GB/T 34047-2017 in English

Overview of the manufacturing process IoT information integration middleware platform

The manufacturing process IoT information integration middleware is a computer software or service program that connects IoT devices and applications, aiming to solve the key problems of data collection, transmission and management in the manufacturing process. Through the middleware platform, the information of the manufacturing IoT system can be efficiently integrated and integrated, and the level of intelligence can be improved.

Background of standard formulation

With the widespread application of RFID technology, Internet of Things technology and service computing in production management, the problems of data collection, processing and integration in the manufacturing process have become increasingly prominent. In order to unify and standardize the collection, transmission and management of IoT information in the manufacturing process, GB/T 34047-2017 "Manufacturing Process IoT Information Integration Middleware Platform Reference System" came into being. This standard is under the jurisdiction of the National Technical Committee for Automation System and Integration Standardization, and was jointly drafted by the Beijing Institute of Automation of the Mechanical Industry and Tsinghua University.

Concept of IoT information integration middleware for manufacturing process

IoT information integration middleware for manufacturing process is the hub connecting IoT devices and applications, and realizes secure and efficient data transmission through asynchronous message transmission. Its core functions include data collection, equipment management, data processing, data transmission and data sharing.

Comparison table of standard frameworks

Technology Evolution Analysis

Implementation suggestions

• Hardware selection:Preferably select reader devices that support multiple communication interfaces (such as USB, RJ45) and multi-protocol compatibility to ensure good adaptation with the middleware platform.
• Data security:During the deployment process, it is necessary to focus on security measures such as identity authentication, permission management, and data encryption to prevent illegal access and data leakage risks.
• System integration:It is recommended to adopt modular design and achieve deep integration with enterprise systems such as ERP and MES through standardized interfaces (such as APIs and adapters).
• Performance optimization:For large-scale manufacturing scenarios, consider distributed architecture deployment and combine load balancing technology to improve data processing capabilities.