鸿蒙跨设备家庭能耗监测系统：分布式电表数据采集与多终端可视化方案原创

进修的泡芙

发布于 2025-6-22 17:30

浏览

0收藏

鸿蒙跨设备家庭能耗监测系统：分布式电表数据采集与多终端可视化方案

一、系统架构设计

!https://example.com/harmonyos-energy-arch.png

采用四层架构：
设备层：智能电表与传感器网络

网关层：鸿蒙设备数据采集与预处理

分析层：分布式能耗计算与异常检测

展示层：多终端实时数据可视化

二、核心模块实现
智能电表数据采集

// EnergyMeterReader.ts
import iot from ‘@ohos.iot’;
import distributedData from ‘@ohos.data.distributedData’;

interface MeterReading {
timestamp: number;
deviceId: string;
voltage: number; // 电压(V)
current: number; // 电流(A)
power: number; // 功率(W)
energy: number; // 累计电量(kWh)
export class EnergyMeterReader {

private kvManager: distributedData.KVManager;
private kvStore?: distributedData.KVStore;

async init() {
const context = getContext(this);
this.kvManager = distributedData.createKVManager({ context });
this.kvStore = await this.kvManager.getKVStore(‘meter_readings’, {
createIfMissing: true,
autoSync: true,
securityLevel: distributedData.SecurityLevel.S2
});

// 注册IoT设备监听
iot.on('deviceStatusChange', this.handleDeviceChange.bind(this));

async startMonitoring(deviceId: string) {

try {
  const config = {
    sampleInterval: 5000, // 5秒采集一次
    metrics: ['voltage', 'current', 'power', 'energy']
  };
  
  await iot.subscribe(deviceId, config, (data) => {
    this.processReading({
      ...data,
      deviceId,
      timestamp: Date.now()
    });
  });

catch (err) {

  console.error(启动监测失败: {err.code}, {err.message});

}

private async processReading(reading: MeterReading) {
// 数据校验
if (reading.power < 0 || reading.energy < 0) return;

// 存储到分布式数据库
const key = reading_{reading.deviceId}_{reading.timestamp};
await this.kvStore?.put(key, reading);

// 触发本地处理
postMessageToUI({
  type: 'new_reading',
  data: reading
});

// 其他方法…

能耗数据分析

// EnergyAnalyzer.ts
import statistical from ‘@ohos.ai.statistical’;

export class EnergyAnalyzer {
async calculateHourlyConsumption(readings: MeterReading[]): Promise<HourlyConsumption> {
const hourlyData: Record<number, number> = {};

readings.forEach(reading => {
  const hour = new Date(reading.timestamp).getHours();
  hourlyData[hour] = (hourlyData[hour] || 0) + 
                     (reading.power * 5 / 3600); // 5秒采样转换为kWh
});

return {
  date: new Date(readings[0].timestamp).toLocaleDateString(),
  hourly: Object.entries(hourlyData).map(([hour, energy]) => ({
    hour: parseInt(hour),
    energy
  }))
};

async detectAnomalies(readings: MeterReading[]): Promise<AnomalyReport[]> {

const powerValues = readings.map(r => r.power);
const stats = statistical.analyze(powerValues);

return readings
  .filter(r => r.power > stats.mean + 3 * stats.stdDev)
  .map(r => ({
    timestamp: r.timestamp,
    deviceId: r.deviceId,
    power: r.power,
    severity: 'high'
  }));

// 其他分析方法…

主页面实现（ArkUI）

// EnergyDashboard.ets
import { EnergyMeterReader } from ‘./EnergyMeterReader’;
import { EnergyAnalyzer } from ‘./EnergyAnalyzer’;

@Entry
@Component
struct EnergyDashboard {
@State currentReadings: MeterReading[] = [];
@State hourlyConsumption?: HourlyConsumption;
@State anomalies: AnomalyReport[] = [];
@State devices: EnergyDevice[] = [];

private meterReader = new EnergyMeterReader();
private analyzer = new EnergyAnalyzer();

async aboutToAppear() {
await this.meterReader.init();
this.loadDevices();

// 监听新数据
eventBus.on('new_reading', (reading: MeterReading) => {
  this.currentReadings = [...this.currentReadings, reading];
  this.updateAnalysis();
});

async loadDevices() {

this.devices = await iot.getDevicesByType('smart_meter');
this.devices.forEach(device => {
  this.meterReader.startMonitoring(device.deviceId);
});

async updateAnalysis() {

if (this.currentReadings.length > 0) {
  this.hourlyConsumption = await this.analyzer.calculateHourlyConsumption(
    this.currentReadings.slice(-24 * 12) // 保留最近12小时数据
  );
  
  this.anomalies = await this.analyzer.detectAnomalies(
    this.currentReadings.slice(-24 * 60) // 检测最近24小时异常
  );

}

build() {
Column() {
// 实时数据展示
RealTimeDisplay({
readings: this.currentReadings.slice(-10).reverse()
})

  // 能耗图表
  if (this.hourlyConsumption) {
    EnergyChart({
      data: this.hourlyConsumption
    })

// 异常警报

  if (this.anomalies.length > 0) {
    AnomalyAlerts({
      reports: this.anomalies
    })

}

@Component
struct RealTimeDisplay {
@Prop readings: MeterReading[];

build() {
Grid() {
GridItem() {
MetricCard(‘当前功率’, ${this.latestReading.power.toFixed(1)}W)
GridItem() {

    MetricCard('今日用电', ${this.calculateTodayUsage().toFixed(2)}kWh)

GridItem() {

    MetricCard('电压', ${this.latestReading.voltage.toFixed(1)}V)

GridItem() {

    MetricCard('电流', ${this.latestReading.current.toFixed(2)}A)

}

.columnsTemplate('1fr 1fr')
.rowsTemplate('1fr 1fr')
.columnsGap(10)
.rowsGap(10)

private get latestReading(): MeterReading {

return this.readings[0] || {
  power: 0, voltage: 0, current: 0, energy: 0, timestamp: 0, deviceId: ''
};

private calculateTodayUsage(): number {

if (this.readings.length < 2) return 0;
return this.readings[0].energy - this.readings[this.readings.length - 1].energy;

}

@Component
struct EnergyChart {
@Prop data: HourlyConsumption;

build() {
Column() {
Text(日期: ${this.data.date})
.fontSize(16)

  // 使用XComponent绘制图表
  XComponent({
    id: 'energy_chart',
    type: 'canvas',
    controller: this.chartController
  })
  .width('100%')
  .height(200)

}

三、跨设备协同关键实现
分布式数据聚合

// 在EnergyMeterReader中添加
async getCombinedReadings(deviceIds: string[]): Promise<MeterReading[]> {
const allReadings: MeterReading[] = [];

await Promise.all(deviceIds.map(async deviceId => {
try {
const remoteStore = await distributedData.getRemoteKVStore(deviceId, ‘meter_readings’);
const entries = await remoteStore.entries(‘reading_’);
allReadings.push(…entries.map(([_, v]) => v as MeterReading));
catch (err) {

  console.error(获取设备${deviceId}数据失败:, err);

}));

return allReadings.sort((a, b) => a.timestamp - b.timestamp);

多设备能耗对比

// 在EnergyAnalyzer中添加
async compareDevices(deviceIds: string[], period: ‘day’ ‘week’
‘month’) {
const now = Date.now();
let startTime: number;

switch (period) {
case ‘day’: startTime = now - 24 3600 1000; break;
case ‘week’: startTime = now - 7 24 3600 * 1000; break;
case ‘month’: startTime = now - 30 24 3600 * 1000; break;
const readings = await this.meterReader.getReadingsAfter(startTime);

const deviceConsumption: Record<string, number> = {};

deviceIds.forEach(id => {
deviceConsumption[id] = readings
.filter(r => r.deviceId === id)
.reduce((sum, r) => sum + r.power * 5 / 3600, 0); // 转换为kWh
});

return deviceConsumption;

异常协同预警

// 新增AnomalyNotifier.ts
export class AnomalyNotifier {
private kvManager: distributedData.KVManager;

async init() {
const context = getContext(this);
this.kvManager = distributedData.createKVManager({ context });
async notifyAnomaly(anomaly: AnomalyReport) {

const kvStore = await this.kvManager.getKVStore('energy_alerts');
await kvStore.put(alert_${anomaly.timestamp}, anomaly);

// 发送推送通知
await notification.publish({
  id: 1,
  content: {
    title: '能耗异常警报',
    text: 检测到${anomaly.power}W的高功耗,
    additionalData: anomaly

});

async getRecentAlerts(hours: number): Promise<AnomalyReport[]> {

const kvStore = await this.kvManager.getKVStore('energy_alerts');
const entries = await kvStore.entries('alert_');

const cutoff = Date.now() - hours  3600  1000;
return entries
  .map(([_, v]) => v as AnomalyReport)
  .filter(a => a.timestamp >= cutoff)
  .sort((a, b) => b.timestamp - a.timestamp);

}

四、性能优化方案
数据采样降频

// 在EnergyMeterReader中添加
private downsample(readings: MeterReading[], interval: number): MeterReading[] {
const sampled: MeterReading[] = [];
let lastTimestamp = 0;

readings.forEach(reading => {
if (reading.timestamp - lastTimestamp >= interval) {
sampled.push(reading);
lastTimestamp = reading.timestamp;
});

return sampled;

分布式计算任务分配

// 新增DistributedComputing.ts
export class DistributedComputing {
static async distributeAnalysisTask(
task: ‘hourly’ ‘daily’
‘anomaly’,
devices: string[]
): Promise<void> {
const manager = await deviceManager.createDeviceManager(‘com.example.energy’);

await Promise.all(devices.map(async deviceId => {
  try {
    await manager.sendMessage(deviceId, 'analysis_task', JSON.stringify({
      type: task,
      timestamp: Date.now()
    }));

catch (err) {

    console.error(分配任务到${deviceId}失败:, err);

}));

}

本地数据缓存

const readingCache = new Map<string, MeterReading[]>();

async getCachedReadings(deviceId: string, hours: number): Promise<MeterReading[]> {
const cacheKey = {deviceId}_{hours};
if (readingCache.has(cacheKey)) {
return readingCache.get(cacheKey)!;
const readings = await this.meterReader.getReadings(deviceId, hours);

readingCache.set(cacheKey, readings);
return readings;

五、应用场景扩展
电费预测系统

class CostPredictor {
async predictMonthlyBill(consumption: number): Promise<number> {
// 基于历史数据和电价计算预测
}

节能建议引擎

class EnergySaver {
async generateSuggestions(patterns: ConsumptionPattern[]): Promise<Suggestion[]> {
// 生成个性化节能建议
}

太阳能集成监控

class SolarIntegration {
async monitorSolarProduction(inverterId: string) {
// 监控太阳能发电与电网交互
}

家庭能源路由器

class EnergyRouter {
async optimizeDeviceUsage(devices: SmartDevice[]) {
// 根据电价和需求优化设备运行时间
}

本系统充分利用HarmonyOS分布式能力，实现了：
多设备数据聚合：整合全屋多个电表数据

实时能耗可视化：秒级数据刷新与图表展示

智能异常检测：基于统计学的异常用电识别