Screen brightness analysis system and method connected with imaging brightness meter and illuminometer

Abstract

The invention discloses a screen brightness analysis system and method connected with an imaging brightness meter and an illuminometer. The system comprises a screen brightness data transmission module for receiving screen brightness measured by an imaging brightness meter; the illumination environment illumination data transmission module is used for receiving the illumination environment illumination measured by the illuminometer; and the screen brightness human eye physiological effect parameter calculation module is used for receiving the screen brightness transmitted by the screen brightness data transmission module and the illumination environment illumination transmitted by the illumination environment illumination data transmission module, and calculating the human eye physiological effect parameter according to the received screen brightness, illumination environment illumination and the like. According to the invention, when the user uses the imaging brightness meter to measure the screen brightness and uses the illuminometer to measure the illumination environment illumination, the physiological effect of the screen brightness can be synchronously calculated, and the risk of asthenopia caused by the screen brightness can be evaluated, so that the user can adjust the eyes in time.

Description

Technical Field

[0001] This invention relates to the field of screen brightness technology, and in particular to a screen brightness analysis system and method connected to an imaging luminance meter and an illuminance meter. Background Technology

[0002] When the human eye performs visual tasks in lighting environments with different photometric properties, it exhibits different physiological responses and accumulates varying degrees of visual fatigue. Although existing studies have confirmed that different photometric parameters have differential effects on ocular physiological indicators, a quantitative correlation between these parameters and specific ocular physiological responses has not yet been established.

[0003] With the widespread use of smartphones, people's work and lifestyles have undergone tremendous changes. At the same time, prolonged viewing of mobile phone screens poses risks to visual health. Screen brightness is a key parameter characterizing the properties of the display lighting environment, and different screen brightness levels have different effects on visual health. Furthermore, the lighting environment in which the eyes view a display screen is also crucial. Studies have shown that the optimal screen brightness for the human eye varies under different lighting conditions. Although researchers recognize the importance of matching screen brightness with ambient lighting for visual health, a systematic quantitative analysis of the impact of screen brightness on the human eye under different lighting conditions is still lacking.

[0004] Therefore, it is necessary to construct a computational model that can quantitatively assess the physiological effects of screen brightness on the human eye under specific lighting conditions. This model should be designed based on the response characteristics of the human eye to different screen brightness under different lighting conditions, and further develop a dedicated device that can apply this model to actually calculate the physiological effects of screen brightness. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a screen brightness analysis system and method connected to an imaging luminance meter and an illuminance meter, which enables users to simultaneously calculate the physiological effects of screen brightness and assess the risk of eye strain caused by such screen brightness while measuring screen brightness with an imaging luminance meter and measuring ambient illuminance with an illuminance meter, so that users can adjust their eye use in a timely manner.

[0006] According to one aspect of the present invention, a screen brightness analysis system connected to an imaging luminance meter and an illuminance meter is provided, the system comprising: The screen brightness data transmission module receives the screen brightness measured by the imaging luminance meter. ; The ambient illuminance data transmission module receives the ambient illuminance measured by the illuminance meter. ; The screen brightness human eye physiological effect parameter calculation module is used to receive the screen brightness transmitted by the screen brightness data transmission module. And the ambient illuminance transmitted by the ambient illuminance data transmission module Based on the received screen brightness and ambient illuminance Obtain screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. and screen brightness contrast sensitivity effect ;in, Based on the obtained screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. Contrast sensitivity effect with screen brightness The formula is shown below:

[0007]

[0008] In the formula, The number of points used to measure brightness on the display screen; For the first on the display screen The brightness of each point The minimum brightness among the points on the display screen where brightness is measured.

[0009] The above technical solution proposes a screen brightness analysis system integrating an imaging luminance meter and an illuminance meter. The aim is to establish a correlation model between screen brightness and ambient illuminance on the physiological effects on the human eye by quantifying specific photometric parameters. The core of this system lies in transforming physical photometric measurements into physiologically meaningful visual effect indicators, providing a methodological basis for objectively assessing the risk of visual fatigue.

[0010] The system mainly consists of three functional modules: the screen brightness data transmission module is responsible for acquiring the screen brightness distribution measured by the imaging brightness meter. The ambient illuminance data transmission module synchronously acquires the ambient illuminance measured by the illuminance meter. The module for calculating the physiological effects of screen brightness on the human eye is responsible for receiving and processing the above data. This module calculates two core indicators—screen brightness adjustment effect—using a pre-set algorithm model. Contrast sensitivity effect with screen brightness This calculation formula not only considers the average brightness of N sampling points on the screen to reflect the overall illumination level, but also introduces a minimum brightness value. The reciprocal of this design aims to compensate for the potential visual load caused by dark areas or poor brightness uniformity on the display, thereby more comprehensively depicting the integrated visual stimulation under complex brightness distribution.

[0011] In summary, the core advantage of this approach lies in its ability to bridge the gap between physical measurement and physiological effect assessment. By simultaneously introducing two variables—screen brightness distribution and ambient illuminance—and defining specific quantitative calculation formulas, this approach lays the technical framework for systematically studying screen visual effects.

[0012] In some embodiments, based on the received ambient illuminance. Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: Different screen brightness and ambient illuminance Human factors experiments were conducted in an environment that measured the amplitude of human eye accommodation. 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 above technical solution aims to establish a quantitative relationship between ambient illuminance and key visual function indicators of the human eye. Its core lies in providing empirical data support and clear numerical ranges for the illuminance adjustment effect parameter and the illuminance contrast sensitivity effect parameter through controlled human factor experiments.

[0014] The specific implementation path of the plan relies on a rigorous human factors experimental design. First, it is necessary to systematically manipulate the two independent variables, screen brightness and ambient illuminance, to simulate complex visual scenarios in the real world. Based on this, dependent variable data are collected through direct physiological measurements of the subjects' eye accommodation amplitude and contrast sensitivity changes. Finally, using data analysis methods, an independent quantitative relationship model from ambient illuminance to the aforementioned physiological changes is established, thereby fitting a functional relationship and determining the numerical range of the illuminance accommodation effect parameter and the illuminance contrast sensitivity effect parameter. This design approach isolates and extracts the influence of a single illuminance factor from the experimental environment of "multiple factors interacting."

[0015] In summary, this approach, through empirical research, provides crucial data foundations and parameter calibration methods for the theoretical model, enhancing the scientific rigor and credibility of the entire evaluation system.

[0016] In some embodiments, the screen brightness adjustment effect parameter is taken. Contrast sensitivity effect parameter with screen brightness The parameter range is as follows:

[0017] The above technical solution provides specific numerical ranges for two physiological effect parameters. The construction of this parameter table provides a direct criterion for assessing the visual health risks of screen brightness under specific lighting conditions. The parameter ranges exhibit typical nonlinear and interactive characteristics. Under a fixed ambient illuminance (I=100 lux), The parameter range does not change with screen brightness ( The monotonous change in brightness as screen brightness increases reflects the complexity of human eye physiological responses. The same screen brightness exhibits significantly different parameter ranges under different ambient illuminance levels (e.g., B=250 cd / m², I=300 lux vs. I=900 lux), validating the core principle that "screen brightness must be evaluated in conjunction with ambient illuminance" and demonstrating the rationality of the selection of the aforementioned two physiological effect parameters.

[0018] 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.

[0019] In the above technical solution, the introduced result transmission module serves as the terminal link of the entire screen brightness analysis system, ensuring the integrity, traceability, and subsequent analysis and application of system data.

[0020] According to another aspect of the present invention, a screen brightness analysis method connected to an imaging luminance meter and an illuminance meter is provided, the method comprising: Receive screen brightness measured by imaging luminance meter ; The lighting environment illuminance data transmission module receives the lighting environment illuminance I measured by the illuminance meter; The screen brightness human eye physiological effect parameter calculation module is used to receive the screen brightness transmitted by the screen brightness data transmission module. And the ambient illuminance transmitted by the ambient illuminance data transmission module Based on the received screen brightness and ambient illuminance Obtain screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. and screen brightness contrast sensitivity effect ;in, Based on the obtained screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. Contrast sensitivity effect with screen brightness The formula is shown below:

[0021]

[0022] In the formula, The number of points used to measure brightness on the display screen; For the first on the display screen The brightness of each point The minimum brightness among the points on the display screen where brightness is measured.

[0023] In the above technical solution, to better utilize the system, this application proposes a screen brightness analysis method connected to an imaging luminance meter and 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.

[0024] According to another aspect of the present invention, a screen brightness analysis device connected to an imaging luminance meter and an illuminance 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.

[0025] 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.

[0026] 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.

[0027] 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

[0028] 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.

[0029] Figure 1 This is a schematic diagram of an embodiment of a screen brightness analysis system connected to an imaging luminance meter and an illuminance meter according to the present invention. Figure 2 This is a schematic flowchart of an embodiment of a screen brightness analysis method connected to an imaging luminance meter and 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 and a display screen in an embodiment of the screen brightness analysis method connected to an imaging luminance meter and an illuminance meter according to the present invention; in the figure, (A) is the spectral power distribution (SPD) of the lighting fixture; (B) is the spectral power distribution (SPD) of the display screen; 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

[0030] 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.

[0031] Example 1 Please see Figure 1 A screen brightness analysis system connected to an imaging luminance meter and an illuminance meter, the system comprising: The screen brightness data transmission module receives the screen brightness measured by the imaging luminance meter. ; The ambient illuminance data transmission module receives the ambient illuminance measured by the illuminance meter. ; The screen brightness human eye physiological effect parameter calculation module is used to receive the screen brightness transmitted by the screen brightness data transmission module. And the ambient illuminance transmitted by the ambient illuminance data transmission module Based on the received screen brightness and ambient illuminance Obtain screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. and screen brightness contrast sensitivity effect ;in, Based on the obtained screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. Contrast sensitivity effect with screen brightness The formula is shown below:

[0032]

[0033] In the formula, The number of points used to measure brightness on the display screen; For the first on the display screen The brightness of each point The minimum brightness among the points on the display screen where brightness is measured.

[0034] In this embodiment, the external luminance meter used is a KONICA MINOLTA LS-160 luminance meter; the external illuminance meter is a SPIC-500AW spectral color illuminance meter. Regarding the issues of standardized measurement point layout, environmental stability requirements, and how to overcome the uncertainties of human operation to ensure the repeatability of results, the following example is provided, combining experimental design and model application logic.

[0035] 1. Spatial layout and operating procedures of measurement points To ensure the consistency and comparability of brightness measurement results, this scheme requires the following spatial layout of the screen measurement points: N points are measured on the display screen, and these points are evenly distributed in a grid pattern. The grid spacing is 1 cm, which is sufficient to capture the brightness distribution characteristics of common displays. The center point of the screen must be included as one of the measurement points, as it usually coincides with the user's primary viewing area. This measurement method conforms to the general practice of measuring the photoelectric performance of displays, and its principle is consistent with the uniformity test requirements in standard SJ / T 11281-2025.

[0036] 2. Environmental and task control in human factors experiments Generate model core parameters and Human-caused experiments were conducted under controlled conditions: Environmental stability: The experiment was conducted in a lighting environment that meets the requirements of lighting standards such as T / SZSA 029 to ensure the stability and uniformity of ambient illuminance and avoid interference with the measurement results caused by fluctuations in ambient light.

[0037] Task and Duration: The experiment was designed according to standard GB / T 44441-2024. Each visual task lasted 45 minutes and consisted of standard Landolt C counting and color recognition. This long-duration, standardized cognitive-visual task was designed to induce a stable and measurable visual load state.

[0038] Participant diversity: The experiment employed a multi-sample (N=88) design, with participants exhibiting a certain distribution range in age and refractive error (as previously mentioned). Although individual transient responses varied during the experiment, the long-duration task integrated transient fluctuations, and the multi-sample data averaged individual differences.

[0039] 3. Mechanisms for ensuring the repeatability and comparability of results The core of ensuring the reproducibility and comparability of results lies in the dual mechanisms of "standardized operation" and "statistical averaging": Standardized Operations: By using the spatial layout of the measurement points (1 cm grid, including the center point), referencing a stable lighting standard environment, and performing standardized visual tasks and durations, the input conditions for data acquisition are unified to the greatest extent possible. This provides clear operating procedures (SOPs) for different users or the same user operating at different times, and is the technical foundation for ensuring the comparability of results.

[0040] Statistical stability of the results: parameters the model ultimately depends on and It is based on the mean or fitted relationship obtained through statistical analysis of multi-sample, multi-round experimental data. Minor operational deviations and instantaneous fluctuations in physiological state during a single measurement are considered random noise. In large-sample statistics, these noises cancel each other out, thus making the obtained statistical relationship stable and robust. Therefore, the model outputs a trend prediction at the population level, rather than a precise diagnosis of an individual's instantaneous state, which precisely reduces its vulnerability to extreme sensitivity to single operations.

[0041] 4. Pathways to overcome the uncertainties of human factors in order to achieve automation This solution, designed as a tool for automation, reduces the uncertainty of human factors through the following design: Process solidification: The standardized operating procedures (gridized measurement points, fixed tasks, standard environment) mentioned above are solidified into the system's hardware and software design and operation guidelines. For example, supporting software can be developed to guide users in completing screen calibration point measurements, or the use of a standard-compliant ambient light sensor can be recommended.

[0042] Robust handling of input parameters: The model's key inputs—average brightness and minimum brightness—are derived from statistical calculations of multiple measurement points. This multi-point averaging itself smooths out the extreme value effects that might arise from slight human placement errors at a single measurement point.

[0043] Application Scope Statement: The system output is applicable to lighting environments and displays that conform to the aforementioned standards. For environments that clearly do not meet the standards (such as extreme low light or strong, uneven lighting), the system may provide a warning that the results are unreliable. This avoids result distortion caused by non-standard operating environments at the application boundary.

[0044] It is important to note that the core formula of this model is: ,in This represents the mean of visual effects (such as the adjustment hysteresis AMP or modulation transfer function MTF attenuation). This represents the average brightness of the screen. This represents the minimum screen brightness. Regarding the mathematical rationality, physiological mechanism correspondence, and formula consistency of this model, the following explanation is provided based on the model's design logic and applicable scope: 1. Regarding the formula Mathematical and physical interpretation of the term In photometry, the uniformity of screen display quality is a key parameter, defined as follows: or In the formula The item is essentially equivalent to This is the reciprocal of screen brightness uniformity. Therefore, the physical meaning of this formula can be expressed as: visual load effect ≈ mean effect × (1 / screen brightness uniformity). This indicates that the model explicitly uses the important physical characteristic of the uniformity of the spatial distribution of screen brightness as a basis for amplifying or reducing the visual effect. The key factor is calculated by measuring the brightness of N points. and This is precisely to quantify these spatial distribution characteristics. The design is supported by clear photometric standards (such as SJ / T 11281-2025) and is not an arbitrary mathematical construct.

[0045] 2. Explanation regarding extreme values ​​and model stability To address this, this model strictly limits its scope of application: it is specifically designed to evaluate the lighting environment of conventional displays that meet lighting standards such as T / SZSA029. These standards specify clear lower limits for screen brightness, contrast ratio, and ambient illuminance, essentially excluding scenarios with extremely dark pixels (brightness approaching zero) or severe measurement noise. Within this standardized scope, It is a meaningful physical quantity that reflects the inherent uniformity of the screen, rather than abnormal noise points, thus ensuring the stability of the model output and its industrial applicability.

[0046] 3. Theoretical basis of the model The current model is not based on a mechanistic model directly derived from underlying visual physiological mechanisms (such as neural signal processing). It does not precisely describe the entire pathway from retinal imaging to brain perception. Instead, it is an empirical statistical model based on human factors experimental data. Its logic is: by controlling experiments, it discovers visual load indicators (…). There is a stable statistical correlation between the input parameters and measurable physical quantities (mean brightness, uniformity), and this correlation can be fitted using mathematical formulas (multiplicative models). Its advantages lie in the ease of measuring input parameters, the intuitiveness of the output, and its suitability for engineering applications; its limitations are that its extrapolation interpretation ability is constrained by the range of the original experimental data. Its value lies in providing a repeatable and computable statistical bridge from easily measurable physical parameters to subjective visual load.

[0047] 4. The rationale for using the same mathematical form for accommodation (screen) and pupillary response (environment). Although the physiological pathways affected by screen brightness (primarily driving accommodation) and ambient illuminance (primarily driving pupillary response) are indeed different, this model does not model isolated basic physiological parameters (such as ciliary muscle contraction or pupil diameter), but rather the comprehensive visual load or discomfort experienced by the terminal. In actual visual tasks, these two physiological responses work together, ultimately converging into a unified visual experience (such as fatigue or blurriness). The formula structure of this model can be understood as: Comprehensive visual load = f(accommodative system load contribution, pupillary adaptation load contribution). The multiplicative form in the formula reflects the potential nonlinear synergistic effect between the two factors, a simplified modeling method for high-dimensional relationships in engineering psychology. Its effectiveness is ultimately verified by the goodness of fit and predictive ability of human factors experimental data.

[0048] 5. Explanation of flicker and spectral (blue light) factors The brightness used in the model of this invention refers to the time-averaged brightness (static brightness) obtained by integrating the measurements with a luminance meter, which already includes the energy integral within the possible flicker period of the light source. Therefore, the model itself does not directly process dynamic flicker parameters; its input is a stable brightness value. The influence of spectral energy distribution, especially the proportion of blue light, is mainly reflected through the parameter correlated color temperature (CCT). This factor has a significant impact on visual fatigue and physiological rhythms. This invention focuses on studying the core interaction effect between brightness and illuminance, separating the study of spectrally related variables such as color temperature; this is an analytical decoupling strategy.

[0049] This model is a practical engineering model based on the correlation between photometric parameters (mean luminance, uniformity) and statistics. Its formula design has a clear photometric interpretation (reciprocal of uniformity), and its stability is ensured by limiting the application scenarios. It is explicitly stated that it is a statistical model rather than a physiological mechanism model. It aggregates complex, multi-pathway visual physiological responses into a comprehensive load index that can be predicted through simple physical measurements. Its core value lies in meeting the urgent need of the healthy lighting industry for operable, measurable, and standardized evaluation tools.

[0050] In this embodiment, based on the received ambient illuminance... Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: Different screen brightness and ambient illuminance Human factors experiments were conducted in an environment that measured the amplitude of human eye accommodation. 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 step of taking screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The parameter range is as follows:

[0052] It is important to note that the core of this approach relies on a set of fixed parameters established through human factors experiments. and ,in Represents screen brightness. This represents ambient illuminance. However, the physiological response of the human eye exhibits significant individual variability; factors such as age and visual acuity can lead to response variations. Furthermore, the current model only considers illuminance as a single factor, neglecting key variables that synergistically influence visual load, including color temperature, color rendering index, light source flicker, spatial distribution, and visual task type (such as reading or screen work). To address the reasons for not considering other factors and how to overcome individual variability, this proposed solution considers the following: Human factors experiments selected The participants, aged [number], were [age]. The subject is [age] years old and their visual acuity meets the requirements of standard GB / T44441-2024. Specifically, the refractive error distribution is as follows: 35% of the subjects' refractive errors fall within [range]. arrive Between, 30% arrive Between, 25% arrive Between, more than 10% This sample design aims to cover a certain range of visual acuity, focusing on the youth population to establish a parameter basis under controlled conditions. This application only considers the combined effects of screen brightness and ambient illuminance, omitting other variables such as color temperature, color rendering index, light source flicker, spatial distribution, and visual task type. This is because this application focuses on modeling the two core variables of brightness and illuminance to simplify the analytical framework and highlight their independent roles, avoiding the complexity brought about by the synergistic effects of multiple variables, thus ensuring the clarity and operability of the model. The results of this application are based on statistical analysis of multiple sample groups, without separate calibration for individual differences (such as age, visual acuity, etc.). This is because individual physiological response differences are difficult to fully control and accurately quantify in industrial applications. In the field of healthy lighting, universality and practicality are the main goals; therefore, population statistical results are used to represent general trends to improve the industrial applicability and generalizability of the model. The visual acuity distribution design of the experimental sample has covered the common range of variations to a certain extent, but individual specificity has not been included in the model, based on considerations of standardization and efficiency in practical applications. The research boundary of this application is limited to the basic brightness-illuminance interaction model to maintain focus. By standardizing human factor experimental samples to control key variables, and using statistical methods to derive fixed parameters from population data, a balance between representativeness and practicality is struck. Simultaneously, other photometric parameters and complex scenarios are removed to ensure the simplicity and industrial applicability of the model, thus constructing a more comprehensive visual load assessment framework overall, rather than including all variables in a single model. While individual differences are not directly overcome, population averaging provides an operational benchmark for healthy lighting applications.

[0053] Further explanation regarding the statistical basis for parameter ranges and the method for continuous operating point determination.

[0054] 1. Statistical meaning of parameter range The parameter range given in the table (e.g., 0.8~1.4) has its lower and upper boundaries corresponding to the minimum and maximum values ​​that may occur in the experimental observation data, respectively. This range is statistically known as the range, which describes the extreme width of the variation in the observed data. It is important to clarify that this range is not based on a confidence interval calculated from the standard error. Confidence intervals are typically used to infer the possible range of population parameters (such as the mean) and include a confidence level (e.g., 95%). The range provided in this solution is a direct description of the dispersion of the sample data itself. This presentation method is chosen because, in industrial applications, the range most intuitively shows users the extreme boundaries of the potential fluctuations of the effect parameter, facilitating risk assessment and design margin considerations. This range is directly derived from the experimental measurement results of the N=88 sample, reflecting the variation observed under these specific controlled experimental conditions.

[0055] 2. Continuous Solution for Discrete Operating Points The parameter table only covers 15 discrete (B, I) operating points, which is insufficient to cover the two-dimensional continuous space consisting of screen brightness (B) and ambient illuminance (I). For any intermediate operating condition not defined in the table (e.g., (180 cd / m², 400 lux)), the system's solution is to construct a continuous prediction function based on the known discrete point values ​​using mathematical interpolation and surface fitting techniques. Specifically: Data basis: The 15 discrete operating point values ​​in the table and their corresponding effect parameter values ​​are regarded as sparse sampling of an unknown continuous function in two-dimensional space.

[0056] Model construction: Using appropriate surface fitting or spatial interpolation algorithms (e.g., bivariate polynomial regression, thin plate spline interpolation, kriging interpolation, etc.), and constrained by these discrete sampling points, construct a smooth, continuous function F_fit(B, I) defined over the entire or most of the effective domain of (B, I).

[0057] Predictive application: For any new working condition (B_new, I_new) that is not in the table, the system will no longer "fail", but will obtain its predicted value by calculating F_fit(B_new, I_new).

[0058] This method is widely used in engineering and scientific computing, and its effectiveness depends on two key premises: Representativeness and rationality of sampling points: The 15 discrete points should have a reasonable distribution in the (B, I) space (such as common screen brightness of 100-500 cd / m², ambient illuminance of 100-1000 lux) and be able to capture the main trend of the function.

[0059] Appropriateness of the fitting model: The form of the selected fitting function should reasonably reflect the physical laws of the changes in visual load parameters with B and I (e.g., usually a monotonic, smooth nonlinear relationship).

[0060] In this embodiment, the system further includes a result transmission module; This module is used to process the calculated results. and The results are stored.

[0061] The experimental design and acquisition process for obtaining the above parameters are as follows: Participants (N=88, age 26.2±6.4 years) underwent human factors experiments under different combinations of lighting intensity and screen brightness. All experiments were conducted in the same indoor environment, and the lighting fixtures and displays were of the same model. The lighting fixtures had 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 in (A); the display screen has a color temperature of 7300±250 K, and the spectral power distribution of the SPD is as follows. Figure 3 As shown in (B).

[0062] 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 and display screen are identical, differing only in the center illuminance of the desktop and the brightness of the center of the display screen during the experiment. Different lighting illuminances are achieved by adjusting the lamp voltage, and different screen brightnesses are achieved by adjusting the display screen settings.

[0063] (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: approximately 500 lux at the center of the desktop and 200 cd / m² at the center of the display screen. The physiological changes in the participants' eyes before and after the visual task were recorded as ΔAMP0 and ΔMTF0.

[0064] (2) Formal Experiment The subjects were placed under different lighting conditions: desktop center illuminance of 100 lux and screen brightness of 50 cd / m², desktop center illuminance of 200 lux and screen brightness of 100 cd / m², desktop center illuminance of 100 lux and screen brightness of 150 cd / m², desktop center illuminance of 300 lux and screen brightness of 150 cd / m², desktop center illuminance of 300 lux and screen brightness of 200 cd / m², desktop center illuminance of 300 lux and screen brightness of 250 cd / m², desktop center illuminance of 500 lux and screen brightness of 200 cd / m², desktop center illuminance of 500 lux and screen brightness of 250 cd / m², desktop center illuminance of 500 lux and screen brightness of 300 cd / m², desktop center illuminance of 700 lux and screen brightness of 250 cd / m², desktop center illuminance of 700 lux and screen brightness of 300 cd / m², and desktop center illuminance of 700 lux and screen brightness of 350 cd / m². Human factors experiments were conducted under various lighting conditions: a desktop center illuminance of 900 lux and a screen brightness of 250 cd / m², a desktop center illuminance of 900 lux and a screen brightness of 300 cd / m², and a desktop center illuminance of 900 lux and a screen brightness of 350 cd / m². 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 visual effects of these combined lighting and screen brightness conditions on the participants' AMP and MTF, respectively.

[0065] Based on the ΔAMP / ΔAMP0 and ΔMTF / ΔMTF0 values ​​under different lighting conditions, the screen brightness adjustment effect parameter F in the invention can be fitted. M,,AMP (B,I) and screen brightness contrast sensitivity effect parameter F B,MTF The value of (B,I).

[0066] Example 2 Please see Figure 2 A screen brightness analysis method connected to an imaging luminance meter and an illuminance meter, the method comprising: Receive screen brightness measured by imaging luminance meter ; The lighting environment illuminance data transmission module receives the lighting environment illuminance I measured by the illuminance meter; The screen brightness human eye physiological effect parameter calculation module is used to receive the screen brightness transmitted by the screen brightness data transmission module. And the ambient illuminance transmitted by the ambient illuminance data transmission module Based on the received screen brightness and ambient illuminance Obtain screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. and screen brightness contrast sensitivity effect ;in, Based on the obtained screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. Contrast sensitivity effect with screen brightness The formula is shown below:

[0067]

[0068] In the formula, The number of points used to measure brightness on the display screen; For the first on the display screen The brightness of each point The minimum brightness among the points on the display screen where brightness is measured.

[0069] In this embodiment, the external luminance meter used is a KONICA MINOLTA LS-160 luminance meter; the external illuminance meter is a SPIC-500AW spectral color illuminance meter.

[0070] In this embodiment, based on the received ambient illuminance... Obtain illuminance adjustment effect parameters Sensitivity effect parameter compared to illuminance , specifically: Different screen brightness and ambient illuminance Human factors experiments were conducted in an environment that measured the amplitude of human eye accommodation. 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.

[0071] In this embodiment, the step of taking screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The parameter range is as follows:

[0072] In this embodiment, the method further includes calculating the... and The results are stored.

[0073] It should be noted that for any other points not fully explained, please refer to one of the embodiments, which will not be repeated here.

[0074] 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) For a user aged 12, a 45-minute paper reading task was performed in three different (B,I) display environments. The changes in the user's accommodative amplitude (AMP) and contrast sensitivity (MTF) were measured in the three (B,I) display environments. In addition, the F values ​​of the three (B,I) display environments were calculated. M,AMP and F M,MTF Numerical results show that the changes in the user's accommodation amplitude (AMP) and contrast sensitivity MTF under different (B,I) display environments are related to the corresponding (B,I) display environment's F... M,AMP and F M,MTF The values ​​show a positive correlation; the F calculated by the user using the system of this invention M,AMP and F M,MTF For visual tasks performed in environments with smaller (B,I) display values, the degree of myopia progression decreased by 0.25D after one year compared to the previous year.

[0075] (2) For a 9-year-old user, a 45-minute paper reading task was performed in three different (B,I) display environments. The changes in the user's accommodative amplitude (AMP) and contrast sensitivity (MTF) were measured in the three (B,I) display environments. In addition, the F values ​​of the three (B,I) display environments were calculated. M,AMP and F M,MTF Numerical results show that the changes in the user's accommodation amplitude (AMP) and contrast sensitivity (MTF) under different (B,I) display environments are related to the F values ​​of the corresponding luminaires. M,AMP and F M,MTF The numerical patterns are consistent; the F calculated by this user using the system of this invention... M,AMP and F M,MTF For visual tasks performed in environments with smaller (B,I) display values, the degree of myopia progression decreased by 0.25 D after one year compared to the previous year.

[0076] Example 3 A screen brightness analysis device connected to an imaging luminance meter and an illuminance 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 above.

[0077] 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.

[0078] Example 4 A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0079] 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.

[0080] 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. A screen brightness analysis system connected to an imaging luminance meter and an illuminance meter, characterized in that, The system includes: The screen brightness data transmission module receives the screen brightness measured by the imaging luminance meter. ; The ambient illuminance data transmission module receives the ambient illuminance measured by the illuminance meter. ; The screen brightness human eye physiological effect parameter calculation module is used to receive the screen brightness transmitted by the screen brightness data transmission module. And the ambient illuminance transmitted by the ambient illuminance data transmission module Based on the received screen brightness and ambient illuminance Obtain screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. and screen brightness contrast sensitivity effect ;in, Based on the obtained screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. Contrast sensitivity effect with screen brightness The formula is shown below: In the formula, The number of points used to measure brightness on the display screen; For the first on the display screen The brightness of each point The minimum brightness among the points on the display screen where brightness is measured.

2. The screen brightness analysis system connected to an imaging luminance meter and an illuminance 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: Different screen brightness and ambient illuminance Human factors experiments were conducted in an environment that measured the amplitude of human eye accommodation. 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 screen brightness analysis system connected to an imaging luminance meter and an illuminance meter as described in claim 2, characterized in that, The parameters of screen brightness adjustment effect are obtained. Contrast sensitivity effect parameter with screen brightness The parameter range is as follows:

4. The screen brightness analysis system connected to an imaging luminance meter and an illuminance 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 screen brightness connected to an imaging luminance meter and an illuminance meter, characterized in that, The method includes: Receive screen brightness measured by imaging luminance meter ; The lighting environment illuminance data transmission module receives the lighting environment illuminance I measured by the illuminance meter; The screen brightness human eye physiological effect parameter calculation module is used to receive the screen brightness transmitted by the screen brightness data transmission module. And the ambient illuminance transmitted by the ambient illuminance data transmission module Based on the received screen brightness and ambient illuminance Obtain screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. and screen brightness contrast sensitivity effect ;in, Based on the obtained screen brightness adjustment effect parameters Contrast sensitivity effect parameter with screen brightness The screen brightness adjustment effect was calculated. Contrast sensitivity effect with screen brightness The formula is shown below: In the formula, The number of points used to measure brightness on the display screen; For the first on the display screen The brightness of each point The minimum brightness among the points on the display screen where brightness is measured.

6. The screen brightness analysis method connected to an imaging luminance meter and 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: Different screen brightness and ambient illuminance Human factors experiments were conducted in an environment that measured the amplitude of human eye accommodation. 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 screen brightness analysis method connected to an imaging luminance meter and an illuminance meter as described in claim 6, characterized in that, The parameters of screen brightness adjustment effect are obtained. Contrast sensitivity effect parameter with screen brightness The parameter range is as follows:

8. The screen brightness analysis method connected to an imaging luminance meter and an illuminance meter as described in claim 5, characterized in that, The method further includes calculating the... and The results are stored.

9. A screen brightness analysis device connected to an imaging luminance meter and an illuminance 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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