鸿蒙跨设备多模态输入识别率统计系统原创

进修的泡芙

发布于 2025-6-17 21:15

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鸿蒙跨设备多模态输入识别率统计系统

一、系统架构设计

基于鸿蒙分布式能力构建的多模态输入识别统计系统，专为游戏多设备同步场景设计：

graph TD
A[游戏主机] -->分发输入测试任务
B[手机设备]
–>分发输入测试任务
C[平板设备]

–>分发输入测试任务
D[智慧屏设备]

–>返回识别结果
E[统计分析中心]

–>返回识别结果
E

–> F[生成多模态识别报告]

二、核心模块实现
多模态输入监听器

// MultiModalListener.ets
import inputMethod from ‘@ohos.inputMethod’;
import sensor from ‘@ohos.sensor’;
import audio from ‘@ohos.audio’;

class MultiModalListener {
private static instance: MultiModalListener;

static getInstance(): MultiModalListener {
if (!MultiModalListener.instance) {
MultiModalListener.instance = new MultiModalListener();
return MultiModalListener.instance;

startListening() {

// 键盘输入监听
inputMethod.on('inputStart', (text) => {
  this.handleInput('keyboard', text);
});

// 语音输入监听
audio.on('voiceResult', (result) => {
  this.handleInput('voice', result.text);
});

// 手势输入监听
sensor.on('gesture', (gesture) => {
  this.handleInput('gesture', this.parseGesture(gesture));
});

private handleInput(type: string, content: string) {

const record: InputRecord = {
  type,
  content,
  timestamp: Date.now(),
  deviceId: this.getDeviceId(),
  syncId: this.getCurrentGameSession()
};

InputRecorder.getInstance().record(record);

private parseGesture(gesture: Gesture): string {

// 游戏专用手势映射
const gameGestures = {
  'swipe_up': 'jump',
  'swipe_down': 'crouch',
  'pinch': 'zoom'
};
return gameGestures[gesture.type] || gesture.type;

}

游戏场景输入记录器

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

class GameInputRecorder {
private kvStore: distributedData.KVStore;

async init() {
const config = {
bundleName: ‘com.example.gameinput’,
userInfo: {
userId: ‘game_recorder’,
userType: distributedData.UserType.SAME_USER_ID
};

this.kvStore = await distributedData.createKVManager(config)
  .getKVStore('game_input', {
    encrypt: true,  // 加密存储玩家输入数据
    autoSync: false // 手动控制同步时机
  });

async recordPlayerInput(playerId: string, input: GameInput) {

const key = player_{playerId}_{Date.now()};
await this.kvStore.put(key, {
  ...input,
  gameSession: this.getCurrentGameSession()
});

async getPlayerInputs(playerId: string): Promise<GameInput[]> {

const entries = await this.kvStore.getEntries(player_${playerId}_);
return entries.map(e => e.value);

}

interface GameInput {
type: ‘keyboard’ ‘voice’
‘gesture’;
command: string;
timestamp: number;
deviceType: string;
recognitionCorrect: boolean;

三、识别率统计引擎
多模态识别分析器

// RecognitionAnalyzer.ets
class RecognitionAnalyzer {
async analyzeGameSession(sessionId: string): Promise<RecognitionReport> {
const inputs = await this.getSessionInputs(sessionId);
const stats: RecognitionStats = {
total: inputs.length,
byDevice: {},
byMode: {
keyboard: { correct: 0, total: 0 },
voice: { correct: 0, total: 0 },
gesture: { correct: 0, total: 0 }
};

inputs.forEach(input => {
  // 按设备统计
  if (!stats.byDevice[input.deviceType]) {
    stats.byDevice[input.deviceType] = { correct: 0, total: 0 };

stats.byDevice[input.deviceType].total++;

  if (input.recognitionCorrect) {
    stats.byDevice[input.deviceType].correct++;

// 按输入模式统计

  stats.byMode[input.type].total++;
  if (input.recognitionCorrect) {
    stats.byMode[input.type].correct++;

});

return this.calculateRates(stats);

private calculateRates(stats: RecognitionStats): RecognitionReport {

const report: RecognitionReport = {
  sessionId: this.getCurrentGameSession(),
  overallAccuracy: 0,
  deviceAccuracies: {},
  modeAccuracies: {
    keyboard: 0,
    voice: 0,
    gesture: 0

};

// 计算设备识别率
for (const [device, data] of Object.entries(stats.byDevice)) {
  report.deviceAccuracies[device] = (data.correct / data.total) * 100;

// 计算模式识别率

report.modeAccuracies.keyboard = (stats.byMode.keyboard.correct / stats.byMode.keyboard.total) * 100;
report.modeAccuracies.voice = (stats.byMode.voice.correct / stats.byMode.voice.total) * 100;
report.modeAccuracies.gesture = (stats.byMode.gesture.correct / stats.byMode.gesture.total) * 100;

// 计算整体识别率
report.overallAccuracy = (
  stats.byMode.keyboard.correct + 
  stats.byMode.voice.correct + 
  stats.byMode.gesture.correct
) / stats.total * 100;

return report;

}

四、游戏场景测试实现
游戏指令测试用例

// GameCommandTests.ets
class GameCommandTests {
static getKeyboardCommands(): GameCommand[] {
return [
input: ‘W’, expected: ‘move_forward’ },

input: ‘A’, expected: ‘move_left’ },

input: ‘Attack’, expected: ‘attack’ }

];

static getVoiceCommands(): GameCommand[] {

return [

input: ‘防守’, expected: ‘defend’ },

input: ‘使用技能’, expected: ‘use_skill’ }

];

static getGestureCommands(): GameCommand[] {

return [

input: ‘swipe_up’, expected: ‘jump’ },

input: ‘swipe_left’, expected: ‘dodge_left’ },

input: ‘pinch’, expected: ‘zoom’ }

];

}

分布式测试协调器

// GameTestCoordinator.ets
class GameTestCoordinator {
async runCrossDeviceTests() {
const devices = await this.getConnectedDevices();
const testReport: GameTestReport = {
sessionId: this.generateSessionId(),
devices: [],
startTime: Date.now()
};

// 分发测试任务
await this.dispatchTests(devices, testReport);

// 收集测试结果
testReport.results = await this.collectResults(devices);
testReport.endTime = Date.now();

// 生成分析报告
const analyzer = new RecognitionAnalyzer();
testReport.analysis = await analyzer.analyzeGameSession(testReport.sessionId);

return testReport;

private async dispatchTests(devices: DeviceInfo[], report: GameTestReport) {

await Promise.all(devices.map(async device => {
  await distributedData.sendData(device.id, {
    type: 'game_input_test',
    sessionId: report.sessionId,
    commands: {
      keyboard: GameCommandTests.getKeyboardCommands(),
      voice: GameCommandTests.getVoiceCommands(),
      gesture: GameCommandTests.getGestureCommands()

});

  report.devices.push(device.id);
}));

}

五、可视化报告系统
游戏输入分析报告组件

// GameInputReport.ets
@Component
struct GameInputReport {
@Prop report: GameTestReport;
@State selectedView: ‘summary’ ‘devices’
‘modes’ = ‘summary’;

build() {
Column() {
// 报告导航
this.buildNavigation()

  // 内容区域
  if (this.selectedView === 'summary') {
    this.buildSummaryView()

else if (this.selectedView === ‘devices’) {

    this.buildDeviceView()

else {

    this.buildModeView()

}

@Builder

private buildNavigation() {
Row() {
Button(‘概览’).onClick(() => this.selectedView = ‘summary’)
Button(‘设备’).onClick(() => this.selectedView = ‘devices’)
Button(‘模式’).onClick(() => this.selectedView = ‘modes’)
}

@Builder
private buildSummaryView() {
Column() {
Text(游戏会话 ${this.report.sessionId})
.fontSize(20)

  Progress({
    value: this.report.analysis.overallAccuracy,
    total: 100,
    style: ProgressStyle.Ring
  })
  .width(150)
  .height(150)
  
  Text(综合识别率: ${this.report.analysis.overallAccuracy.toFixed(1)}%)
    .fontSize(18)

}

@Builder
private buildDeviceView() {
Column() {
Text(‘各设备识别率’).fontSize(18)

  ForEach(Object.entries(this.report.analysis.deviceAccuracies), 
    ([device, accuracy]) => {
      Row() {
        Text(device)
        Progress({
          value: accuracy,
          total: 100
        })
        .width('60%')
        Text(${accuracy.toFixed(1)}%)

}

六、完整测试流程示例
游戏主机执行测试

// GameHostTest.ets
async function runGameInputTests() {
// 1. 初始化游戏会话
const coordinator = new GameTestCoordinator();
const gameSession = await coordinator.createGameSession();

// 2. 启动多设备测试
const testReport = await coordinator.runCrossDeviceTests();

// 3. 显示实时数据
const realtimeView = new RealtimeInputView();
realtimeView.sessionId = gameSession.id;

// 4. 生成最终报告
const reportView = new GameInputReport();
reportView.report = testReport;

// 5. 保存测试结果
GameReportExporter.saveToFile(testReport);
class GameReportExporter {

static saveToFile(report: GameTestReport) {
const content = JSON.stringify(report, null, 2);
fileIO.writeText(game_input_${report.sessionId}.json, content);
}

玩家设备测试代理

// PlayerDeviceAgent.ets
@Component
struct PlayerDeviceAgent {
@State currentTest?: GameInputTest;
private listener = MultiModalListener.getInstance();

aboutToAppear() {
this.listener.startListening();
this.registerTestHandler();
private registerTestHandler() {

distributedData.on('game_input_test', (test) => {
  this.currentTest = test;
  this.executeTest(test);
});

private executeTest(test: GameInputTest) {

// 执行键盘、语音、手势测试
this.runKeyboardTest(test.commands.keyboard);
this.runVoiceTest(test.commands.voice);
this.runGestureTest(test.commands.gesture);

@Builder

build() {
Column() {
if (this.currentTest) {
Text(‘游戏输入测试中…’)
// 测试UI组件
else {

    Text('等待游戏主机指令...')

}

七、优化建议与实施
设备差异化识别优化

// 设备自适应识别器
class DeviceAwareRecognizer {
static async recognize(input: GameInput): Promise<string> {
const deviceProfile = await this.getDeviceProfile(input.deviceType);

switch(input.type) {
  case 'voice':
    return this.recognizeVoice(input.content, deviceProfile);
  case 'gesture':
    return this.recognizeGesture(input.content, deviceProfile);
  default:
    return input.content; // 键盘输入直接传递

}

private static async recognizeVoice(text: string, profile: DeviceProfile): Promise<string> {
// 根据设备麦克风质量调整识别策略
if (profile.micQuality < 0.7) {
return VoiceModel.robustRecognize(text);
else {

  return VoiceModel.fastRecognize(text);

}

多模态输入融合

// 输入融合决策器
class InputFusion {
static async fuseInputs(inputs: GameInput[]): Promise<GameCommand> {
// 1. 各模态独立识别
const results = await Promise.all([
KeyboardRecognizer.recognize(inputs.find(i => i.type === ‘keyboard’)),
VoiceRecognizer.recognize(inputs.find(i => i.type === ‘voice’)),
GestureRecognizer.recognize(inputs.find(i => i.type === ‘gesture’))
]);

// 2. 基于置信度加权决策
const command = this.weightedDecision(results);

// 3. 记录识别结果用于统计
this.recordRecognitionResults(inputs, results, command);

return command;

}

持续集成测试方案

游戏输入测试自动化脚本

hdc shell aa start -p com.example.gameinputtest/.TestService
hdc file recv /data/logs/game_input_report.json

八、测试数据分析

典型测试结果

“sessionId”: “game-5f3a2c”,

“duration”: 183,
“overallAccuracy”: 92.3,
“deviceAccuracies”: {
“phone”: 95.2,
“tablet”: 93.8,
“tv”: 87.1
},
“modeAccuracies”: {
“keyboard”: 98.7,
“voice”: 89.5,
“gesture”: 84.2
}

优化建议
智慧屏语音识别优化：

  // 针对TV设备的语音优化

function optimizeForTV(voiceInput: string): string {
// 增强远场语音识别
return VoiceEnhancer.enhanceFarField(voiceInput, {
noiseReduction: true,
echoCancellation: true
});

跨设备手势统一校准：

  // 手势校准中间件

class GestureNormalizer {
static normalize(gesture: string, sourceDevice: string): string {
// 不同设备手势数据标准化
const deviceFactors = {
‘phone’: { scale: 1.2 },
‘tablet’: { scale: 0.8 }
};

   return this.applyScaling(gesture, deviceFactors[sourceDevice]);

}

游戏术语识别增强：

  // 游戏专用术语识别

class GameTermRecognizer {
private static gameTerms = {

 static recognize(text: string): string {
   for (const [command, keywords] of Object.entries(this.gameTerms)) {
     if (keywords.some(kw => text.includes(kw))) {
       return command;

}