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Rail Transit Exhibition News On August 27, 2025, our country's first full-life cycle health management industry model for rail transit vehicles, "Nine Chapters of Chopping Wheels", was officially released in Changchun. Professor Qian Qingquan of Southwest Jiaotong University, Jia Ning, member of the party group and deputy director of the Jilin Provincial Department of Transportation, Zhong Hua, vice president of the China Transportation Association, Wang Jun, chief scientist of CRRC, and Sha Miao, deputy secretary of the party committee and general manager of CRRC Changchun Rail Bus Co., Ltd., jointly launched the large model.
The large model is the "strongest brain" tailored for the health management business scenario of rail transit vehicles throughout the life cycle on the basis of CRRC's "wheel-cutting" large model, under the unified organization of CRRC Group, CRRC Changke Co., Ltd. and the Qingdao Technology Innovation Center of the National High-speed Train.
With the increase in the speed and number of rail transit vehicles, the industry is facing problems such as difficulty in abnormally accurate perception, reliance on manual experience for diagnosis, and scattered operation of multiple systems. With a hierarchical intelligent architecture as the core, "Nine Chapters of Chopping Wheels" deeply integrates multi-source time series data analysis, intelligent knowledge center, efficient information interaction and intelligent decision-making deduction to build an artificial intelligence health management system covering the entire life cycle of vehicles. To put it simply, it is like a "vehicle doctor", which can not only monitor the health status of the vehicle in real time during the whole life cycle, but also give the best solution for vehicle abnormalities, greatly improve work efficiency, reduce the business cost of the whole life cycle, and make the train run smarter, more economical and more reliable.
"Nine Chapters" applies the self-developed industry timing model to comprehensively perceive and intelligently analyze the multi-dimensional and heterogeneous data generated by thousands of sensors on the train, and automatically gain insight into the changing trends in high-frequency, non-linear, and strongly coupled data such as vibration, current, and temperature.
The model has excellent deduction capabilities, which can realize abnormal early warning and health assessment weeks to months in advance, and realize low-cost migration across systems and platforms through adaptive and self-learning, effectively ensuring driving safety.
When the system detects an abnormality, the staff only needs to ask questions in natural language, such as: "There is an abnormality in a certain part, what could be the reason?" "Nine Chapters of Cutting Wheels" can quickly analyze and give root cause analysis, maintenance suggestions, and even spare parts lists.
It is like an "encyclopedia of vehicle life cycle health management", bringing together massive industry corpora such as vehicle design documents, operation data, maintenance manuals, fault reports, etc., to build a knowledge center for health management throughout the life cycle. Staff can quickly obtain vehicle-related knowledge, real-time status and maintenance suggestions, just as many industry experts are invited to "consult" the train at the same time, significantly improving the efficiency and accuracy of diagnosis and greatly improving work efficiency.
"Nine Chapters of Cutting Wheels" can not only "see" and "think", but also "continue to evolve". It transforms "perception" and "cognition" capabilities into automated and closed-loop execution actions through agent orchestration technology, and builds a smart brain for health management throughout the life cycle of multi-agent collaboration.
Based on the "brain + cerebellum" model, it realizes intelligent perception, intelligent decision-making, and intelligent execution of the whole life cycle of rail transit vehicles, breaks the barriers of system collaborative calling, and closes the whole process from problem discovery, decision-making to task execution, and promotes health management into a new process of independent learning and continuous evolution.
The relevant person in charge of the large model said that through technologies such as timing prediction, autonomous decision-making, intelligent interaction, and multi-agent collaboration, "Nine Chapters" continues to empower the whole chain business of rail transit vehicle design, manufacturing, operation and operation and maintenance, creates a new model of human-machine intelligent collaboration, and leads the health management of the whole life cycle of rail transit vehicles to a new stage of smarter, safer and more efficient.
