Knowledge Graph-Based Method for Intelligent Generation of Emergency Plans for Water Conservancy Projects

In response to the issues of poor content correlation and insufficient intelligent decision support in emergency plans for water conservancy projects, a method for intelligent generation of emergency plans based on knowledge graphs is proposed. Utilizing pre-trained language models (PTM) based on entity masking, the accuracy of entity recognition tasks is enhanced by uncovering contextual features surrounding the masked entities. By employing translations, rotations, and superpositions within the vector space, a multiview convolutional neural network (MCNN) is constructed to enhance the accuracy of relation extraction through complementary and integrated feature representation. Integrating PTM with MCNN enables the construction of an emergency entity relationship extraction method based on PTM-MCNN. Neo4j is utilized for storing entity relationship triplets to construct an emergency knowledge graph. Through the utilization of the mutual information criterion, knowledge retrieval and matching are performed to accomplish the intelligent generation of emergency plans. The results indicate that PTM-MCNN achieves high recognition accuracy (F1 score of 92.2%), ensuring the reliability of the generated emergency plans. Related studies can effectively improve the intelligence of emergency management of water conservancy projects.