CN114388103A - Algorithm for teenager psychological early warning analysis - Google Patents
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Abstract
The invention relates to the technical field of mental health management, in particular to an algorithm for teenager mental early warning analysis. The method comprises the steps of establishing a teenager psychological crisis early warning mechanism, developing a system, collecting data, processing data, analyzing psychological health, constructing a psychological portrait, counting and reporting screening results, feeding back the screening results, dividing risk levels, customizing an intervention scheme, carrying out psychological crisis intervention by a controller with visitor identity reading and judging functions, establishing a psychological crisis intervention file, tracking and feeding back the psychological intervention effect and the like. The design of the invention can analyze and evaluate the mental health condition of the teenagers from multiple aspects, thereby effectively improving the evaluation efficiency of the mental health; the psychological crisis types can be identified and the grades are classified, so that an effective psychological intervention scheme can be customized in time in a targeted manner, and the psychological crisis problem of teenagers is effectively improved; by establishing the systematic psychological crisis intervention file, a campus manager can conveniently carry out psychological health management work, and the probability of the teenagers in generating psychological crisis is effectively reduced.
Description
Technical Field
The invention relates to the technical field of mental health management, in particular to an algorithm for teenager mental early warning analysis.
Background
Education is one of the fundamental motivations for national development and is one of the standards for measuring the comprehensive strength of a country. And teenagers in the basic education stage are more important for education as future builders in China. Research and study on the health problems of teenagers have been concerned about the development of national society. However, the health of the teenager includes not only physiological but also psychological. The psychological problem is different from physiological diseases, has the characteristics of privacy and concealment, and people with psychological problems can suppress the symptoms of the psychological problems and can cause the occurrence of psychological abnormal behaviors once the psychological problems occur. According to investigation, most of the current unit managers still only rely on the traditional means, namely high-frequency psychological safety education, to prevent the psychological safety problem, and the passive preventive measures obviously do not meet the actual situation of the current psychological safety problem.
In daily life, parents and teachers of students are advocated to pay attention to emotion, behavior and speech of the students frequently at ordinary times so as not to wait until problems occur and then seek solution measures. Parents may not pay attention to students, on one hand, the pressure of life causes the parents to be busy with running waves, on the other hand, most parents do not know which points to pay attention to and cannot pay attention to all directions, and most people cannot know the obvious signs of the psychological health risks. Mental health is affected by a series of factors such as the character and the external environment, and the symptom of mental health risk is almost the last warning line before the psychological problem occurs. Therefore, how to carry out mental health management work on the campus so as to screen the mental health problems of teenagers in time and avoid the occurrence of mental crisis is a problem to be solved urgently, and the need of doing mental health education work to make students actively and healthily grow up requires scientific knowledge and long-term effort. However, no algorithm for early warning analysis aiming at the mental health of teenagers based on big data and intelligent algorithm exists at present.
Disclosure of Invention
The invention aims to provide an algorithm for teenager psychological early warning analysis, so as to solve the problems in the background technology.
In order to solve the above technical problems, an object of the present invention is to provide an algorithm for a teenager psychological early warning analysis, comprising the following steps:
s1, establishing a teenager psychological crisis early warning mechanism in the campus, and developing a system for teenager psychological early warning analysis based on a computer platform of a campus management center;
s2, acquiring data related to teenager mental health from multiple aspects through multiple means, including but not limited to regularly conducted mental screening activity reports and psychological test questionnaires;
s3, processing operations such as cleaning, screening, classifying and sorting are carried out on the collected mass data, so that the collected mass data can meet the requirements of subsequent analysis and calculation;
s4, based on a large amount of data, conducting mental health analysis of teenagers through methods such as statistical analysis and intelligent algorithm, and respectively constructing a mental portrait of each teenager according to analysis results so as to rapidly screen out teenagers with possible psychological crises;
s5, judging and identifying the screened psychological crisis types of the teenagers, counting and reporting the screening results, and feeding back the results to educators and parents of students;
s6, dividing the psychological crisis screened and judged into different risk levels according to a certain rule, customizing different intervention schemes according to the different risk levels, inviting professional psychologists, and performing psychological crisis intervention by teachers and parents of students;
s7, establishing a systematic psychological crisis intervention file, so that the psychological health conditions of students in the campus can be relied on, the students continuously track teenagers with psychological crisis, and the psychological intervention effect is fed back in time.
As a further improvement of the technical solution, in S1, a specific method for establishing a teenager psychological crisis early warning mechanism in a campus includes the following steps:
s1.1, establishing a teenager psychological crisis early warning mechanism in a campus, and making regular comprehensive psychological risk screening and psychological health assessment activities;
s1.2, relying on a campus network to comprehensively cover a campus security monitoring network and a campus information comprehensive management platform, developing a system for teenager psychological early warning analysis, wherein the system is loaded in a machine room of a campus management center;
s1.3, the system is connected with a campus information comprehensive management platform and used for sharing student data related to mental health;
s1.4, software applications or small programs which can be loaded on a mobile phone end are researched and developed, and teachers and parents of students can access the system through the mobile phone;
s1.5, real-name authentication is needed when a teacher and parents access the system, and the system opens corresponding operation permission according to the identity of an accessor.
In S1.1, the regular basis may be estimated once a year according to the development stage of the teenager, for example, the teenager aged 5-18 years may be selected.
In S1.5, the authority of the teacher includes organizing students to perform psychological tests in the school, and collecting and reporting psychological test reports, observing, recording and reporting daily and classroom behaviors of all students in the management class, and checking psychological health analysis results of all students in the management class; the parents' rights include assisting the children to complete the psychological test outside the school and report the psychological test report, observing, recording and reporting the daily and family behaviors of the children, checking the psychological health analysis results of the children, and the like.
As a further improvement of the present technical solution, in S2, the specific method for obtaining data related to mental health of teenagers from multiple aspects by multiple means includes the following steps:
s2.1, organizing the psychological screening activities of students in the management class of the teacher to participate in the campus development, collecting psychological test reports of all the students and uploading the psychological test reports to a system data layer;
s2.2, compiling a psychological test questionnaire by professional psychology professionals, wherein simple questionnaires or older students can finish tests by themselves, and teachers or parents upload test reports, and complicated questionnaires or younger students can finish tests instead of parents and directly upload test reports to a system data layer;
s2.3, acquiring daily school activity conditions, learning states and classroom performances of each student based on campus safety monitoring and teacher observation and statistics;
s2.4, directly acquiring the courseware, learning condition and examination condition data of each student from the campus information comprehensive management platform;
s2.5, observing, counting and reporting by parents of students to obtain the family life state, work and rest habits and the physical health condition of each student after class;
and S2.6, combining the observation records of teachers and parents to know the character and personality of each student.
Wherein, in the S2.1 and the S2.2, available psychological screening questionnaires include: MHT test for mental health diagnosis of primary and middle school students, intellectual test (wecker adult intellectual test, combined rewining test, chinese binner test), personality test (minnesota multi-personality test, kanji 16 personality factor test, esseck personality questionnaire), psychological and behavioral assessment (90 symptom list, depression self-rating scale, anxiety self-rating scale), stress and related assessment (life practice scale, social support rating scale, coping style questionnaire), and the like, wherein MHT test for mental health diagnosis of primary and middle school students should be mainly used; meanwhile, it is worth noting that: traditional mental health assessment methods such as depression and anxiety are not suitable for assessing psychological crisis conditions of primary and secondary school students, because the assessment methods are not designed directly based on psychological risk screening and psychological crisis identification, and are aimed at long-term psychological health level under general conditions, so that the assessment methods are not as sensitive and accurate to the psychological crisis.
As a further improvement of the present technical solution, in S3, the specific method for performing processing operation on a large amount of data includes the following steps:
s3.1, the system acquires a large number of open real psychological crisis cases of the teenagers from the network information platform of each empowerment psychological service organization, and stores the accumulated cases in a cloud platform connected with the system after classification and arrangement;
s3.2, the system acquires and arranges a large number of visible signs of mental health risks from the network information platform of each empowerment psychological service organization, and feeds the sign information back to education workers and parents of students for learning reference;
s3.3, according to a standard grading mechanism, grading the psychological health degree of each student from the aspects of intelligence, personality, psychology, behavior, stress ability and the like through a large number of psychological test questionnaires collected by the system, and comprehensively evaluating the psychological health condition of each student according to the psychological health degree;
s3.4, respectively cleaning and screening all related data belonging to the same student, screening out repeated and invalid data, and extracting information data with high correlation;
s3.5, classifying and summarizing the screened effective information, and converting all data into a uniform standard format;
and S3.6, the information data of each student are respectively arranged and stored in a report form graph format.
As a further improvement of the technical solution, in S3.3, the method for comprehensively evaluating the mental health condition of each student comprises:
and comprehensively evaluating through a standard mean value, and then calculating the score according to the following expression:
wherein f is the overall mental health score for each student, x1，x2，...，xnRespectively the scoring values of the psychological test questionnaire of the student on the aspects of intelligence, personality, psychology, behavior, stress ability and the like;
and comprehensively evaluating by using the weighted average, and then calculating the score by the following expression:
wherein F is the overall mental health score for each student, x1，x2，...，xkRespectively the score values of the student psychological test questionnaire in the aspects of intelligence, personality, psychology, behavior, stress ability and the like, f1，f2，...，fkIs called weight, respectively represents the proportion of the health score value of the psychological test questionnaire in the comprehensive mental health degree score, and f1+f2+...+f5＝1，f1，f2，...，fkThe value of (c) can be set by a psychologist.
As a further improvement of the present technical solution, in S4, the specific method for performing mental health analysis of teenagers includes the following steps:
s4.1, establishing a teenager psychological crisis early warning model by relying on a large number of real psychological crisis cases, and improving the prediction accuracy of the model by training and learning the large number of real cases;
s4.2, importing the sorted student information data, and intelligently analyzing psychological crisis conditions which may occur to each student respectively from the aspects of psychological health risks, learning adaptation conditions, problem behaviors and the like by utilizing a learning technology of data fitting;
s4.3, accurately sketching the teenager psychological images of the students respectively by combining scientific and technological means, identifying the personality types of the students according to internationally recognized personality division standards, and carrying out label recording;
and S4.4, combining the teenager psychological images of the same student and the psychological test questionnaire scores and the comprehensive psychological health assessment scores of all aspects of the teenager psychological images, and analyzing the psychological crisis condition according to the scores of all aspects and aiming at the aspect of abnormal scores so as to realize accurate positioning, assessment and screening.
As a further improvement of the present technical solution, in S4.2, a polyfit (X, Y, N) function and a polyfal function may be used for calculating the data fitting, and the operation processes of the two functions are respectively:
the syntax of the operation of the polyfit (X, Y, N) function (polynomial curve fitting) is:
p ═ polyfit (x, y, n) returns the coefficients of the polynomial p (x) of order n, which is the best fit (in least squares) to the data in y; the coefficients in p are arranged in descending power, and the length of p is n + 1;
p(x)＝p1xn+p2xn-1+...+pnx+pn+1；
the method specifically comprises the following steps:
step1, where p is polyfit (x, y, n) returns the coefficients of a polynomial p (x) with the order n;
step2, [ p, S ] ═ polyfit (x, y, n) returns a structure S that can be used as an input to a polyfal to obtain an error estimate;
step3, [ p, S, mu ] ═ polyfit (x, y, n) return value mu is a bivariate vector, representing the mean (x) and variance std (x) distributions of predictor errors;
among them, it should be noted that: the matlab realizes that the polyfit function uses a least square + QR matrix decomposition algorithm, so when fitting is carried out in the order of n, n is not more than length (x);
the syntax of the operation of the poly val function (corresponding to the polynomial fit, polynomial evaluation function) is:
step1, y is poly val (p, x); returning the value of the fitted polynomial;
Step2、[y，delta]＝polyval(p，x，S)；
generating an error estimate delta by using an optional output structure S generated by the polyfit, wherein the delta is an error standard deviation estimate when a future observed value at x is predicted by using p (x), and if a coefficient in p is a least square estimate calculated by the polyfit, errors in the polyfit data input are in independent normal distribution and have constant variance, y +/-delta at least comprises 50% of the future observed value at x;
step3, y ═ poly val (p, x, [ ], mu) or [ y, delta ] ═ poly val (p, x, S, mu);
using x1＝(x-μ1)/μ2Substitution of x, in this equation, μ1Mean (x) and μ2Std (x); centering and scaling parameters mu ═ mu1，μ2]Is an optional output calculated from the polyfit.
As a further improvement of the technical solution, in S5, the specific method for statistically reporting and feeding back the screening result includes the following steps:
s5.1, screening and detecting the possible abnormal aspects in various information data of the students by combining the personality characteristics of the students, and carrying out deep excavation to screen out teenagers with abnormal psychological states;
s5.2, deeply analyzing and screening the abnormal information of the teenagers to identify the possible psychological crisis and intelligently judge the crisis type;
s5.3, respectively sorting the teenager list statistical tables of all groups by taking the class, the grade, the age, the sex, the psychological crisis type and the like as grouping bases;
s5.4, reporting all the statistical tables to educators of a campus management layer to serve as a basis for the management layer to carry out psychological screening activities, strengthen safety education of students and adjust a psychological crisis early warning mechanism;
s5.5, feeding back a statistical table which takes the class as a grouping basis to a teacher managing the class, wherein the teacher needs to strengthen the attention of students screening out possible psychological crises according to a list, including paying attention to the emotion, behavior and speech of the students and comparing psychological crisis signs;
and S5.6, feeding back the psychological state analysis report of each student and the possible psychological crisis condition to parents of the student, reminding the parents to actively pay attention to the daily behaviors, the emotion and the physical health state of children according to the early-warning psychological crisis type, and comparing psychological crisis signs so as to prevent the children from getting worse.
As a further improvement of the present technical solution, in S6, the specific method for performing psychological crisis intervention includes the following steps:
s6.1, grading and judging the possible psychological crisis degree of the teenagers according to the internationally recognized psychological problem evaluation standard;
s6.2, carrying out grade division on the psychological crisis degree of each teenager according to the grading condition on the juvenile population with the same psychological crisis type;
s6.3, combining a large number of psychological crisis examples and suggestions of professional psychological experts, and respectively customizing different universal intervention schemes aiming at different types and psychological crisis conditions with different risk degree grades;
s6.4, inviting professional psychological experts aiming at psychological crisis teenagers with particularly outstanding problems, and adjusting the psychological experts in the general intervention scheme to make an accurate and targeted psychological intervention scheme;
and S6.5, according to the customized scheme, the psychologist, the teacher and the parents of the students cooperate with one another to perform psychological intervention and correction on the teenagers with psychological crisis.
As a further improvement of the present technical solution, in S7, the specific method for continuously tracking teenagers with psychological crisis includes the following steps:
s7.1, respectively establishing a mental health monitoring file for each student, and adding a mental crisis intervention file for the students with mental intervention records;
s7.2, storing all files in a cloud end connected with the system, so that the mental health conditions of all students in the campus can be determined, and a campus manager can follow the mental health conditions;
s7.3, continuously tracking and monitoring teenagers with psychological crisis, recording the implementation process of the intervention scheme, and increasing the frequency of performing psychological tests on the teenagers;
s7.4, respectively recording the previous risk assessment results, the suicide possibility questionnaire score, the psychological consultation record, the life record and the like of the teenager, and drawing a potential risk curve graph to support the establishment of a psychological file of the current state of the teenager;
and S7.5, comparing the test records of the early-warned possibly existing psychological crisis of the teenager with the historical psychological intervention records and risk assessment results, assessing the effect of the psychological intervention, and timely adjusting the intervention scheme according to the intervention effect until the psychological crisis state of the teenager is reduced to be below the early warning threshold value preset by the system.
The invention also provides a system for teenager psychological early warning analysis, which comprises a data acquisition and processing unit, a psychological health analysis unit, a psychological crisis early warning unit, a psychological detection file unit and an intervention tracking feedback unit, wherein the operation process of the system is used for realizing the algorithm steps for teenager psychological early warning analysis.
The present invention also provides a system operating apparatus for a teenager mental early warning analysis, which includes a processor, a memory, and a computer program stored in the memory and operating on the processor, wherein the processor is configured to implement the above algorithm steps for the teenager mental early warning analysis when executing the computer program.
It is a fourth object of the present invention to provide a computer readable storage medium, which stores a computer program, which when executed by a processor implements the above-mentioned algorithm steps for a teenager mental warning analysis.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the algorithm for teenager psychological early warning analysis, a teenager psychological crisis early warning mechanism is established in a campus, professional psychological risk screening and psychological health assessment activities are regularly carried out in the campus, data related to teenager psychological health are comprehensively collected by adopting various means, and the psychological health conditions of the teenagers can be analyzed and assessed from aspects such as psychological health risks, learning adaptation conditions, problem behaviors and the like based on a self-research system and algorithm, so that the psychological health assessment efficiency is effectively improved, and early teenager psychological health problems are prevented;
2. the algorithm for teenager psychological early warning analysis can deeply excavate psychological crises possibly existing in teenager daily performances, identify the type of the psychological crises and carry out grade division, so that an effective psychological intervention scheme can be customized in time in a targeted manner, and psychological experts, education workers and parents cooperate to effectively improve the psychological crises of teenagers;
3. the algorithm for the teenager psychological early warning analysis ensures that the psychological health condition of the teenager can be followed and traced by establishing the systematic psychological crisis intervention file, so that a campus manager can conveniently carry out the psychological health management work, the probability of the teenager generating the psychological crisis is effectively reduced, and the healthy growth of the teenager is promoted.
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FIG. 1 is a block diagram of the overall algorithm flow of the present invention;
FIG. 2 is a block diagram of a partial algorithm flow of the present invention;
FIG. 3 is a second block diagram of the local algorithm of the present invention;
FIG. 4 is a third block diagram of the local algorithm of the present invention;
FIG. 5 is a fourth flowchart of the local algorithm of the present invention;
FIG. 6 is a block diagram of a partial algorithm according to the present invention;
FIG. 7 is a sixth block diagram of the local algorithm of the present invention;
FIG. 8 is a seventh block diagram of the local algorithm flow of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1 to 8, the present embodiment provides an algorithm for a teenager psychological early warning analysis, which includes the following steps:
s1, establishing a teenager psychological crisis early warning mechanism in the campus, and developing a system for teenager psychological early warning analysis based on a computer platform of a campus management center;
s2, acquiring data related to teenager mental health from multiple aspects through multiple means, including but not limited to regularly conducted mental screening activity reports and psychological test questionnaires;
s3, processing operations such as cleaning, screening, classifying and sorting are carried out on the collected mass data, so that the collected mass data can meet the requirements of subsequent analysis and calculation;
s4, based on a large amount of data, conducting mental health analysis of teenagers through methods such as statistical analysis and intelligent algorithm, and respectively constructing a mental portrait of each teenager according to analysis results so as to rapidly screen out teenagers with possible psychological crises;
s5, judging and identifying the screened psychological crisis types of the teenagers, counting and reporting the screening results, and feeding back the results to educators and parents of students;
s6, dividing the psychological crisis screened and judged into different risk levels according to a certain rule, customizing different intervention schemes according to the different risk levels, inviting professional psychologists, and performing psychological crisis intervention by teachers and parents of students;
s7, establishing a systematic psychological crisis intervention file, so that the psychological health conditions of students in the campus can be relied on, the students continuously track teenagers with psychological crisis, and the psychological intervention effect is fed back in time.
In this embodiment, in S1, the specific method for establishing a teenager psychological crisis early warning mechanism in a campus includes the following steps:
s1.1, establishing a teenager psychological crisis early warning mechanism in a campus, and making regular comprehensive psychological risk screening and psychological health assessment activities;
s1.2, relying on a campus network to comprehensively cover a campus security monitoring network and a campus information comprehensive management platform, developing a system for teenager psychological early warning analysis, wherein the system is loaded in a machine room of a campus management center;
s1.3, the system is connected with a campus information comprehensive management platform and used for sharing student data related to mental health;
s1.4, software applications or small programs which can be loaded on a mobile phone end are researched and developed, and teachers and parents of students can access the system through the mobile phone;
s1.5, real-name authentication is needed when a teacher and parents access the system, and the system opens corresponding operation permission according to the identity of an accessor.
In S1.1, the regular basis can be estimated once a year according to the development stage of the teenager, for example, the teenager of 5-18 years old can be selected.
In S1.5, the authority of the teacher includes organizing students to perform psychological tests in the school, collecting and reporting psychological test reports, observing, recording and reporting daily and classroom behaviors of all students in the management class, checking psychological health analysis results of all students in the management class, and the like; the parents' rights include assisting the children to complete the psychological test outside the school and report the psychological test report, observing, recording and reporting the daily and family behaviors of the children, checking the psychological health analysis results of the children, and the like.
In this embodiment, in S2, the specific method for obtaining data related to mental health of teenagers from multiple aspects by multiple means includes the following steps:
s2.1, organizing the psychological screening activities of students in the management class of the teacher to participate in the campus development, collecting psychological test reports of all the students and uploading the psychological test reports to a system data layer;
s2.2, compiling a psychological test questionnaire by professional psychology professionals, wherein simple questionnaires or older students can finish tests by themselves, and teachers or parents upload test reports, and complicated questionnaires or younger students can finish tests instead of parents and directly upload test reports to a system data layer;
s2.3, acquiring daily school activity conditions, learning states and classroom performances of each student based on campus safety monitoring and teacher observation and statistics;
s2.4, directly acquiring the courseware, learning condition and examination condition data of each student from the campus information comprehensive management platform;
s2.5, observing, counting and reporting by parents of students to obtain the family life state, work and rest habits and the physical health condition of each student after class;
and S2.6, combining the observation records of teachers and parents to know the character and personality of each student.
Among S2.1 and S2.2, available psychological screening questionnaires include: MHT test for mental health diagnosis of primary and middle school students, intellectual test (wecker adult intellectual test, combined rewining test, chinese binner test), personality test (minnesota multi-personality test, kanji 16 personality factor test, esseck personality questionnaire), psychological and behavioral assessment (90 symptom list, depression self-rating scale, anxiety self-rating scale), stress and related assessment (life practice scale, social support rating scale, coping style questionnaire), and the like, wherein MHT test for mental health diagnosis of primary and middle school students should be mainly used; meanwhile, it is worth noting that: traditional mental health assessment methods such as depression and anxiety are not suitable for assessing psychological crisis conditions of primary and secondary school students, because the assessment methods are not designed directly based on psychological risk screening and psychological crisis identification, and are aimed at long-term psychological health level under general conditions, so that the assessment methods are not as sensitive and accurate to the psychological crisis.
In this embodiment, in S3, the specific method for performing processing operation on a large amount of data includes the following steps:
s3.1, the system acquires a large number of open real psychological crisis cases of the teenagers from the network information platform of each empowerment psychological service organization, and stores the accumulated cases in a cloud platform connected with the system after classification and arrangement;
s3.2, the system acquires and arranges a large number of visible signs of mental health risks from the network information platform of each empowerment psychological service organization, and feeds the sign information back to education workers and parents of students for learning reference;
s3.3, according to a standard grading mechanism, grading the psychological health degree of each student from the aspects of intelligence, personality, psychology, behavior, stress ability and the like through a large number of psychological test questionnaires collected by the system, and comprehensively evaluating the psychological health condition of each student according to the psychological health degree;
s3.4, respectively cleaning and screening all related data belonging to the same student, screening out repeated and invalid data, and extracting information data with high correlation;
s3.5, classifying and summarizing the screened effective information, and converting all data into a uniform standard format;
and S3.6, the information data of each student are respectively arranged and stored in a report form graph format.
Specifically, in S3.3, the method for comprehensively evaluating the mental health condition of each student is as follows:
and comprehensively evaluating through a standard mean value, and then calculating the score according to the following expression:
wherein f is the overall mental health score for each student, x1，x2，...，xnRespectively the scoring values of the psychological test questionnaire of the student on the aspects of intelligence, personality, psychology, behavior, stress ability and the like;
and comprehensively evaluating by using the weighted average, and then calculating the score by the following expression:
wherein F is the overall mental health score for each student, x1，x2，...，xkRespectively the score values of the student psychological test questionnaire in the aspects of intelligence, personality, psychology, behavior, stress ability and the like, f1，f2，...，fkIs called weight, respectively represents the proportion of the health score value of the psychological test questionnaire in the comprehensive mental health degree score, and f1+f2+...+f5＝1，f1，f2，...，fkThe value of (c) can be set by a psychologist.
In this embodiment, in S4, the specific method for performing mental health analysis of teenagers includes the following steps:
s4.1, establishing a teenager psychological crisis early warning model by relying on a large number of real psychological crisis cases, and improving the prediction accuracy of the model by training and learning the large number of real cases;
s4.2, importing the sorted student information data, and intelligently analyzing psychological crisis conditions which may occur to each student respectively from the aspects of psychological health risks, learning adaptation conditions, problem behaviors and the like by utilizing a learning technology of data fitting;
s4.3, accurately sketching the teenager psychological images of the students respectively by combining scientific and technological means, identifying the personality types of the students according to internationally recognized personality division standards, and carrying out label recording;
and S4.4, combining the teenager psychological images of the same student and the psychological test questionnaire scores and the comprehensive psychological health assessment scores of all aspects of the teenager psychological images, and analyzing the psychological crisis condition according to the scores of all aspects and aiming at the aspect of abnormal scores so as to realize accurate positioning, assessment and screening.
Specifically, in S4.2, the data fitting may be calculated by using a polyfit (X, Y, N) function and a polyfal function, where the two functions are respectively executed as follows:
the syntax of the operation of the polyfit (X, Y, N) function (polynomial curve fitting) is:
p ═ polyfit (x, y, n) returns the coefficients of the polynomial p (x) of order n, which is the best fit (in least squares) to the data in y; the coefficients in p are arranged in descending power, and the length of p is n + 1;
p(x)＝p1xn+p2xn-1+...+pnx+pn+1；
the method specifically comprises the following steps:
step1, where p is polyfit (x, y, n) returns the coefficients of a polynomial p (x) with the order n;
step2, [ p, S ] ═ polyfit (x, y, n) returns a structure S that can be used as an input to a polyfal to obtain an error estimate;
step3, [ p, S, mu ] ═ polyfit (x, y, n) return value mu is a bivariate vector, representing the mean (x) and variance std (x) distributions of predictor errors;
among them, it should be noted that: the matlab realizes that the polyfit function uses a least square + QR matrix decomposition algorithm, so when fitting is carried out in the order of n, n is not more than length (x);
the syntax of the operation of the poly val function (corresponding to the polynomial fit, polynomial evaluation function) is:
step1, y is poly val (p, x); returning the value of the fitted polynomial;
Step2、[y，delta]＝polyval(p，x，S)；
generating an error estimate delta by using an optional output structure S generated by the polyfit, wherein the delta is an error standard deviation estimate when a future observed value at x is predicted by using p (x), and if a coefficient in p is a least square estimate calculated by the polyfit, errors in the polyfit data input are in independent normal distribution and have constant variance, y +/-delta at least comprises 50% of the future observed value at x;
step3, y ═ poly val (p, x, [ ], mu) or [ y, delta ] ═ poly val (p, x, S, mu);
using x1＝(x-μ1)/μ2Substitution of x, in this equation, μ1Mean (x) and μ2Std (x); centering and scaling parameters mu ═ mu1，μ2]Is an optional output calculated from the polyfit.
In this embodiment, in S5, the specific method for statistically reporting and feeding back the screening result includes the following steps:
s5.1, screening and detecting the possible abnormal aspects in various information data of the students by combining the personality characteristics of the students, and carrying out deep excavation to screen out teenagers with abnormal psychological states;
s5.2, deeply analyzing and screening the abnormal information of the teenagers to identify the possible psychological crisis and intelligently judge the crisis type;
s5.3, respectively sorting the teenager list statistical tables of all groups by taking the class, the grade, the age, the sex, the psychological crisis type and the like as grouping bases;
s5.4, reporting all the statistical tables to educators of a campus management layer to serve as a basis for the management layer to carry out psychological screening activities, strengthen safety education of students and adjust a psychological crisis early warning mechanism;
s5.5, feeding back a statistical table which takes the class as a grouping basis to a teacher managing the class, wherein the teacher needs to strengthen the attention of students screening out possible psychological crises according to a list, including paying attention to the emotion, behavior and speech of the students and comparing psychological crisis signs;
and S5.6, feeding back the psychological state analysis report of each student and the possible psychological crisis condition to parents of the student, reminding the parents to actively pay attention to the daily behaviors, the emotion and the physical health state of children according to the early-warning psychological crisis type, and comparing psychological crisis signs so as to prevent the children from getting worse.
In this embodiment, in S6, the specific method for performing psychological crisis intervention includes the following steps:
s6.1, grading and judging the possible psychological crisis degree of the teenagers according to the internationally recognized psychological problem evaluation standard;
s6.2, carrying out grade division on the psychological crisis degree of each teenager according to the grading condition on the juvenile population with the same psychological crisis type;
s6.3, combining a large number of psychological crisis examples and suggestions of professional psychological experts, and respectively customizing different universal intervention schemes aiming at different types and psychological crisis conditions with different risk degree grades;
s6.4, inviting professional psychological experts aiming at psychological crisis teenagers with particularly outstanding problems, and adjusting the psychological experts in the general intervention scheme to make an accurate and targeted psychological intervention scheme;
and S6.5, according to the customized scheme, the psychologist, the teacher and the parents of the students cooperate with one another to perform psychological intervention and correction on the teenagers with psychological crisis.
In this embodiment, in S7, the specific method for continuously tracking teenagers with psychological crisis includes the following steps:
s7.1, respectively establishing a mental health monitoring file for each student, and adding a mental crisis intervention file for the students with mental intervention records;
s7.2, storing all files in a cloud end connected with the system, so that the mental health conditions of all students in the campus can be determined, and a campus manager can follow the mental health conditions;
s7.3, continuously tracking and monitoring teenagers with psychological crisis, recording the implementation process of the intervention scheme, and increasing the frequency of performing psychological tests on the teenagers;
s7.4, respectively recording the previous risk assessment results, the suicide possibility questionnaire score, the psychological consultation record, the life record and the like of the teenager, and drawing a potential risk curve graph to support the establishment of a psychological file of the current state of the teenager;
and S7.5, comparing the test records of the early-warned possibly existing psychological crisis of the teenager with the historical psychological intervention records and risk assessment results, assessing the effect of the psychological intervention, and timely adjusting the intervention scheme according to the intervention effect until the psychological crisis state of the teenager is reduced to be below the early warning threshold value preset by the system.
The embodiment also provides a system for teenager psychological early warning analysis, which comprises a data acquisition and processing unit, a psychological health analysis unit, a psychological crisis early warning unit, a psychological detection file unit and an intervention tracking feedback unit, wherein the operation process of the system is used for realizing the algorithm steps for teenager psychological early warning analysis.
The embodiment also provides a system running device for teenager psychological early warning analysis, which comprises a processor, a memory and a computer program stored in the memory and running on the processor.
The processor comprises one or more processing cores, the processor is connected with the memory through the bus, the memory is used for storing program instructions, and the processor executes the program instructions in the memory to realize the mental early warning analysis algorithm aiming at the teenagers.
Alternatively, the memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
In addition, the present invention also provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the algorithm steps for the teenager mental early warning analysis.
Optionally, the present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the algorithm steps of the above aspects for teenager mental warning analysis.
It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by hardware related to instructions of a program, which may be stored in a computer-readable storage medium, such as a read-only memory, a magnetic or optical disk, and the like.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. An algorithm for teenager psychological early warning analysis is characterized in that: the method comprises the following steps:
s1, establishing a teenager psychological crisis early warning mechanism in the campus, and developing a system for teenager psychological early warning analysis based on a computer platform of a campus management center;
s2, acquiring data related to teenager mental health from multiple aspects through multiple means, including but not limited to regularly conducted mental screening activity reports and psychological test questionnaires;
s3, processing operations such as cleaning, screening, classifying and sorting are carried out on the collected mass data, so that the collected mass data can meet the requirements of subsequent analysis and calculation;
s4, based on a large amount of data, conducting mental health analysis of teenagers through methods such as statistical analysis and intelligent algorithm, and respectively constructing a mental portrait of each teenager according to analysis results so as to rapidly screen out teenagers with possible psychological crises;
s5, judging and identifying the screened psychological crisis types of the teenagers, counting and reporting the screening results, and feeding back the results to educators and parents of students;
s6, dividing the psychological crisis screened and judged into different risk levels according to a certain rule, customizing different intervention schemes according to the different risk levels, inviting professional psychologists, and performing psychological crisis intervention by teachers and parents of students;
s7, establishing a systematic psychological crisis intervention file, so that the psychological health conditions of students in the campus can be relied on, the students continuously track teenagers with psychological crisis, and the psychological intervention effect is fed back in time.
2. The algorithm for juvenile mental warning analysis according to claim 1, wherein: in S1, the specific method for establishing a teenager psychological crisis early warning mechanism in the campus includes the following steps:
s1.1, establishing a teenager psychological crisis early warning mechanism in a campus, and making regular comprehensive psychological risk screening and psychological health assessment activities;
s1.2, relying on a campus network to comprehensively cover a campus security monitoring network and a campus information comprehensive management platform, developing a system for teenager psychological early warning analysis, wherein the system is loaded in a machine room of a campus management center;
s1.3, the system is connected with a campus information comprehensive management platform and used for sharing student data related to mental health;
s1.4, software applications or small programs which can be loaded on a mobile phone end are researched and developed, and teachers and parents of students can access the system through the mobile phone;
s1.5, real-name authentication is needed when a teacher and parents access the system, and the system opens corresponding operation permission according to the identity of an accessor.
3. The algorithm for juvenile mental warning analysis according to claim 2, characterized in that: in S2, the method for obtaining data related to mental health of teenagers from multiple aspects by multiple means includes the following steps:
s2.1, organizing the psychological screening activities of students in the management class of the teacher to participate in the campus development, collecting psychological test reports of all the students and uploading the psychological test reports to a system data layer;
s2.2, compiling a psychological test questionnaire by professional psychology professionals, wherein simple questionnaires or older students can finish tests by themselves, and teachers or parents upload test reports, and complicated questionnaires or younger students can finish tests instead of parents and directly upload test reports to a system data layer;
s2.3, acquiring daily school activity conditions, learning states and classroom performances of each student based on campus safety monitoring and teacher observation and statistics;
s2.4, directly acquiring the courseware, learning condition and examination condition data of each student from the campus information comprehensive management platform;
s2.5, observing, counting and reporting by parents of students to obtain the family life state, work and rest habits and the physical health condition of each student after class;
and S2.6, combining the observation records of teachers and parents to know the character and personality of each student.
4. The algorithm for juvenile mental warning analysis according to claim 3, characterized in that: in S3, the specific method for processing a large amount of data includes the following steps:
s3.1, the system acquires a large number of open real psychological crisis cases of the teenagers from the network information platform of each empowerment psychological service organization, and stores the accumulated cases in a cloud platform connected with the system after classification and arrangement;
s3.2, the system acquires and arranges a large number of visible signs of mental health risks from the network information platform of each empowerment psychological service organization, and feeds the sign information back to education workers and parents of students for learning reference;
s3.3, according to a standard grading mechanism, grading the psychological health degree of each student from the aspects of intelligence, personality, psychology, behavior, stress ability and the like through a large number of psychological test questionnaires collected by the system, and comprehensively evaluating the psychological health condition of each student according to the psychological health degree;
s3.4, respectively cleaning and screening all related data belonging to the same student, screening out repeated and invalid data, and extracting information data with high correlation;
s3.5, classifying and summarizing the screened effective information, and converting all data into a uniform standard format;
and S3.6, the information data of each student are respectively arranged and stored in a report form graph format.
5. The algorithm for juvenile mental warning analysis according to claim 4, wherein: in the step S3.3, the method for comprehensively evaluating the mental health condition of each student comprises the following steps:
and comprehensively evaluating through a standard mean value, and then calculating the score according to the following expression:
wherein f is the overall mental health score for each student, x1，x2，...，xnRespectively the scoring values of the psychological test questionnaire of the student on the aspects of intelligence, personality, psychology, behavior, stress ability and the like;
and comprehensively evaluating by using the weighted average, and then calculating the score by the following expression:
wherein F is the overall mental health score for each student, x1，x2，...，xkRespectively the score values of the student psychological test questionnaire in the aspects of intelligence, personality, psychology, behavior, stress ability and the like, f1,f2,...,fkIs called weight, respectively represents the proportion of the health score value of the psychological test questionnaire in the comprehensive mental health degree score, and f1+f2+...+f5＝1，f1,f2,...,fkThe value of (c) can be set by a psychologist.
6. The algorithm for juvenile mental warning analysis according to claim 4, wherein: in S4, the method for analyzing mental health of a teenager includes the following steps:
s4.1, establishing a teenager psychological crisis early warning model by relying on a large number of real psychological crisis cases, and improving the prediction accuracy of the model by training and learning the large number of real cases;
s4.2, importing the sorted student information data, and intelligently analyzing psychological crisis conditions which may occur to each student respectively from the aspects of psychological health risks, learning adaptation conditions, problem behaviors and the like by utilizing a learning technology of data fitting;
s4.3, accurately sketching the teenager psychological images of the students respectively by combining scientific and technological means, identifying the personality types of the students according to internationally recognized personality division standards, and carrying out label recording;
and S4.4, combining the teenager psychological images of the same student and the psychological test questionnaire scores and the comprehensive psychological health assessment scores of all aspects of the teenager psychological images, and analyzing the psychological crisis condition according to the scores of all aspects and aiming at the aspect of abnormal scores so as to realize accurate positioning, assessment and screening.
7. The algorithm for juvenile mental warning analysis according to claim 6, wherein: in S4.2, the data fitting may be calculated by using a polyfit (X, Y, N) function and a polyfal function, and the operation processes of the two functions are respectively:
the syntax of the operation of the polyfit (X, Y, N) function (polynomial curve fitting) is:
p ═ polyfit (x, y, n) returns the coefficients of the polynomial p (x) of order n, which is the best fit (in least squares) to the data in y; the coefficients in p are arranged in descending power, and the length of p is n + 1;
p(x)＝p1xn+p2xn-1+...+pnx+pn+1；
the method specifically comprises the following steps:
step1, where p is polyfit (x, y, n) returns the coefficients of a polynomial p (x) with the order n;
step2, [ p, S ] ═ polyfit (x, y, n) returns a structure S that can be used as an input to a polyfal to obtain an error estimate;
step3, [ p, S, mu ] ═ polyfit (x, y, n) return value mu is a bivariate vector, representing the mean (x) and variance std (x) distributions of predictor errors;
among them, it should be noted that: the matlab realizes that the polyfit function uses a least square + QR matrix decomposition algorithm, so when fitting is carried out in the order of n, n is not more than length (x);
the syntax of the operation of the poly val function (corresponding to the polynomial fit, polynomial evaluation function) is:
step1, y is poly val (p, x); returning the value of the fitted polynomial;
Step2、[y，delta]＝polyval(p，x，S)；
generating an error estimate delta by using an optional output structure S generated by the polyfit, wherein the delta is an error standard deviation estimate when a future observed value at x is predicted by using p (x), and if a coefficient in p is a least square estimate calculated by the polyfit, errors in the polyfit data input are in independent normal distribution and have constant variance, y +/-delta at least comprises 50% of the future observed value at x;
step3, y ═ poly val (p, x, [ ], mu) or [ y, delta ] ═ poly val (p, x, S, mu);
using x1＝(x-μ1)/μ2Substitution of x, in this equation, μ1Mean (x) and μ2Std (x); centering and scaling parameters mu ═ mu1,μ2]Is an optional output calculated from the polyfit.
8. The algorithm for juvenile mental warning analysis according to claim 6, wherein: in S5, the specific method for statistically reporting and feeding back the screening result includes the following steps:
s5.1, screening and detecting the possible abnormal aspects in various information data of the students by combining the personality characteristics of the students, and carrying out deep excavation to screen out teenagers with abnormal psychological states;
s5.2, deeply analyzing and screening the abnormal information of the teenagers to identify the possible psychological crisis and intelligently judge the crisis type;
s5.3, respectively sorting the teenager list statistical tables of all groups by taking the class, the grade, the age, the sex, the psychological crisis type and the like as grouping bases;
s5.4, reporting all the statistical tables to educators of a campus management layer to serve as a basis for the management layer to carry out psychological screening activities, strengthen safety education of students and adjust a psychological crisis early warning mechanism;
s5.5, feeding back a statistical table which takes the class as a grouping basis to a teacher managing the class, wherein the teacher needs to strengthen the attention of students screening out possible psychological crises according to a list, including paying attention to the emotion, behavior and speech of the students and comparing psychological crisis signs;
and S5.6, feeding back the psychological state analysis report of each student and the possible psychological crisis condition to parents of the student, reminding the parents to actively pay attention to the daily behaviors, the emotion and the physical health state of children according to the early-warning psychological crisis type, and comparing psychological crisis signs so as to prevent the children from getting worse.
9. The algorithm for juvenile mental warning analysis according to claim 8, wherein: in S6, the method for performing psychological crisis intervention includes the following steps:
s6.1, grading and judging the possible psychological crisis degree of the teenagers according to the internationally recognized psychological problem evaluation standard;
s6.2, carrying out grade division on the psychological crisis degree of each teenager according to the grading condition on the juvenile population with the same psychological crisis type;
s6.3, combining a large number of psychological crisis examples and suggestions of professional psychological experts, and respectively customizing different universal intervention schemes aiming at different types and psychological crisis conditions with different risk degree grades;
s6.4, inviting professional psychological experts aiming at psychological crisis teenagers with particularly outstanding problems, and adjusting the psychological experts in the general intervention scheme to make an accurate and targeted psychological intervention scheme;
and S6.5, according to the customized scheme, the psychologist, the teacher and the parents of the students cooperate with one another to perform psychological intervention and correction on the teenagers with psychological crisis.
10. The algorithm for juvenile mental warning analysis according to claim 9, wherein: in S7, the specific method for continuously tracking teenagers with psychological crisis includes the following steps:
s7.1, respectively establishing a mental health monitoring file for each student, and adding a mental crisis intervention file for the students with mental intervention records;
s7.2, storing all files in a cloud end connected with the system, so that the mental health conditions of all students in the campus can be determined, and a campus manager can follow the mental health conditions;
s7.3, continuously tracking and monitoring teenagers with psychological crisis, recording the implementation process of the intervention scheme, and increasing the frequency of performing psychological tests on the teenagers;
s7.4, respectively recording the previous risk assessment results, the suicide possibility questionnaire score, the psychological consultation record, the life record and the like of the teenager, and drawing a potential risk curve graph to support the establishment of a psychological file of the current state of the teenager;
and S7.5, comparing the test records of the early-warned possibly existing psychological crisis of the teenager with the historical psychological intervention records and risk assessment results, assessing the effect of the psychological intervention, and timely adjusting the intervention scheme according to the intervention effect until the psychological crisis state of the teenager is reduced to be below the early warning threshold value preset by the system.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114937501A (en) * | 2022-05-23 | 2022-08-23 | 上海迎智正能文化发展有限公司 | Mental health risk grade assessment system and method based on knowledge graph |
| CN115497587A (en) * | 2022-11-16 | 2022-12-20 | 内江市感官密码科技有限公司 | Smart campus system and mental health monitoring method |
| CN115565643A (en) * | 2022-10-14 | 2023-01-03 | 杭州中暖科技有限公司 | Grading management system for family education and mental health education |
| CN115641236A (en) * | 2022-09-30 | 2023-01-24 | 广州宏途数字科技有限公司 | Campus intelligent security management system based on big data |
| CN117038074A (en) * | 2023-08-01 | 2023-11-10 | 中国工业互联网研究院 | User management method, device, equipment and storage medium based on big data |
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2021
- 2021-12-07 CN CN202111482566.9A patent/CN114388103A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114937501A (en) * | 2022-05-23 | 2022-08-23 | 上海迎智正能文化发展有限公司 | Mental health risk grade assessment system and method based on knowledge graph |
| CN115641236A (en) * | 2022-09-30 | 2023-01-24 | 广州宏途数字科技有限公司 | Campus intelligent security management system based on big data |
| CN115565643A (en) * | 2022-10-14 | 2023-01-03 | 杭州中暖科技有限公司 | Grading management system for family education and mental health education |
| CN115497587A (en) * | 2022-11-16 | 2022-12-20 | 内江市感官密码科技有限公司 | Smart campus system and mental health monitoring method |
| CN117038074A (en) * | 2023-08-01 | 2023-11-10 | 中国工业互联网研究院 | User management method, device, equipment and storage medium based on big data |
| CN117038074B (en) * | 2023-08-01 | 2024-10-01 | 中国工业互联网研究院 | User management method, device, equipment and storage medium based on big data |
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