###### Abstract
By employing a heuristic population initialization strategy and a clustering-based evolutionary strategy, along with the application of a Task Arrangement Algorithm (TAA), the algorithm's adaptability to different scenarios and the likelihood of successful task scheduling have been effectively enhanced. Compared with the traditional multi-satellite network mission planning methods that lack the intricate dependencies among multiple tasks and the allocation of onboard resources, the new generation of satellites with autonomous online mission planning capabilities canfully utilize the individual intelligence [16]. Under this precondition, our study aims to bridge these gaps by proposing an innovative multi-satellite, multi-task aggregation algorithm that not only addresses the dynamic scheduling of tasks but also optimizes resource utilization across the satellite network. This involves creating a strategy based on a multi-task aggregate graph model. By incorporating the principle of minimum energy consumption from graph theory, this strategy is designed to dynamically optimize the task queue. This optimization is based on current task demands and the status of available resources, ensuring a more effective allocation and utilization of satellite capabilities. While CP-TM stands as a solid foundation for maximizing observation opportunities through task merging criteria, it predominantly focuses on static task aggregation. It does not adequately address the dynamic scheduling of emergency tasks or the real-time optimization of task queues based on fluctuating demands and resource availability [17]. Our method extends these concepts by providing a comprehensive framework for dynamic task scheduling. By leveraging CP-TM as a baseline, we highlight the novel contributions of our approach in enhancing resource efficiency and operational effectiveness in satellite networks. As science and technology advance, the need for swiftly addressing complex space tasks escalates. Optimized task aggregation algorithms enhance the execution rate of observation tasks and enable rapid responses to terrestrial or extraterrestrial environmental changes, thereby significantly boosting the efficacy of space missions. In this paper, we address the above shortcomings and investigate the graph-based task aggregation problem under multiple observation satellites to minimize satellite energy resources. The innovative work in this paper is as follows. 1. In-depth analysis of the principle of satellite Earth observation and the aggregation constraints of Earth observation missions, culminating in the creation of an advanced multi-satellite, multi-mission aggregation graph model and its corresponding construction algorithm;
2. Leveraging the aforementioned aggregation graph model, this paper devises a dynamic task merging-based multi-satellite, multi-task aggregation algorithm. The task synthesis priority based on the minimum energy consumption of the aggregated tasks and the minimum energy consumption calculation method of the aggregated tasks based on dynamic planning is designed to realize the fast solution of the task synthesis priority;
3. This paper's algorithm undergoes rigorous theoretical and experimental validation, including a comparative analysis with the aggregation algorithm presented in the literature [17]. The findings reveal that our algorithm can decrease mission size by approximately 45% and reduce the energy consumption needed for mission execution by about 50%. These results significantly enhance satellite operational efficiency and resource utilization.