AI人工智能代理工作流AI Agent WorkFlow：在智能家居场景应用代理技术

    # 使用Kafka实现设备状态的实时监控和报警
    from kafka import KafkaProducer
    import json
    
    # Kafka Producer配置
    producer = KafkaProducer(
    bootstrap_servers=['localhost:9092'],
    value_serializer=lambda v: json.dumps(v).encode('utf-8')
    )
    
    # 设备状态数据结构
    device_status = {
    'device_id': '12345',
    'status': 'ALARM',
    'description': '温度过高，请检查设备'
    }
    
    # 发送设备状态数据到Kafka
    producer.send('device_status_topic', device_status)
    
    # 关闭Kafka Producer
    producer.close()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现设备节能调度算法
    import datetime
    import random
    
    # 设备能耗特性数据
    device_energy = {
    'device_id': '12345',
    'energy_usage': random.uniform(0.1, 0.5),  # 设备能耗（单位：千瓦时）
    'operating_time': datetime.timedelta(hours=8),  # 设备运行时间
    }
    
    # 设备节能调度策略
    def schedule_energy_saving(device_energy):
    # 根据设备能耗和运行时间，计算节能模式下的运行时间
    energy_saving_time = device_energy['operating_time'] * (1 - device_energy['energy_usage'])
    return energy_saving_time
    
    # 调用节能调度策略
    energy_saving_time = schedule_energy_saving(device_energy)
    print(f"设备节能模式下的运行时间：{energy_saving_time}")
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现设备联动规则引擎
    class RuleEngine:
    def __init__(self):
        self.rules = []
    
    def add_rule(self, condition, action):
        self.rules.append({'condition': condition, 'action': action})
    
    def execute_rules(self, device_status):
        for rule in self.rules:
            if rule['condition'](device_status):
                rule['action']()
                break
    
    # 设备联动规则
    def rule1(device_status):
    return device_status['status'] == 'ON'
    
    def rule2(device_status):
    return device_status['status'] == 'OFF'
    
    # 执行设备联动操作
    def action1():
    print("打开照明设备")
    
    def action2():
    print("关闭照明设备")
    
    # 创建规则引擎
    rule_engine = RuleEngine()
    
    # 添加规则
    rule_engine.add_rule(rule1, action1)
    rule_engine.add_rule(rule2, action2)
    
    # 模拟设备状态
    device_status = {'device_id': '12345', 'status': 'ON'}
    
    # 执行规则引擎
    rule_engine.execute_rules(device_status)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现用户行为分析模块和个性化推荐
    from sklearn.cluster import KMeans
    import numpy as np
    
    # 用户行为数据
    user_behavior = [
    [1, 0, 0],  # 用户A浏览了商品A、C
    [0, 1, 1],  # 用户B浏览了商品B、D
    [1, 1, 0],  # 用户C浏览了商品A、D
    [0, 0, 1],  # 用户D浏览了商品C
    ]
    
    # 使用K-Means算法对用户行为数据进行聚类
    kmeans = KMeans(n_clusters=2, random_state=0).fit(user_behavior)
    clusters = kmeans.predict(user_behavior)
    
    # 根据聚类结果，生成个性化推荐列表
    def generate_recommendations(clusters):
    recommendations = []
    for cluster in set(clusters):
        items = [user_behavior[i] for i, c in enumerate(clusters) if c == cluster]
        popular_item = max(set([item for items in items for item in items]), key=items.count)
        recommendations.append(popular_item)
    return recommendations
    
    # 生成个性化推荐列表
    recommendations = generate_recommendations(clusters)
    print("个性化推荐列表：", recommendations)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现数据加密模块
    from Crypto.Cipher import AES
    from Crypto.Random import get_random_bytes
    
    # AES加密函数
    def encrypt_data(data, key):
    cipher = AES.new(key, AES.MODE_CBC)
    ct_bytes = cipher.encrypt(data)
    iv = cipher.iv
    return iv + ct_bytes
    
    # AES解密函数
    def decrypt_data(encrypted_data, key):
    iv = encrypted_data[:16]
    ct = encrypted_data[16:]
    cipher = AES.new(key, AES.MODE_CBC, iv)
    pt = cipher.decrypt(ct)
    return pt
    
    # 生成随机密钥
    key = get_random_bytes(16)
    
    # 加密数据
    data = b"Hello, World!"
    encrypted_data = encrypt_data(data, key)
    print("加密后的数据：", encrypted_data)
    
    # 解密数据
    decrypted_data = decrypt_data(encrypted_data, key)
    print("解密后的数据：", decrypted_data.decode())
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现设备异常检测模块
    import numpy as np
    from sklearn.ensemble import IsolationForest
    
    # 设备运行数据
    device_data = [
    [20, 2],  # 温度20°C，湿度2%
    [22, 4],  # 温度22°C，湿度4%
    [23, 5],  # 温度23°C，湿度5%
    [18, 1],  # 温度18°C，湿度1%（异常值）
    [25, 6],  # 温度25°C，湿度6%
    ]
    
    # 使用Isolation Forest算法进行异常检测
    clf = IsolationForest(contamination=0.1)
    clf.fit(device_data)
    
    # 预测异常值
    device_data_with_labels = clf.predict(device_data)
    print("设备运行数据与异常标签：", device_data_with_labels)
    
    # 自动执行恢复操作
    def recover_device(device_id, label):
    if label == -1:
        print(f"设备{device_id}发生故障，自动执行恢复操作。")
        # 在此处添加设备恢复代码，如重启设备、更换零部件等
    
    # 应用恢复操作
    for device_id, label in zip(range(len(device_data)), device_data_with_labels):
    recover_device(device_id, label)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现语音交互模块
    import speech_recognition as sr
    from gtts import gTTS
    
    # 语音识别
    def recognize_speech_from_mic(recognizer, microphone):
    with microphone as source:
        audio = recognizer.listen(source)
    
    response = {
        "success": True,
        "error": None,
        "transcription": None,
    }
    try:
        response["transcription"] = recognizer.recognize_google(audio)
    except sr.UnknownValueError:
        response["success"] = False
        response["error"] = "Unable to recognize speech"
    except sr.RequestError:
        response["success"] = False
        response["error"] = "API unavailable"
    
    return response
    
    # 语音合成
    def synthesize_speech(text):
    tts = gTTS(text=text, lang="zh-cn")
    tts.save("response.mp3")
    return "response.mp3"
    
    # 语音识别示例
    microphone = sr.Microphone()
    recognizer = sr.Recognizer()
    print(recognize_speech_from_mic(recognizer, microphone))
    
    # 语音合成示例
    text = "你好，智能家居系统，我现在想打开客厅的灯光。"
    audio_file = synthesize_speech(text)
    print("合成的语音文件：", audio_file)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现设备监控模块（基于MQTT协议）
    import paho.mqtt.client as mqtt
    
    # MQTT客户端配置
    client = mqtt.Client()
    
    # 连接到MQTT服务器
    client.connect("mqtt.server.com", 1883, 60)
    
    # 订阅设备状态主题
    client.subscribe("device_status_topic")
    
    # 设备控制主题
    def send_control_command(device_id, command):
    topic = f"device_control_topic/{device_id}"
    client.publish(topic, command)
    
    # 设备状态回调函数
    def on_message(client, userdata, message):
    device_id = message.topic.split('/')[-1]
    device_status = json.loads(message.payload)
    print(f"设备{device_id}的状态：{device_status}")
    
    # 设置消息回调函数
    client.on_message = on_message
    
    # 启动MQTT客户端
    client.loop_start()
    
    # 发送设备控制命令
    send_control_command("12345", "ON")
    
    # 关闭MQTT客户端
    client.loop_stop()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现用户行为分析模块（基于Pandas和Matplotlib）
    import pandas as pd
    import matplotlib.pyplot as plt
    
    # 用户行为数据
    user_behavior = [
    ["用户A", "商品A"],
    ["用户A", "商品B"],
    ["用户B", "商品C"],
    ["用户B", "商品D"],
    ["用户C", "商品A"],
    ["用户C", "商品D"],
    ["用户D", "商品C"],
    ]
    
    # 创建数据框
    df = pd.DataFrame(user_behavior, columns=["用户ID", "商品ID"])
    
    # 用户浏览频次统计
    user_browsing_frequency = df.groupby("用户ID").size().reset_index(name="频次")
    
    # 商品浏览频次统计
    product_browsing_frequency = df.groupby("商品ID").size().reset_index(name="频次")
    
    # 绘制用户浏览频次分布图
    plt.figure(figsize=(10, 5))
    plt.bar(user_browsing_frequency["用户ID"], user_browsing_frequency["频次"])
    plt.xlabel("用户ID")
    plt.ylabel("浏览频次")
    plt.title("用户浏览频次分布图")
    plt.xticks(rotation=45)
    plt.show()
    
    # 绘制商品浏览频次分布图
    plt.figure(figsize=(10, 5))
    plt.bar(product_browsing_frequency["商品ID"], product_browsing_frequency["频次"])
    plt.xlabel("商品ID")
    plt.ylabel("浏览频次")
    plt.title("商品浏览频次分布图")
    plt.xticks(rotation=45)
    plt.show()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 使用Python实现多设备联动模块（基于规则引擎）
    class RuleEngine:
    def __init__(self):
        self.rules = []
    
    def add_rule(self, condition, action):
        self.rules.append({'condition': condition, 'action': action})
    
    def execute_rules(self, device_statuses):
        for rule in self.rules:
            if rule['condition'](device_statuses):
                rule['action']()
                break
    
    # 设备状态数据
    device_statuses = [
    {'device_id': '灯', 'status': '关闭'},
    {'device_id': '空调', 'status': '关闭'},
    {'device_id': '窗帘', 'status': '关闭'},
    ]
    
    # 设备联动规则
    def rule1(device_statuses):
    return all([status['status'] == '关闭' for status in device_statuses])
    
    def rule2(device_statuses):
    return any([status['status'] == '开启' for status in device_statuses])
    
    def action1():
    print("关闭所有设备")
    
    def action2():
    print("开启所有设备")
    
    # 创建规则引擎
    rule_engine = RuleEngine()
    
    # 添加规则
    rule_engine.add_rule(rule1, action1)
    rule_engine.add_rule(rule2, action2)
    
    # 执行规则引擎
    rule_engine.execute_rules(device_statuses)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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