## 1 Introduction  
Against the backdrop of the widespread application of the Fifth Generation (5G) mobile communication technology, the Sixth Generation (6G) mobile communication technology has gradually become a research hotspot for scholars around the world, and the research direction in this field has demonstrated significant commercial potential, attracting extensive attention from the industrial sector, including the State Grid Corporation of China [1]. With the rapid development of new power systems and the widespread coverage of the smart grid, the flexible and random access of massive and diverse entities put higher requirements on power communication. Different business requirements arise in a variety of typical power application scenarios, many of which generate a large amount of real-time data that need to be uploaded to cloud server data centers promptly for grid regulation and control [2, 3]. In the blind area of traditional network coverage or power emergency communication scenarios especially, when ground networks experience damage, faults, or lack signal coverage, the ground network data collection methods may fail to meet the data transmission requirements of GPFs. The establishment of the space-air-ground-integrated power three-dimensional converged communication network, as one of the technological visions for 6G network design, can effectively supplement the existing ground power communication systems and provide reliable support for power data transmission. It will become an effective solution for achieving secure, reliable, and flexible transmission of power business data in the future. The SAGIN can be divided into three parts: a space-based network, an air-based network, and a ground-based network [4]. In the space-based network, satellite constellation systems led by LEO satellites [5] can achieve seamless and extensive coverage and provide cloud server data processing services for GPFs. In the air-based network, auxiliary communication airborne platforms, such as Unmanned Aerial Vehicles (UAVs), can serve as amplifiers in satellite-ground communication, completing operations related to power amplification and forwarding signals. They can also act as microbase stations for the temporary storage and processing of data. To further enhance the application potential of the SAGIN in power emergency communication scenarios, in this paper, we propose an algorithm based on RL for the joint optimization of AP trajectories, GPF associations, and power control strategies under classification-based throughput constraints. The algorithm aims to ensure the completion of communication tasks and maximize the long-term overall system energy efficiency. The main contributions of this paper are as follows:  
1. We integrate APs with the SAGIN to categorize the communication requirements of the GPF into real-time data transmission and non-real-time data transmission. For real-time data transmission, the priority is to maximize system throughput and ensure that communication latency is low enough for the data from the GPFs to be relayed by the APs and eventually uploaded to the cloud server on the LEO satellite for processing. For non-real-time data transmission, the goal is to maximize overall system energy efficiency by storing as much data as possible in the APs. After the communication task is completed, the APs fly to the corresponding ground management facility for data unloading. 2. To solve the optimization problem, this paper employs an RL algorithm that models the AP flight paths as an MDP and proposes a multi-agent iterative optimization algorithm based on comprehensive assessments of GPF positions and communication workload. The GPF associations and power control are modeled as a multi-agent K-armed bandit problem, and we propose an algorithm based on multi-agent Temporal Difference learning to solve this aspect. The two algorithms alternate iterations, ultimately solving for maximum overall system energy efficiency. 3.