鸿蒙跨端智能衣橱搭配系统：多设备协同的个性化穿搭推荐 原创

鸿蒙跨端智能衣橱搭配系统：多设备协同的个性化穿搭推荐

一、项目概述

本系统基于HarmonyOS 5的分布式能力与AI技术，实现以下核心功能：
多端衣橱同步：手机、平板、智慧屏等多设备间实时同步衣物数据

智能搭配推荐：基于机器学习算法生成个性化穿搭方案

场景化推荐：根据天气、场合自动适配不同搭配风格

虚拟试衣间：通过AR技术实现多设备协同的虚拟试穿体验

二、技术架构

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

采用四层架构：
设备层：手机/平板摄像头采集衣物图像

AI层：图像识别+搭配算法

服务层：分布式数据管理+天气服务

展示层：3D渲染+跨设备协同展示

三、核心代码实现
衣物识别与特征提取

// ClothingDetector.ts
import image from ‘@ohos.multimedia.image’;
import imageAnalysis from ‘@ohos.ai.imageAnalysis’;

interface ClothingItem {
id: string;
category: ‘top’ ‘bottom’ ‘dress’ ‘outerwear’ ‘shoes’
‘accessories’;
color: string;
texture: string;
style: string;
imageUri: string;
lastWorn?: number;
export class ClothingDetector {

private analyzer: imageAnalysis.ImageAnalyzer;

async init() {
try {
this.analyzer = await imageAnalysis.createImageAnalyzer();
console.info(‘衣物分析器初始化成功’);
catch (err) {

  console.error(初始化失败: {err.code}, {err.message});

}

async analyzeClothing(image: image.Image): Promise<ClothingItem> {
const options = {
analyzeTypes: imageAnalysis.AnalyzeType.COLOR
imageAnalysis.AnalyzeType.TEXTURE

              imageAnalysis.AnalyzeType.OBJECT
};

const result = await this.analyzer.analyze(image, options);
return {
  id: this.generateId(),
  category: this.detectCategory(result.objects),
  color: this.getDominantColor(result.colors),
  texture: result.textures[0]?.type || 'plain',
  style: this.detectStyle(result),
  imageUri: await this.saveImage(image)
};

private detectCategory(objects: imageAnalysis.DetectedObject[]): ClothingItem[‘category’] {

// 基于AI识别结果判断衣物类型
const detected = objects.find(obj => 
  ['shirt', 'pants', 'dress', 'coat', 'shoe'].includes(obj.type)
);

const map: Record<string, ClothingItem['category']> = {
  'shirt': 'top',
  'pants': 'bottom',
  'dress': 'dress',
  'coat': 'outerwear',
  'shoe': 'shoes'
};

return detected ? map[detected.type] || 'top' : 'top';

// 其他辅助方法…

分布式衣橱数据管理

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

export class WardrobeManager {
private kvManager: distributedData.KVManager;
private kvStore: distributedData.KVStore;
private deviceList: deviceManager.DeviceBasicInfo[] = [];

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

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

this.kvStore = await this.kvManager.getKVStore('wardrobe_data', options);
this.setupDeviceListeners();

private setupDeviceListeners() {

deviceManager.createDeviceManager('com.example.wardrobe', (err, manager) => {
  if (err) return;
  
  manager.on('deviceStateChange', (data) => {
    this.refreshDeviceList();
  });
  this.refreshDeviceList();
});

async addClothingItem(item: ClothingItem) {

try {
  await this.kvStore.put(item.id, item);
  console.info('衣物添加成功');
  this.syncLatestItems();

catch (err) {

  console.error(添加失败: {err.code}, {err.message});

}

async getOutfitRecommendations(context: OutfitContext): Promise<Outfit[]> {
const allItems = await this.getAllClothingItems();
return this.generateOutfits(allItems, context);
private async generateOutfits(items: ClothingItem[], context: OutfitContext): Promise<Outfit[]> {

// 实现搭配算法（示例简化版）
const tops = items.filter(i => i.category === 'top');
const bottoms = items.filter(i => i.category === 'bottom');
const outfits: Outfit[] = [];

// 基础搭配逻辑
tops.forEach(top => {
  bottoms.forEach(bottom => {
    if (this.isColorCompatible(top.color, bottom.color)) {
      outfits.push({
        id: this.generateOutfitId(),
        items: [top.id, bottom.id],
        score: this.calculateMatchScore(top, bottom, context)
      });

});

});

return outfits.sort((a, b) => b.score - a.score).slice(0, 10);

// 其他数据管理方法…

主页面实现（ArkUI）

// OutfitRecommendation.ets
import { WardrobeManager } from ‘./WardrobeManager’;
import { ClothingDetector } from ‘./ClothingDetector’;

@Entry
@Component
struct OutfitApp {
@State outfits: Outfit[] = [];
@State currentContext: OutfitContext = {
weather: ‘sunny’,
temperature: 25,
occasion: ‘casual’
};

private wardrobeManager = new WardrobeManager();
private clothingDetector = new ClothingDetector();

async aboutToAppear() {
await this.wardrobeManager.init();
await this.clothingDetector.init();
this.loadRecommendations();
async onAddClothing(image: image.Image) {

const item = await this.clothingDetector.analyzeClothing(image);
await this.wardrobeManager.addClothingItem(item);
this.loadRecommendations();

async loadRecommendations() {

this.outfits = await this.wardrobeManager.getOutfitRecommendations(this.currentContext);

build() {

Column() {
  // 场景选择器
  ContextSelector({
    context: this.currentContext,
    onContextChange: (newContext) => {
      this.currentContext = newContext;
      this.loadRecommendations();

})

  // 推荐搭配列表
  Grid() {
    ForEach(this.outfits, (outfit) => {
      GridItem() {
        OutfitCard({
          outfit,
          onTryOn: () => this.tryOutfit(outfit)
        })

})

.columnsTemplate(‘1fr 1fr’)

  .rowsGap(20)
  .columnsGap(15)

}

async tryOutfit(outfit: Outfit) {
// 跨设备试衣逻辑
}

@Component
struct OutfitCard {
@Prop outfit: Outfit;
@Prop onTryOn: () => void;

build() {
Column() {
// 搭配预览
Row() {
ForEach(this.outfit.items, (itemId) => {
Image(this.getItemImage(itemId))
.width(80)
.height(80)
.margin(5)
})
// 评分和操作按钮

  Row() {
    Text(匹配度: ${this.outfit.score.toFixed(1)})
      .fontSize(14)
    
    Button('试穿')
      .onClick(() => this.onTryOn())

}

.border({ width: 1, color: '#EEEEEE' })
.borderRadius(8)
.padding(10)

private getItemImage(itemId: string): Resource {

// 从缓存或KVStore获取衣物图片
return $r('app.media.default_clothing');

}

四、跨设备协同关键实现
分布式试衣间功能

// VirtualFittingRoom.ts
import distributedData from ‘@ohos.data.distributedData’;
import xComponent from ‘@ohos.xComponent’;

export class VirtualFittingRoom {
private kvStore: distributedData.KVStore;
private xComponentContext?: xComponent.XComponentContext;

async init() {
const context = getContext(this);
const kvManager = distributedData.createKVManager({ context });
this.kvStore = await kvManager.getKVStore(‘fitting_room’, {
createIfMissing: true,
autoSync: true
});
async startSession(outfit: Outfit) {

// 在分布式数据库中设置当前试穿搭配
await this.kvStore.put('current_outfit', outfit);

// 启动3D渲染
this.xComponentContext = await xComponent.createContext('3d_view');
this.renderOutfit(outfit);

private async renderOutfit(outfit: Outfit) {

// 实现3D渲染逻辑
const items = await Promise.all(
  outfit.items.map(id => this.kvStore.get(id))
);

// 调用Native 3D渲染能力
this.xComponentContext?.sendMessage({
  type: 'render_outfit',
  items: items.filter(i => i) as ClothingItem[]
});

async syncWithDevice(deviceId: string) {

// 将试衣状态同步到指定设备
const outfit = await this.kvStore.get('current_outfit');
if (outfit) {
  await this.kvStore.saveToDevice(deviceId, 'current_outfit', outfit);

}

多设备协同推荐

// 在WardrobeManager类中添加
async getCollaborativeRecommendations(): Promise<Outfit[]> {
const allDevicesItems = await this.getAllDevicesItems();
return this.generateOutfits(allDevicesItems.flat(), {
weather: ‘any’,
temperature: 0,
occasion: ‘any’
});
private async getAllDevicesItems(): Promise<ClothingItem[][]> {

return Promise.all(
this.deviceList.map(async device => {
try {
const items = await this.kvStore.getDeviceData(device.deviceId, ‘clothing_items’);
return items || [];
catch {

    return [];

})

);

实时数据同步优化

// 添加数据变化监听
setupDataSync() {
this.kvStore.on(‘dataChange’, distributedData.SubscribeType.SUBSCRIBE_TYPE_REMOTE, (changes) => {
changes.forEach(({ key, value }) => {
if (key.startsWith(‘clothing_’)) {
this.updateLocalCache(key, value);
});

});
private updateLocalCache(key: string, item: ClothingItem) {

// 更新内存缓存并触发UI更新

五、智能推荐算法增强
基于深度学习的搭配算法

// 在WardrobeManager类中扩展
private async calculateMatchScore(
items: ClothingItem[],
context: OutfitContext
): Promise<number> {
// 调用AI模型计算搭配得分
try {
const input = {
items: items.map(i => ({
category: i.category,
color: i.color,
texture: i.texture,
style: i.style
})),
context
};

const result = await this.callAIModel('outfit_score', input);
return result.score;

catch {

// 回退到基础算法
return this.basicMatchScore(items);

}

private basicMatchScore(items: ClothingItem[]): number {
// 实现基础评分逻辑
let score = 0;

// 颜色搭配评分
const colorScore = this.evaluateColorCombination(
items.map(i => i.color)
);

// 风格一致性评分
const styleConsistency = this.evaluateStyleConsistency(
items.map(i => i.style)
);

return colorScore 0.6 + styleConsistency 0.4;

场景化推荐增强

// 天气服务集成
import weather from ‘@ohos.weather’;

async updateWeatherContext() {
try {
const weatherInfo = await weather.getCurrentWeather();
this.currentContext = {
…this.currentContext,
weather: weatherInfo.weatherType,
temperature: weatherInfo.temperature
};
this.loadRecommendations();
catch (err) {

console.error('获取天气信息失败', err);

}

六、性能优化方案
图像处理优化：

// 在ClothingDetector中添加
private async optimizeImage(image: image.Image): Promise<image.Image> {
const opts = {
format: ‘jpg’,
quality: 70, // 70%质量
size: {
width: 800,
height: 800
};

return image.optimize(opts);

分布式数据分片：

// 大数据分片传输
async syncLargeCloset(items: ClothingItem[]) {
const CHUNK_SIZE = 10;
for (let i = 0; i < items.length; i += CHUNK_SIZE) {
const chunk = items.slice(i, i + CHUNK_SIZE);
await this.kvStore.put(closet_chunk_${i}, chunk);
}

本地缓存策略：

const recommendationCache = new Map<string, Outfit[]>();

async getCachedRecommendations(context: OutfitContext): Promise<Outfit[]> {
const cacheKey = JSON.stringify(context);
if (recommendationCache.has(cacheKey)) {
return recommendationCache.get(cacheKey)!;
const fresh = await this.getOutfitRecommendations(context);

recommendationCache.set(cacheKey, fresh);
return fresh;

七、应用场景扩展
社交穿搭分享：

class OutfitSharing {
async shareToDevice(outfit: Outfit, deviceId: string) {
// 实现搭配方案分享
}

智能洗衣提醒：

class LaundryHelper {
checkWearFrequency(items: ClothingItem[]) {
// 根据穿着频率提醒清洗
}

购物推荐：

class ShoppingAdvisor {
suggestItemsToComplete(outfit: Outfit) {
// 推荐可以完善当前搭配的单品
}

季节转换助手：

class SeasonalTransition {
suggestStorageItems() {
// 根据季节变化建议收纳衣物
}

本系统充分利用HarmonyOS 5的分布式能力，实现了跨设备的智能衣橱管理和穿搭推荐。开发者可以基于此框架扩展更多时尚科技应用场景，打造全新的智能穿衣体验。