CN-121983321-A - Intelligent monitoring method and system for mental health state of teenagers

CN121983321ACN 121983321 ACN121983321 ACN 121983321ACN-121983321-A

Abstract

The invention discloses an intelligent monitoring method and system for mental health states of teenagers, which particularly relate to the technical field of mental health monitoring, and are characterized in that an initial mental state characteristic sequence is constructed by collecting emotion response data and intervention feedback data generated in the psychological intervention process of teenagers, the matching effect between intervention measures and the mental states of the teenagers is quantitatively evaluated based on the initial mental state characteristic sequence to obtain intervention measure dynamic effectiveness evaluation index data, long-term interaction change trend and short-term action effect between the intervention measures and the mental states are analyzed and identified to obtain intervention measure effectiveness attenuation trend prediction data and intervention measure instant effectiveness response characteristic data, psychological state matching adaptation degree data is generated through characteristic combination and optimization, the intervention measures are dynamically optimized, real-time optimized intervention strategies are output, and stability and individuation adaptation degree of the psychological intervention process are improved.

Inventors

HAN WEI
ZHANG YUANCHUN

Assignees

江苏卓顿信息科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (8)

1. An intelligent monitoring method for the mental health state of teenagers is characterized by comprising the following steps: s1, collecting emotion response data and intervention feedback data generated in a heart intervention process of teenager individuals, and performing time sequence alignment and feature extraction to obtain an initial psychological state feature sequence; s2, based on the initial psychological state characteristic sequence, evaluating the matching effect between the intervention measure and individual psychological states of teenagers, and outputting dynamic effectiveness evaluation index data of the intervention measure; S3, identifying a long-term interaction change trend between the intervention measure and the psychological state according to the intervention measure dynamic effectiveness evaluation index data, and outputting intervention measure effectiveness attenuation trend prediction data; s4, analyzing the influence effect of the short-term action of the intervention measure on the psychological state according to the dynamic effectiveness evaluation index data of the intervention measure, and outputting the instant utility response characteristic data of the intervention measure; S5, feature combination and optimization are carried out on the intervention measure utility attenuation trend prediction data and the intervention measure instant utility response feature data, and psychological state matching fitness data is generated; and S6, dynamically optimizing intervention measures based on the psychological state matching adaptation data, and outputting a real-time optimizing intervention strategy.
2. The intelligent adolescent mental health state monitoring method according to claim 1, characterized in that S1 is specifically: continuously collecting emotion response data and intervention feedback data generated by teenager individuals on intervention contents in a psychological intervention process through a human-computer interaction terminal; Carrying out time sequence alignment on emotion response data and intervention feedback data according to a unified time reference; And respectively carrying out feature extraction on the emotion response data and the intervention feedback data after time sequence alignment to form an initial psychological state feature sequence.
3. The intelligent adolescent mental health state monitoring method according to claim 2, characterized in that S2 is specifically: based on the initial psychological state characteristic sequence, establishing a dynamic matching evaluation model between the intervention measure and the individual psychological state of teenagers; Based on the dynamic matching evaluation model, calculating matching probability values between different intervention measures and the initial psychological state feature sequences to obtain a matching probability matrix; Based on the matching probability matrix, quantifying the dynamic matching degree of the intervention measures on the individual psychological states of the teenagers, and outputting the dynamic effectiveness evaluation index data of the intervention measures.
4. The intelligent adolescent mental health monitoring method according to claim 3, characterized in that S3 is specifically: According to the psychological intervention measure category, performing time sequence arrangement on the intervention measure dynamic effectiveness evaluation index data to form an intervention measure dynamic effectiveness evaluation index time sequence corresponding to each intervention measure; based on the intervention measure dynamic effectiveness evaluation index time sequence, trend decomposition is carried out to form a long-term trend component sequence representing the long-term interaction change trend of the intervention measure and the psychological state; and carrying out trend fitting and trend extrapolation based on the long-term trend component sequence to obtain interference measure utility attenuation trend prediction data.
5. The intelligent adolescent mental health monitoring method according to claim 4, characterized in that S4 is specifically: Intercepting an intervention dynamic effectiveness evaluation index time sequence by a fixed-length sliding time window based on the intervention dynamic effectiveness evaluation index data, and calculating an instantaneous increment vector of an evaluation index in each time window; extracting multi-scale change characteristics of the instantaneous increment vector by utilizing wavelet transformation; And positioning the instantaneous peak position and the corresponding amplitude value from the multi-scale change characteristic, and arranging the instantaneous peak position and the corresponding amplitude value according to the time sequence to generate the intervention measure instant utility response characteristic data.
6. The intelligent adolescent mental health monitoring method according to claim 5, characterized in that S5 is specifically: Adopting a multidimensional feature space mapping method to respectively perform unified feature space conversion on the intervention measure utility attenuation trend prediction data and the intervention measure instant utility response feature data; Calculating a cross correlation coefficient between the intervention measure utility attenuation trend prediction data and the intervention measure instant utility response characteristic data after the unified feature space conversion; And carrying out feature weighted fusion on the intervention measure utility attenuation trend prediction data and the intervention measure instant utility response feature data based on the cross-correlation coefficient to generate psychological state matching fitness data.
7. The intelligent adolescent mental health monitoring method according to claim 6, characterized in that S6 is specifically: Adjusting the selection probability distribution of the intervention measures according to the psychological state matching adaptation data; selecting intervention measure combinations currently suitable for individual psychological states of teenagers in real time according to the adjusted intervention measure selection probability distribution; and sequencing the intervention measure combinations selected in real time to form a real-time optimized intervention strategy corresponding to the mental state matching adaptation data.
8. An intelligent adolescent mental health state monitoring system for implementing the intelligent adolescent mental health state monitoring method according to any one of claims 1-7, characterized by comprising: The feature extraction module is used for collecting emotion response data and intervention feedback data generated in the psychological intervention process of teenager individuals, and carrying out time sequence alignment and feature extraction to obtain an initial psychological state feature sequence; The matching evaluation module is used for evaluating the matching effect between the intervention measure and the individual psychological states of teenagers based on the initial psychological state characteristic sequence and outputting the dynamic effectiveness evaluation index data of the intervention measure; The long-term trend module is used for identifying the long-term interaction change trend between the intervention measure and the psychological state according to the intervention measure dynamic effectiveness evaluation index data and outputting intervention measure effectiveness attenuation trend prediction data; The short-term response module is used for analyzing the influence effect of the short-term action of the intervention measure on the psychological state according to the dynamic effectiveness evaluation index data of the intervention measure and outputting immediate utility response characteristic data of the intervention measure; the adaptation generation module is used for carrying out feature combination and optimization on the intervention measure utility attenuation trend prediction data and the intervention measure instant utility response feature data to generate psychological state matching adaptation degree data; and the intervention optimization module is used for dynamically optimizing intervention measures based on the psychological state matching adaptation data and outputting real-time optimization intervention strategies.

Description

Intelligent monitoring method and system for mental health state of teenagers Technical Field The invention relates to the technical field of mental health monitoring, in particular to an intelligent monitoring method and system for mental health status of teenagers. Background The prior teenager mental health state monitoring usually relies on an informatization system to record and analyze emotion related information and feedback related information formed in the intervention interaction process, and accordingly gives out mental state evaluation results and intervention suggestions, and in application scenes such as school mental coaching, medical mental services, on-line mental intervention and the like, a fixed evaluation rule or a preset evaluation flow is adopted to judge the intervention effect, and a staged conclusion is used as the basis of intervention selection, so that the teenager mental state is continuously tracked and managed. The existing teenager mental health state monitoring method is lack of a closed-loop linkage mechanism facing individual mental state change for evaluation of the validity of mental intervention measures and adjustment of intervention strategies, so that the degree of matching between the intervention measures and the individual mental states of the teenagers is difficult to keep stable, and the continuous suitability of the mental intervention process and the reliability of monitoring conclusion are affected. In order to solve the above problems, a technical solution is now provided. Disclosure of Invention In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a method and a system for intelligently monitoring mental health status of teenagers, so as to solve the problems set forth in the background art. In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent monitoring method for mental health state of teenagers comprises the following steps: s1, collecting emotion response data and intervention feedback data generated in a heart intervention process of teenager individuals, and performing time sequence alignment and feature extraction to obtain an initial psychological state feature sequence; s2, based on the initial psychological state characteristic sequence, evaluating the matching effect between the intervention measure and individual psychological states of teenagers, and outputting dynamic effectiveness evaluation index data of the intervention measure; S3, identifying a long-term interaction change trend between the intervention measure and the psychological state according to the intervention measure dynamic effectiveness evaluation index data, and outputting intervention measure effectiveness attenuation trend prediction data; s4, analyzing the influence effect of the short-term action of the intervention measure on the psychological state according to the dynamic effectiveness evaluation index data of the intervention measure, and outputting the instant utility response characteristic data of the intervention measure; S5, feature combination and optimization are carried out on the intervention measure utility attenuation trend prediction data and the intervention measure instant utility response feature data, and psychological state matching fitness data is generated; and S6, dynamically optimizing intervention measures based on the psychological state matching adaptation data, and outputting a real-time optimizing intervention strategy. In a preferred embodiment, S1 is specifically: continuously collecting emotion response data and intervention feedback data generated by teenager individuals on intervention contents in a psychological intervention process through a human-computer interaction terminal; Carrying out time sequence alignment on emotion response data and intervention feedback data according to a unified time reference; And respectively carrying out feature extraction on the emotion response data and the intervention feedback data after time sequence alignment to form an initial psychological state feature sequence. In a preferred embodiment, S2 is specifically: based on the initial psychological state characteristic sequence, establishing a dynamic matching evaluation model between the intervention measure and the individual psychological state of teenagers; Based on the dynamic matching evaluation model, calculating matching probability values between different intervention measures and the initial psychological state feature sequences to obtain a matching probability matrix; Based on the matching probability matrix, quantifying the dynamic matching degree of the intervention measures on the individual psychological states of the teenagers, and outputting the dynamic effectiveness evaluation index data of the intervention measures. In a preferred embodiment, S3 is specifically: According to the psychological intervention