A method for detecting and analyzing mental health based on a large model

Through the mental health detection and analysis method based on big model, the feature extraction and abnormality level division of student data is used using data feature parameters and abnormality evaluation index, which solves the problem of inefficient data visualization methods in the existing technology, and realizes mental health detection Efficient identification and management with analysis.

CN118899065BActive Publication Date: 2024-12-27HUNAN ANZHI NETWORK TECH CO LTD
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Patent Information

Application Number
CN202411394411.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-08
Publication Date
2024-12-27
Estimated Expiration
2044-10-08

AI Technical Summary

Technical Problem

In the prior art, the effectiveness of mental health detection and analysis through data visualization is not high, resulting in wasted time and inefficiency.

Method used

A mental health detection and analysis method based on large models is used to extract student data features, input preset models to obtain data feature parameters, analyze data fluctuation coefficients and abnormality evaluation index, and divide the abnormality levels into combination with evaluation thresholds for data visualization.

Benefits of technology

It realizes accurate identification and hierarchical management of student data, improves the data visualization effectiveness of mental health detection and analysis, ensures data integrity and calculation accuracy, and thus improves analysis efficiency.

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Abstract

The present invention discloses a method for detecting and analyzing mental health based on a large model, and relates to the technical field of electronic digital data processing. The method for detecting and analyzing mental health based on a large model comprises the following steps: extracting features; obtaining a data anomaly evaluation index; and performing anomaly level classification. The present invention obtains student data features by extracting features of student data corresponding to the students to be detected, and then obtains a data fluctuation coefficient by analyzing the data feature parameters of the historical time period, obtains a data anomaly evaluation index by combining the student data features and the data feature parameters obtained by inputting the student data features into a preset model, and finally obtains a data anomaly group by performing an anomaly level classification of the students to be detected in combination with a preset evaluation threshold, thereby improving the effectiveness of data visualization in mental health detection and analysis, and solving the problem of low effectiveness of mental health detection and analysis by data visualization in the prior art.
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Citation Information

Patent Citations

  • A contactless image evaluation method based on a human mind recognition model

    CN113705621B

  • A mental health auxiliary evaluation method based on emotional event knowledge graph

    CN117743586B

  • Student psychological state detection method based on feature interactive learning network

    CN116172556A

  • Student physiological and psychological health screening system based on computer vision and algorithm technology

    CN116741344A

  • College student mental health state assessment model and early warning method based on campus behavior data analysis

    CN116825348A

Cited By

  • Student mental health data processing method based on feature extraction and model analysis

    CN121789913A

  • Student mental health data processing method based on feature extraction and model analysis

    CN121789913B