RGB LED light source calibration mode optimization method and system

Abstract

The invention discloses an RGB LED light source calibration mode optimization method and system, and the method comprises the steps: collecting the color coordinates and brightness data of a three-primary-color light source of an RGB LED, and recording the actual optical characteristics; based on the collected data, establishing a physical model of the light mixing effect of the RGB LED light source, and calculating an initial PWM duty ratio corresponding to a target color coordinate and brightness; inputting the initial PWM duty ratio into the intelligent calibration algorithm model, and optimizing and calculating the optimal PWM duty ratio; the optimized PWM duty ratio is written into an MCU, and the MCU controls mixed light output of the RGB LED according to the written duty ratio; and testing and verifying the calibration effect of the RGB LED light source, and adjusting the parameters of the intelligent calibration algorithm model according to the test result. According to the invention, the PWM duty ratio can be dynamically optimized to realize high-precision light mixing output, and rapid repair and real-time adjustment are supported. The invention belongs to the technical field of photoelectricity, and aims to solve the core problem of the existing RGB LED light source calibration technology and improve the consistency of the light source performance and the production efficiency.

Description

Technical Field

[0001] The present invention relates to the field of optoelectronic technology, and in particular to an RGB LED light source calibration mode optimization method and system. Background Art

[0002] With the continuous development of modern optoelectronic technology, RGB LED light sources have been widely used in display screens, lighting equipment and consumer electronics due to their advantages such as high efficiency and energy saving, rich color expression and long service life. The calibration technology of RGB LED light sources is the key link to achieve its high-quality output. The light mixing effect of the light source is achieved by accurately controlling the color coordinates and brightness of the three-primary color light source. However, the traditional RGB LED light source calibration method usually relies on measuring the three-primary color optical data (such as color coordinates and brightness) of each light source, and storing it in the controller so that the light mixing ratio can be calculated according to the preset algorithm during operation. This method can provide preliminary light mixing calibration in the production stage, but in the face of batch differences in optical components such as light guides and optical lenses in mass production, as well as the influence of ambient light intensity and temperature changes during actual use, the traditional method has certain limitations. Especially in a multi-variety and multi-batch production environment, how to ensure the consistency and accuracy of RGB LED light source output has become an urgent problem to be solved in the industry.

[0003] The main deficiencies of the existing RGB LED light source calibration technology are reflected in the following aspects. First, the light mixing calculation of the controller in the existing method depends on the preset target color coordinates and brightness data, which are usually solidified in the storage module of the device, resulting in the inability to dynamically adjust the light mixing ratio. When there are batch differences in the light guide or lens of the product, the color and brightness of the finished product often deviate from the target value, which directly affects the performance of the light source. Secondly, the calibration method lacks dynamic adaptability and cannot correct the optical property drift caused by changes in the external environment (such as temperature, humidity and light intensity) in real time. In addition, when there is a color or brightness deviation in the finished product, the existing technology usually completes the repair by R&D personnel re-modifying the software parameters and burning them into the controller. This method is inefficient and difficult to meet the needs of large-scale production environments for rapid correction. The above deficiencies make it difficult for the existing RGB LED light source calibration method to meet the growing market demand in terms of stability, consistency and production efficiency. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing RGB LED light source calibration technology has problems such as batch differences leading to inconsistent calibration, low rework efficiency and insufficient dynamic adjustment capability.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: collecting the color coordinates and brightness data of the three-primary color light source of the RGB LED and recording its actual optical characteristics;

[0007] Based on the collected data, a physical model of the mixed light effect of the RGB LED light source is established using virtual simulation technology to calculate the initial PWM duty cycle corresponding to the target color coordinates and brightness;

[0008] The initial PWM duty cycle obtained by simulation calculation is input into the intelligent calibration algorithm model, and the optimal PWM duty cycle is optimized and calculated by combining the target color coordinates, brightness data and historical calibration deviations;

[0009] The optimized PWM duty cycle is written into the MCU, and the MCU controls the mixed light output of the RGB LED according to the written duty cycle value;

[0010] Test and verify the calibration effect of the RGB LED light source, adjust the parameters of the intelligent calibration algorithm model according to the test results, and complete the calibration optimization.

[0011] As a preferred solution of the RGB LED light source calibration mode optimization method described in the present invention, wherein: the collecting of the color coordinates and brightness data of the three-primary color light source of the RGB LED includes using a spectrum measurement device to measure the RGB LED light source point by point to obtain the color coordinate values ​​and brightness values ​​of the three primary colors of red, green and blue;

[0012] Based on the measurement results, data preprocessing is performed on the color coordinate values ​​and brightness values;

[0013] The measured data after data preprocessing is corrected, and the measured value is adjusted according to the equipment calibration curve to obtain the calibrated color coordinates and brightness data.

[0014] As a preferred solution of the RGB LED light source calibration mode optimization method of the present invention, the data preprocessing includes denoising the measurement data, using a median filter or a Gaussian filter algorithm to remove abnormal values ​​caused by ambient light interference;

[0015] Normalize the denoised data to make the brightness values ​​and color coordinate values ​​of the three primary colors of red, green and blue uniform within a predetermined range;

[0016] Based on historical calibration data and the performance characteristics of the measuring equipment, an interpolation algorithm or a regression algorithm is used to correct the nonlinear deviation of the measured data.

[0017] As a preferred solution of the RGB LED light source calibration mode optimization method described in the present invention, wherein: the physical model of the RGB LED light source mixing effect is established, including collating and storing the color coordinates and brightness characteristics of each primary color light source to form an optical parameter data set;

[0018] Based on the optical parameter data set, a light mixing effect model is constructed to describe the superposition effect of different primary color light sources on color coordinates and brightness after mixing in proportion;

[0019] In a virtual simulation environment, by gradually adjusting the mixing ratio of each primary color light source, the mixed output effect is simulated to generate color coordinates and brightness values ​​corresponding to different ratio combinations;

[0020] In the process of model building, actual optical characteristic parameters are added, including the luminous angle distribution of the primary color light source, the law of light intensity attenuation and the influence of ambient light on the mixed light effect;

[0021] The simulation output results of the light mixing effect are verified and compared with the actual measured optical data. The optical parameters and light mixing ratio rules in the model are adjusted to ensure that the model accurately reflects the physical characteristics of the RGB LED light source.

[0022] As a preferred solution of the RGB LED light source calibration mode optimization method of the present invention, wherein: the calculation of the initial PWM duty cycle corresponding to the target color coordinates and brightness includes, based on the optical characteristic data of the three primary colors of the RGB LED light source, constructing an optical characteristic model, including the color coordinates and brightness value of each primary color;

[0023] Using the target color coordinates and brightness values, define the objective function to describe the error between the output optical properties and the target value:

[0024]

[0025] Among them, x output ,y output is the color coordinate of the current mixed light output, L output is the brightness value of the current mixed light output, x target ,y target ,L target is its corresponding target value, ω x ,ω y ,ω L are the corresponding weight factors;

[0026] The gradient optimization method is used to adjust the mixing ratio parameters, which is expressed as:

[0027]

[0028] Among them, α R ,α G ,α B is the initial PWM duty cycle of the three primary colors of red, green and blue, and η is the learning rate.

[0029] As a preferred solution of the RGB LED light source calibration mode optimization method of the present invention, wherein: the optimization calculation of the optimal PWM duty cycle includes: R ,α G ,α B With the target color coordinates and brightness x target ,y target ,L target And the historical calibration deviation data ΔH is input into the intelligent calibration algorithm model;

[0030] The optimal PWM duty cycle is calculated by the following optimization objective function:

[0031]

[0032] The main objective term calculates the dynamic distribution of the output error over time in an integral form, and the smoothing time factor (1+e -βt ) is used to adjust the error weight of the time dimension;

[0033] The regularization constraint term R(α) is used to smooth the change of PWM duty cycle;

[0034] The historical bias correction term Γ(ΔH) is combined with the historical calibration bias to dynamically adjust the optimization process;

[0035] Update the PWM duty cycle through the iterative optimization algorithm, and finally output the optimized optimal PWM duty cycle

[0036] As a preferred solution of the RGB LED light source calibration mode optimization method described in the present invention, wherein: the writing of the optimized PWM duty cycle into the MCU includes: writing the optimal PWM duty cycle Convert to pulse width modulation signal supported by MCU;

[0037] The pulse width modulation signal is input into the driving circuit of the RGB LED in a time series, wherein each primary color light source receives a corresponding PWM signal to control the output power;

[0038] MCU monitors the color coordinates and brightness value (x real ,y real ,L real ), compare the target color coordinates and brightness value (x target ,y target ,L target );

[0039] The duty cycle of the PWM signal is adjusted according to the real-time monitoring data, and the deviation of color coordinates and brightness is dynamically compensated through the feedback control algorithm:

[0040] Δαk =η·(κ x (x real -x target )+κ y (y real -y target )+κ L (L real -L target ))

[0041] Among them, Δα k is the PWM duty cycle compensation value after real-time adjustment, and η is the adjustment step factor;

[0042] The MCU dynamically updates the PWM signal and ensures that the color coordinates and brightness values ​​of the RGB LED output are stable within the target value range through closed-loop feedback control;

[0043] The stable PWM signal is written into the storage module of the MCU for a long time and locked as the default light mixing parameters to ensure the consistency of the device in subsequent use.

[0044] A RGB LED light source calibration mode optimization system, characterized in that: it includes:

[0045] Data acquisition module: collects the color coordinates and brightness data of the three-primary color light source of RGB LED and records its actual optical characteristics;

[0046] Virtual simulation module: Based on the collected data, the physical model of the mixed light effect of the RGB LED light source is established using virtual simulation technology to calculate the initial PWM duty cycle corresponding to the target color coordinates and brightness;

[0047] Intelligent calibration algorithm module: input the initial PWM duty cycle obtained by simulation calculation into the intelligent calibration algorithm model, and optimize the calculation of the optimal PWM duty cycle by combining the target color coordinates, brightness data and historical calibration deviation;

[0048] PWM signal control module: write the optimized PWM duty cycle into the MCU, and the MCU controls the mixed light output of the RGB LED according to the written duty cycle value;

[0049] Test and feedback module: Test and verify the calibration effect of the RGB LED light source, adjust the parameters of the intelligent calibration algorithm model according to the test results, and complete the calibration optimization.

[0050] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0051] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0052] Beneficial effects of the present invention: 1. By introducing an intelligent calibration algorithm and a dynamic feedback control mechanism, the present invention can adjust the PWM duty cycle in real time, and dynamically optimize the mixing effect of the RGB LED light source according to batch differences and environmental changes (such as temperature, humidity, light intensity, etc.), thereby greatly improving the color coordinates and brightness consistency of the product.

[0053] 2. Combined with virtual simulation technology, a physical model of the mixed light effect of the RGB LED light source is established, and precise calibration is performed based on the differences in light guides and lenses from different batches, effectively avoiding the color and brightness deviation problems caused by batch differences in traditional methods.

[0054] 3. Through the automated calibration process, the traditional repair method that relies on manual modification of software parameters and re-burning of the controller is replaced, which significantly improves the efficiency of large-scale production and repair and reduces labor costs.

[0055] 4. The present invention supports dynamic calibration optimization of products that have already left the factory. When color and brightness deviations occur in the finished product, they can be quickly repaired by adjusting the parameters of the intelligent calibration algorithm without replacing hardware or performing complicated repair operations, thereby improving the feasibility and economy of repair.

[0056] 5. By using an intelligent algorithm combined with historical calibration deviation data and real-time optical property feedback, the present invention can more accurately calculate the optimal PWM duty cycle, making the mixed light effect of the RGB LED light source closer to the target color coordinates and brightness value, further improving the calibration accuracy and stability.

[0057] 6. In batch production, the present invention ensures high consistency in color and brightness among different batches of products through a unified calibration model and real-time adjustment strategy, thereby improving product quality and user experience.

[0058] 7. Through an efficient calibration and repair mechanism, the present invention significantly reduces the waste of resources caused by defective color and brightness products. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0060] Figure 1An overall flow chart of an RGB LED light source calibration mode optimization method provided by the first embodiment of the present invention.

[0061] Figure 2 A real-time feedback and control flow chart of an RGB LED light source calibration mode optimization method provided in the first embodiment of the present invention.

[0062] Figure 3 A module connection diagram of an RGB LED light source calibration mode optimization system provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0064] Example 1, reference Figure 1 and Figure 2 , is an embodiment of the present invention, and provides a method for optimizing an RGB LED light source calibration mode, comprising:

[0065] S1: Collect the color coordinates and brightness data of the three primary color light source of RGB LED and record its actual optical characteristics.

[0066] Use a spectrum measurement device to measure the RGB LED light source point by point to obtain the color coordinate values ​​and brightness values ​​of the three primary colors of red, green and blue.

[0067] Based on the measurement results, data preprocessing is performed on the color coordinate values ​​and brightness values.

[0068] Furthermore, the measured data is denoised by using a median filter or a Gaussian filter algorithm to remove outliers caused by ambient light interference; the denoised data is normalized to unify the brightness values ​​and color coordinate values ​​of the three primary colors of red, green, and blue within a predetermined range; and the nonlinear deviation of the measured data is corrected by using an interpolation algorithm or a regression algorithm based on historical calibration data and the performance characteristics of the measuring equipment.

[0069] The measured data after data preprocessing is corrected, and the measured value is adjusted according to the equipment calibration curve to obtain the calibrated color coordinates and brightness data.

[0070] It should be noted that x and y are used to define the position of the RGB LED light source on the chromaticity diagram, which represents the color characteristics; L represents the brightness characteristics of the light source, which affects the visual effect of the light source output. These parameters are key input data for calibration and optimization of mixed light output.

[0071] The combination of interpolation and regression algorithms corrects the measured data, solving the problem of systematic errors caused by the nonlinear characteristics of the measuring equipment. The interpolation algorithm fills the gaps in the sampled data and improves the continuity and integrity of the data; the regression algorithm uses the equipment performance characteristics and historical calibration data to make deeper corrections to the measured data. The introduction of the equipment calibration curve further ensures the accurate mapping between the data and the actual physical quantity. This correction process greatly improves the accuracy of the data and provides high-quality data support for subsequent calibration optimization.

[0072] S2: Based on the collected data, a physical model of the mixed light effect of the RGB LED light source is established using virtual simulation technology to calculate the initial PWM duty cycle corresponding to the target color coordinates and brightness.

[0073] The color coordinates and brightness characteristics of each primary color light source are sorted and stored to form an optical parameter data set, which generally includes the color coordinates, brightness, luminous angle distribution, light intensity attenuation characteristics, ambient light characteristics and equipment characteristics of the RGB LED light source, forming the input basis of the physical model of the mixed light effect. These parameters can fully reflect the optical characteristics of the RGB LED light source, ensuring that the simulation and optimization of the mixed light effect are closer to the actual application scenario.

[0074] Based on the optical parameter data set, a light mixing effect model is constructed to describe the superposition effect of different primary color light sources on color coordinates and brightness after mixing in proportion. Its purpose is to mathematically describe the overall output optical characteristics (color coordinates and brightness) of the RGB three-primary color light sources after combining in different light mixing proportions. The following is a general construction method:

[0075] The RGB LED light mixing effect is usually based on the linear superposition principle of three primary color light sources. Its overall color coordinates and brightness can be expressed as:

[0076]

[0077] L output =α R ·R L +α G ·G L +α B ·B L ,

[0078] Among them, R x ,R y ,R L , Gx ,G y ,G L , B x ,B y ,B L are the color coordinates and brightness values ​​of red, green and blue light sources; α R ,α G ,α B is the mixing ratio of red, green and blue light sources (usually related to the PWM duty cycle); x output ,y output ,L output It is the overall color coordinate and brightness of the mixed light output.

[0079] In practical applications, the light mixing process of RGB LED light sources usually has nonlinear characteristics, such as the angular distribution and attenuation law of light intensity.

[0080] The mixed light effect model needs to consider these factors, including:

[0081] Lighting angle distribution: Lighting angle distribution will affect the superposition effect of primary color light sources. The angle distribution function f(θ) is used to represent the distribution of light intensity.

[0082] Angle changes:

[0083] I R (θ) = R L ·f R (θ),I G (θ) = G L ·f G (θ),I B (θ) = B L ·f B (θ)

[0084] In the mixed light effect model, the light intensity at different angles needs to be integrated:

[0085]

[0086] Light intensity attenuation law: The attenuation characteristics of light intensity with distance (such as the inverse square law) affect the uniformity of light mixing. Use the attenuation function g(d) to correct the light intensity:

[0087]

[0088] Where d is the distance and ∈ is an adjustment factor to avoid the denominator being zero.

[0089] Ambient light effect: Mixed light output needs to be corrected by adding ambient light effect:

[0090] L final =L corrected +L env

[0091] Among them, L env is the ambient light brightness.

[0092] It should be noted that the model parameters are fitted and optimized using the actual measured mixed light output data to correct the assumed values ​​in the model. The model parameters can be adjusted using nonlinear regression algorithms or machine learning methods (such as polynomial fitting or neural networks). The mixed light effects (color coordinates and brightness) of the simulated output are compared with the actual measured data, and the optical characteristic parameters (such as luminous angle distribution, attenuation function, etc.) are iteratively adjusted to improve the accuracy of the model.

[0093] In a virtual simulation environment, by gradually adjusting the mixing ratio of each primary color light source, the mixed output effect is simulated to generate color coordinates and brightness values ​​corresponding to different ratio combinations;

[0094] During the model building process, actual optical characteristic parameters are added, including the luminous angle distribution of the primary light source, the law of light intensity attenuation, and the influence of ambient light on the mixed light effect; by introducing actual optical characteristic parameters such as luminous angle distribution, light intensity attenuation, and the influence of ambient light, the model is closer to the actual application scenario. Especially under complex optical conditions (such as when there are differences in light guides or lens batches), this step significantly improves the adaptability and reliability of the model.

[0095] The simulation output results of the light mixing effect are verified and compared with the actual measured optical data. The optical parameters and light mixing ratio rules in the model are adjusted to ensure that the model accurately reflects the physical characteristics of the RGB LED light source.

[0096] It should be noted that by comparing the simulation output results with the actual measurement data, adjusting the optical parameters and the light mixing ratio rules, the accuracy of the model is further optimized. The verification and adjustment process provides a feedback closed-loop mechanism for the accuracy of the model, which can effectively solve the calibration error problem caused by parameter assumption deviation in the traditional model.

[0097] S3: Input the initial PWM duty cycle obtained by simulation calculation into the intelligent calibration algorithm model, and optimize and calculate the optimal PWM duty cycle by combining the target color coordinates, brightness data and historical calibration deviation.

[0098] Based on the optical characteristic data of the three primary colors of the RGB LED light source, an optical characteristic model is constructed, including the color coordinates and brightness values ​​of each primary color; using the target color coordinates and brightness values, an objective function is defined to describe the error between the output optical characteristics and the target value:

[0099]

[0100] Among them, x output ,y outputis the color coordinate of the current mixed light output, L output is the brightness value of the current mixed light output; x target ,y target ,L target is the corresponding target value, indicating the expected optical properties that the RGB LED light source needs to achieve; ω x ,ω y ,ω L They are corresponding weight factors, which are used to adjust the importance of color coordinates and brightness errors in the objective function, and are dynamically adjusted according to actual needs.

[0101] The gradient optimization method is used to adjust the mixing ratio parameters, which is expressed as:

[0102]

[0103] Among them, α R ,α G ,α B is the initial PWM duty cycle of the three primary colors of red, green and blue, and is the direct control variable of the mixed light ratio; η is the learning rate, which controls the step size of parameter adjustment during the gradient optimization process and is used to balance the convergence speed and optimization stability.

[0104] Set the initial PWM duty cycle α R ,α G ,α B With the target color coordinates and brightness x target ,y target ,L target And the historical calibration deviation data ΔH is input into the intelligent calibration algorithm model;

[0105] The optimal PWM duty cycle is calculated by the following optimization objective function:

[0106]

[0107] The main objective term calculates the dynamic distribution of the output error over time in an integral form, and the smoothing time factor (1+e -βt ) is used to adjust the error weight in the time dimension.

[0108] The regularization constraint term R(α) smoothes the change of PWM duty cycle, suppresses excessive fluctuations, and avoids output instability caused by drastic adjustment of the mixing ratio.

[0109] The historical deviation correction term Γ(ΔH) is combined with the historical calibration deviation data to dynamically adjust the objective function to compensate for the long-term deviation caused by equipment performance or light source batch differences.

[0110] Update the PWM duty cycle through the iterative optimization algorithm, and finally output the optimized optimal PWM duty cycle

[0111] It should be noted that, through the establishment of the optical characteristic model and the definition of the objective function, the adjustment of the mixing ratio of the RGB three-primary color light source is transformed into a mathematical optimization problem. The objective function comprehensively considers the importance of color coordinates and brightness errors, combined with the weight factor ω x ,ω y ,ω L Dynamically allocate optimization weights. Compared with traditional methods, this step upgrades the adjustment of the light mixing ratio from a simple empirical method to a computable theoretical model, significantly improving the optimization accuracy and scientificity.

[0112] The initial PWM duty cycle is gradually adjusted by the gradient optimization method, and the gradient direction is used to find the parameter value that minimizes the error. This method combines the learning rate η to achieve a balance between optimization speed and stability, avoiding the uncertainty of the adjustment process in traditional methods. Compared with simple heuristic methods, gradient optimization has higher efficiency and convergence. It provides an optimization process for the initial PWM duty cycle, significantly improving the speed and accuracy of the light mixing effect approaching the target value.

[0113] S4: Write the optimized PWM duty cycle into the MCU, and the MCU controls the mixed light output of the RGB LED according to the written duty cycle value.

[0114] The optimal PWM duty cycle Convert to pulse width modulation signal supported by MCU;

[0115] The pulse width modulation signal is input into the driving circuit of the RGB LED in a time series, wherein each primary color light source receives a corresponding PWM signal to control the output power;

[0116] MCU monitors the color coordinates and brightness value (x real ,y real ,L real ), compare the target color coordinates and brightness value (x target ,y target ,L target );

[0117] The duty cycle of the PWM signal is adjusted according to the real-time monitoring data, and the deviation of color coordinates and brightness is dynamically compensated through the feedback control algorithm:

[0118] Δα k =μ·(κ x (x real -x target )+κ y (y real -y target )+κ L (L real -Ltarget ))

[0119] Among them, Δα k is the PWM duty cycle compensation value after real-time adjustment, μ is the adjustment step factor; κ x ,κ y ,κ L are weight factors, which adjust the compensation priorities of color coordinates and brightness deviation respectively.

[0120] The MCU dynamically updates the PWM signal and ensures that the color coordinates and brightness values ​​of the RGB LED output are stable within the target value range through closed-loop feedback control.

[0121] Furthermore, the closed-loop feedback control dynamically adjusts the duty cycle of the PWM signal based on the deviation between the real-time monitoring data and the target value, forming a real-time closed-loop control to ensure the stability of the output.

[0122] The stable PWM signal is written into the storage module of the MCU for a long time and locked as the default light mixing parameters to ensure the consistency of the device in subsequent use.

[0123] It should be noted that by converting the optimal PWM duty cycle into a pulse width modulation signal that meets the MCU drive requirements, the optimization result can be directly used to control the RGB LED light source. This process seamlessly connects the output of the optimization algorithm with the hardware control, avoiding the control deviation problem caused by signal format mismatch. It provides precise control signals for the subsequent drive circuit to ensure that the color coordinates and brightness of the mixed light output meet the target values.

[0124] The MCU monitors the color coordinates and brightness values ​​of the RGB LED light source in real time, compares them with the target values, and dynamically adjusts the PWM signal duty cycle according to the formula. This step effectively solves the long-term impact of batch differences and environmental changes on the mixed light effect and improves the system's adaptability.

[0125] S5: Test and verify the calibration effect of the RGB LED light source, adjust the parameters of the intelligent calibration algorithm model according to the test results, and complete the calibration optimization.

[0126] Use precision optical testing equipment to measure the output optical characteristics of RGB LED light sources under different working conditions, including the color coordinates (x real ,y real ) and brightness (L real ) real-time data.

[0127] The test process needs to be carried out in a standardized optical test environment to avoid interference from ambient light and ensure the accuracy and consistency of the data. If necessary, record environmental parameters (such as temperature and humidity) to assist in analyzing changes in light source characteristics.

[0128] Test data and target value (x target ,y target ,L target ) for comparison and calculate the deviation value:

[0129] Δx=x real -x target ,Δy=y real -y target ,ΔL=L real -L target .

[0130] The smaller the deviation value, the closer the calibration effect is to the expected one.

[0131] The overall accuracy and stability of the calibration are evaluated based on the distribution range of the deviation values ​​Δx, Δy, and ΔL. For example, when the deviation values ​​are concentrated within an acceptable range (such as within ±5%), the calibration effect can be considered to meet the requirements.

[0132] For measurement points with large deviations, analyze possible causes, such as equipment errors, ambient light interference, or hardware performance inconsistency, and eliminate or correct abnormal data.

[0133] If the deviation of the color coordinates or brightness is large, the corresponding weight factor is increased to strengthen the calibration process to give priority to correcting the deviation.

[0134] When the deviation is small and evenly distributed, the step size is appropriately reduced to ensure the smoothness of the calibration result; when the deviation is large, the step size is increased to accelerate convergence.

[0135] Using an iterative optimization algorithm, the parameters are gradually updated according to the test results to make the calibration model more consistent with the actual output characteristics.

[0136] Furthermore, the intelligent calibration algorithm model after adjusting the parameters needs to be tested again to ensure the continuous optimization of the calibration results. This process forms a closed-loop calibration verification mechanism. If the deviation value of the verified result meets the expected range, the optimized PWM duty cycle and algorithm parameters are stored as the default values. If it does not meet expectations, it is necessary to further adjust the parameters and repeat the verification process.

[0137] After the calibration optimization is completed, the stable PWM duty cycle is stored in the long-term storage module of the MCU as the default light mixing parameter for subsequent applications. The optimized intelligent calibration algorithm model can be extended to other batches of RGB LED light sources, and the adaptability can be verified through batch testing to ensure the consistency and accuracy of mass-produced products.

[0138] Embodiment 2 is the second embodiment of the present invention, which is different from the previous embodiment in that:

[0139] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art or partly in the current technical solution. The current computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0140] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0141] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0142] Example 3, reference Figure 3 , is an embodiment of the present invention, and provides an RGB LED light source calibration mode optimization system, which is characterized by: including a data acquisition module, a virtual simulation module, an intelligent calibration algorithm module, a PWM signal control module and a test and feedback module.

[0143] Data acquisition module: collects the color coordinates and brightness data of the three-primary color light source of RGB LED and records its actual optical characteristics.

[0144] Virtual simulation module: Based on the collected data, virtual simulation technology is used to establish a physical model of the mixed light effect of the RGB LED light source, and the initial PWM duty cycle corresponding to the target color coordinates and brightness is calculated.

[0145] Intelligent calibration algorithm module: The initial PWM duty cycle obtained by simulation calculation is input into the intelligent calibration algorithm model, and the optimal PWM duty cycle is optimized and calculated by combining the target color coordinates, brightness data and historical calibration deviations.

[0146] PWM signal control module: write the optimized PWM duty cycle into the MCU, and the MCU controls the mixed light output of the RGB LED according to the written duty cycle value.

[0147] Test and feedback module: Test and verify the calibration effect of the RGB LED light source, adjust the parameters of the intelligent calibration algorithm model according to the test results, and complete the calibration optimization.

[0148] Example 4 is an embodiment of the present invention, which provides an RGB LED light source calibration mode optimization method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation / comparative experiments.

[0149] The goal of this example is to verify the feasibility and superiority of the RGB LED light source calibration optimization method. Six test objects were selected in the experiment, including red, green, and blue monochromatic LEDs, and three different mixed light combination LEDs. The specific implementation of the experiment is as follows:

[0150] A high-precision spectrometer is used to measure the optical characteristics of each LED light source point by point, collecting the target color, brightness, and initial PWM duty cycle. In order to simulate the real conditions as much as possible, the experimental environment is added with light guide lenses and the effects of batch differences on the optical characteristics of LEDs.

[0151] Based on the collected data, a physical model of the mixed light effect was constructed using virtual simulation technology. By modeling the light angle distribution and light intensity attenuation law of each LED, the simulation output the target color coordinates and the initial PWM duty cycle required for brightness. The model output results show that the target color coordinates are most sensitive to the proportion of blue LEDs.

[0152] The simulation results are input into the intelligent calibration algorithm, and the mixed light ratio parameters are dynamically adjusted by combining the target value, historical deviation and real-time monitoring data. Through iterative optimization, the optimal PWM duty cycle of each LED is calculated.

[0153] The optimized PWM signal is written into the MCU, which controls the RGB LED drive circuit. The optical characteristics of the light source output are monitored in real time, the target value is compared with the actual output, and the PWM signal is dynamically compensated using a closed-loop feedback system.

[0154] The calibrated optical properties are tested, and the calibration model is optimized by further adjusting the algorithm parameters, and finally the stable PWM signal is stored in the permanent storage module of the MCU.

[0155] For specific experimental data, please refer to Table 1.

[0156] Table 1 Experimental data reference table

[0157]

[0158] From the data, we can see that after optimization and calibration, the output effect of the test object is significantly improved:

[0159] The deviations between the measured color coordinates and the target values ​​are all less than 0.005. Compared with the traditional calibration method where the deviation is often above 0.01, this method can achieve high-precision optical calibration to meet the needs of high-end display and lighting equipment.

[0160] The maximum deviation of the measured brightness is ±1.5cd / m 2 , indicating that the method of the present invention can still maintain the consistency of optical output under batch differences and environmental influences.

[0161] When the initial PWM setting deviation is large, mixed light LED 1 and mixed light LED 2 quickly converge to the target value through real-time feedback adjustment, which proves the efficiency and stability of closed-loop feedback control.

[0162] The fixed PWM duty cycle in the traditional method cannot adapt to batch differences, while this method dynamically compensates for deviations through intelligent calibration and feedback control. Traditional rework requires manual re-programming of parameters, while this method achieves automatic calibration and significantly improves production efficiency.

[0163] The experimental results fully verify the innovation and practicality of the RGB LED light source calibration mode optimization method. This method has significant advantages in color coordinate accuracy, brightness consistency and dynamic adjustment capability, which solves the limitations of traditional methods in batch differences and environmental changes, and provides important technical support for the high-precision application of RGB LED light sources.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing RGB LED light source calibration mode, characterized in that: include: Collect the color coordinates and brightness data of the three primary color light sources of RGB LED and record their actual optical characteristics; Based on the collected data, a physical model of the mixed light effect of the RGB LED light source is established using virtual simulation technology to calculate the initial PWM duty cycle corresponding to the target color coordinates and brightness; The initial PWM duty cycle obtained by simulation calculation is input into the intelligent calibration algorithm model, and the optimal PWM duty cycle is optimized and calculated by combining the target color coordinates, brightness data and historical calibration deviations; The optimized PWM duty cycle is written into the MCU, and the MCU controls the mixed light output of the RGB LED according to the written duty cycle value; Test and verify the calibration effect of the RGB LED light source, adjust the parameters of the intelligent calibration algorithm model according to the test results, and complete the calibration optimization.

2. The RGB LED light source calibration mode optimization method according to claim 1, wherein: The collecting of the color coordinates and brightness data of the three-primary color light source of the RGB LED includes measuring the RGB LED light source point by point using a spectrum measuring device to obtain the color coordinate values ​​and brightness values ​​of the three primary colors of red, green and blue; Based on the measurement results, data preprocessing is performed on the color coordinate values ​​and brightness values; The measured data after data preprocessing is corrected, and the measured value is adjusted according to the equipment calibration curve to obtain the calibrated color coordinates and brightness data.

3. The RGB LED light source calibration mode optimization method according to claim 2, wherein: The data preprocessing includes denoising the measured data by using a median filter or a Gaussian filter algorithm to remove abnormal values ​​caused by ambient light interference; Normalize the denoised data to make the brightness values ​​and color coordinate values ​​of the three primary colors of red, green and blue uniform within a predetermined range; Based on historical calibration data and the performance characteristics of the measuring equipment, an interpolation algorithm or a regression algorithm is used to correct the nonlinear deviation of the measured data.

4. The RGB LED light source calibration mode optimization method according to claim 3, characterized in that: The physical model of the RGB LED light source mixing effect is established by collating and storing the color coordinates and brightness characteristics of each primary color light source to form an optical parameter data set; Based on the optical parameter data set, a light mixing effect model is constructed to describe the superposition effect of different primary color light sources on color coordinates and brightness after mixing in proportion; In a virtual simulation environment, by gradually adjusting the mixing ratio of each primary color light source, the mixed output effect is simulated to generate color coordinates and brightness values ​​corresponding to different ratio combinations; In the process of model building, actual optical characteristic parameters are added, including the luminous angle distribution of the primary color light source, the law of light intensity attenuation and the influence of ambient light on the mixed light effect; The simulation output results of the light mixing effect are verified and compared with the actual measured optical data. The optical parameters and light mixing ratio rules in the model are adjusted to ensure that the model accurately reflects the physical characteristics of the RGB LED light source.

5. The RGB LED light source calibration mode optimization method according to claim 4, characterized in that: The calculating the initial PWM duty cycle corresponding to the target color coordinates and brightness includes constructing an optical characteristic model based on the optical characteristic data of the three primary colors of the RGB LED light source, including the color coordinates and brightness values ​​of each primary color; Using the target color coordinates and brightness values, define the objective function to describe the error between the output optical properties and the target value: Among them, x output ,y output is the color coordinate of the current mixed light output, L output is the brightness value of the current mixed light output, x target ,y target ,L target is its corresponding target value, ω x ,ω y ,ω L are the corresponding weight factors; The gradient optimization method is used to adjust the mixing ratio parameters, which is expressed as: Among them, α R ,α G ,α B is the initial PWM duty cycle of the three primary colors of red, green and blue, and η is the learning rate.

6. The RGB LED light source calibration mode optimization method according to claim 5, characterized in that: The optimization calculation of the optimal PWM duty cycle includes: R ,α G ,α B With the target color coordinates and brightness x target ,y target ,L target And the historical calibration deviation data ΔH is input into the intelligent calibration algorithm model; The optimal PWM duty cycle is calculated by the following optimization objective function: The main objective term calculates the dynamic distribution of the output error over time in an integral form, and the smoothing time factor (1+e -βt ) is used to adjust the error weight of the time dimension; The regularization constraint term R(α) is used to smooth the change of PWM duty cycle; The historical bias correction term Γ(ΔH) is combined with the historical calibration bias to dynamically adjust the optimization process; Update the PWM duty cycle through the iterative optimization algorithm, and finally output the optimized optimal PWM duty cycle 7. The RGB LED light source calibration mode optimization method according to claim 6, characterized in that: Writing the optimized PWM duty cycle into the MCU includes: writing the optimal PWM duty cycle Convert to pulse width modulation signal supported by MCU; The pulse width modulation signal is input into the driving circuit of the RGB LED in a time series, wherein each primary color light source receives a corresponding PWM signal to control the output power; MCU monitors the color coordinates and brightness value (x real ,y real ,L real ), compare the target color coordinates and brightness value (x target ,y target ,L target ); The duty cycle of the PWM signal is adjusted according to the real-time monitoring data, and the deviation of color coordinates and brightness is dynamically compensated through the feedback control algorithm: Da k =η·(κ x (x real -x target )+k y (y real -y target )+k L (L real -L target )) Among them, Δα k is the PWM duty cycle compensation value after real-time adjustment, η is the adjustment step factor; κ x ,κ y ,κ L are weight factors, which adjust the compensation priorities of color coordinates and brightness deviation respectively; The MCU dynamically updates the PWM signal and ensures that the color coordinates and brightness values ​​of the RGB LED output are stable within the target value range through closed-loop feedback control; The stable PWM signal is written into the storage module of the MCU for a long time and locked as the default light mixing parameters to ensure the consistency of the device in subsequent use.

8. A RGB LED light source calibration mode optimization system, used to implement the method according to any one of claims 1 to 7, characterized in that: Data acquisition module: collects the color coordinates and brightness data of the three-primary color light source of RGB LED and records its actual optical characteristics; Virtual simulation module: Based on the collected data, the physical model of the mixed light effect of the RGB LED light source is established using virtual simulation technology to calculate the initial PWM duty cycle corresponding to the target color coordinates and brightness; Intelligent calibration algorithm module: input the initial PWM duty cycle obtained by simulation calculation into the intelligent calibration algorithm model, and optimize the calculation of the optimal PWM duty cycle by combining the target color coordinates, brightness data and historical calibration deviation; PWM signal control module: write the optimized PWM duty cycle into MCU, and MCU controls the mixed light output of RGBLED according to the written duty cycle value; Test and feedback module: Test and verify the calibration effect of the RGB LED light source, adjust the parameters of the intelligent calibration algorithm model according to the test results, and complete the calibration optimization.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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