LED multi-light-color regulation and control method and system based on multi-objective evolutionary algorithm

Abstract

The invention provides an LED multi-light-color regulation and control method and system based on a multi-objective evolutionary algorithm, and the method comprises the steps: firstly obtaining the spectrum and electric power data of each single-color LED, and carrying out the preprocessing of the data through combining a cubic spline interpolation method and a fitting function; secondly, optimizing light color indexes such as color coordinates, color rendering index, color temperature, SDCM value and power consumption by designing an evaluation function; the core of the algorithm is to iteratively optimize the population through individual evaluation, survival of the fittest, heredity and mutation operations by using a multi-target genetic algorithm until an optimal PWM combination meeting all light color index requirements is found. Specifically, the method has the advantages of being high in flexibility, good in expansibility and the like, can adapt to different LED channel configurations, provides a light color regulation and control scheme with high pertinence, is particularly suitable for factory regulation of fluorescent powder-free multi-primary-color LED lamps, and provides an efficient and automatic solution for light color optimization.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control technology, and in particular relates to a method and system for controlling LED multi-light colors based on a multi-objective evolutionary algorithm. Background Art

[0002] With the rapid development of LED (light-emitting diode) technology, LED light sources have gradually become the mainstream light source in the lighting field with its advantages of high efficiency, energy saving, and long life. Multi-primary color LED lamps have attracted much attention in recent years because they can adjust a variety of light colors and realize personalized lighting solutions. Especially in high-demand scenarios such as display technology, plant lighting, and medical lighting, higher requirements are placed on the accuracy of light color regulation. However, due to the complex spectral combination of multi-primary color LED lamps, there are mutual constraints between light color indicators such as power consumption, color rendering index, and color temperature under different light colors. How to achieve efficient and precise regulation of multi-primary color LED lamps has become the difficulty and focus of current research.

[0003] Existing LED light color control methods usually adopt linear adjustment schemes based on empirical formulas, or manually adjust the PWM (pulse width modulation) values ​​of each primary color to obtain the desired light color output. However, the adjustment process of these methods is complicated and time-consuming, and it is difficult to meet all light color index requirements while taking into account the optimization of electrical power. In addition, due to the lack of systematicity of manual adjustment, the control results are often not accurate enough and it is difficult to meet the stringent requirements for light color in certain special application scenarios.

[0004] In response to the above problems, some studies have introduced intelligent optimization methods such as genetic algorithms and multi-objective optimization algorithms to solve the nonlinear and multi-objective problems in the light color regulation of multi-primary color LEDs. These algorithms can optimize power consumption and color rendering performance on the basis of meeting the requirements of light color indicators, and improve the efficiency and accuracy of light color regulation. However, the existing intelligent algorithms still have many shortcomings in practical applications. For example, some algorithms converge slowly in complex scenarios and are prone to falling into local optimal solutions; the evaluation function design of some algorithms is single and cannot take into account the trade-off optimization of multiple light color indicators; and some algorithms are not flexible enough and are difficult to adapt to multi-primary color LED lamps with different configurations.

[0005] Therefore, how to design a light color control scheme that can effectively optimize multi-primary color LED lamps, especially by introducing a multi-objective evolutionary algorithm to comprehensively optimize multiple objectives such as color coordinates, color rendering index, color temperature and power consumption, has become a key problem to be solved in the current technical field. In this context, the present invention proposes an LED multi-light color control optimization method based on a multi-objective evolutionary algorithm, aiming to provide an efficient, automated light color optimization solution that can adapt to different application scenarios. Summary of the invention

[0006] Based on this, an embodiment of the present invention provides an LED multi-light color control method and system based on a multi-objective evolutionary algorithm, aiming to solve the problems in the prior art of low efficiency, poor accuracy and difficulty in taking into account multiple light color indicators at the same time of LED multi-light color control.

[0007] A first aspect of an embodiment of the present invention provides a method for controlling LED multi-color light based on a multi-objective evolutionary algorithm, the method comprising: Collecting spectrum data and electric power data of LED lamps at different PWM values, and smoothing the spectrum data using cubic spline interpolation, and processing the electric power data using a preset fitting function; Calculate the light color index corresponding to each PWM combination, wherein the light color index at least includes color coordinates, color rendering index, color temperature, SDCM value and power consumption, wherein the color rendering index includes color rendering index Ra and color rendering index R9; The initial population is randomly generated, in which each individual represents a PWM combination, and the population is evolved through a multi-objective evolutionary algorithm. The population is gradually optimized through the selection, crossover and mutation operations in the genetic algorithm. Specifically, in the selection operation, a roulette selection strategy is adopted to select the target individual to enter the next generation; in the crossover operation, the single-point crossover method is used to cross the PWM combination of two parent individuals to generate new offspring individuals; in the mutation operation, random disturbances are introduced through a dynamic mutation rate adjustment mechanism to increase the diversity of the population; When all light color indicators meet preset requirements, a target PWM combination is output, and the target PWM combination is used to control the LED lamp.

[0008] An embodiment of the present invention provides an LED multi-light color control method based on a multi-objective evolutionary algorithm. The method first obtains the spectrum and electric power data of each monochromatic LED, and pre-processes the data in combination with the cubic spline interpolation method and the fitting function; then, by designing an evaluation function, various light color indicators such as color coordinates, color rendering index, color temperature, SDCM value and power consumption are optimized; the core of the algorithm is to use a multi-objective genetic algorithm to iteratively optimize the population through individual evaluation, survival of the fittest, inheritance and mutation operations until the optimal PWM combination that meets all light color index requirements is found. Specifically, this method has the advantages of strong flexibility and good scalability, can adapt to different LED channel configurations, and provides a highly targeted light color control solution. It is particularly suitable for factory adjustment of multi-primary color LED lamps without phosphors, and provides an efficient and automated solution for light color optimization. Furthermore, the calculation formula of the cubic spline interpolation method is:

[0009] in, is the input value of the PWM combination, is the corresponding spectral output, , , , Respectively The interpolation coefficients of the sampling points, For the The PWM value of each sampling point.

[0010] Furthermore, the multi-objective evolutionary algorithm is a combination algorithm of a genetic algorithm and a non-dominated sorting algorithm, which gradually selects the optimal balance point between light color index and power consumption by performing multi-objective optimization on the initial population.

[0011] Furthermore, the evaluation function design of the multi-objective evolutionary algorithm takes into account the color coordinates, color rendering index, color temperature, SDCM value and power consumption, and the evaluation function comprehensively calculates each index through a weighted coefficient.

[0012] Furthermore, in the design of the evaluation function, a weighted multi-objective fitness function is adopted. Specifically, the calculation formula of the weighted multi-objective fitness function is:

[0013] in, represents the fitness of the color coordinates, Indicates the adaptability of color rendering index Ra and color rendering index R9, Indicates the adaptability of color temperature. Indicates the adaptability of electric power consumption, represents the fitness of the SDCM value, , , , , is the weight coefficient of each indicator.

[0014] Furthermore, the calculation of the color rendering index Ra and the color rendering index R9 is based on the CIE colorimetry standard, and a dynamic weight adjustment method is used to correct the color rendering index score of each evolved individual in real time to improve the color reproduction of the final generated PWM combination in practical applications.

[0015] Furthermore, the calculation formula of the dynamic mutation rate adjustment mechanism is:

[0016] in, Indicates that in the genetic algorithm The probability of performing mutation operation in each generation, is the current evolutionary generation, is the rate adjustment constant, is the convergence point of the algorithm.

[0017] A second aspect of the embodiments of the present invention provides an LED multi-light color control system based on a multi-objective evolutionary algorithm, which is used to implement the LED multi-light color control method based on a multi-objective evolutionary algorithm described in the first aspect, and the system includes: An acquisition module is used to collect spectrum data and electric power data of LED lamps under different PWM values, and use cubic spline interpolation to smooth the spectrum data and use a preset fitting function to process the electric power data; A calculation module, used for calculating the light color index corresponding to each PWM combination, wherein the light color index at least includes color coordinates, color rendering index, color temperature, SDCM value and power consumption, wherein the color rendering index includes color rendering index Ra and color rendering index R9; The optimization module is used to randomly generate an initial population, in which each individual represents a PWM combination, and the population is evolved through a multi-objective evolutionary algorithm. The population is gradually optimized through the selection, crossover and mutation operations in the genetic algorithm. Specifically, in the selection operation, a roulette selection strategy is adopted to select the target individual to enter the next generation; in the crossover operation, a single-point crossover method is used to cross the PWM combination of two parent individuals to generate a new child individual; in the mutation operation, a dynamic mutation rate adjustment mechanism is used to introduce random disturbances to increase the diversity of the population; The output module is used to output a target PWM combination when all light color indicators meet preset requirements, and the target PWM combination is used to control the LED lamp.

[0018] A third aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the LED multi-light color control method based on a multi-objective evolutionary algorithm provided in the first aspect.

[0019] The fourth aspect of an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the LED multi-light color control method based on a multi-objective evolutionary algorithm provided in the first aspect is implemented.

[0020] The beneficial effects of the LED multi-light color control method based on a multi-objective evolutionary algorithm provided by the present invention are: 1. Through the introduction of multi-objective evolutionary algorithms, the light color control problem of multi-primary color LED lamps is effectively solved. It can simultaneously optimize multiple light color indicators such as color coordinates, color rendering index, color temperature and power consumption, and achieve highly accurate light color control. Compared with traditional manual control methods, this invention greatly improves the control efficiency through intelligent optimization algorithms, reduces human intervention, and the optimization results are more accurate and stable; 2. The cubic spline interpolation method is used to smooth the spectral data, so that the spectral data can transition continuously under different PWM values, reducing the discrete error of the data, thereby improving the accuracy of the spectral data. This data processing method has higher fitting accuracy than the traditional linear interpolation method, and is particularly suitable for multi-primary color LED lamps with nonlinear spectral changes; 3. By introducing a dynamic mutation rate adjustment mechanism, the genetic algorithm is prevented from falling into the local optimal solution. In the early stage of optimization, the diversity of the population is increased by increasing the mutation rate, thereby enhancing the global search capability; in the later stage of optimization, the mutation rate is reduced to speed up the convergence speed and ensure that the algorithm quickly finds the global optimal solution. This dynamic mutation rate strategy significantly improves the efficiency and stability of the optimization algorithm; 4. The multi-objective fitness function is flexibly designed and can dynamically adjust the weight coefficient according to different application requirements, thus realizing adaptive optimization for different scenarios. For example, in indoor lighting, the present invention can give priority to optimizing the color rendering index and power consumption; in display lighting, it can focus on optimizing the color coordinates and color rendering index. This flexible optimization strategy enables the present invention to be applicable to a variety of application scenarios, and has strong practicality and promotion value; 5. Through the operation of inheritance, crossover and mutation, the optimal PWM combination can be automatically generated through multi-generation population evolution, avoiding the tedious manual adjustment process of different light colors in the traditional method, greatly improving the control efficiency. In addition, by saving the best individuals in each generation of population, the global optimality of the final output PWM combination in terms of light color index is guaranteed, so that this method can achieve excellent light color performance in different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of an implementation method of LED multi-color control based on a multi-objective evolutionary algorithm provided in Embodiment 1 of the present invention; Figure 2 A structural block diagram of an LED multi-light color control system based on a multi-objective evolutionary algorithm provided in Embodiment 4 of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 5 of the present invention. DETAILED DESCRIPTION

[0022] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0023] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0025] Embodiment 1 According to an embodiment of the present invention, an embodiment of an LED multi-light color control method based on a multi-objective evolutionary algorithm is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0026] In the first embodiment of the present invention, a method for controlling LED light colors based on a multi-objective evolutionary algorithm is provided, which can be used in electronic devices, such as computers. Figure 1 , Figure 1 The flowchart of the implementation of an LED multi-light color control method based on a multi-objective evolutionary algorithm provided in the first embodiment of the present invention is shown, which specifically includes steps S01 to S04.

[0027] Step S01, collecting spectrum data and electric power data of the LED lamp under different PWM values, and smoothing the spectrum data by using a cubic spline interpolation method, and processing the electric power data by using a preset fitting function.

[0028] First, the system collects the spectrum of the multi-primary color LED lamp and obtains the spectrum data of each primary color LED under different PWM combinations. In order to improve the accuracy of data processing, the present invention uses a cubic spline interpolation algorithm when processing the spectrum data. The advantage of this algorithm is that it can achieve a smooth transition of the spectrum data between discrete PWM combinations, thereby effectively reducing the error caused by the nonlinearity of the LED spectrum change. The calculation formula of the cubic spline interpolation method is as follows:

[0029] in, is the input value of the PWM combination, is the corresponding spectral output, , , , Respectively The interpolation coefficients of the sampling points, For the Through this interpolation method, continuous spectral data under different PWM values ​​can be generated, providing more accurate input for subsequent optimization algorithms.

[0030] Step S02, calculating the light color index corresponding to each PWM combination, wherein the light color index at least includes color coordinates, color rendering index, color temperature, SDCM value and power consumption, wherein the color rendering index includes color rendering index Ra and color rendering index R9.

[0031] In the embodiment of the present invention, the light color index corresponding to each PWM combination is calculated by combining the collected spectral data and electric power data with the calculation method in the CIE standard colorimetric system, wherein the calculation formula of the color rendering index Ra is:

[0032] Among them, ΔE represents the color difference value, which is calculated by the difference between the LED spectrum and the standard light source spectrum. R9, as the color rendering index of high-saturation red, is an important parameter in certain special application scenarios (such as medical lighting and plant lighting). Different from the prior art, the embodiment of the present invention takes R9 and Ra as the optimization targets of the color rendering index at the same time, ensuring that the light color control result not only performs well in conventional application scenarios, but also is suitable for special lighting needs.

[0033] Step S03, randomly generate an initial population, in which each individual represents a PWM combination, and perform population evolution through a multi-objective evolutionary algorithm.

[0034] The process of population evolution is as follows: first, generate the initial population, where each individual represents a PWM combination. Through the selection, crossover and mutation operations in the genetic algorithm, the population is gradually optimized. In the selection operation, the roulette selection strategy is adopted to select individuals with higher fitness to enter the next generation; in the crossover operation, the single-point crossover method is used to cross the PWM combination of two parent individuals to generate new offspring individuals; in the mutation operation, the dynamic mutation rate adjustment mechanism is used to appropriately introduce random disturbances to increase the diversity of the population.

[0035] It should be noted that the multi-objective evolutionary algorithm is a combination of genetic algorithm and non-dominated sorting. By performing multi-objective optimization on the initial population, the optimal balance point between light color index and power consumption is gradually screened out to ensure that the final output PWM combination performs well in all light color indexes. Specifically, the evaluation function design of the multi-objective evolutionary algorithm takes into account color coordinates, color rendering index, color temperature, SDCM value and power consumption. The evaluation function comprehensively calculates each index through a weighted coefficient. According to specific application requirements, the weighted coefficient is adjusted to achieve light color control optimization in different scenarios. The calculation formula of the weighted multi-objective fitness function is:

[0036] in, represents the fitness of the color coordinates, Indicates the adaptability of color rendering index Ra and color rendering index R9, Indicates the adaptability of color temperature. Indicates the adaptability of electric power consumption, represents the fitness of the SDCM value, , , , , is the weight coefficient of each index. It can be understood that by dynamically adjusting the weight coefficient, one or several light color indexes can be optimized first according to actual application requirements. For example, in medical lighting, the focus is on optimizing the color rendering index, while in general lighting, more attention is paid to minimizing power consumption.

[0037] In the embodiment of the present invention, in order to prevent the algorithm from falling into a local optimal solution, the present invention dynamically adjusts the mutation rate according to the fitness distribution of each generation of the population. In the early stage of evolution, the mutation rate is increased to increase the diversity of the population; in the later stage of evolution, the mutation rate is reduced to accelerate the convergence speed. The calculation formula of the dynamic mutation rate adjustment mechanism is:

[0038] in, Indicates that in the genetic algorithm The probability of performing mutation operation in each generation, is the current evolutionary generation, is the rate adjustment constant, is the convergence point of the algorithm. Through this mechanism, the algorithm can avoid premature convergence in the early stage of exploration and speed up the search for the global optimal solution in the later stage.

[0039] Step S04: when all light color indicators meet preset requirements, a target PWM combination is output, and the target PWM combination is used to control the LED lamp.

[0040] In summary, the LED multi-light color control method based on the multi-objective evolutionary algorithm in the above-mentioned embodiment of the present invention first obtains the spectrum and electric power data of each monochromatic LED, and pre-processes the data in combination with the cubic spline interpolation method and the fitting function; then, by designing an evaluation function, various light color indicators such as color coordinates, color rendering index, color temperature, SDCM value and power consumption are optimized; the core of the algorithm is to use a multi-objective genetic algorithm to iteratively optimize the population through individual evaluation, survival of the fittest, inheritance and mutation operations until the optimal PWM combination that meets all light color index requirements is found. Specifically, this method has the advantages of strong flexibility and good scalability, can adapt to different LED channel configurations, and provides a highly targeted light color control solution, which is particularly suitable for factory adjustment of multi-primary color LED lamps without phosphors, and provides an efficient and automated solution for light color optimization.

[0041] Embodiment 2 The second embodiment of the present invention describes how to realize the light color control optimization process of multi-primary color LED lamps through a multi-objective evolutionary algorithm. In the embodiment of the present invention, for a multi-primary color LED lamp without phosphor, it is required to adjust the PWM value to make the lamp meet specific light color index requirements under different color temperature gears (such as 5000K, 4000K, 3000K and 2000K), including optimization of color coordinates, SDCM, color rendering index Ra, color rendering index R9 and power consumption. The whole process runs on a preset machine, which has a built-in control system and is connected to the host computer interface to facilitate data acquisition and transmission. In this embodiment, the algorithm is written in Python, and all data processing and optimization processes are completed in this language environment.

[0042] First, the machine is used to collect the spectrum data and electrical power data of LED lamps at different PWM values. To ensure the accuracy of the data, the measured PWM values ​​cover a certain range of changes, such as the PWM value of the red LED from 50 to 200, with a step size of 20. After obtaining the spectrum data corresponding to these PWM values, it is saved in a data file for subsequent use. In addition, while measuring the spectrum, the electrical power consumption data at different PWM values ​​is also recorded, which is used to fit the power model.

[0043] Next, the system performs fitting and interpolation processing on the collected spectral data and electric power data. Spectral data is usually discrete. In order to obtain accurate spectral output at any PWM value, the embodiment of the present invention uses cubic spline interpolation to preprocess the data, so that all PWM values ​​between 50 and 200 can obtain the corresponding spectrum through interpolation calculation. At the same time, the electric power data is processed by a fitting function to establish a four-channel power estimation model. The model outputs an electric power estimation value based on the PWM values ​​of the four RGBA channels as independent variables. This processing step ensures that the subsequent algorithm can quickly query the spectral data and electric power data during the multi-objective optimization process without the need for re-measurement.

[0044] After completing the data preprocessing, the main process of the multi-objective evolutionary algorithm is entered. The core task of the algorithm is to achieve comprehensive optimization of color coordinates, SDCM values, color rendering index Ra, color rendering index R9 and power consumption through iterative optimization of genetic algorithms. The embodiment of the present invention regards the optimization problem as a multi-input and multi-output optimization problem, that is, the input is the PWM value of the four channels of red light, green light, blue light and yellow light, and the output is light color indicators such as color coordinates, SDCM values, color rendering index Ra and color rendering index R9. In order to ensure the comprehensiveness of the optimization, a variety of objective functions are set in the multi-objective evolutionary algorithm of the embodiment of the present invention, and each objective is scored by the fitness function.

[0045] Specifically, an independent evaluation function is designed for each light color index. For example, the evaluation function of the color coordinate determines the fitness by calculating the Euclidean distance between the LED light color and the target color coordinate under the current PWM combination. The evaluation function of the SDCM value uses a set threshold value to ensure that the highest score is given when the SDCM value is less than the set value. The evaluation functions of the color rendering index Ra and the color rendering index R9 are based on the set numerical requirements (such as the color rendering index Ra and the color rendering index R9 are both greater than 90). As long as this requirement is met, the fitness score is the highest. For the evaluation function of power consumption, the system sets a target power range. As long as the power under the PWM combination falls within this range, it is considered to meet the requirements and obtain a corresponding score. By weighted synthesis of the outputs of all evaluation functions, the embodiment of the present invention ensures that each light color index is fully considered in the optimization process.

[0046] Furthermore, the system begins a multi-generation evolutionary iteration process. First, a set of random PWM combinations is generated as the initial population. Each PWM combination represents an LED spectral output. The spectral data under these combinations is found by cubic spline interpolation, and the corresponding light color index is calculated. Then, the system evaluates the fitness of these individuals. Individuals with high evaluation values ​​are retained, and individuals with low fitness are gradually eliminated. Then, the system performs genetic operations and mutation operations on the retained excellent individuals. Genetic operations generate new PWM combinations by crossing the characteristics of different PWM combinations, while mutation operations increase the diversity of the population by randomly changing certain PWM values. This process continues to iterate until the optimal individuals in the population converge to a stable optimal solution, that is, all light color indicators meet the predetermined requirements.

[0047] In the embodiment of the present invention, the iterative process of the multi-objective evolutionary algorithm also uses a dynamic mutation rate adjustment mechanism. In the early stage of evolution, the diversity of the population is enhanced by increasing the mutation rate to avoid falling into the local optimal solution in the early stage. In the later stage of evolution, as the fitness of the population increases, the mutation rate gradually decreases to accelerate the convergence speed of the algorithm. In addition, the embodiment of the present invention also records the individuals with the highest fitness in each generation and uses them as a reference to prevent the loss of excellent solutions due to subsequent mutations. This mechanism effectively improves the global search capability of the algorithm while ensuring the reliability of the optimal solution.

[0048] When the iteration process is completed, the system outputs the final optimal PWM combination. The light color index corresponding to this combination can meet the requirements under multiple color temperature gears. For example, under 5000K, 4000K, 3000K and 2000K gears, the color coordinates of the LED lamp meet the target color coordinate requirements, the SDCM value is less than 1, the color rendering index Ra and the color rendering index R9 are both greater than 90, and the power consumption is also controlled within a reasonable range. Through the method of the present invention, the optimized PWM combination can ensure that the light color performance of the multi-primary color LED lamp at different color temperatures not only meets the design requirements, but also has higher energy efficiency.

[0049] It should be noted that the embodiment of the present invention demonstrates the flexibility and scalability of the optimization method. Although the embodiment of the present invention is aimed at four-channel (red, green, blue, and yellow) LED lamps, the algorithm can be expanded to the regulation of LED lamps with more channels through simple modifications. In addition, since the multi-objective evolutionary algorithm can be adaptively adjusted according to different optimization goals, this method is also applicable to other application scenarios in the future, such as plant lighting, display backlight, etc., when there are special requirements for color rendering index, power consumption or other light color indicators. By adjusting the weight coefficient in the fitness function, the system can quickly adapt to different optimization goals, thereby enhancing the wide application of the present invention.

[0050] The embodiment of the present invention applies the multi-objective evolutionary algorithm to the light color control of multi-primary color LED lamps, demonstrating its advantages in improving the light color control accuracy, optimizing energy efficiency, and realizing automatic control. The method of the present invention not only effectively simplifies the process of LED light color control, but also greatly improves the control efficiency and light color performance, and has a wide range of application potential.

[0051] Embodiment 3 The third embodiment of the present invention also focuses on factory adjustment for multi-primary color LED lamps without phosphors, but adopts a different algorithm optimization strategy to achieve higher flexibility and adaptability in the multi-objective optimization process. The goal of the embodiment of the present invention is to ensure that the light color indicators (including color coordinates, color rendering index Ra, color rendering index R9 and power consumption) of LED lamps at multiple color temperature levels (such as 5000K, 4000K, 3000K and 2000K) can meet strict standards.

[0052] Specifically, the machine first collects the spectrum data and electric power data of the LED lamp under each PWM combination to ensure the accuracy and representativeness of the data. Compared with the first embodiment, the embodiment of the present invention pays more attention to the extensiveness of the collection scope during the data collection process, which not only includes the PWM values ​​of red light, green light, blue light and yellow light, but also adds a comprehensive measurement of the spectrum under each color temperature level. This means that under different color temperature conditions, the machine will repeat the measurement of the spectrum and electric power to ensure the reliability and accuracy of the light color data.

[0053] In the data processing stage, the embodiment of the present invention also uses the cubic spline interpolation method, but in the process of processing the interpolation data, it focuses on the characteristics of each LED spectrum and uses different interpolation parameters for targeted processing. By analyzing the characteristics of each spectrum and the nonlinear changes in the PWM output, a more refined interpolation strategy is formulated to improve the accuracy of subsequent calculations. Specifically, LEDs of different colors may show different spectral fluctuations at the same PWM value. Therefore, when processing the spectral data of red light, green light, blue light and yellow light, the embodiment of the present invention uses color-specific parameters, which can better adapt to the luminous characteristics of the LED.

[0054] In the implementation process of the multi-objective optimization algorithm, the embodiment of the present invention constructs an improved multi-objective fitness function. This function not only includes the calculation of color coordinates, color rendering index Ra, color rendering index R9 and power consumption, but also introduces a new indicator, namely, user-customized light color preference settings. Users can set the priority of different light colors according to specific application requirements. This flexible optimization method has high adaptability in market applications.

[0055] Specifically, when establishing the fitness function, the design of the embodiment of the present invention takes into account user feedback and market demand, allowing users to set certain light color targets before the algorithm runs. For example, in indoor lighting applications, users may pay more attention to color rendering index and color temperature, while in display screen applications, users may pay priority attention to color coordinates and power consumption. Therefore, the fitness function can dynamically adjust the weight according to the user's input in different application scenarios to ensure that the optimal control effect can be achieved under different requirements.

[0056] The embodiment of the present invention adopts a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization. Compared with the genetic algorithm of the first embodiment, the NSGA-II algorithm in the embodiment of the present invention can handle multiple objectives at the same time, and ensure the diversity of the population during the evolution process, avoiding the individual from converging to the local optimal solution too early. In the initial generation stage of the population, a larger random range is used to ensure that more PWM combinations are covered. By evaluating the initial population, individuals with higher fitness are selected, and crossover and mutation operations are performed on this basis.

[0057] In the crossover operation, the embodiment of the present invention adopts a multi-point crossover method, randomly selecting multiple crossover points for feature mixing to generate new individuals. Compared with single-point crossover, this method can produce more diverse individuals and enhance the genetic diversity of the population. In the mutation operation, the embodiment of the present invention adopts a combination of random mutation and purposeful mutation. For individuals with higher fitness, a small range of random mutation is used to maintain their excellent characteristics; for individuals with lower fitness, a larger range of mutation is introduced to increase their chances of exploring new solutions.

[0058] In the iterative process of each generation, the fitness evaluation of the embodiment of the present invention is more flexible. In addition to the evaluation of fixed target values, a dynamic fitness correction mechanism is also introduced. By real-time feedback of the changes in the current light color index, the fitness function can adjust the weights of each target in time to quickly adapt to the changes in the light color requirements of the current environment. This dynamic correction mechanism ensures that the algorithm can still maintain the optimization effect when facing changes that may occur in actual applications.

[0059] The iterative process of the embodiment of the present invention continues until a preset number of iterations is reached or the fitness of the population is stable. Finally, the final output of the PWM combination is selected by screening the individuals with the best fitness in each generation. This PWM combination should be able to ensure that the light color performance of the LED lamp meets the predetermined standard at the color temperature levels of 5000K, 4000K, 3000K and 2000K. Specifically, in terms of color rendering index, the color rendering index Ra and the color rendering index R9 are both greater than 90; in terms of color coordinates, SDCM is less than the set threshold; in terms of power consumption, it is controlled within a reasonable range.

[0060] It should be noted that the embodiments of the present invention demonstrate the flexibility and scalability of the light color control algorithm. In the embodiments of the present invention, the function of user-customized light color preference settings not only improves the ability to meet personalized needs of control, but also improves market competitiveness. The ability to adjust the optimization target based on user feedback and actual needs ensures that the present invention can achieve the best light color performance in different application scenarios.

[0061] The embodiment of the present invention effectively improves the light color control efficiency and accuracy of the phosphor-free multi-primary color LED lamp by adopting a series of innovative technologies such as improved multi-objective fitness function, non-dominated sorting genetic algorithm, dynamic fitness correction mechanism, etc. Through flexible algorithm design, the embodiment of the present invention not only ensures that the light color index of the LED lamp at different color temperatures meets the expected requirements, but also can adapt to the ever-changing market demand and user personalized requirements, and has good application prospects.

[0062] Embodiment 4 See also Figure 2 , Figure 2 This is a structural block diagram of an LED multi-light color control system based on a multi-objective evolutionary algorithm provided in Example 4 of the present invention. The LED multi-light color control system 200 based on a multi-objective evolutionary algorithm is used to implement the above-mentioned embodiments and preferred implementations, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0063] Specifically, the LED multi-color control system 200 based on the multi-objective evolutionary algorithm includes: a collection module 21, a calculation module 22, an optimization module 23 and an output module 24, wherein: The acquisition module 21 is used to collect the spectrum data and electric power data of the LED lamp under different PWM values, and use the cubic spline interpolation method to smooth the spectrum data, and use a preset fitting function to process the electric power data. The calculation formula of the cubic spline interpolation method is:

[0064] in, is the input value of the PWM combination, is the corresponding spectral output, , , , Respectively The interpolation coefficients of the sampling points, For the PWM value of each sampling point; A calculation module 22, used to calculate the light color index corresponding to each PWM combination, wherein the light color index at least includes color coordinates, color rendering index, color temperature, SDCM value and power consumption, wherein the color rendering index includes color rendering index Ra and color rendering index R9; The optimization module 23 is used to randomly generate an initial population, in which each individual represents a PWM combination, and the population is evolved through a multi-objective evolutionary algorithm, wherein the population is gradually optimized through the selection, crossover and mutation operations in the genetic algorithm. Specifically, in the selection operation, a roulette selection strategy is adopted to select the target individual to enter the next generation; in the crossover operation, a single-point crossover method is used to cross the PWM combination of two parent individuals to generate a new child individual; in the mutation operation, a dynamic mutation rate adjustment mechanism is used to introduce random disturbances to increase the diversity of the population. The multi-objective evolutionary algorithm is a combination algorithm of a genetic algorithm and a non-dominated sorting algorithm. , by performing multi-objective optimization on the initial population, the optimal balance point between the light color index and the power consumption is gradually screened out to ensure that the final output PWM combination performs well in all light color indicators. The evaluation function design of the multi-objective evolutionary algorithm takes into account the color coordinates, color rendering index, color temperature, SDCM value and power consumption. The evaluation function comprehensively calculates each indicator through a weighted coefficient, wherein the weighted coefficient is adjusted according to specific application requirements to achieve light color control optimization in different scenarios. In the design of the evaluation function, a weighted multi-objective fitness function is adopted. Specifically, the calculation formula of the weighted multi-objective fitness function is:

[0065] in, represents the fitness of the color coordinates, Indicates the adaptability of color rendering index Ra and color rendering index R9, Indicates the adaptability of color temperature. Indicates the adaptability of electric power consumption, represents the fitness of the SDCM value, , , , , is the weight coefficient of each index. The calculation of color rendering index Ra and color rendering index R9 is based on CIE colorimetry standard, and a dynamic weight adjustment method is used to correct the color rendering index score of each evolutionary individual in real time to improve the color reproduction of the final generated PWM combination in practical applications. The calculation formula of the dynamic mutation rate adjustment mechanism is:

[0066] in, Indicates that in the genetic algorithm The probability of performing mutation operation in each generation, is the current evolutionary generation, is the rate adjustment constant, is the convergence point of the algorithm; The output module 24 is used to output a target PWM combination when all light color indicators meet preset requirements, and the target PWM combination is used to control the LED lamp.

[0067] Embodiment 5 Another aspect of the present invention provides an electronic device, see Figure 3 , shown is an electronic device in Embodiment 5 of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the LED multi-light color control method based on the multi-objective evolutionary algorithm as described above is implemented.

[0068] In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0069] Among them, the memory 20 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as a hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of an electronic device, such as a plug-in hard disk equipped on the electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Further, the memory 20 can also include both an internal storage unit of the electronic device and an external storage device. The memory 20 can not only be used to store application software and various types of data of the electronic device, but also can be used to temporarily store data that has been output or is to be output.

[0070] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than those shown in the figure, or combine certain components, or arrange the components differently.

[0071] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the LED multi-light color control method based on the multi-objective evolutionary algorithm as described above is implemented.

[0072] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented 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 purposes of this specification, "computer-readable medium" may 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.

[0073] 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.

[0074] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0075] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0076] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A LED multi-color control method based on a multi-objective evolutionary algorithm, characterized in that: The method comprises: Collecting spectrum data and electric power data of LED lamps at different PWM values, and smoothing the spectrum data using cubic spline interpolation, and processing the electric power data using a preset fitting function; Calculate the light color index corresponding to each PWM combination, wherein the light color index at least includes color coordinates, color rendering index, color temperature, SDCM value and power consumption, wherein the color rendering index includes color rendering index Ra and color rendering index R9; The initial population is randomly generated, in which each individual represents a PWM combination, and the population is evolved through a multi-objective evolutionary algorithm, wherein the population is gradually optimized through the selection, crossover and mutation operations in the genetic algorithm. Specifically, in the selection operation, a roulette selection strategy is adopted to select the target individual to enter the next generation; in the crossover operation, the single-point crossover method is used to cross the PWM combination of two parent individuals to generate new offspring individuals; in the mutation operation, random disturbances are introduced through a dynamic mutation rate adjustment mechanism to increase the diversity of the population; When all light color indicators meet preset requirements, a target PWM combination is output, and the target PWM combination is used to control the LED lamp.

2. The LED multi-color control method based on a multi-objective evolutionary algorithm according to claim 1 is characterized in that: The calculation formula of the cubic spline interpolation method is: in, is the input value of the PWM combination, is the corresponding spectral output, , , , Respectively The interpolation coefficients of the sampling points, For the The PWM value of each sampling point.

3. The LED multi-color control method based on multi-objective evolutionary algorithm according to claim 1 is characterized in that: The multi-objective evolutionary algorithm is a combination algorithm of a genetic algorithm and a non-dominated sorting algorithm, which gradually screens out the optimal balance point between light color index and power consumption by performing multi-objective optimization on the initial population.

4. The LED multi-color control method based on a multi-objective evolutionary algorithm according to claim 1, characterized in that: The evaluation function design of the multi-objective evolutionary algorithm takes into account color coordinates, color rendering index, color temperature, SDCM value and power consumption, and the evaluation function comprehensively calculates each index through a weighted coefficient.

5. The LED multi-color control method based on multi-objective evolutionary algorithm according to claim 4 is characterized in that: In the design of the evaluation function, a weighted multi-objective fitness function is adopted. Specifically, the calculation formula of the weighted multi-objective fitness function is: in, represents the fitness of the color coordinates, Indicates the adaptability of color rendering index Ra and color rendering index R9, Indicates the adaptability of color temperature. Indicates the adaptability of electric power consumption, represents the fitness of the SDCM value, , , , , is the weight coefficient of each indicator.

6. The LED multi-color control method based on multi-objective evolutionary algorithm according to claim 5 is characterized in that: The calculation of the color rendering index Ra and the color rendering index R9 is based on the CIE colorimetry standard, and a dynamic weight adjustment method is used to make real-time corrections to the color rendering index score of each evolving individual to improve the color reproduction of the final generated PWM combination in practical applications.

7. The LED multi-color control method based on multi-objective evolutionary algorithm according to claim 1 is characterized in that: The calculation formula of the dynamic mutation rate adjustment mechanism is: in, Indicates that in the genetic algorithm The probability of performing mutation operation in each generation, is the current evolutionary generation, is the rate adjustment constant, is the convergence point of the algorithm.

8. An LED multi-color control system based on a multi-objective evolutionary algorithm, characterized in that: The system is used to implement the LED multi-color control method based on a multi-objective evolutionary algorithm as described in any one of claims 1 to 7, and comprises: An acquisition module is used to collect spectrum data and electric power data of LED lamps under different PWM values, and use cubic spline interpolation to smooth the spectrum data and use a preset fitting function to process the electric power data; A calculation module, used for calculating the light color index corresponding to each PWM combination, wherein the light color index at least includes color coordinates, color rendering index, color temperature, SDCM value and power consumption, wherein the color rendering index includes color rendering index Ra and color rendering index R9; The optimization module is used to randomly generate an initial population, in which each individual represents a PWM combination, and the population is evolved through a multi-objective evolutionary algorithm. The population is gradually optimized through the selection, crossover and mutation operations in the genetic algorithm. Specifically, in the selection operation, a roulette selection strategy is adopted to select the target individual to enter the next generation; in the crossover operation, a single-point crossover method is used to cross the PWM combination of two parent individuals to generate a new child individual; in the mutation operation, a dynamic mutation rate adjustment mechanism is used to introduce random disturbances to increase the diversity of the population; The output module is used to output a target PWM combination when all light color indicators meet preset requirements, and the target PWM combination is used to control the LED lamp.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the LED multi-light color control method based on a multi-objective evolutionary algorithm as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the LED multi-light color control method based on the multi-objective evolutionary algorithm as described in any one of claims 1 to 7 is implemented.

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