The fiber optic sensing perimeter security system has broad application prospects due to its unique characteristics such as long transmission distance, wide monitoring range, high sensitivity, and strong real-time performance. Based on DWT and STFT, this paper extracts the features of the intrusion source signal and uses the CNN-LSTM deep learning algorithm to intelligently classify the signal, improving the separability of disturbance events and accurately judging the intrusion source type. The average accuracy rate of classification can reach more than 96%, with excellent intelligence and low false alarm performance. This technology is a perfect replacement for traditional perimeter alarm systems such as infrared beams and electronic fences, especially applicable for real-time measurement of vibration-related events such as long-distance, omni-directional, and multi-point illegal intrusion, illegal destruction, and structural damage. In addition, the application of distributed optical fiber sensing system in the industrial chain can improve product quality, ensure production safety, and realize production line automation.This research will improve the reliability and accuracy of the perimeter security system, improve the public security environment, save social resources, and improve social security.
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