Employee mental health management system and method based on cloud computing

Abstract

The invention relates to the field of psychological health, and provides an employee psychological health management system and method based on cloud computing, and the system comprises a psychological archive management module which is used for collecting, classifying, storing and updating the psychological health data of employees, and calculating the comprehensive psychological health indexes of the employees; the intelligent psychological assessment module is used for acquiring psychological data of the employees and assessing psychological states of the employees by applying an advanced regression model to obtain psychological assessment results; the remote psychological counseling module is used for evaluating the psychological counseling effect of the employee to obtain a first evaluation value of the psychological counseling effect; the personalized psychological assistance plan generation module is used for generating a psychological assistance plan for the employee; and the virtual reality psychological treatment module is used for providing psychological treatment service for the employees through the virtual reality technology and evaluating the treatment effect to obtain a second evaluation value of the virtual reality psychological treatment effect. According to the invention, the psychological health state of the employee can be comprehensively and accurately improved.

Description

Technical Field

[0001] The present invention relates to the field of mental health, and particularly to an employee mental health management system, method, and electronic device based on cloud computing. Background Art

[0002] Nowadays, enterprises have taken various measures in paying attention to employees' mental health, including providing psychological counseling services, carrying out mental health training, organizing team-building activities, and setting up mental health hotlines. These practices aim to help employees relieve stress, improve their mood, and enhance their overall mental health level. In addition, some enterprises have introduced mental health assessment tools to timely detect employees' mental problems, so as to provide targeted support and intervention.

[0003] However, existing measures are often carried out in a scattered and independent manner, lacking a comprehensive management system and making it difficult to form a comprehensive management of employees' mental health. Secondly, due to the lack of a systematic tracking and feedback mechanism, enterprises are difficult to accurately understand the dynamic changes of employees' mental health and evaluate the effects. In addition, current mental health services mostly focus on the intervention after problems occur, while the prevention and early identification of problems are insufficient, resulting in some employees' mental problems not being able to be processed in a timely and effective manner. Summary of the Invention

[0004] The present invention aims at the technical problems existing in the prior art and provides an employee mental health management system, method, and electronic device based on cloud computing that can comprehensively and accurately improve employees' mental health status.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides an employee mental health management system based on cloud computing, and the system includes:

[0007] A psychological file management module, which is used to collect, classify, store, and update employees' mental health data and calculate employees' comprehensive mental health index;

[0008] An intelligent psychological assessment module, which is used to obtain employees' psychological data and apply an advanced regression model to evaluate the employees' mental state to obtain a psychological assessment result;

[0009] A remote psychological counseling module, which is used to evaluate the psychological counseling effect of the employees to obtain a first evaluation value of the psychological counseling effect;

[0010] A personalized psychological assistance plan generation module, which is used to generate a psychological assistance plan for the employees;

[0011] A virtual reality psychotherapy module, which is used to provide psychotherapy services for the employee through virtual reality technology and evaluate the treatment effect, so as to obtain a second evaluation value of the virtual reality psychotherapy effect.

[0012] Furthermore, the psychological file management module is also used for:

[0013] Obtain multiple mental health indicators of the employee;

[0014] Determine the interaction between different mental health indicators;

[0015] Process each of the mental health indicators based on the non-linear index, process the interaction between different mental health indicators based on the cross-term coefficient, and determine the comprehensive mental health index of the employee.

[0016] Furthermore, the intelligent psychological assessment module is also used for:

[0017] Obtain the regression coefficient;

[0018] Obtain the second-order change rate of the employee's mental state over time;

[0019] Process the second-order change rate, each of the mental health indicators, and the interaction between different mental health indicators based on the regression coefficient processing to obtain the psychological assessment result of the employee.

[0020] Furthermore, the personalized psychological assistance plan generation module is also used for:

[0021] Generate an optimal psychological assistance plan through a multi-objective optimization algorithm under the premise of meeting multiple constraints.

[0022] Furthermore, the employee mental health management system further includes:

[0023] A psychological knowledge training module, which is used to provide online and offline mental health knowledge training resources for the employee.

[0024] Furthermore, the employee mental health management system further includes:

[0025] A career development counseling module, which is used to provide and generate guiding suggestions for the career planning and development of the employee.

[0026] Furthermore, the employee mental health management system further includes:

[0027] A psychological counseling appointment and crisis intervention module, which is used to provide convenient psychological counseling appointment services for the employee and rapid response services in case of crisis.

[0028] The present invention also provides a method for managing employees' mental health based on cloud computing, the method comprising:

[0029] Collecting, classifying, storing and updating employees' mental health data, and calculating employees' comprehensive mental health indices;

[0030] Obtaining employees' psychological data and applying an advanced regression model to evaluate the psychological state of the employees to obtain a psychological evaluation result;

[0031] Evaluating the psychological counseling effect of the employees to obtain a first evaluation value of the psychological counseling effect;

[0032] Generating a psychological assistance plan for the employees;

[0033] Providing psychological treatment services for the employees through virtual reality technology and evaluating the treatment effect to obtain a second evaluation value of the virtual reality psychological treatment effect.

[0034] In addition, to achieve the above object, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; a processor for reading and executing the computer software programs, thereby implementing a method for managing employees' mental health based on cloud computing as described above.

[0035] The beneficial effects of the present invention are as follows:

[0036] (1) The present invention integrates multiple modules such as psychological file management, intelligent psychological evaluation, remote psychological counseling, generation of personalized psychological assistance plans, and virtual reality psychological treatment, providing a comprehensive and systematic solution for managing employees' mental health, which can cover the whole process from mental health data collection, evaluation to personalized intervention and treatment, and significantly improves the overall management level of enterprises for employees' mental health.

[0037] (2) The present invention adopts intelligent technologies such as complex multivariable regression models, non-linear optimization algorithms, and biofeedback analysis. By analyzing employees' psychological data, personalized mental health evaluation reports and assistance plans are automatically generated. The psychological state of each employee can be accurately evaluated, and intervention measures and treatment plans can be customized according to their individual needs, avoiding the "one-size-fits-all" problem in traditional mental health management.

[0038] (3) The present invention can update employees' psychological files in real time and analyze the dynamic changes of employees' psychological states through an intelligent evaluation model. By introducing a historical effect attenuation factor and a time derivative term, the system can reasonably evaluate the effect of long-term mental health management, helping enterprises to grasp the change trend of employees' mental health in real time, so as to carry out timely intervention and adjustment.

[0039] In summary, by combining and collaborating multiple functional modules for managing employees' mental health, the present invention can achieve the intelligent, personalized, and real-time management of employees' mental health, greatly improving the scientificity and effectiveness of employees' mental health management and creating significant value for enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 FIG. is a scenario diagram of a cloud computing-based employee mental health management system provided by the present invention;

[0041] Figure 2 FIG. is a schematic structural diagram of a cloud computing-based employee mental health management system provided by the present invention;

[0042] Figure 3 FIG. is a flowchart of a cloud computing-based employee mental health management method provided by the present invention;

[0043] Figure 4 FIG. is a schematic hardware structure diagram of a possible electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Please refer to Figure 1 , Figure 1 FIG., which is a scenario diagram of a cloud computing-based employee mental health management system provided by the present invention. As Figure 1 shown, the terminal and the server are connected through a network, for example, through a wired or wireless network connection, etc. Among them, the terminal may include, but is not limited to, portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, inquiry machines, and advertising machines. Among them, the server provides various business services for users, including service push servers, user recommendation servers, etc.

[0046] It should be noted that Figure 1 the system diagram of a cloud computing-based employee mental health management method shown in FIG. is only an example. The terminal, server, and application scenarios described in the embodiments of the present invention are for more clearly explaining the technical solutions of the embodiments of the present invention, and do not generate limitations on the technical solutions provided by the embodiments of the present invention. Those skilled in the art know that with the evolution of the system and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.

[0047] Among them, the terminal can be used for:

[0048] Collect, classify, store and update the mental health data of employees, and calculate the comprehensive mental health index of employees;

[0049] Obtain the mental data of employees and apply an advanced regression model to evaluate the mental state of the employees to obtain a mental evaluation result;

[0050] Evaluate the psychological counseling effect of the employees to obtain a first evaluation value of the psychological counseling effect;

[0051] Generate a psychological assistance plan for the employees;

[0052] Provide psychological treatment services for the employees through virtual reality technology and evaluate the treatment effect to obtain a second evaluation value of the virtual reality psychological treatment effect.

[0053] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of an employee mental health management system based on cloud computing provided by the present invention.

[0054] As Figure 2 shown, an employee mental health management system based on cloud computing proposed in an embodiment of the present invention includes: a psychological file management module 201, an intelligent psychological evaluation module 202, a remote psychological counseling module 203, a personalized psychological assistance plan generation module 204, and a virtual reality psychological treatment module 205. The functions and operations of each module will be described in detail below.

[0055] The psychological file management module 201 is used to collect, classify, store and update the mental health data of employees, and calculate the comprehensive mental health index of employees.

[0056] In some embodiments, the psychological file management module 201 is further used for:

[0057] Obtain multiple mental health indicators of the employees;

[0058] Determine the interaction between different mental health indicators;

[0059] Process each mental health indicator based on a non-linear index, process the interaction between different mental health indicators based on a cross-term coefficient, and determine the comprehensive mental health index of the employees.

[0060] In some embodiments, the comprehensive mental health index is expressed as:

[0061]

[0062] Where Fd is the comprehensive mental health index, x i is the i-th mental health indicator, ω i is the non-linear weight of the i-th indicator, α i is the non-linear exponent, β jk is the cross-term coefficient, n is the total number of mental health indicators, and m is the total number of mental health indicators participating in the calculation of the interaction effect.

[0063] In the specific implementation, x i is the i-th mental health indicator, for example, including the anxiety index, depression index, stress level, self-esteem index, etc. Each indicator x i represents an aspect of the mental health of employees.

[0064] ω i is the non-linear weight of the i-th indicator, used to represent the relative importance of each mental health indicator in the overall mental health assessment. For example, in some cases, the anxiety index may be more important than the self-esteem index, so the corresponding weight ω i will be higher.

[0065] ω i is dynamically adjusted. Based on the historical data of the system and the real-time assessment results, the weight can be automatically updated according to the changes in the mental state of employees to reflect the importance of different mental health indicators at different stages.

[0066] α i is the non-linear exponent, used to adjust the contribution of each indicator to the overall mental health. For example, the impact of some mental health indicators may not be linear, that is, the change within a certain range has a greater or smaller impact on mental health. By setting different α i , the impact of x i can be enhanced or weakened.

[0067] When α i > 1, the impact of the indicator is amplified; when α i < 1, the impact of the indicator is weakened; when α i = 1, the impact of the indicator is linear. This non-linear processing enables the formula to more flexibly adapt to complex mental health data.

[0068] Consider the interaction effects between different mental health indicators. For example, the anxiety index and the depression index may have a mutually influential relationship, and this relationship may play an important role in the comprehensive mental health assessment. By multiplying two different indicators x j , x k , and assigning the cross-term coefficient β jk , the interaction effects between these indicators can be quantified.

[0069] β jk is the cross - term coefficient, representing the strength and direction of the interaction between the j - th and k - th indicators. This coefficient can be positive, negative, or zero.

[0070] When β jk > 0, it indicates a positive correlation between the two indicators, and increasing one indicator will enhance the effect of the other indicator; when β jk < 0, it indicates a negative correlation between the two indicators; when β jk = 0, it indicates no significant correlation between the two indicators. This cross - term allows the formula to capture more complex mental health dynamics and is a supplement and enhancement to the influence of individual indicators. n is the total number of mental health indicators, and m is the total number of mental health indicators involved in calculating the interaction.

[0071] This formula provides a comprehensive mental health assessment model through the combination of two parts: the first part focuses on the non - linear contribution of individual mental health indicators, allowing the system to flexibly adapt to different mental health states by adjusting weights and exponents. The second part further refines the overall health assessment by considering the interactions between indicators. This processing enables the system to capture more subtle changes and complex associations in employees' mental health.

[0072] Overall, this calculation method enables the comprehensive mental health index F d to accurately reflect the overall mental health level of employees, dynamically adapt to changes in employees' mental states, and helps enterprises manage employees' mental health more effectively.

[0073] The intelligent psychological assessment module 202 is used to obtain employees' psychological data and apply an advanced regression model to evaluate the mental state of the employees to obtain a psychological assessment result.

[0074] In some embodiments, the intelligent psychological assessment module 202 is further used for:

[0075] Obtaining regression coefficients;

[0076] Obtaining the second - order change rate of the mental state of the employees over time;

[0077] Processing the second - order change rate, each of the mental health indicators, and the interactions between different mental health indicators based on the regression coefficient processing to obtain the psychological assessment result of the employees.

[0078] In some embodiments, the psychological assessment result is expressed as:

[0079]

[0080] where Y is the psychological assessment result, xi , x j , x k , x l are the i-th, j-th, k-th, and l-th mental health indicators respectively, and β i , γ jk , δ l are regression coefficients, is the second-order rate of change of the mental state over time, ∈ is the error term, and β0 is the constant term.

[0081] In a specific implementation, x i , x j , x k , x l are the i-th, j-th, k-th, and l-th mental health indicators respectively, such as the anxiety index, depression index, stress level, etc. Each indicator reflects a certain aspect of the mental health of employees.

[0082] β i , β jk , δ l are regression coefficients, and β i is the regression coefficient of the i-th indicator, indicating the degree of influence of this indicator on the psychological assessment result Y. The sign and magnitude of the coefficient determine how this indicator affects Y. For example, if β i is positive and large, it means that an increase in this indicator will significantly increase the value of Y; if it is negative, it means that an increase in this indicator will decrease the value of Y.

[0083] γ jk is the cross-term coefficient of the j-th and k-th indicators, indicating the influence of the interaction between these two indicators on Y. If γ jk is positive, it means that when the two indicators increase together, it will have a greater positive impact on Y; if γ jk is negative, it means that when the two indicators increase together, it may have an inhibitory effect and decrease the value of Y.

[0084] ∈ is the error term, representing the deviation between the actual data and the model prediction value, including all random factors or noises not explained by the model, and also reflecting the accuracy of the model. In practical applications, the existence of the error term is inevitable, but through appropriate model selection and parameter optimization, the influence of the error term can be minimized as much as possible, thereby improving the prediction accuracy of the model.

[0085] β0 is the constant term, specifically the intercept term of the regression model, indicating the basic level of the assessment result Y when the values of all mental health indicators x i are zero. It usually represents the default value of the mental state when not considering the influence of any indicators. The intercept term provides a benchmark for the model, enabling the model to provide a reasonable baseline assessment value even when the values of some indicators are low or zero when evaluating the mental state.

[0086] are the individual mental health indicators x i 's linear impact on the psychological assessment result Y. For each indicator x i 's value is multiplied by its corresponding regression coefficient β i , and then all these products are summed to obtain the total contribution of each indicator to Y.

[0087] Considers the impact of the interaction between different mental health indicators on the psychological assessment result Y. By introducing the cross-term x j ×x k , the model can capture the impact on Y when two indicators act jointly, and this effect may not be explainable by the linear terms of individual indicators.

[0088] Introduces the dynamic change factor of time, through the second derivative to reflect the impact of the acceleration change of a certain mental health indicator x l over time on Y. The second derivative represents the acceleration of the change in mental state and can capture the rapid change trends and fluctuations of mental health indicators.

[0089] is the second-order change rate of the mental state over time, which is the second derivative of the l-th mental health indicator x l over time and reflects the acceleration of this indicator in time. For example, if x l represents the stress level, then represents the acceleration of the change in stress level, and a larger value may indicate a sharp change in stress.

[0090] δ l is the regression coefficient related to , indicating the degree of impact of this dynamic change on Y. This coefficient can adjust the impact of the dynamic change on the assessment result, making the model more adaptable to the complexity of the change in mental state over time.

[0091] In the above way, the present invention comprehensively evaluates the mental state of employees through the following steps:

[0092] Direct impact of individual indicators: The direct contribution of each mental health indicator x i to the mental state of employees is adjusted by the coefficient β i . Indicators with larger weights have a greater impact on the psychological assessment result Y.

[0093] Interaction between indicators: Different mental health indicators x j and x kThe interaction between them is taken into account. This interaction effect is quantified by the cross - term γ jk ×x j ×x k and can capture more complex mental health dynamics.

[0094] Dynamic changes in mental state: By considering the second - order derivative of the indicator the model can reflect the changing trend and acceleration of the mental state over time, thus providing a deeper dynamic analysis for the evaluation.

[0095] Compensation for overall error: The error term ∈ ensures that the model can adapt to the noise and unexplained variations in the actual data, making the prediction result Y more robust and accurate.

[0096] In the above - mentioned ways, the present invention can help enterprises understand the mental state of employees more precisely and provide more targeted mental health intervention measures.

[0097] The remote psychological counseling module 203 is used to evaluate the psychological counseling effect of the employee and obtain the first evaluation value of the psychological counseling effect.

[0098] In some embodiments, the first evaluation value can be expressed as:

[0099]

[0100] where R t is the mental state evaluation result, that is, the first evaluation value, ΔP(τ) is the change in mental state at time τ, h is the proportionality coefficient, and L{·} is the Laplace transform.

[0101] In specific implementation, R t is the mental state evaluation result, that is, the first evaluation value, which represents the change effect of the employee's current mental state after a period of psychological counseling or intervention. This value reflects the cumulative impact of past mental state changes on the current state. The magnitude and sign of R t can help psychological counselors or enterprise decision - makers judge the effect of the intervention. If R t is positive and large, it indicates that the counseling has produced a significant positive effect; if it is negative, further intervention or adjustment is required.

[0102] ΔP(τ) is the change in mental state at time τ, which refers to the change amplitude of the employee's mental state relative to the previous moment at time τ. This change amount reflects the improvement or deterioration of the employee's mental state, depending on the effect of psychological counseling, intervention or other influencing factors. A large positive value usually means a significant improvement in the mental state, while a negative value may indicate a deterioration of the mental state.

[0103] h is a proportionality coefficient used to adjust the influence degree of the integral result on the final evaluation value R t . By adjusting h, the output value of the overall formula can be controlled to adapt to the psychological evaluation needs in different situations. h is usually determined based on experience or historical data, which enables the R t value output by the formula to reasonably reflect the effect of actual psychological state changes. For example, if a certain psychological consultation has a significant positive effect on the psychological state of employees, then this influence can be reflected by increasing h.

[0104] L{·} is the Laplace transform. It is used to transform the integral result from the time domain to the frequency domain to analyze the influence of different frequency components on the psychological state changes. This transform is particularly suitable for analyzing long-term trends or identifying periodic changes. In the psychological state evaluation, the Laplace transform can help reveal the psychological state fluctuations of different frequencies generated during the consultation process, which is very useful for understanding the effect of long-term mental health improvement. By applying the Laplace transform, the system can better identify and analyze the periodic or long-term trends implicit in the consultation effect, providing data support for further intervention. D represents the domain or feasible region of the decision variable x, indicating that D is a set that contains all possible solutions x that satisfy specific conditions or restrictions.

[0105] represents the cumulative effect of the psychological state changes of employees from the initial time t0 to the current time t. By integrating all state changes within the time interval, a cumulative quantity reflecting the long-term effect can be obtained.

[0106] The concept of time decay is introduced, such that the psychological state changes farther from the current time t have a smaller impact on the current effect R t . q is the decay exponent used to control the rate of this decay.

[0107] q is the decay exponent used to control the rate of this decay. When q > 0, as the time t - τ increases, will decrease, indicating that the influence of past psychological state changes on the current gradually decays. A larger q value means faster decay, indicating that changes far from the current moment have a smaller impact on the current psychological state. When q = 0, the formula simplifies to not considering the decay effect, that is, all past changes have an equal impact on the current. When q < 0, changes farther in time have a greater impact instead. Although this is relatively rare in psychological state evaluation, it can be used to handle specific psychological phenomena.

[0108] This formula comprehensively considers time decay and frequency domain analysis and is used to evaluate the long-term effects of psychological counseling on employees' mental states. Through the integration and Laplace transform of mental state changes, the formula can capture the complex dynamic changes in employees' mental states, thus providing a powerful tool for enterprises or mental health professionals to analyze and improve mental health management strategies.

[0109] Only as an example, assume that an employee has experienced several psychological counseling sessions over a period of time, and the recorded changes in mental state ΔP(τ) are respectively positive and negative values. Assume q = 1, representing that the impact of mental state changes further in the past is smaller, and h = 2 adjusts the overall impact of the formula. After applying the Laplace transform, the long-term trend of the counseling effect can be revealed.

[0110] By calculating R t If the result is positive and large, it indicates that these psychological counseling sessions have effectively improved the employee's mental state. If the result is negative, it indicates that more in-depth psychological interventions may be needed. This kind of analysis can help enterprises continuously optimize the mental health support plan to ensure the effective management of employees' mental health.

[0111] The personalized psychological assistance plan generation module 204 is used to generate a psychological assistance plan for the said employee.

[0112] In some embodiments, the personalized psychological assistance plan generation module 204 can generate an optimal psychological assistance plan by means of a multi-objective optimization algorithm under the premise of meeting multiple constraints.

[0113] In some embodiments, the multi-objective optimization algorithm can be expressed as:

[0114] min x∈D {f1(x)+λ1×g1(x)+λ2×g2(x)+…+λ p ×g p (x)};

[0115] Where f1(x) is the objective function, g(x) is the non-linear constraint condition, λ is the Lagrange multiplier, D represents the domain or feasible region of the decision variable x, indicating that D is a set that contains all possible solutions x that meet specific conditions or restrictions.

[0116] In specific implementation, f1(x) is the objective function and is the main objective to be minimized in the optimization process. In the psychological assistance plan generation module, f1(x) can represent a certain cost or risk related to employees' mental health. For example, f1(x) can represent the total cost of psychological intervention measures, time investment, or the probability of poor mental state.

[0117] The goal of the optimization process is to find the solution x that can minimize f1(x), that is, to minimize the resource input of the enterprise or employees as much as possible while ensuring the mental health effect.

[0118] g(x) is a non - linear constraint condition, and g1(x), g2(x)…, g p (x) are non - linear constraint conditions to be considered in the optimization process. Each constraint condition g(x) defines certain conditions or restrictions that need to be satisfied when solving the optimal solution x. For example, g(x) may represent the effectiveness, safety, employee acceptance, or legal compliance of the psychological assistance plan, etc. Non - linear constraint conditions mean that these restrictions are not simple linear relationships but may involve more complex dependencies or conditions. If these constraint conditions are not met, even if the objective function f1(x) is minimized, a feasible solution may not be obtained. λ is the Lagrange multiplier, and p is the number of constraint conditions.

[0119] The virtual reality psychotherapy module 205 is used to provide psychological therapy services for the employee through virtual reality technology and evaluate the treatment effect, obtaining a second evaluation value of the virtual reality psychotherapy effect.

[0120] In some embodiments, the second evaluation value can be expressed as:

[0121]

[0122] Where S VR is the final evaluation value of the virtual reality treatment effect, that is, the second evaluation value, θ i (t) is the change of the i - th psychological response parameter over time, R i (t) is the i - th physiological response parameter, T is the total treatment duration, and e -iωt is the kernel function of the Fourier transform.

[0123] In specific implementation, S VR is the final evaluation value of the virtual reality treatment effect, that is, the second evaluation value. It is the weighted average result of all psychological and physiological response parameters. By summing and averaging the Fourier transform results of each psychological and physiological response parameter, the obtained S VR value can comprehensively reflect the overall response of the employee during the treatment. The magnitude and sign of S VR can indicate the overall effect of the treatment: a positive value and a larger S VR usually indicate a positive effect of the treatment on the employee, while a negative value or a smaller S VR may indicate poor treatment effect or side effects.

[0124] θ iθ(t) is the variation of the i-th psychological response parameter over time, representing the change of the i-th psychological response parameter over time t during the treatment. The psychological response parameters may include anxiety level, mood swings, attention concentration, etc.

[0125] The psychological response parameter θ varying with time i θ(t) captures the changes in the psychological state of the employee during virtual reality treatment. These changes can be positive (such as reduced anxiety) or negative (such as increased mood swings), and they change continuously over time t.

[0126] R i R(t) is the i-th physiological response parameter, the variation of the i-th physiological response parameter over time t. These physiological response parameters may include heart rate, blood pressure, electro-dermal activity (EDA), etc. The physiological response parameter R i R(t) reflects the changes in the physiological state of the employee during the treatment, providing a comprehensive assessment of the employee's overall state together with the psychological response. By monitoring these parameters, the physiological response of the employee during virtual reality treatment and its impact on mental health can be understood.

[0127] T is the total treatment duration, that is, the time span from the start to the end of the treatment. This parameter determines the upper and lower limits of the integral, ensuring that the formula covers the changes in the psychological and physiological responses of the employee throughout the treatment process. By integrating the responses over the entire treatment duration T, the overall psychological and physiological response characteristics during the treatment can be obtained, thereby quantifying the treatment effect.

[0128] e -iωt is the kernel function of the Fourier transform, where ω is the frequency and i is the imaginary unit. The Fourier transform is a tool for converting a signal from the time domain to the frequency domain, used to analyze the intensity and distribution of different frequency components in the signal.

[0129] In this formula, the kernel function e of the Fourier transform -iωt is used to analyze the frequency characteristics of the psychological and physiological response parameters. Through the Fourier transform, periodic changes or significant responses at specific frequencies that occur during the treatment can be identified, which is very important for understanding the long-term treatment effect.

[0130] This formula comprehensively considers the changes in the psychological and physiological response parameters over time, and analyzes the frequency characteristics of these parameters through the Fourier transform, thereby providing a comprehensive evaluation model for the virtual reality treatment effect. By simultaneously considering the psychological response θ i (t) and the physiological response R i (t), the formula can provide a more comprehensive evaluation of the treatment effect. This combination makes the evaluation result not only depend on the psychological state, but also consider the actual response of the body, ensuring the reliability and comprehensiveness of the evaluation result.

[0131] Through Fourier transform, the formula can identify hidden periodic changes or significant responses at specific frequencies during the treatment process. This is of great significance for analyzing and understanding long-term treatment effects, especially for identifying potential problems or advantages that occur during the treatment process.

[0132] The finally obtained S VR value can help mental health professionals and corporate decision-makers quantify and evaluate the effectiveness of virtual reality therapy, and thus provide data support for formulating the next intervention measures.

[0133] Suppose an employee experiences a 30-minute treatment time (i.e., T = 30 minutes) during a virtual reality therapy session. During this period, the system monitors multiple psychological and physiological response parameters, such as anxiety level θ1(t), heart rate R1(t), mood fluctuations θ2(t), and skin electrical activity R2(t), etc. By analyzing the frequency characteristics of these parameters through Fourier transform, the system can calculate the contribution of each response parameter and finally obtain a comprehensive S VR score.

[0134] If S VR shows a positive and large value, it indicates that the treatment process has a positive impact on the employee's psychological and physiological states, which may lead to reduced anxiety, stable mood, and normalized heart rate; if S VR is negative or small, it may indicate that the treatment effect is not ideal and the treatment plan needs to be adjusted or further intervention is required. This kind of analysis can help enterprises and mental health experts better understand and improve the application of virtual reality therapy.

[0135] In some embodiments, in addition to the above several modules, the employee mental health management system of the present invention may further include a psychological knowledge training module 206, a career development counseling module 207, and a psychological counseling appointment and crisis intervention module 208.

[0136] In some embodiments, the psychological knowledge training module 206 is used to provide online and offline mental health knowledge training resources for the said employees.

[0137] Specifically, the psychological knowledge training module 206 can provide employees with rich mental health knowledge resources, which may include online courses, offline materials, video lectures, audio courses, e-books, interactive learning modules, etc. Through these resources, employees can obtain information about basic concepts, coping skills, preventive measures, etc. of mental health, helping them better understand and manage their mental health.

[0138] This module supports online training functions, and employees can access these resources at any time through the company's internal network or cloud platform. This flexible learning method enables employees to flexibly select learning content according to their own time arrangements and learning progress. Online training can also include live or recorded mental health lectures, online quizzes and assessments, interactive Q&A, etc., enhancing employees' sense of participation and learning effectiveness.

[0139] In addition to online training, the mental health knowledge training module 206 also provides offline resources, such as downloadable learning manuals, PDF materials, and workshop materials that can be used for team building or employee training.

[0140] These offline resources can help employees continue learning without an internet connection and provide the enterprise with opportunities to organize group training, ensuring that every employee can participate in the learning of mental health knowledge.

[0141] In some embodiments, the mental health knowledge training module 206 can, through systematic training, help employees enhance their awareness of mental health, understand common mental health problems and their coping methods. The popularization of this knowledge can effectively prevent the occurrence of mental health problems or enable employees to identify and take actions at the early stage when problems arise. By learning mental health knowledge, employees can better understand their mental states and master self-regulation and stress-coping skills. This self-management ability helps employees maintain a good mental state in work and life, improving work efficiency and quality of life.

[0142] In some embodiments, the mental health knowledge training module 206 is not only about providing knowledge but is also an important part of the enterprise's mental health management system. Through this module, the enterprise can regularly update and release new training content to ensure that employees' mental health knowledge keeps up with the times. At the same time, the enterprise can also analyze employees' learning situations based on training data to evaluate the effectiveness of mental health education.

[0143] Merely as an example, when employees join the company, the enterprise can, through the mental health knowledge training module 206, provide new employees with training courses related to mental health to help them adapt to the work environment as soon as possible and master skills for managing work stress and mental health. The enterprise can regularly provide employees with updated mental health courses through this module, such as new coping strategies, the latest mental health research results in the industry, etc., to support employees' continuous learning and personal development. The mental health knowledge training module 206 can also provide materials and support for team building activities, for example, through team learning, collective workshops, etc., to promote interaction and cooperation among employees, enhancing team cohesion and mental health awareness.

[0144] The Psychological Knowledge Training Module 206 helps employees enhance their mental health literacy, strengthen their self-management abilities, and support the enterprise's overall management strategy in mental health by providing diverse online and offline mental health training resources. This module is a key component of the enterprise mental health management system, aiming to prevent the occurrence of mental health problems and improve the overall mental health level of employees through education and training.

[0145] In some embodiments, the Career Development Counseling Module 207 is used to provide and generate guidance suggestions for the career planning and development of the employees.

[0146] Specifically, the Career Development Counseling Module 207 can formulate personalized career planning suggestions for employees by analyzing their personal interests, professional skills, work experiences, and career goals. The system may help employees clarify their career development directions and provide specific steps and suggestions based on information such as the employees' mental health status and career inclination test results. This module can also include a series of career planning tools, such as career interest assessments, ability evaluations, goal-setting tools, etc., to help employees systematically plan their career paths.

[0147] In addition to planning, the Career Development Counseling Module 207 also provides continuous career development suggestions for employees. These suggestions may include training course recommendations, promotion path planning, skill improvement suggestions, etc., to support the continuous growth of employees in their careers. The module will generate customized development suggestions by combining the employees' current positions, market demands, and future career goals to ensure that the employees' career development meets the enterprise's needs and personal expectations.

[0148] This module not only provides initial career planning and suggestions but also can dynamically adjust the career development path according to the employees' progress, feedback, and market changes. For example, the system can regularly track the employees' career development and provide updated suggestions based on new data or changing environments (such as internal job vacancies in the company, new skill requirements, etc.). Employees can obtain continuous feedback through this module to understand whether their career development is on the expected track and what adjustments need to be made to achieve their career goals.

[0149] In some embodiments, the career development coaching module 207 helps employees clarify their career goals and paths by providing systematic career planning and development advice, which is conducive to enhancing employees' career development awareness and enabling them to more proactively plan and manage their careers. By providing personalized career development advice to employees, enterprises can enhance employees' career satisfaction and job engagement. When employees feel that the enterprise is supporting their career development, they tend to show higher work enthusiasm and loyalty. This module not only focuses on employees' current career needs but also pays attention to their long-term development. By providing targeted career advice, it helps employees grow at all stages of their careers, thereby enhancing their career capabilities and market competitiveness.

[0150] In some embodiments, the career development coaching module 207 is also an important tool for the enterprise's talent management strategy. By providing systematic career development advice to employees, the enterprise can better identify and cultivate high-potential talents, support internal promotion and talent retention strategies, and thus achieve the common growth of the enterprise and employees.

[0151] Merely by way of example, for new employees or employees in the early stage of their careers, the career development coaching module 207 can help them quickly clarify their career directions and provide detailed career planning paths to help them integrate into the corporate culture as soon as possible and give full play to their maximum potential. For employees who wish to enhance their skills or make a career transition, this module can recommend relevant training courses and skill improvement opportunities according to the employees' career goals to help them succeed in new career fields. The module can also provide specific guidance advice for employees with promotion intentions or those who wish to change their career paths, including how to prepare for promotion, which skills need to be improved, and how to adapt to new roles, etc.

[0152] The career development coaching module 207 helps employees set goals, enhance skills, and achieve their personal career aspirations in their careers by providing personalized career planning and career development advice. This module not only supports the personal career growth of employees but also provides a powerful tool for the enterprise's talent management and employee retention. Through dynamic adjustment and continuous feedback, this module can ensure that the employees' career development paths are consistent with the enterprise's long-term development strategy, promoting the common success of employees and the enterprise.

[0153] In some embodiments, the psychological counseling appointment and crisis intervention module 208 is used to provide convenient psychological counseling appointment services and rapid response services in case of crisis for the said employees.

[0154] Specifically, the psychological counseling appointment and crisis intervention module 208 provides employees with a convenient psychological counseling appointment function. Employees can access this module through the system, view the available schedules of psychologists, and select a suitable time to book counseling services. This module may support multiple appointment methods, including online appointment, phone appointment, and direct appointment through the company's internal system, etc. In addition, the system can automatically match the most suitable psychologist according to the employees' needs and preferences, and provide background information of relevant psychologists for employees to make a choice.

[0155] This module not only provides daily psychological counseling services but also pays special attention to the emergency response when employees are facing psychological crises. The crisis intervention service aims to provide professional support promptly when employees encounter serious psychological problems (such as sudden emotional breakdowns, suicidal tendencies, extreme anxiety, etc.). The crisis intervention service may include emergency psychological counseling, 24 / 7 psychological support hotlines, instant messaging services, etc., to ensure that employees can obtain help immediately during a crisis. In addition, the system may also be equipped with an automated early warning mechanism that automatically triggers intervention measures when it detects that an employee may be facing a psychological crisis.

[0156] In some embodiments, the psychological counseling appointment and crisis intervention module 208 may support multi-channel service access, including phone, video, online chat, etc. This multi-channel support ensures that employees can conveniently obtain psychological support services at any time and place. In case of emergencies, the module may also establish connections with external emergency services (such as hospitals, mental health professional institutions) to ensure that employees can quickly obtain the necessary medical or psychological intervention in extreme situations.

[0157] By providing convenient psychological counseling appointment services, the psychological counseling appointment and crisis intervention module 208 enables employees to more easily obtain professional psychological support. This high accessibility helps to provide early intervention in problems, prevent problems from worsening, and improve the mental health level of employees. The crisis intervention service ensures that employees can quickly receive effective support and guidance when facing serious psychological problems. The rapid response mechanism can reduce the negative impact on employees during a crisis and even save employees' lives in critical moments. This module not only focuses on the mental health of individual employees but also provides an enterprise with a systematic mental health management tool. Through the analysis of appointment data and crisis intervention records, the enterprise can better understand the mental health status of employees and optimize psychological support services. By providing continuously available psychological support services, employees will feel the enterprise's attention and care for their mental health. This sense of psychological security not only helps to improve employees' job satisfaction and loyalty but also enhances work efficiency and team cohesion to a certain extent.

[0158] As an example, when employees feel stressed or emotionally unstable, they can easily schedule psychological counseling services through the Psychological Counseling Appointment and Crisis Intervention Module 208 and obtain professional psychological guidance and support. For example, if an employee is experiencing continuous work stress, they can schedule a meeting with a psychologist through this module to discuss coping strategies. If an employee suddenly shows severe signs of a psychological crisis (such as a tendency to self-harm), the system can detect it in a timely manner through the early warning mechanism and trigger the crisis intervention service. For example, when the system detects that an employee expresses extreme anxiety or despair in communication, it will immediately initiate an emergency intervention procedure to contact mental health professionals or emergency services. Enterprises can analyze the usage data of the module to understand the psychological counseling needs of employees and the frequency of crises, so as to optimize the mental health management strategy. For example, enterprises can adjust the available time of psychological counseling services or increase service resources during certain high-demand periods based on the data.

[0159] The Psychological Counseling Appointment and Crisis Intervention Module 208 provides comprehensive support for employees' mental health by offering convenient psychological counseling appointments and urgent crisis intervention services. This module not only improves the convenience of employees obtaining psychological support but also ensures timely and effective intervention in critical moments, maximizing the protection of employees' mental health. Through this module, enterprises can better manage and support the mental health needs of employees, thereby enhancing the safety of the overall work environment and the psychological comfort of employees.

[0160] Please refer to Figure 3 , which provides a flowchart of a cloud computing-based method for managing employees' mental health according to the present invention, including the following steps:

[0161] Step 301: Collect, classify, store, and update employees' mental health data, and calculate the comprehensive mental health index of employees;

[0162] Step 302: Obtain employees' psychological data and apply an advanced regression model to evaluate the psychological state of the employees to obtain a psychological evaluation result;

[0163] Step 303: Evaluate the psychological counseling effect of the employees to obtain a first evaluation value of the psychological counseling effect;

[0164] Step 304: Generate a psychological assistance plan for the employees;

[0165] Step 305: Provide psychological treatment services for the employees through virtual reality technology and evaluate the treatment effect to obtain a second evaluation value of the virtual reality psychological treatment effect.

[0166] For the relevant content of the above steps, please refer to Figure 2 the detailed descriptions of each module in

[0167] Please refer to Figure 4 , Figure 4 , which is a schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As Figure 4 shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored on the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0168] Collect, classify, store, and update the mental health data of employees, and calculate the comprehensive mental health index of employees;

[0169] Obtain the psychological data of employees and apply an advanced regression model to evaluate the psychological state of the employees to obtain a psychological evaluation result;

[0170] Evaluate the psychological counseling effect of the employees to obtain a first evaluation value of the psychological counseling effect;

[0171] Generate a psychological assistance plan for the employees;

[0172] Provide psychological treatment services for the employees through virtual reality technology and evaluate the treatment effect to obtain a second evaluation value of the virtual reality psychological treatment effect.

[0173] It should be noted that in the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0174] Those skilled in the art should understand that the embodiments of the present invention may provide a system, a method, or a computer program product. Therefore, the present invention may be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

Claims

1. An employee mental health management system based on cloud computing, characterized in that, The system includes: A psychological file management module, which is used to collect, classify, store and update the mental health data of employees, and calculate the comprehensive mental health index of employees; An intelligent psychological assessment module, which is used to obtain the psychological data of employees and evaluate the mental state of the employees by applying an advanced regression model to obtain a psychological assessment result; A remote psychological counseling module, which is used to evaluate the psychological counseling effect of the employees to obtain a first evaluation value of the psychological counseling effect; A personalized psychological assistance plan generation module, which is used to generate a psychological assistance plan for the employees; A virtual reality psychotherapy module, which is used to provide psychological treatment services for the employees through virtual reality technology and evaluate the treatment effect to obtain a second evaluation value of the virtual reality psychotherapy effect.

2. The employee mental health management system based on cloud computing according to claim 1, wherein The psychological file management module is further used for: Obtaining multiple mental health indicators of the employees; Determining the interaction between different mental health indicators; Processing each mental health indicator based on a non-linear index, processing the interaction between different mental health indicators based on a cross-term coefficient, and determining the comprehensive mental health index of the employees.

3. The employee mental health management system based on cloud computing according to claim 2, characterized in that, The intelligent psychological assessment module is further used for: Obtaining a regression coefficient; Obtaining the second-order change rate of the mental state of the employees over time; Processing the second-order change rate, each mental health indicator, and the interaction between different mental health indicators based on the regression coefficient to obtain the psychological assessment result of the employees.

4. The employee mental health management system based on cloud computing according to claim 3, wherein The personalized psychological assistance plan generation module is further used for: Generating an optimal psychological assistance plan by means of a multi-objective optimization algorithm on the premise of meeting multiple constraints.

5. The employee mental health management system based on cloud computing according to claim 4, characterized in that, The employee mental health management system further includes: A psychological knowledge training module, which is used to provide online and offline mental health knowledge training resources for the employees.

6. The employee mental health management system based on cloud computing according to claim 5, wherein The employee mental health management system further includes: A career development counseling module, which is used to provide and generate guiding suggestions for the career planning and development of the employees.

7. The employee mental health management system based on cloud computing according to claim 6, wherein, The employee mental health management system further includes: A psychological counseling appointment and crisis intervention module, which is used to provide convenient psychological counseling appointment services for the employees and rapid response services in case of crisis.

8. A method for managing employees' mental health based on cloud computing, which is implemented based on the system described in claim 1, characterized in that, The method includes: Collecting, classifying, storing and updating the mental health data of employees, and calculating the comprehensive mental health index of employees; Obtaining the psychological data of employees and evaluating the mental state of the employees by applying an advanced regression model to obtain a psychological assessment result; Evaluating the psychological counseling effect of the employees to obtain a first evaluation value of the psychological counseling effect; Generating a psychological assistance plan for the employees; Providing psychological treatment services for the employees through virtual reality technology and evaluating the treatment effect to obtain a second evaluation value of the virtual reality psychotherapy effect.

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