基于改进混合粒子群算法和匹配理论的无人机电力巡检卸载策略

无人机搭载深度神经网络进行自主电力巡检时由于受到设备本身计算能力、电池容量、深度神经网络计算负载的限制，无法独立处理巡检任务中产生的海量图像数据。为解决该问题，提出了一种基于改进混合粒子群算法和匹配理论的无人机电力巡检卸载策略，该策略将系统成本最小化问题分解为深度神经网络计算任务协同分割和边缘服务器选择两个子问题。针对协同分割子问题，基于深度神经网络计算任务的执行流程提出了一种错时传输方法，通过改进混合粒子群算法求解多无人机任务协同分割层。针对边缘服务器选择子问题，定义无人机与边缘服务器各自偏好函数，根据偏好函数通过匹配理论建立两者间的稳定匹配，得到边缘服务器选择策略。仿真结果表明，与其他卸载策略相比，所提策略能有效降低无人机能耗和计算任务处理时延，促进边缘服务器负载均衡。

Power inspection and unloading strategy of UAV based on improved hybrid particle swarm algorithm and matching theory

When UAVs are equipped with a deep neural network for power inspection, because the device computing power and battery capacity as well as the deep neural network computation load limit, it cannot independently process the massive amount of image data generated in the inspection task. To address this problem, this paper proposed an unloading strategy for UAV power inspection based on improved hybrid particle swarm algorithm and matching theory, which decomposed the system cost minimization problem into two sub-problems: cooperative partitioning of deep neural network computing tasks and edge server selection. For the collaborative partition problem, it proposed a staggered transmission method based on the execution process of deep neural network computational tasks, and an improved hybrid particle swarm algorithm solved the multi-UAV task collaborative partition layer. For the server selection problem, it defined the respective preference functions of the UAV and the server, and established a stable match between them through matching theory according to the preference functions to obtain the server selection policy. Simulation results show that the proposed strategy can effectively reduce the energy consumption and computational task processing delay of UAVs and promote the load balancing of edge servers compared with other offloading strategies.