Leveraging digital twin technology, we construct digital twin models for the laboratory, factory, and production processes. Through the digital twin of the lab and factory, we accelerate the transition from laboratory testing to mass production, shortening the new product introduction cycle. By leveraging the digital twin of production processes, we predict production trends, intervene in the production process in real - time, optimize production objectives, and enhance product quality and production efficiency. We digitally map the product attributes from the lab and market environment to predict the actual product attributes, accelerating product iteration speed and aligning products more closely with market demands. Employing multi - factor and multi - objective algorithms, we dynamically seek the optimal solution for business value. During the product development process, we comprehensively consider multiple factors such as cost, quality, and performance, achieving optimization in product development.

With the goal of achieving optimal total costs for the supply chain and factory, comprehensively considering capital occupancy costs, variable warehousing costs, NQC (non-quality costs), changeover costs, etc. Through AI algorithms, analyze and process X-week production plans, material and packaging supporting plans, production line information, etc. Under multiple constraints such as production capacity, materials, and personnel, achieve AI-aided decision-making under multi-objective constraints. It can not only generate optimal production scheduling plans that only consider in-factory costs but also provide more global and strategic optimal production scheduling plans from the perspective of overall factory and supply chain costs. Able to quickly adjust production scheduling plans according to periodic triggers or change triggers (such as urgent order insertion, equipment downtime), ensuring smooth production and effective cost control.

Dynamically optimize critical production parameters (e.g., temperature, pressure, speed, time) via data-driven methods to enhance quality, efficiency, and resource utilization. In Industry 4.0 contexts, combine AI and machine learning to transition from "experience-driven" to "data-intelligence-driven" processes, addressing traditional manual tuning issues like low efficiency and poor stability.

Industrial cameras and other devices collect data to be inspected. After undergoing inspection by the prediction engine, the data is input into the intelligent detection model for analysis, which then outputs results such as defect categories. Meanwhile, raw data from the production database flows into the training database, and the training engine conducts model training based on this data. Through an iterative process, the intelligent detection model is continuously optimized, forming a "training-inspection" closed loop. Inspection results are transmitted to the control module to adjust the process parameters of defective sections. Additionally, inspection data is retained to support model iteration and production optimization, thus forming a closed-loop business process of "data collection → inspection → training optimization → process regulation".

The AI quality inspection system adopts a hierarchical collaborative architecture, building end-to-end capabilities from hardware to applications: The bottom hardware access layer collects raw data through imaging systems, industrial controllers, sensors, etc. The model deployment layer supports multi-mode adaptive scenarios such as public cloud, edge computing, and AI Cameras. The algorithm layer integrates deep learning (unsupervised learning, few-shot learning, etc.) with traditional algorithms (Hough transform, corner detection, etc.), complementarily handling complex/regularized quality inspection tasks. The engine layer relies on data annotation, storage, analysis, and training/prediction/decision engines, forming a closed loop of "data processing - model iteration - intelligent decision-making". The application tool layer, through interface customization, industrial cloud connection, device management, and annotation/training/deployment tools, connects algorithm capabilities with user operations. Ultimately, it realizes the full-process intelligence of quality inspection from data collection and intelligent analysis to business implementation, flexibly supporting the diverse inspection needs of production lines.