Machine language large model construction method and system

Through intelligent semantic association network, cross-modal attention mechanism and adversarial generation network, combined with multi-level feedback system and task adaptive module, a machine language big model is built, solving the problems of insufficient deep semantic association and poor adaptability in multi-modal data processing in the existing technology, and achieving more efficient multi-modal data processing and dynamic adaptation.

CN119721118AActive Publication Date: 2025-03-28HUAQING WEIYANG (BEIJING) TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510220620.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-27
Publication Date
2025-03-28
Estimated Expiration
2045-02-27

AI Technical Summary

Technical Problem

Existing multimodal data processing methods fail to fully explore the deep semantic associations between text and visual or auditory data, and the model is less adaptable in the face of new tasks or environmental changes.

Method used

An intelligent semantic association network is adopted to combine cross-modal attention mechanisms and adversarial generation networks to enhance the relationship between text and visual or auditory data, and dynamically adjust model parameters through a multi-level feedback system and task adaptive module to build a machine language large model.

Benefits of technology

The model's understanding and dynamic adaptability of multimodal data is significantly improved, ensuring that the model maintains high performance and flexibility in complex environments.

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Abstract

The invention provides a machine language large model construction method and system, and the method comprises the steps: receiving user input data, defining an intelligent semantic association network, and extracting the semantic association between a text and visual or auditory data; a cross-modal attention mechanism and an adversarial generative network are utilized to enhance the multi-modal data relationship in the association, and a multi-modal perception mechanism is formed; based on the mechanism, a multi-level feedback system is constructed, feedback signals from different levels are processed, and an optimized prediction path is generated; key parameters are dynamically adjusted in combination with a task adaptive module and the optimized prediction path, and an adaptive learning rate adjustment strategy is generated; based on an incremental context extension module, an intelligent semantic association network, a multi-modal sensing mechanism, a multi-level feedback system and a task adaptive module, a machine language large model is constructed. According to the method, the understanding and processing capability of the model on complex multi-modal data is improved.
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