鸿蒙跨设备智能垃圾分类系统：分布式图像识别与多终端协同分类方案原创

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发布于 2025-6-22 17:18

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鸿蒙跨设备智能垃圾分类系统：分布式图像识别与多终端协同分类方案

一、系统架构设计

!https://example.com/harmonyos-trash-classification-arch.png

采用三层架构：
采集层：多设备摄像头图像采集

识别层：分布式图像分类与结果融合

展示层：跨终端分类结果同步展示

二、核心模块实现
垃圾图像识别模块

// TrashClassifier.ts
import image from ‘@ohos.multimedia.image’;
import imageClassification from ‘@ohos.ai.imageClassification’;

export class TrashClassifier {
private classifier: imageClassification.ImageClassifier;

async init() {
try {
this.classifier = await imageClassification.createClassifier({
model: ‘trash_classification’,
device: ‘NPU’
});
console.info(‘垃圾分类模型加载成功’);
catch (err) {

  console.error(模型加载失败: {err.code}, {err.message});

}

async classify(image: image.Image): Promise<TrashCategory> {
const result = await this.classifier.classify(image);
return this.parseResult(result);
private parseResult(result: imageClassification.ClassificationResult): TrashCategory {

const topCategory = result.result.sort((a, b) => b.confidence - a.confidence)[0];
return {
  name: topCategory.name,
  category: this.mapToStandardCategory(topCategory.name),
  confidence: topCategory.confidence
};

private mapToStandardCategory(name: string): ‘recyclable’ ‘hazardous’ ‘household’
‘other’ {

// 简化分类逻辑，实际项目应使用更精确的映射
if (name.includes('plastic') || name.includes('paper')) return 'recyclable';
if (name.includes('battery') || name.includes('medicine')) return 'hazardous';
if (name.includes('food')) return 'household';
return 'other';

}

interface TrashCategory {
name: string;
category: ‘recyclable’ ‘hazardous’ ‘household’
‘other’;
confidence: number;

分布式分类结果管理

// ClassificationSync.ts
import distributedData from ‘@ohos.data.distributedData’;
import deviceManager from ‘@ohos.distributedHardware.deviceManager’;

interface ClassificationResult {
deviceId: string;
timestamp: number;
category: TrashCategory;
image?: string; // Base64缩略图
export class ClassificationSync {

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

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

const options = {
  createIfMissing: true,
  encrypt: false,
  autoSync: true,
  kvStoreType: distributedData.KVStoreType.SINGLE_VERSION
};

this.kvStore = await this.kvManager.getKVStore('trash_classification', options);
this.setupListeners();

async syncResult(result: ClassificationResult) {

const key = result_{result.deviceId}_{result.timestamp};
await this.kvStore.put(key, result);

async getConsensusResult(itemId: string): Promise<TrashCategory | undefined> {

const entries = await this.kvStore.entries(result_${itemId});
if (entries.length === 0) return undefined;

return this.calculateConsensus(entries.map(([_, v]) => v as ClassificationResult));

private calculateConsensus(results: ClassificationResult[]): TrashCategory {

// 选择置信度最高的结果
return results.sort((a, b) => b.category.confidence - a.category.confidence)[0].category;

private setupListeners() {

this.kvStore.on('dataChange', distributedData.SubscribeType.SUBSCRIBE_TYPE_REMOTE, 
  (changes) => {
    changes.forEach(({ key, value }) => {
      if (key.startsWith('result_')) {
        this.handleNewResult(value as ClassificationResult);

});

});

// 其他方法…

主页面实现（ArkUI）

// TrashClassification.ets
import { TrashClassifier } from ‘./TrashClassifier’;
import { ClassificationSync } from ‘./ClassificationSync’;

@Entry
@Component
struct TrashClassificationApp {
@State currentResult?: TrashCategory;
@State consensusResult?: TrashCategory;
@State deviceResults: ClassificationResult[] = [];

private classifier = new TrashClassifier();
private syncManager = new ClassificationSync();
private cameraController?: CameraController;

async aboutToAppear() {
await this.classifier.init();
await this.syncManager.init();
async classifyFromCamera() {

this.cameraController = new CameraController({
  onFrame: async (image: image.Image) => {
    const result = await this.classifier.classify(image);
    this.currentResult = result;
    
    await this.syncManager.syncResult({
      deviceId: 'local_device',
      timestamp: Date.now(),
      category: result,
      image: await this.compressImage(image)
    });
    
    this.consensusResult = await this.syncManager.getConsensusResult('current_item');

});

this.cameraController.start();

private async compressImage(image: image.Image): Promise<string> {

// 图像压缩为Base64
return 'data:image/jpeg;base64,...'; // 实际项目实现压缩逻辑

build() {

Column() {
  // 本地识别结果
  if (this.currentResult) {
    ClassificationResultCard(this.currentResult)

// 协同识别结果

  if (this.consensusResult) {
    ConsensusResultDisplay(this.consensusResult)

// 设备结果列表

  DeviceResultsList(this.deviceResults)
  
  // 控制按钮
  Button('开始识别')
    .onClick(() => this.classifyFromCamera())

}

@Component

struct ClassificationResultCard {
@Prop result: TrashCategory;

build() {
Column() {
Text(识别结果: ${this.result.name})
.fontSize(18)
Text(分类: ${this.getCategoryName(this.result.category)})
.fontSize(16)
Text(置信度: ${(this.result.confidence * 100).toFixed(1)}%)
.fontSize(14)
.padding(10)

.border({ width: 1, color: '#EEEEEE' })

private getCategoryName(category: TrashCategory[‘category’]): string {

const names = {
  'recyclable': '可回收物',
  'hazardous': '有害垃圾',
  'household': '厨余垃圾',
  'other': '其他垃圾'
};
return names[category];

}

@Component
struct ConsensusResultDisplay {
@Prop result: TrashCategory;

build() {
Row() {
Text(‘协同识别结果:’)
.fontSize(16)
Text(this.result.name)
.fontSize(20)
.fontColor(‘#FF0000’)
.margin(10)

}

三、跨设备协同关键实现
多设备图像协同识别

// 在ClassificationSync中添加
async startCollaborativeClassification(itemId: string) {
const devices = await deviceManager.getTrustedDeviceListSync();
await Promise.all(devices.map(device =>
this.requestClassification(device.deviceId, itemId)
));
private async requestClassification(deviceId: string, itemId: string) {

try {
await distributedNotification.publish({
targetDevice: deviceId,
message: JSON.stringify({
type: ‘classification_request’,
itemId
})
});
catch (err) {

console.error(请求设备${deviceId}识别失败:, err);

}

识别结果冲突解决

// 在ClassificationSync中添加
private resolveConflicts(results: ClassificationResult[]): TrashCategory {
// 按类别分组统计
const categories = results.reduce((acc, result) => {
const key = result.category.category;
if (!acc[key]) {
acc[key] = {
count: 0,
totalConfidence: 0,
samples: []
};
acc[key].count++;

acc[key].totalConfidence += result.category.confidence;
acc[key].samples.push(result);
return acc;

}, {} as Record<string, { count: number; totalConfidence: number; samples: ClassificationResult[] }>);

// 选择出现次数最多且平均置信度高的类别
return Object.entries(categories)
.sort((a, b) => b[1].count - a[1].count || b[1].totalConfidence - a[1].totalConfidence)[0][1]
.samples[0].category;

分布式图像缓存策略

// 添加ImageCache.ts
export class ImageCache {
private static cache = new Map<string, string>();

static get(key: string): string | undefined {
return this.cache.get(key);
static set(key: string, image: string): void {

if (this.cache.size > 50) { // 限制缓存大小
  const oldestKey = this.cache.keys().next().value;
  this.cache.delete(oldestKey);

this.cache.set(key, image);

static async cacheRemoteImage(deviceId: string, imageKey: string): Promise<string | undefined> {

const cached = this.get({deviceId}_{imageKey});
if (cached) return cached;

const imageData = await this.fetchRemoteImage(deviceId, imageKey);
if (imageData) {
  this.set({deviceId}_{imageKey}, imageData);
  return imageData;

}

// 其他方法…

四、性能优化方案
图像预处理优化

// 在TrashClassifier中添加
private async preprocessImage(image: image.Image): Promise<image.Image> {
const roi = await this.detectROI(image); // 检测垃圾物品区域
return image.crop({
x: roi.x,
y: roi.y,
width: roi.width,
height: roi.height
});
private async detectROI(image: image.Image): Promise<{ x: number; y: number; width: number; height: number }> {

// 使用轻量级物体检测模型定位垃圾物品
return { x: 0, y: 0, width: 200, height: 200 }; // 示例值

分布式计算负载均衡

// 在ClassificationSync中添加
private async getOptimalDevice(): Promise<string | undefined> {
const devices = await deviceManager.getTrustedDeviceListSync();
if (devices.length === 0) return undefined;

// 简单选择第一个设备，实际应根据设备性能选择
return devices[0].deviceId;

结果缓存策略

// 在ClassificationSync中添加
private resultCache = new Map<string, TrashCategory>();

async getCachedResult(itemId: string): Promise<TrashCategory | undefined> {
if (this.resultCache.has(itemId)) {
return this.resultCache.get(itemId);
const result = await this.getConsensusResult(itemId);

if (result) {
this.resultCache.set(itemId, result);
return result;

五、应用场景扩展
垃圾分类统计看板

class TrashStatistics {
async generateDailyReport() {
// 生成每日垃圾分类统计
}

回收积分系统

class RecyclingPoints {
async awardPoints(category: TrashCategory) {
// 根据分类结果奖励积分
}

AR垃圾分类指导

class ARGuidance {
async showARGuide(category: TrashCategory) {
// 通过AR展示分类指引
}

社区垃圾分类PK

class CommunityChallenge {
async startNeighborhoodChallenge() {
// 发起社区垃圾分类挑战
}

本系统充分利用HarmonyOS分布式能力，实现了：
多视角协同识别：综合多个设备的识别结果提高准确率

实时结果同步：毫秒级的分类结果共享

智能冲突解决：基于置信度的多结果融合算法