Numerical fitting optimization method for detecting service life of LED device

Abstract

The invention relates to the field of semiconductor device testing, in particular to a numerical fitting optimization method for detecting the service life of an LED device, and the method comprises the steps: collecting the luminous flux time sequence data of the LED device; performing luminous flux change analysis on the luminous flux time sequence data to obtain a window adjustment factor, and performing dynamic window division through the window adjustment factor; performing distance optimization factor analysis on any two dynamic windows to obtain a distance optimization factor, and optimizing # imgabs0 # distance measurement between the dynamic windows through the distance optimization factor to obtain an optimized distance; performing neighbor matching by optimizing the distance to obtain a neighbor matching result; arc span number sequence evaluation is carried out through a neighbor matching result and luminous flux time sequence data, and an arc span number sequence is obtained; performing self-adaptive segmentation according to the arc span number sequence, and performing segmentation fitting; and carrying out service life detection analysis according to the segmentation fitting result to obtain a service life detection result of the LED device, thereby improving the service life detection precision of the LED device.

Description

Technical Field

[0001] The invention relates to the technical field of semiconductor detection, and in particular to a numerical fitting optimization method for life detection of LED devices. Background Art

[0002] With the widespread application of LEDs (light emitting diodes) in lighting, display, and electronic equipment, how to accurately evaluate the life of LEDs has become a key issue of concern to the industry. According to the light decay characteristics of LEDs, the life of LEDs is usually measured by the time when the luminous flux decays to the initial value. As the life evaluation standard of LED. Traditional LED life detection methods are mainly based on long-term operation tests, that is, continuously lighting the LED under a specific environment and recording the change of luminous flux over time. However, this method is time-consuming and difficult to meet the needs of rapid evaluation. Therefore, the light decay model based on numerical fitting has become the mainstream life prediction method. Currently common methods include exponential decay model, Weibull distribution fitting, etc. These methods predict the overall life of LED based on the light decay trend within a certain period of time. Although these methods can provide effective predictions to a certain extent, their accuracy is affected by multiple factors, such as LED packaging characteristics, working environment, drive current, junction temperature, etc.

[0003] Existing LED life detection methods mainly rely on the exponential decay model, which assumes that the LED luminous flux decays at a fixed rate. However, in the actual detection process, the early light decay is often affected by the release of package stress, driving current fluctuations and ambient temperature changes, resulting in the light decay curve not conforming to a single exponential decay trend in the initial stage. At present, the segmented fitting method based on time series analysis has become an important means to improve the accuracy of LED life prediction. However, most of the existing segmentation methods use fixed windows or simple sliding window strategies, which cannot fully adapt to the light decay variation characteristics of different LED devices. In addition, there are problems with the alignment of time scales and local features when matching windows of different lengths, which affects the accuracy of the light decay trend. Therefore, how to construct an adaptive dynamic window division method and optimize the window matching strategy to improve the segmented fitting accuracy of LED luminous flux detection time series data is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a numerical fitting optimization method for LED device life detection to solve the problem of low precision of segmented fitting of LED luminous flux detection timing data.

[0005] An embodiment of the present invention provides a numerical fitting optimization method for LED device life detection, and the numerical fitting optimization method for LED device life detection includes the following steps:

[0006] The luminous flux of the LED device is detected to obtain luminous flux time series data; the window adjustment factors at different times are obtained by analyzing the luminous flux change of the luminous flux time series data; the luminous flux time series data is dynamically divided into windows by the window adjustment factor to obtain the dynamic window of the luminous flux time series data; the distance optimization factor is obtained by analyzing the distance optimization factor of any two of the dynamic windows; the distance between any two of the dynamic windows is adjusted by the distance optimization factor. The distance metric is optimized to obtain the optimized distance between any two dynamic windows of the luminous flux time series data; the optimized distance is used to perform nearest neighbor matching on the dynamic windows of the luminous flux time series data to obtain the nearest neighbor matching result; the nearest neighbor matching result is used to evaluate the arc span number sequence with the luminous flux time series data to obtain the arc span number sequence; the arc span number sequence is evaluated in segments to obtain the adaptive segmentation result of the luminous flux time series data; the adaptive segmentation result is fitted in segments to obtain the segment fitting result of the luminous flux time series data; the segment fitting result is analyzed for life detection to obtain the life detection result of the LED device.

[0007] Preferably, the analysis of the luminous flux change of the luminous flux time series data to obtain the window adjustment factor at different times; and the dynamic window division of the time series data by the window adjustment factor to obtain the dynamic window of the luminous flux time series data include:

[0008] Acquire the set initial window length and luminous flux time series data; acquire the local average light decay rate weight factor and the local average light decay rate change rate weight factor; for any time series data point in the luminous flux time series data, use the initial window corresponding to the time series data point as the evaluation window of the time series data point, and use the local average light decay rate weight multiplied by the average light decay rate in the evaluation window of the time series data point as the first dynamic window evaluation factor; use the local average light decay rate change rate weight factor multiplied by the average light decay rate change rate in the evaluation window of the time series data point as the second dynamic window evaluation factor; use the first dynamic window evaluation factor and the second dynamic window evaluation factor as the third dynamic window evaluation factor of the time series data point; substitute the opposite of the third dynamic window evaluation factor into the exponential function with the natural constant e as the base, and obtain the window adjustment factor of the time series data point;

[0009] The product of the window adjustment factor of the time series data point and the set initial window length is used as the dynamic window length of the time series data point; the dynamic window length of each time series data point in the luminous flux time series data point is obtained, and the dynamic window of the luminous flux time series data is divided according to the dynamic window length of each time series data point to obtain the dynamic window of the luminous flux time series data.

[0010] Preferably, the distance optimization factor is obtained by performing distance optimization factor analysis on any two of the dynamic windows, including:

[0011] By performing a time scale difference analysis on any two of the dynamic windows, the time scale matching factor between the dynamic windows of the luminous flux time series data is obtained; by performing a spatial scale difference analysis on any two of the dynamic windows, the spatial scale matching factor between the dynamic windows of the luminous flux time series data is obtained; by performing a fusion evaluation of the time scale matching factor and the spatial scale matching factor, the distance optimization factor between any two dynamic windows of the luminous flux time series data is obtained.

[0012] Preferably, the step of performing time scale difference analysis on any two of the dynamic windows to obtain a time scale matching factor between the dynamic windows of the luminous flux time series data includes:

[0013] Get any two dynamic windows, take the absolute value of the dynamic window length difference between any two dynamic windows as the dynamic window length difference between any two dynamic windows, substitute the opposite of the dynamic window length difference between any two dynamic windows into an exponential function with the natural constant e as the base as the first exponential function result, and use the constant 1 minus the calculation result of the first exponential function as the time scale matching factor between the dynamic windows of the luminous flux timing data.

[0014] Preferably, the spatial scale matching factor between the dynamic windows of the luminous flux time series data is obtained by performing spatial scale difference analysis on any two of the dynamic windows, including:

[0015] The calculation formula of the spatial scale matching factor between the dynamic windows of the luminous flux time series data is:

[0016] ;

[0017] in Indicates The first The spatial scale matching factor between dynamic windows of luminous flux time series data points; Respectively represent The first Dynamic window length of each luminous flux time series data point; Indicates The first The smaller dynamic window length among the dynamic window lengths of the light flux time series data points; Indicates The first A larger dynamic window length among the dynamic window lengths of the light flux time series data points; Indicates The first point in the dynamic window of the luminous flux time series data point The light decay rate corresponding to the data point; Indicates The first point in the dynamic window of the luminous flux time series data point The light decay rate corresponding to the data point; Indicates The first The dynamic window length of the light flux time series data point is the largest in the dynamic window length. The light decay rate corresponding to the data point.

[0018] Preferably, the step of fusing and evaluating the time scale matching factor and the space scale matching factor to obtain a distance optimization factor between any two dynamic windows of light flux time series data includes:

[0019] Obtain a time scale matching factor and a space scale matching factor between any two of the dynamic windows of the luminous flux time series data, and use the calculation result of the product of the time scale matching factor and the space scale matching factor as the distance optimization factor between any two of the dynamic windows of the luminous flux time series data.

[0020] Preferably, the distance between any two of the dynamic windows is optimized by the distance optimization factor. The distance metric is optimized to obtain an optimized distance between any two of the dynamic windows of the luminous flux time series data; the optimized distance performs nearest neighbor matching on the dynamic windows of the luminous flux time series data to obtain a nearest neighbor matching result, including:

[0021] Obtaining a distance optimization factor between any two of the luminous flux time series data dynamic windows and any two of the luminous flux time series data dynamic windows; taking the calculation result of multiplying the distance optimization factor between the luminous flux time series data dynamic windows by the DTW distance between the luminous flux time series data dynamic windows as the optimized distance between any two of the luminous flux time series data dynamic windows;

[0022] For each data point in the luminous flux time series data, the optimized distance between the dynamic window corresponding to the data point and the dynamic window corresponding to any other data point is obtained, and the dynamic window corresponding to any other data point with the smallest optimized distance between the dynamic window corresponding to the data point is taken as the nearest neighbor matching result of the data point.

[0023] Preferably, the nearest neighbor matching result and the luminous flux time series data are evaluated for arc spanning number sequence to obtain the arc spanning number sequence, including:

[0024] Obtain the nearest neighbor matching result corresponding to the dynamic window of each data point in the luminous flux time series data; for any moment in the luminous flux time series data, take the number of corresponding nearest neighbor matching relationships on both sides of the moment as the arc span number corresponding to the moment, and obtain the arc span number sequence according to the arc span number corresponding to each moment in the luminous flux time series data.

[0025] Preferably, the step of obtaining the adaptive segmentation result of the luminous flux time series data by segmentally evaluating the arc span number sequence includes:

[0026] Obtain a segmentation degree threshold of the luminous flux time series data; use the linear normalization result of the arc span number corresponding to each moment of the luminous flux time series data in the arc span number sequence as the segmentation degree corresponding to each moment; compare the segmentation degree corresponding to each moment with the segmentation degree threshold of the luminous flux time series data, and mark the moment as a segmentation point if it is higher than the segmentation degree threshold; obtain all the segmentation points in the luminous flux time series data and segment the luminous flux time series data according to the segmentation points to obtain an adaptive segmentation result of the luminous flux time series data.

[0027] Preferably, the step of performing segmented fitting on the adaptive segmented results to obtain segmented fitting results of the luminous flux time series data; and performing life detection analysis on the segmented fitting results to obtain life detection results of the LED device includes:

[0028] Obtain the adaptive segmentation result of the luminous flux time series data, use the least squares method to perform exponential function fitting on each segmentation result, and obtain the segmentation fitting result of the luminous flux time series data; perform the LED device luminous flux reduction to the initial luminous flux according to the segmentation fitting result of the luminous flux time series data The cumulative time is evaluated and the cumulative time is used as the LED device Life test results.

[0029] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0030] The present invention performs luminous flux detection on LED devices to obtain luminous flux time series data; performs luminous flux change analysis on the luminous flux time series data to obtain window adjustment factors at different times; performs dynamic window division on the luminous flux time series data by using the window adjustment factors to obtain dynamic windows of the luminous flux time series data; performs distance optimization factor analysis on any two of the dynamic windows to obtain a distance optimization factor; and adjusts the distance between any two of the dynamic windows by using the distance optimization factor. The distance metric is optimized to obtain the optimized distance between any two dynamic windows of the luminous flux time series data; the optimized distance is used to perform nearest neighbor matching on the dynamic windows of the luminous flux time series data to obtain the nearest neighbor matching result; the nearest neighbor matching result is used to evaluate the arc span number sequence with the luminous flux time series data to obtain the arc span number sequence; the arc span number sequence is evaluated in segments to obtain the adaptive segmentation result of the luminous flux time series data; the adaptive segmentation result is fitted in segments to obtain the segment fitting result of the luminous flux time series data; the segment fitting result is analyzed for life detection to obtain the life detection result of the LED device. The luminous flux time series data points are dynamically divided into windows through window adjustment factors, so that the neighbor matching relationship between the time series data windows can adapt to the light decay stages of different LEDs, improve the segmentation accuracy of the luminous flux time series data and solve the problem that the fixed window length cannot adapt to the change of the light decay rate; after the dynamic window division, the distance optimization factor is further evaluated through the time scale matching factor and the space scale matching factor between the dynamic windows to solve the scale problem in the neighbor matching process of different dynamic window lengths, ensure the matching accuracy of short windows and long windows, thereby improving the adaptive segmentation accuracy of the luminous flux time series data, thereby improving the segmentation fitting accuracy of the luminous flux time series data. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0032] Figure 1 A method flow chart of a numerical fitting optimization method for LED device life detection according to an embodiment of the present invention; DETAILED DESCRIPTION

[0033] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0034] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this toothpaste can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an unexpected order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0035] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0036] The specific scenario targeted by the present invention is: performing adaptive segmented fitting optimization on the luminous flux time series data of the LED device during the LED device life detection process.

[0037] In addition, the light attenuation mentioned in the embodiments of the present invention refers to the attenuation of light flux.

[0038] See also Figure 1 , is a method flow chart of a numerical fitting optimization method for LED device life detection provided by the first embodiment of the present invention, such as Figure 1 As shown, a numerical fitting optimization method for LED device life detection may include:

[0039] Step S101, performing luminous flux detection on the LED device to obtain luminous flux time series data.

[0040] First, a stable test environment is established to ensure the accuracy and repeatability of the luminous flux data. Usually, an integrating sphere and a spectrometer are used for measurement, where the integrating sphere is used to evenly collect the light emitted by the LED, and the spectrometer is used to accurately measure the luminous flux and record its changes over time. During the detection process, the LED device is continuously lit under constant current drive, and the test system collects the luminous flux data of the LED at a set time interval. In the embodiment of the present invention, the time interval is set to 1 hour, so as to obtain the luminous flux time series data.

[0041] Step S102, analyzing the luminous flux change of the luminous flux time series data to obtain the window adjustment factor at different times, and dynamically dividing the luminous flux time series data into windows according to the window adjustment factor to obtain the dynamic window of the luminous flux detection time series data.

[0042] After obtaining the luminous flux time series data, the luminous flux time series data can be segmented to perform segmented fitting analysis. In the adaptive segmentation process of LED luminous flux detection time series data, the time data can be segmented by the time data fast semantic segmentation FLOSS algorithm. However, in actual scenarios, the luminous flux attenuation process of LED devices is not a simple exponential decline, but is affected by a variety of factors, including the early stage of luminous flux changes slowly, affected by the release of packaging stress and the initial temperature rise, the attenuation trend is not obvious; the transition stage of light decay rate gradually increases, and the luminous flux attenuation gradually approaches the stable model; the long-term stable attenuation stage, the LED performs a stable attenuator, which conforms to the exponential attenuation model; in the mutation stage, the luminous flux may have a local mutation at a specific time point due to high temperature, overload or current fluctuations. Because these change stages are not completely fixed, each LED may have different light decay characteristics. Therefore, the method of segmenting LED luminous flux detection data using a fixed time series data window may lead to the loss of key points of the light decay trend, the failure of matching between the short-term window and the long-term window, and the inaccurate segmentation caused by mismatching between the luminous flux time series data windows.

[0043] Therefore, in the process of adaptive segmentation of LED luminous flux detection data, the core problem that needs to be solved first is how to determine the appropriate window size so that it can adapt to the changes in the LED light decay rate, rather than using fixed window division. First, by analyzing the luminous flux change of the luminous flux time series data, the window adjustment factor at different times is obtained, and the time series data is dynamically divided into windows by the window adjustment factor, and the dynamic window of the luminous flux time series is obtained, including:

[0044] Acquire the set initial window length and luminous flux time series data; acquire the local average light decay rate weight factor and the local average light decay rate change rate weight factor; for any time series data point in the luminous flux time series data, use the initial window corresponding to the time series data point as the evaluation window of the time series data point, and use the local average light decay rate weight multiplied by the local average light decay rate in the evaluation window of the time series data point as the first dynamic window evaluation factor; use the local average light decay rate change rate weight factor multiplied by the average light decay rate change rate in the evaluation window of the time series data point as the second dynamic window evaluation factor; use the first dynamic window evaluation factor and the second dynamic window evaluation factor as the third dynamic window evaluation factor of the time series data point; substitute the opposite of the third dynamic window evaluation factor into the exponential function with the natural constant e as the base, and obtain the window adjustment factor of the time series data point;

[0045] The product of the window adjustment factor of the time series data point and the set initial window length is used as the dynamic window length of the time series data point; the dynamic window length of each time series data point in the luminous flux time series data point is obtained, and the dynamic window of the luminous flux time series data is divided according to the dynamic window length of each time series data point to obtain the dynamic window of the luminous flux time series data.

[0046] In one embodiment, assuming that The local average light decay rate in the dynamic window of time series data points is , No. The local average light decay rate in the dynamic window of time series data points is , then The calculation expression of the window adjustment factor of a time series data point is:

[0047] ;

[0048] in, Indicates The window adjustment factor for each time series data point; Indicates The local average light decay rate in a dynamic window of time series data points; Indicates The local average light decay rate change rate in the dynamic window of time series data points; Represents the weight factor of the local average light decay rate; Represents the weight factor of the change rate of the local average light attenuation rate; Represents a natural constant.

[0049] It should be noted that the trend of LED luminous flux attenuation is not uniform attenuation, but presents different change patterns over time. In the early stage of luminous flux time series data, the luminous flux decays slowly and the local changes are small, which is suitable for segmentation with a larger window to reduce the impact of short-term fluctuations; in the exponential decay stage of luminous flux time series data, the light decay rate gradually accelerates. If a large window is still used, the key turning points of the light decay trend will be ignored. Therefore, the window should be narrowed to more accurately capture the light decay changes; in the case of mutations in the luminous flux time series data, when the LED is affected by external factors such as temperature and current, the luminous flux may undergo local mutations. At this time, the window should be narrowed to increase the sensitivity of detection and ensure that the mutation information is not averaged. Therefore, it is necessary to dynamically adjust the window through the local window light decay rate and the light decay rate change rate of the light flux time series data points. When the local average light decay rate of the light flux time series data points is large, it means that the light decay rate is fast, and the window should be reduced to improve the time resolution. When the local average light decay rate of the light flux time series data points is small, it means that the light decay is slow, and the window should be increased to reduce unnecessary short-term fluctuation interference; when the local average light decay rate change rate of the light flux time series data points is large, it means that the light decay rate is changing rapidly, and the window should be reduced to accurately capture the change trend. When the local average light decay rate change rate of the light flux time series data points is small, it means that the light decay rate is relatively stable, and the window can be appropriately increased to smooth the data. In the embodiment of the present invention, the local average light decay rate weight factor and the local average light decay rate change rate weight factor are set to be , so as to refer to the local average light attenuation rate and the local light attenuation rate change rate to the same extent. The local average light attenuation rate weight factor and the local average light attenuation rate change rate weight factor can be adjusted according to the actual scenario without any requirement.

[0050] In one embodiment, it is assumed that the initial window length is set to , then The calculation expression of the dynamic window length of time series data points is:

[0051] ;

[0052] in, Indicates Dynamic window length of time series data points; Indicates The window adjustment factor for each time series data point; Indicates the initial window length.

[0053] It should be noted that, in the embodiment of the present invention, the initial window length is set to ,The initial window length can be adjusted according to the actual scenario and is not required.

[0054] Step S103, performing distance optimization factor analysis on any two dynamic windows to obtain a distance optimization factor, and optimizing the DTW distance metric between any two dynamic windows using the distance optimization factor to obtain an optimized distance.

[0055] After obtaining the dynamic window division result of the LED luminous flux detection time series data, the dynamic window of the LED luminous flux detection time series data can be used to perform the window nearest neighbor matching process, thereby completing the matrix contour matching of the time series data. In this process, it is necessary to determine the nearest neighbor window of each time series data window in all time series data windows, and form a matching relationship in the nearest neighbor window, so as to perform adaptive segmentation of the LED luminous flux detection time series data through the matching relationship. In the matching process, it is necessary to measure the distance between the dynamic length windows. However, in the process of measuring the distance between two dynamic length windows, there are differences in window lengths between the dynamic windows. At the same time, in the two dynamic windows, the short window may be affected by local fluctuations due to the small time span, while the long window may ignore local details due to the large time span, thereby causing matching errors. In order to ensure the accuracy of the nearest neighbor matching of the dynamic windows of the luminous flux time series data, it is necessary to perform a time scale difference analysis on any two of the dynamic windows to obtain the time scale matching factor between the dynamic windows of the luminous flux time series data, and to perform a spatial scale difference analysis on any two of the dynamic windows to obtain the spatial scale matching factor between the dynamic windows of the luminous flux time series data. Finally, the time scale matching factor and the spatial scale matching factor are fused and evaluated to obtain the distance optimization factor between any two dynamic windows of the luminous flux time series data, so as to optimize the distance evaluation between the dynamic windows through the distance optimization factor.

[0056] First, by performing time scale difference analysis on any two of the dynamic windows, the time scale matching factor between the dynamic windows of the luminous flux time series data is obtained, including:

[0057] Get any two dynamic windows, take the absolute value of the dynamic window length difference between any two dynamic windows as the dynamic window length difference between any two dynamic windows, substitute the opposite of the dynamic window length difference between any two dynamic windows into an exponential function with the natural constant e as the base as the first exponential function result, and use the constant 1 minus the calculation result of the first exponential function as the time scale matching factor between the dynamic windows of the luminous flux timing data.

[0058] In one embodiment, The first The calculation expression of the time scale matching factor between the dynamic windows of the light flux time series data points is:

[0059] ;

[0060] in, Indicates The first The time scale matching factor between the dynamic windows of the light flux time series data points; Indicates The window length of the dynamic window of the light flux time series data points; Indicates The window length of the dynamic window of the light flux time series data points; Indicates absolute value calculation; represents the constant 1; Represents a natural constant.

[0061] After obtaining the time scale matching factor between the dynamic windows of the luminous flux time series data, the spatial scale matching factor between the dynamic windows of the luminous flux time series data can be obtained by performing a spatial scale difference analysis on any two of the dynamic windows.

[0062] In one embodiment, The first The calculation formula of the spatial scale matching factor between the dynamic windows of the light flux time series data points is:

[0063] ;

[0064] in Indicates The first The spatial scale matching factor between dynamic windows of luminous flux time series data points; Respectively represent The first Dynamic window length of each luminous flux time series data point; Indicates The first The smaller dynamic window length among the dynamic window lengths of the light flux time series data points; Indicates The first A larger dynamic window length among the dynamic window lengths of the light flux time series data points; Indicates The first point in the dynamic window of the luminous flux time series data point The light decay rate corresponding to the data point; Indicates The first point in the dynamic window of the luminous flux time series data point The light decay rate corresponding to the data point; Indicates The first The dynamic window length of the light flux time series data point is the largest in the dynamic window length. The light decay rate corresponding to each data point.

[0065] It should be noted that in order to solve this problem, we introduced the time scale matching factor and the spatial scale matching factor to optimize the distance calculation between windows. The time scale matching factor is used to measure whether there is a significant difference in the time span of the two windows, and adjust the similarity calculation between windows through normalization, so that windows of different lengths can be matched at the same scale. The spatial scale matching factor is used to evaluate whether the light attenuation trend within the window is consistent, ensuring that the matching of short windows in long windows will not lead to incorrect matching due to different local trends.

[0066] After obtaining the time scale matching factor and the space scale matching factor, it is necessary to perform a fusion evaluation through the time scale matching factor and the space scale matching factor to obtain a distance optimization factor between any two dynamic windows of light flux time series data, including:

[0067] Obtain a time scale matching factor and a space scale matching factor between any two of the dynamic windows of the luminous flux time series data, and use the calculation result of the product of the time scale matching factor and the space scale matching factor as the distance optimization factor between any two of the dynamic windows of the luminous flux time series data.

[0068] In one embodiment, The first The calculation expression of the distance optimization factor between the dynamic windows of the light flux time series data points is:

[0069] ;

[0070] in, Indicates The first The distance optimization factor between the dynamic windows of the light flux time series data points; Indicates The first The time scale matching factor between the dynamic windows of the light flux time series data points; Indicates The first The spatial scale matching factor between dynamic windows of luminous flux time series data points.

[0071] After obtaining the distance optimization factor, the distance between any two dynamic windows is optimized by the distance optimization factor. The distance metric is optimized to obtain the optimized distance between any two dynamic windows of the luminous flux time series data, including:

[0072] Obtain the distance optimization factor between any two of the luminous flux time series data dynamic windows and any two of the luminous flux time series data dynamic windows; and use the result of multiplying the distance optimization factor between the luminous flux time series data dynamic windows by the DTW distance between the luminous flux time series data dynamic windows as the optimized distance between any two of the luminous flux time series data dynamic windows.

[0073] In one embodiment, assuming that The first The dynamic window between the light flux time series data points Distance is , then The first The calculation expression of the optimized distance between the dynamic windows of the light flux time series data points is:

[0074] ;

[0075] in, Indicates The first Optimized distance between dynamic windows of luminous flux time series data points; Indicates The first The distance optimization factor between the dynamic windows of the light flux time series data points; Indicates The first The dynamic window between the light flux time series data points distance.

[0076] It should be noted that the DTW distance between the long and short windows is weighted and optimized through the distance optimization factor, so that the matching calculation can not only consider the consistency of the global trend, but also ensure the alignment of the local light decay mode, thereby improving the accuracy of the luminous flux timing data segmentation and ensuring that the true characteristics of the LED light decay process can be accurately captured.

[0077] Step S104, performing nearest neighbor matching on the dynamic window of the luminous flux time series data by optimizing the distance, and evaluating the arc spanning number sequence based on the nearest neighbor matching result to obtain the arc spanning number sequence; and adaptively segmenting the luminous flux time series data based on the arc spanning number sequence to obtain an adaptive segmentation result.

[0078] After obtaining the optimized distance between any two dynamic windows of the luminous flux time series data, the dynamic windows of the luminous flux time series data can be nearest neighbor matched by the optimized distance to obtain the nearest neighbor matching results, including:

[0079] For each data point in the luminous flux time series data, the optimized distance between the dynamic window corresponding to the data point and the dynamic window corresponding to any other data point is obtained, and the dynamic window corresponding to any other data point with the smallest optimized distance between the dynamic window corresponding to the data point is taken as the nearest neighbor matching result of the data point.

[0080] After obtaining the nearest neighbor matching result, the arc spanning number sequence is further evaluated by using the nearest neighbor matching result and the luminous flux time series data to obtain the arc spanning number sequence, including:

[0081] Obtain the nearest neighbor matching result corresponding to the dynamic window of each data point in the luminous flux time series data; for any moment in the luminous flux time series data, take the number of corresponding nearest neighbor matching relationships on both sides of the moment as the arc span number corresponding to the moment, and obtain the arc span number sequence according to the arc span number corresponding to each moment in the luminous flux time series data.

[0082] After obtaining the arc span number sequence, the adaptive segmentation results of the luminous flux time series data are obtained by segmentally evaluating the arc span number sequence, including:

[0083] Obtain a segmentation degree threshold of the luminous flux time series data; use the linear normalization result of the arc span number corresponding to each moment of the luminous flux time series data in the arc span number sequence as the segmentation degree corresponding to each moment; compare the segmentation degree corresponding to each moment with the segmentation degree threshold of the luminous flux time series data, and mark the moment as a segmentation point if it is higher than the segmentation degree threshold; obtain all the segmentation points in the luminous flux time series data and segment the luminous flux time series data according to the segmentation points to obtain an adaptive segmentation result of the luminous flux time series data.

[0084] It should be noted that, in the embodiment of the present invention, the segmentation threshold of the luminous flux time series data is set to , can be adjusted according to the actual scenario and is not required.

[0085] Step S105, performing segmented fitting on the adaptive segmented results to obtain segmented fitting results of the luminous flux detection time series data, and performing life detection analysis on the segmented fitting results to obtain life detection results of the LED device.

[0086] After obtaining the adaptive segmentation results of the luminous flux time series data, each segmentation result is fitted with an exponential function using the least squares method to obtain the segmented fitting results of the luminous flux time series data, and the luminous flux of the LED device is reduced to the initial luminous flux according to the segmented fitting results of the luminous flux time series data. The cumulative time evaluation is used as the LED device Life test results.

[0087] In summary, the embodiment of the present invention performs luminous flux detection on the LED device to obtain luminous flux time series data; performs luminous flux change analysis on the luminous flux time series data to obtain window adjustment factors at different times; performs dynamic window division on the luminous flux time series data by using the window adjustment factor to obtain dynamic windows of the luminous flux time series data; performs distance optimization factor analysis on any two of the dynamic windows to obtain the distance optimization factor; and adjusts the distance between any two of the dynamic windows by using the distance optimization factor. The distance metric is optimized to obtain the optimized distance between any two dynamic windows of the luminous flux time series data; the optimized distance is used to perform nearest neighbor matching on the dynamic windows of the luminous flux time series data to obtain the nearest neighbor matching result; the nearest neighbor matching result is used to evaluate the arc span number sequence with the luminous flux time series data to obtain the arc span number sequence; the arc span number sequence is evaluated in segments to obtain the adaptive segmentation result of the luminous flux time series data; the adaptive segmentation result is fitted in segments to obtain the segment fitting result of the luminous flux time series data; the segment fitting result is analyzed for life detection to obtain the life detection result of the LED device. The luminous flux time series data points are dynamically divided into windows through window adjustment factors, so that the neighbor matching relationship between the time series data windows can adapt to the light decay stages of different LEDs, improve the segmentation accuracy of the luminous flux time series data and solve the problem that the fixed window length cannot adapt to the change of the light decay rate; after the dynamic window division, the distance optimization factor is further evaluated through the time scale matching factor and the space scale matching factor between the dynamic windows to solve the scale problem in the neighbor matching process of different dynamic window lengths, ensure the matching accuracy of short windows and long windows, thereby improving the adaptive segmentation accuracy of the luminous flux time series data, thereby improving the segmentation fitting accuracy of the luminous flux time series data.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A numerical fitting optimization method for LED device life detection, characterized in that: include: Performing luminous flux detection on the LED device to obtain luminous flux time series data; performing luminous flux change analysis on the luminous flux time series data to obtain window adjustment factors at different times; The luminous flux time series data is divided into dynamic windows by using the window adjustment factor to obtain the dynamic windows of the luminous flux time series data; the distance optimization factor is obtained by performing distance optimization factor analysis on any two of the dynamic windows; the distance between any two of the dynamic windows is adjusted by using the distance optimization factor The distance metric is optimized to obtain the optimized distance between any two dynamic windows of the luminous flux time series data; the optimized distance is used to perform nearest neighbor matching on the dynamic windows of the luminous flux time series data to obtain the nearest neighbor matching result; the nearest neighbor matching result is used to evaluate the arc span number sequence with the luminous flux time series data to obtain the arc span number sequence; the arc span number sequence is evaluated in segments to obtain the adaptive segmentation result of the luminous flux time series data; the adaptive segmentation result is fitted in segments to obtain the segment fitting result of the luminous flux time series data; the segment fitting result is analyzed for life detection to obtain the life detection result of the LED device.

2. A numerical fitting optimization method for LED device life detection according to claim 1, characterized in that: According to the analysis of the luminous flux change on the luminous flux time series data, the window adjustment factors at different moments are obtained; The time series data is dynamically divided into windows by the window adjustment factor to obtain the dynamic window of the luminous flux time series data. The specific steps include: Acquire the set initial window length and luminous flux time series data; acquire the local average light decay rate weight factor and the local average light decay rate change rate weight factor; for any time series data point in the luminous flux time series data, use the initial window corresponding to the time series data point as the evaluation window of the time series data point, and use the local average light decay rate weight multiplied by the average light decay rate in the evaluation window of the time series data point as the first dynamic window evaluation factor; use the local average light decay rate change rate weight factor multiplied by the average light decay rate change rate in the evaluation window of the time series data point as the second dynamic window evaluation factor; use the first dynamic window evaluation factor and the second dynamic window evaluation factor as the third dynamic window evaluation factor of the time series data point; substitute the opposite of the third dynamic window evaluation factor into the exponential function with the natural constant e as the base, and obtain the window adjustment factor of the time series data point; The product of the window adjustment factor of the time series data point and the set initial window length is used as the dynamic window length of the time series data point; the dynamic window length of each time series data point in the luminous flux time series data point is obtained, and the dynamic window of the luminous flux time series data is divided according to the dynamic window length of each time series data point to obtain the dynamic window of the luminous flux time series data.

3. A numerical fitting optimization method for LED device life detection according to claim 1, characterized in that: According to the distance optimization factor analysis performed on any two of the dynamic windows to obtain the distance optimization factor, the specific steps include: By performing a time scale difference analysis on any two of the dynamic windows, the time scale matching factor between the dynamic windows of the luminous flux time series data is obtained; by performing a spatial scale difference analysis on any two of the dynamic windows, the spatial scale matching factor between the dynamic windows of the luminous flux time series data is obtained; by performing a fusion evaluation of the time scale matching factor and the spatial scale matching factor, the distance optimization factor between any two dynamic windows of the luminous flux time series data is obtained.

4. A numerical fitting optimization method for LED device life detection according to claim 3, characterized in that: According to the time scale difference analysis of any two of the dynamic windows, the time scale matching factor between the dynamic windows of the luminous flux time series data is obtained, and the specific steps include: Get any two dynamic windows, take the absolute value of the dynamic window length difference between any two dynamic windows as the dynamic window length difference between any two dynamic windows, substitute the opposite of the dynamic window length difference between any two dynamic windows into an exponential function with the natural constant e as the base as the first exponential function result, and use the constant 1 minus the calculation result of the first exponential function as the time scale matching factor between the dynamic windows of the luminous flux timing data.

5. A numerical fitting optimization method for LED device life detection according to claim 3, characterized in that: According to the spatial scale difference analysis of any two of the dynamic windows, the spatial scale matching factor between the dynamic windows of the luminous flux time series data is obtained, and the specific steps include: The calculation formula of the spatial scale matching factor between the dynamic windows of the luminous flux time series data is: ； in Indicates The first The spatial scale matching factor between dynamic windows of luminous flux time series data points; Respectively represent The first Dynamic window length of each luminous flux time series data point; Indicates The first The smaller dynamic window length among the dynamic window lengths of the light flux time series data points; Indicates The first A larger dynamic window length among the dynamic window lengths of the light flux time series data points; Indicates The first point in the dynamic window of the luminous flux time series data point The light decay rate corresponding to the data point; Indicates The first point in the dynamic window of the luminous flux time series data point The light decay rate corresponding to the data point; Indicates The first The dynamic window length of the light flux time series data point is the largest in the dynamic window length. The light decay rate corresponding to the data point.

6. A numerical fitting optimization method for LED device life detection according to claim 3, characterized in that: According to the fusion evaluation of the time scale matching factor and the space scale matching factor, the distance optimization factor between any two dynamic windows of light flux time series data is obtained, and the specific steps include: Obtain a time scale matching factor and a space scale matching factor between any two of the dynamic windows of the luminous flux time series data, and use the calculation result of the product of the time scale matching factor and the space scale matching factor as the distance optimization factor between any two of the dynamic windows of the luminous flux time series data.

7. A numerical fitting optimization method for LED device life detection according to claim 1, characterized in that: According to the distance optimization factor, the distance between any two dynamic windows The distance metric is optimized to obtain an optimized distance between any two of the dynamic windows of the luminous flux time series data; the optimized distance performs nearest neighbor matching on the dynamic windows of the luminous flux time series data to obtain a nearest neighbor matching result, and the specific steps include: Obtaining a distance optimization factor between any two of the luminous flux time series data dynamic windows and any two of the luminous flux time series data dynamic windows; taking the calculation result of multiplying the distance optimization factor between the luminous flux time series data dynamic windows by the DTW distance between the luminous flux time series data dynamic windows as the optimized distance between any two of the luminous flux time series data dynamic windows; For each data point in the luminous flux time series data, the optimized distance between the dynamic window corresponding to the data point and the dynamic window corresponding to any other data point is obtained, and the dynamic window corresponding to any other data point with the smallest optimized distance between the dynamic window corresponding to the data point is taken as the nearest neighbor matching result of the data point.

8. A numerical fitting optimization method for LED device life detection according to claim 1, characterized in that: The arc spanning number sequence is evaluated according to the nearest neighbor matching result and the luminous flux time series data to obtain the arc spanning number sequence, and the specific steps include: Obtain the nearest neighbor matching result corresponding to the dynamic window of each data point in the luminous flux time series data; for any moment in the luminous flux time series data, take the number of corresponding nearest neighbor matching relationships on both sides of the moment as the arc span number corresponding to the moment, and obtain the arc span number sequence according to the arc span number corresponding to each moment in the luminous flux time series data.

9. A numerical fitting optimization method for LED device life detection according to claim 1, characterized in that: According to the method of performing segmented evaluation on the arc span number sequence to obtain the adaptive segmented result of the luminous flux time series data, the specific steps include: Obtain a segmentation degree threshold of the luminous flux time series data; use the linear normalization result of the arc span number corresponding to each moment of the luminous flux time series data in the arc span number sequence as the segmentation degree corresponding to each moment; compare the segmentation degree corresponding to each moment with the segmentation degree threshold of the luminous flux time series data, and mark the moment as a segmentation point if it is higher than the segmentation degree threshold; obtain all the segmentation points in the luminous flux time series data and segment the luminous flux time series data according to the segmentation points to obtain an adaptive segmentation result of the luminous flux time series data.

10. A numerical fitting optimization method for LED device life detection according to claim 1, characterized in that: The step of performing segmented fitting on the adaptive segmented results to obtain segmented fitting results of the luminous flux time series data; and performing life detection analysis on the segmented fitting results to obtain life detection results of the LED device include: Obtain the adaptive segmentation result of the luminous flux time series data, use the least squares method to perform exponential function fitting on each segmentation result, and obtain the segmentation fitting result of the luminous flux time series data; perform the LED device luminous flux reduction to the initial luminous flux according to the segmentation fitting result of the luminous flux time series data The cumulative time is evaluated and the cumulative time is used as the LED device Life test results.

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