An elevator fault prediction technology based on knowledge graph embedding and completion is proposed to address issues such as large amounts of elevator fault data,poor data quality,and complex fault relationships.Firstly,taking elevator fault data as the research object,a knowledge graph of elevator faults was constructed,and ontology theory was used to describe elevator fault data;Secondly,an embedded model of elevator fault knowledge graph was designed,and a pre trained model was used for feature extraction and representation;Then,the public relations and semantic relationship information in the knowledge graph were used to complete the embedded model and train the prediction model;Finally,the trained prediction model was used to predict faults in elevator operation.The experimental results indicate that the accuracy of the fault prediction results proposed in this paper is relatively high,and the proposed method can provide reference for elevator fault prediction and maintenance.
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