鸿蒙跨设备手写数字识别系统：分布式协同学习与多终端同步方案 原创

鸿蒙跨设备手写数字识别系统：分布式协同学习与多终端同步方案

一、系统架构设计

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

采用三层架构：
输入层：多设备手写输入采集

处理层：分布式MNIST模型推理

展示层：跨终端实时结果同步与可视化

二、核心模块实现
分布式数字识别引擎

// DigitRecognizer.ts
import image from ‘@ohos.multimedia.image’;
import neuralNetwork from ‘@ohos.ai.neuralNetwork’;
import distributedData from ‘@ohos.data.distributedData’;

interface RecognitionResult {
digit: number;
confidence: number;
deviceId: string;
timestamp: number;
export class DigitRecognizer {

private model!: neuralNetwork.Model;
private kvManager: distributedData.KVManager;
private kvStore!: distributedData.KVStore;

async init() {
// 加载预训练MNIST模型
this.model = await neuralNetwork.loadModel({
modelPath: ‘models/mnist.model’,
device: ‘NPU’
});

// 初始化分布式数据同步
const context = getContext(this);
this.kvManager = distributedData.createKVManager({ context });
this.kvStore = await this.kvManager.getKVStore('digit_recognition', {
  createIfMissing: true,
  autoSync: true
});

async recognize(image: image.Image): Promise<RecognitionResult> {

// 预处理输入图像
const input = this.preprocessImage(image);

// 执行模型推理
const output = await this.model.predict(input);
const digit = this.parseOutput(output);

// 保存识别结果
const result: RecognitionResult = {
  digit,
  confidence: output[digit],
  deviceId: 'local_device',
  timestamp: Date.now()
};

await this.syncResult(result);
return result;

private preprocessImage(img: image.Image): neuralNetwork.Tensor {

// 转换为28x28灰度图并归一化
const pixels = img.toGrayScale().resize(28, 28).getPixels();
return new neuralNetwork.Tensor({
  dataType: 'float32',
  shape: [1, 28, 28, 1],
  data: pixels.map(p => p / 255.0)
});

private parseOutput(output: number[]): number {

return output.indexOf(Math.max(...output));

async syncResult(result: RecognitionResult) {

await this.kvStore.put(result_${result.timestamp}, result);

}

多设备协同学习模块

// CollaborativeLearner.ts
import deviceManager from ‘@ohos.distributedHardware.deviceManager’;

export class CollaborativeLearner {
private deviceList: deviceManager.DeviceBasicInfo[] = [];

async init() {
const manager = await deviceManager.createDeviceManager(‘com.example.digitrecognition’);
manager.on(‘deviceStateChange’, () => this.refreshDeviceList());
await this.refreshDeviceList();
async improveModel(newSamples: RecognitionResult[]) {

// 收集各设备识别结果作为训练数据
const allSamples = await this.collectDeviceSamples();

// 联邦学习更新模型
const updatedModel = await this.federatedUpdate([...allSamples, ...newSamples]);

// 分发新模型到各设备
await this.distributeModel(updatedModel);

private async collectDeviceSamples(): Promise<RecognitionResult[]> {

const samples: RecognitionResult[] = [];

await Promise.all(this.deviceList.map(async device => {
  try {
    const remoteStore = await distributedData.getRemoteKVStore(device.deviceId, 'digit_recognition');
    const entries = await remoteStore.entries('result_');
    samples.push(...entries.map(([_, v]) => v as RecognitionResult));

catch (err) {

    console.error(从设备${device.deviceId}收集数据失败:, err);

}));

return samples;

// 其他方法…

主页面实现（ArkUI）

// DigitRecognition.ets
import { DigitRecognizer } from ‘./DigitRecognizer’;
import { CollaborativeLearner } from ‘./CollaborativeLearner’;

@Entry
@Component
struct DigitRecognitionApp {
@State currentDigit?: number;
@State confidence?: number;
@State deviceResults: Record<string, RecognitionResult> = {};

private recognizer = new DigitRecognizer();
private collaborator = new CollaborativeLearner();
private canvasController?: CanvasController;

async aboutToAppear() {
await this.recognizer.init();
await this.collaborator.init();
this.setupResultListener();
setupResultListener() {

this.recognizer.kvStore.on('dataChange', distributedData.SubscribeType.SUBSCRIBE_TYPE_REMOTE, 
  (changes) => {
    changes.forEach(({ key, value }) => {
      if (key.startsWith('result_')) {
        this.deviceResults[value.deviceId] = value;

});

  });

async onCanvasDraw(image: image.Image) {

const result = await this.recognizer.recognize(image);
this.currentDigit = result.digit;
this.confidence = result.confidence;

// 每识别10个数字触发协同学习
if (Object.keys(this.deviceResults).length % 10 === 0) {
  await this.collaborator.improveModel(Object.values(this.deviceResults));

}

build() {
Column() {
// 画布区域
DrawingCanvas({
onDraw: (image) => this.onCanvasDraw(image)
})

  // 本地识别结果
  if (this.currentDigit !== undefined) {
    Text(识别结果: ${this.currentDigit})
      .fontSize(24)
    Text(置信度: ${this.confidence?.toFixed(2)})
      .fontSize(16)

// 其他设备结果

  if (Object.keys(this.deviceResults).length > 0) {
    Text('其他设备识别结果:')
      .fontSize(18)
    DeviceResultsList({
      results: this.deviceResults
    })

}

}

@Component
struct DrawingCanvas {
@Param onDraw: (image: image.Image) => void;
private canvasRef: CanvasRenderingContext2D | null = null;

build() {
Canvas(this.canvasRef)
.width(‘100%’)
.height(‘60%’)
.backgroundColor(‘#FFFFFF’)
.onTouch((event) => this.handleTouch(event))
private handleTouch(event: TouchEvent) {

// 实现手写绘制逻辑
const image = this.canvasRef?.transferToImage();
if (image) this.onDraw(image);

}

@Component
struct DeviceResultsList {
@Prop results: Record<string, RecognitionResult>;

build() {
List() {
ForEach(Object.entries(this.results), ([deviceId, result]) => {
ListItem() {
Row() {
Text(设备${deviceId.slice(0, 6)})
Text(数字: ${result.digit})
Text(置信度: ${result.confidence.toFixed(2)})
}

  })

.height(‘30%’)

}

三、跨设备协同关键实现
联邦学习模型更新

// 在CollaborativeLearner中添加
private async federatedUpdate(samples: RecognitionResult[]): Promise<neuralNetwork.Model> {
// 创建临时模型
const tempModel = await neuralNetwork.cloneModel(this.recognizer.model);

// 准备训练数据
const { images, labels } = this.prepareTrainingData(samples);

// 执行一轮联邦学习
await tempModel.train({
epochs: 1,
batchSize: 32,
learningRate: 0.001,
loss: ‘categorical_crossentropy’,
metrics: [‘accuracy’],
validationSplit: 0.2,
callbacks: {
onEpochEnd: (epoch, logs) => {
console.info(联邦学习进度: epoch {epoch} - loss: {logs?.loss});
}

});

return tempModel;
private prepareTrainingData(samples: RecognitionResult[]) {

// 将识别结果转换为训练数据
const images: number[][][] = [];
const labels: number[] = [];

samples.forEach(sample => {
if (sample.confidence > 0.7) { // 只使用高置信度样本
images.push(this.loadSampleImage(sample));
labels.push(sample.digit);
});

return { images, labels };

模型差异同步

// 在DigitRecognizer中添加
private modelVersion = 0;
private readonly MODEL_KEY = ‘mnist_model’;

async checkForUpdates() {
const latestVersion = await this.kvStore.get(‘model_version’);
if (latestVersion > this.modelVersion) {
const modelData = await this.kvStore.get(this.MODEL_KEY);
await this.model.loadParameters(modelData);
this.modelVersion = latestVersion;
}

async distributeModel(model: neuralNetwork.Model) {
const params = await model.getParameters();
await this.kvStore.put(this.MODEL_KEY, params);
await this.kvStore.put(‘model_version’, this.modelVersion + 1);

设备能力适配

// 在DigitRecognizer中添加
private optimizeForDevice() {
const deviceInfo = deviceManager.getLocalDeviceInfo();

// 根据设备性能调整模型参数
if (deviceInfo.deviceType === ‘phone’) {
this.model.setPreference(‘inference_precision’, ‘fp16’);
else if (deviceInfo.deviceType === ‘tablet’) {

this.model.setPreference('inference_precision', 'fp32');

}

四、性能优化方案
输入数据压缩

// 在DigitRecognizer中添加
private compressImage(image: image.Image): CompressedImage {
const pixels = image.toGrayScale().resize(14, 14).getPixels(); // 降采样
return {
w: image.width,
h: image.height,
p: pixels.map(p => Math.round(p * 255))
};

分布式推理调度

// 新增DistributedInference.ts
export class DistributedInference {
async balanceLoad(image: image.Image, devices: string[]) {
const chunks = this.splitImage(image, devices.length);
const results = await Promise.all(
chunks.map((chunk, i) =>
this.remoteInference(devices[i], chunk)
)
);
return this.mergeResults(results);
// 其他方法…

本地结果缓存

const resultCache = new Map<string, RecognitionResult>();

async getCachedResult(imageHash: string): Promise<RecognitionResult | undefined> {
if (resultCache.has(imageHash)) {
return resultCache.get(imageHash);
const result = await this.recognizer.recognize(image);

resultCache.set(imageHash, result);
return result;

五、应用场景扩展
教育协作白板

class CollaborativeWhiteboard {
async syncDigitRecognitions(sessionId: string) {
// 多用户手写数字识别协同
}

数学作业批改

class HomeworkGrader {
async gradeWrittenAnswers(image: image.Image) {
// 手写数学答案识别与批改
}

验证码识别工具

class CaptchaSolver {
async recognizeComplexDigits(image: image.Image) {
// 复杂背景下的数字识别
}

银行票据识别

class CheckRecognizer {
async extractAmount(image: image.Image) {
// 票据金额数字识别
}

本系统充分利用HarmonyOS 5的分布式能力，实现了：
多设备协同识别：综合多个设备的识别结果提高准确率

联邦学习优化：保护隐私的同时持续改进模型

实时结果同步：毫秒级的多终端数据同步

自适应推理：根据设备性能动态调整计算策略

开发者可以基于此框架扩展更多数字识别场景，如：
结合AR的手写数字教学

数学公式识别计算

工业仪表数字识别

验证码自动填充工具