Exploration of bidirectional empowerment technology between water conservancy knowledge graph and large models

The illusion problem of large models and the low efficiency of knowledge graph construction have become the main bottlenecks restricting their application in the water conservancy industry. Proposed a bidirectional empowerment technology system for knowledge graphs and large models, with the core logic being that knowledge graphs complete domain cognition for large models, and large models improve construction efficiency for knowledge graphs, and a bidirectional empowerment technology framework is constructed. In terms of empowering large models with knowledge graphs, injecting structured domain knowledge through enhanced pre-training, constructing targeted datasets, clarifying inference paths, tracing knowledge sources and inference links, and quantitatively verifying inference results based on knowledge graphs can suppress the illusion of large models and enhance professionalism and credibility. In terms of empowering knowledge graphs with large models, achieving automatic recognition of entities and relationships, intelligent classification of knowledge annotation, and knowledge supplementation can improve the efficiency of knowledge graph construction and management. By applying the bidirectional empowerment technology of knowledge graphs and large models, the professional accuracy and interpretability of large models in flood control scheduling, water resource management, engineering hazard identification and other scenarios can be significantly improved. At the same time, the automation iteration of knowledge graphs can be achieved, forming a virtuous loop and providing technical support for the intelligent transformation of the water conservancy industry. The relevant ideas and methods provide reference for the value mining of water conservancy knowledge platforms and the application of large model industries.