Illumination visual effect analysis system and method connected with illuminometer

Abstract

The invention discloses an illuminance visual effect analysis system and method connected with an illuminometer. The system comprises an illumination environment illumination data transmission module which receives illumination measured by an illuminometer connected with the illumination environment illumination data transmission module; and the illuminance human eye physiological effect calculation module is used for receiving the illuminance transmitted by the illumination environment illuminance data transmission module, acquiring an illuminance adjustment effect parameter and an illuminance contrast sensitivity effect parameter according to the received illumination environment illuminance, and calculating an illuminance adjustment effect and an illuminance contrast sensitivity effect. The invention aims to provide an illuminance visual effect analysis system and method connected with an illuminometer, which can synchronously calculate the physiological effect of the illuminance and evaluate the risk of asthenopia caused by the illuminance of the illumination environment when a user measures the illuminance of the illumination environment by using the illuminometer, so that the user can adjust the eyes in time.

Description

Technical Field

[0001] This invention relates to the field of lighting technology, and in particular to a system and method for analyzing the visual effects of illuminance connected to a lux meter. Background Technology

[0002] Under varying photometric conditions, the physiological responses of the human eye differ significantly during visual tasks, leading to varying degrees of visual fatigue. Although existing research indicates that different photometric parameters can have specific effects on physiological indicators, the quantitative correspondence between these parameters and specific physiological effects remains unclear.

[0003] As the external visual environment that the human eye frequently encounters daily, the quality of lighting directly affects visual comfort, work efficiency, and long-term eye health. Poor lighting can cause eye strain, dry eyes, decreased concentration, and even disrupt circadian rhythms, affecting sleep quality. Therefore, when choosing lighting fixtures, one should not only focus on appearance but also pay attention to key parameters such as illuminance characteristics.

[0004] A scientifically designed and reasonable lighting environment illuminance not only enhances the visual experience but also serves as a crucial safeguard for human eye health. It is necessary to construct a computational model that can quantify the physiological impact of lighting environment illuminance on the human eye. This model should be designed based on the response characteristics of the human eye to different lighting environment illuminance levels, and further, specialized equipment should be developed to apply this model to the actual calculation of the physiological effects of lighting environment illuminance. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide an illuminance visual effect analysis system and method connected to a lux meter, which enables users to simultaneously calculate the physiological effects of such illuminance while measuring the illuminance of the lighting environment with a lux meter, assess the risk of eye fatigue caused by such lighting environment illuminance, and allow users to adjust their eye use in a timely manner.

[0006] According to one aspect of the present invention, an illuminance visual effect analysis system connected to a lux meter is provided, the system comprising: The ambient illuminance data transmission module receives illuminance measured by the connected illuminance meter. ; Illuminance Human Eye Physiological Effect Calculation Module: Used to receive the illuminance transmitted by the lighting environment illuminance data transmission module. Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance The illuminance adjustment effect was calculated. Contrast sensitivity effect with illuminance ;in, Based on illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance Calculate the illuminance adjustment effect Contrast sensitivity effect with illuminance The calculation formula is as follows:

[0007]

[0008] In the formula, The number of points used to measure illuminance on the desktop; For the desktop Illuminance at a point, The minimum illuminance among the points on the desktop where illuminance is measured.

[0009] The aforementioned technical solution aims to construct an illuminance visual effect analysis system linked to a lux meter. Its core lies in achieving a quantitative mapping from physical illuminance measurement to assessment of physiological effects on the human eye. Through modular design, this system combines environmental parameter acquisition with biological effect calculation, providing a clear technical path for objectively evaluating the visual health risks of lighting environments.

[0010] The technical implementation of this solution primarily relies on the collaborative work of two core modules. First, the "Lighting Ambient Illuminance Data Transmission Module" receives and transmits raw illuminance data I from the physical sensor (lux meter), ensuring the objectivity and reliability of the data input. Second, the "Illuminance Human Eye Physiological Effect Calculation Module" is the computational core of the entire solution, incorporating the "illuminance adjustment effect." And the "illuminance contrast sensitivity effect" Two key physiological effect indicators. Its innovation lies in the algorithm design: the model does not simply use single-point or average illuminance, but comprehensively considers the illuminance distribution of N sampling points within the measurement plane. The final effect value is calculated as the product of three factors: the average physiological effect at each point, the average environmental illuminance level, and the reciprocal of the minimum illuminance. This design can simultaneously reflect the combined effects of average environmental brightness, overall photobiological effects, and illuminance uniformity (penalizing environments with poor uniformity by using the reciprocal of the minimum illuminance) on the visual system, thus more precisely characterizing the comprehensive physiological load under complex lighting conditions.

[0011] In summary, this solution, through a clear system architecture and algorithm model, engineered and quantified the relationship between the abstract lighting environment and the physiological response of the human eye. Its core lies in providing an executable and quantifiable evaluation tool, solving the problem of unclear quantitative relationships between parameters and physiological effects. By outputting physiological effect parameters in two dimensions, this solution can provide direct data support for the optimized design of lighting environments, visual ergonomic evaluation, and health risk warning.

[0012] In some embodiments, based on the received ambient illuminance. Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: At different illuminance Human factors experiments were conducted under specific lighting conditions to measure the human eye's accommodative amplitude. and contrast sensitivity The change in illuminance, and establish the illuminance A quantitative relationship model between the parameter and the change amount is used to obtain the numerical range of the parameter.

[0013] The purpose of the above technical solution is to provide experimentally validated quantitative parameters for the subsequent physiological effect calculation module. First, through the design of human factors engineering experiments covering different illuminance levels I, the dynamic changes in key physiological indicators of the human eye—amplitude of accommodation (AMP) and contrast sensitivity (MTF)—are systematically collected under controlled conditions. Second, using data analysis techniques, a quantitative relationship model between the illuminance value I and the aforementioned physiological changes is constructed, thereby reducing discrete physiological response data to a universal mathematical function. and Ultimately, this model determines the specific numerical ranges of each parameter within the target illuminance range, transforming subjective and difficult-to-quantify physiological sensations into objective parameter values ​​that can be directly accessed by the system. This approach addresses the problem of unclear quantitative relationships between photometric parameters and physiological effects by converting them into concrete and calculable functional relationships.

[0014] In some embodiments, the illuminance adjustment effect parameter Sensitivity effect parameter compared to illuminance The parameter range is as follows:

[0015] In the above technical solution, this step provides the core calculation parameters for the illuminance visual effect analysis system, which have been experimentally calibrated, and clarifies the illuminance adjustment effect parameters. Sensitivity effect parameter compared to illuminance The specific numerical range under different illuminance levels. and The numerical range of the baseline illuminance It exhibits non-monotonic dependence. Specifically, within the test range of 200 lux to 800 lux, the values ​​of the two parameters did not show a simple linear increase or decrease with increasing illuminance; instead, valleys were observed in specific intervals (such as around 500 lux). For example, at 500 lux illuminance, and The lower limits of illuminance decrease to 0.4 and 0.5 respectively, while the upper limits reach 1.8 at 200 lux and 800 lux. This non-linear relationship better reflects the complex dynamic characteristics of human eye physiological response (accommodation amplitude and contrast sensitivity) under different lighting conditions. The above parameter range table transforms the established qualitative and quantitative relationship model into a calculation basis that the system can directly call upon. Its non-monotonic distribution characteristics confirm the necessity of constructing an evaluation model—simply relying on the absolute value of illuminance cannot accurately determine its physiological effects; it is necessary to rely on such empirically calibrated mapping relationships. The establishment of this parameter set enables the system to quickly and objectively output the corresponding physiological effect estimates based on real-time measured ambient illuminance.

[0016] In some embodiments, the system further includes a result transmission module; This module is used to process the calculated results. and The results are stored.

[0017] In the above technical solution, this module is located at the end of the data processing flow. Its core function is to receive and store the quantitative results output by the illuminance human eye physiological effect calculation module, i.e., the illuminance accommodation effect. Contrast sensitivity effect with illuminance By storing the calculation results of each measurement (including timestamps, corresponding illuminance data, and physiological effect values) in a database or file system, it is ensured that all calculated physiological load data are completely preserved, providing the possibility for subsequent traceability, analysis, and application.

[0018] According to another aspect of the present invention, a method for analyzing the visual effect of illuminance connected to a lux meter is provided, the method comprising: Illuminance measured by the lux meter ; Received illuminance Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance The illuminance adjustment effect was calculated. Contrast sensitivity effect with illuminance ;in, Based on illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance Calculate the illuminance adjustment effect Contrast sensitivity effect with illuminance The calculation formula is as follows:

[0019]

[0020] In the formula, The number of points used to measure illuminance on the desktop; For the desktop Illuminance at a point, The minimum illuminance among the points on the desktop where illuminance is measured.

[0021] In order to better utilize the above system, this application proposes an illuminance visual effect analysis method connected to an illuminance meter. Each module corresponds to a step in the above method, and its specific principle has been described above and will not be repeated here.

[0022] According to another aspect of the present invention, an illuminance visual effect analysis device connected to a lux meter is provided, comprising: At least one processor and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0023] In the above technical solution, to better operate and process the method, the method is stored in memory, and the processor executes the stored method. It should be noted that the principle and effect of each step have been described above and will not be elaborated upon here.

[0024] According to another aspect of the present invention, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method.

[0025] In the above technical solution, to better operate and use the method, the method is stored in a computer-readable storage medium and implemented using a processor. It should be noted that the principle and effect of each step have been described above and will not be elaborated upon here. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of an embodiment of the illuminance visual effect analysis system connected to an illuminance meter according to the present invention. Figure 2 This is a flowchart illustrating an embodiment of the illuminance visual effect analysis method connected to an illuminance meter according to the present invention. Figure 3 This is a schematic diagram of the spectral power distribution (SPD) of a lighting fixture in an embodiment of the illuminance visual effect analysis method connected to an illuminance meter according to the present invention; Figure 4 This is a schematic diagram of the human factors experiment process of an embodiment of the illuminance visual effect analysis method connected to an illuminance meter according to the present invention. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The purpose of this invention is to propose an illuminance visual effect analysis system and method connected to a lux meter, which enables users to simultaneously calculate the physiological effects of illuminance while measuring the illuminance of the lighting environment, assess the risk of eye fatigue caused by the illuminance of the lighting environment, and allow users to adjust their eye use in a timely manner.

[0030] Example 1 Please see Figure 1 An illuminance visual effect analysis system connected to an illuminance meter, the method comprising: The ambient illuminance data transmission module receives illuminance measured by the connected illuminance meter. ; In this embodiment, the ambient illuminance data transmission module is used to receive the ambient illuminance I measured by the illuminance meter electrically connected to the system; the external illuminance meter is a SPIC-500AW spectral color illuminance meter. To ensure the consistency and comparability of the brightness measurement results, this solution requires the following spatial layout of the screen measurement points: N points are measured on the desktop, and these points are evenly distributed in a grid pattern. The grid spacing is 10 cm, which is sufficient to capture the brightness distribution characteristics of common displays. The center point of the desktop must be included as one of the measurement points, as it usually coincides with the user's primary line of sight area.

[0031] Illuminance Human Eye Physiological Effect Calculation Module: Used to receive the illuminance transmitted by the lighting environment illuminance data transmission module. Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance The illuminance adjustment effect was calculated. Contrast sensitivity effect with illuminance ;in, Based on illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance Calculate the illuminance adjustment effect Contrast sensitivity effect with illuminance The calculation formula is as follows:

[0032]

[0033] In the formula, The number of points used to measure illuminance on the desktop; For the desktop Illuminance at a point, The minimum illuminance among the points on the desktop where illuminance is measured.

[0034] In this embodiment, to ensure the spatial representativeness and comparability of the illuminance measurement results, the scheme has the following explicit provisions regarding the layout of the desktop measurement points: Layout method: Measure N points on the desktop, and these points are evenly distributed in a grid pattern.

[0035] Grid density: The spacing between grid points is fixed at 10 cm. This density is suitable for typical office and reading desktop sizes (e.g., 80cm × 60cm), effectively capturing the spatial distribution characteristics of illuminance in the work area while ensuring measurement efficiency.

[0036] Key reference point: The center of the desktop must be included as a measurement point. This point is usually associated with the user's core work area and approximate line of sight, and is a key location for assessing the quality of the visual environment.

[0037] 2. Environmental and task control in human factors experiments Generate model core parameters and Human factor experiments are conducted under highly standardized conditions to ensure the quality and consistency of input data: Environmental stability: The experiments were conducted in experimental chambers or rooms that met the requirements of national lighting standards such as GB / T 50034-2024. The illuminance level, color temperature, and uniformity of ambient light remained strictly stable during the experiments, thus eliminating the interference of ambient light fluctuations on the experimental results.

[0038] Task and duration standardization: Experiments were designed according to standard GB / T 44441-2024. Each visual task lasted 45 minutes and consisted of standard Landolt C counting. This long-duration, standardized cognitive-visual task aimed to induce stable and reproducible visual load states.

[0039] Participants and Statistical Processing: The experiment employed a multi-sample design. Although individuals exhibited varying transient states during the experiment, the long-term standardized task integrated these transient fluctuations, while the aggregation of multi-sample data averaged the differences between individuals using statistical methods, ultimately yielding stable parameters that reflect population trends.

[0040] 3. Mechanisms to ensure the repeatability and comparability of results The core of ensuring repeatability and comparability of results lies in combining standardized operating procedures with reliance on statistical stability: a 10-centimeter grid layout including a center point provides objective and reproducible rules for spatial positioning of measurement points, eliminating arbitrariness in point placement. The requirement that the measurement environment conform to national lighting standards provides a stable benchmark condition for measurement. This series of specific and clear regulations constitutes a detailed standard operating procedure. Different users, or the same user at different times, can theoretically have consistent and comparable measurement input conditions as long as they follow the same procedure. The final output of the model relies on statistical relationships fitted based on population experimental data. Minor operational deviations that may occur in a single measurement (such as a deviation of a few millimeters in the location of a measurement point) or instantaneous fluctuations in individual physiology are treated as random errors in the statistical model. When operations generally follow the specifications, the effects of these random errors are smoothed and canceled out during the model's calculation process, thus making the system output robust to minor perturbations.

[0041] 4. Pathways to overcome human factors and achieve automation This solution, designed as an automation tool, reduces reliance on operator skills and overcomes human uncertainty through the following design features: Process solidification and guidance: Solidify the above SOPs into the system's hardware and software design or user interface. For example, develop a supporting application that displays a virtual 10cm grid diagram on the screen to guide the user to measure one by one; or design a simple positioning tool (such as a measuring bracket with a ruler) to assist in the placement of points.

[0042] Robustness handling of input parameters: The key inputs of the model—average illuminance and minimum illuminance—are derived from statistical calculations of N measurement points. The process of averaging or taking extreme values ​​from multiple measurements inherently possesses a certain ability to resist gross errors at individual points.

[0043] Application Boundaries and Quality Control Tips: The system clearly defines its effective application scope as conventional indoor lighting environments that comply with national standards. At the software level, simple data rationality checks can be set up (such as whether the illuminance values ​​at each point are within the common range, and whether the minimum illuminance is too low), and prompts can be given for measurement results that significantly exceed the conventional range, thus avoiding erroneous input caused by seriously non-standard operation or environment from the application end.

[0044] It is important to note that The formula is not an arbitrary mathematical combination, but has a clear photometric interpretation: Core physical meaning: in the formula Item, equivalent to In lighting engineering, illuminance uniformity is a key parameter, typically defined as the ratio of the minimum illuminance to the average illuminance on the working surface. This can be expressed as either the minimum illuminance or the ratio of the maximum illuminance. Therefore, It is essentially a reciprocal form of illuminance uniformity.

[0045] Based on the above understanding, the original formula can be interpreted as: Visual load effect ≈ Mean effect × (1 / Illumination uniformity). This means that the model introduces an amplification factor—the reciprocal of uniformity—on top of the basic visual effect (Ē, determined by the average illuminance level). When the illuminance distribution is more uneven ( Significantly smaller than (A smaller U-value indicates a higher visual load, which is amplified on top of the average effect). This design incorporates the important physical quantity of spatial light distribution quality into the evaluation system, and its measurement method is clearly specified in the national standard GB / T 5700-2023.

[0046] 2. Dimensional Issues and Interpretation of Model Output It is not a purely experimental measurement with independent physical units, but rather a value obtained by fitting experimental data to a specific illuminance. The corresponding scaling factor or weighting coefficient. During the fitting process, its value has been processed to correspond to the effect size. Multiplying them yields a dimensionless number with physical meaning. Therefore, In mathematics, coefficients are used to represent this. For average illuminance Scaling is applied. The overall output F of the formula is a comprehensive index. Its purpose is to rank and compare the relative magnitudes of visual load within its own defined system, rather than directly outputting physiological quantities with classical physical units (such as diopter, contrast threshold). It incorporates the "mean illuminance level" ( ,pass Weighted) and "uniformity of illuminance distribution" Two dimensions. The value of this method of constructing a dimensionless comprehensive index lies in its internal consistency and its ability to aggregate and represent complex effects.

[0047] 3. Consideration of extreme values ​​and model universality when As the value approaches zero, the formula's result amplifies dramatically. To address this, the model limits its application scope. This model is specifically designed to evaluate conventional lighting reading environments that meet the requirements of national standards such as GB / T 50034-2024 and GB / T 9473-2022. These standards specify clear lower limits for minimum illuminance and uniformity of the work surface. Therefore, within the model's pre-defined legal application scenarios, It is a meaningful physical quantity that reflects uniformity and will not exhibit invalid values ​​approaching 0, thus ensuring computational stability. Model validity depends on the initial experimental measurement conditions. Any fundamental change in measurement conditions (such as using a completely different visual task or measuring grid density) may affect the parameters. The value of is a common feature of all empirical models. The universality of this model lies in its applicability to similar lighting reading scenarios that conform to standard specifications, rather than being a universal formula.

[0048] 4. Reasons for not considering flicker and rich blue spectrum Flicker issue: The "illuminance" measured and used in this model refers to the time-averaged illuminance (static illuminance) obtained by integrating the illuminance meter. This value already includes the total energy of the light source within one fluctuation cycle, and therefore does not directly reflect the dynamic characteristics of flicker. The model evaluates the visual load under stable light intensity; flicker, as a time modulation factor, has its impact separated into other specialized studies.

[0049] Blue-rich spectroscopy issues: The influence of spectral power distribution, especially the proportion of short-wavelength blue light, is mainly reflected by parameters such as correlated color temperature (CCT) and spectral irradiance. This application focuses on the core variable of irradiance level and its spatial distribution, which is a decoupling strategy in the research.

[0050] In this embodiment, based on the received ambient illuminance... Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: At different illuminance Human factors experiments were conducted under specific lighting conditions to measure the human eye's accommodative amplitude. and contrast sensitivity The change in illuminance, and establish the illuminance A quantitative relationship model between the parameter and the change amount is used to obtain the numerical range of the parameter.

[0051] In this embodiment, the human eye's accommodation amplitude This is an indicator of the efficiency of human eye dynamic accommodation under the influence of illumination, not a traditional static indicator of ciliary muscle maximum contractile capacity determined solely by age; its calculation formula is:

[0052] in, Calculated by the Hofstetter empirical model, Illuminance correction factor (0 < ≤1, under low light conditions Reduce, under high illuminance Approaching 1), characterizing the neuromuscular dynamics effect of illumination on regulating reaction speed and focusing stability, is also the core measurement object of the experiment in this invention.

[0053] In this embodiment, MTF is the modulation transfer function of the human visual system, which is an objective physiological indicator describing the imaging quality of the human eye and the physical basis of the contrast sensitivity function (CSF). In this embodiment, the indicator is measured by a human eye MTF detection device. Its numerical change directly reflects the change in the contrast sensitivity of the human eye. Therefore, MTF is used to characterize the contrast sensitivity characteristics of the human eye and is a homologous evaluation indicator with CSF in this field. The measurement data can be converted to each other.

[0054] In this embodiment, the illuminance adjustment effect parameter Sensitivity effect parameter compared to illuminance The parameter range is as follows:

[0055] It is important to note that , The numerical range represents the statistical results (95% confidence interval) of human factors experiments on young adults aged 20-40 under corresponding illuminance. Due to the natural dispersion of physiological responses among different individuals under the same illuminance, this range is observed. The mathematical function relationship between illuminance I and the effect parameter, corresponding to the mean value within the range, is uniquely determined. Example values: For routine visual fatigue assessment, the mean value can be selected; for high-risk visual fatigue warning, the upper limit of the range can be selected; for simple and rapid assessment, the middle value of the range can be selected. The system can be customized according to user needs; this embodiment does not impose limitations on the selection mode.

[0056] In this embodiment, the core parameters of the model are used to establish the model. and The human factor experiment had the following subject sample composition: Sample size: Total N = 82 participants.

[0057] Age distribution: The subjects' ages were 28.2 ± 8.1 years (expressed as mean ± standard deviation). This indicates that the experimental sample was mainly concentrated in the youth to early adulthood stage.

[0058] Visual acuity screening criteria: The visual acuity of all subjects strictly complied with the relevant requirements of the Chinese National Standard GB / T44441-2024. Specifically, their refractive error (degree of myopia) was structured and distributed as follows: -1.00D ~ +0.50D (emmetropia to mild myopia) accounted for 35%; -3.00D ~ -1.00D (mild to moderate myopia) accounted for 30%; -5.00D ~ -3.00D (moderate myopia) accounted for 25%; and more than -5.00D (high myopia) accounted for 10%. This design aimed to cover the most common visual acuity range from emmetropia to moderate myopia and included a small number of high myopia samples to ensure the representativeness of the visual acuity distribution of the sample.

[0059] Based on the universality of the parameter range obtained from the above sample, the parameters of this model are derived from a sample of appropriate size (N=82) with a standardized structured distribution on the key variable (visual acuity). This sample can well represent young and middle-aged adults aged 20-40 years with visual acuity conforming to the aforementioned common distribution. Within this target group, the parameter range obtained through statistical methods reflects the central tendency and dispersion of visual effect parameters in this group, possessing statistical universality within this group and serving as a common reference benchmark for this population.

[0060] It is important to note that the wide range listed in the parameter table (e.g., 0.9~1.8) is not directly used in the final calculation. This range is only used to describe the possible fluctuation range of the effect value in the experimental population. In the actual system calculation, for a given ambient illuminance, the system will call the single best estimate (e.g., mean or fitted function value) of the effect parameter corresponding to the ambient illuminance, determined based on the experimental data fitting. For example, when the system outputs... =1.2 and When the value is 1.5, these two values ​​are the specific effect intensity estimates calculated by the model for the currently measured illuminance environment. The output value is a dimensionless relative index. Its core physical meaning is the amplification factor of the current environment's influence on the amplitude of accommodation (AMP) and modulation transfer function (MTF, reflecting contrast sensitivity) compared to the baseline level under a standard reference illuminance environment. The larger the value, the more the current environment tends to exacerbate visual load in this aspect, that is, the more likely it is to cause or aggravate visual fatigue.

[0061] Since the model parameters are derived from statistics of a specific sample group (young adults with standard vision), its output values ​​can indirectly reference the distribution of experimental data. For example, if a large amount of data shows that under generally comfortable lighting conditions, the index value is mostly concentrated in a small range around 1.0, while higher values ​​(e.g., >1.5) mostly occur under conditions where subjects report fatigue, then an empirical reference threshold can be established based on this. Users can compare the output value with 1.0; a value significantly greater than 1.0 indicates a risk of increased environmental load. When a relatively high assessment value is obtained (e.g., greater than 1.5), it indicates that the user's current illumination environment may be putting significant stress on the visual system. In this case, rational coping measures do not simply rely on the numerical value, but rather take action based on this indication, such as: increasing the frequency and duration of breaks during work, actively adjusting the brightness of the environment or screen to improve uniformity, and ensuring compliance with national lighting standards. The core value of the model lies in providing quantitative and comparable risk indications, rather than diagnosis. The model parameters are derived from a multi-sample (N=82) group experiment. During the experimental data processing phase, each participant's raw physiological data (AMP, MTF) were standardized—that is, divided by their baseline value under standard reference illuminance. This "ratio" method essentially eliminates some of the inherent absolute physiological differences between individuals, allowing the data to reflect the relative rate of change caused by environmental variations. This inherently takes into account, to some extent, the differences in individual sensitivity to light response.

[0062] This model aims to provide an objective assessment tool based on common population characteristics for the development of healthy lighting products, environmental assessments, and standard setting. It presents typical reaction trends that a representative population might exhibit under similar conditions. For a user who is extremely sensitive or insensitive to light, the model output may not accurately reflect their individual experience. This is an inherent characteristic of population statistical models. The fundamental solution to individual differences lies in developing personalized models, but this requires obtaining a large amount of baseline physiological data on users beforehand, which currently faces feasibility challenges in industrial applications. The practical strategy of this solution is to prioritize the establishment and promotion of common population models as a universal benchmark and screening tool. For individuals with special needs, the suggestion is to fine-tune this benchmark based on their subjective experiences.

[0063] It should be noted that the model only considers a single variable (illuminance), the handling of individual differences, and the potential decrease in assessment accuracy in specific populations and scenarios. This application takes the following into consideration: This application limits the core variable of the model to ambient illuminance (I), without considering other variables such as color temperature, color rendering index, light source flicker, spatial distribution, and visual task type. This is because the invention adopts a research paradigm of variable separation and control. The core task of this application is to establish and quantify the independent influence of ambient illuminance on specific visual load indicators (amplitude of accommodation (AMP) and modulation transfer function (MTF), avoiding the problem of overly complex models and difficulty in identifying key relationships due to including too many variables.

[0064] 2. Approaches to handling individual differences Significant individual differences exist in the physiological responses of the human eye. This invention addresses this by addressing the following: Modeling approach based on population statistics: Parameters of this invention and It does not originate from individualized fitting, but rather from the population average trend obtained by conducting experiments on a designed population sample and statistically analyzing its data.

[0065] Sample Design and Statistical Stability: The human factors experiment selected N=82 participants, aged 28.2 ± 8.1 years, whose visual acuity was structured according to the requirements of standard GB / T 44441-2024 (emmetropia to moderate myopia). Although this sample did not cover all age groups (e.g., the elderly) or all races, it provided a statistically diverse basis within the target group (young adults). Individual differences were treated as random variation in the sample, and the fixed parameters obtained through the law of large numbers and statistical averaging reflected the systematic regularity after removing individual random noise.

[0066] Trade-offs in industrial practicality: In engineering applications within the healthy lighting industry, pursuing a completely personalized physiological parameter model for each user is neither feasible nor economical at this stage. A standardized model based on representative samples and reflecting common patterns, while unable to accurately predict the absolute response value of each individual, can provide a stable and reliable benchmark and comparison tool for product design, lighting standard setting, and general evaluation. This is the core manifestation of its strong industrial practicality.

[0067] 3. Accuracy of assessment for specific populations and complex scenarios Regarding the potential decrease in model accuracy for older adults or in complex mixed lighting scenarios, this needs to be understood in conjunction with the model's specific target audience: the parameters in this application are derived from a sample within a specific age range (28.2 ± 8.1 years), and do not consider older adults or complex mixed lighting scenarios. Therefore, directly applying this model to scenarios outside these clearly defined ranges will theoretically lead to a decrease in assessment accuracy. The reason for the decrease in accuracy is that the visual system of older adults (such as lens transmittance and pupillary reflex ability) differs physiologically from that of younger adults, while complex mixed lighting scenarios involve multiple light sources and non-uniform spatial distribution, introducing new variables that the model has not learned or included. The solution proposed in this application is to conduct independent, specialized human factors experiments and modeling for these specific scenarios. In other words, coverage of the entire population and all scenarios is achieved through a family of models consisting of multiple models focusing on different variables or populations, rather than relying on a single all-encompassing model.

[0068] In this embodiment, the system further includes a result transmission module; this module is used to transmit the calculated results. and The results are stored.

[0069] The experimental design and acquisition process for obtaining the above parameters are as follows: Subjects (N=82, age 28.2±8.1 years) underwent human factors experiments under different illuminance levels. All experiments were conducted in the same indoor environment, using the same type of lighting fixtures with a color temperature of 4500±250 K, a color rendering index (CRI) > 90, and a spectral power distribution (SPD) as shown in the figure. Figure 3 As shown.

[0070] All human factors experiments were conducted between 7:00 PM and 9:00 PM. To avoid interference from other light sources, all doors and windows were covered with thick curtains during the experiments, and participants were prohibited from using mobile phones or other electronic display devices. Each participant underwent the human factors experiment for 45 minutes each day at a single illuminance level. Experiments under different light environments were scheduled on different days to avoid the residual effects of previous light environments.

[0071] The process of human factor experiments is as follows Figure 4As shown. Before the experiment, participants relaxed their eyes for 20 minutes, then their accommodative amplitude (AMP) and contrast sensitivity (MTF) were measured. After the measurements, participants underwent a 45-minute visual task consisting of counting Landau rings on paper. All Landau rings were identical in size. After 45 minutes, the participants' accommodative amplitude (AMP) and contrast sensitivity (MTF) were measured again. The changes in AMP and MTF during the visual task (ΔAMP and ΔMTF) were obtained by subtracting the values ​​before the visual task from the values ​​after the visual task.

[0072] For each participant, a baseline experiment is conducted first, followed by the formal experiment. The procedures for the baseline experiment and the formal experiment are exactly the same (both are...). Figure 4 As shown in the diagram, the lighting fixtures are exactly the same, the only difference being the illuminance at the center of the table during the experiment. Different illuminance levels are achieved by adjusting the voltage of the lighting fixtures.

[0073] (1) Baseline experiment Subjects underwent human factors experiments under baseline light conditions, and the experimental procedure was as follows: Figure 4 As shown. The baseline lighting environment was: 500 lux at the center of the desktop. The physiological changes in the participants' eyes before and after the visual task were denoted as ΔAMP0 and ΔMTF0.

[0074] (2) Formal Experiment Participants underwent a human factors experiment under lighting conditions with a central illuminance of approximately 200 lux, 300 lux, 400 lux, 500 lux, 600 lux, 700 lux, and 800 lux. The physiological changes in the participants' eyes before and after the visual task were recorded as ΔAMP and ΔMTF. Therefore, ΔAMP / ΔAMP0 and ΔMTF / ΔMTF0 represent the illuminance visual effects of this lighting environment on the participants' AMP and MTF, respectively.

[0075] According to different lighting environments / and / The value can be used to fit the illuminance adjustment effect parameter F in the invention. L,AMP (I) Sensitivity effect parameter F compared to illuminance L,MTF The value of (I). For example, setting a standard reference illuminance. =500 lux, the baseline value of the human eye's dynamic accommodation amplitude was measured under this illuminance. The baseline value of MTF in the human visual system (These are the basic calibration values ​​for the experiments described in the instruction manual). Among them, = AMP measured after baseline task - AMP measured before baseline task = Measured MTF after baseline task - Measured MTF before baseline task, used as the baseline change for subsequent effect comparison. Participants underwent human factors experiments in lighting environments with center illuminance of approximately 200 lux, 300 lux, 400 lux, 500 lux, 600 lux, 700 lux, and 800 lux. The physiological changes in the eyes before and after the visual task were recorded as ΔAMP and ΔMTF (ΔAMP = Measured AMP after target illuminance task - Measured AMP before target illuminance task, ΔMTF = Measured MTF after target illuminance task - Measured MTF before target illuminance task). Therefore, ΔAMP / ΔAMP0 and ΔMTF / ΔMTF0 represent the illuminance visual effects of this lighting environment on the participants' AMP and MTF, respectively. Based on the ΔAMP / ΔAMP0 and ΔMTF / ΔMTF0 values ​​under different lighting environments, the illuminance modulation effect parameters in the invention can be fitted. Sensitivity effect parameter compared to illuminance The value.

[0076] In this invention, the illuminance adjustment effect parameter Illuminance contrast sensitivity effect parameter The ratio of the fitted core variables to the changes in physiological indicators obtained from the visual task in the experiment. / and / Both are physically equivalent normalized values, and both can be used as the basis for fitting. Furthermore, based on experimental data from 82 subjects, there was no significant difference in the fitting results between the two methods. This invention adopts... / and / As the basis for actual fitting, the corresponding fitting formula is as follows (taking the linear fitting formula as an example):

[0077] Where a, b, c, and d are fixed coefficients obtained by fitting experimental data from 82 young and middle-aged subjects.

[0078] Example 2 Please see Figure 2 A method for analyzing the visual effect of illuminance connected to a lux meter, the method comprising: S1, receiving the illuminance measured by the illuminance meter. ; S2, Received Illuminance Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance The illuminance adjustment effect was calculated. Contrast sensitivity effect with illuminance ;in, Based on illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance Calculate the illuminance adjustment effect Contrast sensitivity effect with illuminance The calculation formula is as follows:

[0079]

[0080] In the formula, The number of points used to measure illuminance on the desktop; For the desktop Illuminance at a point, The minimum illuminance among the points on the desktop where illuminance is measured.

[0081] In this embodiment, based on the received ambient illuminance... Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: At different illuminance Human factors experiments were conducted under specific lighting conditions to measure the human eye's accommodative amplitude. and contrast sensitivity The change in illuminance, and establish the illuminance A quantitative relationship model between the parameter and the change amount is used to obtain the numerical range of the parameter.

[0082] In this embodiment, the illuminance adjustment effect parameter Sensitivity effect parameter compared to illuminance The parameter range is as follows:

[0083] In this embodiment, the method further includes: S3, calculate the results and The results are stored.

[0084] Based on the method and system proposed in this invention, a one-year test was conducted on two groups of users, as detailed below: (1) A user aged 30 was asked to perform a 45-minute paper reading task under three different lighting environments. The accommodative amplitude of the user's eyes was measured under the three lighting environments. and contrast sensitivity The change in illuminance; in addition, the changes in illuminance under these three lighting environments were calculated respectively. and Numerical values; the results indicate the range of human eye accommodation under different lighting conditions. and contrast sensitivity The change in the amount of light, and the corresponding ambient illuminance. and The values ​​show a positive correlation; the values ​​calculated by the user using the system of this invention... and When using lamps with smaller numerical values ​​for visual tasks, the degree of myopia progression decreased by 0.50 D after one year compared to the previous year.

[0085] (2) A 24-year-old user was asked to perform a 45-minute paper reading task under three different lighting environments. The accommodative amplitude of the user's eyes was measured under the three lighting environments. and contrast sensitivity The change in illuminance; in addition, the illuminance of these three lighting environments was calculated separately. and Numerical values; the results indicate the range of human eye accommodation under different lighting conditions. and contrast sensitivity The change in the amount of light fixtures and The numerical patterns are consistent; the calculations performed by this user using the system of this invention... and When using lamps with smaller numerical values ​​for visual tasks, the degree of myopia progression decreased by 0.50 D after one year compared to the previous year.

[0086] It should be noted that for other points not fully explained, please refer to one of the embodiments, which will not be repeated here.

[0087] By adopting the above technical solution, the present invention has the following advantages compared with the prior art: This invention provides an illuminance visual effect analysis system and method connected to a lux meter. This system enables users to simultaneously calculate the physiological effects of ambient illuminance while measuring it, assessing the risk of eye strain caused by the ambient illuminance and allowing users to adjust their eye use accordingly. This invention is applicable to lighting-related fields and photometric measurement fields, demonstrating high practicality.

[0088] Example 3 An illuminance visual effect analysis device connected to a lux meter, comprising: At least one processor and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in one of the embodiments.

[0089] In this embodiment, to better run and process the method described in one of the embodiments, the above method is stored in a memory, and the stored method is executed using a processor. It should be noted that the principle and effect of each step have been described above and will not be elaborated further here.

[0090] Example 4 A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in one of the embodiments.

[0091] In this embodiment, to better operate and use the method described in one of the embodiments, the above method is stored in a computer-readable storage medium, and the above method is implemented using a processor. It should be noted that the principle and effect of each step have been described above and will not be elaborated further here.

[0092] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An illuminance visual effect analysis system connected to a lux meter, characterized in that, The system includes: The ambient illuminance data transmission module receives illuminance measured by the connected illuminance meter. ; Illuminance Human Eye Physiological Effect Calculation Module: Used to receive the illuminance transmitted by the lighting environment illuminance data transmission module. Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance The illuminance adjustment effect was calculated. Contrast sensitivity effect with illuminance ;in, Based on illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance Calculate the illuminance adjustment effect Contrast sensitivity effect with illuminance The calculation formula is as follows: In the formula, The number of points used to measure illuminance on the desktop; For the desktop Illuminance at a point, The minimum illuminance among the points on the desktop where illuminance is measured.

2. The illuminance visual effect analysis system connected to a lux meter as described in claim 1, characterized in that, Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: At different illuminance Human factors experiments were conducted under specific lighting conditions to measure the human eye's accommodative amplitude. and contrast sensitivity The change in illuminance, and establish the illuminance A quantitative relationship model between the parameter and the change amount is used to obtain the numerical range of the parameter.

3. The illuminance visual effect analysis system connected to a lux meter as described in claim 2, characterized in that, The illuminance adjustment effect parameter Sensitivity effect parameter compared to illuminance The parameter range is as follows: 。 4. The illuminance visual effect analysis system connected to a lux meter as described in claim 1, characterized in that, The system also includes a result transmission module; This module is used to process the calculated results. and The results are stored.

5. A method for analyzing the visual effect of illuminance connected to a lux meter, characterized in that, The method includes: Illuminance measured by the lux meter ; Received illuminance Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance The illuminance adjustment effect was calculated. Contrast sensitivity effect with illuminance ;in, Based on illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance Calculate the illuminance adjustment effect Contrast sensitivity effect with illuminance The calculation formula is as follows: In the formula, The number of points used to measure illuminance on the desktop; For the desktop Illuminance at a point, The minimum illuminance among the points on the desktop where illuminance is measured.

6. The illuminance visual effect analysis method connected to an illuminance meter as described in claim 5, characterized in that, Based on the received ambient illuminance Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: At different illuminance Human factors experiments were conducted under specific lighting conditions to measure the human eye's accommodative amplitude. and contrast sensitivity The change in illuminance, and establish the illuminance A quantitative relationship model between the parameter and the change amount is used to obtain the numerical range of the parameter.

7. The illuminance visual effect analysis method connected to an illuminance meter as described in claim 6, characterized in that, The illuminance adjustment effect parameter Sensitivity effect parameter compared to illuminance The parameter range is as follows: 。 8. The illuminance visual effect analysis method connected to an illuminance meter as described in claim 5, characterized in that, The method further includes calculating the... and The results are stored.

9. An illuminance visual effect analysis device connected to a lux meter, characterized in that, include: At least one processor and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 5 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 5 to 6.

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