China's National Industrial Data Base Puts Real AI Workloads Within Reach
China has launched a national Industrial Data Resource Base to speed up real-world industrial AI. The platform compiles data across raw materials, parts, and production processes into a standardized system built for training and running industrial AI models.
Backed by large-scale computing and the country's Industrial Internet infrastructure, the goal is straightforward: give engineers clean, consistent "data fuel" and a clear path to deploy AI in factories, supply chains, and maintenance workflows.
What's inside the data resource base
- 230 million digital records of industrial products
- 180,000 specifications of raw material grades
- 35 TB of real-time equipment operation data
- 300,000 entries of manufacturing process data
This isn't a pilot. It's already powering tools for 2 million engineers across 430,000 companies, with use cases spanning smart design, automated process planning, targeted analytics, and predictive equipment maintenance.
Built on national infrastructure
The data base sits on the National Industrial Internet Big Data Center and the National Equipment Manufacturing Digital Supply Chain Platform. That foundation simplifies data exchange and reduces duplicated setups across enterprises.
"The national industrial resource base breaks down data silos across the sector. It ensures efficient integration and secure usage of data while avoiding redundant infrastructure," said Zhang Xu, director of the Network Research Institute of China Academy of Industrial Internet.
The broader context: scale and readiness
China hosts more than 340 influential industrial internet platforms, with over 100 million connected industrial devices. Digital R&D tools have reached 85.4% adoption among core enterprises, and 68.5% of key industrial tools are now digitally controlled.
That footprint creates the conditions for data to move, be priced, and be used as a core asset-making industrial AI more predictable to implement and maintain.
Why this matters for IT and development teams
- Standardized, high-signal data gives you cleaner training sets and smoother fine-tuning for domain models.
- Shared infrastructure reduces integration overhead for cross-plant or cross-supplier projects.
- Real-time equipment streams enable practical MLOps: continuous retraining, drift detection, and faster deployment cycles.
- Centralized governance improves auditability, role-based access, and IP protection across partner networks.
Practical next steps for engineering leaders
- Define model strategy: where to use foundation models plus domain adapters vs. fully custom models for process-specific tasks.
- Build the data layer: event-driven pipelines for equipment telemetry, schema versioning, and lineage tracking from source to model.
- Target high-ROI jobs first: CAD/CAM assist, NC program optimization, scheduling, anomaly detection, and spare-parts analytics.
- Close the loop: integrate inference outputs with MES/SCADA/PLM to trigger workflows, not just dashboards.
- Plan security early: data zoning, secure enclaves for sensitive process data, and clear supplier access policies.
AI agents and compute efficiency
"We'll continue expanding and strengthening the national data resource base. By accelerating the 'Industrial Internet platform plus AI Agent' model, we'll further optimize computing resources and deliver solid data and processing capabilities to support industrial AI," said Tian Chuan, Party chief of the China Academy of Industrial Internet.
For teams, this points to agent-based workflows that connect model outputs to task execution-think auto-generated process plans, parameter checks, and maintenance tickets that route themselves.
What to watch
- APIs and access models for third-party developers
- Reference ontologies and schemas for parts, processes, and machine states
- Service-level guarantees for real-time streams and historical queries
- Compliance standards for inter-company data exchange
If you need an overview of China's industrial internet strategy, see the Ministry of Industry and Information Technology for policy context and updates: MIIT.
Upskilling your team on applied AI for engineering, data, and ops? Explore role-based learning paths here: Complete AI Training - Courses by Job.
Your membership also unlocks:
