Light source quality prediction method and device

Abstract

The invention provides a light source quality prediction method and device, and belongs to the field of optical measurement. The method comprises the following steps: acquiring the central wavelength and bandwidth of a target light source; inputting the central wavelength and the bandwidth into a light source simulation model to obtain a simulation spectrum output by the light source simulation model; determining the proportion of the spectral intensity in a specified wavelength range in all spectral intensities of the target light source in the simulated spectrum; according to the relation between the proportion and the preset proportion threshold value, whether the target light source meets the preset standard or not is judged, the method can provide important reference basis for researchers to select or customize the light source in the initial stage of experimental design, experimental result deviation or invalidation caused by using a non-compliant light source is avoided, and the experimental efficiency is improved. Therefore, the research efficiency and reliability are improved.

Description

Technical Field

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

[0002] Blue light is a kind of visible light with a wavelength between 400 and 500 nanometers, belonging to short-wave light with relatively high energy. Artificial light sources such as LED screens (including mobile phones, computers, televisions, etc.), energy-saving lamps, and fluorescent lamps will emit a large amount of blue light. Research shows that blue light not only affects the visual system, but also can penetrate the skin tissue, induce the production of reactive oxygen species through photosensitization, resulting in cellular oxidative stress, DNA damage, pigmentation, accelerated skin aging, and even may increase the risk of certain skin diseases.

[0003] With the deepening of the understanding of the biological effects of blue light, the development of skin care products, protective materials, or optical filters that can effectively protect against blue light has become a research and development hotspot in the industry. In order to scientifically and accurately evaluate the efficacy of these protective products, relevant biological experiments are required. When designing or selecting a blue light LED light source for such evaluation experiments, researchers need to ensure that the core parameters of its emission spectrum, such as the central wavelength (peak wavelength) and spectral bandwidth, can make its overall spectrum meet the requirements of some industry standards regarding the wavelength range and energy ratio. For example, standards such as T / CAPA13—2024 "Specification for the Evaluation of the Efficacy of Blue Light Protection for Human Skin" in China clearly require that the light source used for evaluation (usually a non-laser light source, such as an LED) should be able to stably emit light in the wavelength range of 400 - 500 nm, and the energy ratio of the light in this wavelength band is not less than 99%, while the energy ratio of the light outside the target wavelength band is not higher than 1%.

[0004] In the existing process, usually, a light source is first selected or customized, and then actual spectrum measurement and analysis are carried out to determine whether it meets the strict requirements regarding the energy ratio in the industry standards. If the selected light source, even though its nominal central wavelength and bandwidth seem appropriate, is found not to meet the specification standards after actual measurement, the light source needs to be reselected or adjusted, which results in a waste of time, cost, and low experimental efficiency. Summary of the Invention

[0005] The present invention provides a method and device for predicting the quality of a light source to solve the defect of resource waste caused by continuously customizing light sources in the prior art, and to achieve determining accurate light source parameters in advance through a model and then configuring an actual light source according to the light source parameters.

[0006] The present invention provides a method for predicting the quality of a light source, including the following steps.

[0007] Obtain the central wavelength and bandwidth of the target light source; 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; Determine 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; Judge whether the target light source meets a preset standard according to the relationship between the proportion and a preset proportion threshold.

[0008] The present invention also provides a method for predicting the quality of a light source, including the following steps: Determine a target bandwidth; Under the constraint of the target bandwidth, input multiple central wavelengths into a light source simulation model to obtain a simulated spectrum corresponding to each of the central wavelengths output by the light source simulation model; Determine the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity corresponding to the simulated spectrum in each simulated spectrum; Determine the central wavelength corresponding to the target light source when the proportion is greater than a preset proportion threshold.

[0009] According to a method for predicting the quality of a light source provided by the present invention, the light source simulation model is obtained according to the following method, including: Obtain preprocessed emission spectrum samples, preprocessed central wavelength samples, and preprocessed bandwidth samples of respective ones of multiple light source samples; After performing weighted summation on an exponential modified Gaussian function and an asymmetric pseudo-Voigt function, obtain a composite simulation function; Input the central wavelength samples and bandwidth samples into the composite simulation function to obtain spectral intensity samples output by the composite simulation function; Adjust model parameters in the composite simulation function according to the comparison result between the spectral intensity samples and the emission spectrum samples of the multiple light source samples to obtain the light source simulation model.

[0010] According to a method for predicting the quality of a light source provided by the present invention, the model parameters include the central wavelength shift, left and right half-width at half maximum, and mixing factor in the asymmetric pseudo-Voigt function.

[0011] According to a method for predicting the quality of a light source provided by the present invention, before obtaining the preprocessed emission spectrum samples, preprocessed central wavelength samples, and preprocessed bandwidth samples of respective ones of the multiple light source samples, it includes: Obtain the original emission spectrum samples, original central wavelength samples, and original bandwidth samples of respective ones of the multiple light source samples; Preprocessing operations are respectively performed on the original emission spectrum sample, the original central wavelength sample, and the original bandwidth sample to obtain the preprocessed emission spectrum sample, the preprocessed central wavelength sample, and the preprocessed bandwidth sample; wherein, the preprocessing operations include peak normalization and / or baseline correction.

[0012] According to a light source quality prediction method provided by the present invention, the target light source is a blue light source.

[0013] The present invention also provides a light source quality prediction device, including the following modules: A data acquisition module, configured to acquire 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 the ratio of the spectral intensity within a specified wavelength range in the simulated spectrum 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 according to the relationship between the ratio and a preset ratio threshold.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the light source quality prediction method described in any one of the above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the light source quality prediction method described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the light source quality prediction method described in any one of the above is implemented.

[0017] A method and device for predicting the quality of a light source provided by the present invention obtain the central wavelength and bandwidth of a target light source; 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; determine 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; and determine whether the target light source meets a preset standard according to the relationship between the proportion and a preset proportion threshold. This method can provide an important reference basis for researchers to select or customize a light source at the initial stage of experimental design, avoid experimental result deviations or invalidation caused by using non-compliant light sources, and thus improve the efficiency and reliability of research. Applying this method to the quality pre-evaluation of blue light sources, especially in fields such as biological effect research (such as skin protection evaluation) that require precise control of the spectral characteristics of the light source, has clear practical significance and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is one of the flow schematic diagrams of a method for predicting the quality of a light source provided by the present invention.

[0020] Figure 2 is a comparison diagram of the spectrum after fitting using the standard Gaussian fitting function and the real spectrum provided by the present invention.

[0021] Figure 3 is the second flow schematic diagram of a method for predicting the quality of a light source provided by the present invention.

[0022] Figure 4 is the determination flow schematic diagram of the light source simulation model provided by the present invention.

[0023] Figure 5 is a comparison diagram of the spectrum after fitting using the EMG function and the real spectrum provided by the present invention.

[0024] Figure 6(a) is one of the comparison diagrams of the spectral intensity sample and the experimental data spectrum provided by the present invention.

[0025] Figure 6(b) is the second comparison diagram of the spectral intensity sample and the experimental data spectrum provided by the present invention.

[0026] Figure 6(c) is the third comparison diagram of the spectral intensity sample and the experimental data spectrum provided by the present invention.

[0027] Figure 6(d) is the fourth comparison graph between the spectral intensity samples provided by the present invention and the experimental data spectra.

[0028] Figure 7 is a schematic structural diagram of the light source quality prediction device provided by the present invention.

[0029] Figure 8 is a schematic structural diagram of the electronic device provided by the present invention. Detailed implementation manners

[0030] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0031] The following combines Figures 1-8 to describe the detailed implementation manners of the present invention.

[0032] Figure 1 is one of the schematic flowcharts of a light source quality prediction method provided by the present invention. As Figure 1 shown, the method includes the following steps: Step 101, obtain the central wavelength and bandwidth of the target light source.

[0033] Among them, the target light source refers to the required light source. For example, in order to perform the efficacy test of sunscreen products, it is necessary to configure a suitable blue light LED light source. Before configuring the blue light LED light source, it is necessary to first determine the parameters of the required target light source.

[0034] Specifically, determine the parameters of the required target light source according to the actual purpose, including the central wavelength and bandwidth. Among them, the central wavelength is the core parameter describing the optical signal or spectral characteristics, referring to the wavelength value with the highest intensity (or the most significant characteristics) in the energy distribution of a certain light wave. For example, the central wavelength of a blue light LED is generally about 450 nm. The bandwidth of the light source is a physical quantity describing the width of the energy distribution of the light emitted by the light source within the wavelength (or frequency) range, usually expressed by the full width at half maximum (FWHM), that is, the wavelength range width corresponding to 50% of the spectral intensity peak. The bandwidth directly reflects the monochromaticity and energy concentration of the light source and is one of the key parameters for evaluating the performance of the light source.

[0035] Step 102, input the central wavelength and the bandwidth into the light source simulation model to obtain the simulated spectrum output by the light source simulation model.

[0036] Among them, the light source simulation model is a model obtained by simulating in advance according to the spectral parameters of a large number of actual light source samples, and can simulate the spectral fitting curves corresponding to light sources of different parameter types.

[0037] Specifically, when the central wavelength and bandwidth of the above-mentioned target light source are input into the above-mentioned light source simulation model, a simulated spectrum output by the light source simulation model can be obtained, and this simulated spectrum is the simulated spectrum of the above-mentioned target light source. Among them, 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 can also be a model obtained by superimposing an exponentially modified Gaussian function (EMG) and an asymmetric pseudo-Voigt function (APV). As Figure 2 shown, Figure 2 shows a comparison chart of the spectrum after fitting using a standard Gaussian fitting function and the true spectrum.

[0038] Step 103, determine 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.

[0039] Specifically, in order to determine whether the target light source meets the preset industry standards. For example, standards such as T / CAPA 13—2024 "Specification for the Evaluation of the Efficacy of Blue Light Protection for Human Skin" in China clearly require that the light source used for evaluation (usually a non-laser light source, such as an LED) should be able to stably emit light in the wavelength range of 400 - 500 nm, and the proportion of the light energy within this wavelength band is not less than 99%, while the proportion of the light energy outside the target wavelength band is not higher than 1%. Therefore, in the above-mentioned simulated spectrum, first determine the proportion of the spectral intensity within a specified wavelength range (such as 400 - 500 nm) in the total spectral intensity.

[0040] Step 104, judge whether the target light source meets the preset standard according to the relationship between the proportion and the preset proportion threshold.

[0041] Specifically, judge whether the above-mentioned proportion is greater than or equal to the preset proportion threshold (such as 99%). If it is greater than or equal to the preset proportion threshold (such as 99%), it is considered that the above-mentioned target light source meets the preset standard. Further, the required actual light source can be configured according to the above-determined light source parameters, including the central wavelength and bandwidth.

[0042] In the above embodiments, by 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 all spectral intensities of the target light source in the simulated spectrum; and judging whether the target light source meets a preset standard according to the relationship between the proportion and a preset proportion threshold, this method can provide an important reference basis for researchers to select or customize a light source at the initial stage of experimental design, avoiding experimental result deviations or invalidation caused by using non-compliant light sources, thereby improving the efficiency and reliability of research. Applying this method to the quality pre-evaluation of blue light sources, especially in fields such as biological effect research (such as skin protection evaluation) that require precise control of the spectral characteristics of the light source, has clear practical significance and application value.

[0043] In one embodiment, as Figure 3 shown, a method for predicting the quality of a light source is also provided, including the following steps.

[0044] Step 301, determine the target bandwidth.

[0045] Specifically, first, determine the target bandwidth of the target light source required. The bandwidth of a light source is a physical quantity that describes the width of the energy distribution of the light emitted by the light source within a wavelength (or frequency) range, usually expressed by the full width at half maximum (FWHM), that is, the width of the wavelength range corresponding to 50% of the spectral intensity peak. The bandwidth directly reflects the monochromaticity and energy concentration of the light source and is one of the key parameters for evaluating the performance of the light source.

[0046] Step 302, under the constraint of the target bandwidth, input multiple central wavelengths into the light source simulation model to obtain the simulated spectrum corresponding to each of the central wavelengths output by the light source simulation model.

[0047] Specifically, keep the above target bandwidth parameter unchanged, and then try to input different central wavelengths through the "trial and error method" to observe the simulated spectrum output by the light source simulation model.

[0048] Step 303, determine the proportion of the spectral intensity within a specified wavelength range in all spectral intensities corresponding to the simulated spectrum in each simulated spectrum.

[0049] Specifically, count the proportion of the spectral intensity within a specified wavelength range in all spectral intensities corresponding to the simulated spectrum in each simulated spectrum, so as to determine the value range of the central wavelength according to the input central wavelength.

[0050] Step 304, determine the central wavelength corresponding to when the proportion is greater than a preset proportion threshold as the central wavelength of the target light source.

[0051] Specifically, the value range of the central wavelength is determined according to the input central wavelength. After multiple attempts to input the central wavelength in the above two steps, the range of the central wavelength that meets the preset standard is finally determined to be 425 - 453 nm. For example, the intensity ratio of the 400 - 500 nm band within the central wavelength range of 425 - 453 nm is greater than 99%.

[0052] In the above embodiment, by determining the target bandwidth; under the constraint of the target bandwidth, inputting multiple central wavelengths into the light source simulation model to obtain the simulated spectrum corresponding to each central wavelength output by the light source simulation model; determining the ratio of the spectral intensity within the specified wavelength range in each simulated spectrum to the total spectral intensity corresponding to the simulated spectrum; and determining the central wavelength corresponding to when the ratio is greater than the preset ratio threshold as the central wavelength of the target light source. This method, by fixing the bandwidth and inputting different central wavelengths, finally obtains the range of the central wavelength of the target light source that meets the preset standard within the fixed bandwidth range, thereby providing direct guidance for the selection and design of the light source.

[0053] In one embodiment, as Figure 4 shown, Figure 4 The schematic diagram of the determination process of the light source simulation model is shown. The above light source simulation model is obtained according to the following method, including the following steps: Step 401, obtain the pre - processed emission spectrum samples, pre - processed central wavelength samples, and pre - processed bandwidth samples of each of the multiple light source samples.

[0054] It is worth mentioning that before this step 401, it also includes: obtaining the original emission spectrum samples, original central wavelength samples, and original bandwidth samples of each of the multiple light source samples; performing pre - processing operations on the original emission spectrum samples, original central wavelength samples, and original bandwidth samples respectively to obtain the pre - processed emission spectrum samples, pre - processed central wavelength samples, and pre - processed bandwidth samples; where the pre - processing operations include peak normalization and / or baseline correction.

[0055] Specifically, obtain the original emission spectrum samples of different actual LED light source samples (specifically, it can be LED blue light sources) in a stable working state, covering a relatively wide range (250 - 800 nm) including the blue light band. The original emission spectrum samples include original central wavelength samples and original bandwidth samples. Perform pre - processing on these original emission spectrum samples, including peak normalization and baseline correction, to eliminate the influence of absolute intensity differences and baseline drift, and use the average value of the obtained blue light emission spectrum samples as the representative spectral data for modeling. Finally, obtain the pre - processed emission spectrum samples, pre - processed central wavelength samples, and pre - processed bandwidth samples.

[0056] Step 402: After performing weighted summation on the exponentially modified Gaussian function and the asymmetric pseudo-Voigt function, a composite simulation function is obtained.

[0057] Specifically, in the experiment, it is observed that the spectrum of a blue LED often exhibits a trailing phenomenon with a steep rising edge and a gentle falling edge. For the asymmetric peak shape often shown by the spectral data obtained from blue LED experiments, using simple models such as a symmetric Gaussian model to simulate this asymmetric spectrum will result in a large deviation in the intensity distribution of the two wings of the spectrum, thus making it impossible to accurately predict the actual energy proportion within a specific wavelength band. As Figure 2 shown, the fitting effect using the standard Gaussian function in Matlab is not good.

[0058] In this application, first, an attempt is made to use an asymmetric function (exponentially modified Gaussian function, EMG) as a mathematical model for simulation in Matlab. EMG is generally applied to describe the trailing state of chromatographic peaks and also has reference value for spectral analysis. The comparison between the simulated spectrum and the experimental data spectrum is as Figure 5 shown. Figure 5 FIG. is a comparison diagram of the spectrum after fitting with the EMG function and the real spectrum.

[0059] Among them, the exponentially modified Gaussian function is generated by the convolution of the Gaussian function and the exponential decay function, and is often used to describe the trailing peak shape of chromatographic peaks. Although it is applied in the chromatographic field, it also has reference value for spectral analysis. The expression form of the exponentially modified Gaussian function (i.e., the EMG function) is as follows: ; 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 peak shape asymmetry. When > 0, a trailing occurs in the direction (long wavelength). When is close to 0, the EMG function approaches a symmetric Gaussian function. The larger the value, the stronger the peak asymmetry and the more obvious the trailing. is the standard deviation of the Gaussian component inside the EMG function, which describes the width of the Gaussian part. The relationship with the full width at half maximum (FWHM_G) of the Gaussian part is FWHM_G≈2.355 σ; is the mean of the Gaussian component inside the EMG function, which roughly corresponds to the position 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.

[0060] 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 show that the peak of the blue light spectrum is consistent with that of the experimental data, while the rising edge trend is steeper than that of the experimental data, and the spectral width of the simulation is narrow. A single correction function model cannot meet all the details of the blue light spectrum. In order to further improve the simulation accuracy, an asymmetric pseudo-Voigt function (APV) is introduced and superimposed as an auxiliary model. The asymmetric pseudo-Voigt function itself is a linear combination of a Gaussian function and a Lorentzian function. The asymmetric characteristics allow different parameters on both sides of the peak.

[0061] Among them, the form of the asymmetric pseudo-Voigt function is as follows: ; ; ; 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. If , the peak shape is asymmetric.

[0062] is the pseudo-Voigt mixing factor. : Pure Gaussian function. : Pure Lorentz function.

[0063] : A mixture of Gaussian and Lorentzian. The larger it is, the more significant the Lorentz characteristics are.

[0064] After multiple inputs of experimental data and comparisons of experimental spectra, the results were confirmed. η=0.

[0065] Here and according to Location determines: when center(left): = / (2 sqrt(2 log(2))); ; When center (right side): ; ; By superimposing the EMG function and the APV function, a composite spectral model is constructed that can more accurately simulate the measured blue light spectrum. The input parameters of the model are the central wavelength and bandwidth of the light source, and the output is the simulated spectral diagram. Finally, the EMG function is normalized and linearly added to the weighted APV function, and the final result is normalized again.

[0066] Step 403, input the central wavelength sample and bandwidth sample into the composite simulation function to obtain the spectral intensity sample output by the composite simulation function; Specifically, when the central wavelength sample and bandwidth sample are input into the composite simulation function, the comparison diagrams of the obtained spectral intensity sample and the experimental data spectrum are shown in FIGS. 6(a), 6(b), 6(c), and 6(d).

[0067] Step 404, according to the comparison result between the spectral intensity sample and the emission spectral samples of the multiple light source samples, adjust the model parameters in the composite simulation function to obtain the light source simulation model.

[0068] Among them, the model parameters include the central wavelength shift, left and right half-width at half maximum, and mixing factor in the asymmetric pseudo-Voigt function.

[0069] Among them, the central wavelength shift refers to the shift of the central wavelength of a certain waveform relative to its original or designed position in the fields of optics, communication, spectroscopy, etc. This shift 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, and its value range is usually 0≤η≤1. Its core role is to describe the symmetry and broadening characteristics of the peak shape.

[0070] Specifically, by comparing with the measured spectral data, adjust the model parameters (such as the central wavelength shift, left and right half-width at half maximum, mixing factor, etc. of the APV function) so that the simulated spectrum is highly consistent with the measured spectrum.

[0071] In the above embodiments, a composite simulation function is constructed by using the EMG function and the APV function, and then the preprocessed emission spectrum samples of each of the multiple light source samples are fitted by using the composite simulation function, so as to obtain a spectral simulation result that fits the experimental data spectrum, which can provide a reliable mathematical model for predicting the light source quality by using the light source simulation model subsequently.

[0072] The light source quality prediction device provided by the present invention will be described below. The light source quality prediction device described below can be correspondingly referred to the light source quality prediction method described above.

[0073] As Figure 7 shown, Figure 7 FIG. 1 is one of the schematic diagrams of the module structure of the light source quality prediction device. The light source quality prediction device includes the following modules: A data acquisition module 701, configured to acquire the central wavelength and bandwidth of a target light source; A simulated spectrum output module 702, 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 703, configured to determine the ratio of the spectral intensity within a specified wavelength range in the simulated spectrum to the total spectral intensity of the target light source; A standard judgment module 704, configured to judge whether the target light source meets a preset standard according to the relationship between the ratio and a preset ratio threshold.

[0074] In one embodiment, the above data acquisition module 701 is further configured to determine a target bandwidth; the above simulated spectrum output module 702 is further configured to: under the constraint of the target bandwidth, input multiple central wavelengths into the light source simulation model to obtain the simulated spectrum corresponding to each of the central wavelengths output by the light source simulation model; the above intensity ratio calculation module 703 is further configured to determine the ratio of the spectral intensity within a specified wavelength range in each simulated spectrum to the total spectral intensity corresponding to the simulated spectrum; in one embodiment, the above standard judgment module 704 is further configured to: determine the central wavelength corresponding to the ratio greater than the preset ratio threshold as the central wavelength of the target light source.

[0075] In one embodiment, the above simulated spectrum output module 702 is further configured to: acquire the preprocessed emission spectrum samples, preprocessed central wavelength samples, and preprocessed bandwidth samples of each of the multiple light source samples; perform weighted summation on the exponential modified Gaussian function and the asymmetric pseudo-Voigt function to obtain a composite simulation function; input the central wavelength samples and bandwidth samples into the composite simulation function to obtain spectral intensity samples output by the composite simulation function; Adjust the model parameters in the composite simulation function according to the comparison result between the spectral intensity sample and the emission spectrum samples of the multiple light source samples, so as to obtain the light source simulation model.

[0076] In one embodiment, the model parameters include the central wavelength shift, the left and right half-width at half maximum, and the mixing factor in the asymmetric pseudo-Voigt function.

[0077] In one embodiment, the above data acquisition module 701 is further configured to: Obtain the original emission spectrum samples, the original central wavelength samples, and the original bandwidth samples of each of the multiple light source samples; Perform preprocessing operations on the original emission spectrum samples, the original central wavelength samples, and the original bandwidth samples respectively to obtain the preprocessed emission spectrum samples, the preprocessed central wavelength samples, and the preprocessed bandwidth samples; wherein, the preprocessing operations include peak normalization and / or baseline correction.

[0078] In one embodiment, the target light source is a blue light source.

[0079] Figure 8 Illustrates a schematic physical structure diagram of an electronic device, as Figure 8 shown. The electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 complete communication with each other through the communication bus 840. The processor 810 can call the logical instructions in the memory 830 to execute the light source quality prediction method, which includes: obtaining the central wavelength and bandwidth of the target light source; inputting the central wavelength and the bandwidth into the light source simulation model to obtain the 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 according to the relationship between the proportion and the preset proportion threshold. Or, determining the target bandwidth; under the constraint of the target bandwidth, inputting multiple central wavelengths into the light source simulation model to obtain the simulated spectrum corresponding to each of the central wavelengths output by the light source simulation model; determining the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity corresponding to the simulated spectrum in each simulated spectrum; determining the central wavelength corresponding to the proportion greater than the preset proportion threshold as the central wavelength of the target light source.

[0080] In addition, when the logical instructions in the above-mentioned memory 830 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0081] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that 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-mentioned various methods. The method includes: obtaining the central wavelength and bandwidth of a 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; and judging whether the target light source meets a preset standard according to 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 the simulated spectrum corresponding to each central wavelength output by the light source simulation model; determining the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity corresponding to each simulated spectrum in each simulated spectrum; and determining the central wavelength corresponding to the proportion greater than the preset proportion threshold as the central wavelength of the target light source.

[0082] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the light source quality prediction method provided by the above-mentioned various methods. The method includes: obtaining the central wavelength and bandwidth of a 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; and judging whether the target light source meets a preset standard according to the relationship between the proportion and a preset proportion threshold. Or, determining a target bandwidth; under the constraint of the target bandwidth, inputting a plurality of central wavelengths into the light source simulation model to obtain the simulated spectrum corresponding to each of the central wavelengths output by the light source simulation model; determining, in each simulated spectrum, the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity corresponding to the 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.

[0083] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0084] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0085] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the quality of a light source, characterized in that, Including: Obtain the central wavelength and bandwidth of the target light source; 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; Determine 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; Judge whether the target light source meets a preset standard according to the relationship between the proportion and a preset proportion threshold.

2. A method for predicting the quality of a light source, characterized in that, Including: Determine the target bandwidth; Under the constraint of the target bandwidth, input multiple central wavelengths into a light source simulation model to obtain the simulated spectrum corresponding to each of the central wavelengths output by the light source simulation model; Determine the proportion of the spectral intensity within a specified wavelength range in the total spectral intensity corresponding to the simulated spectrum in each simulated spectrum; Determine the central wavelength corresponding to when the proportion is greater than the preset proportion threshold as the central wavelength of the target light source.

3. The method for predicting the quality of a light source according to any one of claims 1 to 2, characterized in that, The light source simulation model is obtained by the following method, including: Obtain the preprocessed emission spectrum samples, preprocessed central wavelength samples, and preprocessed bandwidth samples of each of multiple light source samples; After performing weighted summation on an exponential modified Gaussian function and an asymmetric pseudo-Voigt function, obtain a composite simulation function; Input the central wavelength samples and bandwidth samples into the composite simulation function to obtain spectral intensity samples output by the composite simulation function; Adjust the model parameters in the composite simulation function according to the comparison result between the spectral intensity samples and the emission spectrum samples of the multiple light source samples to obtain the light source simulation model.

4. The method for predicting the quality of a light source according to claim 3, wherein, The model parameters include the central wavelength offset, left and right half-width heights, and mixing factor in the asymmetric pseudo-Voigt function.

5. The light source quality prediction method according to claim 3, wherein Before obtaining the preprocessed emission spectrum samples, preprocessed central wavelength samples, and preprocessed bandwidth samples of each of the multiple light source samples, including: Obtain the original emission spectrum samples, original central wavelength samples, and original bandwidth samples of each of the multiple light source samples; Perform preprocessing operations on the original emission spectrum samples, original central wavelength samples, and original bandwidth samples respectively to obtain the preprocessed emission spectrum samples, preprocessed central wavelength samples, and preprocessed bandwidth samples; wherein, the preprocessing operations include peak normalization and / or baseline correction.

6. The method for predicting the quality of a light source according to any one of claims 1 to 2, characterized in that The target light source is a blue light source.

7. A light source quality prediction device, characterized in that, Including: A data acquisition module for obtaining the central wavelength and bandwidth of the target light source; A simulated spectrum output module for 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; An intensity proportion calculation module for 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; A standard judgment module for judging whether the target light source meets a preset standard according to the relationship between the proportion and a preset proportion threshold.

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

9. 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, it implements the light source quality prediction method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the light source quality prediction method according to any one of claims 1 to 6.

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