A method and system for detecting defects in an LED light source

Abstract

The application discloses a kind of LED light source defect detection method and system, it is related to LED light source defect detection technical field.The LED light source defect detection method, obtains the light intensity time sequence image data and temperature time sequence image data of LED light source under the current of several kinds of test is passed in, and the defect index set of LED light source is analyzed, based on the defect index set of LED light source Comprehensive defect evaluation index is analyzed, and with the defect evaluation interval of preestablished is judged and analyzed, the light intensity time sequence image and temperature time sequence image data under different current conditions are acquired simultaneously by the present application, combined with multidimensional feature analysis, the defect of LED light source can be comprehensively evaluated, the performance degradation of LED under different working environments can be effectively revealed, especially the influence of current fluctuation and temperature change on light intensity and stability, by analyzing the defect index set of LED light source, not only single defect can be captured, but also the comprehensive defect of light source can be accurately identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of LED light source defect detection, in particular to an LED light source defect detection method and system. BACKGROUND

[0002] LED (Light Emitting Diode) light sources have been widely used in lighting, display, communication and other fields due to their high efficiency, energy saving and long service life. However, with the expansion of the use range of LED light sources and the continuous improvement of performance requirements, the long-term stability and reliability of LED light sources have become an important research direction for the development of LED technology.

[0003] During the operation of LED light sources, they may experience performance degradation or failure due to environmental factors such as current and temperature. Defects of LED light sources, such as uneven light intensity, brightness attenuation, overheating and other problems, seriously affect their light efficiency, service life and user experience. Therefore, timely and effective detection of defects of LED light sources is of great significance for prolonging their service life, improving work efficiency and reducing maintenance costs.

[0004] Prior art such as the LED light source defect detection method and system disclosed in the patent application with publication number CN117437170A, which includes: turning on the driving power supply of the LED light source to be tested, and obtaining the illumination image of the LED light source; obtaining the light spot profile of the bright area in the illumination image; calculating the area of the light spot profile to obtain the light spot area; comparing the light spot area with the standard area, and detecting the current LED light source according to the comparison result; through the above scheme, the LED light source can be completely replaced by artificial defect detection, effectively saving labor costs, and having the advantages of accurate detection results and fast detection speed.

[0005] Based on the above scheme, it is found that the limitations of the prior art at least include the following problems. First, the existing LED light source defect detection method is often limited to traditional single data source analysis, such as using only static light intensity images or temperature data for evaluation, which makes it difficult for the prior art to comprehensively evaluate the overall defect state of the LED light source under dynamic current and temperature conditions. Since the prior art does not fully utilize the time series data under different current tests, it cannot effectively identify the potential complex defects of the LED light source under various current fluctuations and temperature changes, for example, the prior art cannot accurately capture the relationship between current fluctuations and light intensity differences, nor can it effectively analyze the specific impact of temperature changes and current interaction effects on the performance of the LED light source, resulting in low accuracy of defect identification and difficulty in comprehensively improving the stability evaluation of the LED light source. SUMMARY

[0006] In view of the deficiencies of the prior art, the LED light source defect detection method and system are provided, which solves the problem that the interaction of multi-dimensional factors such as current fluctuation, temperature change and light intensity difference is difficult to consider comprehensively in the prior art, so that the defect cannot be accurately identified.

[0007] To achieve the above object, the present application is implemented by the following technical scheme: an LED light source defect detection method, comprising the following steps: obtaining light intensity time sequence image data and temperature time sequence image data of the LED light source under input of several test currents, and respectively preprocessing; performing feature analysis on the preprocessed light intensity time sequence image data and temperature time sequence image data of the LED light source under input of several test currents, respectively, to obtain a defect index set of the LED light source, including a light intensity difference defect index, a current fluctuation defect index and a local thermal effect defect index; performing comprehensive analysis on the defect index set of the LED light source to obtain a comprehensive defect evaluation index of the LED light source, and the calculation formula is as follows: Wherein, ZqX is the comprehensive defect evaluation index of the LED light source, GqQ is the light intensity difference defect index of the LED light source, λ1 is the light intensity influence coefficient stored in the database, DqQ is the current fluctuation defect index of the LED light source, λ2 is the current influence coefficient stored in the database, RqQ is the local thermal effect defect index of the LED light source, μ1 is the thermal effect adjustment coefficient stored in the database, λ3 is the thermal effect influence coefficient stored in the database, μ2 is the light intensity current interaction adjustment coefficient stored in the database, and λ4 is the light intensity current interaction influence coefficient stored in the database; the comprehensive defect evaluation index of the LED light source is judged and analyzed with the preset defect evaluation interval, and if the comprehensive defect evaluation index of the LED light source is within the preset defect evaluation interval, it is considered that the LED light source has defects.

[0008] Further, the light intensity time sequence image data includes light intensity images at several time points, and each light intensity image includes pixel values of several light source points, and the specific steps of obtaining the light intensity difference defect index of the LED light source are as follows: the light intensity images at several time points of the LED light source under input of several test currents are respectively comprehensively analyzed to obtain a light intensity difference feature set of the LED light source, including average pixel values of the several light source points under input of the several test currents, pixel time variance values, and time pixel mean values of the several time points; the pixel values of the several light source points in the light intensity images at the several time points of the LED light source under input of the several test currents are respectively combined with the light intensity difference feature set of the LED light source for comprehensive analysis to obtain the light intensity difference defect index of the LED light source.

[0009] Further, the specific steps for obtaining the light intensity difference feature set of the LED light source are as follows: reading the pixel values of the light source points in the light intensity images at several time points under several test currents, and performing time point analysis to obtain the average pixel values of the light source points under several test currents; reading the pixel values of the light source points in the light intensity images at several time points under several test currents, and performing variance analysis combined with the average pixel values of the light source points under several test currents to obtain the pixel time variance values of the light source points under several test currents; reading the pixel values of the light source points in the light intensity images at several time points under several test currents, and performing light source point pixel analysis to obtain the time pixel mean values at several time points under several test currents.

[0010] Further, the specific formula for calculating the light intensity difference defect index of the LED light source is as follows: wherein, GqQ is the light intensity difference defect index of the LED light source, XsZ i(u+1)r is the pixel value of the rth light source point in the light intensity image at the u+1th time point under the ith test current, XsZ iur is the pixel value of the rth light source point in the light intensity image at the uth time point under the ith test current, PXS ir is the average pixel value of the rth light source point under the ith test current, XsF ir is the pixel time variance value of the rth light source point under the ith test current, δ1 is the time variance influence coefficient stored in the database, SxS iu is the time pixel mean value at the uth time point under the ith test current, δ2 is the time pixel influence coefficient stored in the database, i = 1, 2, 3, …, i0, i0 is the number of test currents, u = 1, 2, 3, …, u0, u0 is the number of time points, r = 1, 2, 3, …, r0, r0 is the number of light source points.

[0011] Further, the specific steps for obtaining the current fluctuation defect index of the LED light source are as follows: obtaining the test current values at several time points for each test current, and performing comprehensive analysis to obtain several groups of test current difference values for each test current; performing comprehensive analysis on the several groups of test current difference values for each test current combined with the pixel values of the light source points in the light intensity images at several time points under several test currents to obtain the current fluctuation defect index of the LED light source.

[0012] Further, the specific formula of calculating the current fluctuation defect index of the LED light source is as follows: Wherein, DqQ is the current fluctuation defect index of the LED light source, XsZ i(u+1)r is the pixel value of the rth light source point in the light intensity image of the LED light source at the u+1th time point under the input of the ith test current, XsZ iur is the pixel value of the rth light source point in the light intensity image of the LED light source at the u+1th time point under the input of the ith test current, ε is the pixel adjustment factor stored in the database, CfD ia is the a th group of test current differential values of the ith test current, χ is the current adjustment factor stored in the database, β is the differential current adjustment coefficient stored in the database, i = 1, 2, 3, …, i0, i0 is the number of test currents, u = 1, 2, 3, …, u0, u0 is the number of time points, r = 1, 2, 3, …, r0, r0 is the number of light source points, a = 1, 2, 3, …, a0, a0 is the number of test current differential value groups.

[0013] Further, the temperature time sequence image data includes temperature images at several time points, and each temperature image includes temperature values of several light source points, and the specific steps of obtaining the local thermal effect defect index of the LED light source are as follows: performing feature analysis on the temperature time sequence image data of the LED light source under the input of several test currents respectively to obtain a temperature difference feature set of the LED light source, including time temperature mean values and temperature time gradient values at several time points under the input of several test currents; and performing comprehensive analysis on the temperature values of the several light source points in the temperature images at the several time points of the LED light source under the input of the several test currents respectively in combination with the temperature difference feature set of the LED light source to obtain the local thermal effect defect index of the LED light source.

[0014] Further, the specific steps of obtaining the temperature difference feature set of the LED light source are as follows: reading the temperature values of the light source points in the temperature images of the LED light source at a plurality of time points under a plurality of test currents, and respectively performing light source point temperature analysis to obtain the time temperature mean values of the LED light source at a plurality of time points under a plurality of test currents; for each light source point in the temperature images of the LED light source at a plurality of time points under a plurality of test currents, a plurality of adjacent light source points in the set neighborhood range are respectively identified; the temperature values of each light source point in the temperature images of the LED light source at a plurality of time points under a plurality of test currents are respectively analyzed in combination with the temperature values of a plurality of adjacent light source points in the set neighborhood range of the corresponding light source point to obtain the temperature time gradient values of the LED light source at a plurality of time points under a plurality of test currents; the local temperature time gradient values of each light source point in the temperature images of the LED light source at a plurality of time points under a plurality of test currents are analyzed in combination to obtain the temperature time gradient values of the LED light source at a plurality of time points under a plurality of test currents.

[0015] Further, the specific formula for calculating the local thermal effect defect index of the LED light source is as follows: wherein RqQ is the local thermal effect defect index of the LED light source, WdZ iur is the temperature value of the rth light source point in the temperature image of the LED light source at the u th time point under the i th test current, SwZ iu is the time temperature mean value of the LED light source at the u th time point under the i th test current, θ is the temperature adjustment factor stored in the database, SwT iu is the temperature time gradient value of the LED light source at the u th time point under the i th test current, ω is the temperature gradient adjustment coefficient stored in the database, ξ is the temperature gradient influence coefficient stored in the database, i = 1, 2, 3, …, i0, i0 is the number of test currents, u = 1, 2, 3, …, u0, u0 is the number of time points, r = 1, 2, 3, …, r0, r0 is the number of light source points.

[0016] The LED light source defect detection system comprises: an image data acquisition unit for acquiring light intensity time sequence image data and temperature time sequence image data of an LED light source under several test currents and respectively pre-processing; an image feature analysis unit for respectively performing feature analysis on the pre-processed light intensity time sequence image data and temperature time sequence image data of the LED light source under several test currents, obtaining a defect index set of the LED light source, including a light intensity difference defect index, a current fluctuation defect index and a local thermal effect defect index; a comprehensive defect evaluation unit for comprehensively analyzing the defect index set of the LED light source, obtaining a comprehensive defect evaluation index of the LED light source; and a defect judgment unit for judging and analyzing the comprehensive defect evaluation index of the LED light source with a preset defect evaluation interval, and regarding the LED light source as having defects if the comprehensive defect evaluation index of the LED light source is within the preset defect evaluation interval.

[0017] The present application has the following beneficial effects:

[0018] (1) The LED light source defect detection method can comprehensively evaluate the defects of the LED light source by simultaneously collecting light intensity time sequence images and temperature time sequence image data under different current conditions and combining multi-dimensional feature analysis. This method can effectively reveal the performance degradation of the LED under different working environments, especially the influence of current fluctuation and temperature change on light intensity and stability. However, the prior art often relies on only a single data source, such as a static light intensity image or a temperature image, which makes it difficult to comprehensively evaluate the defects of the LED light source under dynamic current and temperature change conditions. Through comprehensive calculation of the light intensity difference defect index, the current fluctuation defect index and the local thermal effect defect index, this method not only can capture single defects, but also can accurately identify the comprehensive defects of the light source, improving the accuracy and comprehensiveness of the detection. This is of great significance for improving the stability and life of the LED light source in practical applications.

[0019] (2) The LED light source defect detection method can accurately analyze the mutual influence of current and temperature by combining multi-dimensional data of current fluctuation, light intensity difference and temperature change, thereby dynamically evaluating the performance of the LED light source. However, the prior art usually ignores the interaction between current fluctuation and temperature change, relying only on static light intensity or temperature data to evaluate the light source state, which leads to the risk of being unable to accurately identify potential defects of the LED light source in practical applications. By introducing adjustment coefficients stored in the database, such as current adjustment factors and temperature gradient adjustment coefficients, this method can accurately identify complex defect patterns by real-time evaluation of the LED light source under different current and temperature conditions, which enables the LED light source to maintain good adaptability in various working environments, ensuring its long-term stability and high efficiency, and effectively avoiding the problem that traditional methods are difficult to reliably detect defects in extreme environments.

[0020] (3), the LED light source defect detection method, through the real-time analysis of light intensity time sequence image and temperature time sequence image, the comprehensive defect evaluation index of LED light source can be calculated, and compared with the preset defect evaluation interval, so as to judge whether the LED light source exists defect in real time, this kind of technology based on time sequence data and dynamic evaluation provides efficient real-time monitoring and early warning mechanism, ensures that the LED light source can be timely feedback and intervention when performance problems appear, however, the prior art usually relies on periodic inspection or manual maintenance, lack of comprehensive monitoring of real-time state of LED light source, this method combines real-time data analysis with automatic judgment, significantly improves the real-time and efficiency of detection, can take measures quickly when any potential failure occurs, reduces the risk of misjudgment and omission, reduces the maintenance cost of equipment, and prolongs the service life of LED light source.

[0021] (4), the LED light source defect detection system, through the organic combination of image data acquisition unit, image feature analysis unit, comprehensive defect evaluation unit and defect judgment unit, realizes the comprehensive monitoring and intelligent evaluation of LED light source, the system can automatically acquire light intensity time sequence image data and temperature time sequence image data under different current conditions, and through automatic preprocessing and feature extraction, reduces human intervention, improves the automation degree of detection process, however, the prior art often relies on manual operation or traditional manual inspection, resulting in tedious detection process and being easily affected by human factors, the system can not only quickly process a large amount of data through automatic image processing and feature analysis, but also dynamically adjust the evaluation parameters according to real-time data, so that the defect detection of LED light source is more accurate and efficient, by completely automating the defect evaluation process, the system can significantly reduce human error, improve detection speed, and reduce labor cost, while enhancing the intelligent level of detection, providing more reliable support for real-time monitoring and maintenance in large-scale LED light source application.

[0022] Of course, implementing any product of the present application does not necessarily require achieving all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A flow chart of the LED light source defect detection method of the present application.

[0024] Figure 2 A light intensity image and temperature image schematic diagram under the test current input to the LED light source in the LED light source defect detection method of the present application.

[0025] Figure 3 A flow chart of the specific steps of obtaining the light intensity difference feature set of the LED light source in the LED light source defect detection method of the present application.

[0026] Figure 4 A LED light source defect detection system block diagram. DETAILED DESCRIPTION

[0027] Please refer to Figure 1 The embodiment of the present application provides a technical scheme: a LED light source defect detection method, comprising the following steps: obtaining the light intensity time sequence image data and temperature time sequence image data of the LED light source under the input of several test currents, and respectively pre-processing; the light intensity time sequence image data and temperature time sequence image data of the LED light source under the input of several test currents are respectively analyzed, and the defect index set of the LED light source is obtained, including the light intensity difference defect index, the current fluctuation defect index and the local thermal effect defect index; the defect index set of the LED light source is analyzed, and the comprehensive defect evaluation index of the LED light source is obtained; the comprehensive defect evaluation index of the LED light source is judged and analyzed with the preset defect evaluation interval, if the comprehensive defect evaluation index of the LED light source is within the preset defect evaluation interval, it is considered that the LED light source has defects, and the light intensity image and temperature image example of a certain time point under the input of test current (0.7A, 1.0A, 1.2A) to the LED light source are respectively input, as shown in Figure 2 .

[0028] The specific formula for calculating the comprehensive defect evaluation index of the LED light source is as follows: Wherein, ZqX is the comprehensive defect evaluation index of the LED light source, GqQ is the light intensity difference defect index of the LED light source, λ1 is the light intensity influence coefficient stored in the database, DqQ is the current fluctuation defect index of the LED light source, λ2 is the current influence coefficient stored in the database, RqQ is the local thermal effect defect index of the LED light source, μ1 is the thermal effect adjustment coefficient stored in the database, λ3 is the thermal effect influence coefficient stored in the database, μ2 is the light intensity current interaction adjustment coefficient stored in the database, and λ4 is the light intensity current interaction influence coefficient stored in the database.

[0029] It needs to be explained that the specific acquisition steps of the light intensity influence coefficient λ1, the current influence coefficient λ2, the thermal effect influence coefficient λ3, and the light intensity-current interaction influence coefficient λ4 stored in the database are as follows: first, in the experimental stage, the light intensity time sequence images of the LED light source under a plurality of different current intensities are recorded and collected, and the current fluctuation and temperature change are monitored synchronously, for the light intensity, temperature and current data under each different current condition, a mathematical model between the current fluctuation and the light intensity difference, temperature change is established by using a regression analysis method (such as multiple linear regression, decision tree, support vector machine, etc.), through these models, the mutual relationship between the current, temperature and light intensity can be extracted, these relationships are stored in the database in the form of coefficients, forming the light intensity influence coefficient, the current influence coefficient and the thermal effect influence coefficient, specifically, the light intensity influence coefficient reflects the influence of different current changes on the LED light intensity; the current influence coefficient reflects the influence of current fluctuation on the stability of the LED; the thermal effect influence coefficient measures the influence of insufficient heat dissipation or local overheating on the performance of the LED through the temperature data; the light intensity-current interaction influence coefficient evaluates the linkage effect of current fluctuation on light intensity change, these coefficients will be stored and updated in real time in the database according to different current, temperature and light intensity conditions.

[0030] The specific acquisition steps of the thermal effect adjustment coefficient μ1 and the light intensity-current interaction adjustment coefficient μ2 stored in the database are as follows: the thermal effect and the interaction effect of current and light intensity of the LED light source under different working environment conditions are obtained through systematic experiments and modeling, in the experimental process, the LED light source runs under different current and temperature environments, collects temperature data and light intensity data at multiple time points, and through statistical analysis and optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm or neural network model) processing of these data, the adjustment effect of temperature fluctuation on the performance of the LED, i.e. the thermal effect adjustment coefficient, can be obtained, in addition, for the interaction effect of current and light intensity, similar methods are used, combined with the light intensity data under different current conditions, a mathematical model of the interaction adjustment between current and light intensity is established, these adjustment coefficients help the system to accurately reflect the complex relationship between current fluctuation and light intensity, through continuous optimization and adjustment, these coefficients are stored in the database, so as to adjust the adjustment parameters of the LED in real time in actual application, to ensure its stability and efficiency, through this method, the database not only stores static influence coefficients, but also records dynamic adjustment parameters in the system running, ensuring more accurate performance evaluation and adjustment.

[0031] The specific implementation example of calculating the comprehensive defect evaluation index of the LED light source is as follows, the following parameters are given:

[0032] The light intensity difference defect index of the LED light source is about 0.763.

[0033] The light intensity influence coefficient stored in the database is about 1.452.

[0034] The current fluctuation defect index of the LED light source is about 0.540.

[0035] The current influence coefficient stored in the database is about 1.621.

[0036] The local thermal effect defect index of the LED light source is about 0.815.

[0037] The thermal effect adjustment coefficient stored in the database is about 1.134.

[0038] The thermal effect influence coefficient stored in the database is about 0.942.

[0039] The light intensity-current interaction adjustment coefficient stored in the database is about 1.365.

[0040] The light intensity-current interaction influence coefficient stored in the database is about 0.890.

[0041] The above data are respectively substituted into the specific formula for calculating the comprehensive defect evaluation index of the LED light source to obtain:

[0042] The comprehensive defect evaluation index of the LED light source is (1.452x0.763)+(1.621x0.540)+(1+0.942x((0.815)^1.134))+(1+0.890x((0.763x0.540)^1.365))≈4.995.

[0043] Specifically, the light intensity time sequence image data includes light intensity images at a plurality of time points, and each light intensity image includes pixel values of a plurality of light source points, i.e. light intensity (brightness) at the light source point position, the light source point positions and quantities in each light intensity image are consistent and one-to-one correspond to the respective light source points in the LED light source, which is used to reflect the brightness intensity distribution of the LED light source points, and the specific steps for obtaining the light intensity difference defect index of the LED light source are as follows: the light intensity images of the LED light source at a plurality of time points under input of a plurality of test currents are respectively comprehensively analyzed to obtain a light intensity difference feature set of the LED light source, including average pixel values, pixel time variance values of a plurality of light source points under input of a plurality of test currents, and time pixel mean values of a plurality of time points; the pixel values of the plurality of light source points in the light intensity images of the LED light source at a plurality of time points under input of a plurality of test currents are respectively combined with the light intensity difference feature set of the LED light source for comprehensive analysis to obtain the light intensity difference defect index of the LED light source.

[0044] The specific formula for calculating the light intensity difference defect index of the LED light source is as follows: Wherein, GqQ is the light intensity difference defect index of the LED light source, XsZ i(u+1)r is the pixel value of the rth light source point in the light intensity image of the LED light source at the u+1th time point under the input of the ith test current, XsZ iur is the pixel value of the rth light source point in the light intensity image of the LED light source at the u+1th time point under the input of the ith test current, XsZ ir is the average pixel value of the rth light source point of the LED light source under the input of the ith test current, XsF ir is the pixel time variance value of the tth light source point of the LED light source under the input of the ith test current, δ1 is the time variance influence coefficient stored in the database, SxS in is the time pixel mean value of the yth time point of the LED light source under the input of the ith test current, δ2 is the time pixel influence coefficient stored in the database, i = 1, 2, 3, …, i0, i0 is the number of test currents, u = 1, 2, 3, …, u0, u0 is the number of time points, r = 1, 2, 3, …, r0, r0 is the number of light source points.

[0045] It needs to be explained that the specific acquisition steps of the time variance influence coefficient δ1 and the time pixel influence coefficient δ2 stored in the database are: by recording the light intensity image data of the LED light source at multiple different time points (such as t1, t2, t3, …), then using variance analysis to calculate the time variance of the light intensity change in the image data, that is, the dispersion degree of the light intensity change between different time points, as the time variance influence coefficient, at the same time, by analyzing the pixel points of the light intensity image at each time point, the performance of each pixel point at different times is calculated, and the time pixel influence coefficient is further obtained. These two coefficients are obtained by regression analysis or data fitting method, and are stored in the database for subsequent use in calculating the light source difference and evaluating the light intensity change.

[0046] In this embodiment, the light intensity difference defect index of the LED light source is defined and calculated in detail, and the performance of the light source is accurately evaluated through the time variance influence coefficient and the time pixel influence coefficient. By analyzing the variance and pixel points of the light intensity image data at multiple different time points, not only the temporal changes of the light source can be effectively captured, but also the light intensity fluctuations and non-uniformity of the LED light source under different current conditions can be revealed, so as to identify potential defects. This method can combine time series data with pixel-level data to provide more comprehensive and accurate defect evaluation than traditional static detection methods. First, by comprehensively analyzing the light intensity time series image data of the LED light source under different current conditions, and combining the influence of time variance and time pixel, the stability and health status of the LED light source under long time and multiple current conditions can be reflected in detail, which is crucial for revealing the brightness non-uniformity or degradation caused by current fluctuation and temperature change. Second, by storing and using the time variance influence coefficient and the time pixel influence coefficient in the database, important references are provided for subsequent light intensity difference defect detection, and the detection method is more automated and intelligent, reducing the deviation and error of human operation. This method not only improves the defect detection accuracy of the LED light source, but also considers the influence of current and temperature change on light intensity through time series data analysis, effectively improving the reliability and applicability of the detection results. By using these coefficients comprehensively, the performance of the LED light source can be dynamically monitored, and scientific basis is provided for subsequent maintenance and performance optimization, enhancing the stability and life of the LED light source in practical application.

[0047] Specifically, as shown in Figure 3 The specific steps of obtaining the light intensity difference feature set of the LED light source are as follows: reading the pixel values of the light source points in the light intensity images at several time points under several test currents, and performing time point analysis (i.e. mean value analysis for several time points) to obtain the average pixel values of the light source points under several test currents; reading the pixel values of the light source points in the light intensity images at several time points under several test currents, and performing variance analysis combined with the average pixel values of the light source points under several test currents to obtain the pixel time variance values of the light source points under several test currents; reading the pixel values of the light source points in the light intensity images at several time points under several test currents, and performing light source point pixel analysis (i.e. pixel mean value analysis for several light source points) to obtain the time pixel mean values (pixel mean values of the light source points in the light intensity images) at several time points under several test currents.

[0048] In this embodiment, through multi-dimensional data analysis method, how to calculate the light intensity difference characteristic set of LED light source is explained in detail, including average pixel value, pixel time variance value and time pixel mean value, so as to provide key data support for accurate evaluation of LED light source defects. First, through mean value analysis of light intensity image at each time point, the light intensity stability of light source point under different current conditions can be understood. Second, through variance analysis, the volatility of light source point between multiple time points can be captured, further revealing whether there is non-uniformity or decay problem of LED light source. Finally, pixel mean value analysis provides data basis for detecting the brightness change trend of each light source point. These steps can help the system to more accurately identify the potential defects of LED light source, and improve the accuracy and comprehensiveness of defect detection.

[0049] Specifically, the specific steps of obtaining the current fluctuation defect index of the LED light source are as follows: obtaining the test current values of each test current at several time points, and comprehensively analyzing the test current difference values of each test current (i.e. the test current difference values of adjacent time points); and comprehensively analyzing the test current difference values of each test current in combination with the pixel values of the light source points in the light intensity images of the LED light source under the input of several test currents at several time points, to obtain the current fluctuation defect index of the LED light source.

[0050] The specific formula for calculating the current fluctuation defect index of the LED light source is as follows: Wherein, DqQ is the current fluctuation defect index of the LED light source, XsZ i(u+1)r is the pixel value of the rth light source point in the light intensity image of the LED light source at the u+1th time point under the input of the ith test current, XsZ iur is the pixel value of the rth light source point in the light intensity image of the LED light source at the uth time point under the input of the ith test current, ε is the pixel adjustment factor stored in the database, which is used to prevent the denominator from being 0, CfD ia is the ath test current difference value of the ith test current, χ is the current adjustment factor stored in the database, which is used to prevent the denominator from being 0, β is the difference current adjustment coefficient stored in the database, i = 1, 2, 3, …, i0, i0 is the number of test currents, u = 1, 2, 3, …, u0, u0 is the number of time points, r = 1, 2, 3, …, r0, r0 is the number of light source points, a = 1, 2, 3, …, a0, a0 is the number of test current difference value groups.

[0051] It needs to be explained that the specific acquisition steps of the differential current adjustment coefficient β stored in the database are as follows: first, record the current fluctuation data of the LED light source at multiple time points under different current intensities, that is, the change amount of current at each time, calculate the differential value of current fluctuation by statistical analysis of these data, evaluate the influence of current fluctuation on LED performance, then use regression analysis or optimization algorithm (such as least squares method, gradient descent, etc.) to fit the relationship between current fluctuation and LED performance indicators (such as brightness change, temperature fluctuation, etc.), thereby obtaining the differential current adjustment coefficient, which reflects the adjustment effect of current fluctuation on LED stability and efficiency, and is stored in the database for subsequent calculation and real-time adjustment.

[0052] In the embodiment, by combining the calculation of the test current differential value and the light intensity image data, the influence of current fluctuation on the performance of the LED light source can be effectively identified, especially whether the light intensity fluctuates at different time points and under current changes. The advantage of this method is that the differential current adjustment coefficient can reflect the specific influence of current fluctuation on the stability and efficiency of the LED light source, further improving the accuracy of LED light source defect detection. The existing technology often ignores the details of current fluctuation, resulting in insufficient evaluation of the stability and efficiency of the LED light source. The current adjustment coefficient obtained by regression analysis or optimization algorithm can combine current fluctuation with factors such as light intensity change and temperature fluctuation of the LED, ensuring that the performance of the LED light source can be accurately evaluated under current changes. In addition, real-time adjustment using the adjustment coefficient in the database not only improves the automation level of the system, but also provides a more scientific basis for subsequent maintenance and adjustment. Overall, this method effectively improves the dynamic stability detection of the LED light source and provides solid technical support for the reliability and efficiency optimization of LED products in practical applications.

[0053] Specifically, the temperature time sequence image data includes temperature images at several time points, and each temperature image includes temperature values of several light source points, that is, the surface temperature at the light source point position. The light source point positions and quantities in each temperature image are consistent and one-to-one correspond to each light source point in the LED light source, which is used to reflect the temperature distribution of the LED light source point. The specific steps to obtain the local thermal effect defect index of the LED light source are as follows: perform feature analysis on the temperature time sequence image data of the LED light source under several test currents to obtain the temperature difference feature set of the LED light source, including the time temperature mean value and temperature time gradient value at several time points under several test currents; and perform comprehensive analysis on the temperature values of the several light source points in the temperature images at the several time points under the several test currents of the LED light source, respectively combined with the temperature difference feature set of the LED light source to obtain the local thermal effect defect index of the LED light source.

[0054] The specific formula for calculating the local thermal effect defect index of the LED light source is as follows: Wherein, RqQ is the local thermal effect defect index of the LED light source, WdZ iur is the temperature value of the rth light source point in the temperature image of the LED light source at the u th time point under the input of the i th test current, SwZ iu is the time temperature mean value of the LED light source at the u th time point under the input of the i th test current, θ is the temperature adjustment factor stored in the database, which is used to prevent the denominator from being 0, SwT iu is the temperature time gradient value of the LED light source at the u th time point under the input of the i th test current, ω is the temperature gradient adjustment coefficient stored in the database, ξ is the temperature gradient influence coefficient stored in the database, i = 1, 2, 3, …, i0, i0 is the number of test currents, u = 1, 2, 3, …, u0, u0 is the number of time points, r = 1, 2, 3, …, r0, r0 is the number of light source points.

[0055] It needs to be explained that the specific acquisition steps of the temperature gradient adjustment coefficient ω and the temperature gradient influence coefficient ξ stored in the database are as follows: first, in the experiment, record the temperature change of the LED light source under different current conditions, especially the temperature gradient at different time points, the temperature image data at each time point will be used to calculate the temperature gradient, that is, the temperature change rate of each pixel point, then, through regression analysis or machine learning method (such as multiple regression or support vector machine), the relationship between temperature gradient and LED performance (such as light intensity, stability, etc.) is analyzed to obtain the temperature gradient adjustment coefficient, which represents the adjustment effect of temperature gradient on LED performance, at the same time, the temperature gradient influence coefficient reflects the influence of temperature gradient under different current and temperature conditions, which is obtained through data fitting or optimization algorithm, finally, these coefficients are stored in the database and used as key parameters in subsequent defect evaluation, ensuring the stability and efficiency of LED under different working conditions.

[0056] In this embodiment, by detailing how to calculate the local thermal effect defect index of LED light source using temperature time series image data, a comprehensive analysis framework is provided to accurately evaluate the thermal effect of LED light source under different current and temperature conditions. First, the temperature time series image data is analyzed by time point by time point analysis of the temperature value of each light source point, combined with the time temperature mean and temperature gradient, which can effectively capture the local temperature fluctuation and thermal inhomogeneity of LED light source, thus providing important data support for defect detection. Through regression analysis or machine learning method, the calculated temperature gradient adjustment coefficient and temperature gradient influence coefficient can accurately reflect the influence of different temperature gradient on the performance of LED light source, providing accurate basis for real-time evaluation. The existing technology usually ignores the complex influence of temperature gradient, while this method makes up for this deficiency through detailed analysis of temperature gradient, providing in-depth understanding of the thermal effect of LED light source. In addition, the storage of temperature gradient adjustment coefficient and temperature gradient influence coefficient in the database makes the dynamic adjustment and real-time feedback of the system possible. In practical application, these coefficients can dynamically adjust the performance evaluation of LED light source according to different current and temperature changes, ensuring the stability and reliability of LED light source under various working conditions.

[0057] Specifically, the specific steps of obtaining the temperature difference feature set of the LED light source are as follows: reading the temperature values of a plurality of light source points in the temperature image of the LED light source at a plurality of time points under a plurality of test currents, and respectively performing light source point temperature analysis (i.e. temperature mean value analysis for a plurality of light source points) to obtain the time temperature mean value (temperature mean value of a plurality of light source points in the temperature image) of the LED light source at a plurality of time points under a plurality of test currents; for each light source point in the temperature image of the LED light source at a plurality of time points under a plurality of test currents, a plurality of adjacent light source points within a set neighborhood range are identified respectively; the temperature values of each light source point in the temperature image of the LED light source at a plurality of time points under a plurality of test currents are respectively analyzed in combination with the temperature values of a plurality of adjacent light source points within the set neighborhood range of the corresponding light source point to obtain the temperature time gradient value of the LED light source at a plurality of time points under a plurality of test currents, specifically, the temperature values of the light source point and the temperature values of a plurality of adjacent light source points within the set neighborhood range are analyzed by absolute difference value to obtain a plurality of temperature absolute difference values, wherein the absolute difference value is the absolute value of the difference value, and the number thereof corresponds to the number of adjacent light source points, and the mean value of a plurality of temperature absolute difference values is calculated to obtain the local temperature time gradient value of the light source point, and the mean value analysis of a plurality of local temperature time gradient values of a plurality of light source points is performed to obtain the temperature time gradient value; the local temperature time gradient value of each light source point in the temperature image of the LED light source at a plurality of time points under a plurality of test currents is analyzed in combination (i.e. mean value analysis) to obtain the temperature time gradient value of the LED light source at a plurality of time points under a plurality of test currents.

[0058] In the embodiment, through detailed analysis steps, the temperature image data and the local temperature time gradient value are used to provide an accurate thermal effect evaluation method for LED light source defect detection. Firstly, through temperature mean value analysis of each light source point, the temperature stability of the LED light source under different current conditions can be identified; secondly, through temperature value difference analysis of adjacent light source points within the neighborhood range, the influence of local temperature fluctuation and temperature gradient on the performance of the LED light source is further revealed; finally, this analysis method based on time temperature mean value and local temperature time gradient value can effectively capture the possible local overheating problem of the LED light source and provide a reliable basis for subsequent defect evaluation, thereby comprehensively and accurately analyzing the thermal effect of the LED light source under different current and time conditions and providing more accurate evaluation results than traditional methods. This method significantly improves the accuracy of thermal effect detection, makes the defect identification of the LED light source more comprehensive, and plays a positive role in performance optimization and stability guarantee of the LED light source in practical applications.

[0059] Please refer to Figure 4The embodiment of the present application provides a technical scheme: a LED light source defect detection system, comprising: an image data acquisition unit, used for acquiring light intensity time sequence image data and temperature time sequence image data of the LED light source under input of several test currents, and respectively performing pretreatment; an image feature analysis unit, used for respectively performing feature analysis on the pretreated light intensity time sequence image data and temperature time sequence image data of the LED light source under input of several test currents, and obtaining a defect index set of the LED light source, including a light intensity difference defect index, a current fluctuation defect index and a local thermal effect defect index; a comprehensive defect evaluation unit, used for performing comprehensive analysis on the defect index set of the LED light source, and obtaining a comprehensive defect evaluation index of the LED light source; and a defect judgment unit, used for performing judgment analysis on the comprehensive defect evaluation index of the LED light source and a preset defect evaluation interval, and regarding the LED light source as having defects if the comprehensive defect evaluation index of the LED light source is within the preset defect evaluation interval.

[0060] Although preferred embodiments of the application have been described herein, after further studying the drawings and disclosure of this application, those skilled in the art will not have any difficulty in making additional modifications and changes to the embodiments. Therefore, the appended claims are intended to cover all such modifications and changes that fall within the scope of the application.

[0061] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, it is intended that the present application cover the modifications and changes as long as they come within the scope of the claims of the present application and their equivalents.

Claims

1. A method for detecting defects in LED light sources, characterized in that, Includes the following steps: Acquire time-series image data of light intensity and temperature of LED light source under several test currents, and perform preprocessing respectively; Feature analysis was performed on the light intensity time-series image data and temperature time-series image data of the preprocessed LED light source under several test currents to obtain the defect index set of the LED light source, including the light intensity difference defect index, the current fluctuation defect index, and the local thermal effect defect index. A comprehensive analysis of the defect index set of LED light sources yields a comprehensive defect assessment index for LED light sources, calculated using the following formula: ； in, , , , In order, they are: LED light source comprehensive defect assessment index, light intensity difference defect index, current fluctuation defect index, and local thermal effect defect index. , , , The following are the light intensity influence coefficient, current influence coefficient, thermal effect influence coefficient, and light intensity-current interaction influence coefficient stored in the database, in that order. , These are, in order, the thermal effect adjustment coefficient and the light intensity-current interaction adjustment coefficient stored in the database; The comprehensive defect assessment index of the LED light source is compared with the preset defect assessment range. If the comprehensive defect assessment index of the LED light source is within the preset defect assessment range, the LED light source is considered to have a defect. The light intensity time-series image data includes light intensity images at several time points, and each light intensity image includes pixel values ​​of several light source points. The specific steps to obtain the light intensity difference defect index of the LED light source are as follows: A comprehensive analysis was performed on the light intensity images of LED light sources at several time points under several test currents to obtain a light intensity difference feature set of LED light sources, including the average pixel value, pixel time variance value, and time pixel mean value at several time points under several test currents. The pixel values ​​of several light source points in the light intensity images of LED light sources at several time points under several test currents are combined with the light intensity difference feature set of LED light sources for comprehensive analysis to obtain the light intensity difference defect index of LED light sources.

2. The LED light source defect detection method according to claim 1, characterized in that, The specific steps to obtain the light intensity difference feature set of LED light sources are as follows: Read the pixel values ​​of several light source points in the light intensity images of the LED light source at several time points under several test currents, and perform time point analysis to obtain the average pixel value of several light source points under several test currents. The pixel values ​​of several light source points in the light intensity images of the LED light source at several time points under several test currents are read, and the variance analysis is performed in combination with the average pixel values ​​of several light source points under several test currents to obtain the pixel time variance value of several light source points under several test currents. The pixel values ​​of several light source points in the light intensity images of the LED light source at several time points under several test currents are read, and the pixel values ​​of the light source points are analyzed to obtain the average pixel value of the LED light source at several time points under several test currents.

3. The LED light source defect detection method according to claim 1, characterized in that, The specific formula for calculating the light intensity difference defect index of an LED light source is as follows: ； in, The light intensity difference defect index for LED light sources. For LED light source in the first The first test current In the light intensity image at the nth time point, the th Pixel values ​​of each light source point For LED light source in the first The first test current In the light intensity image at the nth time point, the th Pixel values ​​of each light source point , The LED light source is introduced in sequence as follows: The first test current Average pixel value and pixel temporal variance of each light source point For LED light source in the first The first test current Average pixel value at each time point , The following are the temporal variance influence coefficient and temporal pixel influence coefficient stored in the database, respectively. , To test the number of current types, , The number of time points, , This represents the number of light source points.

4. The LED light source defect detection method according to claim 1, characterized in that, The specific steps to obtain the current fluctuation defect index of an LED light source are as follows: The test current values ​​of each test current at several time points are obtained and comprehensively analyzed to obtain several sets of test current difference values ​​for each test current. By combining several sets of test current difference values ​​for each test current with the pixel values ​​of several light source points in the light intensity images of the LED light source at several time points under several test currents, a comprehensive analysis is performed to obtain the current fluctuation defect index of the LED light source.

5. The LED light source defect detection method according to claim 4, characterized in that, The specific formula for calculating the current fluctuation defect index of an LED light source is as follows: ； in, This refers to the current fluctuation defect index of LED light sources. For LED light source in the first The first test current In the light intensity image at the nth time point, the th Pixel values ​​of each light source point For LED light source in the first The first test current In the light intensity image at the nth time point, the th Pixel values ​​of each light source point For the first The first test current Group test current differential value, , , The parameters stored in the database are, in order: pixel adjustment factor, current adjustment factor, and differential current adjustment coefficient. , To test the number of current types, , The number of time points, , The number of light source points, , This represents the number of test current differential value groups.

6. The LED light source defect detection method according to claim 1, characterized in that, The temperature time-series image data includes temperature images at several time points, and each temperature image includes temperature values ​​at several light source points. The specific steps for obtaining the local thermal effect defect index of the LED light source are as follows: Feature analysis was performed on the temperature time-series image data of LED light source under several test currents to obtain the temperature difference feature set of LED light source, including the time temperature mean and temperature time gradient value at several time points under several test currents. The temperature values ​​of several light source points in the temperature images of several time points under several test currents of LED light source are combined with the temperature difference feature set of LED light source for comprehensive analysis to obtain the local thermal effect defect index of LED light source.

7. The LED light source defect detection method according to claim 6, characterized in that, The specific steps to obtain the temperature difference feature set of the LED light source are as follows: Read the temperature values ​​of several light source points in the temperature images of several time points under several test currents of the LED light source, and perform temperature analysis of the light source points respectively to obtain the average time temperature of the LED light source at several time points under several test currents. For each light source point in the temperature image of an LED light source at several time points under several test currents, identify several adjacent light source points within a set neighborhood range; The temperature value of each light source point in the temperature image of the LED light source at several time points under several test currents is comprehensively analyzed with the temperature values ​​of several adjacent light source points within a set neighborhood of the corresponding light source point to obtain the temperature time gradient value of the LED light source at several time points under several test currents. By comprehensively analyzing the local temperature time gradient values ​​of each light source point in the temperature images of several time points under several test currents, the temperature time gradient values ​​of the LED light source at several time points under several test currents are obtained.

8. The LED light source defect detection method according to claim 6, characterized in that, The specific formula for calculating the local thermal effect defect index of an LED light source is as follows: ； in, The local thermal effect defect index of LED light source. For LED light source in the first The first test current The temperature image at time point n is the first Temperature value of each light source point , The LED light source is introduced in sequence as follows: The first test current The mean temperature over time and the temperature gradient over time at each time point. , , The parameters stored in the database are, in order: temperature adjustment factor, temperature gradient adjustment coefficient, and temperature gradient influence coefficient. , To test the number of current types, , The number of time points, , This represents the number of light source points.

9. An LED light source defect detection system, employing the LED light source defect detection method according to any one of claims 1-8, characterized in that, include: The image data acquisition unit is used to acquire the light intensity time-series image data and temperature time-series image data of the LED light source under several test currents, and to preprocess them respectively. The image feature analysis unit is used to perform feature analysis on the light intensity time-series image data and temperature time-series image data of the preprocessed LED light source under several test currents to obtain the defect index set of the LED light source, including the light intensity difference defect index, the current fluctuation defect index, and the local thermal effect defect index. The comprehensive defect assessment unit is used to comprehensively analyze the defect index set of LED light sources to obtain the comprehensive defect assessment index of LED light sources. The defect judgment unit is used to judge and analyze the comprehensive defect evaluation index of the LED light source and the preset defect evaluation range. If the comprehensive defect evaluation index of the LED light source is within the preset defect evaluation range, the LED light source is considered to have a defect.

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