## 6 Conclusions  
In this paper, we study the energy efficiency optimization problem of the space-air-ground-integrated power three-dimensional converged communication network in emergency communication scenarios. On the basis of the system model, we establish the optimization problems of joint AP flight path selection, GPF association, and power control. The AP flight path is modeled as an MDP, and the AP path optimization algorithm is proposed based on a comprehensive evaluation of GPF location and workload. The GPF association and power control subproblem is modeled as a multi-agent, time-varying K-arm bandit model. We propose a device association and power control algorithm based on Temporal Difference learning to solve this problem. Combining these two algorithms, we propose a joint optimization strategy for flight path and resource allocation based on RL. Through the collaborative iteration of the two algorithms, the optimization of AP flight paths, GPF association, and power control can be achieved, which can improve the overall energy efficiency of the system while meeting certain throughput requirements. Finally, through computer simulations and comparisons with three other benchmark algorithms, the proposed algorithm's effectiveness in enhancing the overall system energy efficiency is validated. In practical scenarios, adjusting the scaling factor of the AP can adapt to different numbers of GPFs to achieve optimal system performance under current throughput requirements.  
In future work, we will improve the settings of some parameters in the algorithm, integrate deep learning content to further optimize the algorithm iteration process, train neural networks as a framework for strategy selection, and conduct simulations in a larger-scale environment compared with other classic algorithms in the field of reinforcement learning to conduct a more in-depth analysis of the simulation results.  
Conceptualization, Z.C., H.Z., S.D. and J.L.; methodology, Z.C., S.D., J.L. and L.Z.; software, S.D. and J.L.; validation, H.Z., S.D. and L.Z.; formal analysis, J.L.; investigation, Z.C.; resources, Z.C., H.Z. and J.L.; data curation, J.L. and L.Z.; writing--original draft preparation, S.D. and J.L.; writing--review and editing, Q.L., Z.C. and L.Z.; visualization, H.Z.; supervision, Q.L. and J.L.; project administration, Z.C.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Foundation of the State Grid Corporation of China under Grant 5700-202352266A-1-1-ZN.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: Author [PERSON] and [PERSON] was employed by the company State Grid Information & Telecommunication Branch, and author [PERSON] was employed by Beijing Fibrlink Communications Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dependent Variable** & **RP Algorithm** & **EP Algorithm** & **RS Algorithm** \\ \hline noise power & 17.23\% & 162.87\% & 4.45\% \\ channel bandwidth & 16.63\% & 116.42\% & 4.96\% \\ throughput load rate & 7.92\% & 34.71\% & 6.79\% \\ the number of ground devices & 7.13\% & 31.15\% & 4.23\% \\ average value & 12.23\% & 86.29\% & 5.11\% \\ computational complexity & \(O(KTN)\) & \(O(KT(M+N))\) & \(O(KT(M+N))\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative indicator of the energy efficiency increases in the proposed algorithm compared with each algorithm.