Psychological assessment method and device for personnel in closed space based on parent-living thing virtual scene

Abstract

The invention discloses a psychological assessment method and device for personnel in a closed space based on a parent-living thing virtual scene. The method comprises the steps that a user collection information set and display scene information are obtained; generating a virtual reality scene according to the display scene information; displaying the virtual reality scene to a user; preprocessing the user collection information set to obtain a preprocessed user collection information set; and performing evaluation processing on the preprocessed user collection information set to obtain a user psychological evaluation result. According to the method, the virtual parent organism scene suitable for the requirements of the personnel in the closed space can be constructed, and a real scene is provided for psychological assessment, so that the accuracy of psychological assessment is improved.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and in particular to a method and device for psychological assessment of personnel in a closed space based on a biophilic virtual scene. Background Art

[0002] With the rapid development of modern society, the development and utilization of underground and enclosed spaces are becoming increasingly extensive, such as the increasing number of facilities such as subways, underground shopping malls, and underground parking lots. However, long-term work and life in a closed underground environment will have a serious impact on people's physical and mental health. Studies have shown that under conditions such as lack of natural light, limited air circulation, and limited spatial perception, people are prone to psychological problems such as anxiety, depression, and claustrophobia. At the same time, it may also lead to physiological problems such as decreased immunity and circadian rhythm disorders. These problems not only affect the health and work efficiency of the staff, but may also cause safety hazards. Therefore, how to effectively and accurately assess the psychological status of people in underground enclosed spaces has become an important issue that needs to be solved urgently. Summary of the invention

[0003] The present invention mainly solves the problem of effectively and accurately evaluating the psychological status of personnel in underground closed spaces. The present invention discloses a method and device for evaluating the psychological status of personnel in closed spaces based on a biophilic virtual scene.

[0004] In a first aspect of an embodiment of the present application, a method for psychological assessment of personnel in a closed space based on a biophilic virtual scene is disclosed, comprising: acquiring a user collected information set and display scene information; generating a virtual reality scene based on the display scene information; displaying the virtual reality scene to the user; preprocessing the user collected information set to obtain a preprocessed user collected information set; and evaluating the preprocessed user collected information set to obtain a user psychological assessment result.

[0005] The user collected information set includes a heart rate data subset, an electroencephalogram data subset, and a breathing data subset; each type of data subset is a sampling data sequence of a corresponding type obtained at a number of discrete moments.

[0006] The preprocessing of the user collected information set to obtain a preprocessed user collected information set includes:

[0007] S31, performing category discrimination processing on the user collected information set to obtain a first data set;

[0008] S32, performing data cleaning processing on the first data set to obtain a second data set;

[0009] S33, performing pattern checking processing on the second data set to obtain a pre-processed user collected information set.

[0010] The step of evaluating the pre-processed user collected information set to obtain a user psychological evaluation result includes:

[0011] S41, performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector;

[0012] S42, performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector;

[0013] S43, performing weighted calculation processing on the feature vector using the weight vector to obtain a user psychological evaluation result.

[0014] The performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector includes:

[0015] S411, representing the pre-processed user collected information set as a data matrix; the row vector of the data matrix includes a sampling data sequence of heart rate data, a sampling data sequence of EEG data, and a sampling data sequence of respiratory data;

[0016] S412, performing deviation calculation processing on all row vectors of the data matrix to obtain a weight vector;

[0017] The expression of the deviation calculation process is:

[0018]

[0019] Among them, ai is the i-th element of the weight vector, is the mean of the i-th row vector of the data matrix, δ i is the variance of the i-th row vector of the data matrix, x ij is the element in the i-th row and j-th column of the data matrix, and N is the column dimension of the data matrix.

[0020] The performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector includes:

[0021] S421, representing the pre-processed user collected information set as a data matrix; the row vector of the data matrix includes a sampling data sequence of heart rate data, a sampling data sequence of EEG data, and a sampling data sequence of respiratory data;

[0022] S422, performing eigenvalue calculation processing on each row vector of the data matrix to obtain a corresponding eigenvalue;

[0023] S423, constructing a feature vector using all the eigenvalues;

[0024] The characteristic value calculation includes:

[0025] Perform time-frequency transformation on the row vector to obtain the corresponding time-frequency series set;

[0026] Perform distribution calculation processing on the time-frequency series set to obtain the corresponding distribution series set;

[0027] The expression of the distribution calculation process is:

[0028]

[0029] Among them, r k,i represents the distribution value of the i-th value of the k-th distribution sequence in the distribution sequence set, E k (i) represents the i-th value of the k-th time-frequency sequence in the time-frequency sequence set, and N0 is the total number of elements contained in the time-frequency sequence;

[0030] Perform logarithmic accumulation processing on the distribution sequence set corresponding to each row vector to obtain the corresponding eigenvalue;

[0031] The expression of the logarithmic accumulation process is:

[0032]

[0033] Among them, H is the corresponding eigenvalue and m is the number of distribution sequences.

[0034] In a second aspect of an embodiment of the present invention, a closed space personnel psychological assessment device based on a biophilic virtual scene is disclosed, which is used to implement the closed space personnel psychological assessment method based on a biophilic virtual scene, including: a virtual reality scene display module, which is used to receive display scene information, and generate and display a virtual reality scene according to the display scene information;

[0035] An information collection module, connected to the preprocessing module and the virtual reality scene display module respectively, for collecting a user collection information set and display scene information;

[0036] A preprocessing module, used for preprocessing the user collected information set to obtain a preprocessed user collected information set;

[0037] The user psychological evaluation module is used to evaluate the pre-processed user collected information set to obtain the user psychological evaluation result.

[0038] The step of evaluating the pre-processed user collected information set to obtain a user psychological evaluation result includes:

[0039] Performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector;

[0040] Performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector;

[0041] Using the weight vector to perform weighted calculation processing on the feature vector to obtain a user psychological evaluation result;

[0042] In a third aspect of an embodiment of the present invention, a closed space personnel psychological assessment device based on a biophilic virtual scene is disclosed, the device comprising:

[0043] A memory storing executable program code;

[0044] a processor coupled to the memory;

[0045] The processor calls the executable program code stored in the memory to execute the closed space personnel psychological assessment method based on the biophilic virtual scene.

[0046] According to a fourth aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the method for psychological assessment of personnel in a closed space based on a biophilic virtual scene.

[0047] The beneficial effects of the present invention are:

[0048] 1) It can build a virtual biophilic scene that is suitable for the needs of people in closed spaces, provide a real scene for psychological assessment, and thus improve the accuracy of psychological assessment; 2) It has the function of real-time physiological index monitoring to objectively evaluate the normal psychological effect; 3) It uses AI / ML algorithms to achieve intelligent optimization of scenes and personalized recommendations. The development of such a comprehensive system can not only effectively alleviate the psychological pressure of people in underground closed spaces, improve their work efficiency and quality of life, but also provide reference for psychological intervention in other similar environments.

[0049] Through the above technical solutions, the present invention can provide a comprehensive, effective and personalized psychological assessment system for workers in closed spaces. The system can not only accurately assess the user's mental health status and improve work efficiency and safety, but also has good scalability and adaptability, and can be customized according to the needs of different environments and user groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 1 is a flow chart for implementing the method of the present invention;

[0051] Figure 2This is a schematic diagram of the composition of the psychological assessment system provided by the present invention. DETAILED DESCRIPTION

[0052] In order to better understand the content of the present invention, two embodiments are given here.

[0053] Figure 1 It is a flow chart for implementing the method of the present invention. Figure 2 This is a schematic diagram of the composition of the psychological assessment system provided by the present invention.

[0054] Embodiment 1:

[0055] In a first aspect of the embodiment of the present application, a method for psychological assessment of personnel in a closed space based on a biophilic virtual scene is disclosed, comprising:

[0056] S1, obtaining a user collection information set and display scene information;

[0057] S2, generating a virtual reality scene according to the display scene information; and displaying the virtual reality scene to the user;

[0058] S3, preprocessing the user collected information set to obtain a preprocessed user collected information set;

[0059] S4, evaluating the pre-processed user collected information set to obtain a user psychological evaluation result;

[0060] The user collected information set includes a heart rate data subset, an electroencephalogram data subset, and a respiratory data subset; each type of data subset includes a sampled data sequence obtained at a number of discrete moments;

[0061] The generating of the virtual reality scene according to the display scene information is to select a corresponding virtual reality scene from the virtual reality scene library for realization according to the value of the display scene information; the production and realization of the virtual reality scene can be realized by using VR glasses.

[0062] The preprocessing of the user collected information set to obtain a preprocessed user collected information set includes:

[0063] S31, performing category discrimination processing on the user collected information set to obtain a first data set;

[0064] S32, performing data cleaning processing on the first data set to obtain a second data set;

[0065] S33, performing pattern checking processing on the second data set to obtain a pre-processed user collected information set;

[0066] The step of evaluating the pre-processed user collected information set to obtain a user psychological evaluation result includes:

[0067] S41, performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector;

[0068] S42, performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector;

[0069] S43, performing weighted calculation processing on the feature vector using the weight vector to obtain a user psychological evaluation result;

[0070] The performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector includes:

[0071] S411, representing the pre-processed user collected information set as a data matrix; the row vector of the data matrix includes a sampling data sequence of heart rate data, a sampling data sequence of EEG data, and a sampling data sequence of respiratory data;

[0072] S412, performing deviation calculation processing on all row vectors of the data matrix to obtain a weight vector;

[0073] The expression of the deviation calculation process is:

[0074]

[0075] Among them, a i is the i-th element of the weight vector, is the mean of the i-th row vector of the data matrix, δ i is the variance of the i-th row vector of the data matrix, x ij is the element in the i-th row and j-th column of the data matrix, and N is the column dimension of the data matrix.

[0076] The performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector includes:

[0077] S431, representing the pre-processed user collected information set as a data matrix; the row vector of the data matrix includes a sampling data sequence of heart rate data, a sampling data sequence of EEG data, and a sampling data sequence of respiratory data;

[0078] S432, performing eigenvalue calculation processing on each row vector of the data matrix to obtain a corresponding eigenvalue;

[0079] S433, constructing a eigenvector using all eigenvalues.

[0080] The characteristic value calculation includes:

[0081] Perform time-frequency transformation on the row vector to obtain the corresponding time-frequency series set;

[0082] Perform distribution calculation processing on the time-frequency series set to obtain the corresponding distribution series set;

[0083] The expression of the distribution calculation process is:

[0084]

[0085] Among them, r k,i represents the distribution value of the i-th value of the k-th distribution sequence in the distribution sequence set, E k (i) represents the i-th value of the k-th time-frequency sequence in the time-frequency sequence set, and N0 is the total number of elements contained in the time-frequency sequence;

[0086] Perform logarithmic accumulation processing on the distribution sequence set corresponding to each row vector to obtain the corresponding eigenvalue;

[0087] The expression of the logarithmic accumulation process is:

[0088]

[0089] Among them, H is the corresponding eigenvalue and m is the number of distribution sequences.

[0090] In a second aspect of the embodiment of the present application, a closed space personnel psychological assessment device based on a biophilic virtual scene is disclosed, which is used to implement the closed space personnel psychological assessment method based on a biophilic virtual scene, including:

[0091] A virtual reality scene display module, used to receive display scene information, and generate and display a virtual reality scene according to the display scene information;

[0092] An information collection module, connected to the preprocessing module and the virtual reality scene display module respectively, for collecting a user collection information set and display scene information;

[0093] A preprocessing module, used for preprocessing the user collected information set to obtain a preprocessed user collected information set;

[0094] The user psychological evaluation module is used to evaluate the pre-processed user collected information set to obtain the user psychological evaluation result.

[0095] The step of evaluating the pre-processed user collected information set to obtain a user psychological evaluation result includes:

[0096] Performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector;

[0097] Performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector;

[0098] Using the weight vector to perform weighted calculation processing on the feature vector to obtain a user psychological evaluation result;

[0099] After a row vector is transformed in time-frequency, a time-frequency transformation set is obtained;

[0100] The category discrimination process is to discriminate whether the attributes of the data in each category of data subset are within a preset attribute range, and delete the data that are not within the preset attribute range from the data subset.

[0101] The mode checking process comprises:

[0102] S331, for each type of data attribute of the second data set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, regression modeling is performed to obtain regression models of the data attributes; using the regression model, the independent variable is calculated and processed to obtain regression data values; judging whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, the data is deleted from the second data set; if it is less than or equal to the first regression discrimination threshold, the data is not processed;

[0103] S332, performing fusion processing on all data of the second data set after executing S331 to obtain a pre-processed user collection information set;

[0104] The regression modeling can be implemented by using autoregressive-moving average modeling;

[0105] The regression modeling can be achieved by using a function approximation method to perform curve fitting on the curve to be approximated; the function approximation method can be used to perform curve fitting on the curve to be approximated, and the best consistent linear approximation method can be used. The best consistent approximation polynomial f(Ix) is expressed as:

[0106] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α 2 (Ix) 2 +α 1 (Ix)+α 0 ,

[0107] Where P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the optimal consistent approximation polynomial f(Ix);

[0108] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0109] The user psychological evaluation result is used to evaluate the normality of the user's psychology. The larger the value of the user psychological evaluation result is, the more normal the user's psychology is.

[0110] Embodiment 2:

[0111] like Figure 2 As shown, the present invention discloses a closed space personnel psychological assessment system based on virtual reality, including a virtual biophilic scene display module, an interactive psychological intervention module, a user information collection module, a data analysis and effect evaluation module and a scene optimization control module.

[0112] The virtual biophilic scene display module includes:

[0113] Virtual reality content library: stores various virtual reality scene contents, such as natural scenery, urban scenery, water scenes, etc.

[0114] Scenario customization engine: Based on the user's comprehensive psychological state assessment report, select appropriate scenes from the virtual reality content library and customize them.

[0115] The interactive psychological intervention module includes:

[0116] Virtual reality equipment: including virtual reality headsets, controllers, etc., to provide users with an immersive virtual reality experience;

[0117] Interactive psychological intervention content library: stores various psychological intervention activities, such as relaxation training led by virtual counselors, meditation exercises, simulated social interactions, etc.

[0118] Real-time monitoring tool: monitors the user's behavioral data in the virtual scene and combines it with physiological data to dynamically adjust the intervention content.

[0119] The user information collection module includes:

[0120] Physiological data sensors: including heart rate sensors, electrocardiogram sensors, skin conductance sensors, etc., used to obtain real-time physiological data of users;

[0121] Psychological state assessment tools: including questionnaires, psychological scales, facial expression and emotion recognition, etc., used to make a preliminary assessment of the user's psychological state;

[0122] Work environment data collection tool: used to obtain environmental data of enclosed spaces, such as light intensity, noise level, etc.

[0123] The data analysis and effect evaluation module includes:

[0124] Data collection tools: used to collect users’ physiological and behavioral data;

[0125] Data analysis engine: Analyzes the user's physiological and behavioral data and generates a psychological intervention effect report;

[0126] Evaluation report generator: Generate a comprehensive psychological intervention effect evaluation report based on the psychological scale and questionnaire survey results.

[0127] The scene optimization control module includes:

[0128] User management system: used to manage users’ basic information and psychological status assessment reports;

[0129] Intervention plan customization system: Generates personalized intervention plans based on the user's psychological state assessment report and intervention effect evaluation report.

[0130] Biophilic virtual scene construction: This system uses advanced 3D modeling and rendering technology, combined with environmental psychology and biophilic design principles, to construct a series of highly realistic biophilic virtual scenes. These scenes include not only traditional natural environments (such as forests, beaches, mountains, etc.), but also microscopic natural scenes suitable for the characteristics of closed spaces (such as moss gardens, water drop worlds, etc.) and abstract natural elements (such as flowing light and shadow, geometric structures of biological forms, etc.). Each scene has been carefully designed, taking into account multiple dimensions such as color, light, sound, dynamic effects, etc., to maximize its psychological repair effect.

[0131] Multimodal physiological indicator monitoring: In order to objectively evaluate the intervention effect of the system, the present invention integrates a variety of physiological indicator monitoring technologies. Specifically, it includes: electrocardiogram (ECG) sensor for measuring heart rate and heart rate variability (HRV); photoplethysmography (PPG) sensor for non-invasive blood pressure monitoring; and skin electrode activity (EDA) sensor for assessing emotional arousal levels. These sensors are integrated into a lightweight, portable wearable device to ensure that they will not affect the user's VR experience.

[0132] Real-time facial expression emotion recognition: This system uses a high-precision infrared camera and a deep learning algorithm to achieve real-time facial expression capture and emotion recognition. The module is able to recognize seven basic emotions (happy, sad, angry, fearful, disgusted, surprised, and neutral) as well as more subtle emotional changes. By analyzing facial muscle movement patterns and micro-expressions, the system can accurately assess changes in the user's emotional state.

[0133] Data fusion and analysis: This invention uses multi-source data fusion technology to conduct comprehensive analysis of multi-dimensional data such as physiological indicators, facial expressions and user subjective feedback. Specifically including:

[0134] Time series data analysis: Use time series analysis methods to study the dynamic changes of various indicators; Multimodal feature extraction: Extract effective features from different data sources to construct a comprehensive description of the user's psychological state; Machine learning model: Use algorithms such as random forests and support vector machines to establish a psychological state assessment model; Anomaly detection: Monitor data anomalies in real time and promptly discover potential mental health risks.

[0135] AI-driven scene optimization: One of the core innovations of this system is to use AI technology to achieve automatic optimization of virtual scenes. Specifically:

[0136] Reinforcement learning algorithm: uses the user's physiological and emotional feedback as reward signals to train AI agents to optimize scenario parameters; Personalized recommendation system: recommends the most suitable scenarios and intervention plans based on user historical data and preferences; Automatic scenario generation: uses generative adversarial network (GAN) technology to create new and personalized virtual environments; Dynamic difficulty adjustment: automatically adjusts the difficulty of relaxation training according to the user's stress level and recovery ability.

[0137] Interactive experience design: In order to enhance user participation and intervention effect, this system incorporates a variety of interactive experience elements:

[0138] Biofeedback training: users can influence the virtual environment by controlling their own physiological state (such as breathing rate), such as controlling tree growth, changing the weather, etc.; guided meditation: combining professional mindfulness meditation techniques to provide voice-guided relaxation training; virtual social support: allowing users to interact socially with others (real users or AI characters) in a virtual environment to alleviate loneliness; achievement system: setting phased goals and reward mechanisms to encourage users to continue to participate in mental health activities.

[0139] System Integration and Deployment:

[0140] In order to adapt to the special environment of enclosed spaces, this system adopts a modular design and is easy to install and maintain: Portable VR equipment: A lightweight, high-performance integrated VR headset is selected to ensure comfort and mobility; Wireless sensor network: Low-power Bluetooth (BLE) technology is used to achieve wireless connection between physiological sensors and the main system; Edge computing: Edge servers are deployed locally to handle most computing tasks and reduce dependence on external networks; Remote monitoring: A remote monitoring interface is set up to allow mental health experts to remotely evaluate user status and adjust intervention strategies.

[0141] Through the above technical solutions, the present invention can provide a comprehensive, effective and personalized psychological intervention system for workers in closed spaces. The system can not only significantly improve the user's mental health, improve work efficiency and safety, but also has good scalability and adaptability, and can be customized according to the needs of different environments and user groups.

[0142] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for psychological assessment of personnel in closed spaces based on biophilic virtual scenes, characterized in that: include: S1, obtaining a user collection information set and display scene information; S2, generating a virtual reality scene according to the display scene information; Displaying the virtual reality scene to the user; S3, preprocessing the user collected information set to obtain a preprocessed user collected information set; S4, evaluating the pre-processed user collected information set to obtain a user psychological evaluation result.

2. The closed space personnel psychological assessment method based on biophilic virtual scene according to claim 1, characterized in that: The user collected information set includes a heart rate data subset, an electroencephalogram data subset, and a breathing data subset; each type of data subset is a sampling data sequence of a corresponding type obtained at a number of discrete moments.

3. The closed space personnel psychological assessment method based on biophilic virtual scene as claimed in claim 2, characterized in that: The preprocessing of the user collected information set to obtain a preprocessed user collected information set includes: S31, performing category discrimination processing on the user collected information set to obtain a first data set; S32, performing data cleaning processing on the first data set to obtain a second data set; S33, performing pattern checking processing on the second data set to obtain a pre-processed user collected information set.

4. The closed space personnel psychological assessment method based on biophilic virtual scene as claimed in claim 3, characterized in that: The step of evaluating the pre-processed user collected information set to obtain a user psychological evaluation result includes: S41, performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector; S42, performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector; S43, performing weighted calculation processing on the feature vector using the weight vector to obtain a user psychological evaluation result.

5. The closed space personnel psychological assessment method based on biophilic virtual scene as claimed in claim 4, characterized in that: The performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector includes: S411, representing the pre-processed user collected information set as a data matrix; the row vector of the data matrix includes a sampling data sequence of heart rate data, a sampling data sequence of EEG data, and a sampling data sequence of respiratory data; S412, performing deviation calculation processing on all row vectors of the data matrix to obtain a weight vector; The expression of the deviation calculation process is: Among them, a i is the i-th element of the weight vector, is the mean of the i-th row vector of the data matrix, δ i is the variance of the i-th row vector of the data matrix, x ij is the element in the i-th row and j-th column of the data matrix, and N is the column dimension of the data matrix.

6. The closed space personnel psychological assessment method based on biophilic virtual scene according to claim 4, characterized in that: The performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector includes: S421, representing the pre-processed user collected information set as a data matrix; the row vector of the data matrix includes a sampling data sequence of heart rate data, a sampling data sequence of EEG data, and a sampling data sequence of respiratory data; S422, performing eigenvalue calculation processing on each row vector of the data matrix to obtain a corresponding eigenvalue; S423, constructing a feature vector using all the eigenvalues; The characteristic value calculation includes: Perform time-frequency transformation on the row vector to obtain the corresponding time-frequency series set; Perform distribution calculation processing on the time-frequency series set to obtain the corresponding distribution series set; The expression of the distribution calculation process is: Among them, r k,i represents the distribution value of the i-th value of the k-th distribution sequence in the distribution sequence set, E k (i) represents the i-th value of the k-th time-frequency sequence in the time-frequency sequence set, and N0 is the total number of elements contained in the time-frequency sequence; Perform logarithmic accumulation processing on the distribution sequence set corresponding to each row vector to obtain the corresponding eigenvalue; The expression of the logarithmic accumulation process is: Among them, H is the corresponding eigenvalue and m is the number of distribution sequences.

7. A closed space personnel psychological assessment device based on a biophilic virtual scene, characterized in that: A method for psychological assessment of personnel in a closed space based on a biophilic virtual scene according to any one of claims 1 to 6, comprising: A virtual reality scene display module, used to receive display scene information, and generate and display a virtual reality scene according to the display scene information; An information collection module, connected to the preprocessing module and the virtual reality scene display module respectively, for collecting a user collection information set and display scene information; A preprocessing module, used for preprocessing the user collected information set to obtain a preprocessed user collected information set; The user psychological evaluation module is used to evaluate the pre-processed user collected information set to obtain the user psychological evaluation result.

8. The closed space personnel psychological assessment device based on biophilic virtual scene according to claim 7, characterized in that: The step of evaluating the pre-processed user collected information set to obtain a user psychological evaluation result includes: Performing category weight calculation processing on the pre-processed user collected information set to obtain a weight vector; Performing feature vector calculation processing on the pre-processed user collected information set to obtain a feature vector; The weight vector is used to perform weighted calculation processing on the feature vector to obtain a user psychological evaluation result.

9. A closed space personnel psychological assessment device based on a biophilic virtual scene, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the closed space personnel psychological assessment method based on a biophilic virtual scene as described in any one of claims 1 to 6.

10. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the closed space personnel psychological assessment method based on a biophilic virtual scene as described in any one of claims 1 to 6.

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