鸿蒙5智能垃圾分类APP开发实战：图像识别与多设备协同 原创

鸿蒙5智能垃圾分类APP开发实战：图像识别与多设备协同

一、项目概述与架构设计

本智能垃圾分类APP基于鸿蒙5的AI能力与分布式技术实现，主要功能包括：
垃圾图像智能识别分类

分类结果多设备同步展示

垃圾分类知识库共享

多设备协同标注训练

技术架构图

┌─────────────┐ ┌─────────────┐ ┌─────────────┐
手机设备 │ │ 平板设备 │ │ 智慧屏 │

┌────────┐ │ │ ┌────────┐ │ │ ┌────────┐ │

│ 图像采集 │─┼───▶│ │ 结果展示 │ │ │ │ 数据看板 │ │

└────────┘ │ │ └────────┘ │ │ └────────┘ │

└───────┬─────┘ └───────┬─────┘ └───────┬─────┘
│ │

    └─────────┬────────┴─────────┬────────┘

│

      ┌───────▼───────┐   ┌───────▼───────┐

分布式数据服务 │ │ AI图像识别 │

      └───────────────┘   └───────────────┘

二、核心代码实现
图像识别服务封装

// TrashRecognitionService.ets
import image from ‘@ohos.multimedia.image’;
import ai from ‘@ohos.ai.image’;

export class TrashRecognitionService {
private imageReceiver: image.ImageReceiver;
private aiPlugin: ai.ImageRecognition;

async init() {
// 初始化图像接收器
this.imageReceiver = image.createImageReceiver(
640, 480,
image.ImageFormat.JPEG, 2
);

// 初始化AI插件
const config: ai.Config = {
  model: 'trash_classification',
  version: '1.0',
  device: 'NPU'
};
this.aiPlugin = await ai.createImageRecognition(config);

async recognize(imageUri: string): Promise<TrashClassification> {

try {
  const pixelMap = await image.createPixelMapFromUri(imageUri);
  const input: ai.Input = {
    data: pixelMap,
    operation: ai.Operation.CLASSIFY
  };
  
  const result = await this.aiPlugin.run(input);
  return this.parseResult(result);

catch (err) {

  console.error('Recognition failed:', err);
  return { category: '未知', confidence: 0 };

}

private parseResult(result: ai.Output): TrashClassification {
const topResult = result.data[0];
return {
category: this.mapCategory(topResult.label),
confidence: topResult.confidence,
timestamp: new Date().getTime()
};
private mapCategory(label: string): string {

const categories: Record<string, string> = {
  'plastic': '可回收物',
  'paper': '可回收物',
  'kitchen': '厨余垃圾',
  'battery': '有害垃圾',
  'other': '其他垃圾'
};
return categories[label] || '未知';

}

分布式数据同步服务

// TrashDataSyncService.ets
import distributedData from ‘@ohos.data.distributedData’;

export class TrashDataSyncService {
private kvStore: distributedData.KVStore;
private readonly STORE_ID = ‘trash_data_store’;

async init() {
const kvManager = await distributedData.createKVManager({
bundleName: ‘com.example.trashapp’
});

this.kvStore = await kvManager.getKVStore(this.STORE_ID, {
  createIfMissing: true,
  autoSync: true,
  kvStoreType: distributedData.KVStoreType.SINGLE_VERSION
});

async syncClassification(result: TrashClassification) {

try {
  const data = {
    ...result,
    deviceId: deviceInfo.deviceId,
    imageUri: this.generateImageUri()
  };
  
  await this.kvStore.put(result_${Date.now()}, JSON.stringify(data));

catch (err) {

  console.error('Sync failed:', err);

}

subscribeResults(callback: (result: SyncTrashResult) => void) {
this.kvStore.on(‘dataChange’, distributedData.SubscribeType.SUBSCRIBE_TYPE_ALL, (changes) => {
changes.forEach(change => {
if (change.key.startsWith(‘result_’)) {
const data = JSON.parse(change.value);
callback({
…data,
sourceDevice: data.deviceId
});
});

});

}

主页面实现

// TrashRecognitionPage.ets
@Entry
@Component
struct TrashRecognitionPage {
@State currentResult: TrashClassification | null = null;
@State historyResults: Array<SyncTrashResult> = [];
@State isProcessing: boolean = false;

private recognitionService = new TrashRecognitionService();
private syncService = new TrashDataSyncService();

aboutToAppear() {
this.recognitionService.init();
this.syncService.init();
this.syncService.subscribeResults(this.handleNewResult.bind(this));
build() {

Column() {
  // 摄像头预览区域
  CameraPreview({
    onCapture: this.captureImage.bind(this)
  })
  
  // 识别结果展示
  if (this.currentResult) {
    RecognitionResult({
      result: this.currentResult,
      onSave: this.saveResult.bind(this)
    })

// 历史记录

  HistoryPanel({
    items: this.historyResults
  })

}

private async captureImage(imageUri: string) {
this.isProcessing = true;
const result = await this.recognitionService.recognize(imageUri);
this.currentResult = result;
this.isProcessing = false;
private saveResult() {

if (this.currentResult) {
  this.syncService.syncClassification(this.currentResult);

}

private handleNewResult(result: SyncTrashResult) {
this.historyResults = […this.historyResults, result];
}

三、关键技术创新点
多设备协同标注训练

// TrainingService.ets
export class TrainingService {
async submitCorrection(original: SyncTrashResult, correctCategory: string) {
const trainingData = {
imageUri: original.imageUri,
originalLabel: original.category,
correctedLabel: correctCategory,
devices: [original.sourceDevice, deviceInfo.deviceId]
};

// 同步到训练数据集
await this.syncTrainingData(trainingData);

// 触发模型增量训练
if (this.shouldTriggerTraining()) {
  this.startIncrementalTraining();

}

private async startIncrementalTraining() {
const config: ai.TrainingConfig = {
model: ‘trash_classification’,
method: ‘transfer_learning’,
devices: this.getTrainingDevices()
};

const trainer = await ai.createModelTrainer(config);
await trainer.start();

}

垃圾分类知识库共享

// KnowledgeBaseService.ets
export class KnowledgeBaseService {
async getDisposalGuide(category: string): Promise<DisposalGuide> {
const key = guide_${category};
const cached = await this.kvStore.get(key);

if (!cached) {
  const guide = await this.fetchCloudGuide(category);
  await this.kvStore.put(key, JSON.stringify(guide));
  return guide;

return JSON.parse(cached);

async syncPopularCategories() {

const categories = await this.calculatePopularCategories();
await this.kvStore.put('popular_categories', JSON.stringify(categories));

}

视觉增强实现

// AugmentedRealityComponent.ets
@Component
struct ARGuideComponent {
@Prop category: string;

build() {
Stack() {
// 基础摄像头视图
CameraView()

  // AR叠加层
  if (this.category === '可回收物') {
    Image('recycle_ar.png')
      .width(100)
      .height(100)
      .position({ x: 0, y: 0 })

// 交互动画

  if (this.showAnimation) {
    LottieAnimation('bin_guide.json')
      .width(200)
      .height(200)

}

}

四、性能优化方案
图像预处理优化

// 图像预处理管道
private async optimizeImage(pixelMap: image.PixelMap): Promise<image.PixelMap> {
// 1. 降采样
const resized = await image.scalePixelMap(pixelMap, {
width: 256,
height: 256
});

// 2. 直方图均衡化
const equalized = await image.equalizeHistogram(resized);

// 3. 归一化
return image.normalize(equalized, {
mean: [0.485, 0.456, 0.406],
std: [0.229, 0.224, 0.225]
});

模型分级加载策略

// 根据设备能力加载不同模型
private async loadAdaptiveModel() {
const deviceClass = await this.getDeviceClass();

const modelConfig = {
‘high-end’: {
model: ‘trash_resnet50’,
precision: ‘FP16’
},
‘mid-range’: {
model: ‘trash_mobilenet’,
precision: ‘INT8’
},
‘low-end’: {
model: ‘trash_shufflenet’,
precision: ‘INT8’
};

return ai.createImageRecognition(modelConfig[deviceClass]);

数据同步压缩

// 图像数据压缩传输
private async compressImageData(uri: string): Promise<string> {
const options: image.EncodingOptions = {
format: ‘image/webp’,
quality: 80,
size: { width: 320, height: 320 }
};

const compressed = await image.compressImage(uri, options);
return compressed.uri;

五、完整示例代码
摄像头预览组件

// CameraPreview.ets
@Component
struct CameraPreview {
@State cameraId: string = ‘’;
@State isReady: boolean = false;
private cameraManager: camera.CameraManager;

build() {
Stack() {
if (this.isReady) {
// 摄像头预览画面
CameraView({
cameraId: this.cameraId,
onTakePhoto: this.onCapture
})

    // 拍照按钮
    Button('识别垃圾', { type: ButtonType.Circle })
      .width(60)
      .height(60)
      .position({ x: '50%', y: '80%' })

else {

    Progress()

}

.onAppear(() => this.initCamera())

private async initCamera() {

this.cameraManager = await camera.getCameraManager();
const cameras = await this.cameraManager.getSupportedCameras();
this.cameraId = cameras[0].cameraId;
this.isReady = true;

}

分类结果组件

// RecognitionResult.ets
@Component
struct RecognitionResult {
@Prop result: TrashClassification;
@State disposalGuide: DisposalGuide | null = null;

build() {
Column() {
// 分类结果
Text(this.result.category)
.fontSize(24)
.fontColor(this.getCategoryColor())

  // 置信度
  Text(置信度: ${(this.result.confidence * 100).toFixed(1)}%)
    .fontSize(16)
  
  // 处理指南
  if (this.disposalGuide) {
    DisposalGuideView({ guide: this.disposalGuide })

// 操作按钮

  Row() {
    Button('保存结果')
      .onClick(() => this.onSave())
    Button('纠错')
      .margin({ left: 10 })

}

.onAppear(() => this.loadGuide())

private getCategoryColor(): Color {

const colors = {
  '可回收物': Color.Blue,
  '有害垃圾': Color.Red,
  '厨余垃圾': Color.Green,
  '其他垃圾': Color.Gray
};
return colors[this.result.category] || Color.Black;

private async loadGuide() {

this.disposalGuide = await knowledgeBase.getDisposalGuide(this.result.category);

}

六、项目部署与测试
权限配置

在module.json5中添加：

“requestPermissions”: [
“name”: “ohos.permission.CAMERA”

},
“name”: “ohos.permission.READ_MEDIA”

},
“name”: “ohos.permission.DISTRIBUTED_DATASYNC”

},
“name”: “ohos.permission.INTERNET”

]

测试方案

// 图像识别测试
describe(‘TrashRecognition’, () => {
it(‘should classify plastic bottle as recyclable’, async () => {
const service = new TrashRecognitionService();
await service.init();

const result = await service.recognize('plastic_bottle.jpg');
expect(result.category).toEqual('可回收物');
expect(result.confidence).toBeGreaterThan(0.8);

});
});

// 数据同步测试
describe(‘DataSync’, () => {
it(‘should sync classification to other devices’, async () => {
const device1 = new MockDevice(‘device1’);
const device2 = new MockDevice(‘device2’);

await device1.classify('banana_peel.jpg');
await device2.waitForSync();

expect(device2.getLatestResult().category).toEqual('厨余垃圾');

});
});

七、总结与扩展

本方案实现了：
基于鸿蒙AI能力的垃圾图像精准分类

分类结果的多设备实时同步

分布式协同标注训练机制

自适应不同设备的性能优化

扩展方向：
结合AR技术实现垃圾分类实景引导

开发垃圾投放点地图导航

集成语音交互功能

建立用户环保积分系统

鸿蒙的分布式能力与AI技术的结合，为环保类应用开发提供了全新可能。开发者可基于此项目框架，进一步探索更多智能环保场景的创新实现。