期刊文献+

基于大数据分析的电网车辆评价模型的研究应用

Research and application of an evaluation model for vehicles of power grid enterprises based on big data analysis
下载PDF
导出
摘要 地市供电公司涉及基层单位多,每个基层单位根据生产需求需要配置车辆。如何在基层单位间开展车辆的合理分配、跨单位间的调拨、精准报废等工作,需要科学有效的评价手段。本文利用大数据分析技术,挖掘统一车辆管理平台车辆数据价值,提出了单位车辆使用评价指数模型和单个车辆报废评价指数模型,以某供电公司车辆使用数据进行模型验证,以数据结果支撑车辆分配、调拨、报废工作,让车辆分配更合理、报废更精准、调拨更科学,有效提升车辆精益化管理水平。 Urban power supply companies involve large numbers of grass-roots units to which vehicles are allocated as production requires.It needs a scientific and effective evaluation means of rationally allocating new vehicles,transferring vehicles between different units and accurately scrapping old vehicles.This paper uses big data analysis technology to mine the value of vehicle data on the unified vehicle management platform and puts forward two evaluation index models of unit vehicle use and single vehicle scrapping.Moreover,it validates the models with vehicle use data of a power supply company and uses the data results to shore up vehicle allocation,transfer and scrapping,and thus makes vehicle allocation more rational,vehicle scrapping more accurate.The evaluation model can improve lean management level of vehicles.
作者 买波 马一凯 陈昌平 王薇 骆丹 吴海琨 MAI Bo;MA Yikai;CHEN Changping;WANG Wei;LUO Dan;WU Haikun(State Grid Ningxia Yinchuan Power Supply Company,Yinchuan Ningxia 750011,China;Shaanxi Detong Information Technology Co.,Ltd.,Xi’an Shaanxi 710000,China)
出处 《宁夏电力》 2021年第6期20-24,共5页 Ningxia Electric Power
关键词 车辆使用评价指数 车辆报废评价指数 大数据挖掘技术 vehicle use evaluation index vehicle scrapping evaluation index big data mining technology
  • 相关文献

参考文献8

二级参考文献58

  • 1MANYIKA J, CHUI M, BROWN B, etal. Big data: The next frontier for innovation, competition, and productivity [EB/OL]. [2012-10-02].http://www.mckinsey.com/Insight/MGI/ Research/Technology_and_Innovation/ Big_data The next frontier_for_innovation.
  • 2BARWICK H. The "four Vs" of big data. Implementing Information Infrastructure Symposium [EB/OLI. [2012-10-02]. http:// www.computerworld.com.au/article/396198/iiis four vs_big_data/.
  • 3GHEMAWAT S, GOBIOFF H, LEUNG S. The Google file system [CI//Proceedings of the 19th ACM SIGOPS Symposium on Operating Systems Principles (SOSP'03), Oct 19 - 22, 2003, Bolton Landing, NY, USA. New York,NY, USA: ACM, 2003:29-43.
  • 4DEAN J, GHEMAWAT S. MapReduce: Simplified data processing on large clusters [C]//Proceedings of the 6th USENIX Symposium on Operation Systems Design and Implementation (OSDI '04), Dec 6-8, 2004, San Francisco, CA USA. New York, NY USA: ACM. 2004:137-150.
  • 5CHANG F, DEAN J, GHEMAWAT S, et.al. Bigtable: A distributed storage system for structured data [C]//Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI '06), Nov 6-8,2006, Seattle,WA, USA. Berkeley, CA, USA: USENIX Association. 2006:205-218.
  • 6CHAIKEN R, JENKINS B, LARSON P, et al. SCOPE: Easy and efficient parallel processing of massive data sets [J]. Proceedings of the VLDB Endowment (PVLDB), 2008, 1 (2): 1265-1276.
  • 7HDFS Architecture Guide [EB/OL]. [2012-10-02]. http://hadoop.apache.org/docs/ hdfs/r0.22.0/hdfs_design.html.
  • 8FastDFS [EB/OL]. [2012-10-02]. http://code. google .com/p/fastdfs/w/list.
  • 9OpenAFS [EB/OL]. http://www.OpenAFS.org.
  • 10CloudStore [EB/OL]. [2012-10-02]. http://code.google.com/p/kosmosfs/.

共引文献519

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部