Big data-based collage students' psychological behavior abnormal change system scheme design method

Abstract

The invention relates to a big data-based collage students' psychological behavior abnormal change system scheme design method. According to the method, multidisciplinary theories such as system science, information science, medicine and psychology are comprehensively applied; based on multi-source heterogeneous information fusion, virtual-reality space psychological behavioral representation information is pooled; by means of big data association analysis, a mapping relationship model between college students' psychological behaviors and behavioral representation information is established; through pooling smart campus big data, appropriate image processing, text mining, deep learning, incremental learning and other algorithms are selected to mine and establish a psychological behavior abnormal change monitoring and early warning model; and based on a Hadoop and Tensorflow big data analysis platform, college students' psychological behavior abnormal change monitoring and early warningsoftware is researched and developed. Compared with an existing system, the system of the invention scientifically detects individual students with psychological behavior abnormal changes, and has the advantages of high accuracy, good dynamic performance and the like.

Description

technical field [0001] The invention relates to a monitoring, early warning and intervention system, in particular to a method for designing a system scheme for college students' psychological behavior abnormalities based on big data, which belongs to the field of big data applications. Background technique [0002] According to the report of the World Health Organization, the incidence of depression in the world is about 11%, and it has become the fourth largest disease that endangers human health. By 2020, it may become the second largest disease after heart disease. In my country, the incidence rate of depression is as high as 7%, and due to untimely discovery and insufficient understanding, the treatment rate is only 20%. Suicide deaths due to depression are frequent. As an active and sensitive special group in social life, college students are in a period of youth undergoing tremendous changes in physiology and psychology, and their mental health problems are more prom...

AI Technical Summary

Problems solved by technology

The advantage is that it eliminates the influence of subjective factors on the assessment of mental health status. The disadvantage is that the source of behavioral data is single, the mining is not thorough enough, and the accuracy of mental health status assessment cannot be guaranteed.

[0007] To sum up, although mental health research based on big data technology has received some attention, existing efforts and research have not yet established a theoretical model to describe human psychology and behavior. The content is not comprehensiv

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