Deep learning mode recognition method for distributed optical fiber pipeline intrusion detection

Abstract

The invention discloses a deep learning mode recognition method for distributed optical fiber pipeline intrusion detection, and the method comprises the steps: carrying out the wavelet threshold denoising of an original intrusion signal, and carrying out the multi-resolution decomposition through mallat; and mapping the denoised signal into a two-dimensional image through a GAF algorithm, and then reducing the size of the image to meet the requirements of a network model. And the network model is optimized, an Adam optimizer is utilized to optimize the learning rate, a Swsh activation function is utilized to enhance the model performance, and high-speed and high-precision identification of the intrusion event is realized. The GAF facilitates CNN recognition of intrusion events with fine feature differences, and especially has a good anti-interference effect for distributed optical fiber surrounding environment factors. As the GAF does not need to carry out iterative operation, the intrusion identification speed is greatly improved. Meanwhile, the GAF algorithm is insensitive to power fluctuation in an optical path, and the robustness and practicability of the system are effectively improved.

Description

technical field [0001] The invention belongs to the field of deep learning and security, and in particular relates to a deep learning pattern recognition method for distributed optical fiber pipeline intrusion detection. Background technique [0002] In recent years, distributed optical fiber vibration sensing (DOVS) technology has attracted extensive attention in the field of intelligent security due to its high sensitivity, anti-electromagnetic interference, and low price. It has been used in the fields of perimeter safety, oil and gas pipeline safety early warning and structural health monitoring, especially for the protection of long-distance pipelines. However, sensing fibers are susceptible to environmental influences, such as wind and rain, pedestrian walking, or animal activity, so these innocuous events can lead to unexpected false alarms in the system. Furthermore, the complexity and similarity of vibration signals may lead to errors in vibration type identificati...

AI Technical Summary

Problems solved by technology

However, the EMD method can cause the problem of modal mixing of discontinuous signals, seriously affecting the accuracy

In addition, the intrusion recognition time exceeds 7 seconds, making it difficult to deal with emergency response

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