###### Abstract
The scheduling cycles of the satellite's tasks were segmented according to the downlink time windows to establish a mixed-integer programming model for integrated imaging and data transmission scheduling problem with a branch-and-price algorithm to solve it. [PERSON] et al. [17] investigated the integrated scheduling problem of observation data transmission for Earth observation satellites with the involvement of relay satellites, assuming that ground station resources were sufficient to simultaneously meet the data transmission needs of multiple satellites. They used a directed acyclic graph to established a mixed-integer linear programming model, and proposed a two-stage genetic annealing algorithm to solve the model. [PERSON] et al. [18] studied the integrated scheduling problem of Earth observation satellites, proposing an integer linear programming model and an local search algorithm. The algorithm utilizes an efficient constraint handling mechanism to guarantee algorithm efficiency. However, the model proposed in this study does not allocate and schedule tasks for satellites and ground stations, without considering the limitation on ground station antenna resources. [PERSON] et al. [19] studied the integrated scheduling problem of multiple satellites for imaging and data transmission under memory constraints, tracking the capacity changes during the task scheduling process through the sequence of task variables, but only used the Gurobi solver for solving, which is not applicable to large-scale cases and did not consider task allocation for stations, adopting a \"first-come, first-served\" approach for data transmission. [PERSON] et al. [20] abstracted the integrated scheduling problem of imaging and data transmission as a vehicle routing problem with pick up and delivery, establishing a mixed-integer programming model for the integrated scheduling of command uplinks, imaging, and data transmission for multiple satellites, and verified the model's effectiveness through a solver. [PERSON] et al. [21] studied the imaging and data transmission integration scheduling problem for two types of data transmission modes: real-time data transmission and storage distribution. They established a mixed-integer programming model and designed a genetic algorithm based on the earliest start rule for observation tasks and the earliest suitable time rule for data transmission tasks. [PERSON] et al. [22] studied the multi-satellite observation-relay transmission-downloading coupling scheduling problem, and improved the conventional tabu search algorithm for multi-satellite earth observation by integrating the data transmission path planning algorithm to solve and carry out the entire coupling process for earth observation. And, a satellite cluster profit-state evaluation function was provided to enhance the local search process, while a rule-based Dijkstra data transmission path planning method was proposed in the data transmission planning algorithm. Table 1 summarizes the related work. Due to the explosive characteristics of imaging and data transmission scheduling, traditional research mostly separates the scheduling of imaging tasks from that of data transmission tasks, and there is little research that considers both the imaging and data transmission processes simultaneously. Current research on integrated scheduling is mostly based on various simplified conditions such as: discretization of time windows, unlimited ground station antenna resources, and \"first-come, first-served\" execution of data transmission tasks and so on. Therefore, in order to improve the effectiveness of satellite scheduling, it is necessary to conduct more in-depth research on the integrated scheduling problem.