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Chao News Client Reporter Shen Aiqun and Xueyan

On August 11, the first Beijing-Zhejiang Talents Hundred People Conference was held in Hangzhou. At the conference, Yang Huayong, an academician of the Chinese Academy of Engineering, delivered a keynote speech entitled "Exploration of Artificial Intelligence Empowering the Innovation and Development of High-end Equipment".

Academician Yang Huayong delivered a keynote speech. Photo by reporter Guan Xueyan

Professor Yang Huayong is an expert in fluid transmission and control, and an academician of the Chinese Academy of Engineering. He is currently the director of the School of Engineering at Zhejiang University, the director of the National Key Laboratory of Fluid Power Basic Components and Electromechanical Systems, and the dean of the Institute of Advanced Equipment at Zhejiang University.

In his speech that day, he focused on the field of artificial intelligence and talked about the exploration of how artificial intelligence can empower the innovative development of high-end equipment.

Academician Yang pointed out that the industry generally believes that scientific methods based on scientific data for exploration have become an important way to conduct scientific research.

At present, data-driven scientific discovery and engineering simulation are popular. In the future, scientific discovery and engineering simulation driven by the combination of mechanism and data will be more competitive. It can maximize the value of historical data and bring more accurate reasoning with more data. Therefore, in the industrial field, through AI empowerment, the combination of mechanism + data has great potential to overtake others.

Academician Yang also talked about the difficulties in the development of high-end equipment digitalization under the background of new industrialization. For example, it is difficult to split and analyze massive heterogeneous data, difficult to mine and refine unstructured knowledge, complex to integrate multidisciplinary mechanism models, poor balance between calculation accuracy and efficiency, etc., which leads to low efficiency of design collaboration, difficult production and manufacturing scheduling, poor quality of operation and maintenance services and other practical production management problems that need to be broken through. He believes that we urgently need an "integrated digital base to accelerate the transformation of high-end equipment digitalization."

How to change? After analyzing the current status of digital base systems at home and abroad, Academician Yang pointed out that data, computing and AI-driven digital bases are the only way to achieve intelligent manufacturing of high-end equipment. The key direction of the intelligent digital base is: how to deeply integrate industrial data and mechanisms with general large models.

Academician Yang Huayong delivered a keynote speech. Photo by reporter Guan Xueyan

Through his speech, Academician Yang also showed the guests the exploration cases of AI in the scientific research of high-end equipment, including AI deep learning-enabled diesel engine power system combustion simulation, AI deep learning-enabled gas turbine blade flow field analysis, AI deep learning-enabled PTMU (power and thermal management unit) simulation, etc. At the same time, he also introduced to everyone real cases of how AI empowers manufacturing, construction, marketing, operation and maintenance, etc.

"AI big models are profoundly changing the landscape of all walks of life, and more and more companies are actively embracing AI. However, companies generally face difficulties in investing in big model hardware, such as high costs, few options, and difficult procurement." Academician Yang suggested, "Therefore, we must provide the ability to manage data, use knowledge, and unify computing power based on the digital base system; we must also use the digital base to unite manufacturing chain leaders, scientific research, and associations to accumulate industry common data."

"On this basis, we will use an open high-end equipment large model platform to reconstruct the integration of mechanical equipment R&D, design and operation, realize multi-modal data analysis and extraction, cross-modal data precise mining, and optimize knowledge access efficiency, and explore services for 12 categories and 100+ industrial scenarios." said Academician Yang.

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