Construction of knowledge-enhanced large models for water conservancy project safety risk identification

The operational environment of water conservancy projects is complex, with numerous risk factors affecting engineering safety and exhibiting a chain-like propagation pattern, which poses significant challenges for risk prevention and control. This study proposes a knowledge-enhanced large language model to uncover the propagation patterns of risk chains and identify key risks impacting engineering safety. Integrating the large language model ChatGLM4 with the chain-of-thought strategy to identify risk entities and their relationships in inspection texts, thereby constructing a risk knowledge graph for engineering safety. Analyze the chain-like propagation patterns of risks in the knowledge graph and integrate them as structured knowledge into ChatGLM4, identifying triggering points, convergence points, and diffusion points in the risk chain to clarify key risks impacting engineering safety. The results show that the method proposed in this study achieves high accuracy in risk identification (with an average F1-score of 81.43%). Risks such as weed growth in the expansion joints of the lining panels and seepage in the slope or toe of the channel embankment exhibit strong transmissibility and a high degree of uncertainty. These studies can effectively reduce the impact of potential risks on engineering safety, enhancing the operational safety of projects.