diff --git a/Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyClassUserController.java b/Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyClassUserController.java index 45d15f7..691e0a7 100644 --- a/Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyClassUserController.java +++ b/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"); diff --git a/Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyVoiceEvaluationController.java b/Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyVoiceEvaluationController.java index 2bc38be..b76878d 100644 --- a/Study-Vue-redis/ry-study-admin/src/main/java/com/ddnai/web/controller/study/StudyVoiceEvaluationController.java +++ b/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 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 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) diff --git a/Study-Vue-redis/ry-study-admin/src/main/resources/application-druid.yml b/Study-Vue-redis/ry-study-admin/src/main/resources/application-druid.yml index 71d9c81..2f0574b 100644 --- a/Study-Vue-redis/ry-study-admin/src/main/resources/application-druid.yml +++ b/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 diff --git a/Study-Vue-redis/ry-study-common/src/main/java/com/ddnai/common/core/domain/entity/StudentImportData.java b/Study-Vue-redis/ry-study-common/src/main/java/com/ddnai/common/core/domain/entity/StudentImportData.java index ea503c8..a001c5d 100644 --- a/Study-Vue-redis/ry-study-common/src/main/java/com/ddnai/common/core/domain/entity/StudentImportData.java +++ b/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） */ - @Excel(name = "监狱", sort = 3, prompt = "可选，如：第一监狱") + /** 监狱（不导出到模板，仅保留字段用于兼容旧数据导入） */ 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） */ diff --git a/Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/domain/study/StudyVoiceEvaluation.java b/Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/domain/study/StudyVoiceEvaluation.java index 0486d64..6e53cc3 100644 --- a/Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/domain/study/StudyVoiceEvaluation.java +++ b/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） */ diff --git a/Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/service/voice/DeepSeekService.java b/Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/service/voice/DeepSeekService.java index f4df678..d571f97 100644 --- a/Study-Vue-redis/ry-study-system/src/main/java/com/ddnai/system/service/voice/DeepSeekService.java +++ b/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" + - "[识别文本] " + recognizedText + "\n\n" + + "[标准文本] " + cleanStandard + "\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(); - String clean2 = text2.replaceAll("\\s+", "").toLowerCase(); + // ✅ 使用 cleanText 方法，自动去除标点符号和空格 + String clean1 = cleanText(text1); + String clean2 = cleanText(text2); int maxLen = Math.max(clean1.length(), clean2.length()); if (maxLen == 0) return 1.0; diff --git a/Study-Vue-redis/ry-study-ui/src/views/study/voiceEvaluation/index.vue b/Study-Vue-redis/ry-study-ui/src/views/study/voiceEvaluation/index.vue index 33d495c..23d2117 100644 --- a/Study-Vue-redis/ry-study-ui/src/views/study/voiceEvaluation/index.vue +++ b/Study-Vue-redis/ry-study-ui/src/views/study/voiceEvaluation/index.vue @@ -261,7 +261,7 @@ - + {{ form.id }} {{ form.studentName }} @@ -271,19 +271,20 @@ 已提交 未提交 - - {{ parseTime(form.submitTime, '{y}-{m}-{d} {h}:{i}:{s}') }} + + {{ parseTime(form.submitTime, '{y}-{m}-{d} {h}:{i}:{s}') }} + 未提交
{{ form.content }}
 - - {{ form.score || 0 }}分 + + {{ parseFloat(form.score || 0).toFixed(0) }}分 - {{ form.accuracy || 0 }}分 - {{ form.fluency || 0 }}分 - {{ form.completeness || 0 }}分 - {{ form.pronunciation || 0 }}分 + {{ parseFloat(form.accuracy || 0).toFixed(0) }}分 + {{ parseFloat(form.completeness || 0).toFixed(0) }}分 + {{ parseFloat(form.fluency || 0).toFixed(0) }}分 + {{ parseFloat(form.pronunciation || 0).toFixed(0) }}分
@@ -317,7 +318,7 @@
- 
{{ formatJson(form.resultDetail) }}
+ 
{{ formatJson(form.resultDetail) }}
@@ -632,5 +633,38 @@ export default { + + diff --git a/Test/python/whisper_server (1).py b/Test/python/whisper_server (1).py deleted file mode 100644 index dc3aee0..0000000 --- a/Test/python/whisper_server (1).py +++ /dev/null @@ -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) diff --git a/fronted_uniapp/pages/speech/speech.vue b/fronted_uniapp/pages/speech/speech.vue index b5a71f5..6be6bcd 100644 --- a/fronted_uniapp/pages/speech/speech.vue +++ b/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） - console.log('[Speech] 开始上传评测...') - const { uploadAndRecognize } = await import('@/api/study/voiceEvaluation.js') - const evalResult = await uploadAndRecognize( + // 2. ✅ 上传并评测（使用 uploadAndEvaluate 一次性完成识别+评测） + console.log('[Speech] ========================================') + console.log('[Speech] 🎯 新版本代码 v2.0 - 统一评测模式') + 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) { - const data = evalResult.data - this.recognizedText = data.recognizedText || '' - this.scoreResult = { - score: data.totalScore || 0, - pronunciationScore: data.pronunciation || 0, - fluencyScore: data.fluency || 0 - } + // 🔍 调试：输出完整返回数据 + console.log('[Speech] 返回结果 code:', evalResult.code) + console.log('[Speech] 返回结果:', JSON.stringify(evalResult)) + console.log('[Speech] 是否有evaluation:', !!evalResult.evaluation) + + // ✅ 只检查 code === 200，不强制要求 evaluation + if (evalResult.code === 200) { + // 获取评测数据（可能在 evaluation 或 data 中） + const evaluation = evalResult.evaluation || evalResult.data || {} + const recognizedText = evalResult.recognizedText || evaluation.recognizedText || '' - // ✅ 使用服务器返回的相对路径（如 /profile/upload/voice/2025/12/11/xxx.mp3） - if (data.audioPath) { - this.lastRecordedFilePath = data.audioPath - console.log('[Speech] 保存服务器音频路径:', data.audioPath) + // ✅ 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) { - uni.showToast({ title: '请先完成语音识别', icon: 'none' }) + // ✅ 已经在停止录音时完成评分，这里不需要再次评分 + if (this.scoreResult && this.scoreResult.totalScore !== undefined) { + uni.showToast({ title: '已经评分完成', icon: 'none' }) + this.$nextTick(() => { uni.pageScrollTo({ selector: '.score-section', duration: 300 }) }) return } - this.isEvaluating = true - try { - const result = await evaluateSpeechRecognition(this.selectedContent.content, this.recognizedText) - if (result.code === 200 && result.data) { - this.scoreResult = result.data - await this.saveScoreToBackend(result.data) - uni.showToast({ title: '评分完成', icon: 'success', duration: 2000 }) - this.$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 - } + + uni.showToast({ title: '请先录音', icon: 'none' }) }, 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' } diff --git a/fronted_uniapp/新方案-快速开始.md b/fronted_uniapp/新方案-快速开始.md index 54ce467..2df92f0 100644 --- a/fronted_uniapp/新方案-快速开始.md +++ b/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` diff --git a/log/AI指南/1-AI指南.md b/log/AI指南/1-AI指南.md index 10958f6..44256af 100644 --- a/log/AI指南/1-AI指南.md +++ b/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) diff --git a/log/whisper_server (1).py b/log/whisper_server (1).py new file mode 100644 index 0000000..e2d19d1 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/log/whisper_server.py b/log/whisper_server.py deleted file mode 100644 index 04df93b..0000000 --- a/log/whisper_server.py +++ /dev/null @@ -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) diff --git a/log/删除用户-userId大于200.sql b/log/删除用户-userId大于200.sql deleted file mode 100644 index ed48477..0000000 --- a/log/删除用户-userId大于200.sql +++ /dev/null @@ -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; -*/ diff --git a/log/数据库/delete_users_over_200.sql b/log/数据库/delete_users_over_200.sql new file mode 100644 index 0000000..404d00c --- /dev/null +++ b/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; diff --git a/log/测试数据/generate_test_data.py b/log/测试数据/generate_test_data.py index f6cc42b..9bb36e1 100644 --- a/log/测试数据/generate_test_data.py +++ b/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}") diff --git a/log/测试数据/test_data.xlsx b/log/测试数据/test_data.xlsx index bf41fbf..4e8bbc1 100644 Binary files a/log/测试数据/test_data.xlsx and b/log/测试数据/test_data.xlsx differ