AI菜谱一步直达服务：基于鸿蒙跨端技术的智能厨房解决方案原创

进修的泡芙

发布于 2025-6-15 10:13

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AI菜谱一步直达服务：基于鸿蒙跨端技术的智能厨房解决方案

系统概述

AI菜谱一步直达服务利用手机摄像头识别冰箱内食材，通过鸿蒙分布式技术协同多设备，实现食材识别、菜谱推荐、烹饪指导的一站式服务。该系统创新性地将计算机视觉与知识图谱技术相结合，打造智能化的厨房体验。

系统架构与核心技术
多设备协作流程：

手机：食材拍照识别

智慧屏：展示推荐菜谱和烹饪视频

智能音箱：语音指导烹饪步骤
关键技术点：

多模态物体检测（YOLOv5+CLIP）

食材-菜谱知识图谱

跨设备任务调度

分布式状态同步

代码实现
分布式服务管理（核心模块）

public class RecipeDistributedService extends Ability {
private static final String TAG = “RecipeService”;
private DistributedDataManager dataManager;
private KnowledgeGraph kg;

@Override
public void onStart(Intent intent) {
    super.onStart(intent);
    initDistributedEnv();
    loadKnowledgeGraph();

private void initDistributedEnv() {

    // 初始化分布式能力
    dataManager = DistributedDataManager.getInstance(this);
    
    // 注册数据变化监听
    dataManager.registerDataChangeListener(new DataChangeListener() {
        @Override
        public void onDataChanged(String key, String value) {
            if (key.equals("ingredients_detected")) {
                processIngredients(value);

else if (key.equals(“recipe_selected”)) {

                dispatchCookingTask(value);

}

});

private void loadKnowledgeGraph() {

    // 加载预构建的食材-菜谱知识图谱
    kg = new KnowledgeGraph(this);
    kg.loadFromAsset("recipe_kg.bin");

private void processIngredients(String ingredientsJson) {

    // 解析识别到的食材
    Ingredient[] ingredients = new Gson().fromJson(
        ingredientsJson, Ingredient[].class);
    
    // 查询知识图谱获取推荐菜谱
    List<Recipe> recipes = kg.queryRecipes(ingredients);
    
    // 同步到智慧屏展示
    syncToSmartScreen(recipes);

private void syncToSmartScreen(List<Recipe> recipes) {

    String json = new Gson().toJson(recipes);
    dataManager.putString("recommended_recipes", json);

private void dispatchCookingTask(String recipeJson) {

    // 解析选定的菜谱
    Recipe recipe = new Gson().fromJson(recipeJson, Recipe.class);
    
    // 分配任务到各设备
    dataManager.putString("cooking_steps", recipe.getStepsJson());
    dataManager.putString("video_guide", recipe.getVideoUrl());

}

手机端食材识别模块

public class IngredientDetectionAbility extends Ability {
private static final String TAG = “IngredientDetection”;
private DistributedDataManager dataManager;
private FoodDetectionModel detectionModel;

@Override
public void onStart(Intent intent) {
    super.onStart(intent);
    initDetectionModel();
    setupCamera();

private void initDetectionModel() {

    // 加载轻量化食材检测模型
    detectionModel = new FoodDetectionModel(this);
    detectionModel.loadModel("yolov5s_food.tflite");

private void setupCamera() {

    SurfaceProvider provider = new SurfaceProvider(this);
    provider.setSurfaceCallback(new SurfaceCallback() {
        @Override
        public void surfaceCreated(Surface surface) {
            CameraConfig config = new CameraConfig.Builder()
                .setSurface(surface)
                .setFrameCallback(frame -> {
                    Bitmap bitmap = frameToBitmap(frame);
                    detectIngredients(bitmap);
                })
                .build();
            
            CameraKit.getInstance().openCamera(config);

});

private void detectIngredients(Bitmap bitmap) {

    // 执行食材检测
    DetectionResult[] results = detectionModel.detect(bitmap);
    
    // 过滤食材结果（置信度>0.7）
    List<Ingredient> ingredients = Arrays.stream(results)
        .filter(r -> r.getConfidence() > 0.7)
        .map(r -> new Ingredient(
            r.getLabel(), 
            r.getConfidence(),
            estimateQuantity(r.getBoundingBox())
        ))
        .collect(Collectors.toList());
    
    if (!ingredients.isEmpty()) {
        // 同步到分布式服务
        String json = new Gson().toJson(ingredients);
        dataManager.putString("ingredients_detected", json);

}

private float estimateQuantity(Rect boundingBox) {
    // 基于边界框大小估算食材数量（简化实现）
    return boundingBox.width() * boundingBox.height() / 10000f;

}

智慧屏菜谱展示模块

public class RecipeDisplayAbility extends Ability {
private DistributedDataManager dataManager;
private MediaPlayer videoPlayer;

@Override
public void onStart(Intent intent) {
    super.onStart(intent);
    initUIComponents();
    initDistributedListener();

private void initDistributedListener() {

    dataManager = DistributedDataManager.getInstance(this);
    dataManager.registerDataChangeListener(new DataChangeListener() {
        @Override
        public void onDataChanged(String key, String value) {
            if (key.equals("recommended_recipes")) {
                showRecipeList(value);

else if (key.equals(“video_guide”)) {

                playGuideVideo(value);

}

});

private void showRecipeList(String recipesJson) {

    Recipe[] recipes = new Gson().fromJson(recipesJson, Recipe[].class);
    
    getUITaskDispatcher().asyncDispatch(() -> {
        ListContainer list = findComponentById(ResourceTable.Id_recipe_list);
        RecipeItemProvider provider = new RecipeItemProvider(recipes);
        list.setItemProvider(provider);
        
        // 设置点击监听
        list.setItemClickedListener((listContainer, component, position, id) -> {
            selectRecipe(recipes[position]);
        });
    });

private void selectRecipe(Recipe recipe) {

    // 通知系统用户选择了某个菜谱
    String json = new Gson().toJson(recipe);
    dataManager.putString("recipe_selected", json);

private void playGuideVideo(String videoUrl) {

    videoPlayer.setSource(videoUrl);
    videoPlayer.prepare();
    videoPlayer.play();

}

智能音箱烹饪指导模块

public class CookingGuideAbility extends Ability {
private DistributedDataManager dataManager;
private TtsEngine ttsEngine;
private int currentStep = 0;

@Override
public void onStart(Intent intent) {
    super.onStart(intent);
    initTtsEngine();
    initDistributedListener();

private void initDistributedListener() {

    dataManager = DistributedDataManager.getInstance(this);
    dataManager.registerDataChangeListener(new DataChangeListener() {
        @Override
        public void onDataChanged(String key, String value) {
            if (key.equals("cooking_steps")) {
                prepareGuidance(value);

}

});

private void prepareGuidance(String stepsJson) {

    CookingStep[] steps = new Gson().fromJson(stepsJson, CookingStep[].class);
    
    // 开始逐步指导
    guideStepByStep(steps);

private void guideStepByStep(CookingStep[] steps) {

    if (currentStep < steps.length) {
        CookingStep step = steps[currentStep];
        
        // 语音播报当前步骤
        ttsEngine.speak(step.getDescription(), TtsEngine.QUEUE_FLUSH, null);
        
        // 设置定时器进入下一步
        Timer timer = new Timer();
        timer.schedule(new TimerTask() {
            @Override
            public void run() {
                currentStep++;
                guideStepByStep(steps);

}, step.getDuration() * 1000);

}

关键技术解析
多模态食材识别：

  # Python端的融合识别算法（部署为HarmonyOS原子化服务）

def enhanced_food_detection(image):
# 使用YOLOv5检测食材边界框
detections = yolov5_model(image)

   # 使用CLIP模型验证和细化分类
   clip_features = clip_model.encode_image(image)
   for det in detections:
       text_features = clip_model.encode_text(f"a photo of {det.label}")
       similarity = cosine_similarity(clip_features, text_features)
       if similarity < 0.3:  # 验证阈值
           det.label = "unknown"
   
   return detections

知识图谱查询优化：

  // 知识图谱查询实现

public List<Recipe> queryRecipes(Ingredient[] ingredients) {
// 提取食材名称
String[] names = Arrays.stream(ingredients)
.map(Ingredient::getName)
.toArray(String[]::new);

   // 查询包含任意3种食材的菜谱
   String query = "MATCH (r:Recipe)-[:CONTAINS]->(i:Ingredient) " +
                 "WHERE i.name IN $names " +
                 "WITH r, COUNT(i) AS matches " +
                 "WHERE matches >= 3 " +
                 "RETURN r ORDER BY matches DESC LIMIT 5";
   
   return neo4jClient.query(query, Map.of("names", names));

跨设备同步优化：

  // 基于网络状况的自适应同步策略

public void optimizeDataSync() {
NetworkState state = NetworkManager.getCurrentState();

   // 根据网络质量调整同步策略
   SyncPolicy policy = new SyncPolicy()
       .setCompression(state != NetworkState.EXCELLENT)
       .setPriority(SyncPriority.HIGH)
       .setRetryCount(state == NetworkState.POOR ? 3 : 1);
   
   dataManager.setSyncPolicy("recommended_recipes", policy);

与游戏昵称同步技术的关联创新
状态同步机制：

借鉴游戏中的玩家状态同步策略，实现烹饪步骤的跨设备同步

采用类似的分布式数据版本控制保证各设备状态一致
设备角色分配：

  // 类似游戏中的角色分配逻辑

public void assignDeviceRoles() {
List<DeviceInfo> devices = DeviceManager.getConnectedDevices();

   devices.forEach(device -> {
       String role = switch(device.getType()) {
           case PHONE -> "detector";
           case TV -> "display";
           case SPEAKER -> "guide";
           default -> "viewer";
       };
       
       dataManager.putString(device.getId(), "assigned_role", role);
   });

实时性保障：

关键数据（如步骤切换）采用即时同步

非关键数据（如菜谱图片）采用后台异步同步

系统特色功能
智能替代推荐：

  public List<Ingredient> findSubstitutes(Ingredient ingredient) {
   // 在知识图谱中查询可替代食材
   return kg.query("MATCH (i1:Ingredient {name: $name})-[:CAN_REPLACE]->(i2) " +
                  "RETURN i2 ORDER BY i2.similarity DESC LIMIT 3", 
                  Map.of("name", ingredient.getName()));

营养分析：

  def analyze_nutrition(recipe):
   total_calories = sum(ingredient.calories for ingredient in recipe.ingredients)
   nutrition_tags = []
   
   if total_calories < 400:
       nutrition_tags.append("低卡")
   if any(i for i in recipe.ingredients if i.is_high_protein):
       nutrition_tags.append("高蛋白")
       
   return NutritionInfo(total_calories, nutrition_tags)

多用户协作：

支持多个手机同时扫描不同区域的食材

合并所有用户的检测结果生成综合推荐

该AI菜谱一步直达服务系统充分展现了鸿蒙分布式技术的优势，将原本分散的厨房设备整合为智能协同的整体，为智能家居场景提供了创新解决方案。通过借鉴游戏中的状态同步和角色分配机制，实现了厨房场景下的无缝多设备协作体验。

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