Illumination environment evaluation system for simulating underground multi-scene space

Abstract

The invention discloses a lighting environment evaluation system for simulating an underground working space, and relates to the technical field of lighting environments. Comprising an immersive virtual reality environment experience system which is used for experiencing different space illumination scenes by people in an immersive manner; a human body physiological index acquisition system; the human body subjective perception acquisition system is used for acquiring subjective feelings of personnel through questionnaires preset in a virtual environment; the task performance system is determined by the task collection time length of the visual task preset in the virtual environment and the answer condition, and the work efficiency performance is evaluated through the inverse efficiency score; and the comprehensive data processing system is used for receiving, sorting and analyzing the data collected by each system. According to the method, the limitation of traditional single information feedback and qualitative method evaluation is broken through, diachronic and instantaneous perception dynamic changes are considered, a quantitative evaluation system integrating subjective perception, objective physiological indexes and working efficiency performance is established based on multi-source information feedback, and the method can be applied to long-term closed space place illumination environment evaluation.

Description

Technical Field

[0001] The present invention belongs to the technical field of lighting environment, and more specifically, relates to a lighting environment evaluation system for simulating underground multi-functional and multi-scene spaces. Background Art

[0002] Underground spaces lack sunlight, and lighting has significant non-visual biological effects on human alertness, cognition, and mood. Long-term exposure to underground lighting significantly impacts physiology, psychology, and work efficiency. A comfortable lighting environment is crucial for maintaining human health. On-site lighting scenario evaluation requires significant time, manpower, and resources, and cannot be adapted to multiple spaces and scenarios. Setting up laboratory scenarios is complex and expensive.

[0003] Patent application number 201910102998.9 discloses a light environment evaluation method and system based on nonlinear regression, used to calculate the overall satisfaction level of a light environment in a space that relies entirely on artificial lighting. The method includes the following steps: Step 1: Establishing a relationship between a subjective evaluation indicator Y and an objective indicator X; Step 2: Establishing a relationship between the overall satisfaction level F and the subjective evaluation indicator Y; Step 3: Establishing a light environment quality evaluation function, and calculating the overall satisfaction level F based on the value of the objective indicator X. This patent utilizes nonlinear regression and the AHP method to propose an evaluation method for artificial lighting environments. However, this method focuses on objective indicators used in light environment design (such as illumination level, color temperature, and lamp type), lacking consideration of changes in users' physiological indicators. Therefore, it cannot provide a comprehensive comfort assessment based on the user's overall perception.

[0004] Patent application number 201910102977.7 discloses a method and system for evaluating the light environment in ship cabins based on extenics. The system calculates light environment evaluation results using evaluation indicators. The method includes: setting evaluation indicators; establishing an extenics evaluation mechanism based on the evaluation indicators; and calculating and outputting the evaluation results. This patent uses extenics to evaluate the light environment in ship cabins, but focuses solely on the user's subjective perception and targets only a specific environment (ship cabin). It lacks the flexibility to adapt to different types of underground spaces (function, scale, and facility layout), limiting its scope of application.

[0005] In summary, combined with relevant research on underground multi-scene space lighting environment evaluation, the existing technology has the following shortcomings:

[0006] 1) Regarding the evaluation environment, there is a lack of multi-scenario applications for underground space lighting. Existing technologies typically rely on actual space lighting environment evaluation, which is time-consuming, labor-intensive, and resource-intensive. This makes it difficult to achieve comprehensive comparisons of different lighting scenarios, and the methods are not universally applicable.

[0007] 2) Evaluation dimensions ignore human physiological perception. Existing technologies focus on objective lighting environments (illuminance, color temperature, etc.), ignoring the psychological and physiological perceptions of people interacting with space and the environment.

[0008] 3) In terms of evaluation accuracy, the influence of time effect on people’s perception of lighting environment is ignored. The evaluation results are not diachronic, and the long-term performance of work in long-term lighting environment cannot be tracked.

[0009] 4) In terms of evaluation results, decision-making results are difficult to interpret.

[0010] Therefore, this invention proposes a comprehensive, dynamic, and multi-factor quantitative method for evaluating the comfort of underground multi-scene lighting environments, providing a flexible approach for optimizing the lighting design of different types of spaces. This invention strategically supports major national needs such as underground exploration and deep-sea operations, while also providing new research tools and theoretical development for related disciplines such as human factors engineering, architectural environment science, and architectural psychology. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to provide a lighting environment evaluation system that simulates underground multi-scene spaces. It can be applied to the lighting environment evaluation of long-term closed space workplaces such as underground machine rooms and underground operations. It breaks through the limitations of traditional single information feedback and qualitative method evaluation, considers the dynamic changes of diachronic and instantaneous perception, and establishes a quantitative evaluation system based on multi-source information feedback that integrates subjective perception, objective physiological indicators and work efficiency performance to achieve the evaluation of lighting environment effectiveness and provide a quantitative reference for the optimal design of underground multi-scene space lighting environment.

[0012] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0013] A lighting environment evaluation system for simulating underground multi-scene spaces includes an immersive virtual reality environment experience system, a human physiological index collection system, a human subjective perception collection system, a task performance system, and a comprehensive data processing system.

[0014] in:

[0015] 1) Immersive virtual reality environment experience system, including virtual scene construction module, lighting parameter control module, and human-computer interaction module, used for people to immersively experience different spatial lighting scenes.

[0016] The virtual scene construction module, comprised of SketchUp, Radiance, and Enscape, used SketchUp to create the spatial model, then used the Radiance engine and Enscape to render the lighting environment and adjust parameters such as color and reflectivity. The reflectivity of each model surface material was set according to the "Architectural Lighting Design Standard" (GB / T 50034) and Munsell values. The reflectivity of the top and side surfaces was adjusted based on measured data to ensure the virtual scene closely resembled the real-world environment.

[0017] The lighting environment control module primarily controls lamp color temperature and indoor illumination, both of which are set up in Enscape software. Select the light you want to adjust in the model and adjust the light's brightness by adjusting the light's CD parameter in Enscape's Properties panel. You can also adjust the light's color temperature by assigning a color temperature material to the light source component.

[0018] The human-computer interaction module is composed of a computer and a virtual reality device. The person first enters the space lighting scene library through the computer device to select, and then wears the virtual reality device and operates the mouse to enter the preset underground multi-scene space lighting scene for experience.

[0019] 2) The human physiological index acquisition system consists of an electrocardiogram acquisition module, a blood pressure acquisition module, a skin conductance acquisition module, a skin temperature acquisition module, an electroencephalogram acquisition module, and an eye movement acquisition module.

[0020] The heart rate acquisition module uses a portable electrocardiogram monitor as a collector and is set on the wrist of the person. The collected indicators are heart rate and heart rate variability, reflecting the changes in the cardiovascular system of the subject in real time.

[0021] The blood pressure collection module uses a smart bracelet as a data collector and is installed on the user's wrist. The collected indicators are systolic and diastolic blood pressure, which are used to assess the subject's circulatory system status and observe the subject's blood pressure response to changes in lighting environment.

[0022] The galvanic skin response acquisition module uses an Arduino-based galvanic skin sensor (such as the Grove GSR sensor) as the acquisition device. It is placed on the subject's finger. By collecting skin conductivity (GSR) signals, it can reflect the subject's mood swings, tension, and psychological stress in real time.

[0023] The skin temperature acquisition module uses button-type temperature sensors (such as the DS18B20 digital temperature sensor) to monitor the subject's skin temperature in real time. Sensors are placed at six key locations on the subject's body: forehead, behind the ears, wrists, chest, back, and ankles. These sensors reflect both local and overall body temperature trends.

[0024] The heart rate acquisition module, blood pressure acquisition module, skin electrical acquisition module, and skin temperature acquisition module respectively use corresponding data acquisition systems to collect data, and then synchronize the data to the comprehensive data processing system through the data conversion box.

[0025] The EEG acquisition module uses a standard international 10-20 arrangement of conductive paste or saline EEG cap as a collector and is placed on the person's head. The acquisition indicators are α, β brain waves and event-related potentials. The collected data is denoised, filtered and analyzed by independent component analysis using EEG studio and Metlab-EEGlab. The frequency domain characteristics of the EEG signal are further analyzed based on the burg algorithm to obtain the power spectral density (PSD). （α） , PSD （β） To characterize the data, we evaluate the activity of the brain at a specific frequency. The resulting value PSD is calculated. （α） / PSD （β） Synchronize to the integrated data processing system. The EEG data processing steps are as follows:

[0026] ①Autoregressive (AR) model.

[0027]

[0028] Where Xt is the time series data, φk is the model parameter, p is the model parameter, and ϵt is the white noise error term.

[0029] ②For the AR (P) model, the Yule-Walker equation is used for parameter estimation.

[0030]

[0031] Where ϕ is the parameter vector, R is the autocorrelation matrix, and r is the autocorrelation coefficient vector.

[0032] ③Use the Burg algorithm to use the reflection coefficient to recursively estimate the AR model parameters.

[0033]

[0034] Where kj is the jth reflection coefficient and rj is the autocorrelation coefficient.

[0035] ④After the AR model parameters are estimated, power spectrum density estimation is performed.

[0036]

[0037] Where S(f) is the power spectral density at frequency f, σ2 is the variance of white noise, ϕk is the AR model parameter, and J is the imaginary unit.

[0038] ⑤Convert the power spectrum density into decibel (dB) units to facilitate data analysis.

[0039]

[0040] The eye movement acquisition module uses an eye tracker as a collector and is placed at the subject's eye position. It is used to collect eye movement behavior data in real time. This module uses infrared optical tracking technology and a built-in camera to record the subject's eye movement characteristics and transmits the data to an analysis system for processing. Core data collected includes average pupil diameter, blink frequency, saccade frequency, fixation frequency, average fixation duration, and average blink duration, reflecting the subject's attention, emotional state, and mental workload, as well as fatigue status in the lighting environment.

[0041] 3) Human subjective perception collection system, which collects subjective feelings of people through questionnaires preset in a virtual environment.

[0042] The human subjective perception collection system uses a questionnaire to collect users' subjective perceptions of the lighting environment. These include light color perception (the degree of warmth or coolness in the lighting environment), comfort (the degree of comfort in the lighting environment), spatial sense (the degree of enclosure and spaciousness in the lighting environment), relaxation (the degree of tension and relaxation in the lighting environment), and brightness perception (the degree of brightness and darkness in the lighting environment). The subjective perception collection system can reflect users' subjective perceptions of ambient color temperature and color rendering, physical and psychological comfort, spatial depth and width perception, emotional relaxation, and light intensity and distribution uniformity. The questionnaire used in the experiment collected subjects' subjective perception indicators using a Likert scale, with a minimum value of Din and a maximum value of Uax. For example, on a 7-point scale ranging from -3 to 3, Din would be -3 and Uax would be 3. Subjects then rated their perception of the lighting environment on a scale from -3 to 3. The subjective perception questionnaire was embedded in the SU model as an image, rendered synchronously with the virtual underground multi-scene lighting environment, and displayed in the VR scene to facilitate intuitive observation of the questionnaire content. During the experiment, the process of the subjects completing the questionnaire was timed and the answers were recorded in a timely manner.

[0043] 4) Task performance system, which is determined by the duration of the visual task collection task and the answering situation in the virtual environment

[0044] The task performance system uses an inverse efficiency score (IES) to evaluate work efficiency. The IES is an efficiency metric calculated based on the ratio of task completion time to accuracy. It measures participants' work performance under different lighting conditions by comparing the time they spent answering questions with their accuracy. Lower scores indicate higher efficiency. The IES calculation process can be expressed as:

[0045]

[0046] Among them, E(t) represents the accuracy rate of the subjects’ answers when the experience perception time is t.

[0047] 5) Comprehensive data processing system, including data sorting module, weight determination module, key impact indicator screening module, and model building module.

[0048] It is used to receive data collected from the human physiological index acquisition system, the human subjective perception acquisition system, and the task performance system, and to organize and analyze the collected data. The steps are as follows:

[0049] ① In order to eliminate the impact of invalid indicators on the analysis results, the data is cleaned to ensure the validity and accuracy of the data.

[0050] ②The weight determination module, key impact indicator screening module and model construction module are used to establish an underground multi-scene space lighting environment evaluation model based on multi-source information feedback.

[0051] The weight determination module uses factor analysis to process subjective perception indicators. SPSS statistics 26 is used for data statistics and analysis. The main process includes data preparation and applicability testing, calculation of factor loading matrix, extraction of eigenvalues ​​and variance explanation rate, calculation of factor scores and weights, and finally construction of the subjective perception evaluation equation. The specific calculation process is as follows:

[0052] ①Data preparation and applicability testing

[0053] First, the subjective perception data was organized into a scoring matrix, and the matrix was tested for suitability for factor analysis. The KMO test was used to assess the suitability of the sample data. When the KMO value was greater than 0.5, the data was considered suitable for factor analysis. Furthermore, the Bartlett test of sphericity was used to verify the correlation between variables. A significance level of p < 0.05 indicated that the data had strong correlations, making factor analysis suitable.

[0054] ②Calculation of factor loading matrix

[0055] After the data passed the suitability test, factor analysis was performed on the evaluation score matrix to extract the factor loading matrix. To improve the explanatory power of the factor analysis results, the factor loading matrix was rotated to generate a rotated factor loading matrix, thereby optimizing the independence between factors.

[0056] ③ Extraction of eigenvalues ​​and variance explanation rate

[0057] Based on the rotated factor loading matrix, the eigenvalue and variance explained of each factor were extracted. Factors with eigenvalues ​​greater than 1 were selected as the primary analysis objects. The variance explained represents the contribution of each factor to the overall data and can provide a basis for determining the weights of the evaluation dimensions.

[0058] ④ Factor score and weight calculation

[0059] Based on the eigenvalue and variance explanation rate, the linear combination coefficient of each evaluation indicator is calculated, and the indicator weight is further determined. The linear combination coefficient shows the contribution of each indicator to the factor, and the calculation formula is:

[0060]

[0061] Where R is the rotated factor loading matrix and eigen is the characteristic root of the factor. The cumulative factor score is calculated by weighted summation of the combined coefficient and the variance explained by the factor:

[0062]

[0063] in, represents the linear combination coefficient of the j-th indicator on the i-th factor, is the variance explanation rate of the i-th factor. Finally, through normalization processing, the weight of each evaluation index is obtained

[0064]

[0065] in, is the cumulative factor score of the j-th evaluation indicator.

[0066] ⑤Construction of subjective perception scoring model

[0067] Finally, based on the weights and factor scores, a subjective perception rating model is constructed. By summing the factor scores according to the weights, the subjective perception rating equation is obtained:

[0068]

[0069] in, is a subjective perception score, is the weight of the jth factor, is the score of the jth factor.

[0070] The key influencing indicator screening module uses SPSS statistics26 to perform correlation analysis, conducts bivariate correlation analysis on subjective perception scores, eye movement indicators and objective physiological indicators, and confirms whether there is a correlation between each variable (physiological indicator) and the dependent variable (subjective perception score) based on the calculated correlation coefficient and significance level, thereby screening the key influencing indicators of the target model. and various physiological indicators , … The linear correlation between the two groups was calculated using the Pearson correlation coefficient, and the formula is as follows:

[0071]

[0072] in:

[0073] is the subjective perception score of the jth sample; is the physiological index value of the jth sample; 、 are the means of subjective perception scores and physiological indicators respectively; n is the number of samples,

[0074] The calculated correlation coefficient r ranges from [−1,1], and r>0 indicates that and There is a positive correlation between them; r<0 indicates and There is a negative correlation between them; the larger |r| is, the stronger the correlation is.

[0075] During the analysis, each physiological index A significance test was performed to determine whether the correlation was significant using the p-value (p<0.05 was considered a significant correlation). At the same time, key influencing indicators were screened based on the absolute value of the correlation coefficient (|r|>0.5).

[0076] The model building module uses SPSS statistics26 for stepwise regression analysis to build the correlation between the subjective evaluation quantitative results and three types of objective indicator variables (physiological indicators, EEG indicators, and eye movement indicators), and finally obtain the perception score under the objective indicators. The calculation method is as follows:

[0077] ① Perception score under physiological indicators:

[0078]

[0079] in, is the constant term of the regression equation, is the regression coefficient of the ith independent variable. is the value of the i-th objective physiological indicator at the experience perception time t.

[0080] ②Perception score based on EEG indicators:

[0081]

[0082] in, is the constant term of the regression equation, is the regression coefficient of the independent variable. The value of the EEG index when the experience perception time is t.

[0083] ③ Perception score under eye movement indicators:

[0084]

[0085] in, is the constant term of the regression equation, is the regression coefficient of the nth independent variable. is the value of the i-th eye movement index when the experience perception time is t

[0086] 6) Based on the subjective perception scores, the perception scores under objective indicators, and the IES scores, a comprehensive perception evaluation model simulating the lighting environment of multiple underground spaces can be constructed:

[0087]

[0088] in, It is a comprehensive evaluation value of the simulated underground multi-scene space lighting environment. is the weight of the jth subjective perception factor (j=1,2,3,4,5), is the score of the jth subjective perception factor; is the constant term of the regression equation of perception score under physiological indicators, is the regression coefficient of the i-th objective physiological index (i=1,2,…7), is the value of the i-th objective physiological indicator at the experience perception time t; is the constant term of the perception score regression equation under EEG indicators, is the regression coefficient of the EEG index, The value of the EEG index at the experience perception time t; is the constant term of the perceptual score regression equation under the eye movement index, is the regression coefficient of the nth eye movement indicator (n=1,2,…6). is the value of the i-th eye movement index at the experience perception time t. Din is the minimum value of the subjective evaluation scale, and Uax is the maximum value of the subjective evaluation scale.

[0089] Comprehensive evaluation model for simulating underground multi-scene space lighting environment including subjective perception , objective perception , work performance 3 models. Among them: subjective perception model Contains light and color , comfort , sense of space , feeling of relaxation , brightness 5 indicators; objective perception model Including physiological indicator model , EEG indicators , eye movement indicators Three sub-models;

[0090] Physiological indicator model Including heart rate , HR change rate , LF / HF , LF / HF change rate Skin temperature , skin electricity , skin electrical change rate 7 indicators. EEG indicator model Including α wave / β wave R(t)1 indicator, eye movement indicator model Including average pupil diameter , blink frequency , Scan rate , gaze frequency , average fixation duration , average blink duration 6 indicators. Physiological indicator model Used to reflect nervous system activity, individual stress and emotional fluctuations. Refers to the ratio of alpha waves to beta waves, reflecting an individual's state of relaxation and concentration. Used to reflect an individual's attention load, fatigue level, attention allocation and concentration level.

[0091] Job Performance Model Including the accuracy rate E(t), t reflects the user's multi-source perception status at different time lengths, and is the performance of the user's work efficiency and accuracy under the lighting environment.

[0092] Assuming the experience perception time is t, the general analytical expression of Y(t) is:

[0093] )

[0094] Among them, the subjective perception model The formula is as follows:

[0095]

[0096] Objective perception model The formula is as follows:

[0097]

[0098] Physiological indicator model The formula is as follows:

[0099]

[0100] EEG indicators The formula is as follows:

[0101]

[0102] Eye movement indicators The formula is as follows:

[0103]

[0104] Job Performance Model The formula is as follows:

[0105] .

[0106] The beneficial effects of adopting the above technical solution are:

[0107] 1. The present invention proposes a scientific evaluation system for the underground multi-scene space light environment based on multi-source feedback data, which can relatively objectively evaluate the underground multi-scene space light environment from multiple dimensions. Based on this system, a relatively optimal light environment design parameter can be determined through experimental research methods of controlling variables, which helps to guide the relevant design of the underground multi-scene space light environment.

[0108] 2. This invention is based on a multidisciplinary perspective and combines virtual reality technology, and has certain universal application value.

[0109] 3. The present invention's subjective perception collection system uses a questionnaire survey to collect users' subjective perceptions of the lighting environment. This includes light color perception (the degree of warmth or coolness in the lighting environment), comfort (the degree of comfort in the lighting environment), spatial perception (the degree of enclosure and spaciousness in the lighting environment), relaxation (the degree of tension and relaxation in the lighting environment), and brightness perception (the degree of brightness and darkness in the lighting environment). The subjective perception collection system can reflect users' subjective perceptions of the ambient color temperature and color rendering, physical and psychological comfort, spatial depth and breadth perception, emotional relaxation, and light intensity and distribution uniformity. This invention enhances the psychological and physiological perceptions of the interaction between people, space, and environment.

[0110] 4. The immersive virtual reality environment experience system provided by the present invention includes a virtual scene construction module, a lighting parameter control module, and a human-computer interaction module, which is used for people to immersively experience different spatial lighting scenes, realizes a comprehensive comparison of different lighting scenes, and has good universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 This is a schematic diagram of the composition of the underground multi-scene spatial light environment assessment system based on multi-source feedback;

[0112] Figure 2 This is a flow chart of the evaluation method of the underground multi-scene spatial light environment evaluation system based on multi-source feedback; DETAILED DESCRIPTION

[0113] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0114] like Figure 1-2 As shown, the process of using the evaluation system of the present invention to perform evaluation is as follows:

[0115] S1: System layout:

[0116] Based on the experimental design requirements, select a virtual scene from the lighting model scene library. This solution provides five different types of underground multi-scene scenarios: underground fitness spaces in sports buildings, submarines, ship cabins, underground machine rooms, and underground office spaces in high-speed rail stations. Select a specific space from these scenarios. Arrange the relevant equipment and instruments within the experimental site. Set the lighting scene parameters and embed the subjective questionnaire within the virtual lighting environment. Calibrate the virtual scene's lighting environment to the actual experimental requirements to ensure accurate experimental data and a sense of immersion.

[0117] S2: Personnel registration:

[0118] Record the basic information and pre-experiment status of the subjects.

[0119] S3: Wearing the device:

[0120] Help the subject wear the VR device and familiarize them with the virtual experimental environment. Instruct the subject to wear equipment to collect heart rate, blood pressure, electrodermal conductivity, skin temperature, EEG, and eye movement data. Verify the signal reception quality of each module to ensure normal data transmission.

[0121] S4: Data collection:

[0122] During the experiment, multi-source perceptual data from the subjects was collected. A preset experimental scenario was loaded, and the subjects were immersed in the lighting environment and performed a preset task. The experimental time was also recorded. The human subjective data collection system collected the subjects' subjective perceptual data using an embedded questionnaire. The physiological data collection module recorded physiological indicators such as heart rate, blood pressure, electrodermal conductivity, and skin temperature in real time. The eye movement collection module monitored and recorded eye movement data such as pupil diameter, blink frequency, and fixation duration. The task performance module recorded the subjects' reaction time and accuracy during the experimental task, generating the work efficiency performance indicator (IES).

[0123] S5: Data processing:

[0124] First, all collected data are cleaned to remove outliers and invalid data to ensure the accuracy and effectiveness of the analysis.

[0125] In the weight determination module, factor analysis is used to process subjective perception scores and calculate the weight and contribution of each factor. Based on the factor analysis results, the obtained weights are used to construct an evaluation system for subjective scores.

[0126] In the key influencing indicator screening module, through correlation analysis, bivariate analysis is performed on the subjective perception score with physiological indicators, EEG indicators and eye movement indicators to screen out important indicators with significant correlation.

[0127] In the model construction module, based on the stepwise regression analysis method, a regression model between subjective scores and three types of objective indicators (physiological indicators, EEG indicators, and eye movement indicators) was constructed.

[0128] In the comprehensive evaluation module, the contribution of each indicator to the comprehensive score is quantified through model calculation and result integration, and finally a comprehensive perception evaluation model simulating the underground multi-scene space lighting environment is generated to provide optimization suggestions for different lighting conditions.

[0129] Below is an evaluation example of a certain underground enclosed lighting environment.

[0130] Case 1: Lighting environment evaluation of an underground experimental space

[0131] The following example evaluates the lighting environment Y(t) of a confined underground space at t = (60, 80, 100, 120). This evaluation does not include EEG data, so W2(t) data is not included. Correlation testing revealed no significant correlation between the physiological indicator W1(t) and the subjective perception score (the variable with the highest correlation was LF / HF, p = 0.086 > 0.05). Therefore, W1(t) data is not included in the following calculations. Among the eye movement indicators W3(t), the variable that showed a significant correlation with the subjective perception score was saccade frequency (p = 0.02). Saccade frequency refers to the number of times the eye's gaze points land within an area per unit time (typically measured in seconds), reflecting the degree of visual attraction of various environments during visual search. In the experience of a light environment, a higher saccade frequency indicates greater ease of observation and reduced mental workload. Therefore, in this case, the W3(t) data are primarily interpreted based on the variation in saccade frequency.

[0132] Calculate individual assessment values

[0133] Time t <![CDATA[Light color perception F1(t)]]> <![CDATA[Comfort F2(t)]]> <![CDATA[Spatial sense F3(t)]]> <![CDATA[Relaxation feeling F4(t)]]> <![CDATA[Luminance sense F5(t)]]> <![CDATA[Eye movement fraction W3(t)]]> IES 60 0.733 1.267 0.8 0.533 1.733 0.898 51.333 80 0.55 0.7 0.95 0.9 1.15 0.986 68.5 100 1.063 1.188 0.938 0.875 1.313 1.002 88.375 120 1.667 1.667 1.167 1.5 1.333 0.9 109

[0134] From the above data we can see that:

[0135] -The changing trends of the subjective evaluation scores over time are basically the same.

[0136] - Comfort F2(t) was high in the initial stage (t=60), and the subjects showed a high degree of adaptability to the initial environment of the closed lighting environment (F2=1.267). In the middle stage (t=80), it dropped sharply to 0.7, indicating that the subjects experienced temporary discomfort. Then (t=100-120), F2 gradually rebounded to 1.188 (t=100) and reached a peak of 1.667 (t=120), indicating that the subjects readjusted to the environment through psychological adjustments (such as attention allocation).

[0137] - The perceived brightness F5(t) gradually decreased over time, from 1.733 at t=60 to 1.333 at t=120. This suggests that the lighting environment may increase visual fatigue, thereby affecting the subjects' brightness perception.

[0138] - IES increases with time, indicating that the subjects' work efficiency performance decreases after the experimental time is extended. As time goes by, the subjects' fatigue and cognitive load in the underground space lighting environment also increase.

[0139] Calculate Y(t)

[0140] Using the above data and the assumed weight coefficients, we can calculate Y(t) at each time point. The following is a list of the calculation results:

[0141] Time t <![CDATA[Subjective perception score Y1(t)]]> <![CDATA[Objective perception score Y2(t)]]> Inverse efficiency score Y3(t) Total score Y (t) 60 0.99 0.898 51.333 1.292 80 0.863 0.986 68.5 0.962 100 1.055 1.002 88.375 0.764 120 1.4 0.9 109 0.633

[0142] Time t=60: Subjective perception score Y1(t)=0.99. The subjects entered the initial stage of the experiment. Due to their unfamiliarity with the environment, their subjective perception score was low.

[0143] Time t=80~100: The subjective perception evaluation Y1(t) increased from 0.863 to 1.055, reflecting that the subjects gradually adapted to the environment through psychological adjustment.

[0144] At time t = 120, Y1(t) reached a peak of 1.4, indicating that while subjective adaptability may have increased, actual efficiency has significantly decreased (IES = 109). The trends of subjective perception and work efficiency are opposite, indicating that subjective evaluation cannot accurately and comprehensively reflect the subjects' overall perception of the light environment. Conclusions based solely on subjective perception are relatively simplistic and one-sided.

[0145] The objective perception score Y2(t) increased from 0.898 at t=60 to 1.002 at t=100. In the experience of the light environment, a higher scanning frequency indicates easier observation and less psychological stress. This suggests that after a period of adjustment, the subjects' physiological adaptability to the environment gradually increases. However, the physiological score decreased at t=120, indicating that the environmental stress and partial load faced by the subjects increased over time.

[0146] The overall perception score decreased over time, from 1.292 at t = 60 to 0.633 at t = 120. As time went on, the fatigue and stress associated with working in a confined space for extended periods of time gradually reduced the subjects' perceptions.

[0147] Case 2: Lighting environment evaluation of an underground office space

[0148] The following is an evaluation example of the lighting environment Y(t) of an underground office space at t=(4, 8, 12, 16). In this evaluation, the data collection of the eye movement module W3(t) was not involved, so the W3(t) data was not counted. After correlation testing, the physiological index W1(t) was not significantly correlated with the subjective perception score (for example, skin temperature, p=0.12>0.05), so the W1(t) data was not counted in the following calculation process. In the W2(t) EEG index, regression analysis found that the ratio of the power spectral density of α waves to that of β waves was significantly positively correlated with the total subjective perception score (p<0.001), indicating that PSD （α） / PSD (β) The higher the ratio, the higher the overall subjective perception score.（α） / PSD (β) The ratio reflects the emotional impact of various environments on the human body during visual observation. （α） / PSD (β) The larger the ratio, the easier it is for people to observe the light environment and the less psychological burden they have. Therefore, the following analysis is mainly based on EEG PSD. （α） / PSD (β) The changes in the ratio are used to explain the W2(t) data.

[0149] Calculate individual assessment values

[0150] Time t <![CDATA[Light color sensation F1(t)]]> <![CDATA[Light color comfort F2(t)]]> <![CDATA[Brightness perception F3(t)]]> <![CDATA[Brightness comfort F4(t)]]> <![CDATA[Spatial sense F5(t)]]> <![CDATA[Relaxation feeling F5(t)]]> <![CDATA[EEG score W2(t)]]> IES 4 0.194 0.806 0.167 0.556 0.5 0.722 0.579 2.988 8 0.724 0.789 0.566 0.697 0 0.395 0.623 5.724 12 1 0.81 0.905 0.238 0.476 1 0.467 9.519 16 1 0.5 0.833 0.25 0.5 1 0.595 16.893

[0151] According to the data in the table, the changing trends of the subjective evaluation indicators are not completely consistent:

[0152] The light color perception F1(t) and brightness perception F3(t) generally showed an upward trend over time, gradually increasing from 0.194 and 0.167 at t=4 to 1.0 at t=12 and >12, indicating that the subjects' perception of light color and brightness enhanced with adaptation.

[0153] Light color comfort F2(t) initially increased slightly (from 0.806 to 0.81 at t=12), but then decreased to 0.5 at t=16, which may indicate that after a certain period of time, the subjects' tolerance to light color comfort decreased.

[0154] Brightness comfort F4(t) reached its highest value (0.697) at t=8, then began to drop sharply to 0.238 at t=12, and remained at a low level (0.25) at t=16, indicating that fatigue or discomfort caused by brightness intensified over time.

[0155] Spatial sense F5(t) dropped to 0 at t = 8 (possibly indicating that the environment lacked appeal or the subjects felt the space was monotonous at that time), and then recovered slightly in the subsequent stages, resulting in a low overall score.

[0156] The sense of relaxation F6(t) dropped to the lowest value of 0.395 at t=8, and then rose rapidly to 1.0 at t=12 and t=16, indicating that after experiencing the initial discomfort, the subjects' subjective sense of relaxation was significantly enhanced in the later period.

[0157] Furthermore, the inverse efficiency score (IES) continued to increase over time (from 2.988 to 16.893), indicating that participants' work efficiency declined throughout the experiment, with fatigue and cognitive load accumulating. From the perspective of psychological adjustment, during the initial phase (t=4), participants were relatively unfamiliar with the environment, resulting in generally low subjective scores. Over time, participants gradually adapted through strategies such as attention regulation, leading to relatively high values ​​for some subjective indicators at t=12. However, as the duration of the experiment continued to increase, fatigue became more pronounced, leading to a decrease in light color comfort and brightness comfort, as reflected in decreased F2(t) and F4(t) scores.

[0158] Calculate Y(t)

[0159] Using the above data and the assumed weight coefficients, the subjective perception score Y1(t), objective perception score Y2(t), inverse efficiency score Y3(t) and total score Y(t) at each time point are obtained as follows:

[0160] Time t <![CDATA[Subjective perception score Y1(t)]]> <![CDATA[Objective perception score Y2(t)]]> Inverse efficiency score Y3(t) Total score Y (t) 4 0.499 0.579 2.988 20.702 8 0.553 0.623 5.724 10.952 12 0.823 0.467 9.519 9.13 16 0.648 0.595 16.893 3.722

[0161] t = 4: Y1(t) = 0.499, Y2(t) = 0.579, Y3(t) = 2.988, total score Y(t) = 20.702. This is the beginning of the experiment, and the subjects feel relatively unfamiliar with the environment, resulting in low subjective perception scores.

[0162] t=8-12: Y1(t) increased from 0.553 to 0.823, indicating that the subjects were gradually adapting to the environment; Y2(t) decreased from 0.623 to 0.467 and then slightly increased to 0.595, indicating that physiological adaptability first increased and then decreased, possibly due to mid-term fatigue; Y3(t) continued to increase, indicating that the efficiency of task completion decreased.

[0163] t=16: Y1(t) dropped to 0.648, and subjective perception was slightly weakened, which may be due to fatigue caused by the long-term experiment; Y2(t) rebounded to 0.595 but was still lower than the initial level, indicating limited objective adaptability; Y3(t) rose to 16.893, and work efficiency decreased significantly.

[0164] Overall, the total score Y(t) decreased over time (from 20.702 to 3.722), reflecting the cumulative fatigue and decreased overall performance caused by long work hours. This also shows that judging overall condition based solely on subjective feelings can be one-sided and requires a comprehensive assessment incorporating physiological indicators. This confirms that this evaluation system, combining subjective and objective assessments, is more scientific for problem analysis.

[0165] Case 3: Lighting environment evaluation of an underground e-commerce office space

[0166] The following example evaluates the lighting environment Y(t) at t = 60, 80, 100, and 120 for an underground e-commerce office space. This space has limited natural light entering the space. This evaluation did not involve an EEG acquisition module, so W2(t) data is not included. Correlation testing revealed that the LF / HF ratio (W1(t)) physiological indicator showed a significant negative correlation with the overall score (p = 0.011 < 0.005) through regression analysis. Furthermore, the blink frequency (W3(t)) eye movement score showed a significant negative correlation with the overall score (p = 0.045 < 0.005) through regression analysis. Therefore, the following analysis primarily interprets the W1(t) data based on changes in the LF / HF ratio, and the W2(t) data based on changes in blink frequency.

[0167] Calculate individual assessment values

[0168] Time t <![CDATA[Light color perception F1(t)]]> <![CDATA[Brightness sensation F2(t)]]> <![CDATA[Comfort F3(t)]]> <![CDATA[Spatial sense F4(t)]]> <![CDATA[Relaxation feeling F5(t)]]> <![CDATA[Physiological index score W1(t)]]> <![CDATA[Eye movement fraction W3(t)]]> IES 60 1.6 1.9 1.6 1.9 1.6 1.761 2.018 52.3 80 0.429 2 1.429 2.143 2 2.025 2.029 66.429 100 1.778 2.222 1.778 2 1.667 2.12 1.952 86 120 1 2.286 1.571 2.143 1.714 1.92 1.9 121.857

[0169] Judging from the data trends, most subjective evaluation indicators maintained high levels at all time points:

[0170] The light and color comfort F3(t), spatial sense F4(t), and relaxation F5(t) were close to 2 points in all stages, indicating that the experimental environment was generally comfortable and the subjects were subjectively satisfied.

[0171] The brightness perception F2(t) fluctuated slightly over time and was slightly higher after t=80 than in other stages, indicating that the subjects' perception of brightness improved in the middle and late stages.

[0172] The light color perception F1(t) dropped significantly to 0.429 at t=80, and remained between 1 and 1.778 in the rest of the period. This may be due to light color discomfort. Then F1(t) rebounded to 1.778 at t=100 and remained at 1.0 at t=120, indicating that the subjects had re-accepted the light color environment through psychological adjustment.

[0173] The physiological index score W1(t) and the eye movement score W3(t) showed an upward trend from t=60 to t=100, rising from 1.761 to 2.12 and from 2.018 to 2.029, respectively. This indicates that the subjects' physiological and eye movement states adapted and even improved as the experiment progressed. However, at t=120, both W1(t) and W3(t) decreased slightly (to 1.92 and 1.90, respectively), possibly signaling the onset of fatigue. The inverse efficiency score IES increased from 52.3 to 121.857, indicating that work efficiency decreased over time, with a significant cumulative effect of fatigue.

[0174] Calculate Y(t)

[0175] The subjective perception score Y1(t), objective perception score Y2(t), inverse efficiency score Y3(t) and total score Y(t) at each time point are calculated as follows:

[0176] Time t <![CDATA[Subjective perception score Y1(t)]]> <![CDATA[Objective perception score Y2(t)]]> Inverse efficiency score Y3(t) Total score Y (t) 60 1.761 1.944 52.3 1.588 80 2.131 2.027 66.429 1.267 100 1.903 2.036 86 0.977 120 2.045 1.909 121.857 0.693

[0177] t=80: Y1(t) rises to 2.131, and Y2(t) is 2.027, indicating that the subjects' subjective and objective evaluations reach their peak at this time, and they may have a better feeling about the environment after a short period of adaptation.

[0178] t=100: Y1(t) dropped to 1.903, and Y2(t) rose to 2.036, indicating that the subjects' subjective sense of relaxation decreased slightly but physiological adaptation remained at a high level; Y3(t) continued to rise to 86.

[0179] At t=120: Y1(t) rebounded to 2.045, while Y2(t) decreased slightly to 1.909. This further increase in subjective perception may be due to psychological adjustment. Y3(t) increased significantly to 121.857, reflecting increased fatigue at this time. Overall, the total score Y(t) decreased over time (from 1.588 to 0.693), reflecting the persistent impact of fatigue on overall performance. The high values ​​of subjective Y1 at the 80 and 120 minute intervals contradict the increasing trend of objective efficiency Y3(t), indicating that subjective perception did not reflect the decline in efficiency in the later period, highlighting the need for comprehensive evaluation of subjective and objective data.

[0180] Summarize:

[0181] In summary, the three different cases are respectively aimed at the underground multi-scene space lighting environment. The use of this evaluation system can break through the limitations of traditional single-type information feedback and qualitative method evaluation, consider source information feedback, including time effect, subjective perception, objective physiological response and work efficiency performance, and form a quantitative evaluation to achieve the evaluation of lighting environment effectiveness and provide a quantitative reference for the optimal design of underground multi-scene space lighting environment.

[0182] Suggestions on the design of underground multi-scene space lighting environment

[0183] Subjective perception:

[0184] The lighting environment should dynamically adjust brightness and color temperature based on user needs. Initially, soft lighting should be provided to alleviate unfamiliarity, with subsequent optimization to ensure adaptability while maintaining a high level of comfort. To address the issue of decreased brightness over time, dynamic brightness adjustment or a combination of multiple light sources is recommended to reduce visual fatigue and enhance the pleasantness of the environment. To reduce the discomfort associated with prolonged time spent in underground functional spaces, emotional design elements are recommended, such as simulating natural light changes or using warm-toned light sources to enhance relaxation.

[0185] Physiological indicators:

[0186] Monitor changes in physiological data (such as heart rate, skin charge, and skin temperature) and dynamically adjust lighting parameters to ensure the impact of the light environment on physiological status is within an appropriate range. To address trends of physiological fatigue and increased cognitive load, timely reduce lighting intensity or introduce intermittent light environment changes to help relieve physical stress. Introduce intelligent systems to adjust the lighting environment based on real-time data feedback to optimize the stability of physiological indicators.

[0187] Task performance:

[0188] Lighting design should support efficient task completion, providing a high-brightness, low-glare lighting environment in the work area to improve reaction time and accuracy. In response to the significant increase in Inverse Efficiency Score (IES) over time, a visual resting lighting environment should be provided between tasks, such as soft light or short periods of dark adaptation, to reduce the impact of long tasks on work efficiency.

[0189] Comprehensive environmental adaptation:

[0190] Underground functional spaces that are used for long periods of time should incorporate variable light environment designs to enhance users' ability to adapt to the environment and gradually improve the overall score through phased light adjustments.

[0191] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A lighting environment evaluation system for simulating underground multi-scene spaces, characterized in that: Including immersive virtual reality environment experience system, human physiological index collection system, human subjective perception collection system, task performance system, and comprehensive data processing system; The immersive virtual reality environment experience system includes a virtual scene construction module, a lighting environment control module, and a human-computer interaction module, which are used for people to immersively experience different spatial lighting scenes; The human physiological index acquisition system is composed of a heart rate acquisition module, a blood pressure acquisition module, a skin charge acquisition module, a skin temperature acquisition module, an EEG acquisition module, and an eye movement acquisition module; The human subjective perception collection system collects subjective feelings of people through questionnaires preset in a virtual environment; The task performance system is determined by the duration of the visual task collection task and the answering situation in the virtual environment, and the work efficiency performance is evaluated through the inverse efficiency score IES; The comprehensive data processing system includes a data sorting module, a weight determination module, a key influencing indicator screening module, and a model building module, which are used to receive data collected from the human physiological indicator acquisition system, the human subjective perception acquisition system, and the task performance system, and to sort and analyze the collected data.

2. The lighting environment evaluation system for simulating underground multi-scene space according to claim 1, characterized in that: The virtual scene construction module is composed of SketchUp, Radiance, and Enscape software. The space model is built through SketchUp, and the lighting environment is rendered using the Radiance engine and Enscape, and the color and reflectivity parameters are adjusted. The lighting environment control module is used to control the color temperature of lamps and indoor illumination, both of which are set in Enscape software. Select the light to be adjusted in the model, and adjust the brightness value of the light by adjusting the light CD parameter in the Enscape property panel. Adjust the color temperature value of the light by assigning a color temperature material to the light source component. The human-computer interaction module is composed of a computer and a virtual reality device. First, the user enters the space lighting scene library through the computer device and selects it. Then, the user wears the virtual reality device and operates the mouse to enter the preset underground multi-scene space lighting scene for experience.

3. The lighting environment evaluation system for simulating underground multi-scene space according to claim 1, characterized in that: The heart rate acquisition module uses a portable electrocardiogram monitor as a collector, and the acquisition indicators are heart rate and heart rate variability; The blood pressure collection module uses a smart bracelet as a collector; the collected indicators are systolic pressure and diastolic pressure, which are used to evaluate the status of the blood circulation system; The skin electrical collection module uses a skin electrical sensor based on the Arduino platform as a collection device; it collects skin conductivity GSR signals; The skin temperature acquisition module uses button-type temperature sensors as acquisition devices to monitor skin temperature in real time; the sensors are respectively set on the forehead, behind the ears, wrists, chest, back and ankles; The heart rate acquisition module, blood pressure acquisition module, skin electrical acquisition module, and skin temperature acquisition module respectively use corresponding data acquisition systems to collect data, and then synchronize the data to the comprehensive data processing system through the data conversion box; The EEG acquisition module uses a conductive paste or saline EEG cap as a collector and is placed on the head. The acquisition indicators are alpha and beta brain waves and event-related potentials. The collected data is de-noised, filtered, and subjected to independent component analysis using EEG studio and Metlab-EEGlab. The frequency domain characteristics of the EEG signal are analyzed based on the burg algorithm. The power spectral density PSD (α) and PSD (β) are used as characteristic data to evaluate the activity of the brain at specific frequencies. The calculated value PSD (α) / PSD (β) is synchronized to the integrated data processing system. The eye movement acquisition module uses an eye tracker as a collector and is set at the position of the person's eyes to collect eye movement behavior data in real time; The eye movement acquisition module uses infrared optical tracking technology and a built-in camera to record eye movement characteristics and transmit the data to the analysis system for processing; The core data indicators collected include average pupil diameter, blink frequency, scan frequency, gaze frequency, average gaze duration, and average blink duration, which reflect attention, emotional state and psychological load, as well as fatigue status in the lighting environment.

4. The lighting environment evaluation system for simulating underground multi-scene space according to claim 3, characterized in that: EEG data processing includes: ①Autoregressive AR model; Where Xt is the time series data, φk is the model parameter, p is the model parameter, and ϵt is the white noise error term; ② For the AR(P) model, the Yule-Walker equation is used for parameter estimation; Where ϕ is the parameter vector, R is the autocorrelation matrix, and r is the autocorrelation coefficient vector; ③ Using the Burg algorithm to use the reflection coefficient to recursively estimate the AR model parameters, Where kj is the jth reflection coefficient and rj is the autocorrelation coefficient; ④After the AR model parameters are estimated, power spectrum density estimation is performed; Where S(f) is the power spectral density at frequency f, σ2 is the variance of white noise, ϕk is the AR model parameter, and J is the imaginary unit; ⑤Convert the power spectrum density into decibel units for data analysis; 。 5. The lighting environment evaluation system for simulating underground multi-scene space according to claim 1, characterized in that: The human body subjective perception collection system uses a questionnaire survey method to collect people's subjective perception evaluation of the lighting environment in the space they are in, including light color perception, that is, the degree of coldness or warmth in the light environment; comfort, that is, the degree of comfort in the light environment; sense of space, that is, the degree of enclosure and spaciousness in the light environment; sense of relaxation, that is, the degree of tension and relaxation in the light environment; and brightness perception, that is, the degree of brightness and darkness in the light environment. A questionnaire was used to collect subjective perception indicators using the Likert scale. The subjective perception questionnaire was embedded in the SU model in the form of pictures, which were rendered synchronously with the virtual underground multi-scene space lighting environment and displayed in the VR scene.

6. The lighting environment evaluation system for simulating underground multi-scene space according to claim 1, characterized in that: The task performance system evaluates work efficiency performance through the inverse efficiency score IES. The calculation process of IES is expressed as follows: Among them, E(t) represents the accuracy rate of the subjects’ answers when the experience perception time is t.

7. The lighting environment evaluation system for simulating underground multi-scene space according to claim 1, characterized in that: In the comprehensive data processing system, in order to eliminate the influence of invalid indicators on the analysis results, the data is cleaned and processed to ensure the validity and accuracy of the data; a weight determination module, a key impact indicator screening module, and a model construction module are used to establish an underground multi-scene space lighting environment evaluation model based on multi-source information feedback; The weight determination module processes the subjective perception index using factor analysis; SPSS statistics26 was used for data statistics and analysis, including data preparation and applicability testing, calculation of factor loading matrix, extraction of eigenvalues ​​and variance explanation, calculation of factor scores and weights, and finally construction of subjective perception evaluation equation; The specific process is as follows: Step 1: Data preparation and applicability test; First, the subjective perception data were organized into an evaluation score matrix, and the matrix was tested for suitability for factor analysis. The KMO test was used to assess the suitability of the sample data. When the KMO value was greater than 0.5, the data was considered suitable for factor analysis. The Bartlett sphericity test was used to verify the correlation between variables. A significance level of p < 0.05 indicated that the data had a strong correlation, so factor analysis was performed. Step 2, calculation of factor loading matrix; After the data passed the applicability test, factor analysis was performed on the evaluation score matrix to extract the factor loading matrix. To improve the explanatory power of the factor analysis results, the factor loading matrix was rotated to generate a rotated factor loading matrix, thereby optimizing the independence between factors. Step 3, extraction of eigenvalues ​​and variance explanation rate; Based on the rotated factor loading matrix, the eigenvalue and variance explanation rate of each factor were extracted; factors with eigenvalues ​​greater than 1 were selected as the main analysis objects. The variance explanation rate indicates the contribution of each factor to the overall data and can provide a basis for determining the weight of the evaluation dimension. Step 4, factor score and weight calculation; Based on the eigenvalue and variance explanation rate, the linear combination coefficient of each evaluation indicator is calculated, and the indicator weight is further determined; the linear combination coefficient shows the contribution of each indicator to the factor, and the calculation formula is: Where R is the rotated factor loading matrix, eigen is the characteristic root of the factor; the cumulative factor score is calculated by weighted summation of the combined coefficient and the variance explained by the factor: in, represents the linear combination coefficient of the j-th indicator on the i-th factor, is the variance explanation rate of the i-th factor; finally, through normalization processing, the weight of each evaluation index is obtained in, is the cumulative factor score of the jth evaluation indicator; Step 5: Construction of subjective perception scoring model; Finally, based on the weights and factor scores, a subjective perception rating model is constructed; by summing the factor scores according to the weights, the subjective perception rating equation is obtained: in, is a subjective perception score, is the weight of the jth factor, is the score of the jth factor; The key influencing indicator screening module uses SPSS statistics26 to perform correlation analysis, conducts bivariate correlation analysis on subjective perception scores, eye movement indicators and objective physiological indicators, and confirms whether there is a correlation between each variable and the dependent variable based on the calculated correlation coefficient and significance level, thereby screening the key influencing indicators of the target model; and various physiological indicators , … The linear correlation is calculated using the Pearson correlation coefficient, and the formula is as follows: in: is the subjective perception score of the jth sample; is the physiological index value of the jth sample; 、 are the means of subjective perception scores and physiological indicators respectively; n is the number of samples, The calculated correlation coefficient r ranges from [−1,1], and r>0 indicates that and There is a positive correlation between them; r<0 indicates and There is a negative correlation between them; the larger the |r|, the stronger the correlation; During the analysis, each physiological index Conduct a significance test and use the p-value to determine whether the correlation is significant; at the same time, select key influencing indicators based on the absolute value of the correlation coefficient; The model building module uses SPSS statistics26 to perform stepwise regression analysis to build the correlation between the subjective evaluation quantitative results and three types of objective indicator variables, namely physiological indicators, EEG indicators, and eye movement indicators, and finally obtain the perception score under the objective indicators; the calculation method is as follows: ① Perception score under physiological indicators: in, is the constant term of the regression equation, is the regression coefficient of the ith independent variable. is the value of the i-th objective physiological indicator at the experience perception time t; ②Perception score based on EEG indicators: in, is the constant term of the regression equation, is the regression coefficient of the independent variable, The value of the EEG index when the experience perception time is t; ③ Perception score under eye movement indicators: in, is the constant term of the regression equation, is the regression coefficient of the nth independent variable; is the value of the i-th eye movement index when the experience perception time is t; Step 6: Based on the subjective perception scores, the perception scores under objective indicators, and the IES scores, a comprehensive perception evaluation model simulating the underground multi-scene space lighting environment is constructed: in, It is a comprehensive evaluation value of the simulated underground multi-scene space lighting environment. is the weight of the jth subjective perception factor, j=1, 2, 3, 4, 5, is the score of the jth subjective perception factor; is the constant term of the regression equation of perception score under physiological indicators, is the regression coefficient of the i-th objective physiological index, i=1,2,…7, is the value of the i-th objective physiological indicator at the experience perception time t; is the constant term of the perception score regression equation under EEG indicators, is the regression coefficient of the EEG index, The value of the EEG index when the experience perception time is t, is the constant term of the perceptual score regression equation under the eye movement index, is the regression coefficient of the nth eye movement index, n=1,2,…6; is the value of the i-th eye movement index when the experience perception time is t, Din is the minimum value of the subjective evaluation scale, and Uax is the maximum value of the subjective evaluation scale.

8. The lighting environment evaluation system for simulating underground multi-scene space according to claim 7, characterized in that: Comprehensive evaluation model for simulating underground multi-scene space lighting environment including subjective perception , objective perception , work performance 3 models; among them: subjective perception model Contains light and color , comfort , sense of space , feeling of relaxation , brightness 5 indicators; objective perception model Including physiological indicator model , EEG indicators , eye movement indicators Three sub-models; physiological indicator model Including heart rate , HR change rate , LF / HF , LF / HF change rate Skin temperature , skin electricity , skin electrical change rate 7 indicators, EEG indicator model Including α wave / β wave R(t)1 indicator, eye movement indicator model Including average pupil diameter , blink frequency , Scan rate , gaze frequency , average fixation duration , average blink duration 6 indicators; work performance model Including the accuracy E(t), t reflects the user's multi-source perception status at different time lengths; Assuming the experience perception time is t, the analytical expression of Y(t) is: ) Among them, the subjective perception model The formula is as follows: Objective perception model The formula is as follows: Physiological indicator model The formula is as follows: EEG indicators The formula is as follows: Eye movement indicators The formula is as follows: Job Performance Model The formula is as follows: 。

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