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

Identification of oil and gas pipeline third-party damage based on mobile devices location

管道第三方破坏是长输油气管道面临的主要风险之一,第三方破坏活动极强的随机性与不确定性使得相应的防范工作变得非常困难。针对目前人工巡线,光纤振动监测,无人机巡线等安全预警技术存在预警不及时、误报、漏报等问题,结合易获取的具有时空序列的手机位置数据,本文建立了基于手机位置数据的管道第三方破坏行为识别模型。首先,通过将手机位置信息进行预处理,获取更准确的目标管线附近的第三方活动位置信息,根据位置数据的密度在空间层上对轨迹点进行聚类分析,提出一种基于时空聚类的停留点识别方法;结合停留点位置关键特征对其进行语义标记,并基于TF-IDF规则对停留点异常程度进行计算,准确提取出管道监控范围内异常停留点;然后通过对第三方轨迹进行提取与分段,结合轨迹位置特征完成停留点所在轨迹的邻域搜索,根据速度、加速度、转角等多个轨迹移动特征计算近邻轨迹分段的行为差异度;最后基于管道风险特征与第三方人员的行为特征构建管道第三方破坏行为决策树模型,深入分析各类特征与第三方破坏活动类型的相关性,实现管道附近第三方破坏行为类别判断。通过搜集的第三方历史特征数据集进行训练测试,本文建立的识别模型准确率为90.9%,且对某长输管段附近30天内的移动设备信息进行处理,依据获得的53994条有效数据对附近第三方活动异常行为进行监测,结果表明该模型可准确识别出轨迹中的异常行为,有助于及时发现第三方管道破坏行动,为智能防范管道第三方破坏维护管道完整性提供了有效依据。

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.