铁路机车乘务员智能实时监测系统研究

针对铁路机车乘务员工作过程中的重点危险因素，研究开发铁路机车乘务员智能实时监测系统。该系统基于深度学习技术和嵌入式视频分析设备，能够有效识别视线脱离、手比异常、疲劳驾驶等不安全作业行为；基于物联网实时获取机车乘务员健康与备班睡眠数据，及时发现机车乘务员健康问题和睡眠不足；该系统改变了依赖于滞后的人工抽查和事后追责的传统机车乘务员监管模式，代之以自动实时检测与即时告警，检测准确度和时效性均有明显提升。

Study on intelligent real-time monitor system for train driver

To deal with the key hazards existing in the working process of train drivers, an intelligent real-time monitoring system for train driver was studied and developed. By using deep learning technology and embedded video analysis devices, this system can effectively recognize unsafe behaviors such as deviated line of vision, abnormal specific gestures, and fatigue driving. Meanwhile, the health and sleep-before-shift data of the train drivers can be acquired in a real-time manner by using Internet of Things technology so that the train drivers' health troubles and sleep insufficiency can be timely discovered and alarms will be sent out immediately. This system can change the traditional mode of supervision on the train drivers by lagging manual selective examination on train drivers’ bad driving behaviors and health as well as accountability after an event and replace it with real-time automatic machine detection and timely alarming in a more accurate and time-effective manner.