## 1 Introduction
The work in [11] proposes using charging UAVs to recharge mission UAVs without interrupting their communication tasks. It achieves this by employing a deep reinforcement learning-based UAV scheduling algorithm to optimize the flight paths and charging processes of the charging UAVs. In [12], the study maximizes energy charging in UAV-assisted MEC systems with SWIPT, addressing the downlink period for returning computing results to ground equipment, and proposes an alternating optimization algorithm using semidefinite relaxation (SDR), singular value decomposition (SVD), and fractional programming (FP) methods to solve the non-convex problem. The work in [13] considers using UAVs as edge data collection nodes in IoT scenarios. Through regular patrols and recharging, drones provide storage and authentication capabilities to distributed ledgers, enabling long-term network access for IoT devices. The work in [14] introduces a UAV-assisted mobile crowdsensing system enhanced by WPT and adaptive compression, addressing user computing and directional energy transmission. It formulates an optimization to maximize data collection, considering compression, WPT, and UAV trajectory, and solves it with an iterative algorithm using decoupling and SCA methods,outperforming non-compression schemes. In [15], the authors proposed a mobile relay system for rotorcraft UAVs using distributed laser charging. Under constraints of UAV mobility, information causality, and energy causality, they advocated joint optimization of transmit power allocation and UAV trajectory design to maximize information transfer efficiency and power transfer efficiency. The literature reviewed in the third paragraph mainly focuses on the research field of WPT technology for UAVs, but has not been extended to collaborative research on cache mechanisms or incentive strategies. Specifically, the research framework of some studies [11; 13; 14; 15] did not include the design of the caching mechanism, while others [11; 12; 14; 15] did not include user incentive mechanisms in the research category. Research on combining UAV-assisted charging with edge caching has just begun. In [16], the authors considered combining UAV-assisted wireless charging technology with a wireless caching network, proposing that UAVs act as content servers to cache files and serve users, minimized the transmission latency of terrestrial users by optimizing resources such as user pairing, file power allocation, and user power allocation, and improved terrestrial users' service quality. The work in [17] proposed a content-centric wireless cache-charged transmission network supporting ultra-dense UAVs, and gave a joint trajectory and communication scheduling scheme. The problem was formulated as an infinite horizon ergodic stochastic differential game (SDG), which models the stochastic dynamics of the channel state, the mobility of the UAVs, the energy queue, and the content request queue to optimize the user's QoE. To further reduce the solution complexity for each UAV, a model-specific deep neural network (DNN) was proposed to learn the optimal control solution in an online manner. However, in weak-network-coverage scenarios where UAVs may need to be integrated as third-party service carriers for network enhancement, it becomes essential to coordinate multiparty interests through economic incentives. Existing studies [16; 17] treat UAVs, users, and content providers as collaborative systems providing free services, neglecting practical economic transaction challenges arising from the tripartite interests among content providers, UAVs, and users. In reality, UAVs must pay content providers to download files, while users need to compensate UAVs for receiving caching and charging services. To address this gap, this paper introduces an incentive mechanism and proposes a combined optimization framework for UAV positioning, transmission power allocation, and user pairing to maximize UAV revenue. The main contributions of this paper are as follows.