| Abstrakt: |
Deep space exploration activities can effectively promote the development and application of space technology, which holds significant scientific and strategic value. Detectors operating in deep space are of long mission periods and substantial distance from Earth, posing challenges in timely resource replenishment. Moreover, scheduled missions may face external uncertainties, hindering their smooth execution. Therefore, resource constraint processing strategies that possess dynamic adjustment capabilities play a crucial role in ensuring the successful execution of deep space detectors. Existing methods mainly focus on studying fixed mission sequences, and the efficiency of planning methods is not high. Specifically, the single uncertainty is only considered, and there is a lack of detection and processing mechanisms, rendering the requirements of deep space detectors inadequate. In this paper, we propose a reverse chain search algorithm, aiming to efficiently plan missions subject to resource constraints and let the mission sequence to possess a certain degree of dynamic repair capability. The results have demonstrated that the resource allocated to each mission is within the resource value range. Compared with the classic algorithm, the proposed algorithm has fewer iterations and whose maximum flow value is accurate. Meanwhile, the potential resource constraints problem during execution can be alerted with the detection and warning system. The missions that were warned are repaired by a dynamic resource repair system, and the proposed algorithm is of higher efficiency and a certain ability to resist external uncertainties. Overall, this paper lays a solid foundation for the processing of resource constraints for deep space detectors. [ABSTRACT FROM AUTHOR] |
