Sample data intelligent screening enhancement processing method for artificial intelligence model training

By employing a closed-loop framework of dynamic adaptive filtering and knowledge graph-constrained generative enhancement, the disconnect between filtering and enhancement in sample data processing is resolved, thereby improving sample quality and optimizing model performance. This framework adapts to dynamic changes in model training and enhances processing efficiency and accuracy.

CN122090199APending Publication Date: 2026-05-26MAGIC BRUSH MA LIANG ARTIFICIAL INTELLIGENCE (HANGZHOU) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
MAGIC BRUSH MA LIANG ARTIFICIAL INTELLIGENCE (HANGZHOU) CO LTD
Filing Date
2026-01-27
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In existing technologies, the sample data processing suffers from several problems: the screening process relies on static rules, leading to the omission of core difficult samples or an excessive number of redundant samples; the enhancement strategies lack domain knowledge constraints, resulting in sample distortion and semantic shift; the screening and enhancement processes lack synergy, resulting in low processing efficiency and difficulty in meeting the training requirements of high-precision models.

Method used

A dynamic adaptive screening module is used to select samples by combining static quality indicators and dynamic training feedback indicators. Enhanced samples are generated through a knowledge graph-constrained generative enhancement module. The strategy is optimized through a screening and enhancement linkage control module, thus constructing a closed-loop framework for sample processing and model training to ensure the quality of enhanced samples and adapt to dynamic changes in the model.

Benefits of technology

It significantly improved sample quality and processing efficiency, optimized model accuracy and generalization ability, shortened training convergence time, and improved model performance.

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Abstract

The invention discloses a sample data intelligent screening enhancement processing method for artificial intelligence model training, and the method comprises the following core steps: S1, carrying out the preprocessing of an original sample, and obtaining a preprocessed sample and basic features; s2, screening samples through a dynamic adaptive screening module in combination with the static quality index and the dynamic training feedback index, and outputting a sample classification result and sample feature information; s3, through screening, the enhanced linkage control module adapts to a strategy instruction, and the strategy instruction is input into a knowledge graph constraint generation type enhancement module to generate and verify an enhanced sample; and S4, mixing qualified enhanced samples with the screened original samples, inputting a target artificial intelligence model for training, outputting performance indexes and feedback data, and reversely optimizing a screening and enhancing strategy until the model performance reaches the standard. Belongs to the technical field of model training, and can enhance collaborative optimization, guarantee enhanced sample quality and adapt to dynamic change of model training.
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