基于深度挖掘的多维动态数据权限管控系统

Multidimensional dynamic data permission control system based on deep mining

当前采用基于B/S体系结构的数据权限管控系统,由于受动态数据的影响,导致用户登录权限受阻,登录系统要花费大量时间。为此,提出基于深度挖掘的多维动态数据权限管控系统。设置权限管理模块,在界面上添加删改按钮。设置数据中心管控模块,通过主板数据中心服务器电源接口对用户登录状态进行实时监控。在中心处理器中,可以执行特定的指令。建立深入挖掘学习模型,对数据进行训练,通过计算重构后隐藏层节点概率,得到全部动态数据。建立多维动态数据权限管理模型,设计多维动态数据权限管理流程,剔除不相符数据,缩短管控时间。根据权限管理的运行模式,通过指令调整控制数据中心。由实验结果可知,该系统所有验证内容登录密码正确率均高于80%,最短管控所耗费时间为5 s。

At present,the data authority management and control system based on B/S architecture is affected by dynamic data,which leads to the obstruction of user login authority,and it takes a lot of time to log in to the system.Therefore,a multidimensional dynamic data permission management and control system based on deep mining is proposed.Set the authority management module and add the delete button on the interface.The data center management and control module is set,and the user login status is monitored in real time through the power interface of the main board data center server.In the central processor,specific instructions can be executed.A deep mining learning model is established to train the data,and all dynamic data are obtained by calculating the probability of hidden layer nodes after reconstruction.Establish multidimensional dynamic data rights management model,design multi⁃dimensional dynamic data rights management process,eliminate inconsistent data,shorten the control time.According to the operation mode of authority management,the data center is adjusted and controlled by command.The experimental results show that the correct rate of login password for all verification contents of the system is higher than 80%,and the shortest management and control time is 5 s.