智能家居APP开发完整指南

interface SmartDevice {
  id: string;
  name: string;
  type: DeviceType;
  manufacturer: string;
  model: string;
  firmwareVersion: string;
  connectionType: 'wifi' | 'zigbee' | 'bluetooth' | 'ethernet';
  status: DeviceStatus;
  capabilities: DeviceCapability[];
  location: DeviceLocation;
  security: DeviceSecurity;
  metadata: Record<string, any>;
}
 
enum DeviceStatus {
  ONLINE = 'online',
  OFFLINE = 'offline',
  UPDATING = 'updating',
  ERROR = 'error',
  PAIRING = 'pairing'
}
 
interface DeviceCapability {
  type: 'switch' | 'dimmer' | 'sensor' | 'thermostat' | 'camera';
  readable: boolean;
  writable: boolean;
  properties: Record<string, CapabilityProperty>;
}
 
// 设备发现和配对流程
class DeviceManager {
  async discoverDevices(): Promise<DiscoveredDevice[]> {
    const discoveryMethods = [
      this.scanWiFiDevices(),
      this.scanBluetooth(),
      this.scanZigbee(),
      this.queryCloudDevices()
    ];
    
    const results = await Promise.allSettled(discoveryMethods);
    return results
      .filter(result => result.status === 'fulfilled')
      .flatMap(result => result.value);
  }
  
  async pairDevice(deviceId: string, pairingCode?: string): Promise<Device> {
    const device = await this.initiatePairing(deviceId);
    
    // 验证配对码（如果需要）
    if (device.requiresPairingCode && pairingCode) {
      await this.validatePairingCode(deviceId, pairingCode);
    }
    
    // 设备配置初始化
    await this.configureDevice(device);
    
    // 安全密钥交换
    const securityContext = await this.establishSecureConnection(device);
    
    return this.finalizeDeviceSetup(device, securityContext);
  }
}

interface AutomationRule {
  id: string;
  name: string;
  conditions: RuleCondition[];
  actions: RuleAction[];
  priority: number;
  enabled: boolean;
  schedule?: CronExpression;
}
 
interface RuleCondition {
  type: 'device_state' | 'time' | 'location' | 'weather' | 'sensor_threshold';
  deviceId?: string;
  property: string;
  operator: 'eq' | 'ne' | 'gt' | 'lt' | 'gte' | 'lte' | 'contains';
  value: any;
  logicOperator?: 'AND' | 'OR';
}
 
class AutomationEngine {
  private rules: Map<string, AutomationRule> = new Map();
  private ruleProcessor: RuleProcessor;
  
  constructor() {
    this.ruleProcessor = new RuleProcessor();
  }
  
  async evaluateRules(context: AutomationContext): Promise<void> {
    const activeRules = Array.from(this.rules.values())
      .filter(rule => rule.enabled)
      .sort((a, b) => b.priority - a.priority);
    
    for (const rule of activeRules) {
      const shouldExecute = await this.evaluateConditions(rule.conditions, context);
      
      if (shouldExecute) {
        await this.executeActions(rule.actions);
        
        // 记录执行日志
        this.logRuleExecution(rule, context);
      }
    }
  }
  
  private async evaluateConditions(
    conditions: RuleCondition[], 
    context: AutomationContext
  ): Promise<boolean> {
    if (conditions.length === 0) return false;
    
    let result = true;
    let currentLogicOperator: 'AND' | 'OR' = 'AND';
    
    for (const condition of conditions) {
      const conditionResult = await this.evaluateSingleCondition(condition, context);
      
      if (currentLogicOperator === 'AND') {
        result = result && conditionResult;
      } else {
        result = result || conditionResult;
      }
      
      currentLogicOperator = condition.logicOperator || 'AND';
    }
    
    return result;
  }
  
  private async executeActions(actions: RuleAction[]): Promise<void> {
    const actionPromises = actions.map(async (action) => {
      try {
        switch (action.type) {
          case 'device_control':
            await this.controlDevice(action.deviceId!, action.properties);
            break;
          case 'notification':
            await this.sendNotification(action.message);
            break;
          case 'scene_activation':
            await this.activateScene(action.sceneId!);
            break;
        }
      } catch (error) {
        console.error(`动作执行失败 ${action.type}:`, error);
      }
    });
    
    await Promise.allSettled(actionPromises);
  }
}

import { configureStore, createSlice, createAsyncThunk } from '@reduxjs/toolkit';
import { persistStore, persistReducer } from 'redux-persist';
import AsyncStorage from '@react-native-async-storage/async-storage';
 
// 设备状态切片
const deviceSlice = createSlice({
  name: 'devices',
  initialState: {
    items: {} as Record<string, Device>,
    discoveredDevices: [] as DiscoveredDevice[],
    loading: false,
    error: null as string | null,
    lastSync: null as number | null
  },
  reducers: {
    updateDeviceStatus: (state, action) => {
      const { deviceId, status } = action.payload;
      if (state.items[deviceId]) {
        state.items[deviceId].status = status;
        state.lastSync = Date.now();
      }
    },
    addDevice: (state, action) => {
      const device = action.payload;
      state.items[device.id] = device;
    },
    removeDevice: (state, action) => {
      delete state.items[action.payload];
    }
  },
  extraReducers: (builder) => {
    builder
      .addCase(fetchDevices.pending, (state) => {
        state.loading = true;
        state.error = null;
      })
      .addCase(fetchDevices.fulfilled, (state, action) => {
        state.loading = false;
        state.items = action.payload.reduce((acc, device) => {
          acc[device.id] = device;
          return acc;
        }, {});
      })
      .addCase(fetchDevices.rejected, (state, action) => {
        state.loading = false;
        state.error = action.error.message || '加载设备失败';
      });
  }
});
 
// 异步操作
export const fetchDevices = createAsyncThunk(
  'devices/fetchDevices',
  async (userId: string) => {
    const response = await deviceAPI.getDevices(userId);
    return response.data;
  }
);
 
// 中间件配置
const persistConfig = {
  key: 'root',
  storage: AsyncStorage,
  whitelist: ['devices', 'scenes', 'userPreferences']
};
 
export const store = configureStore({
  reducer: persistReducer(persistConfig, {
    devices: deviceSlice.reducer,
    scenes: sceneSlice.reducer,
    auth: authSlice.reducer
  }),
  middleware: (getDefaultMiddleware) =>
    getDefaultMiddleware({
      serializableCheck: {
        ignoredActions: ['persist/PERSIST']
      }
    })
});

import { SpeechRecognition } from 'react-native-speech-recognition';
 
class VoiceController {
  private nlpProcessor: NLPProcessor;
  private isListening: boolean = false;
  
  constructor() {
    this.nlpProcessor = new NLPProcessor();
    this.initializeVoiceRecognition();
  }
  
  private initializeVoiceRecognition(): void {
    SpeechRecognition.onSpeechResults = async (results) => {
      const command = results[0];
      await this.processVoiceCommand(command);
    };
    
    SpeechRecognition.onSpeechError = (error) => {
      console.error('语音识别错误:', error);
      this.stopListening();
    };
  }
  
  async startListening(): Promise<void> {
    if (this.isListening) return;
    
    try {
      await SpeechRecognition.requestPermission();
      await SpeechRecognition.startSpeech({
        locale: 'zh-CN',
        prompt: '请说出您的指令...'
      });
      this.isListening = true;
    } catch (error) {
      throw new Error(`语音识别启动失败: ${error}`);
    }
  }
  
  private async processVoiceCommand(command: string): Promise<void> {
    // 自然语言处理
    const intent = await this.nlpProcessor.extractIntent(command);
    
    switch (intent.action) {
      case 'device_control':
        await this.handleDeviceControl(intent);
        break;
      case 'scene_activation':
        await this.handleSceneActivation(intent);
        break;
      case 'query_status':
        await this.handleStatusQuery(intent);
        break;
      default:
        await this.handleUnknownCommand(command);
    }
  }
  
  private async handleDeviceControl(intent: ParsedIntent): Promise<void> {
    const { deviceName, action, value } = intent.parameters;
    
    // 设备名称模糊匹配
    const device = await this.findDeviceByName(deviceName);
    if (!device) {
      await this.speakResponse(`未找到名为"${deviceName}"的设备`);
      return;
    }
    
    // 执行控制操作
    try {
      await deviceAPI.controlDevice(device.id, { [action]: value });
      await this.speakResponse(`${device.name}已${action === 'on' ? '开启' : '关闭'}`);
    } catch (error) {
      await this.speakResponse(`控制${device.name}失败，请检查设备连接`);
    }
  }
}
 
// 自然语言处理器
class NLPProcessor {
  private patterns: IntentPattern[] = [
    {
      regex: /打开|开启|开.*?(灯|空调|电视|风扇)/,
      action: 'device_control',
      parameters: { action: 'on' }
    },
    {
      regex: /关闭|关.*?(灯|空调|电视|风扇)/,
      action: 'device_control', 
      parameters: { action: 'off' }
    },
    {
      regex: /温度调到(\d+)度/,
      action: 'device_control',
      parameters: { action: 'setTemperature' }
    }
  ];
  
  async extractIntent(command: string): Promise<ParsedIntent> {
    for (const pattern of this.patterns) {
      const match = command.match(pattern.regex);
      if (match) {
        return {
          action: pattern.action,
          parameters: {
            ...pattern.parameters,
            deviceName: this.extractDeviceName(command),
            value: match[1] || true
          },
          confidence: this.calculateConfidence(match)
        };
      }
    }
    
    return { action: 'unknown', parameters: {}, confidence: 0 };
  }
}

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from datetime import datetime, timedelta
 
class SmartRecommendationEngine:
    def __init__(self):
        self.scaler = StandardScaler()
        self.kmeans_model = KMeans(n_clusters=5, random_state=42)
        self.user_patterns = {}
    
    def analyze_usage_patterns(self, user_id: str, history_days: int = 30):
        """分析用户使用模式"""
        # 获取用户操作历史
        end_date = datetime.now()
        start_date = end_date - timedelta(days=history_days)
        
        usage_data = self.fetch_user_actions(user_id, start_date, end_date)
        
        # 特征工程
        features = self.extract_features(usage_data)
        
        # 聚类分析
        clusters = self.kmeans_model.fit_predict(features)
        
        # 生成推荐
        recommendations = self.generate_recommendations(clusters, features)
        
        return {
            'user_profile': self.create_user_profile(features),
            'recommendations': recommendations,
            'energy_insights': self.analyze_energy_patterns(usage_data)
        }
    
    def extract_features(self, usage_data: pd.DataFrame) -> np.ndarray:
        """提取用户行为特征"""
        features = []
        
        # 时间模式特征
        hourly_activity = usage_data.groupby('hour').size()
        daily_activity = usage_data.groupby('day_of_week').size()
        
        # 设备使用频率特征
        device_frequency = usage_data.groupby('device_type').size()
        
        # 场景使用偏好特征
        scene_preference = usage_data.groupby('scene_type').size()
        
        # 归一化处理
        feature_vector = np.concatenate([
            hourly_activity.values,
            daily_activity.values,
            device_frequency.values,
            scene_preference.values
        ])
        
        return self.scaler.fit_transform(feature_vector.reshape(1, -1))
    
    def generate_recommendations(self, user_cluster: int, features: np.ndarray) -> List[Recommendation]:
        """基于聚类结果生成个性化推荐"""
        recommendations = []
        
        # 场景推荐
        if user_cluster == 0:  # 早起型用户
            recommendations.append({
                'type': 'scene',
                'title': '晨间唤醒场景',
                'description': '自动开启窗帘、播放轻音乐，逐渐点亮房间',
                'confidence': 0.85
            })
        elif user_cluster == 1:  # 夜猫子用户
            recommendations.append({
                'type': 'scene',
                'title': '深夜模式',
                'description': '降低所有灯光亮度，开启夜灯，静音模式',
                'confidence': 0.82
            })
        
        # 节能推荐
        energy_savings = self.calculate_energy_savings(features)
        if energy_savings['potential_savings'] > 20:
            recommendations.append({
                'type': 'energy',
                'title': '节能优化建议',
                'description': f'通过智能调度可节省{energy_savings["potential_savings"]:.1f}%能耗',
                'actions': energy_savings['recommended_actions']
            })
        
        return recommendations
 
    async def predict_next_actions(self, user_id: str, context: dict) -> List[PredictedAction]:
        """预测用户可能的下一步操作"""
        current_time = datetime.now()
        
        # 获取类似时间段的历史行为
        similar_contexts = await self.find_similar_contexts(user_id, context)
        
        # 计算各种操作的概率
        action_probabilities = {}
        for ctx in similar_contexts:
            for action in ctx['subsequent_actions']:
                if action['type'] not in action_probabilities:
                    action_probabilities[action['type']] = []
                action_probabilities[action['type']].append(action['probability'])
        
        # 生成预测结果
        predictions = []
        for action_type, probabilities in action_probabilities.items():
            avg_probability = np.mean(probabilities)
            if avg_probability > 0.3:  # 阈值过滤
                predictions.append({
                    'action_type': action_type,
                    'probability': avg_probability,
                    'suggested_devices': self.get_relevant_devices(action_type),
                    'estimated_time': self.estimate_action_time(action_type)
                })
        
        return sorted(predictions, key=lambda x: x['probability'], reverse=True)

import CryptoJS from 'crypto-js';
import { RSA } from 'react-native-rsa-native';
 
class SecurityManager {
  private deviceKeys: Map<string, DeviceKeyPair> = new Map();
  private sessionKeys: Map<string, string> = new Map();
  
  async establishSecureSession(deviceId: string): Promise<SecureSession> {
    // 1. RSA密钥交换
    const { publicKey, privateKey } = await RSA.generateKeyPair();
    const devicePublicKey = await this.getDevicePublicKey(deviceId);
    
    // 2. 生成会话密钥
    const sessionKey = CryptoJS.lib.WordArray.random(256/8).toString();
    const encryptedSessionKey = await RSA.encrypt(sessionKey, devicePublicKey);
    
    // 3. 发送密钥交换请求
    const keyExchange = await this.sendKeyExchangeRequest(deviceId, {
      clientPublicKey: publicKey,
      encryptedSessionKey: encryptedSessionKey
    });
    
    // 4. 验证设备响应
    const deviceSessionKey = await RSA.decrypt(keyExchange.encryptedResponse, privateKey);
    
    if (deviceSessionKey !== sessionKey) {
      throw new Error('密钥交换验证失败');
    }
    
    // 5. 建立安全会话
    this.sessionKeys.set(deviceId, sessionKey);
    
    return {
      deviceId,
      sessionKey,
      expires: Date.now() + (24 * 60 * 60 * 1000) // 24小时过期
    };
  }
  
  encryptCommand(deviceId: string, command: DeviceCommand): string {
    const sessionKey = this.sessionKeys.get(deviceId);
    if (!sessionKey) {
      throw new Error('设备会话未建立');
    }
    
    const commandString = JSON.stringify(command);
    const encrypted = CryptoJS.AES.encrypt(commandString, sessionKey).toString();
    
    return encrypted;
  }
  
  decryptResponse(deviceId: string, encryptedResponse: string): any {
    const sessionKey = this.sessionKeys.get(deviceId);
    if (!sessionKey) {
      throw new Error('设备会话未建立');
    }
    
    const decrypted = CryptoJS.AES.decrypt(encryptedResponse, sessionKey);
    return JSON.parse(decrypted.toString(CryptoJS.enc.Utf8));
  }
}
 
// 生物识别认证
import TouchID from 'react-native-touch-id';
 
class BiometricAuth {
  static async authenticateUser(): Promise<boolean> {
    try {
      const biometryType = await TouchID.isSupported();
      
      if (biometryType) {
        await TouchID.authenticate('验证您的身份以控制智能设备', {
          title: '生物识别验证',
          imageColor: '#FF4F19',
          imageErrorColor: '#ef4444',
          sensorDescription: '请将手指放在传感器上',
          sensorErrorDescription: '验证失败，请重试',
          cancelText: '取消'
        });
        return true;
      }
      
      return false;
    } catch (error) {
      console.error('生物识别验证失败:', error);
      return false;
    }
  }
  
  static async setupBiometricSecurity(userId: string): Promise<void> {
    const authKey = CryptoJS.lib.WordArray.random(256/8).toString();
    
    // 将认证密钥与生物识别绑定
    await AsyncStorage.setItem(`biometric_key_${userId}`, authKey);
    
    // 服务器端注册生物识别
    await authAPI.registerBiometric(userId, {
      deviceId: await this.getDeviceId(),
      publicKey: await this.generateBiometricPublicKey()
    });
  }
}

interface SyncConflict {
  deviceId: string;
  property: string;
  localValue: any;
  remoteValue: any;
  localTimestamp: number;
  remoteTimestamp: number;
}
 
class DataSyncManager {
  private conflictResolver: ConflictResolver;
  private syncQueue: SyncOperation[] = [];
  private isSyncing: boolean = false;
  
  constructor() {
    this.conflictResolver = new ConflictResolver();
  }
  
  async synchronizeDeviceData(): Promise<SyncResult> {
    if (this.isSyncing) return { status: 'already_syncing' };
    
    this.isSyncing = true;
    const conflicts: SyncConflict[] = [];
    const synced: string[] = [];
    const errors: string[] = [];
    
    try {
      const localDevices = await this.getLocalDeviceStates();
      const remoteDevices = await this.getRemoteDeviceStates();
      
      for (const [deviceId, localState] of localDevices) {
        const remoteState = remoteDevices.get(deviceId);
        
        if (!remoteState) {
          // 设备仅存在于本地，推送到服务器
          await this.pushDeviceState(deviceId, localState);
          synced.push(deviceId);
          continue;
        }
        
        // 检查冲突
        const deviceConflicts = this.detectConflicts(deviceId, localState, remoteState);
        
        if (deviceConflicts.length > 0) {
          conflicts.push(...deviceConflicts);
        } else if (remoteState.lastModified > localState.lastModified) {
          // 远程更新，拉取到本地
          await this.updateLocalDeviceState(deviceId, remoteState);
          synced.push(deviceId);
        }
      }
      
      // 解决冲突
      const resolvedConflicts = await this.resolveConflicts(conflicts);
      
      return {
        status: 'completed',
        synced: synced.length,
        conflicts: conflicts.length,
        errors: errors.length,
        resolvedConflicts
      };
      
    } finally {
      this.isSyncing = false;
    }
  }
  
  private async resolveConflicts(conflicts: SyncConflict[]): Promise<ConflictResolution[]> {
    const resolutions: ConflictResolution[] = [];
    
    for (const conflict of conflicts) {
      try {
        const resolution = await this.conflictResolver.resolve(conflict);
        await this.applyResolution(conflict, resolution);
        resolutions.push(resolution);
      } catch (error) {
        console.error(`冲突解决失败 ${conflict.deviceId}:`, error);
      }
    }
    
    return resolutions;
  }
}
 
class ConflictResolver {
  async resolve(conflict: SyncConflict): Promise<ConflictResolution> {
    const strategy = this.selectResolutionStrategy(conflict);
    
    switch (strategy) {
      case 'latest_wins':
        return {
          strategy,
          selectedValue: conflict.localTimestamp > conflict.remoteTimestamp 
            ? conflict.localValue 
            : conflict.remoteValue,
          reason: '选择最新修改的值'
        };
      
      case 'user_preference':
        // 用户设置了优先级策略
        const userPreference = await this.getUserConflictPreference(conflict.deviceId);
        return {
          strategy,
          selectedValue: userPreference === 'local' ? conflict.localValue : conflict.remoteValue,
          reason: '根据用户偏好选择'
        };
      
      case 'device_authoritative':
        // 某些设备状态以设备端为准
        return {
          strategy,
          selectedValue: conflict.remoteValue,
          reason: '设备状态以设备端实际值为准'
        };
      
      default:
        throw new Error(`未支持的冲突解决策略: ${strategy}`);
    }
  }
}

import React, { useMemo, useCallback } from 'react';
import { View, PanGestureHandler, State } from 'react-native-gesture-handler';
import Animated, { 
  useAnimatedStyle, 
  useAnimatedGestureHandler,
  useSharedValue,
  withSpring,
  interpolate
} from 'react-native-reanimated';
 
interface SmartThermostatProps {
  deviceId: string;
  currentTemp: number;
  targetTemp: number;
  minTemp: number;
  maxTemp: number;
  onTemperatureChange: (temp: number) => void;
}
 
const SmartThermostat: React.FC<SmartThermostatProps> = ({
  deviceId,
  currentTemp,
  targetTemp,
  minTemp,
  maxTemp,
  onTemperatureChange
}) => {
  const rotation = useSharedValue(0);
  const scale = useSharedValue(1);
  
  const gestureHandler = useAnimatedGestureHandler({
    onStart: () => {
      scale.value = withSpring(1.1);
    },
    onActive: (event) => {
      const angle = Math.atan2(event.y - 120, event.x - 120);
      rotation.value = angle;
    },
    onEnd: () => {
      scale.value = withSpring(1);
      
      // 计算新的温度值
      const normalizedRotation = (rotation.value + Math.PI) / (2 * Math.PI);
      const newTemp = minTemp + (maxTemp - minTemp) * normalizedRotation;
      onTemperatureChange(Math.round(newTemp));
    }
  });
  
  const animatedStyle = useAnimatedStyle(() => ({
    transform: [
      { rotate: `${rotation.value}rad` },
      { scale: scale.value }
    ]
  }));
  
  const indicatorStyle = useAnimatedStyle(() => {
    const tempRatio = (targetTemp - minTemp) / (maxTemp - minTemp);
    const indicatorRotation = -Math.PI + (2 * Math.PI * tempRatio);
    
    return {
      transform: [{ rotate: `${indicatorRotation}rad` }]
    };
  });
  
  return (
    <View style={styles.thermostatContainer}>
      <PanGestureHandler onGestureEvent={gestureHandler}>
        <Animated.View style={[styles.thermostat, animatedStyle]}>
          {/* 温度刻度圈 */}
          <Svg height="240" width="240" style={styles.svgContainer}>
            {renderTemperatureScale(minTemp, maxTemp)}
            <Circle 
              cx="120" 
              cy="120" 
              r="100" 
              stroke="#374151" 
              strokeWidth="2" 
              fill="none"
            />
          </Svg>
          
          {/* 温度指示器 */}
          <Animated.View style={[styles.indicator, indicatorStyle]}>
            <View style={styles.indicatorDot} />
          </Animated.View>
          
          {/* 中心温度显示 */}
          <View style={styles.centerDisplay}>
            <Text style={styles.currentTemp}>{currentTemp}°</Text>
            <Text style={styles.targetTemp}>目标: {targetTemp}°</Text>
          </View>
        </Animated.View>
      </PanGestureHandler>
    </View>
  );
};
 
// 自适应设备控制面板
const AdaptiveDevicePanel: React.FC<{ device: Device }> = ({ device }) => {
  const ControlComponent = useMemo(() => {
    switch (device.type) {
      case 'light':
        return device.capabilities.includes('dimming') 
          ? DimmableLightControl 
          : SimpleLightControl;
      case 'thermostat':
        return SmartThermostat;
      case 'camera':
        return CameraControl;
      case 'speaker':
        return AudioControl;
      default:
        return GenericDeviceControl;
    }
  }, [device.type, device.capabilities]);
  
  return (
    <View style={styles.devicePanel}>
      <ControlComponent device={device} />
    </View>
  );
};

import * as tf from '@tensorflow/tfjs-react-native';
 
class EdgeAIProcessor {
  private models: Map<string, tf.LayersModel> = new Map();
  
  async loadModel(modelName: string, modelUrl: string): Promise<void> {
    try {
      const model = await tf.loadLayersModel(modelUrl);
      this.models.set(modelName, model);
      console.log(`AI模型 ${modelName} 加载成功`);
    } catch (error) {
      throw new Error(`模型加载失败: ${error}`);
    }
  }
  
  async predictDeviceFailure(deviceId: string, sensorData: SensorReading[]): Promise<FailurePrediction> {
    const model = this.models.get('device_failure_predictor');
    if (!model) {
      throw new Error('设备故障预测模型未加载');
    }
    
    // 数据预处理
    const features = this.preprocessSensorData(sensorData);
    const inputTensor = tf.tensor2d([features]);
    
    // 预测
    const prediction = model.predict(inputTensor) as tf.Tensor;
    const failureProb = await prediction.data();
    
    // 清理内存
    inputTensor.dispose();
    prediction.dispose();
    
    return {
      deviceId,
      failureProbability: failureProb[0],
      riskLevel: this.calculateRiskLevel(failureProb[0]),
      recommendedActions: this.generateMaintenanceRecommendations(failureProb[0])
    };
  }
  
  async optimizeEnergyUsage(
    deviceStates: Record<string, DeviceState>, 
    userPreferences: UserPreferences
  ): Promise<EnergyOptimization> {
    const optimizationModel = this.models.get('energy_optimizer');
    
    // 构建特征向量
    const features = this.buildEnergyFeatures(deviceStates, userPreferences);
    const prediction = await this.runInference(optimizationModel, features);
    
    return {
      estimatedSavings: prediction.savings,
      recommendations: prediction.actions,
      confidence: prediction.confidence
    };
  }
  
  private preprocessSensorData(sensorData: SensorReading[]): number[] {
    const features = [];
    
    // 时间序列特征
    const values = sensorData.map(reading => reading.value);
    features.push(
      Math.mean(values),                    // 均值
      Math.std(values),                     // 标准差
      Math.max(...values) - Math.min(...values), // 极差
      this.calculateTrend(values)           // 趋势
    );
    
    // 频域特征（FFT）
    const fftFeatures = this.extractFFTFeatures(values);
    features.push(...fftFeatures);
    
    return features;
  }
}

class DeviceSimulator {
  private simulatedDevices: Map<string, SimulatedDevice> = new Map();
  
  createSimulatedDevice(type: DeviceType, config: DeviceConfig): SimulatedDevice {
    const device = {
      id: `sim_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
      type,
      name: config.name,
      capabilities: this.getDeviceCapabilities(type),
      state: this.getInitialState(type),
      behaviorModel: this.createBehaviorModel(type, config)
    };
    
    this.simulatedDevices.set(device.id, device);
    return device;
  }
  
  private createBehaviorModel(type: DeviceType, config: DeviceConfig): DeviceBehavior {
    switch (type) {
      case 'thermostat':
        return new ThermostatBehavior({
          heatingRate: 2.5, // 每分钟升温度数
          coolingRate: 1.8,
          ambientTemp: 22,
          thermalMass: 0.9  // 热惯性系数
        });
      
      case 'light':
        return new LightBehavior({
          powerConsumption: config.wattage || 10,
          dimmingCurve: 'linear',
          colorSpace: 'sRGB'
        });
      
      default:
        return new GenericBehavior();
    }
  }
  
  async simulateEnvironmentChange(environmentId: string, changes: EnvironmentChange[]): Promise<void> {
    for (const change of changes) {
      const affectedDevices = this.getDevicesByEnvironment(environmentId);
      
      for (const device of affectedDevices) {
        await device.behaviorModel.respondToEnvironmentChange(change);
      }
    }
  }
}
 
// 压力测试
class LoadTester {
  async simulateHighConcurrency(deviceCount: number, commandsPerSecond: number): Promise<TestResults> {
    const devices = Array.from({ length: deviceCount }, (_, i) => 
      this.simulator.createSimulatedDevice('mixed', { name: `TestDevice${i}` })
    );
    
    const testDuration = 60000; // 1分钟
    const interval = 1000 / commandsPerSecond;
    
    const stats = {
      commandsSent: 0,
      responses: 0,
      errors: 0,
      latencies: [] as number[]
    };
    
    return new Promise((resolve) => {
      const startTime = Date.now();
      
      const sendCommand = async () => {
        if (Date.now() - startTime >= testDuration) {
          clearInterval(commandInterval);
          resolve(this.calculateTestResults(stats));
          return;
        }
        
        const device = devices[Math.floor(Math.random() * devices.length)];
        const command = this.generateRandomCommand(device);
        
        const requestStart = performance.now();
        stats.commandsSent++;
        
        try {
          await deviceAPI.sendCommand(device.id, command);
          stats.responses++;
          stats.latencies.push(performance.now() - requestStart);
        } catch (error) {
          stats.errors++;
        }
      };
      
      const commandInterval = setInterval(sendCommand, interval);
    });
  }
}

name: Smart Home App Deploy
 
on:
  push:
    branches: [main]
  pull_request:
    branches: [main]
 
jobs:
  test:
    runs-on: ubuntu-latest
    
    steps:
    - uses: actions/checkout@v3
    
    - name: Setup Node.js
      uses: actions/setup-node@v3
      with:
        node-version: '18'
        cache: 'npm'
    
    - name: Install dependencies
      run: npm ci
    
    - name: Run unit tests
      run: npm run test:unit
    
    - name: Run integration tests
      run: npm run test:integration
    
    - name: Device simulation tests
      run: npm run test:devices
    
    - name: Security vulnerability scan
      run: npm audit --audit-level moderate
    
  build:
    needs: test
    runs-on: macos-latest
    
    steps:
    - uses: actions/checkout@v3
    
    - name: Setup React Native environment
      uses: ./.github/actions/setup-rn
    
    - name: Build iOS
      run: |
        cd ios
        xcodebuild -workspace SmartHome.xcworkspace \
                   -scheme SmartHome \
                   -configuration Release \
                   -archivePath build/SmartHome.xcarchive \
                   archive
    
    - name: Build Android
      run: |
        cd android
        ./gradlew assembleRelease
        
    - name: Upload to TestFlight
      if: github.ref == 'refs/heads/main'
      run: |
        xcrun altool --upload-app \
                     --type ios \
                     --file build/SmartHome.ipa \
                     --username "${{ secrets.APPLE_ID }}" \
                     --password "${{ secrets.APPLE_PASSWORD }}"
    
    - name: Deploy to Play Store
      if: github.ref == 'refs/heads/main'
      uses: r0adkll/upload-google-play@v1
      with:
        serviceAccountJsonPlainText: ${{ secrets.PLAY_STORE_SERVICE_ACCOUNT }}
        packageName: com.iotteam.smarthome
        releaseFiles: android/app/build/outputs/bundle/release/app-release.aab
        track: internal

import crashlytics from '@react-native-firebase/crashlytics';
import analytics from '@react-native-firebase/analytics';
import perf from '@react-native-firebase/perf';
 
class AppMonitoring {
  static initializeMonitoring(): void {
    // 崩溃报告
    crashlytics().setCrashlyticsCollectionEnabled(true);
    
    // 用户分析
    analytics().setAnalyticsCollectionEnabled(true);
    
    // 性能监控
    this.setupPerformanceMonitoring();
    this.setupDeviceConnectionMonitoring();
  }
  
  static async logDeviceOperation(operation: DeviceOperation): Promise<void> {
    const trace = perf().newTrace(`device_${operation.type}`);
    await trace.start();
    
    try {
      await operation.execute();
      
      // 记录成功指标
      await analytics().logEvent('device_operation_success', {
        device_type: operation.deviceType,
        operation_type: operation.type,
        response_time: Date.now() - operation.startTime
      });
      
    } catch (error) {
      // 记录失败信息
      await analytics().logEvent('device_operation_failed', {
        device_type: operation.deviceType,
        operation_type: operation.type,
        error_code: error.code,
        error_message: error.message
      });
      
      // 发送崩溃报告
      crashlytics().recordError(error);
      
      throw error;
    } finally {
      await trace.stop();
    }
  }
  
  private static setupPerformanceMonitoring(): void {
    // App启动时间监控
    const appStartTrace = perf().newTrace('app_start');
    appStartTrace.start();
    
    // 设备连接性能监控
    const deviceConnectionTrace = perf().newTrace('device_connections');
    deviceConnectionTrace.start();
    
    // 内存使用监控
    setInterval(() => {
      const memoryUsage = performance.memory;
      if (memoryUsage.usedJSHeapSize > 50 * 1024 * 1024) { // 50MB阈值
        analytics().logEvent('high_memory_usage', {
          used_heap: memoryUsage.usedJSHeapSize,
          total_heap: memoryUsage.totalJSHeapSize
        });
      }
    }, 30000);
  }
}
 
// 实时性能指标收集
class PerformanceCollector {
  private metrics: PerformanceMetric[] = [];
  
  async collectDeviceResponseTimes(): Promise<void> {
    const devices = await DeviceManager.getConnectedDevices();
    
    for (const device of devices) {
      const startTime = performance.now();
      
      try {
        await device.ping();
        const responseTime = performance.now() - startTime;
        
        this.metrics.push({
          type: 'device_response_time',
          deviceId: device.id,
          value: responseTime,
          timestamp: Date.now()
        });
        
        // 响应时间异常告警
        if (responseTime > 5000) {
          await this.sendPerformanceAlert(device.id, responseTime);
        }
      } catch (error) {
        this.metrics.push({
          type: 'device_connection_error',
          deviceId: device.id,
          error: error.message,
          timestamp: Date.now()
        });
      }
    }
  }
  
  async generatePerformanceReport(): Promise<PerformanceReport> {
    const last24h = Date.now() - 24 * 60 * 60 * 1000;
    const recentMetrics = this.metrics.filter(m => m.timestamp > last24h);
    
    return {
      averageResponseTime: this.calculateAverageResponseTime(recentMetrics),
      deviceUptime: this.calculateDeviceUptime(recentMetrics),
      errorRate: this.calculateErrorRate(recentMetrics),
      topPerformingDevices: this.getTopPerformingDevices(recentMetrics),
      performanceInsights: this.generateInsights(recentMetrics)
    };
  }
}

abstract class DeviceAdapter {
  abstract async connect(device: Device): Promise<Connection>;
  abstract async sendCommand(command: DeviceCommand): Promise<CommandResult>;
  abstract async getStatus(): Promise<DeviceStatus>;
}
 
class WiFiAdapter extends DeviceAdapter {
  async connect(device: Device): Promise<Connection> {
    const connection = new WiFiConnection(device.ipAddress, device.port);
    await connection.establish();
    
    return connection;
  }
  
  async sendCommand(command: DeviceCommand): Promise<CommandResult> {
    const payload = {
      method: command.method,
      params: command.parameters,
      timestamp: Date.now()
    };
    
    const response = await fetch(`http://${this.device.ipAddress}/api/command`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(payload)
    });
    
    return await response.json();
  }
}
 
class ZigbeeAdapter extends DeviceAdapter {
  private coordinator: ZigbeeCoordinator;
  
  constructor() {
    super();
    this.coordinator = new ZigbeeCoordinator();
  }
  
  async connect(device: Device): Promise<Connection> {
    await this.coordinator.permitJoining(60); // 允许60秒内加入网络
    
    const networkAddress = await this.coordinator.addDevice(device.ieeeAddress);
    device.networkAddress = networkAddress;
    
    return new ZigbeeConnection(device, this.coordinator);
  }
  
  async sendCommand(command: DeviceCommand): Promise<CommandResult> {
    const cluster = this.getClusterForCommand(command);
    const attribute = this.getAttributeForCommand(command);
    
    const result = await this.coordinator.writeAttribute(
      device.networkAddress,
      cluster,
      attribute,
      command.value
    );
    
    return { status: result.status, data: result.data };
  }
}
 
class BluetoothAdapter extends DeviceAdapter {
  async connect(device: Device): Promise<Connection> {
    const bleDevice = await BluetoothSerial.connect(device.address);
    
    // 发现服务和特征
    const services = await bleDevice.discoverServices();
    const characteristics = await this.discoverCharacteristics(services);
    
    return new BluetoothConnection(bleDevice, characteristics);
  }
  
  async sendCommand(command: DeviceCommand): Promise<CommandResult> {
    const characteristic = this.getWriteCharacteristic(command.serviceType);
    const data = this.encodeCommand(command);
    
    await characteristic.write(data);
    
    // 等待响应
    const response = await this.waitForResponse(characteristic, 5000);
    return this.decodeResponse(response);
  }
}
 
// 协议适配器工厂
class AdapterFactory {
  static createAdapter(connectionType: string): DeviceAdapter {
    switch (connectionType) {
      case 'wifi':
        return new WiFiAdapter();
      case 'zigbee':
        return new ZigbeeAdapter();
      case 'bluetooth':
        return new BluetoothAdapter();
      case 'zwave':
        return new ZWaveAdapter();
      default:
        throw new Error(`不支持的连接类型: ${connectionType}`);
    }
  }
}

class OTAManager {
  async checkForUpdates(deviceId: string): Promise<UpdateInfo | null> {
    const device = await DeviceManager.getDevice(deviceId);
    const currentVersion = device.firmwareVersion;
    
    const updateInfo = await this.queryUpdateServer(device.manufacturer, device.model, currentVersion);
    
    if (updateInfo && this.compareVersions(updateInfo.version, currentVersion) > 0) {
      return {
        ...updateInfo,
        isSecurityUpdate: updateInfo.securityFixes.length > 0,
        isCritical: updateInfo.priority === 'critical',
        estimatedDuration: this.estimateUpdateDuration(updateInfo.size)
      };
    }
    
    return null;
  }
  
  async performUpdate(deviceId: string, updateInfo: UpdateInfo): Promise<UpdateResult> {
    const updateSession = await this.createUpdateSession(deviceId, updateInfo);
    
    try {
      // 1. 预更新检查
      await this.preUpdateChecks(updateSession);
      
      // 2. 下载固件
      const firmware = await this.downloadFirmware(updateInfo, (progress) => {
        this.notifyUpdateProgress(deviceId, 'downloading', progress);
      });
      
      // 3. 验证固件签名
      await this.verifyFirmwareSignature(firmware, updateInfo.signature);
      
      // 4. 开始更新过程
      await this.startDeviceUpdate(updateSession, firmware);
      
      // 5. 监控更新进度
      const result = await this.monitorUpdateProgress(updateSession);
      
      // 6. 验证更新结果
      await this.verifyUpdateSuccess(deviceId, updateInfo.version);
      
      return {
        status: 'success',
        oldVersion: updateSession.oldVersion,
        newVersion: updateInfo.version,
        duration: Date.now() - updateSession.startTime
      };
      
    } catch (error) {
      // 更新失败，尝试恢复
      await this.attemptRecovery(updateSession);
      
      return {
        status: 'failed',
        error: error.message,
        recoveryAttempted: true
      };
    }
  }
  
  private async monitorUpdateProgress(session: UpdateSession): Promise<void> {
    return new Promise((resolve, reject) => {
      const progressInterval = setInterval(async () => {
        try {
          const progress = await this.getUpdateProgress(session.deviceId);
          
          this.notifyUpdateProgress(session.deviceId, 'installing', progress.percentage);
          
          if (progress.status === 'completed') {
            clearInterval(progressInterval);
            resolve();
          } else if (progress.status === 'failed') {
            clearInterval(progressInterval);
            reject(new Error(progress.errorMessage));
          }
        } catch (error) {
          clearInterval(progressInterval);
          reject(error);
        }
      }, 5000);
      
      // 超时保护
      setTimeout(() => {
        clearInterval(progressInterval);
        reject(new Error('更新超时'));
      }, 30 * 60 * 1000); // 30分钟超时
    });
  }
}

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
 
class UserBehaviorAnalyzer:
    def __init__(self):
        self.models = {}
        self.feature_importance = {}
    
    def analyze_usage_patterns(self, user_data: pd.DataFrame) -> dict:
        """深度分析用户使用模式"""
        
        # 时间模式分析
        temporal_patterns = self.extract_temporal_patterns(user_data)
        
        # 设备使用偏好分析
        device_preferences = self.analyze_device_preferences(user_data)
        
        # 场景使用分析
        scene_analysis = self.analyze_scene_usage(user_data)
        
        # 能耗分析
        energy_analysis = self.analyze_energy_consumption(user_data)
        
        return {
            'temporal_patterns': temporal_patterns,
            'device_preferences': device_preferences, 
            'scene_analysis': scene_analysis,
            'energy_insights': energy_analysis,
            'recommendations': self.generate_smart_recommendations(user_data)
        }
    
    def extract_temporal_patterns(self, data: pd.DataFrame) -> dict:
        """提取时间使用模式"""
        data['hour'] = pd.to_datetime(data['timestamp']).dt.hour
        data['day_of_week'] = pd.to_datetime(data['timestamp']).dt.dayofweek
        data['is_weekend'] = data['day_of_week'].isin([5, 6])
        
        # 活跃时段分析
        hourly_activity = data.groupby('hour')['action'].count()
        peak_hours = hourly_activity.nlargest(3).index.tolist()
        
        # 工作日vs周末模式
        weekday_pattern = data[~data['is_weekend']].groupby('hour')['action'].count()
        weekend_pattern = data[data['is_weekend']].groupby('hour')['action'].count()
        
        return {
            'peak_hours': peak_hours,
            'weekday_pattern': weekday_pattern.to_dict(),
            'weekend_pattern': weekend_pattern.to_dict(),
            'activity_consistency': self.calculate_consistency_score(hourly_activity)
        }
    
    def predict_next_action(self, user_id: str, current_context: dict) -> dict:
        """预测用户下一步可能的操作"""
        
        # 获取用户历史数据
        user_history = self.get_user_history(user_id, days=90)
        
        # 特征工程
        features = self.build_prediction_features(user_history, current_context)
        
        # 使用训练好的模型预测
        model = self.models.get(user_id) or self.models['global']
        predictions = model.predict_proba(features)
        
        # 解析预测结果
        action_probabilities = {}
        for i, action_type in enumerate(model.classes_):
            action_probabilities[action_type] = float(predictions[0][i])
        
        # 过滤低概率预测
        significant_predictions = {
            action: prob for action, prob in action_probabilities.items() 
            if prob > 0.15
        }
        
        return {
            'predictions': significant_predictions,
            'confidence': max(action_probabilities.values()),
            'context_factors': self.explain_predictions(features, model)
        }
    
    def generate_energy_optimization_report(self, household_data: pd.DataFrame) -> dict:
        """生成能耗优化报告"""
        
        # 设备能耗分析
        device_consumption = self.calculate_device_consumption(household_data)
        
        # 峰谷电价优化
        time_of_use_analysis = self.analyze_time_of_use_opportunities(household_data)
        
        # 自动化节能潜力
        automation_savings = self.calculate_automation_savings_potential(household_data)
        
        return {
            'current_consumption': device_consumption,
            'optimization_opportunities': time_of_use_analysis,
            'automation_potential': automation_savings,
            'monthly_savings_estimate': self.estimate_monthly_savings(household_data),
            'carbon_footprint_reduction': self.calculate_carbon_impact(household_data)
        }