Self-adaptive question-answering system and method based on knowledge distillation and multi-modal dynamic fusion

Through the integration of knowledge distillation and multimodal dynamic technology, an adaptive question-and-answer system was built, which solved the problems of knowledge dispersion and superficial semantic understanding in the communication field, and achieved efficient knowledge management and reasoning capabilities, especially in the iteration of 5G base station operation and maintenance and communication protocol versions.

CN120354944APending Publication Date: 2025-07-22NANJING UNIV OF POSTS & TELECOMM +1
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Patent Information

Application Number
CN202510478223.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-16
Publication Date
2025-07-22

AI Technical Summary

Technical Problem

The communication field has dispersed knowledge, shallow semantic understanding and insufficient adaptability to dynamic scenarios, resulting in lagging knowledge updates, inefficient use of multi-source data, and insufficient complex logical reasoning capabilities. Especially in the operation and maintenance of 5G base stations and the iteration of communication protocol versions.

Method used

Using knowledge distillation and multimodal dynamic fusion technology, through lightweight model compression and adaptive inference path generation, combined with cross-modal semantic alignment and dynamic conflict resolution mechanisms, an adaptive question-and-answer system is built to realize unified management and in-depth inference of multi-source heterogeneous knowledge.

Benefits of technology

It improves the accuracy of cross-modal inference, reduces inference delay, improves the accuracy of equipment fault diagnosis and the efficiency of resource configuration optimization, and enhances the compatibility of multi-vendor equipment and the expansion capabilities of 5G network management.

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Abstract

The invention discloses an adaptive question-answering system based on knowledge distillation and multi-modal dynamic fusion, and the system comprises a knowledge distillation module which is used for migrating knowledge of a teacher model pre-trained on corpora in the communication field to a lightweight student model, achieving model compression through optimizing a distillation loss function, and obtaining a multi-modal dynamic fusion model; the loss function comprises a soft label output by the teacher model and a KL divergence constraint output by the student model; the multi-modal knowledge fusion module comprises a feature extraction unit, a self-adaptive weighting unit and an attention fusion unit; the self-adaptive inference engine comprises a semantic analysis unit; according to the cross-modal reasoning method and system, semantic alignment of equipment parameters, protocol texts and topological graphs is achieved through the multi-modal dynamic fusion technology, and the cross-modal reasoning accuracy is improved; compared with an original model, the lightweight student model has the advantage that the reasoning speed is increased in a protocol analysis task.
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