A conflict clique mitigation method for large-scale satellite mission planning based on heterogeneous graph learning

For the large-scale and intensive demands of satellite remote sensing observation, the complexity of constraint relationships grows explosively with expansion of satellite task scale. How to efficiently deal with the complex and temporal varying constraint conflicts, and mine the implicit knowledge existing among satellite mission constraints, which is significant to enhance scheduling efficiency, however, is also a core difficulty in the satellite scheduling problem. In this paper, we propose a conflict clique mitigation method based on dynamic task-constrained heterogeneous graph learning to solve large-scale satellite mission scheduling. The method exploits the advantage of heterogeneous graphs to characterize multiple unstructured relationships, and projects the temporal-varying features of constraint conflicts to the spatial topology of multiple nodes and edges in a heterogeneous graph. Thus, a dynamic constraints heterogeneous graph model for satellite tasks based on sampling critical conflict cliques is developed. And an improved heterogeneous attention network with quadratic unconstrained binary optimization (HAN-QUBO) is proposed, which is able to deal with the heterogeneous graphs and attempts to represent the implicit principles of multiple constraints of satellite missions, so that the valuable strategies and experiences of conflict mitigation can be extracted. The simulation experiments demonstrate that the method can provide effective empirical guidance for multi-satellite scheduling, greatly relieve the pressure of cumbersome constraint conflict checking process for large-scale tasks. The average number of conflict resolutions has been reduced by about 73.48 % for EOSSPs with tens of thousands tasks, while the quality of solutions is maintained at the same time, which significantly improves the efficiency of multi-satellite scheduling.