GB/T 38666-2020 in English
VALIDInformation technology -- Big data -- Industrial application reference architecture
- Issued on:2020-04-28
- Implemented on:2020-11-01
- File Format:PDF
- Delivery:Via email within 1~3 business days
$194.00
《GB/T 38666-2020信息技术 大数据 工业应用参考架构》由TC28（全国信息技术标准化技术委员会）归口，主管部门为国家标准化管理委员会。
Introduction
Core Framework of Industrial Big Data Reference Architecture
The Industrial Big Data Application Reference Architecture (IBDRA) constructed by GB/T38666-2020 includes five logical functional components:
| Component Name | Core Function | Typical Technology |
|---|---|---|
| System Coordinator | Workload Configuration and Dynamic Resource Allocation | Kubernetes/YARN |
| Data Provider | Industrial Data Collection and Preprocessing | SCADA/PLC/MES |
| Data Consumers | Smart Manufacturing Scenario Applications | Digital Twins/Predictive Maintenance |
Classification and Characteristics of Industrial Data Sources
The standard divides industrial data sources into three types:
- Product Lifecycle Data: including structured and unstructured data such as CAD drawings (Computer-Aided Design), CAE simulation data, etc.
- Industrial IoT equipment data: covers real-time streaming data such as equipment operating parameters and working conditions
- Production and operation system data: includes business process data accumulated by systems such as ERP and MES
Comparison of typical data characteristics:
| Data type | Data volume | Timeliness requirement | Typical system |
|---|---|---|---|
| Product design data | TB-PB level | Non-real-time | PLM system |
| Equipment sensor data | GB-TB/day | Millisecond level | SCADA system |
| Production business data | GB-TB level | Second level | MES/ERP |
Key technology implementation suggestions
Data governance implementation path
- Metadata standardization: Establish a unified equipment coding and product identification system
- Data quality control: Design data cleaning rules based on the characteristics of industrial time series data
- Security hierarchical protection: Implement four-level data security protection according to GB/T35589 requirements
Architecture evolution trend
- Evolution from batch processing architecture to batch and stream integration architecture
- Digital twin technology promotes real-time data fusion analysis
- Hybrid architecture of edge computing and cloud collaboration
Analysis of typical application scenarios
| Scenario type | Data demand | Technical implementation | Value output |
|---|---|---|---|
| Predictive maintenance | Time series data such as equipment vibration and temperature | LSTM neural network | Reduce unplanned downtime by 30%+ |
| Process optimization | Production parameters, quality inspection data | Random forest algorithm | Increase yield rate by 5-15% |
| Supply chain collaboration | Order, logistics, inventory data | Graph computing engine | Reduce inventory turnover days by 20% |
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