Teenager psychological sub-health intelligent early warning system based on multi-source heterogeneous data fusion

The intelligent early warning system for adolescent mental health sub-health, which integrates multi-source heterogeneous data, utilizes campus card consumption records, wearable devices, and social media analysis, combined with weighted averaging and Kalman filtering algorithms, to construct a deep learning early warning model. This enables real-time prediction and dynamic tracking of adolescents' mental state, solving the problems of static assessment and delayed intervention in traditional methods and ensuring timely intervention.

CN121601241APending Publication Date: 2026-03-03YICHUN UNIVERSITY
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Patent Information

Application Number
CN202511801255.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-02
Publication Date
2026-03-03

AI Technical Summary

Technical Problem

Traditional mental health assessment methods are static, have delayed interventions, and lack integration of multi-source data, making it impossible to accurately identify mental health risks in adolescents and to follow up in a timely manner, leading to the deterioration of mental health problems.

Method used

The intelligent early warning system for adolescent mental sub-health, which adopts multi-source heterogeneous data fusion, collects data through campus card consumption records, wearable device monitoring, social media analysis and questionnaire surveys. It combines weighted average, Kalman filter fusion algorithms and deep learning early warning models to construct a three-dimensional psychological profile, realize real-time prediction and dynamic tracking, and provide 24-hour online intervention through an intelligent early warning intervention platform.

Benefits of technology

It enables dynamic tracking and timely intervention of adolescents' mental state, reduces the risk of mental problems worsening due to lack of timely intervention, and overcomes data distortion caused by social desirability bias and test fatigue.

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Abstract

The invention discloses a teenager psychological sub-health intelligent early warning system based on multi-source heterogeneous data fusion, and the system comprises a data collection layer which collects the behavior, physiological and social three-dimensional data of teenagers through the multi-source channels of campus cards, wearable devices, social media and questionnaires, and builds an original data pool through cleaning, denoising and standardization; the data fusion layer is used for integrating multi-source data by adopting weighted average and Kalman filtering, mining psychological sub-health key indexes in combination with a feature selection and extraction technology, and forming a three-dimensional psychological portrait; the deep learning layer is used for constructing a multi-modal fusion early warning model based on a Transform architecture, and carrying out real-time prediction and dynamic tracking of psychological sub-health risks through large-scale data training and cross validation optimization; and the intelligent early warning layer is used for visually displaying an early warning result, integrating three-party linkage of a management end, a teacher end and a parent end, providing 24-hour online intervention by a built-in AI psychological counseling module, and automatically transferring high-risk cases to professional psychological consultants.
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