物联网学报 ›› 2020, Vol. 4 ›› Issue (3): 112-119.doi: 10.11959/j.issn.2096-3750.2020.00174

• 专题:智慧交通物联网 • 上一篇    下一篇

长输油气管道大数据挖掘与应用

于涛1(),刘丽君2,陈泓君1,于瑶1   

  1. 1 中国石油天然气股份有限公司北京油气调控中心,北京 100007
    2 北京中油瑞飞信息技术有限责任公司,北京 100007
  • 修回日期:2020-06-30 出版日期:2020-09-30 发布日期:2020-09-07
  • 作者简介:于涛(1982- ),男,山东青岛人,博士,中国石油天然气股份有限公司北京油气调控中心高级工程师,主要研究方向为长输油气管道调控运行管理、数据挖掘分析与应用等|刘丽君(1982- ),女,山东烟台人,北京中油瑞飞信息技术有限责任公司工程师,主要研究方向为长输油气管道信息化、智能化建设|陈泓君(1989- ),女,山东菏泽人,中国石油天然气股份有限公司北京油气调控中心工程师,主要研究方向为长输油气管道调控运行管理、数据分析|于瑶(1989- ),女,吉林松原人,中国石油天然气股份有限公司北京油气调控中心工程师,主要研究方向为长输油气管道安全监控
  • 基金资助:
    国家自然科学基金资助项目(51774311)

Big data mining and application of long-distance oil and gas pipeline

Tao YU1(),Lijun LIU2,Hongjun CHEN1,Yao YU1   

  1. 1 PetroChina Co.,Ltd.,Beijing Oil &Gas Pipeline Control Center,Beijing 100007,China
    2 Richfit Information Technology Co.,Ltd.,Beijing 100007,China
  • Revised:2020-06-30 Online:2020-09-30 Published:2020-09-07
  • Supported by:
    The National Natural Science Foundation of China(51774311)

摘要:

针对未来长输油气管道智能化建设的需要,结合油气管道数据采集与监视控制(SCADA,supervisory control and data acquisition)系统及运行参数,对比传统理论方法和大数据挖掘方法的特点,提出了大数据推动管道智能化的研究方向以及管道智能化研究的数字信息化、理论化和智能化3个步骤,建立了管道智能化架构,包括物理层、数据层、数据挖掘层、应用层和用户层共5个层次,并确定以数据挖掘层为架构核心。统计分析、时序性预测和工况识别等应用案例表明,利用大数据挖掘可有效解决实际生产的业务需求,指导未来管道智能化的研究与建设。

关键词: 长输油气管道, 大数据, 智能化

Abstract:

In response to the need of the intelligent construction of the long-distance oil and gas pipeline in the future,combining with the supervisory control and data acquisition (SCADA) system and operating parameters of the oil and gas pipeline,comparing with the characteristics of traditional theoretical methods and big data mining methods,the direction of big data to promote intelligent pipeline and three steps of digital informatization,theorization,and intelligence of the pipeline intelligence research were proposed.The pipeline intelligent architecture was established,which included a physical layer,a data layer,a data mining layer,an application layer,and a user layer.The data mining layer was the core of the architecture.The statistical analysis,time series prediction and working condition identification and other application cases showed that the use of the big data mining could effectively solve the actual production business needs and guide the future pipeline intelligent research and construction.

Key words: long-distance oil and gas pipeline, big data, intelligent

中图分类号: