基于BP神经网络的电缆沟监控模式识别方法

为了减少电缆沟盲目施工与偷盗等因素造成的损失和影响，引入一种智能的线路监控技术。该技术基于分布式光纤传感技术，利用BP神经网络对光纤传感器的四种常见的扰动信号进行模式识别。通过将光纤传感器得到的时域扰动信号用特定的程序处理后转换成图像，再经特定的图像处理流程后形成模式识别的样本。利用这些样本训练BP神经网络，并将训练好的模型应用到实际的电缆沟安全监控系统中进行测试。测试结果表明，电缆沟的总体识别成功率为98.16%，该识别方法还可以应用于光纤周界安防系统等领域。

A pattern recognition method of cable trench monitoring based on BP neural network

In order to reduce the loss and impact caused by blind construction and theft of the cable trench, an intelligent line monitoring technology is introduced. Based on distributed optical fiber sensing technology, BP neural network was used to recognize the four common disturbance signals obtained by optical fiber sensors. The time-domain disturbance signal obtained by the optical fiber sensor is processed by a specific program and converted into an image, and then a pattern recognition sample is formed after a specific image processing process. These samples are used to train BP neural network, and the trained model is applied to the actual cable trench safety monitoring system for testing. The test results show that the overall recognition success rate of the cable trench is 98.16%. In addition, the identification method can also be applied to the optical fiber perimeter security system and other fields.