我的AI应用开发实践：从创意到落地的项目展示#

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// 对话管理器核心类
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public class ConversationManager {
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    private final IntentClassifier intentClassifier;
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    private final EntityExtractor entityExtractor;
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    private final KnowledgeGraph knowledgeGraph;
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    private final ResponseGenerator responseGenerator;
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    private final Map<String, ConversationContext> conversationContexts;
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    public ConversationManager(KnowledgeBaseConfig config) {
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        // 初始化各组件
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        this.intentClassifier = new BERTIntentClassifier(config.getModelPath());
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        this.entityExtractor = new CRFEntityExtractor(config.getEntityRulesPath());
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        this.knowledgeGraph = new Neo4jKnowledgeGraph(config.getNeo4jConfig());
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        this.responseGenerator = new TemplateBasedResponseGenerator(config.getResponseTemplatesPath());
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        this.conversationContexts = new ConcurrentHashMap<>();
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    }
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    public Response processUserQuery(String sessionId, String userQuery) {
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        // 获取或创建会话上下文
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        ConversationContext context = getOrCreateContext(sessionId);
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        // 1. 意图识别
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        Intent intent = intentClassifier.classify(userQuery);
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        context.setCurrentIntent(intent);
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        // 2. 实体提取
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        List<Entity> entities = entityExtractor.extract(userQuery);
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        context.updateEntities(entities);
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        // 3. 知识库检索
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        QueryResult knowledgeResult = knowledgeGraph.query(
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            intent, entities, context.getPreviousInteractions());
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        // 4. 生成响应
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        Response response = responseGenerator.generateResponse(
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            intent, entities, knowledgeResult, context);
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        // 5. 更新会话状态
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        context.addInteraction(userQuery, response);
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        return response;
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    }
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    // 上下文管理相关方法
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    private ConversationContext getOrCreateContext(String sessionId) {
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        return conversationContexts.computeIfAbsent(sessionId,
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            id -> new ConversationContext(id, MAX_CONTEXT_LENGTH));
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    }
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    // 其他辅助方法...
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}

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// 代码审查引擎核心类
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public class CodeReviewEngine {
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    private final StaticAnalyzer staticAnalyzer;
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    private final MachineLearningDetector mlDetector;
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    private final BestPracticeChecker bestPracticeChecker;
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    private final SecurityVulnerabilityScanner securityScanner;
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    public CodeReviewEngine(CodeReviewConfig config) {
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        this.staticAnalyzer = new JavaStaticAnalyzer(config.getStaticAnalysisRules());
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        this.mlDetector = new TensorFlowAnomalyDetector(config.getModelPath());
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        this.bestPracticeChecker = new BestPracticeChecker(config.getBestPracticesPath());
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        this.securityScanner = new OWASPSecurityScanner(config.getSecurityRules());
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    }
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    public ReviewReport reviewCode(CodeRepository repository, PullRequest pr) {
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        ReviewReport report = new ReviewReport(pr.getId());
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        // 获取变更的代码文件
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        List<CodeFile> changedFiles = repository.getChangedFiles(pr);
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        // 对每个文件进行审查
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        for (CodeFile file : changedFiles) {
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            // 1. 静态代码分析
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            List<StaticAnalysisIssue> staticIssues = staticAnalyzer.analyze(file);
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            report.addIssues(staticIssues);
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            // 2. 机器学习异常检测
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            List<MLDetectionResult> mlIssues = mlDetector.detectAnomalies(file);
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            report.addIssues(convertToIssues(mlIssues));
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            // 3. 最佳实践检查
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            List<BestPracticeViolation> practiceIssues = bestPracticeChecker.check(file);
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            report.addIssues(practiceIssues);
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            // 4. 安全漏洞扫描
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            List<SecurityVulnerability> securityIssues = securityScanner.scan(file);
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            report.addIssues(securityIssues);
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        }
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        // 生成优化建议
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        report.generateOptimizationSuggestions();
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        return report;
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    }
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    // 自动修复建议生成
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    public List<FixSuggestion> generateFixSuggestions(ReviewReport report, Severity minSeverity) {
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        List<FixSuggestion> suggestions = new ArrayList<>();
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        for (Issue issue : report.getIssues()) {
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            if (issue.getSeverity().compareTo(minSeverity) >= 0) {
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                FixSuggestion suggestion = fixGenerator.generateFix(issue);
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                if (suggestion != null) {
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                    suggestions.add(suggestion);
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                }
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            }
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        }
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        return suggestions;
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    }
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    // 其他辅助方法...
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}

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// 自然语言查询处理器
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public class NLQueryProcessor {
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    private final QueryParser queryParser;
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    private final DataAnalyzer dataAnalyzer;
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    private final VisualizationGenerator vizGenerator;
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    private final QueryHistoryManager historyManager;
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    public NLQueryProcessor(AnalyticsConfig config) {
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        this.queryParser = new NLQueryParser(config.getParsingModelPath());
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        this.dataAnalyzer = new MultiModelAnalyzer(config.getAnalyzerConfig());
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        this.vizGenerator = new DynamicVisualizationGenerator(config.getVizTemplates());
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        this.historyManager = new QueryHistoryManager(config.getHistoryStoragePath());
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    }
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    public AnalysisResult processNaturalLanguageQuery(String userId, String naturalLanguageQuery) {
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        // 1. 解析自然语言查询
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        ParsedQuery parsedQuery = queryParser.parse(naturalLanguageQuery);
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        // 2. 执行数据分析
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        AnalysisResult result = dataAnalyzer.analyze(parsedQuery);
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        // 3. 生成可视化结果
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        Visualization visualization = vizGenerator.generateVisualization(
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            result, detectOptimalVisualizationType(parsedQuery));
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        result.setVisualization(visualization);
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        // 4. 记录查询历史
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        historyManager.saveQuery(userId, naturalLanguageQuery, parsedQuery, result);
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        // 5. 生成见解和建议
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        result.setInsights(generateInsights(result, parsedQuery));
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        return result;
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    }
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    // 自动洞察生成
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    private List<Insight> generateInsights(AnalysisResult result, ParsedQuery query) {
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        List<Insight> insights = new ArrayList<>();
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        // 检测异常值
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        List<Anomaly> anomalies = anomalyDetector.detect(result.getDataPoints());
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        for (Anomaly anomaly : anomalies) {
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            insights.add(new Insight("异常检测", describeAnomaly(anomaly)));
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        }
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        // 识别趋势
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        List<Trend> trends = trendAnalyzer.analyze(result.getTimeSeriesData());
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        for (Trend trend : trends) {
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            insights.add(new Insight("趋势分析", describeTrend(trend)));
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        }
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        // 发现相关性
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        List<Correlation> correlations = correlationAnalyzer.find(result.getDimensions());
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        for (Correlation corr : correlations) {
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            insights.add(new Insight("相关性分析", describeCorrelation(corr)));
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        }
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        return insights;
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    }
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    // 其他辅助方法...
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}

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// 学习路径推荐引擎
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public class LearningPathRecommender {
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    private final UserProfileManager profileManager;
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    private final ContentRepository contentRepo;
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    private final KnowledgeGraph knowledgeGraph;
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    private final CollaborativeFiltering cfEngine;
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    private final ReinforcementLearningOptimizer rlOptimizer;
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    public LearningPathRecommender(LearningSystemConfig config) {
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        this.profileManager = new UserProfileManager(config.getUserProfileStorage());
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        this.contentRepo = new LearningContentRepository(config.getContentDatabase());
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        this.knowledgeGraph = new SubjectKnowledgeGraph(config.getKnowledgeGraphConfig());
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        this.cfEngine = new MatrixFactorizationCF(config.getCfModelPath());
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        this.rlOptimizer = new DeepQNetworkRL(config.getRlModelPath());
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    }
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    public LearningPath recommendLearningPath(String userId, LearningGoal goal) {
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        // 获取用户画像
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        UserProfile profile = profileManager.getUserProfile(userId);
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        // 1. 基于知识图谱的推荐
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        List<LearningNode> knowledgePath = knowledgeGraph.findOptimalPath(
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            profile.getCurrentKnowledgeLevel(), goal.getTargetLevel());
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        // 2. 基于协同过滤的内容推荐
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        List<ContentItem> recommendedContents = cfEngine.recommend(
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            userId, mapNodesToContentIds(knowledgePath), 20);
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        // 3. 个性化难度调整
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        List<ContentItem> personalizedContents = adjustDifficulty(
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            recommendedContents, profile.getLearningAbility());
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        // 4. 使用强化学习优化学习顺序
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        LearningPath optimizedPath = rlOptimizer.optimizeLearningSequence(
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            personalizedContents, profile.getLearningPreferences(), goal);
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        // 5. 添加学习资源和评估点
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        enrichedLearningPath(optimizedPath, profile);
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        return optimizedPath;
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    }
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    // 学习效果预测
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    public LearningProgressPrediction predictLearningProgress(String userId, LearningPath path) {
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        // 基于用户历史表现和内容难度预测完成情况
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        return learningAnalytics.predictProgress(userId, path);
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    }
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    // 实时路径调整
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    public void adjustLearningPath(String userId, LearningActivity activity) {
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        // 根据用户的学习活动和反馈，实时调整学习路径
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        UserProfile updatedProfile = profileManager.updateUserProfile(userId, activity);
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        currentLearningPathManager.adjustPath(userId, updatedProfile);
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    }
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    // 其他辅助方法...
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}