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电力大数据:2018,21(11):-
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基于Power BI的大数据分析在变电运检作业管理中的应用
李博,谢潇磊,王清,魏天航,钱臻,杨文睿
(国网江苏省电力有限公司苏州供电分公司,国网江苏省电力有限公司苏州供电分公司,国网江苏省电力有限公司苏州供电分公司,国网江苏省电力有限公司苏州供电分公司,国网江苏省电力有限公司苏州供电分公司,国网江苏省电力有限公司苏州供电分公司)
Application of Big Data Analysis Based on Power BI in Substation Inspection Operation Management
Li Bo,Xie Xiaolei,Wang Qing,Wei Tianhang,Qian Zhen and Yang Wenrui
(State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Company,State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Company,State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Company,State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Company,State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Company,State Grid Jiangsu Electric Power Co., Ltd. Suzhou Power Supply Company)
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本文已被:浏览 625次   下载 853
投稿时间:2018-07-04    修订日期:2018-08-01
中文摘要: 本文提出一种大数据分析在变电运检作业管理中的应用方案,分析了目前存在的业务增长与人力、物力等资源配置矛盾凸显的问题,积极探索和推动大数据在协调运检人力、物力等资源与业务管控中的作用。借助Power BI大数据分析软件,以运检生产作业管理、人员管理等数据为依托,构建了关键因素全景展示、作业管理优化决策、工作过程柔性管控的多方位“大数据+运维检修”应用研究体系;实现了人员、工作量、变电站等运检生产关键因素的全方位多时空动态展示;提出了以“人站比”、“工作量预测”等指标为基础的人员评估考核、生产计划安排的优化决策方法;挖掘了周期性与非周期、独立性与耦合性工作的可调配度关系,提升了工作全过程管控的柔性。研究结果对于全面提升运检人力、物力等资源的优化配置和业务处理的智能化水平具有借鉴意义和应用价值。
中文关键词: 大数据  变电运检  作业管理  优化决策
Abstract:This paper proposes an application scheme of big data analysis in the management of substation operation and maintenance, analyzes the problems that currently exist between business growth and resource allocations such as human resources and material resources, and actively explored and promoted the role of big data in coordinating resources such as manpower and material resources and business management and control. With the help of Power BI big data analysis software, which constructs a multi-faceted "big data + operation and maintenance" application research system for key factors panoramic display, job management optimization decision, and flexible control of work process based on the data of production management and personnel management, realizes the comprehensive and multi-temporal dynamic display of the key factors of personnel maintenance, personnel, workload and substation, proposes an optimization decision-making method for personnel assessment and production planning based on indicators such as “person-to-station ratio” and “workload forecast”, explores the adjustable relationship between periodicity and aperiodicity, independence and coupling work, and improves the flexibility of the whole process control. The research results have reference significance and application value for comprehensively improving the optimal allocation of resources such as manpower and material resources for transportation inspection and the intelligent level of business processing.
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