由于长距离石油天然气管道分布范围广、背景环境复杂，光纤预警系统在实际环境中对威胁管道安全的破坏性事件的识别具有较高的虚警率，难以达到保护管道安全的预警效果。本文将深度学习应用于长距离的光纤预警系统中，识别出主要影响预警效果的过车信号以降低系统的虚警率。智能光纤预警系统主要分为两个部分：分布式光纤传感系统和信号识别系统。本文在实际环境中从?-OTDR（phase-sensitive optical time domain reflectometry）分布式光纤传感系统采集管道周围的入侵信号，通过CLDNN（convolutional, long short-term memory, fully connected deep neural networks）神经网络建立识别模型实现过车信号的识别。经过训练和盲测，所构建的过车事件的识别模型在实际长距离光纤监测环境下有良好的识别和定位效果，有效地降低了预警系统的误报率。

Long-distance oil and gas pipelines are widely distributed and have complex background environments. Therefore, their optical-fiber pre-warning system experiences a high false-alarm rate in identifying destructive events that threaten pipeline safety in a real-world environment. This makes it challenging for the system to achieve accurate pre-warning results and ensure pipeline safety. This study applies deep learning to a long-distance fiber pre-warning system. Through deep learning, a vehicle-passing signal that mainly affects the pre-warning effect is identified, which effectively reduces the false-alarm rate of the pre-warning system. The intelligent fiber pre-warning system is mainly divided into two parts: the distributed optical-fiber sensing system and the signal-recognition system. In a real-world environment, an intrusion signal around the pipeline is collected by a Φ-OTDR(phase-sensitive optical time domain reflectometry) distributed optical-fiber sensing system. Additionally, a recognition model is established by convolutional long short-term memory and fully connected deep neural networks to detect the vehicle-passing signal. After training and blind testing, the vehicle-passing event recognition model demonstrated a good recognition and positioning effect in a real-world long-distance fiber-monitoring environment and effectively reduced the false positives of the pre-warning system.