Intelligent generation method of emergency plan for hydraulic engineering based on knowledge graph – take the South-to-North Water Diversion Project as an example

There are some problems with the traditional emergency plans of hydraulic engineering, such as low digitisation, poor knowledge relevance, insufficient intelligent decision-making, and so on. This paper proposes an intelligent method for generating the hydraulic engineering emergency plan for patrol text based on knowledge graph and machine learning. Firstly, based on the electronic documents of various plans, the knowledge graph of the emergency plan is constructed to realise the high organisation of scattered knowledge, using the skills of knowledge modelling, knowledge extraction, knowledge fusion, and knowledge storage. Then, based on bidirectional encoder representation from transformers (BERT) and bidirectional long-short-term memory with conditional random fields (BiLSTM+CRF), the entity recognition model is constructed to intelligently recognise dangers, projects, parts, and other entities in the patrol text. The Jaccard entity similarity algorithm based on the word2vec model matches the danger entity with the graph danger entities and generates the emergency plan through knowledge retrieval and reasoning. With the performance of the model and the verification of the “Channel Leakage” example, this method has high accuracy in identifying entities (the F1 value is 96.21%) and has high reliability in the generation of emergency plans, which can be applied to the emergency rescue of hydraulic engineering.