Research and application of fiber optic perimeter vibration monitoring technology

The safety management of parking lots in urban rail transit vehicle depots is an indispensable part of ensuring the smooth and efficient operation of the entire rail transit system. The fiber optic vibration detection system, with its excellent sensitivity and wide monitoring range, has become the preferred solution for the new generation of urban rail transit perimeter security protection. The system captures external vibration signals through fiber optic sensors to achieve real-time monitoring and early warning of intrusion behavior. However, in practical applications, complex and changing operating environments such as wind interference and animal activity often lead to frequent false alarms in the system, affecting its stability and practicality. In response to this challenge, this article innovatively proposes an artificial intelligence (AI) based fiber optic perimeter vibration monitoring algorithm and deeply integrates deep learning (DL) technology to achieve intelligent recognition and analysis of vibration signals. The experimental results show that the algorithm significantly improves the accuracy and stability of the fiber optic vibration detection system, reduces the false alarm rate, and provides more solid technical support for the safety protection of urban rail transit vehicle depot parking lots.