软件定义的无人机集群弹性组网架构OA

To enhance the sustained mission capability of large-scale UAV(unmanned aerial vehicle)swarms facing both soft and hard-kill threats,this research focused on swarm topology reconstruction methods.Addressing the issue of node failures,a dynamic control authority migration mechanism under a centralized-distributed hybrid control architecture was proposed to improve reconstruction efficiency;concurrently,an SDN(software-defined network)-based elastic networking architecture was designed,integrating intent-driven principles to enable intelligent and dynamic configuration of network resources.Simulation comparisons were conducted between dual-mode reconstruction strategies:neighbor autonomous compensation and resource-pool dynamic scheduling.When facing small-scale node loss,neighbor compensation leverages its advantage in local decision-making,reducing the average reconstruction latency by 38.5％compared to resource-pool scheduling.However,as the number of failed nodes increases,the combined centralized-distributed controller strategy achieves the shortest reconstruction time;notably,under persistent electromagnetic interference environments,this combined strategy demonstrates significant advantages,reducing latency by 18.3％compared to a purely distributed approach.This research provides theoretical support and practical reference for topology reconstruction in large-scale UAV swarms.