光时域分布式技术的电缆外破行为检测系统

Detection system of cable breaking behavior based on optical time domain distributed technology

针对现有技术中电缆受到外力破坏影响电缆正常运行的问题,提出了新型的电缆外破行为检测方法,利用光频域分布式技术实现电缆外破行为检测。通过采用BP人工神经网络模型对电缆防外破的各种数据信息进行分类,提高了数据计算的效率。该研究采用BP人工神经网络模型,将造成电缆外破的确定性因素通过属性学习进行故障数据输出,并通过识别与分类算法模型对入侵事件所包含的频率信息进行识别与分类。试验表明,该研究方法正确率达到90%以上,平均偏差为2.3米,定位精准。

To solve the problem that the cable is damaged by external force in the prior art,which affects the operation of the cable,a new type of cable external damage detection method is proposed,which uses the optical frequency domain distributed technology to realize the cable breaking behavior detection.By using the BP artificial neural network model to classify various data information of cable protection against external damage,the efficiency of data calculation is improved.This study uses the BP artificial neural network model to output the fault data which causes cable broken through attribute learning,and identify and classify the frequency information contained in the intrusion event through the recognition and classification algorithm model.Experiments show that the accuracy rate of this research method is over 90%,the average deviation is 2.3 meters,which indicates a accurate positioning.