一、引言

1.1 边缘计算的重要性

1.2 技术价值与市场分析

class HarmonyEdgeComputingAnalysis {
    /** 边缘计算市场分析 */
    static getMarketAnalysis() {
        return {
            '市场规模': '2025年全球边缘计算市场规模将达2500亿美元',
            '增长率': '年复合增长率35%',
            '应用领域': '智能制造40%、智能家居25%、车联网20%、其他15%',
            '鸿蒙份额': '中国边缘计算市场鸿蒙占比预计达30%',
            '技术优势': '比云端推理快3-5倍，延迟降低80%'
        };
    }

    /** 技术方案对比 */
    static getTechnologyComparison() {
        return {
            '云端推理': {
                '延迟': '100-500ms',
                '带宽需求': '高',
                '隐私安全': '数据上传云端',
                '成本': '按使用量计费',
                '适用场景': '非实时、大数据量处理'
            },
            '边缘推理': {
                '延迟': '5-20ms',
                '带宽需求': '低',
                '隐私安全': '数据本地处理',
                '成本': '一次性硬件投入',
                '适用场景': '实时、高隐私要求'
            },
            '端侧推理': {
                '延迟': '1-10ms',
                '带宽需求': '无',
                '隐私安全': '最高级别',
                '成本': '设备集成',
                '适用场景': '极致实时响应'
            }
        };
    }

    /** 鸿蒙边缘计算优势 */
    static getHarmonyAdvantages() {
        return {
            '分布式架构': '多设备协同计算',
            '端侧AI引擎': '专用神经网络处理器',
            '统一开发框架': '一次开发多端部署',
            '安全沙箱': '硬件级安全隔离',
            '动态部署': '根据设备能力自适应'
        };
    }
}

1.3 性能基准对比

二、技术背景

2.1 鸿蒙边缘计算架构

graph TB
    A[鸿蒙边缘计算架构] --> B[硬件层]
    A --> C[系统层]
    A --> D[框架层]
    A --> E[应用层]
    
    B --> B1[CPU/GPU/NPU]
    B --> B2[内存管理]
    B --> B3[存储系统]
    B --> B4[网络模块]
    
    C --> C1[分布式软总线]
    C --> C2[设备虚拟化]
    C --> C3[任务调度]
    C --> C4[安全沙箱]
    
    D --> D1[AI推理框架]
    D --> D2[模型管理]
    D --> D3[数据流水线]
    D --> D4[性能监控]
    
    E --> E1[智能视觉]
    E --> E2[语音交互]
    E --> E3[行为预测]
    E --> E4[环境感知]
    
    C1 --> F[低延迟通信]
    D1 --> F
    B1 --> F
    
    F --> G[毫秒级推理]

2.2 核心技术栈

public class HarmonyEdgeTechStack {
    // AI推理引擎
    public static class AIEngine {
        public static final String[] SUPPORTED_ARCH = {
            "CPU", "GPU", "NPU", "DSP"  // 多硬件加速
        };
        
        public static final String[] MODEL_FORMATS = {
            "HIAI", "ONNX", "TFLite", "Caffe"  // 多格式支持
        };
    }
    
    // 分布式计算
    public static class DistributedComputing {
        public static final String[] TECHNOLOGIES = {
            "分布式软总线", "设备虚拟化", "能力调度", "数据同步"
        };
    }
    
    // 实时处理
    public static class RealTimeProcessing {
        public static final Map<String, Integer> PERFORMANCE_TARGETS = Map.of(
            "图像分类", 10,      // 10ms内完成
            "目标检测", 30,     // 30ms内完成  
            "语义分割", 50,     // 50ms内完成
            "语音识别", 20,     // 20ms内完成
            "自然语言处理", 100 // 100ms内完成
        );
    }
}

三、环境准备与开发配置

3.1 开发环境搭建

// build.gradle 配置
public class BuildConfiguration {
    dependencies {
        // 鸿蒙AI引擎
        implementation 'ohos:ai-engine:1.0.0'
        implementation 'ohos:neural-networks:1.0.0'
        implementation 'ohos:computer-vision:1.0.0'
        
        // 边缘计算框架
        implementation 'ohos:edge-computing:1.0.0'
        implementation 'ohos:distributed-hardware:1.0.0'
        implementation 'ohos:real-time-scheduler:1.0.0'
        
        // 工具库
        implementation 'ohos:model-optimizer:1.0.0'
        implementation 'ohos:performance-monitor:1.0.0'
    }
}

3.2 模型配置管理

// src/main/resources/rawfile/model_config.json
public class ModelConfiguration {
    public static final String CONFIG = """
    {
        "models": {
            "image_classification": {
                "name": "mobilenet_v2",
                "format": "HIAI",
                "input_size": [224, 224, 3],
                "output_size": [1000],
                "quantization": "int8",
                "hardware_preference": ["NPU", "GPU", "CPU"],
                "max_latency": 15,
                "accuracy_threshold": 0.8
            },
            "object_detection": {
                "name": "yolo_v5s", 
                "format": "HIAI",
                "input_size": [640, 640, 3],
                "output_size": [25200, 85],
                "quantization": "int8",
                "hardware_preference": ["NPU", "GPU"],
                "max_latency": 30,
                "confidence_threshold": 0.5
            },
            "speech_recognition": {
                "name": "deepspeech2",
                "format": "ONNX",
                "input_size": [16000],
                "output_size": [5000],
                "quantization": "int16", 
                "hardware_preference": ["DSP", "CPU"],
                "max_latency": 20,
                "sample_rate": 16000
            }
        },
        "performance": {
            "target_fps": 30,
            "max_memory_mb": 50,
            "max_power_mw": 500,
            "quality_priority": "balanced"
        }
    }
    """;
}

四、核心架构实现

4.1 边缘AI推理引擎

// src/main/java/com/example/edgeai/EdgeAIEngine.java
package com.example.edgeai;

import ohos.ai.cv.common.ConnectionCallback;
import ohos.ai.cv.common.VisionConfiguration;
import ohos.ai.cv.common.VisionManager;
import ohos.ai.engine.*;
import ohos.ai.engine.utils.HwChain;
import ohos.app.Context;
import ohos.hiviewdfx.HiLog;
import ohos.hiviewdfx.HiLogLabel;

import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.*;

/**
 * 边缘AI推理引擎 - 核心类
 */
public class EdgeAIEngine {
    private static final HiLogLabel LABEL = new HiLogLabel(HiLog.LOG_APP, 0, "EdgeAIEngine");
    
    // 单例实例
    private static volatile EdgeAIEngine instance;
    
    // AI引擎组件
    private AIService aiService;
    private ModelManager modelManager;
    private HardwareManager hardwareManager;
    
    // 性能监控
    private PerformanceMonitor performanceMonitor;
    
    // 线程池
    private ExecutorService inferenceExecutor;
    private ScheduledExecutorService monitorExecutor;
    
    // 模型缓存
    private Map<String, ModelInstance> modelCache;
    
    // 配置
    private EngineConfig engineConfig;
    
    private EdgeAIEngine(Context context) {
        init(context);
    }
    
    public static EdgeAIEngine getInstance(Context context) {
        if (instance == null) {
            synchronized (EdgeAIEngine.class) {
                if (instance == null) {
                    instance = new EdgeAIEngine(context);
                }
            }
        }
        return instance;
    }
    
    private void init(Context context) {
        HiLog.info(LABEL, "初始化边缘AI推理引擎");
        
        try {
            // 初始化AI服务
            initAIService(context);
            
            // 初始化硬件管理器
            initHardwareManager();
            
            // 初始化模型管理器
            initModelManager();
            
            // 初始化性能监控
            initPerformanceMonitor();
            
            // 初始化线程池
            initExecutors();
            
            // 加载模型配置
            loadModelConfig();
            
            HiLog.info(LABEL, "边缘AI推理引擎初始化完成");
            
        } catch (Exception e) {
            HiLog.error(LABEL, "边缘AI推理引擎初始化失败: %{public}s", e.getMessage());
            throw new RuntimeException("AI引擎初始化失败", e);
        }
    }
    
    private void initAIService(Context context) {
        // 连接AI引擎
        int result = VisionManager.init(context, new ConnectionCallback() {
            @Override
            public void onServiceConnect() {
                HiLog.info(LABEL, "AI引擎服务连接成功");
            }
            
            @Override
            public void onServiceDisconnect() {
                HiLog.warn(LABEL, "AI引擎服务断开连接");
            }
        });
        
        if (result != 0) {
            throw new RuntimeException("AI引擎连接失败，错误码: " + result);
        }
        
        // 创建AI服务实例
        aiService = AIService.create(context);
    }
    
    private void initHardwareManager() {
        hardwareManager = new HardwareManager();
        hardwareManager.scanAvailableHardware();
        
        HiLog.info(LABEL, "可用硬件加速器: %{public}s", 
            hardwareManager.getAvailableAccelerators());
    }
    
    private void initModelManager() {
        modelManager = new ModelManager(aiService);
        modelCache = new ConcurrentHashMap<>();
    }
    
    private void initPerformanceMonitor() {
        performanceMonitor = new PerformanceMonitor();
        monitorExecutor = Executors.newSingleThreadScheduledExecutor();
        
        // 定期监控性能指标
        monitorExecutor.scheduleAtFixedRate(() -> {
            performanceMonitor.collectMetrics();
            checkPerformanceThresholds();
        }, 1, 5, TimeUnit.SECONDS);
    }
    
    private void initExecutors() {
        // 根据CPU核心数创建线程池
        int coreCount = Runtime.getRuntime().availableProcessors();
        inferenceExecutor = new ThreadPoolExecutor(
            coreCount, 
            coreCount * 2,
            60L, TimeUnit.SECONDS,
            new LinkedBlockingQueue<>(100),
            new InferenceThreadFactory(),
            new InferenceRejectedHandler()
        );
    }
    
    private void loadModelConfig() {
        try {
            engineConfig = EngineConfig.loadFromAssets();
            HiLog.info(LABEL, "模型配置加载成功");
        } catch (Exception e) {
            HiLog.error(LABEL, "模型配置加载失败: %{public}s", e.getMessage());
            engineConfig = EngineConfig.getDefault();
        }
    }
    
    /**
     * 异步推理请求
     */
    public CompletableFuture<InferenceResult> inferenceAsync(
        String modelName, 
        InferenceData inputData
    ) {
        return CompletableFuture.supplyAsync(() -> {
            try {
                return inferenceSync(modelName, inputData);
            } catch (Exception e) {
                throw new CompletionException(e);
            }
        }, inferenceExecutor);
    }
    
    /**
     * 同步推理请求
     */
    public InferenceResult inferenceSync(String modelName, InferenceData inputData) {
        long startTime = System.currentTimeMillis();
        
        try {
            // 获取或加载模型
            ModelInstance model = getModelInstance(modelName);
            
            // 预处理输入数据
            PreprocessedData processedData = preprocessData(inputData, model);
            
            // 执行推理
            InferenceResult result = model.inference(processedData);
            
            // 后处理结果
            result = postprocessResult(result, model);
            
            // 记录性能指标
            long latency = System.currentTimeMillis() - startTime;
            performanceMonitor.recordInference(modelName, latency, result.getConfidence());
            
            HiLog.debug(LABEL, "推理完成 - 模型: %{public}s, 延迟: %{public}dms", 
                modelName, latency);
            
            return result;
            
        } catch (Exception e) {
            HiLog.error(LABEL, "推理失败 - 模型: %{public}s, 错误: %{public}s", 
                modelName, e.getMessage());
            performanceMonitor.recordError(modelName);
            throw new InferenceException("推理执行失败", e);
        }
    }
    
    /**
     * 批量推理
     */
    public List<InferenceResult> batchInference(
        String modelName, 
        List<InferenceData> batchData
    ) {
        return batchData.parallelStream()
            .map(data -> inferenceSync(modelName, data))
            .collect(Collectors.toList());
    }
    
    private ModelInstance getModelInstance(String modelName) {
        return modelCache.computeIfAbsent(modelName, name -> {
            try {
                ModelConfig config = engineConfig.getModelConfig(name);
                return modelManager.loadModel(config);
            } catch (Exception e) {
                HiLog.error(LABEL, "模型加载失败: %{public}s", name);
                throw new InferenceException("模型加载失败: " + name, e);
            }
        });
    }
    
    private PreprocessedData preprocessData(InferenceData input, ModelInstance model) {
        Preprocessor preprocessor = new Preprocessor(model.getConfig());
        return preprocessor.process(input);
    }
    
    private InferenceResult postprocessResult(InferenceResult rawResult, ModelInstance model) {
        Postprocessor postprocessor = new Postprocessor(model.getConfig());
        return postprocessor.process(rawResult);
    }
    
    private void checkPerformanceThresholds() {
        PerformanceMetrics metrics = performanceMonitor.getCurrentMetrics();
        
        for (Map.Entry<String, ModelMetrics> entry : metrics.getModelMetrics().entrySet()) {
            String modelName = entry.getKey();
            ModelMetrics modelMetrics = entry.getValue();
            
            // 检查延迟阈值
            if (modelMetrics.getAverageLatency() > 
                engineConfig.getMaxLatency(modelName)) {
                HiLog.warn(LABEL, "模型 %{public}s 延迟超标: %{public}.2fms", 
                    modelName, modelMetrics.getAverageLatency());
                
                // 触发优化策略
                optimizeModelPerformance(modelName);
            }
            
            // 检查准确率阈值
            if (modelMetrics.getAverageAccuracy() < 
                engineConfig.getAccuracyThreshold(modelName)) {
                HiLog.warn(LABEL, "模型 %{public}s 准确率过低: %{public}.2f", 
                    modelName, modelMetrics.getAverageAccuracy());
            }
        }
    }
    
    private void optimizeModelPerformance(String modelName) {
        HiLog.info(LABEL, "开始优化模型性能: %{public}s", modelName);
        
        // 动态切换硬件加速器
        hardwareManager.optimizeHardwareSelection(modelName);
        
        // 调整模型参数
        modelManager.optimizeModel(modelName);
        
        // 清理缓存
        modelCache.remove(modelName);
    }
    
    /**
     * 释放资源
     */
    public void release() {
        HiLog.info(LABEL, "释放边缘AI推理引擎资源");
        
        // 关闭线程池
        inferenceExecutor.shutdown();
        monitorExecutor.shutdown();
        
        try {
            if (!inferenceExecutor.awaitTermination(5, TimeUnit.SECONDS)) {
                inferenceExecutor.shutdownNow();
            }
            if (!monitorExecutor.awaitTermination(5, TimeUnit.SECONDS)) {
                monitorExecutor.shutdownNow();
            }
        } catch (InterruptedException e) {
            inferenceExecutor.shutdownNow();
            monitorExecutor.shutdownNow();
            Thread.currentThread().interrupt();
        }
        
        // 释放模型
        modelCache.values().forEach(ModelInstance::release);
        modelCache.clear();
        
        // 断开AI服务
        VisionManager.destroy();
        
        instance = null;
        HiLog.info(LABEL, "边缘AI推理引擎资源释放完成");
    }
    
    // 内部类：推理线程工厂
    private static class InferenceThreadFactory implements ThreadFactory {
        private final AtomicInteger threadNumber = new AtomicInteger(1);
        
        @Override
        public Thread newThread(Runnable r) {
            Thread thread = new Thread(r, 
                "inference-thread-" + threadNumber.getAndIncrement());
            thread.setPriority(Thread.MAX_PRIORITY);
            thread.setDaemon(true);
            return thread;
        }
    }
    
    // 内部类：拒绝策略
    private static class InferenceRejectedHandler implements RejectedExecutionHandler {
        @Override
        public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
            HiLog.error(LABEL, "推理任务被拒绝，线程池已满");
            throw new RejectedExecutionException("推理任务队列已满");
        }
    }
}

4.2 硬件加速管理器

// src/main/java/com/example/edgeai/HardwareManager.java
package com.example.edgeai;

import ohos.ai.engine.HardwareInfo;
import ohos.hiviewdfx.HiLog;
import ohos.hiviewdfx.HiLogLabel;

import java.util.*;

/**
 * 硬件加速管理器 - 管理多硬件加速器
 */
public class HardwareManager {
    private static final HiLogLabel LABEL = new HiLogLabel(HiLog.LOG_APP, 0, "HardwareManager");
    
    // 可用硬件加速器
    private List<HardwareAccelerator> availableAccelerators;
    private Map<String, HardwareAccelerator> acceleratorMap;
    
    // 性能统计
    private Map<String, AcceleratorMetrics> performanceMetrics;
    
    public HardwareManager() {
        availableAccelerators = new ArrayList<>();
        acceleratorMap = new HashMap<>();
        performanceMetrics = new HashMap<>();
        scanAvailableHardware();
    }
    
    /**
     * 扫描可用硬件加速器
     */
    public void scanAvailableHardware() {
        HiLog.info(LABEL, "开始扫描可用硬件加速器");
        
        availableAccelerators.clear();
        acceleratorMap.clear();
        
        // 扫描NPU
        if (hasNPUSupport()) {
            NPUAccelerator npu = new NPUAccelerator();
            availableAccelerators.add(npu);
            acceleratorMap.put("NPU", npu);
            HiLog.info(LABEL, "发现NPU加速器: %{public}s", npu.getInfo());
        }
        
        // 扫描GPU
        if (hasGPUSupport()) {
            GPUAccelerator gpu = new GPUAccelerator();
            availableAccelerators.add(gpu);
            acceleratorMap.put("GPU", gpu);
            HiLog.info(LABEL, "发现GPU加速器: %{public}s", gpu.getInfo());
        }
        
        // 扫描DSP
        if (hasDSPSupport()) {
            DSPAccelerator dsp = new DSPAccelerator();
            availableAccelerators.add(dsp);
            acceleratorMap.put("DSP", dsp);
            HiLog.info(LABEL, "发现DSP加速器: %{public}s", dsp.getInfo());
        }
        
        // 总是包含CPU
        CPUAccelerator cpu = new CPUAccelerator();
        availableAccelerators.add(cpu);
        acceleratorMap.put("CPU", cpu);
        HiLog.info(LABEL, "发现CPU加速器: %{public}s", cpu.getInfo());
        
        // 按性能排序
        availableAccelerators.sort(Comparator
            .comparingDouble(HardwareAccelerator::getPerformanceScore)
            .reversed());
        
        HiLog.info(LABEL, "硬件扫描完成，发现 %{public}d 个加速器", 
            availableAccelerators.size());
    }
    
    /**
     * 为指定模型选择最佳硬件加速器
     */
    public HardwareAccelerator selectBestAccelerator(String modelName, ModelConfig config) {
        HiLog.debug(LABEL, "为模型 %{public}s 选择硬件加速器", modelName);
        
        // 获取模型硬件偏好
        List<String> hardwarePreference = config.getHardwarePreference();
        
        // 按偏好顺序选择第一个可用的加速器
        for (String hardwareType : hardwarePreference) {
            HardwareAccelerator accelerator = acceleratorMap.get(hardwareType);
            if (accelerator != null && accelerator.isAvailable()) {
                HiLog.info(LABEL, "为模型 %{public}s 选择加速器: %{public}s", 
                    modelName, hardwareType);
                return accelerator;
            }
        }
        
        // 回退到CPU
        HiLog.warn(LABEL, "无偏好加速器可用，回退到CPU");
        return acceleratorMap.get("CPU");
    }
    
    /**
     * 动态优化硬件选择
     */
    public void optimizeHardwareSelection(String modelName) {
        HiLog.info(LABEL, "开始优化模型 %{public}s 的硬件选择", modelName);
        
        AcceleratorMetrics currentMetrics = performanceMetrics.get(modelName);
        if (currentMetrics == null) {
            return;
        }
        
        // 检查当前硬件性能
        String currentHardware = currentMetrics.getCurrentHardware();
        double currentLatency = currentMetrics.getAverageLatency();
        
        // 寻找更好的硬件选择
        for (HardwareAccelerator accelerator : availableAccelerators) {
            if (!accelerator.getType().equals(currentHardware) {
                // 评估新硬件的预期性能
                double expectedPerformance = estimatePerformance(
                    modelName, accelerator.getType());
                
                if (expectedPerformance < currentLatency * 0.8) { // 预期提升20%以上
                    HiLog.info(LABEL, "检测到更优硬件: %{public}s -> %{public}s", 
                        currentHardware, accelerator.getType());
                    // 触发硬件切换
                    switchHardware(modelName, accelerator.getType());
                    break;
                }
            }
        }
    }
    
    /**
     * 切换硬件加速器
     */
    private void switchHardware(String modelName, String newHardware) {
        HiLog.info(LABEL, "切换模型 %{public}s 的硬件: %{public}s", 
            modelName, newHardware);
        
        // 在实际实现中，这里会重新加载模型到新硬件
        // 并更新性能监控配置
    }
    
    /**
     * 估计性能
     */
    private double estimatePerformance(String modelName, String hardwareType) {
        // 基于历史数据和硬件能力进行性能估计
        // 简化实现：返回基准性能数据
        Map<String, Double> baselinePerformance = Map.of(
            "NPU", 5.0,
            "GPU", 10.0, 
            "DSP", 15.0,
            "CPU", 30.0
        );
        
        return baselinePerformance.getOrDefault(hardwareType, 30.0);
    }
    
    /**
     * 记录硬件性能指标
     */
    public void recordPerformance(String modelName, String hardwareType, 
                                long latency, double accuracy) {
        AcceleratorMetrics metrics = performanceMetrics
            .computeIfAbsent(modelName, k -> new AcceleratorMetrics());
        
        metrics.recordInference(hardwareType, latency, accuracy);
    }
    
    /**
     * 检查NPU支持
     */
    private boolean hasNPUSupport() {
        try {
            // 调用系统API检查NPU支持
            HardwareInfo hardwareInfo = HardwareInfo.getInstance();
            return hardwareInfo.hasNeuralNetworkAccelerator();
        } catch (Exception e) {
            HiLog.error(LABEL, "检查NPU支持失败: %{public}s", e.getMessage());
            return false;
        }
    }
    
    /**
     * 检查GPU支持
     */
    private boolean hasGPUSupport() {
        // 简化实现，实际应调用系统API
        return true; // 大多数设备支持GPU
    }
    
    /**
     * 检查DSP支持
     */
    private boolean hasDSPSupport() {
        // 简化实现，实际应调用系统API  
        return false; // 多数移动设备不支持DSP
    }
    
    /**
     * 获取可用加速器列表
     */
    public List<String> getAvailableAccelerators() {
        return availableAccelerators.stream()
            .map(HardwareAccelerator::getType)
            .collect(Collectors.toList());
    }
    
    /**
     * 获取硬件信息
     */
    public String getHardwareInfo() {
        StringBuilder info = new StringBuilder("可用硬件加速器:\n");
        for (HardwareAccelerator accelerator : availableAccelerators) {
            info.append(String.format("- %s: %s (性能分数: %.1f)\n", 
                accelerator.getType(), 
                accelerator.getInfo(), 
                accelerator.getPerformanceScore()));
        }
        return info.toString();
    }
}

/**
 * 硬件加速器基类
 */
abstract class HardwareAccelerator {
    public abstract String getType();
    public abstract String getInfo();
    public abstract boolean isAvailable();
    public abstract double getPerformanceScore();
}

/**
 * NPU加速器
 */
class NPUAccelerator extends HardwareAccelerator {
    @Override
    public String getType() { return "NPU"; }
    
    @Override
    public String getInfo() { 
        return "神经网络处理器 - 专为AI计算优化"; 
    }
    
    @Override
    public boolean isAvailable() { 
        return true; // 实际应检查硬件支持
    }
    
    @Override
    public double getPerformanceScore() { 
        return 10.0; // 最高性能分数
    }
}

/**
 * GPU加速器
 */
class GPUAccelerator extends HardwareAccelerator {
    @Override
    public String getType() { return "GPU"; }
    
    @Override
    public String getInfo() { 
        return "图形处理器 - 并行计算能力强"; 
    }
    
    @Override
    public boolean isAvailable() { 
        return true; 
    }
    
    @Override
    public double getPerformanceScore() { 
        return 8.0; 
    }
}

/**
 * CPU加速器
 */
class CPUAccelerator extends HardwareAccelerator {
    @Override
    public String getType() { return "CPU"; }
    
    @Override
    public String getInfo() { 
        return "中央处理器 - 通用计算"; 
    }
    
    @Override
    public boolean isAvailable() { 
        return true; // 总是可用
    }
    
    @Override
    public double getPerformanceScore() { 
        return 5.0; // 基础性能分数
    }
}

4.3 性能监控系统

// src/main/java/com/example/edgeai/PerformanceMonitor.java
package com.example.edgeai;

import ohos.hiviewdfx.HiLog;
import ohos.hiviewdfx.HiLogLabel;

import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicLong;

/**
 * 性能监控系统 - 实时监控推理性能
 */
public class PerformanceMonitor {
    private static final HiLogLabel LABEL = new HiLogLabel(HiLog.LOG_APP, 0, "PerformanceMonitor");
    
    // 性能数据存储
    private Map<String, ModelMetrics> modelMetrics;
    private SystemMetrics systemMetrics;
    
    // 性能阈值
    private PerformanceThresholds thresholds;
    
    // 监控回调
    private List<PerformanceListener> listeners;
    
    public PerformanceMonitor() {
        modelMetrics = new ConcurrentHashMap<>();
        systemMetrics = new SystemMetrics();
        thresholds = new PerformanceThresholds();
        listeners = new ArrayList<>();
        
        HiLog.info(LABEL, "性能监控系统初始化完成");
    }
    
    /**
     * 记录推理性能
     */
    public void recordInference(String modelName, long latency, double confidence) {
        ModelMetrics metrics = modelMetrics
            .computeIfAbsent(modelName, k -> new ModelMetrics(modelName));
        
        metrics.recordInference(latency, confidence);
        
        // 检查阈值
        checkThresholds(modelName, metrics);
        
        // 通知监听器
        notifyListeners(modelName, metrics);
        
        HiLog.debug(LABEL, "记录推理性能 - 模型: %{public}s, 延迟: %{public}dms", 
            modelName, latency);
    }
    
    /**
     * 记录错误
     */
    public void recordError(String modelName) {
        ModelMetrics metrics = modelMetrics.get(modelName);
        if (metrics != null) {
            metrics.recordError();
        }
    }
    
    /**
     * 收集系统指标
     */
    public void collectMetrics() {
        systemMetrics.collect();
        
        // 检查系统级阈值
        checkSystemThresholds();
    }
    
    /**
     * 检查性能阈值
     */
    private void checkThresholds(String modelName, ModelMetrics metrics) {
        // 检查延迟阈值
        if (metrics.getAverageLatency() > thresholds.getMaxLatency(modelName)) {
            HiLog.warn(LABEL, "模型 %{public}s 延迟超标: %{public}.2fms > %{public}dms", 
                modelName, metrics.getAverageLatency(), thresholds.getMaxLatency(modelName));
            
            // 触发性能告警
            triggerPerformanceAlert(modelName, "HIGH_LATENCY", 
                metrics.getAverageLatency());
        }
        
        // 检查准确率阈值
        if (metrics.getAverageAccuracy() < thresholds.getMinAccuracy(modelName)) {
            HiLog.warn(LABEL, "模型 %{public}s 准确率过低: %{public}.2f < %{public}.2f", 
                modelName, metrics.getAverageAccuracy(), thresholds.getMinAccuracy(modelName));
            
            triggerPerformanceAlert(modelName, "LOW_ACCURACY", 
                metrics.getAverageAccuracy());
        }
        
        // 检查错误率阈值
        if (metrics.getErrorRate() > thresholds.getMaxErrorRate(modelName)) {
            HiLog.warn(LABEL, "模型 %{public}s 错误率过高: %{public}.2f%%", 
                modelName, metrics.getErrorRate() * 100);
            
            triggerPerformanceAlert(modelName, "HIGH_ERROR_RATE", 
                metrics.getErrorRate());
        }
    }
    
    /**
     * 检查系统阈值
     */
    private void checkSystemThresholds() {
        // 检查内存使用
        if (systemMetrics.getMemoryUsage() > thresholds.getMaxMemoryUsage()) {
            HiLog.warn(LABEL, "系统内存使用过高: %{public}.2f%%", 
                systemMetrics.getMemoryUsage() * 100);
        }
        
        // 检查CPU使用
        if (systemMetrics.getCpuUsage() > thresholds.getMaxCpuUsage()) {
            HiLog.warn(LABEL, "系统CPU使用过高: %{public}.2f%%", 
                systemMetrics.getCpuUsage() * 100);
        }
        
        // 检查温度
        if (systemMetrics.getTemperature() > thresholds.getMaxTemperature()) {
            HiLog.warn(LABEL, "系统温度过高: %{public}.2f°C", 
                systemMetrics.getTemperature());
        }
    }
    
    /**
     * 触发性能告警
     */
    private void triggerPerformanceAlert(String modelName, String alertType, double value) {
        PerformanceAlert alert = new PerformanceAlert(modelName, alertType, value);
        
        for (PerformanceListener listener : listeners) {
            try {
                listener.onPerformanceAlert(alert);
            } catch (Exception e) {
                HiLog.error(LABEL, "性能告警监听器执行失败: %{public}s", e.getMessage());
            }
        }
    }
    
    /**
     * 添加性能监听器
     */
    public void addListener(PerformanceListener listener) {
        listeners.add(listener);
    }
    
    /**
     * 移除性能监听器
     */
    public void removeListener(PerformanceListener listener) {
        listeners.remove(listener);
    }
    
    /**
     * 获取当前性能指标
     */
    public PerformanceMetrics getCurrentMetrics() {
        return new PerformanceMetrics(
            new HashMap<>(modelMetrics),
            systemMetrics.copy()
        );
    }
    
    /**
     * 生成性能报告
     */
    public PerformanceReport generateReport() {
        PerformanceReport report = new PerformanceReport();
        
        for (ModelMetrics metrics : modelMetrics.values()) {
            report.addModelReport(metrics.generateReport());
        }
        
        report.setSystemReport(systemMetrics.generateReport());
        report.setTimestamp(System.currentTimeMillis());
        
        return report;
    }
    
    /**
     * 重置性能统计
     */
    public void resetStatistics() {
        modelMetrics.clear();
        systemMetrics.reset();
        HiLog.info(LABEL, "性能统计已重置");
    }
}

/**
 * 模型性能指标
 */
class ModelMetrics {
    private String modelName;
    private AtomicLong totalInferences;
    private AtomicLong totalErrors;
    private double totalLatency;
    private double totalConfidence;
    
    // 滑动窗口统计（最近100次推理）
    private Deque<Long> recentLatencies;
    private Deque<Double> recentConfidences;
    private int windowSize = 100;
    
    public ModelMetrics(String modelName) {
        this.modelName = modelName;
        this.totalInferences = new AtomicLong(0);
        this.totalErrors = new AtomicLong(0);
        this.totalLatency = 0;
        this.totalConfidence = 0;
        this.recentLatencies = new ArrayDeque<>(windowSize);
        this.recentConfidences = new ArrayDeque<>(windowSize);
    }
    
    public void recordInference(long latency, double confidence) {
        totalInferences.incrementAndGet();
        totalLatency += latency;
        totalConfidence += confidence;
        
        // 更新滑动窗口
        synchronized (this) {
            if (recentLatencies.size() >= windowSize) {
                recentLatencies.removeFirst();
                recentConfidences.removeFirst();
            }
            recentLatencies.addLast(latency);
            recentConfidences.addLast(confidence);
        }
    }
    
    public void recordError() {
        totalErrors.incrementAndGet();
    }
    
    public double getAverageLatency() {
        long count = totalInferences.get();
        return count > 0 ? totalLatency / count : 0;
    }
    
    public double getAverageAccuracy() {
        long count = totalInferences.get();
        return count > 0 ? totalConfidence / count : 0;
    }
    
    public double getErrorRate() {
        long total = totalInferences.get() + totalErrors.get();
        return total > 0 ? (double) totalErrors.get() / total : 0;
    }
    
    public double getRecentAverageLatency() {
        synchronized (this) {
            if (recentLatencies.isEmpty()) return 0;
            return recentLatencies.stream().mapToLong(Long::longValue).average().orElse(0);
        }
    }
    
    public double getRecentAverageAccuracy() {
        synchronized (this) {
            if (recentConfidences.isEmpty()) return 0;
            return recentConfidences.stream().mapToDouble(Double::doubleValue).average().orElse(0);
        }
    }
    
    public PerformanceReport generateReport() {
        PerformanceReport report = new PerformanceReport();
        report.setModelName(modelName);
        report.setTotalInferences(totalInferences.get());
        report.setTotalErrors(totalErrors.get());
        report.setAverageLatency(getAverageLatency());
        report.setAverageAccuracy(getAverageAccuracy());
        report.setErrorRate(getErrorRate());
        report.setRecentAverageLatency(getRecentAverageLatency());
        report.setRecentAverageAccuracy(getRecentAverageAccuracy());
        return report;
    }
}

五、实际应用场景

5.1 智能视觉应用

// src/main/java/com/example/edgeai/vision/SmartCameraService.java
package com.example.edgeai.vision;

import com.example.edgeai.EdgeAIEngine;
import com.example.edgeai.InferenceData;
import com.example.edgeai.InferenceResult;
import ohos.ai.cv.common.*;
import ohos.ai.cv.docrefine.DocRefine;
import ohos.ai.cv.docrefine.DocRefineConfiguration;
import ohos.ai.cv.docrefine.IDocRefine;
import ohos.ai.cv.qrcode.QRCode;
import ohos.ai.cv.qrcode.QRCodeConfiguration;
import ohos.ai.cv.qrcode.IQRCode;
import ohos.app.Context;
import ohos.media.image.ImageSource;
import ohos.media.image.PixelMap;
import ohos.miscservices.download.DownloadSession;

import java.util.concurrent.CompletableFuture;

/**
 * 智能相机服务 - 实时图像分析
 */
public class SmartCameraService {
    private EdgeAIEngine aiEngine;
    private Context context;
    
    // 视觉分析器
    private IQRCode qrCodeDetector;
    private IDocRefine docRefine;
    
    public SmartCameraService(Context context) {
        this.context = context;
        this.aiEngine = EdgeAIEngine.getInstance(context);
        initVisionAnalyzers();
    }
    
    private void initVisionAnalyzers() {
        // 初始化二维码检测器
        QRCodeConfiguration qrConfig = new QRCodeConfiguration.Builder()
            .setMode(QRCodeConfiguration.MODE_IN)
            .build();
        qrCodeDetector = QRCode.getInstance(qrConfig);
        
        // 初始化文档增强器
        DocRefineConfiguration docConfig = new DocRefineConfiguration.Builder()
            .setMode(DocRefineConfiguration.MODE_IN)
            .build();
        docRefine = DocRefine.getInstance(docConfig);
    }
    
    /**
     * 实时物体检测
     */
    public CompletableFuture<DetectionResult> detectObjects(PixelMap image) {
        InferenceData imageData = ImageData.fromPixelMap(image);
        
        return aiEngine.inferenceAsync("object_detection", imageData)
            .thenApply(result -> {
                DetectionResult detection = new DetectionResult();
                
                // 解析检测结果
                for (Detection detection : result.getDetections()) {
                    if (detection.getConfidence() > 0.5) { // 置信度阈值
                        detection.addObject(
                            detection.getClassName(),
                            detection.getConfidence(),
                            detection.getBoundingBox()
                        );
                    }
                }
                
                return detection;
            });
    }
    
    /**
     * 实时场景识别
     */
    public CompletableFuture<SceneRecognitionResult> recognizeScene(PixelMap image) {
        InferenceData imageData = ImageData.fromPixelMap(image);
        
        return aiEngine.inferenceAsync("scene_classification", imageData)
            .thenApply(result -> {
                SceneRecognitionResult sceneResult = new SceneRecognitionResult();
                sceneResult.setSceneType(result.getTopClassification());
                sceneResult.setConfidence(result.getTopConfidence());
                return sceneResult;
            });
    }
    
    /**
     * 二维码检测与识别
     */
    public CompletableFuture<QRCodeResult> detectQRCode(PixelMap image) {
        return CompletableFuture.supplyAsync(() -> {
            try {
                VisionImage visionImage = VisionImage.fromPixelMap(image);
                QRCode.Result[] results = qrCodeDetector.detect(visionImage);
                
                QRCodeResult result = new QRCodeResult();
                if (results.length > 0) {
                    result.setContent(results[0].getText());
                    result.setPoints(results[0].getCornerPoints());
                }
                
                return result;
            } catch (Exception e) {
                throw new RuntimeException("二维码检测失败", e);
            }
        });
    }
    
    /**
     * 文档扫描与增强
     */
    public CompletableFuture<PixelMap> enhanceDocument(PixelMap documentImage) {
        return CompletableFuture.supplyAsync(() -> {
            try {
                VisionImage visionImage = VisionImage.fromPixelMap(documentImage);
                PixelMap enhanced = docRefine.refine(visionImage);
                return enhanced;
            } catch (Exception e) {
                throw new RuntimeException("文档增强失败", e);
            }
        });
    }
    
    /**
     * 人脸检测与分析
     */
    public CompletableFuture<FaceAnalysisResult> analyzeFaces(PixelMap image) {
        InferenceData imageData = ImageData.fromPixelMap(image);
        
        return aiEngine.inferenceAsync("face_detection", imageData)
            .thenApply(result -> {
                FaceAnalysisResult faceResult = new FaceAnalysisResult();
                
                for (FaceDetection face : result.getFaces()) {
                    FaceAnalysis analysis = new FaceAnalysis();
                    analysis.setBoundingBox(face.getBoundingBox());
                    analysis.setLandmarks(face.getLandmarks());
                    analysis.setAttributes(face.getAttributes()); // 年龄、性别、表情等
                    
                    faceResult.addFace(analysis);
                }
                
                return faceResult;
            });
    }
}

5.2 语音交互应用

// src/main/java/com/example/edgeai/voice/VoiceAssistantService.java
package com.example.edgeai.voice;

import com.example.edgeai.EdgeAIEngine;
import com.example.edgeai.InferenceData;
import com.example.edgeai.InferenceResult;
import ohos.media.audio.AudioCapturer;
import ohos.media.audio.AudioRenderer;
import ohos.media.audio.AudioStreamInfo;

import java.nio.ByteBuffer;
import java.util.concurrent.CompletableFuture;

/**
 * 语音助手服务 - 实时语音处理
 */
public class VoiceAssistantService {
    private EdgeAIEngine aiEngine;
    private AudioCapturer audioCapturer;
    private AudioRenderer audioRenderer;
    private boolean isListening = false;
    
    public VoiceAssistantService() {
        this.aiEngine = EdgeAIEngine.getInstance(null); // 需要Context
        initAudioSystem();
    }
    
    private void initAudioSystem() {
        // 初始化音频采集器
        AudioStreamInfo captureInfo = new AudioStreamInfo.Builder()
            .encodingFormat(AudioStreamInfo.EncodingFormat.ENCODING_PCM_16BIT)
            .channelMask(AudioStreamInfo.ChannelMask.CHANNEL_IN_MONO)
            .sampleRate(16000)
            .build();
        
        audioCapturer = new AudioCapturer(captureInfo);
        
        // 初始化音频渲染器
        AudioStreamInfo renderInfo = new AudioStreamInfo.Builder()
            .encodingFormat(AudioStreamInfo.EncodingFormat.ENCODING_PCM_16BIT)
            .channelMask(AudioStreamInfo.ChannelMask.CHANNEL_OUT_MONO)
            .sampleRate(16000)
            .build();
        
        audioRenderer = new AudioRenderer(renderInfo);
    }
    
    /**
     * 开始语音识别
     */
    public void startListening() {
        if (isListening) return;
        
        isListening = true;
        audioCapturer.start();
        
        // 启动语音识别线程
        new Thread(this::voiceRecognitionLoop).start();
    }
    
    /**
     * 停止语音识别
     */
    public void stopListening() {
        isListening = false;
        audioCapturer.stop();
    }
    
    /**
     * 语音识别循环
     */
    private void voiceRecognitionLoop() {
        ByteBuffer buffer = ByteBuffer.allocateDirect(1600); // 100ms音频数据
        
        while (isListening) {
            int bytesRead = audioCapturer.read(buffer, buffer.capacity());
            if (bytesRead > 0) {
                // 处理音频数据
                processAudioChunk(buffer.array(), bytesRead);
            }
        }
    }
    
    /**
     * 处理音频块
     */
    private void processAudioChunk(byte[] audioData, int length) {
        InferenceData audioInput = AudioData.fromPcm(audioData, length, 16000);
        
        aiEngine.inferenceAsync("speech_recognition", audioInput)
            .thenAccept(result -> {
                String text = result.getText();
                if (text != null && !text.trim().isEmpty()) {
                    // 处理识别结果
                    onSpeechRecognized(text, result.getConfidence());
                }
            })
            .exceptionally(e -> {
                System.err.println("语音识别失败: " + e.getMessage());
                return null;
            });
    }
    
    /**
     * 语音合成
     */
    public CompletableFuture<Void> textToSpeech(String text) {
        InferenceData textInput = TextData.fromString(text);
        
        return aiEngine.inferenceAsync("text_to_speech", textInput)
            .thenAccept(result -> {
                byte[] audioData = result.getAudioData();
                if (audioData != null) {
                    // 播放合成音频
                    playAudio(audioData);
                }
            });
    }
    
    /**
     * 播放音频
     */
    private void playAudio(byte[] audioData) {
        audioRenderer.start();
        audioRenderer.write(audioData, 0, audioData.length);
        audioRenderer.stop();
    }
    
    /**
     * 语音指令识别
     */
    public CompletableFuture<VoiceCommand> recognizeCommand(String speechText) {
        InferenceData textInput = TextData.fromString(speechText);
        
        return aiEngine.inferenceAsync("command_recognition", textInput)
            .thenApply(result -> {
                VoiceCommand command = new VoiceCommand();
                command.setAction(result.getAction());
                command.setParameters(result.getParameters());
                command.setConfidence(result.getConfidence());
                return command;
            });
    }
    
    /**
     * 语音唤醒词检测
     */
    public boolean detectWakeWord(byte[] audioChunk) {
        // 简单的唤醒词检测实现
        InferenceData audioInput = AudioData.fromPcm(audioChunk, audioChunk.length, 16000);
        
        try {
            InferenceResult result = aiEngine.inferenceSync("wake_word_detection", audioInput);
            return result.getConfidence() > 0.8; // 置信度阈值
        } catch (Exception e) {
            return false;
        }
    }
    
    /**
     * 语音识别回调
     */
    private void onSpeechRecognized(String text, double confidence) {
        // 通知监听器
        for (VoiceListener listener : voiceListeners) {
            listener.onSpeechRecognized(text, confidence);
        }
        
        // 如果置信度足够高，尝试识别为命令
        if (confidence > 0.7) {
            recognizeCommand(text).thenAccept(command -> {
                if (command.getConfidence() > 0.6) {
                    for (VoiceListener listener : voiceListeners) {
                        listener.onCommandRecognized(command);
                    }
                }
            });
        }
    }
}

六、测试与验证

6.1 性能测试

// src/test/java/com/example/edgeai/PerformanceTest.java
public class PerformanceTest {
    private EdgeAIEngine aiEngine;
    private PerformanceMonitor performanceMonitor;
    
    @Before
    public void setUp() {
        // 初始化测试环境
        aiEngine = EdgeAIEngine.getInstance(context);
        performanceMonitor = new PerformanceMonitor();
    }
    
    /**
     * 延迟测试
     */
    @Test
    public void testInferenceLatency() {
        // 准备测试数据
        InferenceData testData = createTestData();
        
        // 执行多次推理并统计延迟
        List<Long> latencies = new ArrayList<>();
        for (int i = 0; i < 100; i++) {
            long startTime = System.nanoTime();
            aiEngine.inferenceSync("test_model", testData);
            long latency = System.nanoTime() - startTime;
            latencies.add(latency);
        }
        
        // 计算统计信息
        double avgLatency = latencies.stream().mapToLong(Long::longValue).average().orElse(0);
        double maxLatency = latencies.stream().mapToLong(Long::longValue).max().orElse(0);
        double minLatency = latencies.stream().mapToLong(Long::longValue).min().orElse(0);
        
        // 验证性能要求
        assertThat(avgLatency).isLessThan(10_000_000); // 10ms
        assertThat(maxLatency).isLessThan(50_000_000); // 50ms
    }
    
    /**
     * 准确率测试
     */
    @Test 
    public void testInferenceAccuracy() {
        // 使用标准测试数据集
        TestDataset dataset = loadTestDataset();
        int correct = 0;
        int total = 0;
        
        for (TestData testData : dataset) {
            InferenceResult result = aiEngine.inferenceSync("test_model", testData.getInput());
            if (result.getTopClassification().equals(testData.getExpectedOutput())) {
                correct++;
            }
            total++;
        }
        
        double accuracy = (double) correct / total;
        assertThat(accuracy).isGreaterThan(0.8); // 80%准确率
    }
    
    /**
     * 压力测试
     */
    @Test
    public void testStressPerformance() {
        // 模拟高并发场景
        int concurrentRequests = 10;
        ExecutorService executor = Executors.newFixedThreadPool(concurrentRequests);
        
        List<CompletableFuture<InferenceResult>> futures = new ArrayList<>();
        for (int i = 0; i < 1000; i++) {
            futures.add(CompletableFuture.supplyAsync(() -> 
                aiEngine.inferenceSync("test_model", createTestData()), executor));
        }
        
        // 等待所有任务完成
        CompletableFuture.allOf(futures.toArray(new CompletableFuture[0])).join();
        
        // 验证系统稳定性
        PerformanceReport report = performanceMonitor.generateReport();
        assertThat(report.getErrorRate()).isLessThan(0.01); // 错误率低于1%
    }
}

七、部署与优化

7.1 模型优化配置

// src/main/resources/rawfile/optimization_config.json
public class OptimizationConfig {
    public static final String CONFIG = """
    {
        "model_optimization": {
            "quantization": {
                "enabled": true,
                "precision": "int8",
                "calibration_dataset": "representative_dataset.npy"
            },
            "pruning": {
                "enabled": true,
                "sparsity": 0.5,
                "method": "magnitude"
            },
            "clustering": {
                "enabled": true,
                "num_clusters": 16
            }
        },
        "hardware_optimization": {
            "operator_fusion": true,
            "memory_optimization": true,
            "parallel_execution": true
        },
        "runtime_optimization": {
            "adaptive_batching": true,
            "dynamic_shape": true,
            "memory_pooling": true
        }
    }
    """;
}

八、总结

8.1 技术成果总结

性能指标达成

8.2 核心优势

1. 1.
   极致性能：NPU加速实现毫秒级推理
2. 2.
   隐私安全：数据完全本地处理，不出设备
3. 3.
   低功耗：专用硬件优化，续航提升明显
4. 4.
   高可靠：离线工作，网络波动无影响
5. 5.
   易开发：统一API，快速集成AI能力

8.3 应用前景