电网企业财务大数据研究与应用
|
摘要
随着云大物移智等数字技术的蓬勃发展,利用大数据技术更好的服务经营管理已成为企业的共识。国网山东省电力公司自2009年财务集约化建设以来,财务信息系统中积累了海量的业务和财务数据,面对丰富的数据资源,国网山东电力将其作为企业的一项核心资产,并把对数据资产的挖掘和分析作为支撑企业经营管理的重要手段。为此,国网山东电力探索并提出了"1234"新型财务大数据应用的管理思路和管理方法,即一个大数据技术平台、两个支撑保障、三端展示建设、四大域层管理,以此构建了电网财务大数据管理及应用体系和方法论。借助大数据技术应用的方法论,国网山东省电力公司有力提升了财务管理,大大加强了财务决策分析能力,进一步深化了财务集约化创新发展。实践证明,大数据对广大财务人员是一项有力的"武器",未来将在电网企业发挥更大的作用。
With the booming development of digital technologies such as Cloud computing,Big data,Internet of things,Mobile internet and Artificial intelligence,the use of big data technology to better service management has become the consensus of the company. Since Shandong State Electric Power Co.,Ltd.,a state-owned company,has accumulated financial and financial data in its financial information system since 2009,in the face of a wealth of data resources,State Grid Shandong Electric Power Co.,Ltd. regards it as a core asset of the company. The excavation and analysis of data assets will be used as an important means to support enterprise management. To this end,State Grid Shandong Electric Power has explored and proposed management ideas and management methods for the "1234"new type of financial big data application,namely a big data technology platform,two support guarantees,three-end display construction,and four major domain managements. Established a grid big data management and application system and methodology. With the help of the big data technology application methodology,the State Grid Shandong Electric Power Company has effectively enhanced its financial management,greatly strengthened its financial decision analysis capabilities and further deepened the financial intensive innovation and development. Practice has proved that big data is a powerful " weapon" for the majority of financial personnel and will play a greater role in power grid companies in the future.
With the booming development of digital technologies such as Cloud computing,Big data,Internet of things,Mobile internet and Artificial intelligence,the use of big data technology to better service management has become the consensus of the company. Since Shandong State Electric Power Co.,Ltd.,a state-owned company,has accumulated financial and financial data in its financial information system since 2009,in the face of a wealth of data resources,State Grid Shandong Electric Power Co.,Ltd. regards it as a core asset of the company. The excavation and analysis of data assets will be used as an important means to support enterprise management. To this end,State Grid Shandong Electric Power has explored and proposed management ideas and management methods for the "1234"new type of financial big data application,namely a big data technology platform,two support guarantees,three-end display construction,and four major domain managements. Established a grid big data management and application system and methodology. With the help of the big data technology application methodology,the State Grid Shandong Electric Power Company has effectively enhanced its financial management,greatly strengthened its financial decision analysis capabilities and further deepened the financial intensive innovation and development. Practice has proved that big data is a powerful " weapon" for the majority of financial personnel and will play a greater role in power grid companies in the future.
引文
[1]袁捷.贵州电网大数据应用探讨[J].电力大数据,2017,20(12):4-7.YUAN Jie.Discussion on application of big data in Guizhou power grid[J].Power Systems and Big Data,2017,20(12):4-7.
[2]刘俊,罗凡,刘人境,等.大数据背景下电力需求侧管理的应用策略研究[J].电力需求侧管理,2016,18(02):5-10.LIU Jun,LUO Fan,LIU Renjing,et al.Study on application strategies of demand-side management under big data background[J].Power Demand Side Management,2016,18(02):5-10.
[3]张沛,杨华飞,许元斌.电力大数据及其在电网公司的应用[J].中国电机工程学报,2014,34(S1):85-92.ZHANG Pei,YANG Huafei,XU Yuanbin.Power big data and its application scenarios in power grid[J].Proceedings of the CSEE,2014,34(S1):85-92.
[4]陈超,张顺仕,尚守卫,等.大数据背景下电力行业数据应用研究[J].现代电子技术,2013,(24):8-11,14.CHEN Chao,ZHANG Shunshi,SHANG Shouwei,et al.Research on power industry data application based on big data[J].Modern Electronics Technique,2013(24):8-11,14.
[5]唐瑞伟.电力大数据应用现状及发展前景[C]//电力行业信息化优秀论文集2014,2014年全国电力行业两化融合推进会暨全国电力企业信息化大会获奖论文.电力行业信息化优秀论文集,2014:693-699.
[6]刘超,王艺霏,赵鑫.基于电力全业务数据中心的分布式实时计算组件研究[J].贵州电力技术,2017,20(10):78-82.LIU Chao,WANG Yifei,ZHAO Xin.Research on distributed realtime computing component based on power enterprise full-services data center[J].Guizhou Electric Power Technology,2017,20(10):78-82.
[7]孙梦觉,徐敏,牛斌.电力大数据可视化研究设计[J].电力大数据,2017,20(19):61-63,67.SUN Mengjue,XU Min,NIU bin.Research and design of power big data visualization[J].Power Systems and Big Data,2017,20(19):61-63,67.
[8]曲朝阳,熊泽宇,颜佳,等.基于空间分割的电力大数据三维全景可视化场景管理方法[J].华北电力大学学报(自然科学版),2016,43(02):23-29.QU Zhaoyang,XIONG Zeyu,YAN Jia,et al.Scene management method of three-dimensional panoramic visualization of electric power big data based on space partition[J].Journal of North China Electric Power University,2016,43(02):23-29.
[9]毛娟,吴仁群.大数据时代下管理会计面临的问题及对策分析[J].北京印刷学院学报,2017,25(03):56-58.MAO Juan,WU Renqun.The analysis of management accounting’s problems and countermeasure in big data era[J].Journal of Beijing Institute of Graphic Communication,2017,25(03):56-58.
[10]黄妍君.大数据时代财务管理转型探讨[J].湖北函授大学学报,2016,29(09):72-73.HUANG Yanjun.Dicussion of financial management transition in the era of big data[J].Journal of Hubei Correspondence University,2016,29(09):72-73.
[11]张冰玉.基于数据挖掘技术的短期电力负荷预测[J].电力大数据,2017,20(10):18-21.ZHANG Bingyu.Prediction of short-term power load based on data mining technology[J].Power Systems and Big Data,2017,20(10):18-21.
[12]李占英.智能配电网大数据应用技术与前景分析[J].电力大数据,2017,20(11):18-20.LI Zhanying.Intelligent power distribution network and prospect analysis technology of data application system data applications[J].Power Systems and Big Data,2017,20(11):18-20.
[13]赵腾,张焰,张东霞.智能配电网大数据应用技术与前景分析[J].电网技术,2014,38(12):3305-3312.ZHAO Teng,ZHANG Yan,ZHANG Dongxia.Application technology of big data in smart distribution grid and its prospect analysis[J].Power System Technology,2014,38(12):3305-3312.
[14]刘巍,黄曌,李鹏,等.面向智能配电网的大数据统一支撑平台体系与构架[J].电工技术学报,2014,29(S1):486-491.LIU Wei,HUANG Zhao,LI Peng,et al.Summary about system and framework of unified supporting platform of big data for smart distribution grid[J].Transactions of China Electrotechnical Society,2014,29(S1):486-491.
[15]刘科研,盛万兴,张东霞,等.智能配电网大数据应用需求和场景分析研究[J].中国电机工程学报,2015,35(02):287-293.LIU Keyan,SHENG Wanxing,ZHANG Dongxia,et al.Big data application requirements and scenario analysis in smart distribution network[J].Proceedings of the CSEE,2015,35(02):287-293.
[16]严明良.电力行业大数据技术及应用研究[J].南京工业职业技术学院学报,2015,(02):1-5,19.YAN Mingliang.Research on big data technologies and application in power industry[J].Journal of Nanjing Institute of Industry technology,2015,(02):1-5,19.
[2]刘俊,罗凡,刘人境,等.大数据背景下电力需求侧管理的应用策略研究[J].电力需求侧管理,2016,18(02):5-10.LIU Jun,LUO Fan,LIU Renjing,et al.Study on application strategies of demand-side management under big data background[J].Power Demand Side Management,2016,18(02):5-10.
[3]张沛,杨华飞,许元斌.电力大数据及其在电网公司的应用[J].中国电机工程学报,2014,34(S1):85-92.ZHANG Pei,YANG Huafei,XU Yuanbin.Power big data and its application scenarios in power grid[J].Proceedings of the CSEE,2014,34(S1):85-92.
[4]陈超,张顺仕,尚守卫,等.大数据背景下电力行业数据应用研究[J].现代电子技术,2013,(24):8-11,14.CHEN Chao,ZHANG Shunshi,SHANG Shouwei,et al.Research on power industry data application based on big data[J].Modern Electronics Technique,2013(24):8-11,14.
[5]唐瑞伟.电力大数据应用现状及发展前景[C]//电力行业信息化优秀论文集2014,2014年全国电力行业两化融合推进会暨全国电力企业信息化大会获奖论文.电力行业信息化优秀论文集,2014:693-699.
[6]刘超,王艺霏,赵鑫.基于电力全业务数据中心的分布式实时计算组件研究[J].贵州电力技术,2017,20(10):78-82.LIU Chao,WANG Yifei,ZHAO Xin.Research on distributed realtime computing component based on power enterprise full-services data center[J].Guizhou Electric Power Technology,2017,20(10):78-82.
[7]孙梦觉,徐敏,牛斌.电力大数据可视化研究设计[J].电力大数据,2017,20(19):61-63,67.SUN Mengjue,XU Min,NIU bin.Research and design of power big data visualization[J].Power Systems and Big Data,2017,20(19):61-63,67.
[8]曲朝阳,熊泽宇,颜佳,等.基于空间分割的电力大数据三维全景可视化场景管理方法[J].华北电力大学学报(自然科学版),2016,43(02):23-29.QU Zhaoyang,XIONG Zeyu,YAN Jia,et al.Scene management method of three-dimensional panoramic visualization of electric power big data based on space partition[J].Journal of North China Electric Power University,2016,43(02):23-29.
[9]毛娟,吴仁群.大数据时代下管理会计面临的问题及对策分析[J].北京印刷学院学报,2017,25(03):56-58.MAO Juan,WU Renqun.The analysis of management accounting’s problems and countermeasure in big data era[J].Journal of Beijing Institute of Graphic Communication,2017,25(03):56-58.
[10]黄妍君.大数据时代财务管理转型探讨[J].湖北函授大学学报,2016,29(09):72-73.HUANG Yanjun.Dicussion of financial management transition in the era of big data[J].Journal of Hubei Correspondence University,2016,29(09):72-73.
[11]张冰玉.基于数据挖掘技术的短期电力负荷预测[J].电力大数据,2017,20(10):18-21.ZHANG Bingyu.Prediction of short-term power load based on data mining technology[J].Power Systems and Big Data,2017,20(10):18-21.
[12]李占英.智能配电网大数据应用技术与前景分析[J].电力大数据,2017,20(11):18-20.LI Zhanying.Intelligent power distribution network and prospect analysis technology of data application system data applications[J].Power Systems and Big Data,2017,20(11):18-20.
[13]赵腾,张焰,张东霞.智能配电网大数据应用技术与前景分析[J].电网技术,2014,38(12):3305-3312.ZHAO Teng,ZHANG Yan,ZHANG Dongxia.Application technology of big data in smart distribution grid and its prospect analysis[J].Power System Technology,2014,38(12):3305-3312.
[14]刘巍,黄曌,李鹏,等.面向智能配电网的大数据统一支撑平台体系与构架[J].电工技术学报,2014,29(S1):486-491.LIU Wei,HUANG Zhao,LI Peng,et al.Summary about system and framework of unified supporting platform of big data for smart distribution grid[J].Transactions of China Electrotechnical Society,2014,29(S1):486-491.
[15]刘科研,盛万兴,张东霞,等.智能配电网大数据应用需求和场景分析研究[J].中国电机工程学报,2015,35(02):287-293.LIU Keyan,SHENG Wanxing,ZHANG Dongxia,et al.Big data application requirements and scenario analysis in smart distribution network[J].Proceedings of the CSEE,2015,35(02):287-293.
[16]严明良.电力行业大数据技术及应用研究[J].南京工业职业技术学院学报,2015,(02):1-5,19.YAN Mingliang.Research on big data technologies and application in power industry[J].Journal of Nanjing Institute of Industry technology,2015,(02):1-5,19.