A method and system for evaluating students' psychological quality

Abstract

The present invention discloses a method and system for evaluating students' psychological quality, and relates to the technical field of health data analysis. The entire evaluation system is divided into four parts, namely a health data aggregation module, a health evaluation module, a data storage and analysis module, and a statistical adjustment module. The technical points are as follows: corresponding data are collected according to different types of students, so that the mental health needs of students of different age groups can be more comprehensively understood and supported; based on multiple factors of students' mental health reflected in the corresponding data, comprehensive evaluation is performed through analysis and calculation, so that multi-dimensional information of students' mental health can be more comprehensively captured. When a psychological counseling mechanism is subsequently triggered, it is helpful to improve the efficiency and effect of coping with students' mental health problems; according to the ratio of the number of students who trigger the psychological counseling mechanism to the total number of students in the school, the number of psychological teachers employed is dynamically adjusted, thereby realizing effective allocation and utilization of resources.

Description

Technical Field

[0001] The present invention relates to the technical field of health data analysis, and in particular to a method and system for evaluating students' psychological quality. Background Art

[0002] Health data analysis refers to the process of collecting, processing and analyzing health-related data to reveal the health status, trends and influencing factors of individuals or groups. This analysis usually involves the use of statistics, machine learning and data mining. In the field of mental health, health data analysis can help schools or medical institutions better understand the mental state of students or patients, identify early signs of mental health problems, and design personalized intervention and treatment strategies. How to evaluate and guide students' mental health is an urgent issue that needs to be addressed.

[0003] The document with the existing authorization announcement number CN113141246B discloses a system design and implementation method of homomorphic encryption in the student mental health survey. In the present invention, the user first uses a key generator to generate an identity authentication code, and then the user logs in to the system to fill out the survey report. Then the system uses the fully homomorphic algorithm somewhat algorithm constructed by Paillier addition homomorphism and RSA multiplication homomorphism to ensure fully homomorphic calculation in the cloud. Finally, the user detects the ciphertext result through ciphertext index query, derives and uses the private key to decrypt to obtain the mental health assessment. The present invention can guide students to trust the mental health assessment system and obtain real test results. Therefore, this system can meet the privacy protection needs of mental health assessment in an online environment, better protect the privacy of users, and can be applied not only at the mental health assessment level, but also in various online surveys such as professional quality evaluation. It has good application prospects, but it cannot cope with students at different stages.

[0004] In combination with the above documents and prior art:

[0005] At present, traditional mental health assessments often adopt universal standards, which cannot accurately reflect the mental health status of students at different stages. The "one-size-fits-all" approach can easily cause large errors in the analysis results. For students at different stages, the problems and dimensions of mental health are also different. For example, for elementary school students, the mental health performance of the corresponding elementary school students can be clearly observed through their daily life performance, such as the number of quarrels. However, when they reach junior high school or high school, due to the existence of puberty, the dimension of their mental health performance will be more reflected in their study pressure. Therefore, there are problems of generalization, singleness and low response efficiency in traditional mental health assessments. For the management of the students' schools, the mental health management solutions it can provide are also of lower quality and lower efficiency. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the deficiencies in the prior art, the present invention provides a method and system for assessing students' psychological literacy, which collects corresponding data according to the types of different students, can more comprehensively understand and support the mental health needs of students of different age groups, and conducts comprehensive assessment based on multiple factors of students' mental health reflected in the corresponding data through analysis and calculation, thereby more comprehensively capturing the multi-dimensional information of students' mental health. When the psychological counseling mechanism is subsequently triggered, it helps to improve the efficiency and effectiveness of coping with students' mental health problems. The number of psychological teachers employed is dynamically adjusted according to the ratio of the number of students that trigger the psychological counseling mechanism to the total number of students in the school, thereby achieving effective allocation and utilization of resources and solving the problems raised in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] A system for evaluating students' psychological quality, comprising:

[0011] The health data summary module obtains the basic information of the students to be evaluated and determines the type of the student;

[0012] When the student to be evaluated is determined to be a primary school student, the student's emotional fluctuation data, interaction frequency data, and home visit score data for this semester are collected; when the student to be evaluated is determined to be a middle school student, the student's academic burden data and adolescent psychological assessment data for this semester are collected; when the student to be evaluated is determined to be a college student, the student's psychological counseling data and mental health self-assessment data for this semester are collected;

[0013] The health assessment module builds an assessment calculation model according to the type of students, obtains the psychological state assessment value of the corresponding students based on the corresponding pre-processed data, and compares the psychological state assessment value with the preset assessment threshold of the corresponding type. When the psychological state assessment value of any student exceeds the assessment threshold of the corresponding type, the psychological counseling mechanism is triggered;

[0014] The data storage and analysis module uses the database to count the psychological state evaluation values ​​of each different type of student each time, and synchronously records the fluctuation of each psychological state evaluation value of each different type of student within the preset period T, and uses linear regression analysis to determine the changing trend of the psychological state evaluation value over time. When the changing trend is upward, the psychological treatment strategy is triggered;

[0015] The statistical adjustment module, under the conditions of dealing with any school, counts the number of students who trigger the psychological counseling mechanism in the current semester, and displays the ratio of the number of students who trigger the psychological counseling mechanism to the total number of students in the school in the form of a report at the end of the semester. It builds a rule engine to adjust the number of psychological teachers in the next semester based on the ratio of the previous semester.

[0016] Furthermore, the emotion fluctuation data and interaction frequency data are both obtained using the school's video surveillance system and sound surveillance system, and the emotion fluctuation data include the number of conflicts the student has had this semester, the interaction frequency data include the average number of times the student interacts with other students each week, and the home visit score data represent the average of each score given by the head teacher after a home visit to the corresponding student this semester.

[0017] Furthermore, the health data aggregation module also includes a built-in home visit setting unit;

[0018] Based on the normalized emotional fluctuation data and interaction frequency data, a weighted calculation is performed to obtain the corresponding value for the student, and the priority of the home visit is determined in the following way:

[0019]

[0020] In the formula, R represents the priority of home visits, Respectively represent emotional fluctuation data and interaction frequency data, weight coefficient: , ; The class teacher arranges home visits for the corresponding primary school students based on the priority of home visits from high to low.

[0021] Furthermore, the academic burden data include the corresponding students' over-budget study time and negative fluctuation values ​​of test scores this semester; the adolescent psychological assessment data include the corresponding students' psychological test scores this semester; the psychological counseling data include the number of times students sought psychological counseling this semester; the mental health self-assessment data include the average value of the scores that exceed the standard in each dimension of the mental health self-assessment form.

[0022] Furthermore, when the student type is a primary school student, the evaluation calculation model is as follows:

[0023]

[0024] In the formula, represents the psychological state evaluation value, Indicates the number of conflicts that occurred to the student this semester. Indicates the set conflict count threshold. represents the average number of interactions this student has with other students per week. It means the maximum number of interactions between the student and other students per week in this semester. A score indicating the student's mental health status.

[0025] Furthermore, when the student type is a middle school student, the evaluation calculation model is as follows:

[0026]

[0027] In the formula, Indicates the duration of study that exceeds the budget for the corresponding student in this semester. Indicates the maximum acceptable value of the set over-budget learning time, which is a fixed value. Indicates the negative fluctuation value of the student's test score during this semester, and the preset proportional coefficient: , Indicates the score of a psychological test taken by the corresponding student during this semester.

[0028] Furthermore, when the student type is a college student, the evaluation calculation model is as follows:

[0029]

[0030] In the formula, Indicates the number of times the student sought psychological counseling this semester. Indicates the expected number of psychological consultations. is a constant correction factor, and The value of is 1~5. It means that the student's mental health self-assessment form this semester exceeds the average value of the standard score in each dimension.

[0031] Further, the triggered psychotherapy strategies are:

[0032] Arrange a psychologist to see a psychologist for treatment, and generate an estimated treatment frequency based on the corresponding psychological state assessment value each time within the preset period T, and give guidance on adjusting the treatment frequency based on the estimated treatment frequency; the formula for calculating the estimated treatment frequency is as follows:

[0033]

[0034] In the formula, represents the estimated frequency of treatment, The i in represents the frequency number, and i=1, 2, ..., n, n=T, and n is a positive integer. represents the psychological state evaluation value calculated during the ith psychotherapy, It represents the average value of the psychological state evaluation obtained each time in period T, Error correction factor, The value range is 0~2.

[0035] Furthermore, the process of building a rule engine is as follows:

[0036] Data collection: Obtain the proportion of students who trigger psychological counseling mechanisms;

[0037] Determine the number of basic teachers: Determine the basic number of psychological teachers when the psychological counseling mechanism is not triggered;

[0038] Rule engine settings: Set different condition thresholds according to the trigger ratio value. When the ratio value is below 0.1, the basic number of psychology teachers remains unchanged; when the ratio value is between 0.1 and 0.3, the first adjustment strategy is triggered; when the ratio value exceeds 0.3, the second adjustment strategy is triggered;

[0039] The first adjustment strategy triggered is:

[0040] Based on the basic number and proportion of psychology teachers, a formula is established to calculate the number of psychology teachers, and the value of the dynamic adjustment parameter is set to 1. The formula is as follows:

[0041]

[0042] In the formula, Yr represents the number of psychology teachers employed, represents the number of psychology teachers, e is a natural constant, and k is a dynamic adjustment parameter used to control exponential growth. Indicates the ratio value, int is the rounding function;

[0043] The second adjustment strategy triggered is:

[0044] Based on the basic number and proportion of psychology teachers, a formula is established to calculate the number of psychology teachers needed, and the value of the dynamic adjustment parameter is set to 2. The formula is the same as that in the first adjustment strategy.

[0045] A method for evaluating students' psychological quality includes the following steps:

[0046] S1. Obtain basic information of the student to be evaluated and determine the type of the student;

[0047] When the student to be evaluated is determined to be a primary school student, the student's emotional fluctuation data, interaction frequency data, and home visit score data for this semester are collected; when the student to be evaluated is determined to be a middle school student, the student's academic burden data and adolescent psychological assessment data for this semester are collected; when the student to be evaluated is determined to be a college student, the student's psychological counseling data and mental health self-assessment data for this semester are collected;

[0048] S2. According to the type of students, an evaluation calculation model is built, and based on the corresponding data after preprocessing, the psychological state evaluation value of the corresponding student is obtained, and the psychological state evaluation value is compared with the preset evaluation threshold of the corresponding type. When the psychological state evaluation value of any student exceeds the evaluation threshold of the corresponding type, the psychological counseling mechanism is triggered;

[0049] S3. Using the database to count the psychological state evaluation values ​​of each different type of student each time, and synchronously recording the fluctuation of each psychological state evaluation value of each different type of student each time within the preset period T, using linear regression analysis to determine the changing trend of the psychological state evaluation value over time, and when the changing trend is upward, triggering the psychological treatment strategy;

[0050] S4. Under the condition of dealing with any school, count the number of students who triggered the psychological counseling mechanism in the current semester, and present the ratio of the number of students who triggered the psychological counseling mechanism to the total number of students in the school in the form of a report at the end of the semester, build a rule engine, and adjust the number of psychological teachers in the next semester according to the ratio of the previous semester.

[0051] (III) Beneficial effects

[0052] The present invention provides a method and system for evaluating students' psychological quality, which has the following beneficial effects:

[0053] 1. The present invention collects corresponding data according to different types of students, and realizes automatic data collection. In combination with the use of psychological assessment tools, the assessment of primary school students focuses on social and conflict situations, the assessment of middle school students considers academic pressure and psychological test results, and the assessment of college students focuses on the number of psychological consultations and the scores exceeding the standard. It can more comprehensively understand and support the mental health needs of students of different age groups, and facilitate the subsequent mental health assessment work;

[0054] 2. When evaluating students' mental health, the present invention takes into account a variety of factors that affect students' mental health, such as social interaction, learning pressure, and self-cognition, and conducts a comprehensive evaluation through analysis and calculation, which can more comprehensively capture the multi-dimensional information of students' mental health, reduce the one-sidedness caused by single-factor evaluation, and set an evaluation threshold for each type of student. When the evaluation value exceeds the threshold, the psychological counseling mechanism is triggered, which helps to improve the efficiency and effectiveness of coping with students' mental health problems;

[0055] 3. The present invention utilizes a database to manage each student's psychological state assessment value and its changing trend, realizes the ability of real-time monitoring and long-term trend analysis, and distinguishes the strategies of psychological counseling and psychotherapy according to the changing trend of the student's psychological state assessment value. This personalized intervention can respond to students' mental health needs more accurately and adjust intervention strategies in a timely manner, from simple psychological counseling to professional psychotherapy; at the same time, the number of psychological teachers employed is dynamically adjusted according to the proportion of historical data, realizing the effective allocation and utilization of resources, which can ensure that sufficient psychological support is provided when needed, while also avoiding waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a modular schematic diagram of a student psychological quality assessment system in the present invention;

[0057] Figure 2 The present invention is a schematic diagram of the steps of a method for assessing students' psychological literacy. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Example 1

[0060] See also Figure 1 ,This embodiment provides a system for evaluating students' psychological quality;

[0061] The development concept of this system is:

[0062] In order to solve the students' mental health problems, it is necessary to collect and process the students' health data related information to realize the assessment of students' mental health literacy. However, the current assessment methods are all for a selected group of students or only for the corresponding types of students. For students, different stages cover primary school students, middle school students and college students. For students at different stages, the psychological assessment methods need to maintain specific targeting. Therefore, the mental health data used in the subsequent assessment process are also different, which ensures the rationality and comprehensiveness of the establishment of the assessment system. After evaluating the psychological literacy of students at different stages, how to continue to track and solve students' psychological abnormalities is also extremely important. Therefore, this system adjusts the process of psychological intervention and treatment according to the assessment results of students' mental health literacy, solves the current growing mental health problems, and ensures that students maintain a healthy mental state.

[0063] The entire evaluation system is divided into four parts, namely health data aggregation module, health evaluation module, data storage and analysis module and statistical adjustment module. After the modules work together, a student psychological literacy evaluation system is obtained. The psychological development characteristics of students at different age stages are considered, and the corresponding mental health data are collected and processed at different stages. The specific design plan is as follows:

[0064] The health data summary module obtains the basic information of the students to be evaluated and determines the type of the student;

[0065] When the student to be evaluated is determined to be a primary school student, the student's emotional fluctuation data, interaction frequency data, and home visit score data for this semester are collected; when the student to be evaluated is determined to be a middle school student, the student's academic burden data and adolescent psychological assessment data for this semester are collected; when the student to be evaluated is determined to be a college student, the student's psychological counseling data and mental health self-assessment data for this semester are collected;

[0066] The basic information of the students to be evaluated includes age, gender, school and grade. The grade here refers to the grade. Students from grade one to grade six are elementary school students, students from grade one to grade three are middle school students, and students who continue to study from freshman to senior high school are college students. Therefore, the type of the student can be clearly determined from the grade in the basic information.

[0067] The emotional fluctuation data and interaction frequency data are obtained by using the video surveillance system and sound surveillance system on campus. The emotional fluctuation data include the number of conflicts that occurred with the student this semester, and the interaction frequency data include the average number of interactions that the student has with other students each week. It should be noted that conflicts include fighting and quarreling.

[0068] The video surveillance system is described as:

[0069] Install cameras in public areas of the school (such as classrooms, corridors, and playgrounds) to record students' behavior through a video surveillance system. When a fight or quarrel occurs, it can be automatically identified and recorded; the specific methods are: Install cameras: Install high-definition cameras in key areas of the school to ensure that all public activity areas can be covered; Real-time monitoring and recording: The camera monitors and records video data in real time; Motion recognition software: Use motion recognition software to analyze video streams and identify conflict behaviors between students, including physical conflicts and fierce verbal quarrels; Automatic alarm and recording: Once a fight or quarrel is identified, the system automatically records the time and location of the incident and sends an alarm to notify relevant personnel;

[0070] Example: A camera is installed in the corridor of a primary school. The motion recognition software analyzes the video stream. When it detects a physical conflict between two students, the system automatically records the time of the incident as 10:30 AM and the location as the corridor on the third floor. The video clip is saved and the school administrator is notified.

[0071] The sound monitoring system is described as:

[0072] Install microphones in the public areas of the school or in the same location as the video surveillance system, and record students' verbal conflicts through the sound monitoring system. When a fierce quarrel occurs, the system can automatically identify and record it; the specific method is: Install microphones: Install high-sensitivity microphones in key areas of the school to ensure that students' verbal interactions can be clearly captured; Sound analysis software: Use sound analysis software to analyze the captured audio data and identify high-decibel, fierce verbal conflicts; Automatic alarm and recording: Once a quarrel is identified, the system automatically records the time and location of the incident and sends an alarm to notify relevant personnel; no examples are given here;

[0073] The behavior analysis software installed in the camera of the video surveillance system is used. The software detects the students' movements and behaviors through image recognition and analysis technology, and records the number of interactions. By counting the number of interactions per week, and then accumulating the number of interactions per week in this semester, the average number of interactions with students per week can be obtained by adding the accumulated value to the number of weeks in this semester. It should be noted that the number of weeks mentioned above refers to the number of weeks in this semester.

[0074] The home visit score data represents the average of each score given by the head teacher after the home visit to the corresponding students during this semester. After the head teacher visits the students at home, the student's mental health status is scored;

[0075] The health data aggregation module also includes a built-in home visit setup unit;

[0076] Based on the normalized emotional fluctuation data and interaction frequency data, a weighted calculation is performed to obtain the corresponding value for the student, and the priority of the home visit is determined in the following way:

[0077]

[0078] In the formula, R represents the priority of home visits, Respectively represent emotional fluctuation data and interaction frequency data, weight coefficient: , ,and The class teacher arranges home visits for the corresponding primary school students according to the priority of home visits from highest to lowest;

[0079] It should be noted that, unlike middle school students and college students, the mental development of primary school students is not complete. At the same time, their behavior needs to be effectively monitored, and their mental health is easily affected by their families. Therefore, when conducting mental health scoring, the class teacher needs to take the initiative to visit the home to understand the family environment of the primary school students and achieve a comprehensive assessment of the mental health of the primary school students.

[0080] The academic burden data includes the time spent studying over budget and the negative fluctuation value of the test scores of the corresponding students in this semester; the academic burden data is obtained through the recognition software built into the camera; the adolescent psychological assessment data includes the psychological test scores of the corresponding students in this semester. At the end of the semester, a psychological assessment questionnaire is distributed to each student, and the corresponding psychological test scores of each student are obtained based on the questionnaire results;

[0081] The over-budget study time means that one or more students continue to study during the break time or rest time in this semester, and the cumulative study time in this semester is recorded. The method of obtaining the over-budget study time is as follows:

[0082] Timetable integration: Integrate the school timetable with cameras to mark the end of get out of class time and rest time;

[0083] Behavior recognition: Using computer vision technology, based on the real-time video stream of the camera, it detects and recognizes students' specific behaviors, including flipping through books and writing at a desk. After identifying specific behaviors, it can be judged as continuing learning behavior;

[0084] Facial recognition: Use facial recognition technology to identify the corresponding students and accurately record the study time of each student;

[0085] Data recording and analysis: record the detected continued learning behaviors and accumulate them to obtain the cumulative duration;

[0086] The recognition software used includes Behavior Analysis Software and AI-based VideoAnalytics Platforms. Behavior Analysis Software includes Deep Vision AI and AxxonNext, which use computer vision technology to analyze behaviors in video streams. It can be configured to identify students' continued learning behaviors and record relevant data. AI-based Video Analytics Platforms include AnyVision and BriefCam, which use artificial intelligence and machine learning to accurately identify and analyze specific behaviors. After configuring the timetable, students' learning behaviors can be monitored during recess.

[0087] Example process:

[0088] 1. Install cameras: Install cameras in appropriate locations in the classroom to cover all students' activity areas; 2. Software configuration: Configure the software to identify learning behaviors, such as sitting posture, desktop activities (writing, reading), etc., and input the school's course schedule so that the software can start detection at the end of get out of class; 3. Data processing and storage: Analyze video streams in real time, identify and record students who continue to study and the duration, store data and generate reports regularly to provide to teachers or administrators; 4. Reports and feedback: Generate reports every day, week or month to record the cumulative duration of each student's continued study after class, and provide a data visualization interface to facilitate teachers to view and analyze students' learning habits;

[0089] Through the above methods and software, students' behavior of continuing to study during break time can be effectively monitored and recorded, and valuable reference data can be provided for schools and teachers.

[0090] The negative fluctuation value of the test score indicates the maximum negative fluctuation value of the comprehensive total score of each monthly exam of the corresponding student in this semester, that is, the difference between the highest and lowest values ​​of the comprehensive total score of the monthly exam in this semester is the maximum negative fluctuation value, that is, the negative fluctuation value of the test score; the required negative fluctuation value of the test score can be obtained by statistical calculation, which will not be elaborated here;

[0091] Psychological assessment questionnaires are psychological assessment tools;

[0092] Including anxiety self-rating form and depression self-rating form, regularly assess students' mood swings and self-identity; specific example: a middle school uses the anxiety self-rating scale and depression self-rating scale to conduct psychological assessments on students once a semester. The results show that a student's mood swing score is 6 points (out of 10 points), and the self-identity score is 7 points (out of 10 points). The average score is 6.5 points, which is the student's psychological test score;

[0093] The psychological counseling data include the number of times students have sought psychological counseling this semester, which can be directly obtained from the work records of the psychological counselors in the school; the mental health self-assessment data include the average value of each dimension of the mental health self-assessment form that exceeds the standard score. The mental health self-assessment form adopts the SCL-90 Mental Health Self-Assessment Scale, which covers psychological assessment data of several dimensions. I will not elaborate on it here. After the corresponding students fill in the form and obtain the assessment of each dimension, they select the difference that exceeds the corresponding standard score and add up the differences. The accumulated value is the number of differences, and the average value of each dimension that exceeds the standard score can be obtained, that is, the mental health self-assessment data.

[0094] By adopting the above-mentioned technical solution, corresponding data is collected according to the types of different students, and the data is collected automatically. Combined with the use of psychological assessment tools, the assessment of primary school students focuses on social and conflict situations, the assessment of middle school students considers academic pressure and psychological test results, and the assessment of college students focuses on the number of psychological consultations and scores exceeding the standard. This can more comprehensively understand and support the mental health needs of students of different age groups, and facilitate subsequent mental health level assessment work.

[0095] The health assessment module builds an assessment calculation model according to the type of students, and obtains a psychological state assessment value reflecting the psychological health level of the corresponding students based on the corresponding pre-processed data, and compares the psychological state assessment value with the preset assessment threshold of the corresponding type. When the psychological state assessment value of any student exceeds the assessment threshold of the corresponding type, it means that the student's psychological health is abnormal, triggering the psychological counseling mechanism; when the psychological state assessment value of any student does not exceed the assessment threshold of the corresponding type, it means that the student's psychological health is normal, and no response action is taken;

[0096] The corresponding data is:

[0097] If the student to be evaluated is determined to be a primary school student, the corresponding data are the student's emotional fluctuation data, interaction frequency data, and home visit score data during the semester; if the student to be evaluated is determined to be a middle school student, the corresponding data are the student's academic burden data and adolescent psychological assessment data during the semester; if the student to be evaluated is determined to be a college student, the corresponding data are the student's psychological counseling data and mental health self-assessment data during the semester;

[0098] Among them, the corresponding data is preprocessed by normalization;

[0099] When the student type is a primary school student, the evaluation calculation model is as follows:

[0100]

[0101] In the formula, represents the psychological state evaluation value, Indicates the number of conflicts that occurred to the student this semester. Indicates the set conflict count threshold. represents the average number of interactions this student has with other students per week. It means the maximum number of interactions between the student and other students per week in this semester. A score indicating the student's mental health status;

[0102] It should be noted that in the above formula, the impact of the number of conflicts is amplified by the square to reflect its importance, while the lack of the number of interactions is supplemented by a linear method. The score of the mental health status is accumulated with other data to affect the final mental state assessment value. The settings of multiplying by 10 and multiplying by 5 are convenient for dealing with various types of students, so that the obtained mental state assessment values ​​can be compared according to the same standard in the future;

[0103] Here are some examples:

[0104] if , then the psychological state assessment value is 2.5+2.5+0=5;

[0105] When the student type is a middle school student, the evaluation calculation model is as follows:

[0106]

[0107] In the formula, Indicates the duration of study that exceeds the budget for the corresponding student in this semester. Indicates the maximum acceptable value of the set over-budget learning time, which is a fixed value. Indicates the negative fluctuation value of the student's test score during this semester, and the preset proportional coefficient: , It indicates the score of a psychological test taken by the corresponding student in this semester. This score is a standardized score, so it is directly included in the calculation;

[0108] It should be noted that for middle school students, it is necessary to take into account the study pressure and psychological problems of adolescence. For the study pressure, the data reflecting the degree of mental health from the side is added into the formula as a correction value for the psychological test score for calculation and processing;

[0109] Here are some examples:

[0110] if Hour, Set to 100 hours, point, The values ​​are 0.2 and 0.1 respectively. , then the psychological state assessment value is 0.05+0.8+4=4.85;

[0111] When the student type is college students, the evaluation calculation model is as follows:

[0112]

[0113] In the formula, Indicates the number of times the student sought psychological counseling this semester. Indicates the expected number of psychological consultations. is a constant correction coefficient, the specific value of which can be adjusted by the user or generated by analytical function fitting, and The value of is 1~5. It means that the student's scores on each dimension of the self-assessment form for mental health this semester exceed the average of the standard scores;

[0114] It should be noted that the expected number of psychological consultations is For standardization The effect of , because too many psychological consultations may indicate serious problems, but it is not a linear relationship, so the square root is used to smooth this effect;

[0115] Here are some examples:

[0116] If L=4 times, =2 times, =2, =2, then the psychological state assessment value is 4.83;

[0117] In summary, the data used to calculate the psychological status assessment values ​​of different types of students are different, mainly because of their different living environments, stressors and psychological development characteristics; the research direction in this application is more in line with reality. Primary school students pay more attention to social interaction and conflict resolution, middle school students face the challenges of learning pressure and grade fluctuations, and college students may pay more attention to self-cognition and proactive help-seeking. This differentiated treatment can more accurately reflect the mental health status of various types of students, thereby providing more targeted help and support; in terms of effect, this type of categorized assessment can improve the pertinence and effectiveness of mental health services and promote the all-round healthy development of students;

[0118] The preset assessment thresholds for the corresponding types are actually set independently based on historical data and are used to determine whether students have mental health problems. The triggered psychological counseling mechanism is to arrange for the school’s psychological teachers to provide psychological counseling.

[0119] By adopting the above technical solution, when evaluating the mental health of students, various factors that affect their mental health, such as social interaction, learning pressure, and self-cognition, are taken into account, and a comprehensive evaluation is performed through analytical calculations, which can more comprehensively capture the multi-dimensional information of students' mental health and reduce the one-sidedness caused by single-factor evaluation. An evaluation threshold is set for each type of student. When the evaluation value exceeds the threshold, the psychological counseling mechanism is triggered, which helps to improve the efficiency and effectiveness of coping with students' mental health problems.

[0120] In summary, this technical solution effectively solves the problems of generalization, singleness and low response efficiency in traditional mental health assessment through personalized and comprehensive assessment methods and trigger mechanisms, providing schools with a more scientific and intelligent mental health management solution, which helps to improve students' mental health level and academic achievement.

[0121] The data storage and analysis module uses the database to count the psychological state evaluation values ​​of each different type of student each time, and synchronously records the fluctuation of each psychological state evaluation value of each different type of student within the preset period T, and uses linear regression analysis to determine the change trend of the psychological state evaluation value over time. When the change trend is upward, it means that the student has a problem with his or her mental health. Unlike abnormalities that require psychological counseling, when there is a problem, a psychological treatment strategy is triggered, otherwise, no response action is taken;

[0122] The process of linear regression analysis is as follows:

[0123] Use linear regression analysis tools, including the `scikit-learn` library in Python, to calculate the trend of psychological status assessment values ​​over frequency; an example is as follows: Assume that a student's psychological health assessment value is as follows (in units of frequency):

[0124] The relevant code examples are as follows:

[0125] import numpy as np

[0126] from sklearn.linear_model import LinearRegression

[0127] # Frequency (times)

[0128] months = np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1)

[0129] # Mental state evaluation value

[0130] values ​​= np.array([6.5, 7.2, 7.8, 8.5, 8.9, 9.2])

[0131] # Create a linear regression model

[0132] model = LinearRegression()

[0133] model.fit(months, values)

[0134] # Print regression coefficients and intercept

[0135] print("Regression coefficient:", model.coef_[0])

[0136] print("Intercept:", model.intercept_);

[0137] The trend direction and rate of the mental health assessment value can be determined based on the sign and size of the regression coefficient. A positive coefficient indicates an upward trend, and a negative coefficient indicates a downward trend. It should be noted that linear regression is suitable for analyzing long-term trends, and the trend of changes in mental health assessment values ​​can be determined by the regression coefficient. The above-mentioned method can be selected and used according to the specific data characteristics and analysis needs to help understand the trend of changes in students' mental health assessment values, so as to take appropriate intervention measures and support strategies.

[0138] The psychotherapy strategies triggered were:

[0139] Arrange for psychologists to see and treat, rather than simple psychological counseling, and generate an estimated treatment frequency based on the corresponding psychological state assessment value each time within the preset period T, and give guidance on adjusting the treatment frequency based on the estimated treatment frequency; the formula for calculating the estimated treatment frequency is as follows:

[0140]

[0141] In the formula, represents the estimated frequency of treatment, The i in represents the frequency number, and i=1, 2, ..., n, n=T, and n is a positive integer. represents the psychological state evaluation value calculated during the ith psychotherapy, It represents the average value of the psychological state evaluation obtained each time in period T, Error correction factor, the specific value of which can be adjusted by the user or generated by analytical function fitting. The value range is 0~2.

[0142] The statistical adjustment module, under the condition of dealing with any school, counts the number of students who triggered the psychological counseling mechanism in the current semester, and displays the ratio of the number of students who triggered the psychological counseling mechanism to the total number of students in the school in the form of a report at the end of the semester, builds a rule engine, and adjusts the number of psychological teachers in the next semester according to the ratio of the previous semester;

[0143] The process of building a rule engine is as follows:

[0144] Data collection: Obtain the proportion of students who trigger psychological counseling mechanisms;

[0145] Determine the number of basic teachers: Determine the basic number of psychological teachers when the psychological counseling mechanism is not triggered;

[0146] Rule engine settings: Set different condition thresholds according to the trigger ratio value. When the ratio value is below 0.1, the basic number of psychology teachers remains unchanged; when the ratio value is between 0.1 and 0.3, the first adjustment strategy is triggered; when the ratio value exceeds 0.3, the second adjustment strategy is triggered;

[0147] The first adjustment strategy triggered is:

[0148] Based on the basic number and proportion of psychology teachers, a formula is established to calculate the number of psychology teachers, and the value of the dynamic adjustment parameter is set to 1. The formula is as follows:

[0149]

[0150] In the formula, Yr represents the number of psychology teachers employed, represents the number of psychology teachers, e is a natural constant, and k is a dynamic adjustment parameter used to control exponential growth. Indicates the ratio value, int is the rounding function;

[0151] The second adjustment strategy triggered is:

[0152] Based on the basic number and proportion of psychology teachers, a formula is established to calculate the number of psychology teachers, and the value of the dynamic adjustment parameter is set to 2. The formula is as follows:

[0153]

[0154] It should be noted that the purpose of +1 in the formula is to ensure that the value after the decimal point is rounded off to reduce the calculation error; the example is as follows: Assuming that the number of basic teachers is 10, and the proportion of students who triggered the psychological counseling mechanism last semester is 0.25 (25%), set k=1, then calculate the number of psychological teachers Therefore, according to the actual data, the number of psychology teachers for the next semester is adjusted from the original 10 to 13 according to the proportion value of the previous semester; through such a rule engine design, the school can flexibly adjust resource allocation according to actual conditions to ensure effective support and protection of students' mental health.

[0155] By adopting the above technical solution, the database is used to manage the psychological state assessment value of each student and its changing trend, and the ability of real-time monitoring and long-term trend analysis is achieved. The strategies of psychological counseling and psychotherapy are distinguished according to the changing trend of students' psychological state assessment values. This personalized intervention can respond to students' mental health needs more accurately and adjust intervention strategies in time, from simple psychological counseling to professional psychotherapy. At the same time, the number of psychological teachers employed is dynamically adjusted according to the proportion of historical data, realizing the effective allocation and utilization of resources. This can not only ensure that sufficient psychological support is provided when needed, but also avoid waste of resources.

[0156] Embodiment 2:

[0157] See also Figure 2 Based on Example 1, this embodiment also provides a method for evaluating students' psychological quality, including the following specific steps:

[0158] S1. Obtain basic information of the student to be evaluated and determine the type of the student;

[0159] When the student to be evaluated is determined to be a primary school student, the student's emotional fluctuation data, interaction frequency data, and home visit score data for this semester are collected; when the student to be evaluated is determined to be a middle school student, the student's academic burden data and adolescent psychological assessment data for this semester are collected; when the student to be evaluated is determined to be a college student, the student's psychological counseling data and mental health self-assessment data for this semester are collected;

[0160] S2. According to the type of students, an evaluation calculation model is built, and based on the corresponding data after preprocessing, the psychological state evaluation value of the corresponding student is obtained, and the psychological state evaluation value is compared with the preset evaluation threshold of the corresponding type. When the psychological state evaluation value of any student exceeds the evaluation threshold of the corresponding type, the psychological counseling mechanism is triggered;

[0161] S3. Using the database to count the psychological state evaluation values ​​of each different type of student each time, and synchronously recording the fluctuation of each psychological state evaluation value of each different type of student each time within the preset period T, using linear regression analysis to determine the changing trend of the psychological state evaluation value over time, and when the changing trend is upward, triggering the psychological treatment strategy;

[0162] S4. Under the condition of dealing with any school, count the number of students who triggered the psychological counseling mechanism in the current semester, and present the ratio of the number of students who triggered the psychological counseling mechanism to the total number of students in the school in the form of a report at the end of the semester, build a rule engine, and adjust the number of psychological teachers in the next semester according to the ratio of the previous semester.

[0163] In the application, the several formulas involved are all calculated by removing the dimensions and taking their numerical values. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The formula is set by technical personnel in this field according to actual conditions.

[0164] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0165] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A system for evaluating students' psychological quality, characterized in that: The system includes: The health data summary module obtains the basic information of the students to be evaluated and determines the type of the student; When the student to be evaluated is determined to be a primary school student, the student's emotional fluctuation data, interaction frequency data and home visit score data for this semester are collected; when the student to be evaluated is determined to be a middle school student, the student's academic burden data and adolescent psychological assessment data for this semester are collected; when the student to be evaluated is determined to be a college student, the student's psychological counseling data and mental health self-assessment data for this semester are collected; the academic burden data include the student's over-budget study time and negative fluctuation value of the test score for this semester, and the adolescent psychological assessment data include the student's psychological test score for this semester; the psychological counseling data include the number of times the student sought psychological counseling this semester, and the mental health self-assessment data include the average value of the scores exceeding the standard in each dimension of the mental health self-assessment form; The health assessment module builds an assessment calculation model according to the type of students, obtains the psychological state assessment value of the corresponding students based on the corresponding pre-processed data, and compares the psychological state assessment value with the preset assessment threshold of the corresponding type. When the psychological state assessment value of any student exceeds the assessment threshold of the corresponding type, the psychological counseling mechanism is triggered; When the student type is a primary school student, the evaluation calculation model is as follows: In the formula, represents the psychological state evaluation value, Indicates the number of conflicts that occurred to the student this semester. Indicates the set conflict count threshold. represents the average number of interactions this student has with other students per week. It means the maximum number of interactions between the student and other students per week in this semester. A score indicating the student's mental health status; When the student type is a middle school student, the evaluation calculation model is as follows: In the formula, It indicates the duration of study that exceeds the budget of the corresponding student in this semester. Indicates the maximum acceptable value of the set over-budget learning time, which is a fixed value. Indicates the negative fluctuation value of the student's test score during this semester, and the preset proportional coefficient: , It indicates the score of a psychological test taken by the corresponding student in this semester; When the student type is college students, the evaluation calculation model is as follows: In the formula, Indicates the number of times the student sought psychological counseling this semester, Indicates the expected number of psychological consultations. is a constant correction factor, and The value of is 1~5. It means that the student's scores on each dimension of the self-assessment form for mental health this semester exceed the average of the standard scores; The data storage and analysis module uses the database to count the psychological state evaluation values ​​of each different type of student each time, and synchronously records the fluctuation of each psychological state evaluation value of each different type of student within the preset period T, and uses linear regression analysis to determine the changing trend of the psychological state evaluation value over time. When the changing trend is upward, the psychological treatment strategy is triggered; The statistical adjustment module, under the conditions of dealing with any school, counts the number of students who trigger the psychological counseling mechanism in the current semester, and displays the ratio of the number of students who trigger the psychological counseling mechanism to the total number of students in the school in the form of a report at the end of the semester. It builds a rule engine to adjust the number of psychological teachers in the next semester based on the ratio of the previous semester.

2. A student psychological quality assessment system according to claim 1, characterized in that: The emotional fluctuation data and interaction frequency data are both obtained using the school's video surveillance system and sound surveillance system. The emotional fluctuation data include the number of conflicts the student has had this semester. The interaction frequency data include the average number of times the student interacts with other students each week. The home visit score data represent the average of each score given by the head teacher after a home visit to the corresponding student this semester.

3. A system for evaluating students' psychological quality according to claim 2, characterized in that: The health data aggregation module also includes a built-in home visit setup unit; Based on the normalized emotional fluctuation data and interaction frequency data, a weighted calculation is performed to obtain the corresponding value for the student, and the priority of the home visit is determined in the following way: In the formula, R represents the priority of home visits, Respectively represent emotional fluctuation data and interaction frequency data, weight coefficient: , ; The class teacher arranges home visits for the corresponding primary school students based on the priority of home visits from high to low.

4. A student psychological quality assessment system according to claim 1, characterized in that: The psychotherapy strategies triggered were: Arrange a psychologist to see a psychologist for treatment, and generate an estimated treatment frequency based on the corresponding psychological state assessment value each time within the preset period T, and give guidance on adjusting the treatment frequency based on the estimated treatment frequency; the formula for calculating the estimated treatment frequency is as follows: In the formula, represents the estimated frequency of treatment, The i in represents the frequency number, and i=1, 2, ..., n, n=T, and n is a positive integer. represents the psychological state evaluation value calculated during the ith psychotherapy, It represents the average value of the psychological state evaluation obtained each time in period T, Error correction factor, The value range is 0~2.

5. A system for evaluating students' psychological quality according to claim 4, characterized in that: The process of building a rule engine is as follows: Data collection: Obtain the proportion of students who trigger psychological counseling mechanisms; Determine the number of basic teachers: Determine the basic number of psychological teachers when the psychological counseling mechanism is not triggered; Rule engine settings: Set different condition thresholds according to the trigger ratio value. When the ratio value is below 0.1, the basic number of psychology teachers remains unchanged; When the ratio value is in the range of 0.1 to 0.3, the first adjustment strategy is triggered; when the ratio value exceeds 0.3, the second adjustment strategy is triggered; The first adjustment strategy triggered is: Based on the basic number and proportion of psychology teachers, a formula is established to calculate the number of psychology teachers, and the value of the dynamic adjustment parameter is set to 1. The formula is as follows: In the formula, Yr represents the number of psychology teachers employed, represents the number of psychology teachers, e is a natural constant, and k is a dynamic adjustment parameter used to control exponential growth. Indicates the ratio value, int is the rounding function; The second adjustment strategy triggered is: Based on the basic number and proportion of psychology teachers, a formula is established to calculate the number of psychology teachers needed, and the value of the dynamic adjustment parameter is set to 2. The formula is the same as that in the first adjustment strategy.

6. A method for assessing students' psychological quality, using any system described in claims 1 to 5, characterized in that: The steps include: S1. Obtain basic information of the student to be evaluated and determine the type of the student; When the student to be evaluated is determined to be a primary school student, the student's emotional fluctuation data, interaction frequency data and home visit score data for this semester are collected; when the student to be evaluated is determined to be a middle school student, the student's academic burden data and adolescent psychological assessment data for this semester are collected; when the student to be evaluated is determined to be a college student, the student's psychological counseling data and mental health self-assessment data for this semester are collected; the academic burden data include the student's over-budget study time and negative fluctuation value of the test score for this semester, and the adolescent psychological assessment data include the student's psychological test score for this semester; the psychological counseling data include the number of times the student sought psychological counseling this semester, and the mental health self-assessment data include the average value of the scores exceeding the standard in each dimension of the mental health self-assessment form; S2. According to the type of students, an evaluation calculation model is built, and based on the corresponding data after preprocessing, the psychological state evaluation value of the corresponding student is obtained, and the psychological state evaluation value is compared with the preset evaluation threshold of the corresponding type. When the psychological state evaluation value of any student exceeds the evaluation threshold of the corresponding type, the psychological counseling mechanism is triggered; When the student type is a primary school student, the evaluation calculation model is as follows: In the formula, represents the psychological state evaluation value, Indicates the number of conflicts that occurred to the student this semester. Indicates the set conflict count threshold. represents the average number of interactions this student has with other students per week. It means the maximum number of interactions between the student and other students per week in this semester. A score indicating the student's mental health status; When the student type is a middle school student, the evaluation calculation model is as follows: In the formula, It indicates the duration of study that exceeds the budget of the corresponding student in this semester. Indicates the maximum acceptable value of the set over-budget learning time, which is a fixed value. Indicates the negative fluctuation value of the student's test score during this semester, and the preset proportional coefficient: , It indicates the score of a psychological test taken by the corresponding student in this semester; When the student type is college students, the evaluation calculation model is as follows: In the formula, Indicates the number of times the student sought psychological counseling this semester, Indicates the expected number of psychological consultations. is a constant correction factor, and The value of is 1~5. It means that the student's scores on each dimension of the self-assessment form for mental health this semester exceed the average of the standard scores; S3. Using the database to count the psychological state evaluation values ​​of each different type of student each time, and synchronously recording the fluctuation of each psychological state evaluation value of each different type of student each time within the preset period T, using linear regression analysis to determine the changing trend of the psychological state evaluation value over time, and when the changing trend is upward, triggering the psychological treatment strategy; S4. Under the condition of dealing with any school, count the number of students who triggered the psychological counseling mechanism in the current semester, and present the ratio of the number of students who triggered the psychological counseling mechanism to the total number of students in the school in the form of a report at the end of the semester, build a rule engine, and adjust the number of psychological teachers in the next semester according to the ratio of the previous semester.

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