面向预警场景的大规模星座协同调度标准建模与求解方法

Standard modeling and solving methods for large-scale constellation collaborative scheduling for early warning scenarios

针对大规模星座任务协同调度具有卫星节点数量多、任务需求规模大、资源使用约束条件复杂,对多节点间协同调度的建模和求解要求较高等特点,设计了一种“任务预处理——统一化建模——规范优化求解——在轨指令生成”的阶段式统一化建模与求解顶层框架,在此顶层框架之下提出了一种基于改进合同网的多节点即时协同调度算法(CSA-ICNP),利用模糊寻优结合局部搜索策略提高算法的整体寻优能力。通过开展大量仿真实验,与随机搜索算法、贪婪搜索算法、基于冲突度的任务分配算法、最小负载最先分配算法和基于改进合同网协议的分布式卫星资源调度算法进行结果对比和性能分析,每个实验用例都获得了最佳目标函数值,平均提升了42.13%、41.51%、37.93%、37.53%和18.57%。

Large-scale constellation coordination scheduling is characterized by a large number of remote satellite nodes,large task demands,complex resource utilization constraints,and high requirements for modeling and solving collaborative scheduling between multiple nodes.This paper proposes a top-level framework for phased unified modeling and solving,which includes“task pre-processing,unified modeling,optimization solving,on-orbit instruction generation”.Under the top-level framework,a multi node real-time Collaborative Scheduling Algorithm based on the Improved Contract Network(CSA-ICNP)is proposed for space-based early warning application scenarios,which utilizes fuzzy optimization combined with the local search strategy to improve the overall optimization ability of the algorithm.The correctness and effectiveness of the proposed algorithm are verified by conducting a large number of simulation experiments.Comparison of the method proposed with Random Search algorithm(RS),Greedy Search algorithm(GS),Task Allocation Algorithm based on Conflict Degree(TAACD),Minimum Load First Allocation algorithm(MLFA)and Distributed Satellite Resource Scheduling based on Improved Contract Network Protocol(DSRS-ICNP)shows that each experimental case achieves the optimal objective function value,with an average improvement of 42.13%,41.51%,37.93%,37.53%and18.57%,respectively.