星地协同中基于多智能体的时敏任务调度优化策略OA

With the deep integration of intelligent Internet of things technology and 5G/6G communication technology,satellite edge computing(SatEC)offers new computational services to areas with weak terrestrial network coverage through its aerospace collaborative computing network.However,the SatEC system faces dual challenges of unbalanced dynamic resource allocation between satellite and ground and insufficient task priority control under multi-dimensional spatiotemporal constraints.Existing methods have defects in hierarchical decision-making,spatiotemporal feature extrac-tion,and task urgency quantification mapping,which limit the efficiency of time-sensitive task processing.To address this problem,a multi-agent deep reinforcement learning algorithm based on self-attention temporal convolutional networks was proposed in this paper.The algorithm achieved joint optimization of task prioritization and resource allocation by con-structing a multi-agent architecture,employed a hybrid neural network integrating spatiotemporal features to accurately extract dynamic correlation characteristics of satellite-ground collaboration scenarios,and established a dynamic schedul-ing mechanism based on a probabilistic model to synergistically optimize latency constraints and task completion rates.Simulation results show that,compared with the baseline algorithm,the proposed algorithm achieves significant improve-ments in both task completion rate and delay control,demonstrating its effectiveness and superiority in complex satellite edge computing scenarios.