基于图数据库的电力设备全生命周期管理技术研究
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摘要
电力设备作为电网公司的重要资产,其质量直接影响电网的运行水平,基于实际运行信息和检修信息对电力设备进行客观评价对于提升电力设备的运行水平意义重大。尽管电力设备制造企业(产业集团)拥有大量的供应商、产品、用户等信息,电网公司拥有大量产品设备运营信息,但受技术手段限制,不仅设备制造企业和电力公司之间的数据缺乏交流,企业内部的数据竖井化问题也非常严重,造成信息重复存储、更新不同步、关联性缺失、系统功能单一等问题。文章提出一种基于图数据库的电力设备全生命周期管理系统,借助图数据库将设备的技术参数、供应链信息以及企业信息高效整合,极大地提升了复杂关联数据的操作性能,实现了生产、物流、库存等供应链信息的快速查询、以技术参数为依据的电力产品精准推荐以及电力设备的全供应链质量追溯功能。
Power equipment is the most important asset of electrical companies and directly affects the operating level of power grids. Objective evaluation of power equipment based on actual operational and maintenance information can significantly improve the operational level of power equipment. Power equipment manufacturers(industry groups) have a huge amount of information for suppliers, products, and users, while electrical companies have detailed equipment operation information. Because of the limitation of technical means, not only the lack of data exchange between equipment manufacturing companies and power enterprises,the internal data silencing problem is also very serious, which may cause duplicated information storage,asynchronous update, weak connections between databases, and function singleness. This paper proposes an enhancement of power equipment life-cycle management(PLM) system, which uses the graph database to efficiently integrate the complete technical parameters, supply chain and enterprise information of the equipment to greatly improves the operational performance of complex associated data. Comparing with the existing asset management systems, the PLM realizes rapid inquiry of supply chain data, such as manufacture,logistics, and inventory, the accurate recommendation of power equipment based on technical parameters, and comprehensive quality traceability of power equipment.
Power equipment is the most important asset of electrical companies and directly affects the operating level of power grids. Objective evaluation of power equipment based on actual operational and maintenance information can significantly improve the operational level of power equipment. Power equipment manufacturers(industry groups) have a huge amount of information for suppliers, products, and users, while electrical companies have detailed equipment operation information. Because of the limitation of technical means, not only the lack of data exchange between equipment manufacturing companies and power enterprises,the internal data silencing problem is also very serious, which may cause duplicated information storage,asynchronous update, weak connections between databases, and function singleness. This paper proposes an enhancement of power equipment life-cycle management(PLM) system, which uses the graph database to efficiently integrate the complete technical parameters, supply chain and enterprise information of the equipment to greatly improves the operational performance of complex associated data. Comparing with the existing asset management systems, the PLM realizes rapid inquiry of supply chain data, such as manufacture,logistics, and inventory, the accurate recommendation of power equipment based on technical parameters, and comprehensive quality traceability of power equipment.
引文
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[2]沈建新,周儒荣.产品全生命周期管理系统框架及关键技术研究[J].南京航空航天大学学报,2003,35(5):565-571.SHEN Jianxin,ZHOU Rurong.Reseach on PLM system framework and key technologies[J].Journal of Nanjing University of Aeronautics&Astronautics,2003,35(5):565-571.
[3]李云峰,张勇.国家电网公司资产全寿命周期管理框架体系研究[J].华东电力,2010,38(8):1126-1131.LI Yunfeng,ZHANG Yong.A study of framework of assets life cycle management in state grid corporation of China[J].East China Electric Power,2010,38(8):1126-1131.
[4]刘己未.全寿命周期管理支持下的电网资产管理[J].现代工业经济和信息化,2018,8(17):89-90.LIU Jiwei.Management of power grid assets supported by life cycle management[J].Modern Industrial Economy and Informationization,2018,8(17):89-90.
[5]费思源.大数据技术在配电网中的应用综述[J].中国电机工程学报,2018,38(1):85-96,345.FEI Siyuan.Overview of application of big data technology in power distribution system[J].Proceedings of the CSEE,2018,38(1):85-96,345.
[6]张一泓,徐旸,黎翡娟,等.电网企业资产全寿命周期管理模式综述[J].电力信息与通信技术,2016,14(5):28-33.ZHANG Yihong,XU Yang,LI Feijuan,et al.Review of LCAMmode in power grid enterprises[J].Electric Power Information and Communication Technology,2016,14(5):29-33.
[7]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(2):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(2):287-293.
[8]MANYIKAJ,CHUI M,BROWN B,et al.Big data:the next frontier for innovation,competition,and productivity[R].USA:McKinsey Global Institude,2011.
[9]邱胜海,王云霞,樊树海,等.云环境下图数据库建模技术及其应用研究[J].计算机应用研究,2016,33(3):794-797.QIU Shenghai,WANG Yunxia,FAN Shuhai,et al.Study on application of modeling technology of graph database on cloud environment[J].Application Research of Computers,2016,33(3):794-797.
[10]PLM-Product Lifecycle Management:Siemens PLM software[EB/OL].(2018-08-14)[2018-08-18].https://www.plm.automation.siemens.com.
[11]王余蓝.图形数据库NEO4J与关系据库的比较研究[J].现代电子技术,2012,35(20):77-79.WANG Yulan.Comparison of graphic database NEO4J and relational database[J].Modern Electronics Technique,2012,35(20):77-79.
[12]MOHANC.History repeats itself:sensible and nonsenSQL aspects of the NoSQL hoopla[C]//In Proceedings of the16th International Conference on Extending Database Technology(EDBT’13),New York,USA:ACM,2013:11-16.
[13]ANGLES R,GUTIERREZ C.Survey of graph database models[J].ACM Computing Surveys,2008,40(1):1-39.
[14]DB-Engines Ranking of Graph DBMS[EB/OL].[2018-09-15].https://db-engines.com/en/ranking/graph+dbms.
[15]TigerGraph[EB/OL].(2018-08-29)[2018-09-15].https://www.tigergraph.com.
[16]中国的行政区划概述[EB/OL].(2009-04-17)[2018-08-26].http://www.gov.cn/test/2009-04/17/content_1288030.htm.
[17]滕雅琳.基于知识图谱的国内外供应链管理研究热点分析及前沿探索[D].吉林:吉林大学,2017.
[18]王仁武,袁毅,袁旭萍.基于深度学习与图数据库构建中文商业知识图谱的探索研究[J].图书与情报,2016(1):110-117.WANG Renwu,YUAN Yi,YUAN Xuping.Study on the construction of Chinese knowledge graph based on deep learning and graph database[J].Library&Information,2016(1):110-117.