基于混合式架构城市轨道交通大数据平台与运营决策应用
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摘要
在分析城市轨道交通信息化系统共享数据特征的基础上,结合数据仓库应用与业务数据体系不匹配等问题,提出构建混合式结构的大数据平台。通过建立数据分级分类融合、数据流规划,设计实时数据处理及计算服务标准化等算法,形成多源异构数据应用与大数据平台适配的环境。探讨运营信息化大数据平台的构成和混合模式下的分层部署和逻辑递阶管控等关键技术。通过将数据挖掘方法引入运营决策业务,以客流数据画像和特殊运营场景下动态客流大数据与列车运能匹配度的评估为例,实现了该模式下数据应用扩展的适应性提升和增值效用。案例应用表明能够解决多种业务交叉多维数据管理的问题,提高信息共享和数据提取服务的效率。
Based on the analysis of shared data characteristics of urban rail transit information system,combined with the problems of mismatch between data warehouse application and business data system,we proposed a big data platform with hybrid structure.Through the establishment of data classification fusion,data flow planning,real-time data processing and standardization of computing services and other algorithms,a multi-source heterogeneous data application and big data platform adapted environment was formed.We discussed the key technologies such as the composition of the big data platform of operation informatization,the layered deployment and logical hierarchical management and control under the hybrid mode.By introducing data mining method into operation decision-making business,taking the portrait of passenger flow data and the evaluation of matching degree between dynamic passenger flow data and train capacity under special operation scene as an example,the adaptability and value-added utility of data application expansion under this mode were realized.Case application shows that it can solve the problem of multi-service cross-dimensional data management and improve the efficiency of information sharing and data extraction services.
Based on the analysis of shared data characteristics of urban rail transit information system,combined with the problems of mismatch between data warehouse application and business data system,we proposed a big data platform with hybrid structure.Through the establishment of data classification fusion,data flow planning,real-time data processing and standardization of computing services and other algorithms,a multi-source heterogeneous data application and big data platform adapted environment was formed.We discussed the key technologies such as the composition of the big data platform of operation informatization,the layered deployment and logical hierarchical management and control under the hybrid mode.By introducing data mining method into operation decision-making business,taking the portrait of passenger flow data and the evaluation of matching degree between dynamic passenger flow data and train capacity under special operation scene as an example,the adaptability and value-added utility of data application expansion under this mode were realized.Case application shows that it can solve the problem of multi-service cross-dimensional data management and improve the efficiency of information sharing and data extraction services.
引文
[1]张义鑫,杨军,张静.基于数据仓库的北京轨道交通数据中心测试平台[J].都市快轨交通,2012,25(5):109-114.
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[4]杨燕,朱焱,戴齐.智慧轨道交通---实现更深入的智能化[J].计算机应用,2012,32(5):1205-1207.
[5]夏靖波,韦泽鲲,付凯.云计算中Hadoop技术研究与应用综述[J].计算机科学,2016,43(11):6-11.
[6]邓仲华,刘伟伟,陆颖隽.基于云计算的大数据挖掘内涵及解决方案研究[J].情报理论与实践,2015,38(7):103-108.
[7]池亚平,杨垠坦,许萍,等.基于Hadoop的监控数据存储与处理方案设计和实现[J].计算机应用与软件,2018,35(6):58-63,157.
[8]李敏,倪少权,邱小平.物联网环境下基于上下文的Hadoop大数据处理系统模型[J].计算机应用,2015,35(5):1267-1272.
[9]张铭,王富章,程超.城市轨道交通设备故障聚类与贝叶斯网络预警[J].计算机工程与应用,2016,52(11):259-264.
[10]李得伟,张天宇,周玮腾,等.轨道交通大数据运用现状及发展趋势研究[J].都市快轨交通,2016,29(6):1-7.
[2]郭鹏,陈川,任红波.数据仓库在轨道交通数据资源整合中的应用[J].城市轨道交通研究,2010,13(10):48-52.
[3]何友全,肖建,黄碧霞.用于铁道牵引供电系统故障诊断的数据仓库挖掘技术[J].计算机工程,2004,30(2):169-188.
[4]杨燕,朱焱,戴齐.智慧轨道交通---实现更深入的智能化[J].计算机应用,2012,32(5):1205-1207.
[5]夏靖波,韦泽鲲,付凯.云计算中Hadoop技术研究与应用综述[J].计算机科学,2016,43(11):6-11.
[6]邓仲华,刘伟伟,陆颖隽.基于云计算的大数据挖掘内涵及解决方案研究[J].情报理论与实践,2015,38(7):103-108.
[7]池亚平,杨垠坦,许萍,等.基于Hadoop的监控数据存储与处理方案设计和实现[J].计算机应用与软件,2018,35(6):58-63,157.
[8]李敏,倪少权,邱小平.物联网环境下基于上下文的Hadoop大数据处理系统模型[J].计算机应用,2015,35(5):1267-1272.
[9]张铭,王富章,程超.城市轨道交通设备故障聚类与贝叶斯网络预警[J].计算机工程与应用,2016,52(11):259-264.
[10]李得伟,张天宇,周玮腾,等.轨道交通大数据运用现状及发展趋势研究[J].都市快轨交通,2016,29(6):1-7.