## 1 Introduction  
The mission scheduling of Earth observation satellites is performed to determine the execution order, execution time and corresponding satellite resources in the case of a fixed number of satellites and limited storage capacity. In the process of scheduling, it is necessary to eliminate the timing conflicts between different missions, improve the utilization of satellite resources and maximize the needs of users. In recent years, with the wide application of Earth observation satellites in emergency conditions, such as natural disaster monitoring, accident disasters and public health incidents, the scheduling of the Earth observation satellite has faced some new challenges. There are many uncertainties in the scheduling of Earth observation satellites under emergency conditions, including the user needs, weather conditions, and the satellite status [1]. In addition, emergency missions submitted by users usually arrive dynamically, and the number and arrival time of missions are uncertain. Due to the characteristics of dynamic real-time scheduling, multiple objectives need to be taken into account simultaneously, such as scheduling revenue, stability and energy conservation. All of these constraints and uncertainties make emergency scheduling an NP-hard combinatorial optimization problem [2]. Therefore, it is of great significance to propose a fast and efficient multi-satellite dynamic emergency scheduling strategy. Recently, some scholars have carried out extensive research on satellite scheduling problems. Under the condition of satisfying multi-resource constraints, the satellite resources and observation period are allotted for imaging observation missions in order to improve the efficiency of mission execution. However, in taking the resources contention into account, it is more complicated and challengeable to reasonably assign multiple satellites to targets [3]. The methods used to solve this problem include priority-based heuristics [4], local search [5], tabu search [6; 7], genetic algorithms [2] and ant colony algorithms [8]. [PERSON] et al. [9] used greedy algorithm for satellite imaging scheduling in the ASTER system. [PERSON] et al. [10] indicated that a greedy algorithm can provide approximately optimal results in a permissible time and is commonly used in solving relevant problems. However, the algorithm performance is sensitive to the heuristic strategies. The aforementioned satellite scheduling strategies are proposed in the framework of static scheduling, which is not applicable to multi-satellite dynamic scheduling problems under emergency conditions. For dynamic emergency scheduling, one remarkable feature is the arrival of new emergency imaging missions. Some scholars have used the complete reprogramming algorithm to remodel and solve this problem [11; 12; 13]. However, the new mission scheduling plan generated is greatly different from the original one, which leads to difficulty in satellite rescheduling. [PERSON] et al. [14] summarized four factors that can result in changes to the satellite mission scheduling plan: mission observation opportunity changes, resource changes, new mission insertion, and scheduling parameter changes. However, no specific model or algorithm was presented. [PERSON] et al. [15] proposed a satellite dynamic mission scheduling model based on the dynamic constraint satisfaction problem (DCSP), but did not consider the adjustability of the initial plan. [PERSON] et al. [16] presented a novel task dynamic merging strategy, and retracting and rearranging operations were conducted to make room for incorporating the newly arrived missions. [PERSON] et al. [17] proposed a hierarchical scheduling method based on an ant colony algorithm for the real-time scheduling problem, but did not consider observation requirements, image data type or acquisition. Considering the real-time satellite scheduling and arrival of new missions, [PERSON] et al.