自然言語処理による状況認識¶

// 感情分析による状況認識システム
interface SentimentAnalysisResult {
  sentiment: 'positive' | 'negative' | 'neutral';
  confidence: number;
  emotions: EmotionScores;
  aspectSentiments: AspectSentiment[];
  intensity: number;
}

interface EmotionScores {
  joy: number;
  anger: number;
  fear: number;
  sadness: number;
  surprise: number;
  disgust: number;
}

interface AspectSentiment {
  aspect: string;
  sentiment: string;
  confidence: number;
}

class SentimentBasedSituationMonitor {
  private sentimentAnalyzer: MultiModalSentimentAnalyzer;
  private trendAnalyzer: SentimentTrendAnalyzer;
  private anomalyDetector: SentimentAnomalyDetector;
  private alertSystem: SentimentAlertSystem;

  constructor() {
    this.initializeAnalyzers();
  }

  private initializeAnalyzers(): void {
    // 複数手法を組み合わせた感情分析エンジン
    this.sentimentAnalyzer = new MultiModalSentimentAnalyzer({
      models: [
        new BERTSentimentModel({ language: 'ja' }),
        new RoBERTaSentimentModel({ language: 'ja' }),
        new LexiconBasedAnalyzer({ 
          lexicon: 'japanese_emotion_lexicon',
          weightingStrategy: 'tf-idf'
        })
      ],
      ensembleStrategy: 'weighted_voting',
      weights: [0.5, 0.3, 0.2]
    });

    this.trendAnalyzer = new SentimentTrendAnalyzer({
      windowSize: '1hour',
      trendDetectionThreshold: 0.15,
      seasonalityAdjustment: true
    });

    this.anomalyDetector = new SentimentAnomalyDetector({
      method: 'isolation_forest',
      contamination: 0.05
    });
  }

  // リアルタイム感情監視
  monitorSentimentStream(
    dataStream: Observable<TextData>
  ): Observable<SentimentSituationUpdate> {
    return dataStream.pipe(
      // バッチ処理用のバッファリング
      bufferTime(30000), // 30秒ごとのバッチ

      // 感情分析実行
      mergeMap(async (textBatch) => {
        const sentimentResults = await Promise.all(
          textBatch.map(text => this.analyzeSentiment(text))
        );

        return {
          timestamp: new Date(),
          batchSize: textBatch.length,
          sentiments: sentimentResults,
          aggregatedSentiment: this.aggregateSentiments(sentimentResults)
        };
      }),

      // トレンド分析
      map(batchResult => ({
        ...batchResult,
        trendAnalysis: this.trendAnalyzer.analyzeTrend(
          batchResult.aggregatedSentiment
        )
      })),

      // 異常検知
      map(result => ({
        ...result,
        anomalyScore: this.anomalyDetector.detectAnomaly(
          result.aggregatedSentiment
        )
      })),

      // アラート判定
      tap(result => {
        if (this.shouldTriggerAlert(result)) {
          this.alertSystem.triggerAlert(result);
        }
      }),

      // エラーハンドリング
      catchError(error => {
        console.error('感情分析エラー:', error);
        return of({
          timestamp: new Date(),
          error: error.message,
          fallbackAnalysis: this.getFallbackAnalysis()
        } as SentimentSituationUpdate);
      })
    );
  }

  // 個別テキストの感情分析
  private async analyzeSentiment(textData: TextData): Promise<SentimentAnalysisResult> {
    // 前処理
    const preprocessedText = await this.preprocessText(textData.content);

    // 複数モデルでの分析実行
    const modelResults = await this.sentimentAnalyzer.analyze(preprocessedText);

    // アスペクトベース感情分析
    const aspectSentiments = await this.analyzeAspectSentiments(
      preprocessedText
    );

    // 感情強度の計算
    const intensity = this.calculateEmotionIntensity(modelResults);

    return {
      sentiment: modelResults.primarySentiment,
      confidence: modelResults.confidence,
      emotions: modelResults.emotions,
      aspectSentiments: aspectSentiments,
      intensity: intensity
    };
  }

  // 感情集約処理
  private aggregateSentiments(
    sentiments: SentimentAnalysisResult[]
  ): AggregatedSentiment {
    // 重み付き平均による集約
    const weights = sentiments.map(s => s.confidence);
    const totalWeight = weights.reduce((sum, w) => sum + w, 0);

    const aggregated = {
      overallSentiment: this.calculateWeightedSentiment(sentiments, weights),
      sentimentDistribution: this.calculateSentimentDistribution(sentiments),
      dominantEmotions: this.identifyDominantEmotions(sentiments),
      polarization: this.calculatePolarization(sentiments),
      volatility: this.calculateVolatility(sentiments)
    };

    return aggregated;
  }

  // 地域・時間別感情分析
  async analyzeSentimentByRegionAndTime(
    timeRange: TimeRange,
    regions: string[]
  ): Promise<SpatioTemporalSentimentAnalysis> {
    const analyses = await Promise.all(
      regions.map(async (region) => {
        const regionalData = await this.getRegionalTextData(region, timeRange);
        const sentimentTimeSeries = await this.analyzeTimeSeriesSentiment(
          regionalData
        );

        return {
          region: region,
          timeRange: timeRange,
          sentimentTimeSeries: sentimentTimeSeries,
          keyEvents: this.identifyKeyEvents(sentimentTimeSeries),
          regionalTrends: this.analyzeRegionalTrends(sentimentTimeSeries)
        };
      })
    );

    // 地域間比較分析
    const crossRegionalAnalysis = this.compareRegionalSentiments(analyses);

    return {
      regionalAnalyses: analyses,
      crossRegionalComparison: crossRegionalAnalysis,
      globalTrends: this.identifyGlobalTrends(analyses)
    };
  }

  // アラート条件判定
  private shouldTriggerAlert(result: SentimentSituationUpdate): boolean {
    const conditions = [
      // 感情の急激な変化
      result.trendAnalysis.changeRate > 0.3,
      // 異常スコアの閾値超過
      result.anomalyScore > 0.8,
      // ネガティブ感情の急増
      result.aggregatedSentiment.overallSentiment < -0.7,
      // 感情の極端な二極化
      result.aggregatedSentiment.polarization > 0.9
    ];

    return conditions.some(condition => condition);
  }
}

// 結果インターフェース
interface SentimentSituationUpdate {
  timestamp: Date;
  batchSize?: number;
  sentiments?: SentimentAnalysisResult[];
  aggregatedSentiment?: AggregatedSentiment;
  trendAnalysis?: TrendAnalysis;
  anomalyScore?: number;
  error?: string;
  fallbackAnalysis?: any;
}

interface AggregatedSentiment {
  overallSentiment: number;
  sentimentDistribution: SentimentDistribution;
  dominantEmotions: string[];
  polarization: number;
  volatility: number;
}

// リアルタイム音声分析による状況認識システム
interface AudioAnalysisResult {
  transcript: string;
  speakerInfo: SpeakerInfo;
  emotionAnalysis: AudioEmotionAnalysis;
  stressLevel: number;
  urgencyScore: number;
  voiceQuality: VoiceQualityMetrics;
}

interface SpeakerInfo {
  id: string;
  confidence: number;
  gender: 'male' | 'female' | 'unknown';
  estimatedAge: number;
  voicePrint: number[];
}

interface AudioEmotionAnalysis {
  primaryEmotion: string;
  emotionIntensity: number;
  emotionConfidence: number;
  detailedEmotions: { [emotion: string]: number };
  arousal: number; // 覚醒度
  valence: number; // 感情価
}

interface VoiceQualityMetrics {
  pitch: number;
  volume: number;
  speechRate: number;
  pauseFrequency: number;
  jitter: number; // 音声の揺らぎ
  shimmer: number; // 振幅の揺らぎ
}

class RealTimeAudioSituationAnalyzer {
  private audioContext: AudioContext;
  private speechRecognizer: SpeechRecognizer;
  private speakerIdentifier: SpeakerIdentifier;
  private emotionAnalyzer: AudioEmotionAnalyzer;
  private stressDetector: StressDetector;
  private urgencyClassifier: UrgencyClassifier;

  constructor() {
    this.initializeAudioProcessing();
  }

  private initializeAudioProcessing(): void {
    // ブラウザ音声処理の初期化
    this.audioContext = new (window.AudioContext || window.webkitAudioContext)();

    // 音声認識エンジン
    this.speechRecognizer = new AdvancedSpeechRecognizer({
      language: 'ja-JP',
      continuous: true,
      interimResults: true,
      confidence: true
    });

    // 話者識別システム
    this.speakerIdentifier = new DeepSpeakerIdentifier({
      embeddingSize: 256,
      threshold: 0.85
    });

    // 音声感情分析
    this.emotionAnalyzer = new AudioEmotionAnalyzer({
      model: 'wav2vec2_emotion',
      frameSize: 1024,
      hopLength: 512
    });

    // ストレス検出器
    this.stressDetector = new VoiceStressAnalyzer({
      features: ['fundamental_frequency', 'jitter', 'shimmer', 'mfcc'],
      model: 'svm_stress_classifier'
    });
  }

  // リアルタイム音声ストリーム分析
  async analyzeAudioStream(
    audioStream: MediaStream
  ): Promise<Observable<AudioSituationUpdate>> {
    return new Observable<AudioSituationUpdate>(observer => {
      const mediaRecorder = new MediaRecorder(audioStream, {
        mimeType: 'audio/webm;codecs=opus'
      });

      const audioChunks: Blob[] = [];

      // 音声データの収集
      mediaRecorder.ondataavailable = async (event) => {
        if (event.data.size > 0) {
          audioChunks.push(event.data);

          try {
            // 音声分析実行
            const analysisResult = await this.analyzeAudioChunk(event.data);

            // 状況評価
            const situationAssessment = this.assessSituation(analysisResult);

            observer.next({
              timestamp: new Date(),
              audioAnalysis: analysisResult,
              situationAssessment: situationAssessment,
              alerts: this.generateAlerts(analysisResult, situationAssessment)
            });
          } catch (error) {
            observer.error(error);
          }
        }
      };

      // 定期的な音声キャプチャ（1秒間隔）
      mediaRecorder.start(1000);

      return () => {
        mediaRecorder.stop();
        audioStream.getTracks().forEach(track => track.stop());
      };
    });
  }

  // 音声チャンクの分析
  private async analyzeAudioChunk(audioBlob: Blob): Promise<AudioAnalysisResult> {
    // 音声バッファの取得
    const audioBuffer = await this.blobToArrayBuffer(audioBlob);
    const audioData = await this.audioContext.decodeAudioData(audioBuffer);

    // 並行して各種分析を実行
    const [
      transcript,
      speakerInfo,
      emotionAnalysis,
      stressLevel,
      voiceQuality
    ] = await Promise.all([
      this.speechRecognizer.recognize(audioBlob),
      this.speakerIdentifier.identify(audioData),
      this.emotionAnalyzer.analyze(audioData),
      this.stressDetector.detectStress(audioData),
      this.extractVoiceQuality(audioData)
    ]);

    // 緊急度スコアの計算
    const urgencyScore = this.calculateUrgencyScore({
      transcript,
      emotionAnalysis,
      stressLevel,
      voiceQuality
    });

    return {
      transcript: transcript.text,
      speakerInfo,
      emotionAnalysis,
      stressLevel,
      urgencyScore,
      voiceQuality
    };
  }

  // 状況評価
  private assessSituation(analysis: AudioAnalysisResult): SituationAssessment {
    // キーワードベースの状況分類
    const situationKeywords = this.extractSituationKeywords(analysis.transcript);

    // 音声特徴からの状況推定
    const audioSituation = this.inferSituationFromAudio(analysis);

    // 総合的な状況評価
    const overallAssessment = {
      situationType: this.classifySituationType(situationKeywords, audioSituation),
      severity: this.calculateSeverity(analysis),
      confidence: this.calculateConfidence(analysis),
      recommendations: this.generateRecommendations(analysis)
    };

    return overallAssessment;
  }

  // 緊急度計算
  private calculateUrgencyScore(analysisData: {
    transcript: TranscriptResult;
    emotionAnalysis: AudioEmotionAnalysis;
    stressLevel: number;
    voiceQuality: VoiceQualityMetrics;
  }): number {
    let urgencyScore = 0;

    // テキスト内容からの緊急度
    const urgentKeywords = ['助けて', '緊急', '危険', '事故', '怪我'];
    const transcriptUrgency = urgentKeywords.some(keyword => 
      analysisData.transcript.text.includes(keyword)
    ) ? 0.8 : 0;

    // 感情からの緊急度
    const emotionUrgency = Math.max(
      analysisData.emotionAnalysis.detailedEmotions['fear'] || 0,
      analysisData.emotionAnalysis.detailedEmotions['anger'] || 0
    );

    // ストレスレベルからの緊急度
    const stressUrgency = Math.min(analysisData.stressLevel, 1.0);

    // 音声品質からの緊急度（話速、音量変化等）
    const voiceUrgency = this.calculateVoiceUrgency(analysisData.voiceQuality);

    // 重み付き平均
    urgencyScore = (
      transcriptUrgency * 0.4 +
      emotionUrgency * 0.3 +
      stressUrgency * 0.2 +
      voiceUrgency * 0.1
    );

    return Math.min(urgencyScore, 1.0);
  }

  // 会話分析（複数話者対応）
  async analyzeConversation(
    audioStream: MediaStream,
    duration: number
  ): Promise<ConversationAnalysis> {
    const conversationData: AudioAnalysisResult[] = [];

    // 指定時間の音声データ収集
    const analysisStream = await this.analyzeAudioStream(audioStream);

    return new Promise((resolve) => {
      const subscription = analysisStream.subscribe({
        next: (update) => {
          conversationData.push(update.audioAnalysis);
        },
        complete: () => {
          const analysis = this.analyzeConversationPatterns(conversationData);
          resolve(analysis);
        }
      });

      // 指定時間後に分析終了
      setTimeout(() => {
        subscription.unsubscribe();
      }, duration);
    });
  }

  // 会話パターン分析
  private analyzeConversationPatterns(
    conversationData: AudioAnalysisResult[]
  ): ConversationAnalysis {
    // 話者変化の検出
    const speakerChanges = this.detectSpeakerChanges(conversationData);

    // 感情変化の追跡
    const emotionFlow = this.trackEmotionFlow(conversationData);

    // 会話の質的分析
    const conversationQuality = this.assessConversationQuality(conversationData);

    // 重要な発言の抽出
    const keyMoments = this.identifyKeyMoments(conversationData);

    return {
      duration: conversationData.length,
      speakerChanges,
      emotionFlow,
      conversationQuality,
      keyMoments,
      summary: this.generateConversationSummary(conversationData)
    };
  }
}

// 結果インターフェース
interface AudioSituationUpdate {
  timestamp: Date;
  audioAnalysis: AudioAnalysisResult;
  situationAssessment: SituationAssessment;
  alerts: Alert[];
}

interface ConversationAnalysis {
  duration: number;
  speakerChanges: SpeakerChange[];
  emotionFlow: EmotionTimeline[];
  conversationQuality: ConversationQuality;
  keyMoments: KeyMoment[];
  summary: string;
}

// 大規模言語モデルによる統合状況理解システム
interface MultiModalInput {
  text?: string;
  image?: ImageData;
  audio?: AudioData;
  structured?: StructuredData;
  metadata: InputMetadata;
}

interface SituationUnderstandingResult {
  contextualAnalysis: ContextualAnalysis;
  situationInference: SituationInference;
  scenarioGeneration: GeneratedScenario[];
  riskAssessment: RiskAssessment;
  actionRecommendations: ActionRecommendation[];
  confidence: number;
}

interface ContextualAnalysis {
  mainEntities: Entity[];
  relationships: Relationship[];
  temporalSequence: TimelineEvent[];
  spatialContext: SpatialInfo;
  emotionalContext: EmotionalContext;
}

class LLMSituationUnderstandingSystem {
  private textLLM: LargeLanguageModel;
  private multiModalLLM: MultiModalLanguageModel;
  private reasoningEngine: CausalReasoningEngine;
  private scenarioGenerator: ScenarioGenerator;
  private knowledgeBase: DynamicKnowledgeBase;

  constructor() {
    this.initializeLLMComponents();
  }

  private initializeLLMComponents(): void {
    // GPT-4系モデルの初期化
    this.textLLM = new OpenAIGPTModel({
      model: 'gpt-4-turbo',
      temperature: 0.2,
      maxTokens: 4000,
      systemPrompt: this.createSituationAnalysisPrompt()
    });

    // マルチモーダルモデル
    this.multiModalLLM = new GPT4VisionModel({
      model: 'gpt-4-vision-preview',
      maxTokens: 2000
    });

    // 因果推論エンジン
    this.reasoningEngine = new CausalReasoningEngine({
      knowledgeGraph: 'situation_causality_graph',
      inferenceMethod: 'probabilistic_reasoning'
    });

    // シナリオ生成器
    this.scenarioGenerator = new AdvancedScenarioGenerator({
      baseModel: this.textLLM,
      creativityLevel: 0.3,
      scenarios: ['best_case', 'worst_case', 'most_likely']
    });
  }

  // 統合状況理解の実行
  async understandSituation(
    inputs: MultiModalInput[]
  ): Promise<SituationUnderstandingResult> {
    // 入力データの前処理と統合
    const integratedContext = await this.integrateMultiModalInputs(inputs);

    // 段階的分析の実行
    const [
      contextualAnalysis,
      situationInference,
      scenarioGeneration,
      riskAssessment
    ] = await Promise.all([
      this.performContextualAnalysis(integratedContext),
      this.performSituationInference(integratedContext),
      this.generateScenarios(integratedContext),
      this.assessRisks(integratedContext)
    ]);

    // 行動推奨の生成
    const actionRecommendations = await this.generateActionRecommendations({
      contextualAnalysis,
      situationInference,
      riskAssessment
    });

    // 信頼度計算
    const confidence = this.calculateConfidence({
      contextualAnalysis,
      situationInference,
      inputs
    });

    return {
      contextualAnalysis,
      situationInference,
      scenarioGeneration,
      riskAssessment,
      actionRecommendations,
      confidence
    };
  }

  // マルチモーダル入力の統合
  private async integrateMultiModalInputs(
    inputs: MultiModalInput[]
  ): Promise<IntegratedContext> {
    const integrationTasks = inputs.map(async (input) => {
      let analysis: any = {};

      // テキスト分析
      if (input.text) {
        analysis.textAnalysis = await this.analyzeText(input.text);
      }

      // 画像分析
      if (input.image) {
        analysis.imageAnalysis = await this.analyzeImage(input.image);
      }

      // 音声分析
      if (input.audio) {
        analysis.audioAnalysis = await this.analyzeAudio(input.audio);
      }

      return {
        inputId: input.metadata.id,
        timestamp: input.metadata.timestamp,
        source: input.metadata.source,
        analysis
      };
    });

    const individualAnalyses = await Promise.all(integrationTasks);

    // クロスモーダル統合
    const crossModalAnalysis = await this.performCrossModalAnalysis(
      individualAnalyses
    );

    return {
      individualAnalyses,
      crossModalAnalysis,
      temporalSequence: this.constructTemporalSequence(individualAnalyses),
      consistencyCheck: this.checkCrossModalConsistency(individualAnalyses)
    };
  }

  // 文脈分析
  private async performContextualAnalysis(
    context: IntegratedContext
  ): Promise<ContextualAnalysis> {
    // LLMプロンプトの構築
    const analysisPrompt = this.buildContextAnalysisPrompt(context);

    // GPT-4による高度文脈分析
    const llmResponse = await this.textLLM.generate(analysisPrompt, {
      responseFormat: 'structured_json',
      schema: this.getContextAnalysisSchema()
    });

    // 構造化された結果の解析
    const structuredResult = JSON.parse(llmResponse.content);

    // エンティティと関係の抽出・拡張
    const enhancedEntities = await this.enhanceEntities(
      structuredResult.entities
    );

    const enhancedRelationships = await this.enhanceRelationships(
      structuredResult.relationships
    );

    return {
      mainEntities: enhancedEntities,
      relationships: enhancedRelationships,
      temporalSequence: structuredResult.timeline,
      spatialContext: structuredResult.spatial,
      emotionalContext: structuredResult.emotional
    };
  }

  // 状況推論
  private async performSituationInference(
    context: IntegratedContext
  ): Promise<SituationInference> {
    // 因果推論の実行
    const causalAnalysis = await this.reasoningEngine.analyzeCausality(context);

    // 状況分類
    const situationClassification = await this.classifySituation(context);

    // 意図・目的の推定
    const intentAnalysis = await this.analyzeIntent(context);

    // 影響度分析
    const impactAnalysis = await this.analyzeImpact(context);

    return {
      situationType: situationClassification.primaryType,
      subTypes: situationClassification.subTypes,
      causalChain: causalAnalysis.causalChain,
      rootCauses: causalAnalysis.rootCauses,
      keyActors: intentAnalysis.keyActors,
      objectives: intentAnalysis.objectives,
      potentialImpacts: impactAnalysis.impacts,
      uncertainty: this.calculateUncertainty(causalAnalysis)
    };
  }

  // インタラクティブ質問応答
  async answerSituationQuestion(
    situation: SituationUnderstandingResult,
    question: string
  ): Promise<SituationQAResult> {
    // 質問の分析
    const questionAnalysis = await this.analyzeQuestion(question);

    // 関連情報の抽出
    const relevantContext = this.extractRelevantContext(
      situation,
      questionAnalysis
    );

    // LLMによる回答生成
    const answerPrompt = this.buildQAPrompt(
      question,
      relevantContext,
      questionAnalysis
    );

    const response = await this.textLLM.generate(answerPrompt, {
      temperature: 0.1,
      maxTokens: 1000
    });

    // 回答の検証と補強
    const verifiedAnswer = await this.verifyAnswer(
      response.content,
      relevantContext
    );

    return {
      question: question,
      answer: verifiedAnswer.answer,
      confidence: verifiedAnswer.confidence,
      sources: relevantContext.sources,
      followUpQuestions: this.generateFollowUpQuestions(
        questionAnalysis,
        verifiedAnswer
      )
    };
  }

  // 継続的状況更新
  async updateSituationUnderstanding(
    currentSituation: SituationUnderstandingResult,
    newInputs: MultiModalInput[]
  ): Promise<SituationUpdateResult> {
    // 新規情報の分析
    const newAnalysis = await this.understandSituation(newInputs);

    // 既存理解との統合
    const integratedUnderstanding = await this.integrateSituationUpdates(
      currentSituation,
      newAnalysis
    );

    // 変化の検出
    const changes = this.detectSituationChanges(
      currentSituation,
      integratedUnderstanding
    );

    // 更新の重要度評価
    const updateSignificance = this.assessUpdateSignificance(changes);

    return {
      updatedSituation: integratedUnderstanding,
      changes: changes,
      significance: updateSignificance,
      recommendations: this.generateUpdateRecommendations(changes)
    };
  }

  // システムプロンプトの構築
  private createSituationAnalysisPrompt(): string {
    return `
    あなたは高度な状況分析AIアシスタントです。以下の能力を持っています。

    1. 複雑な状況の文脈理解と要素分析
    2. 因果関係の特定と推論
    3. リスク評価と将来予測
    4. 実用的な行動提案

    入力された情報を基に、以下の観点から状況を分析してください。

    - 主要な要素と関係性
    - 時系列での変化
    - 潜在的なリスクと機会
    - 推奨される対応策

    分析は論理的で客観的に、根拠とともに提示してください。
    `.trim();
  }
}

// 結果インターフェース
interface SituationInference {
  situationType: string;
  subTypes: string[];
  causalChain: CausalLink[];
  rootCauses: Cause[];
  keyActors: Actor[];
  objectives: Objective[];
  potentialImpacts: Impact[];
  uncertainty: number;
}

interface SituationQAResult {
  question: string;
  answer: string;
  confidence: number;
  sources: Source[];
  followUpQuestions: string[];
}