## 1 Introduction
In addition, MSIMPLTS can use an intelligent scheduling strategy to adjust satellite resource allocation in real time to enhance system adaptability in the face of large-scale target mission environments. Therefore, the investigation of MSIMPLTS provides a new solution to the practical difficulties in large-scale mission planning and is of great significance in solving the conflict between limited satellite resources and the explosive growth of target observation demand. In actual applications, remote sensing technology has been widely used to provide strong support for mission scheduling and emergency response in complex scenarios. A large amount of the literature has shown that remote sensing data have important roles in areas such as disaster emergency response, urban infrastructure monitoring, and climate change assessment. For example, using satellite rainfall products to evaluate the impacts of natural disasters [10], using General Circulation Models (GCMs) to quantify climate change [11], and investigating the impacts of manmade infrastructures on river morphology under global warming, as well as applications in environmental protection [12] and disaster emergency response [13], have fully demonstrated the great potential of satellite imaging technology. Particularly in enhancing human resilience in coastal communities [14], these applications further demonstrate the critical role of satellite imaging technology in addressing global environmental and disaster challenges. Therefore, in-depth research on MSIMPLTS will provide new ways to solve these practical problems and promote mission planning for efficient and intelligent scheduling in a wider range of application scenarios. Presently, MSIMPLTS can be classified into two parts: task planning models and task planning algorithms, according to the type of target task, constraints of task planning, and scale of planning scenarios. Research on MSIMPLTS task planning models includes the integer planning model [15], constraint satisfaction model [16], agent model [17], graph theory model [18], Markov decision model [19], Graph Neural Network (GNN) model [20], etc. [PERSON] et al. [21] further applied the Mixed Integer Linear Programming (MILP) model to solve the computational efficiency problem of task assignment in large-scale scenarios. [PERSON] et al. [22] proposed an intelligent task-solving framework based on the constraint satisfaction model, which realized the autonomous adjustment of the observation sequence of the target task in large-scale scenarios. [PERSON] et al. [23] combined the description methods of attributes, interactions, rules, and states through agent technology to solve the complexity problem of multi-satellite resource allocation and coordination. [PERSON] et al. [24] proposed a graph theoretical model for the automatic aggregation of MSIMPLTS problems, modeling MSIMPLTS problems as heterogeneous graphs following various constraints, which effectively improves large-scale target scenario mission planning in terms of task benefits. [PERSON] et al. [25] modeled the MSIMPLTS problem as a finite Markov decision process with a continuous state space and a discrete action space, which solved the task planning problem under uncertainty and diverse demands. [PERSON] [26] proposed a graph neural network model based on MSIMPLTS, which improves the speed of reaching the optimal task planning solution for large-scale scenarios. Research on algorithms for MSIMPLTS task planning includes the greedy algorithm [27], mixed integer programming algorithm [28], local search algorithm [29], intelligent optimization algorithms [30], heuristic algorithms [31], and deep learning methods [32]. [PERSON] et al. [33] proposed a greedy algorithm considering both observation task priority and task congestion, which improves the image duration of observation tasks in large-scale scenarios. [PERSON] et al.