Teenager mental health risk assessment method and system

Abstract

The invention relates to the technical field of mental health management, in particular to a teenager mental health risk assessment method and system, and the method comprises the steps: obtaining basic information parameters of teenagers to be subjected to mental health assessment; analyzing psychological state related factors based on the basic information parameters of the teenagers to obtain a psychological influence factor set; based on the psychological influence factor set, matching a plurality of factor evaluation indexes corresponding to the psychological influence factor set; carrying out data acquisition on the plurality of factor evaluation indexes to obtain a psychological state associated data list; obtaining a standard psychological state data list corresponding to the basic information parameters based on the basic information parameters; constructing a psychological state difference matrix through the collected psychological state associated data list and the standard psychological state data list; inputting the psychological state difference matrix into a preset psychological health risk prediction model to generate a psychological health risk probability; and comparing the psychological health risk probability with a preset health risk threshold, and generating a psychological health assessment result of the teenager.

Description

Technical Field

[0001] The present invention relates to the technical field of mental health management, and particularly to a method and system for assessing the mental health risks of teenagers. Background Art

[0002] In today's society, the mental health problems of teenagers have received increasing widespread attention. With the rapid development of society and the continuous increase in competition pressure, teenagers are facing challenges from multiple aspects such as academics, social interactions, and families. These factors are intertwined and have a profound impact on the mental health of teenagers.

[0003] Existing means for assessing the mental health of teenagers usually rely on single - dimensional observations or simple questionnaires, lacking a comprehensive consideration of the complex life backgrounds and diverse psychological influencing factors of teenagers. For teenagers of different ages, genders, family structures, and school types, the formation mechanisms of their mental states are significantly different, but existing methods are difficult to effectively capture these differences. For example, when assessing the impact of learning pressure on mental health, only the grades themselves are concerned, while key indicators such as grade fluctuations and the duration of homework completion are ignored; in considering social relationships, there is a lack of in - depth analysis of different types of social relationships; due to the lack of comprehensive and accurate assessment techniques, many potential mental health problems of teenagers have not been detected in time, delaying the best intervention opportunity.

[0004] To meet the urgent need for accurate and systematic assessment of the mental health status of teenagers, there is an urgent need for a method for assessing the mental health risks of teenagers. Summary of the Invention

[0005] To solve the above - mentioned technical problems, the present invention provides a method and system for assessing the mental health risks of teenagers that can accurately and systematically assess the mental health status of teenagers.

[0006] In the first aspect, the present invention provides a method for assessing the mental health risks of teenagers, and the method includes: Obtain the basic information parameters of the teenager to be mentally health - evaluated; Based on the basic information parameters of the teenager, analyze the factors related to the mental state to obtain a set of psychological influencing factors; Based on the set of psychological influencing factors, match multiple factor assessment indicators corresponding thereto; Collect data for the multiple factor assessment indicators to obtain a list of data related to the mental state; Based on the basic information parameters, obtain a corresponding standard list of mental state data; Construct a mental state difference matrix through the collected list of data related to the mental state and the standard list of mental state data; Input the mental state difference matrix into a preset mental health risk prediction model to generate a mental health risk probability; Compare the mental health risk probability with a preset health risk threshold to generate a mental health assessment result for the adolescent.

[0007] Furthermore, the set of psychological influencing factors at least includes factors affecting mental health such as learning stress sources, types of social relationships, and family atmosphere factors.

[0008] Furthermore, the matching of the factor evaluation indicators includes: Associate the learning stress source with the achievement volatility, the standard deviation of the homework completion duration, and the self - assessment score of test anxiety; Associate the family atmosphere factor with the parental communication frequency, the record of family conflict events, and the economic stress perception index; Associate the type of social relationship with the online social duration, the participation degree in offline peer activities, and the number of reported bullying incidents.

[0009] Furthermore, the method for constructing the mental state difference matrix includes: Align the indicators in the list of mental state - related data of the adolescent to be evaluated with the list of standard mental state data; For each corresponding indicator, calculate the difference between the actual value and the standard value of the adolescent to be evaluated; Fill the calculated difference values into the mental state difference matrix in the order of the indicators.

[0010] Furthermore, the number of rows of the mental state difference matrix is equal to the number of psychological influencing factors, and the number of columns is equal to the total number of evaluation indicators corresponding to all factors.

[0011] Furthermore, the formula for the mental health risk prediction model to calculate the mental health risk probability is: Where P(y = 1∣x) represents the probability that the adolescent belongs to the category with high mental health risk under the given input feature vector x; x1, x2, …, xn represent various factor evaluation indicators extracted from the mental state difference matrix; β0, β1, …, βn represent model parameters, representing the influence degree of each factor on the mental health risk.

[0012] Furthermore, the health risk threshold includes a low - risk threshold, a medium - risk threshold, and a high - risk threshold.

[0013] On the other hand, this application also provides an adolescent mental health risk assessment system, and the system includes: A basic information acquisition module that acquires the basic information parameters of the adolescent to be mentally health - evaluated, at least including age, gender, family structure, and school type; A psychological influence factor analysis module analyzes factors related to the psychological state based on the basic information parameters of the adolescents, and obtains a set of psychological influence factors, which at least include factors affecting mental health such as learning stress sources, types of social relationships, and family atmosphere factors; A factor evaluation index matching module matches a plurality of corresponding factor evaluation indexes based on the set of psychological influence factors, and the plurality of factor evaluation indexes include indexes that need to be monitored and evaluated for each influence factor; A data collection module collects data for the plurality of factor evaluation indexes to obtain a list of data related to the psychological state; A standard psychological state data acquisition module obtains a corresponding list of standard psychological state data based on the basic information parameters as a reference for the normal psychological state of adolescents of the same type; A difference matrix construction module constructs a psychological state difference matrix through the collected list of data related to the psychological state and the list of standard psychological state data to quantify the difference degree of the psychological state between this adolescent and adolescents of the same type; A mental health risk prediction module inputs the psychological state difference matrix into a preset mental health risk prediction model to generate a mental health risk probability; An evaluation result generation module compares the mental health risk probability with a preset health risk threshold to generate a mental health evaluation result for this adolescent.

[0014] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus, and when the computer program is executed by the processor, the steps in any one of the above methods are implemented.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in any one of the above methods are implemented.

[0016] The beneficial effects of the present invention compared with the prior art are as follows: The method obtains basic information such as age, gender, family structure, and school type at the beginning, laying a solid foundation for subsequent in-depth analysis; in the analysis link of psychological state-related factors, based on this multivariate information, it can comprehensively analyze many factors affecting the mental health of teenagers, such as learning stress sources, types of social relationships, and family atmosphere factors, breaking through the limitation of traditional assessment from only a single dimension and fully taking into account the complex life background and diverse psychological influencing factors of teenagers; for the set of psychological influencing factors, the method will match corresponding multiple factor assessment indicators; in terms of learning stress assessment, it not only focuses on grades, but also incorporates key indicators such as grade fluctuations and homework completion time; when considering social relationships, it deeply analyzes different types of social relationships; making the assessment indicators more targeted and accurate, making up for the deficiencies in indicator selection of existing methods; by collecting data for specific assessment indicators, comprehensive and accurate psychological state-related data can be obtained; at the same time, a standard data list of normal psychological states of the same type is generated based on the basic information of teenagers as a reference basis; and by constructing a psychological state difference matrix, the differences in psychological states between individuals and teenagers of the same type are quantified. Compared with traditional methods, the data acquisition and comparison process is more scientific and rigorous; using a preset mental health risk prediction model, combining the difference matrix to generate a risk probability, and then comparing it with a preset health risk threshold to generate an assessment result; the entire process is based on scientific models and quantitative analysis, which can timely and accurately detect potential mental health problems of teenagers and effectively avoid delays in intervention opportunities caused by insufficient assessment technology; From the perspective of the continuity of information collection, by obtaining basic information parameters, a comprehensive basis is provided for the subsequent analysis of factors related to mental state; these basic information are like the basic pieces of a puzzle, supporting the subsequent links; the psychological influencing factor analysis based on this information can accurately locate the key influencing factors, making the matching of factor evaluation indicators more targeted; this process is like a series of progressive gears, one link after another, closely connected, ensuring a comprehensive review of the factors affecting adolescent mental health from the root, avoiding the one-sidedness of information collection, and is the cornerstone of subsequent accurate evaluation; in terms of data processing, data collection collects data for specific factor evaluation indicators, and combines it with the standard mental state data list to construct a difference matrix, which produces a powerful synergistic effect; instead of looking at a certain data in isolation, individual data is compared and quantified with the same type of standard data, so as to clearly present the psychological status of the adolescent in the group The degree of state deviation; it provides high-quality data input for the mental health risk prediction model, so that the model can generate reliable risk probabilities based on accurate and meaningful data; from the perspective of the overall assessment process, the preset mental health risk prediction model, the difference matrix and the preset health risk threshold are combined to achieve a scientific transformation from data to results; the entire process is completed in one go, through multi-dimensional considerations of the mental health status of adolescents, accurate data collection and comparison, and ultimately generates reliable assessment results; it can not only effectively capture the differences in the formation mechanism of the mental state of adolescents of different ages, genders, family structures and school types, but also promptly detect mental health risks, and provide strong support for early intervention, and in meeting the needs of accurate and systematic assessment of the mental health status of adolescents, it has demonstrated strong effectiveness and innovation; this adolescent mental health risk assessment method can accurately and systematically assess the mental health status of adolescents. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the present invention; Figure 2 It is the structural diagram of the youth mental health risk assessment system. DETAILED DESCRIPTION

[0018] In the description of this application, those skilled in the art should know that this application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, this application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0019] The above computer-readable storage medium may adopt any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0020] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws.

[0021] The present application describes the provided method, apparatus, and electronic device through flowcharts and / or block diagrams.

[0022] It should be understood that each block of the flowchart and / or block diagram, as well as the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by a computer or other programmable data processing devices to produce a device that realizes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0023] These computer-readable program instructions can also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that includes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0024] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing devices, or other devices, so that a series of operation steps are executed on the computer, other programmable data processing devices, or other devices to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing devices to provide a process that realizes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0025] The present application will be described below with reference to the accompanying drawings in the present application.

[0026] Embodiment 1: As Figure 1 shown, the method for assessing the mental health risks of teenagers according to the present invention specifically includes the following steps: S1. Obtain basic information parameters of adolescents to be evaluated for mental health, including at least age, gender, family structure and school type; Age information: Age is one of the key factors that affect adolescent psychological development. Adolescents at different ages have significant differences in cognitive ability, emotional regulation ability, social adaptability, etc. Students in lower grades of primary school will face more psychological pressure to adapt to the school environment and learn basic skills; while adolescents in middle school will face more complex psychological challenges such as pressure to enter higher education and self-identity development. Obtaining age information can help mental health management workers more accurately understand the psychological problems that adolescents may encounter at a specific age, so as to conduct targeted assessments and interventions. Gender information: Gender differences are also more obvious in the psychological development of adolescents. Boys and girls differ in emotional expression, social methods, interests and hobbies. Girls are more delicate in emotional communication and more easily affected by interpersonal relationships. Boys are more inclined to solve problems independently when facing setbacks. Understanding gender information can help assessors take these differences into account when analyzing psychological influencing factors and formulating intervention strategies, thereby improving the accuracy of assessments and the effectiveness of interventions. Family structure information: Family is an important environment for adolescents to grow up, and family structure has a profound impact on adolescents' mental health. Common family structures include nuclear families, single-parent families, remarried families, and extended families. Different family structures will bring different family atmospheres, parent-child relationships, and educational methods. Adolescents from single-parent families are relatively weak in emotional support and face more life pressures. Adolescents in extended families need to deal with more complex interpersonal relationships. Obtaining family structure information can help assessors gain an in-depth understanding of the adolescents' family environment, analyze the impact of family factors on their mental health, and provide important references for subsequent psychological interventions. School type information: School is an important place for teenagers to learn and socialize. The school type will affect the teenagers' learning pressure, campus culture and social circle. Students in key schools will face greater academic competition pressure, while students in vocational schools are more concerned about the cultivation of professional skills and employment pressure. Understanding school type information can help assessors better understand the challenges that teenagers may face in the school environment, analyze the impact of school factors on their mental health, and thus develop a mental health management plan that is more in line with the actual situation. When obtaining these basic information parameters, we must pay attention to protecting the privacy of young people, ensure that the information collection process is legal and compliant, and in the subsequent evaluation process, use this information in strict accordance with relevant regulations and ethical requirements.

[0027] In this step, by obtaining basic information parameters such as the age, gender, family structure, and school type of adolescents, mental health management workers can gain a deeper understanding of the background characteristics and psychological development characteristics of adolescents; it helps evaluators more accurately understand the psychological problems that adolescents may encounter in specific situations, so as to be able to conduct targeted mental health assessments and interventions, improving the accuracy and effectiveness of the assessments; understanding the basic information parameters of adolescents can help evaluators fully consider these differences when analyzing psychological influencing factors and formulating intervention strategies; it helps to develop intervention measures that better meet the individual needs of adolescents, improving the pertinence and effectiveness of the interventions, and promoting the mental health development of adolescents; in the process of obtaining the basic information parameters of adolescents, attention is paid to protecting the privacy of adolescents to ensure that the information collection process is legal and compliant; this not only helps to establish a trust relationship with adolescents, but also ensures the ethics and legality of the assessment process, laying a solid foundation for subsequent mental health management work; at the same time, strictly using this information in accordance with relevant regulations and ethical requirements can also effectively avoid risks such as information leakage and abuse.

[0028] S2. Based on the basic information parameters of the adolescents, analyze the factors related to the mental state to obtain a set of psychological influencing factors, including at least factors affecting mental health such as learning stress sources, types of social relationships, and family atmosphere factors. Analysis of learning stress sources: According to the age and school type of adolescents, the learning stress sources they may face can be analyzed; for middle school students, the pressure of entrance examinations, course difficulty, amount of homework, and competitive environment are all important stress sources; while for vocational school students, the mastery of vocational skills, internship experience, and employment prospects will become their main sources of stress; specific indicators such as grade fluctuations and homework completion duration will also be considered, and these indicators can reflect the learning burden and stress level of adolescents. Analysis of types of social relationships: The social relationships of adolescents have an important impact on their mental health; according to the gender and family structure of adolescents, analyze the types of social relationships they form; adolescents from families with a harmonious atmosphere may be more likely to form positive peer relationships, while adolescents from single-parent families may be more cautious or lonely in social interactions; deeply examine the specific impacts of different types of social relationships on the mental health of adolescents, such as the quality of friendship, teacher-student relationship, and family support. Analysis of family atmosphere factors: According to the family structure of adolescents, analyze various aspects of the family atmosphere, such as the educational methods of parents, family communication patterns, and frequency of family conflicts; these factors can reflect the stability and supportiveness of the family environment and have a profound impact on the emotional development, self-identity, and stress-coping ability of adolescents. Consideration of other psychological influencing factors: including personal interests and hobbies, personality traits, living habits, etc.; Although these factors may not be as direct as learning pressure, social relationships, and family atmosphere, they also affect the mental health status of teenagers to a certain extent.

[0029] In this step, a comprehensive analysis is carried out from aspects such as learning stress sources, types of social relationships, family atmosphere factors, and others such as personal interests and hobbies. It comprehensively covers multiple dimensions that have an important impact on the mental health of teenagers, avoids the limitations of only focusing on a single factor, and can more comprehensively understand various factors affecting the mental health of teenagers, providing a rich and comprehensive information basis for subsequent evaluations; In the analysis of learning stress sources, based on the different ages and school types of teenagers, the main learning stress sources faced by middle school students and vocational school students are accurately identified, and specific indicators such as grade fluctuations and homework completion time are considered, making the evaluation of learning stress more accurate and helping to formulate targeted strategies to relieve learning stress; Analyze the types of social relationships based on the gender and family structure of teenagers, deeply explore the impact of family atmosphere on social relationships, and the specific effects of different types of social relationships on the mental health of teenagers, which can better understand the role of social factors in the mental health of teenagers and provide guidance for improving the social situation of teenagers and promoting mental health; Analyze all aspects of the family atmosphere according to the family structure, clearly showing the profound impact of the family environment on the emotional development, self-identity, and stress-coping ability of teenagers, helping to discover possible problems in the family environment and providing directions for family intervention and support; Considering other psychological influencing factors such as personal interests and hobbies, personality traits, and living habits, although these factors are relatively indirect, they can also more comprehensively grasp the mental health status of teenagers, avoid missing potential factors that may affect their mental health, and make the evaluation more perfect and scientific; Through a comprehensive, in-depth, and accurate analysis of various psychological influencing factors, it provides a strong basis for formulating targeted mental health intervention measures in the future. According to the specific situations of different teenagers, more effective intervention means can be adopted to improve the effect of mental health management and better meet the needs of the mental health development of teenagers.

[0030] S3. Based on the set of psychological influencing factors, match multiple corresponding factor evaluation indicators, where the multiple factor evaluation indicators include indicators that need to be monitored and evaluated for each influencing factor; The matching of the factor evaluation indicators includes: Associate the learning stress source with the grade volatility rate, standard deviation of homework completion time, and self-assessment score of test anxiety; Grade volatility is an important indicator to measure the stability of students' academic performance. In mental health management, large fluctuations in grades often indicate that students may be facing learning difficulties or pressure. Grade volatility is obtained by calculating the standard deviation of students' grades in various subjects over multiple semesters or academic years. A higher volatility means that students find it difficult to maintain a stable learning state during the learning process, or are greatly affected by external factors, which in turn leads to psychological problems such as anxiety and tension. The time it takes to complete an assignment reflects the time students spend on their learning tasks; the standard deviation of the time it takes to complete an assignment reflects the stability of the time it takes students to complete their assignments. If the standard deviation of a student's time to complete an assignment is large, it means that the time it takes to complete the assignment fluctuates significantly. This may be due to unstable learning efficiency, large differences in assignment difficulty, or other external interference factors. If students are in this state for a long time, they may feel increased learning pressure and even develop a dislike for learning. In mental health assessments, this indicator can help understand the time pressure students are under during their studies. Test anxiety is a common psychological reaction of students when facing exams. Moderate test anxiety can stimulate students' learning motivation, but excessive test anxiety will have a negative impact on students' mental health. Through the test anxiety self-assessment scale, students can self-assess their anxiety levels before, during and after the exam to obtain a test anxiety self-assessment score. This score can intuitively reflect students' psychological feelings and anxiety levels about the exam, and help evaluate the impact of learning pressure on students' mental health. The family atmosphere factors were associated with the frequency of parental communication, the record of family conflict events, and the perceived economic stress index; The frequency of communication between parents and children directly affects the psychological state of children; a higher frequency of communication means that parents can understand their children's needs, confusion and emotional changes in a timely manner, and provide appropriate support and guidance; on the contrary, a lower frequency of communication will cause children to feel neglected and lack emotional support, thus causing psychological problems such as loneliness and anxiety; in mental health management, questionnaires or interviews can be used to understand the number and duration of communication between parents and children each week, so as to evaluate the impact of family atmosphere on children's mental health; Family conflict is an inevitable part of family life, but frequent and intense family conflict can have a serious negative impact on the mental health of adolescents; record incidents of conflict in the family, including the cause of the conflict, the participants, the intensity of the conflict, and the way the conflict was resolved; through the analysis of these incidents, we can understand the frequency and characteristics of family conflicts, assess the tension of the family atmosphere, and the potential impact of family conflicts on the psychological state of adolescents; The economic pressure perception index is an indicator to measure adolescents' subjective feelings about their family economic situation. Through questionnaire surveys, we can understand adolescents' perception of family economic pressure, such as whether they worry about the family's economic burden and whether they give up certain learning or entertainment activities due to economic reasons. A higher economic pressure perception index may lead to psychological problems such as inferiority, anxiety, and depression in adolescents, affecting their mental health development. Associate the types of social relationships with the online social duration, the participation degree in offline peer activities, and the number of reported bullying incidents. In today's digital age, online socializing has become an important part of adolescents' social lives. However, excessive online socializing may have a negative impact on adolescents' mental health. By recording the time adolescents spend on various online social platforms every day or week, we can obtain the online social duration. An overly long online social duration may cause adolescents to become addicted to the virtual world, neglecting social relationships in real life, thus resulting in psychological problems such as loneliness and social anxiety. At the same time, problems such as bad information and cyberbullying in online socializing may also pose a threat to adolescents' mental health. Adolescents with a high participation degree in offline peer activities usually have better social skills and mental health levels. By investigating the frequency and enthusiasm of adolescents' participation in various offline peer activities, we can evaluate their participation degree in offline peer activities. Adolescents who actively participate in offline peer activities are more likely to obtain social support, enhancing their self-confidence and sense of belonging. Adolescents with a low participation degree may feel lonely and excluded, affecting their mental health. Bullying incidents can cause serious harm to the mental health of victims. Recording the number of reported bullying incidents that adolescents encounter or witness in schools or other social settings can reflect the safety and harmony of their social environment. Frequent bullying incidents not only cause psychological trauma to victims but also have a negative impact on the mental health of the entire social group. By analyzing the number of reported bullying incidents, we can timely discover and intervene in potential mental health problems, creating a good social environment.

[0031] In this step, multiple evaluation indicators in different dimensions are respectively matched for various psychological influencing factors such as learning stressors, family atmosphere factors, and types of social relationships; from the perspectives of academic performance stability, homework time stability, exam anxiety level, parental communication, family conflicts, economic stress perception, as well as online and offline social interaction duration and activity participation, bullying incidents, etc., it comprehensively covers the key factors affecting the mental health of teenagers, avoids the limitations of single-dimensional evaluation, and can understand the mental health status of teenagers more comprehensively; the evaluation indicators matched for each factor have strong pertinence and accuracy; the self-rating scale for exam anxiety is used to obtain the self-rating score of exam anxiety, which intuitively reflects the students' psychological feelings and anxiety levels towards exams; it helps to capture the changes in the psychological states of teenagers in different aspects more accurately and provides a reliable basis for subsequent evaluations; these evaluation indicators can help detect potential hidden dangers in the mental health of teenagers in a timely manner; through the monitoring and analysis of these indicators, it is possible to detect and take intervention measures before the mental health problems develop to a serious level and grasp the best intervention opportunity; the matched evaluation indicators can deeply analyze the reasons affecting the mental health of teenagers; considering that there are differences in the formation mechanisms of the mental states of teenagers of different ages, genders, family structures, and school types, through multi-dimensional factor evaluation indicators, it is possible to better adapt to these diverse differences, effectively capture the characteristics and problems of different teenage groups in terms of mental health, and make the evaluation results more personalized and targeted; through the analysis of indicators such as the number of reported bullying incidents, it is possible to detect problems existing in the social environment in a timely manner and take measures to create a safe and harmonious social environment, which is not only beneficial to the mental health recovery of the victims but also contributes to the mental health development of the entire social group.

[0032] S4. Collect data for the multiple factor evaluation indicators to obtain a list of data related to mental states. Data collection for indicators related to learning stressors: The academic performance volatility obtains the academic performance data of each subject of students in multiple semesters or school years from the school's academic performance management system; ensure the accuracy and integrity of the data, and then calculate the standard deviation of the academic performance of each subject according to statistical methods to obtain the academic performance volatility; during the collection process, pay attention to the time range and subject coverage of the academic performance data to ensure that it can comprehensively reflect the academic performance stability of students. The standard deviation of the homework completion duration collects data through the following two methods; one is to set up a special homework completion time recording system in the school and let students record the start and end times by themselves when completing their homework; the other is that when teachers assign homework, they require students to record the homework completion time and collect and summarize it regularly; for the collected homework completion time data, calculate its standard deviation to reflect the stability of students' homework completion time; during the collection process, remind students to record the time truthfully to avoid data fraud. Self-assessment score of test anxiety: Use a professional self-assessment scale for test anxiety and arrange students to conduct self-assessment before, during or after the test; Before administering the test, explain to students how to fill in the scale and what to note to ensure that students can understand the meaning of the questions and answer them truthfully; Collect the scales filled in by students and score them according to the scoring criteria of the scale to obtain the self-assessment score of test anxiety; Data collection for indicators related to family atmosphere factors: Frequency of parental communication: Use questionnaires or interviews to ask parents and children about their communication situation. The questionnaires or interviews may include the number of communications per week, the approximate duration of each communication, the main topics of communication, etc. To improve the accuracy of the data, the answers of parents and children are mutually verified. Records of family conflict events: interview family members to understand conflict events in the family; during the interview, ask in detail the cause of the conflict, the participants, the intensity of the conflict, and the way to resolve the conflict; at the same time, ask family members to record family conflict events that occurred over a period of time, and summarize and analyze them regularly; during the collection process, pay attention to protecting the privacy of family members to avoid causing unnecessary conflicts; Economic stress perception index: a special questionnaire survey is designed to understand teenagers' subjective feelings about their family's economic situation. The questionnaire may include questions such as whether they are worried about the family's economic burden, whether they have given up certain learning or entertainment activities due to economic reasons, and whether the family's economic situation has an impact on their psychology. Teenagers are asked to answer the questions according to their actual situation, and the economic stress perception index is calculated based on the answers. Data collection for indicators related to social relationship types: The length of time spent on online social networking can be recorded by installing time monitoring software on students' electronic devices to record the time students spend on various online social platforms every day or week. Before collecting data, the purpose of the software and the confidentiality of the data should be explained to students and their consent should be obtained. At the same time, the accuracy and stability of the software should be ensured to avoid data loss or errors. The data on offline peer activity participation are collected through questionnaire surveys, interviews and observations. The questionnaire survey can understand the frequency and types of offline peer activities that students participate in. The interview can provide an in-depth understanding of the students' enthusiasm in the activities and their interactions with their peers. The observation can directly record the students' performance and participation in the activities. The data are combined to evaluate the students' offline peer activity participation. The number of reported bullying incidents, establish a reporting mechanism for bullying incidents, encourage students to report in a timely manner when they encounter or witness bullying incidents; at the same time, record the reported incidents in detail, including information such as the time, location, participants, and process of the incident; during the collection process, protect the privacy of the whistleblowers, and seriously handle bullying incidents to ensure that students can report with confidence; Sort and verify all the collected data to ensure the accuracy and integrity of the data; for missing data, supplement or explain it; According to the requirements of associating data with mental states, classify and summarize the sorted data to generate a list of data associated with mental states.

[0033] In this step, for the psychological influencing factors in different aspects such as learning stress sources, family atmosphere factors, and social relationship types, a variety of data collection methods were adopted to ensure that all kinds of data related to the psychological state of teenagers could be comprehensively obtained, covering multiple life fields such as academics, family, and social interactions, avoiding the limitations of a single data source, and making the evaluation results more able to reflect the true situation of teenagers' mental health; in the process of data collection, a series of measures were taken to ensure the accuracy of the data; for the grade volatility rate, it was emphasized to obtain accurate and complete data from the school grade management system, and pay attention to the time range and subject coverage; when collecting the standard deviation of the homework completion duration, students were reminded to record the time truthfully, and data was collected through various methods for mutual verification; before administering the self-assessment score of test anxiety, students were explained the filling methods and precautions; the frequency of communication with parents was mutually verified through the answers of parents and children, etc.; these measures effectively reduced data errors and false information, improved the reliability of the data, and provided a solid data foundation for subsequent mental health evaluations; when collecting data related to family atmosphere factors and social relationship types, full consideration was given to the protection of personal privacy and rights; making the data collection process more user-friendly, helping to gain the trust and cooperation of participants, and thus ensuring the authenticity and effectiveness of the data; through detailed data collection of specific indicators in different aspects, it was possible to analyze according to the unique situation of each teenager; different teenagers have differences in aspects such as learning stress, family environment, and social relationships, and these detailed data can better capture these personalized characteristics, providing rich data support for subsequent construction of a mental state difference matrix, quantification of the degree of mental state differences, and generation of personalized mental health evaluation results, making the evaluation more targeted and accurate; accurate and comprehensive data collection provides a strong basis for timely discovering teenagers' mental health problems and formulating effective intervention measures; through the analysis of data related to learning stress sources, it is possible to understand the stress situation of students in academics and take timely measures to relieve stress; the analysis of data on family atmosphere factors helps to discover problems in the family environment and guide the family to improve communication and atmosphere; the analysis of data on social relationship types can identify the difficulties and risks faced by students in social interactions and conduct targeted social skills training and psychological counseling, etc.; thus better promoting the mental health development of teenagers.

[0034] S5. Based on the basic information parameters, obtain the corresponding standard psychological state data list as a reference basis for the normal psychological state of teenagers of the same type; Using the existing large-scale database related to adolescent mental health, national or regional adolescent health survey projects will conduct surveys on the mental health status of a large number of adolescents of different ages, genders, family structures and school types, covering data in multiple dimensions such as learning pressure, social relationships, and family atmosphere; screening out data that matches the basic information parameters of the adolescent to be evaluated from the database related to adolescent mental health as one of the sources of standard mental state data; Cooperating with multiple schools, psychological counseling institutions, community service centers, etc. to jointly collect the mental state data of adolescents of the same type; these institutions have accumulated rich information related to adolescent mental health in their daily work, and integrate data resources from all parties by establishing a data sharing mechanism; Consulting relevant professional research literature to obtain research results on the mental health of adolescents of specific ages, genders, family structures and school types; According to the age of the adolescent to be evaluated, screening out the data of adolescents in a similar age range from the collected data; considering the differences in the psychological development characteristics of adolescents at different age stages, setting the age range within a reasonable interval, with a difference of no more than 2 years; Dividing the data into two groups of male and female according to the gender of the adolescent to be evaluated, and respectively selecting the corresponding gender data; According to the family structure of the adolescent to be evaluated, screening out the data of adolescents with the same or similar family structure from the data; If the adolescent to be evaluated comes from different types of schools such as public schools, private schools, vocational schools, etc., corresponding matching is also required during data screening; the educational concepts, teaching methods, learning atmosphere, etc. of different school types may have an impact on the mental state of adolescents; Cleaning the screened data of adolescents of the same type, removing outliers, missing values and incorrect data; for the missing data, using methods such as mean filling and regression prediction to supplement it; Using statistical methods to analyze the cleaned data, calculating statistics such as the mean, standard deviation, median of various mental state indicators; these statistics can reflect the general level and dispersion degree of adolescents of the same type in each mental state indicator; Integrating the calculated statistics according to the relevant factors of mental state to form a standard mental state data list; in the list, clearly list the evaluation indicators corresponding to each factor and their statistical values for subsequent comparative analysis with the associated data of the mental state of the adolescent to be evaluated.

[0035] In this step, data is collected through various means such as leveraging large-scale databases related to adolescent mental health, collaborating with multiple institutions, and consulting professional research literature; it covers data from national or regional health survey projects, daily accumulated information from schools and various institutions, and professional research results, ensuring the diversity of data sources, comprehensively covering multiple dimensions such as learning pressure, social relationships, and family atmosphere, enabling the standard psychological state data list to comprehensively reflect all aspects of the psychological state of adolescents of the same type and avoiding the one-sidedness of data; precise screening and matching are carried out according to the age, gender, family structure, and school type of the adolescents to be evaluated; a reasonable age range is set, taking into account the differences in psychological development at different age stages; gender differentiation can take into account the influence of gender factors on the psychological state; the matching of family structure and school type fully considers the effects of family environment and school educational background on adolescents' psychology, making the selected data more in line with the actual situation of the adolescents to be evaluated, enhancing the pertinence and personalization of the standard data, and improving the accuracy of the evaluation; the selected data is cleaned to remove outliers, missing values, and incorrect data, and scientific methods are used to supplement the missing data, ensuring the accuracy and integrity of the data; subsequently, statistical methods are used to analyze the data, calculating statistics such as mean, standard deviation, and median, further improving the quality and reliability of the data, being able to accurately reflect the general level and dispersion degree of adolescents of the same type on each psychological state index, providing a solid data basis for the evaluation; the calculated statistics are integrated according to psychological state-related factors to form a standard psychological state data list, clearly listing the evaluation indicators corresponding to each factor and their statistical values; the structured list form makes it more intuitive and convenient to conduct comparative analysis with the psychological state-related data of the adolescents to be evaluated later, helping to quickly and accurately discover the differences in the psychological states between the adolescents to be evaluated and those of the same type, thus providing strong support for the scientific evaluation of adolescents' mental health status; collaborating with multiple schools, psychological counseling institutions, community service centers, etc. to collect data and establishing a data sharing mechanism promotes communication and cooperation between different institutions, integrating the resources and knowledge of all parties in the field of adolescent mental health; at the same time, consulting professional research literature also enables academic research results to be applied to actual evaluations, achieving the combination of theory and practice, and being conducive to promoting the development of the field of adolescent mental health management; due to the wide range of data sources and the ability to be updated regularly, the standard psychological state data list can adapt to the changes in the living environment and social background of adolescents, timely reflecting the normal psychological state of current adolescents of the same type, ensuring the timeliness and effectiveness of the evaluation reference basis.

[0036] S6. Construct a psychological state difference matrix through the collected psychological state-related data list and the standard psychological state data list to quantify the degree of difference in the psychological states between the adolescent and adolescents of the same type. The method for constructing the psychological state difference matrix includes: Align the indicators in the list of associated data on the mental state of the adolescents to be evaluated with the list of standard mental state data; if there is no direct correspondence for a certain indicator between the two, perform appropriate conversion or approximate matching; For each corresponding indicator, calculate the difference between the actual value and the standard value of the adolescents to be evaluated; methods such as absolute difference, relative difference or standardized difference can be used for difference calculation; when selecting the difference calculation method, the nature of the indicator and the evaluation purpose should be considered; Fill the calculated difference values into the mental state difference matrix in the order of the indicators; the number of rows of the matrix is equal to the number of psychological influencing factors, and the number of columns is equal to the total number of evaluation indicators corresponding to all factors; Assign weights to each element in the difference matrix according to the importance of the indicator to mental health.

[0037] In this step, by aligning the indicators in the list of associated data on the mental state of the adolescents to be evaluated with the list of standard mental state data, even in the case of no direct correspondence of indicators, appropriate conversion or approximate matching can be performed, ensuring the comparability of the data; on this basis, differences are calculated, enabling the specific differences between the adolescents to be evaluated and their peers in each mental state indicator to be accurately found, avoiding incorrect comparisons caused by data mismatch, and providing a reliable basis for subsequent evaluations; multiple methods are provided for difference calculation, such as absolute difference, relative difference or standardized difference, etc., and the appropriate method can be selected according to the nature of the indicator and the evaluation purpose; this flexibility can adapt to different types of mental state indicators, making the difference calculation more in line with the actual situation and more accurately reflecting the degree of change of the indicator and its impact on the mental health of adolescents, thereby improving the scientificity and rationality of difference quantification; the calculated difference values are filled into the mental state difference matrix in the order of the indicators, and the number of rows and columns of the matrix correspond to the psychological influencing factors and evaluation indicators; it shows the difference situations of different indicators under each psychological influencing factor, facilitating the intuitive observation and analysis of the difference distribution of the mental states of the adolescents to be evaluated and their peers, helping to quickly locate the factors and indicators with larger differences, and providing a clear direction for further intervention and guidance; weights are assigned to each element in the difference matrix according to the importance of the indicator to mental health, highlighting the role of key indicators in the evaluation; different mental state indicators have different degrees of impact on the mental health of adolescents. Through weight assignment, the differences of each indicator can be more reasonably considered comprehensively, avoiding excessive influence on the evaluation results due to large differences in secondary indicators, making the finally quantified degree of difference more able to reflect the true mental health status of adolescents, and improving the accuracy and reliability of the evaluation results.

[0038] S7. Input the mental state difference matrix into a preset mental health risk prediction model to generate a mental health risk probability; The mental health risk prediction model is constructed based on a large amount of historical data, professional psychological theories, and advanced machine learning or artificial intelligence algorithms; the historical data includes basic information parameters, psychological state-related factors, corresponding psychological state correlation data, and their true mental health status of numerous different teenagers; through the analysis and mining of these data, machine learning algorithms are used to train the model so that it can learn the internal relationships and patterns between different factors and mental health risks; when constructing the model using neural network algorithms, a large amount of historical data will be input into the network, and each neuron in the network adjusts its weights and thresholds continuously to fit the rules in the data, so that the model can make accurate predictions for the newly input data. During the evaluation process, the mental state difference matrix is provided as input data to the constructed mental health risk prediction model; this matrix quantifies the degree of difference in the mental states between the current teenager to be evaluated and teenagers of the same type, contains multi-dimensional information, and these information reflect the deviation of this teenager from the standard state in various psychological influencing factors. After receiving the mental state difference matrix, the model processes and analyzes the input data based on the patterns and rules it has learned; through calculation and reasoning, the model outputs a probability value, that is, the mental health risk probability of this teenager; the mental health risk probability value reflects the likelihood of this teenager having mental health problems in the current state. The formula for the mental health risk prediction model to calculate the mental health risk probability is as follows: Among them, P(y = 1|x) represents the probability that a teenager belongs to the category with high mental health risk given the input feature vector x; x1, x2,..., x n represent various factor evaluation indicators extracted from the mental state difference matrix; β0, β1,..., β n represent model parameters, representing the influence degree of each factor on mental health risk.

[0039] In this step, by using a mental health risk prediction model constructed based on a large amount of historical data, professional psychological theories, and advanced machine learning or artificial intelligence algorithms, the mental health risk probability of adolescents can be accurately quantified, improving the accuracy and reliability of the prediction. The mental state difference matrix, as input data, comprehensively reflects the deviation of adolescents from the standard state in various psychological influencing factors, making the evaluation process more scientific and objective, and helping to more comprehensively understand the mental state of adolescents. By learning and analyzing the internal relationships and patterns in historical data, the model can identify the associations between different factors and mental health risks, thus making accurate predictions for newly input data. Furthermore, it can adapt to the individual differences of different adolescents and provide personalized risk assessments. The output of the mental health risk probability provides strong decision-making support for relevant decision-makers. Corresponding intervention measures can be taken or necessary psychological support can be provided according to the magnitude of the risk probability to prevent or mitigate the occurrence of mental health problems. Through the construction and application of the mental health risk prediction model in this step, the accurate prediction and scientific evaluation of adolescents' mental health risks are achieved, providing strong decision-making support for relevant decision-makers and helping to promote the mental health development of adolescents.

[0040] S8. Compare the mental health risk probability with a preset health risk threshold to generate a mental health assessment result for this adolescent; Before starting the comparison, set one or more health risk thresholds, which are comprehensively determined based on a large amount of historical data, clinical research, and expert opinions. Set a low risk threshold indicating good mental health status; a medium risk threshold indicating that there may be certain mental health hidden dangers and further observation is needed; and a high risk threshold meaning there is a relatively high mental health risk and immediate action is required. Compare the mental health risk probability with the preset health risk threshold. When the mental health risk probability is lower than the set low risk threshold, it is considered that the mental health status of this adolescent is good and no special attention or intervention measures are needed. When the risk probability is between the low risk and medium risk thresholds, it indicates that this adolescent is facing minor mental health challenges. It is recommended to regularly monitor the changes in their mental state and consider providing some basic support services. When the risk probability is close to but does not exceed the high risk threshold, it indicates that the adolescent is on the verge of relatively serious mental health problems. At this time, monitoring should be strengthened and professional psychological counseling or treatment should be arranged as soon as possible. When the mental health risk probability exceeds the high risk threshold, it strongly indicates that this adolescent is experiencing serious mental health problems and an emergency intervention procedure must be immediately initiated, including but not limited to contacting parents, school counselors, and professional mental health service providers to ensure that necessary support and help can be provided in a timely manner. Generate a detailed mental health assessment report for teenagers automatically according to the above comparison results; the mental health assessment report should not only include the final risk level assessment, but also cover specific recommended measures.

[0041] In this step, by setting multiple health risk thresholds determined comprehensively based on a large amount of historical data, clinical research, and expert opinions, the mental health risks of teenagers can be accurately graded; the detailed grading method can give corresponding assessment conclusions according to different risk levels, making the assessment results more targeted and accurate, and avoiding the drawbacks of general assessment; corresponding clear intervention suggestions are provided for different risk probability intervals; for teenagers with low risks, it can be determined that no special attention is needed, saving resources; for those between low and medium risks, it is recommended to monitor regularly and provide basic support services for early prevention and timely attention; for those close to the high-risk threshold, enhanced monitoring and prompt arrangement for professional counseling or treatment are required to prevent the problem from deteriorating; for those exceeding the high-risk threshold, an emergency intervention procedure is immediately initiated to ensure that serious mental health problems are dealt with in a timely manner; this helps to reasonably allocate mental health management resources and improve the effectiveness and timeliness of intervention; automatically generate a detailed mental health assessment report for teenagers, and the report content not only includes the risk level assessment, but also covers specific recommended measures; on the one hand, this provides intuitive and clear information for relevant personnel to comprehensively understand the mental health status of teenagers, facilitating them to make appropriate decisions; on the other hand, the specific recommended measures provide clear operation guidelines for the subsequent management and intervention of teenagers' mental health, helping to form a systematic and scientific mental health management process and promoting the healthy development of teenagers' mental health.

[0042] Embodiment 2: As Figure 2 shown, the mental health risk assessment system for teenagers of the present invention specifically includes the following modules; Basic information acquisition module, which acquires the basic information parameters of teenagers to be mentally health assessed, including at least age, gender, family structure, and school type; Psychological influencing factor analysis module, which analyzes the factors related to the mental state based on the basic information parameters of the teenagers to obtain a set of psychological influencing factors, including at least factors affecting mental health such as learning stress sources, types of social relationships, and family atmosphere factors; Factor assessment index matching module, which matches a plurality of corresponding factor assessment indexes based on the set of psychological influencing factors, and the plurality of factor assessment indexes include the indexes that need to be monitored and evaluated for each influencing factor; Data collection module, which collects data for the plurality of factor assessment indexes to obtain a list of data related to the mental state; The standard psychological state data acquisition module obtains a list of standard psychological state data corresponding to the basic information parameters as a reference for the normal psychological state of adolescents of the same type; The difference matrix construction module constructs a psychological state difference matrix through the collected list of psychological state associated data and the list of standard psychological state data to quantify the difference degree of the psychological state between this adolescent and adolescents of the same type; The mental health risk prediction module inputs the psychological state difference matrix into a preset mental health risk prediction model to generate a mental health risk probability; The evaluation result generation module compares the mental health risk probability with a preset health risk threshold to generate a mental health evaluation result for this adolescent.

[0043] Through the basic information acquisition module and the psychological influence factor analysis module, the system comprehensively considers the multi-dimensional basic information of adolescents, as well as various psychological influence factors such as learning stress sources, social relationship types, and family atmosphere factors; it helps to capture the complexity and diversity of adolescents' mental health more accurately; the factor evaluation index matching module matches specific evaluation indexes for each psychological influence factor, which can more accurately reflect the psychological state of adolescents in different aspects, thereby improving the accuracy of evaluation; the data acquisition module is responsible for collecting data related to the psychological state, providing a basis for the acquisition of standard psychological state data and the construction of the difference matrix; making the evaluation result more objective and reliable and reducing the deviation caused by subjective judgment; The system obtains a list of standard psychological state data through basic information parameters as a reference for the normal psychological state of adolescents of the same type; making the evaluation result more targeted and able to more accurately reflect the mental health status of each adolescent; the difference matrix construction module quantifies the difference degree of the psychological state between adolescents and adolescents of the same type, making the evaluation result more intuitive and easy to understand; helping to more accurately judge the mental health risk level of adolescents; the mental health risk prediction module uses a preset mental health risk prediction model to generate a mental health risk probability; being able to detect potential mental health problems of adolescents in advance and providing strong support for timely intervention; The entire system forms a complete evaluation process from basic information acquisition to evaluation result generation; making the evaluation process more standardized and efficient and improving the accuracy and reliability of evaluation; this adolescent mental health risk assessment system has multiple advantages such as comprehensiveness, refinement, data-driven, personalization, quantitative evaluation, predictability, and systematicness, can effectively solve the problems existing in the existing adolescent mental health assessment methods, and provide strong support for the mental health management of adolescents.

[0044] The various variations and specific embodiments of the adolescent mental health risk assessment method in the foregoing Embodiment 1 are equally applicable to the adolescent mental health risk assessment system of this embodiment. Through the foregoing detailed description of the adolescent mental health risk assessment method, those skilled in the art can clearly know the implementation method of the adolescent mental health risk assessment system in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here.

[0045] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it realizes each process of the method embodiment for controlling the output data, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0046] The foregoing is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for assessing the mental health risks of adolescents, characterized in that, The method includes: Obtaining the basic information parameters of the adolescents to be psychologically evaluated; Based on the basic information parameters of the adolescents, analyzing the factors related to the psychological state to obtain a set of psychological influencing factors; Based on the set of psychological influencing factors, matching multiple corresponding factor evaluation indicators; Collecting data for the multiple factor evaluation indicators to obtain a list of data related to the psychological state; Based on the basic information parameters, obtaining a corresponding standard psychological state data list; Constructing a psychological state difference matrix through the collected list of data related to the psychological state and the standard psychological state data list; Inputting the psychological state difference matrix into a preset mental health risk prediction model to generate a mental health risk probability; Comparing the mental health risk probability with a preset health risk threshold to generate a mental health evaluation result for the adolescent.

2. The adolescent mental health risk assessment method according to claim 1, wherein, The set of psychological influencing factors at least includes factors affecting mental health such as learning stress sources, types of social relationships, and family atmosphere factors.

3. The adolescent mental health risk assessment method according to claim 2, wherein The matching of the factor evaluation indicators includes: For the learning stress source, correlating the achievement volatility, the standard deviation of homework completion duration, and the self-assessment score of test anxiety; For the family atmosphere factor, correlating the parental communication frequency, the record of family conflict events, and the economic stress perception index; For the type of social relationship, correlating the online social duration, the participation degree in offline peer activities, and the number of reported bullying incidents.

4. The adolescent mental health risk assessment method according to claim 1, characterized in that, The method for constructing the psychological state difference matrix includes: Aligning the indicators in the list of data related to the psychological state of the adolescent to be evaluated and the standard psychological state data list; For each corresponding indicator, calculating the difference between the actual value and the standard value of the adolescent to be evaluated; Filling the calculated difference values into the psychological state difference matrix in the order of the indicators.

5. The adolescent mental health risk assessment method according to claim 4, wherein The number of rows of the psychological state difference matrix is equal to the number of psychological influencing factors, and the number of columns is equal to the total number of evaluation indicators corresponding to all factors.

6. The adolescent mental health risk assessment method according to claim 1, wherein, The formula for the mental health risk prediction model to calculate the mental health risk probability is as follows: Among them, P(y = 1∣x) represents the probability that an adolescent belongs to the category with high mental health risk under the given input feature vector x; x1, x2, …, xn represent various factor evaluation indicators extracted from the mental state difference matrix; β0, β1, …, βn represent model parameters, representing the influence degree of each factor on mental health risk.

7. The adolescent mental health risk assessment method according to claim 1, wherein The health risk threshold includes a low risk threshold, a medium risk threshold, and a high risk threshold.

8. A mental health risk assessment system for teenagers, characterized in that, The system includes: A basic information acquisition module that obtains the basic information parameters of the adolescents to be psychologically evaluated, at least including age, gender, family structure, and school type; A psychological influencing factor analysis module that, based on the basic information parameters of the adolescents, analyzes the factors related to the psychological state to obtain a set of psychological influencing factors, at least including factors affecting mental health such as learning stress sources, types of social relationships, and family atmosphere factors; A factor evaluation indicator matching module that, based on the set of psychological influencing factors, matches multiple corresponding factor evaluation indicators, and the multiple factor evaluation indicators include the indicators that need to be monitored and evaluated for each influencing factor; A data collection module that collects data for the multiple factor evaluation indicators to obtain a list of data related to the psychological state; A standard psychological state data acquisition module that, based on the basic information parameters, obtains a corresponding standard psychological state data list as a reference basis for the normal psychological state of adolescents of the same type; A difference matrix construction module constructs a mental state difference matrix by using the collected list of mental state correlation data and the standard mental state data list, and quantifies the degree of difference in the mental state between this adolescent and adolescents of the same type; A mental health risk prediction module inputs the mental state difference matrix into a preset mental health risk prediction model to generate a mental health risk probability; An evaluation result generation module compares the mental health risk probability with a preset health risk threshold to generate a mental health evaluation result for this adolescent.

9. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected through the bus, and is characterized in that, When the computer program is executed by the processor, it implements the steps in the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the method according to any one of claims 1-7.

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