鸿蒙智能插座用电统计系统开发指南 原创

鸿蒙智能插座用电统计系统开发指南

一、系统架构设计

基于HarmonyOS的分布式能力和硬件加速特性，我们设计了一套智能插座用电统计系统，主要功能包括：
硬件加速电流检测：利用芯片专用模块实现高精度测量

用电模式学习：AI算法分析用户用电习惯

批量数据上传：优化网络传输减少功耗

跨设备同步：多终端实时查看用电数据

用电异常告警：实时监测异常用电情况

!https://example.com/harmony-smart-outlet-arch.png

二、核心代码实现
硬件加速电流检测服务

// CurrentDetectionService.ets
import driver from ‘@ohos.driver’;
import power from ‘@ohos.power’;

class CurrentDetectionService {
private static instance: CurrentDetectionService;
private adcController: driver.AdcController | null = null;
private samplingInterval: number = 1000; // 默认1秒采样一次
private calibrationFactor: number = 1.0;
private currentReadings: number[] = [];
private powerMode: power.Mode = power.Mode.NORMAL;

private constructor() {
this.initAdcController();
this.initPowerListener();
private initAdcController(): void {

try {
  this.adcController = driver.createAdcController();
  this.adcController.init({
    channel: 0, // 使用ADC通道0
    samplingRate: 1000, // 1kHz采样率
    precision: 12 // 12位精度
  });
  
  // 加载校准数据
  this.loadCalibration();

catch (error) {

  console.error('ADC controller initialization failed:', error);

}

private initPowerListener(): void {
power.on(‘powerModeChange’, (mode) => {
this.powerMode = mode;
this.adjustSamplingRate();
});
private adjustSamplingRate(): void {

switch (this.powerMode) {
  case power.Mode.POWER_SAVE:
    this.samplingInterval = 5000; // 5秒采样一次
    break;
  case power.Mode.PERFORMANCE:
    this.samplingInterval = 200; // 0.2秒采样一次
    break;
  default:
    this.samplingInterval = 1000; // 1秒采样一次

this.startSampling();

private loadCalibration(): void {

// 从持久化存储加载校准因子
const calibration = storageService.get('currentCalibration');
if (calibration) {
  this.calibrationFactor = calibration.factor;

}

public static getInstance(): CurrentDetectionService {
if (!CurrentDetectionService.instance) {
CurrentDetectionService.instance = new CurrentDetectionService();
return CurrentDetectionService.instance;

public startSampling(): void {

setInterval(() => {
  this.takeSample();
}, this.samplingInterval);

private takeSample(): void {

if (!this.adcController) return;

try {
  // 使用硬件加速读取ADC值
  const rawValue = this.adcController.read();
  const current = this.convertToAmperes(rawValue);
  
  // 保存读数
  this.currentReadings.push(current);
  
  // 计算功率
  const voltage = 220; // 假设电压220V
  const power = current * voltage;
  
  // 更新用电统计
  powerStatsService.updatePowerUsage(current, power);
  
  // 检查异常
  if (this.checkAbnormalCurrent(current)) {
    alertService.triggerCurrentAlert(current);

} catch (error) {

  console.error('Failed to read current:', error);

}

private convertToAmperes(rawValue: number): number {
// 转换为实际电流值(安培)
// 公式: (rawValue / 4095) 3.3V / 0.185V/A calibrationFactor
return (rawValue / 4095) 3.3 / 0.185 this.calibrationFactor;
private checkAbnormalCurrent(current: number): boolean {

// 简单异常检测: 电流超过10A或比平均值高3倍标准差
const avg = this.currentReadings.reduce((a, b) => a + b, 0) / this.currentReadings.length;
const stddev = Math.sqrt(
  this.currentReadings.reduce((sq, n) => sq + Math.pow(n - avg, 2), 0) / 
  this.currentReadings.length
);

return current > 10 || current > avg + 3 * stddev;

public calibrate(knownCurrent: number): void {

if (!this.adcController || this.currentReadings.length === 0) return;

const rawValue = this.adcController.read();
const measuredCurrent = (rawValue / 4095) * 3.3 / 0.185;
this.calibrationFactor = knownCurrent / measuredCurrent;

// 保存校准数据
storageService.set('currentCalibration', { factor: this.calibrationFactor });

public getCurrentReadings(count: number = 10): number[] {

return this.currentReadings.slice(-count);

}

export const currentDetection = CurrentDetectionService.getInstance();

用电模式学习服务

// PowerPatternService.ets
import neuralNetwork from ‘@ohos.ai.neuralNetwork’;
import power from ‘@ohos.power’;

class PowerPatternService {
private static instance: PowerPatternService;
private model: neuralNetwork.Model | null = null;
private powerPatterns: PowerPattern[] = [];
private powerMode: power.Mode = power.Mode.NORMAL;

private constructor() {
this.initModel();
this.initPowerListener();
private async initModel(): Promise<void> {

try {
  // 加载轻量级LSTM模型
  this.model = await neuralNetwork.loadModel({
    modelPath: 'models/power_pattern_lstm.nn',
    quantization: true
  });
  
  // 加载已有模式
  this.loadPatterns();

catch (error) {

  console.error('Failed to load power pattern model:', error);

}

private initPowerListener(): void {
power.on(‘powerModeChange’, (mode) => {
this.powerMode = mode;
});
private async loadPatterns(): Promise<void> {

const patterns = await storageService.get('powerPatterns');
if (patterns) {
  this.powerPatterns = patterns;

}

public static getInstance(): PowerPatternService {
if (!PowerPatternService.instance) {
PowerPatternService.instance = new PowerPatternService();
return PowerPatternService.instance;

public async analyzeUsage(data: PowerData[]): Promise<PowerPattern | null> {

if (!this.model || data.length < 24) return null; // 至少需要24小时数据

try {
  // 准备输入数据
  const input: neuralNetwork.Tensor = {
    data: this.normalizeData(data),
    shape: [1, data.length, 2] // [批次, 时间步, 特征(功率,时间)]
  };
  
  // 运行模型
  const output = await this.model.run(input);
  
  // 解析输出
  const pattern = this.parsePattern(output);
  
  // 保存新模式
  if (this.isNewPattern(pattern)) {
    this.powerPatterns.push(pattern);
    await storageService.set('powerPatterns', this.powerPatterns);

return pattern;

catch (error) {

  console.error('Power pattern analysis failed:', error);
  return null;

}

private normalizeData(data: PowerData[]): number[] {
// 简单归一化: 功率除以最大值, 时间转换为0-1
const maxPower = Math.max(…data.map(d => d.power));
return data.flatMap(d => [
d.power / maxPower,
(d.timestamp % 86400000) / 86400000 // 一天中的时间
]);
private parsePattern(output: neuralNetwork.Tensor): PowerPattern {

// 解析模型输出为用电模式
const data = output.data as number[];
return {
  id: generateId(),
  typicalPower: data[0] * 1000, // 转换为瓦
  peakHours: data.slice(1, 25).map((v, i) => ({
    hour: i,
    intensity: v
  })),
  createdAt: Date.now()
};

private isNewPattern(pattern: PowerPattern): boolean {

if (this.powerPatterns.length === 0) return true;

// 检查是否与现有模式相似
const similar = this.powerPatterns.some(p => 
  Math.abs(p.typicalPower - pattern.typicalPower) < 100 && // 功率差小于100W
  this.comparePeakHours(p.peakHours, pattern.peakHours) < 0.2 // 峰值时间相似度
);

return !similar;

private comparePeakHours(a: PeakHour[], b: PeakHour[]): number {

// 计算两个峰值时间分布的差异
const diff = a.reduce((sum, hour, i) => 
  sum + Math.abs(hour.intensity - b[i].intensity), 0);
return diff / a.length;

public async predictUsage(): Promise<PowerPrediction> {

if (!this.model || this.powerPatterns.length === 0) {
  return { predictedPower: 0, confidence: 0 };

// 获取最近24小时数据

const recentData = powerStatsService.getHourlyData(24);
if (recentData.length < 24) return { predictedPower: 0, confidence: 0 };

// 找到最匹配的模式
const bestMatch = this.findBestMatch(recentData);

// 基于模式预测
return {
  predictedPower: bestMatch.pattern.typicalPower,
  confidence: bestMatch.similarity,
  patternId: bestMatch.pattern.id
};

private findBestMatch(data: HourlyPowerData[]): { pattern: PowerPattern, similarity: number } {

// 计算当前数据与各模式的相似度
const currentPeakHours = this.calculateCurrentPeaks(data);

const matches = this.powerPatterns.map(pattern => ({
  pattern,
  similarity: 1 - this.comparePeakHours(currentPeakHours, pattern.peakHours)
}));

// 返回最相似的模式
return matches.reduce((best, current) => 
  current.similarity > best.similarity ? current : best
);

private calculateCurrentPeaks(data: HourlyPowerData[]): PeakHour[] {

// 计算当前数据的峰值时间分布
const maxPower = Math.max(...data.map(d => d.power));
return data.map(d => ({
  hour: d.hour,
  intensity: d.power / maxPower
}));

public getPowerPatterns(): PowerPattern[] {

return [...this.powerPatterns];

}

export const powerPattern = PowerPatternService.getInstance();

批量数据上传服务

// DataUploadService.ets
import http from ‘@ohos.net.http’;
import power from ‘@ohos.power’;

class DataUploadService {
private static instance: DataUploadService;
private uploadQueue: PowerData[] = [];
private isUploading: boolean = false;
private lastUploadTime: number = 0;
private powerMode: power.Mode = power.Mode.NORMAL;

private constructor() {
this.initPowerListener();
this.startUploadTimer();
private initPowerListener(): void {

power.on('powerModeChange', (mode) => {
  this.powerMode = mode;
  this.adjustUploadStrategy();
});

private adjustUploadStrategy(): void {

// 根据电源模式调整上传策略
switch (this.powerMode) {
  case power.Mode.POWER_SAVE:
    // 省电模式下延长上传间隔
    this.startUploadTimer(3600000); // 1小时
    break;
  case power.Mode.PERFORMANCE:
    // 高性能模式下缩短上传间隔
    this.startUploadTimer(300000); // 5分钟
    break;
  default:
    // 正常模式
    this.startUploadTimer(900000); // 15分钟

}

private startUploadTimer(interval: number = 900000): void {
// 先清除现有定时器
if (this.uploadTimer) {
clearInterval(this.uploadTimer);
// 设置新定时器

this.uploadTimer = setInterval(() => {
  this.processUploadQueue();
}, interval);

public static getInstance(): DataUploadService {

if (!DataUploadService.instance) {
  DataUploadService.instance = new DataUploadService();

return DataUploadService.instance;

public addToUploadQueue(data: PowerData): void {

this.uploadQueue.push(data);

// 高性能模式下立即上传
if (this.powerMode === power.Mode.PERFORMANCE && this.uploadQueue.length > 10) {
  this.processUploadQueue();

}

private async processUploadQueue(): Promise<void> {
if (this.isUploading || this.uploadQueue.length === 0) return;

this.isUploading = true;

try {
  // 准备批量数据
  const batch = this.prepareBatch();
  
  // 上传数据
  const response = await this.uploadData(batch);
  
  if (response.responseCode === 200) {
    // 上传成功，清空已上传数据
    this.uploadQueue = this.uploadQueue.slice(batch.length);
    this.lastUploadTime = Date.now();

} catch (error) {

  console.error('Upload failed:', error);

finally {

  this.isUploading = false;

}

private prepareBatch(): PowerData[] {
// 准备要上传的批次数据
const batchSize = this.getOptimalBatchSize();
return this.uploadQueue.slice(0, batchSize);
private getOptimalBatchSize(): number {

switch (this.powerMode) {
  case power.Mode.POWER_SAVE: return 100; // 大批次减少上传频率
  case power.Mode.PERFORMANCE: return 10; // 小批次快速上传
  default: return 50; // 中等批次

}

private async uploadData(data: PowerData[]): Promise<http.HttpResponse> {
const httpRequest = http.createHttp();
const url = ‘https://api.example.com/power/upload’;

// 准备请求数据
const requestData = {
  deviceId: deviceManager.getLocalDevice().id,
  timestamp: Date.now(),
  data
};

// 发送请求
return await httpRequest.request(url, {
  method: 'POST',
  header: { 'Content-Type': 'application/json' },
  extraData: JSON.stringify(requestData)
});

public async forceUpload(): Promise<void> {

await this.processUploadQueue();

public getQueueSize(): number {

return this.uploadQueue.length;

public getLastUploadTime(): number {

return this.lastUploadTime;

}

export const dataUpload = DataUploadService.getInstance();

三、主界面实现
用电统计主界面

// PowerStatsView.ets
@Component
struct PowerStatsView {
@State currentPower: number = 0;
@State dailyUsage: number = 0;
@State patterns: PowerPattern[] = [];
@State uploadStatus: string = ‘已同步’;
@State alertCount: number = 0;

aboutToAppear() {
this.loadData();
EventBus.on(‘powerUpdate’, (data) => this.updatePower(data));
EventBus.on(‘alertTriggered’, () => this.alertCount++);
build() {

Column() {
  // 当前功率
  Text(当前功率: ${this.currentPower.toFixed(1)}W)
    .fontSize(24)
    .fontWeight(FontWeight.Bold)
    .margin({ top: 16, bottom: 8 })
  
  // 今日用电
  Text(今日用电: ${this.dailyUsage.toFixed(2)}kWh)
    .fontSize(18)
    .margin({ bottom: 24 })
  
  // 用电趋势图
  PowerChartView()
    .height(200)
    .margin({ bottom: 24 })
  
  // 用电模式
  Text('用电模式')
    .fontSize(18)
    .fontWeight(FontWeight.Bold)
    .margin({ bottom: 8 })
  
  PatternList({ patterns: this.patterns })
    .margin({ bottom: 16 })
  
  // 状态栏
  Row() {
    Text(this.uploadStatus)
      .fontSize(14)
      .layoutWeight(1)
    
    if (this.alertCount > 0) {
      Badge({ count: this.alertCount })
        .margin({ right: 16 })

}

.padding(16)

private loadData(): void {

this.currentPower = powerStats.getCurrentPower();
this.dailyUsage = powerStats.getDailyUsage();
this.patterns = powerPattern.getPowerPatterns();
this.updateUploadStatus();

private updatePower(data: PowerUpdate): void {

this.currentPower = data.power;
this.dailyUsage = data.dailyUsage;

private updateUploadStatus(): void {

const lastUpload = dataUpload.getLastUploadTime();
const queueSize = dataUpload.getQueueSize();

if (queueSize === 0) {
  this.uploadStatus = 已同步 ${this.formatTime(lastUpload)};

else {

  this.uploadStatus = 待同步 ${queueSize}条;

}

private formatTime(timestamp: number): string {
if (!timestamp) return ‘’;

const date = new Date(timestamp);
return {date.getHours().toString().padStart(2, '0')}:{date.getMinutes().toString().padStart(2, '0')};

}

@Component
struct PowerChartView {
@State powerData: HourlyPowerData[] = [];

aboutToAppear() {
this.loadData();
build() {

Canvas({ context: this.getContext() })
  .width('100%')
  .height('100%')
  .onReady(() => this.drawChart())

private getContext(): CanvasRenderingContext2D {

// 创建并返回Canvas上下文
return new CanvasRenderingContext2D();

private loadData(): void {

this.powerData = powerStats.getHourlyData(24);

private drawChart(): void {

// 实现绘制用电曲线图的逻辑
// 实际项目中可以使用第三方图表库

}

@Component
struct PatternList {
private patterns: PowerPattern[];

build() {
Column() {
ForEach(this.patterns, (pattern) => {
PatternItem({ pattern })
.margin({ bottom: 8 })
})
}

@Component

struct PatternItem {
private pattern: PowerPattern;

build() {
Row() {
Column() {
Text(典型功率: ${pattern.typicalPower.toFixed(0)}W)
.fontSize(14)

    Text(峰值时段: ${this.getPeakHours()})
      .fontSize(12)
      .fontColor('#666666')

.layoutWeight(1)

  Button('详情')
    .width(60)

.padding(8)

.backgroundColor('#FFFFFF')
.borderRadius(4)

private getPeakHours(): string {

const peak = this.pattern.peakHours.reduce((max, hour) => 
  hour.intensity > max.intensity ? hour : max
);
return {peak.hour}:00-{peak.hour + 1}:00;

}

设置界面

// SettingsView.ets
@Component
struct SettingsView {
@State uploadInterval: number = 15;
@State currentCalibration: number = 1.0;
@State powerMode: string = ‘normal’;
@State isLearning: boolean = true;

build() {
Column() {
// 上传间隔
Text(数据上传间隔: ${this.uploadInterval}分钟)
.fontSize(16)
.margin({ top: 16, bottom: 8 })

  Slider({
    value: this.uploadInterval,
    min: 5,
    max: 120,
    step: 5
  })
  .onChange((value: number) => {
    this.changeUploadInterval(value);
  })
  .margin({ bottom: 24 })
  
  // 电流校准
  Text(电流校准因子: ${this.currentCalibration.toFixed(2)})
    .fontSize(16)
    .margin({ bottom: 8 })
  
  Slider({
    value: this.currentCalibration,
    min: 0.8,
    max: 1.2,
    step: 0.01
  })
  .onChange((value: number) => {
    this.changeCalibration(value);
  })
  .margin({ bottom: 24 })
  
  // 电源模式
  Text('电源模式')
    .fontSize(16)
    .margin({ bottom: 8 })
  
  RadioGroup({ initial: this.powerMode })
    .onChange((value: string) => {
      this.changePowerMode(value);
    })
    .margin({ bottom: 24 })

Radio({ value: ‘power_save’ }).text(‘省电模式’)

    Radio({ value: 'normal' }).text('普通模式')
    Radio({ value: 'performance' }).text('高性能模式')

// 学习模式

  Row() {
    Text('用电模式学习')
      .fontSize(16)
      .layoutWeight(1)
    
    Toggle({ type: ToggleType.Switch, isOn: this.isLearning })
      .onChange((isOn: boolean) => {
        this.toggleLearning(isOn);
      })

.margin({ bottom: 24 })

  // 校准按钮
  Button('校准电流')
    .onClick(() => this.startCalibration())
    .width('90%')
    .margin({ bottom: 16 })
  
  // 手动上传
  Button('立即上传数据')
    .onClick(() => this.forceUpload())
    .width('90%')

.padding(16)

private changeUploadInterval(minutes: number): void {

this.uploadInterval = minutes;
dataUpload.setUploadInterval(minutes * 60000);

private changeCalibration(factor: number): void {

this.currentCalibration = factor;
currentDetection.setCalibrationFactor(factor);

private changePowerMode(mode: string): void {

this.powerMode = mode;
powerOptimization.setPowerMode(mode);

private toggleLearning(enabled: boolean): void {

this.isLearning = enabled;
powerPattern.setLearningEnabled(enabled);

private startCalibration(): void {

// 提示用户连接已知负载
prompt.showDialog({
  message: '请连接已知电流的负载后点击确定',
  buttons: [{ text: '确定' }, { text: '取消' }]
}).then((result) => {
  if (result.index === 0) {
    const knownCurrent = 5.0; // 应从用户输入获取实际值
    currentDetection.calibrate(knownCurrent);
    this.currentCalibration = currentDetection.getCalibrationFactor();

});

private forceUpload(): void {

dataUpload.forceUpload();
prompt.showToast({ message: '开始上传数据' });

}

四、高级功能实现
用电统计服务

// PowerStatsService.ets
import { currentDetection } from ‘./CurrentDetectionService’;

class PowerStatsService {
private static instance: PowerStatsService;
private currentPower: number = 0;
private dailyUsage: number = 0;
private hourlyData: HourlyPowerData[] = [];
private lastUpdateTime: number = 0;

private constructor() {
this.initEventListeners();
private initEventListeners(): void {

currentDetection.onCurrentUpdate((current) => {
  this.updatePower(current);
});

public static getInstance(): PowerStatsService {

if (!PowerStatsService.instance) {
  PowerStatsService.instance = new PowerStatsService();

return PowerStatsService.instance;

private updatePower(current: number): void {

const voltage = 220; // 假设电压220V
this.currentPower = current * voltage;

// 计算用电量
const now = Date.now();
if (this.lastUpdateTime > 0) {
  const hours = (now - this.lastUpdateTime) / 3600000;
  this.dailyUsage += this.currentPower * hours / 1000; // 转换为kWh

this.lastUpdateTime = now;

// 更新小时数据
this.updateHourlyData();

// 触发事件
EventBus.emit('powerUpdate', {
  power: this.currentPower,
  dailyUsage: this.dailyUsage
});

private updateHourlyData(): void {

const now = new Date();
const currentHour = now.getHours();

if (this.hourlyData.length === 0 || 
    this.hourlyData[this.hourlyData.length - 1].hour !== currentHour) {
  // 新小时开始
  this.hourlyData.push({
    hour: currentHour,
    power: this.currentPower,
    timestamp: now.getTime()
  });

else {

  // 更新当前小时数据
  const lastHour = this.hourlyData[this.hourlyData.length - 1];
  lastHour.power = (lastHour.power + this.currentPower) / 2; // 平均功率

// 保留最近7天数据

if (this.hourlyData.length > 168) { // 24*7
  this.hourlyData.shift();

}

public getCurrentPower(): number {
return this.currentPower;
public getDailyUsage(): number {

return this.dailyUsage;

public getHourlyData(hours: number = 24): HourlyPowerData[] {

return this.hourlyData.slice(-hours);

public resetDailyUsage(): void {

this.dailyUsage = 0;
this.lastUpdateTime = Date.now();

}

export const powerStats = PowerStatsService.getInstance();

告警服务

// AlertService.ets
import { currentDetection } from ‘./CurrentDetectionService’;
import { notificationService } from ‘./NotificationService’;

class AlertService {
private static instance: AlertService;
private alertThreshold: number = 10; // 默认10A告警阈值
private alertCount: number = 0;

private constructor() {
this.initEventListeners();
private initEventListeners(): void {

currentDetection.onAbnormalCurrent((current) => {
  this.handleAbnormalCurrent(current);
});

public static getInstance(): AlertService {

if (!AlertService.instance) {
  AlertService.instance = new AlertService();

return AlertService.instance;

private handleAbnormalCurrent(current: number): void {

this.alertCount++;

// 本地通知
EventBus.emit('alertTriggered', {
  type: 'highCurrent',
  current,
  timestamp: Date.now()
});

// 发送到其他设备
notificationService.sendAlert({
  type: 'highCurrent',
  message: 异常电流: ${current.toFixed(2)}A,
  severity: 'high'
});

public setAlertThreshold(threshold: number): void {

this.alertThreshold = threshold;
currentDetection.setAlertThreshold(threshold);

public getAlertCount(): number {

return this.alertCount;

public resetAlertCount(): void {

this.alertCount = 0;

}

export const alertService = AlertService.getInstance();

电源优化服务

// PowerOptimizationService.ets
import power from ‘@ohos.power’;
import { currentDetection } from ‘./CurrentDetectionService’;
import { dataUpload } from ‘./DataUploadService’;
import { powerPattern } from ‘./PowerPatternService’;

class PowerOptimizationService {
private static instance: PowerOptimizationService;
private currentMode: power.Mode = power.Mode.NORMAL;

private constructor() {
this.initPowerListener();
private initPowerListener(): void {

power.on('powerModeChange', (mode) => {
  this.handlePowerModeChange(mode);
});

public static getInstance(): PowerOptimizationService {

if (!PowerOptimizationService.instance) {
  PowerOptimizationService.instance = new PowerOptimizationService();

return PowerOptimizationService.instance;

public setPowerMode(mode: string): void {

let powerMode: power.Mode;
switch (mode) {
  case 'power_save': powerMode = power.Mode.POWER_SAVE; break;
  case 'performance': powerMode = power.Mode.PERFORMANCE; break;
  default: powerMode = power.Mode.NORMAL;

power.setMode(powerMode);

private handlePowerModeChange(mode: power.Mode): void {

this.currentMode = mode;

// 调整各服务参数
switch (mode) {
  case power.Mode.POWER_SAVE:
    this.enablePowerSaveMode();
    break;
  case power.Mode.PERFORMANCE:
    this.enablePerformanceMode();
    break;
  default:
    this.enableNormalMode();

}

private enablePowerSaveMode(): void {
// 降低电流检测频率
currentDetection.setSamplingInterval(5000); // 5秒

// 减少数据上传频率
dataUpload.setUploadInterval(3600000); // 1小时

// 禁用模式学习
powerPattern.setLearningEnabled(false);

private enablePerformanceMode(): void {

// 提高电流检测频率
currentDetection.setSamplingInterval(200); // 0.2秒

// 增加数据上传频率
dataUpload.setUploadInterval(300000); // 5分钟

// 启用模式学习
powerPattern.setLearningEnabled(true);

private enableNormalMode(): void {

// 恢复默认设置
currentDetection.setSamplingInterval(1000); // 1秒
dataUpload.setUploadInterval(900000); // 15分钟
powerPattern.setLearningEnabled(true);

public getCurrentMode(): power.Mode {

return this.currentMode;

}

export const powerOptimization = PowerOptimizationService.getInstance();

五、总结

本智能插座用电统计系统实现了以下核心价值：
高精度电流检测：硬件加速ADC采样实现精确测量

智能用电分析：LSTM模型学习用户用电模式

高效数据传输：批量上传和自适应间隔优化网络使用

实时异常告警：电流异常即时通知多设备

自适应电源管理：根据场景动态调整性能与功耗

扩展方向：
增加电能质量分析功能

开发用电预测和节能建议

集成到智能家居能源管理系统

支持太阳能发电监测

添加电价计算和电费预估

注意事项：
需要校准电流检测电路以获得准确测量

用电模式学习需要至少一周数据

省电模式可能影响数据实时性

异常告警阈值需根据实际负载调整

首次使用建议进行完整校准