A system and method for spectral visual effect analysis in conjunction with a spectrometer

Abstract

The application discloses a spectrum visual effect analysis system and method connected with a spectrometer. The system comprises a spectrum power distribution data transmission module for receiving spectrum power distribution data measured by the spectrometer; and a spectrum human eye physiological effect calculation module for receiving the spectrum power distribution data transmitted by the spectrum power distribution data transmission module, and acquiring spectrum contrast sensitivity effect parameters, spectrum aberration effect parameters, spectrum ocular fundus blood flow effect parameters, spectrum choroid effect parameters and the like according to the received spectrum power distribution data. The application can enable a user to calculate physiological effects of spectrum in real time and synchronously while measuring spectrum power distribution of a lamp by using the spectrometer, so as to evaluate the risk of visual fatigue caused by the spectrum of the lamp, and to adjust the use of eyes in time.

Description

Technical Field

[0001] This invention relates to the field of lighting technology, and in particular to a spectral visual effect analysis system and method connected to a spectrometer. Background Technology

[0002] The human eye produces different responses and accumulates varying degrees of visual fatigue when performing visual tasks in lighting environments with different photometric parameters. Although studies have shown that different photometric parameters have different effects on human eye physiological quantities, a quantitative relationship between photometric parameters and the corresponding eye physiological responses has not been established.

[0003] Spectral power distribution is an important photometric parameter describing spectral characteristics. Different wavelength ranges of the spectrum play different roles in the visual and non-visual health of the human eye. Studies have shown that blue light in the 420-440nm range poses a greater risk of retinal damage, blue light around 460nm has a strong inhibitory effect on melatonin, yellow-green light has significant effects on both visual and non-visual aspects, red light in the 605-635nm range has a melatonin compensating effect, and red light at 650nm plays an important role in myopia treatment. However, to date, no studies have systematically quantified the effects of different wavelengths across the entire visible light range on the human eye.

[0004] Therefore, it is necessary to develop a computational model that can quantitatively calculate the impact of a certain spectral power distribution on the human eye based on the characteristics of the human eye's response to visible light of different wavelengths, as well as a device that can use this model to calculate spectral physiological effects. Summary of the Invention

[0005] In view of this, the purpose of this invention is to propose a spectral visual effect analysis system and method connected to a spectrometer, which enables users to simultaneously calculate the physiological effects of the spectrum while measuring the spectral power distribution of a lamp using a spectrometer, assess the risk of eye fatigue caused by the lamp spectrum, and allow users to adjust their eye use in a timely manner.

[0006] According to one aspect of the present invention, a spectral visual effect analysis system connected to a spectrometer is provided, the system comprising:

[0007] The spectral power distribution data transmission module is used to receive spectral power distribution data measured by the spectrometer. ;

[0008] The spectral human eye physiological effect calculation module is used to receive spectral power distribution data transmitted by the spectral power distribution data transmission module. Based on the received spectral power distribution data, the spectral contrast sensitivity effect parameter is obtained. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The spectral contrast sensitivity effect of the lighting fixtures was calculated. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect ;in,

[0009] Based on the spectral contrast sensitivity effect parameters Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Calculate the spectral contrast sensitivity effect of lighting fixtures Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The calculation formula is as follows:

[0010]

[0011]

[0012] In the formula, the integral range of the numerator is 440nm~660nm, and the integral range of the denominator is 380nm~780nm.

[0013] In the above technical solution, compared with the prior art, its significant advantage lies in providing a complete, quantitative, and executable technical path, transforming the abstract theory of spectral-physiological correlation into an analytical system with clearly defined inputs, processing flows, and output results. This solution effectively overcomes the technical dilemma in existing technologies that cannot quantitatively assess the multidimensional physiological effects of spectroscopy on the human eye. Specifically, the advantages of this solution are reflected in the following three aspects:

[0014] First, a multi-dimensional and quantifiable physiological effect assessment model was constructed. This scheme creatively defines four core physiological effect parameters: spectral contrast sensitivity effect. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect These parameters correspond to key physiological functions of the human eye, enabling the analysis of single spectral physical parameters. This enables a multi-dimensional mapping of complex physiological responses. By using the provided normalized integral calculation formula, the influence of each wavelength is integrated into a single quantitative index, making the physiological effects of different lamp spectra comparable, thereby establishing a quantitative relationship missing in the background technology.

[0015] Secondly, it achieves a highly integrated and automated analysis workflow. This solution constructs a closed-loop processing system through the collaborative work of the spectral power distribution data transmission module and the spectral human eye physiological effect calculation module. This system can seamlessly connect with the spectrometer, realizing full automation from data acquisition, transmission, calculation to result output. This design enables real-time analysis, allowing users to obtain scientific evaluation reports directly on-site without complex data post-processing, greatly improving efficiency and practicality.

[0016] Third, it provides a unified and accurate calculation paradigm. The integral calculation formula used in this scheme defines different effective wavelength bands for the numerator and denominator (numerator: 440nm-660nm; denominator: 380nm-780nm). This design not only has clear physical meaning, highlighting the key role of specific wavelength bands (such as the mid-visible band), but also eliminates the influence of the absolute brightness of the light source by normalizing the energy across the entire wavelength band, ensuring the accuracy and consistency of the evaluation results, and providing a reliable algorithm core for the standardized manufacturing and application of equipment.

[0017] In summary, the fundamental advantage of this technical solution lies in its successful integration of theoretical models, algorithms, and hardware systems. It provides a method and system capable of quantitatively calculating the multidimensional physiological effects of the spectrum on the human eye, offering direct technical support for objectively assessing visual health risks and guiding the construction of healthy lighting environments.

[0018] In some embodiments, the spectral contrast sensitivity effect parameter is obtained based on the received spectral power distribution data. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters , specifically:

[0019] Data on power distribution in different spectra Human factors experiments were conducted under specific lighting conditions to measure the contrast sensitivity of the human eye. aberrations retinal blood flow Choroid thickness The change in the amount of spectral power distribution data was established. A quantitative relationship model between the parameter and the change is used to obtain the numerical range of the parameter.

[0020] The advantage of the above technical solution lies in establishing a quantitative relationship based on human factors experiments for the entire computational model, anchoring the theoretical model from a mathematical abstraction level to real physiological response data, thereby ensuring the reliability and validity of the final evaluation results. Specifically, the advantages of this solution are reflected in the following two aspects:

[0021] First, it establishes a reliable, empirically-based source of parameters. The plan explicitly states that all core parameters (such as...) The numerical range of (etc.) is determined by different spectral power distributions. This method involves obtaining data through human factor experiments. The key physiological indicators of the human eye (…) , , , The actual measurement of the changes in the spectrum correlates the physical stimulation of the spectrum with observable physiological changes. This avoids the biases that may arise from subjective assumptions in parameter setting or theoretical deductions, providing a real and reliable data foundation for the entire system and significantly improving the interpretability of the model.

[0022] Second, a complete technical chain from data to model was constructed. The scheme not only describes the experimental measurements, but more importantly, it establishes spectral power distribution data. A quantitative relationship model between the changes is established. By constructing this model, the system can predict the potential physiological effects of any newly input spectral data, providing algorithmic support for the system's automated and general-purpose computational functions.

[0023] In summary, the fundamental contribution of this technical solution lies in the fact that, through systematic human factor experiments and modeling, it endows the previously defined mathematical model with physiologically meaningful and experimentally verified parameter values, thus enabling the entire analysis system to be built on the foundation of experimental science and possess practical value and technical credibility.

[0024] In some embodiments, the spectral contrast sensitivity effect parameter Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Their respective numerical ranges are as follows:

[0025]

[0026] The core technical feature enabling the precise implementation of this system and method lies in clearly defining the specific numerical ranges of each physiological effect parameter at different wavelengths. Its key advantage is transforming the aforementioned theoretical model and experimental relationship into a quantitative spectral-physiological response database that can be directly accessed and executed by computing devices, thereby ensuring the uniformity, repeatability, and engineering practicality of the system output. Specifically, the advantages of this technical solution are reflected in the following two aspects:

[0027] First, it achieves the quantification and operability of core parameters. The scheme clearly lists the numerical ranges of four key effect parameters corresponding to multiple characteristic wavelength points (such as 450nm, 455nm, etc.) in tabular form. This design concretizes the abstract "quantitative relationship model" into a series of discrete but key numerical nodes, giving the calculation module clear and quantitative data basis when performing integration calculations, and solving the uncertainty of evaluation results caused by parameter ambiguity.

[0028] Second, it provides reliable data support that balances universality and specificity. The listed parameter values ​​are all given within a reasonable numerical range (rather than a single fixed value). This range is based on statistical results of individual physiological differences in human factors experiments, encompassing both general response patterns and reasonable individual fluctuations, thereby enhancing the robustness and reliability of the model in practical applications. Simultaneously, the data clearly demonstrates the differences in the impact of different wavelengths on various physiological effects (for example, the influence range of the 560nm wavelength on aberrations is significantly lower than other bands).

[0029] In summary, the specific parameter ranges provided in this section constitute the database of the entire analysis system, significantly improving the industrial feasibility of this technical solution.

[0030] In some embodiments, the system further includes a result transmission module;

[0031] This module is used to calculate the spectral contrast sensitivity effect. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The results are stored.

[0032] In the above technical solution, the added result transmission module has the core advantage of completing the final closed loop of the system data flow, transforming the calculated quantitative physiological effect data into information assets that can be used persistently, and ensuring the storability and traceability of the calculation results.

[0033] Specifically, the advantages of this module are reflected in the following two aspects:

[0034] First, it ensures the persistence and integrity of analysis results. This module is responsible for storing the calculated spectral physiological effect values, effectively preventing the instantaneous loss of data. This function ensures that the results of each measurement and calculation are completely recorded, forming a queryable and traceable historical database.

[0035] Secondly, it enhances the system's functional scalability and application potential. Data storage provides a fundamental prerequisite for subsequent in-depth analysis and applications. Based on the stored data, users can perform advanced functions such as historical comparison, trend analysis, and statistical evaluation. For example, they can track changes in the spectral physiological effects of the same luminaire at different times, or conduct large-scale horizontal comparisons of different luminaire models. This greatly expands the system's application scenarios and provides crucial data support for long-term visual health management and lighting product quality monitoring.

[0036] In summary, the results transmission module ensures the retention and added value of the system's output results, enabling the entire technical solution not only to solve the problem of real-time calculation, but also to serve long-term, systematic visual health research and applications.

[0037] According to another aspect of the present invention, a method for analyzing spectral visual effects connected to a spectrometer is provided, the method comprising:

[0038] Spectral power distribution data measured by the receiving spectrometer ;

[0039] Receive spectral power distribution data transmitted by the spectral power distribution data transmission module Based on the received spectral power distribution data, the spectral contrast sensitivity effect parameter is obtained. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The spectral contrast sensitivity effect of the lighting fixtures was calculated. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect ;in,

[0040] Based on the spectral contrast sensitivity effect parameters Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Calculate the spectral contrast sensitivity effect of lighting fixtures Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The calculation formula is as follows:

[0041]

[0042]

[0043] In the formula, the integral range of the numerator is 440nm~660nm, and the integral range of the denominator is 380nm~780nm.

[0044] In order to better utilize the above system, this application proposes a spectral visual effect analysis method connected to a spectrometer. Each module corresponds to a step of the above method, and its specific principle has been described above and will not be repeated here.

[0045] According to another aspect of the present invention, a spectral visual effect analysis device connected to a spectrometer is provided, comprising:

[0046] At least one processor and a memory communicatively connected to said at least one processor;

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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

[0051] 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.

[0052] Figure 1 This is a schematic diagram of an embodiment of the spectral visual effect analysis system connected to a spectrometer according to the present invention;

[0053] Figure 2 This is a flowchart illustrating an embodiment of the spectral visual effect analysis method connected to a spectrometer according to the present invention.

[0054] Figure 3 This is a schematic diagram of the spectral power distribution (SPD) of a lighting fixture in an embodiment of the spectral visual effect analysis method connected to a spectrometer according to the present invention;

[0055] Figure 4 This is a schematic diagram of the human factors experiment process of an embodiment of the spectral visual effect analysis method connected to a spectrometer according to the present invention. Detailed Implementation

[0056] 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.

[0057] Example 1

[0058] Please see Figure 1 A spectral visual effect analysis system connected to a spectrometer, the system comprising:

[0059] The spectral power distribution data transmission module is used to receive spectral power distribution data measured by the spectrometer. ;

[0060] It is important to note that the spectral measurement system consists of an integrating sphere (500mm in diameter, internally coated with barium sulfate), an ultraviolet-visible-near-infrared fiber optic cable, and a Hamamatsu C12666MA spectrometer, and is executed in accordance with the national standard GB / T 5700-2023 "Methods for Illumination Measurement". This standard clearly specifies the instrument calibration, measurement environment, and operating procedures, fundamentally ensuring the accuracy and validity of the acquired spectral data S(λ). By enforcing the use of standard-compliant hardware and procedures, the system preemptively eliminates significant errors, noise, or invalid data introduced by non-standard equipment, improper operation, or out-of-range measurements. Therefore, the data input to the calculation core can be considered as validated results. Minor fluctuations that may still occur within the standard measurement procedure will be smoothed out by subsequent statistical models. To ensure the consistency and comparability of the spectral data S(λ), this embodiment is limited as follows:

[0061] Measurement conditions: Performed according to GB / T 5700-2023 standard, including the luminaire's stability in the integrating sphere, ambient temperature, warm-up time, etc. This ensures that the results of the same luminaire measured at different times and by different operators are repeatable, as long as the same standard is followed.

[0062] Key measurement parameters: The above limits the hardware configuration (such as integrating sphere size, coating, spectrometer model C12666MA) and the applicable wavelength range of the optical fiber (340-780nm). The wavelength resolution and other performance characteristics of the spectrometer are guaranteed by this specific module. Using equipment with different resolutions or models will introduce deviations and therefore are not within the standard measurement conditions defined in this solution.

[0063] The spectral human eye physiological effect calculation module is used to receive spectral power distribution data transmitted by the spectral power distribution data transmission module. Based on the received spectral power distribution data, the spectral contrast sensitivity effect parameter is obtained. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The spectral contrast sensitivity effect of the lighting fixtures was calculated. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect ;in,

[0064] Based on the spectral contrast sensitivity effect parameters Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Calculate the spectral contrast sensitivity effect of lighting fixtures Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The calculation formula is as follows:

[0065]

[0066]

[0067] In the formula, the integral range of the numerator is 440nm~660nm, and the integral range of the denominator is 380nm~780nm.

[0068] It is important to note that the system outputs four independent physiological effect values, not a single integrated index. This design in this embodiment is based on the following considerations: Accommodative lag, contrast sensitivity, aberration, and pupillary response are relatively independent and parallel physiological processes in the visual system. Their contribution mechanisms to visual load differ. Quantifying them separately provides a more refined and diagnostically valuable visual state profile, helping professional users (such as optometrists and lighting designers) identify the dominant factors causing discomfort. Because these physiological processes are not linearly correlated and vary between individuals, simply weighting and merging them into a single "risk level" would lose crucial information and may be misleading. For example, "significant accommodative lag but good contrast sensitivity" and "both slightly decreased" may correspond to completely different visual task performances or sources of fatigue, requiring different intervention strategies. Therefore, the system retains multi-dimensional outputs to support more professional judgment and decision-making. In this scheme, "real-time" specifically refers to the output of spectral data. Once acquired by the system, the calculation of the four physiological effector quantities is instantaneous and automated. This does not include the sampling and data transmission time of the spectrometer itself. The core value of the system lies in rapidly resolving the physiological potential of known spectra.

[0069] Furthermore, considering the potential limitations of the linear weighted average integral model in describing physiological responses, and the rationale for normalization using full-band optical flux, this embodiment takes the following considerations:

[0070] 1. The universality and modeling logic of linear weighted integral models

[0071] The physiological processes of human vision involve nonlinear components. However, the current model is an engineering and standardized modeling method widely accepted and used in studies of non-visual and partial visual effects of illumination. Its rationale is based on the following logic:

[0072] In studies of the physiological effects of light, particularly in the quantification of non-visual effects (such as rhythm and pupillary response), linear weighted integrals are the core computational method. When converting continuous spectra into scalar effect values, linear weighted integrals are currently the mainstream simplification tool used by the international academic and engineering communities to balance accuracy and operability. This model is positioned as a practical, population-averaged predictive tool, rather than a physiological mechanism model attempting to fully simulate the details of retinal neural circuits or photochemical dynamics. Its goal is to establish a robust and computable relationship from easily measurable physical quantities (spectral power distribution) to intermediate physiological effect indices. The model assumes that "under population statistical mean and moderate light levels, the contributions of different wavelengths of light to the specific endpoint effects of interest (such as changes in hysteresis and pupillary constriction amplitude) can be approximated as independent and linearly superimposed." This does not deny the nonlinearity at the physiological level, but rather assumes that at the system input-output level, this nonlinearity may be smoothed after population averaging, or that the linear approximation already possesses sufficient engineering accuracy for the main range of variation predicted by the model. This simplified model provides sufficiently reliable and highly practical predictions.

[0073] 2. The physical meaning and purpose of luminous flux normalization

[0074] The denominator, when integrated within the range of 380-780 nm, corresponds to either luminous flux (if S(λ) is a photometric quantity) or radiant flux (if S(λ) is a radiometric quantity). It is used for "normalization" primarily for the following reasons:

[0075] Achieving standardized comparison of effect intensity: The core purpose of normalization is to transform the result of weighted integration into the intensity of the physiological effect produced per unit incident light energy (or luminous flux). This decouples the calculation results from the absolute brightness or illuminance of the light source, allowing direct comparison of the "efficacy" or "quality" of the physiological effect of different light source spectral compositions, unaffected by differences in the total output power of the light source. Taking the fundus blood flow effect as an example, the question arises: "Why is normalization by total luminous flux necessary?" The answer lies in the fact that research focuses on the impact of spectral composition on vascular modulation efficiency. Without normalization, a brighter light source (with a larger total luminous flux) will always calculate a larger effect value, confusing the two factors of "brightness" and "spectral efficacy." After normalization, the obtained value more purely reflects the ability of that specific spectrum to drive changes in blood flow per unit of light energy, which is more meaningful for comparing spectral characteristics.

[0076] In this embodiment, the spectral contrast sensitivity effect parameter is obtained based on the received spectral power distribution data. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters , specifically:

[0077] Data on power distribution in different spectra Human factors experiments were conducted under specific lighting conditions to measure the contrast sensitivity of the human eye. aberrations retinal blood flow Choroid thickness The change in the amount of spectral power distribution data was established. A quantitative relationship model between the parameter and the change is used to obtain the numerical range of the parameter.

[0078] In this embodiment, the spectral contrast sensitivity effect parameter Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Their respective numerical ranges are as follows:

[0079]

[0080] It should be noted that the detailed explanations regarding human factors experimental design, model variable control, and wavelength interpolation methods are as follows:

[0081] 1. Details of the participants in this human factors experiment are as follows:

[0082] Sample size: N = 82.

[0083] Age distribution: Age was 24.9 ± 3.0 years (mean ± standard deviation). The experiment focused on young adults.

[0084] Visual health status: The visual acuity of all subjects strictly complied with the requirements of GB / T 44441-2024 standard. Specifically, the refractive error distribution was as follows: 35% in the range of -1.00D to +0.50D, 30% in the range of -3.00D to -1.00D, 25% in the range of -5.00D to -3.00D, and 10% exceeding -5.00D. This design covered common visual acuity ranges from emmetropia to moderate myopia.

[0085] The parameters of this model are derived from and primarily applicable to the aforementioned youth group and the corresponding vision distribution range. This positioning is based on the industry reality that healthy lighting products initially primarily target the mainstream working-age population. The experiment, using a large sample size (N=82) and a standardized vision distribution, aims to obtain statistically stable parameter estimates within this target group.

[0086] 2. Controlling confounding variables and establishing causal relationships

[0087] To ensure that the measured physiological changes were primarily attributable to spectral variations, the following measures were taken in the experiment:

[0088] Variable isolation design: This application addresses the independent effects of spectral wavelength distribution (S(λ)). To achieve this, all experiments in this human factors study were conducted in highly controlled darkrooms or standard illumination chambers to eliminate interference from ambient stray light. Visual tasks (such as specific target recognition) were standardized, and subjects' head positions and viewing distances were fixed to minimize variability caused by accommodation and fixation fluctuations. The experiment employed a multi-sample, repeated-measures design. Inherent individual physiological fluctuations (such as attentional variability) were treated as random errors. By comparing the responses of the same subject to different spectral stimuli (within-group comparisons) and averaging the responses of all subjects (between-group averaging), individual random fluctuations were largely offset, thus highlighting the systematic trends caused by spectral variations. While this cannot achieve absolute causal isolation like in physical experiments, it is a mainstream and reliable method for establishing strong correlations and inferring causal relationships in human factors physiological research.

[0089] 3. Interpolation methods for discrete wavelength parameters

[0090] For wavelengths not listed in the parameter table, the system uses spline interpolation instead of nonlinear interpolation or taking neighboring values.

[0091] Method Selection: Spline interpolation is a widely used technique for processing spectral data and biological effect curves. It estimates the values ​​at intermediate points by constructing a smooth piecewise polynomial curve that passes through all known data points. Compared to linear interpolation, it can better fit the smooth nonlinear characteristics that physiological response curves may have (such as gentle changes near the peak wavelength).

[0092] Error control: Under the premise that the known data points are dense and reasonably distributed (such as the 5nm interval in this parameter table), spline interpolation introduces far less error than the simple neighbor-taking method, and can ensure the continuity of the curve derivative, which is more in line with the physical interpretation of physiological response changes. The discrete points provided in the parameter table are the key node data for performing this type of scientific interpolation.

[0093] In this embodiment, the system further includes a result transmission module;

[0094] This module is used to calculate the spectral contrast sensitivity effect. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The results are transmitted to the computer and automatically saved to the computer's hard drive in txt format.

[0095] The experimental design and acquisition process for obtaining the above parameters are as follows:

[0096] Participants (N=82, age 24.9±3.0 years) underwent human factors experiments in different spectral environments, all conducted in the same indoor setting. For each participant, a baseline experiment was performed first, followed by an exploration experiment, and finally a fitting experiment. 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 participated in human factors experiments for 45 minutes in only one light environment per day, with experiments under different light environments scheduled on different days to avoid residual effects from previous light environments.

[0097] (1) Baseline experiment

[0098] In the baseline experiment, participants' visual task was to count Randall rings on paper under illumination. The illumination was 500 lux, color temperature 4500±250 K, color rendering index (CRI) > 90, and spectral power distribution (SPD) as shown in the figure. Figure 3 As shown. The process of the human factor experiment is as follows. Figure 4 As shown. Before the experiment, participants relaxed their eyes for 20 minutes. Then, their contrast sensitivity (MTF), higher-order aberration (HOA), fundus blood flow density (FVD), and choroidal thickness (CT) were measured. After the measurements, participants underwent a 45-minute visual task, which involved counting Landau rings on paper. After 45 minutes, the MTF, HOA, FVD, and CT were measured again. The changes in MTF, HOA, FVD, and CT during the visual task were obtained by subtracting the values ​​before the task from the values ​​after the task (denoted as ΔMTF0, ΔHOA0, ΔFVD0, and ΔCT0).

[0099] (2) Inquiry Experiment

[0100] The exploratory experiment consisted of 11 groups, with lighting environments combining standard lighting fixtures and different external monochromatic LED light strips. The lighting fixtures were the same as those used in the baseline experiment. A total of 11 external monochromatic LED light strips were used in the exploratory experiment, all with a spectral half-width of approximately 20 nm and peak wavelengths of 450 nm, 455 nm, 465 nm, 475 nm, 525 nm, 560 nm, 590 nm, 605 nm, 620 nm, 630 nm, and 660 nm. In each exploratory experiment, the brightness of the external monochromatic LED light strips was the same and significantly lower than that of the standard lighting fixtures. The participants' visual task was to count Randall rings on paper under the combined lighting environment of the standard lighting fixtures and their corresponding external monochromatic LED light strips.

[0101] The process of human factor experiments is still as follows Figure 4 As shown. Before the experiment, participants relaxed their eyes for 20 minutes. Then, their contrast sensitivity (MTF), higher-order aberration (HOA), fundus blood flow density (FVD), and choroidal thickness (CT) were measured. After the measurements, participants underwent a 45-minute visual task, which involved counting Landau rings on paper. After 45 minutes, the MTF, HOA, FVD, and CT were measured again. Subtracting the pre-visual values ​​from the post-visual task values ​​(ΔMTF, ΔHOA, ΔFVD, ΔCT) yields the changes in these values ​​during the visual task (denoted as ΔMTF, ΔHOA, ΔFVD, ΔCT). Therefore, ΔMTF / ΔMTF0, ΔHOA / ΔHOA0, ΔFVD / ΔFVD0, and ΔCT / ΔCT0 represent the effects of different spectral combinations on human eye MTF, HOA, FVD, and CT, respectively. However, this effect value only provides a relative magnitude and is not the final value of the spectral physiological effect on the eye. To obtain the final value of the spectral physiological effect on the eye, fitting experiments using real lighting fixtures are required.

[0102] (3) Fitting experiment

[0103] The fitting experiment used four different SPD lighting fixtures with an illuminance of 500 lux, a color temperature of 4500±250 K, a color rendering index (CRI) > 90, and spectral power distributions as shown in the figure. Figure 3 As shown. These four types of lighting fixtures are manufactured using the same packaging process, and the only difference can be considered to be in the SPD (Surface Mount Device).

[0104] The exploratory experiment consisted of four groups. In each group, participants conducted a human factors experiment under one type of lighting fixture. The process of the human factors experiment was as follows: Figure 4As shown. Before the experiment, participants relaxed their eyes for 20 minutes. Then, their contrast sensitivity (MTF), higher-order aberration (HOA), fundus blood flow density (FVD), and choroidal thickness (CT) were measured. After the measurements, participants underwent a 45-minute visual task, which involved counting Landau rings on paper. After 45 minutes, the MTF, HOA, FVD, and CT were measured again. The changes in MTF, HOA, FVD, and CT during the visual task were obtained by subtracting the values ​​before the visual task from the values ​​after the task (denoted as ΔMTF, ΔHOA, ΔFVD, and ΔCT).

[0105] By combining the relative effects of different wavelengths on human eye physiological parameters obtained from the exploratory experiments, and the absolute effects of different spectra on human eye physiological parameters obtained from the fitting experiments, the spectral contrast sensitivity effect parameter F mentioned in the invention patent can be finally obtained. S,MTF (λ), spectral aberration effect parameter F S,HOA (λ), spectral fundus blood flow effect parameter F S,FVD (λ), spectral vesicle effect parameter F S,CT The value of (λ).

[0106] Example 2

[0107] Please see Figure 2 A method for analyzing spectral visual effects connected to a spectrometer, the method comprising:

[0108] S1, Receive spectral power distribution data measured by the spectrometer. ;

[0109] S2. Receive spectral power distribution data transmitted by the spectral power distribution data transmission module. Based on the received spectral power distribution data, the spectral contrast sensitivity effect parameter is obtained. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The spectral contrast sensitivity effect of the lighting fixtures was calculated. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect ;in,

[0110] Based on the spectral contrast sensitivity effect parameters Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Calculate the spectral contrast sensitivity effect of lighting fixtures Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The calculation formula is as follows:

[0111]

[0112]

[0113] In the formula, the integral range of the numerator is 440nm~660nm, and the integral range of the denominator is 380nm~780nm.

[0114] In this embodiment, the spectral contrast sensitivity effect parameter is obtained based on the received spectral power distribution data. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters , specifically:

[0115] Data on power distribution in different spectra Human factors experiments were conducted under specific lighting conditions to measure the contrast sensitivity of the human eye. aberrations retinal blood flow Choroid thickness The change in the amount of spectral power distribution data was established. A quantitative relationship model between the parameter and the change is used to obtain the numerical range of the parameter.

[0116] In this embodiment, the spectral contrast sensitivity effect parameter Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Their respective numerical ranges are as follows:

[0117]

[0118] In this embodiment, the method further includes:

[0119] The calculated spectral contrast sensitivity effect Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The results are stored.

[0120] Example 3,

[0121] Based on the method and system proposed in this invention, a one-year test was conducted on two groups of users, as detailed below:

[0122] (1) An 11-year-old user was asked to perform a 45-minute paper reading task under three different lighting fixtures with S(λ). The changes in contrast sensitivity (MTF), aberration (HOA), fundus blood flow (FVD), and choroidal thickness (CT) of the user's eyes were measured under the three lighting fixtures. In addition, the F values ​​of the three lighting fixtures were calculated. S,MTF F S,HOA F S,FVD F S,CT Numerical results showed that the changes in contrast sensitivity (MTF), aberration (HOA), fundus blood flow (FVD), and choroidal thickness (CT) of the user's eye under different lighting fixtures were related to the FVD of the corresponding lighting fixtures. S,MTF F S,HOA F S,FVD F S,CT The values ​​show a positive correlation; the F calculated by the user using the system of this invention S,MTF F S,HOA F S,FVD F S,CT 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.

[0123] (2) A 13-year-old user was asked to perform a 45-minute paper reading task under three different lighting fixtures with S(λ). The changes in contrast sensitivity (MTF), aberration (HOA), fundus blood flow (FVD), and choroidal thickness (CT) of the user's eyes were measured under the three lighting fixtures. In addition, the F values ​​of the three lighting fixtures were calculated. S,MTF F S,HOA F S,FVD F S,CT Numerical results showed that the changes in contrast sensitivity (MTF), aberration (HOA), fundus blood flow (FVD), and choroidal thickness (CT) of the user's eye under different lighting fixtures were related to the FVD of the corresponding lighting fixtures. S,MTF F S,HOA F S,FVD F S,CT The numerical patterns are consistent; the F calculated by this user using the system of this invention... S,MTF F S,HOA F S,FVD F S,CT When using lamps with smaller numerical values ​​for visual tasks, the degree of myopia progression decreased by 0.25 D after one year compared to the previous year.

[0124] The system and method proposed in this invention, when used in conjunction with a spectrometer, enable dynamic calculation and analysis of the visual effects of luminaire spectra. Its advantages lie in the fact that, simultaneously measuring the spectral power distribution, the system can quantitatively calculate the physiological effects of the spectrum and determine the risk level of visual fatigue. This technical approach forms an efficient "measurement-calculation-feedback" closed loop, strongly supporting visual health management based on real-time data.

[0125] Example 4

[0126] A spectral visual effect analysis device connected to a spectrometer, comprising:

[0127] At least one processor and a memory communicatively connected to said at least one processor;

[0128] 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 Embodiment 2.

[0129] In the above technical solution, to better operate and process the method described in Embodiment 2, the 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.

[0130] Example 5,

[0131] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 2.

[0132] In the above technical solution, to better operate and use the method described in Embodiment 2, 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 further here.

[0133] 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 spectral visual effect analysis system connected to a spectrometer, characterized in that, The system includes: The spectral power distribution data transmission module is used to receive spectral power distribution data measured by the spectrometer. ; The spectral human eye physiological effect calculation module is used to receive spectral power distribution data transmitted by the spectral power distribution data transmission module. Based on the received spectral power distribution data, the spectral contrast sensitivity effect parameter is obtained. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The spectral contrast sensitivity effect of the lighting fixtures was calculated. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect ;in, Based on the spectral contrast sensitivity effect parameters Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Calculate the spectral contrast sensitivity effect of lighting fixtures Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The calculation formula is shown below. In the formula, the integral range of the numerator is 440nm~660nm, and the integral range of the denominator is 380nm~780nm; Based on the received spectral power distribution data, obtain the spectral contrast sensitivity effect parameters. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The specific method is as follows: Data on power distribution in different spectra Human factors experiments were conducted under specific lighting conditions to measure the contrast sensitivity of the human eye. aberrations retinal blood flow choroidal thickness The change in the amount of spectral power distribution data was established. A quantitative relationship model between the parameter and the change was established to obtain the numerical range of the parameter. The spectral contrast sensitivity effect parameter Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Their respective numerical ranges are as follows: 。 2. The spectral visual effect analysis system connected to a spectrometer as described in claim 1, characterized in that, The system also includes a result transmission module; This module is used to calculate the spectral contrast sensitivity effect. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The results are stored.

3. A method for analyzing spectral visual effects connected to a spectrometer, characterized in that, The method includes: Spectral power distribution data measured by the receiving spectrometer ; Receive spectral power distribution data transmitted by the spectral power distribution data transmission module Based on the received spectral power distribution data, the spectral contrast sensitivity effect parameter is obtained. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The spectral contrast sensitivity effect of the lighting fixtures was calculated. Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect ;in, Based on the spectral contrast sensitivity effect parameters Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Calculate the spectral contrast sensitivity effect of lighting fixtures Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The calculation formula is shown below. In the formula, the integral range of the numerator is 440nm~660nm, and the integral range of the denominator is 380nm~780nm; Based on the received spectral power distribution data, obtain the spectral contrast sensitivity effect parameters. Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters The specific method is as follows: Data on power distribution in different spectra Human factors experiments were conducted under specific lighting conditions to measure the contrast sensitivity of the human eye. aberrations retinal blood flow choroidal thickness The change in the amount of spectral power distribution data was established. A quantitative relationship model between the parameter and the change was established to obtain the numerical range of the parameter. The spectral contrast sensitivity effect parameter Spectral aberration effect parameters Spectral fundus blood flow effect parameters Spectral vesicle effect parameters Their respective numerical ranges are as follows: 。 4. The method for analyzing spectral visual effects connected to a spectrometer as described in claim 3, characterized in that, The method further includes: The calculated spectral contrast sensitivity effect Spectral aberration effects Spectral fundus blood flow effect Spectral vesicle effect The results are stored.

5. A spectral visual effect analysis device connected to a spectrometer, 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 3 to 4.

6. 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 3 to 4.

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