Construction and application of mine electromechanical equipment accident knowledge graph

Abstract

It is difficult to judge the root cause of equipment accident from the appearance of coal mine electromechanical equipment accident and part of monitoring data, and there is a lack of effective methods to improve the efficiency of equipment accident treatment by using historical data and experience knowledge. In order to solve the above problems, the mine electromechanical equipment accident knowledge graph is constructed. Firstly, the data relationships of the four-group ontology model are designed, and the ontology and the relationship types between the ontologies are determined. Secondly, according to the designed data relationships, a combination method of machine learning and rule templates is used to extract entities, relationships and attributes from databases and texts. Finally, based on the Python language, through the py2neo library, the entities, relationships and attributes are created and stored in the Neo4j graph database with Cypher statements, so as to realize the construction and update of the knowledge graph. The application of mine electromechanical equipment accident knowledge graph in mine electromechanical equipment accident diagnosis, risk management and intelligent question and answer can enable users to effectively use related knowledge of mine electromechanical equipment accident, help equipment maintenance personnel to quickly find the accident chain, locate the cause of the accident and put forward maintenance schemes, so as to achieve the purpose of reducing the accident rate and the accident handling time.

Keywords

• coal mine electromechanical equipment
• knowledge graph
• neo4j
• accident diagnosis
• risk management
• intelligent question and answer