Disease and pest question answering system fusing knowledge graph and large model

By integrating knowledge graphs and large models into a pest and disease question-and-answer system, the accuracy and rigor of pest and disease control knowledge acquisition in existing technologies have been solved. This system enables accurate understanding and professional answers to farmers' colloquial questions, thereby improving the accuracy and practicality of pest and disease control.

CN121958508APending Publication Date: 2026-05-01YUNNAN AGRICULTURAL UNIVERSITY
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Patent Information

Application Number
CN202610417767.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-01
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Existing methods for acquiring knowledge about pest and disease control are insufficient in understanding user questions and the rigor of answers is poor. Traditional knowledge graph systems have low recall rates, while large language model systems lack rigor.

Method used

This pest and disease question-answering system integrates knowledge graphs and large-scale models. It extracts corpora from multi-source data and constructs vertical domain knowledge graphs through a knowledge base construction module. It then uses a large-scale model semantic parsing module to extract entities and relationships and a collaborative retrieval module to perform semantic matching to generate structured answers.

Benefits of technology

It achieves accurate understanding and professional responses to farmers' colloquial expressions, improves the accuracy and rigor of pest and disease control recommendations, reduces the rate of misdiagnosis and missed detection, and outputs results that are logically fluent and conform to human language habits.

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Abstract

The invention relates to the field of knowledge questions and answers, in particular to a disease and insect pest questions and answers system fusing a knowledge graph and a large model. The uniqueness and accuracy of underlying facts are ensured through an automatic extraction and disambiguation mechanism driven by a large model, and the illusion phenomenon of the large model is effectively inhibited in combination with hard constraints of a knowledge graph, so that the accuracy and preciseness of disease and pest control suggestions are superior to those of a traditional data-driven method; the deep semantic understanding capability of the large language model is utilized, oral expressions, wrongly written characters and implicit intentions of peasant households are accurately recognized, compared with syntactic rule matching in the prior art, the misdiagnosis and omission ratio can be greatly reduced, and higher practicability is shown in a complex consultation scene; structured multi-source data representing expert knowledge is seamlessly fused into a large model generation process, an output result has professional constraints and conforms to human language habits, logic is smooth, the sense of speech is natural, and the interactive experience is far better than that of stiff template reply in the prior art.
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