エッジAI・リアルタイム処理による状況認識¶

// ブラウザベースエッジAI統合システム
interface EdgeAIInferenceResult {
  inferenceId: string;
  timestamp: Date;
  modelType: 'tensorflow' | 'onnx' | 'webgl' | 'wasm';
  predictions: Prediction[];
  processingTime: number;
  memoryUsage: MemoryUsage;
  confidence: number;
  metadata: InferenceMetadata;
}

interface Prediction {
  class: string;
  confidence: number;
  boundingBox?: BoundingBox;
  segmentationMask?: Uint8Array;
  embeddings?: Float32Array;
}

interface MemoryUsage {
  heap: number;
  gpu: number;
  buffer: number;
  peak: number;
}

interface ModelConfiguration {
  modelPath: string;
  runtime: 'tfjs' | 'onnx' | 'webgl' | 'wasm';
  quantization: 'fp32' | 'fp16' | 'int8';
  batchSize: number;
  inputShape: number[];
  outputShape: number[];
}

class BrowserEdgeAISystem {
  private tfModels: Map<string, tf.LayersModel> = new Map();
  private onnxSessions: Map<string, InferenceSession> = new Map();
  private webglBackend: WebGLBackend;
  private wasmBackend: WebAssemblyBackend;
  private workerPool: WorkerPool;
  private performanceMonitor: PerformanceMonitor;
  private memoryManager: MemoryManager;

  constructor() {
    this.initializeRuntimes();
    this.setupPerformanceMonitoring();
    this.initializeWorkerPool();
  }

  private async initializeRuntimes(): Promise<void> {
    // TensorFlow.js バックエンド初期化
    await tf.ready();
    await tf.setBackend('webgl');

    // WebGL バックエンドの設定
    this.webglBackend = new WebGLBackend({
      precision: 'highp',
      textureDownloadOptimization: true,
      parallelCompilation: true
    });

    // WebAssembly バックエンドの設定
    this.wasmBackend = new WebAssemblyBackend({
      simdSupport: this.checkSIMDSupport(),
      threadsSupport: this.checkThreadsSupport(),
      optimization: 'speed'
    });

    // ONNX Runtime Web の初期化
    ort.env.wasm.wasmPaths = '/wasm/';
    ort.env.webgl.contextId = 'webgl2';

    // メモリ管理システム
    this.memoryManager = new MemoryManager({
      maxHeapSize: 512 * 1024 * 1024, // 512MB
      gcThreshold: 0.8,
      preallocation: true
    });
  }

  // 複数モデルの並列読み込み
  async loadModels(
    modelConfigs: ModelConfiguration[]
  ): Promise<ModelLoadResult[]> {
    const loadPromises = modelConfigs.map(async (config) => {
      try {
        const startTime = performance.now();

        let model: any;
        switch (config.runtime) {
          case 'tfjs':
            model = await this.loadTensorFlowModel(config);
            break;
          case 'onnx':
            model = await this.loadONNXModel(config);
            break;
          case 'webgl':
            model = await this.loadWebGLModel(config);
            break;
          case 'wasm':
            model = await this.loadWASMModel(config);
            break;
        }

        const loadTime = performance.now() - startTime;

        return {
          modelId: config.modelPath,
          runtime: config.runtime,
          model: model,
          loadTime: loadTime,
          memoryFootprint: await this.calculateModelMemoryFootprint(model),
          success: true
        };

      } catch (error) {
        return {
          modelId: config.modelPath,
          runtime: config.runtime,
          error: error.message,
          success: false
        };
      }
    });

    return Promise.all(loadPromises);
  }

  // TensorFlow.js モデルの読み込み
  private async loadTensorFlowModel(
    config: ModelConfiguration
  ): Promise<tf.LayersModel> {
    // モデルの読み込み
    const model = await tf.loadLayersModel(config.modelPath);

    // モデルの最適化
    if (config.quantization !== 'fp32') {
      return await this.quantizeModel(model, config.quantization);
    }

    // モデルのウォームアップ
    await this.warmupModel(model, config.inputShape);

    this.tfModels.set(config.modelPath, model);
    return model;
  }

  // ONNX モデルの読み込み
  private async loadONNXModel(
    config: ModelConfiguration
  ): Promise<InferenceSession> {
    const sessionOptions: InferenceSession.SessionOptions = {
      executionProviders: ['webgl', 'cpu'],
      graphOptimizationLevel: 'all',
      enableCpuMemArena: true,
    };

    // WebGL プロバイダーの設定
    if (this.webglBackend.isSupported()) {
      sessionOptions.executionProviders = ['webgl'];
    }

    const session = await InferenceSession.create(
      config.modelPath,
      sessionOptions
    );

    this.onnxSessions.set(config.modelPath, session);
    return session;
  }

  // リアルタイム推論の実行
  async performRealtimeInference(
    inputData: any,
    modelId: string,
    options?: InferenceOptions
  ): Promise<EdgeAIInferenceResult> {
    const inferenceId = this.generateInferenceId();
    const startTime = performance.now();

    // メモリ使用量の監視開始
    const memoryBefore = await this.measureMemoryUsage();

    try {
      // 入力データの前処理
      const preprocessedData = await this.preprocessInput(
        inputData,
        modelId,
        options?.preprocessing
      );

      // モデルの種類に応じた推論実行
      let predictions: Prediction[];
      const model = this.getModel(modelId);

      if (this.tfModels.has(modelId)) {
        predictions = await this.runTensorFlowInference(
          model as tf.LayersModel,
          preprocessedData
        );
      } else if (this.onnxSessions.has(modelId)) {
        predictions = await this.runONNXInference(
          model as InferenceSession,
          preprocessedData
        );
      } else {
        throw new Error(`Model not found: ${modelId}`);
      }

      // 後処理
      const postprocessedPredictions = await this.postprocessPredictions(
        predictions,
        options?.postprocessing
      );

      const processingTime = performance.now() - startTime;
      const memoryAfter = await this.measureMemoryUsage();

      return {
        inferenceId,
        timestamp: new Date(),
        modelType: this.getModelType(modelId),
        predictions: postprocessedPredictions,
        processingTime,
        memoryUsage: this.calculateMemoryDelta(memoryBefore, memoryAfter),
        confidence: this.calculateOverallConfidence(postprocessedPredictions),
        metadata: {
          modelId,
          inputShape: preprocessedData.shape,
          batchSize: preprocessedData.batchSize,
          backend: await tf.getBackend()
        }
      };

    } catch (error) {
      throw new EdgeAIInferenceError(
        `推論エラー: ${error.message}`,
        inferenceId,
        modelId
      );
    } finally {
      // メモリクリーンアップ
      await this.memoryManager.cleanup();
    }
  }

  // ストリーミング推論（RxJS統合）
  createInferenceStream(
    inputStream: Observable<any>,
    modelId: string,
    options?: StreamInferenceOptions
  ): Observable<EdgeAIInferenceResult> {
    return inputStream.pipe(
      // バッファリング（バッチ処理用）
      bufferTime(options?.bufferTime || 100),
      filter(buffer => buffer.length > 0),

      // 並列推論実行
      mergeMap(batch => 
        from(this.processBatch(batch, modelId, options)).pipe(
          // タイムアウト処理
          timeout(options?.timeout || 5000),
          catchError(error => of({
            error: error.message,
            modelId,
            timestamp: new Date()
          } as EdgeAIInferenceResult))
        )
      ),

      // パフォーマンス監視
      tap(result => this.performanceMonitor.recordInference(result)),

      // メモリ使用量チェック
      tap(() => this.memoryManager.checkUsage()),

      share()
    );
  }

  // バッチ推論の処理
  private async processBatch(
    batch: any[],
    modelId: string,
    options?: StreamInferenceOptions
  ): Promise<EdgeAIInferenceResult[]> {
    // バッチサイズの最適化
    const optimalBatchSize = await this.calculateOptimalBatchSize(
      modelId,
      batch.length
    );

    const results: EdgeAIInferenceResult[] = [];

    // バッチをチャンクに分割
    for (let i = 0; i < batch.length; i += optimalBatchSize) {
      const chunk = batch.slice(i, i + optimalBatchSize);

      // 並列処理での推論実行
      const chunkResults = await Promise.all(
        chunk.map(input => 
          this.performRealtimeInference(input, modelId, options)
        )
      );

      results.push(...chunkResults);
    }

    return results;
  }

  // Web Workers での並列処理
  async performParallelInference(
    inputs: any[],
    modelId: string
  ): Promise<EdgeAIInferenceResult[]> {
    const workerTasks = inputs.map((input, index) => ({
      taskId: `inference_${index}`,
      input: input,
      modelId: modelId,
      workerId: index % this.workerPool.size
    }));

    return this.workerPool.executeTasks(workerTasks);
  }

  // プログレッシブローディング（段階的モデル読み込み）
  async loadModelProgressively(
    modelConfig: ModelConfiguration,
    onProgress?: (progress: LoadProgress) => void
  ): Promise<void> {
    const totalLayers = await this.getModelLayerCount(modelConfig.modelPath);
    let loadedLayers = 0;

    // レイヤーごとの段階的読み込み
    for (const layerInfo of await this.getLayerInfo(modelConfig.modelPath)) {
      await this.loadLayer(layerInfo);
      loadedLayers++;

      if (onProgress) {
        onProgress({
          loaded: loadedLayers,
          total: totalLayers,
          percentage: (loadedLayers / totalLayers) * 100,
          currentLayer: layerInfo.name
        });
      }
    }
  }

  // モデル性能の動的最適化
  async optimizeModelPerformance(
    modelId: string,
    performanceTargets: PerformanceTargets
  ): Promise<OptimizationResult> {
    const currentPerformance = await this.measureModelPerformance(modelId);

    const optimizations: OptimizationStrategy[] = [];

    // レイテンシ最適化
    if (currentPerformance.latency > performanceTargets.maxLatency) {
      optimizations.push(new LatencyOptimization({
        quantization: 'int8',
        pruning: 0.3,
        batchOptimization: true
      }));
    }

    // メモリ最適化
    if (currentPerformance.memoryUsage > performanceTargets.maxMemory) {
      optimizations.push(new MemoryOptimization({
        layerFusion: true,
        constantFolding: true,
        bufferReuse: true
      }));
    }

    // 精度最適化
    if (currentPerformance.accuracy < performanceTargets.minAccuracy) {
      optimizations.push(new AccuracyOptimization({
        ensembling: true,
        dataAugmentation: true,
        fineTuning: true
      }));
    }

    return this.applyOptimizations(modelId, optimizations);
  }
}

// Web Worker 統合クラス
class EdgeAIWorkerPool {
  private workers: Worker[] = [];
  private taskQueue: TaskQueue = new TaskQueue();

  constructor(workerCount: number = navigator.hardwareConcurrency || 4) {
    this.initializeWorkers(workerCount);
  }

  private initializeWorkers(count: number): void {
    for (let i = 0; i < count; i++) {
      const worker = new Worker('/workers/edge-ai-worker.js');
      worker.onmessage = this.handleWorkerMessage.bind(this);
      worker.onerror = this.handleWorkerError.bind(this);
      this.workers.push(worker);
    }
  }

  async executeTasks(tasks: WorkerTask[]): Promise<any[]> {
    return Promise.all(
      tasks.map(task => this.executeTask(task))
    );
  }

  private async executeTask(task: WorkerTask): Promise<any> {
    return new Promise((resolve, reject) => {
      const worker = this.getAvailableWorker();
      const timeout = setTimeout(() => {
        reject(new Error(`Worker task timeout: ${task.taskId}`));
      }, 30000);

      worker.postMessage({
        taskId: task.taskId,
        type: 'inference',
        data: task.input,
        modelId: task.modelId
      });

      const handleResponse = (event: MessageEvent) => {
        if (event.data.taskId === task.taskId) {
          clearTimeout(timeout);
          worker.removeEventListener('message', handleResponse);

          if (event.data.error) {
            reject(new Error(event.data.error));
          } else {
            resolve(event.data.result);
          }
        }
      };

      worker.addEventListener('message', handleResponse);
    });
  }

  private getAvailableWorker(): Worker {
    // 単純なラウンドロビン選択
    return this.workers[Math.floor(Math.random() * this.workers.length)];
  }
}

// 結果インターフェース
interface StreamInferenceOptions extends InferenceOptions {
  bufferTime?: number;
  timeout?: number;
  maxConcurrency?: number;
  backpressureStrategy?: 'drop' | 'buffer' | 'latest';
}

interface LoadProgress {
  loaded: number;
  total: number;
  percentage: number;
  currentLayer: string;
}

interface PerformanceTargets {
  maxLatency: number; // milliseconds
  maxMemory: number; // bytes
  minAccuracy: number; // 0-1
  minThroughput: number; // inferences per second
}

interface OptimizationResult {
  originalPerformance: PerformanceMetrics;
  optimizedPerformance: PerformanceMetrics;
  appliedOptimizations: OptimizationStrategy[];
  improvement: PerformanceImprovement;
}

// PWA統合エッジAI状況認識システム
interface PWAEdgeAIConfiguration {
  modelCaching: ModelCachingConfig;
  offlineCapabilities: OfflineConfig;
  backgroundProcessing: BackgroundProcessingConfig;
  pushNotifications: PushNotificationConfig;
  syncStrategy: SyncStrategyConfig;
}

interface SituationalAwareness {
  id: string;
  timestamp: Date;
  situationType: 'normal' | 'warning' | 'critical' | 'emergency';
  confidence: number;
  details: SituationDetails;
  actions: RecommendedAction[];
  offlineGenerated: boolean;
}

interface OfflineInferenceResult {
  inferenceId: string;
  modelVersion: string;
  result: EdgeAIInferenceResult;
  cachedAt: Date;
  syncStatus: 'pending' | 'synced' | 'failed';
}

class PWAEdgeAISituationalAwarenessSystem {
  private serviceWorkerManager: ServiceWorkerManager;
  private indexedDBManager: IndexedDBManager;
  private cacheManager: CacheManager;
  private backgroundSync: BackgroundSyncManager;
  private pushManager: PushNotificationManager;
  private edgeAISystem: BrowserEdgeAISystem;
  private connectivityMonitor: ConnectivityMonitor;

  constructor(config: PWAEdgeAIConfiguration) {
    this.initializePWAComponents(config);
    this.setupOfflineCapabilities();
    this.initializeEdgeAI();
  }

  private async initializePWAComponents(
    config: PWAEdgeAIConfiguration
  ): Promise<void> {
    // Service Worker の登録
    if ('serviceWorker' in navigator) {
      const registration = await navigator.serviceWorker.register(
        '/sw-edge-ai.js',
        { scope: '/' }
      );

      this.serviceWorkerManager = new ServiceWorkerManager(registration);
    }

    // IndexedDB の初期化
    this.indexedDBManager = new IndexedDBManager({
      dbName: 'EdgeAI_SituationalAwareness',
      dbVersion: 2,
      stores: [
        'models',
        'inferences',
        'situations',
        'cache_metadata',
        'sync_queue'
      ]
    });

    await this.indexedDBManager.initialize();

    // キャッシュマネージャー
    this.cacheManager = new CacheManager({
      cacheName: 'edge-ai-models-v1',
      maxCacheSize: 100 * 1024 * 1024, // 100MB
      ttl: 7 * 24 * 60 * 60 * 1000 // 7 days
    });

    // バックグラウンド同期
    this.backgroundSync = new BackgroundSyncManager({
      syncInterval: 30000, // 30秒
      maxRetries: 3,
      exponentialBackoff: true
    });

    // プッシュ通知
    this.pushManager = new PushNotificationManager({
      vapidKey: config.pushNotifications.vapidKey,
      applicationServerKey: config.pushNotifications.serverKey
    });

    // 接続監視
    this.connectivityMonitor = new ConnectivityMonitor();
  }

  // モデルの事前キャッシュ
  async precacheModels(modelConfigs: ModelConfiguration[]): Promise<void> {
    const cachePromises = modelConfigs.map(async (config) => {
      try {
        // モデルファイルの取得
        const modelResponse = await fetch(config.modelPath);
        const modelBlob = await modelResponse.blob();

        // IndexedDB に保存
        await this.indexedDBManager.put('models', {
          id: config.modelPath,
          data: modelBlob,
          config: config,
          cachedAt: new Date(),
          version: this.generateModelVersion(config)
        });

        // Cache API にも保存（高速アクセス用）
        await this.cacheManager.put(config.modelPath, modelResponse);

        console.log(`モデル ${config.modelPath} のキャッシュ完了`);

      } catch (error) {
        console.error(`モデル ${config.modelPath} のキャッシュ失敗:`, error);
      }
    });

    await Promise.all(cachePromises);
  }

  // オフライン状況認識の実行
  async performOfflineSituationalAwareness(
    inputData: any,
    contextInfo?: ContextualInfo
  ): Promise<SituationalAwareness> {
    const isOnline = this.connectivityMonitor.isOnline();

    try {
      // オフライン時はキャッシュからモデルを読み込み
      const model = isOnline 
        ? await this.loadModelOnline('primary_situation_model')
        : await this.loadModelFromCache('primary_situation_model');

      // エッジAI推論の実行
      const inferenceResult = await this.edgeAISystem.performRealtimeInference(
        inputData,
        'primary_situation_model'
      );

      // 状況評価の生成
      const situationAssessment = this.assessSituation(
        inferenceResult,
        contextInfo
      );

      // 推奨アクションの生成
      const recommendedActions = await this.generateRecommendedActions(
        situationAssessment
      );

      const awareness: SituationalAwareness = {
        id: this.generateSituationId(),
        timestamp: new Date(),
        situationType: situationAssessment.type,
        confidence: situationAssessment.confidence,
        details: situationAssessment.details,
        actions: recommendedActions,
        offlineGenerated: !isOnline
      };

      // 結果の保存（オンライン時は同期、オフライン時はキューイング）
      if (isOnline) {
        await this.saveSituationOnline(awareness);
      } else {
        await this.queueSituationForSync(awareness);
      }

      // 緊急度が高い場合は即座に通知
      if (awareness.situationType === 'critical' || awareness.situationType === 'emergency') {
        await this.sendImmediateNotification(awareness);
      }

      return awareness;

    } catch (error) {
      // エラー時のフォールバック処理
      return this.generateFallbackAssessment(inputData, error);
    }
  }

  // キャッシュからのモデル読み込み
  private async loadModelFromCache(modelId: string): Promise<any> {
    // IndexedDB からモデルデータを取得
    const cachedModel = await this.indexedDBManager.get('models', modelId);

    if (!cachedModel) {
      throw new Error(`Cached model not found: ${modelId}`);
    }

    // キャッシュ有効期限をチェック
    const cacheAge = Date.now() - cachedModel.cachedAt.getTime();
    if (cacheAge > this.cacheManager.ttl) {
      console.warn(`Cached model ${modelId} is expired, but using for offline operation`);
    }

    // Blob からモデルを復元
    const modelUrl = URL.createObjectURL(cachedModel.data);

    try {
      const model = await this.edgeAISystem.loadModel({
        ...cachedModel.config,
        modelPath: modelUrl
      });

      return model;
    } finally {
      URL.revokeObjectURL(modelUrl);
    }
  }

  // バックグラウンド処理の設定
  setupBackgroundProcessing(): void {
    // Service Worker でのバックグラウンド処理
    this.serviceWorkerManager.onMessage('background-inference', async (data) => {
      const result = await this.performOfflineSituationalAwareness(
        data.inputData,
        data.contextInfo
      );

      // 結果をメインスレッドに送信
      return { result };
    });

    // バックグラウンド同期の設定
    this.backgroundSync.register('sync-situations', async () => {
      await this.syncPendingSituations();
    });

    // 定期的なモデル更新チェック
    this.backgroundSync.register('model-updates', async () => {
      await this.checkModelUpdates();
    });
  }

  // バックグラウンド同期の実行
  private async syncPendingSituations(): Promise<void> {
    const pendingSituations = await this.indexedDBManager.getAll('sync_queue');

    for (const situation of pendingSituations) {
      try {
        // オンラインサーバーへの同期
        await this.syncSituationToServer(situation.data);

        // 同期完了後、キューから削除
        await this.indexedDBManager.delete('sync_queue', situation.id);

      } catch (error) {
        // 同期失敗の記録
        situation.retryCount = (situation.retryCount || 0) + 1;
        situation.lastAttempt = new Date();

        if (situation.retryCount < 3) {
          await this.indexedDBManager.put('sync_queue', situation);
        } else {
          console.error(`Sync failed for situation ${situation.id} after 3 attempts`);
          await this.indexedDBManager.delete('sync_queue', situation.id);
        }
      }
    }
  }

  // リアルタイム状況監視ストリーム
  createSituationalAwarenessStream(
    inputStream: Observable<any>,
    options?: StreamOptions
  ): Observable<SituationalAwareness> {
    return inputStream.pipe(
      // オフライン対応バッファリング
      buffer(this.connectivityMonitor.connectionChanges$.pipe(
        startWith(this.connectivityMonitor.isOnline()),
        distinctUntilChanged()
      )),

      // 状況認識処理
      mergeMap(bufferedInputs => 
        from(bufferedInputs).pipe(
          mergeMap(input => 
            from(this.performOfflineSituationalAwareness(input))
          ),
          // エラーハンドリング
          catchError(error => {
            console.error('Situational awareness error:', error);
            return of(this.generateErrorAwareness(error));
          })
        )
      ),

      // 重複除去
      distinctUntilChanged((a, b) => 
        a.id === b.id && a.timestamp.getTime() === b.timestamp.getTime()
      ),

      // 優先度フィルタリング
      filter(awareness => 
        !options?.minSeverity || 
        this.getSeverityLevel(awareness.situationType) >= options.minSeverity
      ),

      // 通知送信
      tap(awareness => {
        if (this.shouldNotify(awareness)) {
          this.sendNotification(awareness);
        }
      }),

      share()
    );
  }

  // プッシュ通知の送信
  private async sendNotification(awareness: SituationalAwareness): Promise<void> {
    if (!('Notification' in window)) {
      return;
    }

    const permission = await Notification.requestPermission();
    if (permission !== 'granted') {
      return;
    }

    const notificationOptions: NotificationOptions = {
      body: this.generateNotificationBody(awareness),
      icon: this.getNotificationIcon(awareness.situationType),
      badge: '/icons/badge-72x72.png',
      vibrate: this.getVibrationPattern(awareness.situationType),
      data: { situationId: awareness.id },
      actions: [
        {
          action: 'view',
          title: '詳細を確認',
          icon: '/icons/view-action.png'
        },
        {
          action: 'dismiss',
          title: '閉じる',
          icon: '/icons/dismiss-action.png'
        }
      ]
    };

    if (this.serviceWorkerManager.registration) {
      await this.serviceWorkerManager.registration.showNotification(
        this.getNotificationTitle(awareness.situationType),
        notificationOptions
      );
    } else {
      new Notification(
        this.getNotificationTitle(awareness.situationType),
        notificationOptions
      );
    }
  }

  // PWA インストール支援
  setupPWAInstallation(): void {
    let deferredPrompt: any;

    window.addEventListener('beforeinstallprompt', (e) => {
      e.preventDefault();
      deferredPrompt = e;

      // インストールボタンの表示
      this.showInstallButton();
    });

    document.getElementById('install-button')?.addEventListener('click', async () => {
      if (deferredPrompt) {
        deferredPrompt.prompt();
        const { outcome } = await deferredPrompt.userChoice;
        console.log(`PWA installation ${outcome}`);
        deferredPrompt = null;
        this.hideInstallButton();
      }
    });
  }

  // システム状態の監視ダッシュボード
  createSystemDashboard(): SystemDashboard {
    return new SystemDashboard({
      metrics: {
        modelCacheStatus: this.getModelCacheStatus(),
        inferencePerformance: this.getInferenceMetrics(),
        offlineCapability: this.getOfflineStatus(),
        syncQueueStatus: this.getSyncQueueStatus(),
        memoryUsage: this.getMemoryUsage()
      },
      realTimeUpdates: true,
      exportCapability: true
    });
  }
}

// Service Worker マネージャー
class ServiceWorkerManager {
  constructor(private registration: ServiceWorkerRegistration) {
    this.setupMessageHandling();
  }

  private setupMessageHandling(): void {
    navigator.serviceWorker.addEventListener('message', (event) => {
      this.handleServiceWorkerMessage(event);
    });
  }

  async onMessage(type: string, handler: (data: any) => Promise<any>): Promise<void> {
    // Service Worker からのメッセージハンドリング
    navigator.serviceWorker.addEventListener('message', async (event) => {
      if (event.data.type === type) {
        try {
          const result = await handler(event.data.payload);
          event.ports[0]?.postMessage({ success: true, result });
        } catch (error) {
          event.ports[0]?.postMessage({ success: false, error: error.message });
        }
      }
    });
  }
}

// 結果インターフェース
interface StreamOptions {
  minSeverity?: number;
  bufferSize?: number;
  debounceTime?: number;
  enableNotifications?: boolean;
}

interface SystemDashboard {
  metrics: SystemMetrics;
  realTimeUpdates: boolean;
  exportCapability: boolean;
  render(): HTMLElement;
  updateMetrics(newMetrics: SystemMetrics): void;
  exportData(format: 'json' | 'csv'): Blob;
}

interface SystemMetrics {
  modelCacheStatus: CacheStatus;
  inferencePerformance: PerformanceMetrics;
  offlineCapability: OfflineStatus;
  syncQueueStatus: SyncStatus;
  memoryUsage: MemoryMetrics;
}