class SmartHomeMCP:    def __init__(self, temperature_sensor, light_sensor, user_preferences):        self.temperature_sensor = temperature_sensor        self.light_sensor = light_sensor        self.user_preferences = user_preferences
    def adjust_temperature(self):        temperature = self.temperature_sensor.get_current_temperature()        if temperature < self.user_preferences["preferred_temperature"]:            self.temperature_sensor.increase_temperature()
    def adjust_lights(self):        light_level = self.light_sensor.get_current_light_level()        if light_level < self.user_preferences["preferred_light_level"]:            self.light_sensor.turn_on_lights()
    def manage_home(self):        self.adjust_temperature()        self.adjust_lights()
# 假设我们有温度传感器、光传感器和用户偏好设置home_mcp = SmartHomeMCP(temperature_sensor, light_sensor, {"preferred_temperature": 22, "preferred_light_level": 300})home_mcp.manage_home()

class TrafficSystemMCP:    def __init__(self, vehicle_list, traffic_data):        self.vehicle_list = vehicle_list        self.traffic_data = traffic_data
    def optimize_traffic_flow(self):        for vehicle in self.vehicle_list:            # 根据交通数据优化车辆行驶路线            vehicle.update_route(self.traffic_data.get_best_route(vehicle))
    def manage_traffic(self):        self.optimize_traffic_flow()
# 假设我们有多个智能车辆和交通数据traffic_system = TrafficSystemMCP(vehicle_list, traffic_data)traffic_system.manage_traffic()

   import torch   import torch.nn as nn   import torch.optim as optim
   class DRLAgent(nn.Module):       def __init__(self, input_size, output_size):           super(DRLAgent, self).__init__()           self.fc = nn.Sequential(               nn.Linear(input_size, 128),               nn.ReLU(),               nn.Linear(128, output_size)           )
       def forward(self, x):           return self.fc(x)
   agent = DRLAgent(input_size=4, output_size=2)   optimizer = optim.Adam(agent.parameters(), lr=0.01)

   class MultimodalFusion(nn.Module):       def __init__(self, image_model, text_model):           super(MultimodalFusion, self).__init__()           self.image_model = image_model           self.text_model = text_model           self.fc = nn.Linear(512, 2)  # 假设融合后的特征大小为512
       def forward(self, image_input, text_input):           image_features = self.image_model(image_input)           text_features = self.text_model(text_input)           combined_features = torch.cat((image_features, text_features), dim=-1)           return self.fc(combined_features)
   # 假设 image_model 和 text_model 已经是预训练模型   multimodal_model = MultimodalFusion(image_model, text_model)

   class Attention(nn.Module):       def __init__(self, input_size, attention_size):           super(Attention, self).__init__()           self.attention_weights = nn.Parameter(torch.randn(input_size, attention_size))
       def forward(self, x):           attention_score = torch.matmul(x, self.attention_weights)           attention_weights = torch.softmax(attention_score, dim=-1)           return torch.matmul(attention_weights.transpose(1, 2), x)
   attention_layer = Attention(input_size=128, attention_size=64)

class MultiTaskModel(nn.Module):    def __init__(self):        super(MultiTaskModel, self).__init__()        self.shared_fc = nn.Linear(128, 64)        self.temperature_fc = nn.Linear(64, 1)        self.light_fc = nn.Linear(64, 1)
    def forward(self, x):        shared_features = torch.relu(self.shared_fc(x))        temperature_output = self.temperature_fc(shared_features)        light_output = self.light_fc(shared_features)        return temperature_output, light_output
# 多任务模型同时预测温度和光照调节multi_task_model = MultiTaskModel()

class CrossDomainCollaboration:    def __init__(self, domain_a, domain_b):        self.domain_a = domain_a        self.domain_b = domain_b
    def execute_task(self, task):        # 在不同领域之间执行任务，并共享信息        result_a = self.domain_a.process_task(task)        result_b = self.domain_b.process_task(task)        return result_a, result_b
# 假设我们有两个不同领域的代理collaboration = CrossDomainCollaboration(domain_a, domain_b)collaboration.execute_task(task)