## 2 Related Work  
The construction of multi-satellite mission queues plays a crucial role in the complex scheduling relationships between rapidly growing, intricate user demands and limited on-board resources. In the current environment of multiple satellites and ground stations, the ability to reasonably allocate payload resources, improve resource utilization efficiency, and maximally meet the timeliness requirements of observation missions presents a valuable research point. At present, most research on multi-satellite mission scheduling is based on the dynamic programming algorithm, greedy algorithm, and heuristic algorithm [18; 19; 20; 21]. In recent years, the exploration of multi-satellite mission scheduling has made continuous progress. [PERSON] et al. [20], by designing preprocessing models for different types of targets to decompose complex scenes, and using dynamic task planning algorithms to optimize the resource allocation for multi-satellite cooperative observation, effectively improved the efficiency of dynamic observation by remote sensing satellites. For the dynamic area task scheduling problem, [PERSON] and [PERSON] [18] proposed an emergency response satellite scheduling model, which, using dynamic segmentation algorithm and dynamic Greedy state algorithm, can intelligently assign satellite imaging tasks and has a good application prospect. Furthermore, [PERSON] et al. [19] proposed a semi-Markov decision process (SMDP) method to solve the satellite task scheduling problem, which reduces the operational complexity and the number of steps of task planning, and achieves higher acceleration in the single target scenario. For complex constraint conflicts, [PERSON] et al. [21] proposes a large-scale task multi-satellite cooperative scheduling method based on a hybrid graph neural network (GNN) and meta-heuristic algorithms. By leveraging the GNN's capability to represent and extract task relationships, combined with the optimization framework of meta-heuristic algorithms, the computational efficiency of large-scale task planning has been substantially improved. In addition to the classical algorithms and their variants, there exist search methods based on complex neural network architectures. For instance, in the paper by [PERSON] et al. [22], a new satellite mission planning algorithm leveraging symmetric recurrent neural networks is introduced. This algorithm demonstrates the capability to promptly respond to various task requirements, aligning with business needs. On a related note, [PERSON] et al. [23] introduced EfficientNet, a novel CNN model scaling method. This approach optimizes model channels in terms of number, depth, and width through neural structure search and composite coefficients. While these approaches effectively enhance resource utilization,satisfy user demands, and manage both urgent and routine task scheduling, they encounter challenges in adapting to the time-varying characteristics of satellite networks. However, their computational complexity often results in increased time consumption, limiting their applicability and efficiency in real-world scenarios. In recent years, numerous scholars have delved into the intricate interrelationships among multiple tasks using graph theory, leading to advancements in both theory and practical applications in the field [24; 25; 26; 27; 28]. [PERSON] et al. [24] introduces the Multi-Aspect Extended Hypergraph (MAEH) model, which accurately describes the spatiotemporal characteristics and functional attributes of resources. Through a two-stage scheme, they achieve rapid and low-complexity scheduling of cross-domain resources, thereby effectively enhancing resource utilization. To overcome the limitations of traditional Time Expanded Graphs (TEG) in representing observation, energy, and transceiver resources, [PERSON] et al. [25] proposes an Enhanced Time Expanded Graph (ETEG).