Fire knowledge question and answer data set construction method and device, electronic equipment and medium

By constructing a structured prior knowledge framework for all fire protection scenarios, the problems of high cost, confusion of professional logic, and insufficient traceability in the construction of fire protection knowledge question and answer datasets in existing technologies are solved. This enables the generation of high-quality, professionally consistent question and answer datasets, which are suitable for multi-condition reasoning and compliance review.

CN122240789APending Publication Date: 2026-06-19HEFEI INST FOR PUBLIC SAFETY RES TSINGHUA UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HEFEI INST FOR PUBLIC SAFETY RES TSINGHUA UNIV
Filing Date
2026-04-08
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies for constructing fire safety knowledge question-and-answer datasets suffer from problems such as high cost, long cycle time, limited coverage, confusion of professional logic, difficulty in fine-grained identification, and lack of traceability and compliance assurance of the generated results.

Method used

We adopt a structured prior knowledge framework for all fire protection scenarios, and construct fire protection knowledge clusters through text segmentation, annotation, semantic feature extraction, nearest neighbor filtering and clustering to generate a professional, consistent and traceable question-and-answer dataset.

Benefits of technology

It improves the professionalism and accuracy of fire safety knowledge Q&A, meets the fire safety field's requirements for high quality, explainability, and compliance traceability, and is applicable to multi-condition and multi-step reasoning questions.

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Abstract

This invention discloses a method, apparatus, electronic device, and medium for constructing a fire safety knowledge question-and-answer dataset, comprising: acquiring fire safety-related data and segmenting the data into text to obtain fire safety knowledge fragments; labeling the fire safety knowledge fragments based on a prior knowledge framework to obtain multiple types of labels for the fire safety knowledge fragments; extracting semantic features from the fire safety knowledge fragments to obtain semantic feature vectors, and performing nearest neighbor filtering on the semantic feature vectors to obtain a semantic nearest neighbor set; performing professional fusion processing on the semantic nearest neighbor pairs in the semantic nearest neighbor set based on the multiple types of labels corresponding to the fire safety knowledge fragments to obtain the edge weights of the semantic nearest neighbor pairs; clustering the fire safety knowledge fragments based on the edge weights of the semantic nearest neighbor pairs to obtain fire safety knowledge clusters; and constructing a fire safety knowledge question-and-answer dataset based on the fire safety knowledge clusters. The method of this invention deeply integrates professional knowledge in the fire safety field, improving the professionalism and accuracy of fire safety knowledge question-and-answer.
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