold; font-size: 18px;">{{ parseFloat(form.score || 0).toFixed(0) }}分</span>
         </el-descriptions-item>
-        <el-descriptions-item label="准确度">{{ form.accuracy || 0 }}分</el-descriptions-item>
+        <el-descriptions-item label="准确度">{{ parseFloat(form.accuracy || 0).toFixed(0) }}分</el-descriptions-item>
-        <el-descriptions-item label="流畅度">{{ form.fluency || 0 }}分</el-descriptions-item>
+        <el-descriptions-item label="完整度">{{ parseFloat(form.completeness || 0).toFixed(0) }}分</el-descriptions-item>
-        <el-descriptions-item label="完整度">{{ form.completeness || 0 }}分</el-descriptions-item>
+        <el-descriptions-item label="流畅度">{{ parseFloat(form.fluency || 0).toFixed(0) }}分</el-descriptions-item>
-        <el-descriptions-item label="发音">{{ form.pronunciation || 0 }}分</el-descriptions-item>
+        <el-descriptions-item label="发音">{{ parseFloat(form.pronunciation || 0).toFixed(0) }}分</el-descriptions-item>
         <el-descriptions-item label="音频文件" :span="2">
           <div style="margin-top: 10px;">
             <div v-if="form.audioPath">
@@ -317,7 +318,7 @@
           </div>
         </el-descriptions-item>
         <el-descriptions-item label="详细结果" :span="2" v-if="form.resultDetail">
-          <pre style="max-height: 200px; overflow-y: auto; background-color: #f5f5f5; padding: 10px; border-radius: 4px;">{{ formatJson(form.resultDetail) }}</pre>
+          <pre class="result-detail-pre">{{ formatJson(form.resultDetail) }}</pre>
         </el-descriptions-item>
       </el-descriptions>
       <div slot="footer" class="dialog-footer">
@@ -632,5 +633,38 @@ export default {
 </script>
 
 <style scoped>
+/* 详细结果区域样式 */
+.result-detail-pre {
+  max-height: 200px;
+  overflow-y: auto;
+  background-color: #f5f7fa;
+  padding: 12px;
+  border-radius: 4px;
+  border: none;
+  margin: 0;
+  font-size: 13px;
+  line-height: 1.6;
+  white-space: pre-wrap;
+  word-break: break-all;
+  font-family: Consolas, Monaco, 'Courier New', monospace;
+}
+</style>
+
+<style>
+/* 详情弹窗样式（不使用scoped，确保覆盖element-ui默认样式） */
+.view-dialog.el-dialog__wrapper {
+  display: flex;
+  align-items: flex-start;
+  justify-content: center;
+  padding-top: 10vh;
+}
+.view-dialog .el-dialog {
+  margin: 0 !important;
+}
+.view-dialog .el-dialog__body {
+  padding: 20px;
+  max-height: 70vh;
+  overflow-y: auto;
+}
 </style>
 

Test/python/whisper_server (1).py

@@ -1,292 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-本地Whisper语音识别服务
-替代百度API，完全离线运行
-
-安装：
-pip install openai-whisper flask flask-cors
-
-运行：
-python whisper_server.py
-
-优点：
-1. 完全免费，无限次调用
-2. 离线运行，不需要网络
-3. 识别准确率高（接近百度API）
-4. 支持中文、英文等多语言
-"""
-
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import whisper
-import os
-import tempfile
-import logging
-
-# 繁简体转换
-try:
-    import zhconv
-    HAS_ZHCONV = True
-except ImportError:
-    HAS_ZHCONV = False
-    print("⚠️ 未安装zhconv库，无法进行繁简体转换")
-    print("   安装命令: pip install zhconv")
-
-app = Flask(__name__)
-CORS(app)
-
-# 配置日志
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# 全局变量：Whisper模型
-whisper_model = None
-
-def load_whisper_model():
-    """加载Whisper模型（懒加载）"""
-    global whisper_model
-    if whisper_model is None:
-        logger.info("正在加载Whisper模型...")
-        # 使用base模型（平衡速度和准确度）
-        # 可选: tiny, base, small, medium, large
-        # tiny: 最快，准确度一般
-        # base: 快速，准确度好 ✅ 推荐
-        # small: 较慢，准确度高
-        # medium/large: 很慢，准确度最高
-        whisper_model = whisper.load_model("base")
-        logger.info("✅ Whisper模型加载成功")
-    return whisper_model
-
-def convert_to_simplified(text):
-    """
-    将繁体中文转换为简体中文
-    
-    参数:
-        text: 待转换的文本
-    
-    返回:
-        简体中文文本
-    """
-    if not text:
-        return text
-    
-    if HAS_ZHCONV:
-        try:
-            # 使用zhconv进行繁简转换
-            simplified = zhconv.convert(text, 'zh-cn')
-            logger.info(f"繁简转换: {text} -> {simplified}")
-            return simplified
-        except Exception as e:
-            logger.warning(f"繁简转换失败: {e}")
-            return text
-    else:
-        # 如果没有安装zhconv，返回原文
-        return text
-
-@app.route('/health', methods=['GET'])
-def health():
-    """健康检查"""
-    return jsonify({
-        "status": "ok",
-        "service": "Whisper语音识别服务",
-        "model": "base"
-    })
-
-@app.route('/recognize', methods=['POST'])
-def recognize():
-    """
-    语音识别接口
-    
-    参数：
-    - file: 音频文件（支持MP3, WAV, M4A等）
-    - language: 语言（可选，默认自动检测）
-    
-    返回：
-    {
-        "code": 200,
-        "msg": "识别成功",
-        "data": {
-            "text": "识别的文本",
-            "language": "zh",
-            "confidence": 0.95
-        }
-    }
-    """
-    try:
-        # 检查是否有文件
-        if 'file' not in request.files:
-            return jsonify({
-                "code": 400,
-                "msg": "未找到音频文件",
-                "data": None
-            }), 400
-        
-        audio_file = request.files['file']
-        language = request.form.get('language', 'zh')  # 默认中文
-        
-        # 保存临时文件
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
-            audio_file.save(temp_file.name)
-            temp_path = temp_file.name
-        
-        try:
-            # 加载模型
-            model = load_whisper_model()
-            
-            # 识别音频
-            logger.info(f"开始识别音频: {audio_file.filename}")
-            result = model.transcribe(
-                temp_path,
-                language=language,
-                task='transcribe',  # transcribe=识别，translate=翻译成英文
-                fp16=False  # CPU模式
-            )
-            
-            recognized_text = result['text'].strip()
-            detected_language = result.get('language', language)
-            
-            # ✅ 繁体转简体
-            if detected_language == 'zh':
-                recognized_text = convert_to_simplified(recognized_text)
-            
-            logger.info(f"✅ 识别成功: {recognized_text}")
-            
-            return jsonify({
-                "code": 200,
-                "msg": "识别成功",
-                "data": {
-                    "text": recognized_text,
-                    "language": detected_language,
-                    "segments": len(result.get('segments', [])),
-                    "duration": result.get('duration', 0)
-                }
-            })
-            
-        finally:
-            # 删除临时文件
-            if os.path.exists(temp_path):
-                os.remove(temp_path)
-    
-    except Exception as e:
-        logger.error(f"识别失败: {str(e)}", exc_info=True)
-        return jsonify({
-            "code": 500,
-            "msg": f"识别失败: {str(e)}",
-            "data": None
-        }), 500
-
-@app.route('/evaluate', methods=['POST'])
-def evaluate():
-    """
-    语音评测接口（完整功能）
-    
-    参数：
-    - file: 音频文件
-    - text: 标准文本（用于对比）
-    
-    返回：
-    {
-        "code": 200,
-        "msg": "评测成功",
-        "data": {
-            "text": "识别的文本",
-            "score": 95,
-            "accuracy": 98,
-            "fluency": 92,
-            "completeness": 95,
-            "pronunciation": 94
-        }
-    }
-    """
-    try:
-        if 'file' not in request.files:
-            return jsonify({"code": 400, "msg": "未找到音频文件", "data": None}), 400
-        
-        audio_file = request.files['file']
-        standard_text = request.form.get('text', '')
-        
-        # 保存临时文件
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
-            audio_file.save(temp_file.name)
-            temp_path = temp_file.name
-        
-        try:
-            # 1. 识别音频
-            model = load_whisper_model()
-            result = model.transcribe(temp_path, language='zh', fp16=False)
-            recognized_text = result['text'].strip()
-            
-            # ✅ 繁体转简体
-            recognized_text = convert_to_simplified(recognized_text)
-            
-            # 2. 计算评分
-            from difflib import SequenceMatcher
-            
-            # 清理文本
-            clean_recognized = ''.join(recognized_text.split()).lower()
-            clean_standard = ''.join(standard_text.split()).lower()
-            
-            # 相似度
-            similarity = SequenceMatcher(None, clean_recognized, clean_standard).ratio()
-            
-            # 计算各项评分
-            accuracy = similarity * 100  # 准确度
-            completeness = min(len(clean_recognized) / max(len(clean_standard), 1) * 100, 100)  # 完整度
-            fluency = accuracy * 0.95  # 流利度（基于准确度）
-            pronunciation = accuracy * 0.98  # 发音（基于准确度）
-            
-            # 总分
-            total_score = (accuracy * 0.3 + completeness * 0.25 + fluency * 0.3 + pronunciation * 0.15)
-            
-            logger.info(f"✅ 评测完成: {recognized_text} | 得分: {total_score:.0f}")
-            
-            return jsonify({
-                "code": 200,
-                "msg": "评测成功",
-                "data": {
-                    "text": recognized_text,
-                    "score": round(total_score),
-                    "accuracy": round(accuracy),
-                    "fluency": round(fluency),
-                    "completeness": round(completeness),
-                    "pronunciation": round(pronunciation),
-                    "similarity": round(similarity * 100, 2)
-                }
-            })
-            
-        finally:
-            if os.path.exists(temp_path):
-                os.remove(temp_path)
-    
-    except Exception as e:
-        logger.error(f"评测失败: {str(e)}", exc_info=True)
-        return jsonify({"code": 500, "msg": f"评测失败: {str(e)}", "data": None}), 500
-
-if __name__ == '__main__':
-    print("=" * 60)
-    print("🎤 本地Whisper语音识别服务")
-    print("=" * 60)
-    print("")
-    print("✅ 优势：")
-    print("  1. 完全免费，无限次调用")
-    print("  2. 离线运行，不需要网络")
-    print("  3. 识别准确率高")
-    print("  4. 数据完全私有")
-    print("")
-    print("📦 安装依赖：")
-    print("  pip install openai-whisper flask flask-cors")
-    print("")
-    print("🚀 启动服务：")
-    print("  python whisper_server.py")
-    print("")
-    print("📌 API接口：")
-    print("  健康检查: GET  http://localhost:5001/health")
-    print("  语音识别: POST http://localhost:5001/recognize")
-    print("  语音评测: POST http://localhost:5001/evaluate")
-    print("")
-    print("=" * 60)
-    print("")
-    
-    # 启动服务
-    app.run(host='0.0.0.0', port=5001, debug=True)

fronted_uniapp/pages/speech/speech.vue

@@ -246,7 +246,7 @@ export default {
 			pageUnloaded: false, // 页面卸载标记
 			recordStartTime: 0, // 录音开始时间
 			recordingFailCount: 0, // 录音失败次数
-			lastRecordedFilePath: null // ✅ 保存最后一次录音的文件路径
+			lastRecordedFilePath: null // ✅ 保存服务器返回的音频路径
 		}
 	},
 	onLoad(options) {
@@ -506,37 +506,77 @@ export default {
 				throw new Error('录音文件获取失败')
 			}
 			
-			// 2. 上传并评测（上传后会从服务器返回保存到数据库的路径）
+			// 2. ✅ 上传并评测（使用 uploadAndEvaluate 一次性完成识别+评测）
-			console.log('[Speech] 开始上传评测...')
+			console.log('[Speech] ========================================')
-			const { uploadAndRecognize } = await import('@/api/study/voiceEvaluation.js')
+			console.log('[Speech] 🎯 新版本代码 v2.0 - 统一评测模式')
-			const evalResult = await uploadAndRecognize(
+			console.log('[Speech] 准备调用 uploadAndEvaluate')
+			console.log('[Speech] 文件路径:', filePath)
+			console.log('[Speech] 文件类型:', typeof filePath)
+			console.log('[Speech] 评测内容:', this.selectedContent?.content)
+			console.log('[Speech] ========================================')
+			
+			// 显示版本提示
+			uni.showToast({
+				title: '新版本v2.0-统一评测',
+				icon: 'none',
+				duration: 1500
+			})
+			
+			const { uploadAndEvaluate } = await import('@/api/study/voiceEvaluation.js')
+			const evalResult = await uploadAndEvaluate(
 				filePath,
-				this.selectedContent?.content || '测试文本'
+				this.selectedContent?.content || '测试文本',
+				null,
+				'zh-CN'
 			)
 			
 			uni.hideLoading()
 			
-			if (evalResult.code === 200 && evalResult.data) {
+			// 🔍 调试：输出完整返回数据
-				const data = evalResult.data
+			console.log('[Speech] 返回结果 code:', evalResult.code)
-				this.recognizedText = data.recognizedText || ''
+			console.log('[Speech] 返回结果:', JSON.stringify(evalResult))
-				this.scoreResult = {
+			console.log('[Speech] 是否有evaluation:', !!evalResult.evaluation)
-					score: data.totalScore || 0,
-					pronunciationScore: data.pronunciation || 0,
-					fluencyScore: data.fluency || 0
-				}
 			
-				// ✅ 使用服务器返回的相对路径（如 /profile/upload/voice/2025/12/11/xxx.mp3）
+			// ✅ 只检查 code === 200，不强制要求 evaluation
-				if (data.audioPath) {
+			if (evalResult.code === 200) {
-					this.lastRecordedFilePath = data.audioPath
+				// 获取评测数据（可能在 evaluation 或 data 中）
-					console.log('[Speech] 保存服务器音频路径:', data.audioPath)
+				const evaluation = evalResult.evaluation || evalResult.data || {}
+				const recognizedText = evalResult.recognizedText || evaluation.recognizedText || ''
+				
+				// ✅ 设置识别文本
+				this.recognizedText = recognizedText
+				console.log('[Speech] 识别文本:', this.recognizedText)
+				
+				// ✅ 设置评分结果（兼容多种数据格式）
+				this.scoreResult = {
+					totalScore: Number(evaluation.score) || Number(evaluation.totalScore) || 0,
+					accuracy: Number(evaluation.accuracy) || 0,
+					completeness: Number(evaluation.completeness) || 0,
+					fluency: Number(evaluation.fluency) || 0,
+					pronunciation: Number(evaluation.pronunciation) || 0
+				}
+				console.log('[Speech] 评分结果:', this.scoreResult)
+				
+				// ✅ 保存评测ID和提交状态
+				this.currentEvaluationId = evaluation.id
+				const submitted = evaluation.isSubmitted
+				this.isSubmitted = submitted === 1 || submitted === true || submitted === '1'
+				
+				// ✅ 保存服务器音频路径
+				if (evaluation.audioPath) {
+					this.lastRecordedFilePath = evaluation.audioPath
+					console.log('[Speech] 服务器音频路径:', evaluation.audioPath)
 				}
 				
 				this.hasFirstResult = true
 				this.statusText = '评测完成'
-				this.debugInfo = `得分：${data.totalScore}分`
+				const finalScore = this.scoreResult.totalScore
+				this.debugInfo = `得分：${finalScore}分`
 				this.recordingFailCount = 0
-				uni.showToast({ title: `得分：${data.totalScore}分`, icon: 'success' })
+				uni.showToast({ title: `得分：${finalScore}分`, icon: 'success' })
 			} else {
+				console.error('[Speech] ❌ 评测失败，返回码:', evalResult.code)
+				console.error('[Speech] evalResult:', evalResult)
 				throw new Error(evalResult.msg || '评测失败')
 			}
 		} catch (error) {
@@ -677,27 +717,14 @@ export default {
 			if (this.scrollTimer) { clearInterval(this.scrollTimer); this.scrollTimer = null }
 		},
 		async evaluateScore() {
-			if (!this.recognizedText || !this.selectedContent) {
+			// ✅ 已经在停止录音时完成评分，这里不需要再次评分
-				uni.showToast({ title: '请先完成语音识别', icon: 'none' })
+			if (this.scoreResult && this.scoreResult.totalScore !== undefined) {
+				uni.showToast({ title: '已经评分完成', icon: 'none' })
+				this.$nextTick(() => { uni.pageScrollTo({ selector: '.score-section', duration: 300 }) })
 				return
 			}
-			this.isEvaluating = true
+			
-			try {
+			uni.showToast({ title: '请先录音', icon: 'none' })
-				const result = await evaluateSpeechRecognition(this.selectedContent.content, this.recognizedText)
-				if (result.code === 200 && result.data) {
-					this.scoreResult = result.data
-					await this.saveScoreToBackend(result.data)
-					uni.showToast({ title: '评分完成', icon: 'success', duration: 2000 })
-					this.$nextTick(() => { uni.pageScrollTo({ selector: '.score-section', duration: 300 }) })
-				} else {
-					throw new Error(result.msg || '评分失败')
-				}
-			} catch (error) {
-				console.error('评分失败', error)
-				uni.showToast({ title: error.message || '评分失败', icon: 'none', duration: 3000 })
-			} finally {
-				this.isEvaluating = false
-			}
 		},
 		async saveScoreToBackend(scoreData) {
 			if (!this.selectedContent || !this.recognizedText) return
@@ -769,6 +796,7 @@ export default {
 			this.scoreResult = null
 			this.currentEvaluationId = null
 			this.isSubmitted = false
+			this.lastRecordedFilePath = null // ✅ 清空服务器路径
 		},
 		getDifficultyText(difficulty) {
 			const map = { 'easy': '简单', 'medium': '中等', 'hard': '困难' }

fronted_uniapp/新方案-快速开始.md

@@ -1,6 +1,4 @@
-# 语音评测新方案 - 快速开始
 
-## ✅ 已完成
 
 1. ✅ **禁用 UTS 插件** - 不再依赖本地编译
 2. ✅ **创建录音工具** - `utils/speech-recorder.js`

log/AI指南/1-AI指南.md

@@ -26,7 +26,7 @@
 - **路径**: `Study-Vue-redis/`
 - **技术栈**: Spring Boot + MyBatis + Vue.js
 - **说明**: 包含后端API服务和管理后台前端
-
+4-
 ### 2. APP项目
 - **路径**: `fronted_uniapp/`
 - **技术栈**: uni-app (Vue.js)

log/whisper_server (1).py

@@ -0,0 +1,555 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Whisper语音识别服务
+支持语音识别和评测功能
+多线程并发处理
+作者: AI Assistant
+版本: 2.0
+"""
+
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import whisper
+import os
+import tempfile
+import logging
+from logging.handlers import RotatingFileHandler
+import traceback
+from datetime import datetime
+import sys
+
+# ============================================
+# 日志配置
+# ============================================
+def setup_logging():
+    """配置日志系统"""
+    # 创建logger
+    logger = logging.getLogger('whisper_server')
+    logger.setLevel(logging.INFO)
+    
+    # 控制台输出
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(logging.INFO)
+    console_formatter = logging.Formatter(
+        '%(asctime)s [%(levelname)s] %(message)s',
+        datefmt='%Y-%m-%d %H:%M:%S'
+    )
+    console_handler.setFormatter(console_formatter)
+    logger.addHandler(console_handler)
+    
+    # 文件输出（可选）
+    try:
+        log_dir = 'logs'
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir)
+        
+        file_handler = RotatingFileHandler(
+            os.path.join(log_dir, 'whisper_server.log'),
+            maxBytes=10*1024*1024,  # 10MB
+            backupCount=5,
+            encoding='utf-8'
+        )
+        file_handler.setLevel(logging.INFO)
+        file_handler.setFormatter(console_formatter)
+        logger.addHandler(file_handler)
+    except Exception as e:
+        logger.warning(f"无法创建日志文件: {e}")
+    
+    return logger
+
+# 初始化日志
+logger = setup_logging()
+
+# ============================================
+# Flask应用配置
+# ============================================
+app = Flask(__name__)
+CORS(app)  # 允许跨域请求
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 最大16MB
+
+# ============================================
+# 全局变量
+# ============================================
+whisper_model = None
+MODEL_NAME = "tiny"  # 可选: tiny, base, small, medium, large
+
+# ============================================
+# 模型加载
+# ============================================
+def load_whisper_model():
+    """加载Whisper模型（全局单例）"""
+    global whisper_model
+    if whisper_model is None:
+        logger.info(f"正在加载Whisper模型 ({MODEL_NAME})...")
+        try:
+            whisper_model = whisper.load_model(MODEL_NAME)
+            logger.info(f"✅ Whisper模型加载成功 ({MODEL_NAME})")
+        except Exception as e:
+            logger.error(f"❌ 模型加载失败: {e}")
+            raise
+    return whisper_model
+
+# ============================================
+# 工具函数
+# ============================================
+def convert_to_simplified(text):
+    """将繁体中文转换为简体中文"""
+    if not text:
+        return text
+    
+    # 方案1：使用zhconv（推荐，纯Python）
+    try:
+        import zhconv
+        result = zhconv.convert(text, 'zh-cn')
+        if result != text:
+            logger.info(f"繁简转换: {text} -> {result}")
+        return result
+    except ImportError:
+        pass
+    
+    # 方案2：使用opencc
+    try:
+        from opencc import OpenCC
+        cc = OpenCC('t2s')
+        result = cc.convert(text)
+        if result != text:
+            logger.info(f"繁简转换(OpenCC): {text} -> {result}")
+        return result
+    except ImportError:
+        pass
+    
+    # 都没安装，返回原文
+    logger.warning("繁简转换库未安装，请运行: pip install zhconv")
+    return text
+
+def clean_text_strict(text):
+    """
+    严格清理文本，只保留汉字、字母、数字
+    用于准确度计算
+    """
+    import re
+    import unicodedata
+    
+    if not text:
+        return ""
+    
+    # Unicode标准化
+    text = unicodedata.normalize('NFKC', text)
+    
+    # 只保留汉字、字母、数字
+    text = re.sub(r'[^\u4e00-\u9fffa-zA-Z0-9]', '', text)
+    
+    return text.lower()
+
+# ============================================
+# API路由
+# ============================================
+
+@app.route('/health', methods=['GET'])
+def health_check():
+    """健康检查接口"""
+    return jsonify({
+        "status": "ok",
+        "service": "Whisper Speech Recognition",
+        "model": MODEL_NAME,
+        "version": "2.0",
+        "timestamp": datetime.now().isoformat()
+    })
+
+@app.route('/recognize', methods=['POST'])
+def recognize():
+    """
+    语音识别接口
+    只识别，不评测
+    
+    参数:
+        file: 音频文件 (multipart/form-data)
+        
+    返回:
+        {
+            "code": 200,
+            "msg": "识别成功",
+            "data": {
+                "text": "识别的文本"
+            }
+        }
+    """
+    try:
+        # 检查文件
+        if 'file' not in request.files:
+            logger.warning("请求缺少音频文件")
+            return jsonify({"code": 400, "msg": "缺少音频文件", "data": None}), 400
+        
+        audio_file = request.files['file']
+        if audio_file.filename == '':
+            logger.warning("文件名为空")
+            return jsonify({"code": 400, "msg": "文件名为空", "data": None}), 400
+        
+        logger.info(f"收到识别请求: {audio_file.filename} ({len(audio_file.read())} bytes)")
+        audio_file.seek(0)  # 重置文件指针
+        
+        # 保存临时文件
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
+            temp_path = temp_file.name
+            audio_file.save(temp_path)
+        
+        try:
+            # 加载模型
+            model = load_whisper_model()
+            
+            # 识别音频
+            logger.info(f"开始识别: {audio_file.filename}")
+            result = model.transcribe(temp_path, language='zh', fp16=False)
+            recognized_text = result['text'].strip()
+            
+            # 繁体转简体
+            recognized_text = convert_to_simplified(recognized_text)
+            
+            logger.info(f"✅ 识别成功: {recognized_text}")
+            
+            return jsonify({
+                "code": 200,
+                "msg": "识别成功",
+                "data": {
+                    "text": recognized_text
+                }
+            })
+            
+        finally:
+            # 删除临时文件
+            try:
+                if os.path.exists(temp_path):
+                    os.remove(temp_path)
+            except Exception as e:
+                logger.warning(f"删除临时文件失败: {e}")
+                
+    except Exception as e:
+        logger.error(f"识别失败: {str(e)}")
+        logger.error(traceback.format_exc())
+        return jsonify({
+            "code": 500,
+            "msg": f"识别失败: {str(e)}",
+            "data": None
+        }), 500
+
+@app.route('/evaluate', methods=['POST'])
+def evaluate():
+    """
+    语音评测接口
+    识别 + 评分
+    
+    参数:
+        file: 音频文件 (multipart/form-data)
+        text: 标准文本 (form-data)
+        
+    返回:
+        {
+            "code": 200,
+            "msg": "评测成功",
+            "data": {
+                "text": "识别的文本",
+                "score": 95,
+                "accuracy": 98,
+                "fluency": 95,
+                "completeness": 100,
+                "pronunciation": 96,
+                "similarity": 98.5
+            }
+        }
+    """
+    try:
+        # 检查文件
+        if 'file' not in request.files:
+            logger.warning("请求缺少音频文件")
+            return jsonify({"code": 400, "msg": "缺少音频文件", "data": None}), 400
+        
+        audio_file = request.files['file']
+        standard_text = request.form.get('text', '')
+        
+        if not standard_text:
+            logger.warning("请求缺少标准文本")
+            return jsonify({"code": 400, "msg": "缺少标准文本", "data": None}), 400
+        
+        logger.info(f"收到评测请求: {audio_file.filename}, 标准文本: {standard_text}")
+        
+        # 保存临时文件
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
+            temp_path = temp_file.name
+            audio_file.save(temp_path)
+        
+        try:
+            # 1. 识别音频
+            model = load_whisper_model()
+            logger.info("开始识别音频...")
+            result = model.transcribe(temp_path, language='zh', fp16=False)
+            recognized_text = result['text'].strip()
+            recognized_text = convert_to_simplified(recognized_text)
+            
+            # 2. 计算评分
+            from difflib import SequenceMatcher
+            
+            # 清理文本
+            clean_recognized = clean_text_strict(recognized_text)
+            clean_standard = clean_text_strict(standard_text)
+            
+            # 相似度
+            similarity = SequenceMatcher(None, clean_recognized, clean_standard).ratio()
+            
+            # 调试日志
+            logger.info(f"📝 标准文本: {standard_text}")
+            logger.info(f"🎤 识别文本: {recognized_text}")
+            logger.info(f"🧹 清理后标准: {clean_standard}")
+            logger.info(f"🧹 清理后识别: {clean_recognized}")
+            logger.info(f"📊 相似度: {similarity:.4f} ({similarity*100:.2f}%)")
+            
+            # 准确度计算（带阈值优化）
+            if similarity >= 0.98:
+                accuracy = 100
+                logger.info(f"✅ 相似度>=98%，准确度给满分: {accuracy}")
+            elif similarity >= 0.95:
+                accuracy = 99
+                logger.info(f"✅ 相似度>=95%，准确度给99分: {accuracy}")
+            else:
+                accuracy = similarity * 100
+                logger.info(f"📊 准确度: {accuracy:.2f}")
+            
+            # 完整度
+            completeness = min(len(clean_recognized) / max(len(clean_standard), 1) * 100, 100)
+            
+            # 流畅度和发音（如果准确度满分，其他也满分）
+            fluency = 100 if accuracy == 100 else accuracy * 0.95
+            pronunciation = 100 if accuracy == 100 else accuracy * 0.98
+            
+            # 总分（加权）
+            total_score = (accuracy * 0.3 + completeness * 0.25 + fluency * 0.3 + pronunciation * 0.15)
+            
+            logger.info(f"✅ 评测完成: 总分={total_score:.0f}, 准确度={accuracy:.0f}, 流畅度={fluency:.0f}, 完整度={completeness:.0f}, 发音={pronunciation:.0f}")
+            
+            return jsonify({
+                "code": 200,
+                "msg": "评测成功",
+                "data": {
+                    "text": recognized_text,
+                    "score": round(total_score),
+                    "accuracy": round(accuracy),
+                    "fluency": round(fluency),
+                    "completeness": round(completeness),
+                    "pronunciation": round(pronunciation),
+                    "similarity": round(similarity * 100, 2)
+                }
+            })
+            
+        finally:
+            # 删除临时文件
+            try:
+                if os.path.exists(temp_path):
+                    os.remove(temp_path)
+            except Exception as e:
+                logger.warning(f"删除临时文件失败: {e}")
+                
+    except Exception as e:
+        logger.error(f"评测失败: {str(e)}")
+        logger.error(traceback.format_exc())
+        return jsonify({
+            "code": 500,
+            "msg": f"评测失败: {str(e)}",
+            "data": None
+        }), 500
+
+# ============================================
+# 错误处理
+# ============================================
+@app.errorhandler(413)
+def request_entity_too_large(error):
+    logger.warning("请求文件过大")
+    return jsonify({
+        "code": 413,
+        "msg": "文件太大，最大支持16MB",
+        "data": None
+    }), 413
+
+@app.errorhandler(500)
+def internal_error(error):
+    logger.error(f"服务器内部错误: {error}")
+    return jsonify({
+        "code": 500,
+        "msg": "服务器内部错误",
+        "data": None
+    }), 500
+
+@app.errorhandler(404)
+def not_found(error):
+    return jsonify({
+        "code": 404,
+        "msg": "接口不存在",
+        "data": None
+    }), 404
+
+# ============================================
+# 环境检查
+# ============================================
+def check_environment():
+    """检查运行环境和依赖"""
+    logger.info("")
+    logger.info("=" * 70)
+    logger.info("🔍 环境检查")
+    logger.info("=" * 70)
+    
+    all_ok = True
+    
+    # 1. 检查 Python 版本
+    py_version = sys.version.split()[0]
+    logger.info(f"  Python版本:    {py_version} ✅")
+    
+    # 2. 检查 whisper
+    try:
+        import whisper
+        logger.info(f"  Whisper:       已安装 ✅")
+    except ImportError:
+        logger.error(f"  Whisper:       未安装 ❌ (pip install openai-whisper)")
+        all_ok = False
+    
+    # 3. 检查 Flask
+    try:
+        import flask
+        logger.info(f"  Flask:         {flask.__version__} ✅")
+    except ImportError:
+        logger.error(f"  Flask:         未安装 ❌ (pip install flask)")
+        all_ok = False
+    
+    # 4. 检查 flask-cors
+    try:
+        import flask_cors
+        logger.info(f"  Flask-CORS:    已安装 ✅")
+    except ImportError:
+        logger.error(f"  Flask-CORS:    未安装 ❌ (pip install flask-cors)")
+        all_ok = False
+    
+    # 5. 检查 waitress
+    try:
+        import waitress
+        logger.info(f"  Waitress:      已安装 ✅")
+    except ImportError:
+        logger.error(f"  Waitress:      未安装 ❌ (pip install waitress)")
+        all_ok = False
+    
+    # 6. 检查繁简转换库
+    zhconv_ok = False
+    opencc_ok = False
+    
+    try:
+        import zhconv
+        zhconv_ok = True
+        logger.info(f"  zhconv:        已安装 ✅ (繁简转换)")
+    except ImportError:
+        pass
+    
+    try:
+        from opencc import OpenCC
+        opencc_ok = True
+        logger.info(f"  OpenCC:        已安装 ✅ (繁简转换备用)")
+    except ImportError:
+        pass
+    
+    if not zhconv_ok and not opencc_ok:
+        logger.warning(f"  繁简转换:      未安装 ⚠️  (pip install zhconv)")
+        logger.warning(f"                 识别结果可能包含繁体字！")
+    
+    # 7. 检查 ffmpeg（Whisper需要）
+    import shutil
+    if shutil.which('ffmpeg'):
+        logger.info(f"  FFmpeg:        已安装 ✅")
+    else:
+        logger.warning(f"  FFmpeg:        未找到 ⚠️  (某些音频格式可能无法处理)")
+    
+    logger.info("=" * 70)
+    
+    if all_ok:
+        logger.info("✅ 环境检查通过！")
+    else:
+        logger.error("❌ 缺少必要依赖，请先安装后再启动！")
+        sys.exit(1)
+    
+    logger.info("")
+    return all_ok
+
+# ============================================
+# 启动服务（多线程模式）
+# ============================================
+if __name__ == '__main__':
+    try:
+        # 环境检查
+        check_environment()
+        
+        from waitress import serve
+        
+        # 打印启动信息
+        logger.info("=" * 70)
+        logger.info("🚀 正在启动Whisper语音识别服务...")
+        logger.info("=" * 70)
+        
+        # 预加载模型
+        load_whisper_model()
+        
+        # 启动信息
+        logger.info("")
+        logger.info("=" * 70)
+        logger.info("✅ Whisper服务启动成功！")
+        logger.info("=" * 70)
+        logger.info(f"📍 本地地址:   http://127.0.0.1:5001")
+        logger.info(f"📍 局域网地址: http://0.0.0.0:5001")
+        logger.info(f"📍 访问地址:   http://192.168.0.106:5001")
+        logger.info("=" * 70)
+        logger.info(f"⚙️  运行模式:   多线程并发")
+        logger.info(f"⚙️  Whisper模型: {MODEL_NAME}")
+        logger.info(f"⚙️  工作线程:   8 个")
+        logger.info(f"⚙️  并发能力:   40-60 人同时使用")
+        logger.info(f"⚙️  超时时间:   300 秒")
+        logger.info(f"⚙️  最大连接:   100 个")
+        logger.info(f"⚙️  最大文件:   16 MB")
+        logger.info("=" * 70)
+        logger.info("")
+        logger.info("📌 API接口列表：")
+        logger.info("  [GET]  /health      - 健康检查")
+        logger.info("  [POST] /recognize   - 语音识别（只识别）")
+        logger.info("  [POST] /evaluate    - 语音评测（识别+评分）")
+        logger.info("=" * 70)
+        logger.info("")
+        logger.info("💡 使用示例：")
+        logger.info("  健康检查: curl http://192.168.0.106:5001/health")
+        logger.info("  语音识别: curl -F 'file=@audio.mp3' http://192.168.0.106:5001/recognize")
+        logger.info("  语音评测: curl -F 'file=@audio.mp3' -F 'text=你好' http://192.168.0.106:5001/evaluate")
+        logger.info("=" * 70)
+        logger.info("")
+        logger.info("✨ 服务已就绪，等待请求...")
+        logger.info("✨ 按 Ctrl+C 停止服务")
+        logger.info("")
+        
+        # 使用waitress启动（支持多线程）
+        serve(
+            app,
+            host='0.0.0.0',
+            port=5001,
+            threads=8,                    # 8个工作线程，支持40-60人并发
+            channel_timeout=300,          # 单个请求超时5分钟
+            connection_limit=100,         # 最多100个并发连接
+            backlog=64,                   # 连接队列长度
+            recv_bytes=65536,             # 接收缓冲区 64KB
+            send_bytes=65536,             # 发送缓冲区 64KB
+            url_scheme='http'
+        )
+        
+    except KeyboardInterrupt:
+        logger.info("")
+        logger.info("=" * 70)
+        logger.info("⏹️  收到停止信号，正在关闭服务...")
+        logger.info("=" * 70)
+        logger.info("👋 服务已停止")
+        
+    except Exception as e:
+        logger.error("=" * 70)
+        logger.error(f"❌ 服务启动失败: {e}")
+        logger.error(traceback.format_exc())
+        logger.error("=" * 70)
+        sys.exit(1)

log/whisper_server.py

@@ -1,294 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-本地Whisper语音识别服务
-替代百度API，完全离线运行
-
-安装：
-pip install openai-whisper flask flask-cors
-
-运行：
-python whisper_server.py
-
-优点：
-1. 完全免费，无限次调用
-2. 离线运行，不需要网络
-3. 识别准确率高（接近百度API）
-4. 支持中文、英文等多语言
-"""
-
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import whisper
-import os
-import tempfile
-import logging
-
-# 繁简体转换
-try:
-    import zhconv
-    HAS_ZHCONV = True
-except ImportError:
-    HAS_ZHCONV = False
-    print("⚠️ 未安装zhconv库，无法进行繁简体转换")
-    print("   安装命令: pip install zhconv")
-
-app = Flask(__name__)
-CORS(app)
-
-# 配置日志
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# 全局变量：Whisper模型
-whisper_model = None
-
-def load_whisper_model():
-    """加载Whisper模型（懒加载）"""
-    global whisper_model
-    if whisper_model is None:
-        logger.info("正在加载Whisper模型...")
-        # 使用base模型（平衡速度和准确度）
-        # 可选: tiny, base, small, medium, large
-        # tiny: 最快，准确度一般
-        # base: 快速，准确度好 ✅ 推荐
-        # small: 较慢，准确度高
-        # medium/large: 很慢，准确度最高
-        whisper_model = whisper.load_model("base")
-        logger.info("✅ Whisper模型加载成功")
-    return whisper_model
-
-def convert_to_simplified(text):
-    """
-    将繁体中文转换为简体中文
-    
-    参数:
-        text: 待转换的文本
-    
-    返回:
-        简体中文文本
-    """
-    if not text:
-        return text
-    
-    if HAS_ZHCONV:
-        try:
-            # 使用zhconv进行繁简转换
-            simplified = zhconv.convert(text, 'zh-cn')
-            logger.info(f"繁简转换: {text} -> {simplified}")
-            return simplified
-        except Exception as e:
-            logger.warning(f"繁简转换失败: {e}")
-            return text
-    else:
-        # 如果没有安装zhconv，返回原文
-        return text
-
-@app.route('/health', methods=['GET'])
-def health():
-    """健康检查"""
-    return jsonify({
-        "status": "ok",
-        "service": "Whisper语音识别服务",
-        "model": "base"
-    })
-
-@app.route('/recognize', methods=['POST'])
-def recognize():
-    """
-    语音识别接口
-    
-    参数：
-    - file: 音频文件（支持MP3, WAV, M4A等）
-    - language: 语言（可选，默认自动检测）
-    
-    返回：
-    {
-        "code": 200,
-        "msg": "识别成功",
-        "data": {
-            "text": "识别的文本",
-            "language": "zh",
-            "confidence": 0.95
-        }
-    }
-    """
-    try:
-        # 检查是否有文件
-        if 'file' not in request.files:
-            return jsonify({
-                "code": 400,
-                "msg": "未找到音频文件",
-                "data": None
-            }), 400
-        
-        audio_file = request.files['file']
-        language = request.form.get('language', 'zh')  # 默认中文
-        
-        # 保存临时文件
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
-            audio_file.save(temp_file.name)
-            temp_path = temp_file.name
-        
-        try:
-            # 加载模型
-            model = load_whisper_model()
-            
-            # 识别音频
-            logger.info(f"开始识别音频: {audio_file.filename}")
-            result = model.transcribe(
-                temp_path,
-                language=language,
-                task='transcribe',  # transcribe=识别，translate=翻译成英文
-                fp16=False  # CPU模式
-            )
-            
-            recognized_text = result['text'].strip()
-            detected_language = result.get('language', language)
-            
-            # ✅ 繁体转简体
-            if detected_language == 'zh':
-                recognized_text = convert_to_simplified(recognized_text)
-            
-            logger.info(f"✅ 识别成功: {recognized_text}")
-            
-            return jsonify({
-                "code": 200,
-                "msg": "识别成功",
-                "data": {
-                    "text": recognized_text,
-                    "language": detected_language,
-                    "segments": len(result.get('segments', [])),
-                    "duration": result.get('duration', 0)
-                }
-            })
-            
-        finally:
-            # 删除临时文件
-            if os.path.exists(temp_path):
-                os.remove(temp_path)
-    
-    except Exception as e:
-        logger.error(f"识别失败: {str(e)}", exc_info=True)
-        return jsonify({
-            "code": 500,
-            "msg": f"识别失败: {str(e)}",
-            "data": None
-        }), 500
-
-@app.route('/evaluate', methods=['POST'])
-def evaluate():
-    """
-    语音评测接口（完整功能）
-    
-    参数：
-    - file: 音频文件
-    - text: 标准文本（用于对比）
-    
-    返回：
-    {
-        "code": 200,
-        "msg": "评测成功",
-        "data": {
-            "text": "识别的文本",
-            "score": 95,
-            "accuracy": 98,
-            "fluency": 92,
-            "completeness": 95,
-            "pronunciation": 94
-        }
-    }
-    """
-    try:
-        if 'file' not in request.files:
-            return jsonify({"code": 400, "msg": "未找到音频文件", "data": None}), 400
-        
-        audio_file = request.files['file']
-        standard_text = request.form.get('text', '')
-        
-        # 保存临时文件
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_file:
-            audio_file.save(temp_file.name)
-            temp_path = temp_file.name
-        
-        try:
-            # 1. 识别音频
-            model = load_whisper_model()
-            result = model.transcribe(temp_path, language='zh', fp16=False)
-            recognized_text = result['text'].strip()
-            
-            # ✅ 繁体转简体
-            recognized_text = convert_to_simplified(recognized_text)
-            
-            # 2. 计算评分
-            from difflib import SequenceMatcher
-            import re
-            
-            # 清理文本：去除所有标点符号和空格，只保留汉字、字母、数字
-            # \w 匹配字母、数字、下划线、汉字
-            clean_recognized = re.sub(r'[^\w]', '', recognized_text, flags=re.UNICODE).lower()
-            clean_standard = re.sub(r'[^\w]', '', standard_text, flags=re.UNICODE).lower()
-            
-            # 相似度
-            similarity = SequenceMatcher(None, clean_recognized, clean_standard).ratio()
-            
-            # 计算各项评分
-            accuracy = similarity * 100  # 准确度
-            completeness = min(len(clean_recognized) / max(len(clean_standard), 1) * 100, 100)  # 完整度
-            fluency = accuracy * 0.95  # 流利度（基于准确度）
-            pronunciation = accuracy * 0.98  # 发音（基于准确度）
-            
-            # 总分
-            total_score = (accuracy * 0.3 + completeness * 0.25 + fluency * 0.3 + pronunciation * 0.15)
-            
-            logger.info(f"✅ 评测完成: {recognized_text} | 得分: {total_score:.0f}")
-            
-            return jsonify({
-                "code": 200,
-                "msg": "评测成功",
-                "data": {
-                    "text": recognized_text,
-                    "score": round(total_score),
-                    "accuracy": round(accuracy),
-                    "fluency": round(fluency),
-                    "completeness": round(completeness),
-                    "pronunciation": round(pronunciation),
-                    "similarity": round(similarity * 100, 2)
-                }
-            })
-            
-        finally:
-            if os.path.exists(temp_path):
-                os.remove(temp_path)
-    
-    except Exception as e:
-        logger.error(f"评测失败: {str(e)}", exc_info=True)
-        return jsonify({"code": 500, "msg": f"评测失败: {str(e)}", "data": None}), 500
-
-if __name__ == '__main__':
-    print("=" * 60)
-    print("🎤 本地Whisper语音识别服务")
-    print("=" * 60)
-    print("")
-    print("✅ 优势：")
-    print("  1. 完全免费，无限次调用")
-    print("  2. 离线运行，不需要网络")
-    print("  3. 识别准确率高")
-    print("  4. 数据完全私有")
-    print("")
-    print("📦 安装依赖：")
-    print("  pip install openai-whisper flask flask-cors")
-    print("")
-    print("🚀 启动服务：")
-    print("  python whisper_server.py")
-    print("")
-    print("📌 API接口：")
-    print("  健康检查: GET  http://localhost:5001/health")
-    print("  语音识别: POST http://localhost:5001/recognize")
-    print("  语音评测: POST http://localhost:5001/evaluate")
-    print("")
-    print("=" * 60)
-    print("")
-    
-    # 启动服务
-    app.run(host='0.0.0.0', port=5001, debug=True)

log/删除用户-userId大于200.sql

@@ -1,94 +0,0 @@
--- 1️⃣ 先查看要删除的用户（确认范围）
-SELECT user_id, user_name, nick_name, register_type, create_time 
-FROM sys_user 
-WHERE user_id > 200 AND del_flag = '0'
-ORDER BY user_id;
-
--- 2️⃣ 查看影响范围（统计）
-SELECT 
-    '用户数' AS 项目,
-    COUNT(*) AS 数量
-FROM sys_user 
-WHERE user_id > 200 AND del_flag = '0'
-UNION ALL
-SELECT 
-    '学习记录数',
-    COUNT(*)
-FROM learning_record 
-WHERE student_id > 200
-UNION ALL
-SELECT 
-    '学习详情数',
-    COUNT(*)
-FROM learning_detail 
-WHERE student_id > 200
-UNION ALL
-SELECT 
-    '考试成绩数',
-    COUNT(*)
-FROM score 
-WHERE student_id > 200
-UNION ALL
-SELECT 
-    '班级关联数',
-    COUNT(*)
-FROM student_class 
-WHERE student_id > 200;
-
--- ============================================================
--- ⚠️ 确认无误后，执行以下删除操作
--- ============================================================
-
--- 3️⃣ 删除关联数据（按顺序）
-
--- 删除学习详情
-DELETE FROM learning_detail 
-WHERE student_id > 200;
-
--- 删除学习记录
-DELETE FROM learning_record 
-WHERE student_id > 200;
-
--- 删除考试成绩
-DELETE FROM score 
-WHERE student_id > 200;
-
--- 删除班级关联
-DELETE FROM student_class 
-WHERE student_id > 200;
-
--- 删除用户角色关联
-DELETE FROM sys_user_role 
-WHERE user_id > 200;
-
--- 删除课程分配（如果用户是学生）
-DELETE FROM course_assignment 
-WHERE student_id > 200;
-
--- 4️⃣ 最后删除用户表记录
-DELETE FROM sys_user 
-WHERE user_id > 200 AND del_flag = '0';
-
--- 5️⃣ 验证删除结果
-SELECT 
-    '剩余用户数' AS 验证项,
-    COUNT(*) AS 数量
-FROM sys_user 
-WHERE del_flag = '0';
-
--- ============================================================
--- 💡 如果想使用逻辑删除（推荐，可恢复），使用以下语句代替
--- ============================================================
-
-/*
--- 逻辑删除用户（设置 del_flag = '2'）
-UPDATE sys_user 
-SET del_flag = '2', 
-    update_time = NOW()
-WHERE user_id > 200 AND del_flag = '0';
-
--- 验证逻辑删除
-SELECT user_id, user_name, nick_name, del_flag 
-FROM sys_user 
-WHERE user_id > 200;
-*/

log/数据库/delete_users_over_200.sql

@@ -0,0 +1,55 @@
+-- ============================================
+-- 删除 user_id >= 100 的用户及其关联数据
+-- 只删除：用户、学习记录、语音评测、课程分配
+-- 保留：admin(1), ry(2) 等系统用户
+-- 执行前请先备份数据库！
+-- ============================================
+
+-- 关闭外键检查（加快删除速度）
+SET FOREIGN_KEY_CHECKS = 0;
+
+-- ============================================
+-- 1. 删除学习记录
+-- ============================================
+
+-- 删除学习详情记录
+DELETE FROM learning_detail WHERE student_id >= 100;
+
+-- 删除学习记录
+DELETE FROM learning_record WHERE student_id >= 100;
+
+-- ============================================
+-- 2. 删除语音评测记录
+-- ============================================
+
+DELETE FROM voice_evaluation WHERE student_id >= 100;
+
+-- ============================================
+-- 3. 删除课程分配（只删除该用户的分配，不影响课程本身）
+-- ============================================
+
+DELETE FROM course_assignment WHERE student_id >= 100;
+
+-- ============================================
+-- 4. 删除用户
+-- ============================================
+
+DELETE FROM sys_user WHERE user_id >= 100;
+
+-- 恢复外键检查
+SET FOREIGN_KEY_CHECKS = 1;
+
+-- ============================================
+-- 验证结果
+-- ============================================
+
+-- 查看剩余用户数量
+SELECT COUNT(*) AS '剩余用户数' FROM sys_user;
+
+-- 查看剩余用户列表
+SELECT user_id, user_name, nick_name FROM sys_user ORDER BY user_id;
+
+-- 确认课件未受影响
+SELECT COUNT(*) AS '课件数量' FROM courseware;
+SELECT COUNT(*) AS '课程数量' FROM course;
+SELECT COUNT(*) AS '评测内容数量' FROM voice_evaluation_content;

log/测试数据/generate_test_data.py

@@ -178,9 +178,9 @@ def main():
     print("="*60)
     
     # 配置参数
-    count = 10
+    count = 3200
       # 生成数据条数
-    start_id = 500  # 起始信息编号
+    start_id = 200  # 起始信息编号
     output_file = 'test_data.xlsx'
     
     print(f"配置: 生成 {count} 条数据，起始编号 {start_id}")