## 1 Introduction  
Industry 4.0, first proposed in Germany, aims to create new intelligent factories; integrate traditional factories with intelligent equipment, the Internet of Things, artificial intelligence, big data technology, and cloud computing; and promote the transformation of the traditional manufacturing industry toward flexible manufacturing, green manufacturing, and intelligent manufacturing [1]. With the support of digital twins, CPS, intelligent technology, big data technology, and cloud computing, virtual modeling of the production line in a manufacturing workshop can be achieved, and the production line can be monitored in real time using big data technology, truly realizing the industrial potential of the manufacturing factory [2]. Through research on intelligent technology for factories, the manufacturing efficiency of the production line in a manufacturing workshop can be improved, which is significant for research on intelligent factories [3]. The intelligent factory takes the cyber-physical production system (CPPS) as the core and is characterized by the deep integration of information technology and manufacturing technology. It embeds the new generation of information technology, such as the Internet of Things, big data, and cloud computing, into different links of the manufacturing process to achieve the efficient production of intelligent products characterized by customization [4]. For smart factories, the introduction of intelligent equipment has made the processes within systems more convenient, but at the same time, the presence of a large number of devices such as sensors in the workshop will lead to the generation of massive amounts of data in real time, so it is still challenging to build a real-time data transmission management system. In addition, due to the large scale and relatively scattered production workshops of modern factories, and the fact that factories generally use intranets to control the entire workshop system to ensure security, the existing production collection system has limitations in processing big data. How to efficiently and securely integrate and manage this information has been a difficult problem faced by factories undergoing digital transformation in recent years [5]. The efficiency of traditional workshop production line data processing can no longer meet production needs. With the rapid development of technologies such as the Internet of Things and AI in recent years, the amount of data from edge devices in workshop production lines has doubled, which has brought huge challenges to the centralized big data processing mode of traditional cloud computing. Massive data need to be transmitted through the network to the computing center for centralized processing, resulting in inefficient data processing of edge devices, which reduces the real-time capabilities and scalability of the entire system [6]. [PERSON] [7] pointed out that OPC UAover TSN (Time Sensitive Network), can support horizontal and vertical communication in smart factories, effectively reducing communication latency and improving system performance, providing a powerful solution for control-level communication in Industry 4.0. [PERSON] [8] proposed an industrial IoT fieldbus solution based on OPC UA over TSN, and the experimental results verified the potential of OPC UA over TSN to achieve sub-millisecond latency in smart factory scenarios, meaning that it can meet the stringent requirements of smart factories in terms of the high real-time performance of industrial IoT. These studies show that the introduction of TSN technology significantly enhances the real-time performance of OPC UA in edge scenarios, but most of the studies focus on the communication level, and discussions of combination with intelligent inference or a data processing layer are limited. [PERSON] [9] and [PERSON] [10] systematically evaluated Kafka, MQTT, and REST API in terms of latency, throughput, scalability, and replication performance.