08,12,2026 7 views
Currently, artificial intelligence technology is comprehensively reshaping the development of the manufacturing industry, promoting the iterative upgrading of traditional industries towards intelligence, high-end, and high efficiency. As the core support of advanced manufacturing, the intelligent transformation of the measurement control and instrumentation industry is a key link in China's new industrialization construction. In the high-end interview segment of the 2026 China Conference on Measurement, Control and Instrumentation Industry (CIC),Gao Lei, Secretary of the Party Committee and General Manager of Shanghai Automation Instrument Co., Ltd；Wang Li, Deputy General Manager of Beijing Jingyi Group Co., Ltd；Shao Chunju, Vice President of China Mobile Research Institute；Zhu Shiming, Chief Engineer of Phoenix (China) Investment Co., Ltd；Zhang Yuncai, General Manager of Endress House (China) Automation Co., LtdFive industry heavyweight guestsDeep integration of new generation artificial intelligence and advanced manufacturingAs the core theme, in-depth discussions will be held around core issues such as pain points in industrial landing, technological innovation practices, co construction of industrial chain ecology, and future trends of the industry. Industry consensus will be gathered, practical experience will be shared, and a new direction will be pointed out for the high-quality development of China's instrumentation and intelligent manufacturing industry.
High end Interview Host Wei Min, Director of the "the Belt and Road" Joint Laboratory of Industrial IoT of Chongqing University of Posts and Telecommunications and Dean of the Graduate School
High end interview site
Facing the pain points of the industry: Breaking down the difficulties of integrating AI and process industries for implementation
Gao Lei, Secretary of the Party Committee and General Manager of Shanghai Automation Instrument Co., Ltd
The process industry has complex processes, strong continuous operation, and strict requirements for system reliability, stability, and real-time performance. It is the most valuable and difficult field for the integration of artificial intelligence and automation measurement and control.Gao Lei, Secretary of the Party Committee and General Manager of Shanghai Automation Instrument Co., LtdHaving been deeply involved in the industry for many years, we have accurately identified the three core pain points of current industry integration and directly hit the bottleneck of industry development.
Firstly, there is a lack of effective training data. Industry enterprises have accumulated a massive amount of normal working condition data, but there is a serious shortage of data in special scenarios such as fault states and extreme working conditions, which leads to the good performance of AI models in regular operation. Once equipment failures or working condition fluctuations occur, the adaptability will significantly decrease, making it difficult to meet the safety needs of industrial production. Secondly, there are breakpoints in system integration. Industrial production prioritizes ensuring process safety and stable operation. Existing AI models cannot fully adapt to special process scenarios, and it is difficult to quickly switch to the original automation system after model failure, which poses safety control risks. Thirdly, it is difficult to replicate the project on a large scale. The equipment system and data standards in different production scenarios are not unified, and mature solutions are difficult to quickly promote. The replication cost is high and the landing efficiency is low, which restricts the overall intelligent upgrading of the industry.
In response to industry pain points, Shangziyi has explored a gradual and steady transformation path. The enterprise relies on its own advantages in instruments, systems, actuators, and industrial scenarios, and collaborates with Shanghai Electric Group Data Science and Technology Co., Ltd. to carry out division of labor and focus on organizing basic data, classifying and labeling core data, and reserving them in advance, laying a solid foundation for model training. At the same time, we adhere to the strategy of lightweight implementation, prioritizing the layout of low-risk and high-value application modules such as fault diagnosis, alarm optimization, and energy management, so that enterprises can intuitively feel the effectiveness of intelligent transformation. Through scenario nesting and module integration, we gradually build systematic intelligent solutions. At present, the enterprise has implemented the top ten intelligent modules in the largest Chengdu garbage incineration power plant in Asia, and it is expected to be fully put into use by the end of 2026, providing a benchmark example for the integration of process industry AI.
Refactoring the R&D ecosystem: Accelerating domestic instrument autonomy and controllability through full chain innovation
Wang Li, Deputy General Manager of Beijing Jingyi Group Co., Ltd
The rapid iteration of AI technology has completely overturned the research and development mode and product form of traditional instruments and meters, promoting the industry to upgrade from hardware iteration to scenario based, intelligent, and systematic.Wang Li, Deputy General Manager of Beijing Jingyi Group Co., LtdIn the context of independent and controllable industrial development, the upgrading of domestic intelligent instruments requires coordinated research and development innovation, industrial chain matching, and market promotion, and the construction of a full chain intelligent development system.
On the R&D side, Jingyi Group is based on the layout of the "15th Five Year Plan" and has established a two-level R&D system to achieve precise empowerment. Building a high-end innovation institute at the group level, focusing on platform technology, basic technology, and research and development of AI models for instruments and meters, providing core technical support for subordinate enterprises; At the enterprise level, relying on Jingyi Intelligent Technology Company, we focus on industrial implementation, deeply cultivate the scenario based application of "AI+instruments", completely break away from the traditional hardware iteration of single R&D thinking, and shift towards a scenario driven global R&D model. With an innovative research and development system, the company has successfully launched a high-resolution real-time online mass spectrometry system, achieving high-speed analysis and processing of hundreds of spectra per second, and adapting to high-end industrial detection needs; Winning the bid for the 47 million yuan oilfield intelligent energy-saving renovation project, this project utilizes high-precision sensors to collect multidimensional operational data and relies on AI algorithms to achieve precise energy consumption analysis and intelligent regulation, upgrading from a single equipment supplier to a comprehensive solution service provider.
At the end of the industrial chain, Jingyi Group closely adheres to the requirements of independent and controllable development of the entire industry chain, relies on the advantages of local industrial policies in Beijing, and focuses on the construction of high-end instrument innovation platforms. Leading the establishment of the Zhongguancun Yichuang Smart Laboratory Alliance, breaking down industry barriers, linking multiple entities such as the Institute of Information and Communications Technology, automation enterprises, and communication operators, promoting the standardization of instrument data interfaces and industry standards, building an industrial ecosystem for data sharing, technological collaboration, and joint research and development, and solving industry chain shortcomings through cross-border integration, accelerating the localization and scale replacement of domestic high-end instruments.
Building a strong digital foundation: Integrated computing and intelligence empowers industrial AI for large-scale implementation
Shao Chunju, Vice President of China Mobile Research Institute
The deep implementation of industrial AI relies on comprehensive support from network connectivity, computing power, and intelligent operation and maintenance.Shao Chunju, Vice President of China Mobile Research InstituteFrom the perspective of digital infrastructure, the integration path of communication computing power base and intelligent manufacturing is explained, and it is proposed to empower industrial digital transformation with "network foundation and full stack innovation".
At the connectivity level, with the rapid development of industrial flexible manufacturing, the demand for wireless connectivity in factories continues to rise. China Mobile relies on 5GA technology iteration and innovative technologies such as fixed mobile fusion, pre scheduling, and ultra short frames to achieve 20 millisecond latency and 99.99% reliability in industrial scenarios, completely solving the pain point of insufficient stability in traditional wireless networks and adapting to the vast majority of industrial production scenarios. In the transformation project of Zhongtian Iron and Steel's 5G factory, more than 2000 devices rely on 5G fully connected technology to operate stably, with a 60% increase in production efficiency. The production line switching time has been reduced from 2 hours to 15 minutes, greatly improving production flexibility and efficiency.
At the level of computing power, in response to the localization processing of industrial field data and the deployment needs of end side models, China Mobile has launched a sinking private network and 5G industrial private network all-in-one machine, integrating network access and core computing power into a single device, shortening the originally several week network construction cycle to the hour level, improving business opening efficiency by 50% and reducing construction costs by 30%. It has been implemented and applied in enterprises such as Hengtong Group, with significant results. At the level of intelligent operation and maintenance, enterprises will transfer their experience in intelligent operation in the CT and IT fields to the OT industrial field, creating an industrial intelligent operation and maintenance system, and realizing the transformation of network operation and maintenance from expert exclusive skills to full staff management and maintenance. In the Xiaomi factory car monitoring and data collection project, the accuracy of network fault perception has been improved from the hour level to the minute level, greatly reducing the difficulty of industrial operation and maintenance. In the future, China Mobile will continue to promote full stack innovation in connectivity, computing power, and intelligence, relying on the "Tiangong" platform to promote the popularization of AI technology and assist large, medium, and small manufacturing enterprises in their digital transformation.
Deeply cultivating local adaptation: integrating global experience to create localized intelligent solutions
Zhang Yuncai, General Manager of Endress House (China) Automation Co., Ltd
As a leading global enterprise in high-end process measurement instruments,Zhang Yuncai, General Manager of Endress House (China) Automation Co., LtdBased on global industry development trends, it is proposed that the integration of AI in China's process industry is at the forefront of the world. Currently, China accounts for over 35% of the global process industry production capacity, and is expected to increase it to 45% by 2030. The vast industrial scale and rich application scenarios provide excellent soil for the integration and innovation of AI and automation.
Zhang Yuncai believes that there are still shortcomings in the underlying infrastructure for the integrated development of industrial AI. Currently, most industrial AI only implements "on-site applications" and has not yet achieved "online real-time iteration". The speed of control system operation and data transmission efficiency still cannot fully match the real-time computing needs of AI. The implementation of digital twin simulation, intelligent security monitoring and other scenarios can effectively promote the deep empowerment of AI in industrial production. Through process simulation, continuous learning and optimization of models can be achieved, relying on AI video monitoring and leak perception to replace traditional manual inspections, and building a solid defense line for industrial production safety.
Based on the Chinese market, Endress House firmly adheres to the development strategy of "in China, for China" and continues to increase investment in localization. The enterprise has invested a total of 1.5 billion yuan in China, and will increase its investment by 2 to 2.5 billion yuan in the next five years. It will not only expand its local production base, but also deploy its core research and development capabilities in China. With the accumulation of advanced global technology and combined with the needs of domestic manufacturing scenarios, it will carry out localized adaptation to help upgrade the entire process intelligence of China's process industry.
Breaking down data barriers: deepening the integration of OT and consolidating the foundation of industrial AI development
Zhu Shiming, Chief Engineer of Phoenix (China) Investment Co., Ltd
The uneven quality of industrial data and the poor integration of IT and OT are the core challenges that restrict the implementation of industrial AI.Zhu Shiming, Chief Engineer of Phoenix (China) Investment Co., LtdBased on the practice of Industry 4.0 in enterprises and the experience of building lighthouse factories, it is proposed that the transformation of industrial intelligence should focus on breaking through the OT end, breaking through data silos and technological integration barriers.
In the current digital transformation of the manufacturing industry, there is a clear disconnect between IT and OT: the OT side is deeply involved in production processes and familiar with equipment logic, while the IT side is proficient in digital technology but disconnected from production scenarios, resulting in messy and low-quality uploaded data that cannot support accurate training of AI models. In response to this pain point, Phoenix Innovation proposes the idea of "technology sinking", which sinks IT digital technology, computing power, and data processing capabilities to the OT front-end, completes data cleaning, labeling, and structured processing on the production site, synchronously deploys lightweight machine learning functions, achieves preliminary iterative optimization on the data side, uploads high-quality data to industrial brains and intelligent agents, and greatly improves AI application efficiency.
At the same time, enterprises continue to develop lightweight data platforms to provide low-cost and practical digital transformation tools for small and medium-sized enterprises, lowering the threshold for industrial AI applications. Zhu Shiming emphasized that there are essential differences between industrial big models and general big models. Industrial AI places more emphasis on real-time, safety, and reliability. Manufacturing enterprises need to abandon the misconception of blindly applying general models, rely on high-quality data accumulation, deeply cultivate industrial mechanism models, gradually promote intelligent upgrading, and build a solid foundation for industrial development.
Unity Future Outlook: Diversified Collaboration to Embark on a New Journey of Industrial Intelligence
At the end of the interview, five industry experts, based on the new starting point of the 15th Five Year Plan, gave core messages and prospects around the development of inclusive, ecological, and high-end industries, anchoring the direction of industry development. Gao Lei reminds small and medium-sized process enterprises that intelligent upgrading needs to be gradual, prioritizing the establishment of a solid foundation for equipment safety, system stability, and accurate data. They should promote intelligent transformation within their capabilities, adapt to their own development pace, and achieve maximum benefits.
Wang Li pointed out that in the future, Jingyi Group will promote the upgrading of its research and development paradigm, shifting from a single experience driven approach to a dual experience+data driven approach. Leveraging industry niche models, it will continuously optimize its product system and build a new intelligent development pattern that integrates technology, products, and ecology. Shao Chunju proposed that the core of industrial development lies in "integration" and "inclusiveness", continuously promoting the integration of CT, IT, and OT, and driving the iteration of industrial intelligence from embodied intelligence to embodied intelligence and endogenous intelligence, so that the dividends of AI transformation can benefit the vast number of small and medium-sized enterprises.
Zhang Yuncai stated that every industrial revolution has been driven by cross-border technology, and he firmly believes that AI technology will bring disruptive changes to the automation industry. Enterprises need to focus on real application scenarios, create practical value, and seize opportunities for industrial transformation. Zhu Shiming once again emphasizes the professionalism and rigor of industrial AI, adheres to the core logic of data foundation and mechanism empowerment, eliminates blind conformity, and steadily promotes the iteration and upgrading of industrial intelligence.
Interview guests' group photo
This high-end interview gathered multiple perspectives from upstream and downstream of the industrial chain, deeply analyzed the pain points, paths, and future of the integration of new generation artificial intelligence and advanced manufacturing, and condensed the core consensus of industrial coordinated development. In the future, with the continuous technological innovation of industry enterprises, deep collaboration of industrial chains, and continuous improvement of ecological systems, China's measurement control and instrumentation industry will continue to break through technological bottlenecks, consolidate industrial foundations, accelerate the transition from an industrial power to an industrial powerhouse, and inject strong intelligent momentum into the construction of new industrialization.
