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％.
关键词
深度强化学习/数字电网数据/安全迁移/数字电网集群/纹理基元理论/阈值范围/敏感度参数
Key words
deep reinforcement learning/digital grid data/secure migration/digital grid cluster/texture basis element theory/threshold range/sensitivity parameters
维普
万方数据