## 1 Introduction
As for the meta-heuristic scheduling algorithms, the neighborhood space of the solution is searched according to the search rules to solve the scheduling problem. These methods obtain a good and feasible solution, but the computation time taken is comparatively long due to the iterative process. [PERSON] [17] applied the genetic algorithm to the satellite mission scheduling problem while [PERSON] [18] proposed a large neighborhood search algorithm for the mission scheduling of the AEOS. [PERSON] [19] proposed an ant colony algorithm based on the sensational and consciousness strategy to solve the area target observation mission scheduling problem. Furthermore, [PERSON] [20] designed a modified ant colony optimization algorithm with tabu lists to solve the problem of video satellites for ground multi-object staring imaging. [PERSON] [21] improved the immune clonal selection algorithm, genetic algorithm, ant colony algorithm, and particle swarm algorithm and applied them in mission scheduling. [PERSON] [22] proposed a vehicle routing problem with the time window-based hybrid genetic algorithm to tackle the single objective static multi-satellite collection scheduling problem. [PERSON] [23] developed an improved genetic algorithm to solve the integrated satellite imaging and data transmission scheduling problem. [PERSON] [24] proposed an improved adaptive ant colony algorithm to solve the scheduling problem of multiple observations with variable duration. Meta-heuristic methods can find a satisfactory solution in the solution space, however, due to the increased coupling among satellite subsystems, the computational complexity of single-stepping in the search process also increases, resulting in a more time-consuming computation. As the planning strategy of the heuristic methods cannot be adjusted to different working conditions, knowledge reasoning methods can be introduced as an improvement. By representing knowledge and recognized rules with a knowledge base, knowledge reasoning methods are capable of judging different situations and complying with the reasoning rules to infer and obtain classification results, decision rules, and suggestions. Knowledge reasoning methods have been widely used in medical diagnosis, knowledge interpretation, motion scheduling, and other fields [25; 26; 27]. In aspects of motion scheduling and control, knowledge reasoning methods are used in robot path scheduling or control processes. Researchers have applied knowledge reasoning methods to motion schedulingand the control of underwater robotic vehicles [28; 29], four-legged robots [30], and omnidirectional robots [31]. According to the predefined reasoning rules, knowledge reasoning methods can infer the actions of a controlled object in different states based on the obtained state information. In this paper, we researched the satellite onboard mission scheduling problem. With the real-time requirement and onboard computing resource prioritization, the scheduling algorithm is required to obtain better scheduling results with a shorter computation time. In this case, we introduced the knowledge reasoning method to tackle the mission scheduling problem of the AEOS. After classifying various coupling states according to the coupling relationships of the satellite subsystems, we established the reasoning rules to select applicable scheduling strategies in different coupling states. By using the knowledge reasoning method, the computation speed can be increased, and the adaptability of the scheduling method to different working conditions can be enhanced. Although researchers have introduced reasoning methods to the satellite scheduling problem, our proposed method is unlike the others. [PERSON] [32] applied the reasoning method to modify the genetic algorithm and used knowledge reasoning to select the rules for various population initialization, crossover, and mutation.