Abstract:In order to solve the problems of traditional offloading algorithm only involves user equipment and edge computing resources, and there are some limitations in the utilization of cloud resources. By effective use of computing task delay, energy consumption and computing resource allocation, the computing task offloading strategy and resource allocation optimization algorithm based on deep reinforcement learning algorithm was proposed, and the models of the edge cloud collaboration delay time, energy consumption and energy efficiency were established. The influence of the number of user equipment, task quantity, and task priority on the delay, energy consumption and energy efficiency was studied. The results show that the edge computing server resource is reasonably configured to 30 GHz. The advanced computing task priority processing strategy and computing resource optimization allocation result in low delay time and energy consumption. The proposed optimization algorithm is better than the other three comparison algorithms in terms of delay time, energy consumption and energy efficiency, and the optimization algorithm and the established model proposed in this paper can more effectively realize the computing task offloading strategy and resource allocation optimization for the power Internet of Things in scenarios of different users devices and calculation task volume.