Scientific problem value quantification method embedded with multi-dimensional mixed expert mechanism

Through the quantitative method of scientific problem value embedded in a multi-dimensional hybrid expert mechanism, the problems of inconsistency and inefficiency in the evaluation standards caused by relying on expert evaluation and subjective judgment in the existing technology are solved, and a more efficient and objective evaluation of scientific problem value is achieved.

CN119647483AActive Publication Date: 2025-03-18DOCUMENT & INFORMATION CENT OF CHINESE ACAD OF SCI
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Patent Information

Application Number
CN202411805341.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-03-18
Estimated Expiration
2044-12-10

AI Technical Summary

Technical Problem

Reliance on expert evaluation and subjective judgment in the existing technology leads to inconsistent evaluation standards for scientific issues and low efficiency.

Method used

The scientific problem value quantification method is adopted with a multi-dimensional hybrid expert mechanism. By reading predetermined quantization dimensions, a target instruction fine-tuning data set is constructed, and the parameter quantization is used using the QLoRA large language model fine-tuning principle, and combined with the gated network automation analysis of text features, the weight coefficients of scientific problem text on each target quantitative dimension are determined.

Benefits of technology

It improves the efficiency of quantitative evaluation of scientific issues, ensures that the evaluation process is more objective and systematic, and can comprehensively evaluate the value of scientific issues.

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Abstract

The invention provides a scientific problem value quantification method embedded with a multi-dimensional hybrid expert mechanism, and relates to the technical field of data processing, and the method comprises the steps: reading a predetermined quantification dimension; introducing a predetermined triple strategy to construct a target instruction fine tuning data set; performing parameter quantization based on a QLoRA large language model fine tuning principle; obtaining a scientific problem text, performing semantic feature analysis through a gating network, and obtaining a target weight coefficient of the scientific problem text according to text semantics; and analyzing the scientific problem text through the target identification large model, and obtaining a target quantification result in combination with the target weight coefficient. Through the method and the device, the technical problem of relatively low scientific problem value evaluation efficiency caused by different evaluation standards due to dependence on expert evaluation and subjective judgment in the prior art can be solved, internal evaluation of scientific problem values is realized by integrating multiple dimensions and combining a large-scale language model for fine adjustment, and the evaluation efficiency of the scientific problem values is improved. And the scientific problem value evaluation efficiency is improved.
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