Spectrum scoring method and device based on illumination scene, equipment and medium

Abstract

The invention belongs to the technical field of LED illumination, and provides a spectrum scoring method and device based on an illumination scene, equipment and a medium. The method comprises the following steps: acquiring a target spectrum of a target LED and a standard spectrum of a standard illuminant; and obtaining a corresponding similarity deviation interval according to the spectral characteristics of the standard spectrum in each preset wavelength interval. And scene information of a target LED is acquired, an adjustment factor of each preset wavelength interval is acquired according to the scene information, each similarity deviation interval is adjusted according to the adjustment factor, and a target similarity deviation interval corresponding to the illumination scene is obtained. And finally, combining the spectrum similarity and the target similarity deviation interval to obtain a spectrum similarity score of the target spectrum. The method comprehensively considers the spectral characteristics and similarity deviation of each wavelength interval in different scenes, specifically evaluates the spectral performance of the LED product, can perform customized evaluation according to the requirements of different scenes, improves the accuracy and applicability of the spectral evaluation result, and provides a method for design and optimization of the LED lighting product.

Description

[0001] This application is a divisional application of the invention patent application filed on March 26, 2024, with the invention name “Evaluation method, device, equipment and medium based on spectral similarity” and application number 2024103472559. Technical Field

[0002] The present invention relates to the field of LED lighting technology, and in particular to a spectral scoring method, device, equipment and medium based on lighting scenes. Background Art

[0003] In the light-emitting semiconductor lighting industry, LEDs have been widely used as an efficient and energy-saving light source. However, due to the diversity and complexity of LED spectra, it is crucial to evaluate the quality and performance of LED products. Spectral similarity is an indicator for evaluating the similarity between two spectra, and is usually used to compare the color quality of different light sources or to judge the degree of spectrum matching. In the fields of lighting, plant growth, etc., accurate evaluation of spectral similarity is crucial to ensure the required light quality.

[0004] Existing spectral similarity calculation schemes are mainly based on the comparison of spectral shape and amplitude information, which often ignores the characteristic information of the spectrum and the different spectral similarity requirements for different wavelength ranges under different lighting scenarios. Therefore, the methods in related technologies have certain limitations when evaluating the color quality of LED light sources, resulting in inaccurate spectral evaluation results. Summary of the invention

[0005] In view of this, the embodiments of the present invention provide a spectral scoring method, device, equipment and medium based on lighting scenes, so as to solve the problem that the spectral similarity calculation scheme in the prior art does not consider the spectral characteristics and related factors of the lighting scene, resulting in inaccurate evaluation results.

[0006] In a first aspect, an embodiment of the present invention provides a spectral scoring method based on a lighting scene, the method comprising:

[0007] Obtain the relative spectrum of the target LED and the relative spectrum of the standard illuminant, which are recorded as the target spectrum and the standard spectrum respectively;

[0008] According to the spectral characteristics of the standard spectrum in each preset wavelength interval, the similarity deviation interval corresponding to each preset wavelength interval is obtained; wherein the preset wavelength intervals have no intersection and belong to the visible light wavelength range, and different preset wavelength intervals correspond to different similarity deviation intervals;

[0009] Acquire scene information of the target LED lighting scene, and acquire adjustment factors corresponding to each of the preset wavelength intervals according to the scene information;

[0010] Adjusting each of the similarity deviation intervals according to the adjustment factor to obtain a target similarity deviation interval corresponding to the lighting scene;

[0011] According to the spectral similarity of each of the preset wavelength intervals and the corresponding target similarity deviation interval, a spectral similarity score of the target spectrum of the target LED is obtained.

[0012] Preferably, the preset wavelength range includes a red light wavelength range of 622 to 700 nm, an orange light wavelength range of 597 to 622 nm, a yellow light wavelength range of 577 to 597 nm, a green light wavelength range of 492 to 577 nm, a cyan light wavelength range of 475 to 492 nm, a blue light wavelength range of 450 to 475 nm and a violet light wavelength range of 400 to 450 nm.

[0013] Preferably, the step of obtaining the similarity deviation interval corresponding to each of the preset wavelength intervals according to the spectral characteristics of the standard spectrum in each of the preset wavelength intervals comprises:

[0014] Acquiring the spectral characteristics of the standard spectrum in each of the preset wavelength ranges;

[0015] According to the spectral energy distribution, the spectral gradient angle and the human eye visibility, determine the first deviation threshold and the second deviation threshold corresponding to each of the preset wavelength intervals, wherein the first deviation threshold is a negative number, which is used to characterize the maximum deviation allowed when the light intensity of the target spectrum in the corresponding preset wavelength interval is lower than the standard spectrum; the second deviation threshold is a positive number, which is used to characterize the maximum deviation allowed when the light intensity of the target spectrum in the corresponding preset wavelength interval is higher than the standard spectrum;

[0016] According to the first deviation threshold and the second deviation threshold, the similarity deviation interval corresponding to each of the preset wavelength intervals is obtained.

[0017] Preferably, the step of obtaining scene information of the target LED lighting scene, and obtaining the adjustment factor corresponding to each of the preset wavelength intervals according to the scene information, includes:

[0018] Acquire scene information of the target LED lighting scene;

[0019] According to the scene information, a first threshold adjustment factor and a second threshold adjustment factor corresponding to each of the preset wavelength intervals are obtained; wherein the first threshold adjustment factor is used to adjust the first deviation threshold of the preset wavelength interval, and the second threshold adjustment factor is used to adjust the second deviation threshold of the preset wavelength interval.

[0020] Preferably, the step of acquiring the first threshold adjustment factor and the second threshold adjustment factor corresponding to each of the preset wavelength intervals according to the scene information includes:

[0021] Filter the preset wavelength range according to the scene information to obtain a key wavelength range;

[0022] According to the scene information, setting a first threshold adjustment factor and a second threshold adjustment factor for each of the key wavelength intervals;

[0023] The first threshold adjustment factor and the second threshold adjustment factor of the preset wavelength interval outside the key wavelength interval are both set to 1.

[0024] Preferably, the step of screening the preset wavelength range according to the scene information to obtain the key wavelength range includes:

[0025] When the scene information indicates that the lighting scene is a classroom scene, the key wavelength range obtained includes a green light wavelength range of 492 to 577 nm and a blue light wavelength range of 450 to 475 nm;

[0026] When the scene information indicates that the lighting scene is an office scene, the key wavelength range obtained includes a green light wavelength range of 492 to 577 nm and a yellow light wavelength range of 577 to 597 nm;

[0027] When the scene information indicates that the lighting scene is factory lighting, the acquired key wavelength range includes a blue light wavelength range of 450-475 nm.

[0028] Preferably, obtaining the spectral similarity score of the target spectrum of the target LED according to the spectral similarity of each of the preset wavelength intervals and the corresponding target similarity deviation interval includes:

[0029] Determining whether the spectral similarity of the preset wavelength interval belongs to the corresponding target similarity deviation interval;

[0030] If yes, then the similarity score of the preset wavelength interval is a preset score, wherein the preset score is a positive number;

[0031] Otherwise, the similarity score of the preset wavelength interval is zero;

[0032] According to the similarity score of each of the preset wavelength intervals, the similarity score of the target LED is obtained.

[0033] In a second aspect, an embodiment of the present invention provides a spectral scoring device based on a lighting scene, the device comprising:

[0034] A spectrum acquisition module is used to acquire the relative spectrum of the target LED and the relative spectrum of the standard illuminant, which are recorded as the target spectrum and the standard spectrum respectively;

[0035] A deviation interval acquisition module, used for acquiring the similarity deviation interval corresponding to each preset wavelength interval according to the spectral characteristics of the standard spectrum in each preset wavelength interval; wherein the preset wavelength intervals have no intersection and belong to the visible light wavelength range, and different preset wavelength intervals correspond to different similarity deviation intervals;

[0036] A scene acquisition module, used to acquire scene information of the target LED lighting scene, and acquire adjustment factors corresponding to each of the preset wavelength intervals according to the scene information;

[0037] An adjustment module, configured to adjust each of the similarity deviation intervals according to the adjustment factor to obtain a target similarity deviation interval corresponding to the lighting scene;

[0038] The scoring module is used to obtain the spectral similarity score of the target spectrum of the target LED according to the spectral similarity of each preset wavelength interval and the corresponding target similarity deviation interval.

[0039] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect in the above-mentioned embodiment.

[0040] In a fourth aspect, an embodiment of the present invention provides a storage medium having computer program instructions stored thereon, which implements the method of the first aspect in the above-mentioned embodiment when the computer program instructions are executed by a processor.

[0041] In summary, the beneficial effects of the present invention are as follows:

[0042] The lighting scene provided by the embodiment of the present invention is based on the spectral scoring method, device, equipment and storage medium of the lighting scene, obtains the relative spectrum of the target LED and the standard illuminant, which are recorded as the target spectrum and the standard spectrum respectively, accurately understand their spectral distribution, and provide basic data for the subsequent similarity evaluation; according to the spectral characteristics of each preset wavelength interval, determining the similarity deviation interval helps to establish a reasonable evaluation system, which fully considers the spectral characteristics of different wavelength intervals. The scene information of the target LED lighting scene is obtained and the adjustment factors corresponding to each preset wavelength interval are obtained accordingly, so that each similarity deviation interval is adjusted according to the adjustment factor to obtain the target similarity deviation interval corresponding to the lighting scene, thereby combining different lighting scenes to more accurately perform spectral evaluation and make the scoring more convincing. Finally, the LED spectral similarity score of the target LED target spectrum can be obtained by combining the spectral similarity of each preset wavelength interval and the target similarity deviation interval. This score comprehensively considers the spectral characteristics and similarity deviation of each wavelength interval in different lighting scenes, and specifically evaluates the spectral performance of LED lighting products. It can be customized according to the needs of different application scenarios, improve the accuracy and applicability of spectral evaluation results, and provide a method for the design and optimization of LED lighting products. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the embodiment of the present invention will be briefly introduced below. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0044] Figure 1 The figure is a flow chart of an evaluation method based on spectral similarity according to an embodiment of the present invention.

[0045] Figure 2 The figure is a schematic diagram of a process of obtaining a similarity deviation interval based on spectral features according to an embodiment of the present invention.

[0046] Figure 3 It is a structural schematic diagram of an evaluation device based on spectral similarity according to an embodiment of the present invention.

[0047] Figure 4 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The features and exemplary embodiments of various aspects of the present invention will be described in detail below. In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present invention by illustrating examples of the present invention.

[0049] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0050] Example 1

[0051] See also Figure 1 The embodiment of the present invention provides an evaluation method based on spectral similarity, the method comprising:

[0052] S1. Obtain the relative spectrum of the target light-emitting semiconductor and the relative spectrum of the standard illuminant, which are recorded as the target spectrum and the standard spectrum respectively;

[0053] Specifically, in order to evaluate the spectral similarity between the target light-emitting semiconductor and the standard illuminant, it is first necessary to obtain the spectra of the two, wherein the standard illuminant is a standard spectrum used as a comparison object, usually an ideal spectrum specified by the International Organization for Standardization or other industry standards; the target light-emitting semiconductor refers to the light-emitting semiconductor light source to be evaluated or compared, which may be a full-spectrum LED device or other types of LED devices. In the LED lighting industry, different types of LED devices have different spectral characteristics. For example, a full-spectrum LED device can emit light in the entire visible spectrum, while other LED devices may only emit light in a specific wavelength range. The quality and performance evaluation of LED lighting products needs to consider whether the spectrum it emits is similar to the spectrum of the standard light source. The standard illuminant usually represents an ideal spectral distribution, such as the D40 standard light source represents a daylight spectrum of white light. By comparing with the standard illuminant, it is possible to evaluate whether the spectrum of the target light-emitting semiconductor meets the expected standard, thereby determining the quality and performance of the target light-emitting semiconductor.

[0054] The target spectrum and standard spectrum data are obtained through a spectrometer or other spectral testing equipment. The target spectrum and standard spectrum are usually presented in the form of wavelength and relative light intensity, thereby providing basic data of the LED device to be evaluated and the standard lighting object, and providing a basis for subsequent spectral similarity evaluation.

[0055] S2. According to the target spectrum and the standard spectrum, obtaining deviation values ​​between the preset wavelength intervals, wherein the preset wavelength intervals have no intersection and belong to the visible light wavelength range;

[0056] Specifically, the preset wavelength interval is a wavelength range determined in advance for analyzing spectral characteristics, which is usually a different range divided by color or other characteristics, and each of the preset wavelength intervals has no intersection and belongs to the visible light wavelength range. According to the spectral deviation calculation formula, the deviation value of the target spectrum and the standard spectrum in each preset wavelength interval can be calculated;

[0057] In a preferred embodiment, the preset wavelength range includes a red light wavelength range of 622 to 700 nm, an orange light wavelength range of 597 to 622 nm, a yellow light wavelength range of 577 to 597 nm, a green light wavelength range of 492 to 577 nm, a cyan light wavelength range of 475 to 492 nm, a blue light wavelength range of 450 to 475 nm and a purple light wavelength range of 400 to 450 nm, wherein the red light wavelength range includes a first red light range of 680 to 700 nm, a second red light range of 650 to 680 nm and a third red light range of 622 to 640 nm.

[0058] Specifically, the purpose of setting the preset wavelength range in this way is to consider the main colors of the visible spectrum and distinguish the wavelength ranges of light of different colors. By dividing the visible spectrum into wavelength ranges of different colors such as red, orange, yellow, green, cyan, blue and purple, the similarity between the target spectrum and the standard spectrum can be compared more accurately;

[0059] Furthermore, different wavelength intervals correspond to spectra of different colors and have distinct spectral characteristics. This subdivision can better capture the differences in spectra and help accurately evaluate spectral similarity; dividing the visible spectrum into wavelength intervals of different colors helps to quantitatively compare the similarities between different colors. This quantitative comparison can provide more specific information to guide the design and manufacture of LED devices; and the spectral intervals of red, orange, yellow, green, cyan, blue and purple colors cover the main range of the visible spectrum, which is suitable for most spectral comparison scenarios and has a wide range of applicability.

[0060] In one embodiment, the spectrum deviation calculation formula is expressed as follows:

[0061]

[0062] The formula is more complete

[0063] In the formula, λ is the wavelength value, M is the deviation value, λmin and λmax are the lower limit and upper limit of the corresponding preset wavelength range, A(λ) is the target spectrum, and S(λ) is the standard spectrum.

[0064] Specifically, the two variables, target spectrum A(λ) and standard spectrum S(λ), respectively represent the target spectrum to be evaluated and the standard spectrum. The preset wavelength interval refers to the spectral wavelength range set in advance according to specific requirements or standards, which is used to divide the spectrum into different intervals. In the spectral deviation calculation, the target spectrum and the standard spectrum will be compared in each preset wavelength interval; the deviation is a measure of the difference between the target spectrum and the standard spectrum in each preset wavelength interval. When the deviation value is positive, it means that the light intensity of the target spectrum in this interval is higher than that of the standard spectrum. By checking the positive and negative values ​​of the deviation value, the direction of the difference between the target spectrum and the standard spectrum in different wavelength intervals can be understood, so as to better evaluate the similarity between them.

[0065] S3, according to the spectral characteristics of the standard spectrum in each of the preset wavelength intervals, obtaining the similarity deviation intervals corresponding to each of the preset wavelength intervals, wherein the spectral characteristics include: spectral energy distribution, spectral gradient angle, and preset wavelength intervals with different human eye visibility have different similarity deviation intervals;

[0066] Specifically, the deviation interval is used to determine the allowable similarity deviation range between the target spectrum and the standard spectrum. The main purpose of setting a different deviation interval for each preset wavelength interval is to take into account the different spectral characteristics of different wavelength intervals;

[0067] Since the setting of the deviation threshold is usually a more general process, it depends more on the spectral characteristics of the standard illumination object. Therefore, this step sets the similarity deviation interval based on the spectral characteristics of the standard illumination object as a reference. The spectral characteristics include but are not limited to spectral peaks and troughs, spectral polarization properties and spectral frequency characteristics. The peaks and troughs in the spectrum reflect the sudden changes in light intensity at certain specific wavelengths. The positions and amplitudes of these peaks and troughs can be used to measure the characteristics of the spectrum, thereby affecting the similarity assessment of the spectrum; the polarization property of the spectrum refers to the orientation of the vibration direction of light with respect to the propagation direction of light. In some cases, the polarization property of the spectrum may affect the similarity of the spectrum, so it can be considered in the similarity assessment; the frequency characteristics of the spectrum describe the frequency distribution of light waves, including the ratio of high-frequency and low-frequency components. The frequency characteristics of the spectrum can also be used as one of the important indicators for evaluating spectral similarity;

[0068] Taking into account the above spectral characteristics and possible other factors, the appropriate range is determined through analysis and experimental verification to ensure that the target spectrum has sufficient similarity with the standard spectrum within this range. The similarity between the target spectrum and the standard spectrum can be more comprehensively evaluated, and the similarity deviation interval can be further determined to achieve a more accurate spectral similarity assessment.

[0069] Preferably, see Figure 2 The step of obtaining the similarity deviation interval corresponding to each of the preset wavelength intervals according to the spectral characteristics of the standard spectrum in each of the preset wavelength intervals comprises:

[0070] S31, obtaining the spectral characteristics of the standard spectrum in each of the preset wavelength ranges;

[0071] Specifically, spectral energy distribution is the energy distribution of light within a specified wavelength range, that is, the shape and amplitude of the spectral curve. Spectral energy distribution is one of the key parameters for describing spectral characteristics, which can reflect the relationship between light intensity and wavelength; spectral gradient angle refers to the slope or rate of change between adjacent wavelength points on the spectral curve. It reflects the speed of change of the spectrum at different wavelengths, which is very important for understanding the transition and change trend of the spectrum;

[0072] The spectral energy distribution can be obtained by measuring the light intensity of a standard spectrum at different wavelengths using a spectroradiometer or a spectrum analyzer, processing the measured spectral data, and calculating the light intensity or radiation energy distribution at each wavelength; the spectral gradient angle represents the rate of change or slope of the spectrum at different wavelengths, and can be obtained by calculating the difference in spectral intensity at adjacent wavelengths; the human eye visibility is expressed in a standard CIE1931 chromaticity diagram, and the human eye visibility data at each wavelength can be obtained by referring to the CIE1931 colorimetry standard.

[0073] S32. Determine a first deviation threshold and a second deviation threshold corresponding to each of the preset wavelength intervals according to the spectral energy distribution, the spectral gradient angle, and the human eye visibility, wherein the first deviation threshold is a negative number, which is used to characterize the maximum deviation allowed when the light intensity of the target spectrum in the corresponding preset wavelength interval is lower than that of the standard spectrum; the second deviation threshold is a positive number, which is used to characterize the maximum deviation allowed when the light intensity of the target spectrum in the corresponding preset wavelength interval is higher than that of the standard spectrum, and the absolute value of the first deviation threshold is greater than or equal to the absolute value of the second deviation threshold;

[0074] Specifically, the first deviation threshold and the second deviation threshold are determined to evaluate the similarity or degree of deviation between the target spectrum and the standard spectrum. These thresholds are determined based on the spectral characteristics (spectral energy distribution, spectral gradient angle) of the standard spectrum and the visibility of the human eye to ensure that the light intensity of the target spectrum in each preset wavelength range is sufficiently similar to that of the standard spectrum;

[0075] The spectral energy distribution reflects the intensity distribution of light at different wavelengths. When determining the first deviation threshold and the second deviation threshold, the spectral energy distribution of the standard spectrum can be used to determine the light intensity level of the target spectrum in each wavelength range. The advantage of this is that the threshold can be set according to the overall brightness of the standard spectrum to ensure that the difference in light intensity between the target spectrum and the standard spectrum does not exceed the acceptable range.

[0076] The spectral gradient angle indicates the rate of change of the spectrum over the wavelength, that is, the slope of the spectrum. By analyzing the spectral gradient angle of the standard spectrum, the spectral change of the target spectrum in each wavelength range can be determined. This helps to determine the size and direction of the first deviation threshold and the second deviation threshold, ensuring that the similarity evaluation takes into account the trend of spectrum change, thereby more accurately judging the similarity between the target spectrum and the standard spectrum.

[0077] The human eye visibility reflects the sensitivity of the human eye to light at different wavelengths. Considering the perceptual characteristics of the human eye, the threshold in the similarity evaluation can be adjusted according to the human eye visibility of the standard spectrum, so that the evaluation result is more in line with the actual situation of human eye perception. This ensures that the similarity evaluation is more real and reliable, and better reflects the sensitivity of the human eye to spectral differences.

[0078] Taking these three parameters into consideration, we can have a more comprehensive understanding of the characteristics of the spectrum, and determine the first deviation threshold and the second deviation threshold based on the characteristics of the standard spectrum. Doing so helps ensure that the similarity assessment is more objective and accurate, and more in line with the actual human eye perception, thereby improving the ability to judge the similarity between the target spectrum and the standard spectrum;

[0079] The first deviation threshold is a negative number, which is used to indicate the maximum deviation allowed when the light intensity of the target spectrum is lower than that of the standard spectrum within a preset wavelength range. In other words, when the light intensity of the target spectrum is lower than that of the standard spectrum, the absolute value of the deviation value should not exceed the first deviation threshold;

[0080] The second deviation threshold is a positive number, which is used to indicate the maximum deviation allowed when the light intensity of the target spectrum is higher than that of the standard spectrum within the preset wavelength range. Contrary to the first deviation threshold, when the light intensity of the target spectrum is higher than that of the standard spectrum, the absolute value of the deviation value should not exceed the second deviation threshold;

[0081] The absolute value of the first deviation threshold is set to be greater than or equal to the absolute value of the second deviation threshold to ensure that deviations in both directions are properly considered. A larger absolute value of the first deviation threshold means that we are more tolerant of light intensity lower than the standard spectrum, that is, the light intensity of the target spectrum is allowed to be lower than the standard spectrum to a certain extent. The human visual system is highly adaptable to light, and we can adapt and carry out normal activities even in relatively dark environments. Therefore, a certain degree of insufficient light may not significantly affect our normal activities and experience, and excessive light may cause glare or visual discomfort, and even affect health. In contrast, dim light conditions usually do not cause discomfort or health problems;

[0082] The absolute value of the second deviation threshold is relatively small, which means that the tolerance is relatively low for light intensity higher than the standard spectrum. Excessive light may cause glare, visual discomfort, and even headaches, affecting visual comfort and health. Excessive light may affect people's overall perception and experience of the environment. For example, too bright light may make the environment look dazzling or unnatural. In addition, too high light intensity will lead to unnecessary waste of energy, which is not in line with the principle of energy conservation and emission reduction. When the light intensity is sufficient to meet the demand, further increasing the light intensity will only increase unnecessary energy consumption.

[0083] Preferably, the step of determining the first deviation threshold and the second deviation threshold corresponding to each of the preset wavelength intervals according to the spectral energy distribution, the spectral gradient angle and the human eye visibility comprises:

[0084] S321. Obtain a first deviation threshold value for each of the preset wavelength intervals according to a preset first deviation threshold value calculation formula, wherein the expression of the first deviation threshold value calculation formula is as follows:

[0085]

[0086] Wherein, λ is the wavelength value, T1 is the first deviation threshold, E(λ) represents the spectral energy distribution of wavelength λ, F(λ) represents the spectral gradient angle of wavelength λ, V(λ) represents the human eye visibility of wavelength λ, α 1 is the first coefficient, which is used to characterize the influence of spectral energy distribution and spectral gradient angle on the first deviation threshold, β 1 is the second coefficient, used to characterize the influence of the human eye visibility on the first deviation threshold;

[0087] Specifically, the first deviation threshold represents the maximum deviation allowed when the light intensity of the target spectrum in a preset wavelength range is lower than that of the standard spectrum. Therefore, in this formula, an increase in the spectral energy distribution will cause the first deviation threshold to increase, because when the spectral energy distribution increases, it means that the light intensity in a certain wavelength range has increased. In the spectral similarity evaluation, if the light intensity of the target spectrum in a preset wavelength range is lower than that of the standard spectrum, it means that the target spectrum is insufficient in this range. In order to ensure the rigor and reliability of the evaluation, it is necessary to set the first deviation threshold to tolerate this deficiency. When the spectral energy distribution increases, it means that the light intensity of the target spectrum in this range increases, so the tolerance for spectral deficiencies should also increase accordingly to adapt to this change. Therefore, as the spectral energy distribution increases, the first deviation threshold will also increase to ensure the accuracy and rationality of the evaluation;

[0088] The spectral gradient angle indicates the rate of change of the spectrum, that is, the degree of change of the light intensity between adjacent wavelengths. When the spectral gradient angle increases, it means that the light intensity of the target spectrum in a certain wavelength range has changed significantly, that is, the rate of change of the spectrum is large. In the spectral similarity evaluation, if the light intensity of the target spectrum in a certain preset wavelength range changes significantly, a larger deviation threshold needs to be set to accommodate this change to ensure the accuracy and rationality of the evaluation. Therefore, when the gradient spectral angle increases, the first deviation threshold will also increase to adapt to the larger change rate of the target spectrum and ensure the rigor of the evaluation.

[0089] The human eye visibility rate indicates the sensitivity of the human eye to light of different wavelengths, that is, the degree of visual impact of light of different wavelengths on the human eye. When the human eye visibility rate increases, it means that the human eye is more sensitive to areas with higher spectral energy distribution, that is, it is easier to perceive light of these wavelengths. In the spectral similarity evaluation, if the light intensity of the target spectrum in a certain preset wavelength range is low, but the human eye has a high sensitivity to the light in this wavelength range, then a smaller deviation threshold needs to be set to accommodate this sensitivity to ensure the objectivity and accuracy of the evaluation.

[0090] α 1 is the first coefficient, which indicates the influence of spectral energy distribution and gradient spectral angle on the first deviation threshold. 1 When it increases, it indicates that the importance attached to the spectral energy distribution and the gradient spectral angle increases, thus making the adjustment of the deviation threshold more sensitive.

[0091] β 1 It shows the inverse effect of human eye visibility on the first deviation threshold. 1 When it increases, it means that the influence of the human eye visibility on the first deviation threshold is weakened, that is, the evaluation of spectral similarity depends more on energy distribution and gradient angle.

[0092] S322. Obtain a second deviation threshold value for each of the preset wavelength intervals according to a preset second deviation threshold value calculation formula, wherein the expression of the second deviation threshold value calculation formula is as follows:

[0093]

[0094] In the formula, α 2 β is the third coefficient used to characterize the influence of the spectral gradient angle on the second deviation threshold; 2 is the fourth coefficient, which is used to characterize the influence of the spectral gradient angle on the second deviation threshold; γ 2 is the fifth coefficient, used to characterize the influence of the human eye visibility on the second deviation threshold;

[0095] Specifically, when the spectral energy distribution increases, it means that the light intensity in a certain wavelength range is relatively high. In the spectral similarity evaluation, if the light intensity of the target spectrum in a certain preset wavelength range is higher than that of the standard spectrum, but the spectral energy distribution in this wavelength range is wider, that is, the spectral distribution range is wider, then in order to keep the light intensity of the target spectrum in this range higher than that of the standard spectrum, a smaller second deviation threshold needs to be set;

[0096] The increase of the spectral gradient angle indicates that the rate of change of the spectrum within a certain wavelength range is greater, that is, the slope of the spectrum is steeper. In the spectral similarity evaluation, if the slope of the target spectrum within a certain preset wavelength range is larger, that is, the gradient spectral angle is larger, it means that the rate of change of the spectrum within this range is faster.

[0097] When the gradient spectrum angle increases, a larger deviation is required to accommodate the spectrum with a faster rate of change. Because a larger gradient spectrum angle means that the spectrum changes more dramatically within the wavelength range, a larger deviation is required to allow for the difference between the target spectrum and the standard spectrum. Therefore, as the gradient spectrum angle increases, the second deviation threshold may increase to adapt to the rate of change of the target spectrum, ensuring that the evaluation results are more accurate and reliable.

[0098] The increase in human eye visibility indicates that the human eye's sensitivity to light at a specific wavelength increases, that is, the human eye is more sensitive to light at that wavelength. In the spectral similarity evaluation, if the human eye visibility of the target spectrum increases within a certain preset wavelength range, that is, the human eye is more sensitive to light within that range, this means that changes in light intensity within this range may be more easily perceived by the human eye.

[0099] When the human eye visibility increases, the deviation tolerance of the target spectrum decreases to more accurately reflect the sensitivity of the human eye to spectral differences. Because a higher human eye visibility means that the human eye is more sensitive to spectral differences, a smaller deviation is required to ensure that the difference between the target spectrum and the standard spectrum is not perceived by the human eye. Therefore, when the human eye visibility increases, the second deviation threshold may be reduced to better reflect the human eye's ability to perceive spectral differences, thereby improving the accuracy and reliability of spectral similarity evaluation.

[0100] α 2 This coefficient represents the influence of the gradient spectrum angle on the second deviation threshold. 2 When it increases, it means that the importance attached to the gradient spectrum angle increases, making the adjustment of the deviation threshold more sensitive.

[0101] γ 2 and β 2 These two indices respectively represent the inverse effects of spectral energy distribution and human eye visibility on the second deviation threshold. 2 and β 2 When it increases, it means that the influence of spectral energy distribution and human eye visibility on the second deviation threshold is weakened, that is, the evaluation of spectral similarity depends more on the spectral gradient angle.

[0102] Wherein, the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient are all obtained by statistically analyzing the historical spectrum test data;

[0103] Specifically, collect a sufficient amount of historical spectral test data, including comparison data between the target spectrum and the standard spectrum. These data should cover a variety of spectral features and conditions to ensure the comprehensiveness and accuracy of the analysis, and extract the required spectral features from the historical spectral data, including spectral energy distribution, gradient spectral angle, and human eye visibility. These features can be extracted and calculated by mathematical methods or professional tools. Perform statistical analysis on the extracted spectral features to explore the relationship between them and their influence on the spectral similarity evaluation. Various statistical methods, such as correlation analysis, regression analysis, factor analysis, etc., can be used to determine the correlation and influence between the features and the similarity evaluation results. Based on the results of the statistical analysis, a mathematical model or fitting method can be used to fit the values ​​of the first coefficient, the second coefficient, the third coefficient, the fourth coefficient, and the fifth coefficient. The values ​​of these coefficients can best describe the relationship between the spectral features and the similarity evaluation.

[0104] S33. Obtain, according to the first deviation threshold and the second deviation threshold, a similarity deviation interval corresponding to each of the preset wavelength intervals.

[0105] Specifically, according to the calculated first deviation threshold and the second deviation threshold, the similarity deviation interval of each preset wavelength interval can be obtained, and each preset wavelength interval may have different spectral characteristics, such as spectral energy distribution, wavelength range, etc. By setting different similarity deviation intervals according to these characteristics, the similarity between the target spectrum and the standard spectrum can be evaluated more finely. In this way, it is possible to avoid simply treating the entire spectrum as a whole, but to perform more detailed analysis and evaluation for different wavelength ranges. Different wavelength ranges may have different types of spectral problems, for example, there may be obvious spectral shifts or intensity anomalies in certain wavelength ranges. By setting different similarity deviation intervals, these problem areas can be better identified, and targeted corrections or adjustments can be made to improve the spectral quality of LED devices. By setting different similarity deviation intervals according to these spectral characteristics, the similarity between the target spectrum and the standard spectrum can be evaluated more finely. In this way, it is possible to avoid simply treating the entire spectrum as a whole, but to perform more detailed analysis and evaluation for different wavelength ranges.

[0106] In a specific embodiment, the similarity deviation interval corresponding to the first red light interval is (-0.6, 0.11), the similarity deviation interval corresponding to the second red light interval is (-0.38, 0.10), the similarity deviation interval corresponding to the third red light interval is (-0.2, 0.19), the similarity deviation interval corresponding to the orange light wavelength interval is (-0.16, 0.16), the similarity deviation interval corresponding to the yellow light wavelength interval is (-0.19, 0.19), the similarity deviation interval corresponding to the green light wavelength interval is (-0.26, 0.1), the similarity deviation interval corresponding to the cyan wavelength interval is (-0.39, 0.1), the similarity deviation interval corresponding to the blue light wavelength interval is (-0.46, 0.18), and the similarity deviation interval corresponding to the purple light wavelength interval is (-1, 1).

[0107] These similarity deviation intervals provide specific indicators for spectral similarity evaluation, which can help analysts understand the similarity between the target spectrum and the standard spectrum, so as to better evaluate the spectral quality. The upper and lower limits of each interval determine the allowable similarity deviation range, making the evaluation results more accurate and reliable;

[0108] In one embodiment, after step S33, the method further includes:

[0109] S34, obtaining application scenario information of the target light-emitting semiconductor;

[0110] Specifically, this step obtains application scenario information of the target light-emitting semiconductor, and the application scenario includes the environment and conditions in which the light source will be used, such as indoor lighting, outdoor lighting, etc., and the indoor lighting includes but is not limited to classroom lighting, office lighting, factory lighting, living room lighting, and bedroom lighting; the application scenario information can be obtained through the product manual or technical documentation of the target light-emitting semiconductor. The product manual and technical documentation usually provide detailed information about the light-emitting semiconductor, including its applicable scenarios, performance characteristics, optoelectronic parameters, etc. By reading the product manual and technical documentation, you can understand which scenarios and application fields the light-emitting semiconductor is suitable for. Different application scenarios have different requirements for spectral similarity in different wavelength ranges, so understanding the application scenario can better adjust the parameters of the similarity evaluation;

[0111] S35. According to the application scenario, obtain a first threshold adjustment factor and a second threshold adjustment factor corresponding to each of the preset wavelength intervals;

[0112] According to the application scenario information obtained, the first threshold adjustment factor and the second threshold adjustment factor corresponding to each preset wavelength interval can be determined. These adjustment factors are used to appropriately adjust the similarity deviation interval according to the application scenario. For example, in an indoor lighting scenario, for some wavelength intervals, it is not particularly critical to a comfortable environment feeling, and a larger deviation can be tolerated, while for some wavelength intervals, spectral similarity may be more important, and the similarity deviation interval can be reduced;

[0113] In one embodiment, the step S35 specifically includes:

[0114] S351, screening the preset wavelength range according to the application scenario to obtain a key wavelength range;

[0115] In this step, the key wavelength ranges are selected according to the specific application scenarios. These wavelength ranges are of special importance in the scenario and are essential for achieving specific lighting effects. For different scenarios, different wavelength ranges may be of concern because they have an important impact on people's perception and comfort in the scenario. Different application scenarios have different requirements for spectral characteristics. Only adjusting the thresholds of the key wavelength ranges can better meet the needs of different scenarios, thereby improving the adaptability and flexibility of the LED lighting system.

[0116] By way of example and not limitation, for classroom scenarios, the key wavelength ranges include the green wavelength range (492-577nm) and the blue wavelength range (450-475nm), where the green wavelength range helps improve students’ attention and concentration while the blue wavelength range can enhance clarity and alertness;

[0117] For office scenes, the key wavelength ranges include the green wavelength range (492-577nm) and the yellow wavelength range (577-597nm). The green wavelength range helps improve employees' attention and work efficiency, and the yellow wavelength range helps create a warm and comfortable atmosphere.

[0118] For factory lighting, the key wavelength range includes the blue light wavelength range (450-475nm), which is conducive to improving alertness and work efficiency;

[0119] S352. According to the application scenario information, set a first threshold adjustment factor and a second deviation adjustment factor for each key wavelength interval;

[0120] First and second threshold adjustment factors are set for each critical wavelength interval. These factors are used to adjust the calculation of the spectral similarity score to take into account the importance of a specific wavelength interval to the scene.

[0121] S353, setting the first threshold adjustment factor and the second threshold adjustment factor of the preset wavelength interval outside the key wavelength interval to 1;

[0122] In this step, first and second threshold adjustment factors are set for each critical wavelength interval. These factors are used to adjust the calculation of the spectral similarity score to take into account the importance of a specific wavelength interval for the scene;

[0123] For example, but not limitation, for the green wavelength range in office lighting scenarios, the first threshold adjustment factor may be increased to ensure that the light intensity of the target light-emitting semiconductor is lower than to ensure that sufficiently bright lighting is provided, which helps to improve employees' attention and work efficiency. For the yellow wavelength range, we may adjust the second threshold adjustment factor to ensure that sufficiently soft and warm lighting is provided;

[0124] In a specific embodiment, when the scene information is classroom lighting, the first threshold adjustment factor of the green light wavelength range is 1.2, and the second threshold adjustment factor is 0.9; the first threshold adjustment factor of the blue light wavelength range is 0.9, and the second threshold adjustment factor is 1.1.

[0125] This setting will increase the tolerance of the target light-emitting semiconductor to light intensities higher than the standard spectrum, so that in classroom lighting scenarios, the spectral deviation in the green wavelength range is slightly more tolerant, while the tolerance in the blue wavelength range is slightly reduced. Such adjustments help optimize the lighting effect, making the light in the classroom more suitable for learning and concentration. Increasing the tolerance in the green wavelength range can provide softer lighting, help reduce eye fatigue and improve students' attention; while reducing the tolerance in the blue wavelength range can reduce the stimulation of blue light on the retina, help reduce visual fatigue of the eyes, and improve students' comfort and learning efficiency.

[0126] When the scene information is office lighting, the first threshold adjustment factor of the red light wavelength range is 1.1, and the second threshold adjustment factor is 0.95; the first threshold adjustment factor of the blue light wavelength range is 0.95, and the second threshold adjustment factor is 1.05.

[0127] This setting helps improve the comfort and suitability of light in office lighting scenarios. Increasing the tolerance of the red light wavelength range can make the light softer, which is helpful to reduce eye fatigue and improve employee work efficiency. Reducing the tolerance of the blue light wavelength range can reduce eye stimulation, help reduce visual fatigue and improve the comfort of the working environment.

[0128] When the scene information is factory lighting, the first threshold adjustment factor of the yellow light wavelength range is 1.2, and the second threshold adjustment factor is 0.9; the first threshold adjustment factor of the green light wavelength range is 1.1, and the second threshold adjustment factor is 0.95.

[0129] In factory lighting scenarios, increasing the tolerance for yellow wavelengths can provide warmer and more comfortable lighting, helping to create a more suitable working environment. A slightly lower tolerance for green wavelengths can make lighting brighter, helping to increase worker alertness and work efficiency.

[0130] When the scene information is living room lighting, the first threshold adjustment factor of the blue light wavelength range is 0.9, and the second threshold adjustment factor is 1.1; the first threshold adjustment factor of the green light wavelength range is 1.1, and the second threshold adjustment factor is 0.95.

[0131] In the living room lighting scene, increasing the tolerance of the green light wavelength range can provide softer and more comfortable lighting, making the space more suitable for relaxation and entertainment. Reducing the tolerance of the blue light wavelength range can reduce eye stimulation, help reduce visual fatigue and improve comfort.

[0132] When the scene information is bedroom lighting, the first threshold adjustment factor of the red light wavelength range is 1.2, and the second threshold adjustment factor is 0.9; the first threshold adjustment factor of the yellow light wavelength range is 1.1, and the second threshold adjustment factor is 0.95.

[0133] In bedroom lighting scenarios, increasing the tolerance of the red wavelength range can create a warmer and more comfortable atmosphere, which helps promote sleep. At the same time, increasing the tolerance of the yellow wavelength range can also provide softer and more comfortable lighting, which helps to relax the body and mind and enter a sleep state.

[0134] S36. Adjust each of the similarity deviation intervals according to the first threshold adjustment factor and the second factor adjustment factor;

[0135] Specifically, the first and second threshold adjustment factors of the non-critical wavelength intervals are set to 1. This is done to keep the similarity scores of these wavelength intervals from being affected by additional adjustments in the calculation.

[0136] According to different application scenarios, the threshold adjustment factor of the key wavelength range is adjusted to make the spectral similarity score more suitable for the needs of specific scenarios. This customized adaptability can improve the accuracy and reliability of the evaluation.

[0137] By adjusting the threshold adjustment factor according to actual needs, the evaluation process is more in line with actual usage, thereby improving the accuracy of the evaluation. In particular, the adjustment of the key wavelength range can more accurately reflect the similarity of the spectrum in the important wavelength range.

[0138] By only adjusting the threshold adjustment factor of the key wavelength interval and setting the adjustment factors of other wavelength intervals to 1, computing resources and time costs can be saved and evaluation efficiency can be improved.

[0139] In summary, the overall process from S34 to S36 improves the accuracy and reliability of the evaluation by customizing the threshold adjustment factor, while saving computing resources, thereby providing an effective method and tool for the spectral similarity evaluation between the target light-emitting semiconductor and the standard illuminant.

[0140] S4. Obtain a spectral similarity score between the target light-emitting semiconductor and a standard illuminant according to the spectral similarity of each of the preset wavelength intervals and the corresponding similarity deviation interval.

[0141] Specifically, based on the spectral similarity and similarity deviation interval of each preset wavelength interval calculated previously, the spectral similarity score between the target light-emitting semiconductor and the standard illuminant can be calculated. This score reflects the degree of similarity between the target spectrum and the standard spectrum in the entire spectral range, and can quickly and accurately evaluate the spectral quality, providing a basis for quality control and optimization of LED devices;

[0142] Preferably, the standard lighting body is standard lighting body D40, which is an artificial light source with a specific spectral power distribution, and its spectral curve is adjusted to simulate the spectral characteristics of natural daylight. As one of the standard lighting bodies, D40 is widely used in colorimetry research and experiments. Its relative spectral power distribution is relatively close to the spectral characteristics of natural daylight, and is particularly suitable for simulation and experiments of indoor lighting environments.

[0143] The step of obtaining the spectral similarity score between the target light-emitting semiconductor and the standard illuminant according to the spectral similarity of each preset wavelength interval and the corresponding similarity deviation interval comprises:

[0144] S41, determining whether the spectral similarity of the preset wavelength interval belongs to the corresponding similarity deviation interval;

[0145] For each preset wavelength interval, the relationship between the spectral similarity of the target spectrum and the standard spectrum in the interval and the similarity deviation interval is compared. If the similarity of the target spectrum falls within the similarity deviation interval, it means that the target spectrum has sufficient similarity with the standard spectrum in the interval and they can be considered similar. If the similarity of the target spectrum exceeds the similarity deviation interval, it means that the difference between the target spectrum and the standard spectrum in the interval exceeds the tolerance range and is considered dissimilar.

[0146] S42: If yes, the similarity score of the preset wavelength interval is a preset score, wherein the preset score is a positive number;

[0147] If the similarity of the target spectrum falls within the similarity deviation interval, a preset score is assigned to the similarity score of the preset wavelength interval. This preset score is usually a positive number, indicating the similarity of the target spectrum with the standard spectrum within the interval.

[0148] S43, otherwise, the similarity score of the preset wavelength interval is zero;

[0149] If the similarity of the target spectrum exceeds the similarity deviation interval, the similarity score of the preset wavelength interval is set to zero, indicating that the similarity between the target spectrum and the standard spectrum in this interval is not sufficient to be considered similar.

[0150] S44. Obtain a similarity score of the target light-emitting semiconductor according to the similarity score of each of the preset wavelength intervals.

[0151] For all preset wavelength ranges, their similarity scores are weighted and summed to obtain the overall similarity score of the target light-emitting semiconductor. This overall score represents the overall similarity between the target spectrum and the standard spectrum.

[0152] Example 2

[0153] See also Figure 3 The embodiment of the present invention provides an evaluation device based on spectral similarity, the device comprising:

[0154] A spectrum acquisition module is used to acquire the relative spectrum of the target light-emitting semiconductor and the standard illuminant, which are recorded as the target spectrum and the standard spectrum respectively;

[0155] A deviation value calculation module, used for obtaining the deviation values ​​between the preset wavelength intervals according to the target spectrum and the standard spectrum, wherein the preset wavelength intervals have no intersection and belong to the visible light wavelength range;

[0156] A deviation interval acquisition module, used for acquiring a similarity deviation interval corresponding to each preset wavelength interval according to the spectral characteristics of the standard spectrum in each preset wavelength interval, wherein different preset wavelength intervals correspond to different similarity deviation intervals;

[0157] The similarity evaluation module is used to obtain the spectral similarity score between the target light-emitting semiconductor and the standard illuminant according to the spectral similarity of each preset wavelength interval and the corresponding similarity deviation interval.

[0158] It should be noted that the modules and units in the evaluation device based on spectral similarity in this embodiment correspond one-to-one to the steps in the evaluation method based on spectral similarity in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the aforementioned implementation of spectral similarity evaluation based on spectral features, which will not be repeated here.

[0159] Example 3

[0160] In addition, combined Figure 1 The evaluation method based on spectral similarity in the described embodiment of the present invention may be implemented by an electronic device. Figure 4 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention is shown.

[0161] The electronic device may include a processor and a memory storing computer program instructions.

[0162] Specifically, the processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present invention.

[0163] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside a data processing device. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0164] The processor implements any one of the evaluation methods based on spectral similarity in the above embodiments by reading and executing computer program instructions stored in the memory.

[0165] In one example, the electronic device may further include a communication interface and a bus. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0166] The communication interface is mainly used to implement communication between the modules, devices, units and / or equipment in the embodiments of the present invention.

[0167] Bus includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In suitable cases, bus may include one or more buses. Although the embodiment of the present invention describes and shows a specific bus, the present invention considers any suitable bus or interconnection.

[0168] Example 4

[0169] In addition, in combination with the evaluation method based on spectral similarity in the above embodiments, the embodiment of the present invention can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any evaluation method based on spectral similarity in the above embodiments is implemented.

[0170] In summary, the embodiments of the present invention provide an evaluation method, apparatus, device and storage medium based on spectral similarity.

[0171] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0172] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0173] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0174] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A spectral scoring method based on lighting scenes, characterized in that: The method comprises: Obtain the relative spectrum of the target LED and the relative spectrum of the standard illuminant, which are recorded as the target spectrum and the standard spectrum respectively; According to the spectral characteristics of the standard spectrum in each preset wavelength interval, the similarity deviation interval corresponding to each preset wavelength interval is obtained; wherein the preset wavelength intervals have no intersection and belong to the visible light wavelength range, and different preset wavelength intervals correspond to different similarity deviation intervals; Acquire scene information of the target LED lighting scene, and acquire adjustment factors corresponding to each of the preset wavelength intervals according to the scene information; Adjusting each of the similarity deviation intervals according to the adjustment factor to obtain a target similarity deviation interval corresponding to the lighting scene; According to the spectral similarity of each of the preset wavelength intervals and the corresponding target similarity deviation interval, a spectral similarity score of the target spectrum of the target LED is obtained.

2. The spectral scoring method based on lighting scene according to claim 1, characterized in that: The preset wavelength ranges include a red light wavelength range of 622 to 700 nm, an orange light wavelength range of 597 to 622 nm, a yellow light wavelength range of 577 to 597 nm, a green light wavelength range of 492 to 577 nm, a cyan light wavelength range of 475 to 492 nm, a blue light wavelength range of 450 to 475 nm, and a violet light wavelength range of 400 to 450 nm.

3. The spectral scoring method based on lighting scenes according to claim 2, characterized in that: The step of obtaining the similarity deviation interval corresponding to each preset wavelength interval according to the spectral characteristics of the standard spectrum in each preset wavelength interval comprises: Acquire the spectral characteristics of the standard spectrum in each of the preset wavelength ranges; wherein the spectral characteristics include: spectral energy distribution, spectral gradient angle and human eye visibility; According to the spectral energy distribution, the spectral gradient angle and the human eye visibility, determine the first deviation threshold and the second deviation threshold corresponding to each of the preset wavelength intervals; wherein the first deviation threshold is a negative number, which is used to represent the maximum deviation allowed when the light intensity of the target spectrum in the corresponding preset wavelength interval is lower than the standard spectrum; the second deviation threshold is a positive number, which is used to represent the maximum deviation allowed when the light intensity of the target spectrum in the corresponding preset wavelength interval is higher than the standard spectrum; According to the first deviation threshold and the second deviation threshold, the similarity deviation interval corresponding to each of the preset wavelength intervals is obtained.

4. The spectral scoring method based on lighting scenes according to claim 3, characterized in that: The step of obtaining scene information of the target LED lighting scene, and obtaining the adjustment factor corresponding to each of the preset wavelength intervals according to the scene information, comprises: Acquire scene information of the target LED lighting scene; According to the scene information, a first threshold adjustment factor and a second threshold adjustment factor corresponding to each of the preset wavelength intervals are obtained; wherein the first threshold adjustment factor is used to adjust the first deviation threshold of the preset wavelength interval, and the second threshold adjustment factor is used to adjust the second deviation threshold of the preset wavelength interval.

5. The spectral scoring method based on lighting scenes according to claim 4, characterized in that: The step of acquiring the first threshold adjustment factor and the second threshold adjustment factor corresponding to each of the preset wavelength intervals according to the scene information comprises: Filter the preset wavelength range according to the scene information to obtain a key wavelength range; According to the scene information, setting a first threshold adjustment factor and a second threshold adjustment factor for each of the key wavelength intervals; The first threshold adjustment factor and the second threshold adjustment factor of the preset wavelength interval outside the key wavelength interval are both set to 1.

6. The spectral scoring method based on lighting scenes according to claim 5, characterized in that: The step of screening the preset wavelength range according to the scene information to obtain the key wavelength range includes: When the scene information indicates that the lighting scene is a classroom scene, the key wavelength range obtained includes a green light wavelength range of 492 to 577 nm and a blue light wavelength range of 450 to 475 nm; When the scene information indicates that the lighting scene is an office scene, the key wavelength range obtained includes a green light wavelength range of 492 to 577 nm and a yellow light wavelength range of 577 to 597 nm; When the scene information indicates that the lighting scene is factory lighting, the acquired key wavelength range includes a blue light wavelength range of 450-475 nm.

7. The spectral scoring method based on lighting scenes according to any one of claims 1 to 6, characterized in that: The step of obtaining the spectral similarity score of the target spectrum of the target LED according to the spectral similarity of each of the preset wavelength intervals and the corresponding target similarity deviation interval includes: Determining whether the spectral similarity of the preset wavelength interval belongs to the corresponding target similarity deviation interval; If yes, then the similarity score of the preset wavelength interval is a preset score, wherein the preset score is a positive number; Otherwise, the similarity score of the preset wavelength interval is zero; According to the similarity score of each of the preset wavelength intervals, the similarity score of the target LED is obtained.

8. A spectral scoring device based on lighting scenes, characterized in that: The device comprises: A spectrum acquisition module is used to acquire the relative spectrum of the target LED and the relative spectrum of the standard illuminant, which are recorded as the target spectrum and the standard spectrum respectively; A deviation interval acquisition module, used for acquiring the similarity deviation interval corresponding to each preset wavelength interval according to the spectral characteristics of the standard spectrum in each preset wavelength interval; wherein the preset wavelength intervals have no intersection and belong to the visible light wavelength range, and different preset wavelength intervals correspond to different similarity deviation intervals; A scene acquisition module, used to acquire scene information of the target LED lighting scene, and acquire adjustment factors corresponding to each of the preset wavelength intervals according to the scene information; An adjustment module, configured to adjust each of the similarity deviation intervals according to the adjustment factor to obtain a target similarity deviation interval corresponding to the lighting scene; The scoring module is used to obtain the spectral similarity score of the target spectrum of the target LED according to the spectral similarity of each preset wavelength interval and the corresponding target similarity deviation interval.

9. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, implement the method according to any one of claims 1 to 7.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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