Quotation analysis model construction method and device, equipment and medium

By structuring the repurchase market quotation data, cleaning dirty data and comprehensive processing, a quotation analysis model is built, which solves the problems of insufficient understanding of professional fields, difficulty in format control and too long response time in the existing technology, and efficient and accurate quotation analysis and real-time response are achieved.

CN119887257APending Publication Date: 2025-04-25E FUND MANAGEMENT CO LTD
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Patent Information

Application Number
CN202411913928.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-24
Publication Date
2025-04-25

AI Technical Summary

Technical Problem

The prior art has problems such as insufficient understanding of professional fields, difficulty in format control and excessive response time in the analysis of repurchase market quotation, especially in adaptability, real-time and format control.

Method used

By obtaining the original full data and performing structured annotation, dirty data cleaning and outlier data screening, comprehensive processing is carried out in combination with open source unstructured data, and finally using the target training data to train the large language model to build a quotation analysis model.

Benefits of technology

It realizes the generation and real-time response of standard structured text, improves the diversity and comprehensiveness of data, enhances the adaptability and accuracy of the model, and reduces the need for manual intervention.

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Abstract

The invention relates to the technical field of data processing, and discloses a quotation analysis model construction method and device, equipment and a medium, and the method comprises the steps: obtaining original full-amount data, and labeling the original full-amount data according to a preset structured format to obtain initial full-amount data; wherein the original total data represents complete data including historical quotation data and newly added quotation data on that day; cleaning dirty data based on the initial total data to obtain processed total data; wherein the dirty data cleaning comprises the step of carrying out outlier data screening by utilizing a trained instruction fine tuning model; determining open source data as unstructured data, and comprehensively processing the unstructured data and the processed total data to obtain target training data; and training a large language model by using the target training data to obtain a quotation analysis model. The method has the beneficial effects of standard structured text and real-time response.
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