A method and system for controlling an LED lighting component driving IC and a driving IC

Abstract

The present invention belongs to the field of display technology. The present invention discloses a method, a system and a driving IC for controlling an LED light-emitting component, including receiving an external control signal; preprocessing the received external control signal to obtain a standardized digital control signal; collecting the working state data of the LED light-emitting component; preprocessing the working state data of the LED light-emitting component to obtain a working state feature data set; training and obtaining a strategy optimization model based on the standardized digital control signal and the working state feature data set, and predicting to obtain a driving IC adjustment signal; adjusting the working state of the LED light-emitting component according to the driving IC adjustment signal; monitoring the real-time light intensity output of the LED light-emitting component through a light sensor, comparing the real-time light intensity output with a preset light intensity output, and judging whether a deviation occurs; which helps to extend the service life of the LED light-emitting component and the performance of the overall system.

Description

Technical Field

[0001] The present invention relates to the field of display technologies, and more specifically, to a method and system for controlling an LED light-emitting component driving IC and a driving IC. Background Art

[0002] The patent with the application publication number CN105405412A discloses a backlight driving control method and system, which is applied to a display terminal having a liquid crystal display screen. The display terminal includes an MCU and a backlight source. The backlight source includes a plurality of light bars formed by LED lights and a plurality of driving ICs. Each driving IC controls one or more light bars. The method includes the steps of: when the MCU receives the electrical signals of the respective LED lights, demodulating the electrical signals of the respective LED lights to obtain the brightness values of the respective LED lights; comparing the brightness values of the respective LED lights in each driving IC to obtain the maximum brightness value of the LED lights in each driving IC; dividing the maximum brightness value of the LED lights in each driving IC into several brightness intervals, determining the current values corresponding to the respective brightness intervals, and controlling the brightness of the LED lights in the driving IC according to the current values. The present invention improves the energy-saving effect of Local Dimming driving and the contrast of the screen display.

[0003] The existing methods, systems and driving ICs for controlling LED light-emitting component driving ICs have the following main problems:

[0004] The working state data of the LED light-emitting component is not divided into multiple signal channels according to the data type. Different types of data may interfere with each other, resulting in difficult data parsing and inability to accurately reflect the actual working state of the LED light-emitting component; without wavelet decomposition, the main trend and rapid changes or detailed information of the data cannot be accurately extracted, which limits the in-depth understanding of the data characteristics and may lead to inaccurate analysis of the performance of the LED light-emitting component; without using covariance matrix and eigenvalue decomposition for multi-channel joint analysis, the cross-channel correlation between signal channels cannot be captured, so multi-channel joint denoising cannot be achieved, which may lead to loss of important information during processing or retention of too much noise; without considering using a soft threshold processing method to threshold the eigenvalue matrix, the noise characteristics cannot be effectively removed while retaining the important features; without combining and reconstructing the denoised low-frequency approximation coefficient matrix and high-frequency detail coefficient matrix through inverse wavelet transform, a clearer and more accurate working state feature data set cannot be restored;

[0005] The lack of utilization of the bacterial foraging optimization algorithm may lead to inefficiency in exploring and optimizing the driving IC regulation signal configuration. Traditional methods may require longer computing time and higher computing resources to find a solution close to the optimal one. Without an adjustment mechanism for the adaptive step size formula, the algorithm may prematurely fall into a local optimal solution and be unable to continue exploring the globally better solution, resulting in limited optimization of the driving IC regulation signal configuration and the inability to achieve the best light intensity output of the LED lighting component. Without a mechanism to gradually reduce the step size, the algorithm may not be able to finely search the solution space, leading to insufficient search accuracy, affecting the precise control of the light intensity output of the LED lighting component and reducing the system stability and reliability. Without the support of the adjustment factor limit formula and the random walk formula, the algorithm may not be able to effectively balance the requirements of the exploration and exploitation phases, resulting in the inability to quickly traverse the solution space in the initial stage of the search and the inability to finely search for potential optimal solutions in the later stage of the search.

[0006] In view of this, the present invention proposes a method, a system and a driving IC for controlling a LED lighting component to solve the above problems. Summary of the Invention

[0007] To overcome the above defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A method for controlling a driving IC of a LED lighting component, comprising:

[0008] S1. Receiving an external control signal;

[0009] S2. Preprocessing the received external control signal to obtain a standardized digital control signal;

[0010] S3. Collecting the working state data of the LED lighting component; preprocessing the working state data of the LED lighting component to obtain a working state feature data set;

[0011] S4. Training and obtaining a strategy optimization model based on the standardized digital control signal and the working state feature data set, predicting to obtain a driving IC regulation signal; adjusting the working state of the LED lighting component according to the driving IC regulation signal;

[0012] S5. Monitoring the real-time light intensity output of the LED lighting component through a light sensor, comparing the real-time light intensity output with a preset light intensity output to determine whether a deviation occurs; if a deviation occurs, calculating the light intensity deviation amount, and further optimizing the driving IC regulation signal based on the light intensity deviation amount until the light intensity output of the LED lighting component reaches the optimum.

[0013] Further, the external control signal includes a brightness control signal, a color temperature control signal, a working mode control signal, a current control signal, a voltage control signal, an environment monitoring signal and a safety control signal;

[0014] The working state data of the LED lighting component includes electrical parameter data, temperature parameter data, optical parameter data, and operating time; the electrical parameter data includes current and voltage; the temperature parameter data includes the temperature of the LED lighting component and the temperature of the driving IC; the optical parameter data includes brightness and color temperature.

[0015] Further, the method for preprocessing the received external control signal to obtain a standardized digital control signal includes:

[0016] Using an analog-to-digital converter to convert the received external control signal into a digital signal, and performing normalization processing to normalize it to a unified digital range; using a low-pass filter to denoise the normalized digital signal to obtain a denoised digital signal; decoding the denoised digital signal into a working mode instruction, mapping the obtained working mode instruction into control parameters, and further obtaining a standardized digital control signal.

[0017] Further, the method for preprocessing the working state data of the LED lighting component to obtain a working state feature data set includes:

[0018] Dividing the working state data of the LED lighting component into N signal channels according to different data types, performing wavelet decomposition on each signal channel to obtain low-frequency approximation coefficients and high-frequency detail coefficients; through covariance matrix and eigenvalue decomposition, jointly analyzing the working state data of the LED lighting component in N signal channels, capturing the cross-channel correlation between the N signal channels, and performing multi-channel joint denoising; through inverse wavelet transform, combining and reconstructing the denoised low-frequency approximation coefficient matrix and high-frequency detail coefficient matrix to further obtain a working state feature data set; the specific method steps are as follows:

[0019] S41. Divide the working state data of the LED lighting component into N signal channels according to different data types, and each signal channel contains a set of independent time series signals; perform timestamp alignment and standard normalization processing on the independent time series signals contained in the N signal channels, and integrate them into a time series vector containing N signal channels; for the independent time series signals contained in each signal channel, use discrete wavelet transform to perform wavelet decomposition, and decompose it layer by layer into low-frequency approximation coefficients and high-frequency detail coefficients;

[0020] S42. Collect the high-frequency detail coefficients obtained by wavelet decomposition of each layer in the N signal channels, and construct a high-frequency detail transform matrix for inter-channel joint analysis; the high-frequency detail transform matrix is ; where is the high-frequency detail coefficient matrix obtained by wavelet decomposition of the first signal channel in the th layer; is the The high-frequency detail coefficient matrix obtained by performing wavelet decomposition on the th layer of the th signal channel; The high-frequency detail coefficient matrix obtained by performing wavelet decomposition on the th layer of the

[0021] S43. Calculate the covariance matrix of the high-frequency detail coefficient matrix for each layer ; where is the number of high-frequency detail coefficients in the high-frequency detail coefficient matrix of the th layer; is the transposed matrix of the high-frequency detail coefficient matrix ; is the transpose operation symbol; perform eigenvalue decomposition on the covariance matrix of the high-frequency detail coefficient matrix obtained for each layer ; the covariance matrix after eigenvalue decomposition is ; where is the covariance matrix after eigenvalue decomposition; is the eigenvector matrix of the covariance matrix ; is the eigenvalue matrix of the covariance matrix ; is the transposed matrix of the eigenvector matrix of the covariance matrix ;

[0022] S44. Adopt a soft-threshold processing method to perform thresholding on the eigenvalue matrix ; preset the threshold , and set to zero the part of the eigenvalue matrix where the eigenvalues are less than the preset threshold to obtain a new eigenvalue matrix ; the thresholding formula is ; where is the th eigenvalue after thresholding; is the sign function, indicating the positive or negative nature of the eigenvalue , ; is the eigenvalue that retains , and sets the eigenvalue of to zero; is the preset threshold, obtained through the threshold adjustment formula; the threshold adjustment formula is ; where is the standard deviation of the noise; log is the logarithm function symbol;

[0023] S45. Through the new eigenvalue matrix , obtain the denoised covariance matrix ; Based on the denoised covariance matrix , use the reconstruction formula to calculate and obtain the denoised high-frequency detail coefficient matrix; the reconstruction formula is ; where is to take the square root of each term one by one;

[0024] S46. Collect the low-frequency approximation coefficients obtained by wavelet decomposition of N signal channels at each layer, construct a low-frequency approximation transformation matrix for joint analysis between channels, and repeat steps S42 - S45 to further obtain the denoised low-frequency approximation coefficient matrix;

[0025] S47. Through inverse wavelet transform, combine and reconstruct the denoised low-frequency approximation coefficient matrix and high-frequency detail coefficient matrix to further obtain the working state feature dataset.

[0026] Further, the training method of the policy optimization model includes:

[0027] Divide the dataset into a training set, a validation set, and a test set; the dataset includes historical standardized digital control signals, the working state feature dataset, and the corresponding drive IC adjustment signals; construct a policy optimization model, which includes an input layer, a hidden layer, and an output layer; the hidden layer uses the ReLU activation function; the input layer of the model is used to input the historical standardized digital control signals and the working state feature dataset; the output layer of the model is used to output the drive IC adjustment signal, and a prediction value is output through a neuron; the policy optimization model is a fully connected neural network model;

[0028] Use the mean absolute error as the loss function to measure the error between the predicted value and the actual value of the model;

[0029] Use the training set data to train the model, and use the Adam optimizer to minimize the loss function; use the validation set to evaluate the performance of the model, and tune the hyperparameters of the model until the model performance no longer improves or reaches the preset stop condition;

[0030] Use the test set to evaluate the performance of the model in the prediction task, and input the current standardized digital control signal and the working state feature dataset into the trained policy optimization model to obtain the drive IC adjustment signal.

[0031] Further, the method for adjusting the working state of the LED lighting component according to the drive IC adjustment signal includes:

[0032] The driving IC adjusts the driving IC adjustment signal predicted by the policy optimization model. The driving IC adjustment signal includes the current, voltage, and PWM signal adjustment parameters required by the LED lighting component; converts the driving IC adjustment signal into an actual output signal, and adjusts the working state of the LED lighting component according to the actual output signal.

[0033] Further, the method for comparing the real-time light intensity output with the preset light intensity output and determining whether there is a deviation includes:

[0034] If the real-time light intensity output is greater than or less than the preset light intensity output, it is determined that there is a deviation;

[0035] If the real-time light intensity output is equal to the preset light intensity output, it is determined that there is no deviation.

[0036] Further, the method for calculating the light intensity deviation amount if there is a deviation and further optimizing the driving IC adjustment signal based on the light intensity deviation amount until the light intensity output of the LED lighting component reaches the optimum includes:

[0037] Further optimize the driving IC adjustment signal based on the light intensity deviation amount through the bacterial foraging optimization algorithm until the light intensity output of the LED lighting component reaches the optimum; the specific method steps are as follows:

[0038] S81. Calculate the light intensity deviation amount between the real-time light intensity output and the preset light intensity output of the LED lighting component through the light intensity deviation amount calculation formula; the light intensity deviation amount calculation formula is: ; where is the preset light intensity output; is the real-time light intensity output;

[0039] S82. Further optimize the driving IC adjustment signal based on the light intensity deviation amount through the bacterial foraging optimization algorithm until the light intensity output of the LED lighting component reaches the optimum; initialize the bacterial positions, and each bacterium represents a driving IC adjustment signal configuration; preset the population size as and represent the position of each bacterium with a vector, denoted as ; where is the position signal of the th bacterium; is the index of the bacterium; the position of each bacterium corresponds to a driving IC adjustment signal configuration, and the real-time light intensity output of the LED is calculated through this configuration, and the light intensity deviation amount is further calculated: ;

[0040] S83. Define a fitness function to evaluate the quality of each bacterium to minimize the light intensity deviation amount; the fitness function is: ; among them, is the fitness value of the th bacterium;

[0041] S84. Bacteria explore the solution space by swimming and update their positions according to the current fitness value and the states of neighboring bacteria through the swimming update formula to find a better solution; the swimming update formula is: ; among them, is the new position of the th bacterium; is the old position of the th bacterium; is the step size factor for controlling the swimming amplitude; is the global optimal position, that is, the driving IC adjustment signal configuration corresponding to the current optimal solution; is a random factor between ;

[0042] S85. Dynamically adjust the step size factor for controlling the swimming amplitude through the adaptive step size formula; the adaptive step size formula is: ; among them, is the step size factor after dynamic adjustment; is the light intensity deviation of the current optimal solution; is the maximum light intensity deviation; is the adjustment factor for controlling the step size adjustment amplitude;

[0043] S86. Limit the adjustment factor for controlling the step size adjustment amplitude through the adjustment factor limit formula; the adjustment factor limit formula is: ; among them, is the adjusted adjustment factor; is the number of current bacterium positions; is the number of times the current bacterium position is updated; is the bacterium position number influence constant; is the bacterium position update influence constant;

[0044] S87. Simulate the random swimming of bacteria in an unknown area through the random swimming formula; the random swimming formula is: ; among them, is the random swimming step size factor, which is used to control the jumping amplitude of bacteria in space; is the optimal position of the bacterial population;

[0045] S88. After each bacterium updates its position, it recalculates its fitness, that is, calculates the new light intensity deviation amount, and evaluates its new fitness value; by comparing the fitness values, the current optimal solution is updated; if the fitness of a certain bacterium is better, that is, the light intensity deviation amount is smaller, then update the position and fitness of the optimal solution; a minimum threshold of the light intensity deviation amount is preset. When the light intensity deviation amount reaches the minimum threshold of the light intensity deviation amount, the algorithm stops; the finally optimized driving IC adjustment signal configuration is output, that is, the adjustment signal corresponding to the minimum light intensity deviation. At this time, the light intensity output of the LED lighting component reaches the optimal.

[0046] A driving IC control system for an LED lighting component, comprising:

[0047] A signal receiving module, configured to receive an external control signal;

[0048] A signal processing module, configured to preprocess the received external control signal to obtain a standardized digital control signal;

[0049] A data acquisition and processing module, configured to acquire the working state data of the LED lighting component; preprocess the working state data of the LED lighting component to obtain a working state feature data set;

[0050] An optimization strategy module, training a strategy optimization model based on the standardized digital control signal and the working state feature data set, predicting a driving IC adjustment signal; adjusting the working state of the LED lighting component according to the driving IC adjustment signal;

[0051] A feedback adjustment module, monitoring the real-time light intensity output of the LED lighting component through a light sensor, comparing the real-time light intensity output with a preset light intensity output to determine whether there is a deviation; if there is a deviation, calculate the light intensity deviation amount, and further optimize the driving IC adjustment signal based on the light intensity deviation amount until the light intensity output of the LED lighting component reaches the optimal.

[0052] A driving IC, the driving IC is located on the LED lighting component, and a circuit for implementing a control method of a driving IC for an LED lighting component is built-in, capable of receiving a driving IC adjustment signal from a driving IC control system for an LED lighting component, converting the driving IC adjustment signal into an actual output signal, and adjusting the working state of the LED lighting component according to the actual output signal; when the actual output signal is executed, a control method of a driving IC for an LED lighting component is implemented.

[0053] The present invention provides a control method, a system and a driving IC for an LED lighting component, having the following beneficial effects:

[0054] The working state data of the LED light-emitting component is divided into multiple signal channels according to the data type, enabling different types of data to be processed independently and avoiding interference between data types. Through wavelet decomposition, the main trend (low-frequency approximation coefficient) and fast-changing or detailed information (high-frequency detail coefficient) of the working state data of the LED light-emitting component can be accurately extracted, which helps to understand the data characteristics more deeply. Multichannel joint analysis is carried out using the covariance matrix and eigenvalue decomposition to capture the cross-channel correlation between signal channels, and then multichannel joint denoising is achieved. It is more effective than single-channel denoising, can retain useful information more accurately, and remove noise. The eigenvalue matrix is thresholded using the soft thresholding method, which can effectively remove noise features while retaining important features, improving the signal-to-noise ratio of the data. By combining and reconstructing the denoised low-frequency approximation coefficient matrix and high-frequency detail coefficient matrix through inverse wavelet transform, a clearer and more accurate working state feature data set can be restored.

[0055] Using the bacterial foraging optimization algorithm, by simulating the swimming and foraging behaviors of bacteria in the solution space, the configuration of the driving IC adjustment signal can be explored and optimized quickly and efficiently. An adaptive step size formula is introduced, which can dynamically adjust the size of the step size factor according to the light intensity deviation amount of the current optimal solution, the maximum light intensity deviation amount, and the adjustment factor of the control step size adjustment amplitude. It helps to quickly traverse the solution space in the initial stage of the search and avoid premature convergence to local optimal solutions. In the later stage of the search, it can gradually reduce the step size and improve the accuracy of local search. Through the adjustment factor limit formula and the random walk formula, the method can balance the requirements of the exploration and exploitation stages. By continuously optimizing the configuration of the driving IC adjustment signal, the light intensity output of the LED light-emitting component can be precisely controlled to reach the preset optimal value. By optimizing the configuration of the driving IC adjustment signal, the light intensity deviation amount of the LED light-emitting component can be reduced, improving the stability and reliability of the system. It helps to extend the service life of the LED light-emitting component, reduce the maintenance cost of the system, and improve the performance of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the method flow of an LED light-emitting component driving IC control method, system, and driving IC of the present invention;

[0057] Figure 2 Schematic diagram of the storage system structure of an LED light-emitting component driving IC control method, system, and driving IC of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1

[0060] Please refer to Figure 1 , in this embodiment, a method for controlling an LED light-emitting component driving IC includes:

[0061] S1. Receive an external control signal;

[0062] S2. Preprocess the received external control signal to obtain a standardized digital control signal;

[0063] S3. Collect the working state data of the LED light-emitting component; preprocess the working state data of the LED light-emitting component to obtain a working state feature data set;

[0064] S4. Train and obtain a strategy optimization model based on the standardized digital control signal and the working state feature data set, and predict to obtain a driving IC adjustment signal; adjust the working state of the LED light-emitting component according to the driving IC adjustment signal;

[0065] S5. Monitor the real-time light intensity output of the LED light-emitting component through a light sensor, compare the real-time light intensity output with the preset light intensity output to determine whether there is a deviation; if there is a deviation, calculate the light intensity deviation amount, and further optimize the driving IC adjustment signal based on the light intensity deviation amount until the light intensity output of the LED light-emitting component reaches the optimum.

[0066] The external control signal includes a brightness control signal, a color temperature control signal, a working mode control signal, a current control signal, a voltage control signal, an environmental monitoring signal, and a safety control signal;

[0067] The working state data of the LED light-emitting component includes electrical parameter data, temperature parameter data, optical parameter data, and operating time; the electrical parameter data includes current and voltage; the temperature parameter data includes the LED light-emitting component temperature and the driving IC temperature; the optical parameter data includes brightness and color temperature.

[0068] The working state data of the LED light-emitting component is collected through an embedded sensor, specifically including a current sensor, a voltage sensor, a temperature sensor, and a light sensor.

[0069] The method for preprocessing the received external control signal to obtain a standardized digital control signal includes:

[0070] The received external control signal is converted into a digital signal using an analog-to-digital converter, and is normalized to a unified digital range through normalization processing; the normalized digital signal is denoised using a low-pass filter to obtain a denoised digital signal; the denoised digital signal is decoded into a working mode instruction, and the obtained working mode instruction is mapped into control parameters such as brightness level, color temperature setting, current magnitude, etc., and then a standardized digital control signal is obtained.

[0071] The method for preprocessing the working state data of the LED lighting component to obtain a working state feature data set includes:

[0072] The working state data of the LED lighting component is divided into N signal channels according to different data types, and wavelet decomposition is performed on each signal channel to obtain low-frequency approximation coefficients and high-frequency detail coefficients; through covariance matrix and eigenvalue decomposition, the working state data of the LED lighting component in N signal channels is jointly analyzed to capture the cross-channel correlation between the N signal channels and perform multi-channel joint denoising; the denoised low-frequency approximation coefficient matrix and high-frequency detail coefficient matrix are combined and reconstructed through inverse wavelet transform, and then a working state feature data set is obtained; the specific method steps are:

[0073] S41. The working state data of the LED lighting component is divided into N signal channels according to different data types, and each signal channel contains a set of independent time series signals; time stamp alignment and standard normalization processing are performed on the independent time series signals included in the N signal channels, and they are integrated into a time series vector containing N signal channels; the time series vector is ; where is the working state data of the LED lighting component at the th moment in the th signal channel; is the working state data of the LED lighting component at the th moment in the th channel; and are the indexes of the signal channel types, ; is the index of the moment; for the independent time series signals included in each signal channel, discrete wavelet transform is used for wavelet decomposition, and it is decomposed layer by layer into low-frequency approximation coefficients and high-frequency detail coefficients;

[0074] The low-frequency approximation coefficients reflect the main trends and large-scale features of the operating state data of the LED lighting components; the high-frequency detail coefficients reflect the rapid changes or detailed information of the operating state data of the LED lighting components; the decomposition level represents the number of repeated decompositions. The more the number of decomposition levels, the narrower the frequency band into which the independent time series signal is segmented, and the higher the resolution.

[0075] S42. Collect the high-frequency detail coefficients obtained by wavelet decomposition of N signal channels at each layer, and construct a high-frequency detail transformation matrix for joint analysis between channels; the high-frequency detail transformation matrix is ; where is the high-frequency detail coefficient matrix obtained by wavelet decomposition of the first signal channel at the th layer; is the high-frequency detail coefficient matrix obtained by wavelet decomposition of the th signal channel at the th layer; is the high-frequency detail coefficient matrix obtained by wavelet decomposition of the th signal channel at the th layer;

[0076] S43. Calculate the covariance matrix of the high-frequency detail coefficient matrix at each layer to describe the correlation between high-frequency coefficients; ; where is the number of high-frequency detail coefficients in the high-frequency detail coefficient matrix at the th layer; is the transpose matrix of the high-frequency detail coefficient matrix ; is the transpose operation symbol; perform eigenvalue decomposition on the covariance matrix of the high-frequency detail coefficient matrix obtained at each layer; the covariance matrix after eigenvalue decomposition is ; where is the covariance matrix after eigenvalue decomposition; is the eigenvector matrix of the covariance matrix ; is the eigenvalue matrix of the covariance matrix , usually a diagonal matrix, and the corresponding eigenvalues are arranged in descending order on the diagonal; is the transpose matrix of the eigenvector matrix of the covariance matrix , used to reconstruct the result of eigenvalue decomposition back to the original space;

[0077] S44. Adopt a soft-threshold processing method to perform thresholding on the eigenvalue matrix ; preset a threshold , and make the eigenvalues in the eigenvalue matrix less than the preset threshold Set the corresponding part to zero to obtain a new eigenvalue matrix ; The thresholding formula is ; where is the -th eigenvalue after thresholding; is the sign function, indicating the positive or negative nature of the eigenvalue ; ; is the eigenvalue that retains , and set the eigenvalue of to zero; is the preset threshold, which is obtained through the threshold adjustment formula; The threshold adjustment formula is ; where is the standard deviation of the noise; log is the symbol of the logarithmic function;

[0078] S45. Through the new eigenvalue matrix , obtain the denoised covariance matrix ; Based on the denoised covariance matrix , use the reconstruction formula to calculate and obtain the denoised high-frequency detail coefficient matrix; The reconstruction formula is ; where is to take the square root of each term of ;

[0079] S46. Collect the low-frequency approximation coefficients obtained by wavelet decomposition of N signal channels at each layer, construct a low-frequency approximation transformation matrix for inter-channel joint analysis, and repeat steps S42 - S45 to obtain the denoised low-frequency approximation coefficient matrix;

[0080] S47. Through inverse wavelet transform, combine and reconstruct the denoised low-frequency approximation coefficient matrix and high-frequency detail coefficient matrix to obtain the working state feature dataset.

[0081] The training method of the strategy optimization model includes:

[0082] Divide the dataset into a training set, a validation set, and a test set; The dataset includes historical standardized digital control signals, the working state feature dataset, and the corresponding drive IC adjustment signals; Construct a strategy optimization model, which includes an input layer, a hidden layer, and an output layer; The hidden layer uses the ReLU activation function; The input layer of the model is used to input the historical standardized digital control signals and the working state feature dataset; The output layer of the model is used to output the drive IC adjustment signals, and a single neuron is used to output the predicted value; The strategy optimization model is a fully connected neural network model;

[0083] Use the mean absolute error as the loss function to measure the error between the predicted value and the actual value of the model;

[0084] Use the training set data for model training and minimize the loss function through the Adam optimizer; use the validation set to evaluate the performance of the model, tune the hyperparameters of the model until the model performance no longer improves or reaches the preset stopping condition, and then stop;

[0085] Use the test set to evaluate the performance of the model in the prediction task. Input the current normalized digital control signal and the working state feature data set into the trained policy optimization model to obtain the driving IC adjustment signal.

[0086] The method for adjusting the working state of the LED lighting component according to the driving IC adjustment signal includes:

[0087] The driving IC drives according to the driving IC adjustment signal predicted by the policy optimization model. The driving IC adjustment signal includes the current, voltage, and PWM signal adjustment parameters required by the LED lighting component; convert the driving IC adjustment signal into an actual output signal, and adjust the working state of the LED lighting component according to the actual output signal.

[0088] The method for comparing the real-time light intensity output with the preset light intensity output to determine whether there is a deviation includes:

[0089] If the real-time light intensity output is greater than or less than the preset light intensity output, it is determined that a deviation occurs;

[0090] If the real-time light intensity output is equal to the preset light intensity output, it is determined that no deviation occurs;

[0091] The preset light intensity output is set by the staff. Collect different light intensity outputs through the driving IC control terminal, and take the average value of multiple light intensity outputs as the preset light intensity output; similarly, set the minimum threshold of the preset light intensity deviation amount.

[0092] If a deviation occurs, calculate the light intensity deviation amount, and further optimize the driving IC adjustment signal based on the light intensity deviation amount until the light intensity output of the LED lighting component reaches the optimal. The method includes:

[0093] Further optimize the driving IC adjustment signal based on the light intensity deviation amount through the bacterial foraging optimization algorithm until the light intensity output of the LED lighting component reaches the optimal; the specific method steps are:

[0094] S81. Calculate the light intensity deviation amount between the real-time light intensity output and the preset light intensity output of the LED lighting component through the light intensity deviation amount calculation formula; the light intensity deviation amount calculation formula is: ; where, is the preset light intensity output; is the real-time light intensity output;

[0095] S82. Further optimize the driving IC adjustment signal based on the light intensity deviation through the bacterial foraging optimization algorithm until the light intensity output of the LED lighting component reaches the optimum; initialize the bacterial positions, where each bacterium represents a driving IC adjustment signal configuration; preset the population size as , and represent the position of each bacterium with a vector, denoted as ; where is the position signal of the -th bacterium; is the index of the bacterium; the position of each bacterium corresponds to a driving IC adjustment signal configuration, and the real-time light intensity output of the LED is calculated through this configuration, and further calculate the light intensity deviation : ;

[0096] S83. Define a fitness function to evaluate the quality of each bacterium to minimize the light intensity deviation; the fitness function is: ; where is the fitness value of the -th bacterium;

[0097] S84. The bacteria explore the solution space by swimming, and update their positions according to the current fitness value and the states of neighboring bacteria through the swimming update formula to find a better solution; the swimming update formula is: ; where is the new position of the -th bacterium; is the old position of the -th bacterium; is the step size factor that controls the swimming amplitude; is the global optimal position, that is, the driving IC adjustment signal configuration corresponding to the current optimal solution; is a random factor between ;

[0098] S85. Dynamically adjust the step size factor that controls the swimming amplitude through the adaptive step size formula; the adaptive step size formula is: ; where is the step size factor after dynamic adjustment; is the light intensity deviation of the current optimal solution; is the maximum light intensity deviation; is the adjustment factor that controls the step size adjustment amplitude;

[0099] S86. Limit the adjustment factor that controls the step size adjustment amplitude through the adjustment factor limit formula; the adjustment factor limit formula is: ; where is the adjusted adjustment factor; is the number of current bacterial positions; is the number of times the current bacterial position is updated; is the bacterial position quantity influence constant, used to balance the influence degree of the number of current bacterial positions on ; is the bacterial position update influence constant, used to balance the influence degree of the number of current bacterial position updates on ;

[0100] The increase in the number of bacterial positions and the increase in the number of bacterial updates mean that the search process gradually deepens and the exploration accuracy gradually improves. Therefore, the step size should gradually decrease as these parameters increase to avoid the large step size "skipping" the potential solution space in the later stage.

[0101] Each term in the formula ( and ) decreases as the number of bacteria and the number of updates increase, which represents the gradual convergence and refinement of the step size, conforming to the common "progressive convergence" characteristic in optimization algorithms.

[0102] In heuristic or swarm optimization algorithms, a larger step size is required in the exploration stage (initial stage) to quickly traverse the solution space and avoid premature convergence to local optimal solutions; while in the exploitation stage (later stage), a smaller step size is needed to finely search the solution space, improve accuracy, and avoid missing potential optimal solutions.

[0103] Through ( and ) in the formula, the step size is larger in the initial stage (when and are smaller), and gradually decreases as the evolution process proceeds. This dynamic adjustment fits well with the strategy of balancing exploration and exploitation in the optimization process.

[0104] A larger initial step size allows bacteria to quickly move around in the solution space and avoid premature convergence. For example, when the number of bacterial positions is small ( is small), the number of updates is small, and the step size is large, enabling extensive exploration of the solution space.

[0105] Exploitation stage: As the evolution progresses, the step size decreases, and bacteria will gradually concentrate in better regions and search for more precise solutions through small step size adjustments. For example, when increases to a certain extent, the step size will decrease due to ( ), making the search more refined.

[0106] Constants and The influence degrees of the number of bacteria and the number of updates on the step size are controlled. This enables the algorithm to flexibly adjust the rate at which the step size changes with the number of bacteria and the number of updates, and it can be optimized according to the needs of the actual problem.

[0107] For example, if it is desired that the step size decreases more rapidly when the number of bacteria increases, then increase ; if it is desired that the step size decreases more rapidly with the increase in the number of updates, then increase . This provides degrees of freedom for the adjustment of the optimization algorithm, ensuring adaptation to different environments and requirements.

[0108] As and increase, each term in the formula will gradually approach 0, thereby causing the step size factor to gradually approach 0. This naturally limits the size of the step size, ensuring that the step size will not be too large in the later stage of the algorithm, and thus guaranteeing the fineness of the search. As the number of iterations increases or the search process deepens, the step size is gradually reduced to improve the accuracy of local search.

[0109] For example, the adjustment factor controlling the step size adjustment amplitude is 0.8, the current number of bacteria positions is 30, the current number of updates of the bacteria positions is 20, the influence constant of the number of bacteria positions is 0.02, and the influence constant of the update of the bacteria positions is 0.01. Then the restricted adjustment factor .

[0110] S87. Simulate the random movement of bacteria in the unknown area through the random walk formula; the random walk formula is: ; where is the random walk step size factor, used to control the jump amplitude of bacteria in space; is the optimal position of the bacterial population;

[0111] S88. After each bacterium updates its position, recalculate its fitness, that is, calculate the new light intensity deviation amount and evaluate its new fitness value; by comparing the fitness values, update the current optimal solution; if the fitness of a certain bacterium is better, that is, the light intensity deviation amount is smaller, then update the position and fitness of the optimal solution; preset the minimum threshold of the light intensity deviation amount, and stop the algorithm when the light intensity deviation amount reaches the minimum threshold of the light intensity deviation amount; output the finally optimized driving IC adjustment signal configuration, that is, the adjustment signal corresponding to the minimum light intensity deviation, and at this time, the light intensity output of the LED lighting component reaches the optimal.

[0112] A method for controlling a driving IC of an LED lighting component, including:

[0113] A signal receiving module, used to receive an external control signal;

[0114] A signal processing module for preprocessing the received external control signal to obtain a standardized digital control signal;

[0115] A data acquisition and processing module for acquiring the working state data of the LED light-emitting component; preprocessing the working state data of the LED light-emitting component to obtain a working state feature data set;

[0116] An optimization strategy module that trains and obtains a strategy optimization model based on the standardized digital control signal and the working state feature data set, and predicts a driving IC adjustment signal; adjusts the working state of the LED light-emitting component according to the driving IC adjustment signal;

[0117] A feedback adjustment module monitors the real-time light intensity output of the LED light-emitting component through a light sensor, compares the real-time light intensity output with the preset light intensity output to determine whether there is a deviation; if there is a deviation, calculates the light intensity deviation amount, and further optimizes the driving IC adjustment signal based on the light intensity deviation amount until the light intensity output of the LED light-emitting component reaches the optimum.

[0118] The driving IC is located on the LED light-emitting component and incorporates a circuit for implementing a method for controlling a driving IC of an LED light-emitting component. It can receive the driving IC adjustment signal from a driving IC control system of an LED light-emitting component, convert the driving IC adjustment signal into an actual output signal, and adjust the working state of the LED light-emitting component according to the actual output signal; when the actual output signal is executed, a method for controlling a driving IC of an LED light-emitting component is implemented.

[0119] In this embodiment, the working state data of the LED light-emitting component is divided into multiple signal channels according to the data type, enabling different types of data to be processed independently and avoiding interference between data types; through wavelet decomposition, the main trend (low-frequency approximation coefficient) and fast-changing or detailed information (high-frequency detail coefficient) of the working state data of the LED light-emitting component can be accurately extracted, which helps to understand the data characteristics more deeply; multi-channel joint analysis is performed using the covariance matrix and eigenvalue decomposition to capture the cross-channel correlation between signal channels, thereby realizing multi-channel joint denoising; it is more effective than single-channel denoising, can more accurately retain useful information and remove noise; the eigenvalue matrix is thresholded using a soft thresholding method, which can effectively remove noise features while retaining important features, improving the signal-to-noise ratio of the data; by combining and reconstructing the denoised low-frequency approximation coefficient matrix and high-frequency detail coefficient matrix through inverse wavelet transform, a clearer and more accurate working state feature data set can be restored;

[0120] Using the bacterial foraging optimization algorithm, by simulating the swimming and foraging behaviors of bacteria in the solution space, it can quickly and efficiently explore and optimize the configuration of the driving IC adjustment signal; an adaptive step size formula is introduced, which can dynamically adjust the size of the step size factor according to the light intensity deviation of the current optimal solution, the maximum light intensity deviation, and the adjustment factor for controlling the step size adjustment amplitude; it helps to quickly traverse the solution space in the initial stage of the search and avoid premature convergence to local optimal solutions; in the later stage of the search, it can gradually reduce the step size and improve the accuracy of local search; through the adjustment factor limit formula and the random walk formula, the method can balance the requirements of the exploration and exploitation stages; by continuously optimizing the configuration of the driving IC adjustment signal, it can precisely control the light intensity output of the LED lighting component to reach the preset optimal value; by optimizing the configuration of the driving IC adjustment signal, it can reduce the light intensity deviation of the LED lighting component and improve the stability and reliability of the system; it helps to extend the service life of the LED lighting component, reduce the maintenance cost of the system, and improve the performance of the overall system.

[0121] Embodiment 2

[0122] Please refer to Figure 2 as shown. For the parts not described in detail in this embodiment, refer to the description in Embodiment 1. A driving IC control system for an LED lighting component is provided, including:

[0123] A signal receiving module for receiving external control signals;

[0124] A signal processing module for preprocessing the received external control signals to obtain standardized digital control signals;

[0125] A data acquisition and processing module for acquiring the working state data of the LED lighting component; preprocessing the working state data of the LED lighting component to obtain a working state feature data set;

[0126] An optimization strategy module for training a strategy optimization model based on the standardized digital control signals and the working state feature data set, predicting the driving IC adjustment signal; adjusting the working state of the LED lighting component according to the driving IC adjustment signal;

[0127] A feedback adjustment module for monitoring the real-time light intensity output of the LED lighting component through a light sensor, comparing the real-time light intensity output with the preset light intensity output to determine whether there is a deviation; if there is a deviation, calculating the light intensity deviation, and further optimizing the driving IC adjustment signal based on the light intensity deviation until the light intensity output of the LED lighting component reaches the optimum.

[0128] Those of ordinary skill in the art will realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0129] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0130] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0131] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included in the protection scope of the present invention.

Claims

1. A method for controlling an LED light emitting component driver IC, characterized in that: include: S1, receiving external control signal; S2, preprocessing the received external control signal to obtain a standardized digital control signal; S3, collecting working status data of LED light-emitting components; The working state data of the LED light-emitting component is preprocessed to obtain a working state feature data set, including: The working state data of the LED light-emitting component is divided into N signal channels according to different data types, and each signal channel is subjected to wavelet decomposition to obtain low-frequency approximate coefficients and high-frequency detail coefficients; the working state data of the LED light-emitting component of the N signal channels are jointly analyzed through covariance matrix and eigenvalue decomposition to capture the cross-channel correlation between the N signal channels and perform multi-channel joint denoising; the denoised low-frequency approximate coefficient matrix and high-frequency detail coefficient matrix are combined and reconstructed through inverse wavelet transform to obtain the working state feature data set; S4. Based on the standardized digital control signal and the working state characteristic data set, the acquisition strategy optimization model is trained to predict the driver IC adjustment signal; and the working state of the LED light-emitting component is adjusted according to the driver IC adjustment signal; S5. Monitor the real-time light intensity output of the LED light-emitting component through a light sensor, compare the real-time light intensity output with the preset light intensity output, and determine whether a deviation occurs; if a deviation occurs, calculate the light intensity deviation, and further optimize the driver IC adjustment signal based on the light intensity deviation until the light intensity output of the LED light-emitting component reaches the optimal value, including: The driver IC adjustment signal is further optimized based on the light intensity deviation through the bacterial foraging optimization algorithm until the light intensity output of the LED light-emitting component reaches the optimal value. The specific method steps are as follows: initialize the bacterial position, each bacterium represents a driver IC adjustment signal configuration; define a fitness function to evaluate the quality of each bacterium to minimize the light intensity deviation; bacteria explore the solution space by swimming, and update the position through the swimming update formula according to the current fitness value and the state of neighboring bacteria to find a better solution; After updating the position of each bacterium, its fitness is recalculated, that is, the new light intensity deviation is calculated, and its new fitness value is evaluated; by comparing the fitness values, the current optimal solution is updated; if the fitness of a certain bacterium is better, that is, the light intensity deviation is smaller, the position and fitness of the optimal solution are updated; the minimum threshold of the light intensity deviation is preset, and the algorithm is stopped when the light intensity deviation ΔI reaches the minimum threshold of the light intensity deviation; the final optimized driver IC adjustment signal configuration is output, that is, the adjustment signal corresponding to the minimum light intensity deviation, at which time the light intensity output of the LED light-emitting component reaches the optimal level.

2. The LED light emitting component driver IC control method according to claim 1, characterized in that: The external control signals include brightness control signals, color temperature control signals, working mode control signals, current control signals, voltage control signals, environment monitoring signals and safety control signals; The working status data of the LED light-emitting component includes electrical parameter data, temperature parameter data, optical parameter data and operating time; the electrical parameter data includes current and voltage; the temperature parameter data includes the LED light-emitting component temperature and the driver IC temperature; the optical parameter data includes brightness and color temperature.

3. The LED light emitting component driver IC control method according to claim 2, characterized in that: The method of preprocessing the received external control signal to obtain a standardized digital control signal includes: The received external control signal is converted into a digital signal using an analog-to-digital converter, and is normalized to a uniform digital range by performing standardization processing; the normalized digital signal is denoised using a low-pass filter to obtain a denoised digital signal; the denoised digital signal is decoded into an operating mode instruction, and the obtained operating mode instruction is mapped into control parameters to obtain a standardized digital control signal.

4. The LED light emitting component driver IC control method according to claim 3, characterized in that: The method of preprocessing the working state data of the LED light-emitting component to obtain a working state feature data set also includes: S41, dividing the working state data of the LED light-emitting component into N signal channels according to different data types, each signal channel containing a group of independent time series signals; performing timestamp alignment and normalization processing on the independent time series signals contained in the N signal channels, and integrating them into a time series vector containing the N signal channels; for the independent time series signals contained in each signal channel, using discrete wavelet transform to perform wavelet decomposition, and decompose them layer by layer into low-frequency approximate coefficients and high-frequency detail coefficients; S42, collect the high-frequency detail coefficients obtained by wavelet decomposition of N signal channels at each layer, and construct a high-frequency detail transformation matrix for joint analysis between channels; the high-frequency detail transformation matrix is in, The high-frequency detail coefficient matrix obtained by wavelet decomposition of the first signal channel at layer L; is the high-frequency detail coefficient matrix obtained by wavelet decomposition of the Cth signal channel at the Lth layer; is the high-frequency detail coefficient matrix obtained by wavelet decomposition of the Nth signal channel at the Lth layer; S43, calculate the covariance matrix of each layer of high-frequency detail coefficient matrix Among them, N' L is the number of high-frequency detail coefficients in the L-th layer high-frequency detail coefficient matrix; (D L ) T is the high frequency detail coefficient matrix D L The transposed matrix; T is the transposed operation symbol; the covariance matrix R of each layer of high-frequency detail coefficient matrix obtained L Perform eigenvalue decomposition; the covariance matrix after eigenvalue decomposition is Among them, R' L is the covariance matrix after eigenvalue decomposition; U L is the covariance matrix R L The eigenvector matrix of L is the covariance matrix R L The eigenvalue matrix of is the covariance matrix R L The transposed matrix of the eigenvector matrix of ; S44, using soft threshold processing method, the eigenvalue matrix Σ L Perform threshold processing; preset threshold θ, and transform the eigenvalue matrix Σ L The part of the eigenvalue less than the preset threshold θ is set to zero, and a new eigenvalue matrix Σ' is obtained. L ; The threshold processing formula is: in, is the i-th eigenvalue after thresholding; is a symbolic function, representing the eigenvalue The positive and negative To keep The characteristic value of The characteristic value of is set to zero; θ is the preset threshold, which is obtained through the threshold adjustment formula; the threshold adjustment formula is where σ N is the standard deviation of the noise; log is the sign of the logarithmic function; S45, through the new eigenvalue matrix Σ' L , get the denoised covariance matrix Based on the denoised covariance matrix R" L , use the reconstruction formula to calculate the high-frequency detail coefficient matrix after denoising; the reconstruction formula is in, For Σ' L Take the square root of each term; S46, collecting low-frequency approximate coefficients obtained by wavelet decomposition of N signal channels at each layer, constructing a low-frequency approximate transformation matrix for joint analysis between channels, repeating steps S42-S45, and then obtaining a denoised low-frequency approximate coefficient matrix; S47. Combining and reconstructing the denoised low-frequency approximate coefficient matrix and the high-frequency detail coefficient matrix through inverse wavelet transform, thereby obtaining a working status feature data set.

5. The LED light emitting component driver IC control method according to claim 4, characterized in that: The training method of the strategy optimization model includes: The data set is divided into a training set, a validation set and a test set; the data set includes a historical standardized digital control signal and a working state feature data set and a corresponding driver IC adjustment signal; a strategy optimization model is constructed, and the strategy optimization model includes an input layer, a hidden layer and an output layer; the hidden layer uses a ReLU activation function; the input layer of the model is used to input the historical standardized digital control signal and the working state feature data set; the output layer of the model is used to output the driver IC adjustment signal, and the predicted value is output through a neuron; the strategy optimization model is a fully connected neural network model; Use mean absolute error as the loss function to measure the error between the model's predicted value and the actual value; Use the training set data to train the model and minimize the loss function using the Adam optimizer. Use the validation set to evaluate the performance of the model and tune the model's hyperparameters until the model performance stops improving or reaches the preset stopping condition. The test set is used to evaluate the performance of the model in the prediction task. The current standardized digital control signal and working state feature data set are input into the trained strategy optimization model to obtain the driver IC adjustment signal.

6. The LED light emitting component driver IC control method according to claim 5, characterized in that: The method for adjusting the working state of the LED light emitting component according to the driving IC adjustment signal includes: The driver IC predicts the driver IC regulation signal through the strategy optimization model, and the driver IC regulation signal includes the current, voltage and PWM signal regulation parameters required by the LED light-emitting component; the driver IC regulation signal is converted into an actual output signal, and the working state of the LED light-emitting component is adjusted according to the actual output signal.

7. The LED light emitting component driver IC control method according to claim 6, characterized in that: The method of comparing the real-time light intensity output with the preset light intensity output to determine whether a deviation occurs includes: If the real-time light intensity output is greater than or less than the preset light intensity output, it is determined that a deviation occurs; If the real-time light intensity output is equal to the preset light intensity output, it is determined that no deviation occurs.

8. The LED light emitting component driver IC control method according to claim 7, characterized in that: If a deviation occurs, the light intensity deviation is calculated, and the driver IC adjustment signal is further optimized based on the light intensity deviation until the light intensity output of the LED light emitting component reaches the optimal value. The method also includes: S81. Calculate the light intensity deviation between the real-time light intensity output of the LED light-emitting component and the preset light intensity output using a light intensity deviation calculation formula; the light intensity deviation calculation formula is: ΔI=|I ta -I ac |; Among them, I ta is the preset light intensity output; I ac It is real-time light intensity output; S82. Further optimize the driver IC adjustment signal based on the light intensity deviation through the bacterial foraging optimization algorithm until the light intensity output of the LED light-emitting component reaches the optimal value; the preset population size is B, and each bacterial position is represented by a vector, denoted as a i' ; Among them, a i' is the position signal of the i'th bacterium; i' is the index of the bacterium; the position a of each bacterium i' Corresponding to a driver IC adjustment signal configuration, the real-time light intensity output I of the LED is calculated through this configuration ac , and further calculate the light intensity deviation ΔI: ΔI=|I ta -I ac (a i' )|; S83, fitness function is: F(a i' )=ΔI=|I ta -I ac (a i' )|; where F(a i' ) is the fitness value of the i'th bacterium; S84, the swimming update formula is: in, is the new position of the i'th bacterium; is the old position of the i'th bacterium; E is the step factor that controls the swimming amplitude; a best is the global optimal position, that is, the driver IC adjustment signal configuration corresponding to the current optimal solution; rand() is a random factor between [0,1]; S85. Dynamically adjust the step length factor for controlling the swimming amplitude through an adaptive step length formula; the adaptive step length formula is: Among them, E' is the dynamically adjusted step size factor; ΔI best is the light intensity deviation of the current optimal solution; ΔI max is the maximum light intensity deviation; α is the adjustment factor for controlling the step adjustment amplitude; S86. Limit the adjustment factor of the control step length adjustment range through the adjustment factor limiting formula; the adjustment factor limiting formula is: α'=α·(1-β1·M re )·(1-β2·G re ), where α' is the adjustment factor after restriction; M re is the number of current bacterial positions; G re is the number of times the current bacterial position is updated; β1 is the influence constant of the number of bacterial positions; β2 is the influence constant of the bacterial position update; S87. Use the random walk formula to simulate the random walk of bacteria in an unknown area; the random walk formula is: Among them, F is the random walk step factor, which is used to control the jumping amplitude of bacteria in space; a gp It is the optimal position for the bacterial colony.

9. An LED light emitting component driver IC control system, used to implement the LED light emitting component driver IC control method according to any one of claims 1 to 8, characterized in that: include: A signal receiving module, used for receiving an external control signal; A signal processing module, used for preprocessing the received external control signal to obtain a standardized digital control signal; A data acquisition and processing module is used to collect working status data of the LED light-emitting component; Preprocessing the working state data of the LED light-emitting component to obtain a working state feature data set; The optimization strategy module trains and acquires the strategy optimization model based on the standardized digital control signal and the working state characteristic data set, and predicts the driver IC adjustment signal; according to the driver IC adjustment signal, the working state of the LED light-emitting component is adjusted; The feedback adjustment module monitors the real-time light intensity output of the LED light-emitting component through a light sensor, compares the real-time light intensity output with the preset light intensity output, and determines whether a deviation occurs; if a deviation occurs, the light intensity deviation amount is calculated, and the driver IC adjustment signal is further optimized based on the light intensity deviation amount until the light intensity output of the LED light-emitting component reaches the optimal level.

10. A driver IC, characterized in that: The driver IC is located on the LED light-emitting component, and has a built-in circuit for implementing a method for controlling a driver IC of an LED light-emitting component. The driver IC can receive a driver IC adjustment signal from a control system of a driver IC of an LED light-emitting component, convert the driver IC adjustment signal into an actual output signal, and adjust the working state of the LED light-emitting component according to the actual output signal; when the actual output signal is executed, the method for controlling the driver IC of an LED light-emitting component described in any one of claims 1 to 8 is implemented.

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