## 1 Introduction
In [17], considering UAVs with microwave power transfer (MPT), an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices by optimizing the device charging and interrogating for data collection and the instantaneous patrol velocity of the UAV through a Markov decision process (MDP). The MDP algorithm can be regarded as an extension of the Lyapunov optimization algorithm, has an excellent performance in solving optimal decision problems, and is also widely used in solving machine learning optimization algorithms. The above two kinds of UAV functions have some limitations. The first kind of UAV as a terminal only undertakes the data acquisition function. Given its own hardware constraints, the UAV can neither process nor store (promptly and for an extended time) numerous video and sensor data. Such a UAV is a burden on the network, can only collect data flexibly, and fails to realize flexible network connectivity. Another kind of UAV functions as a server or auxiliary server in the MEC network to enable computation and provide storage for the terminal, but the limitation of the UAV itself cannot be ignored. Limited energy, computing resources, and substantial hovering mechanical energy consumption render the UAV unable to handle large data processing or can only assume the auxiliary computing function. Accordingly, the edge network still has limitations and is unable to deploy the network flexibly and quickly. The problem of the power supply is always the key to solving issues when the UAV acts as the MEC server. Given that the UAV's own energy is insufficient, its MEC function must be weakened to allow the UAV to relay data. The UAV acts as a relay to assist information transmission in wireless networks and generates new ideas for the integration of UAV and MEC networks. On the basis of considering propagation loss, shadow, and multipath fading, a UAV-based multi-hop relay channel model is established in [18]. In addition, the cascade propagation loss and cascade probability density function of the channel fading of the relay link are derived. Considering the limited energy characteristics of the UAV, UAV, [PERSON], [PERSON] et al. [19] studied the energy collection problem of multiple UAV relay networks in the Internet of things system to overcome the large-scale fading between the source and receiver, thereby realizing green collaborative communication. In [20], a framework for MEC-enabled cognitive radio (CR) networks is proposed, which integrates three technologies: MEC, CR, and wireless power transfer (WPT), in which the UAV performs collaborative relay during communication. WPT technology can use solar or wind energy to charge the UAV and provide a controllable and stable energy supply, which can significantly improve the performance of access to the MEC network. This study considers WPT technology to realize UAV green energy charging. On the one hand, with the UAV as the relay node, the energy consumption and the task burden of the UAV is limited, and the network coverage and the long-distance communication ability are increased. On the other hand, the task is transmitted to the remote MEC server through the relay, and the task runs on the resource-rich fixed server, thereby reducing the task processing delay and energy consumption. However, considering the increase of one-hop forwarding, the network reliability of the edge network, including relay, may be reduced. In addition, the bit-error rate (BER) and interrupt probability problems cannot be ignored, and the transmission distance of the relay communication is relatively large. Such factors will cause a certain transmission delay. How to balance the demand for reliability and delay is an important research direction. In addition, how the UAV relay function and the MEC service function of the UAV are used in coordination to improve the overall system performance is also of practical significance.