第二十四讲:基于多源数据融合的管网智能站场设备管理方法研究  

Chapter 24:Research on Equipment Management Method for Intelligent Station of Pipeline Network Based on Multi-Source Data Fusion

作　　者：孙志成 朱悦铭 曾鹏[1] Sun Zhicheng;Zhu Yueming;Zeng Peng(Shenyang Institute of Automation,Chinese Academy of Science,Shenyang 110016)

机构地区：[1]中国科学院沈阳自动化研究所,沈阳市110016

出　　处：《仪器仪表标准化与计量》2023年第6期12-14,共3页Instrument Standardization & Metrology

摘　　要：油气长输管道是国家能源供应大动脉和国民经济的重点工程,设备管理作为油气管网的重要组成部分,越来越受到人们的重视。由于多源数据融合方法能更准确的反映设备的运行状态,也引起了人们广泛的兴趣。本文探讨了基于多源数据融合的管网智能站场设备管理的优势与方法,通过采集气象、地理、环境和设备等多种数据源,并结合数据挖掘和机器学习技术进行分析,可以对设备的运行状态进行更加准确和可靠的分析,提高设备的可靠性和稳定性,进而降低管理成本,提高工作效率。The oil and gas long-distance pipeline is a major artery of national energy supply and a key project of the national economy.Equipment management,as an important component of the oil and gas pipeline network,is increasingly receiving people’s attention.Due to the fact that multi-source data fusion methods can more accurately reflect the operational status of devices,it has also attracted widespread interest.The paper explores the advantages and methods of intelligent station equipment management in pipeline networks based on multi-source data fusion.By collecting multiple data sources such as meteorology,geography,environment,and equipment,and combining data mining and machine learning technology for analysis,the operational status of equipment can be analyzed more accurately and reliably,improving the reliability and stability of equipment,thereby reducing management costs and improving work efficiency.

关 键 词：油气管网 智能站场 多源数据融合 设备管理 

分 类 号：TE978[石油与天然气工程—石油机械设备]