Deeply integrating cutting-edge technologies such as artificial intelligence, image recognition, machine learning, and predictive algorithms, we have constructed a real-time, data-driven closed-loop process control system.

Relying on big data mining technology, this system precisely identifies improvement opportunities and underlying patterns within the production process. Simultaneously, by combining digital twin and automated fault diagnosis technologies, we have successfully built an intelligent expert support system, which efficiently diagnoses and resolves quality anomalies and equipment failures, significantly enhancing operational efficiency and product reliability.

We have established an intelligent quality control system covering the entire process from materials and manufacturing to finished products.

Supported by advanced technologies like machine vision and knowledge graphs, this system fully replaces manual inspection with automated intelligent detection. It also ensures full traceability of all quality information, laying a solid foundation for exceptional products.

Applying Al and deep learning technologies to improve cell visual inspection capabilities.

Applying large data model prediction technologies to perform quality trend analysis and early warning.

Applying closed-loop feedback control technologies for processes such as coating, cold pressing, winding, and injection to improve process capabilities.

Applying warehouse management and logistics technologies based on cloud-edge collaborative MES, big data closed-loop control, machine learning detection and measurement , and optimal scheduling to create an industry-leading intelligent manufacturing system.

In manufacturing logistics, integrated AGVs, smart automated storage and retrieval systems (AS/RS), and a Warehouse Management System (WMS) to achieve automated, digitalized, and lean management of warehousing and distribution.