Meal nutrition scientific evaluation and improvement method and system based on AI large model

By using an AI-based large-scale model for meal nutrition assessment, a closed-loop system was constructed, which solved the problem of inaccurate assessments caused by incomplete dietary data, enabling personalized health advice and resource optimization, and improving the effectiveness of health management.

CN120878077APending Publication Date: 2025-10-31杭州祐全科技发展有限公司
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Patent Information

Application Number
CN202511318123.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-16
Publication Date
2025-10-31

AI Technical Summary

Technical Problem

Existing technologies cannot dynamically adjust judgment criteria when dietary data records are incomplete or missing, resulting in insufficient accuracy of assessment results. Furthermore, they lack an effective closed-loop optimization mechanism, making it impossible to provide personalized and accurate recommendations.

Method used

We employ an AI-based large-scale model-based food nutrition assessment method. Through integrity and reliability scoring, dynamic threshold gating, adaptive sampling intensity scheduling, uncertainty gating compensation, and adaptive parameter updates, we construct a linked closed-loop system to achieve data integrity assessment, resource management, and personalized recommendations.

Benefits of technology

In cases of incomplete or missing data, more accurate assessments, reasonable compensation, and personalized recommendations are provided, significantly improving the effectiveness of health management and ensuring a balance between computational efficiency and user experience.

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Abstract

The invention discloses a meal nutrition scientific evaluation and improvement method and system based on an AI large model, relates to the technical field of scientific evaluation, and is used for solving the problem that the existing method generally adopts a fixed threshold value to judge whether food group intake reaches the standard or not and cannot dynamically adjust a judgment standard according to data integrity under the condition that records are incomplete, so that the efficiency is high. And misjudgment is easily caused. A linkage closed-loop food intake record optimization method is constructed by introducing an integrity credibility scoring mechanism and taking the integrity credibility scoring mechanism as core input. According to the method, data integrity evaluation, dynamic threshold gating, adaptive sampling intensity scheduling, uncertainty gating compensation, double-factor suggestion intensity mapping and parameter adaptive updating form a closely connected necessary chain, and system-level optimization is achieved.
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