Method and device for calculating light duty cycle, and lamp

Abstract

The application provides a light duty cycle calculation method and device and a lamp. The method comprises the following steps: acquiring a first color coordinate and a first luminous flux of light; determining a first duty cycle of the light according to the first color coordinate and the first luminous flux; and correcting the first duty cycle based on a neural network fitting mode to obtain a corrected target duty cycle. The application can calculate the corrected duty cycle of the non-linear change of the luminous flux with the duty cycle by using the neural network fitting mode, the calculation method is simple, a more accurate duty cycle can be obtained, the safety of color calculation is effectively increased, and the product value of the lamp is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of light control, and in particular to a light duty cycle calculation method and device and a light. BACKGROUND

[0002] With the improvement of people's living standards, people's requirements for the quality of life are also getting higher and higher. In order to meet people's different scenes in life, color light is usually used to create atmosphere. When different colors of light are mixed together, different colors can be presented due to different duty cycles. Color light lamps decorated on the outer surface of a building are particularly beautiful when they flicker in the night sky.

[0003] The existing RGB color light mixing lamp calculates the duty cycle based on the Grassmann mixing law, but the Grassmann mixing formula has a hypothetical condition that the chromaticity value of the RGB three light sources remains constant, and the luminous flux changes linearly with the input current.

[0004] However, due to the characteristics of the RGB color light source itself, the chromaticity parameter is relatively unstable, and the luminous flux is not linearly changed with the input current signal, and the chromaticity value also changes with the current. Therefore, the duty cycle calculated by the existing Grassmann mixing formula has a large deviation. SUMMARY

[0005] The embodiments of the present application provide a light duty cycle calculation method, device and light to solve the problem of large deviation of the duty cycle calculated by the existing Grassmann mixing formula in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a light duty cycle calculation method, comprising:

[0007] obtaining a first color coordinate and a first luminous flux of light;

[0008] determining a first duty cycle of the light according to the first color coordinate and the first luminous flux;

[0009] correcting the first duty cycle based on a neural network fitting method to obtain a corrected target duty cycle.

[0010] In a possible implementation, the correcting the first duty cycle based on the neural network fitting method to obtain the corrected target duty cycle comprises:

[0011] determining a fitting relationship of the color coordinate and the luminous flux of the light according to the parameters of the neural network;

[0012] determining a neural network fitting formula according to the fitting relationship and the Grassmann mixing formula;

[0013] The first duty cycle and the first color coordinate are input into the neural network fitting formula to obtain a corrected target duty cycle.

[0014] In a possible implementation, the fitting relationship is:

[0015]

[0016] where Y represents a first luminous flux of a certain mixed light, x and y represent a first color coordinate of the certain mixed light in RGB light, D i represents a duty cycle of the i-th mixed light in RGB light, i=1, 2, 3, w j represents a weight from an input layer to a hidden layer in a neural network, j=1, 2, …, n, v j represents a weight from the hidden layer to an output layer in the neural network, λ represents a constant, f x (D i ) represents an x coordinate in a color coordinate of the i-th mixed light after fitting, f y (D i ) represents a y coordinate in the color coordinate of the i-th mixed light after fitting, f Y (D i ) represents a luminous flux of the i-th mixed light after fitting,

[0017] In a possible implementation, the neural network fitting formula is:

[0018]

[0019] where, represents a luminous flux of D′ i required for a color coordinate of a mixed light, D′ i represents a corrected duty cycle of the i-th mixed light, x i and y i respectively represent a first color coordinate of the i-th mixed light in RGB light, x' and y' respectively represent a target color coordinate corresponding to the first color coordinate, Y m represents a stimulus value of a light source RGB.

[0020] In a possible implementation, after the first duty cycle is corrected based on the neural network fitting manner to obtain a corrected target duty cycle, the method further includes:

[0021] The target duty cycle is input into the fitting relationship to obtain a second color coordinate;

[0022] The second color coordinate is input into the neural network fitting formula for secondary fitting processing to obtain a duty cycle after secondary correction.

[0023] In a possible implementation, after the first duty cycle of the light is determined according to the first color coordinate and the first luminous flux, the method further includes:

[0024] normalizing the first duty cycle to obtain a normalized first duty cycle;

[0025] The first duty cycle is corrected based on the neural network fitting mode to obtain a corrected target duty cycle, including:

[0026] The normalized first duty cycle is corrected based on the neural network fitting mode to obtain a corrected target duty cycle.

[0027] In a possible implementation, the first duty cycle is normalized to obtain a normalized first duty cycle, including:

[0028] determining a second luminous flux when the duty cycle of the light RGB three-way is a full duty cycle;

[0029] determining a maximum duty cycle in the first duty cycle;

[0030] determining a normalized luminous flux according to the second luminous flux, the maximum duty cycle and the Grassmann light mixing formula;

[0031] inputting the normalized luminous flux into the neural network fitting formula to obtain a normalized first duty cycle.

[0032] In a possible implementation, the normalized luminous flux is determined according to the second luminous flux, the maximum duty cycle and the Grassmann light mixing formula, including:

[0033] determining a normalized luminous flux according to

[0034] wherein, Y nor represents the normalized luminous flux, Y maxi represents the second luminous flux when the duty cycle of the i-way in the RGB three-way is a full duty cycle, D max represents the maximum duty cycle, D i represents the duty cycle of the i-way mixed light.

[0035] In a second aspect, an embodiment of the present application provides a light duty cycle calculation device, including:

[0036] an acquisition module configured to acquire a first color coordinate and a first luminous flux of light;

[0037] a calculation module configured to determine a first duty cycle of the light according to the first color coordinate and the first luminous flux.​

[0038] The computing module is further configured to correct the first duty cycle based on a neural network fitting method to obtain a corrected target duty cycle.

[0039] In a third aspect, an embodiment of the present application provides a lamp, comprising a controller, the controller comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the light duty cycle calculation method according to the first aspect or any possible implementation manner of the first aspect.

[0040] The embodiment of the present application provides a light duty cycle calculation method, device and lamp. The first color coordinate and the first luminous flux of the light are obtained. The first duty cycle of the light is determined according to the first color coordinate and the first luminous flux. The first duty cycle is corrected based on a neural network fitting method to obtain a corrected target duty cycle. The embodiment of the present application calculates the calibration duty cycle of the non-linear change of the luminous flux with the duty cycle by using the neural network. The calculation method is simple, and the more accurate duty cycle can be obtained. The safety of color calculation is effectively increased, and the product value of the lamp is improved. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0042] Figure 1 is the implementation flowchart of the light duty cycle calculation method provided by the embodiment of the present application;

[0043] Figure 2 is a schematic diagram of the neural network model provided by the embodiment of the present application;

[0044] Figure 3 is the implementation flowchart of the light duty cycle calculation method provided by another embodiment of the present application;

[0045] Fig. 4(a) is a fitting schematic diagram of the x coordinate of the R channel changing with the duty cycle provided by the embodiment of the present application;

[0046] Fig. 4(b) is a fitting schematic diagram of the y coordinate of the R channel changing with the duty cycle provided by the embodiment of the present application;

[0047] Fig. 4(c) is a fitting schematic diagram of the luminous flux of the R channel changing with the duty cycle provided by the embodiment of the present application;

[0048] FIG. 5(a) is a fitting diagram of the x coordinate of the G channel varying with the duty cycle according to an embodiment of the present application;

[0049] FIG. 5(b) is a fitting diagram of the y coordinate of the G channel varying with the duty cycle according to an embodiment of the present application;

[0050] FIG. 5(c) is a fitting diagram of the luminous flux of the G channel varying with the duty cycle according to an embodiment of the present application;

[0051] FIG. 6(a) is a fitting diagram of the x coordinate of the B channel varying with the duty cycle according to an embodiment of the present application;

[0052] FIG. 6(b) is a fitting diagram of the y coordinate of the B channel varying with the duty cycle according to an embodiment of the present application;

[0053] FIG. 6(c) is a fitting diagram of the luminous flux of the B channel varying with the duty cycle according to an embodiment of the present application;

[0054] Figure 7 is a structural diagram of a light duty cycle calculation device according to an embodiment of the present application;

[0055] Figure 8 is a structural diagram of a light duty cycle calculation system according to an embodiment of the present application;

[0056] Figure 9 is a diagram of a controller according to an embodiment of the present application. DETAILED DESCRIPTION

[0057] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular sequences of acts, to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, and circuits are omitted so as not to obscure the description of the present application with unnecessary detail.

[0058] In order to make the objects, technical solutions and advantages of the present application clearer, the following will be described with reference to the accompanying drawings and specific embodiments.

[0059] The prior art calculates the duty cycle of color light mixing based on the Grassmann light mixing formula, assuming that the chrominance values of RGB three light sources remain constant and the luminous flux changes linearly with the input current. However, in fact, the output of the optical parameter is relatively unstable, and the luminous flux is not linearly changed with the input current signal, and the chrominance value also changes with the current. Therefore, the embodiment of the present application provides a light duty cycle calculation method, which corrects the duty cycle by using a neural network to obtain a corrected duty cycle, which can realize the calibration calculation of the nonlinear change of color coordinates and luminous flux, and effectively increase the accuracy of color calculation.

[0060] Figure 1 The implementation flowchart of the light duty cycle calculation method provided by the embodiment of the present application is described in detail as follows:

[0061] Step 101, obtaining a first color coordinate and a first luminous flux of the light.

[0062] When the user turns on the light, adjusts the brightness of the light through the light control APP or the remote controller, and the control module or the calculation module of the light obtains the current color coordinate and luminous flux of the light. Here, the first color coordinate is not for sorting the color coordinates, but for distinguishing the color coordinates calculated subsequently. Similarly, the first luminous flux is not for sorting the luminous flux, but for distinguishing the luminous flux calculated subsequently.

[0063] Step 102, determining a first duty cycle of the light according to the first color coordinate and the first luminous flux.

[0064] Optionally, determining the first duty cycle of the light according to the first color coordinate and the first luminous flux can include: determining the first duty cycle of the light by using a three-primary-color Grassmann light mixing formula.

[0065] Determining the first duty cycle of the light by using the three-primary-color Grassmann light mixing formula can include:

[0066] According to the three-primary-color Grassmann light mixing formula, the first duty cycle of the light is determined as follows:

[0067] Wherein, D i represents the duty cycle of the i-th mixed light in the RGB three light paths of the light, i=1, 2, 3, x i ,y i respectively represent the first color coordinates of the i-th mixed light in the RGB three light paths of the light, x', y' respectively represent the target color coordinates corresponding to the first color coordinates, Y m represents the stimulus value of the light source RGB, Y i represents the Y stimulus value of the i-th light path of the light under full current working.

[0068] ​The target color coordinate can be obtained as follows: when the user turns on the lamp, the control module or the calculation module of the lamp obtains the expected brightness input by the user after adjusting the brightness of the lamp through the lamp control APP or the remote controller, and obtains the color coordinate corresponding to the expected brightness according to the correspondence between the brightness and the color wheel stored in the storage module of the lamp, which is determined as the target color coordinate in this embodiment. The first color coordinate is the color coordinate of the light actually emitted by the lamp.

[0069] In step 103, the first duty cycle is corrected based on a neural network fitting mode to obtain a corrected target duty cycle.

[0070] With the rise of artificial intelligence, neural networks are being applied to more and more fields. Neural networks theoretically have the ability to fit any function. A neuron is the basic computing unit of a neural network, which can accept input from other neurons or external data, and then calculate an output. Each input value has a weight, and then a specific function f is executed on the neuron, which operates on all input values and their weights of the neuron. Referring to Figure 2 As shown, the input parameters of the input layer are x1, x2…x n , and the output parameters of the output layer are y1, y2…y p …y m , p = 1, 2, … m, m represents the number of output parameters, the weight of the input layer connected to the hidden layer is w i , i = 1, 2, 3…n, the bias is β i , the weight of the hidden layer connected to the output layer is v i , and the bias is λ i . The activation functions of the hidden layer and the output layer are Loss is the loss function.

[0071] The specific derivation from the input layer to the output layer is shown as follows:

[0072] The output value of the hidden layer is: q i = xw i ;

[0073] Input q i as an independent variable into the activation function to obtain h i ,

[0074] The output value of the output layer is: μ = h1v1 + h2v2 + … + h i v i - λ; wherein λ represents a constant;

[0075] Input μ as an independent variable into the activation function to obtain

[0076] The average error of each calculated output parameter with respect to the known output parameter is: Loss represents the average error value.

[0077] The color coordinates and luminous flux of the light in the light RGB three-way change nonlinearly with the duty cycle, and it is difficult to fit the curve using a conventional method, therefore, in this embodiment, the arbitrary fitting characteristics of the neural network can be used to well fit the nonlinear change trend. Therefore, in an embodiment, referring to Figure 3 The first duty cycle is corrected based on the neural network fitting method to obtain a corrected target duty cycle, which can include:

[0078] According to the parameters of the neural network, fitting relationship formulas of the color coordinates and luminous flux of the light are determined respectively;

[0079] According to the fitting relationship formulas and the Grassmann light mixing formula, a neural network fitting formula is determined;

[0080] The first duty cycle and the first color coordinates are input into the neural network fitting formula to obtain the corrected target duty cycle.

[0081] Optionally, the fitting relationship formula is:

[0082]

[0083] wherein Y represents the first luminous flux of a certain mixed light, x and y represent the first color coordinates of a certain mixed light in the light RGB three-way, Di represents the duty cycle of the i-th mixed light in the light RGB three-way, i = 1, 2, 3, wi represents the weight of the i-th mixed light in the light RGB three-way, j = 1, 2, …, n, vi represents the weight of the i-th mixed light in the light RGB three-way, v = 1, 2, …, n, λ represents a constant, and f represents a function. i j j x i y i Y i

[0084] In this embodiment, the nonlinear variation of the luminous flux with the duty cycle can be calibrated to obtain an accurate duty cycle. Therefore, according to the fitting relationship formulas and the Grassmann light mixing formula, a neural network fitting formula is determined, which can include:

[0085] ​​​​​​​​​The light flux fitting relationship in the fitting relationship is deformed, and is brought into the three primary color Grassmann mixed light formula to obtain a neural network fitting formula.

[0086] Optionally, the light flux fitting relationship in the fitting relationship is deformed to obtain f Y (D i )=Y i / D i .

[0087] Therefore, the neural network fitting formula is:

[0088]

[0089] Wherein, represents the light flux required by the mixed light color coordinate D′ i , D′ i represents the duty cycle of the corrected i-th mixed light, x i , y i respectively represent the first color coordinate of the i-th mixed light in the RGB three-way light, x', y' respectively represent the target color coordinate corresponding to the first color coordinate, Y m represents the stimulus value of the light source RGB.

[0090] In the neural network fitting formula, the left side of the equal sign is the light flux required by the mixed light color point under the corresponding duty cycle of the RGB three channels, and the right side is the light flux required by the mixed light.

[0091] Optionally, after inputting the first duty cycle and the first color coordinate into the neural network fitting formula, the neural network fitting formula is calculated to obtain the corrected target duty cycle.

[0092] When the neural network fitting formula is calculated, the term on the right side of the equal sign can be moved to the left side of the equal sign, and the above formula is rewritten as:

[0093]

[0094] Then the corrected target duty cycle D′ i is obtained by solving.

[0095] In order to further improve the accuracy of the duty cycle, the color coordinates of the light can also be calibrated with the non-linear variation of the duty cycle.

[0096] Optionally, as shown in Figure 3 , after correcting the first duty cycle based on the neural network fitting method to obtain the corrected target duty cycle, it can further include:

[0097] The target duty cycle is input into the fitting relationship to obtain the second color coordinate.

[0098] The second color coordinates are input into the neural network fitting formula for secondary fitting to obtain the duty cycle after secondary correction.

[0099] Optionally, the target duty cycle can be input into the color coordinate fitting formula to obtain the accurate color coordinates of the three channels under a single calibrated duty cycle, i.e., the second color coordinates.

[0100] Optionally, the second color coordinates can be input into the neural network fitting formula for secondary fitting to obtain the duty cycle after secondary correction. This can include:

[0101]

[0102] in, The required D to represent the mixed light color coordinates i i Luminous flux, D i i x represents the duty cycle of the i-th mixed light after secondary correction. i ',y i 'x' and y' represent the second color coordinates of the i-th mixed light in the RGB three-way light path, respectively, and represent the target color coordinates corresponding to the second color coordinates, respectively. m This indicates the RGB stimulus value of the light source.

[0103] The duty cycle obtained at this time is a quadratic intersection duty cycle that takes into account the nonlinear changes in luminous flux and chromatic coordinates with the duty cycle, resulting in a higher accuracy of the duty cycle.

[0104] In one embodiment, after determining the first duty cycle of the light based on the first color coordinates and the first luminous flux, the method further includes:

[0105] The first duty cycle is normalized to obtain the normalized first duty cycle.

[0106] Normalizing the first duty cycle ensures that one of the calculated duty cycles is always 100%, maximizing the power of the lamp and ensuring the lamp is brightest while maintaining its color.

[0107] Optionally, normalizing the first duty cycle to obtain a normalized first duty cycle may include:

[0108] Determine the second luminous flux when the duty cycle of the three RGB light channels is full;

[0109] Determine the maximum duty cycle within the first duty cycle;

[0110] The normalized luminous flux is determined based on the second luminous flux, the maximum duty cycle, and the Grassmann mixing formula.

[0111] The normalized light flux is input into a neural network fitting formula to obtain a normalized first duty cycle.

[0112] Optionally, the second light flux is Y max (D) = R Y + G Y + B Y .

[0113] wherein R Y represents the light flux when the duty cycle of the R channel of the RGB light is the largest, G Y represents the light flux when the duty cycle of the G channel of the RGB light is the largest, and B Y represents the light flux when the duty cycle of the B channel of the RGB light is the largest.

[0114] Optionally, determining the normalized light flux according to the second light flux, the maximum duty cycle, and the Grassmann light mixing formula can include:

[0115] determining the normalized light flux according to .

[0116] wherein Y nor represents the normalized light flux, Y maxi represents the second light flux when the duty cycle of the i-th channel of the RGB light is the full duty cycle, D max represents the maximum duty cycle, and D i represents the duty cycle of the i-th mixed light.

[0117] Optionally, the normalized light flux is input into a neural network fitting formula to obtain a normalized first duty cycle, i.e., a normalized duty cycle combination in which the duty cycle of one channel of RGB reaches the full duty cycle.

[0118] Optionally, the first duty cycle is corrected based on a neural network fitting method to obtain a corrected target duty cycle, which can include:

[0119] The normalized first duty cycle is corrected based on the neural network fitting method to obtain the corrected target duty cycle.

[0120] The following is an exemplary description.

[0121] In the existing RGB bulb lamp, the color coordinates and light flux of the R channel change with the duty cycle as shown in the neural network fitting formula as shown in Figures 4(a)-4(c) , the color coordinates and light flux of the G channel change with the duty cycle as shown in the neural network fitting formula as shown in Figures 5(a)-5(c) , and the color coordinates and light flux of the B channel change with the duty cycle as shown in the neural network fitting formula as shown in Figures 6(a)-6(c)As shown, by using the light duty cycle calculation method provided in the scheme, the following color point calibration duty cycle is obtained, and the color tolerance between the measured color coordinates and the target color coordinates is shown in Table 1.

[0122] Table 1

[0123]

[0124] After two times of fitting calibration by the neural network fitting formula, the maximum color tolerance of the test color point is 1.8, and the average color tolerance is 1.2, which is much smaller than 12.72 without calibration.

[0125] The embodiment of the application obtains the first color coordinates and the first luminous flux of the light; determines the first duty cycle of the light according to the first color coordinates and the first luminous flux; and corrects the first duty cycle based on a neural network fitting method to obtain a corrected target duty cycle. The embodiment of the application calculates the calibration duty cycle of the non-linear change of the luminous flux with the duty cycle by using the neural network, the calculation method is simple, and more accurate duty cycle can be obtained, which effectively increases the safety of color calculation and improves the product value of the lamp.

[0126] Further, the neural network is used to perform secondary calibration calculation on the non-linear change of the color coordinates with the duty cycle, which can further improve the accuracy of the duty cycle.

[0127] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the application.

[0128] The following is a device embodiment of the application, and for details not described in detail, reference can be made to the corresponding method embodiments described above.

[0129] Figure 7 The structure of the light duty cycle calculation device provided by the embodiment of the application is shown, only the part related to the embodiment of the application is shown for convenience, and the details are as follows:

[0130] As Figure 7 shown, the light duty cycle calculation device 7 comprises an acquisition module 71 and a calculation module 72.

[0131] The acquisition module 71 is used to acquire the first color coordinates and the first luminous flux of the light;

[0132] The calculation module 72 is used to determine the first duty cycle of the light according to the first color coordinates and the first luminous flux.

[0133] The calculation module 72 is also used to correct the first duty cycle based on a neural network fitting method to obtain a corrected target duty cycle.

[0134] In a possible implementation, the calculation module 72 corrects the first duty cycle based on the neural network fitting manner to obtain a corrected target duty cycle, which can be used for:

[0135] According to the parameters of the neural network, a fitting relationship of color coordinates and luminous flux of the light is determined respectively;

[0136] According to the fitting relationship and the Grassmann light mixing formula, a neural network fitting formula is determined;

[0137] The first duty cycle and the first color coordinate are input into the neural network fitting formula to obtain the corrected target duty cycle.

[0138] In a possible implementation, the fitting relationship is:

[0139]

[0140] wherein Y represents the first luminous flux of a certain mixed light, x and y represent the first color coordinates of a certain mixed light in the RGB three lights of the light, D i represents the duty cycle of the i-th mixed light in the RGB three lights of the light, i = 1, 2, 3, w j represents the weight from the input layer to the hidden layer in the neural network, j = 1, 2, …, n, v j represents the weight from the hidden layer to the output layer in the neural network, λ represents a constant, f x (D i ) represents the x coordinate in the color coordinates of the i-th mixed light after fitting, f y (D i ) represents the y coordinate in the color coordinates of the i-th mixed light after fitting, f Y (D i ) represents the luminous flux of the i-th mixed light after fitting,

[0141] In a possible implementation, the neural network fitting formula is:

[0142]

[0143] wherein, represents the luminous flux of D' i required for the color coordinates of the mixed light, D' i represents the duty cycle of the i-th mixed light after correction, x i , y i represent the first color coordinates of the i-th mixed light in the RGB three lights of the light respectively, x' and y' represent the target color coordinates corresponding to the first color coordinates, Y m represents the stimulus value of the RGB light source.

[0144] In one possible implementation, after the calculation module 72 corrects the first duty cycle based on a neural network fitting method to obtain the corrected target duty cycle, it is further used for:

[0145] Input the target duty cycle into the fitting formula to obtain the second color coordinates;

[0146] The second color coordinates are input into the neural network fitting formula for secondary fitting to obtain the duty cycle after secondary correction.

[0147] In one possible implementation, after the calculation module 72 determines the first duty cycle of the light based on the first color coordinates and the first luminous flux, it is further used for:

[0148] The first duty cycle is normalized to obtain the normalized first duty cycle;

[0149] The first duty cycle is corrected based on a neural network fitting method to obtain the corrected target duty cycle, including:

[0150] The normalized first duty cycle is corrected using a neural network fitting method to obtain the corrected target duty cycle.

[0151] In one possible implementation, the calculation module 72 normalizes the first duty cycle, and the normalized first duty cycle can be used for:

[0152] Determine the second luminous flux when the duty cycle of the three RGB light channels is full;

[0153] Determine the maximum duty cycle within the first duty cycle;

[0154] The normalized luminous flux is determined based on the second luminous flux, the maximum duty cycle, and the Grassmann mixing formula.

[0155] The normalized luminous flux is input into the neural network fitting formula to obtain the normalized first duty cycle.

[0156] In one possible implementation, when the calculation module 72 determines the normalized luminous flux based on the second luminous flux, the maximum duty cycle, and the Grassmann mixing formula, it can be used for:

[0157] according to Determine the normalized luminous flux;

[0158] Among them, Y nor Y represents the normalized luminous flux. maxi D represents the second luminous flux of the i-th channel in the RGB three-channel RGB array when the duty cycle is full. max D represents the maximum duty cycle. iDuty cycle of the i-th mixed light.

[0159] The light duty cycle calculation device obtains the first color coordinates and the first luminous flux of the light through an acquisition module; a calculation module determines the first duty cycle of the light according to the first color coordinates and the first luminous flux, and corrects the first duty cycle based on a neural network fitting mode to obtain a corrected target duty cycle. The embodiment of the application calculates the calibrated duty cycle of the non-linear change of the luminous flux with the duty cycle by using the neural network, the calculation method is simple, the more accurate duty cycle can be obtained, the safety of color calculation is effectively increased, and the product value of the lamp is improved.

[0160] Further, the non-linear change of the color coordinates with the duty cycle is calculated by using the neural network for secondary calibration, and the accuracy of the duty cycle can be further improved.

[0161] The embodiment of the application provides a light duty cycle calculation system, referring to Figure 8 as shown, comprising a light duty cycle calculation device 7 and a command input module 81, an LED driving module 82, an LED light source module 83 and a storage module 84.

[0162] The command input module 81 is used for inputting commands.

[0163] The LED light source module 83 includes RGB three kinds of light sources.

[0164] The LED driving module 82 is used for generating at least 3 PWM signals and outputting to the LED light source module 84, so as to control the LED light source, wherein the adjustable depth of the PWM signal can reach 0.1% and below;

[0165] The storage module 84 is used for storing the basic information of the LED light source module and the preset color point information, the reference white point information and the like, including color coordinates x, y and brightness Y;

[0166] The light duty cycle calculation device 7 is used for retrieving the information in the storage module 85 according to the instruction of the command input module 81, and calculating the PWM signal required to be output by the LED driving module 83.

[0167] The embodiment of the application provides a lamp, comprising a controller, Figure 9 is a schematic diagram of the controller provided by the embodiment of the application. As Figure 9 shown, the controller 9 of the embodiment comprises a processor 90, a memory 91 and a computer program 92 stored in the memory 91 and executable on the processor 90. The processor 90 implements the steps in each of the above light duty cycle calculation method embodiments when executing the computer program 92, for example Figure 1The steps 101 to 103 are shown. Alternatively, the processor 90, when executing the computer program 92, implements the functions of the various modules / units in the above-mentioned apparatus embodiments, for example Figure 7 The functions of the various modules / units are shown.

[0168] For example, the computer program 92 can be divided into one or more modules / units, which are stored in the memory 91 and executed by the processor 90 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 92 in the controller 9. For example, the computer program 92 can be divided into Figure 7 The functions of the various modules / units are shown.

[0169] The controller 9 can include, but is not limited to, the processor 90, the memory 91. Those skilled in the art can understand that the controller 9 can include more or fewer components than those shown, or combine certain components, or different components, for example, the controller can also include an input / output device, a network access device, a bus, etc. Figure 9 The controller 9 is only an example and does not constitute a limitation on the controller 9, and can include more or fewer components than those shown, or combine certain components, or different components, for example, the controller can also include an input / output device, a network access device, a bus, etc.

[0170] The processor 90 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0171] The memory 91 can be an internal storage unit of the controller 9, such as a hard disk or a memory of the controller 9. The memory 91 can also be an external storage device of the controller 9, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the controller 9. Further, the memory 91 can also include both the internal storage unit and the external storage device of the controller 9. The memory 91 is used to store the computer program and other programs and data required by the controller. The memory 91 can also be used to temporarily store data that has been output or is to be output.

[0172] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0173] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0174] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0175] In the embodiments of the present application, it should be understood that the disclosed apparatuses / controllers and methods can be implemented in other manners. For example, the described apparatus / controller embodiments are merely schematic. The division of the modules or units is merely logical function division. There can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0176] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0177] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically independent unit, or two or more units can be integrated into a unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0178] The integrated module / unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of the above-described various light duty cycle calculation method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0179] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for calculating the duty cycle of a light source, characterized in that, include: Obtain the first color coordinates and first luminous flux of the light; The first duty cycle of the light is determined based on the first color coordinates and the first luminous flux; The first duty cycle is corrected based on a neural network fitting method to obtain the corrected target duty cycle; The step of correcting the first duty cycle based on a neural network fitting method to obtain the corrected target duty cycle includes: Based on the parameters of the neural network, the fitting formulas for the color coordinates and luminous flux of the light are determined respectively; Based on the aforementioned fitting relationship and the Grassmann light mixing formula, the neural network fitting formula is determined; The first duty cycle and the first color coordinates are input into the neural network fitting formula to obtain the corrected target duty cycle.

2. The method for calculating the duty cycle of lighting according to claim 1, characterized in that, The fitting relationship is: ； in, This represents the first luminous flux of a certain mixed light source. This represents the coordinates of the first color of a single mixed light source from one of the RGB light sources. This indicates the third of the three RGB lighting channels. Duty cycle of the mixed light path, , This represents the weights from the input layer to the hidden layer in a neural network. , This represents the weights from the hidden layer to the output layer in a neural network. Describe a constant. Represents the fitted first... In the color coordinates of the mixed light coordinate, Represents the fitted first... In the color coordinates of the mixed light coordinate, Represents the fitted first... The luminous flux of the mixed light path .

3. The method for calculating the duty cycle of lighting according to claim 2, characterized in that, The neural network fitting formula is: ； in, The coordinates required to represent the mixed light color luminous flux, Indicates the corrected number Duty cycle of the mixed light path, These represent the third of the three RGB lighting channels. The first color coordinate of the mixed light path These represent the target color coordinates corresponding to the first color coordinates. This indicates the RGB stimulus value of the light source.

4. The method for calculating the lamp duty cycle according to claim 2 or 3, characterized in that, After correcting the first duty cycle based on the neural network fitting method to obtain the corrected target duty cycle, the method further includes: Input the target duty cycle into the fitting formula to obtain the second color coordinates; The second color coordinates are input into the neural network fitting formula for secondary fitting to obtain the duty cycle after secondary correction.

5. The method for calculating the duty cycle of lighting according to claim 1, characterized in that, After determining the first duty cycle of the light source based on the first color coordinates and the first luminous flux, the method further includes: The first duty cycle is normalized to obtain the normalized first duty cycle; The step of correcting the first duty cycle based on a neural network fitting method to obtain the corrected target duty cycle includes: The normalized first duty cycle is corrected based on a neural network fitting method to obtain the corrected target duty cycle.

6. The method for calculating the lamp duty cycle according to claim 5, characterized in that, The first duty cycle is normalized to obtain the normalized first duty cycle, including: Determine the second luminous flux when the duty cycle of the three RGB light channels is full; Determine the maximum duty cycle among the first duty cycles; The normalized luminous flux is determined based on the second luminous flux, the maximum duty cycle, and the Grassmann mixing formula. The normalized luminous flux is input into the neural network fitting formula to obtain the normalized first duty cycle.

7. The method for calculating the lamp duty cycle according to claim 6, characterized in that, Based on the second luminous flux, the maximum duty cycle, and the Grassmann mixing formula, the normalized luminous flux is determined, including: according to Determine the normalized luminous flux; in, This represents the normalized luminous flux. Indicates the third of the RGB three channels The path duty cycle is the second luminous flux at full duty cycle. Indicates the maximum duty cycle. Indicates the first Duty cycle of mixed light.

8. A device for calculating the duty cycle of a light source, characterized in that, include: The acquisition module is used to acquire the first color coordinates and first luminous flux of the light. The calculation module is used to determine the first duty cycle of the light based on the first color coordinates and the first luminous flux; The calculation module is also used to correct the first duty cycle based on a neural network fitting method to obtain the corrected target duty cycle; The calculation module is further configured to determine the fitting relationship between the color coordinates of the light and the luminous flux according to the parameters of the neural network; determine the neural network fitting formula according to the fitting relationship and the Grassmann mixing formula; and input the first duty cycle and the first color coordinates into the neural network fitting formula to obtain the corrected target duty cycle.

9. A lamp, the lamp comprising a controller, the controller comprising a memory and a processor, the memory for storing a computer program, the processor for calling and running the computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the steps of the method for calculating the lamp duty cycle as described in any one of claims 1 to 7.

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