Construction techniques for knowledge graphs in the field of combine harvester fault diagnosis and their question and answer applications

As an effective mechanized harvesting equipment, the combine harvester can greatly improve the harvesting efficiency of crops. However, it is inevitable that some mechanical failures will occur during harvesting operations. Since the driver lacks specialized maintenance experience, he does not know the cause of the failure and how to repair the machine when the failure occurs. This will seriously affect the harvest of crops, and even it may also cause safety accidents. Since knowledge graphs can use graph databases to store unstructured data such as expert knowledge in a standardized manner, knowledge graphs have good application prospects in the field of fault diagnosis question and answer. Based on this, a set of knowledge graphs for combine harvester fault diagnosis is proposed. Firstly, the entities and entity relationship types required in the knowledge graph are clarified based on expert knowledge, the entity extraction model of the Bidirectional Gated Recurrent Unit（BiGRU） and the Transformer encoder is combined with the RoBERTa-wwm-ext pre-training model to extract entities from unstructured text. Secondly, the RoBERTa-wwm-ext pre-training model is again used to fuse the recurrent neural network（RNN） model to conduct entity review of the extracted entities. Thirdly, after the entity review is completed, the RoBERTa-wwm-ext pre-training model is used to extract the entity relationships existing between the head entity and the tail entity, by combining the relationship between the Bidirectional Gated Recurrent Unit（BiGRU） and the attention mechanism. Finally, the extracted entities and entity relationships are formed into triples, and the triples are used to build a knowledge graph, so that the knowledge graph can be used to implement intelligent question and answer.