Light source color evaluation method and apparatus, and computer device, computer storage medium and computer program product

Abstract

Provided in the present application are a light source color evaluation method and apparatus, and a computer device, a computer storage medium and a computer program product. The method comprises: acquiring a color temperature value CCT and a chromaticity deviation value Duv of a target light source; determining a color temperature range type corresponding to the color temperature value CCT; on the basis of the color temperature range type, determining a target light source color evaluation model corresponding thereto; and inputting the color temperature value CCT and the chromaticity deviation value Duv into the target light source color evaluation model, so as to obtain a color evaluation result of the target light source. The present application can quantify a lighting effect presented by the irradiation of a light source, and establish an evaluation standard for predicting the degree of preference of human eyes for the light source, so as to adjust and control the lighting effect of the light source in a lighting scenario on the basis of the evaluation standard.

Description

Light source color evaluation methods, apparatus, computer equipment, computer storage media and computer program products

[0001] This application claims priority to Chinese Patent Application No. 202411648898.3, filed on November 18, 2024, entitled “Method and Apparatus for Evaluating the Color of a Light Source”, parts of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of intelligent lighting technology, and in particular to light source color evaluation methods, devices, computer equipment, computer storage media, and computer program products. Background Technology

[0003] Compared to traditional light sources, LED light sources have many advantages such as high luminous efficiency, low radiation, and long lifespan. Furthermore, their adjustable light color technology points the way for the development of intelligent lighting, enabling them to meet people's personalized lighting needs and adapt to various intelligent application scenarios.

[0004] Light quality research has become a hot topic in the lighting field. The actual use of light sources is often in situations where there are no objects being illuminated, and the light source is used to directly illuminate the entire space. However, in these situations, due to the discrepancy between the parameters of the light source itself and the actual perception of the human eye, the lighting setup may not match the user's needs. Therefore, there is an urgent need for a method to accurately predict the illumination effect of a light source, in order to predict the degree of human visual preference for the color of the light source. Summary of the Invention

[0005] This application proposes a method, apparatus, computer device, computer storage medium, and computer program product for evaluating the color of a light source. These methods can quantify the lighting effect presented by a light source and establish an evaluation standard to predict the human eye's preference for a light source. Based on this evaluation standard, the lighting effect of the light source in a lighting scene can be controlled.

[0006] On one hand, embodiments of this application provide a method for evaluating the color of a light source, including:

[0007] Obtain the color temperature (CCT) and color deviation (Duv) of the target light source;

[0008] Determine the color temperature range type corresponding to the color temperature value CCT, wherein the color temperature range type includes a first preset color temperature range, a second preset color temperature range, and a third preset color temperature range;

[0009] Determine the corresponding target light source color evaluation model based on the color temperature range type;

[0010] The color temperature value (CCT) and the color deviation value (Duv) are input into the target light source color evaluation model to obtain the color evaluation result of the target light source.

[0011] In one possible implementation, the first preset color temperature range, the second preset color temperature range, and the third preset color temperature range are 2500–3500K, 3500–5000K, and 5000–7000K, respectively.

[0012] In one possible implementation, inputting the color temperature value (CCT) and the color deviation value (Duv) into the target light source color evaluation model includes:

[0013] When the color temperature value (CCT) is determined to be within the range of 2500–3500K.

[0014] The color temperature value (CCT) is input into the first light source color evaluation model to obtain the color evaluation result of the target light source. The first light source color evaluation model is: M1 = p1 + p2 * Ln(CCT) + p3 * Duv + p4 * Duv 2

[0015] Where, p1 = -4.728 × 10 1 , p2=6.415, p3=-1.079×10 2 p4 = -6.181 × 10 3 .

[0016] In one possible implementation, inputting the color temperature value (CCT) and the color deviation value (Duv) into the target light source color evaluation model includes:

[0017] When the color temperature value (CCT) is determined to be within the range of 3500–5000K.

[0018] The color temperature value (CCT) is input into the second light source color evaluation model to obtain the color evaluation result of the target light source. The second light source color evaluation model is as follows:

[0019] Where p5 = 3.538, p6 = 4.735 × 10 -4 p7 = -5.782 × 10 -1 p8 = -9.420 × 10 -3 .

[0020] In one possible implementation, inputting the color temperature value (CCT) and the color deviation value (Duv) into the target light source color evaluation model includes:

[0021] When the color temperature value (CCT) is determined to be within the range of 5000–7000K.

[0022] The color temperature value (CCT) is input into the third light source color evaluation model to obtain the color evaluation result of the target light source. The third light source color evaluation model is: M3 = p9 + p10 * CCT + p11 * Duv + p12 * Duv 2

[0023] Where p9 = 6.173, p10 = -1.489 × 10 -4 p11 = -3.584 × 10 1 p12 = -5.473 × 10 3 .

[0024] One possible implementation also includes:

[0025] Determine the corresponding target light source quality quantification model based on the color temperature range type;

[0026] The color temperature value (CCT), the color deviation value (Duv), and the maximum color deviation value are input into the target light source quality quantification model to obtain the quality quantification result of the target light source.

[0027] In one possible implementation, the quality quantification result of the target light source is the ratio between the evaluation result obtained by inputting the color deviation value (Duv) of the target light source into the target light source color evaluation model and the evaluation result obtained by inputting the maximum color deviation value.

[0028] In one possible implementation, the step of obtaining the color temperature value (CCT) and color deviation value (Duv) of the target light source then includes:

[0029] Determine whether the color temperature value CCT is within a preset color temperature range, and determine whether the color deviation value Duv is within a preset color deviation range;

[0030] If both are within their corresponding preset ranges, then determine the color temperature range type corresponding to the color temperature value CCT.

[0031] One possible implementation also includes:

[0032] The spectral power distribution information of the target light source is obtained, wherein the wavelength of the target light source is 400–700 nm;

[0033] The color temperature (CCT) and color deviation (Duv) of the target light source are calculated based on the spectral power distribution information.

[0034] On one hand, embodiments of this application provide a light source color evaluation device, including:

[0035] Color temperature range determination unit: used to obtain the color temperature value (CCT) and color deviation value (Duv) of the target light source;

[0036] Determine the color temperature range type corresponding to the color temperature value CCT, wherein the color temperature range type includes a first preset color temperature range, a second preset color temperature range, and a third preset color temperature range;

[0037] Color evaluation quantization unit: used to determine the corresponding target light source color evaluation model according to the color temperature range type;

[0038] The color temperature value (CCT) and the color deviation value (Duv) are input into the target light source color evaluation model to obtain the color evaluation result of the target light source.

[0039] On one hand, embodiments of this application provide a computer device, including: a storage device and a processor;

[0040] A memory, wherein one or more computer programs are stored;

[0041] A processor is used to load one or more computer programs to implement the above-described light source color evaluation method.

[0042] On one hand, embodiments of this application provide a computer storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, can perform the aforementioned light source color evaluation method.

[0043] On one hand, embodiments of this application provide a computer program product, including computer instructions, which are stored in a computer storage medium. The processor of a computer device reads and executes the computer instructions from the computer storage medium, enabling the computer device to perform the aforementioned light source color evaluation method.

[0044] In this embodiment, the color temperature value (CCT) and color deviation value (Duv) of the target light source are first obtained; the color temperature range type corresponding to the CCT is determined, including a first preset color temperature range, a second preset color temperature range, and a third preset color temperature range; then, the corresponding target light source color evaluation model is determined based on the color temperature range type; finally, the CCT and Duv are input into the target light source color evaluation model to obtain the color evaluation result of the target light source. Thus, this application divides the color temperature value into multiple different color temperature ranges using corresponding boundary values ​​and introduces the color deviation value as a parameter to obtain color evaluation models for light sources under different color temperature conditions. This quantifies the different lighting quality evaluation results generated by users under different color temperature ranges due to the different degrees of color deviation of the light source, thereby reliably predicting the human eye's preference for the color of the light source in a specific lighting scenario, and achieving the control and optimization of the light source lighting scheme under different demand scenarios. Attached Figure Description

[0045] To more clearly illustrate the technical solutions and advantages in the embodiments or conventional technologies of this specification, the accompanying drawings used in the description of the embodiments or conventional technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 is a schematic flowchart of a light source color evaluation method provided in an embodiment of this specification;

[0047] Figure 2 is a schematic diagram of the structure of a light source color evaluation device provided in an embodiment of this specification. Detailed Implementation

[0048] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0049] In the field of lighting, color temperature and color deviation are two important indicators that have a significant impact on human color perception. They directly reflect the relative position of the light source's color point to the blackbody locus in the chromaticity diagram. Since they are the basic parameters of light sources in the industry, using them as a starting point for judging the color preference of light sources has a strong theoretical basis.

[0050] However, under different color temperature conditions, users' perception and mood will vary, resulting in different abilities to distinguish and accept color deviation, which directly affects users' judgment of the lighting effect and light source quality of the lighting scene.

[0051] Therefore, in order to solve the above-mentioned technical problems, this application proposes a light source color evaluation method, which quantifies the impact of light source lighting scenarios with different color temperature ranges and color deviations on users, thereby accurately assessing the user's preference for the lighting effect based on the current lighting scheme, and providing guidance for the evaluation and control optimization of light source color quality in the field of lighting applications.

[0052] As shown in Figure 1, this application provides a method for evaluating the color of a light source, including:

[0053] S100: Obtain the color temperature (CCT) and color deviation (Duv) of the target light source;

[0054] In one embodiment, before obtaining the color temperature value CCT and color deviation value Duv of the target light source in step S100, the method includes: obtaining the spectral power distribution information of the target light source, wherein the wavelength of the target light source is 400-700nm.

[0055] The color temperature (CCT) and color deviation (Duv) of the target light source are calculated based on the spectral power distribution information.

[0056] Specifically, the above method is implemented based on a light source color evaluation device, which includes a spectral reflectance acquisition module for measuring the spectral reflectance of the light source to be used; and a Duv value calculation module for calculating the Duv value of the light source to be used. The light source to be used is the target light source in this application.

[0057] In one implementation, step S100 acquires the color temperature value (CCT) and color deviation value (Duv) of the target light source, followed by:

[0058] Determine whether the color temperature value CCT is within a preset color temperature range, and determine whether the color deviation value Duv is within a preset color deviation range;

[0059] If both are within their corresponding preset ranges, then determine the color temperature range type corresponding to the color temperature value CCT.

[0060] The process involves determining whether the Duv value of the light source to be used is within the chromaticity shift range applicable to this application, specifically whether Duv1 ≤ Duv ≤ Duv2 holds true. If not, this application is not applicable; otherwise, the process proceeds to the next step. Here, Duv1 = -0.01 and Duv2 = 0.01.

[0061] The correlated color temperature (CCT) of the light source to be used is determined to be within the range of correlated color temperatures applicable to this application, i.e., whether CCT1 ≤ CCT ≤ CCT4 holds true. If not, this application is not applicable; if true, proceed to the next step. Wherein, CCT1 = 2500k and CCT4 = 7000k.

[0062] S200: Determine the color temperature range type corresponding to the color temperature value CCT, wherein the color temperature range type includes a first preset color temperature range, a second preset color temperature range, and a third preset color temperature range;

[0063] The first preset color temperature range CCT1≤CCT≤CCT2, the second preset color temperature range CCT2≤CCT≤CCT3, and the third preset color temperature range CCT3≤CCT≤CCT4 are 2500~3500K, 3500~5000K, and 5000~7000K, respectively.

[0064] S300: Determine the corresponding target light source color evaluation model based on the color temperature range type;

[0065] S400: Input the color temperature value CCT and the color deviation value Duv into the target light source color evaluation model to obtain the color evaluation result of the target light source.

[0066] In one implementation, step S400 inputs the color temperature value CCT and the color deviation value Duv into the target light source color evaluation model, including:

[0067] When the color temperature value (CCT) is determined to be within the range of 2500–3500K.

[0068] The color temperature value (CCT) is input into the first light source color evaluation model to obtain the color evaluation result of the target light source. The first light source color evaluation model is: M1 = p1 + p2 * Ln(CCT) + p3 * Duv + p4 * Duv 2

[0069] Where, p1 = -4.728 × 10 1 , p2=6.415, p3=-1.079×10 2 p4 = -6.181 × 10 3 .

[0070] In one implementation, step S400 inputs the color temperature value CCT and the color deviation value Duv into the target light source color evaluation model, including:

[0071] When the color temperature value (CCT) is determined to be within the range of 3500–5000K.

[0072] The color temperature value (CCT) is input into the second light source color evaluation model to obtain the color evaluation result of the target light source. The second light source color evaluation model is as follows:

[0073] Where p5 = 3.538, p6 = 4.735 × 10 -4 p7 = -5.782 × 10 -1 p8 = -9.420 × 10 -3 .

[0074] In one implementation, step S400 inputs the color temperature value CCT and the color deviation value Duv into the target light source color evaluation model, including:

[0075] When the color temperature value (CCT) is determined to be within the range of 5000–7000K.

[0076] The color temperature value (CCT) is input into the third light source color evaluation model to obtain the color evaluation result of the target light source. The third light source color evaluation model is: M3 = p9 + p10 * CCT + p11 * Duv + p12 * Duv 2

[0077] Where p9 = 6.173, p10 = -1.489 × 10 -4 p11 = -3.584 × 10 1 p12 = -5.473 × 10 3 .

[0078] Specifically, M1, M2 and M3 represent absolute color preference models of light sources under different color temperature conditions, which are suitable for estimating the absolute value of color preference of light sources. In the formula, CCT is the correlated color temperature of the light source to be used, and Duv is the color difference value between the color point of the light source to be used and the point of the same color temperature on the blackbody trajectory.

[0079] Based on the corresponding CCT range that meets the conditions, the Duv value and correlated color temperature (CCT) of the light source to be used are input into the light source color evaluation models M1, M2 and M3 constructed in this application, so as to calculate the absolute preference quantification value of the light source color.

[0080] In one embodiment, the light source color evaluation method of this application further includes:

[0081] Determine the corresponding target light source quality quantification model based on the color temperature range type;

[0082] The color temperature value (CCT), the color deviation value (Duv), and the maximum color deviation value are input into the target light source quality quantification model to obtain the quality quantification result of the target light source.

[0083] The quality quantification result of the target light source is the ratio between the evaluation result obtained by inputting the color deviation value (Duv) of the target light source into the target light source color evaluation model and the evaluation result obtained by inputting the maximum color deviation value.

[0084] The light source quality quantification model includes a first light source quality quantification model, a second light source quality quantification model, and a third light source quality quantification model, which correspond to the first preset color temperature range, the second preset color temperature range, and the third preset color temperature range, respectively.

[0085] M4 is a quantification model for light source quality in the 2500K-3500K range, and its specific form is as follows:

[0086] M5 is a quantification model for light source quality in the 3500K-5000K range, and its specific form is as follows:

[0087] M6 is a quantification model for light source quality in the 5000K-7000K range, and its specific form is as follows:

[0088] Since different CCT light sources cannot be directly compared in actual production applications, it is necessary to develop a relative model for the same CCT light source.

[0089] Among them, the relative models M4, M5 and M6 correspond to the absolute models M1, M2 and M3 respectively, representing the relative color preference models of light sources under different color temperature conditions, which are suitable for estimating the relative values ​​of light source color preferences.

[0090] In the formula, CCT is the correlated color temperature of the light source to be used, Duv is the color difference between the color point of the light source to be used and the point of the same color temperature on the blackbody trajectory, and Duv_max is the maximum color deviation of the light source to be used, which is -0.00873 in this embodiment.

[0091] This application employs an absolute value quantification method based on the correlated color temperature (CCT) and color deviation (Duv) of the light source, as well as a relative value quantification method with CCT as the boundary. Relying on the correlated color temperature (CCT) and Duuv indices of the light source to be used, and using multiple predictive quantification models, it achieves a comprehensive and accurate quantitative assessment of the color preference of the light source to be used based on the CCT and Duuv of the light source, thereby providing a comprehensive and targeted light source color preference quantification scheme for the field.

[0092] To further demonstrate the technical advantages of the light source color preference prediction model in the quantitative evaluation scheme for light source color preference described in this application, a psychophysical experiment was conducted. The PEARSON correlation coefficients between the human eye's preference for color point light sources of different color temperatures and the absolute color preference models M1, M3, and M5 of the light source were calculated.

[0093] In the embodiments, the correlated color temperatures of the light sources to be used are 2700K, 3000K, 4000K, 5000K, 5500K and 6500K, and the Duv values ​​of the light sources are -0.01, -0.007, -0.004, -0.002, 0, 0.002, 0.004, 0.007 and 0.01, respectively.

[0094] The specific implementation is as follows: Using 54 color point light sources of the above-mentioned 6 types of CCT and 9 types of Duv combinations as experimental light sources, observers with normal color vision and who have passed the Ishihara test are asked to carefully observe the empty light box and take the light color as the object of observation, and conduct a psychophysical preference evaluation experiment. The specific experimental method is as follows:

[0095] 1) The lightbox, as the sole emitting object, will be used throughout the entire process in a completely dark laboratory. Observers will need to observe the color of the light from the empty lightbox and use a Likert scale to rate their preference, assigning a score from +1 to +7 on a seven-point scale.

[0096] 2) The color temperature of the light source during the experiment is divided into three categories: 2500K-3500K, 3500K-5000K and 5000K-7000K. These belong to the conditional color temperatures of the light source color absolute preference models M1, M3 and M5, respectively. They are suitable for quantitative estimation of people's absolute preference under different light sources. The Duv value and the corresponding CCT value are obtained by substituting them into the calculation.

[0097] 3) During the experiment, the order in which each light source appears is random. Observers will be required to observe carefully for at least 5 seconds before making their evaluation. Observers need to record their evaluation of the light color in the empty lightbox for each experimental scenario in the experimental record sheet, according to the evaluation rules specified in the experiment. The specific evaluation rules are as follows:

[0098] A seven-point rating system is used, with 1 point for "strongly dislike" and 7 points for "strongly like," ranging from 1 to 7, with higher numbers indicating greater liking. After observing the color of the light in the empty lightbox for a period of time, observers can give their rating. Meanwhile, other experimenters will continuously remind observers to focus on the color of the light in the lightbox and eliminate interference from external environmental factors.

[0099] 4) A total of 31 observers were selected for the experiment, all young students aged 18-24 years old, with a mean age of 22.13 and a standard deviation of 1.70, and all had normal vision. Before each experiment, the experimenters verbally explained the experimental procedure. During the experiment, observers observed the color of the light source in the empty lightbox under the corresponding light source, gave their preference rating, and then closed their eyes until the experimenters changed to the next set of experimental light sources. Observers then observed for a period of time again and gave their preference rating, and this process was repeated until the last set of experimental scenarios. Each observer needed to observe 60 color points of light sources (54+5 replicates), with approximately 20 seconds spent under each light source, for a total of 20 minutes per person.

[0100] 5) After the experiment, the observers' ratings of their preferences for the light color in the lightbox were statistically analyzed in order to calculate the PEARSON correlation coefficients between the subjective evaluation values ​​and the absolute preference models for light source color M1, M3, and M5. The experimental parameters and calculation results are shown in the table below.

[0101] Observer Preference Subjective Evaluation Experiment and Calculation Results

[0102] The aforementioned psychophysical experiments yielded subjective evaluations of light source preferences by different observers under varying CCT (Color Correlated Color Temperature) conditions. Furthermore, the PEARSON correlation coefficients between these nine scores at each color temperature and the absolute color preference models M1, M2, and M3 constructed in this application were calculated, as shown in the table below. The results show that the correlation coefficients between the nine subjective evaluations at each color temperature and the absolute color preference models M4, M5, and M6 are all greater than 0.85, demonstrating that the light source color preference prediction model based on CCT and Duv constructed in this application has extremely high accuracy. This proves that the method described in this application has a strong technical advantage in predicting light source color preferences.

[0103] The correlation coefficients between the average preference scores under different color temperatures and the absolute preference models M1, M2, and M3 for light source colors.

[0104] Referring to Figure 2, which is a schematic diagram of a light source color evaluation device provided in an embodiment of this application, the light source color evaluation device 200 may specifically include:

[0105] Color temperature range determination unit 201: used to obtain the color temperature value CCT and color deviation value Duv of the target light source;

[0106] Determine the color temperature range type corresponding to the color temperature value CCT, wherein the color temperature range type includes a first preset color temperature range, a second preset color temperature range, and a third preset color temperature range;

[0107] Color evaluation quantization unit 202: used to determine the target light source color evaluation model according to the color temperature range type;

[0108] The color temperature value (CCT) and the color deviation value (Duv) are input into the target light source color evaluation model to obtain the color evaluation result of the target light source.

[0109] An embodiment of this application provides a computer device, including: a storage device and a processor;

[0110] A memory, wherein one or more computer programs are stored;

[0111] A processor is used to load one or more computer programs to implement the light source color evaluation method in this application.

[0112] Furthermore, it should be noted that this application embodiment also provides a computer storage medium, which stores a computer program, and the computer program includes program instructions. When the processor executes the above program instructions, it can execute the methods in the corresponding embodiments described above. Therefore, it will not be described again here.

[0113] For technical details not disclosed in the embodiments of the computer storage medium involved in this application, please refer to the description of the method embodiments of this application. As an example, program instructions may be deployed on a computer device, or executed on multiple computer devices located in one location, or executed on multiple computer devices distributed in multiple locations and interconnected through a communication network.

[0114] According to one aspect of this application, embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, enabling the computer device to perform the methods described in the preceding embodiments; therefore, further details will not be provided here.

[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0117] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method of evaluating the color of a light source, wherein, include: Obtain the color temperature (CCT) and color deviation (Duv) of the target light source; Determine the color temperature range type corresponding to the color temperature value CCT; Determine the corresponding target light source color evaluation model based on the color temperature range type; The color temperature value (CCT) and the color deviation value (Duv) are input into the target light source color evaluation model to obtain the color evaluation result of the target light source.

2. The light source color evaluation method according to claim 1, wherein The color temperature range type includes at least a first preset color temperature range, a second preset color temperature range, and a third preset color temperature range.

3. The light source color evaluation method according to claim 2, wherein The first preset color temperature range, the second preset color temperature range, and the third preset color temperature range are 2500~3500K, 3500~5000K, and 5000~7000K, respectively.

4. The light source color evaluation method according to claim 2 or 3, wherein, The step of inputting the color temperature value (CCT) and the color deviation value (Duv) into the target light source color evaluation model includes: When the color temperature value CCT is determined to be within the first preset color temperature range... The color temperature value (CCT) is input into the first light source color evaluation model to obtain the color evaluation result of the target light source. The first light source color evaluation model is as follows: M1 = p1 + p2*Ln(CCT) + p3*Duv + p4*Duv 2 where p1 = -4.728 x 10 1 p2 = 6.415, p3 = -1.079 x 10 2 p4 = -6.181 x 10 3 .

5. The light source color evaluation method according to claim 2 or 3, wherein The step of inputting the color temperature value (CCT) and the color deviation value (Duv) into the target light source color evaluation model includes: When the color temperature value CCT is determined to be within the second preset color temperature range... inputting the color temperature value CCT into a second light source color evaluation model to obtain a color evaluation result of the target light source, the second light source color evaluation model being: where p5 = 3.538, p6 = 4.735 x 10 -4 , p7 = -5.782 x 10 -1 , and p8 = -9.420 x 10 -3 .

6. The light source color evaluation method according to claim 2 or 3, wherein The step of inputting the color temperature value (CCT) and the color deviation value (Duv) into the target light source color evaluation model includes: When the color temperature value CCT is determined to be within the third preset color temperature range The color temperature value (CCT) is input into the third light source color evaluation model to obtain the color evaluation result of the target light source. The third light source color evaluation model is as follows: M3 = p9 + p10*CCT + p11*Duv + p12*Duv 2 where p9 = 6.173, p10 = -1.489 x 10 -4 , p11 = -3.584 x 10 1 , and p12 = -5.473 x 10 3 .

7. The light source color evaluation method according to claim 1, wherein Also includes: Determine the corresponding target light source quality quantification model based on the color temperature range type; The color temperature value (CCT), the color deviation value (Duv), and the maximum color deviation value are input into the target light source quality quantification model to obtain the quality quantification result of the target light source.

8. The light source color evaluation method according to claim 7, wherein The quality quantification result of the target light source is the ratio between the evaluation result obtained by inputting the color deviation value (Duv) of the target light source into the target light source color evaluation model and the evaluation result obtained by inputting the maximum color deviation value.

9. The light source color evaluation method according to claim 1, wherein, The process of obtaining the color temperature (CCT) and color deviation (Duv) of the target light source then includes: Determine whether the color temperature value CCT is within a preset color temperature range, and determine whether the color deviation value Duv is within a preset color deviation range; If both are within their corresponding preset ranges, then determine the color temperature range type corresponding to the color temperature value CCT.

10. A light source color evaluation apparatus, wherein, include: Color temperature range determination unit: used to obtain the color temperature value (CCT) and color deviation value (Duv) of the target light source; Determine the color temperature range type corresponding to the color temperature value CCT; Color evaluation quantization unit: used to determine the corresponding target light source color evaluation model according to the color temperature range type; The color temperature value (CCT) and the color deviation value (Duv) are input into the target light source color evaluation model to obtain the color evaluation result of the target light source.

11. A computer device, wherein, include: Storage devices and processors; A memory, wherein one or more computer programs are stored; A processor for loading one or more computer programs to implement the light source color evaluation method as described in any one of claims 1-9.

12. A computer storage medium, wherein, The computer storage medium stores a computer program, which includes program instructions. When the processor executes the program instructions, it is able to perform the light source color evaluation method as described in any one of claims 1-9.

13. A computer program product, wherein, The method includes computer instructions stored in a computer storage medium, which are read from and executed by the processor of the computer device, enabling the computer device to perform the light source color evaluation method as described in any one of claims 1-9.

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