The embodiment of the invention provides an
urban rail signal system fault diagnosis method based on a
knowledge graph and a
large model, and the method comprises the steps: collecting
system operation logs, state parameters and fault information in real time, and carrying out the data preprocessing and
standardization; based on historical fault data, a fault classification and prediction model is constructed through
feature extraction and mode recognition, and automatic
fault recognition and risk early warning are achieved; constructing a fault diagnosis
knowledge graph, and establishing an association relationship among entities such as equipment, faults, reasons, maintenance schemes and the like; a pre-training large
language model and a LoRA technology are adopted for efficient
fine tuning, a fault diagnosis reasoning model is trained, and end-to-end generation from fault description to diagnosis and maintenance suggestions is achieved; the
knowledge graph and
large model output are fused, real-time and historical data are combined, multi-path
fault analysis and comprehensive diagnosis are carried out, and a diagnosis report is generated and displayed. The intelligent level, accuracy and efficiency of fault diagnosis can be improved, and
technical support is provided for intelligent operation and
maintenance management.