Sunlight spectrum similarity evaluation method and system considering human eye visual characteristics

Abstract

The application discloses a sunlight spectrum similar degree evaluation method and system considering human eye visual characteristics, which comprises the following steps: obtaining the spectral power distribution of a reference daylight source; collecting the spectral power distribution information of a to-be-tested light source; calculating a spectral power distribution similarity fitting degree coefficient combined with spectral luminous efficiency; calculating an illumination color preference quantitative index of the to-be-tested light source; calculating an illumination color resolution quantitative index of the to-be-tested light source; calculating comprehensive color restoration; calculating a comprehensive evaluation index of the to-be-tested light source quality; and obtaining a corresponding similarity quantitative value by using a sunlight spectrum similar degree evaluation model considering human eye visual characteristics according to the spectral power distribution similarity fitting degree coefficient and the comprehensive evaluation index of the to-be-tested light source quality, so as to realize comprehensive and overall evaluation and quantification of the sunlight spectrum similarity considering the visual characteristics of the human eye, and further provide a comprehensive and innovative sunlight spectrum similarity quantification scheme in the field.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of human factors lighting, and particularly relates to a sunlight spectrum similarity evaluation method and system based on spectral power distribution of a light source, which takes into account both human eye visual characteristics and comprehensive evaluation of light source quality. BACKGROUND

[0002] With the improvement of material living standards, consumers pay more and more attention to healthy lighting. Healthy lighting refers to reducing the impact of light pollution on human health while providing a good light environment. The core of healthy lighting is to provide a lighting environment that meets the physiological cycle of the human body, has high quality and balanced spectrum.

[0003] Conventional white light LEDs are almost suitable for various life occasions due to their high light efficiency, energy saving, long service life and strong plasticity. However, since the conventional white LED uses high-energy short-wave blue light as the excitation light source, the blue light with a peak wavelength of 465 nm is one of the most vulnerable wavebands to cause photochemical damage to the retina, which is one of the main reasons for current retinal photochemical damage. Today's lighting industry uses sunlight as the standard for evaluating light source quality because the spectrum of sunlight is continuous and the intensity is uniform, which is suitable for the normal development of the human eye and the circadian rhythm. Sunlight spectrum LED is a new type of LED that simulates the spectrum of sunlight. It has lower blue light content and enhances the continuity and integrity of the spectrum, becoming a research hotspot in the current intelligent LED field. Studies have shown that light under the sunlight spectrum can improve key learning abilities such as working memory, cognitive processing speed, and test accuracy. Therefore, in the process of realizing healthy lighting, the sunlight spectrum plays an important role.

[0004] The human eye is mainly used to receive light and transmit visual information to the brain. When calculating the approximation of the sunlight spectrum, in addition to calculating the physical differences between the spectra, the visual characteristics of the human eye, such as light-dark vision, color preference, and color resolution ability, should also be considered to comprehensively evaluate their similarity. However, the current sunlight LED fitting calculation only uses formulas such as RMS (root mean square method) and GFC (fitting coefficient) to simply calculate the similarity between the measured spectrum and the daylight spectrum. This method only considers the physical differences between different spectra and ignores the characteristics of human eye vision. SUMMARY

[0005] The purpose of the present application is to solve the problems described in the background art, and to propose a sunlight spectrum similarity evaluation method and system based on spectral power distribution, taking into account both human eye visual characteristics and comprehensive evaluation of light source quality, which is of great significance for healthy lighting and can play a key role in creating a healthy lighting environment.

[0006] The technical scheme of the present application provides a kind of sun-like spectrum similarity evaluation method considering human eye visual characteristics, comprising the following steps:

[0007] Step 1, determine the reference daylight source and obtain its spectral power distribution S Reference ;

[0008] Step 2, measure the spectral power distribution S of the light source to be measured LED ;

[0009] Step 3, respectively calculate the product of the spectral power distribution of the reference daylight source, the spectral power distribution of the light source to be measured and the spectral luminous efficiency of the human eye under photopic vision , representing wavelength;

[0010] Step 4, calculate the spectral power distribution similarity fitting degree coefficient GFC combined with spectral luminous efficiency V(λ) ;

[0011] Step 5, calculate the illumination color preference quantitative index according to the spectral power distribution of the light source to be measured measured in step 2 ;

[0012] Step 6, calculate the illumination color resolution quantitative index according to the spectral power distribution of the light source to be measured measured in step 2 ;

[0013] Step 7, calculate the comprehensive color restoration of the light source to be measured according to the spectral power distribution obtained in steps 1 and 2 and the spectral reflectivity of a plurality of color samples ;

[0014] Step 8, use the obtained illumination color preference quantitative index , illumination color resolution quantitative index , comprehensive color restoration , to fit the light source quality comprehensive evaluation index ;

[0015] Step 9, according to the spectral power distribution similarity fitting degree coefficient GFC combined with spectral luminous efficiency obtained in step 4 V(λ) and the light source quality comprehensive evaluation index obtained in step 8 , fit the sun-like spectrum similarity evaluation model considering human eye visual characteristics Ms , obtain the sun-like spectrum similarity value of the light source to be measured through the sun-like spectrum similarity evaluation model Ms .

[0016] Further, the calculation formula of the spectral power distribution similarity fitting degree coefficient in step 4 is as follows;

[0017]

[0018] wherein and is the wavelength;

[0019]

[0020]

[0021] wherein, denotes the wavelength, is the product of the spectral power distribution of the reference daylight illuminant and the spectral luminous efficiency of the human eye in photopic vision , and is the product of the spectral power distribution of the light source to be measured and the spectral luminous efficiency of the human eye in photopic vision .

[0022] Further, the calculation formula of the lighting color preference quantitative index in step 5 is as follows:

[0023] wherein, is the lighting color preference index measurement value, is the light color quality index, CDI is the color resolution index, and are constants.

[0024] Further, the calculation formula of the light color quality index is as follows:

[0025]

[0026]

[0027]

[0028] When

[0029]

[0030] When

[0031]

[0032] wherein is the CCT factor, is a parameter for converting into 0-100, T is the correlated color temperature CCT of the light source to be measured, K is the color temperature unit, is the saturation factor, is an intermediate variable;

[0033] The specific calculation method of the color resolution index CDI is as follows:

[0034]

[0035] Wherein, CDI is the color resolution index, is the color gamut space area under the irradiation of the light source to be measured, is the color gamut space area under the irradiation of the reference light source.

[0036] Further, the illumination color resolution quantification index The calculation formula is as follows:

[0037] Wherein, is the color resolution index measurement value, S neutral is the whiteness index of the light source, is the hue dislocation index of the light source, and is a constant.

[0038] Further, the calculation formula of the whiteness index of the light source S neutral is as follows:

[0039]

[0040]

[0041]

[0042] Wherein, x and y are independent variables, is the whiteness index of the light source, and are the chromaticity coordinates of the light source to be measured in the CIE1976 UCS uniform color space;

[0043] The specific formula of the hue dislocation index of the light source is as follows:

[0044]

[0045]

[0046] Wherein, is the total hue dislocation score of the light source, which is used to measure the number of chess piece dislocations in the FM-100 hue chess caused by the light source; 85 samples of the FM-100 hue chess are divided into 4 long strip chessboards A, B, C and D; iThe numbers represent the four lines of the FM-100 color chessboard. i =1 represents chessboard A. i =2 represents chessboard B. i =3 represents chessboard C. i =4 represents chessboard D; For testing the first light source j The position of each chess piece; For testing the first light source j The misalignment score of each piece; n is the number of movable pieces on each chessboard, where chessboard A has... n =22, in chessboards B, C, and D n =21.

[0047] Furthermore, overall color reproduction The specific calculation method is as follows:

[0048]

[0049]

[0050] in, R i Indicates the first i The color reproduction value was calculated from each of the 21 color samples. For the first i The color difference of each color sample under the light source to be tested and the selected reference daylight light source. and It is a constant.

[0051] Furthermore, the 21 color samples include 14 basic color samples and 7 supplementary color samples. The 14 basic color samples are: light gray-red R1, dark gray-yellow R2, saturated yellow-green R3, medium yellow-green R4, light blue-green R5, light blue R6, light purple-blue R7, light purple-red R8, saturated red R9, saturated yellow R10, saturated green R11, saturated blue R12, light skin tone R13, and leaf green R14. The 7 supplementary color samples are: pork color sample, bread color sample, green pepper color sample, lipstick color sample, jeans color sample, Mirinda color sample, and male skin tone color sample.

[0052] Furthermore, step 9 involves a solar-like spectral similarity evaluation model that takes into account the characteristics of human visual perception. The calculation method is as follows:

[0053]

[0054]

[0055] in, It is the similarity coefficient of spectral power distribution fitting. is a light source quality index, , are constants.

[0056] The application also provides a sunlight spectrum similarity evaluation system considering human eye visual characteristics, comprising the following modules:

[0057] A reference daylight source selection module is configured to determine a reference daylight source and obtain its spectral power distribution S Reference ;

[0058] A to-be-measured light source measurement module is configured to measure the spectral power distribution S LED of a to-be-measured light source;

[0059] A product calculation module is configured to calculate the product of the spectral power distribution of the reference daylight source, the spectral power distribution of the to-be-measured light source and the spectral luminous efficiency of the human eye under photopic vision , respectively, wherein λ represents wavelength;

[0060] A spectral power distribution similarity fitting degree coefficient calculation module is configured to calculate the spectral power distribution similarity fitting degree coefficient GFC V(λ) combined with the spectral luminous efficiency;

[0061] A lighting color preference quantitative index calculation module is configured to calculate the lighting color preference quantitative index according to the measured spectral power distribution of the to-be-measured light source; ;

[0062] A lighting color resolution quantitative index calculation module is configured to calculate the lighting color resolution quantitative index according to the measured spectral power distribution of the to-be-measured light source; ;

[0063] A comprehensive color restoration calculation module is configured to calculate the comprehensive color restoration of the to-be-measured light source according to the obtained spectral power distribution and the spectral reflectivity of a plurality of color samples; ;

[0064] A light source quality index calculation module is configured to obtain the lighting color preference quantitative index , the lighting color resolution quantitative index , and the comprehensive color restoration , and fit to obtain a light source quality comprehensive evaluation index ;

[0065] A sunlight spectrum similarity evaluation module is configured to obtain the spectral power distribution similarity fitting degree coefficient GFC V(λ) combined with the spectral luminous efficiency and the light source quality comprehensive evaluation index ;A solar-like spectral similarity evaluation model that takes into account the characteristics of human visual perception was obtained through fitting. Ms The solar-like spectral similarity evaluation model was used. Ms Obtain the solar-like spectral similarity evaluation value of the light source to be tested.

[0066] Compared with the prior art, the advantages of the present invention are as follows:

[0067] Based on the physical similarity of the spectral power distributions of the light source under test and the reference sunlight source, and taking into account advanced evaluation indicators that consider both human visual characteristics and light source quality, an optimized spectral power distribution similarity fitting coefficient (GFC) combining spectral luminous efficiency was used. V(λ) Light color preference quantitative index Quantitative indicators of color resolution under illumination Overall color reproduction These indicators enable a comprehensive and integrated assessment and quantification of solar-like spectral similarity, thus providing a comprehensive and innovative quantification scheme for solar-like spectral similarity in this field. Attached Figure Description

[0068] Figure 1 This is a flowchart of an embodiment of the present invention;

[0069] Figure 2 The spectral reflectance information of the seven supplementary color samples provided for the embodiments of the present invention. Detailed Implementation

[0070] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0071] This invention provides a solar-like spectral similarity evaluation model that takes into account the characteristics of human vision. Based on the physical similarity of the spectral power distribution of the light source under test and the reference sunlight source, it incorporates advanced evaluation indicators that consider both human visual characteristics and light source quality. This is achieved through an optimized spectral power distribution similarity fitting coefficient (GFC) that combines spectral luminous efficiency. V(λ) Light color preference quantitative index Quantitative indicators of color discrimination under illumination Overall color reproduction These indicators enable a comprehensive and integrated assessment and quantification of solar-like spectral similarity.

[0072] The embodiment uses five LED light sources with different correlated color temperatures as the to-be-tested light sources: the correlated color temperatures are 2500K, 3500K, 4500K, 5500K and 6500K respectively, and the Duv values are all close to 0; the D65 standard illuminant is used as the reference daylight light source; and the novel sunlight spectrum similarity evaluation method and system considering the visual characteristics of human eyes are used for use instruction. It should be noted that the application is not limited to the above to-be-tested light sources and reference daylight light sources, and is also applicable to other to-be-tested light sources and reference daylight light sources.

[0073] The technical scheme of the application can be automatically run by a person skilled in the art using computer software technology when implemented. As shown in the accompanying drawings, Figure 1 The sunlight spectrum similarity evaluation method considering the visual characteristics of human eyes provided by the embodiment includes the following steps:

[0074] 1) Obtain the spectral power distribution of the reference daylight light source;

[0075] In the embodiment, the standard illuminant D65 is used as the reference daylight light source, and the spectral power distribution information of 400nm-700nm is obtained.

[0076] 2) Measure the spectral power distribution of the to-be-tested light source;

[0077] In the embodiment, an illuminometer is used to measure the to-be-used light source, and the spectral power distribution information of 400nm-700nm is obtained. In the embodiment, the correlated color temperatures of the to-be-tested light source are 2500K, 3500K, 4500K, 5500K and 6500K respectively, and the Duv values are all close to 0.

[0078] 3) Calculate the product of the spectral power distribution of the reference daylight light source and the spectral luminous efficiency under photopic vision, respectively;

[0079] In the embodiment, the spectral luminous efficiency data in the 400nm-700nm waveband are used.

[0080] 4) Calculate the spectral power distribution similarity fitting degree coefficient GFC V(λ) combined with the spectral luminous efficiency;

[0081] In the embodiment, the product of the spectral power distribution of the reference daylight light source and the spectral power distribution of the to-be-tested light source under photopic vision can be used to calculate the spectral power distribution similarity fitting degree coefficient GFC V(λ) combined with the spectral luminous efficiency, and the specific calculation method is as follows:

[0082]

[0083] wherein and for wavelength;

[0084]

[0085]

[0086] wherein, represents wavelength, is the product of the spectral power distribution of the reference daylight source and the spectral power distribution of the light source under test and the spectral luminous efficiency under photopic vision , and is the product of the spectral power distribution of the light source under test and the spectral luminous efficiency under photopic vision .

[0087] 5) Calculate the lighting color preference metric of the light source under test ;

[0088] In an embodiment, the color discrimination index can be calculated by using the spectral power distribution of the light source under test obtained in 2), and the specific calculation method is as follows:

[0089]

[0090]

[0091]

[0092] wherein, is the lighting color preference index metric value, and CDI are both existing indexes, which can be calculated from the spectral power distribution of the light source under test.

[0093] The calculation formula of the light color quality index is as follows:

[0094]

[0095]

[0096]

[0097] When

[0098]

[0099] When

[0100]

[0101] wherein is the CCT factor, is used to convert the parameter into 0-100, T is the correlated color temperature CCT of the light source to be measured, K is the color temperature unit, is a saturation factor, is an intermediate variable;

[0102] The specific calculation method of the color resolution index CDI is as follows:

[0103]

[0104] Wherein, CDI is the color resolution index, is the color gamut space area under the illumination of the light source to be measured, is the color gamut space area under the illumination of the reference light source.

[0105] 6) Calculate the illumination color resolution quantification index of the light source to be measured ;

[0106] In the embodiment, the color resolution index can be calculated by the light source spectral power distribution to be used obtained in 2), and the specific calculation method is as follows:

[0107]

[0108]

[0109]

[0110] Wherein, is the color resolution index value, and are existing indexes, which can be calculated from the spectral power distribution of the light source to be measured.

[0111] The whiteness index of the light source S neutral The calculation formula is as follows:

[0112]

[0113]

[0114]

[0115] Wherein, x and y are independent variables, is the light source whiteness index, and are the chromaticity coordinates of the light source to be measured in the CIE1976 UCS uniform color space;

[0116] ​Hue misalignment index of light source The specific formula is as follows:

[0117]

[0118]

[0119] in, The total hue misalignment score of the light source is used to measure the number of misalignments of pieces in the FM-100 hue chess caused by the light source; 85 samples of the FM-100 hue chess are packaged in four rectangular chessboards A, B, C, and D; i The numbers represent the four lines of the FM-100 color chessboard. i =1 represents chessboard A. i =2 represents chessboard B. i =3 represents chessboard C. i =4 represents chessboard D; For testing the first light source j The position of each chess piece; For testing the first light source j The misalignment score of each piece; n is the number of movable pieces on each chessboard, where chessboard A has... n =22, in chessboards B, C, and D n =21.

[0120] 7) Calculate the overall color reproduction of the light source under test. ;

[0121] In the embodiment, the spectral power distribution of the reference sunlight source and the spectral power distribution of the light source under test obtained through 1) and 2), as well as the spectral reflectance information of color samples 1 to 14 provided by R1 to R14 and the spectral reflectance information of 7 common color samples provided by the present invention (see appendix) Figure 2 The first column represents the wavelength, and columns 2-8 represent the spectral reflectance values ​​of the seven color samples. This allows for the calculation of the overall color reproduction of the light source under test. The specific calculation method is as follows:

[0122]

[0123]

[0124]

[0125]

[0126] in, To comprehensively measure color fidelity, R iThe method comprises two parts. One part is to calculate the color reduction value according to 14 standard color samples selected from R1 to R14. The 14 basic color samples include: light gray red R1, dark gray yellow R2, saturated yellow green R3, medium yellow green R4, light blue green R5, light blue R6, light purple blue R7, light purple red R8, saturated red R9, saturated yellow R10, saturated green R11, saturated blue R12, light skin color R13, and leaf green R14. The other part is to calculate the color reduction value of 7 color samples supplemented by the application. The 7 supplemented color samples include: pork color sample, bread color sample, green pepper color sample, lipstick color sample, jeans color sample, Mayonnaise color sample, and male skin color sample. The method is the first i The color difference of the 7 color samples under the to-be-measured light source and the selected reference daylight light source can be obtained according to the spectral power distribution of the to-be-measured light source and the reference daylight light source and the spectral reflectivity of the 21 color samples.

[0127] 8) Calculate the comprehensive evaluation index CQ of light source quality.

[0128] In the embodiment, the illumination color preference quantitative index of the to-be-measured light source obtained by 5), 6), and 7) , the illumination color resolution quantitative index , and the comprehensive color reduction are used to calculate the comprehensive evaluation index CQ of light source quality , so as to obtain the quality estimation value of the sunlight spectrum-like light source. The specific calculation method is as follows:

[0129]

[0130]

[0131]

[0132]

[0133] 9) Calculate the sunlight spectrum-like similarity considering the visual characteristics of the human eye.

[0134] In the embodiment, the spectral power distribution similarity fitting degree coefficient GFC considering the spectral luminous efficiency obtained by 4) V(λ) and the comprehensive evaluation index CQ of light source quality obtained by 8) can be used to obtain the sunlight spectrum-like similarity evaluation model considering the visual characteristics of the human eye . The specific calculation method is as follows:

[0135]

[0136]

[0137] The similar-to-sunlight spectrum similarity evaluation model Ms Obtain the similar-to-sunlight spectrum similarity value of the to-be-tested light source; finally, the similar-to-sunlight spectrum similarity calculation results of the five experimental light sources taking into account the visual characteristics of the human eye are as shown in Table 1:

[0138] Table 1 Similar-to-sunlight spectrum similarity calculation results

[0139]

[0140] The results of this embodiment show that, compared with the reference light source D65, in the five to-be-tested light sources, 6500K is the highest similar-to-sunlight spectrum similarity under the condition of taking into account the visual characteristics of the human eye; at the same time, from low correlated color temperature to high correlated color temperature, the similarity is improved in turn.

[0141] In another embodiment, the present application provides a similar-to-sunlight spectrum similarity evaluation system taking into account the visual characteristics of the human eye, comprising the following modules:

[0142] The reference daylight light source selection module is used to determine the reference daylight light source and obtain its spectral power distribution S Reference ;

[0143] The to-be-tested light source measurement module is used to measure the spectral power distribution S LED of the to-be-tested light source;

[0144] The product calculation module is used to calculate the product of the spectral power distribution of the reference daylight light source, the spectral power distribution of the to-be-tested light source and the spectral luminous efficiency of the human eye under photopic vision , respectively, represents the wavelength;

[0145] The spectral power distribution similarity fitting degree coefficient calculation module is used to calculate the spectral power distribution similarity fitting degree coefficient GFC V(λ) combined with the spectral luminous efficiency;

[0146] The illumination color preference quantitative index calculation module is used to calculate the illumination color preference quantitative index according to the measured spectral power distribution of the to-be-tested light source ;

[0147] The illumination color resolution quantitative index calculation module is used to calculate the illumination color resolution quantitative index according to the measured spectral power distribution of the to-be-tested light source ;

[0148] The comprehensive color restoration calculation module is used to calculate the comprehensive color restoration of the to-be-tested light source according to the obtained spectral power distribution and the spectral reflectivity of a plurality of color samples ;

[0149] The light source quality index calculation module is configured to obtain the light color preference quantitative index , the light color resolution quantitative index , the comprehensive color restoration , and the light source quality comprehensive evaluation index ;

[0150] The sunlight spectrum similarity evaluation module is configured to obtain the sunlight spectrum similarity evaluation model by fitting the spectrum power distribution similarity fitting degree coefficient GFC V(λ) obtained in combination with the spectrum luminous efficiency and the light source quality comprehensive evaluation index , and obtain the sunlight spectrum similarity value of the to-be-tested light source through the sunlight spectrum similarity evaluation model Ms . Ms

[0151] The specific implementation of each module and the corresponding steps are not described herein.

[0152] The specific embodiments described herein are merely illustrative of the spirit of the present application. Those skilled in the art of the present application can modify or supplement the described specific embodiments or replace them with similar ways, but will not deviate from the spirit of the present application or exceed the scope defined by the appended claims.​

Claims

1. A method for evaluating solar-like spectral similarity that takes into account the characteristics of human visual perception, characterized in that, Includes the following steps: Step 1: Determine the reference sunlight source and obtain its spectral power distribution S Reference ; Step 2, measure the spectral power distribution S of the light source under test. LED ; Step 3: Calculate the spectral power distribution of the reference sunlight source, the spectral power distribution of the light source under test, and the spectral luminous efficiency of the human eye under photopic vision. The product of Indicates wavelength; Step 4: Calculate the spectral power distribution similarity fitting coefficient (GFC) combining spectral luminous efficiency. V(λ) ; Step 5: Calculate the quantitative index of light color preference based on the spectral power distribution of the light source measured in Step 2. ; Step 6: Calculate the illumination color resolution quantization index based on the spectral power distribution of the light source measured in Step 2. ; Step 7: Based on the spectral power distribution obtained in Steps 1 and 2 and the spectral reflectance of several color samples, calculate the overall color reproduction of the light source under test. ; Step 8: Quantify the obtained light color preference index Quantitative indicators of color discrimination under illumination Overall color reproduction The comprehensive evaluation index of light source quality was obtained by fitting. ; Step 9: Based on the spectral power distribution similarity fitting coefficient (GFC) obtained in Step 4, combine the spectral luminous efficiency with the spectral power distribution. V(λ) The comprehensive evaluation index of light source quality obtained in step 8 A solar-like spectral similarity evaluation model that takes into account the characteristics of human visual perception was obtained through fitting. Ms The solar-like spectral similarity evaluation model was used. Ms Obtain the solar-like spectral similarity value of the light source to be tested.

2. The solar-like spectral similarity evaluation method that takes into account the characteristics of human visual perception as described in claim 1, characterized in that: The formula for calculating the similarity fitting coefficient of spectral power distribution in step 4 is as follows; in and Wavelength; in, Indicates wavelength. To reference the spectral power distribution of sunlight sources and the spectral luminous efficiency of the human eye under photopic vision. The product of The spectral power distribution of the light source under test and the spectral luminous efficiency of the human eye under photopic vision. The product of.

3. The solar-like spectral similarity evaluation method that takes into account the characteristics of human visual perception as described in claim 1, characterized in that: Step 5: Quantification of Light Color Preferences The calculation formula is as follows: in, This is a measurement value for the color preference index under light. CDI is the color quality index, while CDI is the color resolution index. and It is a constant.

4. The solar-like spectral similarity evaluation method that takes into account the characteristics of human visual perception as described in claim 3, characterized in that: Light and color quality index The calculation formula is as follows: when when in It is the CCT factor. It is used to Convert the parameters to the range of 0-100, where T is the correlated color temperature (CCT) of the light source under test, and K is the unit of color temperature. It is a saturation factor. It is an intermediate variable; The specific calculation method for the Color Discrimination Index (CDI) is as follows: CDI is the color discrimination index. It is the area of ​​the color gamut space under the illumination of the light source to be tested. It is the area of ​​the color gamut space under the illumination of the reference light source.

5. The solar-like spectral similarity evaluation method that takes into account the characteristics of human visual perception according to claim 1, characterized in that: Light color resolution quantification index The calculation formula is as follows: in, For color discrimination index measurement value, S neutral The whiteness index of the light source. This is an index of hue misalignment in light sources. and It is a constant.

6. The solar-like spectral similarity evaluation method that takes into account the characteristics of human visual perception according to claim 5, characterized in that: Whiteness index of light source S neutral The calculation formula is as follows: in, x and y As the independent variable, The whiteness index of the light source and Here are the chromaticity coordinates of the light source under test in the CIE1976 UCS uniform color space; Hue misalignment index of light source The specific formula is as follows: in, The total hue misalignment score of the light source is used to measure the number of misalignments of pieces in the FM-100 hue chess caused by the light source; 85 samples of the FM-100 hue chess are packaged in four rectangular chessboards A, B, C, and D; i The numbers represent the four lines of the FM-100 color chessboard. i =1 represents chessboard A. i =2 represents chessboard B. i =3 represents chessboard C. i =4 represents chessboard D; For testing the first light source j The position of each chess piece; For testing the first light source j The misalignment score of each piece; n is the number of movable pieces on each chessboard, where chessboard A has... n =22, in chessboards B, C, and D n =21.

7. The solar-like spectral similarity evaluation method according to claim 1, which takes into account the characteristics of human visual vision, is characterized in that: Overall color reproduction The specific calculation method is as follows: in, R i Indicates the first i The color reproduction value was calculated from each of the 21 color samples. For the first i The color difference of each color sample under the light source to be tested and the selected reference daylight light source. and It is a constant.

8. The solar-like spectral similarity evaluation method that takes into account the characteristics of human visual perception according to claim 7, characterized in that: The 21 color samples include 14 basic color samples and 7 supplementary color samples. The 14 basic color samples are: light gray-red R1, dark gray-yellow R2, saturated yellow-green R3, medium yellow-green R4, light blue-green R5, light blue R6, light purple-blue R7, light purple-red R8, saturated red R9, saturated yellow R10, saturated green R11, saturated blue R12, light skin tone R13, and leaf green R14.

9. The solar-like spectral similarity evaluation method that takes into account the characteristics of human visual perception according to claim 1, characterized in that: Step 9: A solar-like spectral similarity evaluation model that takes into account the characteristics of human visual perception. The calculation method is as follows: in, It is the similarity coefficient of spectral power distribution fitting. It is a quantitative indicator of light source quality. , All are constants.

10. A solar-like spectral similarity evaluation system that takes into account the characteristics of human visual perception, characterized in that, Includes the following modules: The reference sunlight source selection module is used to determine the reference sunlight source and obtain its spectral power distribution S. Reference ; The test light source measurement module is used to measure the spectral power distribution S of the test light source. LED ; The product calculation module is used to calculate the spectral power distribution of a reference sunlight source, the spectral power distribution of the light source under test, and the spectral luminous efficiency of the human eye under photopic vision, respectively. The product of Indicates wavelength; The spectral power distribution similarity fitting coefficient calculation module is used to calculate the spectral power distribution similarity fitting coefficient (GFC) in conjunction with spectral luminous efficiency. V(λ) ; The module for calculating quantitative indicators of light color preference is used to calculate quantitative indicators of light color preference based on the measured spectral power distribution of the light source under test. ; The illumination color resolution quantification module is used to calculate the illumination color resolution quantification index based on the measured spectral power distribution of the light source under test. ; The comprehensive color reproduction calculation module is used to calculate the comprehensive color reproduction of the light source under test based on the obtained spectral power distribution and the spectral reflectance of several color samples. ; The light source quality quantification index calculation module is used to quantify the obtained light color preference index. Quantitative indicators of color discrimination under illumination Overall color reproduction The comprehensive evaluation index of light source quality was obtained by fitting. ; The solar-like spectral similarity evaluation module is used to evaluate the similarity of the spectral power distribution based on the obtained combined spectral luminous efficiency (GFC) fitting coefficient. V(λ) and the comprehensive evaluation index of the obtained light source quality A solar-like spectral similarity evaluation model that takes into account the characteristics of human visual perception was obtained through fitting. Ms The solar-like spectral similarity evaluation model was used. Ms Obtain the solar-like spectral similarity value of the light source to be tested.

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