GB/T 42130-2022 in English
VALIDIntelligent manufacturing—Industry big data system functional requirement
- Issued on:2022-12-30
- Implemented on:2023-07-01
- File Format:PDF
- Delivery:Via email within 1~3 business days
$117.00
《GB/T 42130-2022智能制造 工业大数据系统功能要求》由339-1（工业和信息化部（电子））归口，主管部门为工业和信息化部（电子）。
Introduction
Analysis of Functional Requirements of Industrial Big Data System for Intelligent Manufacturing
1. Background and Significance of Standard Formulation
With the rapid development of intelligent manufacturing, the role of industrial big data in enterprise production and management is becoming increasingly important. The release of GB/T42130-2022 "Functional Requirements of Industrial Big Data System for Intelligent Manufacturing" aims to standardize the construction and application of industrial big data systems and provide technical support for the digital transformation of the manufacturing industry.
2. Comparison of standard frameworks
| Module name | Function description | Comparison with GB/T38673-2020 |
|---|---|---|
| Data collection module | Supports the collection of structured, time series and real-time data, and is compatible with multiple protocols such as Syslog, SNMP, etc. | Adds high-frequency time series data processing capabilities and greater scalability. |
| Real-time computing module | Supports distributed stream data computing framework and has industrial enhanced algorithm functions. | Added support for multiple coded character sets (such as GB/T1988). |
| Data analysis modeling module | Covering machine learning, deep learning, and natural language processing functions, supporting the development of dedicated business models. | Expanded online analysis (OLAP) and real-time data statistics capabilities. |
3. Analysis of standard technology evolution
From GB/T38673-2020 to GB/T42130-2022, the functional requirements of industrial big data systems have undergone significant technical upgrades:
- Data processing capability improvement: Added support for cross-domain correlation analysis of multi-source heterogeneous data.
- Algorithm optimization: Introduced more advanced industrial enhancement algorithms and streaming data analysis functions.
- Security enhancement: Strengthened sensitive data management and secure transmission mechanisms.
4. Implementation recommendations and best practices
When implementing industrial big data systems, companies should focus on the following aspects:
- Data quality management: Establish a sound metadata management mechanism to ensure data accuracy and consistency.
- System integration capabilities: Give priority to products that support multiple protocols and interfaces (such as JDBC, JSON).
- Security protection: Strengthen data encryption and access control functions to ensure the security of industrial data.
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