With the development of the society, highway traffic safety is gradually valued by the world. However, due to the complicated state of the highway roads, the faster road speeds and the different types of vehicles, the problem of highway safety warning is an extremely complex system engineering problem faced by the entire society. In view of the characteristics of the problem studied, this study firstly conducted a simple analysis of the design goals and overall architecture of the highway traffic safety early warning system; on this basis, the various components of the system-road information collection, road information processing analysis and road the early warning information release and other functional modules have been elaborated and analyzed accordingly. The road information collection and cloud architecture are combined to solve the problem of excessive data generation. Finally, the important link of analysis and early warning-highway status classification Problem, the BP neural network algorithm is proposed. Through the BP neural network algorithm, the road nodes are classified, and then the safety warnings are generated according to the road status information. The safety warnings are divided into four levels: the first level is particularly dangerous and vehicle traffic is strictly prohibited; the second level is more dangerous and requires vehicles to bypass; the third level is a certain danger, the vehicle is required to pay attention to the prompt information, and you must go to the service area to rest for a long time Through the BP neural network algorithm, the efficiency of node classification is improved by 13%.

随着社会的发展，公路交通安全逐渐被世界所重视。但是，由于公路状态复杂，路速更快，车辆种类不同，高速公路安全预警问题是整个社会面临的极为复杂的系统工程问题。鉴于所研究问题的特点，本研究首先对高速公路交通安全预警系统的设计目标和总体架构进行了简单分析。在此基础上，对道路信息采集系统，道路信息处理分析系统，道路预警信息发布系统等各个功能模块进行了详细的分析。道路信息采集与云架构相结合，解决了数据生成过多的问题。最后，提出了分析与预警高速公路状态分类问题的重要环节，提出了BP神经网络算法。通过BP神经网络算法对道路节点进行分类，然后根据道路状态信息生成安全警告。安全警告分为四个等级：第一等级特别危险，严格禁止车辆通行。第二级更危险，需要车辆绕行；第三级是一定的危险，要求车辆注意提示信息，必须长时间到服务区休息。通过BP神经网络算法，