Multi-modal data quality evaluation method based on deep learning

By using deep learning methods to extract semantic features and perform cross-modal joint encoding of multimodal data, the limitations of traditional methods in multimodal data evaluation are overcome, and highly accurate multimodal data quality assessment is achieved.

CN120804084AActive Publication Date: 2025-10-17GUANGZHOU PRINCIPAL DATA CO LTD
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Patent Information

Application Number
CN202511299320.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-12
Publication Date
2025-10-17
Estimated Expiration
2045-09-12

AI Technical Summary

Technical Problem

Traditional database data quality assessment methods have limitations when facing multimodal data. They are unable to handle the complex relationships between multimodal data, lack fine-grained hash cross-modal encoding, have difficulty adapting to dynamically changing data, are highly subjective, and lack standardization and automation.

Method used

A deep learning-based method is used to obtain the first modality recording data and the second modality recording data marked as aligned through the acquisition engine, and semantic feature extraction and cross-modal joint encoding are performed. The fine-grained hash algorithm and bidirectional reconstruction offset evaluation are used to generate multimodal data quality assessment results.

Benefits of technology

It improves the accuracy of multimodal data quality assessment, implements an end-to-end deep learning framework, addresses the shortcomings of traditional methods in multimodal data assessment, and enhances the standardization and automation capabilities of assessment.

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Abstract

The invention discloses a multi-modal data quality evaluation method based on deep learning. The method comprises the steps of obtaining first modal record data and second modal record data which are marked to be aligned in a database; respectively carrying out semantic feature extraction on the first modal record data and the second modal record data to obtain a text structured data semantic coding feature tensor and a metadata description semantic coding feature vector; performing cross-modal joint coding on the text structured data semantic coding feature tensor and the metadata description semantic coding feature vector to obtain a structured-metadata cross-modal quality evaluation joint coding feature tensor; generating reconstruction record data based on the structured-metadata cross-modal quality evaluation joint coding feature tensor, and calculating offset features of the reconstruction record data; performing quality evaluation on the offset features through a first deep learning model; according to the method, through semantic coding feature extraction, fine-grained hash cross-modal coding and bidirectional reconstruction offset evaluation, the accuracy of data quality evaluation is improved.
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