## 2 Related Work  
Currently, research on the SAGIN has made significant progress and produced valuable results. In terms of satellite-fused communication, reference [6] proposed using satellites as cloud servers to provide remote computing services for ground users and jointly optimized communication and computing resources to minimize overall system energy consumption. Reference [7] investigated the feasibility of seamless and efficient connections between ground communication systems and satellite networks. In terms of UAV trajectory optimization, reference [8] proposed an algorithm that utilizes the successive convex approximation (SCA) technique to optimize UAV trajectories and minimize overall system energy consumption. Reference [9] introduced an algorithm to discretize the UAV and user movement process in a multi-UAV scenario to maximize the minimum user transmission rate. Reference [10] presented a trajectory optimization algorithm for minimizing the number of UAVs deployed in a vehicular network by solving the optimization problem using the SCA technique. Reference [11] proposed an algorithm using RL with the Q-learning algorithm to optimize the trajectory of a single UAV to minimize average communication latency. In addition to the research directions mentioned in the above literature, another focus is on optimizing device association and power control strategies to enhance system performance and communication quality. Reference [12] proposed an algorithm based on block coordinate descent (BCD) and SCA theory for the joint optimization of device association, UAV trajectories, and power control to maximize the minimum user rate. Reference [13] described an energy consumption minimization algorithm that jointly optimized user association matching and power allocation using BCD and SCA theory. In the aforementioned studies, optimizations were conducted considering parameters such as energy consumption, throughput, minimum user rate, and average communication latency in communication systems. However, the analyses were limited to models with a single constraint and lacked consideration of specific business communication requirements in practical scenarios. For instance, in power emergency communication scenarios, different services may have varying communication throughput requirements while considering system energy consumption. Consequently, a comprehensive optimization of system efficiency considering both throughput and energy consumption becomes an effective solution. Reference [14] proposed an algorithm utilizing the genetic algorithm and simulated annealing to separately optimize device association and power selection to maximize the overall system energy efficiency. Reference [15] presented a method for power allocation and wireless backhaul bandwidth allocation under specific quality of service (QoS) constraints, aiming to maximize the system energy efficiency of downlink heterogeneous networks. Most of the resource allocation optimization algorithms mentioned above employ traditional heuristic algorithms or SCA techniques based on BCD, requiring multiple iterations for solving, leading to a significant increase in the computational complexity of the system. Additionally, as the network scale increases, the high-speed dynamic characteristics of the SAGIN result in continuous changes in wireless environment parameters, and the adaptability of algorithms becomes a challenge. Considering the use of RL algorithms to solve the resource allocation problem as an effective solution, reference [16] proposed an algorithm utilizing RL to jointly optimize UAV paths, user device selection, and power allocation, ensuring fairness in user throughput. Reference [17] employed the Q-learning algorithm to jointly optimize UAV paths, user selections, and power allocation to maximize network capacity. Reference [18] proposed a power control scheme based on the Deep Q Network (DQN) to improve the system-level energy efficiency of two-layer 5G heterogeneous and multi-channel cells.