A method, system and storage medium for debugging the spectrum of fresh produce

Abstract

The present invention discloses a method, system and storage medium for debugging the spectrum of fresh produce. The method includes: finding the color coordinates, the offset index of the color vector diagram, and the Duv range that can enhance the visual freshness of the fresh produce model. By determining the color coordinates, the offset index of the color vector diagram, and the Duv range, the spectrum that can enhance the visual freshness of the fresh produce model can be efficiently found, thereby improving the efficiency of spectrum determination. At the same time, a system for executing the method and a corresponding storage medium are also provided. The present invention is mainly used in the technical field of semiconductor light sources.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor light sources, and particularly to a method, a system and a storage medium for adjusting the spectrum of fresh food lighting. Background Art

[0002] When specifying the spectrum of a light source, it is necessary to adjust the light source multiple times to obtain the optimal spectrum of the light source. However, the existing adjustment methods generally adjust the color temperature or color saturation, etc. However, for fresh food products, there is no very systematic debugging method. Therefore, for the lighting of fresh food products, relatively complex debugging methods are often required. Summary of the Invention

[0003] The object of the present invention is to provide a method, a system and a storage medium for adjusting the spectrum of fresh food lighting to solve one or more technical problems existing in the prior art, and at least provide a beneficial choice or create conditions.

[0004] The solution of the present invention to solve its technical problems is: providing a method for adjusting the spectrum of fresh food lighting, including: within a specific color temperature range, respectively irradiating a fresh food model with basic white light that conforms to the color temperature range, and determining the target color temperature for enhancing the visual freshness of the fresh food model according to the irradiation result; adjusting the color temperature of the basic white light to the target color temperature to form the first white light; finding the corresponding color coordinates according to the target color temperature to obtain the second set of color coordinates; analyzing the spectra corresponding to each color coordinate in the second set of color coordinates to obtain a fidelity index Rf > 90, a saturation index Rg ≈ 100, and the color coordinates corresponding to the spectra in the color vector image that are close to the shape of the reference light source, and the set of the color coordinates is denoted as the third set of color coordinates; adjusting the spectra of the first white light to the spectra corresponding to the color coordinates in the third set of color coordinates respectively, and respectively irradiating the fresh food model, comparing the irradiation results to obtain the color coordinates and the Duv range for enhancing the visual freshness of the fresh food model, the set of the color coordinates is denoted as the fourth set of color coordinates, and the Duv range is denoted as the target Duv range; keeping the parameter value of the saturation index Rg of the first white light unchanged, changing the offset index of the color vector diagram, and obtaining the offset index of the color vector diagram for enhancing the visual freshness of the fresh food model by comparison, and denoting the offset index of the color vector diagram as the offset index of the target color vector diagram; respectively adjusting the spectra of the first white light according to the offset index of the target color vector diagram, the fourth set of color coordinates and the target Duv range to obtain a plurality of spectra, and the set of the spectra is denoted as the target spectrum set; adjusting the spectra of the first white light to the spectra in the target spectrum set respectively, and respectively irradiating the fresh food model, comparing the irradiation results to obtain the spectrum for enhancing the visual freshness of the fresh food model, and the spectrum is denoted as the output spectrum.

[0005] Further, the step of finding the corresponding color coordinates according to the target color temperature to obtain the second set of color coordinates specifically includes: finding the corresponding color coordinates according to the target color temperature, where the set of color coordinates is denoted as the first set of color coordinates, and limiting the first set of color coordinates by the color tolerance step size that meets the general lighting requirements, and narrowing the range of the first set of color coordinates to obtain the second set of color coordinates.

[0006] Further, the specific color temperature range is 2700K - 6500K.

[0007] Further, the offset index of the target color vector diagram includes: Rg cs,h1 ≥17%, Rg cs,h16 ≥17%.

[0008] Further, the offset index of the target color vector diagram includes: Rg cs,h6 ≥12%, Rg cs,h7 ≥7%, Rg cs,h8 ≥ - 4%.

[0009] Further, this fresh food light spectrum debugging method further includes: performing dimming verification on the output spectrum, specifically including: generating a target white light with the output spectrum through a colorimetric light box, and applying the target white light to the fresh food model to verify the fresh food lighting effect.

[0010] Further, the quantization standard of the color rendering index adopted by the colorimetric light box is ANSI / IES TM - 30 - 20.

[0011] Further, the general color rendering index Ra of the basic white light is > 70.

[0012] On the other hand, a fresh food light spectrum debugging system is provided, including: a target color temperature determination module, a first white light generation module, a target spectrum set determination module, and an output spectrum determination module;

[0013] The target color temperature determination module is used for: within a specific color temperature range, respectively irradiating the fresh food model with the basic white light that meets the color temperature range, and determining the target color temperature that enhances the visual freshness of the fresh food model according to the irradiation result;

[0014] The first white light generation module is used for: adjusting the color temperature of the basic white light to the target color temperature to form the first white light;

[0015] The target spectrum set determination module is used for: keeping the Rg parameter value of the first white light unchanged, changing the offset index of the color vector diagram, obtaining the offset index of the color vector diagram that enhances the visual freshness of the fresh food model through comparison, and denoting the offset index of the color vector diagram as the offset index of the target color vector diagram;

[0016] Find the corresponding color coordinates according to the target color temperature to obtain a second set of color coordinates;

[0017] Analyze the spectra corresponding to each color coordinate in the second set of color coordinates to obtain a fidelity index Rf > 90, a saturation index Rg ≈ 100, and the color coordinates corresponding to the spectra in the color vector image that are close to the shape of the reference light source. The set of these color coordinates is denoted as the third set of color coordinates;

[0018] Adjust the spectrum of the first white light to the spectra corresponding to the color coordinates in the third set of color coordinates respectively,

[0019] And irradiate the fresh food model respectively, compare the irradiation results, and obtain the color coordinates and the Duv range that can enhance the visual freshness of the fresh food model. The set of these color coordinates is denoted as the fourth set of color coordinates, and the Duv range is denoted as the target Duv range;

[0020] Adjust the spectrum of the first white light according to the offset index of the target color vector map, the fourth set of color coordinates and the target Duv range respectively to obtain a plurality of spectra. The set of these spectra is denoted as the target spectrum set;

[0021] An output spectrum determination module is used for: adjusting the spectrum of the first white light to the spectra in the target spectrum set respectively, irradiating the fresh food model respectively, comparing the irradiation results, and obtaining the spectrum that can enhance the visual freshness of the fresh food model. This spectrum is denoted as the output spectrum.

[0022] On the other hand, a computer-readable storage medium is provided, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to implement the fresh food light spectrum debugging method described in any one of the above technical solutions.

[0023] The beneficial effects of the present invention are: The method of the present invention can efficiently find the spectrum that can enhance the visual freshness of the fresh food model by determining the color coordinates, the offset index of the color vector map and the Duv range. It improves the efficiency of spectrum determination. At the same time, a system for executing the method and a corresponding storage medium are also provided. The beneficial effects of the system and the storage medium are similar to those of the method and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly describe the drawings required for the description of the embodiments. Obviously, the described drawings are only a part of the embodiments of the present invention, rather than all the embodiments. Those skilled in the art can also obtain other design solutions and drawings based on these drawings without creative efforts.

[0025] Figure 1It is a step flowchart of the debugging method for the fresh food light spectrum;

[0026] Figure 2 It is a collection of effect pictures of the spectrum that can enhance the visual freshness of meat irradiated onto the meat;

[0027] Figure 3 It is the spectral curve of white light for sample 1 in the meat embodiment;

[0028] Figure 4 It is the spectral curve of white light for sample 2 in the meat embodiment;

[0029] Figure 5 It is the spectral curve of white light for sample 3 in the meat embodiment;

[0030] Figure 6 It is the spectral curve of white light for sample 4 in the meat embodiment;

[0031] Figure 7 It is the spectral curve of white light for sample 5 in the meat embodiment;

[0032] Figure 8 It is the spectral curve of white light for sample 6 in the meat embodiment;

[0033] Figure 9 It is the spectral curve of white light for sample 7 in the meat embodiment;

[0034] Figure 10 It is the spectral curve of white light for sample 8 in the meat embodiment;

[0035] Figure 11 It is a collection of effect pictures of the spectrum that can enhance the visual freshness of vegetables irradiated onto the vegetables;

[0036] Figure 12 It is the spectral curve of white light for sample 1 in the vegetable embodiment;

[0037] Figure 13 It is the spectral curve of white light for sample 2 in the vegetable embodiment;

[0038] Figure 14 It is the spectral curve of white light for sample 3 in the vegetable embodiment;

[0039] Figure 15 It is the spectral curve of white light for sample 4 in the vegetable embodiment;

[0040] Figure 16 It is the spectral curve of white light for sample 5 in the vegetable embodiment;

[0041] Figure 17 It is the spectral curve of white light for sample 6 in the vegetable embodiment;

[0042] Figure 18 It is the spectral curve of white light of sample 7 in the vegetable example;

[0043] Figure 19 It is the spectral curve of white light of sample 8 in the vegetable example;

[0044] Figure 20 It is the schematic diagram of the system structure of the fresh food light spectrum debugging system. Detailed implementation manners

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] It should be noted that although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the system or the flowchart in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects and do not have to be used to describe a specific order or sequence.

[0047] Before further elaborating on the embodiments of the present application, the nouns and terms involved in the embodiments of the present application are described. The nouns and terms involved in the embodiments of the present application are applicable to the following explanations.

[0048] Ra represents the general color rendering index. Rg represents the saturation index. The color tolerance refers to the difference between the calculated formula by the computer and the target standard. Calculated under a single illumination source, the smaller the value, the higher the accuracy. However, it should be noted that it only represents the color comparison under a certain light source and cannot detect the deviation under different light sources. The difference between the spectrum emitted by the light source and the standard spectrum. CCT, Correlated color temperature, is expressed as the correlated color temperature. The colorimetric light box, also known as the visual colorimetric box, the color comparison light box, the standard light source color comparison light box, English name: ColorController Light Box or ColorAssessment Cabinet. It is a lighting device used to detect the color deviation of goods. The ANSI / IES TM-30-20 standard, the ANSI / IES TM-30-20 standard is a national standard method for evaluating the color rendering of light sources issued by the Illuminating Engineering Society of the United States. It is a system composed of several related metrics and graphics that can be used to effectively evaluate and communicate the color rendering characteristics of light sources. The spectral camera refers to a camera that can obtain the spectrum of the received light.

[0049] Reference Figure 1 , Figure 1 is a flowchart of the steps of a fresh food light spectrum debugging method.

[0050] A fresh food light spectrum debugging method is provided, including: Step 1, within a specific color temperature range, irradiate a fresh food model with basic white light that meets the color temperature range respectively, and determine the target color temperature for enhancing the visual freshness of the fresh food model according to the irradiation result. Among them, the basic white light is required to be white light, and the general color rendering index Ra>70. The specific color temperature range includes: 2700K - 6500K, but is not limited to this. In this specific embodiment, a colorimetric light box is used to assist in the debugging of the fresh food spectrum. Place the fresh food model in the colorimetric light box, and then determine the target color temperature by adjusting the output white light of the colorimetric light box. It can be searched one by one in the range of 2700K to 6500K, and by comparing the irradiation results, the color temperature that can enhance the visual freshness of the fresh food model is determined, and this color temperature is recorded as the target color temperature.

[0051] Step 2, adjust the color temperature of the basic white light to the target color temperature to form the first white light; in Step 2, use the colorimetric light box to adjust the color temperature of the basic white light so that the color temperature of the basic white light is the target color temperature, and the adjusted basic white light is the first white light. Use the colorimetric light box to output the first white light.

[0052] Step 3, keep the parameter value of the saturation index Rg of the first white light unchanged, change the offset index of the color vector diagram, and obtain the offset index of the color vector diagram for enhancing the visual freshness of the fresh food model by comparison, and record the offset index of the color vector diagram as the target offset index of the color vector diagram. The purpose of Step 3 is to determine the offset index of the color vector diagram that can enhance the visual freshness of the fresh food model. Specifically, the colorimetric light box can be used to keep the Rg parameter value of the first white light unchanged, and then use the spectral curve of the first white light output by the colorimetric light box to be changed. It should be noted that when the spectral curve of the first white light changes, the Rg parameter value of the first white light needs to be maintained unchanged. Obviously, when the spectral curve of the first white light changes, the obtained offset index of the color vector diagram will also change accordingly. By comparing the irradiation results of the first white light on the fresh food model, the offset index of the color vector diagram that can enhance the visual freshness of the fresh food model can be determined. Record this offset index and record it as the target offset index of the color vector diagram.

[0053] Step 4: Find the corresponding color coordinates according to the target color temperature. The set of these color coordinates is denoted as the first color coordinate set. Narrow down the range of the first color coordinate set to obtain the second color coordinate set. Among them, in some further specific embodiments, the first color coordinate set is narrowed down with a color tolerance less than 5 SDCM. The purpose of step 4 is to find color coordinates. It should be noted that there is no clear order relationship between the execution of step 4 and step 3. Step 4 can be executed before step 3 or after step 3. This specific embodiment only takes the execution of step 4 after step 3 as an example for illustration.

[0054] Under the target color temperature, there will be several color coordinates, and each color coordinate corresponds to some spectra. For the sake of convenience of description, the set of these color coordinates is denoted as the first color coordinate set. Therefore, to find the appropriate spectra, it is necessary to determine the appropriate color coordinates. To reduce the search range, the range of the first color coordinate set is narrowed down with a color tolerance less than 5 SDCM, so as to obtain the narrowed first color coordinate set. For the sake of convenience of description, the narrowed first color coordinate set is denoted as the second color coordinate set.

[0055] Step 5: Analyze the spectra corresponding to each color coordinate in the second color coordinate set to obtain the color coordinates corresponding to the spectra with a fidelity index Rf > 90, a saturation index Rg ≈ 100, and a shape close to the reference light source in the color vector image. The set of these color coordinates is denoted as the third color coordinate set. The function of step 5 is to screen the second color coordinate set, obtain the spectra corresponding to each color coordinate in the second color coordinate set, and then analyze these spectra to screen out the spectra with Rf > 90, Rg ≈ 100, and a color vector image close to the shape of the reference light source. The color coordinates corresponding to these spectra are the color coordinates we need to care about. Mark the set of these color coordinates as the third color coordinate set.

[0056] Step 6: Adjust the spectrum of the first white light to the spectra corresponding to the color coordinates in the third color coordinate set respectively, irradiate the fresh food model respectively, compare the irradiation results, and obtain the color coordinates and Duv range that enhance the visual freshness of the fresh food model. The set of the color coordinates is denoted as the fourth color coordinate set, and the Duv range is denoted as the target Duv range. The function of Step 6 is to find the Duv range and color coordinates. Among them, the spectrum of the first white light is adjusted respectively by using a colorimetric light box. Among them, different Duv index gradients need to be set, and then the spectra corresponding to the color coordinates in the third color coordinate set are determined. Then, the first white light is adjusted to the corresponding spectrum by using a colorimetric light box, these spectra are output, and irradiated onto the fresh food model. According to the irradiation results, it is thus determined which color coordinates are beneficial to enhancing the visual freshness of the fresh food model. Which Duv ranges are beneficial to enhancing the visual freshness of the fresh food model. Therefore, the fourth color coordinate set and the target Duv range are obtained.

[0057] Step 7: Adjust the spectrum of the first white light respectively according to the offset index of the target color vector diagram, the fourth color coordinate set and the target Duv range to obtain a plurality of spectra. The set of the spectra is denoted as the target spectrum set. The function of Step 7 is to determine the target spectrum set. From Step 1 to Step 6, the offset index of the target color vector diagram, the fourth color coordinate set and the target Duv range can be found. With these as limiting conditions, the spectra that meet the requirements of these limiting conditions can be found. For the convenience of description, the set of these spectra is recorded as the target spectrum set.

[0058] Step 8: Adjust the spectrum of the first white light to the spectra in the target spectrum set respectively, irradiate the fresh food model respectively, compare the irradiation results, and obtain the spectrum that enhances the visual freshness of the fresh food model. The spectrum is denoted as the output spectrum.

[0059] There is no strict order requirement for Step 3 and Steps 4 to 6. In some embodiments, the steps of Step 3 can be executed first, or the steps of Steps 4 to 6 can be executed first. Of course, the steps of Step 3 and the steps of Steps 4 to 6 can also be executed simultaneously.

[0060] After obtaining the target spectrum set, the target spectrum set can be screened again. The spectrum of the first white light can be adjusted respectively by using a colorimetric light box to be the spectra in the target spectrum set. Then the adjusted white light is irradiated onto the fresh food model, and the irradiation results are compared. Thus, it is determined which spectrum can enhance the visual freshness of the fresh food model. The spectrum that can enhance the visual freshness of the fresh food model is denoted as the output spectrum. Thus, the debugging of the output spectrum is completed, and the spectrum suitable for enhancing the visual freshness of the fresh food model is found.

[0061] By determining the color coordinates, the offset index of the color vector diagram, and the Duv range, the present invention can efficiently find the spectrum that can enhance the visual freshness of the fresh food model, thus improving the efficiency of spectrum determination.

[0062] To reduce the complexity of debugging and improve the debugging efficiency, in some further improvable specific embodiments, a specific color temperature range is set to 2700K - 6500K. Moreover, when selecting the color temperature within the specific color temperature range, common color temperatures can be selected. For example, 3000K, 4000K, 5000K, and 6500K. Of course, to ensure the diversity of samples during the selection process, in some further improvable specific embodiments, at least two color temperatures need to be selected within the specific color temperature range for debugging to verify the preference of the fresh food model for which color temperatures.

[0063] In some further improvable specific embodiments, the offset index of the target color vector diagram includes: Rg cs,h1 ≥17%, Rg cs,h16 ≥17%. Through research, it is found that when the offset index of the target color vector diagram shows: Rg cs,h1 ≥17%, Rg cs,h16 ≥17%, at this time, the spectrum has a good effect on enhancing the visual freshness of fresh meat products.

[0064] In some further improvable specific embodiments, the offset index of the target color vector diagram includes: Rg cs,h6 ≥12%, Rg cs,h7 ≥7%, Rg cs,h8 ≥ - 4%. Through research, it is found that when the offset index of the target color vector diagram shows: Rg cs,h6 ≥12%, Rg cs,h7 ≥7%, Rg cs,h8 ≥ - 4%, at this time, the spectrum has a good effect on enhancing the visual freshness of fresh vegetable products.

[0065] In order to verify the obtained output spectrum, therefore, it is necessary to perform dimming verification on the output spectrum. Among them, the specific method for dimming verification of the output spectrum includes: generating target white light with the output spectrum through a colorimetric light box. Acting these target white lights on the fresh food model, then the fresh food model will produce reflection, thereby obtaining a spectral reflection spectrum. Extracting the spectral reflection spectrum through a spectral camera and then inputting it to a computer. The computer is internally integrated with a simulation model, and a rendering effect diagram can be obtained through the simulation model. Verifying the output spectrum through the rendering effect diagram. Through the computer verification method, the output spectrum can be verified more accurately.

[0066] In addition, after obtaining the output spectrum, it can also be used to control lighting fixtures. Specifically, the lighting fixtures are controlled according to the output spectrum so that the lighting fixtures emit light with the output spectrum. It should be noted that those skilled in the art can modulate the spectrum by any means well-known in the art on the basis of knowing the output spectrum proposed by the present invention; for example, a light source of the lighting fixture of the present invention can be composed of multiple lamp beads that emit different monochromatic lights, such as red lamp beads, blue lamp beads, and green lamp beads, etc. By adjusting the light flux ratio of each lamp bead, the light source outputs light with this spectrum.

[0067] To better illustrate the debugging method of the present application, the debugging of the spectrum of a fresh meat lamp will be described below.

[0068] Step 1: First, ensure that the spectrum of the selected basic white light satisfies: general color rendering index Ra > 70. At this time, the fresh food model is selected as fresh meat, and the fresh meat is placed in a colorimetric light box. By setting the satisfaction conditions of the spectrum, it is beneficial to quickly debug the spectrum. Among them, fresh meat generally includes fresh red meat or white meat. Four color temperatures of 3000K, 4000K, 5000K, and 6500K are selected within the common color temperature range of 2700K - 6500K, and the color temperature is changed in the colorimetric light box. Through discovery and display, the color temperatures of 4000K and 5000K are beneficial to improving the freshness of fresh meat, that is, enhancing the visual freshness of fresh meat. Therefore, the target color temperatures are 4000K and 5000K. Take 4000K as the target color temperature.

[0069] Step 2: Adjust the color temperature of the basic white light of the colorimetric light box to 4000K to form the first white light.

[0070] Step 3: For the first white light, ensure that its saturation index Rg ≈ 100; change the offset index of the color vector diagram so that the offset index of the color vector diagram satisfies: Rg cs,h1 ≥17%, Rg cs,h16 ≥17%. That is, the offset index of the target color vector diagram is obtained to satisfy: Rg cs,h1 ≥17%, Rg cs,h16 ≥17%.

[0071] Step 4: Find the corresponding color coordinates through the target color temperature of 4000K. Since there are many corresponding color coordinates for 4000K, therefore, the set of corresponding color coordinates is denoted as the first color coordinate set. At the same time, the first color coordinate set is reduced with a color tolerance of less than 5 SDCM as the limit to obtain the second color coordinate set.

[0072] Step 5: By analyzing the spectra corresponding to the second set of color coordinates, find the color coordinates corresponding to the spectra with a fidelity index Rf > 90, a saturation index Rg ≈ 100, and a shape close to that of the reference light source in the color vector image. The set of these color coordinates is denoted as the third set of color coordinates.

[0073] Step 6: Set different Duv index gradients, adjust the spectrum of the first white light to the spectra corresponding to the color coordinates in the third set of color coordinates respectively, irradiate the fresh food model respectively, compare the irradiation results, and obtain the color coordinates and Duv range that enhance the visual freshness of the fresh food model. The set of these color coordinates is denoted as the fourth set of color coordinates, and the Duv range is denoted as the target Duv range. Among them, for the Duv range, by testing the comparison effects in three cases of Duv > 0, Duv = 0, and Duv < 0, it can be seen that when Duv < 0, the effect of enhancing the visual freshness of the fresh food model is the best, which is denoted as the target Duv range.

[0074] Step 7: Adjust the spectrum of the first white light according to the offset index of the target color vector map, the fourth set of color coordinates, and the target Duv range obtained in Steps 1 to 6 to obtain a plurality of spectra, which are denoted as the target spectrum set.

[0075] Step 8: Adjust the spectrum of the first white light to the spectra in the target spectrum set of Step 7 respectively, irradiate the fresh meat respectively, and compare the irradiation results. The obtained results are as Figure 2 shown.

[0076] The spectral curves of 8 samples are as follows:

[0077] The spectral curve of the white light of Sample 1 is as Figure 3 shown. From Figure 3 the spectral curve, it can be seen that the spectrum of Sample 1 starts to climb from 380 nm, reaches the first peak near 440 nm, that is, the first peak. After the first peak, the curve starts to decline, reaches a trough near 480 nm, then rises, reaches the second peak near 530 nm, that is, the second peak. Then it declines again, reaches a trough near 610 nm. Then it rises, reaches the third peak near 660 nm, that is, the third peak. From Figure 3 it can be seen that the spectrum of Sample 1 has the first peak, the second peak, and the third peak. Among them, the first peak is between 380 nm and 480 nm. The second peak is between 480 nm and 620 nm. The third peak is between 620 nm and 780 nm. If the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.4, and the spectral intensity at the peak of the second peak is 0.25. The Rg of the white light of this Sample 1 cs,h1 = 8%, Rg cs,h16= 16%, Rg = 112.

[0078] The spectral curve of the white light of Sample 2 is as Figure 4 shown. From Figure 4 the spectral curve, it can be seen that the spectral curve of Sample 2 starts from 380 nm, climbs, forms the first peak near 440 nm, that is, the first peak. After reaching the first peak, the spectral curve starts to decline, forms a trough near 480 nm. Then it rises again, forms the second peak near 540 nm, that is, the second peak. After that, it declines again, forms a trough near 590 nm. Then it rises again, forms the third peak near 620 nm, that is, the third peak. From Figure 4 it can be known that the spectrum of Sample 2 has the first peak, the second peak and the third peak. Among them, the first peak of Sample 2 is between 380 nm and 480 nm, the second peak is between 480 nm and 590 nm, and the third peak is between 590 nm and 780 nm. If the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.84, and the spectral intensity at the peak of the second peak is 0.59. The Rg of the white light of this Sample 2 cs,h1 = -8%, Rg cs,h16 = 2%, Rg = 105.

[0079] The spectral curve of the white light of Sample 3 is as Figure 5 shown. From Figure 5 the spectral curve, it can be seen that the spectrum of Sample 3 starts from 380 nm, climbs, forms the first peak near 445 nm, that is, the first peak. After the first peak, the curve starts to decline, forms a trough near 475 nm, then rises, forms the second peak near 522 nm, that is, the second peak. After that, it declines again, forms a trough near 584 nm, then rises, forms the third peak near 658 nm, that is, the third peak. From Figure 5 it can be known that the spectrum of Sample 3 has the first peak, the second peak and the third peak. Among them, the first peak of Sample 3 is between 380 nm and 475 nm, the second peak is between 475 nm and 584 nm, and the third peak is between 584 nm and 780 nm. If the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.31, and the spectral intensity at the peak of the second peak is 0.3. The Rg of the white light of this Sample 3 cs,h1 = 18%, Rg cs,h16 = 20%, Rg = 116.

[0080] The spectral curve of the white light of Sample 4 is as Figure 6 shown. From Figure 6From the spectral curve, it can be seen that the spectrum of Sample 4 starts from 380 nm and climbs, forming the first peak, i.e., the first peak, near 440 nm. After the first peak, the curve starts to decline, forming a trough near 480 nm, then rises again, forming the second peak, i.e., the second peak, near 520 nm. Then it declines again, forming a trough near 580 nm, and then rises again, forming the third peak, i.e., the third peak, near 620 nm. From Figure 6 it can be seen that the spectrum of Sample 4 has the first peak, the second peak and the third peak. The first peak of Sample 4 is between 380 nm and 476 nm, the second peak is between 476 nm and 580 nm, and the third peak is between 580 nm and 780 nm. If the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.43, and the spectral intensity at the peak of the second peak is 0.58. The Rg of the white light of this Sample 4 cs,h1 = 9%, Rg cs,h16 = 12%, Rg = 115.

[0081] The spectral curve of the white light of Sample 5 is as Figure 7 shown. From Figure 7 the spectral curve, it can be seen that the spectrum of Sample 5 starts from 380 nm and climbs, forming the first peak, i.e., the first peak, near 420 nm. After the first peak, the curve starts to decline, forming a trough near 480 nm, then rises again, forming the second peak, i.e., the second peak, near 540 nm. Then it declines again, forming a trough near 580 nm, and then rises again, forming the third peak, i.e., the third peak, near 660 nm. From Figure 7 it can be seen that the spectrum of Sample 5 has the first peak, the second peak and the third peak. The first peak of Sample 5 is between 380 nm and 474 nm, the second peak is between 474 nm and 620 nm, and the third peak is between 620 nm and 780 nm. If the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.82, and the spectral intensity at the peak of the second peak is 0.31. The Rg of the white light of this Sample 5 cs,h1 = 7%, Rg cs,h16 = 12%, Rg = 113.

[0082] The spectral curve of the white light of Sample 6 is as Figure 8 shown. From Figure 8From the spectral curve, it can be seen that the spectrum of sample 6 starts from 380 nm and climbs, forming the first peak, i.e., the first peak, near 420 nm. After the first peak, the curve starts to decline, forming a trough near 480 nm, then rises again, forming the second peak, i.e., the second peak, near 540 nm. Then it declines again, forming a trough near 600 nm, then rises again, forming the third peak, i.e., the third peak, near 620 nm. From Figure 8 it can be seen that the spectrum of sample 6 has the first peak, the second peak, and the third peak. The first peak of sample 6 is between 380 nm and 476 nm, the second peak is between 476 nm and 600 nm, and the third peak is between 600 nm and 780 nm. The spectral intensity at the peak of the first peak is 1, the spectral intensity at the peak of the second peak is 0.38, and the spectral intensity at the peak of the third peak is 0.59. The Rg of the white light of this sample 6 cs,h1 =-8%, Rg cs,h16 =-1%, Rg = 106.

[0083] The spectral curve of the white light of sample 7 is as Figure 9 shown. From Figure 9 the spectral curve, it can be seen that the spectrum of sample 7 starts from 380 nm and climbs, forming the first peak, i.e., the first peak, near 420 nm. After the first peak, the curve starts to decline, forming a small trough near 439 nm, then makes a callback, and then continues to decline and forms a trough near 475 nm. Then it rises again, forming the second peak, i.e., the second peak, near 530 nm. Then it declines again, forming a trough near 583 nm, then rises again, forming the third peak, i.e., the third peak, near 660 nm. From Figure 9 it can be seen that the spectrum of sample 7 has the first peak, the second peak, and the third peak. The first peak of sample 7 is between 380 nm and 475 nm. The second peak is between 475 nm and 583 nm. The third peak is between 583 nm and 780 nm.

[0084] The spectral intensity at the peak of the third peak is 1, the spectral intensity at the peak of the first peak is 0.35, and the spectral intensity at the peak of the second peak is 0.33. The Rg of the white light of this sample 7 cs,h1 = 17%, Rg cs,h16 = 17%, Rg = 116.

[0085] The spectral curve of the white light of sample 8 is as Figure 10 shown. From Figure 10As can be seen from the spectral curve, the spectrum of Sample 8 starts to climb from 380 nm, reaches the vicinity of 420 nm to form the first peak, i.e., the first peak. After the first peak, the curve starts to decline, reaches the vicinity of 480 nm to form a trough, then rises, reaches the vicinity of 520 nm to form the second peak, i.e., the second peak. Then it declines again, reaches the vicinity of 580 nm to form a trough, and then rises, reaching the vicinity of 620 nm to form the third peak, i.e., the third peak. From Figure 10 As can be seen, the spectrum of Sample 8 has the first peak, the second peak and the third peak. The first peak of Sample 8 is between 380 nm and 480 nm, the second peak is between 480 nm and 580 nm, and the third peak is between 580 nm and 780 nm. If the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.68, and the spectral intensity at the peak of the second peak is 0.58. The Rg of the white light of this Sample 8 cs,h1 = 8%, Rg cs,h16 = 9%, Rg = 117.

[0086] The white lights of 8 samples were respectively irradiated on the same piece of meat under the same environment, and 8 photos were obtained under the same shooting conditions. Among them, the 8 photos are all assembled in Figure 2 In Figure 2 In, the photo of Sample 1 is marked with 1#, the photo of Sample 2 is marked with 2#, the photo of Sample 3 is marked with 3#, the photo of Sample 4 is marked with 4#, the photo of Sample 5 is marked with 5#, the photo of Sample 6 is marked with 6#, the photo of Sample 7 is marked with 7#, and the photo of Sample 8 is marked with 8#. From the comparison of the 8 photos, the visual effects of the overall meat in the photos obtained from Sample 3 (3#) and Sample 7 (7#) are the best, which can greatly improve the visual freshness of the meat.

[0087] Among them, the spectral effects of the white lights that can improve the visual freshness of the meat are Sample 3# and Sample 7#.

[0088] The common characteristics of the spectral curves of the white lights of Sample 3# and Sample 7# are as follows: the white light spectrum has the first spectral characteristic, the second spectral characteristic and the third spectral characteristic; the first spectral characteristic includes the first peak in the wavelength range of 380 nm - 476 nm; the second spectral characteristic includes the second peak in the wavelength range of 476 nm - 589 nm; the third spectral characteristic includes the third peak in the wavelength range of 589 nm - 780 nm; the spectral intensity at the peak of the first peak is 23% - 40% of the spectral intensity at the peak of the third peak; the spectral intensity at the peak of the second peak is 27% - 38% of the spectral intensity at the peak of the third peak.

[0089] To better illustrate the debugging method of this application, the debugging of the spectrum of a green vegetable fresh lamp will be described below.

[0090] Step 1: First, ensure that the spectrum of the selected basic white light meets the following conditions: the general color rendering index Ra > 70. At this time, the fresh model is selected as fresh vegetables, and the fresh vegetables are placed in a colorimetric light box. By setting the satisfaction conditions of the spectrum, it is beneficial to quickly debug the spectrum.

[0091] Among them, fresh vegetables generally include fresh red vegetables or green vegetables. Four color temperatures of 3000K, 4000K, 5000K, and 6500K are selected within the common color temperature range of 2700K - 6500K, and the color temperature is changed in the colorimetric light box. Through discovery, it is found that the color temperatures of 4000K and 5000K are beneficial to improving the freshness of fresh meat, that is, enhancing the visual freshness of fresh meat. Therefore, the target color temperatures are 4000K and 5000K. Take 5000K as the target color temperature.

[0092] Step 2: Adjust the color temperature of the basic white light in the colorimetric light box to 5000K to form the first white light.

[0093] Step 3: For the first white light, ensure that its saturation index Rg ≈ 100; change the offset index of the color vector diagram so that the offset index of the color vector diagram meets the following conditions: Rg cs,h6 ≥ 12%, Rg cs,h7 ≥ 7%, Rg cs,h8 ≥ -4%. That is, the offset index of the target color vector diagram is obtained to meet the following conditions: Rg cs,h6 ≥ 12%, Rg cs,h7 ≥ 7%, Rg cs,h8 ≥ -4%.

[0094] Step 4: Find the corresponding color coordinates through the target color temperature of 5000K. Since there are many corresponding color coordinates for 5000K, the set of corresponding color coordinates is denoted as the first color coordinate set. At the same time, the first color coordinate set is reduced with a color tolerance of less than 5 SDCM to obtain the second color coordinate set.

[0095] Step 5: Through the analysis of the spectrum corresponding to the second color coordinate set, find the color coordinates corresponding to the spectrum with a fidelity index Rf > 90, a saturation index Rg ≈ 100, and a shape close to the reference light source in the color vector image. The set of these color coordinates is denoted as the third color coordinate set.

[0096] Step 6: Set different Duv index gradients, adjust the spectrum of the first white light to the spectra corresponding to the color coordinates in the third color coordinate set respectively, irradiate the fresh food model respectively, compare the irradiation results, and obtain the color coordinates and Duv range that enhance the visual freshness of the fresh food model. The set of the color coordinates is denoted as the fourth color coordinate set, and the Duv range is denoted as the target Duv range. Among them, for the Duv range, by testing the three cases of Duv>0, Duv = 0, and Duv<0 and comparing the effects, it can be known that when Duv>0, the effect of enhancing the visual freshness of the fresh food model is the best, which is denoted as the target Duv range.

[0097] Step 7: Adjust the spectrum of the first white light according to the offset index of the target color vector map, the fourth color coordinate set, and the target Duv range obtained in Steps 1 to 6 to obtain a plurality of spectra, which are denoted as the target spectrum set.

[0098] Step 8: Adjust the spectrum of the first white light to the spectra in the target spectrum set of Step 7 respectively, irradiate the vegetables respectively, and compare the irradiation results. The obtained results are as Figure 11 shown.

[0099] The spectral curves of 8 samples are as follows:

[0100] The spectral curve of the white light of Sample 1 is as Figure 12 shown; from Figure 12 the spectral curve, it can be seen that the spectral curve of Sample 1 starts to climb from 380 nm, reaches the vicinity of 445 nm to form the first peak, that is, the first peak. After the first peak, the spectral curve starts to decline, reaches the vicinity of 480 nm to form a trough, then rises, reaches the vicinity of 545 nm to form the second peak, that is, the second peak. Then it declines again, reaches the vicinity of 622 nm to form a trough. Then it rises, reaches the vicinity of 657 nm to form the third peak, that is, the third peak. Among them, the first peak is located between 380 nm and 480 nm, the second peak is located between 480 nm and 622 nm, and the third peak is located between 622 nm and 780 nm. Among them, the spectral intensity relationships at the peaks of each peak are as follows: the spectral intensity at the peak of the third peak is 0.97, then the spectral intensity at the peak of the first peak is 1, and the spectral intensity at the peak of the second peak is 0.61. The Rg cs,h6 of the white light of this Sample 1 cs,h7 = 16%, the Rg cs,h8 of the white light

[0101] The spectral curve of the white light of Sample 2 is as Figure 13 shown. From Figure 13From the spectral curve, it can be seen that the spectral curve of Sample 2 starts to climb from 380 nm, reaches the vicinity of 440 nm to form the first peak, namely the first peak. After reaching the first peak, the spectral curve starts to decline, reaches the vicinity of 480 nm to form a trough. Then it rises again, reaches the vicinity of 540 nm to form the second peak, namely the second peak. Then it declines again, reaches the vicinity of 600 nm to form a trough. Then it rises again, reaches the vicinity of 620 nm to form the third peak, namely the third peak. Among them, the first peak of Sample 2 is located between 380 nm and 480 nm, the second peak is located between 480 nm and 605 nm, and the third peak is located between 605 nm and 780 nm. Among them, the spectral intensity relationships at the peaks of each peak are as follows: the spectral intensity at the peak of the third peak is 0.58, then the spectral intensity at the peak of the second peak is 0.6, and the spectral intensity at the peak of the first peak is 1. The Rg of the white light of this Sample 2 cs,h6 = 15%, and the Rg of the white light cs,h7 = 6%, and the Rg of the white light cs,h8 = -6%, and the Rg of the white light = 99.

[0102] The spectral curve of the white light of Sample 3 is as shown in Figure 14 shown. From Figure 14 the spectral curve, it can be seen that the spectral curve of Sample 3 starts to climb from 380 nm, reaches the vicinity of 445 nm to form the first peak, namely the first peak. After reaching the first peak, the spectral curve starts to decline, reaches the vicinity of 474 nm to form a trough. Then it rises again, reaches the vicinity of 522 nm to form the second peak, namely the second peak. Then it declines again, reaches the vicinity of 589 nm to form a trough. Then it rises again, reaches the vicinity of 659 nm to form the third peak, namely the third peak. Among them, the first peak of Sample 3 is located between 380 nm and 474 nm, the second peak is located between 474 nm and 589 nm, and the third peak is located between 589 nm and 780 nm. Among them, the spectral intensity relationships at the peaks of each peak are as follows: the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.42, and the spectral intensity at the peak of the second peak is 0.41. The Rg of the white light of this Sample 3 cs,h6 = 12%, and the Rg of the white light cs,h7 = 15%, and the Rg of the white light cs,h8 = 11%, and the Rg of the white light = 117.

[0103] The spectral curve of the white light of Sample 4 is as shown in Figure 15 shown. From Figure 15From the spectral curve, it can be seen that the spectral curve of Sample 4 starts to climb from 380 nm, reaches the vicinity of 440 nm to form the first peak, i.e., the first peak. After reaching the first peak, the spectral curve starts to decline, reaches the vicinity of 480 nm to form a trough. Then it rises again, reaches the vicinity of 520 nm to form the second peak, i.e., the second peak. After that, it declines again, reaches the vicinity of 620 nm to form the third peak, i.e., the third peak. Among them, the first peak of Sample 4 is located between 380 nm and 480 nm, the second peak is located between 480 nm and 580 nm, and the third peak is located between 580 nm and 780 nm. Among them, the spectral intensity relationships at the peaks of each peak are as follows: If the spectral intensity at the peak of the third peak is 1, then the spectral intensity at the peak of the first peak is 0.65, and the spectral intensity at the peak of the second peak is 0.79. The Rg of the white light of this Sample 4 cs,h6 = 15%, and the Rg of the white light cs,h7 = 20%, and the Rg of the white light cs,h8 = 16%, and the Rg of the white light = 117.

[0104] The spectral curve of the white light of Sample 5 is as shown in Figure 16 the figure. From Figure 16 the spectral curve, it can be seen that the spectral curve of Sample 5 starts to climb from 380 nm, reaches the vicinity of 425 nm to form the first peak, i.e., the first peak. After reaching the first peak, the spectral curve starts to decline, reaches the vicinity of 471 nm to form a trough. Then it rises again, reaches the vicinity of 549 nm to form the second peak, i.e., the second peak. After that, it declines again, reaches the vicinity of 639 nm to form a trough. Then it rises again, reaches the vicinity of 653 nm to form the third peak, i.e., the third peak. Among them, the first peak of Sample 5 is located between 380 nm and 471 nm, the second peak is located between 471 nm and 639 nm, and the third peak is located between 639 nm and 780 nm. Among them, the spectral intensity relationships at the peaks of each peak are as follows: If the spectral intensity at the peak of the third peak is 0.27, then the spectral intensity at the peak of the first peak is 1, and the spectral intensity at the peak of the second peak is 0.37. The Rg of the white light of this Sample 5 cs,h6 = 22%, and the Rg of the white light cs,h7 = 14%, and the Rg of the white light cs,h8 = 0%, and the Rg of the white light = 101.

[0105] The spectral curve of the white light of Sample 6 is as shown in Figure 17 the figure. From Figure 17From the spectral curve, it can be seen that the spectral curve of Sample 6 starts to climb from 380 nm, forms the first peak near 425 nm, i.e., the first peak. After reaching the first peak, the spectral curve starts to decline, forms a trough near 480 nm. Then it rises again, forms the second peak near 550 nm, i.e., the second peak. Then it slowly declines until 780 nm. Among them, the first peak of Sample 6 is between 380 nm and 480 nm, and the second peak is between 480 nm and 780 nm. Among them, the spectral intensity relationship at the peaks of the first peak and the second peak is as follows: the spectral intensity at the peak of the first peak is 1, and the spectral intensity at the peak of the second peak is 0.39. The Rg of the white light of this Sample 6 cs,h6 = 22%, the Rg of the white light cs,h7 = 15%, the Rg of the white light cs,h8 = 0%, the Rg of the white light = 101.

[0106] The spectral curve of the white light of Sample 7 is as Figure 18 shown. From Figure 18 the spectral curve, it can be seen that the spectral curve of Sample 7 starts to climb from 380 nm, forms the first peak near 426 nm, i.e., the first peak. After reaching the first peak, the spectral curve starts to decline, forms a trough near 471 nm. Then it rises again, forms the second peak near 520 nm, i.e., the second peak. Then it declines again, forms a trough near 593 nm. Then it rises again, forms the third peak near 656 nm, i.e., the third peak. Among them, the first peak of Sample 7 is between 380 nm - 471 nm, the second peak is between 471 nm - 593 nm, and the third peak is between 593 nm - 780 nm. Among them, the spectral intensity relationship at the peaks of each peak is as follows: the spectral intensity at the peak of the third peak is 1, the spectral intensity at the peak of the first peak is 0.66, and the spectral intensity at the peak of the second peak is 0.48. The Rg of the white light of this Sample 7 cs,h6 = 19%, the Rg of the white light cs,h7 = 21%, the Rg of the white light cs,h8 = 12%, the Rg of the white light = 116.

[0107] The spectral curve of the white light of Sample 8 is as Figure 19 shown. From Figure 19From the spectral curve, it can be seen that the spectral curve of sample 8 starts to climb from 380 nm, reaches the first peak near 425 nm, that is, the first peak. After reaching the first peak, the spectral curve starts to decline, and reaches a trough near 480 nm. Then it rises again and reaches the second peak near 525 nm, that is, the second peak. Then it declines again and reaches a trough near 580 nm. Then it rises again and reaches the third peak near 620 nm, that is, the third peak. Among them, the first peak of sample 8 is between 380 nm and 480 nm, the second peak is between 480 nm and 580 nm, and the third peak is between 580 nm and 780 nm. Among them, the spectral intensity relationships at the peaks of each peak are as follows: the spectral intensity at the peak of the third peak is 0.9, then the spectral intensity at the peak of the first peak is 1, and the spectral intensity at the peak of the second peak is 0.72. The Rg of the white light of this sample 8 cs,h6 = 24%, and the Rg of the white light cs,h7 = 29%, and the Rg of the white light cs,h8 = 20%, and the Rg of the white light = 118.

[0108] The white lights of 8 samples were respectively irradiated on the same portion of vegetables under the same environment, and 8 photos were obtained under the same shooting conditions. Among them, the 8 photos are all collected in Figure 11 In Figure 11 In, the photo obtained by labeling sample 1 with 1# is obtained, the photo obtained by labeling sample 2 with 2# is obtained, the photo obtained by labeling sample 3 with 3# is obtained, the photo obtained by labeling sample 4 with 4# is obtained, the photo obtained by labeling sample 5 with 5# is obtained, the photo obtained by labeling sample 6 with 6# is obtained, the photo obtained by labeling sample 7 with 7# is obtained, and the photo obtained by labeling sample 8 with 8# is obtained. From the comparison of the 8 photos, the visual effects of the overall vegetables in the photos obtained from sample 1 (1#), sample 3 (3#), sample 5 (5#), and sample 7 (7#) are the best, which can greatly improve the visual freshness of the vegetables.

[0109] Among them, the effects of finding the spectra of white light that enhance the visual freshness of vegetables are samples 1#, 3#, 5#, and 7#. The common features of the white light spectra curves of samples 3# and 7# are as follows: the white light spectrum has a first spectral feature, a second spectral feature, and a third spectral feature; the first spectral feature includes a first peak within the wavelength range of 380 nm - 474 nm; the second spectral feature includes a second peak within the wavelength range of 474 nm - 589 nm; the third spectral feature includes a third peak within the wavelength range of 589 nm - 780 nm; where the spectral intensity at the peak of the first peak is 37% - 97% of the spectral intensity at the peak of the third peak, and the spectral intensity at the peak of the second peak is 36% - 56% of the spectral intensity at the peak of the third peak; or the spectral intensity at the peak of the second peak is 28% - 66% of the spectral intensity at the peak of the first peak, and the spectral intensity at the peak of the third peak is 22% - 50% or 90% - 98% of the spectral intensity at the peak of the first peak.

[0110] Reference Figure 20 Second, a fresh food light spectrum debugging system of the present application includes: a target color temperature determination module, a first white light generation module, a target spectrum set determination module, and an output spectrum determination module. Among them, the target color temperature determination module is used to: within a specific color temperature range, respectively irradiate a fresh food model with basic white light that meets the color temperature range, and determine the target color temperature that enhances the visual freshness of the fresh food model according to the irradiation results. The first white light generation module is used to: adjust the color temperature of the basic white light to the target color temperature to form the first white light.

[0111] The target spectrum set determination module is used to include: determining the offset index of the color vector diagram that can enhance the visual freshness of the fresh food model. Specifically, the Rg parameter value of the first white light can be kept unchanged by a colorimetric light box, and then the spectral curve of the first white light output by the colorimetric light box is changed. It should be noted that when the spectral curve of the first white light changes, the Rg parameter value of the first white light needs to be maintained unchanged. Obviously, when the spectral curve of the first white light changes, the offset index of the obtained color vector diagram will also change accordingly. By comparing the irradiation results of the first white light on the fresh food model, the offset index of the color vector diagram that can enhance the visual freshness of the fresh food model can be determined. Record this offset index and denote it as the offset index of the target color vector diagram.

[0112] Find the corresponding color coordinates according to the target color temperature, and the set of the color coordinates is denoted as the first color coordinate set. With the color tolerance less than 5 SDCM as the limit, narrow down the range of the first color coordinate set to obtain the second color coordinate set. Since there will be several color coordinates under the target color temperature, and each color coordinate corresponds to some spectra. For the sake of convenience of description, the set of these color coordinates is denoted as the first color coordinate set. Therefore, to find the appropriate spectra, it is necessary to determine the appropriate color coordinates. In order to reduce the search range, with the color tolerance less than 5 SDCM as the limit, narrow down the range of the first color coordinate set, so as to obtain the narrowed first color coordinate set. For the sake of convenience of description, the narrowed first color coordinate set is denoted as the second color coordinate set.

[0113] Screen the second color coordinate set, obtain the spectra corresponding to each color coordinate in the second color coordinate set, and then analyze these spectra to screen out the spectra with Rf > 90, Rg ≈ 100, and the color vector image close to the shape of the reference light source. The color coordinates corresponding to these spectra are the color coordinates we need to care about. Mark the set of these color coordinates as the third color coordinate set. Control the colorimetric light box to adjust the spectra of the first white light respectively. Among them, different Duv index gradients need to be set, and then use the color coordinates in the third color coordinate set to determine the corresponding spectra. Then, use the colorimetric light box to adjust the first white light to the corresponding spectra, output these spectra, and irradiate them on the fresh food model. According to the irradiation results, determine which color coordinates are beneficial to improving the visual freshness of the fresh food model. Which Duv ranges are beneficial to improving the visual freshness of the fresh food model. Therefore, obtain the fourth color coordinate set and the target Duv range. Adjust the spectra of the first white light according to the offset index of the target color vector diagram, the fourth color coordinate set and the target Duv range respectively to obtain a plurality of spectra, and the set of the spectra is denoted as the target spectrum set.

[0114] An output spectrum determination module, configured to: adjust the spectra of the first white light to the spectra in the target spectrum set respectively, irradiate the fresh food model respectively, compare the irradiation results, and obtain the spectra that can improve the visual freshness of the fresh food model, and the spectra are denoted as output spectra.

[0115] In a third aspect, the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored, and when the program executable by the processor is executed by the processor, it is used to implement the fresh food light spectrum debugging method described in any of the previous embodiments.

[0116] The embodiments of the present application also disclose a computer program product, including a computer program or computer instructions. The computer program or computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the fresh food light spectrum debugging method as described in any of the previous embodiments.

[0117] The terms "first", "second", "third", "fourth", etc. (if any) in the description of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0118] Of course, in some embodiments, the fresh food light spectrum debugging method can also be implemented by forming a computer program and executing the steps of the fresh food light spectrum debugging method in the form of a computer program. At this time, the device for executing the fresh food light debugging method includes: a processor and a memory; the memory is used to store a computer-readable program; when the computer-readable program is executed by the processor, the processor is enabled to implement the fresh food light spectrum debugging method as described in any of the above specific embodiments.

[0119] Those of ordinary skill in the art will appreciate that all or some of the steps and systems disclosed above in the methods can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that a communication medium typically contains computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0120] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can also make various equivalent variations or substitutions without departing from the spirit of the present invention, and these equivalent variations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for debugging the spectrum of a fresh food lamp, characterized in that: include: Within a specific color temperature range, the freshness model is illuminated with basic white light that meets the color temperature range, and a target color temperature that enhances the visual freshness of the freshness model is determined based on the illumination results; Adjusting the color temperature of the basic white light to the target color temperature to form the first white light; Finding corresponding color coordinates according to the target color temperature to obtain a second color coordinate set; Analyze the spectrum corresponding to each color coordinate in the second color coordinate set to obtain the color coordinates corresponding to the spectrum with a fidelity index Rf >90, a saturation index Rg ≈100, and a color vector image close to the shape of the reference light source. The set of color coordinates is recorded as the third color coordinate set. The spectra of the first white light are adjusted to spectra corresponding to the color coordinates in the third color coordinate set, and the fresh food models are illuminated respectively. The illumination results are compared to obtain color coordinates and Duv ranges that enhance the visual freshness of the fresh food models. The color coordinate set is recorded as a fourth color coordinate set, and the Duv range is recorded as a target Duv range. Maintaining the parameter value of the saturation index Rg of the first white light unchanged, changing the offset index of the color vector graph, obtaining the offset index of the color vector graph that improves the visual freshness of the fresh model through comparison, and marking the offset index of the color vector graph as the offset index of the target color vector graph; Adjusting the spectrum of the first white light according to the offset index of the target color vector diagram, the fourth color coordinate set, and the target Duv range to obtain a plurality of spectra, where the set of spectra is recorded as a target spectrum set; The spectra of the first white light are adjusted to spectra in the target spectrum set, and the fresh models are irradiated respectively. The irradiation results are compared to obtain spectra that enhance the visual freshness of the fresh models. The spectra are recorded as output spectra.

2. A method for debugging the spectrum of a fresh food lamp according to claim 1, characterized in that: Finding corresponding color coordinates according to the target color temperature to obtain a second color coordinate set specifically includes: finding corresponding color coordinates according to the target color temperature, recording the set of color coordinates as a first color coordinate set, and limiting the first color coordinate set to a color tolerance step size that meets general lighting requirements, narrowing the range of the first color coordinate set to obtain the second color coordinate set.

3. A method for debugging the spectrum of a fresh food lamp according to claim 1, characterized in that: The specific color temperature range is 2700K-6500K.

4. A method for debugging the light spectrum of fresh produce according to claim 1, characterized in that, The offset index of the target color vector diagram includes: Rg cs,h1 ≥ 17%, Rg cs,h16 ≥ 17%.

5. The method for adjusting the spectrum of a fresh food lamp according to claim 1, wherein: The offset index of the target color vector diagram includes: Rg cs,h6 ≥12%, Rg cs,h7 ≥7%, Rg cs,h8 ≥ -4%.

6. A method for debugging the spectrum of fresh produce lights, characterized in that, Also includes: Dimming verification of the output spectrum, including: A target white light with the output spectrum is generated by a colorimetric light box, and the target white light is applied to a fresh food model to verify the fresh food lighting effect.

7. A method for debugging the light spectrum of fresh produce according to claim 6, characterized in that, The color rendering index quantitative standard adopted by the colorimetric light box is ANSI / IES TM-30-20.

8. A method for debugging the spectrum of fresh produce lights according to claim 1, characterized in that The general color rendering index of the basic white light is Ra>70.

9. A fresh food light spectrum debugging system, characterized in that, include: A target color temperature determination module is configured to: illuminate the freshness model with basic white light that meets the color temperature range within a specific color temperature range, and determine a target color temperature that enhances the visual freshness of the freshness model based on the illumination results; A first white light generating module is configured to adjust the color temperature of the basic white light to a target color temperature to form a first white light; a target spectrum set determination module, configured to maintain the Rg parameter value of the first white light unchanged, change the offset index of the color vector graph, obtain the offset index of the color vector graph that improves the visual freshness of the fresh model through comparison, and mark the offset index of the color vector graph as the offset index of the target color vector graph; Finding corresponding color coordinates according to the target color temperature to obtain a second color coordinate set; Analyze the spectrum corresponding to each color coordinate in the second color coordinate set to obtain the color coordinates corresponding to the spectrum with a fidelity index Rf >90, a saturation index Rg ≈100, and a color vector image close to the shape of the reference light source. The set of color coordinates is recorded as the third color coordinate set. The spectrum of the first white light is adjusted to the spectrum corresponding to the color coordinates in the third color coordinate set, The fresh food models are irradiated respectively, and the irradiation results are compared to obtain the color coordinates and Duv range that improve the visual freshness of the fresh food models. The set of color coordinates is recorded as the fourth color coordinate set, and the Duv range is recorded as the target Duv range. Adjusting the spectrum of the first white light according to the offset index of the target color vector diagram, the fourth color coordinate set, and the target Duv range to obtain a plurality of spectra, where the set of spectra is recorded as a target spectrum set; The output spectrum determination module is used to: adjust the spectrum of the first white light to a spectrum in the target spectrum set, irradiate the fresh model respectively, compare the irradiation results, and obtain a spectrum that improves the visual freshness of the fresh model, and record the spectrum as the output spectrum.

10. A computer-readable storage medium, characterized in that: A processor-executable program is stored therein, and when the processor-executable program is executed by the processor, it is used to implement the fresh food lamp spectrum debugging method as described in any one of claims 1 to 8.

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