基于5G的编组站咽喉区异常检测系统

Research and implementation of 5G-based anomaly detection system for marshalling station throat

针对编组站咽喉区存在的道岔异物入侵、工器具遗留等问题,研究并实现一种基于无监督学习的智能视觉异常检测系统。运用第5代移动通信技术(5G)切片技术实现咽喉区4 K超高清视频的传输,通过运营商用户面功能(UPF)下沉实现数据不出站,保障网络安全。针对异常的不确定性,采用无监督学习的师生特征金字塔匹配算法,通过比较教师模型与学生模型的得分,实现咽喉区异常识别。该系统在怀化西编组站现场试验取得良好效果,能够有效提高咽喉区的安全防范能力。

To cope with the anormalities within the throat of a marshalling station,such as the intrusion of foreign objects and the left of tools and instruments,an intelligent visual anomaly detection system based on unsupervised learning is studied and implemented.The 5th generation mobile communication technology(5G)slicing technology is used to transimmit 4K ultra-high definition video data collected from the throat and the operator's User Plane Function(UPF)sinks to keep the data not outbound so as to ensure network security.In view of the uncertainty of the anomalities,the student-teacher feature pyramid matching algorith,which is an unsupervised learning method,is adopted to recognize the abnormalities within the throat by comparing the score of the teacher network and that of the student network.The system has achieved good results in the on-site test at Huaihua West marshalling station and can effectively improve the safety prevention ability of marshalling station throat.