电力通信运行管理中典型业务数据的智能关联方法

电力通信运行管理过程中,会产生和存储各类相对独立的业务数据(如故障工单、值班日志、检修工单、巡检记录等),这些业务数据为电力通信网运行管理提供了重要支撑。目前大多数业务数据的统计过程相对独立,后期较少人工加以关联。选取了电力通信运行管理中值班日志与故障工单两种典型的业务数据,采用文本挖掘技术,构建无监督召回和监督分类相结合的机器学习模型,提出值班日志与故障工单的智能关联方法,并利用电力通信运行管理系统中相关历史业务数据,对智能关联方法进行实验验证,达到较好的关联效果。

Intelligent correlation method of typical business data in power communication operation management

In the process of power communication operation management,various independent business data,such as trouble tickets,duty logs,maintenance tickets,and inspection records,are generated and stored.These business data provide important support for the operation management of the power communication network.At present,the statistical process of most business data is relatively independent,and there is less manual correlation in the later stage.Two typical business data of duty log and trouble ticket in power communication operation management were selected,text mining technology was used to build a machine learning model combining unsupervised recall and supervised classification,and the intelligent association method between duty log and trouble ticket was proposed.Besides,the relevant historical business data in the electric power communication operation management system was used to do the experimental verification of the intelligent association method.The results show that it can achieve positive effect.