面向6G的跨域知识驱动网络元智能算法框架OA

A cross-domain knowledge-driven meta-intelligent network algorithm framework was proposed in this paper to address the limitations of existing automated operation and maintenance models in supporting multi-scenario and real-time intelligent management in 6G networks.Traditional approaches often rely on static rules or single-domain optimiza-tion,which are insufficient for 6G demands such as heterogeneous perception,dynamic policy adaptation,and multi-objective scheduling.The framework modeled network states across environmental,network,and user behavior domains,leveraging lightweight models and graph neural networks for high-level intent parsing and online knowledge fusion.A global knowledge base was dynamically updated via knowledge distillation.Multi-layer meta-intelligent agents formed a closed-loop control process of perception,reasoning,knowledge generation,decision issuance,validation,and memory re-trieval.Self-supervised learning,reinforcement learning,and meta-learning techniques were integrated to support rapid policy adaptation and continual optimization.Centered on a low-altitude traffic control scenario,the framework was evaluated through three tasks:knowledge-driven networking,intent-guided agent management,and swarm path planning.Experimental results show that the proposed method consistently outperforms baseline approaches in throughput,failure recovery time,traffic prediction accuracy,decision latency,execution success rate,resource fairness,path efficiency,and task success rate.