云边协同背景下基于融合RF算法的电网数据资产综合处理技术

Integrated processing technology based on fusion RF algorithm for power grid data assets under background of cloud edge collaboration

针对现有大多数方法难以充分挖掘出电网数据潜在价值的问题,提出了一种云边协同背景下基于随机森林算法结合BP神经网络的电网数据资产综合处理技术。该技术在靠近电网数据源一侧部署边缘计算节点,以构建云边协同环境下的电网数字化资产管理系统。利用随机森林算法设计分类器完成电网数据类型的划分,并将各类型数据输入至BP神经网络中进行学习,通过不断地迭代优化输出相应的综合处理结果。基于Python平台进行的实验分析结果表明,所提技术的分类准确率均超过了90%,能够有效提升电网数据资产的处理效率。

Aiming at the problem that most existing methods are difficult to mine the potential value of power grid data,an integrated processing technology based on random forest algorithm and BP neural network for power grid data assets under the background of cloud edge collaboration was proposed.The edge computing nodes close to the grid data source were deployed to build a grid digital asset management system in the cloud edge collaborative environment.At the same time,a classifier was designed by using the random forest algorithm to complete the classification of power grid data types,each type of data was input into the BP neural network for learning,and the corresponding comprehensive processing results were output through continuous iterative optimization.The experimental analysis of as-proposed technology based on Python platform shows that the classification accuracy is more than 90%,adequately effective to improve the processing efficiency of power grid data assets.