基于大数据运输集团生产运营决策系统的构建及应用

李卫华; 李志猛

doi:10.12052/gdutxb.180022

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于大数据运输集团生产运营决策系统的构建及应用

综合研究 | 更新时间：2026-05-27

- 基于大数据运输集团生产运营决策系统的构建及应用
- Construction and Application of the Production Operation & Decision-making System Based on Big Data for Transport Group
- 广东工业大学学报 2018年35卷第3期页码：113-118
- 作者机构：
  
  1. 惠州学院信息科学技术学院,广东,惠州,516015
  2. 广东韵为大数据技术咨询有限公司,广东,广州,510640
- 作者简介：
  
  李卫华(1977–)，女，讲师，主要研究方向为机器学习、数据挖掘、大数据. E-mai：410128774@qq.com
- 基金信息：
- DOI：10.12052/gdutxb.180022
  中图分类号： TP399
- 网络首发：2018-05-09，
  
  纸质出版：2018
- 稿件说明：
移动端阅览
李卫华, 李志猛. 基于大数据运输集团生产运营决策系统的构建及应用[J]. 广东工业大学学报, 2018, 35(3): 113-118.

Li Wei-hua, Li Zhi-meng. Construction and Application of the Production Operation & Decision-making System Based on Big Data for Transport Group[J]. 2018, 35(3): 113-118.
李卫华, 李志猛. 基于大数据运输集团生产运营决策系统的构建及应用[J]. 广东工业大学学报, 2018, 35(3): 113-118. DOI： 10.12052/gdutxb.180022.

Li Wei-hua, Li Zhi-meng. Construction and Application of the Production Operation & Decision-making System Based on Big Data for Transport Group[J]. 2018, 35(3): 113-118. DOI： 10.12052/gdutxb.180022.

摘要

为实现南方某运输集团公司生产运营数据的统一采集、处理、分析与决策

本文构建基于大数据的生产运营决策系统. 该系统可使管理者快速了解分析各业务部门的生产运营状态

使决策者借助可视化功能强大的管理驾驶舱分析面向生产运营主题的多维数据集

提高运输集团的信息化水平. 在此基础上进行各种大数据分析应用

可有效促进运输集团生产运营智能化水平

为相关系统的数据挖掘和分析提供有益的借鉴.

Abstract

To achieve the unified collecting

processing

analyzing and decision of the production and operation data for the Southern Transport Group

the production operation and decision-making system based on big data was constructed. It can help the administrators rapidly analyze the production and operation status of each department. With the help of the powerful visual function of the management cockpit

the decision makers can analyze the multidimensional datasets of the production and operation. It can also raise the level of informatization. On this basis

analysis and implements for sorts of big data were carried out to improve the intelligent level of the production and operation for the transport group. It can be useful for data mining and analysis of related study.

关键词

Keywords

references

MANYIKA J, CHUI M, BROWN B, et al. Bigdata: the next frontier for innovation, competition, andproductivity[R]. Las Vegas: The Mc Kinsey Global Institute, 2012.

XIE Z D, WU J C, LI Z M, et al . A study of construction and cultivation of big data capacity of enterprise [J ] . Journal of Guangdong University of Technology, 2017, 34(3): 110-114.

SHEN Z. Study on early warning system of coal mine accidents basedon cloud computing and big data crunching platform [J]. China Coal, 2017, 43(12): 109-114.

HUANG X Q, CHEN J, TIAN S M, et al . Big data integration for optimal planning and operation of electric vehicle charging stations [J ] . Power System Technology, 2016, 40(3): 762-767.

DU H C, DING L, FENG Y J. Research on business intelligence system with the big data for retail industry [J]. Journal of Guangdong University of Technology, 2014, 31(4): 41-45.

XIAO R M, LI B, CHEN Y S. Trend analysis of expressway transportation based on big data [J]. Journal of Traffic and Transportation Engineering, 2015, 15(5): 85-90.

LU W C. On the financial analysis development of highway transport group in the era of big data [J]. Accounting Learning, 2017(11): 21-22.

杨竞. 基于云理论的公路客运服务旅客满意度测评研究[D]. 西安: 长安大学公路学院, 2015.

皮瑞麟. 数据可视化技术在客运信息监测系统中的研究与应用[D]. 广州: 华南理工大学计算机科学与工程学院, 2016.

LI H, LI N. The application of internet of things technology in the management of long-distance bus transport [J]. Science and Technology of West China, 2015, 14(6): 5-7.

WANG Q F. Research on user interest model construction based on bayes network [J]. Wireless Internet Technology, 2016(12): 101-102.

MENG W, WU X X, LI J, et al . Power user portraits based on big data technology [J ] . Telecommunications Science, 2017, 33(S1): 15-20.

SHANG W. Analysis and suggestions of causes of frequent incidence of passenger traffic accidents [J]. Road Traffic Managemen, 2006(4): 36-41.

WANG G J, ZHAN Y Z, LIU Z Q. Implementation of embedded real-time driving fatiguemonitoring systembased on DM642 [J]. Journal of Computer Applications, 2007, 27(10): 2612-2614.

GUO D F, Li Y. Design of the embedded drunk driving automatic test system based on internet of things [J]. Computer Measurement Control, 2013, 21(3): 594-596.

刘兰芳. 大数据背景下道路运输运营管理数据挖掘研究与应用[D]. 长春: 吉林大学交通学院, 2017.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

并行最小割算法及其在金融社交网络中的应用

利用消费者浏览行为识别品牌竞争关系研究

基于交通一卡通数据的交通状态分析及动态控制研究

车用催化剂的台架快速老化研究

用信息流和知网构建大数据语义共享通道研究