Data value evaluation method and device, computer equipment and storage medium

By performing feature analysis and transfer learning on low-frequency trading data, combined with graph neural network and pre-trained value evaluation model, the problem of deviation in low-frequency trading data value evaluation results is solved, and the accurate quantification of the value of low-frequency trading data is achieved.

CN119669891APending Publication Date: 2025-03-21CHINA PING AN LIFE INSURANCE CO LTD
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Patent Information

Application Number
CN202411640172.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-15
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

When conducting low-frequency trading data value evaluation, due to the small amount of data and limited information coverage, it is difficult to effectively capture the overall picture of user behavior and market dynamic changes, resulting in large deviations in the evaluation results.

Method used

By obtaining transaction data and transaction frequency impact factors, a data relationship map of high-frequency transaction data is constructed, and an implicit correlation characteristics between users and commodities are extracted using graph neural network algorithm. Then, these implicit features are transferred to low-frequency trading data through transfer learning, enhancing their feature representation capabilities, and ultimately inputting low-frequency data features into the pre-trained value evaluation model to obtain more accurate evaluation results.

Benefits of technology

It effectively alleviates the problem of insufficient coverage of low-frequency data, reduces evaluation bias, and realizes accurate quantification of the value of low-frequency trading data.

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Abstract

The invention discloses a data value evaluation method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of classifying transaction data to obtain high-frequency transaction data and low-frequency transaction data; obtaining high-frequency transaction data features; based on the high-frequency transaction data features, constructing a data relation graph of the high-frequency transaction data; performing graph traversal operation on the data relation graph by adopting a graph neural network algorithm to obtain implicit association features between the user and the commodity; based on the implicit association features, performing feature analysis on the low-frequency transaction data by using a transfer learning algorithm to obtain low-frequency transaction data features; and importing the low-frequency transaction data features into the data value evaluation model to obtain a value evaluation result. The invention also relates to the technical field of block chains, and the transaction data is stored on a block chain network. The problem of insufficient coverage of low-frequency data is effectively relieved, evaluation deviation is reduced, and accurate quantification of low-frequency transaction data value is realized.
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