Abstract: In response to the diverse types of equipment and scattered data in the field of railway signaling, this paper explored the path of using artificial intelligence large model technology to implement intelligent upgrading of traditional signal operation and maintenance. The paper analyzed the core issues of railway signal operation and maintenance in data governance, fault diagnosis, and other aspects, chose DeepSeek-R1 as the basic model to implement standardized governance of multi-source heterogeneous data through interface extension, and built a unified data processing flow, adopted a hierarchical learning mechanism and a hybrid fine-tuning strategy, improved the adaptability of the basic model to dynamic data and the diagnostic ability for rare faults by combining techniques such as incremental learning and small sample learning. It designed three core application scenarios, including intelligent diagnosis of equipment, intelligent question and answer assistant, and preventive maintenance to promote the practical implementation of large models in railway signal operation and maintenance, and reduce manual operation and maintenance costs. The research results provide feasible technical solutions and practical references for the intelligent upgrade of railway signal system.