基于大数据技术的铁路货运价格策略应用研究
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摘要
精准制定价格策略,对于实现企业效益最大化、保持企业竞争力具有重要意义。随着信息化蓬勃发展,日趋成熟的大数据技术为铁路货运价格策略的制定提供了新的思路和工具。从铁路货运价格决策的业务需求分析出发,借鉴主流大数据Lambda架构思想,搭建铁路货运价格策略大数据应用平台,针对数据采集、数据分析挖掘等关键技术进行分析,通过设计货运市场运行情况分析、货运价格策略订制、效果预测及监控预警等核心功能,利用大数据为铁路货运价格策略的制定和管理提供辅助支撑。
Targeted pricing policies are of great significance for enterprises to maximize their benefits and remain competitive. With flourishing development of the information technology, more and more advanced big data technologies have begun to provide the policy-makers of railway freight pricing with new ideas and tools. According to a business requirement analysis of railway freight pricing policies,relied on the mainstream big data framework of Lambda, this paper puts forward a big data application platform for making railway freight pricing policies. To shore up policy making and management, this paper also puts forward the core functions of the platform: market performance analysis, freight pricing policy-making, effect prediction and monitoring and early warning based on the analysis of the key technologies including data collection and digging.
Targeted pricing policies are of great significance for enterprises to maximize their benefits and remain competitive. With flourishing development of the information technology, more and more advanced big data technologies have begun to provide the policy-makers of railway freight pricing with new ideas and tools. According to a business requirement analysis of railway freight pricing policies,relied on the mainstream big data framework of Lambda, this paper puts forward a big data application platform for making railway freight pricing policies. To shore up policy making and management, this paper also puts forward the core functions of the platform: market performance analysis, freight pricing policy-making, effect prediction and monitoring and early warning based on the analysis of the key technologies including data collection and digging.
引文
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[5]南森·马茨,詹姆斯·沃伦.大数据系统构建:可拓展实时数据系统构建原理与最佳实践[M].北京:机械工业出版社,2016.
[6]李琛轩.面向推荐的大数据计算与存储平台设计与实现[D].哈尔滨:哈尔滨工业大学,2016.LI Chenxuan.Design and Implementation of Recommended Big Data Computing and Storage Platform[D].Harbin:Harbin Institute of Technology,2016.
[7]陈烘.基于Storm的大数据流式计算关键技术研究及应用[D].杭州:浙江工业大学,2017.CHEN Hong.Research and Application of Key Technology of Big Data Streaming Calculation based on Storm[D].Hangzhou:Zhejiang University of Technology,2017.
[8]张斌,彭其渊.基于大数据的铁路客户关系管理系统设计研究[J].铁道运输与经济,2017,39(6):42-48.ZHANG Bin,PENG Qiyuan.Design of Railway Customer Relationship Management System based on Big Data[J].Railway Transport and Economy,2017,39(6):42-48.
[9]李涛,曾春秋,周武柏,等.大数据时代的数据挖掘:从应用的角度看大数据挖掘[J].大数据,2015(4):57-80.LI Tao,ZENG Chunqiu,ZHOU Wubo,et al.Data Mining in the Big Data Era:Big Data Mining from the Perspective of Application[J].Big Data,2015(4):57-80.