智能体5G/6G网络优化技术详解

# AI智能体5G/6G网络优化核心代码示例 import asyncio import numpy as np import torch import torch.nn as nn from ai_agent import NetworkOptimizationAgent from network_slice import NetworkSliceManager from edge_computing import EdgeComputingScheduler class AI5G6GNetworkOptimizer: def __init__(self): self.ai_agent = NetworkOptimizationAgent() self.slice_manager = NetworkSliceManager() self.edge_scheduler = EdgeComputingScheduler() self.performance_monitor = NetworkPerformanceMonitor() async def start_optimization(self, config): """启动5G/6G网络优化系统""" # 初始化AI智能体 await self.ai_agent.initialize( model_path=config['model_path'], optimization_target=config['target'], network_type=config['network_type'] ) # 启动网络切片管理 await self.slice_manager.start_slicing( slice_config=config['slice_config'], qos_requirements=config['qos'] ) # 启动边缘计算调度 await self.edge_scheduler.start_scheduling( edge_nodes=config['edge_nodes'], compute_resources=config['resources'] ) # 主优化循环 while True: # 采集网络数据 network_data = await self.performance_monitor.collect_data() # AI智能体分析 optimization_decision = await self.ai_agent.analyze_and_decide(network_data) # 执行网络优化 if optimization_decision: await self.execute_optimization(optimization_decision) # 控制优化频率 await asyncio.sleep(config['optimization_interval']) async def execute_optimization(self, decision): """执行网络优化决策""" # 网络切片优化 if decision['slice_optimization']: await self.slice_manager.optimize_slices(decision['slice_params']) # 边缘计算调度优化 if decision['edge_optimization']: await self.edge_scheduler.optimize_scheduling(decision['edge_params']) # 路由优化 if decision['routing_optimization']: await self.optimize_routing(decision['routing_params']) async def predict_network_traffic(self, historical_data): """预测网络流量""" # 使用LSTM模型预测流量 prediction = await self.ai_agent.predict_traffic(historical_data) return prediction async def optimize_resource_allocation(self, current_load): """优化资源分配""" # 基于当前负载优化资源分配 allocation = await self.ai_agent.optimize_allocation(current_load) return allocation class NetworkOptimizationAgent: def __init__(self): self.lstm_model = self.build_lstm_model() self.optimization_model = self.build_optimization_model() def build_lstm_model(self): """构建LSTM流量预测模型""" model = nn.LSTM(input_size=10, hidden_size=64, num_layers=2, batch_first=True) return model def build_optimization_model(self): """构建网络优化模型""" model = nn.Sequential( nn.Linear(20, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 10) ) return model async def analyze_and_decide(self, network_data): """分析网络数据并做出优化决策""" # 数据预处理 processed_data = self.preprocess_data(network_data) # 预测网络状态 prediction = self.predict_network_state(processed_data) # 生成优化决策 decision = self.generate_optimization_decision(prediction) return decision # 使用示例 async def main(): config = { 'model_path': '/models/5g_6g_optimizer_v2.0', 'target': 'latency_minimization', 'network_type': '5G', 'slice_config': {'eMBB': 0.6, 'uRLLC': 0.3, 'mMTC': 0.1}, 'qos': {'latency': '<1ms', 'throughput': '>10Gbps'}, 'edge_nodes': ['node1', 'node2', 'node3'], 'resources': {'cpu': 100, 'memory': 200, 'bandwidth': 1000}, 'optimization_interval': 1.0 } optimizer = AI5G6GNetworkOptimizer() await optimizer.start_optimization(config) if __name__ == "__main__": asyncio.run(main())