LED fluorescent material quality management system based on emission spectrum data

Abstract

The invention relates to the technical field of quality management, and discloses an LED fluorescent material quality management system based on emission spectrum data, and the system comprises a trajectory analysis unit, a spectral analysis unit, a high temperature maintenance unit and a quality management unit. A spectral response track is generated through parameters such as a spectral intrinsic purity factor and a thermal evolution incidence matrix, the spectral drift and thermal quenching process is reduced, the spectral analysis unit carries out analysis based on the spectral response track to obtain a dynamic spectral response vector representing dynamic thermal stability, and meanwhile, the high-temperature performance maintenance index generated by the high-temperature maintenance unit is used for improving the dynamic thermal stability. The performance maintenance capability of the LED fluorescent material at a high temperature can be reflected, the instruction of the LED fluorescent material can be effectively judged through the quality management identifier generated by the quality management unit, the quality misjudgment is effectively avoided, and the quality management accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of quality management technology, and more specifically to a quality management system for LED fluorescent materials based on emission spectrum data. Background Technology

[0002] Currently, when evaluating the quality of LED fluorescent materials, the standard emission spectrum data measured at room temperature is usually used. The emission spectrum curve can be plotted using this standard spectrum, and key spectral parameters such as peak wavelength, half width at half maximum (WHM), and relative intensity can be extracted. These parameters are then compared with preset acceptable thresholds to determine whether the quality of the fluorescent material meets the requirements, thereby achieving quality control of the product.

[0003] However, the above quality management methods still have the following drawbacks: In industrial high-temperature lighting scenarios, the optical properties of fluorescent materials, including peak wavelength and luminous intensity, will change significantly with ambient temperature and working time, exhibiting temperature-dependent spectral drift and thermal quenching effects. If the quality judgment standards at room temperature are still used, the quality judgment results will not match the actual quality of LED fluorescent materials, reducing the accuracy of quality control. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an LED fluorescent material quality management system based on emission spectrum data, which solves the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A quality management system for LED fluorescent materials based on emission spectrum data, comprising: The trajectory analysis unit is used to acquire the emission spectrum of the target object under different temperatures, analyze the two emission spectra, and obtain the spectral response trajectory. The target object under management is an LED fluorescent material. The spectral analysis unit is used to calculate the thermal mobility of the spectral centroid and the retention rate of the characteristic peaks of the target object based on the spectral response trajectory, and obtain the dynamic spectral response vector. The high-temperature maintenance unit is used to analyze the dynamic spectral response vector to obtain the high-temperature performance maintenance index, which represents the performance maintenance capability of the target managed object at high temperatures. The quality management unit is used to determine the quality management identifier based on the high-temperature performance maintenance index.

[0006] Furthermore, different temperatures include: The different temperatures are room temperature and high temperature. Room temperature is 25℃, and high temperature is ≥80℃.

[0007] Furthermore, the two emission spectra were analyzed to obtain the spectral response trajectories, including: Interference removal is performed on the preprocessed emission spectrum to generate the intrinsic purity factor of the spectrum. The preprocessed emission spectrum was analyzed to obtain the intrinsic emission components and their weighting coefficients; The relationship between the weighting coefficients of the two intrinsic emission components and temperature is analyzed to form a thermal evolution correlation matrix, which is used to represent the cooperative or competitive relationship between different emission centers under thermal perturbation.

[0008] Furthermore, analysis of the two emission spectra yielded the spectral response trajectories, which also included: The direction of the maximum variance of the dominant spectral shape change in the thermal evolution correlation matrix is ​​analyzed to obtain the principal evolution axis vector. The projection value of the spectrum at each temperature point onto the principal evolution axis is calculated to form a projection evolution sequence. Based on the projection evolution sequence, the spectral coordinates of each temperature point are interpolated to generate high-density intermediate trajectory nodes. The spatial arrangement of nodes in the intermediate trajectory nodes is optimized by combining the spectral intrinsic purity factor, and the trajectory smoothness convergence of the entire interpolation path is calculated. Based on the trajectory smoothness convergence, the nodes in the high-density trajectory nodes are curve fitted and connected to generate a spectral response trajectory that continuously changes from room temperature to high temperature.

[0009] Furthermore, based on the spectral response trajectory, the thermal mobility of the spectral centroid of the target object and the retention rate of its characteristic peak shape are calculated to obtain the dynamic spectral response vector, including: The spectral response trajectory is analyzed, and its curvature is calculated as a function of temperature to obtain the trajectory thermal perturbation entropy value. In two-dimensional space, the spectral response trajectory is projected to obtain a two-dimensional path of spectral migration with temperature. The rate of change of the angle of the tangent direction of the two-dimensional path at each temperature point is calculated to generate a centroid migration trend vector representing the migration direction and rate.

[0010] Furthermore, based on the spectral response trajectory, the thermal mobility of the spectral centroid of the target object and the retention rate of its characteristic peak shape are calculated to obtain the dynamic spectral response vector, which also includes: The emission spectrum shape of the spectral response trajectory at various temperatures is analyzed to obtain the spectral morphology fidelity factor; For the key bands in the emission spectrum, the variation of the fluctuation amplitude of the spectral response trajectory in the key bands with temperature is analyzed to obtain the characteristic band trajectory stiffness.

[0011] Furthermore, based on the spectral response trajectory, the thermal mobility of the spectral centroid of the target object and the retention rate of its characteristic peak shape are calculated to obtain the dynamic spectral response vector, which also includes: The thermal perturbation entropy of the trajectory and the centroid migration trend vector are calculated to obtain the spectral centroid thermal mobility. The spectral morphology fidelity factor and the trajectory stiffness of the characteristic band are analyzed to obtain the characteristic peak shape retention rate. A two-dimensional dynamic spectral response vector is constructed by using the thermal mobility of the spectral centroid and the retention rate of characteristic peaks as components.

[0012] Furthermore, by analyzing the dynamic spectral response vector, a high-temperature performance maintenance index is obtained, which represents the performance maintenance capability of the target managed object at high temperatures. This index includes: The dynamic spectral response vector is analyzed to generate a high-temperature phase trajectory stability index; The attenuation tendency of the dynamic spectral response vector is evaluated to obtain the vector thermal attenuation coefficient.

[0013] Furthermore, by analyzing the dynamic spectral response vector, a high-temperature performance maintenance index is obtained, which represents the performance maintenance capability of the target managed object at high temperatures. This also includes: By analyzing the directional shift of the dynamic spectral response vector, the vector thermal drift tolerance is obtained; The interaction between the thermal mobility of the spectral centroid and the retention rate of characteristic peaks at high temperatures was analyzed to generate a high-temperature component coupling factor. Using the high-temperature phase trajectory stability index as the stability weight, the vector thermal decay coefficient, vector thermal drift tolerance, and high-temperature component coupling factor are fused to generate the high-temperature performance maintenance index.

[0014] Furthermore, based on the high-temperature performance maintenance index, quality management indicators are determined, including: By analyzing the changes in the high-temperature performance maintenance index, the curvature at the end of the performance degradation was obtained. By synergistically analyzing the high-temperature performance maintenance index, the curvature at the end of performance degradation, and the intrinsic purity factor of the spectrum, a quality management identifier is obtained.

[0015] In summary, the present invention has the following main beneficial effects: The emission spectra at room temperature and high temperature are obtained through the trajectory analysis unit. After interference stripping, a spectral intrinsic purity factor is generated. A thermal evolution correlation matrix is ​​constructed by combining the intrinsic emission components and their weighting coefficients to accurately capture the cooperative or competitive relationships under thermal perturbations of different luminescent centers. Then, through optimization of the principal evolution axis vector, projected evolution sequence, and trajectory smoothing convergence, a continuous spectral response trajectory is generated, comprehensively reflecting the temperature-dependent spectral drift and thermal quenching process. Based on the spectral response trajectory, the spectral analysis unit obtains the light emission spectrum through multi-dimensional calculations of trajectory thermal perturbation entropy, centroid migration trend vector, spectral morphology fidelity factor, and characteristic band trajectory stiffness. The dynamic spectral response vector constructed by the spectral centroid thermal mobility and characteristic peak shape retention rate can reflect the thermal stability of LED phosphor material quality dynamics. At the same time, the high-temperature maintenance unit integrates the high-temperature phase trajectory stability index, vector thermal decay coefficient, vector thermal drift tolerance, and high-temperature component coupling factor to generate a high-temperature performance maintenance index, which accurately reflects the performance maintenance capability of LED phosphor material at high temperatures. Finally, the quality management unit combines the performance decay end curvature and spectral intrinsic purity factor to generate a quality management label, effectively avoiding misjudgment of LED phosphor material quality in high-temperature scenarios and achieving effective management of LED phosphor material quality. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an LED fluorescent material quality management system based on emission spectrum data according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] refer to Figure 1 A quality management system for LED fluorescent materials based on emission spectral data, comprising: The trajectory analysis unit is used to acquire the emission spectrum of the target object under different temperatures, analyze the two emission spectra, and obtain the spectral response trajectory. The target object under management is an LED fluorescent material. The spectral analysis unit is used to calculate the thermal mobility of the spectral centroid and the retention rate of the characteristic peaks of the target object based on the spectral response trajectory, and obtain the dynamic spectral response vector. The high-temperature maintenance unit is used to analyze the dynamic spectral response vector to obtain the high-temperature performance maintenance index, which represents the performance maintenance capability of the target managed object at high temperatures. The quality management unit is used to determine the quality management identifier based on the high-temperature performance maintenance index.

[0019] In one embodiment, different temperatures include: The different temperatures are room temperature and high temperature. Room temperature is 25℃, and high temperature is ≥80℃.

[0020] In one embodiment, the two emission spectra are analyzed to obtain the spectral response trajectory, including: Interference removal is performed on the preprocessed emission spectra to generate a spectral intrinsic purity factor. Specifically, this involves: for each preprocessed emission spectrum, identifying the characteristic emission peak with the highest intensity; dividing the wavelength range of the entire characteristic emission peak into a left and right half-region, with the wavelength position corresponding to the peak point of the characteristic emission peak as the boundary; integrating the areas under the spectral curve for the left and right half-regions respectively to obtain the integrated intensity of the left and right sides; dividing the absolute value of the difference between the integrated intensity of the left and right sides by the total integrated intensity of the entire characteristic emission peak, and normalizing the calculation result to the 0-1 interval to obtain a symmetry index. The closer the symmetry index is to 0, the more symmetrical the peak shape. For the same characteristic emission peak, the intensity value at its highest point is the peak intensity, and the intensity value corresponding to half of the peak intensity is taken as the half-maximum intensity. On the spectral curve of the emission spectrum, find two points with an intensity equal to the half-maximum intensity. These two points are located to the left and right of the highest point of the characteristic emission peak, respectively. The difference in wavelengths corresponding to these two points is the full width at half maximum (FWHM) of the characteristic emission peak. Data points 10 nm before the start and 10 nm after the end of the emission spectrum are selected at the beginning and end of the emission spectrum, respectively, which are the starting data point and the ending data point. Linear fitting is performed on the starting data point to obtain a fitted straight line, and the same linear fitting is performed on the ending data point to obtain another fitted straight line. The two fitted straight lines are extended to the middle wavelength point of the entire emission spectrum, and the intensity difference between the two lines at the middle wavelength point is calculated to obtain the absolute intensity value of the baseline drift. Multiply the symmetry index by the full width at half maximum (FWHM) and divide by the sum of the absolute intensity value and 1. Then normalize the calculation result to the 0-1 interval to obtain the intrinsic spectral purity factor corresponding to the emission spectrum. Then obtain the intrinsic spectral purity factors corresponding to room temperature and high temperature. The intrinsic spectral purity factor is used to eliminate interference signals in the spectrum and purify the intrinsic emission characteristics of the fluorescent material itself.

[0021] The preprocessed emission spectrum is analyzed to obtain the intrinsic emission components and their weighting coefficients. Specifically, for the characteristic emission peaks in the preprocessed emission spectrum, the wavelength corresponding to the local maximum point of the characteristic emission peak is taken as its center wavelength. The center wavelength is used as the center of symmetry of the Gaussian peak shape, and its peak position is locked. The full width at half maximum (FWHM) is divided into two segments to obtain the FWHM offset. The FWHM offset is shifted to both sides based on the center wavelength to determine the half-high points on both sides. The peak position is smoothly reduced to the half-high point on both sides without abrupt changes. Then, the half-high point on both sides continues to extend smoothly towards the baseline of the emission spectrum at a gradually decreasing rate, forming a symmetrical and standardized curve. This curve is the Gaussian peak shape of the characteristic emission peak constructed by the center wavelength and the full width at half maximum (FWHM). The Gaussian peak shape is the intrinsic emission component, and the amplitude of the Gaussian peak shape is the weighting coefficient of the intrinsic emission component. Thus, the intrinsic emission components and their weighting coefficients at room temperature and high temperature are obtained respectively. The intrinsic emission components and their weighting coefficients are used to decompose the complex spectrum, identify different luminescent centers in the LED phosphor material, and reflect the intensity changes of the luminescent centers of the LED phosphor material at different temperatures.

[0022] The relationship between the weighting coefficients of the two intrinsic emission components and temperature is analyzed to form a thermal evolution correlation matrix. Specifically, this includes: for the two intrinsic emission components and their weighting coefficients, calculating the individual temperature response coefficient of each intrinsic emission component: subtracting the weighting coefficient of the other intrinsic emission component from the weighting coefficient of the first intrinsic emission component yields the temperature response coefficient of that intrinsic emission component. Then, a two-dimensional matrix is ​​constructed, with its rows and columns corresponding to the two intrinsic emission components. The element values ​​on the diagonal of the matrix are 1 minus the absolute value of the temperature response coefficient of the intrinsic emission component. The element values ​​on the off-diagonal of the matrix are obtained by multiplying the two temperature response coefficients and dividing by twice the sum of the absolute values ​​of the two temperature response coefficients. This forms the thermal evolution correlation matrix, which is used to represent the cooperative or competitive relationship between different luminescent centers in the target management object under thermal disturbance.

[0023] In one embodiment, analyzing two emission spectra to obtain spectral response trajectories further includes: The maximum variance direction of the dominant spectral shape change in the thermal evolution correlation matrix is ​​analyzed to obtain the principal evolution axis vector. The projection value of the spectrum at each temperature point onto the principal evolution axis vector is calculated to form a projection evolution sequence. Specifically, this includes: setting a two-dimensional unit vector with each component of 0.7, multiplying the two-dimensional unit vector with the thermal evolution correlation matrix to obtain a new vector; normalizing the new vector to make its magnitude 1, and repeating the above multiplication and normalization process. When the cosine of the angle between two consecutive new vectors is greater than 0.9, the iteration stops. The normalized vector obtained at this time is the principal evolution axis vector. A two-dimensional weight vector is constructed by weighting coefficients corresponding to the intrinsic emission components at room temperature and high temperature. The dot product of the two-dimensional weight vector and the principal evolution axis is calculated to obtain the projection value of the temperature point. Finally, the projection values ​​of the two temperature points at room temperature and high temperature are arranged in ascending order of temperature to form a projection evolution sequence containing two elements. The projection evolution sequence is used to represent the thermal evolution trajectory of the emission spectrum shape along the principal evolution axis.

[0024] Based on the projection evolution sequence, the spectral coordinates of each temperature point are interpolated to generate a high density of intermediate trajectory nodes. The spatial arrangement of the nodes in the intermediate trajectory nodes is optimized by combining the spectral intrinsic purity factor, and the trajectory smoothness convergence of the entire interpolation path is calculated. Specifically, the two temperature points, room temperature and high temperature, are respectively constructed as room temperature endpoint and high temperature endpoint in two-dimensional space, and the line connecting the room temperature endpoint and the high temperature endpoint is the straight path. On the straight path, the difference between the two projection values ​​in the projection evolution sequence is calculated, and it is divided into nine equal parts to determine the interpolation step size. Linear interpolation is performed from the room temperature endpoint to the high temperature endpoint to generate the preliminary coordinates of seven intermediate trajectory nodes in sequence, and then a high density of intermediate trajectory nodes is generated. Each intermediate trajectory node corresponds to a temperature value. Set an optimization reference direction, which is the vector direction obtained by subtracting the normal temperature end from the high temperature end, that is, the main trend direction of thermal evolution; Using the mean of two spectral intrinsic purity factors as adjustment weights, the initial coordinates of each intermediate trajectory node are finely offset along the direction perpendicular to the straight path towards the optimization reference direction. The offset distance is equal to 5% of the total length of the straight path. After optimizing the arrangement of all intermediate trajectory nodes, the direction vector of the line connecting every two adjacent intermediate trajectory nodes is calculated for the entire interpolation path formed by the room temperature endpoint, the seven intermediate trajectory nodes, and the high temperature endpoint. The arithmetic mean of the cosine values ​​of the angles between all adjacent direction vectors is used as the average directional consistency. The ratio of the total length of the entire interpolation path to the length of the straight path is calculated to obtain the path elongation rate. Subtracting the absolute value of the difference between the average directional consistency and 1, and normalizing the calculation result to the 0-1 interval, yields the trajectory smoothness convergence of the entire interpolation path. The trajectory smoothness convergence is mainly used to reflect the continuity and stability of the trajectory.

[0025] Based on the trajectory smoothness convergence, curve fitting and connection are performed on the nodes in the high-density trajectory nodes to generate a spectral response trajectory that continuously changes from room temperature to high temperature. Specifically, the trajectory smoothness convergence is used as the node adaptive flexibility coefficient. Starting from the room temperature endpoint, every two adjacent intermediate trajectory nodes are processed sequentially, the direction vector of the line connecting these two intermediate trajectory nodes is calculated, and it is rotated 90 degrees as the initial normal of the segment. The adaptive flexibility coefficient of the node is multiplied by the adjustment weight to serve as the local bending weight of the curve in that section. Using the midpoint of the line connecting two intermediate trajectory nodes as the base point, offset along the initial normal direction by a distance equal to the average Euclidean distance between all intermediate trajectory nodes multiplied by 15% of the local curvature weight, resulting in a curve control point. Based on the two curve control points, the room temperature endpoint, and the high temperature endpoint, a smooth circular arc is constructed to connect them. This process is repeated for all adjacent point pairs, and all the circular arcs that connect end to end are smoothly connected to form a spectral response trajectory that continuously changes from room temperature to high temperature. The spectral response trajectory is used to reflect the temperature-dependent spectral drift and thermal quenching process.

[0026] By calculating the intrinsic purity factor of LED fluorescent materials at room temperature and high temperature, spectral interference signals are effectively eliminated, ensuring the accuracy of spectral analysis. Furthermore, by calculating the intrinsic emission components and weighting coefficients, different luminescent centers are clearly identified and their intensity variation patterns are reflected. At the same time, based on the thermal evolution correlation matrix, the cooperative or competitive relationship of luminescent centers under thermal perturbation can be clarified. Combined with the projection evolution sequence and optimized intermediate trajectory nodes, a continuous spectral response trajectory is generated, accurately capturing the temperature-dependent spectral drift and thermal quenching process. This achieves comprehensive and accurate identification of the optical performance of fluorescent materials in high-temperature scenarios, facilitating the evaluation and management of fluorescent material quality in industrial high-temperature lighting scenarios.

[0027] In one embodiment, based on the spectral response trajectory, the spectral centroid thermal mobility and characteristic peak shape retention rate of the target managed object are calculated to obtain a dynamic spectral response vector, including: The spectral response trajectory is analyzed to calculate the curvature change with temperature, and the trajectory thermal perturbation entropy value is obtained. Specifically, for the arc curve segment connecting every two adjacent nodes in the spectral response trajectory, the straight-line distance between the two ends of the arc curve segment is taken as the chord length, and the perpendicular distance from the midpoint of the arc curve segment to the line connecting its two ends is taken as the arc height. The arc height is divided by the chord length and then multiplied by the local curvature weight to obtain the curvature characterization value of the arc curve segment. For the arc curve segment connecting the room temperature endpoint and the first intermediate trajectory node, the arc curve segment connecting adjacent points between the seven intermediate trajectory nodes, and the arc curve segment connecting the last intermediate trajectory node and the high temperature endpoint, the rate of change of the curvature characterization value of two adjacent arc curve segments is calculated in turn. Each rate of change is multiplied by the average curvature characterization value of two adjacent arc curve segments to obtain a weighted change. The sum of all the weighted changes in the spectral response trajectory and the result divided by the total number of arc curve segments yields the trajectory thermal perturbation entropy value. The trajectory thermal perturbation entropy value is used to reflect the overall fluctuation of the trajectory curvature under thermal perturbation. The higher the value, the worse the spectral stability of the material.

[0028] In two-dimensional space, the spectral response trajectory is projected to obtain a two-dimensional path of spectral migration with temperature. The rate of change of the angle of the tangent direction of the two-dimensional path at each temperature point is calculated to generate a centroid migration trend vector representing the migration direction and rate. Specifically, in two-dimensional space, each node on the spectral response trajectory is regarded as a two-dimensional data point. Each node on the spectral response trajectory includes a normal temperature endpoint, seven intermediate trajectory nodes, and a high temperature endpoint. The two dimensions of the two-dimensional data point are the weighting coefficients of the two intrinsic emission components, respectively. Connect all nodes to form a two-dimensional path of spectral migration with temperature. On this two-dimensional path, except for the first and last nodes, take three points for each node: itself and the points before and after it. Calculate the angle between the two continuous line segments formed by these three points as the path direction angle at that node. Calculate the difference between the path direction angle at that node and the path direction angle at the previous node. Divide the difference by the absolute value of the temperature difference between the two nodes to obtain the rate of change of the path direction at that node; calculate the Euclidean distance from each node to the previous node, and divide the Euclidean distance by the absolute value of the temperature difference between the two nodes to obtain the migration rate at that node. For each node, the rate of change of angle and the instantaneous rate are combined to form a two-dimensional vector. This two-dimensional vector is the centroid migration trend vector representing the direction of spectral centroid migration and the instantaneous rate for that node.

[0029] In one embodiment, based on the spectral response trajectory, the thermal mobility of the spectral centroid of the target managed object and the characteristic peak shape retention rate are calculated to obtain the dynamic spectral response vector, which further includes: Analyze the emission spectrum shape of the spectral response trajectory at various temperatures to obtain the spectral morphology fidelity factor. Specifically, this includes: for the room temperature endpoint and the high temperature endpoint on the spectral response trajectory, for the room temperature endpoint, add the weight coefficients of the room temperature endpoint and the high temperature endpoint and divide by 2 to obtain the weight mean. Calculate the absolute difference between the weight mean and the weight coefficient of the room temperature endpoint to obtain the weight offset. Subtract the weight offset from 1 to obtain the first weight fidelity. Divide the weighting coefficient of the room temperature endpoint by the intrinsic purity factor of the spectrum at room temperature. If the result is greater than 1, take its reciprocal to obtain the second weighted fidelity; otherwise, use the result directly as the second weighted fidelity. Calculate the arithmetic mean of the first and second weighted fidelities to obtain the spectral morphology fidelity factor of the room temperature endpoint. Following the same calculation method, the spectral morphology fidelity factor of the high temperature endpoint can be calculated. The two spectral morphology fidelity factors can reflect the similarity between the spectral peak shape at high temperature and the peak shape at room temperature.

[0030] For the key bands in the emission spectrum, the amplitude of the spectral response trajectory fluctuation in the key bands is analyzed as a function of temperature to obtain the characteristic band trajectory stiffness. Specifically, the wavelength range covered by the characteristic emission peak with the highest intensity in the room temperature emission spectrum is the key band. For this key band, its center wavelength and the wavelength corresponding to 35% of the center wavelength are respectively used as two fixed analysis wavelength points. Divide the spectral intensities at the room temperature endpoint and the high temperature endpoint at these two fixed wavelengths in the order of dividing the room temperature spectral intensity by the high temperature spectral intensity to obtain the intensity ratio; Calculate the rate of change of the intensity ratio between the ambient temperature endpoint and the high temperature endpoint, then multiply this rate of change by the trajectory thermal perturbation entropy value, and normalize the calculation result to the 0-1 interval, which is the characteristic band trajectory stiffness. The higher the value of the characteristic band trajectory stiffness, the stronger the ability of the spectral shape of the key band to resist changes under thermal perturbation, and the better the thermal stability.

[0031] In one embodiment, based on the spectral response trajectory, the thermal mobility of the spectral centroid of the target managed object and the characteristic peak shape retention rate are calculated to obtain the dynamic spectral response vector, which further includes: The thermal mobility of the spectral centroid is obtained by calculating the trajectory thermal perturbation entropy and the centroid migration trend vector. The characteristic peak shape retention rate is obtained by analyzing the spectral morphology fidelity factor and the trajectory stiffness of the characteristic band. Specifically, the calculation includes: calculating the mean of the instantaneous velocities in all centroid migration trend vectors to obtain the average instantaneous migration rate; multiplying the average instantaneous migration rate by the trajectory thermal perturbation entropy to obtain an intermediate value representing the unstable migration tendency under thermal perturbation; subtracting the intermediate value from 1 and normalizing the calculation result to the 0-1 interval to obtain the spectral centroid thermal mobility. Calculate the mean value of the spectral morphology fidelity factor at the room temperature endpoint and the high temperature endpoint, multiply the mean value by the characteristic band trajectory stiffness, and normalize the calculation result to the 0-1 interval to obtain the characteristic peak shape retention rate.

[0032] A two-dimensional dynamic spectral response vector is constructed using the spectral centroid thermal mobility and the characteristic peak shape retention rate as components. Specifically, this involves: subtracting the spectral centroid thermal mobility from 1 to obtain the migration stability component; then subtracting the characteristic peak shape retention rate from 1 to obtain the morphology degradation tendency component; finally, multiplying the migration stability component by 0.65 to obtain the weighted migration component; and multiplying the morphology degradation tendency component by 0.35 to obtain the weighted degradation component. The weighted migration component is multiplied by the product of the trajectory smoothing convergence and the mean values ​​of the intrinsic purity factors of the two spectra at room temperature and high temperature to obtain the modulated migration component. The modulated migration component is used as the first coordinate value, and the weighted degradation component is used as the second coordinate value. They are combined to form a two-dimensional coordinate point. The directed line segment from the origin of the two-dimensional coordinate system to this coordinate point is the dynamic spectral response vector. The dynamic spectral response vector is used to represent the dynamic thermal stability of the LED phosphor material.

[0033] By calculating the thermal perturbation entropy of the spectral response trajectory, the overall fluctuation of the trajectory curvature under thermal perturbation can be accurately reflected. Furthermore, the centroid migration trend vector can clearly represent the direction and instantaneous rate of spectral centroid migration. Combined with the spectral morphology fidelity factor, the similarity of the peak shapes at high and low temperatures can be reflected. The thermal stability resistance of key bands can be evaluated through the trajectory stiffness of characteristic bands. Finally, the dynamic spectral response vector can accurately reflect the dynamic thermal stability of LED fluorescent materials, enabling precise capture and comprehensive evaluation of the dynamic changes in the optical performance of fluorescent materials in high-temperature scenarios. This facilitates the judgment of the quality of LED fluorescent materials and improves the quality control of LED fluorescent materials in industrial high-temperature lighting scenarios.

[0034] In one embodiment, the dynamic spectral response vector is analyzed to obtain a high-temperature performance maintenance index, which represents the performance maintenance capability of the target managed object at high temperatures. This index includes: The dynamic spectral response vector is analyzed to generate a high-temperature phase trajectory stability index. Specifically, this includes: calculating the modulus of the dynamic spectral response vector, which represents the overall variation of spectral performance at high temperatures; subtracting the modulus from 1 and multiplying it by the trajectory smoothing convergence to obtain a stable value modulated by smoothing. Divide the stable value by 1 and the sum of the trajectory thermal perturbation entropy, and normalize the result to the 0-1 range to obtain the high-temperature phase trajectory stability index. The high-temperature phase trajectory stability index is used to determine the overall stability of the spectral trajectory at high temperatures.

[0035] The attenuation tendency of the dynamic spectral response vector is evaluated to obtain the vector thermal attenuation coefficient. Specifically, for the two-dimensional phase space composed of the modulation migration component and the weighted degradation component in the dynamic spectral response vector, two key reference directions are set: the first reference direction is the positive direction of the vertical axis orthogonal to the horizontal axis in the two-dimensional phase space, which corresponds to the ideal performance state of 0 spectral centroid thermal mobility and 1 characteristic peak shape retention rate; the second reference direction is the ray direction in the two-dimensional phase space that forms a 30-degree clockwise angle with the positive vertical axis, representing the attenuation trend of spectral morphology degradation accompanied by centroid migration. Calculate the angle between the dynamic spectral response vector and the first reference direction, and the angle between the dynamic spectral response vector and the second reference direction; calculate the difference between these two angles, and divide this difference by 30 to obtain the degree ratio; multiply the degree ratio by the magnitude of the dynamic spectral response vector, and the resulting product is the vector thermal decay coefficient, which is used to reflect the decay trend of spectral performance at high temperatures.

[0036] In one embodiment, analyzing the dynamic spectral response vector to obtain a high-temperature performance maintenance index, which represents the performance maintenance capability of the target managed object at high temperatures, further includes: The direction shift of the dynamic spectral response vector is analyzed to obtain the vector thermal drift tolerance. Specifically, for the two-dimensional phase space composed of the modulation migration component and the weighted degradation component in the dynamic spectral response vector, a reference ray passing through the origin and with a slope of 0.5 is set. The reference ray represents the equilibrium state reference line of moderate spectral centroid migration and moderate morphological degradation. Calculate the angle between the dynamic spectral response vector and the reference ray; subtract the ratio of the angle to 90 degrees from 1 to obtain an index representing the degree of deviation of the vector direction from the equilibrium state; multiply the degree index by the trajectory smoothness convergence and then divide by the magnitude of the dynamic spectral response vector, and normalize the calculation result to the 0-1 interval to obtain the vector thermal drift tolerance. The vector thermal drift tolerance is used to reflect the balance between spectral centroid shift and peak shape deterioration, and is mainly used to judge whether the changes in LED fluorescent materials are within the controllable range.

[0037] The interaction between the thermal mobility of the spectral centroid and the retention rate of characteristic peaks at high temperatures is analyzed to generate a high-temperature component coupling factor. Specifically, this includes: calculating the arithmetic mean of the thermal mobility of the spectral centroid and the retention rate of characteristic peaks to obtain a preliminary performance benchmark; calculating the mean of the trajectory smoothing convergence and the two intrinsic purity factors of the spectrum; multiplying the preliminary performance benchmark by the trajectory smoothing convergence and then by the mean to obtain a comprehensive adjustment value; and normalizing the comprehensive adjustment value to the 0-1 interval to obtain the critical threshold. If the thermal mobility of the spectral centroid is less than the critical threshold, it is marked as a controlled state; otherwise, it is marked as a state with significant migration. If the feature peak shape retention rate is greater than the critical threshold, it is marked as a retention state; otherwise, it is marked as a deterioration state. If the two states of spectral centroid thermal mobility and characteristic peak shape retention rate are controlled and maintained, respectively, it is a cooperative mode with a basic coupling strength of 0.8; if the two states of spectral centroid thermal mobility and characteristic peak shape retention rate are significant migration and deterioration, respectively, it is an antagonistic mode with a basic coupling strength of 0.2; in any other state, it is a transitional mode with a basic coupling strength of 0.5. The absolute value of the difference between the thermal mobility of the spectral centroid and the retention rate of the characteristic peak shape is taken as the state separation degree. The basic coupling strength is multiplied by the mean of the two spectral intrinsic purity factors, then divided by the sum of the state separation degree and 1. The result is then multiplied by the trajectory smoothing convergence degree and normalized to the 0-1 interval to obtain the high-temperature component coupling factor. The high-temperature component coupling factor mainly reflects the interaction strength between the thermal mobility of the spectral centroid and the retention rate of the characteristic peak shape.

[0038] Using the high-temperature phase trajectory stability index as the stability weight, the vector thermal decay coefficient, vector thermal drift tolerance, and high-temperature component coupling factor are fused to generate a high-temperature performance maintenance index. Specifically, this includes: adding the vector thermal decay coefficient and the vector thermal drift tolerance, and then subtracting the product of the vector thermal decay coefficient and the vector thermal drift tolerance to obtain an intermediate fused value representing the combined effect of decay and drift; multiplying the high-temperature component coupling factor by 2, and then subtracting the square of the high-temperature component coupling factor itself to obtain the modulation value. Multiply the intermediate fusion value by the modulation value to obtain the preliminary comprehensive performance score; use half of the high-temperature phase trajectory stability index as the stability adjustment benchmark; subtract the stability adjustment benchmark from the comprehensive performance score to obtain the correction score; multiply the correction score by the high-temperature phase trajectory stability index and normalize the calculation result to the 0-1 range to obtain the high-temperature performance maintenance index.

[0039] The high-temperature phase trajectory stability index accurately represents the overall stability of the spectral trajectory at high temperatures, while the vector thermal decay coefficient reflects the spectral performance decay trend. The vector thermal drift tolerance is used to assess the controllability of spectral changes, and the high-temperature component coupling factor quantifies the interaction strength between the thermal mobility of the spectral centroid and the retention rate of characteristic peaks. Finally, the high-temperature performance maintenance index is obtained by fusing multiple parameters, realizing a comprehensive evaluation of the performance maintenance capability of LED fluorescent materials in high-temperature scenarios. This effectively avoids quality misjudgments caused by spectral drift and thermal quenching at high temperatures, and facilitates quality control of LED fluorescent materials in industrial high-temperature lighting scenarios.

[0040] In one embodiment, the quality management identifier is determined based on the high-temperature performance maintenance index, including: The changes in the high-temperature performance maintenance index are analyzed to obtain the curvature at the end of the performance degradation. Specifically, this includes: calculating the angle between the dynamic spectral response vector and the first reference direction, and dividing the angle by 90 to obtain the directional deviation; and calculating the product of the vector thermal decay coefficient and the vector thermal drift tolerance to obtain the combined effect value. Among them, if the high temperature performance maintenance index is >0.5, the state is maintained; otherwise, the state is decayed. The intermediate parameter value is obtained by adding the directional deviation and the combined effect value and multiplying it by the high temperature phase trajectory stability index. If the state is maintained, the intermediate parameter value is multiplied by the high temperature component coupling factor and then divided by the modulus of the dynamic spectral response vector. The calculation result is then normalized to the 0-1 interval to obtain the curvature at the end of the performance degradation. If the state is decaying, the intermediate parameter value is multiplied by the trajectory thermal perturbation entropy value and divided by the magnitude of the dynamic spectral response vector, and the calculation result is normalized to the 0-1 interval to obtain the curvature at the end of the performance decay.

[0041] By conducting a synergistic analysis of the high-temperature performance maintenance index, the curvature at the end of performance degradation, and the intrinsic purity factor of the spectrum, a quality management identifier is obtained. Specifically, the mean values ​​of the high-temperature performance maintenance index, the curvature at the end of performance degradation, and the intrinsic purity factor of the spectrum at room temperature and high temperature are sorted from high to low. The parameter with the highest value is marked as the dominant state, the lowest value is marked as the weak state, and the middle value is marked as the auxiliary state. If the high-temperature performance maintenance index is dominant and the mean is not weak, it is initially judged as having a tendency to be qualified; if the curvature at the end of the performance degradation is dominant and the high-temperature performance maintenance index is weak, it is initially judged as having a tendency to be unqualified; all other combinations are initially judged as pending. The consensus strength is obtained by subtracting the parameter value in the weak state from the parameter value in the dominant state, multiplying it by the parameter value in the auxiliary state, and normalizing the calculation result to the 0-1 range. If the initial assessment indicates a tendency towards qualification or disqualification, and the consensus strength is greater than 0.3, then the assessment remains unchanged; if the initial assessment indicates a tendency towards qualification or disqualification, and the consensus strength is less than or equal to 0.3, then the final assessment is pending. When the condition is judged to be in a state of acceptance, the quality management indicator is 1; when the condition is judged to be in a state of non-acceptance, the quality management indicator is 0; and in the pending state, the quality management indicator is forced to be 0, thus obtaining the quality management indicator representing the quality of the LED fluorescent material.

[0042] By calculating the curvature at the end of performance degradation, the performance degradation characteristics of LED phosphor materials at high temperatures are accurately captured. Combined with the high-temperature performance maintenance index and the average intrinsic purity factors of room-temperature and high-temperature spectra, a synergistic analysis is performed. A quality management identifier is generated through the classification of dominant, weak, and auxiliary states and consensus intensity verification. This effectively avoids quality misjudgments caused by spectral drift and thermal quenching effects in high-temperature scenarios, achieving accurate quality grading for high-temperature adaptability and facilitating precise quality control of LED phosphor materials in industrial high-temperature lighting scenarios.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A quality management system for LED fluorescent materials based on emission spectral data, characterized in that, include: The trajectory analysis unit is used to acquire the emission spectrum of the target object under different temperatures, analyze the two emission spectra, and obtain the spectral response trajectory. The target object under management is an LED fluorescent material. The spectral analysis unit is used to calculate the thermal mobility of the spectral centroid and the retention rate of the characteristic peaks of the target object based on the spectral response trajectory, and obtain the dynamic spectral response vector. The high-temperature maintenance unit is used to analyze the dynamic spectral response vector to obtain the high-temperature performance maintenance index, which represents the performance maintenance capability of the target managed object at high temperatures. The quality management unit is used to determine the quality management identifier based on the high-temperature performance maintenance index.

2. The LED fluorescent material quality management system based on emission spectral data according to claim 1, characterized in that, Different temperatures include: The different temperatures are room temperature and high temperature. Room temperature is 25℃, and high temperature is ≥80℃.

3. The LED fluorescent material quality management system based on emission spectrum data according to claim 1, characterized in that, Analysis of the two emission spectra yielded the spectral response trajectories, including: Interference removal is performed on the preprocessed emission spectrum to generate the intrinsic purity factor of the spectrum. The preprocessed emission spectrum was analyzed to obtain the intrinsic emission components and their weighting coefficients; The relationship between the weighting coefficients of the two intrinsic emission components and temperature is analyzed to form a thermal evolution correlation matrix, which is used to represent the cooperative or competitive relationship between different emission centers under thermal perturbation.

4. The LED fluorescent material quality management system based on emission spectral data according to claim 3, characterized in that, Analysis of the two emission spectra yielded the spectral response trajectories, which also included: The direction of the maximum variance of the dominant spectral shape change in the thermal evolution correlation matrix is ​​analyzed to obtain the principal evolution axis vector. The projection value of the spectrum at each temperature point onto the principal evolution axis is calculated to form a projection evolution sequence. Based on the projection evolution sequence, the spectral coordinates of each temperature point are interpolated to generate high-density intermediate trajectory nodes. The spatial arrangement of nodes in the intermediate trajectory nodes is optimized by combining the spectral intrinsic purity factor, and the trajectory smoothness convergence of the entire interpolation path is calculated. Based on the trajectory smoothness convergence, the nodes in the high-density trajectory nodes are curve fitted and connected to generate a spectral response trajectory that continuously changes from room temperature to high temperature.

5. The LED fluorescent material quality management system based on emission spectrum data according to claim 4, characterized in that, Based on the spectral response trajectory, the spectral centroid thermal mobility and characteristic peak shape retention rate of the target object are calculated to obtain the dynamic spectral response vector, including: The spectral response trajectory is analyzed, and its curvature is calculated as a function of temperature to obtain the trajectory thermal perturbation entropy value. In two-dimensional space, the spectral response trajectory is projected to obtain a two-dimensional path of spectral migration with temperature. The rate of change of the angle of the tangent direction of the two-dimensional path at each temperature point is calculated to generate a centroid migration trend vector representing the migration direction and rate.

6. The LED fluorescent material quality management system based on emission spectrum data according to claim 5, characterized in that, Based on the spectral response trajectory, the thermal mobility of the spectral centroid and the characteristic peak shape retention rate of the target object are calculated to obtain the dynamic spectral response vector, which also includes: The emission spectrum shape of the spectral response trajectory at various temperatures is analyzed to obtain the spectral morphology fidelity factor; For the key bands in the emission spectrum, the variation of the fluctuation amplitude of the spectral response trajectory in the key bands with temperature is analyzed to obtain the characteristic band trajectory stiffness.

7. The LED fluorescent material quality management system based on emission spectrum data according to claim 6, characterized in that, Based on the spectral response trajectory, the thermal mobility of the spectral centroid and the characteristic peak shape retention rate of the target object are calculated to obtain the dynamic spectral response vector, which also includes: The thermal perturbation entropy of the trajectory and the centroid migration trend vector are calculated to obtain the spectral centroid thermal mobility. The spectral morphology fidelity factor and the trajectory stiffness of the characteristic band are analyzed to obtain the characteristic peak shape retention rate. A two-dimensional dynamic spectral response vector is constructed by using the thermal mobility of the spectral centroid and the retention rate of characteristic peaks as components.

8. The LED fluorescent material quality management system based on emission spectrum data according to claim 7, characterized in that, Analysis of the dynamic spectral response vector yields the high-temperature performance maintenance index, which represents the performance maintenance capability of the target managed object at high temperatures. This index includes: The dynamic spectral response vector is analyzed to generate a high-temperature phase trajectory stability index; The attenuation tendency of the dynamic spectral response vector is evaluated to obtain the vector thermal attenuation coefficient.

9. The LED fluorescent material quality management system based on emission spectrum data according to claim 8, characterized in that, Analysis of the dynamic spectral response vector yields a high-temperature performance maintenance index, which represents the performance maintenance capability of the target managed object at high temperatures. This also includes: By analyzing the directional shift of the dynamic spectral response vector, the vector thermal drift tolerance is obtained; The interaction between the thermal mobility of the spectral centroid and the retention rate of characteristic peaks at high temperatures was analyzed to generate a high-temperature component coupling factor. Using the high-temperature phase trajectory stability index as the stability weight, the vector thermal decay coefficient, vector thermal drift tolerance, and high-temperature component coupling factor are fused to generate the high-temperature performance maintenance index.

10. The LED fluorescent material quality management system based on emission spectrum data according to claim 9, characterized in that, Based on the high-temperature performance maintenance index, quality management indicators are determined, including: By analyzing the changes in the high-temperature performance maintenance index, the curvature at the end of the performance degradation was obtained. By synergistically analyzing the high-temperature performance maintenance index, the curvature at the end of performance degradation, and the intrinsic purity factor of the spectrum, a quality management identifier is obtained.

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