基于大数据深度挖掘电网设备缺陷体外循环的模型研制与应用

Model Development and Application Based on Big Data Deep Mining of Extracorporeal Circulation of Grid Equipment Defects

为提升电网设备缺陷文本的完整性、及时性、规范性,改善因缺陷数据不完整而导致缺陷管理上存在的管控模式粗放、事后管控、数据不完整、消缺不及时、缺陷分析不到位等情况,防止缺陷数据存在体外循环的现象,因此本文提出了一种基于大数据深度挖掘电网设备缺陷体外循环的模型研制与应用。以大量的历史缺陷数据为载体,基于TF-IDF算法对庞大的历史缺陷数据进行识别,提取出属于缺陷的关键词,通过缺陷关键词筛选出属于缺陷的工作票,再运用pairletters similarity算法和JaroWinkler算法将缺陷工作票与已有的缺陷数据做匹配,最终输出无法匹配的数据为缺陷体外循环数据。实验表明,本研究模型有效提高了缺陷数据的完整性、数据填报的及时性。

In order to improve the integrity,timeliness and standardization of the defect text of power grid equipment,and improve the defects management caused by incomplete defect data,such as extensive control mode,post control,incomplete data,incomplete defect elimination and incomplete defect analysis,the phenomenon of cardiopulmonary bypass existed in the defective data.data.This paper proposes the development and application of a model for deep mining grid equipment defects cardiopulmonary bypass based on big data.With a large number of historical defect data as the carrier,based on TF-IDF algorithm to identify the huge historical defect data,extract the keywords belonging to the defect,through the defect keywords to screen out the work tickets belonging to the defect.Then pair letters similarity algorithm and Jaro Winkler algorithm are used to match the defect work ticket with the existing defect data.Finally,the unmatched data is the defect cardiopulmonary bypass data.The test results show that the research model effectively improves the integrity of defect data,and has obvious advantages in the integrity of defect data and the timeliness of data filling.