鸿蒙Flutter性能优化与高级特性实战：构建复杂应用

本文深入探讨鸿蒙Flutter在企业级应用中的性能优化策略，以视频会议应用为例，构建了完整的性能优化体系。文章首先提出多维度性能监控框架，包括渲染性能、内存效率、启动速度等关键指标，并配套实时监控系统。通过详细配置文件展示了硬件加速、内存管理、网络优化等具体实施方案。在实战部分，设计了视频会议应用的架构，重点优化视频引擎、编解码和网络传输等核心模块。整套方案实现了从毫秒级响应到分布式渲染的全方位性

昼-枕

620人浏览 · 2025-12-07 12:59:28

昼-枕 · 2025-12-07 12:59:28 发布

鸿蒙Flutter性能优化与高级特性实战：构建复杂应用

一、引言：从功能实现到极致性能

在前三篇文章中，我们探讨了鸿蒙Flutter的基础集成、全场景开发和生态融合。然而，在企业级应用中，性能往往是决定成败的关键因素。本文将深入鸿蒙Flutter的性能优化核心，通过构建一个复杂的企业级视频会议应用，展示如何实现从毫秒级响应到分布式渲染的全方位性能优化。

二、鸿蒙Flutter性能优化全景

2.1 性能优化架构体系

// 性能优化监控体系
class PerformanceOptimizationFramework {
  // 性能监控维度
  static const List<PerformanceDimension> dimensions = [
    PerformanceDimension(
      name: '渲染性能',
      metrics: ['FPS', 'UI线程耗时', 'GPU内存'],
      target: {'FPS': '≥58', 'UI耗时': '<16ms'},
    ),
    PerformanceDimension(
      name: '内存效率',
      metrics: ['堆内存', 'Native内存', '缓存命中率'],
      target: {'堆内存': '<200MB', '泄漏': '0'},
    ),
    PerformanceDimension(
      name: '启动速度',
      metrics: ['冷启动', '温启动', '首帧渲染'],
      target: {'冷启动': '<1.5s', '首帧': '<400ms'},
    ),
    PerformanceDimension(
      name: '分布式性能',
      metrics: ['同步延迟', '带宽占用', '设备协同效率'],
      target: {'延迟': '<50ms', '协同效率': '>95%'},
    ),
    PerformanceDimension(
      name: '能耗优化',
      metrics: ['CPU占用', '网络请求', '屏幕功耗'],
      target: {'CPU峰值': '<30%', '能耗': '降低20%'},
    ),
  ];

  // 优化策略库
  final Map<PerformanceIssue, OptimizationStrategy> strategies = {
    PerformanceIssue.highMemory: MemoryOptimizationStrategy(),
    PerformanceIssue.lowFps: RenderingOptimizationStrategy(),
    PerformanceIssue.slowStartup: StartupOptimizationStrategy(),
    PerformanceIssue.networkLatency: NetworkOptimizationStrategy(),
    PerformanceIssue.batteryDrain: PowerOptimizationStrategy(),
  };
}

// 实时性能监控器
class RealtimePerformanceMonitor {
  final _performanceStream = StreamController<PerformanceMetrics>.broadcast();
  final Map<String, PerformanceCollector> _collectors = {};
  
  Future<void> initialize() async {
    // 初始化鸿蒙性能采集器
    await HarmonyPerformance.init();
    
    // 注册各类性能采集器
    _collectors['render'] = RenderPerformanceCollector();
    _collectors['memory'] = MemoryPerformanceCollector();
    _collectors['network'] = NetworkPerformanceCollector();
    _collectors['distributed'] = DistributedPerformanceCollector();
    _collectors['power'] = PowerPerformanceCollector();
    
    // 启动性能监控
    for (final collector in _collectors.values) {
      collector.start();
      collector.metricsStream.listen(_onMetricsCollected);
    }
    
    // 启动性能分析引擎
    _startPerformanceAnalysis();
  }
  
  void _onMetricsCollected(PerformanceMetrics metrics) {
    // 实时性能分析
    final analysis = _analyzeMetrics(metrics);
    
    if (analysis.hasIssue) {
      // 自动触发优化策略
      _applyOptimizationStrategy(analysis);
    }
    
    // 推送性能数据到开发者平台
    _reportToDevPlatform(metrics);
    
    // 用户端性能提示（开发模式）
    if (kDebugMode && analysis.needsAlert) {
      _showPerformanceAlert(analysis);
    }
  }
}

2.2 多维度优化配置

# performance_config.yaml - 性能优化配置文件
performance:
  # 渲染优化配置
  rendering:
    enable_hardware_acceleration: true
    vsync_strategy: adaptive
    frame_scheduling: predictive
    texture_cache_size: 128MB
    layer_caching: true
    raster_cache_strategy: smart
    shader_warmup: true
    
  # 内存优化配置
  memory:
    heap_size_limit: 256MB
    image_cache_strategy: lru
    image_cache_size: 64MB
    bitmap_pool_enabled: true
    native_memory_monitoring: true
    leak_detection:
      enabled: true
      check_interval: 30s
      max_allowed_leaks: 5
      
  # 启动优化配置
  startup:
    pre_warm_engine: true
    lazy_initialization: true
    deferred_loading:
      routes: true
      plugins: true
      assets: true
    splash_screen_optimization: enhanced
    initial_route_cache: true
    
  # 网络优化配置
  network:
    connection_pool_size: 10
    keep_alive: true
    dns_prefetch: true
    http2_enabled: true
    request_compression: true
    response_caching:
      enabled: true
      max_age: 300s
      strategy: aggressive
      
  # 分布式优化配置
  distributed:
    sync_strategy: delta_compression
    conflict_resolution: last_write_wins
    compression_enabled: true
    max_sync_delay: 100ms
    batch_updates: true
    priority_queues: true
    
  # 能耗优化配置
  power:
    cpu_throttling: adaptive
    background_restrictions: strict
    sensor_optimization: true
    wake_lock_management: smart
    network_power_saving: true
    
  # 监控配置
  monitoring:
    sampling_rate: 60hz
    alert_thresholds:
      fps: 45
      memory_growth: 10MB/s
      startup_time: 2000ms
      network_latency: 500ms
    reporting:
      enabled: true
      endpoint: https://metrics.example.com
      batch_interval: 30s

三、企业级视频会议应用实战

3.1 应用架构设计

video_conference_harmony/
├── lib/
│   ├── core/
│   │   ├── performance/          # 性能优化核心
│   │   │   ├── optimizers/      # 优化器实现
│   │   │   ├── monitors/        # 监控器
│   │   │   ├── analyzers/       # 分析器
│   │   │   └── profilers/       # 性能分析器
│   │   ├── video/
│   │   │   ├── engine/          # 视频引擎
│   │   │   ├── codec/           # 编解码
│   │   │   ├── render/          # 渲染器
│   │   │   └── network/         # 网络传输
│   │   └── audio/
│   │       ├── processing/      # 音频处理
│   │       ├── spatial/         # 空间音频
│   │       └── noise_cancel/    # 降噪
│   ├── features/
│   │   ├── meeting/
│   │   ├── recording/
│   │   ├── collaboration/
│   │   └── distributed/
│   └── infrastructure/
│       ├── harmony_bridge/      # 鸿蒙桥接
│       ├── native_integration/  # 原生集成
│       └── platform_channels/   # 平台通道
├── native/
│   ├── harmony/
│   │   ├── video_processing/    # 原生视频处理
│   │   ├── audio_engine/        # 原生音频引擎
│   │   └── hardware_accel/      # 硬件加速
│   └── ffi/
│       └── bindings/            # FFI绑定
└── packages/
    ├── video_optimization/      # 视频优化包
    └── realtime_performance/    # 实时性能包

3.2 核心性能优化实现

1. 高性能视频渲染引擎

// lib/core/video/engine/harmony_video_engine.dart
import 'dart:ffi';
import 'dart:typed_data';
import 'package:flutter/foundation.dart';
import 'package:ffi/ffi.dart';
import 'package:harmony_hardware/harmony_hardware.dart';

/// 鸿蒙高性能视频渲染引擎
class HarmonyVideoEngine with PerformanceOptimizer {
  static final HarmonyVideoEngine _instance = HarmonyVideoEngine._internal();
  factory HarmonyVideoEngine() => _instance;
  HarmonyVideoEngine._internal();
  
  late Pointer<NativeVideoEngine> _nativeEngine;
  late Pointer<NativeRenderContext> _renderContext;
  final List<VideoFrameBuffer> _frameBuffers = [];
  final PerformanceMetricsCollector _metrics = PerformanceMetricsCollector();
  
  // 渲染统计
  int _renderedFrames = 0;
  int _droppedFrames = 0;
  double _averageRenderTime = 0;
  
  Future<void> initialize({
    required VideoConfig config,
    bool enableHardwareAccel = true,
  }) async {
    _metrics.start();
    
    // 1. 初始化原生视频引擎
    _nativeEngine = await _initializeNativeEngine(config);
    
    // 2. 创建渲染上下文
    _renderContext = await _createRenderContext(
      config: config,
      hardwareAccel: enableHardwareAccel,
    );
    
    // 3. 初始化帧缓冲区
    await _initializeFrameBuffers(config.bufferSize);
    
    // 4. 设置性能监控回调
    _setupPerformanceCallbacks();
    
    // 5. 预热渲染管线
    if (config.preWarmPipeline) {
      await _preWarmRenderPipeline();
    }
    
    // 6. 启动自适应优化
    _startAdaptiveOptimization();
  }
  
  Future<void> _initializeNativeEngine(VideoConfig config) async {
    // 使用FFI调用鸿蒙原生视频引擎
    final nativeLib = DynamicLibrary.open('libharmony_video.so');
    
    final initFunc = nativeLib.lookupFunction<
      Pointer<NativeVideoEngine> Function(
        Int32 width,
        Int32 height,
        Int32 fps,
        Int32 bitrate,
        Int32 codec,
      ),
      Pointer<NativeVideoEngine> Function(
        int width,
        int height,
        int fps,
        int bitrate,
        int codec,
      )
    >('harmony_video_engine_create');
    
    final engine = initFunc(
      config.width,
      config.height,
      config.fps,
      config.bitrate,
      config.codec.index,
    );
    
    // 设置硬件加速
    if (config.enableHardwareAccel) {
      final hwAccelFunc = nativeLib.lookupFunction<
        Void Function(Pointer<NativeVideoEngine>, Int32),
        void Function(Pointer<NativeVideoEngine>, int)
      >('harmony_video_enable_hardware_acceleration');
      
      hwAccelFunc(engine, 1);
    }
    
    return engine;
  }
  
  /// 渲染视频帧（高性能实现）
  Future<void> renderFrame(VideoFrame frame) async {
    final startTime = DateTime.now().microsecondsSinceEpoch;
    
    try {
      // 1. 帧预处理（降噪、增强等）
      final processedFrame = await _preprocessFrame(frame);
      
      // 2. 使用硬件纹理渲染
      await _renderWithHardwareTexture(processedFrame);
      
      // 3. 性能统计
      final renderTime = (DateTime.now().microsecondsSinceEpoch - startTime) / 1000.0;
      _updateRenderStats(renderTime);
      
      // 4. 自适应质量调整
      if (renderTime > _calculateMaxRenderTime()) {
        _adjustQualityLevel();
      }
      
      // 5. 缓存管理
      _manageFrameCache(processedFrame);
      
    } catch (e) {
      _droppedFrames++;
      _handleRenderError(e);
    }
  }
  
  Future<void> _renderWithHardwareTexture(VideoFrame frame) async {
    // 使用鸿蒙硬件纹理API
    final textureId = await HarmonyHardwareTexture.createTexture(
      width: frame.width,
      height: frame.height,
      format: TextureFormat.nv21,
    );
    
    // 上传帧数据到GPU
    await HarmonyHardwareTexture.updateTexture(
      textureId: textureId,
      data: frame.data,
      width: frame.width,
      height: frame.height,
    );
    
    // 渲染到Flutter
    await TextureWidgetController.renderTexture(
      textureId: textureId,
      width: frame.width,
      height: frame.height,
    );
    
    // 异步释放纹理
    scheduleMicrotask(() {
      HarmonyHardwareTexture.releaseTexture(textureId);
    });
  }
  
  /// 自适应质量调整算法
  void _adjustQualityLevel() {
    final currentQuality = _getCurrentQualityLevel();
    final targetFps = _calculateTargetFps();
    final metrics = _metrics.currentMetrics;
    
    // 基于多因素的质量调整
    final adjustment = QualityAdjustmentAlgorithm.calculate(
      currentQuality: currentQuality,
      targetFps: targetFps,
      renderTime: metrics.averageRenderTime,
      memoryPressure: metrics.memoryPressure,
      thermalState: DeviceThermal.currentState,
      networkCondition: NetworkMonitor.currentCondition,
    );
    
    if (adjustment.shouldAdjust) {
      _applyQualityAdjustment(adjustment);
      _logQualityChange(adjustment);
    }
  }
  
  /// 分布式视频渲染优化
  Future<void> renderDistributedVideo({
    required List<VideoSource> sources,
    required LayoutConfiguration layout,
    bool enableCrossDeviceSync = true,
  }) async {
    // 1. 计算最优设备分配
    final deviceAllocation = await _calculateOptimalDeviceAllocation(sources);
    
    // 2. 建立分布式渲染通道
    final renderChannels = await _establishRenderChannels(deviceAllocation);
    
    // 3. 同步渲染状态
    if (enableCrossDeviceSync) {
      await _syncRenderStateAcrossDevices(renderChannels);
    }
    
    // 4. 启动协同渲染
    await _startCollaborativeRendering(
      sources: sources,
      layout: layout,
      channels: renderChannels,
    );
    
    // 5. 监控分布式性能
    _monitorDistributedPerformance(renderChannels);
  }
  
  Future<Map<DeviceInfo, List<VideoSource>>> _calculateOptimalDeviceAllocation(
    List<VideoSource> sources,
  ) async {
    final availableDevices = await DistributedDeviceManager.getAvailableDevices();
    final deviceCapabilities = await Future.wait(
      availableDevices.map((device) => DeviceCapabilityProfiler.profile(device))
    );
    
    // 使用匈牙利算法进行最优分配
    return DeviceAllocationOptimizer.allocate(
      sources: sources,
      devices: availableDevices,
      capabilities: deviceCapabilities,
      optimizationGoal: OptimizationGoal.balanced,
    );
  }
}

// 视频帧缓冲区管理
class VideoFrameBuffer with PerformanceMonitor {
  static const int maxBuffers = 3;
  final List<VideoFrame> _buffers = [];
  final CircularBuffer<FrameStats> _frameStats = CircularBuffer(size: 60);
  
  Future<void> pushFrame(VideoFrame frame) async {
    // 缓冲区管理策略
    if (_buffers.length >= maxBuffers) {
      // 根据策略丢弃最合适的帧
      final frameToDrop = _selectFrameToDrop();
      _buffers.remove(frameToDrop);
      _recordDroppedFrame(frameToDrop);
    }
    
    // 性能标记
    final startTime = PerformanceTracer.mark('frame_buffer_push');
    
    // 异步解码（如果编码）
    if (frame.isEncoded) {
      frame = await _asyncDecodeFrame(frame);
    }
    
    // 添加帧统计
    _frameStats.add(FrameStats(
      timestamp: DateTime.now(),
      size: frame.data.length,
      type: frame.type,
    ));
    
    _buffers.add(frame);
    
    PerformanceTracer.measure('frame_buffer_push', startTime);
  }
  
  VideoFrame? popFrame() {
    if (_buffers.isEmpty) return null;
    
    final frame = _buffers.removeAt(0);
    
    // 记录缓冲区延迟
    final bufferTime = DateTime.now().difference(frame.timestamp);
    _recordBufferDelay(bufferTime);
    
    return frame;
  }
  
  VideoFrame _selectFrameToDrop() {
    // 智能丢帧策略
    if (_shouldDropOldestFrame()) {
      return _buffers.first;
    } else if (_shouldDropLowPriorityFrame()) {
      return _findLowPriorityFrame();
    } else {
      // 使用预测性丢帧
      return _predictiveDropFrame();
    }
  }
  
  bool _shouldDropOldestFrame() {
    // 检查缓冲区是否过载
    if (_buffers.length > maxBuffers * 1.5) return true;
    
    // 检查帧延迟
    final oldestFrame = _buffers.first;
    final age = DateTime.now().difference(oldestFrame.timestamp);
    return age > Duration(milliseconds: 100);
  }
  
  VideoFrame _predictiveDropFrame() {
    // 基于机器学习的预测性丢帧
    final prediction = FrameDropPredictor.predict(
      frames: _buffers,
      stats: _frameStats.toList(),
      targetFps: 60,
      currentLoad: PerformanceMonitor.currentLoad,
    );
    
    return _buffers[prediction.bestFrameToDrop];
  }
}

2. 实时音频处理引擎

// lib/core/audio/processing/realtime_audio_engine.dart
import 'dart:ffi';
import 'dart:typed_data';
import 'package:ffi/ffi.dart';
import 'package:harmony_audio/harmony_audio.dart';

/// 鸿蒙实时音频处理引擎
class RealtimeAudioEngine with AudioPerformanceOptimizer {
  late Pointer<NativeAudioEngine> _nativeEngine;
  final AudioProcessingPipeline _pipeline = AudioProcessingPipeline();
  final AudioQualityMonitor _qualityMonitor = AudioQualityMonitor();
  final SpatialAudioProcessor _spatialProcessor = SpatialAudioProcessor();
  
  // 音频统计
  final AudioStatistics _statistics = AudioStatistics();
  final CircularBuffer<AudioMetrics> _metricsHistory = CircularBuffer(size: 1000);
  
  Future<void> initialize({
    required AudioConfig config,
    List<AudioEffect> effects = const [],
  }) async {
    // 1. 初始化原生音频引擎
    _nativeEngine = await _initializeNativeAudioEngine(config);
    
    // 2. 构建处理流水线
    await _buildProcessingPipeline(config, effects);
    
    // 3. 初始化空间音频
    if (config.enableSpatialAudio) {
      await _spatialProcessor.initialize(config.spatialConfig);
    }
    
    // 4. 启动音频质量监控
    _qualityMonitor.start();
    
    // 5. 预热音频处理
    await _preWarmAudioProcessing();
  }
  
  Future<void> processAudioFrame(AudioFrame frame) async {
    final startTime = PerformanceTracer.mark('audio_processing');
    
    try {
      // 1. 前置处理（降噪、回声消除）
      var processedFrame = await _pipeline.preProcess(frame);
      
      // 2. 空间音频处理
      if (_spatialProcessor.isInitialized) {
        processedFrame = await _spatialProcessor.process(processedFrame);
      }
      
      // 3. 音效处理
      processedFrame = await _pipeline.applyEffects(processedFrame);
      
      // 4. 后置处理（压缩、均衡）
      processedFrame = await _pipeline.postProcess(processedFrame);
      
      // 5. 质量监测
      final quality = _qualityMonitor.analyze(processedFrame);
      _statistics.update(quality);
      
      // 6. 性能统计
      final processTime = PerformanceTracer.measure('audio_processing', startTime);
      _recordProcessingTime(processTime);
      
      return processedFrame;
      
    } catch (e) {
      _handleAudioProcessingError(e, frame);
      // 降级处理：返回原始帧
      return frame;
    }
  }
  
  /// 分布式音频同步处理
  Future<DistributedAudioResult> processDistributedAudio({
    required Map<DeviceInfo, AudioSource> sources,
    required AudioMixConfig mixConfig,
  }) async {
    final result = DistributedAudioResult();
    
    // 1. 收集所有设备音频
    final collectedAudio = await _collectDistributedAudio(sources);
    
    // 2. 时间对齐（消除网络延迟）
    final alignedAudio = await _timeAlignAudioFrames(collectedAudio);
    
    // 3. 智能混音
    final mixedAudio = await _intelligentAudioMixing(alignedAudio, mixConfig);
    
    // 4. 空间音频合成
    final spatialAudio = await _synthesizeSpatialAudio(mixedAudio);
    
    // 5. 分发给各设备
    await _distributeProcessedAudio(spatialAudio, sources.keys.toList());
    
    // 6. 性能优化反馈
    _optimizeDistributedAudioProcessing(collectedAudio.metrics);
    
    return result..audio = spatialAudio;
  }
  
  Future<List<AudioFrame>> _timeAlignAudioFrames(
    List<CollectedAudioFrame> frames,
  ) async {
    // 使用NTP协议进行时间同步
    final ntpTimestamps = await NTPSynchronizer.synchronize(frames.map((f) => f.deviceId));
    
    // 计算网络延迟补偿
    final latencyCompensation = await _calculateLatencyCompensation(frames);
    
    // 应用时间对齐
    return frames.map((frame) {
      final deviceTimestamp = ntpTimestamps[frame.deviceId]!;
      final compensation = latencyCompensation[frame.deviceId] ?? Duration.zero;
      
      return frame.align(
        referenceTime: deviceTimestamp,
        compensation: compensation,
      );
    }).toList();
  }
  
  Future<AudioFrame> _intelligentAudioMixing(
    List<AudioFrame> frames,
    AudioMixConfig config,
  ) async {
    // 基于AI的智能混音
    final mixingStrategy = await AIMixingStrategySelector.select(
      frames: frames,
      context: MeetingContext.current,
      config: config,
    );
    
    switch (mixingStrategy) {
      case MixingStrategy.speechPriority:
        return _speechPriorityMixing(frames, config);
      case MixingStrategy.spatialBalance:
        return _spatialBalanceMixing(frames, config);
      case MixingStrategy.adaptiveDynamic:
        return _adaptiveDynamicMixing(frames, config);
      default:
        return _defaultMixing(frames, config);
    }
  }
}

// 音频处理流水线
class AudioProcessingPipeline with PerformanceOptimizer {
  final List<AudioProcessor> _processors = [];
  final Map<String, AudioProcessor> _processorCache = {};
  
  Future<AudioFrame> preProcess(AudioFrame frame) async {
    var processedFrame = frame;
    
    // 1. 噪声抑制
    if (_shouldApplyNoiseSuppression()) {
      processedFrame = await _getProcessor<NoiseSuppressor>().process(processedFrame);
    }
    
    // 2. 回声消除
    if (_shouldApplyEchoCancellation()) {
      processedFrame = await _getProcessor<EchoCanceller>().process(processedFrame);
    }
    
    // 3. 自动增益控制
    processedFrame = await _getProcessor<AutoGainControl>().process(processedFrame);
    
    // 4. 语音活动检测
    final vadResult = await _getProcessor<VoiceActivityDetector>().detect(processedFrame);
    processedFrame = processedFrame.copyWith(vadResult: vadResult);
    
    return processedFrame;
  }
  
  Future<AudioFrame> applyEffects(AudioFrame frame) async {
    if (!frame.hasVoiceActivity) return frame;
    
    var processedFrame = frame;
    
    // 应用音效链
    for (final effect in _activeEffects) {
      processedFrame = await effect.process(processedFrame);
    }
    
    return processedFrame;
  }
  
  T _getProcessor<T extends AudioProcessor>() {
    final typeName = T.toString();
    
    if (!_processorCache.containsKey(typeName)) {
      _processorCache[typeName] = _createProcessor<T>();
    }
    
    return _processorCache[typeName] as T;
  }
  
  bool _shouldApplyNoiseSuppression() {
    // 基于环境噪声水平的智能决策
    final noiseLevel = EnvironmentNoiseMonitor.currentLevel;
    return noiseLevel > NoiseLevel.moderate;
  }
}

3. 智能内存管理优化

// lib/core/performance/memory/harmony_memory_manager.dart
import 'dart:developer';
import 'package:flutter/foundation.dart';
import 'package:harmony_memory/harmony_memory.dart';

/// 鸿蒙智能内存管理器
class HarmonyMemoryManager with ChangeNotifier {
  static final HarmonyMemoryManager _instance = HarmonyMemoryManager._internal();
  factory HarmonyMemoryManager() => _instance;
  HarmonyMemoryManager._internal();
  
  final MemoryPool _memoryPool = MemoryPool();
  final LeakDetector _leakDetector = LeakDetector();
  final MemoryCompressor _compressor = MemoryCompressor();
  final MemoryPredictor _predictor = MemoryPredictor();
  
  // 内存状态
  MemoryState _currentState = MemoryState.normal;
  double _memoryPressure = 0.0;
  final List<MemoryEvent> _events = [];
  
  Future<void> initialize() async {
    // 1. 初始化鸿蒙内存监控
    await HarmonyMemory.initMonitoring();
    
    // 2. 设置内存警告阈值
    HarmonyMemory.setWarningThresholds(
      warning: 0.7,  // 70%内存使用
      critical: 0.85, // 85%内存使用
    );
    
    // 3. 启动内存监控
    HarmonyMemory.onMemoryWarning.listen(_handleMemoryWarning);
    HarmonyMemory.onMemoryCritical.listen(_handleMemoryCritical);
    
    // 4. 初始化内存池
    await _memoryPool.initialize(
      strategy: PoolStrategy.dynamic,
      maxSize: 256 * 1024 * 1024, // 256MB
    );
    
    // 5. 启动泄漏检测
    _leakDetector.start();
    
    // 6. 启动内存预测
    _predictor.start();
  }
  
  /// 智能内存分配
  Future<MemoryBlock> allocate({
    required int size,
    required MemoryType type,
    bool zeroed = false,
    int alignment = 16,
  }) async {
    final startTime = DateTime.now();
    
    try {
      // 检查内存压力
      if (_memoryPressure > 0.8) {
        await _performMemoryCleanup();
      }
      
      // 尝试从内存池分配
      MemoryBlock? block = await _memoryPool.tryAllocate(
        size: size,
        type: type,
        alignment: alignment,
      );
      
      // 如果内存池不足，使用系统分配
      if (block == null) {
        block = await _allocateFromSystem(
          size: size,
          type: type,
          zeroed: zeroed,
          alignment: alignment,
        );
      }
      
      // 记录分配
      _recordAllocation(block, startTime);
      
      // 更新内存预测
      _predictor.recordAllocation(size, type);
      
      return block;
      
    } catch (e) {
      // 内存分配失败处理
      await _handleAllocationFailure(size, type, e);
      rethrow;
    }
  }
  
  /// 智能内存释放
  Future<void> release(MemoryBlock block) async {
    final startTime = DateTime.now();
    
    try {
      // 检查是否应该缓存
      if (_shouldCacheBlock(block)) {
        await _memoryPool.cache(block);
      } else {
        // 直接释放回系统
        await _releaseToSystem(block);
      }
      
      // 记录释放
      _recordRelease(block, startTime);
      
      // 更新内存预测
      _predictor.recordRelease(block.size, block.type);
      
    } catch (e) {
      debugPrint('内存释放失败: $e');
      // 尝试强制释放
      await _forceRelease(block);
    }
  }
  
  /// 内存压力处理
  Future<void> _handleMemoryWarning(double pressure) async {
    _memoryPressure = pressure;
    _currentState = MemoryState.warning;
    
    // 执行内存优化策略
    await _executeMemoryOptimization(MemoryOptimizationLevel.moderate);
    
    // 通知观察者
    notifyListeners();
  }
  
  Future<void> _handleMemoryCritical(double pressure) async {
    _memoryPressure = pressure;
    _currentState = MemoryState.critical;
    
    // 执行激进内存优化
    await _executeMemoryOptimization(MemoryOptimizationLevel.aggressive);
    
    // 触发GC（如果可用）
    if (HarmonyMemory.supportsManualGC) {
      await HarmonyMemory.performGarbageCollection();
    }
    
    // 通知观察者
    notifyListeners();
  }
  
  Future<void> _executeMemoryOptimization(MemoryOptimizationLevel level) async {
    final strategy = MemoryOptimizationStrategy.forLevel(level);
    
    for (final action in strategy.actions) {
      switch (action) {
        case MemoryAction.clearImageCache:
          await ImageCacheManager.clearCache(level: level);
          break;
          
        case MemoryAction.compressInactiveData:
          await _compressor.compressInactiveMemory();
          break;
          
        case MemoryAction.releaseUnusedNative:
          await HarmonyMemory.releaseUnusedNativeMemory();
          break;
          
        case MemoryAction.dropCachedRoutes:
          await RouteCacheManager.clear();
          break;
          
        case MemoryAction.reduceRenderQuality:
          await RenderQualityManager.adjustForMemory(level);
          break;
      }
    }
  }
  
  /// 内存泄漏检测与处理
  Future<void> detectAndFixLeaks() async {
    final leaks = await _leakDetector.detect();
    
    if (leaks.isNotEmpty) {
      debugPrint('检测到 ${leaks.length} 个内存泄漏');
      
      // 记录泄漏信息
      for (final leak in leaks) {
        await _logLeak(leak);
      }
      
      // 尝试修复泄漏
      if (_leakDetector.canAutoFix) {
        await _leakDetector.attemptFix(leaks);
      }
      
      // 发送泄漏报告
      if (kReleaseMode) {
        await _sendLeakReport(leaks);
      }
    }
  }
}

// 预测性内存管理
class MemoryPredictor with PerformanceMonitor {
  final TimeSeriesAnalyzer _analyzer = TimeSeriesAnalyzer();
  final PatternRecognizer _recognizer = PatternRecognizer();
  final ResourceUsagePredictor _resourcePredictor = ResourceUsagePredictor();
  
  MemoryPrediction _currentPrediction = MemoryPrediction();
  
  Future<void> start() async {
    // 收集历史数据
    await _collectHistoricalData();
    
    // 训练预测模型
    await _trainPredictionModel();
    
    // 启动预测循环
    _startPredictionLoop();
  }
  
  Future<MemoryPrediction> predict({
    required Duration timeAhead,
    required PredictionContext context,
  }) async {
    // 1. 基于历史趋势预测
    final trendPrediction = await _predictBasedOnTrend(timeAhead);
    
    // 2. 基于模式识别预测
    final patternPrediction = await _predictBasedOnPattern(context);
    
    // 3. 基于资源使用预测
    final resourcePrediction = await _predictBasedOnResourceUsage(context);
    
    // 4. 融合预测结果
    final fusedPrediction = _fusePredictions([
      trendPrediction,
      patternPrediction,
      resourcePrediction,
    ]);
    
    // 5. 更新当前预测
    _currentPrediction = fusedPrediction;
    
    return fusedPrediction;
  }
  
  Future<MemoryPrediction> _predictBasedOnTrend(Duration timeAhead) async {
    final historicalData = await _analyzer.getHistoricalData(
      period: Duration(hours: 24),
      granularity: Duration(minutes: 5),
    );
    
    // 使用ARIMA模型预测
    return await ARIMAPredictor.predict(
      data: historicalData,
      stepsAhead: (timeAhead.inMinutes / 5).ceil(),
    );
  }
  
  Future<MemoryPrediction> _predictBasedOnPattern(PredictionContext context) async {
    // 识别当前使用模式
    final currentPattern = await _recognizer.identifyPattern(
      currentUsage: MemoryMonitor.currentUsage,
      context: context,
    );
    
    // 基于模式预测
    return await PatternBasedPredictor.predict(
      pattern: currentPattern,
      context: context,
    );
  }
}

4. 分布式渲染同步优化

// lib/core/performance/distributed/render_sync_engine.dart
import 'dart:async';
import 'package:harmony_distributed/harmony_distributed.dart';

/// 分布式渲染同步引擎
class DistributedRenderSyncEngine {
  final SyncCoordinator _coordinator = SyncCoordinator();
  final FrameSynchronizer _frameSynchronizer = FrameSynchronizer();
  final StateReconciler _stateReconciler = StateReconciler();
  final BandwidthOptimizer _bandwidthOptimizer = BandwidthOptimizer();
  
  final Map<String, RenderSession> _sessions = {};
  final PriorityQueue<SyncTask> _syncQueue = PriorityQueue();
  
  Future<void> initialize() async {
    // 1. 初始化分布式同步
    await _coordinator.initialize();
    
    // 2. 设置帧同步策略
    await _frameSynchronizer.configure(SyncStrategy.adaptive);
    
    // 3. 启动带宽优化
    await _bandwidthOptimizer.start();
    
    // 4. 注册设备变化监听
    DistributedDeviceManager.onDeviceChange.listen(_handleDeviceChange);
  }
  
  /// 创建分布式渲染会话
  Future<RenderSession> createSession({
    required String sessionId,
    required List<DeviceInfo> devices,
    required RenderConfig config,
  }) async {
    final session = RenderSession(
      id: sessionId,
      devices: devices,
      config: config,
    );
    
    // 1. 建立设备连接
    await _establishDeviceConnections(devices);
    
    // 2. 协商渲染参数
    final negotiatedConfig = await _negotiateRenderConfig(devices, config);
    session.config = negotiatedConfig;
    
    // 3. 分配渲染角色
    await _assignRenderRoles(session);
    
    // 4. 建立同步通道
    await _establishSyncChannels(session);
    
    // 5. 启动心跳检测
    _startHeartbeat(session);
    
    _sessions[sessionId] = session;
    return session;
  }
  
  /// 同步渲染状态
  Future<void> syncRenderState({
    required String sessionId,
    required RenderState state,
    SyncPriority priority = SyncPriority.normal,
  }) async {
    final session = _sessions[sessionId];
    if (session == null) throw SessionNotFoundException(sessionId);
    
    // 创建同步任务
    final task = SyncTask(
      session: session,
      state: state,
      priority: priority,
      timestamp: DateTime.now(),
    );
    
    // 添加到同步队列
    _syncQueue.add(task);
    
    // 触发同步处理
    _processSyncQueue();
  }
  
  Future<void> _processSyncQueue() async {
    while (_syncQueue.isNotEmpty) {
      final task = _syncQueue.removeFirst();
      
      try {
        await _executeSyncTask(task);
      } catch (e) {
        await _handleSyncError(task, e);
      }
    }
  }
  
  Future<void> _executeSyncTask(SyncTask task) async {
    final session = task.session;
    
    // 1. 状态压缩
    final compressedState = await _compressRenderState(task.state);
    
    // 2. 差异计算（如果可能）
    final stateDiff = await _calculateStateDiff(
      session.lastSyncedState,
      compressedState,
    );
    
    // 3. 选择同步策略
    final syncStrategy = _selectSyncStrategy(
      stateDiff: stateDiff,
      networkCondition: NetworkMonitor.currentCondition,
      priority: task.priority,
    );
    
    // 4. 执行同步
    switch (syncStrategy.type) {
      case SyncType.full:
        await _syncFullState(session, compressedState);
        break;
      case SyncType.delta:
        await _syncDeltaState(session, stateDiff);
        break;
      case SyncType.predictive:
        await _syncPredictiveState(session, compressedState);
        break;
    }
    
    // 5. 更新会话状态
    session.lastSyncedState = compressedState;
    session.lastSyncTime = DateTime.now();
    
    // 6. 记录同步指标
    _recordSyncMetrics(session, syncStrategy);
  }
  
  SyncStrategy _selectSyncStrategy({
    required StateDiff stateDiff,
    required NetworkCondition networkCondition,
    required SyncPriority priority,
  }) {
    // 基于多因素的选择算法
    final factors = {
      'diff_size': stateDiff.size,
      'network_bandwidth': networkCondition.bandwidth,
      'network_latency': networkCondition.latency,
      'priority': priority.value,
      'device_battery': DeviceBattery.currentLevel,
    };
    
    return SyncStrategySelector.select(factors);
  }
  
  /// 自适应帧同步
  Future<void> syncFrames({
    required String sessionId,
    required List<VideoFrame> frames,
  }) async {
    final session = _sessions[sessionId];
    if (session == null) return;
    
    // 1. 帧预处理
    final processedFrames = await _preprocessFrames(frames);
    
    // 2. 时间戳同步
    final syncedFrames = await _synchronizeTimestamps(processedFrames);
    
    // 3. 设备能力适配
    final adaptedFrames = await _adaptToDeviceCapabilities(
      syncedFrames,
      session.devices,
    );
    
    // 4. 分发到设备
    await _distributeFrames(session, adaptedFrames);
    
    // 5. 反馈优化
    _optimizeFrameSync(session, adaptedFrames);
  }
  
  Future<List<VideoFrame>> _adaptToDeviceCapabilities(
    List<VideoFrame> frames,
    List<DeviceInfo> devices,
  ) async {
    final adaptedFrames = <VideoFrame>[];
    
    for (final frame in frames) {
      // 为每个设备创建适配版本
      final deviceFrames = await Future.wait(
        devices.map((device) async {
          return await _adaptFrameForDevice(frame, device);
        }),
      );
      
      adaptedFrames.addAll(deviceFrames);
    }
    
    return adaptedFrames;
  }
  
  Future<VideoFrame> _adaptFrameForDevice(
    VideoFrame frame,
    DeviceInfo device,
  ) async {
    final capabilities = await DeviceCapabilityProfiler.profile(device);
    
    // 根据设备能力调整帧参数
    return frame.adaptedCopy(
      resolution: _calculateOptimalResolution(capabilities),
      bitrate: _calculateOptimalBitrate(capabilities, NetworkMonitor.currentCondition),
      codec: _selectOptimalCodec(capabilities),
      framerate: _calculateOptimalFramerate(capabilities),
    );
  }
}

// 智能带宽优化器
class BandwidthOptimizer with PerformanceMonitor {
  final AdaptiveBitrateController _bitrateController = AdaptiveBitrateController();
  final CompressionOptimizer _compressionOptimizer = CompressionOptimizer();
  final TrafficShaper _trafficShaper = TrafficShaper();
  
  Future<void> start() async {
    // 监控网络条件
    NetworkMonitor.onConditionChange.listen(_handleNetworkChange);
    
    // 启动自适应优化
    _startAdaptiveOptimization();
  }
  
  Future<void> _handleNetworkChange(NetworkCondition condition) async {
    // 基于网络条件调整优化策略
    final optimizationLevel = _calculateOptimizationLevel(condition);
    
    switch (optimizationLevel) {
      case OptimizationLevel.aggressive:
        await _applyAggressiveOptimization(condition);
        break;
      case OptimizationLevel.moderate:
        await _applyModerateOptimization(condition);
        break;
      case OptimizationLevel.conservative:
        await _applyConservativeOptimization(condition);
        break;
    }
  }
  
  Future<List<int>> optimizeDataTransfer({
    required List<int> data,
    required DataType type,
    required NetworkCondition condition,
  }) async {
    // 1. 选择压缩算法
    final compressionAlgorithm = _selectCompressionAlgorithm(type, condition);
    
    // 2. 应用压缩
    var compressedData = await compressionAlgorithm.compress(data);
    
    // 3. 数据分块（如果需要）
    if (compressedData.length > _calculateOptimalChunkSize(condition)) {
      compressedData = await _chunkData(compressedData, condition);
    }
    
    // 4. 添加纠错码（在弱网络条件下）
    if (condition.quality < NetworkQuality.good) {
      compressedData = await _addErrorCorrection(compressedData);
    }
    
    // 5. 流量整形
    compressedData = await _trafficShaper.shape(compressedData, condition);
    
    return compressedData;
  }
  
  CompressionAlgorithm _selectCompressionAlgorithm(DataType type, NetworkCondition condition) {
    // 基于数据类型和网络条件的智能选择
    final candidates = CompressionAlgorithmRegistry.getCandidates(type);
    
    return CompressionAlgorithmSelector.select(
      candidates: candidates,
      networkCondition: condition,
      dataCharacteristics: _analyzeDataCharacteristics(type),
      deviceCapabilities: DeviceCapabilityProfiler.currentCapabilities,
    );
  }
}

四、高级测试与监控体系

4.1 全方位性能测试框架

// 性能测试套件
class ComprehensivePerformanceTestSuite {
  final List<PerformanceTestCase> _testCases = [];
  final PerformanceTestRunner _runner = PerformanceTestRunner();
  final TestResultAnalyzer _analyzer = TestResultAnalyzer();
  
  Future<void> runAllTests() async {
    // 1. 基准测试
    await _runBenchmarkTests();
    
    // 2. 压力测试
    await _runStressTests();
    
    // 3. 稳定性测试
    await _runStabilityTests();
    
    // 4. 兼容性测试
    await _runCompatibilityTests();
    
    // 5. 分布式测试
    await _runDistributedTests();
  }
  
  Future<void> _runBenchmarkTests() async {
    final benchmarks = [
      BenchmarkTest(
        name: '启动性能测试',
        description: '测量应用启动时间',
        runner: () => StartupBenchmark.run(),
        metrics: ['冷启动时间', '温启动时间', '首帧时间'],
      ),
      BenchmarkTest(
        name: '渲染性能测试',
        description: '测量UI渲染性能',
        runner: () => RenderingBenchmark.run(),
        metrics: ['FPS', '丢帧率', 'UI线程耗时'],
      ),
      BenchmarkTest(
        name: '内存性能测试',
        description: '测量内存使用效率',
        runner: () => MemoryBenchmark.run(),
        metrics: ['堆内存', 'Native内存', '泄漏检测'],
      ),
      BenchmarkTest(
        name: '网络性能测试',
        description: '测量网络请求性能',
        runner: () => NetworkBenchmark.run(),
        metrics: ['延迟', '吞吐量', '错误率'],
      ),
    ];
    
    for (final benchmark in benchmarks) {
      final result = await _runner.runBenchmark(benchmark);
      await _analyzer.analyzeBenchmarkResult(result);
    }
  }
  
  Future<void> _runStressTests() async {
    // 高负载压力测试
    await StressTestRunner.run(
      test: MemoryStressTest(
        duration: Duration(minutes: 30),
        memoryAllocationRate: 100, // MB/s
      ),
    );
    
    await StressTestRunner.run(
      test: CPUBurnTest(
        duration: Duration(minutes: 15),
        targetUtilization: 0.9,
      ),
    );
    
    await StressTestRunner.run(
      test: NetworkStressTest(
        duration: Duration(minutes: 10),
        requestRate: 1000, // 请求/秒
      ),
    );
  }
}

// 实时性能监控面板
class RealTimePerformanceDashboard extends StatefulWidget {
  const RealTimePerformanceDashboard({super.key});

  @override
  State<RealTimePerformanceDashboard> createState() => _RealTimePerformanceDashboardState();
}

class _RealTimePerformanceDashboardState extends State<RealTimePerformanceDashboard> {
  final PerformanceMonitor _monitor = PerformanceMonitor();
  final Map<String, PerformanceChart> _charts = {};
  
  @override
  void initState() {
    super.initState();
    _initializeDashboard();
  }
  
  Future<void> _initializeDashboard() async {
    await _monitor.initialize();
    
    // 创建监控图表
    _charts['fps'] = PerformanceChart(
      title: '帧率监控',
      metric: 'fps',
      maxValue: 60,
      warningThreshold: 45,
      criticalThreshold: 30,
    );
    
    _charts['memory'] = PerformanceChart(
      title: '内存使用',
      metric: 'memory_usage',
      maxValue: 256, // MB
      warningThreshold: 180,
      criticalThreshold: 220,
    );
    
    _charts['network'] = PerformanceChart(
      title: '网络延迟',
      metric: 'network_latency',
      maxValue: 1000, // ms
      warningThreshold: 300,
      criticalThreshold: 500,
    );
    
    // 订阅性能数据流
    _monitor.metricsStream.listen(_updateCharts);
  }
  
  void _updateCharts(PerformanceMetrics metrics) {
    if (!mounted) return;
    
    setState(() {
      for (final chart in _charts.values) {
        chart.addDataPoint(metrics);
      }
    });
  }
  
  @override
  Widget build(BuildContext context) {
    return Container(
      decoration: BoxDecoration(
        color: Colors.black87,
        borderRadius: BorderRadius.circular(12),
      ),
      padding: const EdgeInsets.all(16),
      child: Column(
        crossAxisAlignment: CrossAxisAlignment.start,
        children: [
          const Text(
            '实时性能监控',
            style: TextStyle(
              color: Colors.white,
              fontSize: 18,
              fontWeight: FontWeight.bold,
            ),
          ),
          const SizedBox(height: 16),
          
          // 性能图表网格
          GridView.builder(
            shrinkWrap: true,
            physics: const NeverScrollableScrollPhysics(),
            gridDelegate: const SliverGridDelegateWithFixedCrossAxisCount(
              crossAxisCount: 2,
              crossAxisSpacing: 12,
              mainAxisSpacing: 12,
              childAspectRatio: 1.8,
            ),
            itemCount: _charts.length,
            itemBuilder: (context, index) {
              final chart = _charts.values.elementAt(index);
              return _buildChartWidget(chart);
            },
          ),
          
          const SizedBox(height: 16),
          
          // 性能建议
          _buildPerformanceSuggestions(),
        ],
      ),
    );
  }
  
  Widget _buildChartWidget(PerformanceChart chart) {
    return Container(
      decoration: BoxDecoration(
        color: Colors.grey[900],
        borderRadius: BorderRadius.circular(8),
      ),
      padding: const EdgeInsets.all(12),
      child: Column(
        crossAxisAlignment: CrossAxisAlignment.start,
        children: [
          Text(
            chart.title,
            style: const TextStyle(
              color: Colors.white,
              fontSize: 14,
              fontWeight: FontWeight.bold,
            ),
          ),
          const SizedBox(height: 8),
          Expanded(
            child: PerformanceChartWidget(
              chart: chart,
              showLegend: true,
              animate: true,
            ),
          ),
          const SizedBox(height: 4),
          _buildChartStatus(chart),
        ],
      ),
    );
  }
  
  Widget _buildChartStatus(PerformanceChart chart) {
    final latestValue = chart.latestValue;
    final status = _getPerformanceStatus(chart.metric, latestValue);
    
    return Row(
      children: [
        Container(
          width: 8,
          height: 8,
          decoration: BoxDecoration(
            color: status.color,
            shape: BoxShape.circle,
          ),
        ),
        const SizedBox(width: 6),
        Text(
          '${latestValue.toStringAsFixed(1)} ${chart.unit}',
          style: TextStyle(
            color: status.color,
            fontSize: 12,
          ),
        ),
        const Spacer(),
        if (status.suggestion != null)
          Text(
            status.suggestion!,
            style: const TextStyle(
              color: Colors.grey,
              fontSize: 10,
            ),
          ),
      ],
    );
  }
  
  Widget _buildPerformanceSuggestions() {
    final suggestions = _analyzePerformanceSuggestions();
    
    return Column(
      crossAxisAlignment: CrossAxisAlignment.start,
      children: [
        const Text(
          '优化建议',
          style: TextStyle(
            color: Colors.white,
            fontSize: 16,
            fontWeight: FontWeight.bold,
          ),
        ),
        const SizedBox(height: 8),
        ...suggestions.map((suggestion) {
          return Padding(
            padding: const EdgeInsets.symmetric(vertical: 4),
            child: Row(
              children: [
                Icon(
                  suggestion.icon,
                  size: 16,
                  color: suggestion.priority.color,
                ),
                const SizedBox(width: 8),
                Expanded(
                  child: Text(
                    suggestion.description,
                    style: TextStyle(
                      color: Colors.white.withOpacity(0.8),
                      fontSize: 12,
                    ),
                  ),
                ),
              ],
            ),
          );
        }),
      ],
    );
  }
}

五、部署与持续优化

5.1 智能部署流水线

# deploy_pipeline.yaml
name: 鸿蒙Flutter性能优化部署流水线

stages:
  - name: 代码质量检查
    steps:
      - name: 静态分析
        run: flutter analyze --performance
        
      - name: 代码复杂度检查
        run: dart metric --complexity
        
      - name: 性能模式检测
        run: flutter analyze --performance-patterns
  
  - name: 性能基准测试
    steps:
      - name: 启动性能测试
        run: flutter test integration_test/startup_test.dart
        
      - name: 渲染性能测试
        run: flutter test integration_test/rendering_test.dart
        
      - name: 内存泄漏测试
        run: flutter test integration_test/memory_test.dart
        
      - name: 分布式性能测试
        run: flutter test integration_test/distributed_test.dart
  
  - name: 构建优化
    steps:
      - name: Tree Shaking优化
        run: flutter build harmony --tree-shaking=aggressive
        
      - name: 代码分割
        run: flutter build harmony --split-per-feature
        
      - name: 资源优化
        run: flutter build harmony --compress-assets
        
      - name: 原生库优化
        run: flutter build harmony --strip-native-symbols
  
  - name: 发布前验证
    steps:
      - name: A/B性能测试
        run: ./scripts/ab_performance_test.sh
        
      - name: 兼容性验证
        run: ./scripts/compatibility_test.sh
        
      - name: 安全扫描
        run: ./scripts/security_scan.sh
        
      - name: 合规性检查
        run: ./scripts/compliance_check.sh

  - name: 智能发布
    steps:
      - name: 版本分析
        run: ./scripts/version_analysis.sh
        
      - name: 灰度发布
        run: ./scripts/canary_release.sh
        
      - name: 性能监控部署
        run: ./scripts/deploy_monitoring.sh
        
      - name: 回滚准备
        run: ./scripts/prepare_rollback.sh

  - name: 发布后监控
    steps:
      - name: 实时性能监控
        run: ./scripts/monitor_performance.sh
        
      - name: 错误率监控
        run: ./scripts/monitor_error_rate.sh
        
      - name: 用户体验监控
        run: ./scripts/monitor_user_experience.sh
        
      - name: 自动优化反馈
        run: ./scripts/auto_optimization_feedback.sh

5.2 持续优化反馈循环

// 智能优化反馈系统
class ContinuousOptimizationFeedbackSystem {
  final PerformanceDataCollector _collector = PerformanceDataCollector();
  final OptimizationAnalyzer _analyzer = OptimizationAnalyzer();
  final AITrainingPipeline _trainingPipeline = AITrainingPipeline();
  final OptimizationRecommender _recommender = OptimizationRecommender();
  
  Future<void> initialize() async {
    // 1. 建立数据收集管道
    await _collector.initialize();
    
    // 2. 启动实时分析
    await _analyzer.start();
    
    // 3. 训练AI优化模型
    await _trainingPipeline.initialize();
    
    // 4. 建立反馈循环
    _establishFeedbackLoop();
  }
  
  void _establishFeedbackLoop() {
    // 实时收集性能数据
    _collector.dataStream.listen((data) async {
      // 1. 分析性能模式
      final patterns = await _analyzer.analyzePatterns(data);
      
      // 2. 识别优化机会
      final opportunities = await _analyzer.identifyOpportunities(patterns);
      
      // 3. 生成优化建议
      final recommendations = await _recommender.generateRecommendations(
        opportunities: opportunities,
        context: OptimizationContext.current,
      );
      
      // 4. 应用优化（如果是自动模式）
      if (recommendations.canAutoApply) {
        await _applyOptimizations(recommendations);
      }
      
      // 5. 训练AI模型
      await _trainingPipeline.trainWithData(data, recommendations);
      
      // 6. 更新优化策略
      await _updateOptimizationStrategies(recommendations);
    });
  }
  
  Future<void> _applyOptimizations(OptimizationRecommendations recommendations) async {
    for (final recommendation in recommendations.items) {
      switch (recommendation.type) {
        case OptimizationType.memory:
          await MemoryOptimizer.apply(recommendation.config);
          break;
        case OptimizationType.rendering:
          await RenderingOptimizer.apply(recommendation.config);
          break;
        case OptimizationType.network:
          await NetworkOptimizer.apply(recommendation.config);
          break;
        case OptimizationType.distributed:
          await DistributedOptimizer.apply(recommendation.config);
          break;
        case OptimizationType.startup:
          await StartupOptimizer.apply(recommendation.config);
          break;
      }
    }
  }
}

// AI驱动的优化建议生成器
class AIOptimizationRecommender {
  final DeepLearningModel _model;
  final OptimizationKnowledgeBase _knowledgeBase;
  final ContextAnalyzer _contextAnalyzer;
  
  Future<OptimizationRecommendations> generateRecommendations({
    required PerformanceData data,
    required OptimizationContext context,
  }) async {
    // 1. 特征提取
    final features = await _extractFeatures(data, context);
    
    // 2. AI模型预测
    final predictions = await _model.predict(features);
    
    // 3. 知识库查询
    final historicalData = await _knowledgeBase.querySimilarCases(features);
    
    // 4. 上下文分析
    final contextAnalysis = await _contextAnalyzer.analyze(context);
    
    // 5. 生成最终建议
    return await _generateFinalRecommendations(
      predictions: predictions,
      historicalData: historicalData,
      contextAnalysis: contextAnalysis,
    );
  }
  
  Future<Map<String, dynamic>> _extractFeatures(
    PerformanceData data,
    OptimizationContext context,
  ) async {
    return {
      // 性能特征
      'fps_trend': data.fpsTrend.toList(),
      'memory_pattern': data.memoryPattern.toList(),
      'network_quality': data.networkQuality.index,
      
      // 设备特征
      'device_type': context.deviceType.index,
      'device_capabilities': context.deviceCapabilities.toFeatureVector(),
      'thermal_state': context.thermalState.index,
      
      // 使用特征
      'usage_pattern': context.usagePattern.toFeatureVector(),
      'time_of_day': context.timeOfDay.value,
      'battery_level': context.batteryLevel,
      
      // 环境特征
      'network_type': context.networkType.index,
      'signal_strength': context.signalStrength,
    };
  }
}