面向衛(wèi)星在軌處理的強化學習任務調度算法
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中圖分類號：TP391 文獻標志碼：A DOI：10.12305/j.issn.1001-506X.2025.06.20
Abstract：As satellite earth observation enters anera multiple satelites，high resolution，real-time response，and global observation，satelite on-orbit data processg has become one the ma methods to improve the real-time characteristic remote sensg data processg.In scenarios where satelite resources are limited，data transmission lk channels are constraed，and opportunisticobservation tasks are unpredictable， real-time schedulg data processg tasks faces significant challenges.An optimization problem model with thegoal maximizg the system's averagedata processg throughput rate is firstly constructed.Secondly，an onle task schedulg algorithm thatcombes deep reforcement learng（DRL） is proposed.DRL algorithm enablesreal-timecalculation task schedulg strategies，and Lagrangian dual optimizationalgorithm can accurately computes the optimal resource allcation.Fally，simulation experiments are conducted to evaluate the effectiveness and data processg throughput rate the proposed algorithm.Results show that the proposed algorithm can converge and approach the optimal solution，improvg data processng throughput rate by approximately 8% compared to existg algorithms，and demonstratg scalability as the satelite data arrival speed and the number satelite computg nodes crease.Theproposed algorithm can maximize the average data processg throughput rate the system while ensurg the stability and convergence task queue length and average energy consumption a high-dynamic environment.
Keywords：satelite on-orbit processg；task schedulg；resource allocation；deep reforcement learng （DRL）；Lyapunov optimization
0 引言
近年來，在全球遙感監(jiān)測中出現(xiàn)許多由多顆遙感衛(wèi)星構成的星座，其能夠實現(xiàn)覆蓋全球的高分辨率對地觀測。（剩余24190字）