AI人工智能领域多智能体系统：提升智能安防的水平

    import numpy as np
    from collections import defaultdict
    
    class MultiAgentPerception:
    def __init__(self, agents):
        self.agents = agents  # 智能体列表
        self.shared_memory = defaultdict(dict)  # 共享记忆体
        
    def update_perception(self, agent_id, data):
        """更新单个智能体的感知数据"""
        self.shared_memory[agent_id] = data
        self._data_fusion()
        
    def _data_fusion(self):
        """多源数据融合算法"""
        fused_data = {}
        for agent_id, data in self.shared_memory.items():
            for key, value in data.items():
                if key in fused_data:
                    # 加权融合策略
                    fused_data[key] = 0.7*fused_data[key] + 0.3*value
                else:
                    fused_data[key] = value
        return fused_data
    
    def consensus_check(self, threshold=0.8):
        """一致性检查算法"""
        alerts = defaultdict(list)
        for agent_id, data in self.shared_memory.items():
            for key, value in data.items():
                if value > threshold:
                    alerts[key].append(agent_id)
        return alerts

    class DistributedDecision:
    def __init__(self, agents):
        self.agents = agents
        self.q_tables = {agent: {} for agent in agents}
        
    def learn(self, state, actions, rewards, next_state, alpha=0.1, gamma=0.9):
        """多智能体Q学习算法"""
        for agent in self.agents:
            current_q = self.q_tables[agent].get(state, 0)
            max_next_q = max(self.q_tables[agent].get(next_state, {}).values(), default=0)
            new_q = current_q + alpha * (rewards[agent] + gamma * max_next_q - current_q)
            self.q_tables[agent][state] = new_q
            
    def decide(self, state):
        """联合决策"""
        decisions = {}
        for agent in self.agents:
            if state in self.q_tables[agent]:
                # 选择Q值最高的动作
                decisions[agent] = max(self.q_tables[agent][state].items(), 
                                     key=lambda x: x[1])[0]
            else:
                # 随机探索
                decisions[agent] = np.random.choice(['patrol', 'alert', 'ignore'])
        return decisions

    import socket
    import json
    from threading import Thread
    
    class AgentCommunication:
    def __init__(self, host, port):
        self.host = host
        self.port = port
        self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
        self.socket.bind((host, port))
        self.received_messages = []
        
    def start_receiver(self):
        """启动消息接收线程"""
        Thread(target=self._receive_messages, daemon=True).start()
        
    def _receive_messages(self):
        """接收消息"""
        while True:
            data, addr = self.socket.recvfrom(1024)
            message = json.loads(data.decode())
            self.received_messages.append(message)
            
    def send_message(self, target_host, target_port, message):
        """发送消息"""
        self.socket.sendto(json.dumps(message).encode(), 
                          (target_host, target_port))
        
    def broadcast(self, message, network):
        """广播消息"""
        for agent in network:
            if agent != (self.host, self.port):
                self.send_message(agent[0], agent[1], message)

    # 创建Python虚拟环境
    python -m venv mas_security
    source mas_security/bin/activate
    
    # 安装核心依赖
    pip install numpy opencv-python tensorflow scikit-learn flask-socketio

    class SecurityAgent:
    def __init__(self, agent_id, agent_type):
        self.id = agent_id
        self.type = agent_type  # 'camera', 'sensor', 'drone'
        self.location = self._init_location()
        self.communication = AgentCommunication('localhost', 5000 + agent_id)
        self.perception = self._init_perception()
        self.decision_maker = DistributedDecision([self.id])
        
    def _init_location(self):
        """初始化智能体位置"""
        if self.type == 'camera':
            return np.random.uniform(0, 10, size=2)
        elif self.type == 'sensor':
            return np.random.uniform(0, 10, size=2)
        else:  # drone
            return np.array([5, 5])  # 起始于中心位置
            
    def _init_perception(self):
        """初始化感知模块"""
        if self.type == 'camera':
            return CameraPerception()
        elif self.type == 'sensor':
            return SensorPerception()
        else:
            return DronePerception()
            
    def run_cycle(self):
        """执行一个感知-决策-行动周期"""
        # 1. 感知环境
        observation = self.perception.observe()
        
        # 2. 共享信息
        self.communication.broadcast({
            'sender': self.id,
            'type': 'observation',
            'data': observation
        }, self.known_agents)
        
        # 3. 接收其他智能体信息
        messages = self.communication.received_messages
        state = self._build_state(observation, messages)
        
        # 4. 做出决策
        action = self.decision_maker.decide(state)
        
        # 5. 执行动作
        self._execute_action(action)
        
    def _build_state(self, observation, messages):
        """构建决策状态"""
        state = {
            'self_obs': observation,
            'other_obs': [msg['data'] for msg in messages 
                         if msg['type'] == 'observation'],
            'time': time.time()
        }
        return str(state)  # 转换为字符串作为Q表的键
        
    def _execute_action(self, action):
        """执行决策动作"""
        if action == 'patrol':
            self._move_to(self._random_point())
        elif action == 'alert':
            self._send_alert()
        elif action == 'track':
            self._track_target()
            
    def _send_alert(self):
        """发送警报"""
        self.communication.broadcast({
            'sender': self.id,
            'type': 'alert',
            'priority': 'high'
        }, self.known_agents)

 * 决策能力下沉到终端设备
 * 减少通信延迟
 * 增强隐私保护

 * 人类安防人员与AI系统协作
 * 混合增强智能
 * 可解释决策支持

 * 动态调整智能体角色
 * 自组织网络拓扑
 * 弹性容错机制

 * 对抗性攻击防范
 * 通信安全加密
 * 系统完整性保护

 * 大规模系统协调
 * 异构设备集成
 * 实时性保证

 * 隐私保护平衡
 * 决策责任归属
 * 算法偏见消除

 * 量子通信保障安全
 * 量子算法加速决策
 * 量子传感增强感知

 * 结合深度学习与符号推理
 * 可解释的威胁评估
 * 因果推理能力

 * 数字孪生仿真
 * 虚拟训练环境
 * 跨虚实边界安防