基于大数据的电力企业财务数据管理系统研究
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摘要
当前,在各类电力生产经营财务数据管理活动中产生的海量、高频数据,具有实时性、突发性、易失性、无序性、无限性等特征。为解决电力企业接入的财务数据的不一致、不规范的问题,以及如何快速从这些海量高频数据中获取有价值的信息、提高数据资产带来的经济效益和数据管理效率,本文在分析目前电力大数据应用现状的基础上,构建了基于大数据的电力企业财务数据管理系统,同时从基于NoSQL的大数据存储管理、基于Hadoop的财务大数据预处理以及财务大数据分析挖掘算法3个方面详细描述了电力企业财务数据管理的关键技术,大幅提升了电力企业财务数据的管理效率和准确性,为在线决策分析提供理论依据及基础技术支撑。
At present,the massive and high-frequent data generated in the financial data management activities of various types of production and operation in power enterprises have the characteristics of real-time,suddenness,volatility,disorder,infiniteness and so on. In order to solve the problems that the financial data power enterprises access exist inconsistency,standards lack normative and to quickly obtain valuable information from these massive and high-frequent data,and improve the economic benefits brought by data assets and the efficiency of data management. Based on the analysis of the current status of power big data application,this paper constructs a financial data management system for power enterprises based on big data. At the same time,the key technologies of financial data management in power enterprises are descripted in detail from big data storage based on NoSQL,big data pretreatment based on Hadoop and high efficient big data analysis algorithms. The management efficiency and accuracy of financial data of power enterprises have been greatly improved,and it provides theoretical basis and basic technical support for online decision analysis.
At present,the massive and high-frequent data generated in the financial data management activities of various types of production and operation in power enterprises have the characteristics of real-time,suddenness,volatility,disorder,infiniteness and so on. In order to solve the problems that the financial data power enterprises access exist inconsistency,standards lack normative and to quickly obtain valuable information from these massive and high-frequent data,and improve the economic benefits brought by data assets and the efficiency of data management. Based on the analysis of the current status of power big data application,this paper constructs a financial data management system for power enterprises based on big data. At the same time,the key technologies of financial data management in power enterprises are descripted in detail from big data storage based on NoSQL,big data pretreatment based on Hadoop and high efficient big data analysis algorithms. The management efficiency and accuracy of financial data of power enterprises have been greatly improved,and it provides theoretical basis and basic technical support for online decision analysis.
引文
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[11]王栋.大数据可视化技术在电网企业的应用[J].江苏电机工程,2014,33(06):82-84WANG Dong. Application of big data visualization technique inpower grid enterprise[J]. Jiangsu Electrical Engineering,2014,33(06):82-84.
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[2]尚学伟,赵林,范泽龙.基于调度数据网的广域数据总线体系架构和关键技术[J].电力系统自动化,2018,42(11):109-114.SHANG Xuewei,ZHAO Lin,FAN Zelong,et al. Architecture andkey technologies of wide-area data bus based on dispatching datanetwork[J]. Automation of Electric Power System,2018,42(11):109-114.
[3]王保义,胡恒,张少敏.差分隐私保护下面向海量用户的用电数据聚类分析[J].电力系统自动化,2018,42(02):121-127.WANG Baoyi,HU Heng,ZHANG Shaomin. Differential privacyprotection based clustering analysis of electricity consumption datafor massive consumers[J]. Autiomation of Electric Power Systems,2018,42(02):121-127.
[4]刘羽霄,张宁,康重庆.数据驱动的电力网络分析与优化研究综述[J].电力系统自动化,2018,42(06):157-167.LIU Yuxiao,ZHANG Ning,KANG Chongqing. A review on data-driven analysis and optimization of power grid[J]. Autiomation ofElectric Power Systems,2018,42(06):157-167.
[5]冷喜武,陈国平,白静洁,等.智能电网监控运行大数据分析系统总体设计[J].电力系统自动化,2018,42(12):160-166.LENG Xiwu,CHEN Guoping,BAI Jingjie et al. General design ofsmart grid monitoring operation big data analysis system[J].Autiomation of Electric Power Systems,2018,42(12):160-166.
[6]耿俊成.基于大数据分析的电网设备质量评价[J].电力大数据,2018(05):36-40.GENG Juncheng. Quality evaluation of power grid equipment basedon big data analysis[J]. Power Systems And Big Data,2018(05):36-40.
[7]王光宏,蒋平.数据挖掘综述[J].同济大学学报. 2004,32(02):246-252.WANG Guanghong,JIANG Ping. Survey of data mining[J].Journal of Tongji University. 2004,32(02):246-252
[8]孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,50(01):146-169.MENG Xiaofeng,CI Xiang. Big data management:concepts,techniques and challenges[J]. Journal of Computer Research andDevelopment,2013,50(01):146-169.
[9]冯芷艳,郭迅华,曾大军,等.大数据背景下商务管理研究若干前沿课题[J].管理科学学报2013,16(01):1-9FENG Zhiyan,GUO Xunhua,ZENG Dajun et al. On the researchfrontiers of business management in the context of big data[J].Journal of Management Sciences in China. 2013,16(01):1-9.
[10]田华.大数据与可视化在电力设备管理中的创新应用[J].电力大数据. 2018,21(09):32-35.TIAN Hua. Innovative application of big data and visualization inpower equipment management[J]. Power Systems And Big Data,2018,21(09):32-35.
[11]王栋.大数据可视化技术在电网企业的应用[J].江苏电机工程,2014,33(06):82-84WANG Dong. Application of big data visualization technique inpower grid enterprise[J]. Jiangsu Electrical Engineering,2014,33(06):82-84.
[12] HAFIZ A,LUKUMON O,MUHAMMAD B,et al. Bankruptcy predictionof construction businesses:towards a big data analytics approach[C].IEEE First International Conference on Big Data Computing Serviceand Applications. IEEE Computer Society,2015,39(156):347-352.
[13] COSTA P A R S,RAMOS F M V,CORREIA M. On the design ofresilient multicloud MAPREDUce[J]. IEEE Cloud Computing,2017,4(04):74-82.
[14] CAO Z,LIN J,WAN C,et al. Hadoop-based framework for bigdata analysis of synchronized harmonics in active distributionnetwork[J]. IET Generation Transmission&Distribution,2017,11(16)::3930-3937.
[15] ABDEL-BASSET M,MAI M,SMARANDACHE F,et al. Neutrosophicassociation rule mining algorithm for big data analysis[J]. Symmetry,2018,10(04):106.
[16]李长青,王志国等.电网企业财务大数据研究与应用[J].电力大数据2018,20(08):14-18.LI Changqing,WANG Zhiguo,et al. Application and research onbig data using in power grid financial management[J]. Powersystems and big data. 2018,20(08):14-18.