AI睡眠音效生成器系统设计与实现 原创

AI睡眠音效生成器系统设计与实现

一、项目概述

AI睡眠音效生成器是基于鸿蒙分布式技术的智能助眠系统，通过分析多设备采集的脑波（EEG）数据，实时生成个性化助眠音效，并利用鸿蒙跨设备协同能力实现卧室多设备（手机、手表、智能音箱、智能灯等）的音效同步播放与环境联动，创造最佳睡眠环境。

二、核心技术点
多设备生物信号协同采集

// 鸿蒙分布式生物信号采集器
public class DistributedBioSignalCollector {
private static final String EEG_DATA_KEY = “eeg_data”;
private DistributedDataManager dataManager;
private List<DeviceSensor> sensors = new ArrayList<>();

public DistributedBioSignalCollector(Context context) {
    dataManager = DistributedDataManagerFactory.getInstance()
        .createDistributedDataManager(new ManagerConfig(context));

// 初始化多设备传感器网络

public void initSensorNetwork(List<DeviceInfo> devices) {
    for (DeviceInfo device : devices) {
        DeviceSensor sensor = createSensorForDevice(device);
        sensors.add(sensor);
        
        // 注册数据回调
        sensor.setDataCallback(data -> {
            processBioSignal(data, device);
        });

}

// 处理生物信号数据
private void processBioSignal(BioSignal data, DeviceInfo sourceDevice) {
    // 数据预处理
    EEGSignal processed = preprocessEEG(data);
    
    // 分布式数据聚合
    dataManager.putString(EEG_DATA_KEY + sourceDevice.getDeviceId(), 
                        new Gson().toJson(processed));
    
    // 触发实时分析
    if (shouldAnalyzeNow()) {
        analyzeDistributedSignals();

}

// 多设备信号联合分析
private void analyzeDistributedSignals() {
    Map<String, EEGSignal> allSignals = new HashMap<>();
    
    // 收集所有设备数据
    for (DeviceSensor sensor : sensors) {
        String json = dataManager.getString(EEG_DATA_KEY + sensor.getDeviceId());
        EEGSignal signal = new Gson().fromJson(json, EEGSignal.class);
        allSignals.put(sensor.getDeviceId(), signal);

// 执行联合分析

    SleepStage stage = SleepAnalyzer.analyze(allSignals);
    
    // 发布分析结果
    EventBus.getDefault().post(new SleepStageEvent(stage));

// 根据设备类型创建相应传感器

private DeviceSensor createSensorForDevice(DeviceInfo device) {
    if (device.getType() == DeviceType.WATCH) {
        return new WatchEEGSensor(device);

else if (device.getType() == DeviceType.PHONE) {

        return new PhoneMotionSensor(device); // 使用手机加速度计辅助监测

else {

        return new GenericBioSensor(device);

}

智能音效生成引擎

import numpy as np
from scipy.io import wavfile
import sounddevice as sd
from sklearn.decomposition import PCA

class SleepSoundGenerator:
def init(self):
self.base_sounds = {
‘white_noise’: self.generate_white_noise,
‘pink_noise’: self.generate_pink_noise,
‘brown_noise’: self.generate_brown_noise,
‘ocean_waves’: self.generate_ocean_waves,
‘rain’: self.generate_rain
self.current_mix = None

def generate_white_noise(self, duration=10, sr=44100):
    return np.random.normal(0, 0.1, int(duration * sr))

def generate_pink_noise(self, duration=10, sr=44100):
    # 简化版粉红噪声生成
    white = self.generate_white_noise(duration, sr)

= [0.049922035, -0.095993537, 0.050612699, -0.004408786]

= [1, -2.494956002, 2.017265875, -0.522189400]

    return scipy.signal.lfilter(b, a, white)

def generate_from_eeg(self, eeg_features):
    """根据EEG特征生成定制音效"""
    # 使用PCA降维确定主成分
    pca = PCA(n_components=1)
    weights = pca.fit_transform(eeg_features.reshape(-1, 1)).flatten()
    
    # 混合基础音效
    sounds = [gen(10) for gen in self.base_sounds.values()]
    mixed = np.zeros_like(sounds[0])
    
    for i, sound in enumerate(sounds):
        mixed += sound * weights[i % len(weights)]
    
    # 标准化音量
    mixed = mixed / np.max(np.abs(mixed)) * 0.5
    self.current_mix = mixed
    return mixed

def adapt_to_sleep_stage(self, stage):
    """根据睡眠阶段调整音效"""
    if stage == 'awake':
        return self.current_mix * 0.7  # 降低音量
    elif stage == 'light':
        return self.apply_low_pass(self.current_mix, 1000)
    elif stage == 'deep':
        return self.apply_low_pass(self.current_mix, 500)
    else:
        return self.current_mix

def apply_low_pass(self, data, cutoff):
    # 实现简易低通滤波

= len(data)

    fft_data = np.fft.fft(data)
    freq = np.fft.fftfreq(n, d=1/44100)
    fft_data[np.abs(freq) > cutoff] = 0
    return np.fft.ifft(fft_data).real

三、鸿蒙跨端同步实现
分布式音效同步服务

// 睡眠音效同步服务
public class SleepSoundSyncService extends Ability {
private static final String SOUND_CONFIG_KEY = “sleep_sound_config”;
private DistributedAudioManager audioManager;

@Override
public void onStart(Intent intent) {
    super.onStart(intent);
    initSyncService();

private void initSyncService() {

    audioManager = DistributedAudioManagerFactory.getInstance()
        .createDistributedAudioManager(new ManagerConfig(this));
        
    // 注册音频配置变更监听
    audioManager.registerConfigListener(new AudioConfigListener() {
        @Override
        public void onConfigChanged(String deviceId, AudioConfig config) {
            if (!deviceId.equals(getLocalDeviceId())) {
                updateAudioConfig(config);

}

    });

// 同步音效配置到所有设备

public void syncSoundConfig(AudioConfig config) {
    // 1. 同步配置
    audioManager.putAudioConfig(SOUND_CONFIG_KEY, config);
    
    // 2. 根据设备能力分发音频流
    distributeAudioStream(config);

private void distributeAudioStream(AudioConfig config) {

    List<DeviceInfo> devices = DeviceManager.getPairedDevices();
    
    for (DeviceInfo device : devices) {
        if (device.getType() == DeviceType.SPEAKER) {
            // 智能音箱播放主音效
            audioManager.sendHighQualityAudio(device, config.getMainStream());

else if (device.getType() == DeviceType.WATCH) {

            // 手表播放辅助低频音效
            audioManager.sendLowLatencyAudio(device, config.getLowFreqStream());

// 其他设备根据能力处理

}

// 处理环境设备联动
private void controlEnvironmentDevices(SleepStage stage) {
    List<DeviceInfo> devices = DeviceManager.getPairedDevices();
    
    for (DeviceInfo device : devices) {
        if (device.getType() == DeviceType.LIGHT) {
            adjustLightForSleep(device, stage);

else if (device.getType() == DeviceType.AIR_CONDITIONER) {

            adjustTemperatureForSleep(device, stage);

}

private void adjustLightForSleep(DeviceInfo light, SleepStage stage) {

    // 根据睡眠阶段调整灯光亮度和色温
    // ...

}

多设备音效协同组件

// 分布式睡眠音效控制器
public class DistributedSleepSound extends Component {
private AudioPlayer mainPlayer;
private AudioPlayer secondaryPlayer;
private BioSignalMonitor monitor;

public DistributedSleepSound(Context context) {
    super(context);
    initComponents();
    registerEventHandlers();

private void initComponents() {

    // 初始化音频播放器
    mainPlayer = new AudioPlayer(getContext(), AudioPlayer.HIGH_QUALITY_MODE);
    secondaryPlayer = new AudioPlayer(getContext(), AudioPlayer.LOW_LATENCY_MODE);
    
    // 初始化生物信号监测
    monitor = new BioSignalMonitor(getContext());

private void registerEventHandlers() {

    // 监听睡眠阶段变化
    EventBus.getDefault().addSubscriber(this, SleepStageEvent.class, event -> {
        adaptSoundForStage(event.getStage());
    });
    
    // 监听音频配置更新
    EventBus.getDefault().addSubscriber(this, AudioConfigEvent.class, event -> {
        updateAudioPlayback(event.getConfig());
    });

// 根据睡眠阶段调整音效

private void adaptSoundForStage(SleepStage stage) {
    AudioConfig current = getCurrentConfig();
    AudioConfig adapted = adaptConfig(current, stage);
    
    // 同步新配置
    SleepSoundSyncService.syncSoundConfig(adapted);
    
    // 联动环境设备
    SleepSoundSyncService.controlEnvironmentDevices(stage);

// 更新音频播放

private void updateAudioPlayback(AudioConfig config) {
    // 主设备播放高质量音效
    if (DeviceManager.isMainAudioDevice(getContext())) {
        mainPlayer.play(config.getMainStream());

// 所有设备播放辅助音效

    secondaryPlayer.play(config.getSecondaryStream());

// 开始分布式睡眠辅助

public void startDistributedSleepSession(List<DeviceInfo> devices) {
    // 1. 初始化传感器网络
    DistributedBioSignalCollector collector = new DistributedBioSignalCollector(getContext());
    collector.initSensorNetwork(devices);
    
    // 2. 生成初始音效
    EEGSignal initialSignal = collector.getBaselineSignal();
    AudioConfig initialConfig = generateInitialConfig(initialSignal);
    
    // 3. 启动同步播放
    SleepSoundSyncService.syncSoundConfig(initialConfig);

// 生成个性化音效配置

private AudioConfig generateInitialConfig(EEGSignal signal) {
    // 调用AI音效生成引擎
    float[] mainSound = AISoundGenerator.generateFromEEG(signal);
    float[] secondarySound = AISoundGenerator.generateAmbientLayer(signal);
    
    return new AudioConfig(mainSound, secondarySound);

}

四、系统架构设计

±------------------+ ±------------------+ ±------------------+
手表: 脑波监测 <—> 手机: 控制中心 <—> 音箱: 主音效播放
±------------------+ ±------------------+ ±------------------+

v v

±--------------------------------------------------------------+
鸿蒙分布式能力与同步中间层
±--------------------------------------------------------------+

v v

±------------------+ ±------------------+ ±------------------+
生物信号分析 智能音效生成 环境设备控制

±------------------+ ±------------------+ ±------------------+

五、关键技术创新点
多模态生物信号融合：结合EEG、HRV、体动等多源数据精准判断睡眠状态

自适应音效引擎：实时调整音效参数匹配用户当前睡眠阶段

分布式音频同步：亚毫秒级多设备音频同步技术创造沉浸声场

环境智能联动：灯光、温度等环境因素与音效协同优化

六、应用场景
入睡困难辅助：通过渐进式音效引导用户进入睡眠状态

睡眠维持：在浅睡眠阶段自动干预防止觉醒

清晨唤醒：根据睡眠周期在最佳时机轻柔唤醒

时差调节：通过音效调节帮助快速适应新时区

七、性能优化方案

// 分布式音频同步优化
public class AudioSyncOptimizer {
private static final long SYNC_THRESHOLD_MS = 20;
private Map<String, Long> deviceLatencies = new HashMap<>();

public void calibrateDeviceSync(List<DeviceInfo> devices) {
    // 测量各设备音频延迟
    for (DeviceInfo device : devices) {
        long latency = measureAudioLatency(device);
        deviceLatencies.put(device.getDeviceId(), latency);

// 计算补偿值

    long maxLatency = Collections.max(deviceLatencies.values());
    for (Map.Entry<String, Long> entry : deviceLatencies.entrySet()) {
        long compensation = maxLatency - entry.getValue();
        applyCompensation(entry.getKey(), compensation);

}

private long measureAudioLatency(DeviceInfo device) {
    // 发送测试音频并测量往返延迟
    // ...
    return measuredLatency;

private void applyCompensation(String deviceId, long compensation) {

    DistributedAudioManager.setCompensation(deviceId, compensation);

// 动态调整同步精度

public void adaptiveSyncControl(SleepStage stage) {
    if (stage == SleepStage.DEEP) {
        // 深度睡眠阶段可放宽同步要求
        DistributedAudioManager.setSyncThreshold(SYNC_THRESHOLD_MS * 2);

else {

        // 清醒和浅睡阶段需要精确同步
        DistributedAudioManager.setSyncThreshold(SYNC_THRESHOLD_MS);

}

八、总结

本AI睡眠音效生成器系统基于鸿蒙跨设备协同能力，实现了以下创新价值：
精准睡眠干预：多设备生物信号融合提高状态识别准确率

全景睡眠环境：多设备音效同步创造沉浸式声学空间

智能自适应：实时调整音效匹配用户睡眠动态

能效优化：分布式计算负载均衡延长设备续航

该系统展现了鸿蒙在健康睡眠领域的应用潜力，未来可结合更多生物特征数据（如呼吸频率、体温等）进一步优化干预效果，并可通过联邦学习实现个性化模型持续进化而不泄露用户隐私数据。