Data grading method, system and related device

By using the preset classification model and the combination of quantitative and quantitative elements, the data and sensitive information judgment of the data solve the problem of data grading in the existing technology, and realize the rapid, accurate and security of the data grading.

CN119377792AInactive Publication Date: 2025-01-28CETC BIGDATA RES INST CO LTD +1

Patent Information

Application Number
CN202411960028.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-30
Publication Date
2025-01-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the prior art, data grading mainly relies on manual operations, is time-consuming and labor-intensive, and is prone to errors, making it difficult to meet the government departments' needs for fast and accurate data grading.

Method used

By obtaining the source information of the data, the target data is used to use the preset classification model, combined with the qualitative and quantitative elements to classify, determine whether it contains sensitive information, and perform secondary leveling according to the weight of the factor, and finally adjust the target level.

Benefits of technology

Realize the rapid and accuracy of data grading, reduce manual intervention, improve the consistency and accuracy of data grading results, and avoid the risk of data leakage.

✦ Generated by Eureka AI based on patent content.

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Abstract

The invention discloses a data grading method, a data grading system and a related device, which are used for quickly and accurately grading data. The method comprises the steps of obtaining data source information, and obtaining a data list based on the data source information; extracting target data through a preset classification model according to the data list; obtaining qualitative elements and quantitative elements, and grading the target data based on the qualitative elements and the classification model to obtain an initial level; judging whether the target data after the initial level is obtained contains sensitive information or not; if yes, element weights are set, secondary grading of target data is obtained for the target data based on the quantitative elements and the element weights, after the secondary grading of the target data is obtained, the secondary grading is adjusted in combination with the initial grade, and finally the target grade of the target data is obtained; and if not, determining the initial level of the target data as the target level.
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Citation Information

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