AI・未来技術と状況認識¶

// 統合AI状況認識システムの基本アーキテクチャ
interface AISituationalAwarenessSystem {
    dataIngestor: MultiModalDataIngestor;
    preprocessor: AIDataPreprocessor;
    mlPipeline: MachineLearningPipeline;
    fusionEngine: MultiModalFusionEngine;
    explainer: ExplainableAIEngine;
    decisionSupport: AIDecisionSupport;
    feedbackLoop: ContinuousLearningEngine;
}

interface SituationalPrediction {
    id: string;
    timestamp: Date;
    scenario: string;
    confidence: number;
    predictions: PredictionComponent[];
    explanations: ExplanationComponent[];
    recommendations: ActionRecommendation[];
    uncertainties: UncertaintyMetrics;
}

interface PredictionComponent {
    type: PredictionType;
    value: any;
    confidence: number;
    timeHorizon: number; // minutes
    dataSource: string[];
    methodology: AlgorithmType;
}

enum PredictionType {
    CLASSIFICATION = 'classification',
    REGRESSION = 'regression',
    ANOMALY_DETECTION = 'anomaly_detection',
    TIME_SERIES_FORECAST = 'time_series_forecast',
    REINFORCEMENT_LEARNING = 'reinforcement_learning'
}

enum AlgorithmType {
    RANDOM_FOREST = 'random_forest',
    NEURAL_NETWORK = 'neural_network',
    SVM = 'svm',
    LSTM = 'lstm',
    TRANSFORMER = 'transformer',
    ENSEMBLE = 'ensemble'
}

class IntegratedAISituationalAwareness {
    private dataStreams: Map<string, Observable<any>> = new Map();
    private mlModels: Map<string, MLModel> = new Map();
    private fusionEngine: MultiModalFusionEngine;
    private explainabilityEngine: ExplainableAIEngine;
    private continuousLearning: ContinuousLearningEngine;

    constructor() {
        this.fusionEngine = new MultiModalFusionEngine();
        this.explainabilityEngine = new ExplainableAIEngine();
        this.continuousLearning = new ContinuousLearningEngine();
        this.initializeSystem();
    }

    // システム初期化
    private initializeSystem(): void {
        // データストリーム設定
        this.setupDataStreams();

        // 機械学習モデル読み込み
        this.loadMLModels();

        // リアルタイム処理パイプライン構築
        this.setupRealTimePipeline();

        // 継続学習メカニズム活性化
        this.enableContinuousLearning();
    }

    // マルチモーダルデータストリーム設定
    private setupDataStreams(): void {
        // テキストデータストリーム
        this.dataStreams.set('text', 
            this.createTextStream().pipe(
                map(data => this.preprocessText(data)),
                share()
            )
        );

        // 画像データストリーム
        this.dataStreams.set('image',
            this.createImageStream().pipe(
                map(data => this.preprocessImage(data)),
                share()
            )
        );

        // センサーデータストリーム
        this.dataStreams.set('sensor',
            this.createSensorStream().pipe(
                map(data => this.preprocessSensorData(data)),
                bufferTime(1000), // 1秒バッファ
                share()
            )
        );

        // 構造化データストリーム
        this.dataStreams.set('structured',
            this.createStructuredDataStream().pipe(
                map(data => this.preprocessStructuredData(data)),
                share()
            )
        );
    }

    // 統合AI状況認識実行
    async performIntegratedSituationalAwareness(
        timeHorizon: number = 60
    ): Promise<SituationalPrediction> {
        // 現在のデータスナップショット取得
        const currentData = await this.getCurrentDataSnapshot();

        // 各モダリティでのAI分析
        const textAnalysis = await this.analyzeTextData(currentData.text);
        const imageAnalysis = await this.analyzeImageData(currentData.image);
        const sensorAnalysis = await this.analyzeSensorData(currentData.sensor);
        const structuredAnalysis = await this.analyzeStructuredData(currentData.structured);

        // マルチモーダル統合
        const fusedAnalysis = await this.fusionEngine.fuseAnalyses([
            textAnalysis,
            imageAnalysis,
            sensorAnalysis,
            structuredAnalysis
        ]);

        // 予測実行
        const predictions = await this.generatePredictions(fusedAnalysis, timeHorizon);

        // 説明生成
        const explanations = await this.explainabilityEngine.generateExplanations(
            predictions,
            fusedAnalysis
        );

        // 行動推奨生成
        const recommendations = await this.generateActionRecommendations(
            predictions,
            explanations
        );

        // 不確実性定量化
        const uncertainties = await this.quantifyUncertainties(predictions);

        return {
            id: generateUUID(),
            timestamp: new Date(),
            scenario: this.determineScenario(fusedAnalysis),
            confidence: this.calculateOverallConfidence(predictions),
            predictions,
            explanations,
            recommendations,
            uncertainties
        };
    }

    // テキストデータ分析
    private async analyzeTextData(textData: TextData[]): Promise<TextAnalysisResult> {
        const model = this.mlModels.get('nlp_transformer') as NLPModel;

        // 感情分析
        const sentimentAnalysis = await model.analyzeSentiment(textData);

        // エンティティ抽出
        const entityExtraction = await model.extractEntities(textData);

        // トピックモデリング
        const topicModeling = await model.extractTopics(textData);

        // 要約生成
        const summarization = await model.generateSummary(textData);

        // 異常テキスト検出
        const anomalyDetection = await model.detectAnomalousText(textData);

        return {
            sentiment: sentimentAnalysis,
            entities: entityExtraction,
            topics: topicModeling,
            summary: summarization,
            anomalies: anomalyDetection,
            confidence: this.calculateTextAnalysisConfidence({
                sentiment: sentimentAnalysis,
                entities: entityExtraction,
                topics: topicModeling
            })
        };
    }

    // 画像データ分析
    private async analyzeImageData(imageData: ImageData[]): Promise<ImageAnalysisResult> {
        const cnnModel = this.mlModels.get('cnn_vision') as VisionModel;

        // オブジェクト検出
        const objectDetection = await cnnModel.detectObjects(imageData);

        // シーン分類
        const sceneClassification = await cnnModel.classifyScenes(imageData);

        // 異常検知
        const anomalyDetection = await cnnModel.detectAnomalies(imageData);

        // 特徴抽出
        const featureExtraction = await cnnModel.extractFeatures(imageData);

        // セマンティックセグメンテーション
        const segmentation = await cnnModel.segmentImages(imageData);

        return {
            objects: objectDetection,
            scenes: sceneClassification,
            anomalies: anomalyDetection,
            features: featureExtraction,
            segmentation: segmentation,
            confidence: this.calculateImageAnalysisConfidence({
                objects: objectDetection,
                scenes: sceneClassification
            })
        };
    }

    // 予測生成
    private async generatePredictions(
        fusedAnalysis: FusedAnalysisResult,
        timeHorizon: number
    ): Promise<PredictionComponent[]> {
        const predictions: PredictionComponent[] = [];

        // 時系列予測
        const timeSeriesModel = this.mlModels.get('lstm_forecaster') as TimeSeriesModel;
        const timeSeriesPrediction = await timeSeriesModel.forecast(
            fusedAnalysis.temporalFeatures,
            timeHorizon
        );

        predictions.push({
            type: PredictionType.TIME_SERIES_FORECAST,
            value: timeSeriesPrediction.forecast,
            confidence: timeSeriesPrediction.confidence,
            timeHorizon,
            dataSource: ['sensor', 'structured'],
            methodology: AlgorithmType.LSTM
        });

        // 分類予測
        const classificationModel = this.mlModels.get('ensemble_classifier') as ClassificationModel;
        const classificationPrediction = await classificationModel.predict(fusedAnalysis.features);

        predictions.push({
            type: PredictionType.CLASSIFICATION,
            value: classificationPrediction.class,
            confidence: classificationPrediction.confidence,
            timeHorizon: 0, // 即座の分類
            dataSource: ['text', 'image', 'sensor', 'structured'],
            methodology: AlgorithmType.ENSEMBLE
        });

        // 異常検知
        const anomalyModel = this.mlModels.get('isolation_forest') as AnomalyDetectionModel;
        const anomalyPrediction = await anomalyModel.detectAnomalies(fusedAnalysis.features);

        predictions.push({
            type: PredictionType.ANOMALY_DETECTION,
            value: anomalyPrediction.anomalies,
            confidence: anomalyPrediction.confidence,
            timeHorizon: 5, // 5分先までの異常予測
            dataSource: ['sensor', 'image'],
            methodology: AlgorithmType.RANDOM_FOREST
        });

        return predictions;
    }

    // アクション推奨生成
    private async generateActionRecommendations(
        predictions: PredictionComponent[],
        explanations: ExplanationComponent[]
    ): Promise<ActionRecommendation[]> {
        const recommendations: ActionRecommendation[] = [];

        for (const prediction of predictions) {
            switch (prediction.type) {
                case PredictionType.ANOMALY_DETECTION:
                    if (prediction.value.length > 0) {
                        recommendations.push({
                            action: 'INVESTIGATE_ANOMALY',
                            priority: this.calculatePriority(prediction.confidence),
                            description: '異常が検出されました。詳細調査を推奨します。',
                            evidence: explanations.filter(e => e.predictionId === prediction.type),
                            timeConstraint: 300, // 5分以内
                            resources: ['investigation_team', 'monitoring_tools']
                        });
                    }
                    break;

                case PredictionType.TIME_SERIES_FORECAST:
                    if (prediction.value.trend === 'declining') {
                        recommendations.push({
                            action: 'PREVENTIVE_MEASURES',
                            priority: 'MEDIUM',
                            description: '予測トレンドに基づく予防措置の実施を推奨します。',
                            evidence: explanations.filter(e => e.predictionId === prediction.type),
                            timeConstraint: timeHorizon * 60, // 予測時間内
                            resources: ['preventive_team']
                        });
                    }
                    break;

                case PredictionType.CLASSIFICATION:
                    if (prediction.confidence > 0.8) {
                        recommendations.push({
                            action: 'CONFIRM_CLASSIFICATION',
                            priority: 'LOW',
                            description: '高信頼度の分類結果の確認を推奨します。',
                            evidence: explanations.filter(e => e.predictionId === prediction.type),
                            timeConstraint: 600, // 10分以内
                            resources: ['validation_team']
                        });
                    }
                    break;
            }
        }

        return recommendations;
    }

    // 継続学習実行
    private enableContinuousLearning(): void {
        // フィードバックループ設定
        this.continuousLearning.setupFeedbackLoop();

        // 定期的なモデル更新
        interval(3600000).subscribe(async () => { // 1時間ごと
            await this.updateModelsWithNewData();
        });

        // パフォーマンス監視
        this.monitorModelPerformance().subscribe(performance => {
            if (performance.accuracy < 0.85) {
                this.triggerModelRetraining(performance.modelId);
            }
        });
    }

    // モデルパフォーマンス監視
    private monitorModelPerformance(): Observable<ModelPerformance> {
        return merge(
            this.monitorClassificationAccuracy(),
            this.monitorPredictionError(),
            this.monitorAnomalyDetectionRate()
        ).pipe(
            throttleTime(60000) // 1分に1回最大
        );
    }

    // リアルタイム状況認識ダッシュボード
    getRealTimeSituationalDashboard(): Observable<SituationalDashboard> {
        return combineLatest([
            this.performIntegratedSituationalAwareness(),
            this.getSystemHealth(),
            this.getModelPerformanceMetrics()
        ]).pipe(
            map(([awareness, health, performance]) => ({
                timestamp: new Date(),
                situationalAwareness: awareness,
                systemHealth: health,
                modelPerformance: performance,
                alerts: this.generateSystemAlerts(awareness, health),
                insights: this.generateKeyInsights(awareness),
                recommendedActions: awareness.recommendations
            }))
        );
    }
}

interface ActionRecommendation {
    action: string;
    priority: 'LOW' | 'MEDIUM' | 'HIGH' | 'CRITICAL';
    description: string;
    evidence: ExplanationComponent[];
    timeConstraint: number; // seconds
    resources: string[];
}

interface ExplanationComponent {
    predictionId: string;
    explanation: string;
    importance: number;
    visualizations: VisualizationData[];
}

interface UncertaintyMetrics {
    aleatoric: number; // データの不確実性
    epistemic: number; // モデルの不確実性
    overall: number;
    sources: UncertaintySource[];
}

interface SituationalDashboard {
    timestamp: Date;
    situationalAwareness: SituationalPrediction;
    systemHealth: SystemHealthStatus;
    modelPerformance: ModelPerformanceMetrics;
    alerts: SystemAlert[];
    insights: KeyInsight[];
    recommendedActions: ActionRecommendation[];
}