鸿蒙5智能垃圾分类APP开发实战：基于图像识别的多设备协同分类系统 原创

鸿蒙5智能垃圾分类APP开发实战：基于图像识别的多设备协同分类系统

一、项目概述与架构设计

本智能垃圾分类APP基于鸿蒙5的图像识别和分布式能力实现，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

垃圾分类知识库共享

分类历史记录云端同步

技术架构图

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

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

│ 图像识别 │─┼───▶│ │ 分类看板 │ │ │ │ 数据展示 │ │

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

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

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

│

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

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’;
import distributedData from ‘@ohos.data.distributedData’;

export class TrashRecognitionService {
private kvStore: distributedData.KVStore;
private readonly STORE_ID = ‘trash_classify_store’;
private aiModel: ai.ImageClassification;

async init() {
// 初始化AI模型
const config: ai.Config = {
model: ‘trash_classification’,
version: ‘2.0’,
device: ‘NPU’
};
this.aiModel = await ai.createImageClassification(config);

// 初始化分布式数据
const kvManager = await distributedData.createKVManager({
  bundleName: 'com.example.trashapp'
});
this.kvStore = await kvManager.getKVStore(this.STORE_ID, {
  createIfMissing: true,
  autoSync: true
});

async classifyImage(pixelMap: image.PixelMap): Promise<TrashClassification> {

const input: ai.Input = {
  data: pixelMap,
  operation: ai.Operation.CLASSIFY
};

const result = await this.aiModel.run(input);
const topResult = result.data[0];

const classification: TrashClassification = {
  category: this.mapCategory(topResult.label),
  confidence: topResult.confidence,
  timestamp: new Date().getTime(),
  deviceId: deviceInfo.deviceId
};

await this.syncClassification(classification);
return classification;

private mapCategory(label: string): TrashCategory {

const mapping: Record<string, TrashCategory> = {
  'bottle': 'recyclable',
  'banana': 'kitchen',
  'battery': 'hazardous',
  'tissue': 'other'
};
return mapping[label] || 'other';

private async syncClassification(data: TrashClassification) {

await this.kvStore.put(classification_${data.timestamp}, JSON.stringify(data));

subscribeClassifications(callback: (data: TrashClassification) => void) {

this.kvStore.on('dataChange', distributedData.SubscribeType.SUBSCRIBE_TYPE_ALL, (changes) => {
  changes.forEach(change => {
    if (change.key.startsWith('classification_')) {
      const data = JSON.parse(change.value);
      if (data.deviceId !== deviceInfo.deviceId) {
        callback(data);

}

  });
});

}

垃圾分类数据模型

// TrashModel.ets
export type TrashCategory = ‘recyclable’ ‘kitchen’ ‘hazardous’
‘other’;

export interface TrashClassification {
category: TrashCategory;
confidence: number;
timestamp: number;
deviceId?: string;
imageUri?: string;
export interface TrashItem {

id: string;
name: string;
category: TrashCategory;
tips: string;

分类结果展示组件

// ClassificationResult.ets
@Component
struct ClassificationResult {
@Prop classification: TrashClassification;
@State relatedItems: TrashItem[] = [];

private knowledgeService = new TrashKnowledgeService();

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

Column() {
  // 分类结果卡片
  Card({
    title: this.getCategoryName(this.classification.category),
    subtitle: 置信度: ${(this.classification.confidence * 100).toFixed(1)}%
  }) {
    Image(this.classification.imageUri)
      .width(120)
      .height(120)
      .margin(10)

// 分类知识

  Text('投放指南')
    .fontSize(18)
    .margin({ top: 15 })
  
  ForEach(this.relatedItems, (item) => {
    Text({item.name}: {item.tips})
      .fontSize(14)
      .margin({ top: 5 })
  })

.padding(15)

private getCategoryName(category: TrashCategory): string {

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

private async loadRelatedItems() {

this.relatedItems = await this.knowledgeService.getItemsByCategory(
  this.classification.category
);

}

三、关键技术创新点
多设备协同分类算法

// 协同分类决策
class CollaborativeClassifier {
async resolveClassification(classification: TrashClassification): Promise<TrashCategory> {
// 1. 获取其他设备的分类结果
const results = await this.getDeviceClassifications();
results.push(classification);

// 2. 应用决策算法
return this.applyDecisionAlgorithm(results);

private async getDeviceClassifications(): Promise<TrashClassification[]> {

const entries = await this.kvStore.getEntries('classification_');
return entries.map(entry => JSON.parse(entry.value));

private applyDecisionAlgorithm(results: TrashClassification[]): TrashCategory {

// 策略1: 高置信度优先
const highConfidence = results.reduce((prev, current) => 
  current.confidence > prev.confidence ? current : prev
);

if (highConfidence.confidence > 0.9) {
  return highConfidence.category;

// 策略2: 多数投票

const voteCount: Record<TrashCategory, number> = {
  recyclable: 0,
  kitchen: 0,
  hazardous: 0,
  other: 0
};

results.forEach(result => {
  voteCount[result.category]++;
});

return Object.entries(voteCount).reduce((a, b) => 
  a[1] > b[1] ? a : b
)[0] as TrashCategory;

}

图像预处理管道

// 图像预处理优化
class ImagePreprocessor {
static async preprocessImage(pixelMap: image.PixelMap): Promise<image.PixelMap> {
// 1. 调整大小
const resized = await image.scalePixelMap(pixelMap, {
width: 224,
height: 224
});

// 2. 增强对比度
const enhanced = await image.enhanceContrast(resized, 1.2);

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

}

分类结果验证机制

// 分类结果验证
class ClassificationValidator {
private static readonly CATEGORY_RULES: Record<TrashCategory, string[]> = {
recyclable: [‘plastic’, ‘paper’, ‘glass’, ‘metal’],
kitchen: [‘food’, ‘fruit’, ‘vegetable’],
hazardous: [‘chemical’, ‘battery’, ‘medicine’],
other: [‘tissue’, ‘diaper’, ‘ceramic’]
};

static validate(label: string, category: TrashCategory): boolean {
return ClassificationValidator.CATEGORY_RULES[category].includes(label);
}

四、性能优化方案
模型分级加载

// 设备自适应模型加载
class ModelLoader {
static async loadAdaptiveModel(): Promise<ai.ImageClassification> {
const deviceClass = await this.getDeviceClass();
const modelConfig = {
‘high-end’: {
model: ‘trash_classification_high’,
precision: ‘FP16’
},
‘mid-range’: {
model: ‘trash_classification_mid’,
precision: ‘INT8’
},
‘low-end’: {
model: ‘trash_classification_lite’,
precision: ‘INT8’
};

return ai.createImageClassification(modelConfig[deviceClass]);

private static async getDeviceClass(): Promise<string> {

const perf = await deviceInfo.getPerformance();
if (perf.score > 80) return 'high-end';
if (perf.score > 50) return 'mid-range';
return 'low-end';

}

分布式数据压缩

// 分类结果压缩传输
class DataCompressor {
static compressClassification(data: TrashClassification): Uint8Array {
const jsonStr = JSON.stringify({
c: data.category, // 缩写字段名
cf: data.confidence,
ts: data.timestamp,
d: data.deviceId
});

return zlib.deflateSync(new TextEncoder().encode(jsonStr));

static decompressClassification(data: Uint8Array): TrashClassification {

const decompressed = zlib.inflateSync(data);
const json = JSON.parse(new TextDecoder().decode(decompressed));

return {
  category: json.c,
  confidence: json.cf,
  timestamp: json.ts,
  deviceId: json.d
};

}

智能缓存策略

// 分类结果缓存
class ClassificationCache {
private memoryCache: Map<string, TrashClassification> = new Map();
private diskCache: CacheStorage;

async getClassification(imageHash: string): Promise<TrashClassification | null> {
// 1. 检查内存缓存
if (this.memoryCache.has(imageHash)) {
return this.memoryCache.get(imageHash);
// 2. 检查磁盘缓存

const diskData = await this.diskCache.match(imageHash);
if (diskData) {
  const classification = JSON.parse(diskData);
  this.memoryCache.set(imageHash, classification);
  return classification;

return null;

async cacheClassification(imageHash: string, data: TrashClassification) {

// 内存缓存
this.memoryCache.set(imageHash, data);

// 磁盘缓存
await this.diskCache.put(imageHash, JSON.stringify(data));

}

五、完整UI组件实现
垃圾分类主界面

// TrashClassificationPage.ets
@Entry
@Component
struct TrashClassificationPage {
@State classifications: TrashClassification[] = [];
@State isProcessing: boolean = false;

private recognitionService = new TrashRecognitionService();
private cameraService = new CameraService();

aboutToAppear() {
this.recognitionService.init();
this.loadHistory();
build() {

Column() {
  // 相机预览
  CameraPreview({
    onCapture: this.classifyImage.bind(this)
  })
  
  // 处理状态指示器
  if (this.isProcessing) {
    ProgressIndicator()
      .margin(10)

// 分类历史

  ClassificationHistory({
    items: this.classifications,
    onSelect: this.showDetails.bind(this)
  })

}

private async classifyImage(imageUri: string) {
this.isProcessing = true;

try {
  const pixelMap = await image.createPixelMapFromUri(imageUri);
  const classification = await this.recognitionService.classifyImage(pixelMap);
  
  this.classifications = [classification, ...this.classifications];

catch (err) {

  console.error('分类失败:', err);

finally {

  this.isProcessing = false;

}

private loadHistory() {
this.recognitionService.subscribeClassifications((data) => {
this.classifications = [data, …this.classifications];
});
}

相机控制组件

// CameraControl.ets
@Component
struct CameraControl {
@State isActive: boolean = false;
@State flashMode: ‘off’ ‘on’
‘auto’ = ‘off’;

private camera: camera.CameraDevice;

build() {
Column() {
// 相机预览
CameraView({
isActive: this.isActive,
flashMode: this.flashMode
})
.height(‘60%’)

  // 控制按钮
  Row() {
    Button(this.isActive ? '停止' : '启动', { type: ButtonType.Capsule })
      .onClick(() => this.toggleCamera())
    
    Button('拍照', { type: ButtonType.Circle })
      .margin({ left: 20, right: 20 })
      .onClick(() => this.captureImage())
      .disabled(!this.isActive)
    
    Button('闪光灯', { type: ButtonType.Normal })
      .onClick(() => this.cycleFlashMode())

.margin({ top: 20 })

}

private toggleCamera() {
this.isActive = !this.isActive;
if (this.isActive) {
this.camera.start();
else {

  this.camera.stop();

}

private cycleFlashMode() {
const modes: Array<‘off’ ‘on’
‘auto’> = [‘off’, ‘on’, ‘auto’];
const currentIndex = modes.indexOf(this.flashMode);
this.flashMode = modes[(currentIndex + 1) % modes.length];
this.camera.setFlashMode(this.flashMode);
}

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.DISTRIBUTED_DATASYNC”

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

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

]

测试方案

// 图像分类测试
describe(‘TrashClassification’, () => {
it(‘should classify plastic bottle as recyclable’, async () => {
const service = new TrashRecognitionService();
await service.init();

const testImage = await loadTestImage('bottle.jpg');
const result = await service.classifyImage(testImage);

expect(result.category).toEqual('recyclable');
expect(result.confidence).toBeGreaterThan(0.8);

});
});

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

await device1.classifyImage('banana.jpg');
await device2.waitForSync();

expect(device2.getLastClassification().category).toEqual('kitchen');

});
});

七、总结与扩展

本方案实现了：
基于鸿蒙AI的图像分类能力

多设备分类结果协同决策

垃圾分类知识库集成

高性能图像处理流程

扩展方向：
添加AR垃圾分类指引

开发垃圾回收点地图

集成语音查询功能

支持垃圾分类游戏化

鸿蒙的分布式AI能力为环保类应用开发提供了创新可能，开发者可基于此项目框架构建更丰富的垃圾分类应用场景。