摘要
为客观、高效地评价压力管道腐蚀状态,预测管道腐蚀速率,从而采用大数据分析技术,其为管道腐蚀状态评估提供了全新的思路和手段。结合大数据分析在管道腐蚀状态方面的应用,说明了压力管道腐蚀状态大数据分析的内涵和目的,数据的来源和类型。总结了单一数据分析模型存在的缺点和不足,探讨了优化单一数据分析模型或结合多种数据分析模型进行压力管道腐蚀状态的应用。提出了压力管道数据分析模型中的主要问题,并对数据分析模型改进发展方向进行了展望,对于压力管道腐蚀状态的分析和预测具有重大意义。
In order to objectively and efficiently evaluate the corrosion status of pressure pipelines and predict the corrosion rate of pipelines, big data analysis technology is applied, which provides a new idea and means for the evaluation of pipeline corrosion status. Combined with the application of big data analysis in pipeline corrosion state, it explains the connotation and purpose of big data analysis on pipeline corrosion state, as well as the source and type of data. The defects and shortcomings of single data analysis model were summarized, and optimizing single data analysis model or combining multiple data analysis models in the application of the corrosion state of pressure pipeline was discussed. The main problems in the data analysis model of pressure pipeline were put forward, and the development direction of the data analysis model was prospected, which was of great significance to the analysis and prediction of the corrosion state of pressure pipeline.
作者
凌晓
徐鲁帅
高甲程
马娟娟
余建平
马贺清
LING Xiao;XU Lushuai;GAO Jiacheng;MA Juanjuan;YU Jianping;MA Heqing(College of Petroleum and Chemical Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Petro China Gansu Lanzhou Marketing Company,Lanzhou 730050,China)
出处
《热加工工艺》
北大核心
2021年第12期34-38,共5页
Hot Working Technology
基金
甘肃省自然科学基金项目(20JR5RA451)
甘肃省重点研发计划项目-工业类(1604GKCA022)
甘肃省高等学校创新能力提升项目(2020A-019)。
关键词
大数据
压力管道
腐蚀
机器学习
数据分析模型
big data
pressure pipeline
corrosion
machine learning
data analysis model
