Enhanced decision-making method and device based on thinking chain labeling, equipment and medium

Through thought chain annotation and multimodal fusion technology, the problems of integration and dynamic adaptation of multi-source heterogeneous data are solved, and the decision-making transparency and adaptability of the model in the fields of financial technology and healthcare are improved.

CN120745823APending Publication Date: 2025-10-03PING AN TECH (SHENZHEN) CO LTD
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Patent Information

Application Number
CN202510862163.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-25
Publication Date
2025-10-03

AI Technical Summary

Technical Problem

Existing technologies are unable to effectively integrate multi-source heterogeneous data, lack the ability to dynamically adapt to complex business scenarios and transparent decision-making capabilities, resulting in limited applications in the fields of financial technology and healthcare.

Method used

Through thought chain annotation, core information features are extracted from multi-source heterogeneous data, basic language models are loaded for supervised fine-tuning, multimodal input data are integrated and a simulation environment is constructed, a dual reward mechanism is designed for model optimization, and a real-time data monitoring module is deployed for dynamic adjustment to generate an adaptive decision-making model.

Benefits of technology

It realizes the structured expression, explainable reasoning and dynamic adaptability of complex data, and improves the application effect of the model in high-demand fields.

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Abstract

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an enhanced decision method, device, equipment and medium based on thinking chain annotation, which comprises the following steps: extracting core information features to generate a structured data set, loading a basic language model and executing supervision fine tuning to generate a fine-tuned model, fusing multi-modal input to generate fusion features, constructing a state observation space to receive the fusion features as input, generating reward signals based on a double reward mechanism and optimizing model parameters to generate an optimized model, deploying a monitoring module to dynamically adjust parameter configuration to generate an adaptive decision model, and outputting a decision response result. According to the method, multi-source data extraction, structured expression, multi-modal fusion, reinforcement learning optimization and dynamic adaptive mechanism fusion are carried out, so that the understanding ability of the model to complex data, reasoning transparency and the adaptive ability of the model to coping with environmental changes are remarkably improved.
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