Research On Data Governance Based On Edge Computing For Power Dat

With the development of smart grids and edge computing,power data governance and resource allocation have become urgent issues to be addressed.This thesis investigates power data governance,the integration of edge computing and blockchain,task offloading,and deep reinforcement learning-based resource allocation.The specific work of this paper is as follows:(1)we introduce the importance and research status of power data governance and systematically analyze domestic and international research.(2)The background knowledge involved in this research is introduced in detail,including blockchain technology,mobile edge computing and deep reinforcement learning.(3)we propose a lightweight privacy-protected data aggregation scheme for smart grids assisted by edge blockchain,named BLEP,and design an efficient distributed adaptive edge offloading(DAEO)scheme to minimize task duration under the power consumption constraints of user equipment(UE).(4)we propose a deep reinforcement learning-based resource allocation mechanism aimed at effectively allocating resources while meeting the needs of mobile users.The results of this research contribute to improving the data security,resource allocation efficiency,and energy utilization of smart grids.