## 6 Conclusions  
This paper addresses the problem of limited resources of Earth observation satellites in space information networks and the difficulty of satisfying many observation tasks. The aggregation method of observation tasks under multi-satellite cooperative observation is studied to improve the utilization rate of satellite resources. An in-depth analysis of the principle of satellite Earth observation and the aggregation constraints of Earth observation tasks is presented, a multi-satellite multi-task aggregation graph model is constructed, and a model construction algorithm is given. Based on the multi-satellite multi-task aggregation graph model, a multi-satellite multi-task aggregation algorithm is designed based on the dynamic merging of tasks. The task merging strategy in the aggregation algorithm is studied in depth. 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. The paper concludes with a complete theoretical and experimental validation of the algorithm and a comparative analysis with the aggregation algorithm proposed in the literature [17]. The experiments show that the task size can be reduced by about 45%. The energy required for task execution can be reduced by about 50% after aggregation by the algorithm in this paper. The algorithm in this paper is slightly higher than the aggregation algorithm proposed in [17] in terms of running time. Still, the reduction in aggregated task size obtained using the algorithm in this paper is 25% more than using the algorithm in [17]. The sum of the energy consumption required for aggregated task execution is 20% less.  
The observation task aggregation method proposed in this paper has some practical significance. However, there is room for improvement. To better match the work of observation satellites, the next step is to study the real-time task aggregation scheme during the execution of observation tasks.  
Conceptualization, J.L.; methodology, S.W. and L.L.; software, S.W.; validation, Y.H., J.L. and L.L.; formal analysis, S.W. and Y.H.; investigation, H.F. and J.L.; resources, S.W. and L.L.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, H.F. and J.L.; visualization, S.W.; supervision, L.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant U2003208, in part by the Science and Technology Plan of Hunan under Grant No. 2016 TP1003, in part by the Key Technology R&D Program of Hunan Province under Grant No. 2018 GK2052, in part by the Foundation of Preview of Equipment (No. 6141A020227), in part by the Theory and Method of Multi-Satellite Collaborative Application (No. 2019-JCQ-ZD-342-00) and in part by the National Natural Science Foundation of China (No. 61972398).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.