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the implementation of ai in industrial scenarios has ushered in another explosive period, and a new round of competition among related industry chain companies has also begun.

"traditional industrial software often focuses on basic functions such as process management and data analysis, which cannot meet the flexible response needs of enterprises in the rapidly changing market. the introduction of ai technology has injected new capabilities into industrial software, making it more intelligent "recently, pei jing, vice president of dingjie digital intelligence (sz300378, stock price 22.68 yuan, market value 6.2 billion yuan), accepted the "daily economy" during the 24th china international industrial expo. news" reporter said in an exclusive interview.

in pei jing's view, the application of large ai models in the manufacturing industry has significant industry characteristics. data is the key to the effectiveness of large ai models. although the current large ai models and algorithms are mainly based on general open source platforms, to truly adapt to the subdivided industries of the manufacturing industry, they must rely on industry data and professional knowledge accumulated over many years for training.

in fact, as the manufacturing industry accelerates its transformation towards intelligence and digitalization, the importance of industrial software as a bridge connecting the physical world and the digital world has become increasingly prominent. according to the "china industrial software industry development research report (2024)", the global industrial software market size in 2023 will be approximately us$502.8 billion, equivalent to approximately 3.56 trillion rmb. the size of my country's industrial software market is approximately 241.4 billion yuan, a year-on-year increase of 12.3%, which is higher than the average growth level of the software industry.

ai supports industrial manufacturing r&d, production and after-sales

the integration of ai technology not only brings new business growth points to industrial software companies, but also has a profound impact on the future development of the entire industry.

from the specific process point of view, pei jing said that during the product development and design stage, ai technology can use machine learning algorithms to deeply mine massive design data and discover potential design rules and optimization space.

for example, using ai for simulation can predict product performance before manufacturing, reducing trial and error costs; at the same time, ai can also automatically adjust design plans based on market demand and user feedback to achieve rapid iteration and optimization of products.

in the production control process, the application of ai technology has achieved precise management of the production process. pei jing introduced that by collecting production data in real time, the ai ​​system can quickly identify abnormalities in the production process and automatically adjust production parameters to ensure the stable operation of the production line.

"in addition, ai can intelligently recommend the optimal production process route based on order information, and realize one-click generation of process information, parameter settings and working hours standards, which greatly improves production efficiency and flexibility. this kind of precise production the control method enables enterprises to better respond to market changes and meet customers' individual needs," pei jing said.

in the after-sales operation and maintenance stage, ai technology also plays an important role.

pei jing told reporters that by building an intelligent operation and maintenance platform, companies can achieve remote monitoring, fault diagnosis and predictive maintenance of equipment. the ai ​​system can use big data analysis technology to monitor and analyze equipment operating data in real time, discover potential faults in advance, and give corresponding maintenance suggestions. this intelligent after-sales operation and maintenance model not only reduces the enterprise's operation and maintenance costs, but also improves customer satisfaction and loyalty.

taking the chemical industry, food and other industries as examples, formula research and development is a key link in product innovation. the traditional formula development process often requires a lot of experimentation and trial and error, which is costly and inefficient. by combining ai with ar (augmented reality) technology, companies can build a virtual recipe r&d environment to achieve rapid iteration and optimization of recipes.

on the one hand, ar technology allows r&d personnel to intuitively see the effect of formula adjustments, while on the other hand, ai can automatically recommend the optimal formula combination based on experimental data. this scenario-based application greatly improves the efficiency and accuracy of formula development.

another scenario is route selection in a factory environment. pei jing said that in the manufacturing industry, the choice of process route directly affects the production cost and quality of the product. traditional process route planning often relies on manual experience and trial and error methods, and is difficult to adapt to rapidly changing market demands. however, by introducing ai technology, companies can automatically recommend the optimal process route based on order information, and generate corresponding process information and parameter settings with one click.

in addition, in the daily operations of enterprises, "problem handling" is an important and tedious task. traditional problem-solving methods often require manual review of large amounts of information or consultation with experts, which is inefficient and error-prone.

in this context, by building an ai-based problem-handling knowledge base, enterprises can achieve rapid response and accurate resolution of problems. pei jing said that the ai ​​system can use big data analysis technology to extract useful information from massive data and provide solutions based on industry knowledge and expert experience. this intelligent problem handling method not only improves the efficiency and accuracy of problem handling but also reduces the company's operating costs.

intelligent transformation of manufacturing industry accelerates

in addition to the blessing of ai, technologies such as the industrial internet, big data, and cloud computing are also maturing. the intelligent transformation of the manufacturing industry has become the only way to enhance industrial competitiveness and achieve high-quality development.

in pei jing's view, intelligent transformation can not only significantly improve production efficiency and product quality, but also optimize resource allocation, reduce operating costs, and enhance an enterprise's market response and innovation capabilities.

however, in the process of promoting intelligent transformation, enterprises also face many challenges.

pei jing told reporters that first of all, due to the numerous manufacturing subdivisions and the huge differences in the informatization foundation, business models and transformation needs of different enterprises, it is difficult to find a completely universal solution. this requires enterprises to carry out personalized top-level design and planning based on their own actual conditions when carrying out intelligent transformation.

secondly, the existing information systems of enterprises are diverse and complex. how to effectively integrate these systems with intelligent technology to achieve data interoperability and business process reengineering has become another major problem faced by enterprises. in addition, intelligent transformation also involves large amounts of capital investment and talent training, which places higher demands on the company's financial strength and talent reserves.

pei jing said that based on this background, dingjie digital also focuses on scenario-based applications and develops corresponding intelligent products and services based on specific needs in different scenarios to help enterprises achieve transformation.

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