Continuously Deepening Innovation Practices, Jiaxun Feihong Helps Beijing Subway Initiate the "AI+" Speed of Intelligent Operation and Maintenance
Continuously Deepening Innovation Practices, Jiaxun Feihong Helps Beijing Subway Initiate the "AI+" Speed of Intelligent Operation and Maintenance


On April 3, the kick-off review meeting for the Beijing Subway project "Research and Application of Urban Rail Transit Civil Engineering Professional Knowledge Q&A Assistant Technology Based on Large Models," undertaken by Jiaxun Feihong (Beijing) Institute of Intelligent Technology Co., Ltd. (hereinafter referred to as "Jiaxun Feihong," stock code: 300213), a wholly-owned subsidiary of Beijing Jiaxun Feihong Electrical Co., Ltd., was successfully held in Beijing. Heads of relevant departments such as the Beijing Subway Asset Maintenance and Management Department, Cyber Information Department, Technology Department, Construction and Maintenance Company, Line Company, and Innovation Research Institute attended the meeting.
This project is an important practice of Jiaxun Feihong's application innovation in the subway large - model field. Relying on large - language - model technologies such as DeepSeek, it constructs an intelligent Q&A system for the urban rail transit civil engineering field, aiming to solve core problems existing in the traditional knowledge management system, such as low retrieval efficiency and limited experience inheritance.
Based on its self-developed "Feihong Inquiry" industry - large - model application, Jiaxun Feihong systematically integrates data resources such as Beijing Subway's regulations, technical standards, and maintenance cases to create a professional knowledge engine with semantic understanding and intelligent reasoning capabilities. This significantly improves the knowledge acquisition and sharing efficiency of management and technical personnel and realizes a paradigm shift in knowledge management from "passive search" to "active service."
At the review meeting, Jiaxun Feihong's technical solution received high praise from authoritative experts from units such as the China Academy of Railway Sciences, Beijing Jiaotong University, and the Chinese Academy of Sciences. The experts unanimously believed that the project innovatively combines large - language - model technology with urban rail transit civil engineering business. Its knowledge base construction method and intelligent reasoning mechanism are at the industry - leading level and will set a new benchmark for the digital transformation of urban rail transit civil engineering management.

As an intelligent solution provider that has led innovation in the rail transit field for many years, Jiaxun Feihong has been deeply involved in the industry and has been engaged in the research and application of artificial intelligence technologies since its early days. It has actively practiced and explored in the field of large models. Currently, it has built an industry knowledge base system for multiple professions and scenarios in rail transit operation and maintenance, safety management, and production operations. It has also developed several industry - large - model innovative applications, such as "Feihong Inquiry" and "Feihong Intelligent Training," and actively participated in and promoted the innovation of artificial intelligence in the rail transit field.
As the contractor of this project, Jiaxun Feihong will also continue to leverage its technical and professional strengths to ensure the high - quality implementation of the project. The completion of the system will not only significantly improve the operation and maintenance management efficiency of Beijing Subway but also form a replicable industry solution, providing strong technical support and a demonstration case for expanding the national rail transit market. This project is also a further deepening of the cooperation between Jiaxun Feihong and Beijing Subway. Based on collaborative innovation and intelligent collision, it injects new momentum into the future high - quality development of the rail transit industry.
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