A metamerism-based spectral optimization method and related products

Abstract

The application provides a metamerism-based spectral optimization method and related products, and relates to the optical technology field, and the specific implementation scheme is: according to the light color index corresponding to the set light source hue of a multi-spectral light source, the different spectral channels of the multi-spectral light source are proportioned to obtain an initial spectral data set, wherein the number of spectral channels of the multi-spectral light source is greater than or equal to the number of spectral channels of the lighting light source required to achieve the metamerism effect under the actual lighting environment; the initial spectral data set, the object spectral reflection curve and the light color index corresponding to the set object hue are combined to obtain the initial spectral data set; and the target spectral data set is selected from the initial spectral data set to form an optimized spectral data set according to the reference lighting environment demand index value. Through the implementation of the application scheme, the spectral level optimization of the light source is adapted to the light source color, the object color and the object reflection characteristics, the more precise light source control is realized by comprehensively considering multiple factors, and the environmental lighting demand under the complex scene can be effectively met.

Description

Technical Field

[0001] This application relates to the field of optical technology, and more particularly to the field of light source control technology, and can be applied to spectral optimization scenarios for multispectral light sources. More specifically, this application discloses a spectral optimization method based on metamerism and related products. Background Technology

[0002] Multispectral light sources possess excellent spectral tunability, enabling them to adapt to diverse user lighting needs in various scenarios and leading to their widespread application in practical lighting applications. Currently, to create a comfortable and healthy lighting environment, adjustments are typically made to multispectral light sources. However, current technologies for optimizing light sources often only consider the colorimetric characteristics of the light source, thus only meeting simple lighting environment requirements and failing to achieve the desired environmental lighting effects in complex scenarios.

[0003] It is important to note that the techniques described in this section are not necessarily those previously conceived or adopted. Unless otherwise specified, no technique described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be recognized in any prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a spectral optimization method and related products based on metamerism, which can at least solve the problem that the multispectral light source optimization schemes provided by related technologies cannot meet the desired environmental lighting effects in complex scenarios.

[0005] The first aspect of this application provides a spectral optimization method based on metamerism, comprising: allocating different spectral channels of the multispectral light source according to a first color index corresponding to a set light source color of the multispectral light source to obtain an initial spectral dataset; wherein the number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels of the lighting source required to achieve metamerism in actual lighting environment; combining the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and a second color index corresponding to a set object color of the target illuminated object to obtain an intermediate spectral dataset; and selecting target spectral data from the intermediate spectral dataset with reference to the lighting environment requirement index value to form an optimized spectral dataset.

[0006] A second aspect of this application provides a spectral optimization device based on metamerism, comprising: a matching module, configured to match different spectral channels of the multispectral light source according to a first color index corresponding to a set light source color of the multispectral light source, to obtain an initial spectral dataset; wherein the number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels of the lighting source required to achieve metamerism in actual lighting environments; an acquisition module, configured to combine the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and a second color index corresponding to a set object color of the target illuminated object to obtain an intermediate spectral dataset; and an optimization module, configured to select target spectral data from the intermediate spectral dataset according to the lighting environment requirement index value, to form an optimized spectral dataset.

[0007] A third aspect of this application provides an electronic device, including a memory and a processor, wherein the processor is configured to execute a computer program stored in the memory, and when the processor executes the computer program, it implements the steps of the metamerism-based spectral optimization method provided in the first aspect of the embodiments of this application.

[0008] The fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the metamerism-based spectral optimization method provided in the first aspect of the embodiments of this application.

[0009] As can be seen from the above, according to the metamerism-based spectral optimization method and related products provided in this application, the different spectral channels of the multispectral light source are matched according to the first light color index corresponding to the set light source color of the multispectral light source to obtain an initial spectral dataset. The number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels required to achieve metamerism in actual lighting environments. Combining the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and the second light color index corresponding to the set object color of the target illuminated object, an intermediate spectral dataset is obtained. Target spectral data is selected from the intermediate spectral dataset with reference to the lighting environment requirement index value to form the optimized spectral dataset. Through the implementation of this application, the spectral optimization of the light source is adapted to the light source color, object color, and object reflectance characteristics, comprehensively considering multiple factors to achieve more refined light source control, effectively meeting the environmental lighting needs in complex scenarios.

[0010] It should be understood that the description in this section is not intended to identify key or important features of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings exemplify embodiments and form part of the specification, working together with the textual description to explain exemplary implementations of the embodiments. The drawings shown are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0012] Figure 1 A schematic diagram of the basic process of a metamerism-based spectral optimization method provided in an embodiment of this application; Figure 2 A schematic diagram of a spectral reflectance curve provided in an embodiment of this application; Figure 3 A detailed flowchart illustrating a metamerism-based spectral optimization method provided in an embodiment of this application; Figure 4 A schematic diagram of the functional modules of a metamerism-based spectral optimization device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] In the description of the embodiments of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, the term "multiple" means two or more, unless otherwise explicitly specified.

[0015] To address the issue that multispectral light source optimization schemes provided by related technologies cannot meet the desired environmental lighting effects in complex scenarios, an embodiment of this application provides a spectral optimization method based on metamerism, such as... Figure 1 The following is a basic flowchart of the metamerism-based spectral optimization method provided in this embodiment. The metamerism-based spectral optimization method includes the following steps: Step 101: Based on the first light color index corresponding to the set light source color of the multispectral light source, the different spectral channels of the multispectral light source are matched to obtain the initial spectral dataset.

[0016] Specifically, in this embodiment, the first light color index that the light source color needs to achieve can be preset based on the CIE 1931 XYZ, 1976 UCS chromaticity diagram or other chromaticity systems that can be converted to each other. The light color index can be chromaticity coordinates or a color gamut within a certain range.

[0017] It is worth noting that in this embodiment, the number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels of the lighting source required to achieve metamerism in actual lighting conditions. In practical applications, if the actual lighting environment is a lighting environment without a reference light source, that is, there are no other light sources in the lighting environment where the multispectral light source is located, then the number of spectral channels of the multispectral light source is greater than or equal to 4, and metamerism is achieved by the multispectral light source itself. On the other hand, if the actual lighting environment is a lighting environment with a reference light source, that is, there are other lighting sources in the lighting environment where the multispectral light source is located, then the number of spectral channels is greater than or equal to 2, and metamerism is achieved by combining the multispectral light source with other light sources. For example, if there is a red-green channel light source in the actual lighting environment, then the multispectral light source in this embodiment can be set as a yellow-blue channel light source.

[0018] In one optional embodiment of this example, before the step of matching different spectral channels of the multispectral light source according to the first light color index corresponding to the set light source color of the multispectral light source, the method further includes: determining the corresponding range of multiple spectral channels according to the spectral reflectance curve; and setting different spectral channels according to the range of multiple spectral channels.

[0019] Specifically, this embodiment tests the spectral reflectance curve characteristics of the illuminated object to obtain the spectral reflectance curve. Then, based on the spectral reflectance curve, it obtains the spectral reflectance characteristics of the illuminated object. Next, based on the spectral reflectance characteristics, it determines the range of multiple spectral channels of the multispectral light source. For example, for an illuminated object with a yellowish material color, the spectral channel range of the light source can be set according to the long wavelength spectral band in the spectral reflectance curve. Finally, based on the determined spectral channel range, it adaptively sets the required optimized spectral channels. Figure 2 The diagram shown is a schematic of a spectral reflectance curve provided in this embodiment. It provides spectral reflectance curves of paper materials of different colors, and the spectral reflectance characteristics of the illuminated object can be effectively characterized by the spectral reflectance curve.

[0020] In one optional embodiment of this example, if the number of spectral channels is 4, the above-mentioned step of allocating different spectral channels of the multispectral light source may include, for example, using a preset channel ratio calculation formula to allocate different spectral channels of the multispectral light source; the channel ratio calculation formula is expressed as:

[0021] in, α 1. α 2. α 3. α 4 represents the channel ratio of the four spectral channels. l =X + Y + Z, where X, Y, and Z represent the tristimulus values. l 1 = X1 + Y1 + Z1 l 2 = X² + Y² + Z² l 3 = X³ + Y³ + Z³ l 4 = X⁴ + Y⁴ + Z⁴, ( x , y ) respectively represent the chromaticity coordinates of the set light source color, ( x 1, y 1) ( x 2, y 2), ( x 3, y 3), ( x 4, y 4) These represent the chromaticity coordinates corresponding to the four channel light sources.

[0022] This embodiment uses a four-channel example to illustrate the principle of metaspectral analysis. The above formula contains three equations and four unknowns, so there are infinitely many solutions. In practical applications, if the ratio of one channel is set to t, then the ratios of the other channels can be expressed as a linear function of t, thereby obtaining the initial spectral dataset.

[0023] Step 102: Combine the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and the second light color index corresponding to the set object color of the target illuminated object to obtain the intermediate spectral dataset.

[0024] It is worth noting that in this embodiment, the target illuminated object, i.e., the object being illuminated, is preferably not a completely reflective object or an object that is close to a reflective object, as it would be difficult to achieve the metamerism effect in this case. Furthermore, the object color in this embodiment refers to the color of the object under illumination, which is different from the inherent color of the object under natural light. The selection of the corresponding colorimetric indices for the object color, such as chromaticity coordinates or color gamut, can be done using CIE L. * a * b * The above will be carried out.

[0025] In one optional embodiment of this example, the step of obtaining an intermediate spectral dataset by combining an initial spectral dataset, the spectral reflectance curve of the target illuminated object, and a second color index corresponding to a set object color of the target illuminated object includes: performing weighted summation calculations on each group of channel spectra in the initial spectral dataset according to the corresponding spectral coefficients to obtain the corresponding synthetic spectra; calculating the corresponding chromaticity of the target illuminated object based on each synthetic spectrum and the spectral reflectance curve of the target illuminated object; and combining all target synthetic spectra whose chromaticity of the target illuminated object conforms to the second color index into an intermediate spectral dataset; wherein, the second color index is a color index corresponding to a set object color of the target illuminated object.

[0026] Specifically, each spectrum in the initial spectral dataset consists of multiple channel spectra P1(λ), ​​P2(λ), P3(λ), ..., P n The spectrum is composed of (λ), where the coefficients corresponding to the synthesized spectrum of a certain spectrum P(λ) are k1, k2, k3, …, k n , that is, P (λ)= k1P1(λ)+ k2P2(λ)+ k3P3(λ) +…+ k n P n (λ). Since the composite chromaticity formula includes three equations, the actual degrees of freedom for the corresponding coefficients are n-3, meaning that n-3 channel coefficients are independent of each other. The spectral reflectance curve of the target illuminated object is ρ(λ), then the obtained target object chromaticity is determined by P(λ)*ρ(λ), still having a degree of freedom of n-3. If the second color index is a definite chromaticity value, the degrees of freedom are further reduced by 3 during chromaticity synthesis, becoming n-6. In this case, the spectral channels must be at least greater than or equal to 6. If the second color index is a color gamut range, then the spectrum of the composite chromaticity within that color gamut range meets the requirements, thus combining all spectra that meet the second color index into an intermediate spectral dataset.

[0027] For example, taking the illumination effect of white paper under a D65 light source as an example, each channel of the three-channel RGB LED light source corresponds to a certain LED selectivity. For example, the red LED includes selectable light sources with a peak wavelength range of 600-650 nm. In this embodiment, to achieve the following target effects: 1) the light source color is presented as D65, and 2) the object color is presented as an eye-friendly paper effect with a slight yellow or green tint, this embodiment tests the paper's reflectivity and then selects one channel spectrum from each of the selectable RGB channels within the visible light spectrum to form an RGB channel spectral combination.

[0028] Furthermore, this embodiment further illustrates the concept using a four-channel spectrum. The light source color is a fixed color temperature, such as one of several color temperatures including 3000 K, 4000 K, 5000 K, and 6000 K. The object color is presented as a light color effect similar to eye-friendly paper on a preset paper surface. When the four channels are used for spectral synthesis, metamerism will occur within certain color gamuts. This effect is then positively vectorized, meaning that the light source color range and corresponding spectral composition ratios are visualized by deriving from a forward mechanism. This embodiment obtains the spectral reflectance characteristics of the paper and quantifies the effect of combining these characteristics with the paper's reflectance ρ(λ). This is also positively vectorized to obtain brightness and color quantification indicators, resulting in a spectral dataset that meets the specified conditions.

[0029] Step 103: Select the target spectral data from the intermediate spectral dataset based on the reference lighting environment requirement index value to form the optimized spectral dataset.

[0030] Specifically, the lighting environment requirement indicators in this embodiment include at least one of the following: lighting environment clarity, lighting environment comfort, and lighting environment health characteristics. Lighting environment clarity is mainly related to brightness and brightness contrast (text and reading background). Generally, higher brightness results in greater contrast, leading to greater clarity for the same illuminated object. Color rendering is also a related parameter. Lighting environment comfort is mainly related to brightness, brightness contrast, and chromaticity contrast. While meeting clarity requirements, brightness and brightness contrast must be within appropriate ranges. Brightness and chromaticity contrast are used to evaluate the contrast between the region of interest (i.e., the area on the illuminated object that the user is focused on or the user's work area) and the background area, as well as the contrast between the illuminated object and the environment. Lighting environment health characteristics are related to circadian rhythm health and blue light damage. Circadian rhythm health is mainly related to the spectral band with a peak wavelength of 490 nm, while blue light damage is mainly related to the spectral band around 460 nm. Under appropriate lighting conditions, certain dose setting requirements must be met. It should be noted that the lighting environment comfort in this embodiment can be calculated based on brightness, brightness contrast, chromaticity contrast, and saturation. The value range of brightness contrast is typically from 1:1 to 3:1.

[0031] In one optional embodiment of this example, the step of selecting target spectral data from the intermediate spectral dataset to form an optimized spectral dataset based on the reference lighting environment demand index value includes: calculating the corresponding actual lighting evaluation index value for all spectral data in the intermediate spectral dataset; and obtaining target spectral data from the intermediate spectral dataset that match the actual lighting evaluation index value and the lighting environment demand index value to form an optimized spectral dataset.

[0032] Specifically, in practical applications, the lighting effects of multispectral light sources vary depending on the spectral data in the intermediate spectral dataset. This embodiment calculates the corresponding actual lighting evaluation index value for each spectral data point based on the type of lighting environment requirement index needed for the actual lighting scenario. Then, it matches the actual lighting evaluation index value with the lighting environment requirement index value, and aggregates the matched spectral data from the intermediate spectral dataset into an optimized spectral dataset. For example, if the current lighting scenario focuses on the health characteristics of the light environment, then the lighting environment requirement index value could be blue light damage dose. This embodiment calculates the corresponding actual blue light damage dose for each spectral data point in the intermediate spectral dataset, compares it with the blue light damage dose reference value, and adds the spectral data whose actual blue light damage dose is less than or equal to the blue light damage dose reference value as matched spectral data to the optimized spectral dataset.

[0033] In one optional implementation of this embodiment, the step of selecting target spectral data from the intermediate spectral dataset to form the optimized spectral dataset based on the reference lighting environment demand index values ​​includes: obtaining a priority ranking of multiple different index types; filtering out spectral data in the intermediate spectral dataset that do not conform to the corresponding index type according to the priority ranking; and obtaining the optimized spectral dataset based on the final remaining target spectral data in the intermediate spectral dataset.

[0034] Specifically, in practical applications, the types of lighting environment requirement indicators that need to be considered in actual lighting scenarios can include multiple types. In this embodiment, multiple indicator types can be prioritized. This priority ranking indicates the optimization order of the spectral dataset. That is, for the intermediate spectral dataset, the lighting environment requirement indicator type with the highest priority is used for optimization first, filtering out spectral data that does not meet the lighting environment requirement indicator values, that is, retaining spectral data that does meet the lighting environment requirement indicator values, to obtain an optimized intermediate spectral dataset. Next, the intermediate spectral dataset is optimized using the lighting environment requirement indicator type with the next lower priority, and so on, until the optimized spectral dataset is obtained. As a preferred implementation of this embodiment, the priorities of light environment clarity, light environment comfort, and light environment health characteristics are arranged from high to low. Light environment clarity and light environment comfort are visual characteristic indicators, while light environment health characteristics are non-visual characteristic indicators.

[0035] It should be noted that, in this embodiment, the types of lighting environment demand indicators to be focused on, the values ​​of lighting environment demand indicators, and the priority order of multiple different indicator types can be determined according to the target lighting scene information.

[0036] In one optional implementation of this embodiment, for the aforementioned lighting environment demand index value, visual perception attribute information of the target user group relative to the characteristic information of the target lighting object can be obtained. Then, by combining the characteristic information of the target lighting object and the visual perception attribute information, the corresponding lighting environment demand index value can be determined. The aforementioned characteristic information of the lighting object may include material characteristic information, color characteristic information, etc.

[0037] Specifically, different user groups have different visual perception attributes of the same illuminated object, so their lighting environment requirements will also differ accordingly. It should be understood that user groups can be categorized by gender or age group. This embodiment combines user group type and the attributes of the illuminated object to set the required lighting environment index values ​​for the current lighting environment, which can effectively improve lighting friendliness.

[0038] Finally, it should be noted that this embodiment combines preset light source color, object color, object reflection characteristics, and lighting environment requirements to perform spectral optimization. That is, while keeping the light source color unchanged, the light environment effect is adjusted through spectral optimization. The resulting optimized spectral dataset is preset to the lighting equipment system. In practical applications, the system can call the spectral data from the optimized spectral dataset to control the light source according to the actual application scenario or actual usage requirements, so as to adaptively achieve a clear, comfortable, and healthy light environment effect.

[0039] Figure 3 The method described in this application is a refined metamerism-based spectral optimization method, specifically including the following steps: Step 301: Determine the corresponding range of multiple spectral channels based on the spectral reflectance curve of the target illuminated object; Step 302: Set multiple different spectral channels of the multispectral light source according to multiple spectral channel ranges; Step 303: Based on the first light color index corresponding to the set light source color of the multispectral light source, the different spectral channels of the multispectral light source are matched to obtain the initial spectral dataset; Step 304: Perform a weighted summation of the channel spectra in the initial spectral dataset according to the corresponding spectral coefficients to obtain the corresponding synthetic spectra; Step 305: Calculate the corresponding chromaticity of the target illuminated object based on the synthesized spectra and the spectral reflectance curves of the target illuminated object; Step 306: Combine the target composite spectra of all the target illuminated objects whose chromaticity matches the second light color index corresponding to the set object color of the target illuminated object into an intermediate spectral dataset; Step 307: Based on the target lighting scene information, determine the types of lighting environment demand indicators to be focused on, the values ​​of the lighting environment demand indicators, and the priority order of multiple different indicator types. Step 308: Sort by priority and filter out the spectral data in the intermediate spectral dataset that do not meet the lighting environment requirement index values ​​of the corresponding index type. Step 309: Based on the remaining target spectral data in the intermediate spectral dataset, obtain the optimized spectral dataset.

[0040] It should be understood that the sequence number of each step in this embodiment does not imply the order in which the steps are executed. The execution order of each step should be determined by its function and internal logic, and should not constitute a unique limitation on the implementation process of this application embodiment.

[0041] Figure 4 An embodiment of this application provides a metamerism-based spectral optimization device, which can be used to implement the metamerism-based spectral optimization method in the foregoing embodiments, and mainly includes: The proportioning module 401 is used to proportion different spectral channels of the multispectral light source according to the first light color index corresponding to the set light source color of the multispectral light source, so as to obtain an initial spectral dataset; wherein, the number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels of the lighting source required to achieve metamerism in actual lighting environment; The acquisition module 402 is used to acquire an intermediate spectral dataset by combining the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and the second light color index corresponding to the set object color of the target illuminated object. The optimization module 403 is used to select target spectral data from the intermediate spectral dataset based on the reference lighting environment demand index value to form an optimized spectral dataset.

[0042] In one optional embodiment of this invention, the metamerism-based spectral optimization device further includes a setting module for determining the corresponding range of multiple spectral channels based on the spectral reflectance curve; and setting different spectral channels based on the range of multiple spectral channels.

[0043] In one optional embodiment of this example, the acquisition module is specifically used to: perform weighted summation calculation on each group of channel spectra in the initial spectral dataset according to the corresponding spectral coefficients to obtain the corresponding synthetic spectrum; calculate the corresponding chromaticity of the target illuminated object based on the spectral reflectance curve of each synthetic spectrum and the target illuminated object; and combine the target synthetic spectra whose chromaticity of all target illuminated objects conforms to the second light color index into an intermediate spectral dataset; wherein, the second light color index is a light color index corresponding to the set object color of the target illuminated object.

[0044] In one optional implementation of this embodiment, the optimization module is specifically used to: calculate the corresponding actual lighting evaluation index value for all spectral data in the intermediate spectral dataset; and obtain target spectral data from the intermediate spectral dataset that match the actual lighting evaluation index value with the lighting environment requirement index value to form an optimized spectral dataset.

[0045] In another optional implementation of this embodiment, the optimization module is specifically used to: obtain a priority ranking of multiple different index types; according to the priority ranking, sequentially filter out the spectral data in the intermediate spectral dataset that do not meet the lighting environment requirement index values ​​of the corresponding index types; and obtain the optimized spectral dataset based on the final remaining target spectral data in the intermediate spectral dataset.

[0046] Furthermore, in an optional embodiment of this example, the metamerism-based spectral optimization device further includes a sorting module, which is used to determine the types of lighting environment demand indicators to be focused on, the values ​​of lighting environment demand indicators, and the priority sorting of multiple different indicator types according to the target lighting scene information.

[0047] It should be noted that the spectral optimization methods based on metamerism in the foregoing embodiments can all be implemented based on the spectral optimization device based on metamerism provided in this embodiment. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the spectral optimization device based on metamerism described in this embodiment can be implemented by referring to the corresponding working process in the foregoing method embodiments, and will not be repeated here.

[0048] Based on the technical solution of the above embodiments of this application, according to the first light color index corresponding to the set light source color of the multispectral light source, the different spectral channels of the multispectral light source are matched to obtain an initial spectral dataset. The number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels of the lighting source required to achieve metamerism in actual lighting environments. Combining the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and the second light color index corresponding to the set object color of the target illuminated object, an intermediate spectral dataset is obtained. Target spectral data is selected from the intermediate spectral dataset with reference to the lighting environment requirement index value to form an optimized spectral dataset. Through the implementation of this application solution, spectral-level optimization of the light source is achieved based on light source color, object color, and object reflectance characteristics. This comprehensively considers multiple factors to achieve more refined light source control, effectively meeting the environmental lighting needs in complex scenarios.

[0049] Figure 5An electronic device is provided as an embodiment of this application. This electronic device can be used to implement the metamerism-based spectral optimization method described in the foregoing embodiments, and mainly includes: a memory 501 and a processor 502. The memory 501 stores a computer program 503 that can run on the processor 502. The memory 501 and the processor 502 are communicatively connected. When the processor 502 executes the computer program 503, it implements the metamerism-based spectral optimization method described in the foregoing embodiments. The number of processors 502 can be one or more.

[0050] Memory 501 can be high-speed random access memory (RAM) or non-volatile memory, such as disk storage. Memory 501 is used to store executable program code, and processor 502 is coupled to memory 501.

[0051] Furthermore, embodiments of this application also provide a computer-readable storage medium, which may be disposed in the electronic device described in the above embodiments, and the computer-readable storage medium may be as described above. Figure 5 The memory in the illustrated embodiment.

[0052] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the metamerism-based spectral optimization method described in the foregoing embodiments. Furthermore, the computer-readable storage medium can also be any medium capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), RAM, magnetic disk, or optical disk.

[0053] It should be understood that the apparatus and methods disclosed in the embodiments provided in this application can also be implemented in any other equivalent manner. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0054] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0055] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0056] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, external hard drives, ROM, RAM, magnetic disks, or optical disks.

[0057] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0058] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0059] The above is a description of the spectral optimization method and related products based on metamerism provided in this application. For those skilled in the art, based on the ideas of the embodiments of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A spectral optimization method based on metamerism, characterized in that, include: Based on the first color index corresponding to the set light source color of the multispectral light source, the different spectral channels of the multispectral light source are matched to obtain an initial spectral dataset; wherein, the number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels of the lighting source required to achieve metamerism in actual lighting environment; By combining the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and the second light color index corresponding to the set object color of the target illuminated object, an intermediate spectral dataset is obtained. Target spectral data are selected from the intermediate spectral dataset based on the reference lighting environment requirement index values ​​to form an optimized spectral dataset; The step of obtaining an intermediate spectral dataset by combining the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and a second light color index corresponding to the set object color of the target illuminated object includes: The corresponding synthetic spectrum is obtained by weighting and summing the channel spectra in the initial spectral dataset according to the corresponding spectral coefficients. Based on the synthesized spectra and the spectral reflectance curves of the target illuminated object, the corresponding chromaticity of the target illuminated object is calculated; Combine the target synthetic spectra of all the target illuminated objects whose chromaticity conforms to the second light color index into an intermediate spectral dataset; wherein, the second light color index is a light color index corresponding to the set object color of the target illuminated object.

2. The spectral optimization method based on metamerism according to claim 1, characterized in that, Before the step of allocating different spectral channels of the multispectral light source according to a first color index corresponding to a set light source color of the multispectral light source, the method further includes: Determine the corresponding range of multiple spectral channels based on the spectral reflectance curve; Different spectral channels are set according to multiple spectral channel ranges.

3. The spectral optimization method based on metamerism according to claim 1, characterized in that, If the actual lighting environment is a lighting environment without a reference light source, then the number of spectral channels is greater than or equal to 4; if the actual lighting environment is a lighting environment with a reference light source, then the number of spectral channels is greater than or equal to 2.

4. The spectral optimization method based on metamerism according to claim 1, characterized in that, The step of selecting target spectral data from the intermediate spectral dataset to form the optimized spectral dataset, based on the reference lighting environment requirement index value, includes: Calculate the corresponding actual lighting evaluation index value for all spectral data in the intermediate spectral dataset; Target spectral data matching the actual lighting evaluation index value and the lighting environment requirement index value are obtained from the intermediate spectral dataset to form the optimized spectral dataset.

5. The spectral optimization method based on metamerism according to claim 1, characterized in that, The types of indicators corresponding to the lighting environment demand index values ​​include at least one of the following: light environment clarity, light environment comfort, and light environment health characteristics.

6. The spectral optimization method based on metamerism according to claim 1, characterized in that, The step of selecting target spectral data from the intermediate spectral dataset to form the optimized spectral dataset, based on the reference lighting environment requirement index value, includes: Obtain the priority sort of multiple different indicator types; According to the priority order, spectral data in the intermediate spectral dataset that do not conform to the lighting environment requirement index values ​​of the corresponding index type are filtered out in turn. Based on the final remaining target spectral data in the intermediate spectral dataset, the optimized spectral dataset is obtained.

7. The spectral optimization method based on metamerism according to claim 6, characterized in that, Before the step of selecting target spectral data from the intermediate spectral dataset to form the optimized spectral dataset, the method further includes: Based on the target lighting scene information, determine the types of lighting environment demand indicators to focus on, the values ​​of the lighting environment demand indicators, and the priority order of multiple different indicator types.

8. A spectral optimization device based on metamerism, characterized in that, include: The matching module is used to match different spectral channels of the multispectral light source according to a first light color index corresponding to the set light source color of the multispectral light source, so as to obtain an initial spectral dataset; wherein, the number of spectral channels of the multispectral light source is greater than or equal to the number of spectral channels of the lighting source required to achieve metamerism in actual lighting environment; The acquisition module is used to acquire an intermediate spectral dataset by combining the initial spectral dataset, the spectral reflectance curve of the target illuminated object, and a second color index corresponding to a set object color of the target illuminated object. Specifically, this includes: weighting and summing the channel spectra in the initial spectral dataset according to their respective spectral coefficients to obtain the corresponding synthetic spectra; calculating the corresponding chromaticity of the target illuminated object based on each synthetic spectrum and the spectral reflectance curve of the target illuminated object; and combining all the target synthetic spectra whose chromaticity conforms to the second color index into an intermediate spectral dataset; wherein the second color index is a color index corresponding to a set object color of the target illuminated object. The optimization module is used to select target spectral data from the intermediate spectral dataset based on the reference lighting environment requirement index value, and form an optimized spectral dataset.

9. An electronic device, characterized in that, Includes memory and processor, of which: The processor is used to execute computer programs stored in the memory; When the processor executes the computer program, it implements the steps in the spectral optimization method based on metamerism as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the metamerism-based spectral optimization method according to any one of claims 1 to 7.

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