Light source quality prediction method and device

Abstract

The present invention provides a method and device for predicting light source quality, belonging to the field of optical measurement. The method comprises: obtaining the central wavelength and bandwidth of a target light source; inputting the central wavelength and bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; determining, in the simulated spectrum, the proportion of the spectral intensity within a specified wavelength range to the total spectral intensity of the target light source; and judging whether the target light source meets a preset standard based on the relationship between the proportion and a preset ratio threshold. This method can provide an important reference for researchers to select or customize light sources in the early stages of experimental design, avoiding deviations or invalidation of experimental results caused by the use of non-compliant light sources, thereby improving the efficiency and reliability of research.

Description

Technical Field

[0001] The present invention relates to the field of optical measurement technology, and in particular to a light source quality prediction method and device. Background Art

[0002] Blue light is a type of visible light with a wavelength between 400 and 500 nanometers, a high-energy, short-wavelength light. Artificial light sources such as LED screens (including mobile phones, computers, and televisions), energy-saving lamps, fluorescent lamps, and other electronic devices emit significant amounts of blue light. Studies have shown that blue light not only affects the visual system but also penetrates skin tissue, inducing the production of reactive oxygen species through photosensitization. This can lead to cellular oxidative stress, DNA damage, hyperpigmentation, accelerated skin aging, and may even increase the risk of certain skin diseases.

[0003] As understanding of the biological effects of blue light deepens, the development of skincare products, protective materials, or optical filters that can effectively protect against blue light has become a hot topic in industry research and development. To scientifically and accurately evaluate the efficacy of these protective products, relevant biological experiments are necessary. When designing or selecting blue light LED light sources for such evaluation experiments, researchers need to ensure that the core parameters of their emission spectra, such as the center wavelength (peak wavelength) and spectral bandwidth, ensure that their overall spectrum meets the wavelength range and energy content requirements of certain industry-developed standards. For example, China's T / CAPA13-2024 "Specifications for the Evaluation of the Blue Light Protection Efficacy of Human Skin" and other standards clearly require that the light source used for evaluation (usually a non-laser light source, such as an LED) must be able to stably emit light in the 400-500nm wavelength range, with the energy content within this band accounting for no less than 99%, and the energy content outside the target band no more than 1%.

[0004] The existing process typically involves first selecting or customizing a light source, then conducting actual spectral measurement and analysis to determine whether it meets the stringent energy-fraction requirements of industry standards. If the selected light source, even if its nominal central wavelength and bandwidth appear appropriate, is found to not meet the specifications after actual measurement, the light source must be reselected or adjusted, resulting in wasted time, cost, and inefficient experiments. Summary of the Invention

[0005] The present invention provides a light source quality prediction method and device to solve the defect of resource waste caused by continuous customization of light sources in the prior art, realize accurate light source parameters determined in advance through a model, and then configure the actual light source according to the light source parameters.

[0006] The present invention provides a light source quality prediction method, comprising the following steps.

[0007] Obtain the central wavelength and bandwidth of the target light source;

[0008] Inputting the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model;

[0009] Determining, in the simulated spectrum, the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity of the target light source;

[0010] According to the relationship between the proportion and the preset ratio threshold, it is determined whether the target light source meets the preset standard.

[0011] The present invention also provides a light source quality prediction method, comprising the following steps:

[0012] Determine target bandwidth;

[0013] Under the constraint of the target bandwidth, multiple center wavelengths are input into a light source simulation model to obtain a simulated spectrum corresponding to each center wavelength output by the light source simulation model;

[0014] Determine, in each simulated spectrum, the proportion of the spectral intensity within the specified wavelength range in the total spectral intensity corresponding to the simulated spectrum;

[0015] The central wavelength corresponding to when the proportion is greater than a preset ratio threshold is determined as the central wavelength of the target light source.

[0016] According to a light source quality prediction method provided by the present invention, the light source simulation model is obtained according to the following method, including:

[0017] Obtaining a preprocessed emission spectrum sample, a preprocessed central wavelength sample, and a preprocessed bandwidth sample for each of the plurality of light source samples;

[0018] The composite simulation function is obtained by weighted summing the exponential modified Gaussian function and the asymmetric pseudo-Voigt function;

[0019] Inputting the central wavelength sample and the bandwidth sample into the composite simulation function to obtain a spectrum intensity sample output by the composite simulation function;

[0020] According to the comparison result of the spectral intensity sample and the emission spectrum samples of the multiple light source samples, the model parameters in the composite simulation function are adjusted to obtain the light source simulation model.

[0021] According to a light source quality prediction method provided by the present invention, the model parameters include the center wavelength offset, the left and right half-width heights, and the mixing factor in the asymmetric pseudo-Voigt function.

[0022] According to a light source quality prediction method provided by the present invention, before obtaining the preprocessed emission spectrum samples, preprocessed center wavelength samples, and preprocessed bandwidth samples of each of the multiple light source samples, the method includes:

[0023] Acquire an original emission spectrum sample, an original center wavelength sample, and an original bandwidth sample of each of the plurality of light source samples;

[0024] The original emission spectrum sample, the original center wavelength sample, and the original bandwidth sample are respectively preprocessed to obtain the preprocessed emission spectrum sample, the preprocessed center wavelength sample, and the preprocessed bandwidth sample; wherein the preprocessing operation includes peak normalization and / or baseline correction.

[0025] According to a light source quality prediction method provided by the present invention, the target light source is a blue light source.

[0026] The present invention also provides a light source quality prediction device, comprising the following modules:

[0027] A data acquisition module, used to obtain the central wavelength and bandwidth of the target light source;

[0028] A simulated spectrum output module, configured to input the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model;

[0029] an intensity ratio calculation module, configured to determine, in the simulated spectrum, the ratio of the spectral intensity within a specified wavelength range to the total spectral intensity of the target light source;

[0030] The standard judgment module is used to judge whether the target light source meets the preset standard according to the relationship between the proportion and the preset ratio threshold.

[0031] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any one of the above-described light source quality prediction methods is implemented.

[0032] The present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which implements any of the above-mentioned light source quality prediction methods when executed by a processor.

[0033] The present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the above-mentioned light source quality prediction methods.

[0034] The present invention provides a light source quality prediction method and device. These methods obtain the central wavelength and bandwidth of a target light source; input these central wavelength and bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; determine, in the simulated spectrum, the proportion of the spectral intensity within a specified wavelength range to the total spectral intensity of the target light source; and, based on the relationship between this proportion and a preset ratio threshold, determine whether the target light source meets preset standards. This method provides an important reference for researchers selecting or customizing light sources in the early stages of experimental design, avoiding biased or invalid experimental results caused by the use of non-compliant light sources, thereby improving research efficiency and reliability. This method has clear practical significance and application value when applied to the quality pre-assessment of blue light sources, particularly in fields such as biological effects research (such as skin protection evaluation) that require precise control of the light source's spectral characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is one of the flow charts of a light source quality prediction method provided by the present invention.

[0037] Figure 2 This is a comparison diagram of the spectrum fitted by the standard Gaussian fitting function provided by the present invention and the true spectrum.

[0038] Figure 3 This is the second flow chart of a light source quality prediction method provided by the present invention.

[0039] Figure 4 It is a schematic diagram of the determination process of the light source simulation model provided by the present invention.

[0040] Figure 5 This is a comparison diagram of the spectrum fitted using the EMG function provided by the present invention and the true spectrum.

[0041] FIG6( a ) is one of the comparison diagrams of the spectrum intensity sample provided by the present invention and the spectrum of the experimental data.

[0042] FIG6( b ) is the second comparison diagram between the spectrum intensity sample provided by the present invention and the spectrum of experimental data.

[0043] FIG6( c ) is the third comparison diagram between the spectrum intensity sample provided by the present invention and the spectrum of experimental data.

[0044] FIG6( d ) is a fourth comparison diagram of the spectrum intensity sample provided by the present invention and the spectrum of experimental data.

[0045] Figure 7 It is a structural schematic diagram of the light source quality prediction device provided by the present invention.

[0046] Figure 8 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0048] The following combination Figures 1-8 Specific embodiments of the present invention are described.

[0049] Figure 1 This is one of the flow charts of a light source quality prediction method provided by the present invention, such as Figure 1 As shown, the method includes the following steps:

[0050] Step 101: Obtain the central wavelength and bandwidth of a target light source.

[0051] The target light source refers to the required light source. For example, in order to test the efficacy of sunscreen products, it is necessary to configure a suitable blue LED light source. Before configuring the blue LED light source, it is necessary to first determine the parameters of the required target light source.

[0052] Specifically, the parameters of the target light source, including its center wavelength and bandwidth, are determined based on the intended purpose. The center wavelength is a key parameter describing the characteristics of an optical signal or spectrum. It refers to the wavelength with the highest intensity (or most pronounced characteristic) within a light wave's energy distribution. For example, the center wavelength of a blue LED is generally around 450nm. The bandwidth of a light source is a physical quantity that describes the width of the energy distribution of the light emitted by the source within a wavelength (or frequency) range. It is typically expressed as the full width at half maximum (FWHM), which is the width of the wavelength range corresponding to 50% of the peak spectral intensity. Bandwidth directly reflects the monochromaticity and energy concentration of a light source and is a key parameter for evaluating light source performance.

[0053] Step 102: input the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model.

[0054] The light source simulation model is a model obtained by simulating the spectral parameters of a large number of actual light source samples in advance, and can simulate the spectral fitting curves corresponding to light sources of different parameter types.

[0055] Specifically, when the central wavelength and bandwidth of the target light source are input into the light source simulation model, a simulated spectrum output by the light source simulation model can be obtained, and the simulated spectrum is the simulated spectrum of the target light source. The light source simulation model can be a model obtained by simulating using a standard Gaussian function or an exponentially modified Gaussian function (EMG) as a mathematical model, or a model obtained by superimposing an EMG function and an asymmetric pseudo-Voigt function (APV). Figure 2 As shown, Figure 2 The figure shows the comparison between the spectrum fitted by the standard Gaussian fitting function and the true spectrum.

[0056] Step 103 : determining, in the simulated spectrum, the proportion of the spectral intensity within the specified wavelength range in the total spectral intensity of the target light source.

[0057] Specifically, to determine whether the target light source meets pre-set industry standards, for example, China's T / CAPA 13-2024 "Specifications for the Evaluation of Blue Light Protection Effectiveness of Human Skin" explicitly requires that the light source used for evaluation (typically a non-laser light source, such as an LED) must be able to stably emit light in the 400-500nm wavelength range, with the light energy within this band accounting for no less than 99%, and the light energy outside the target band accounting for no more than 1%. Therefore, in the above simulated spectrum, the proportion of spectral intensity within the specified wavelength range (e.g., 400-500nm) to the total spectral intensity is first determined.

[0058] Step 104 : judging whether the target light source meets a preset standard based on the relationship between the proportion and a preset ratio threshold.

[0059] Specifically, a determination is made as to whether the percentage is greater than or equal to a preset threshold (e.g., 99%). If so, the target light source is deemed to meet the preset criteria. Furthermore, the actual light source can be configured based on the determined light source parameters, including the center wavelength and bandwidth.

[0060] The above embodiment obtains the central wavelength and bandwidth of a target light source; inputs these central wavelength and bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; determines, in the simulated spectrum, the proportion of the spectral intensity within a specified wavelength range to the total spectral intensity of the target light source; and, based on the relationship between this proportion and a preset ratio threshold, determines whether the target light source meets the preset standard. This method provides an important reference for researchers to select or customize light sources in the early stages of experimental design, avoiding biased or invalid experimental results caused by the use of non-compliant light sources, thereby improving research efficiency and reliability. This method has clear practical significance and application value when applied to the quality pre-assessment of blue light sources, particularly in fields such as biological effects research (such as skin protection evaluation) that require precise control of the light source's spectral characteristics.

[0061] In one embodiment, if Figure 3 As shown, a light source quality prediction method is also provided, which includes the following steps.

[0062] Step 301: Determine the target bandwidth.

[0063] Specifically, first determine the target bandwidth of the desired light source. A light source's bandwidth is a physical quantity that describes the width of the energy distribution within the wavelength (or frequency) range of the light it emits. It's typically expressed as the Full Width at Half Maximum (FWHM), which is the width of the wavelength range corresponding to 50% of the peak spectral intensity. Bandwidth directly reflects a light source's monochromaticity and energy concentration, and is a key parameter for evaluating light source performance.

[0064] Step 302: Under the constraint of the target bandwidth, multiple center wavelengths are input into a light source simulation model to obtain a simulated spectrum corresponding to each center wavelength output by the light source simulation model.

[0065] Specifically, the target bandwidth parameters are fixed, and then different central wavelengths are input through the “trial and error method” to observe the simulated spectrum output by the light source simulation model.

[0066] Step 303 : determining, in each simulated spectrum, the proportion of the spectral intensity within the specified wavelength range in the total spectral intensity corresponding to the simulated spectrum.

[0067] Specifically, in each simulated spectrum, the proportion of the spectral intensity within the specified wavelength range in the total spectral intensity corresponding to the simulated spectrum is counted, so as to determine the value range of the central wavelength according to the input central wavelength.

[0068] Step 304 : determining the central wavelength corresponding to when the proportion is greater than a preset ratio threshold as the central wavelength of the target light source.

[0069] Specifically, the range of the center wavelength is determined based on the input center wavelength. After multiple attempts to enter the center wavelength in the previous two steps, the center wavelength range that meets the preset criteria is ultimately determined to be 425-453nm. For example, within the 425-453nm center wavelength range, the intensity of the 400-500nm band accounts for greater than 99%.

[0070] The above embodiment determines a target bandwidth; within the constraints of the target bandwidth, multiple center wavelengths are input into a light source simulation model to obtain a simulated spectrum corresponding to each of the center wavelengths output by the light source simulation model; within each simulated spectrum, the proportion of the spectral intensity within a specified wavelength range to the total spectral intensity corresponding to the simulated spectrum is determined; and the center wavelength corresponding to the point where the proportion exceeds a preset ratio threshold is determined as the center wavelength of the target light source. This method, by fixing the bandwidth and inputting different center wavelengths, ultimately obtains a center wavelength range of the target light source that meets preset standards within the fixed bandwidth range, providing direct guidance for light source selection and design.

[0071] In one embodiment, if Figure 4 As shown, Figure 4 The following is a schematic diagram of the process for determining a light source simulation model. The light source simulation model is obtained according to the following method, including the following steps:

[0072] Step 401 : obtaining a preprocessed emission spectrum sample, a preprocessed center wavelength sample, and a preprocessed bandwidth sample of each of a plurality of light source samples.

[0073] It is worth mentioning that before step 401, it also includes: obtaining the original emission spectrum samples, original center wavelength samples and original bandwidth samples of each of the multiple light source samples; performing preprocessing operations on the original emission spectrum samples, original center wavelength samples and original bandwidth samples respectively to obtain the preprocessed emission spectrum samples, preprocessed center wavelength samples and preprocessed bandwidth samples; wherein the preprocessing operation includes peak normalization and / or baseline correction.

[0074] Specifically, raw emission spectrum samples are obtained from different actual LED light source samples (particularly, LED blue light sources) under stable operating conditions, covering a wide range (250-800nm) including the blue light band. The raw emission spectrum samples include raw center wavelength samples and raw bandwidth samples. These raw emission spectrum samples are preprocessed, including peak normalization and baseline correction, to eliminate the effects of absolute intensity differences and baseline drift. The average value of the acquired blue light emission spectrum samples is used as representative spectral data for modeling, ultimately obtaining preprocessed emission spectrum samples, preprocessed center wavelength samples, and preprocessed bandwidth samples.

[0075] Step 402 : Perform weighted summation on the exponentially modified Gaussian function and the asymmetric pseudo-Voigt function to obtain a composite simulation function.

[0076] Specifically, experiments have observed that blue LED spectra often exhibit a tailing phenomenon with a steep rising edge and a gentle falling edge. Given the asymmetric peak shape often exhibited by spectra obtained from blue LED experimental data, using simple models such as the symmetrical Gaussian model to simulate this asymmetric spectrum will produce large deviations in the intensity distribution on both sides of the spectrum, making it impossible to accurately predict the actual energy proportion within a specific band. Figure 2 As shown in Figure 2, the effect of fitting with a standard Gaussian function using Matlab is not good.

[0077] In this application, we first try to simulate the asymmetric function (exponentially modified Gaussian, EMG) in Matlab as a mathematical model. EMG is generally used to describe the tailing state of chromatographic peaks and is also of reference value for spectral analysis. The simulated spectrum is compared with the experimental data spectrum. Figure 5 As shown, Figure 5 This is a comparison diagram between the spectrum fitted using the EMG function and the true spectrum.

[0078] The exponentially modified Gaussian function is generated by convolving a Gaussian function with an exponential decay function and is often used to describe the tailing of chromatographic peaks. Although used in chromatography, it is also valuable for spectral analysis. The exponentially modified Gaussian function (EMG function) is expressed as follows:

[0079] ;

[0080] Where x is the independent variable, which represents the wavelength (unit: nm) in this model; A is the amplitude factor, which affects the total area and height of the peak; is the time constant of the exponential decay function, which is the key parameter controlling the asymmetry of the peak shape. >0, generates direction (long wavelength). When it is close to 0, the EMG function approaches a symmetrical Gaussian function. The larger the value, the stronger the peak asymmetry and the more obvious the tailing. is the standard deviation of the Gaussian component within the EMG function, which describes the width of the Gaussian part and is related to the full width at half maximum (FWHM_G) of the Gaussian part as FWHM_G≈2.355 σ; is the mean of the Gaussian components within the EMG function, which roughly corresponds to the location of the peak but is not equal to the exact peak wavelength of the final EMG peak, unless =0; () is the complementary error function. It is defined as , which is a form of Gaussian function integral, appears in the analytical solution of the convolution of Gaussian and exponential function.

[0081] By observation Figure 5 The simulated spectrum is basically consistent with the experimental data in the trend of the falling edge. Figure 1 The simulation results are consistent with the experimental data, while the rising edge is steeper than the experimental data, and the spectral width of the simulation is narrow. A single correction function model cannot well meet all the details of the blue light spectrum. To further improve the simulation accuracy, an asymmetric pseudo-Voigt function (APV) is introduced and superimposed as an auxiliary model. The APV is a linear combination of a Gaussian function and a Lorentzian function. Its asymmetric nature allows different parameters on both sides of the peak.

[0082] Among them, the form of the asymmetric pseudo-Voigt function is as follows:

[0083] ;

[0084] ;

[0085] ;

[0086] in, is the wavelength, and center is the center position of the APV peak. In the code, center=(center wavelength x in the EMG function)-14; is the full width at half maximum of the APV to the left of center. is the full width at half maximum of the APV to the right of the center. , the peak shape is asymmetric.

[0087] is the pseudo-Voigt mixing factor. : Pure Gaussian function. : Pure Lorentz function.

[0088] : A mixture of Gaussian and Lorentzian. The larger it is, the more pronounced the Lorentz characteristics are.

[0089] After multiple inputs of experimental data and comparisons of experimental spectra, it was determined η=0.

[0090] Here and according to Location determines:

[0091] when center (left): = / (2 sqrt(2 log(2)));

[0092] ;

[0093] when center (right):

[0094] ;

[0095] ;

[0096] By superimposing the EMG and APV functions, a composite spectral model is constructed that more accurately simulates the measured blue light spectrum. The model takes the central wavelength and bandwidth of the light source as input parameters and outputs a simulated spectrum. The model ultimately linearly adds the normalized EMG function to the weighted APV function, and the resulting result is further normalized.

[0097] Step 403: input the central wavelength sample and the bandwidth sample into the composite simulation function to obtain a spectrum intensity sample output by the composite simulation function;

[0098] Specifically, the central wavelength sample and the bandwidth sample are input into the composite simulation function, and the comparison diagrams of the obtained spectral intensity sample and the experimental data spectrum are shown in Figures 6(a), 6(b), 6(c), and 6(d).

[0099] Step 404 : According to the comparison result of the spectral intensity sample and the emission spectrum samples of the plurality of light source samples, the model parameters in the composite simulation function are adjusted to obtain the light source simulation model.

[0100] The model parameters include the center wavelength offset, left and right half-width heights, and a mixing factor in the asymmetric pseudo-Voigt function.

[0101] Among them, center wavelength offset refers to the movement of the center wavelength of a waveform relative to its original or designed position in fields such as optics, communications, or spectroscopy. This offset may be caused by environmental, physical, or design factors; the mixing factor (η) is a parameter used to adjust the relative weights of the Gaussian component and the Lorentzian component. Its value range is usually 0≤η≤1. Its core function is to describe the symmetry and broadening characteristics of the peak shape.

[0102] Specifically, by comparing with the measured spectral data, the model parameters (such as the central wavelength offset of the APV function, the left and right half-width heights, the mixing factor, etc.) are adjusted so that the simulated spectrum is highly consistent with the measured spectrum.

[0103] In the above embodiment, a composite simulation function is constructed by using the EMG function and the APV function, and then the composite simulation function is used to fit the preprocessed emission spectrum samples of each of the multiple light source samples, thereby obtaining a spectral simulation result that fits the experimental data spectrum, which can provide a reliable mathematical model for the subsequent use of the light source simulation model to predict the light source quality.

[0104] The light source quality prediction device provided by the present invention is described below. The light source quality prediction device described below and the light source quality prediction method described above can be referenced to each other.

[0105] like Figure 7 As shown, Figure 7 This is a schematic diagram of the module structure of a light source quality prediction device, which includes the following modules:

[0106] Data acquisition module 701, used to obtain the central wavelength and bandwidth of the target light source;

[0107] A simulated spectrum output module 702 is configured to input the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model;

[0108] an intensity ratio calculation module 703 for determining, in the simulated spectrum, a ratio of the spectral intensity within a specified wavelength range to the total spectral intensity of the target light source;

[0109] The standard judgment module 704 is configured to judge whether the target light source meets a preset standard based on a relationship between the proportion and a preset ratio threshold.

[0110] In one embodiment, the data acquisition module 701 is also used to determine the target bandwidth; the simulated spectrum output module 702 is also used to: under the constraint of the target bandwidth, input multiple center wavelengths into the light source simulation model to obtain a simulated spectrum corresponding to each center wavelength output by the light source simulation model; the intensity proportion calculation module 703 is also used to determine the proportion of the spectral intensity within the specified wavelength range in each simulated spectrum in the total spectral intensity corresponding to the simulated spectrum; in one embodiment, the standard judgment module 704 is also used to: determine the center wavelength corresponding to the proportion greater than a preset proportion threshold as the center wavelength of the target light source.

[0111] In one embodiment, the simulated spectrum output module 702 is further configured to:

[0112] Obtaining a preprocessed emission spectrum sample, a preprocessed central wavelength sample, and a preprocessed bandwidth sample for each of the plurality of light source samples;

[0113] The composite simulation function is obtained by weighted summing the exponential modified Gaussian function and the asymmetric pseudo-Voigt function;

[0114] Inputting the central wavelength sample and the bandwidth sample into the composite simulation function to obtain a spectrum intensity sample output by the composite simulation function;

[0115] According to the comparison result of the spectral intensity sample and the emission spectrum samples of the multiple light source samples, the model parameters in the composite simulation function are adjusted to obtain the light source simulation model.

[0116] In one embodiment, the model parameters include a central wavelength offset, left and right half-widths, and a mixing factor in an asymmetric pseudo-Voigt function.

[0117] In one embodiment, the data acquisition module 701 is further configured to:

[0118] Acquire an original emission spectrum sample, an original center wavelength sample, and an original bandwidth sample of each of the plurality of light source samples;

[0119] The original emission spectrum sample, the original center wavelength sample, and the original bandwidth sample are respectively preprocessed to obtain the preprocessed emission spectrum sample, the preprocessed center wavelength sample, and the preprocessed bandwidth sample; wherein the preprocessing operation includes peak normalization and / or baseline correction.

[0120] In one embodiment, the target light source is a blue light source.

[0121] Figure 8 An example of a physical structure diagram of an electronic device is shown below. Figure 8As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communications bus 840. The processor 810, the communications interface 820, and the memory 830 communicate with each other via the communications bus 840. The processor 810 may invoke logic instructions in the memory 830 to execute a light source quality prediction method, which includes: obtaining a central wavelength and bandwidth of a target light source; inputting the central wavelength and bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; determining, in the simulated spectrum, the proportion of spectral intensity within a specified wavelength range to the total spectral intensity of the target light source; and determining, based on a relationship between the proportion and a preset proportion threshold, whether the target light source meets a preset standard. Alternatively, a target bandwidth may be determined; subject to the target bandwidth, multiple central wavelengths may be input into the light source simulation model to obtain a simulated spectrum corresponding to each central wavelength output by the light source simulation model; determining, in each simulated spectrum, the proportion of spectral intensity within the specified wavelength range to the total spectral intensity corresponding to the simulated spectrum; and determining the central wavelength corresponding to the target light source when the proportion is greater than the preset proportion threshold as the central wavelength of the target light source.

[0122] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0123] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the light source quality prediction method provided by the above methods, which includes: obtaining the central wavelength and bandwidth of the target light source; inputting the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; determining the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity of the target light source in the simulated spectrum; judging whether the target light source meets the preset standard based on the relationship between the proportion and a preset proportion threshold. Alternatively, determining a target bandwidth; under the constraint of the target bandwidth, inputting multiple central wavelengths into the light source simulation model to obtain a simulated spectrum corresponding to each central wavelength output by the light source simulation model; determining the proportion of the spectral intensity within the specified wavelength range in the total spectral intensity corresponding to the simulated spectrum in each simulated spectrum; and determining the central wavelength corresponding to when the proportion is greater than the preset proportion threshold as the central wavelength of the target light source.

[0124] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the light source quality prediction method provided by the above-mentioned methods, the method comprising: obtaining the central wavelength and bandwidth of the target light source; inputting the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; determining the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity of the target light source in the simulated spectrum; judging whether the target light source meets the preset standard based on the relationship between the proportion and a preset ratio threshold. Alternatively, determining a target bandwidth; under the constraint of the target bandwidth, inputting multiple central wavelengths into the light source simulation model to obtain a simulated spectrum corresponding to each of the central wavelengths output by the light source simulation model; determining the proportion of the spectral intensity within the specified wavelength range in the total spectral intensity corresponding to the simulated spectrum in each simulated spectrum; determining the central wavelength corresponding to when the proportion is greater than the preset ratio threshold as the central wavelength of the target light source.

[0125] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0126] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A light source quality prediction method, characterized in that: include: Obtain the central wavelength and bandwidth of the target light source; Inputting the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; Determining, in the simulated spectrum, the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity of the target light source; Determining whether the target light source meets a preset standard based on a relationship between the proportion and a preset ratio threshold; The light source simulation model is obtained according to the following method, including: Obtaining a preprocessed emission spectrum sample, a preprocessed central wavelength sample, and a preprocessed bandwidth sample for each of the plurality of light source samples; The composite simulation function is obtained by weighted summing the exponential modified Gaussian function and the asymmetric pseudo-Voigt function; Inputting the central wavelength sample and the bandwidth sample into the composite simulation function to obtain a spectrum intensity sample output by the composite simulation function; According to the comparison results of the spectral intensity sample and the emission spectrum samples of the multiple light source samples, the model parameters in the composite simulation function are adjusted to obtain the light source simulation model; the model parameters include the center wavelength offset, the left and right half-width heights and the mixing factor in the asymmetric pseudo-Voigt function.

2. A light source quality prediction method, characterized in that: include: Determine target bandwidth; Under the constraint of the target bandwidth, multiple center wavelengths are input into a light source simulation model to obtain a simulated spectrum corresponding to each center wavelength output by the light source simulation model; Determine, in each simulated spectrum, the proportion of the spectral intensity within the specified wavelength range in the total spectral intensity corresponding to the simulated spectrum; Determine one or more central wavelengths corresponding to when the proportion is greater than a preset ratio threshold as the central wavelength range of the target light source; The light source simulation model is obtained according to the following method, including: Obtaining a preprocessed emission spectrum sample, a preprocessed central wavelength sample, and a preprocessed bandwidth sample for each of the plurality of light source samples; The composite simulation function is obtained by weighted summing the exponential modified Gaussian function and the asymmetric pseudo-Voigt function; Inputting the central wavelength sample and the bandwidth sample into the composite simulation function to obtain a spectrum intensity sample output by the composite simulation function; According to the comparison results of the spectral intensity sample and the emission spectrum samples of the multiple light source samples, the model parameters in the composite simulation function are adjusted to obtain the light source simulation model; the model parameters include the center wavelength offset, the left and right half-width heights and the mixing factor in the asymmetric pseudo-Voigt function.

3. The light source quality prediction method according to claim 2, characterized in that: Before obtaining the preprocessed emission spectrum samples, preprocessed center wavelength samples, and preprocessed bandwidth samples of each of the plurality of light source samples, the method includes: Acquire an original emission spectrum sample, an original center wavelength sample, and an original bandwidth sample of each of the plurality of light source samples; The original emission spectrum sample, the original center wavelength sample, and the original bandwidth sample are respectively preprocessed to obtain the preprocessed emission spectrum sample, the preprocessed center wavelength sample, and the preprocessed bandwidth sample; wherein the preprocessing operation includes peak normalization and / or baseline correction.

4. The light source quality prediction method according to any one of claims 1 to 2, characterized in that: The target light source is a blue light source.

5. A light source quality prediction device, characterized in that: include: A data acquisition module, used to obtain the central wavelength and bandwidth of the target light source; A simulated spectrum output module, configured to input the central wavelength and the bandwidth into a light source simulation model to obtain a simulated spectrum output by the light source simulation model; an intensity ratio calculation module, configured to determine, in the simulated spectrum, the ratio of the spectral intensity within a specified wavelength range to the total spectral intensity of the target light source; a standard judgment module, configured to judge whether the target light source meets a preset standard based on a relationship between the proportion and a preset ratio threshold; Among them, the simulated spectrum output module is also used to obtain the preprocessed emission spectrum samples, preprocessed center wavelength samples and preprocessed bandwidth samples of each of the multiple light source samples; after weighted summation of the exponentially corrected Gaussian function and the asymmetric pseudo-Voigt function, a composite simulation function is obtained; the center wavelength samples and bandwidth samples are input into the composite simulation function to obtain the spectral intensity samples output by the composite simulation function; according to the comparison results of the spectral intensity samples with the emission spectrum samples of the multiple light source samples, the model parameters in the composite simulation function are adjusted to obtain the light source simulation model; the model parameters include the center wavelength offset, left and right half-width heights and mixing factor in the asymmetric pseudo-Voigt function.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the light source quality prediction method according to any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the light source quality prediction method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the light source quality prediction method according to any one of claims 1 to 4 is implemented.

Images