Psychological crisis early warning method based on text and image information joint calculation

Abstract

The invention discloses a psychological crisis early warning method based on text and image information joint calculation, which comprises the steps: S1, establishing and training a psychological health automatic evaluation model, S2, selecting network content data of a certain tested student from the step S1, sequentially preprocessing each text and a corresponding image, respectively obtaining a text representation matrix and an image representation matrix; S3, sequentially inputting the text representation matrix and the image representation matrix into a text sentiment calculation model and an image sentiment calculation model according to a row sequence to respectively obtain a text sentiment tendency matrix and an image sentiment tendency matrix, sequentially calculating the two matrixes according to a row sequence by adopting a maximum decision rule to obtain a comprehensive emotional tendency vector sequence of the tested student; and S4, inputting the comprehensive emotional tendency vector sequence into an automatic psychological health assessment model, and judging the psychological health grade of the tested student according to an output result to complete automatic psychological health assessment. The psychological health level of students can be quickly and accurately recognized.

Description

technical field [0001] The invention belongs to the technical field of psychological crisis early warning, and in particular relates to a psychological crisis early warning method based on joint calculation of text and image information. Background technique [0002] As social competition becomes more intense and interpersonal relationships become increasingly complex, college students who are under multiple pressures are prone to psychological problems such as anxiety and depression. However, the current mental health education work in colleges and universities is directional, failing to pay attention to all students and satisfy their individuality. Therefore, the realization of rapid and accurate assessment of the mental health status of college students is not only the premise and guarantee for students to carry out their learning activities smoothly, but also the basis for precise intervention and personalized educational services for college psychological work. [0003]...

AI Technical Summary

Problems solved by technology

[0004] Mental health assessment based on explicit acquisition methods is mostly a single census for freshmen in practical applications, and continuous follow-up investigations cannot be carried out; there are social approval and intrusive effects of scales in the implementation process; the assessment work is subject to artificial Dealing with speed limits and limits on the autonomy of the assessee

Although the automatic assessment of mental health based on implicit acquisition methods can effectively improve the above problems, most of the existing mental health assessments are based on network behavior data or text analysis, ignoring the relationship between different modal data. Second, mental health is a state-type psychological characteristic that changes greatly over time. Existing research ignores the error caused by time contingency and does not consider the changes in students' mental health over a period of time. , so the accuracy rate of the automatic assessment of mental health has been reduced, and there is still a lot of room for improvement in the accuracy rate, and it is impossible to make an accurate early warning analysis for the psychological crisis of college students

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