## 1 Introduction
[18] proposed a dynamic scheduling algorithm based on constraint satisfaction, but the efficiency of this method needs to be further improved. The satellite scheduling for emergency missions is different from that for conventional observations. Emergency missions have more stringent time constraints and require observations in a limited visible time window before the cut-off time. [PERSON] et al. [19] proposed a spatial optimization model to solve the large-area satellite image acquisition planning problem in the context of hazard emergency response, and the priority of satellite image acquisition for different portions of impact areas were addressed. [PERSON] et al. [20] expressed the multi-satellite tasking problem as a multi-objective integer-programming model, including optimizing objectives of the coverage rate, the imaging completion time, the average spatial resolution and the average slewing angle. A real disaster scenario was revisited in terms of satellite image acquisition in the context of emergency response. However, these methods mainly focuses on the acquisition of satellite images in emergency situations, and there are no detailed methods regarding the problem of satellite scheduling. In addition, due to the differences in the importance of missions, the order of execution varies, so a reasonable and effective priority algorithm is important for improving the efficiency of mission execution. Most scholars study satellite networks by setting the mission priority to evaluate the revenues of scheduling and regard the priority as the main optimization objective. [PERSON] et al. [21] applied a new tabu search algorithm and addressed a scheduling problem for LEO satellite missions, assigning satellites to the greatest number of requested missions by considering the mission priority and satisfying multi-resource constraints. In terms of solution algorithms, there is no universal algorithm for all problems since the satellite mission scheduling problem involves a large number of non-linear constraints and the solution objectives are not unique. [PERSON] et al. [22] proposed a constraint-based model and a description method for the scheduling of Earth observation satellites. [PERSON] et al. [23] studied the multi-satellite joint model and applied genetic algorithm to solve the problem. Mission priority was taken into account in this model, and each satellite has constraint condition, but factors such as the storage capacity limitation and data downlink were not taken into account. [PERSON] et al. [24] introduced the Clustered Maximum Weight Clique Problem (CCP) which was derived from the Satellite Image Acquisition Scheduling Problem (SIASP), and proposed a novel model-based matheuristic which took advantage of the power of the modern mixed-integer programming solvers and boosted the performance by including appropriate constraints. For optimization objective function, [PERSON] et al. [25] presented an indicator-based multi-objective local search method to solve a multi-objective optimization problem, which was to maximize the total revenue of the selected acquisitions and simultaneously ensure the fair resource sharing by minimizing the maximum revenue difference among users. [PERSON] et al. [26] studied the image acquisition scheduling for the Canadian RADARSAT Constellation Mission (RCM). An integer programming model was proposed to employ a piecewise linear objective function to favor completion of an image acquisition request and has achieved good results. [PERSON] et al. [27] proposed a heuristic algorithm based on minimum resource competition with the objective of minimizing scheduling costs and maximizing mission revenues. However, most of the methods above were based on general missions and are difficult to apply in emergency mission scheduling.