Creating a "super doctor" for intelligent operation and maintenance of railway signals - the large model of rail transit makes operation and maintenance more efficient

From March 15th to 16th, 2025, the event was hosted by the China Transport and Logistics Association and organized by the Office of Science and Technology Awards of the China Transport and Logistics Association, the People's Government of Yongchuan District, Chongqing Municipality, the New Technology Promotion Branch of the China Transport and Logistics Association, etc. The 2024 China Transport and Logistics Association Science and Technology Award Commendation Conference and the Promotion Conference of New Technologies and Achievements in Transportation, with the theme of "Empowering New Quality Productivity with Science and Technology and Driving High-Quality Development through Innovation", was successfully held at the Yongchuan International Convention and Exhibition Center in Chongqing. This conference aims to promote the exchange and application of new technologies and achievements in the transportation sector, and to facilitate the intelligent and green development of the transportation industry.

With the increasing complexity and diversity of rail transit transportation, traditional operation and maintenance methods have been difficult to meet the current demands. The release of "Intelligent Railway 2.0" has put forward new technological innovation requirements for the improvement of railway transportation organization efficiency, construction and operation safety, and passenger service levels. Based on this, a large model of rail transit was built. With mature and open-source general large models such as DeepSeek R1 and Qwen as the base, and externally connected to the knowledge base of the rail transit industry, through technologies such as big data analysis, machine learning, computer vision, and natural language processing, the capabilities of data processing and model training were further enhanced, and the intelligence level of the system was strengthened. Make railway operation and maintenance more efficient, thereby enhancing the operational efficiency and safety of rail transit.

Facing application scenarios, a multi-mode technical architecture is adopted to break through the intelligent mode of "general large model foundation + industry data setting". Based on the large model of rail transit, the collaborative integration of language large model, visual large model and multi-modal large model technologies has been achieved, providing customized intelligent solutions for the rail transit industry.

Traditional intelligent operation and maintenance of railways mainly rely on rule judgment, regular inspection and expert experience, lacking self-learning and reasoning capabilities, and thus unable to achieve predictive maintenance. After the introduction of large model technology, thinking and reasoning can be carried out, with self-learning ability. The data processing range is expanded, and voltage, current, text and image data can all be analyzed. It comprehensively covers the key equipment of the railway signal system, realizes the full-process intelligence from monitoring to maintenance, and completes the transformation from "fault repair" to "condition-based maintenance". At the same time, through data-driven and AI-assisted approaches, the efficiency of operation and maintenance and the scientific nature of decision-making are enhanced.

Railway signal large Model intelligent diagnosis system: Integrates monitoring data to achieve centralized management and rule judgment, and expands real-time monitoring functions, such as turnout force analysis and signal machine fault prediction, to build a full-dimensional intelligent monitoring and diagnosis system, significantly improving the accuracy of equipment status perception and fault prediction capabilities.

AI diagnosis technology for equipment status: It comprehensively covers key equipment such as turnouts and track circuits, and can provide full life cycle health management from early anomaly detection to remaining life prediction, offering intelligent decision support for the operation and maintenance of railway signal equipment.

AI scheduling based on device status: By real-time analysis of device health data, it intelligently generates skylight maintenance plans and optimizes resource allocation to achieve precise task dispatch. 

On-site operation AI assistant: Integrates historical fault database and can achieve intelligent case matching. 

AI emergency auxiliary dispatching: Through intelligent question-and-answer, path planning and real-time risk alerts, it provides efficient emergency command support for sudden failures.

AI Management Assistant: Generate operation and maintenance reports through intelligent statistical analysis to help management optimize decision-making and resource allocation. 

At present, the large model of rail transit has been applied to the entire business process of intelligent transportation business products, including research and development, engineering design, safety testing, and engineering services, and has been applied and promoted in internal operation, market, human resources and other management processes. In the engineering services of national railways, the large model of rail transit has provided targeted solutions for engineering personnel to quickly handle on-site problems by analyzing the technical regulations, operation instructions and historical fault handling records of on-board products (such as ATP, ATO, etc.) and ground train control products (such as TCC, TSRS, RBC, CBI, etc.), and has generated fault analysis reports in a timely manner. Comprehensively enhance the efficiency and service level of signal operation and maintenance.

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