A method for detecting uniformity and color deviation of an LED light source

Abstract

The application provides a kind of LED light source uniformity and color deviation detection method, comprising: setting the environmental parameter of constant temperature and humidity test chamber, the environment in the box is monitored in real time by temperature and humidity sensor, when detecting that temperature and humidity deviate from set value, trigger temperature and humidity control system to adjust;According to the initial color characteristic database, LED light source is placed in the constant temperature and humidity test chamber to carry out accelerated aging test, and the spectral data of light source at different time points are obtained by using spectral analyzer;The chromaticity coordinates, color temperature and color rendering index corresponding to the spectral data at different time points are calculated, the color deviation amount of LED light source is calculated, and the change curve of color deviation with time is fitted;According to the accelerated aging test result of LED light source, the color maintenance life of light source under high temperature and high humidity environment is calculated, and the color deviation detection is completed through the color stability uniformity index of LED light source.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a method for detecting uniformity and color deviation of an LED light source. BACKGROUND

[0002] In some high-temperature and high-humidity indoor environments, such as kitchens, bathrooms, etc., LED lamps may be affected by high humidity and high temperature, leading to accelerated aging of the packaging material, and thus affecting its performance and lifespan. During the manufacturing process of LED light sources, the influence of high-temperature and high-humidity environments on color stability is a technical problem that needs to be solved urgently. As the temperature and humidity increase, the quantum well structure inside the LED chip will change microscopically, causing the emission wavelength to shift. This color deviation not only exists between different saturations, but also changes over time, posing challenges to product quality control. Traditional accelerated aging test methods are difficult to fully simulate the complex factors in actual use environments, such as temperature and humidity cycle changes, light intensity fluctuations, etc. In addition, there is currently a lack of unified standards and measurement methods for accurately quantifying LED light source color deviation, making it difficult to directly compare data between different batches and different manufacturers. Under high-temperature and high-humidity environments, the aging rates of LED chips and packaging materials differ, and this asynchronous aging further exacerbates the unpredictability of color deviation. How to accurately simulate the complex factors of the actual use environment in accelerated aging tests, establish a scientific and reasonable color deviation evaluation system, and on this basis optimize the material selection and structural design of LED light sources to improve their color stability in high-temperature and high-humidity environments is an important technical challenge facing the current LED industry. SUMMARY

[0003] The present application provides a method for detecting the uniformity and color deviation of an LED light source, mainly comprising:

[0004] Setting the environmental parameters of a constant temperature and humidity test chamber, and monitoring the chamber environment in real time through a temperature and humidity sensor. When the temperature and humidity deviate from the set values, triggering the temperature and humidity control system to adjust;

[0005] Performing initial spectral measurement on the LED light source to obtain the chromaticity coordinates, color temperature, and color rendering index of the light source. Converting the measurement data into numerical values in the standard color space, and establishing an initial color characteristic database of the LED light source;

[0006] According to the initial color characteristic database, placing the LED light source in the constant temperature and humidity test chamber for accelerated aging test, and using a spectral analyzer to obtain the spectral data of the light source at different time points;

[0007] Calculating the chromaticity coordinates, color temperature, and color rendering index corresponding to the spectral data at different time points, calculating the color deviation of the LED light source, and fitting the color deviation curve over time;

[0008] The color deviation data of the LED light source is processed by dimension reduction, factors affecting color stability are extracted, a color compensation model is established, working time and environmental parameters of the LED light source are input, color compensation parameters are output, and the color of the LED light source is dynamically compensated;

[0009] The current and voltage parameters of the light source are adjusted by the LED driving circuit, the dynamic compensation of the color of the LED light source is adjusted, the intensity ratio of light of different wavelengths is adjusted, the color rendering index and color saturation of the LED light source are improved, the color output of the LED light source is monitored in real time, and when the color saturation parameter deviates from the target value, corresponding correction is performed;

[0010] According to the accelerated aging test results of the LED light source, the color maintenance life of the light source in a high-temperature and high-humidity environment is calculated, and the color deviation detection is completed through the color stability and uniformity index of the LED light source.

[0011] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0012] The present application discloses a detection method for LED light source uniformity and color deviation. The method simulates different environmental conditions through a constant temperature and humidity test chamber, performs accelerated aging test on the LED light source, and monitors the spectral changes in real time. Colorimetric parameters are calculated using color analysis software, a color deviation change curve over time is fitted, main factors affecting color stability are extracted, and a color compensation model is established. According to the working time and environmental parameters of the LED, the present application dynamically outputs color compensation parameters, adjusts the driving circuit parameters to realize color compensation, and improves the color rendering index and color saturation. At the same time, multiple small test chambers are used for parallel test to expand the test capacity. The present application realizes accurate test and dynamic compensation of the color stability of the LED light source, effectively prolongs the color maintenance life of the LED light source, improves the color stability and uniformity, and provides technical support for LED product quality control. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The flowchart of the LED light source uniformity and color deviation detection method of the present application.

[0014] Figure 2 The schematic diagram of the LED light source uniformity and color deviation detection method of the present application. DETAILED DESCRIPTION

[0015] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described in the following with reference to the drawings in the embodiments of the specification. Obviously, the described embodiments are only part of the embodiments of the specification, not all. Based on the embodiments in the specification, all other embodiments obtained by those of ordinary skill in the art without creative labor should belong to the protection scope of the specification.

[0016] As Figure 1 -2, the LED light source uniformity and color deviation detection method can specifically include:

[0017] Step S101, set the environmental parameters of the constant temperature and humidity test chamber, and monitor the environment in the chamber in real time through the temperature and humidity sensor. When the detected temperature and humidity deviates from the set value, trigger the temperature and humidity control system to adjust.

[0018] Obtain the environmental parameter setting value, which is read from a pre-established database; collect real-time temperature and humidity data in the test chamber, which is collected by a temperature and humidity sensor array at multiple points; perform sliding average filtering on the real-time temperature and humidity data to obtain temperature and humidity measured values. Calculate the deviation between the temperature and humidity measured values and the temperature and humidity set values; determine whether the deviation exceeds a preset threshold value, if the deviation exceeds the preset threshold value, trigger parameter control. According to the deviation value and the change trend, calculate the heater power adjustment amount and the humidifier water adjustment amount, which are output by a PID controller.

[0019] Exemplarily, according to the preset temperature and humidity range of the constant temperature and humidity test chamber, the corresponding environmental parameter setting value is obtained from the database, the temperature setting value and the humidity setting value are input to the temperature and humidity control system through the control panel, and the control system initializes the heater power and the humidifier water amount according to the setting value. A high-precision temperature and humidity sensor array is used to collect real-time temperature and humidity data at multiple points in the test chamber, and the sensor collection frequency is set to 1 time per second. The collected temperature and humidity values are subjected to sliding average filtering, and the average value within 5 seconds is taken as the current temperature and humidity measured value. The deviation between the measured value and the setting value is calculated by a comparison algorithm, and if the temperature deviation exceeds ±0.05℃ or the humidity deviation exceeds ±0.5%RH, the incremental PID controller is triggered. The proportional coefficient Kp of the PID controller is set to 0.5, the integral coefficient Ki is set to 0.1, and the differential coefficient Kd is set to 0.05. The controller calculates the heater power adjustment amount and the humidifier water amount adjustment amount according to the deviation value and the change trend. The temperature and humidity control actuator receives the adjustment amount output by the PID controller. For the heater, the PWM modulation method is used to control the power output, the PWM period is set to 1 second, and the duty cycle accuracy is 0.1%. For the humidifier, a stepper motor is used to control the water pump speed, the minimum step angle of the stepper motor is 1.8°, and 128 subdivision control is realized through micro-step driving. The response time of the control actuator is set to 1 second, and the adjustment accuracy can reach ±0.05℃ for temperature and ±0.5%RH for humidity. The control system of the constant temperature and humidity test chamber uses a MySQL-based relational database to store the preset temperature and humidity range parameters. The control panel communicates with the database server through the TCP / IP protocol to obtain the temperature setting value 25℃ and the humidity setting value 60%RH required by the current test. The control system uses a 32-bit ARM processor and runs the real-time operating system FreeRTOS. The output 0-5V analog signal initializes the heater power to 50% and the humidifier water amount to 30%. Eight SHT35 digital temperature and humidity sensors are arranged in the test chamber, and data transmission is performed through the I2C bus, with data collected once per second. The temperature and humidity values are represented by 32-bit floating-point numbers, and the last 5 seconds of data are subjected to sliding average filtering. The filtered temperature value is 25.03℃, and the humidity value is 59.8%RH. The comparison algorithm uses simple subtraction operation to calculate the temperature deviation 0.03℃ and the humidity deviation -0.2%RH. Since the temperature deviation does not exceed the ±0.05℃ threshold and the humidity deviation does not exceed the ±0.5%RH threshold, the PID controller is not triggered. If the temperature is found to rise to 25.06℃ in subsequent monitoring, exceeding the threshold, the incremental PID controller is triggered. The PID controller uses floating-point operation to calculate that the heater power needs to be reduced by 0.3% according to the set proportional coefficient 0.5, integral coefficient 0.1 and differential coefficient 0.05.The heater in the temperature and humidity control executor adopts MOSFET as the power output element, and the PWM signal is generated by a timer, with a clock frequency of 10 MHz and a PWM period of 1 second, corresponding to 10000000 clock cycles. The duty cycle accuracy is 0.1%, which is equivalent to 10000 discrete levels. The humidifier water pump is driven by a 42-step motor, using an A4988 stepper motor drive chip to achieve 128 subdivision control by setting MS1, MS2 and MS3 pins, with a minimum adjustment angle of 0.014°. The response time of the control executor is 1 second, during which the PWM duty cycle adjustment and the speed change of the stepper motor are completed, realizing accurate adjustment of temperature and humidity.

[0020] In step S102, initial spectrum measurement is performed on the LED light source to obtain the chromaticity coordinates, color temperature and color rendering index of the light source, the measured data is converted into numerical values in the standard color space, and an initial color characteristic database of the LED light source is established.

[0021] Spectrum data of the LED light source is obtained, which is collected by a high-precision spectrometer in a preset wavelength range. According to the spectrum data, tristimulus values X, Y and Z are calculated; the correlated color temperature is determined by the MacAdam method; a plurality of standard color samples are selected, and the color rendering index Ra is calculated, which is the average value of the color rendering indexes of the plurality of standard color samples. The chromaticity coordinates are converted into coordinates in the uniform color space. An initial color characteristic data table of the LED light source is created, which includes the fields of LED number, spectrum data, chromaticity coordinates, uniform color space coordinates, correlated color temperature and color rendering index Ra. A unique index of the LED number is established to improve the query efficiency of the data table.

[0022] Spectrometer, the wavelength range of which is 380-780 nm, the wavelength resolution is 1 nm, and the spectral collection time is set to 100 ms. The spectral data of the LED light source is obtained by measuring each LED light source for 10 times and calculating the average spectral data of the 10 measurements by Simpson integral method with an integral interval of 1 nm. According to the obtained spectral data, the tristimulus values X, Y, and Z are calculated by using the CIE1931 colorimetric standard observer function, and then the chromaticity coordinates (x, y) of the LED light source are calculated, wherein x = X / (X+Y+Z) and y = Y / (X+Y+Z). The color temperature is calculated by using the MacAdam method, and the closest point on the Planck locus is found by iterative calculation. The color rendering index Ra is calculated by using the CIE1995 method, and the color rendering index of each color sample is calculated by selecting 15 standard color samples, and the average value is taken. The measured chromaticity coordinates (x, y) are converted into (u', v') coordinates in the CIE1976 uniform color space, and the conversion formula is u' = 4x / (-2x+12y+3) and v' = 9y / (-2x+12y+3). Meanwhile, the L*, a*, and b* values in the CIELAB color space are calculated, and the conversion formula is L = 116f(Y / Yn)-16, a* = 500[f(X / Xn)-f(Y / Yn)], and b* = 200[f(Y / Yn)-f(Z / Zn)], wherein Xn, Yn, and Zn are the tristimulus values of the standard white point. The color temperature and color rendering index Ra are rounded to two decimal places to form a numerical set of the standard color space. An initial color characteristic data table of the LED light source is created by using a MySQL relational database, and the table structure includes the primary key ID (integer, self-increasing), LED number (character type, 20 bits), spectral data (BLOB type), chromaticity coordinates x and y (double-precision floating point type), u' and v' coordinates (double-precision floating point type), correlated color temperature (integer), color rendering index Ra (double-precision floating point type), L*, a*, and b* values (double-precision floating point type), and the like. The converted numerical set of the standard color space is written into the data table by using a preprocessing statement, and a unique index of the LED number is established to improve the query efficiency, thereby forming an initial color characteristic database of the LED light source. In the process of measuring the LED light source, a spectral radiometer OL770 is used for spectral measurement, the wavelength range is set to 380-780 nm, the wavelength resolution is 1 nm, and the integration time is set to 100 ms. Each LED light source is measured for 10 times, and the spectral data of 401 wavelength points are obtained in each measurement. The Simpson integral method is used to average the 10 measurement results to obtain the final spectral data. Taking a typical white LED as an example, the main wavelength is 455 nm, and the phosphor peak value is about 565 nm. The spectral data is integrated and calculated by using the CIE1931 standard color observer function to obtain the tristimulus values X = 3256, Y = 3412, and Z = 1576.The chromaticity coordinates x = 0.3952, y = 0.4141 are calculated. The correlated color temperature is calculated by the MacAdam method, and through 5 iterations, the correlated color temperature of the LED is 3985K. The color rendering index is calculated by selecting 15 standard color samples, and the average color rendering index Ra is 82. The chromaticity coordinates are converted to CIE1976 UCS chromaticity diagram coordinates, and u' = 0.2247, v' = 0.5298 are obtained. The CIELAB color space values are also calculated, and L* = 65.32, a* = -12.45, b* = 14.78 are obtained. All calculation results are rounded to two decimal places. The measurement results are stored in the MySQL database, and a table named led_color_characteristics is created, which includes id (primary key, auto-increment), led_code (varchar (20)), spectrum_data (blob), x (double), y (double), u_prime (double), v_prime (double), cct (int), cri (double), l_star (double), a_star (double), b_star (double) and other fields. The data is inserted into the table using a preprocessor statement, and a unique index is created on the led_code field to improve subsequent query efficiency.

[0023] In step S103, according to the initial color characteristic database, the LED light source is placed in the constant temperature and humidity test box for accelerated aging test, and the spectrum analyzer is used to obtain the spectrum data of the light source at different time points.

[0024] The LED light source number list stored in the initial color characteristic database is obtained, and the LED light source is placed on the preset coordinates in the constant temperature and humidity test box according to the LED light source number list; the accelerated aging test program is executed, and the total test time and the sampling time interval are set; the spectrum analyzer is controlled, and if the sampling time point is reached, the spectrum measurement is triggered, and the electrical parameters of the LED light source are monitored; the spectrum analyzer measures each LED light source multiple times according to the preset measurement parameters, and processes the multiple measurement data by using the moving average filtering algorithm; the measured spectrum data, cumulative aging time and environmental parameter information are associated with the LED light source number and inserted into the database table, and the database table includes LED number, measurement time point, spectrum data, temperature, humidity and light source state fields.

[0025] Exemplarily, a list of LED light source numbers is read from an initial color characteristic database, an automatic mechanical arm is used to place the LED light sources on a fixed support in a constant temperature and humidity test box according to preset coordinates, the support is provided with individually numbered interfaces, ensuring that the position of each LED light source is unique and traceable, the temperature of the test box is set to 85 DEG C, the relative humidity is set to 85%, and the environmental parameters are monitored and recorded in real time through a temperature and humidity sensor. An accelerated aging test program written based on Python is started, the total test time is set to 1000 hours, the sampling time interval is 50 hours, the program controls the spectrum analyzer through serial communication, triggers the spectrum measurement at each sampling time point, and simultaneously monitors the electrical parameters of the LED light source in real time. If an abnormal current is detected, the LED is immediately recorded and marked as a fault state. The spectrum analyzer measurement parameter settings are a wavelength range of 380-780 nm, a resolution of 1 nm, and an integration time of 100 ms. Each LED light source is measured continuously for 3 times, the moving average filtering algorithm is used to process the data of 3 measurements to remove possible noise interference, and the stable spectrum data at the time point is obtained. At the same time, the accurate time stamp of the spectrum measurement is recorded to the millisecond level, and the current cumulative aging time is recorded. A MySQL relational database is used to create an aging test data table, the table structure includes the fields of primary key ID (integer, self-increasing), LED number (character type, 20 bits), measurement time point (timestamp type), spectrum data (BLOB type), temperature (floating point type), humidity (floating point type), light source state (integer) and the like. The measured spectrum data, cumulative aging time, environmental parameters and the like are associated with the LED light source number and inserted into the data table using a pre-processing statement, and a composite index is established on the LED number and the measurement time point to improve the subsequent query and analysis efficiency. After completing the measurement of each time point, the corresponding record in the database is updated. In the LED accelerated aging test, first, the initial color characteristic table is read from the MySQL database to obtain the LED number list, such as LED001 to LED100. Using a KUKA KR3R540 automatic mechanical arm with an accuracy of ±0.02 mm, the LEDs are placed on the 100 numbered interfaces in the constant temperature and humidity test box according to the preset coordinates (X: 100 mm, Y: 100 mm, Z: 50 mm). The test box model is ESPEC PSL-2KPH, the temperature is set to 85±0.3 DEG C, the relative humidity is 85±1%RH, and the environmental data is collected every 10 seconds through a PT100 temperature sensor and a high-molecular humidity sensor. The test program written in Python is started, and the total time is set to 1000 hours, and the spectrum measurement is performed every 50 hours. The program communicates with the Ocean Optics QE65000 spectrum analyzer through the RS-232 serial port at a baud rate of 9600, and triggers the measurement. At the same time, the program monitors the LED current through the Keithley 2400 source table at a sampling rate of 1 Hz, and if the current fluctuation exceeds ±5%, the LED is marked as a fault state.The spectral measurement parameters are set as wavelength range 380-780 nm, resolution 1 nm, and integration time 100 ms. Each LED is measured for 3 times continuously, and the data is processed using a 5-point moving average filter algorithm to obtain a smooth spectral curve. A timestamp with a precision of 1 ms is used to record the measurement time. The processed data is stored in the aging_test table of the MySQL database, and the table structure includes id (INT), led_code (VARCHAR (20)), measure_time (TIMESTAMP), spectrum_data (BLOB), temperature (FLOAT), humidity (FLOAT), and led_status (TINYINT) fields. A pre-processing statement is used to insert data, and a composite index BTREE is established on the led_code and measure_time paths to optimize query speed.

[0026] Different LED light sources are tested using a multi-constant temperature and humidity test chamber in parallel. According to the model and specifications of the LED light source, the standard spectral data of the corresponding LED light source is obtained from the pre-established initial color characteristic data. Multiple different environmental parameter combinations are set in the constant temperature and humidity test chamber, and test chambers of corresponding sizes are selected for testing.

[0027] The standard spectral data corresponding to the model and specifications of the LED light source is obtained from the pre-established initial color characteristic database. The spectral peak wavelength and color temperature of the LED light source are determined according to the standard spectral data. A clustering algorithm is used to classify the LED light source to obtain a group of LED light sources with similar spectral characteristics. Environmental parameter combinations are generated for the LED light source group, and different environmental parameter combinations are set in each test chamber. The spatial position information of the LED light source is obtained. The LED light source is placed in the pre-set position in the test chamber. Parallel testing of multiple test chambers is performed, including synchronous aging testing of each test chamber. The environmental parameters and spectral data sent by the local data acquisition node are received.

[0028] Exemplarily, according to the model and specifications of the LED light source, the standard spectral data is extracted from the pre-established initial color characteristic database, the LED light source is classified by K-means clustering algorithm, the spectral peak wavelength and color temperature are taken as characteristic values, the number of clusters is set to 5, and the iteration number is set to 100, 5 groups of LED light sources with similar spectral characteristics are obtained, and each group is allocated a small constant temperature and humidity test chamber. For each group of LED light sources, L9(3^4) orthogonal table is designed for environmental parameter combination, factors include temperature 25℃, 55℃, 85℃ and humidity 45%, 65%, 85%, 9 kinds of environmental parameter combinations are generated, different environmental parameter combinations are set in each test chamber. Select the constant temperature and humidity test chamber with the internal size of 40cm×40cm×50cm, place 100 LED light sources in each test chamber, use the six-axis mechanical arm to accurately grab the LED light source and place it in the pre-set fixture position in the test chamber, the fixture spacing is 2cm, each test chamber is equipped with an independent temperature and humidity control unit and a spectral measurement device, and the parallel testing of multiple test chambers is realized. Start the parallel test program, and each test chamber synchronously performs the aging test, a distributed data acquisition system based on MQTT protocol is adopted, each test chamber is equipped with a Raspberry Pi as a local data acquisition node, the data is transmitted to the central server through MQTTBroker, the environmental parameters and spectral data are stored in the InfluxDB time series database, the real-time data visualization is realized through Grafana, and the centralized management and analysis of multiple test chamber data are realized. In the LED light source accelerated aging test, first, the standard spectral data of 1000 LED light sources is extracted from the MySQL database, and the K-means clustering algorithm is used for classification. The spectral peak wavelength such as 450nm, 550nm and the color temperature such as 3000K, 6500K are taken as characteristic values, the number of cluster centers k is set to 5, and the maximum iteration number is set to 100, 5 groups of LED light sources are obtained. Each group is equipped with an ESPECSH-242 constant temperature and humidity test chamber with an internal size of 40cm×40cm×50cm. L9(3^4) orthogonal table is used to design 9 kinds of environmental parameter combinations, the temperature is 25℃, 55℃, 85℃, and the humidity is 45%, 65%, 85%. KUKAKR3R540 six-axis mechanical arm cooperates with Konica Minolta In-Sight 7000 vision system to accurately grab the LED light source and put it into the test chamber, the fixture spacing is 2cm, and there are 100 in each chamber. The test chamber is equipped with Vaisala HMT330 temperature and humidity sensor with accuracy ±0.1℃ and ±1%RH, and Ocean Optics QE65000 spectrometer for spectral measurement. A distributed data acquisition system based on MQTT protocol is adopted, Raspberry Pi4B is used as a local node in each test chamber, and the data is transmitted to the central server through EMQXBroker.The time series data is stored using InfluxDB, the retention policy is set to 30 days, and Grafana creates a real-time dashboard to display temperature, humidity and spectral variation trends. Through this parallel test method, the accelerated aging test of 5000 LED samples is completed within 1000 hours, greatly improving the test efficiency and the comprehensiveness of data acquisition.

[0029] In step S104, the chromaticity coordinates, color temperature and color rendering index corresponding to the spectral data at different time points are calculated, the color deviation of the LED light source is calculated, and the change curve of the color deviation with time is fitted.

[0030] The spectral data of the LED light source at different time points is obtained, the tristimulus values X, Y and Z corresponding to the spectral data are calculated, and the chromaticity coordinates (x, y) are obtained; after the chromaticity coordinates (x, y) are determined, the correlated color temperature CCT and the color rendering index Ra are calculated; according to the chromaticity coordinates (x, y), the correlated color temperature CCT and the color rendering index Ra, the chromaticity coordinate deviation Δu'v', the color temperature deviation ΔCCT and the color rendering index deviation ΔRa are calculated; for the changes of the Δu'v', ΔCCT and ΔRa with time, the least square method is used for 3 times polynomial fitting to obtain the fitting function; according to the fitting function, the corresponding color deviation value is calculated through the pre-defined time points.

[0031] For example, the spectral data of the LED light source at different time points is extracted from the database, the tristimulus values X, Y and Z are calculated using the CIE1931 colorimetric calculation formula, and then the chromaticity coordinates (x, y) are obtained. The correlated color temperature CCT is calculated using the MacAdam formula, and the color rendering index Ra is calculated using the CIE13.3-1995 method, 15 standard color samples are selected to calculate the special color rendering index Ri, and the average value is taken to obtain Ra. The calculation results are stored in the result data table. According to the initial color characteristic data and the calculation results at each time point, the chromaticity coordinate deviation Δu'v' is calculated, the CIE1976UCS color diagram is used for calculation, the color temperature deviation ΔCCT is calculated through

[0032] | CCT test - CCT initial | Get, color rendering index deviation ΔRa by | Ra test - Ra initial | Calculate, associate deviation data with corresponding time point, store in color deviation data table. Use numpy library polyfit function for least squares polynomial fitting, respectively Δu'v', ΔCCT and ΔRa change with time 3 times polynomial fitting, get the form like at^3+bt^2+c*t+d fitting function, t is time, a, b, c, d is constant, store fitting parameters and fitting degree R2 in fitting results table. Based on the fitting function, use numpy poly1d function to create polynomial object, calculate the corresponding color deviation value by passing in the predefined time point, such as 100 hours, 1000 hours, 10000 hours, at the same time use scipy.optimize library fsolve function to solve when the fitting function reaches the preset deviation threshold, save the prediction results to the prediction data table, provide data support for subsequent analysis and decision making. In the process of LED light source color performance analysis, first extract the spectral data of 0 hours, 100 hours, 500 hours and 1000 hours from the MySQL database. Use CIE1931 colorimetric formula to calculate the three stimulus values, such as X=3256, Y=3412, Z=1576 at 0 hours, get the chromaticity coordinates x=0.3952, y=0.4141. Calculate the correlated color temperature CCT using the MacAdam formula, which is 3985K at 0 hours. Calculate the color rendering index Ra by selecting 15 standard color samples, Ra is 82 at 0 hours. Store the results in the results data table. Calculate the color deviation, such as Δu'v'=0.0056, ΔCCT=127K, ΔRa=5 at 1000 hours, store in deviations data table. Use numpy library polyfit function for 3 times polynomial fitting, such as Δu'v'(t)=2e-11t^3-3e-8t^2+2e-5t+0.0001, R2=0.9985, e is constant, store in fitting_results data table. Based on the fitting function, predict the color deviation at 10000 hours, such as Δu'v'=0.0189, ΔCCT=423K, ΔRa=16. Use scipy.optimize.fsolve function to solve the time point when Δu'v' reaches 0.01, get 5783 hours. All prediction results are stored in predictions prediction data table.

[0033] Step S105, the color deviation data of the LED light source is processed by dimension reduction, the factors affecting the color stability are extracted, the color compensation model is established, the working time and environmental parameters of the LED light source are input, and the color compensation parameters are output. Dynamic compensation of LED light source color.

[0034] Obtaining color deviation data of an LED light source and corresponding working time and environmental parameters, the color deviation data including chromaticity coordinate deviation, color temperature deviation and color rendering index deviation; performing dimension reduction processing on the color deviation data, and determining main components if an accumulated contribution rate of eigenvalues and eigenvectors reaches a preset threshold; constructing a nonlinear color compensation model according to the main components, the nonlinear color compensation model adopting a support vector regression algorithm and selecting a radial basis function as a kernel function; converting the nonlinear color compensation model into a lightweight format, downloading the nonlinear color compensation model in the lightweight format to an LED drive controller; receiving working time and environmental parameters sent by the LED drive controller, calculating color compensation parameters according to the nonlinear color compensation model, and adjusting duty cycles of LED drive currents of three RGB channels.

[0035] Exemplarily, color deviation data of LED light sources, including chromaticity coordinate deviation, color temperature deviation, color rendering index deviation, and corresponding working time and environmental parameters, are extracted from the database, principal component analysis algorithm is used for dimension reduction processing of the data, PCA method is used for implementation, 95% of the variance information is set to be retained, the main components are selected by calculating the eigenvalues and eigenvectors, and the main factors affecting color stability are extracted. Based on the dimension-reduced data, a nonlinear color compensation model is constructed by using a support vector regression algorithm, an SVR algorithm is used, an RBF kernel function is selected, the hyperparameters C and gamma are optimized by grid search, the working time and environmental parameters are used as input variables, and the color compensation parameters are used as output variables. The constructed color compensation model is converted into a lightweight format, the model parameters are downloaded to an LED driving controller based on an ARM Cortex-M4 through serial communication, the controller uses a real-time operating system, the working time and environmental parameters are sampled once every 100 ms, the working time and environmental parameter information of the LED light source is received in real time, and the color compensation parameters are calculated by inputting the model. According to the calculated color compensation parameters, the PWM technology is used to adjust the LED driving current of the RGB three channels, the PWM frequency is set to 20 kHz, the resolution is 12 bits, the duty cycle is adjusted to realize 0.025% fine adjustment, the dynamic compensation of the color of the LED light source is realized, and the stability of the color output is maintained. In the LED color compensation system, firstly, 1000 groups of color deviation data of LED light sources are extracted from the MySQL database, including Δu'v', ΔCCT, ΔRa, and corresponding working time 0-10000 hours and environmental temperature-20℃ to 60℃. The PCA class of the sklearn library is used for principal component analysis, and n_components=0.95 is set to retain 95% of the variance information. After PCA processing, the original 5-dimensional data is reduced to 3-dimensional, and the main influencing factors are working time, environmental temperature and Δu'v'. Based on the dimension-reduced data, the SVR class of the sklearn library is used to construct a nonlinear color compensation model, the RBF kernel function is selected, and the parameters are optimized by GridSearchCV. Finally, C=10 and gamma=0.1 are determined. After the model training is completed, the model is converted into an 8-bit quantized.tflite format by using TensorFlowLite, and the size is compressed from the original 5MB to 500KB. The model is downloaded to an LED driving controller based on an STM32F407 through a UART interface at a baud rate of 115200, the controller runs in a FreeRTOS environment, the environmental temperature is sampled by an ADC analog-digital converter every 100 ms, and the working time is read from an RTC real-time clock. The sampling data is input into the nonlinear color compensation model, and the compensation parameters of the RGB three channels are calculated.The controller uses Timer1, Timer2, Timer3 clock to generate 20kHz, 12-bit resolution PWM signal, according to the compensation parameter dynamic adjustment duty cycle, realize 0.025% fine adjustment. In this way, it can compensate the color deviation of LED light source in real time, control Δu'v' within 0.003 in 10000 hours of use cycle, ensure the long-term color stability of LED light source.

[0036] Step S106, through the LED drive circuit adjustment light source current and voltage parameters, dynamic compensation of LED light source color, adjust the intensity ratio of different wavelength light, improve the color rendering index and color saturation of LED light source; real-time monitoring of LED light source color output, when the color saturation parameter deviates from the target value, the corresponding correction is carried out.

[0037] Obtain the resistance range and adjustment series of the digital potentiometer, the digital potentiometer is used for controlling the output current of the driving chip; the brightness of the LED is controlled according to the output current of the driving chip, and the brightness of the LED is adjusted by PWM modulation; the spectral data of the LED light source is collected by using a spectral analyzer, and the spectral data is used to calculate the color rendering index and color saturation; a PID control algorithm is run, the PID algorithm takes the target color rendering index and color saturation as the set value, and takes the measured value as the feedback input; it is judged whether the output compensation value of the PID algorithm is in the preset range; if the compensation value is in the preset range, the compensation value is mapped to the resistance value of the digital potentiometer and the PWM duty cycle; adjust the resistance value and PWM duty cycle of the digital potentiometer, the adjustment is used to change the current and brightness of each channel of the LED.

[0038] An example of a multi-channel LED driving circuit is designed using MCP4151 digital potentiometer with a resistance range of 0-10 kΩ and 256 levels of adjustment. The LED current is controlled by LM3405 driving chip with a range of 0-1 A. The brightness of the LED is controlled by PWM modulation with a frequency of 20 kHz and a resolution of 12 bits, achieving a fine adjustment of 0.025%. The spectral data of the LED light source is collected in real time using Ocean Optics QE65000 spectral analyzer with a measurement range of 350-1000 nm and a resolution of 0.8 nm. The color rendering index is calculated using CIE13.3-1995 method, and the color saturation is calculated using CIECAM02 color appearance model. The data is transmitted to the control unit through USB interface with a sampling frequency of 10 Hz. The incremental PID control algorithm is run in the Arduino Mega2560 control unit with a frequency of 16 MHz, Kp=0.5, Ki=0.2, and Kd=0.1. The target color rendering index and color saturation are set as the set value, and the measured value is fed back as the input. The compensation value for each LED channel is calculated, and the PID parameters are determined by Ziegler-Nichols tuning method. According to the compensation value output by the PID algorithm, the output range of -100 to 100 is linearly mapped to the resistance range of 0-10 kΩ of the digital potentiometer and the duty cycle range of 0-100% of the PWM. The PWM signal is generated using the analogWrite() function of Arduino, and the resistance of the MCP4151 digital potentiometer and the duty cycle of the PWM are adjusted in real time through the SPI interface, thereby changing the current and brightness of each channel of the LED and achieving dynamic compensation of the color of the LED light source and improvement of the color rendering index and color saturation. In the dynamic compensation of LED color, a four-channel LED driving circuit is designed to control red, green, blue, and white LEDs respectively. Each channel uses MCP4151 digital potentiometer and LM3405 driving chip with a current range of 0-1 A, a PWM frequency of 20 kHz, and a resolution of 12 bits. For example, the red LED is initially set to 700 mA with a duty cycle of 50%. The LED spectrum is collected in real time using Ocean Optics QE65000 spectral analyzer with a measurement range of 350-1000 nm and a sampling frequency of 100 ms. The color rendering index Ra is calculated using CIE13.3-1995 method with an initial value of 82, and the color saturation s is calculated using CIECAM02 model with an initial value of 1.2. The Arduino Mega2560 is used as the control unit to run the incremental PID algorithm with Kp=0.5, Ki=0.2, and Kd=0.1, and the target Ra is set to 90 and s is set to 1.5. When the actual Ra=85 and s=1.3 are detected, the PID output compensation value is -20. The -20 is linearly mapped to an increase of 1.6 kΩ in the resistance of the digital potentiometer, and the initial 5 kΩ becomes 6.6 kΩ. The PWM duty cycle decreases by 10%, from 50% to 40%.The MCP4151 is updated through the SPI interface at a rate of 1MHz, and the PWM is adjusted through the analogWrite() function. This causes the red LED current to drop to about 530mA, and the brightness to weaken. At the same time, other channels are adjusted accordingly, such as the blue LED current increasing to 850mA, and the duty cycle rising to 60%. After several iterations of adjustment, the final Ra=89.5, s=1.48, close to the target value, completing the dynamic color compensation of the LED light source.

[0039] In step S107, according to the results of the LED light source accelerated aging test, the color maintenance life of the light source in a high temperature and high humidity environment is calculated, and the color deviation detection is completed through the color stability uniformity index of the LED light source.

[0040] A constant temperature and humidity test chamber is used to set the LED light source accelerated aging test environment, the temperature of the test environment is a preset temperature value, the relative humidity of the test environment is a preset humidity value, and the test duration is a preset duration value; according to the preset time interval, the spectrometer is used to collect the spectral data of the LED light source, the spectral data including luminous flux, chromaticity coordinates, correlated color temperature and color rendering index; according to the spectral data, the color difference value of the LED light source at different time points is calculated, if the color difference value exceeds the preset threshold value, it is determined that the color maintenance life of the LED light source is the end point; the light emitting surface image of the LED light source is obtained, and the chroma uniformity index is calculated according to the light emitting surface image, the chroma uniformity index is used to represent the color stability uniformity of the LED light source; according to the spectral data, the maximum color difference, the average color difference and the color difference standard deviation of the LED light source in the whole test process are calculated, and the maximum color difference, the average color difference and the color difference standard deviation are used to evaluate the color deviation of the LED light source.

[0041] Exemplarily, an LED light source is set in a constant temperature and humidity test chamber to accelerate the aging test environment, the temperature is 85°C, the relative humidity is 85%, the test duration is set to 1000 hours, every 50 hours uses OceanOptics QE65000 spectrometer to collect the spectral data of LED light source, wavelength range 380-780nm, resolution 0.8nm, records the luminous flux, chromaticity coordinates, correlated color temperature and color rendering index. According to the collected spectral data, the color difference value Δu'v' of LED light source at different time points is calculated, when Δu'v' exceeds 0.007, it is determined that the color maintenance life of LED light source ends, the linear interpolation method is used to calculate the accurate color maintenance life, find the two adjacent time points of Δu'v' just exceeding 0.007, and the accurate life time is obtained by linear interpolation. The LED light emitting surface image is taken by using Allied Vision Prosilica GT2000 high resolution CCD camera, the chromaticity uniformity index CUI is calculated, the image is divided into 9 equal area regions, the average chromaticity coordinates of each region are calculated, then the maximum deviation of the 9 coordinates and the overall average coordinates is calculated to obtain the CUI value, the closer the CUI value to 1, the better the color uniformity, and the CUI is used as the color stability uniformity index of LED light source. Based on the collected spectral data, the maximum color difference Δu'v'max, the average color difference Δu'v'avg and the color difference standard deviation σΔu'v' of LED light source in the whole test process are calculated, the color deviation of LED light source is evaluated comprehensively by these three parameters, the color deviation detection report is generated by using Python pandas library, including time series data, statistical charts and summary data, the report is output in HTML format, which is convenient for viewing and sharing. In the accelerated aging test of LED light source, the ESPEC PL-3KPH constant temperature and humidity test chamber is set to 85°C, 85%RH, and the test lasts for 1000 hours. The OceanOptics QE65000 spectrometer collects spectral data every 50 hours, the wavelength range is 380-780nm, and the integration time is 100ms. The initial measurement shows that the LED luminous flux is 1000lm, the chromaticity coordinates are (0.3127, 0.3290), the CCT is 6500K, and the CRI is 80. At 850 hours, the measured Δu'v' is 0.0068, and at 900 hours, it is 0.0074. Through linear interpolation, the color maintenance life is calculated to be 875 hours. The Allied Vision Prosilica GT2000 camera takes a 16M pixel image of the LED light emitting surface with an exposure time of 10ms. The image processing algorithm divides it into a 3x3 grid, calculates the average (u', v') of each grid. The center region (0.2103, 0.4997), the edge region (0.2116, 0.5012), the maximum deviation is 0.0022, and the CUI is 0.9978.Using the pandas library to process 21 sets of measurement data over 1000 hours, the calculated Δu'v'max = 0.0074, Δu'v'avg = 0.0042, σΔu'v' = 0.0015. The HTML report contains time series charts, such as 0-1000 hours Δu'v'.

[0042] The change curve, and the CUI, CCT correlated color temperature, CRI color rendering index change column chart over time. The summary data table lists the key parameters for color maintenance life 875h, the final CUI 0.9978, Δu'v'max 0.0074. The report is published through a web server, making it easy to access remotely and share analysis results with the team.

[0043] The above only lists some preferred embodiments of the present application, but the present application is not limited thereto, and many improvements and changes can be made. As long as the improvements and changes are made on the basis of the basic principles of the present application, they should be considered to fall within the scope of the present application.

Claims

1. A method for detecting the uniformity and color deviation of an LED light source, characterized in that, The method includes: setting the environmental parameters of the constant temperature and humidity test chamber, monitoring the environment inside the chamber in real time through temperature and humidity sensors, and triggering the temperature and humidity control system to adjust when the temperature and humidity deviate from the set values. Initial spectral measurements were performed on the LED light source to obtain its chromaticity coordinates, color temperature, and color rendering index. The measurement data were then converted into values ​​in a standard color space to establish an initial color characteristic database for the LED light source. Based on the initial color characteristic database, the LED light source was placed in the constant temperature and humidity test chamber for accelerated aging test, and the spectral data of the light source at different time points were obtained using a spectral analyzer. Calculate the chromaticity coordinates, color temperature, and color rendering index corresponding to the spectral data at different time points, calculate the color deviation of the LED light source, and fit the curve of color deviation changing over time. Dimensionality reduction is performed on the color deviation data of LED light sources to extract factors affecting color stability, and a color compensation model is established. The model takes the LED light source's operating time and environmental parameters as input and outputs color compensation parameters to dynamically compensate for the LED light source's color. This includes: acquiring color deviation data of the LED light source and corresponding operating time and environmental parameters, where the color deviation data includes chromaticity coordinate deviation, color temperature deviation, and color rendering index deviation; performing dimensionality reduction on the color deviation data, and determining the principal components if the cumulative contribution rate of eigenvalues ​​and eigenvectors reaches a preset threshold; constructing a nonlinear color compensation model based on the principal components, where the nonlinear color compensation model uses a support vector regression algorithm and selects a radial basis function as the kernel function; converting the nonlinear color compensation model into a lightweight format and downloading the lightweight nonlinear color compensation model to the LED driver controller; receiving the operating time and environmental parameters sent by the LED driver controller, calculating the color compensation parameters based on the nonlinear color compensation model, and adjusting the duty cycle of the LED driving current for the three RGB channels. By adjusting the current and voltage parameters of the LED light source through the LED driver circuit, dynamic compensation of the LED light source color is achieved, the intensity ratio of different wavelengths of light is adjusted, the color rendering index and color saturation of the LED light source are improved, and the color output of the LED light source is monitored in real time. When the color saturation parameter is detected to deviate from the target value, corresponding corrections are made. Based on the accelerated aging test results of LED light sources, the color maintenance life of the light source under high temperature and high humidity environment is calculated, and the color deviation is detected by the color stability and uniformity index of LED light sources.

2. The method according to claim 1, wherein, The environmental parameters of the constant temperature and humidity test chamber are set by real-time monitoring of the environment inside the chamber using temperature and humidity sensors. When the temperature and humidity deviate from the set values, the temperature and humidity control system is triggered to adjust them. This includes: acquiring the environmental parameter setting values, which are read from a pre-established database; collecting real-time temperature and humidity data inside the test chamber, which are collected from multiple points by a temperature and humidity sensor array; performing a moving average filter on the real-time temperature and humidity data to obtain the measured temperature and humidity values; calculating the deviation between the measured temperature and humidity values ​​and the set values; determining whether the deviation exceeds a preset threshold, and if the deviation exceeds the preset threshold, triggering parameter control; and calculating the heater power adjustment and humidifier water volume adjustment based on the magnitude and trend of the deviation, which are output by a PID controller.

3. The method according to claim 1, wherein, The initial spectral measurement of the LED light source, obtaining its chromaticity coordinates, color temperature, and color rendering index (CRI), converting the measurement data into values ​​in a standard color space, and establishing an initial color characteristic database for the LED light source includes: acquiring spectral data of the LED light source, which is collected by a high-precision spectrometer within a preset wavelength range; calculating tristimulus values ​​X, Y, and Z based on the spectral data; determining the correlated color temperature using the McAdam method; selecting several standard color samples and calculating the CRI Ra, where the CRI Ra is the average of the CRIs of the several standard color samples; converting the chromaticity coordinates to coordinates in a uniform color space; creating an initial color characteristic data table for the LED light source, the data table containing fields for LED number, spectral data, chromaticity coordinates, uniform color space coordinates, correlated color temperature, and CRI Ra; and establishing a unique index for the LED number to improve the query efficiency of the data table.

4. The method according to claim 1, wherein, The process involves placing LED light sources in a constant temperature and humidity test chamber for accelerated aging testing based on the initial color characteristic database. A spectrometer is used to acquire spectral data of the light sources at different time points. This includes: acquiring a list of LED light source numbers stored in the initial color characteristic database; placing the LED light sources at preset coordinates within the constant temperature and humidity test chamber according to the LED light source number list; executing an accelerated aging test program, which sets the total test duration and sampling time interval; controlling the spectrometer to trigger spectral measurement when a sampling time point is reached, while simultaneously monitoring the electrical parameters of the LED light sources; and the spectrometer measuring each LED light source according to preset measurement parameters. The LED light source was measured multiple times, and the measurement data was processed using a moving average filtering algorithm. The measured spectral data, cumulative aging time, and environmental parameter information were associated with the LED light source number and inserted into a database table. The database table contained fields for LED number, measurement time point, spectral data, temperature, humidity, and light source status. The test also included: testing different LED light sources in parallel using multiple constant temperature and humidity test chambers. Based on the LED light source model and specifications, standard spectral data for the corresponding LED light source model was obtained from pre-established initial color characteristic data. Multiple different combinations of environmental parameters were set in the constant temperature and humidity test chambers, and test chambers of corresponding sizes were selected for testing.

5. The method according to claim 4, wherein, The method employs parallel testing in multiple constant temperature and humidity test chambers to test different LED light sources. Based on the model and specifications of the LED light source, standard spectral data corresponding to the model is obtained from pre-established initial color characteristic data. Multiple different environmental parameter combinations are set within the constant temperature and humidity test chambers, and test chambers of corresponding sizes are selected for testing. This includes: obtaining standard spectral data from a pre-established initial color characteristic database, where the standard spectral data corresponds to the model and specifications of the LED light source; determining the spectral peak wavelength and color temperature of the LED light source based on the standard spectral data; classifying the LED light sources using a clustering algorithm to obtain groups of LED light sources with similar spectral characteristics; generating environmental parameter combinations for the LED light source groups, with different environmental parameter combinations set in each test chamber; acquiring the spatial location information of the LED light source; placing the LED light source in a preset position within the test chamber; conducting parallel testing in multiple test chambers, including simultaneous aging tests in each test chamber; and receiving environmental parameters and spectral data sent by a local data acquisition node.

6. The method according to claim 1, wherein, The calculation of chromaticity coordinates, color temperature, and color rendering index corresponding to spectral data at different time points, the calculation of color deviation of the LED light source, and the fitting of the color deviation change curve over time include: acquiring spectral data of the LED light source at different time points, calculating the tristimulus values ​​X, Y, and Z corresponding to the spectral data to obtain chromaticity coordinates (x, y); after the chromaticity coordinates (x, y) are determined, calculating the correlated color temperature (CCT) and color rendering index (Ra); based on the chromaticity coordinates (x, y), CCT, and Ra, calculating the chromaticity coordinate deviation Δu'v', color temperature deviation ΔCCT, and color rendering index deviation ΔRa; for the changes of Δu'v', ΔCCT, and ΔRa over time, performing a third-order polynomial fitting using the least squares method to obtain a fitting function; and based on the fitting function, calculating the corresponding color deviation value at predefined time points.

7. The method according to claim 1, wherein, The process involves adjusting the current and voltage parameters of the LED light source via an LED driving circuit to dynamically compensate for the LED light source's color, adjust the intensity ratio of different wavelengths of light, improve the color rendering index and color saturation of the LED light source, and monitor the color output of the LED light source in real time. When the color saturation parameter deviates from the target value, corresponding corrections are performed. This includes: acquiring the resistance range and adjustment levels of a digital potentiometer, which is used to control the output current of the driving chip; controlling the LED brightness based on the output current of the driving chip, whereby the LED brightness is adjusted via PWM modulation; collecting spectral data of the LED light source using a spectral analyzer, whereby the spectral data is used to calculate the color rendering index and color saturation; running a PID control algorithm, where the PID algorithm uses the target color rendering index and color saturation as setpoints and the measured values ​​as feedback inputs; determining whether the output compensation value of the PID algorithm is within a preset range; if the compensation value is within the preset range, mapping the compensation value to the digital potentiometer resistance and PWM duty cycle; and adjusting the digital potentiometer resistance and PWM duty cycle, whereby the adjustment is used to change the current and brightness of each LED channel.

8. The method according to claim 1, wherein, The process of calculating the color maintenance lifespan of the LED light source under high temperature and high humidity conditions based on the accelerated aging test results, and detecting color deviation through the color stability and uniformity index of the LED light source, includes: setting up the accelerated aging test environment of the LED light source using a constant temperature and humidity test chamber, wherein the temperature of the test environment is a preset temperature value, the relative humidity of the test environment is a preset humidity value, and the test duration is a preset duration value; collecting spectral data of the LED light source using a spectrometer at preset time intervals, wherein the spectral data includes luminous flux, chromaticity coordinates, correlated color temperature, and color rendering index; calculating the color difference value of the LED light source at different time points based on the spectral data, and determining the end point of the color maintenance lifespan of the LED light source if the color difference value exceeds a preset threshold; acquiring an image of the emitting surface of the LED light source, calculating the chromaticity uniformity index based on the emitting surface image, wherein the chromaticity uniformity index is used to characterize the color stability and uniformity of the LED light source; and calculating the maximum color difference, average color difference, and standard deviation of the color difference of the LED light source throughout the entire test process based on the spectral data, wherein the maximum color difference, the average color difference, and the standard deviation of the color difference are used to evaluate the color deviation of the LED light source.

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