Research on Digital Grid Data Security Migration Based on Deep Reinforcement Learning
Due to the dynamic development characteristic of digital power grid data timing mean in the state of redundancy,it is difficult to effectively control the packet loss rate in the migration process.Therefore,the study on digital power grid data security migration based on deep reinforcement learning is proposed.From the perspective of cluster(Cluster),the digital grid data modeling based on deep reinforcement learning is carried out,which defines the specific state of the cluster in the case of vacancies in the digital grid cluster Que.In the specific migration process,the texture basis theory is introduced.After calculating the change characteristics of the digital grid data within the threshold range,the sensitivity parameters are set for the digital grid data migration,and the digital grid data migration function is constructed.In this test results,the packet loss rate of the whole migration process is only 6.75％,and the highest packet loss rate in each stage of the specific migration process is only 2.03％.
deep reinforcement learningdigital grid datasecure migrationdigital grid clustertexture basis element theorythreshold rangesensitivity parameters
万方数据
维普
