多源异构环境下的车联网大数据混合属性特征检测方法

doi:10.11896/jsjkx.220300273

计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 108-112.doi: 10.11896/jsjkx.220300273

• 数据库&大数据&数据科学^* • 上一篇下一篇

多源异构环境下的车联网大数据混合属性特征检测方法

陈晶¹, 吴玲玲²

1 中冶赛迪工程技术股份有限公司重庆 401122
2 重庆交通大学重庆 400074

收稿日期:2022-03-09 修回日期:2022-05-25 发布日期:2022-08-02
通讯作者: 吴玲玲(330931228@qq.com)
作者简介:(patton_g_2000@163.com)
基金资助:
国家重点研发计划(2018YFB1601001)

Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment

CHEN Jing¹, WU Ling-ling²

1 CISDI Engineering Co.,Ltd,Chongqing 401122,China
2 Chongqing Jiaotong University,Chongqing 400074,China

Received:2022-03-09 Revised:2022-05-25 Published:2022-08-02
About author:CHEN Jing,born in 1982,master,engineer.His main research interests include digitalization and informatization of engineering industry.
WU Ling-ling,born in 1976,Ph.D,associate professor.Her main research interests include transport engineering and so on.
Supported by:
National Key Research and Development Program of China(2018YFB1601001).

摘要/Abstract

摘要： 现有的车联网大数据特征检测方法忽略了数据属性权重,导致效率偏低,无法在车辆运行中提供高效服务。为此,提出了多源异构环境下的车联网大数据混合属性特征检测方法。该方法利用集成模型集成车联网多源异构数据,并对集成数据进行标准化和属性约简处理;同时,通过加权主成分分析法提取集成数据的属性特征,并利用聚类方法实现特征聚类,完成车联网大数据混合属性特征检测。实验结果表明,与现有方法相比,所提方法在评价指标敏感性指数上取值更高,时间复杂度更低,能更高效地完成车联网大数据混合属性特征提取任务。

关键词: 车联网大数据, 多源异构环境, 混合属性, 特征检测方法

Abstract: Current feature detection methods for big data of Internet of vehicles ignore the data attribute weight,resulting in low efficiency and fail to provide efficient services in vehicle operation.Therefore,a hybrid attribute feature detection method for big data of Internet of vehicles in multi-source heterogeneous environment is proposed.Middleware is used to build an integration model to integrate multi-source heterogeneous data of the Internet of vehicles,and standardization and attribute reduction of integrated data are completed.With pre-processed data as input,attribute features are extracted by weighted principal component analysis,and feature clustering is realized by clustering method to complete the feature detection of mixed attribute of Internet of vehicles big data.Experimental results show that compared with existing methods,the sensitivity index of the proposed method is higher and the time complexity is lower,which indicates that the proposed feature detection method is more efficient and can more accurately complete the feature extraction task of mixed attributes of the Internet of vehicles big data.

Key words: Feature detection method, Internet of vehicles big data, Mixed attributes, Multi-source heterogeneous environment

中图分类号:

TP368.6

陈晶, 吴玲玲. 多源异构环境下的车联网大数据混合属性特征检测方法[J]. 计算机科学, 2022, 49(8): 108-112. https://doi.org/10.11896/jsjkx.220300273

CHEN Jing, WU Ling-ling. Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment[J]. Computer Science, 2022, 49(8): 108-112. https://doi.org/10.11896/jsjkx.220300273

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多源异构环境下的车联网大数据混合属性特征检测方法

Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment

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