数据标准化建设中电网数据认责体系的优化分析

Optimization analysis of power grid data accountability system in data standardization construction

数据标准化是反映数据治理水平的重要指标,但在数据治理过程中,数据标准化会出现认责不清、缺乏认责主体的问题,降低数据标准化水平,并增加数据治理成本,浪费大量的人力和物力,以致占用相关的系统资源。基于此,文章提出一种基于回归分析方法的数据认责体系优化模型。首先,对电网中的治理数据进行映射处理,降低数据认责复杂度,缩短数据治理时间。其次,对数据治理程度、数据可信度、数据来源进行标准化分析,并赋予治理权重。最后,利用回归分析方法对数据认责中争议问题进行剖析,并得出最终数据认责结果。研究结果显示,回归分析方法能快速、准确地寻找到争议问题,降低数据复杂度对数据治理的影响。其中,数据标准化处理的误差小于10%、认责准确率和数据完整率均大于90%,优于人工数据认责方法。所以,回归分析方法能优化电网数据认责体系,满足数据治理中的标准化分析要求。

Data standardization is an important index of the level of data governance,but in the process of data governance,data standardization will appear unclear accountability,lack of accountability subjects,reduce the level of data standardization,and increase the cost of data governance,waste a lot of manpower and material resources,and occupy the relevant system resources.Based on this,this paper proposes an optimization model of data accountability systetm based on regression analysis.Firstly,the governance data in the power grid is mapped to reduce the complexity of data accountability and shorten the time of data governance.Then the degree of data governance,data credibility,data sources are standardized analysis,and the governance weight.Finally,the regression analysis method is used to analyze the controversial issues in the data recognition,and the final data recognition results are obtained.The results show that the regression analysis method can quickly and accurately find the controversial issues and reduce the impact of data complexity on data governance.Among them,the error of data standardization processing is less than 10%,the accuracy rate of recognition and the integrity rate of data are more than 90%,which is superior to manual data recognition method.Therefore,the regression analysis method can optimize the power grid data accountability system and meet the requirements of standardized amalysis in data governance.