Deep integration of large AI models into the core of industrial control systems
Evolution from decision support to autonomous decision-making loops, covering scenarios such as dynamic process parameter optimization, visual quality inspection, predictive maintenance, and holistic production scheduling, thereby enabling high levels of autonomy in "lights-out factories."
Deployment of VLA (Vision-Language-Action) multimodal embodied AI technology
Robots can directly interpret natural language commands, recognize complex unstructured environments, and autonomously plan operational paths, making them suitable for flexible scenarios that are difficult for traditional automation to handle.
Maturation of few-shot transfer learning technology
Quality inspection robots require only 10 samples to identify entirely new types of defects with over 99.5% accuracy, significantly reducing the cost of adapting to new production line scenarios.
Efficiency gains and downtime reduction
AI-driven quality inspection has boosted efficiency by 50% on photovoltaic and lithium-battery production lines, while predictive maintenance in automotive manufacturing has reduced unplanned downtime by 70%, making these the most widely adopted AI automation use cases.
