On the production floor, equipment protocols are complex and data collection is incomplete. ERP, MES, WMS, and other systems are isolated from each other, leaving real-time production status opaque. Management cannot make precise decisions based on complete data, and delayed response to abnormalities severely affects resource utilization and order delivery.
SOLUTION INTRODUCTION
Solution introduction
Inject AI capabilities into the entire manufacturing process to enable real-time sensing, intelligent analysis, and precise control. Help enterprises build flexible, efficient, and predictable smart factories, making AI a core engine for improving quality, reducing costs, and increasing efficiency.As products become increasingly precise and defects more complex, manual visual inspection and traditional rule-based vision systems can no longer meet high-precision, fast-cycle requirements. When AI vision and process models are introduced for intelligent analysis, the lack of powerful and elastic AI computing power often leads to long model training cycles and high inference latency, blocking intelligent upgrades.
Critical equipment is still maintained mainly on a scheduled basis. Massive sensor data remains underutilized, making it impossible to predict remaining useful life or early signs of failure. Sudden downtime paralyzes production lines, causing high repair costs and order delay penalties. AI-based predictive maintenance is urgently needed.
SOLUTION ADVANTAGES
Solution advantages
The following points are replaceable demonstration content based on the current solution structure.Powerful AI computing injects intelligence into every process
The core AI computing servers support ultra-high-density GPU deployment and high-speed interconnection, delivering extreme performance for industrial-grade model training and high-concurrency inference. This enables millisecond-level response and high accuracy in complex defect detection, multi-factor process optimization, and other scenarios, moving AI from the lab to the production line and directly improving yield and efficiency.
Integrated general-purpose computing, AI computing, and storage for optimal total cost
Flexibly combine general-purpose computing, AI computing, and storage servers. Compute and storage can be decoupled on demand and scaled elastically. General business, AI innovation, and data asset accumulation each receive appropriate resources, greatly improving resource utilization. The solution can scale smoothly from a single production line to multiple factories across a group, reducing initial investment and operating costs.
Low delivery threshold and faster return on smart manufacturing
An integrated solution with industrial AI frameworks, pre-trained models, and application templates, combined with automated deployment and O&M tools, allows manufacturers to quickly launch applications such as intelligent quality inspection and equipment prediction without building a top-tier AI team. This significantly shortens implementation cycles and delivers quantifiable ROI within weeks.
SOLUTION ARCHITECTURE
Solution architecture
This staged architecture visualizes the current planning path and is not a delivery commitment.- 01
Multi-source perception and full connectivity
Connect various equipment and sensors through industrial gateways and intelligent acquisition modules. Use general-purpose computing servers for edge computing to achieve data cleaning, protocol conversion, and millisecond-level local response, building a solid digital foundation.
- 02
Data fusion and data lake governance
Deploy highly reliable storage servers to build a unified manufacturing data lake. Aggregate multi-source data from production lines, quality, equipment, and environment; establish data standards and catalogs; break down cross-system data silos; and enable historical and real-time data to serve analysis and modeling securely and efficiently.
- 03
AI computing and model-defined manufacturing
Deploy AI computing servers at scale to provide powerful parallel training and low-latency inference services. Support rapid iteration and deployment of AI models such as surface defect detection, process parameter self-optimization, and equipment life prediction, turning industrial knowledge into reusable algorithm assets.
- 04
Intelligent applications and closed-loop decision-making
Run MES, APS, digital twin, and other systems on general-purpose computing server clusters. Deeply integrate AI inference results to enable intelligent scheduling, online quality prediction and alerting, predictive maintenance scheduling, and 3D visualization of production processes, driving continuous optimization of business management.
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