Multi-wavelength spectrum adjustable method and system for LED light source

Abstract

The invention relates to the technical field of light source adjustment, and discloses a multi-wavelength spectrum adjustable method and system for an LED light source, and the method comprises the steps: processing real-time spectrum data according to a self-adaptive filtering algorithm, obtaining precise spectrum features, carrying out the independent component analysis of the precise spectrum features, and carrying out the independent component analysis of the precise spectrum features; the method comprises the following steps: acquiring a single spectral feature corresponding to each light source, determining a contribution coefficient corresponding to the single spectral feature through a least square method, comparing different contribution coefficients according to a minimum value method, determining an overlapping integral, and adjusting real-time spectral data according to the overlapping integral. Therefore, the accuracy and the stability of the spectral data are improved, interference caused by spectral overlapping is avoided, the real-time spectral data are adjusted according to the overlapping integral, spectral output is optimized, more accurate color mixing and a uniform lighting effect are ensured, and the adaptability and the precision of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of light source adjustment, and more specifically, to a multi-wavelength spectrum adjustable method and system for LED light sources. Background Art

[0002] With the rapid development of modern lighting technology, LED (light-emitting diode) has been widely used in various lighting scenarios due to its high efficiency, low energy consumption, long life and environmental protection characteristics. However, traditional LED lighting systems usually use LED modules with fixed wavelengths or limited wavelength combinations. Their fixed spectral output has poor adaptability under different environmental conditions and cannot dynamically adjust the color of light according to the actual use environment.

[0003] For example, the patent application with publication number CN106793326A provides a high color rendering multi-spectrum LED light source, exhibition lighting for museum exhibits, and exhibition and lighting methods. The technical solution includes a variety of different LED light source chips, each of which has different spectral wavelengths and spectral bandwidths. It can achieve precise control of the light intensity of different bands in the lighting source through spectral independent driving technology, multi-spectral light source testing and automatic control systems to meet the needs of different cultural relics.

[0004] In the prior art, although some high-end LED systems achieve a certain degree of color temperature and color change by adjusting the driving current of LEDs of different colors, these systems often rely on a preset spectral characteristic database and lack real-time environmental perception and adaptive adjustment capabilities. This reliance on a preset database not only limits the flexibility of spectral output, but may also produce spectral overlap and interference areas in multi-wavelength LED combinations, thereby affecting the stability and uniformity of the overall spectrum. In addition, spectral overlap will lead to inaccurate color mixing and reduce the accuracy of the lighting effect.

[0005] In view of this, the present invention proposes a multi-wavelength spectrum tunable method and system for LED light source to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a multi-wavelength spectrum tunable method and system for an LED light source.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, a multi-wavelength spectrum tunable method for an LED light source is provided, comprising:

[0009] Acquire real-time spectral data, process it according to the adaptive filtering algorithm, and obtain accurate spectral features;

[0010] Perform independent component analysis on the precise spectral features to obtain a single spectral feature corresponding to each light source, and determine a contribution coefficient corresponding to the single spectral feature by a least squares method, wherein the contribution coefficient represents the weight of each light source in the current spectral output;

[0011] In a preset wavelength range, different contribution coefficients are compared according to a minimum value method to determine an overlap integral, and real-time spectral data is adjusted according to the overlap integral, wherein the overlap integral characterizes the degree of overlap of different spectra in the preset wavelength range.

[0012] In some embodiments, a method for processing real-time spectral data according to an adaptive filtering algorithm to obtain accurate spectral features includes:

[0013] Acquire real-time environmental data, process the real-time spectral data according to an adaptive filtering algorithm to obtain initial spectral features, determine filter estimation parameters in the adaptive filtering algorithm according to the real-time environmental data, correct the initial spectral features according to the filter estimation parameters, and obtain accurate spectral features.

[0014] In some embodiments, a method for determining a filter estimation parameter in an adaptive filtering algorithm according to real-time environmental data includes:

[0015] The real-time environmental data is input into the pre-trained neural network model to obtain the filter estimation parameters, which include signal power spectral density and noise power spectral density.

[0016] In some embodiments, the method of correcting the initial spectral feature according to the filter estimation parameter to obtain the accurate spectral feature includes:

[0017] The frequency response coefficient is calculated according to the filter estimation parameters, and the frequency response coefficient is subjected to inverse Fourier transform to obtain the impulse response function. The initial spectral features are convolved according to the impulse response function to obtain the accurate spectral features.

[0018] In some embodiments, the method of calculating the frequency response coefficient according to the filter estimation parameter includes:

[0019] The sum of the signal power spectrum density and the noise power spectrum density is calculated, and the ratio of the signal power spectrum density to the sum of the signal power spectrum density and the noise power spectrum density is calculated, and the ratio is used as the frequency response coefficient.

[0020] In some embodiments, a method of performing independent component analysis on precise spectral features to obtain a single spectral feature corresponding to each light source includes:

[0021] The precise spectral features are centralized and decomposed using the ICA algorithm to obtain a single spectral feature corresponding to each light source.

[0022] In some embodiments, the method of determining the contribution coefficient corresponding to a single spectral feature by the least squares method includes:

[0023] A spectral feature model is constructed based on the precise spectral features and the single spectral features corresponding to each light source, a first feature matrix is ​​constructed based on the single spectral features, a second feature vector is constructed based on the precise spectral features, a linear equation group is constructed according to the first feature matrix and the second feature vector, and the linear equation group is solved by the least squares method to obtain the contribution coefficient corresponding to the single spectral feature.

[0024] In some embodiments, the method of comparing different contribution coefficients according to the minimum method to determine the overlap integral includes:

[0025] Determine each wavelength point within the preset wavelength range, traverse each wavelength point, determine the minimum contribution coefficient of all light sources at the wavelength point by the minimum value method, traverse the wavelength range, integrate the minimum contribution coefficient of each wavelength point, and obtain the overlapping integral value within the wavelength range.

[0026] In some embodiments, the method of determining the minimum contribution coefficient of all light sources at the wavelength point by the minimum value method includes:

[0027] For each wavelength point, the contribution coefficients of all light sources at that wavelength point are compared, the minimum value is selected, and the corresponding minimum contribution coefficient is determined.

[0028] In a second aspect, a multi-wavelength spectrum tunable system for an LED light source is provided, which is used to implement the above-mentioned multi-wavelength spectrum tunable method for an LED light source, including:

[0029] Filter module: used to obtain real-time spectral data, process the real-time spectral data according to the adaptive filtering algorithm, and obtain accurate spectral characteristics;

[0030] Data processing module: used to perform independent component analysis on precise spectral features, obtain a single spectral feature corresponding to each light source, and determine the contribution coefficient corresponding to the single spectral feature by the least square method, wherein the contribution coefficient represents the weight of each light source in the current spectral output;

[0031] Overlap analysis module: used to compare different contribution coefficients within a preset wavelength range according to the minimum method, determine the overlap integral, and adjust the real-time spectral data according to the overlap integral, wherein the overlap integral represents the degree of overlap of different spectra within the preset wavelength range.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention first processes the real-time spectral data according to an adaptive filtering algorithm to obtain accurate spectral features, then performs independent component analysis on the accurate spectral features to obtain a single spectral feature corresponding to each light source, determines the contribution coefficient corresponding to the single spectral feature by the least squares method, and finally compares different contribution coefficients according to the minimum value method to determine the overlapping integral, and adjusts the real-time spectral data according to the overlapping integral. The present invention processes the real-time spectral data by an adaptive filtering algorithm, thereby improving the accuracy and stability of the spectral data, avoiding interference caused by spectral overlap, extracting the single spectral feature of each light source by independent component analysis, and determining the contribution coefficient by the least squares method, solving the problem of poor flexibility of spectral output in traditional systems, and realizing dynamic adjustment. Finally, the real-time spectral data is adjusted according to the overlapping integral, the spectral output is optimized, more accurate color mixing and uniform lighting effects are ensured, and the adaptability and accuracy of the system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of a process of a multi-wavelength spectrum tunable method for an LED light source in the present invention;

[0035] Figure 2 It is a structural schematic diagram of a multi-wavelength spectrum tunable system for LED light source in the present invention;

[0036] Figure 3 It is a flow chart of a method for processing real-time spectral data according to an adaptive filtering algorithm to obtain accurate spectral features in the present invention;

[0037] Figure 4 The figure is a flow chart of a method for correcting initial spectral features according to filter estimation parameters to obtain accurate spectral features in the present invention. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. In the following detailed description, many specific details are elaborated to provide a thorough understanding of the described exemplary embodiments. However, it is obvious to those skilled in the art that the described embodiments can be practiced without some or all of these specific details. In other exemplary embodiments, well-known structures are not described in detail to avoid unnecessarily blurring the concepts of the present disclosure. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. At the same time, the various aspects described in the embodiments may be arbitrarily combined without conflict.

[0039] Example 1

[0040] See also Figure 1As shown, this embodiment discloses a multi-wavelength spectrum tunable method for an LED light source, comprising:

[0041] S10: Acquire real-time spectral data, and process the real-time spectral data according to an adaptive filtering algorithm to obtain accurate spectral features;

[0042] In this embodiment, the real-time spectral data includes at least a spectral intensity value, a spectral wavelength value and a spectral change rate value. The spectral change rate value refers to the rate at which the spectral intensity changes with the wavelength. Specifically, the spectral change rate value quantifies the changing trend of the spectral intensity with the wavelength, and represents the slope or curvature of the spectral curve.

[0043] like Figure 3 As shown, the method of processing real-time spectral data according to the adaptive filtering algorithm to obtain accurate spectral features includes:

[0044] Acquire real-time environmental data, process the real-time spectral data according to an adaptive filtering algorithm to obtain initial spectral features, determine filter estimation parameters in the adaptive filtering algorithm according to the real-time environmental data, correct the initial spectral features according to the filter estimation parameters, and obtain accurate spectral features.

[0045] It can be understood that the initial spectral characteristics include but are not limited to spectral peak position, spectral peak intensity, spectral width value and spectral contrast. The spectral peak position refers to the position of the maximum absorption or emission wavelength in the spectrum, the spectral peak intensity refers to the light intensity at a wavelength point in the spectrum, the spectral width value represents the light intensity distribution within a specific wavelength range, and the spectral contrast represents the degree of difference between the spectral signal and the background or noise, specifically measuring the significance or clarity of the signal.

[0046] It should be added that the adaptive filtering algorithm is an algorithm that can dynamically adjust its filtering parameters according to the characteristics of the input signal. Unlike the traditional fixed filtering method, the adaptive filtering algorithm can adjust the filter parameters in real time when the signal changes or the noise environment changes, thereby optimizing the signal extraction effect. In this embodiment, the purpose of processing real-time spectral data according to the adaptive filtering algorithm is to optimize the extraction of spectral features, reduce noise interference, and improve signal quality, thereby improving the accuracy and reliability of subsequent spectral analysis.

[0047] The method for determining the filter estimation parameters in the adaptive filtering algorithm according to the real-time environmental data includes:

[0048] The real-time environmental data is input into the pre-trained neural network model to obtain the filter estimation parameters, which include signal power spectral density and noise power spectral density.

[0049] It can be understood that the signal power spectral density represents the power size of the signal distributed in different frequency components, and the noise power spectral density represents the power distribution of the noise signal at different frequencies. The power spectral density is usually obtained by converting the time domain signal into the frequency domain signal through Fourier transform. It can show the energy distribution of the signal in each frequency component. The training method of the neural network model can be based on the fully connected neural network as the basic model. The input layer of the fully connected neural network receives historical environmental data, and the output layer of the fully connected neural network outputs historical filtering estimation parameters. When training the fully connected neural network, the cross entropy loss function is selected as the loss function, and the loss function is minimized by the gradient descent method. The weight parameters of the fully connected neural network are updated, and the neural network model is obtained through iterative training.

[0050] It should be noted that real-time environmental data includes but is not limited to real-time environmental temperature and real-time environmental humidity. Taking real-time environmental temperature and real-time environmental humidity as examples, as the temperature increases, the electronic noise of the sensor will generally increase. This is because the increase in temperature increases the thermal motion of the sensor elements, thereby increasing the noise level. Therefore, in a high temperature environment, the filter estimation parameters should be adjusted higher. Increased humidity will cause the electrical characteristics of the sensor to change, thereby affecting the signal quality and noise level. When the humidity is too high, water vapor will adhere to the sensor surface to form a thin film, which will cause additional interference to the signal. In a high humidity environment, the filter estimation parameters need to be adjusted higher. It should be added that in the adaptive filtering algorithm, the filter estimation parameters (such as signal power spectral density and noise power spectral density) are adjusted according to the real-time environmental data in order to optimize the signal extraction effect, reduce noise interference and improve signal quality.

[0051] like Figure 4 As shown, the method of correcting the initial spectral features according to the filter estimation parameters to obtain accurate spectral features includes:

[0052] The frequency response coefficient is calculated according to the filter estimation parameters, and the frequency response coefficient is subjected to inverse Fourier transform to obtain the impulse response function. The initial spectral features are convolved according to the impulse response function to obtain the accurate spectral features.

[0053] Methods for calculating frequency response coefficients based on filter estimation parameters include:

[0054] The sum of the signal power spectrum density and the noise power spectrum density is calculated, and the ratio of the signal power spectrum density to the sum of the signal power spectrum density and the noise power spectrum density is calculated, and the ratio is used as the frequency response coefficient.

[0055] The specific methods for calculating the frequency response coefficient include:

[0056]

[0057] Where H(ω) is the frequency response coefficient, S xx (ω) is the signal power spectrum density, S nn (ω) is the noise power spectral density, where ω represents the frequency.

[0058] In this embodiment, the frequency response coefficient represents the response of the filter to signals of different frequencies. Inverse Fourier transform of the frequency response coefficient refers to converting the frequency response coefficient in the frequency domain back to the impulse response function in the time domain. The impulse response function represents the time domain signal. The impulse response function describes the response of the filter to a unit pulse signal and is one of the most basic characteristics of the filter. The transformation of the frequency response coefficient and the impulse response function is a prior art and will not be elaborated in this embodiment.

[0059] Methods for convolving the initial spectral features according to the impulse response function to obtain accurate spectral features include:

[0060]

[0061] Where RSF(t0) represents the precise spectral feature, ISF(t) represents the initial spectral feature, H(·) represents the impulse response function, and τ is the time delay variable.

[0062] It should be added that the time delay variable refers to the time variable used to shift the impulse response function in the convolution operation. Specifically, it represents the time offset of the impulse response function relative to the initial signal when the signal is convolved. In this embodiment, the filter estimation parameters are adjusted according to the real-time environmental data. This process helps the system to better cope with environmental changes and optimize the signal extraction process. Through these dynamic adjustments, the system can more accurately extract spectral features, reduce noise interference, and ensure the accuracy of subsequent analysis.

[0063] S20: Performing independent component analysis on the precise spectral features to obtain a single spectral feature corresponding to each light source, and determining a contribution coefficient corresponding to the single spectral feature by a least square method, wherein the contribution coefficient represents the weight of each light source in the current spectral output;

[0064] Methods for performing independent component analysis on precise spectral features to obtain a single spectral feature corresponding to each light source include:

[0065] The precise spectral features are centralized and decomposed using the ICA algorithm to obtain a single spectral feature corresponding to each light source.

[0066] In this embodiment, the precise spectral features may be centralized by calculating the mean of the precise spectral features and subtracting it from each data point, thereby ensuring that the mean is zero before independent component analysis (ICA) is performed. This helps the ICA algorithm to more effectively extract the single spectral features of each light source. The ICA algorithm is a technique used in signal processing and statistics, which aims to separate independent components from multiple signal sources. The light sources mentioned above may be LED lights of different wavelengths. Each LED light source emits light in a different wavelength range. The single spectral features of these light sources can be separated by the ICA algorithm.

[0067] Methods for determining the contribution coefficient corresponding to a single spectral feature by the least squares method include:

[0068] A spectral feature model is constructed based on the precise spectral features and the single spectral features corresponding to each light source, a first feature matrix is ​​constructed based on the single spectral features, a second feature vector is constructed based on the precise spectral features, a linear equation group is constructed according to the first feature matrix and the second feature vector, and the linear equation group is solved by the least squares method to obtain the contribution coefficient corresponding to the single spectral feature.

[0069] Methods for constructing a spectral feature model based on accurate spectral features and a single spectral feature corresponding to each light source include:

[0070]

[0071] Where Y(λ) represents the precise spectral characteristics, X j (λ) is characterized by the single spectral feature corresponding to the j-th light source, a j It is represented as the contribution coefficient corresponding to the jth light source, M is the total number of light sources, and ∈(λ) is represented as the error term.

[0072] It should be noted that the error term refers to the difference between the weighted sum of each single spectral feature and the precise spectral feature, which is caused by multiple factors. This difference usually reflects the fitting error of the model, including the influence of measurement noise, model incompleteness and other factors.

[0073] It should be added that the first feature matrix is ​​formed by combining the single spectral features corresponding to each light source to form a matrix representing the spectrum of the light source, and the second feature vector is formed by combining the precise spectral features to form a vector, which is exemplified by the spectral peak intensity in the precise spectral features:

[0074]

[0075] Among them, X(λ) represents the first characteristic matrix.

[0076] The method of constructing a linear system of equations according to the first characteristic matrix and the second characteristic vector includes:

[0077] a=[X(λ) T X(λ)] -1 X(λ) T Y(λ);

[0078] Among them, a represents the contribution coefficient, X(λ) T is the transposed matrix of the first characteristic matrix, [X(λ) T X(λ)] -1 is X(λ) T The inverse matrix of X(λ).

[0079] It can be understood that the least squares method is used to solve the linear equations in order to find the optimal solution to a set of linear equations, thereby minimizing the error term. Specifically, the least squares method solves the coefficients of a set of unknown variables by minimizing the objective function, ensuring that the difference between the weighted sum of each single spectral feature and the precise spectral feature is minimized, thereby achieving the best fit. For example, the a obtained by the solution is shown below:

[0080]

[0081] Among them, a 1 With X 1 (λ) corresponds to a 2 With X 2 (λ) corresponds.

[0082] This embodiment first adjusts the filtering parameters through an adaptive filtering algorithm, optimizes the processing of spectral data, reduces the impact of environmental noise, and thus obtains more accurate initial spectral features. Then, by centralizing the precise spectral features and using the ICA algorithm to extract the single spectral features of each light source, the contribution of each light source can be effectively separated, thereby eliminating interference between different light sources. The contribution coefficient is further solved by combining the least squares method, and the weight of each light source in the current spectral output can be accurately calculated, further optimizing the adjustment and control of the light source.

[0083] S30: Within a preset wavelength range, different contribution coefficients are compared according to a minimum value method to determine an overlap integral, and the real-time spectrum data is adjusted according to the overlap integral, wherein the overlap integral represents the degree of overlap of different spectra within the preset wavelength range;

[0084] In this embodiment, the wavelength range refers to a specific wavelength interval selected during the spectral data analysis process. The spectral data will be analyzed and processed within this interval to determine the contribution coefficient of the light source and its weight in different spectral outputs. The purpose of the preset wavelength range is to reduce the calculation complexity by selecting a suitable wavelength range in actual operation. Limiting the range of analysis wavelengths helps to reduce the amount of calculation, thereby improving analysis efficiency.

[0085] Methods for comparing different contribution coefficients according to the minimum method to determine overlap integrals include:

[0086] Determine each wavelength point within the preset wavelength range, traverse each wavelength point, determine the minimum contribution coefficient of all light sources at the wavelength point by the minimum value method, traverse the wavelength range, integrate the minimum contribution coefficient of each wavelength point, and obtain the overlapping integral value within the wavelength range.

[0087] In this embodiment, the minimum method refers to calculating the minimum value of the contribution coefficients of different light sources at each wavelength point, which means that for each wavelength point, the contribution coefficients of all light sources at this wavelength are selected, and the minimum value is taken to represent the degree of overlap of different light sources at this wavelength. Then, the entire preset wavelength range is traversed, and this process is repeated, and the minimum contribution coefficient of each wavelength point is integrated to obtain the overlap integral within the wavelength range.

[0088] The method of determining the minimum contribution coefficient of all light sources at this wavelength point by the minimum value method includes:

[0089] For each wavelength point, the contribution coefficients of all light sources at that wavelength point are compared, the minimum value is selected, and the corresponding minimum contribution coefficient is determined.

[0090] It should be noted that the overlap integral represents the degree of overlap between the spectral outputs of different light sources within a preset wavelength range. If the overlap integral is high (i.e., the overlap degree is large), the contribution coefficients of certain light sources need to be corrected or adjusted to reduce unnecessary overlap and optimize the output effect of the light source. The adjustment method can be weighted adjustment, filtering, or optimizing the overall spectral data by adjusting the output intensity of the light source. For example, the output intensity of different light sources is adjusted to optimize the contribution of different light sources within a specific wavelength range. If the contribution coefficient of a certain light source is high, resulting in an increase in the overlap integral, the contribution of the light source can be reduced by adjusting the output intensity of the light source, thereby reducing overlap and making the contribution of each light source more balanced.

[0091] In this embodiment, the real-time spectral data is first processed according to an adaptive filtering algorithm to obtain accurate spectral features, and then an independent component analysis is performed on the accurate spectral features to obtain a single spectral feature corresponding to each light source, and the contribution coefficient corresponding to the single spectral feature is determined by the least squares method. Finally, different contribution coefficients are compared according to the minimum method to determine the overlapping integral, and the real-time spectral data is adjusted according to the overlapping integral. In this embodiment, the real-time spectral data is processed by an adaptive filtering algorithm, thereby improving the accuracy and stability of the spectral data and avoiding interference caused by spectral overlap. The single spectral feature of each light source is extracted by independent component analysis, and the contribution coefficient is determined by the least squares method, which solves the problem of poor flexibility of spectral output in traditional systems and realizes dynamic adjustment. Finally, the real-time spectral data is adjusted according to the overlapping integral, the spectral output is optimized, more accurate color mixing and uniform lighting effects are ensured, and the adaptability and accuracy of the system are improved.

[0092] Example 2

[0093] See also Figure 2 As shown, based on the same inventive concept, this embodiment discloses a multi-wavelength spectrum tunable system for an LED light source. For details not provided in this embodiment, please refer to the description of the relevant parts in Embodiment 1. The system includes:

[0094] Filter module: used to obtain real-time spectral data, process the real-time spectral data according to the adaptive filtering algorithm, and obtain accurate spectral characteristics;

[0095] In this embodiment, the real-time spectral data includes at least a spectral intensity value, a spectral wavelength value and a spectral change rate value. The spectral change rate value refers to the rate at which the spectral intensity changes with the wavelength. Specifically, the spectral change rate value quantifies the changing trend of the spectral intensity with the wavelength, and represents the slope or curvature of the spectral curve.

[0096] Methods for processing real-time spectral data according to an adaptive filtering algorithm to obtain accurate spectral features include:

[0097] Acquire real-time environmental data, process the real-time spectral data according to an adaptive filtering algorithm to obtain initial spectral features, determine filter estimation parameters in the adaptive filtering algorithm according to the real-time environmental data, correct the initial spectral features according to the filter estimation parameters, and obtain accurate spectral features.

[0098] The method for determining the filter estimation parameters in the adaptive filtering algorithm according to the real-time environmental data includes:

[0099] The real-time environmental data is input into the pre-trained neural network model to obtain the filter estimation parameters, which include signal power spectral density and noise power spectral density.

[0100] The method of correcting the initial spectral features according to the filter estimation parameters to obtain accurate spectral features includes:

[0101] The frequency response coefficient is calculated according to the filter estimation parameters, and the frequency response coefficient is subjected to inverse Fourier transform to obtain the impulse response function. The initial spectral features are convolved according to the impulse response function to obtain the accurate spectral features.

[0102] Data processing module: used to perform independent component analysis on precise spectral features, obtain a single spectral feature corresponding to each light source, and determine the contribution coefficient corresponding to the single spectral feature by the least square method, wherein the contribution coefficient represents the weight of each light source in the current spectral output;

[0103] Methods for performing independent component analysis on precise spectral features to obtain a single spectral feature corresponding to each light source include:

[0104] The precise spectral features are centralized and decomposed using the ICA algorithm to obtain a single spectral feature corresponding to each light source.

[0105] Methods for determining the contribution coefficient corresponding to a single spectral feature by the least squares method include:

[0106] A spectral feature model is constructed based on the precise spectral features and the single spectral features corresponding to each light source, a first feature matrix is ​​constructed based on the single spectral features, a second feature vector is constructed based on the precise spectral features, a linear equation group is constructed according to the first feature matrix and the second feature vector, and the linear equation group is solved by the least squares method to obtain the contribution coefficient corresponding to the single spectral feature.

[0107] Methods for constructing a spectral feature model based on accurate spectral features and a single spectral feature corresponding to each light source include:

[0108]

[0109] Where Y(λ) represents the precise spectral characteristics, X j (λ) is characterized by the single spectral feature corresponding to the j-th light source, a j It is represented as the contribution coefficient corresponding to the jth light source, M is the total number of light sources, and ∈(λ) is represented as the error term.

[0110] Overlap analysis module: used to compare different contribution coefficients within a preset wavelength range according to the minimum value method, determine the overlap integral, and adjust the real-time spectral data according to the overlap integral, wherein the overlap integral represents the degree of overlap of different spectra within the preset wavelength range;

[0111] In this embodiment, the wavelength range refers to a specific wavelength interval selected during the spectral data analysis process. The spectral data will be analyzed and processed within this interval to determine the contribution coefficient of the light source and its weight in different spectral outputs. The purpose of the preset wavelength range is to reduce the calculation complexity by selecting a suitable wavelength range in actual operation. Limiting the range of analysis wavelengths helps to reduce the amount of calculation, thereby improving analysis efficiency.

[0112] Methods for comparing different contribution coefficients according to the minimum method to determine overlap integrals include:

[0113] Determine each wavelength point within the preset wavelength range, traverse each wavelength point, determine the minimum contribution coefficient of all light sources at the wavelength point by the minimum value method, traverse the wavelength range, integrate the minimum contribution coefficient of each wavelength point, and obtain the overlapping integral value within the wavelength range.

[0114] The method of determining the minimum contribution coefficient of all light sources at this wavelength point by the minimum value method includes:

[0115] For each wavelength point, the contribution coefficients of all light sources at that wavelength point are compared, the minimum value is selected, and the corresponding minimum contribution coefficient is determined.

[0116] The detailed description set forth above in conjunction with the accompanying drawings describes examples and does not represent all examples that can be implemented or fall within the scope of the claims. The terms "example" and "exemplary" when used in this specification mean "used as an example, instance, or illustration" and do not mean "better than or superior to other examples."

[0117] References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the use of these phrases may refer to more than just one embodiment. Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0118] It should also be noted that these embodiments may be described as a process depicted as a flowchart, structure diagram, or block diagram, and although the flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently, and the order of the operations may be rearranged.

Claims

1. A multi-wavelength spectrum tunable method for LED light source, characterized in that: include: Acquire real-time spectral data, process it according to the adaptive filtering algorithm, and obtain accurate spectral features; Perform independent component analysis on the precise spectral features to obtain a single spectral feature corresponding to each light source, and determine a contribution coefficient corresponding to the single spectral feature by a least squares method, wherein the contribution coefficient represents the weight of each light source in the current spectral output; In a preset wavelength range, different contribution coefficients are compared according to a minimum value method to determine an overlap integral, and real-time spectral data is adjusted according to the overlap integral, wherein the overlap integral characterizes the degree of overlap of different spectra in the preset wavelength range.

2. A multi-wavelength spectrum tunable method for LED light source according to claim 1, characterized in that: The method of processing real-time spectral data according to the adaptive filtering algorithm to obtain accurate spectral features includes: Acquire real-time environmental data, process the real-time spectral data according to an adaptive filtering algorithm to obtain initial spectral features, determine filter estimation parameters in the adaptive filtering algorithm according to the real-time environmental data, correct the initial spectral features according to the filter estimation parameters, and obtain accurate spectral features.

3. A multi-wavelength spectrum tunable method for LED light source according to claim 2, characterized in that: The method for determining the filter estimation parameters in the adaptive filtering algorithm according to the real-time environmental data comprises: The real-time environmental data is input into the pre-trained neural network model to obtain the filter estimation parameters, which include signal power spectral density and noise power spectral density.

4. A multi-wavelength spectrum tunable method for LED light source according to claim 3, characterized in that: The method of correcting the initial spectral features according to the filter estimation parameters to obtain accurate spectral features includes: The frequency response coefficient is calculated according to the filter estimation parameters, and the frequency response coefficient is subjected to inverse Fourier transform to obtain the impulse response function. The initial spectral features are convolved according to the impulse response function to obtain the accurate spectral features.

5. A multi-wavelength spectrum tunable method for LED light source according to claim 4, characterized in that: The method for calculating the frequency response coefficient according to the filtering estimation parameter comprises: The sum of the signal power spectrum density and the noise power spectrum density is calculated, and the ratio of the signal power spectrum density to the sum of the signal power spectrum density and the noise power spectrum density is calculated, and the ratio is used as the frequency response coefficient.

6. A multi-wavelength spectrum tunable method for LED light source according to claim 4, characterized in that: The method of performing independent component analysis on the precise spectral features to obtain a single spectral feature corresponding to each light source comprises: The precise spectral features are centralized and decomposed using the ICA algorithm to obtain a single spectral feature corresponding to each light source.

7. A multi-wavelength spectrum tunable method for LED light source according to claim 6, characterized in that: The method for determining the contribution coefficient corresponding to a single spectral feature by the least squares method comprises: A spectral feature model is constructed based on the precise spectral features and the single spectral features corresponding to each light source, a first feature matrix is ​​constructed based on the single spectral features, a second feature vector is constructed based on the precise spectral features, a linear equation group is constructed according to the first feature matrix and the second feature vector, and the linear equation group is solved by the least squares method to obtain the contribution coefficient corresponding to the single spectral feature.

8. A multi-wavelength spectrum tunable method for LED light source according to claim 7, characterized in that: The method of comparing different contribution coefficients according to the minimum value method to determine the overlap integral includes: Determine each wavelength point within the preset wavelength range, traverse each wavelength point, determine the minimum contribution coefficient of all light sources at the wavelength point by the minimum value method, traverse the wavelength range, integrate the minimum contribution coefficient of each wavelength point, and obtain the overlapping integral value within the wavelength range.

9. A multi-wavelength spectrum tunable method for LED light source according to claim 8, characterized in that: The method for determining the minimum contribution coefficient of all light sources at the wavelength point by the minimum value method comprises: For each wavelength point, the contribution coefficients of all light sources at that wavelength point are compared, the minimum value is selected, and the corresponding minimum contribution coefficient is determined.

10. A multi-wavelength spectrum tunable system for an LED light source, which is used to implement a multi-wavelength spectrum tunable method for an LED light source according to any one of claims 1 to 9, characterized in that: include: Filter module: used to obtain real-time spectral data, process the real-time spectral data according to the adaptive filtering algorithm, and obtain accurate spectral characteristics; Data processing module: used to perform independent component analysis on precise spectral features, obtain a single spectral feature corresponding to each light source, and determine the contribution coefficient corresponding to the single spectral feature by the least square method, wherein the contribution coefficient represents the weight of each light source in the current spectral output; Overlap analysis module: used to compare different contribution coefficients within a preset wavelength range according to the minimum method, determine the overlap integral, and adjust the real-time spectral data according to the overlap integral, wherein the overlap integral represents the degree of overlap of different spectra within the preset wavelength range.

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