A multi-
satellite task scheduling
hybrid control method driven by a large
language model comprises the steps that a
natural language task instruction input by a user is subjected to semantic analysis through an adaptive large
language model, and structured scheduling parameters,
heuristic scheduling
algorithm control parameters and multi-target optimization weights are automatically generated; the method is used for guiding dynamic allocation and
resource scheduling of
satellite tasks. The method is combined with a
heuristic algorithm to execute multi-objective optimization calculation, and a search strategy and a convergence behavior are adjusted according to task requirements and constraint conditions, so that the optimization effect of a scheduling scheme is ensured. Furthermore, the method supports a user to perform semantic-driven re-optimization based on
natural language feedback of an initial scheduling result, realizes iterative update of a scheduling scheme, forms a closed-
loop control process from
natural language input to optimization result to feedback adjustment, and gives consideration to scheduling intelligence,
controllability and multi-objective optimization performance. The method solves the problems that a traditional method is insufficient in environment dynamic
adaptation and high in initial parameter and weight dependence, efficient multi-
satellite task scheduling with controllable
semantics and man-
machine cooperation is achieved, and the method is suitable for scenes such as satellite
group cooperation observation and real-time task planning.