## 1 Introduction  
Multi-satellite Imaging Mission Planning (MSIMP) refers to the priority scheduling of high-priority tasks to maximize the number of task completions and mission benefits in a complex environment by efficiently allocating limited satellite resources within a limited time window to meet the diversified image requirements of multi-source users [1, 2]. At present, it has been widely used in many fields, such as disaster emergency response and assessment [3], urban infrastructure monitoring [4], climate change and environmental protection assessment [5], etc. However, with the increasing number of satellites in orbit and the gradual expansion of application scenarios, the demand for observation targets has shown exponential growth. In recent years, the demand for image data in disaster emergency response has increased by more than 20% in recent years [6], placing higher demands on mission response timeliness, mission benefits, and mission completion. To be specific, response timeliness requires rapid access to image data during emergencies, task gain requires more efficient resource utilization through optimized task scheduling, and task completion requires that all planned tasks are completed as far as possible to meet the needs of different priorities. In MSIMP on a regular scale (more than tens of targets), there are difficulties such as a long response time, low mission benefit, and low mission execution rate due to multiple factors, such as limited satellite resources, spatial and temporal inhomogeneity of orbit coverage, and restricted time window for observation tasks. Taking disaster emergency response as an example, flooding in a certain region requires that high-resolution satellite images be acquired within three hours for rescue deployment; however, due to the time window of satellite orbit coverage and other tasks, the satellite fails to acquire key images in time, making the delayed images lose their actual decision-making value. At the same time, the observation requirements for multiple tasks in the region resulted in some low-priority tasks not being able to be performed or insufficiently observed, reducing the task execution rate [7,8]. These problems are particularly acute in large-scale target mission scenarios. The simultaneous demand for hundreds of observation targets exacerbates satellite resource conflicts and competition for time windows, leading to an exponential increase in the complexity of mission planning. In a global climate monitoring mission, multiple countries simultaneously demand satellite observation images of different regions, and 300 targets must be observed globally within 24 h, while the execution priorities and time windows of the targets are different, which greatly increases the response time of the MSIMP system, and in addition, the limitations of the number of satellites and orbital coverage may delay some of the low-priority tasks, which has an impact on the mission benefit mission revenue and mission completion [9]. Multi-satellite imaging mission planning in large-scale target scenarios (MSIMPLTS) aims to optimize satellite resource allocation and mission scheduling through a systematic approach, which improves the mission response speed, increases the mission execution rate, and maximizes the mission benefit to cope with the complexity of large-scale target missions. MSIMPLTS can practically solve the key challenges in mission planning by improving the optimization algorithms and dynamic scheduling strategies: firstly, MSIMPLTS can quickly match satellite resources and mission requirements to significantly shorten the response time; secondly, MSIMPLTS can reduce resource conflicts and scheduling bottlenecks by reasonably allocating mission priorities and time windows and improve the mission response time; secondly, it can reduce the resource conflicts and scheduling bottlenecks by reasonably allocating mission priorities and time windows and improve the mission response time by reasonably allocating satellite resources and mission requirements.