## 4 Conclusions and Future Work  
Dynamic emergency scheduling of Earth observation satellites is of great significance for the efficient use of satellite resources to obtain ground image data under emergency conditions. This paper studies the satellite imaging scheduling problem under emergency conditions and proposes a set of reasonable mission priority calculation method based on seven impact factors. Considering the constraints in the scheduling process, a multi-objective mathematical programming model of multi-satellite dynamic emergency scheduling based on mission priority is established. This model can be used to solve the scheduling problem of newly arrived emergency missions. In the simulation experiment, complex scenarios with different mission types and different satellites were set, and the priority levels of different missions were obtained with a calculation model of mission priority. It was also used as the heuristic information to solve the constrained scheduling problem. In the multi-objective optimization, the hybrid genetic tabu search algorithm used in this paper effectively overcomes the disadvantages of the weak local search ability of the genetic algorithm and strong dependence on the initial solution of tabu search. Therefore, this hybrid algorithm avoids a large number of useless iterations when approaching the best solution in the late stage, thereby improving the efficiency of the algorithm. In the simulation scenarios with different numbers of initial missions and newly arrived missions, the analysis results show that the probability of executing high-priority missions increases because the model considers the calculation of mission priority. Thus, a reasonable initial scheduling plan for mission request can be established under emergency conditions, and the effect of satellite resource scheduling can be improved. In case of newly arrived emergency missions, a feasible solution with low time cost can be given by using dynamic scheduling algorithm based on the mission priority. Overall, the proposed model yields high scheduling revenues and low scheduling costs, maintains the stability of the initial scheduling plan under the condition of fast scheduling, so it is suitable for solving multi-satellite dynamic mission scheduling problems. In practical applications, the constraints of satellite mission scheduling are very complex. In some cases, manual intervention is needed for obtaining the current satellite scheduling plan, such as the collection of mission request lists, the selection of satellite resources, and the weight setting of optimization objective function. In the emergency situation, the operators may need to adjust the scheme according to the latest planning configuration before it releases, including deleting observation missions and adding new missions. However, finding the balance between manual intervention and automation is a perennial challenge. In future work, if some manual intervention can be transformed into decision-making conditions of the scheduling system through learning mechanism, it will be more capable to meet the demand. Furthermore, we will analyze more complex scheduling problems on this basis and further refine the model. Additionally, more constraints will be considered to make the scheduling problems more comprehensive. [PERSON] and [PERSON] are the directors of the corresponding contents. [PERSON] and [PERSON] designed the methodology and performed the experiments. [PERSON] completed data processing, formal analysis and the manuscript. [PERSON] checked the writing and completed the manuscript polishing. The study is funded by the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA20020101), the National Key R & D Programs of China (No. 2017 YFB0504201), and the Natural Science Foundation of China (No. 61473286 and No. 61375002). The authors would like to thank the Satellite System Technology Department, Aerospace Information Research Institute for providing the technical guidance.