# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 6 Conclusions  
This paper studies the cooperative scheduling problem of space information network resources. Aiming at the pain points of the strong dependence of resource scheduling on ground base stations, weak autonomous coordination of resources, and low resource utilization, we propose a low time complexity algorithm based on the related theory of graphs to achieve dynamic reconfiguration of satellite resource request queues and inter-satellite resource collaboration. We compare the time complexity of several baseline algorithms for resource scheduling, analyze the computation time of multiple algorithms in spatial information network scenarios, and compare the performance of the algorithms. In future research, there are still some issues to be addressed. First, the model constraints constructed are not perfect, such as power and radiation constraints. The time complexity of the maximum flow algorithm determines the time complexity of the algorithm designed in this paper. Optimization of the maximum traffic algorithm for dynamic networks can effectively improve the computational efficiency of the whole algorithm. In addition, the threshold setting of the number of coordinated satellites is not considered in this paper. If the coordinated satellite resources are abundant, it will generate much link resource  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Time Consumption**} \\ \cline{2-6}  & **-** & **P1** & **P2** & **P3** & **P4** \\ \hline RQRCS & 133 & - & - & - & - \\ PSO & - & 230 & 230 & 230 & 230 \\ GA & - & 291 & 302 & 288 & 283 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the time consumption of the three algorithm scheduling resource processes.  
Figure 10: GA solution process based on different parameter settings. (**a**) GA process based on parameter P1, (**b**) GA process based on parameter P2, (**c**) GA process based on parameter P3, (**d**) GA process based on parameter P4.  
Figure 9: PSO solution process based on different parameter settings. (**a**) PSO process based on parameter P1, (**b**) PSO process based on parameter P2, (**c**) PSO process based on parameter P3, (**d**) PSO process based on parameter P4.  
occupation and path optimization time consumption. If the coordinated satellite resources are too few, the resource request and pressure will not be significantly relieved. Therefore, how to select the number of peripheral satellites needs further study.  
Conceptualization, J.L.; methodology, H.F., L.L.; software, H.F.; validation, S.W., J.L. and L.L.; formal analysis, H.F., X.Z. and S.W.; investigation, Z.Y.; resources, H.F. and Z.Y.; data curation, H.F.; writing--original draft preparation, H.F.; writing--review and editing, H.F., S.W.; visualization, H.F.; supervision, Z.Y.; 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), and in part by the National Natural Science Foundation of China (No. 61972398).  
Not applicable.  
The authors declare no conflict of interest.