Design and Research of M2M Message Transfer Mechanism of Looms for Information Transmission

Machine-to-machine (M2M) communication is a key implementation technology for future Industrial Internet of Things (IIoT) applications. The requirements for flexibility, efficiency, and cross-platform compatibility in intermodule communication between the connected machines raise challenges for the M2M messaging mechanism. For the loom message transmission mechanism, it is necessary to have certain flexibility and meet certain M2M communication requirements to understand each other. In this paper, we analyze and study the data model, data exchange language, MQTT protocol and logo resolution technology of looms to address the current problems of low data transmission efficiency, poor flexibility and lack of flexibility in the network. By establishing the data model of looms and classifying them by topic, selecting JSON format as the transmission format of data, and using MQTT protocol to realize data transmission between loom networks, an MQTT-based messaging mechanism is established. The data involved in the communication of the loom equipment are grouped according to the communication requirements, and only the loom data required by the application are subscribed. At the same time, the hardware and software parts of the communication module are designed with ESP32 as the core, so that it can complete the functions of loom network communication, data analysis, format conversion and so on. In addition, the real-time requirements of the loom network communication and the network requirements for future intelligent manufacturing must be met. This paper uses the BP neural network to intelligently optimize the loom network, and the existing problems related to performance indices are analyzed. The original loom network is optimized by integrating the RSSI, QoS requirements, transmission priority, real-time communication and data transmission volume of nodes. In the meantime, the particle swarm optimization algorithm is introduced to optimize the BP neural network to avoid the problem that it easily can fall into the local optimal solution and effectively improving the network optimization efficiency. Finally, the feasibility of this method is proven by using the experimental platform, which can solve the problems of low network transmission efficiency and difficult network optimization of looms and provide an effective technical example of loom information transmission and network optimization for industry.