AI-based multi-model collaborative multi-modal data intelligent screening and deep analysis method

By employing an AI-driven multi-model collaborative approach for intelligent screening and deep analysis of multimodal data, the problems of high computational resource consumption and insufficient screening accuracy in multimodal data processing have been solved. This approach enables efficient, accurate, and flexible multimodal data processing, and enhances the ability to mine cross-modal associations and the interpretability of analysis results.

CN122087375APending Publication Date: 2026-05-26BEIJING XINLIU WANLIAN NETWORK TECH CO LTD
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Patent Information

Application Number
CN202610556221.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-24
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies for multimodal data processing suffer from problems such as high computational resource consumption, low processing efficiency, insufficient screening accuracy, difficulty in cross-modal alignment, and insufficient in-depth analysis capabilities, making it difficult to meet the needs for efficient, accurate, and flexible multimodal data processing.

Method used

We employ an AI-based multi-model collaborative approach for intelligent screening and deep analysis of multimodal data. Through differentiated and standardized preprocessing, multi-dimensional indicator evaluation, importance and diversity coupling optimization, cross-modal fusion, and multi-dimensional analysis, combined with domain transfer learning and blockchain evidence storage technology, we achieve efficient screening and deep analysis.

Benefits of technology

It significantly reduces computational overhead, improves screening and modality alignment accuracy, enhances data quality, improves cross-modal association mining capabilities, ensures the interpretability and flexibility of analysis results, and supports applications in multiple fields.

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Abstract

The invention provides an AI-based multi-model collaborative multi-modal data intelligent screening and deep analysis method, which comprises the following steps: carrying out differential preprocessing on modal differences of multi-modal samples, and dynamically adjusting preprocessing parameters through sample metadata analysis; evaluating the quality of the sample data by adopting a multi-dimensional index to obtain high-value data; screening high-value data by using an importance and diversity coupling optimization strategy to obtain an effective sample; modal alignment is carried out on the screened samples, cross-modal feature fusion is completed, and a unified fusion feature vector is generated; multi-dimensional deep analysis is carried out on the fusion feature vector, inter-modal associated information is mined, and result generalization is realized in combination with domain transfer learning; the analysis result is evaluated in real time based on a preset index, an optimization instruction is fed back according to the evaluation result, and whole-process core parameters are dynamically adjusted. The method has the beneficial effects that the problems of high screening cost, low precision, poor fusion effect, weak analysis capability, poor interpretability, inflexible deployment and the like in the prior art are solved.
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