Abstract
In recent years, the process of industrial modernization has intensified, traditional industrial control has been improved and rapidly developed, industrial automation and intelligent unmanned production lines have become a new development trend, and the Internet of Things has become the basic direction of industrial development. In order to improve the effect of safe transmission and industrial IoT traffic detection, this study uses a neural network to improve the industrial IoT traffic detection algorithm. In order to improve the visualization effect of monitoring, this study uses computer vision technology to construct a traffic detection system of secure transmission industrial Internet of Things and builds an intelligent detection model. Finally, this study combines experimental research to verify the performance of the system. From the statistical point of view, it can be seen that the system’s security detection and traffic detection effects are very good.
Highlights
In an industrial production environment, IoT sensor devices are often used to capture data to monitor and adjust the production operation process. e data generated by these devices are collected and organized in different ways and used for various purposes [1]. e transmission speed of IoTsensors is very fast, and the application of a large number of sensor devices will definitely lead to a substantial increase in the output of industrial data. e Internet of ings and big data are closely linked, and the data generated by sensors can be processed by the big data platform [2]
It can be seen from the above research that the effects of security detection and traffic detection are very good, so the traffic detection method of computer vision-based secure transmission industrial Internet of ings proposed in this study is very effective
The application of big data processing and analysis technology is mostly focused on the Internet field, but there is a lack of rational use of massive industrial data in industrial production scenarios. erefore, it is necessary to use the existing big data technology to design a better data processing platform suitable for industrial IoT big data
Summary
In an industrial production environment, IoT sensor devices are often used to capture data to monitor and adjust the production operation process. e data generated by these devices are collected and organized in different ways and used for various purposes [1]. e transmission speed of IoTsensors is very fast, and the application of a large number of sensor devices will definitely lead to a substantial increase in the output of industrial data. e Internet of ings and big data are closely linked, and the data generated by sensors can be processed by the big data platform [2]. E transmission speed of IoTsensors is very fast, and the application of a large number of sensor devices will definitely lead to a substantial increase in the output of industrial data. Erefore, traditional Internet big data processing methods are not fully applicable, and new solutions need to be designed to properly analyze IoT data and extract more important information from IoT monitoring equipment. In order to comply with the intelligent development of the new generation of industries, the number of IoT devices used in industrial production environments is increasing. The amount of data collected by sensors is exponentially increasing [4], so it is necessary to find more effective processing methods for these large-scale industrial data. Based on the above analysis, this study combines computer vision to conduct research on safe transmission and industrial Internet of ings traffic detection methods to further improve industrial production safety and enhance the application effect of the Internet of ings in the industry
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