Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Railway slope monitoring and early warning equipment
According to the railway slope early warning equipment, the differential base station with differential calculation capability is fixed on the railway video rod, and a plurality of monitoring units are arranged on the railway slope to form a monitoring matrix, so that under the calibration of the differential base station, the positioning precision of each monitoring terminal is greatly improved, and the accuracy of early warning of the railway slope is improved. The distortion phenomenon caused by collapse and relaxation of the side slope can be accurately monitored in real time; in addition, a camera device is additionally arranged on a video rod, external invasion can be detected, recognized and classified by means of a deep learning network model, and compared with a traditional foreign matter recognition method, the self-developed image recognition algorithm is used, and the recognition efficiency is improved on the basis of analysis of image features such as rectangle, aspect ratio, compactness and gradient of foreign matter and the good or bad degree of feature extraction information. And the identification efficiency and accuracy are greatly improved. Finally, when abnormity is monitored, the early warning equipment sends warning information to the monitoring platform, and after confirmation of monitoring personnel, railway inspection maintenance personnel or driving drivers are notified and take corresponding measures, so that major accidents are prevented.
一种铁路边坡预警设备通过在铁路视频杆固定具备差分解算能力的差分基准站，并在铁路边坡布设多个监测单元，形成监测矩阵，使其在差分基准站的校准下，极大提升了各监测终端的定位精度，做到对边坡的坍塌和松驰产生的畸变现象进行实时准确的监测；此外在视频杆上加装的摄像装置，依托深度学习网络模型，能够对外来入侵进行检测、识别和分类，使用自研图像识别算法，比传统的异物识别方法在基于对异物矩形、纵横比、紧凑度、倾斜度等图像特征的分析以及特征提取信息的好坏程度上，极大提升了识别效率和准确性。最终做到在监测有异常时，预警设备向监测平台发送告警信息，同时通过监测人员确认后，对铁路巡检维护人员或行驶中的驾驶人员进行通知并采取相应措施，防止重大事故的发生。
Railway slope monitoring and early warning equipment
According to the railway slope early warning equipment, the differential base station with differential calculation capability is fixed on the railway video rod, and a plurality of monitoring units are arranged on the railway slope to form a monitoring matrix, so that under the calibration of the differential base station, the positioning precision of each monitoring terminal is greatly improved, and the accuracy of early warning of the railway slope is improved. The distortion phenomenon caused by collapse and relaxation of the side slope can be accurately monitored in real time; in addition, a camera device is additionally arranged on a video rod, external invasion can be detected, recognized and classified by means of a deep learning network model, and compared with a traditional foreign matter recognition method, the self-developed image recognition algorithm is used, and the recognition efficiency is improved on the basis of analysis of image features such as rectangle, aspect ratio, compactness and gradient of foreign matter and the good or bad degree of feature extraction information. And the identification efficiency and accuracy are greatly improved. Finally, when abnormity is monitored, the early warning equipment sends warning information to the monitoring platform, and after confirmation of monitoring personnel, railway inspection maintenance personnel or driving drivers are notified and take corresponding measures, so that major accidents are prevented.
一种铁路边坡预警设备通过在铁路视频杆固定具备差分解算能力的差分基准站，并在铁路边坡布设多个监测单元，形成监测矩阵，使其在差分基准站的校准下，极大提升了各监测终端的定位精度，做到对边坡的坍塌和松驰产生的畸变现象进行实时准确的监测；此外在视频杆上加装的摄像装置，依托深度学习网络模型，能够对外来入侵进行检测、识别和分类，使用自研图像识别算法，比传统的异物识别方法在基于对异物矩形、纵横比、紧凑度、倾斜度等图像特征的分析以及特征提取信息的好坏程度上，极大提升了识别效率和准确性。最终做到在监测有异常时，预警设备向监测平台发送告警信息，同时通过监测人员确认后，对铁路巡检维护人员或行驶中的驾驶人员进行通知并采取相应措施，防止重大事故的发生。
Railway slope monitoring and early warning equipment
一种铁路边坡监测预警设备
ZHANG YAN (Autor:in) / PENG CHAOLIANG (Autor:in) / ZHOU JIN (Autor:in) / WANG XU (Autor:in) / LIU CHUNBO (Autor:in) / DONG RONGHUA (Autor:in) / ZHANG YUPENG (Autor:in) / WANG SHENG (Autor:in) / LUO YING (Autor:in) / YIN YIFAN (Autor:in)
13.01.2023
Patent
Elektronische Ressource
Chinesisch
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