A method for testing optical performance of LED lamps

Abstract

The present application relates to the field of optical performance testing technology, and specifically to a method for testing the optical performance of LED lamps, the method comprising: collecting the color coordinate position of each LED lamp at each moment, and the standard color coordinate position of a standard LED lamp; obtaining overall color coordinate data; constructing standard visual sensitivity data and an approximate MacAdam ellipse; calculating the visual perception weight of the color coordinate position of each LED lamp at each moment; determining the perceived offset distance of each LED lamp at each moment; training a prediction model; and completing the optical performance test of the LED lamp. The present application aims to consider the offset of the color coordinate position of the LED lamp due to the human eye's perception of chromaticity, and improve the accuracy of the test results.

Description

Technical Field

[0001] The present application relates to the technical field of optical performance testing, and in particular to a method for testing the optical performance of an LED lamp. Background Art

[0002] LED lamps are widely used in industries, daily lighting, artistic photography and other fields to meet lighting needs due to their advantages such as high efficiency, energy saving and good chromaticity controllability. Among them, LED lamps have higher requirements for light in the field of industrial visual inspection or artistic photography in the hope of obtaining better object reflected light and more object visual information. Therefore, when testing the optical performance of LED lamps, it is particularly important to test the optical color performance of LED lamps. In terms of the life test of LED lamps, one of the tests related to the optical color performance of LED lamps is the chromaticity shift life, which characterizes the change in color accuracy of LED lamps during long-term use. The current chromaticity shift life uses neural networks, linear regression and other methods to directly process the measurement data of LED lamps, and uses prediction models to perceive the degradation characteristics of LED lamps and predict the life of LED lamps.

[0003] This method does not consider the visual perception of the human eye in the measurement data of LED lamps, resulting in a large difference between the measured chromaticity deviation and the observation of the human eye, affecting the accuracy of the final prediction model. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an optical performance testing method of an LED lamp to solve the existing problems.

[0005] The optical performance testing method of an LED lamp of the present application adopts the following technical solution:

[0006] An embodiment of the present application provides a method for testing the optical performance of an LED lamp, the method comprising the following steps:

[0007] Collect the color coordinate position of each LED lamp at each moment, as well as the standard color coordinate position of the standard LED lamp;

[0008] The function after Gaussian fitting of the color coordinate positions of all LED lamps at each moment is recorded as the overall color coordinate data at each moment;

[0009] According to the distance between the standard color coordinate position in the MacAdam ellipse diagram and the center of each ellipse, standard visual sensitivity data is constructed; the standard visual sensitivity data within a preset range is used to construct an approximate MacAdam ellipse in the MacAdam ellipse diagram;

[0010] Based on the difference between the standard visual sensitivity data and the overall color coordinate data at each moment, the visual perception weight of the color coordinate position of each LED lamp at each moment is calculated;

[0011] Obtain the intersection of the color coordinate position of each LED lamp and the line connecting the center of the approximate MacAdam ellipse at each moment, and determine the perceived offset distance of each LED lamp at each moment according to the distance difference between the center of the approximate MacAdam ellipse and the color coordinate position and the intersection, respectively, in combination with the visual perception weight;

[0012] The prediction model is trained by combining the perceived offset distance and color coordinate position of each LED lamp at each moment, as well as its color coordinate position at the next moment;

[0013] The color coordinate position of the LED lamp to be tested during the life test and the perceived offset distance calculated are used as the input of the prediction model to test the optical performance of the LED lamp.

[0014] Preferably, the process of constructing the standard visual sensitivity data includes:

[0015] The distance between each ellipse in the MacAdam ellipse diagram and the standard color coordinate position is used as the weight of the covariance matrix of the corresponding ellipse, and the covariance matrices of all ellipses in the MacAdam ellipse diagram are weighted summed to obtain the covariance matrix of the standard color coordinate position;

[0016] The function obtained by Gaussian fitting the standard color coordinate position and its covariance matrix is ​​recorded as standard visual sensitivity data.

[0017] Preferably, the calculation process of the visual perception weight of the color coordinate position of each LED lamp at each moment includes:

[0018] Obtain the function value of the color coordinate position of each LED lamp in the standard visual sensitivity data at each moment, and record it as the standard function value;

[0019] Obtain the function value of the color coordinate position of each LED lamp in the overall color coordinate data at each moment, and record it as the measurement function value;

[0020] The normalized value of the ratio between the measured function value and the standard function value is used as the visual perception weight.

[0021] Preferably, the intersection point is the intersection point closest to the color coordinate position.

[0022] Preferably, the method for determining the perceived offset distance of each LED lamp at each moment includes:

[0023] Determine the actual offset distance of each LED lamp at each moment according to the distance difference between the center of the approximate MacAdam ellipse and the color coordinate position and the intersection point;

[0024] According to the actual offset distance and visual perception weight of each LED lamp at each moment, the perceived offset distance of each LED lamp at each moment is determined.

[0025] Preferably, the method for determining the actual offset distance of each LED lamp at each moment includes:

[0026] Calculate the distance between the center of the approximate MacAdam ellipse and the color coordinate position as a numerator;

[0027] Calculate the distance between the center of the approximate MacAdam ellipse and the intersection point as the denominator;

[0028] Calculate the ratio of the numerator to the denominator; and use the difference between the ratio and the value 1 as the actual offset distance.

[0029] Preferably, the perceived offset distance of each LED lamp at each moment further comprises: taking the product of the actual offset distance of each LED lamp at each moment and the visual perception weight as the perceived offset distance of each LED lamp at each moment.

[0030] Preferably, the process of training the prediction model is:

[0031] The perceived offset distance and color coordinate position of each LED lamp at each moment are used as input data, and the color coordinate position of each LED lamp at the next moment is used as label data. A neural network is used to train the prediction model, and the output is the color coordinate predicted position of each LED lamp at the next moment.

[0032] The loss function of the neural network is: the mean square error between the predicted color coordinate position and the color coordinate position of each LED lamp at the next moment.

[0033] Preferably, the process of testing the optical performance of the LED lamp includes:

[0034] If the perceived offset distance is greater than 0, the time when the perceived offset distance is greater than 0 is used as the end time of the life span;

[0035] Otherwise, all color coordinate positions of the LED lamp to be tested during the life test and all perceived offset distances calculated are used as inputs of the prediction model, and the color coordinate predicted position at the next moment is output, wherein, when the color coordinate predicted position at the next moment is greater than 0, the moment is used as the end of life moment; otherwise, the color coordinate predicted position and perceived offset distance at the next moment are used as inputs of the prediction model, the input data is updated, and the color coordinate predicted position is iteratively predicted.

[0036] Preferably, the method further comprises: multiplying the end-of-life time by the equivalent time set in the accelerated life test to obtain the chromaticity shift life of the lamp to be tested.

[0037] This application has at least the following beneficial effects:

[0038] In view of the problem that the test results are inaccurate due to insufficient data feature extraction and a large gap between the data and the human eye perception when testing the chromaticity shift life of LED lamps, this application proposes a method for testing the optical performance of LED lamps, including: collecting life test data of LED lamps, extracting features of the statistical features of the color coordinate position of each LED lamp at each moment in the test data, obtaining overall color coordinate data, and eliminating statistical errors; constructing standard visual sensitivity data based on the MacAdam ellipse diagram and the standard color coordinate position, and obtaining an approximate MacAdam ellipse within a preset range; comparing the standard visual sensitivity data with the overall color coordinate data to obtain visual perception weights, characterizing some measurement data that are obvious in both statistical features and visual perception, and giving higher weights to important data; calculating the perception offset distance based on the standard visual sensitivity data and the visual perception weights, and characterizing the chromaticity shift of the LED lamp. Finally, a prediction model is used to predict the color coordinate position of the LED lamp to complete the chromaticity shift life detection of the LED lamp. Since this embodiment extracts the statistical characteristics of the color coordinate position of the lamp based on the human eye's perception of chromaticity and the lamp, a characteristic value characterizing the offset of the lamp's color coordinate position is obtained, redundant information is screened out, and a faster and more accurate LED lamp chromaticity offset life detection result can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A flowchart of a method for testing the optical performance of an LED lamp provided in this application;

[0041] Figure 2 Chromaticity shift life test flow chart provided for this application. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the optical performance test method of an LED lamp proposed in the present application, its specific implementation method, structure, features and effects are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0044] The specific scheme of the optical performance testing method of an LED lamp provided by the present application is described in detail below with reference to the accompanying drawings.

[0045] An embodiment of the present application provides a method for testing the optical performance of an LED lamp.

[0046] The present embodiment includes a method for testing the optical performance of a model of LED lamps, and the process is as follows: first, collecting optical data of a complete life test process of a standard lamp; combining the deviation between human eye perception and actual measurement, constructing a prediction model for chromaticity shift life; finally, performing sampling inspection on lamps of the same batch, obtaining optical data of the life test of the lamps to be tested, inputting the prediction model to obtain the predicted color coordinate position, and then calculating the chromaticity shift life of the lamps, thereby completing the life test of the sampled lamps.

[0047] Specifically, the following optical performance test method for LED lamps is provided. Figure 1 , the method comprises the following steps:

[0048] The first step is to collect the color coordinate position of each LED lamp at each moment, as well as the standard color coordinate position of the standard LED lamp.

[0049] Collecting optical data of LED lamps during life test: Setting the test conditions for life test is a common technology in the field of optical measurement; the equipment for collecting life test data of LED lamps includes: integrating sphere intelligent sensor, photoelectric color fast measurement system and terminal computer, which have the following specific functions:

[0050] Integrating sphere intelligent sensor: responsible for collecting optical data of LED lamps;

[0051] Photoelectric color fast measurement system: responsible for converting the optical data of LED lamps collected by the integrating sphere intelligent sensor into intuitive optical data, including the color coordinate position of the LED lamp at a certain moment;

[0052] Terminal computer: responsible for collecting, displaying and processing optical data of the photoelectric color rapid measurement system.

[0053] In this embodiment, N LED lamps are tested for life and the optical data of each LED lamp at each moment in the life test process is collected. In this embodiment, a total of N=100 LED lamps of the same model are tested for life. During the test, the accelerated life test method is used for measurement. In this embodiment, data is collected from the nth lamp every 10 minutes, for a total of 150 times. The data collected each time is the color coordinate position (x t,n ,y t,n ), (x t,n ,y t,n ) represents the color coordinate position of the nth LED lamp at time t.

[0054] Since the accelerated life test is used for measurement, the LED lamp in this embodiment is tested every 10 minutes, which is equivalent to the LED lamp working for 100 hours under normal conditions. Among them, the accelerated life test is a common technology in the measurement field and will not be described in detail, and the number of times and time intervals for collecting the life test data of the lamp can be set by yourself.

[0055] The optical data of the nth LED lamp during the life test includes a color coordinate measurement matrix. The color coordinate measurement matrix is ​​a matrix with 150 columns and 2 rows, where the first element x in the tth column is t,n and the second element y t,n The binary group represents the color coordinate position (x t,n ,y t,n ). At the same time, the color coordinate position measured by the LED lamp that meets the optical performance test standard is used as the standard color coordinate position.

[0056] It should be noted that the color coordinate position is represented by the cie1931XYZ standard colorimetric system in this embodiment, so the data format for expressing color is (x, y, z), where (x, y) represents the color coordinate position, and z represents the color brightness. This embodiment performs performance testing on the optical data of the LED lamp at the color coordinate position. The chromaticity shift phenomenon described in this embodiment refers to the shift of the color coordinate position of the LED lamp from the original position as the lamp is used. Among them, the cie1931XYZ standard colorimetric system is a common technology in the field of optical measurement and will not be repeated.

[0057] At this point, the color coordinate position of each LED lamp at each moment and the standard color coordinate position of the standard LED lamp can be obtained through the above method.

[0058] The second step is to perform Gaussian fitting on the color coordinate positions of all LED lamps at each moment, and record them as the overall color coordinate data at each moment.

[0059] This embodiment mainly analyzes the chromaticity shift life of LED lamps and completes the optical performance test of LED lamps. The traditional chromaticity shift life does not take into account the large gap between human eye perception and actual measurement, which leads to insufficient data feature extraction and inaccurate test results. Accordingly, this application aims to analyze the relationship between optical data and human eye perception and chromaticity aging, extract effective information of optical data during the life test, and thus improve the accuracy of the test results.

[0060] When measuring different LED lamps, due to slight differences in the test environment, the color coordinate positions of LED lamps of the same quality measured at the same time will be offset to varying degrees. To eliminate this statistical error, repeated tests are usually performed during life tests. Usually, the color coordinate position deviations of N lamps should conform to the statistical law of Gaussian distribution.

[0061] Therefore, this embodiment performs preliminary processing on the data of the LED lamps, and constructs the color coordinate positions of the N lamps at time t into a Gaussian distribution for characterizing the color coordinate position of the LED lamps as a whole to eliminate statistical errors. The larger the standard deviation of the obtained Gaussian distribution, and the larger the Euclidean distance between the mean coordinate of the Gaussian distribution and the standard color coordinate position, the more serious the chromaticity deviation of the LED lamps as a whole at time t.

[0062] The specific process of Gaussian fitting for the color coordinate positions of all LED lamps at each moment is as follows:

[0063] First, the color coordinate positions of N lamps at time t are obtained respectively to form a color coordinate position data set at time t.

[0064] Further, the color coordinate position data set is used as input and the least square method is used for calculation. The necessary parameters are the fitting function: two-dimensional Gaussian function; the output is the variable mean of the two-dimensional Gaussian function, denoted as (μx t ,μy t ), the covariance matrix is ​​recorded as The least squares method and two-dimensional Gaussian function are commonly used techniques in the field of data processing and will not be elaborated on here.

[0065] It should be understood that the greater the distance between the variable mean and the standard color coordinate position, and the greater the element value in the covariance matrix, the more serious the chromaticity deviation of the LED lamp is.

[0066] Then, take the variable mean (μx t ,μy t ) and the covariance matrix As input, a two-dimensional Gaussian function construction method is used, and the output is a two-dimensional Gaussian function, recorded as the overall color coordinate data f at time t t (x,y).

[0067] Among them, the two-dimensional Gaussian function construction method is a commonly used technology in the field of data processing and will not be elaborated on here.

[0068] The third step is to construct standard visual sensitivity data according to the distance between the standard color coordinate position and the center of each ellipse in the MacAdam ellipse diagram; and to construct an approximate MacAdam ellipse in the MacAdam ellipse diagram using the standard visual sensitivity data within a preset range.

[0069] In the existing chromaticity shift life prediction model, the Euclidean distance between the standard color coordinate position and the measured color coordinate position is used as the standard for judging chromaticity shift. For example, according to the IE LM-79-08 standard, if the Euclidean distance between the standard color coordinate position and the measured color coordinate position is greater than 0.007, it is considered that the chromaticity has seriously deviated and the lamp cannot be used; however, according to the MacAdam ellipse diagram, in the CIE1931XYZ standard color system, the human eye has different perception abilities for chromaticity shifts of different colors. Therefore, the calculation of the Euclidean distance as an indicator for judging chromaticity shift is quite different from the human eye's perception, and it is easy to have inaccurate chromaticity life representation problems. Among them, the MacAdam ellipse diagram is a common technology in the field of optical measurement and will not be repeated.

[0070] Based on this, this embodiment constructs standard visual sensitivity data according to the distance between the standard color coordinate position and each ellipse in the MacAdam ellipse diagram, compares the standard visual sensitivity data with the overall color coordinate data, obtains visual perception weights, and characterizes the offset of the color coordinate position. It is used to adjust the calculation method of the position difference between the standard color coordinate position and the measured color coordinate position to make it more consistent with the human eye's perception of chromaticity offset, and obtain a more accurate chromaticity offset judgment.

[0071] In this embodiment, the standard visual sensitivity data is constructed specifically for the distance between the standard color coordinate data in the MacAdam ellipse diagram and the centers of each ellipse as follows:

[0072] First, the data form of the MacAdam ellipse is G = 24 six-element arrays, where the g-th array is (μx g ,μy g ,σx g 2 ,σxy g ,σyx g ,σyg 2 ).

[0073] It should be noted that the visualization representation of the MacAdam ellipse diagram in the cie1931XYZ standard colorimetry system is 24 ellipses, where any two color coordinate positions represent two colors within the range of the g-th ellipse, and the human eye is unlikely to find the difference between the two. The g-th ellipse corresponds to the g-th sextuple, and the g-th sextuple represents a two-dimensional Gaussian function. If a color coordinate position is within the 3σ range of the two-dimensional Gaussian function represented by the g-th sextuple, then the color coordinate position is in the g-th ellipse; the MacAdam ellipse diagram is a common technology in the field of optical measurement, and the 3σ range of the two-dimensional Gaussian function is a common technology in the field of data processing, which will not be repeated in this application.

[0074] Since the MacAdam ellipse diagram corresponds to 24 ellipses, and the distance between the standard color coordinate position and the center of any ellipse in the MacAdam ellipse diagram is calculated as the weight, the visual sensitivity range of the current model of LED lamps is characterized by the MacAdam ellipse diagram, and the MacAdam ellipse at the standard color coordinate position can be approximated by data processing of the MacAdam ellipse diagram. The specific processing method is:

[0075] First, calculate the distance from the center of the g-th ellipse to the standard color coordinate position and record it as the ellipse distance δ 0,g , the calculation method is: obtain the first and second elements of the six-element array corresponding to the g-th ellipse, which represent the horizontal and vertical coordinates of the center of the g-th MacAdam ellipse, respectively, and represent the center position. Further, the ellipse distance δ is calculated by comparing the center position with the standard color coordinate position. 0,g ; Optionally, the distance calculation method includes but is not limited to Euclidean distance, Manhattan distance, and Chebyshev distance. The above distance calculation methods are well-known technologies and will not be repeated here.

[0076] The G ellipse distances are further normalized, and the normalized value of the g-th ellipse distance is recorded as the g-th ellipse weight β g ; Finally, the covariance matrix of the standard color coordinate position is constructed by combining the elliptical distances between the centers of all ellipses in the MacAdam ellipse diagram and the standard color coordinate position:

[0077]

[0078] in, is the covariance matrix of the standard color coordinate position, which together with the standard color coordinate position constitutes a two-dimensional Gaussian function, which can approximately characterize the MacAdam ellipse at the standard color coordinate position; G is the number of ellipse centers in the MacAdam ellipse diagram; β g is the g-th ellipse distance; is the covariance matrix of the g-th ellipse in the MacAdam ellipse diagram.

[0079] It should be understood that the closer the center of the g-th ellipse in the MacAdam ellipse diagram is to the standard color coordinate position, the more similar the human eye's perception of chromaticity deviation at the standard color coordinate position is to the g-th ellipse in the MacAdam ellipse diagram, and the more the g-th ellipse should be used to construct the ellipse at the standard color coordinate position and be regarded as the ellipse at the standard color coordinate position in the MacAdam ellipse diagram.

[0080] At the same time, the standard color coordinate position and its corresponding calculated covariance matrix are used as input, and a two-dimensional Gaussian function construction method is adopted, and the output is a two-dimensional Gaussian function. The two-dimensional Gaussian function is an ellipse of the standard color coordinate position in the MacAdam ellipse diagram, which is recorded as the standard visual sensitivity data f0(x,y).

[0081] Then, with the standard visual sensitivity data f0(x, y) as input, according to the 3σ range calculation method of the two-dimensional Gaussian function, an ellipse equation g0(x, y) is output, which is recorded as an approximate MacAdam ellipse, and the center of the ellipse is at the standard color coordinate position. The calculation method of obtaining the 3σ range of the two-dimensional Gaussian function is a common technology in the field of data processing and will not be repeated here. Among them, the 3σ range of the two-dimensional Gaussian function is a preset range in this embodiment, and other ranges can be set in other embodiments.

[0082] The fourth step is to calculate the visual perception weight of the color coordinate position of each LED lamp at each moment based on the difference between the standard visual sensitivity data and the overall color coordinate data at each moment.

[0083] For the color coordinate position (x t,n ,y t,n ) is analyzed in the standard visual sensitivity data and the overall color coordinate data at the corresponding moment, and the color coordinate position (x t,n ,y t,n ) is substituted into the standard visual sensitivity data f0(x,y). The larger the function value is, the less likely it is that the offset of the color coordinate position of the nth LED lamp at time t is visually perceived, and the less likely it is that the data can represent the color coordinate offset well. t,n ,y t,n ) Substitute the overall color coordinate data f at time t t (x, y), the larger the function value is, the more the offset of the color coordinate position of the nth lamp at time t is consistent with the overall color coordinate offset, and the more important data is to characterize the color coordinate offset.

[0084] Based on this, this embodiment constructs the visual perception weight of the color coordinate position of the nth LED lamp at time t: Among them, α t,n is the visual perception weight of the color coordinate position of the nth LED lamp at time t, norm is the normalization function, and f t (x t,n ,y t,n )、f0(x t,n ,y t,n ) are the color coordinate position (x t,n ,y t,n ) Substitute the overall color coordinate data f at time t t The function values ​​in (x, y) and the standard visual sensitivity data f0(x, y) are recorded as the measured function value and the standard function value, respectively.

[0085] It should be understood that the larger the function value of the color coordinate position of the nth LED lamp at time t when substituted into the overall color coordinate data at time t, and the smaller the function value when substituted into the standard visual sensitivity data, the more important the color coordinate position is for characterizing the color coordinate offset at time t.

[0086] The fifth step is to obtain the intersection of the color coordinate position of each LED lamp at each moment and the line connecting the center of the approximate MacAdam ellipse, and determine the perceived offset distance of each LED lamp at each moment based on the distance difference between the center of the approximate MacAdam ellipse and the color coordinate position and the intersection, combined with the visual perception weight.

[0087] Further obtain the color coordinate position A of the nth lamp at time t t,n =(x t,n ,y t,n ), and obtain a path that passes through (x t,n ,y t,n ) and A0=(x0,y0), the straight line and the approximate MacAdam ellipse center g0(x,y) will have two intersection points, and the color coordinate position A t,n The nearest intersection point is recorded as the perception boundary point B t,n In particular, the two intersection points to the color coordinate position A t,n When the distances are the same, a random intersection is obtained and recorded as the perception boundary point B t,n .

[0088] According to the distance difference between the center of the approximate MacAdam ellipse and the color coordinate position and the intersection point, combined with the visual perception weight, the perceived offset distance of each LED lamp at each moment is determined. The specific calculation formula is: Among them, γ t,n is the perceived offset distance of the nth LED lamp at time t, δ(At,n ,A0),δ(B t,n , A0) are the approximate MacAdam ellipse center A0 and the color coordinate position A of the nth LED lamp at time t t,n , perception boundary point B t,n The distance between t,n is the visual perception weight of the color coordinate position of the nth LED lamp at time t. Recorded as the actual offset distance of the nth LED lamp at time t.

[0089] It should be understood that if δ(A t,n ,A0) is less than δ(B t,n ,A0), then it indicates that the chromaticity offset of the nth LED lamp at time t is not easy to be perceived, otherwise it is easier to be detected. Therefore, the ratio of the two represents the actual chromaticity offset distance based on actual perception. The ratio between 0 and 1 means that the current chromaticity offset is less perceptible in actual circumstances. The smaller the value, the smaller the chromaticity offset, and the calculated perceptual offset distance is a negative number; the larger the ratio is, the more serious the chromaticity offset is and the easier it is to be perceived, and the calculated perceptual offset distance is a positive number.

[0090] The perceived offset distance is obtained by multiplying the actual offset distance by the visual perception weight. The positive or negative value represents whether the chromaticity offset can be visually perceived or not, and the size of the value represents the strength of the chromaticity offset. Among them, the visual perception weight is used to numerically adjust the actual offset distance, expand the actual offset distance of important offset data, and reduce the actual offset distance of unimportant offset data, so as to avoid unimportant data from having too much impact on the final prediction model and improve the accuracy of the prediction model.

[0091] The sixth step is to train the prediction model by combining the perceived offset distance and color coordinate position of each LED lamp at each moment, as well as its color coordinate position at the next moment.

[0092] Finally, for the nth LED fixture at time t, a set of input data can be obtained, including: the perceived offset distance γ of the nth LED fixture at time t t,n and color coordinate position (x t,n ,y t,n );

[0093] At the same time, the color coordinate position (x t+1,n ,y t+1,n ) as the label data of the nth LED lamp at time t.

[0094] Therefore, at time t, the nth LED lamp has a set of training data, including: γ t,n ,x t,n,y t,n ,x t+1,n ,y t+1,n .

[0095] Further obtain all the training data of N LED lamps at each moment to form a training data set, and use the training data set to build a prediction model. Optionally, this embodiment uses LSTM neural network as a data processing technology for building a prediction model. Other embodiments of the present application may also use other neural network models for training, such as NLS neural network (nonlinear least squares neural network) and RNN neural network (recurrent neural network). The specific construction process of the model is:

[0096] The training data set is used as input and the LSTM neural network model is used for training. The output is the color coordinate predicted position of the lamp at time t+1. The activation function uses the ReLU function, the optimizer uses the Adam optimizer, and the loss function is the predicted color coordinate position of the lamp at time t+1 and the color coordinate position of the nth lamp at time t+1 (x t+1,n ,y t+1,n ) is obtained by performing mean square error, and finally a trained LSTM neural network model is obtained, which is recorded as a prediction model. Among them, the training process of the LSTM neural network is a common technology in the data processing field and will not be repeated here.

[0097] Finally, when using the trained prediction model, the life test of the lamp to be tested is carried out and the life test data is collected. During the test, the accelerated life test method is used for measurement. In this embodiment, data is collected every 10 minutes for the lamp to be tested, and a total of S=30 times are collected. The data collected each time is the color coordinate position (x t,n ,y t,n ), because the accelerated life test method is used for measurement, each 10-minute interval during the lamp test is equivalent to the lamp working for 100 hours under normal conditions. Accelerated life test is a common technology in the measurement field and will not be described in detail.

[0098] In the seventh step, the color coordinate position of the LED lamp to be tested during the life test and the calculated perceived offset distance are used as inputs of the prediction model to test the optical performance of the LED lamp.

[0099] In this embodiment, the life test data collected for the lamp to be tested is:

[0100] A color coordinate test matrix: a matrix with S columns and 2 rows, where the first element x in the sth column is s and the second element y s It means that after the lamp under test works normally for t×100 hours, its color coordinate position is (x s ,y s ).

[0101] Details are as attached Figure 2 As shown, Figure 2 This is the chromaticity shift life test flow chart, specifically:

[0102] Step 1: According to the color coordinate test matrix of the lamp to be tested, the perceived offset distances at all moments in the life test process are calculated in sequence according to the method described in this embodiment. If the sth perceived offset distance is calculated to be greater than 0, the moment s at which it is located is taken as the end of life, which means that the chromaticity offset life of the lamp to be tested is s×100 hours, and the chromaticity offset life test is completed; otherwise, that is, all the calculated perceived offset distances are less than or equal to 0, proceed to step 2;

[0103] Step 2: All perceived offset distances and their corresponding color coordinate positions are used as the input of the prediction model, and the output is the predicted color coordinate position at the next moment;

[0104] Step 3: Calculate the perceived offset distance at the next moment based on the predicted position of the color coordinates at the next moment. If the perceived offset distance at the next moment is greater than 0, it is considered that the lamp under test has chromaticity offset, and the next moment is taken as the end of life moment. The end of life moment is multiplied by 100 hours to obtain the chromaticity offset life of the lamp under test, and the life test is completed. Otherwise, proceed to step 4;

[0105] Step 4: At this time, it is considered that the lamp under test has not yet experienced chromaticity shift, so the predicted color coordinate position and perceived shift distance at the next moment are used as inputs of the prediction model, the input data is updated, and step 2 is entered.

[0106] Finally, the chromaticity shift life of the lamp under test was obtained, and the optical performance test of the LED lamp was completed.

[0107] Since this embodiment extracts the statistical characteristics of the color coordinate position of the lamp based on the human eye's perception of chromaticity and the lamp, a characteristic value characterizing the offset of the lamp's color coordinate position is obtained, redundant information is screened out, and a faster and more accurate LED lamp chromaticity offset life detection result can be provided.

[0108] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0109] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0110] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0111] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for testing the optical performance of an LED lamp, characterized in that: The method comprises the following steps: Collect the color coordinate position of each LED lamp at each moment, as well as the standard color coordinate position of the standard LED lamp; The function after Gaussian fitting of the color coordinate positions of all LED lamps at each moment is recorded as the overall color coordinate data at each moment; According to the distance between the standard color coordinate position in the MacAdam ellipse diagram and the center of each ellipse, standard visual sensitivity data is constructed; the standard visual sensitivity data within a preset range is used to construct an approximate MacAdam ellipse in the MacAdam ellipse diagram; Based on the difference between the standard visual sensitivity data and the overall color coordinate data at each moment, the visual perception weight of the color coordinate position of each LED lamp at each moment is calculated; Obtain the intersection of the color coordinate position of each LED lamp and the line connecting the center of the approximate MacAdam ellipse at each moment, and determine the perceived offset distance of each LED lamp at each moment according to the distance difference between the center of the approximate MacAdam ellipse and the color coordinate position and the intersection, respectively, in combination with the visual perception weight; The prediction model is trained by combining the perceived offset distance and color coordinate position of each LED lamp at each moment, as well as its color coordinate position at the next moment; The color coordinate position of the LED lamp under test during the life test and the calculated perceived offset distance are used as inputs of the prediction model to test the optical performance of the LED lamp; The process of testing the optical performance of the LED lamp includes: If the perceived offset distance is greater than 0, the time when the perceived offset distance is greater than 0 is used as the end time of the life span; Otherwise, all color coordinate positions of the LED lamp to be tested during the life test and all perceived offset distances calculated are used as inputs of the prediction model, and the color coordinate predicted position at the next moment is output, wherein, when the color coordinate predicted position at the next moment is greater than 0, the moment is used as the end of life moment; otherwise, the color coordinate predicted position and perceived offset distance at the next moment are used as inputs of the prediction model, the input data is updated, and the color coordinate predicted position is iteratively predicted.

2. The optical performance testing method of an LED lamp according to claim 1, characterized in that: The construction process of the standard visual sensitive data includes: The distance between each ellipse in the MacAdam ellipse diagram and the standard color coordinate position is used as the weight of the covariance matrix of the corresponding ellipse, and the covariance matrices of all ellipses in the MacAdam ellipse diagram are weighted summed to obtain the covariance matrix of the standard color coordinate position; The function obtained by Gaussian fitting the standard color coordinate position and its covariance matrix is ​​recorded as standard visual sensitivity data.

3. The optical performance testing method of an LED lamp according to claim 1, characterized in that: The calculation process of the visual perception weight of the color coordinate position of each LED lamp at each moment includes: Obtain the function value of the color coordinate position of each LED lamp in the standard visual sensitivity data at each moment, and record it as the standard function value; Obtain the function value of the color coordinate position of each LED lamp in the overall color coordinate data at each moment, and record it as the measurement function value; The normalized value of the ratio between the measured function value and the standard function value is used as the visual perception weight.

4. The optical performance testing method of an LED lamp according to claim 1, characterized in that: The intersection point is the intersection point closest to the color coordinate position.

5. The optical performance testing method of an LED lamp according to claim 1, characterized in that: The method for determining the perceived offset distance of each LED lamp at each moment includes: Determine the actual offset distance of each LED lamp at each moment according to the distance difference between the center of the approximate MacAdam ellipse and the color coordinate position and the intersection point; According to the actual offset distance and visual perception weight of each LED lamp at each moment, the perceived offset distance of each LED lamp at each moment is determined.

6. The optical performance testing method of an LED lamp according to claim 5, characterized in that: The method for determining the actual offset distance of each LED lamp at each moment includes: Calculate the distance between the center of the approximate MacAdam ellipse and the color coordinate position as a numerator; Calculate the distance between the center of the approximate MacAdam ellipse and the intersection point as the denominator; Calculate the ratio of the numerator to the denominator; and use the difference between the ratio and the value 1 as the actual offset distance.

7. The optical performance testing method of an LED lamp according to claim 5, characterized in that: The perceived offset distance of each LED lamp at each moment further includes: taking the product of the actual offset distance of each LED lamp at each moment and the visual perception weight as the perceived offset distance of each LED lamp at each moment.

8. The optical performance testing method of an LED lamp according to claim 1, characterized in that: The process of training the prediction model is as follows: The perceived offset distance and color coordinate position of each LED lamp at each moment are used as input data, and the color coordinate position of each LED lamp at the next moment is used as label data. A neural network is used to train the prediction model, and the output is the color coordinate predicted position of each LED lamp at the next moment. The loss function of the neural network is: the mean square error between the predicted color coordinate position and the color coordinate position of each LED lamp at the next moment.

9. The optical performance testing method of an LED lamp according to claim 1, characterized in that: Also includes: The chromaticity shift life of the lamp to be tested is obtained by multiplying the end-of-life time by the equivalent time set in the accelerated life test.

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