Research On Express Security Monitoring And Control System Based On Big Data Analysis

With the rapid development of the express industry and the lack of express security monitoring and control,the related issues become more prominent.Benefited from the continuous progress of computer intelligent system technology and the wide application of big data analysis technology,a good solution for the express security problems is provided.In this paper,an express security monitoring and control system based on big data analysis is proposed to efficiently solves these problems from the technical aspects.In order to monitor the express security,the proposed system build delivery risk level prediction module and dangerous goods detection and alarm module by means of deep learning and machine vision.It is of great significance that suitable solutions are carried out without affecting the efficiency.In addition,a more intuitive user experience is provided by various data visualizations,which enhances the decision-making management.The main research work of this paper is as follows:1.The overall architecture design of the express security monitoring and control system based on big data analysis is completed.The system is consisted of user management module,basic data statistical analysis module,send-receive process risk level prediction module and dangerous goods detection and identification module.All the function modules are realized;2.The send-receive process risk level prediction module is completed.On the basis of demand analysis,data preparation,rules making,algorithm selection and model optimization are executed,and finally good prediction results are obtained.3.The detailed design of the detection and identification module of dangerous goods is completed.The application of convolution neural network to the identification of dangerous goods in X-ray security image is proposed,which has some novelty.The discriminant formula of the classification has been modified so that it has more excellent performance in this situation.