Distributed optical fiber circumference vibration signal processing and recognizing method based on image

Abstract

The invention discloses a distributed optical fiber circumference vibration signal processing and recognizing method based on an image. The distributed optical fiber circumference vibration signal processing and recognizing method comprises the following steps that disturbance data of all detection point positions within a whole distributed circumference detection range are collected in real time; when the disturbance data of a certain detection point position are larger than a set threshold value, the disturbance data of the detection point and the disturbance data within the M*N regional range around the detection point are collected, and the numerical values of the disturbance data are mapped in the range of 0-255; a grey level co-occurrence matrix is calculated according to the mapping values, and texture feature statistic parameters in multiple directions in the grey level co-occurrence matrix are calculated; the texture feature statistic parameter numerical values in multiple directions are compared, and if the texture feature statistic parameter numerical values in multiple directions are similar, it is determined that variable system noise is generated in the section; if deviation exceeds a preset value, it is determined that intrusion disturbance occurs at the moment; mode recognition is carried out on the intrusion disturbance signals so as to judge the variety of intrusion behaviors.

Description

technical field [0001] The invention relates to the technical field of optical fiber perimeter security, in particular to an image-based distributed optical fiber perimeter vibration signal processing and identification method. Background technique [0002] Distributed optical fiber sensing technology continuously and real-time measures the environmental physical parameters distributed along the optical fiber geometric path, which is very suitable for a large spatial range. The optical fiber is used as both a sensing element and a transmission element. Its advantages are high sensitivity, low cost, continuous real-time, long monitoring distance, high concealment, and anti-electromagnetic and other environmental factors. In order to further meet the requirements of security performance, it is necessary to discriminate the behavior information that triggers the monitoring area of ​​the system, and at the same time shield the impact of wind and rain environment and system noise...

AI Technical Summary

Problems solved by technology

Features based on single-point signal extraction are easily affected by the above factors, which interfere with the recognition effect.

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