Knowledge-driven recommended method for inspection information of South-to-North Water Transfers Project
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Abstract:
There are many risks in the operation of the South-to-North Water Transfers Project, and the inspection work of the South-to-North Water Transfers Project is of great significance to ensure the safety and stability of the project. Traditional project inspection methods mainly rely on manual experience and have a low degree of digitalization. Due to the uneven professional level of inspection personnel, it is difficult to form a unified record standard, which in turn leads to redundant inspection special report information, among which the accessibility of effective information is poor, making the traditional project inspection method inefficient.As a new generation of information technology, knowledge graph is a powerful tool for knowledge organization and management. In order to alleviate the limitations of traditional project inspection methods and improve the efficiency of project inspection, knowledge graph with deep learning technology was combined, and using knowledge graph to empower intelligent inspection of the South-to-North Water Transfers Project was proposed. Specifically, the inspection knowledge graph was constructed based on the project inspection text, and an project risk information recommendation method was designed based on the inspection knowledge graph. In the process of building the inspection knowledge graph, the conceptual model of the inspection knowledge graph is defined based on expert experience, and on this basis, the entity relationship joint extraction framework is used to extract the structured triplet knowledge from the unstructured project inspection text, and the knowledge visualization is carried out with the Neo4j graph database as the carrier. The inspection knowledge graph clearly presents inspection information such as engineering sites, parts, risk events, and disposal measures in the form of entity-relationship-entity triples, and supports knowledge visualization and knowledge retrieval, which alleviates the limitation of poor accessibility of effective information in inspection reports. In the project risk information recommendation method, the Bert pre-training model and twin network framework were used to design the Bert twin network, and recommends the project risk information of the entities and entities associated with the current part in the inspection knowledge graph to the inspectors by calculating the string similarity of the part entities, and assists the inspectors in the project risk level diagnosis. The quality of the knowledge graph and the effectiveness of the method are evaluated experimentally. The experimental results show that the average F1 value of various relational triples extracted is 88.42%, and the knowledge extraction results have high accuracy, and the quality of the knowledge graph is considered to be reliable. The F1 value of the candidate entity ranking model designed reaches 86%, which is higher than that of the traditional Jaccard algorithm and Word2Vec model.In general, the Bert twin network designed shows good performance in the string similarity calculation task, and the project risk information recommendation results based on patrol inspection knowledge graph are basically reliable. Knowledge graph and deep learning technology were introduced into the intelligent application of project inspection, which realizes the deep correlation and effective use of inspection knowledge, which can provide reference significance for improving the operation and maintenance efficiency of the South-to-North Water Transfers Project and strengthening the risk management capability of the project.
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