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首页» 过刊浏览» 2022» Vol.7» Issue(2) 261-269     DOI : 10.3969/j.issn.2096-1693.2022.02.024
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基于移动设备位置数据的油气管道第三方破坏行为识别研究
张行,凌嘉瞳,刘思敏,董绍华
1 中国石油大学(北京)管道技术与安全研究中心,北京 102249 2 中油国际管道公司,北京 102206
Identification of oil and gas pipeline third-party damage based on mobile devices location
ZHANG Hang, LING Jiatong, LIU Simin, DONG Shaohua
1 Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China 2 Sino-pipeline International Company Limited, Beijing 102206, Chin

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摘要  基于移动设备位置数据建立了油气长输管道第三方破坏行为识别模 型,并通过历史数据对此模型进行训练测试,结果表明该模型准确率 高达 90.9%,最后建立了异常活动类型判断决策图,决策树各分枝判 断依据及模型的准确率将根据数据量的变化实时更新。
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关键词 : 长输油气管道;第三方破坏;位置数据;安全预警;异常检测
Abstract

For the long-distance oil and gas pipelines, the third-party damage (TPD) is a main risk, which is randomness and uncertainty, and difficult to prevent. At present, the safety early warning technologies such as line patrol, fiber-optical vibration and Unmanned Aerial Vehicle (UAV) line patrol are mainly methods adopted by the TPD. But that has many problems such as untimely warning, false alarm and missed report. Combined with the easily obtained mobile phone location data with time-space sequence, a third-party damage behavior identification model for pipelines based on mobile phone location data was established here. Firstly, the mobile phone location information was preprocessed to obtain more accurate third-party activity location information near the target pipeline. The trajectory points were clustered and analyzed based on the density of data, and a stop recognition method based on spatiotemporal clustering was proposed. The key features of the staying point were semantically marked, and the abnormality degree of the staying point is calculated based on the TF IDF rule to accurately extract the abnormal staying point within the pipeline monitoring range. Secondly, extracted and segmented the third-party trajectory, completed the neighborhood search of the trajectory where the stay point located in accordance with trajectory location characteristics, and calculated the behavior difference degree of the neighbor trajectory segment according to multiple trajectory movement characteristics such as velocity, acceleration and rotation angle. Finally, established the model of pipeline TPD decision tree based on the pipeline risk characteristics, and in depth analysis the correlation between various characteristics and the types of third-party sabotage activities. In the end, used the behavior characteristics of the third party to judge the type of TPD. Through the training and testing of the collected historical characteristic data set of the third-party, the accuracy of the identification model established in this paper is 90.9%, and the mobile equipment information in the vicinity of a long-distance pipeline section within 30 days was processed, and the abnormal activities of nearby third parties were monitored according to the 53994 valid data obtained. The results show that the model can accurately identify abnormal behaviors based on the trajectory, it is helpful for timely detection of TPD damage activities such as private excavation, engineering damage and oil theft by drilling, which provides an effective basis for intelligently PTD damage to the pipeline and maintaining the integrity of the pipeline.

Key words: long distance oil and gas pipeline; third-party damage; location data; safety warning; abnormal detection
收稿日期: 2022-06-29     
PACS:    
基金资助:中国石油天然气股份有限公司—中国石油大学( 北京) 战略合作科技专项(ZLZX2020-05) 和中国石油科技创新基金研究项目(2018D-5007- 0601) 联合资助
通讯作者: zhanghang@cup.edu.cn.
引用本文:   
张行, 凌嘉瞳, 刘思敏, 董绍华. 基于移动设备位置数据的油气管道第三方破坏行为识别研究. 石油科学通报, 2022, 02: 261-269 ZHANG Hang, LING Jiatong, LIU Simin, DONG Shaohua. Identification of oil and gas pipeline third-party damage based on mobile devices location. Petroleum Science Bulletin, 2022, 02: 261-269.
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