基于半监督学习的新能源运行错误数据辨识系统

New energy operation error data identification system based on semi supervised learning

为增强系统主机对于新能源运行错误数据的准确辨别能力,避免数据信息错误分拣,设计了基于半监督学习的新能源运行错误数据辨识系统。借助PT-LAB处理平台,平衡数据分辨模块与运行指令分析模块间的新能源运行错误数据传输关系,实现辨识系统的硬件运行环境搭建。根据半监督支持向量取值结果,求解UCI学习参数的计算数值,并根据参数向量之间的实时映射关系,确定数据库E-R图的连接形式,完成辨识系统的软件运行环境搭建。结合相关硬件设备结构,实现基于半监督学习的新能源运行错误数据辨识系统设计。对比实验结果表明,在半监督学习算法的影响下,系统主机对于待传输新能源运行错误数据的提取准确度超过了90.0%,能够较好地解决数据信息的错误分拣问题。

In order to enhance the accurate discrimination ability of the system host for the new energy operation error data and avoid the wrong sorting of data information,a new energy operation error data identification system based on semi supervised learning is designed.With the help of PT-LAB processing platform,the new energy operation error data transmission relationship between the data resolution module and the operation instruction analysis module is balanced,and the hardware operation environment of the identification system is built.According to the results of semi supervised support vector,the calculated values of UCI learning parameters are solved,and the connection form of database E-R diagram is determined according to the real⁃time mapping relationship between parameter vectors,so as to build the software running environment of the identification system.Combined with the structure of relevant hardware equipment,the design of new energy operation error data identification system based on semi supervised learning is realized.The comparative experimental results indicate that under the influence of semi supervised learning algorithm,the extraction accuracy of system host for new energy operation error data to be transmitted exceeds 90.0%,which can better solve the problem of wrong sorting of data information.