###### Abstract
However, their mathematical models primarily considered the bottleneck constraints in the problem, namely the cumulative imaging count and cumulative imaging time constraints related to imaging. For data transmission processing, they adopted a simplified mode where data is transmitted back when there is a data transmission window and storage capacity available, and for the contention of multiple satellites returning data to a single ground station, they employed a simple first-come, first-served conflict resolution strategy. [PERSON] et al. (2019) abstracted the satellite integration scheduling problem as a variant form of a transceiver problem, establishing a constrained planning model for the multi-satellite multi-station task planning problem, and employed a tabu search algorithm to solve the issue. [PERSON] (2019) studied a comprehensive task scheduling model that only considered satellite task allocation without accounting for the scheduling of task start times, while considering both imaging and data transmission tasks, and proposed two modes for solving the problem based on global optimization and phase optimization. In the phase optimization mode, a task scheduling algorithm for emergency conditions based on the concept of imaging path delay dominance and the idea of label updating is proposed. [PERSON] et al. (2019) designed a task scheduling strategy for multi-satellite emergency mission planning that combines fixed-period decision planning with local adjustment planning, and proposed a two-phase algorithm to address the dynamic programming problem of emergency tasks with uncertain arrival times. However, this paper, when studying the scheduling problem, only considers the selection of task time windows, without scheduling the specific start and end times of the tasks. [PERSON] [13] modeled the task planning problem into a heterogeneous multi-vehicle delivery problem with multiple time windows and complementary resource constraints, and introduced the branch-and-price algorithm into the solution process of this problem. However, this study strictly limited the order of satellite data downlinking to follow the imaging sequence. Additionally, during the modeling process, to facilitate the solution of the branch-and-price method, the start time of tasks was restricted to integer values, and the satellite could only perform one action at the same time, meaning imaging and data transmission could not be executed simultaneously. [PERSON] et al. [14] analyzed the integrated scheduling problem of satellite observation and data transmission in the context of satellite storage environments with partially erasable memory (PEM) and wholly erasable memory (HEM), proposed an integrated scheduling model suitable for both types of storage environments, and employed constructive heuristics and metaheuristics, using Tabu Simulated Annealing (TSA) and Tabu Late Acceptance (TLA) algorithms to solve the model. In this paper, the imaging and non-real-time data transmission actions of the satellite cannot be executed simultaneously. [PERSON] et al. [15] studied the emergency task scheduling problem of agile satellites while simultaneously considering imaging and data transmission tasks.They accurately took into account the attitude adjustment of agile satellites and proposed a new time window partitioning method that transforms variable-spacing visible time windows into multiple fixed-spacing visible time windows and presented a scheduling algorithm based on merging insertion, direct insertion, shifting insertion, deletion insertion, and reinsertion strategies. In this study, the satellite can perform imaging and data transmission tasks simultaneously, and the storage is only required to not exceed the maximum storage capacity limit. [PERSON] et al. [16] assumes that data transmission occurs when the satellite passes over the ground station, with the data transmission time being the visible window time of the ground station and that the amount of data transmitted is variable, which is independent of the transmission time.