###### Abstract  
The purpose of constructing onboard observation mission queues is to improve the execution efficiency of onboard tasks and reduce energy consumption, representing a significant challenge in achieving efficient global military reconnaissance and target tracking. Existing research often focuses on the aspect of task scheduling, aiming at optimizing the efficiency of single-task execution, while neglecting the complex dependencies that might exist between multiple tasks and payloads. Moreover, traditional task scheduling schemes are no longer suitable for large-scale tasks. To effectively reduce the number of tasks within the network, we introduce a network aggregation graph model based on multiple satellites and tasks, and propose a task aggregation priority dynamic calculation algorithm based on graph computations. Subsequently, we present a dynamic merging-based method for multi-satellite, multi-task aggregation, a novel approach for constructing onboard mission queues that can dynamically optimize the task queue according to real-time task demands and resource status. Simulation experiments demonstrate that, compared to baseline algorithms, our proposed task aggregation method significantly reduces the task size by approximately 25% and effectively increases the utilization rate of onboard resources. Earth observation satellite; resource management; mission queue construction; graph computation; dynamic merging +
Footnote †: journal: _Mathematics_  
0000-0002-4000-0002-3499-9999-9999-9999-99999-99999-99999-99999-99999-99999-99999-999999-9-exigent challenge amid dynamic and multifaceted task environments. Most current mission aggregation algorithms concentrate on observation time windows and sidelobe angles, frequently overlooking the critical aspect of satellite resource utilization before and after aggregation [8]. In the network environment of spatio-temporal dynamic evolution, there are problems [9] such as large task scale, complex execution sequence relationship, dynamic queue growth, strict execution time, and inclusion of tasks. These characteristics make the calculation of the task execution queue particularly complicated. In addition, the traditional fixed task scheduling scheme will no longer adapt to a large-scale task and the dynamic changes of task requirements. The complex dynamic topology and complex constraint relationships of satellite information networks result in distributed scheduling architectures that are not suitable for on-satellite mission scheduling scenarios. The importance of in-depth research on task aggregation algorithms has become increasingly prominent. At present, the task scheduling method based on spatial information network integration strategy is mainly applied to remote sensing observation tasks. This method interprets the issue as a clustering problem by constructing a task graph model based on clusters. The model is then solved using the appropriate approximation algorithm [10; 11; 12; 13]. For example, [PERSON] et al. [10], and improved cluster partitioning algorithm was designed to achieve the clustering of imaging reconnaissance tasks based on the approximate cluster partitioning algorithm proposed by [PERSON]. [PERSON] combined with two merging criteria proposed by [PERSON] [11]; [PERSON] et al. [12] designed a clustering algorithm based on the maximum-minimum ant colony algorithm aiming to effectively accomplish the observation mission clustering of agile imaging satellites; in addition, more common are task aggregation methods based on task insertion. For example, [PERSON] et al. [14] proposed an enhanced single-track optimal cluster partitioning clustering method for remote sensing satellite imaging missions; task aggregation methods based on task insertion are commonly employed. For example, [PERSON] et al. [15] introduces a Cluster-Based Genetic Algorithm (C-BGA) using the k-means clustering method to address satellite scheduling issues.