AI与物联网

```python
   # 伪代码示例：智能音箱控制智能灯光
   import speech_recognition as sr
 
   def recognize_speech():
       recognizer = sr.Recognizer()
       with sr.Microphone() as source:
           print("Say something!")
           audio = recognizer.listen(source)
       try:
           return recognizer.recognize_google(audio)
       except sr.UnknownValueError:
           return "Could not understand audio"
       except sr.RequestError:
           return "Could not request results"
 
   def control_light(command):
       if "turn on the light" in command:
           print("Light is turned on.")
           # 发送控制信号到智能灯光设备
       elif "turn off the light" in command:
           print("Light is turned off.")
           # 发送控制信号到智能灯光设备
       else:
           print("Command not recognized.")
 
   command = recognize_speech()
   control_light(command)
   ```

```python
   # 伪代码示例：预测性维护
   import numpy as np
   from sklearn.ensemble import RandomForestClassifier
 
   # 示例数据
   data = np.array([
       [1, 85, 0],
       [2, 90, 1],
       [3, 78, 0],
       # 更多设备数据
   ])
   features = data[:, :-1]
   labels = data[:, -1]
 
   # 训练模型
   model = RandomForestClassifier()
   model.fit(features, labels)
 
   # 新设备数据
   new_data = np.array([4, 88])
   prediction = model.predict([new_data])
   if prediction == 1:
       print("设备需要维护")
   else:
       print("设备运行正常")
   ```

```python
   # 伪代码示例：健康监测与分析
   import random
 
   def get_health_data():
       heart_rate = random.randint(60, 100)  # 模拟心率数据
       blood_pressure = random.randint(110, 140)  # 模拟血压数据
       return heart_rate, blood_pressure
 
   def analyze_health_data(heart_rate, blood_pressure):
       if heart_rate > 90 or blood_pressure > 130:
           return "需要注意健康，建议就医"
       else:
           return "健康状况良好"
 
   heart_rate, blood_pressure = get_health_data()
   analysis = analyze_health_data(heart_rate, blood_pressure)
   print(f"心率: {heart_rate}, 血压: {blood_pressure}")
   print(f"健康分析: {analysis}")
   ```