Application of Intelligent Safety Monitoring System Based on Distributed Fiber Optic Vibration Sensing in Oil and Gas Pipelines

在广域油气输送网络中，埋地管道长期面临着复杂地质条件与城市化进程的双重挑战。为提升监测系统的预警效能与运行可靠性，本研究对油气管道运营过程中的监测需求和常见干扰源进行了分析，引入深度学习架构对光纤预警系统的探测信号进行识别和判断，开发出具有动态自适应能力的光纤振动信号分类模型。并且通过开发清管器自动识别跟踪功能和振动源距离估算方法，进一步优化光纤测振预警系统的应用效果。In the wide area oil and gas transmission network, the buried pipeline has long been faced with the dual challenges of complex geological conditions and urbanization. In order to improve the early warning efficiency and operational reliability of the monitoring system, this study analyzed the monitoring requirements and common interference sources during the operation of oil and gas pipelines, introduced deep learning architecture to identify and judge the detection signals of the fiber optic early warning system, and developed a fiber optic vibration signal classification model with dynamic adaptive ability. The application effect of the fiber-optic vibration measurement early warning system is further optimized by developing the automatic identification and tracking function of pig and the estimation method of vibration source distance.