A wear resistance testing method and system for optical coating products

Abstract

The present application provides a method and system for testing the wear resistance of an optical coating product. The present application obtains a test torque autoregression model of the optical coating product by performing autoregression analysis on a test torque sequence; performs wear anomaly detection on a regional temperature characteristic image in a coating temperature characteristic, thereby obtaining the wear anomaly degree corresponding to each coating temperature characteristic; then obtains the test pressure label corresponding to each coating temperature characteristic, and fits the temperature influence coefficient to obtain a temperature influence function of the optical coating product; based on the temperature influence function, the test torque autoregression model is segmentedly corrected to obtain a corrected friction coefficient of the optical coating product under different test pressures, and the wear resistance test results are obtained according to the corrected friction coefficient under different test pressures. The present application reduces the influence of high friction temperature on the wear resistance test results, and improves the accuracy of the wear resistance test of the optical coating product.

Description

Technical Field

[0001] The present application relates to the technical field of wear resistance testing, and more specifically, to a wear resistance testing method and system for optical coating products. Background Art

[0002] Optical coating refers to the process of coating a layer (or multiple layers) of metal dielectric film on the surface of optical parts. The purpose of coating the surface of optical parts is to change the reflection and transmission characteristics of the material surface, so as to reduce or increase the reflection, filtering, polarization and other requirements of light. Long-term use of the coating can easily cause the coating to wear and tear, making it difficult to meet the use requirements of optical products. Therefore, the wear resistance test of the surface coating of optical coating products is very critical.

[0003] The existing wear resistance test method is mainly equipped with appropriate friction wheels and weights. The wear resistance of optical coating products is tested by testing the friction torque of the friction wheel. Usually, the high surface heat generated by the friction test will affect the wear resistance of the coating. For example, the dynamic friction coefficient of most metal coatings will show a downward trend under high friction temperature, while the working environment of most optical coating products is a normal temperature environment. The high temperature generated by friction during the test will be significantly different from the actual working conditions, thereby affecting the representativeness and accuracy of the test results. Therefore, how to eliminate the influence of high friction temperature on the wear resistance test results has become an urgent problem to be solved. Summary of the invention

[0004] The present application provides a method and system for testing the wear resistance of optical coating products, which can reduce the impact of high friction temperature on the wear resistance test results and improve the accuracy of the wear resistance test of optical coating products.

[0005] In a first aspect, the present application provides a method for testing the wear resistance of an optical coating product. The method can be executed by a network device, or can be executed by a chip configured in the network device, and the present application does not limit this.

[0006] Specifically, the method includes:

[0007] Obtain the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures, and obtain the test torque sequence and coating temperature characteristic sequence;

[0008] Performing autoregressive analysis on the test torque sequence to obtain a test torque autoregressive model of the optical coating product;

[0009] For any coating temperature feature in the coating temperature feature sequence, performing wear anomaly detection on the regional temperature feature image in the coating temperature feature to obtain the wear anomaly degree corresponding to each coating temperature feature;

[0010] When the wear anomaly of all coating temperature characteristics is lower than a preset threshold, after extracting the temperature influence coefficient corresponding to each coating temperature characteristic in the coating temperature characteristic sequence, the test pressure label corresponding to each coating temperature characteristic is obtained to fit the temperature influence coefficient to obtain the temperature influence function of the optical coating product;

[0011] The test torque autoregressive model is segmentedly corrected based on the temperature influence function to obtain the corrected friction coefficient of the optical coating product under different test pressures, and the wear resistance test results are obtained according to the corrected friction coefficient under different test pressures.

[0012] In combination with the first aspect, in certain implementations of the first aspect, performing autoregressive analysis on the test torque sequence to obtain a test torque autoregressive model of the optical coating product specifically includes:

[0013] Obtaining a preset autoregressive period, obtaining a historical test moment sequence, and then determining an autocorrelation coefficient diagram and a partial autocorrelation coefficient diagram of the test moment sequence based on the historical test moment sequence;

[0014] A moving average autoregressive model of the historical test moment sequence is established according to the autocorrelation coefficient diagram and the partial autocorrelation coefficient diagram as the test moment autoregressive model.

[0015] In combination with the first aspect, in some implementations of the first aspect, before performing wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic, the method further includes:

[0016] Acquire a surface infrared image in the coating temperature feature, perform color space mapping on the surface infrared image, and obtain a grayscale image converted from the surface infrared image;

[0017] Threshold segmentation is performed based on the grayscale image converted from the surface infrared image to determine the regional temperature characteristic image of the coating temperature characteristic.

[0018] In combination with the first aspect, in certain implementations of the first aspect, performing wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic to obtain the wear anomaly degree corresponding to each coating temperature characteristic specifically includes:

[0019] Acquire a regional temperature characteristic image of the coating temperature characteristic;

[0020] Obtaining the test environment temperature in the coating temperature characteristic;

[0021] Determine the detection window scale based on the grayscale mean of the regional temperature characteristic image of the coating temperature characteristic and the test environment temperature, and segment the regional temperature characteristic image according to the detection window scale to obtain a plurality of detection windows;

[0022] Performing local temperature uniformity determination in each detection window to obtain the local temperature uniformity coefficient corresponding to each detection window;

[0023] Determining the wear abnormality of the coating temperature characteristic based on the local temperature uniformity coefficient corresponding to each detection window;

[0024] The wear anomaly degrees corresponding to other coating temperature characteristics in the coating temperature characteristic sequence are obtained in the same manner.

[0025] In combination with the first aspect, in certain implementations of the first aspect, when there is a wear abnormality of the coating temperature characteristic that is higher than a preset threshold, the wear resistance test result of the optical coating product is determined to be unqualified.

[0026] In combination with the first aspect, in some implementations of the first aspect, extracting the temperature influence coefficients corresponding to the respective coating temperature features in the coating temperature feature sequence specifically includes:

[0027] For any coating temperature feature in the coating temperature feature sequence, obtaining the test environment temperature and regional temperature feature image corresponding to the coating temperature feature;

[0028] Extracting the test temperature difference between the test environment temperature and the regional temperature characteristic image, obtaining a preset reference temperature difference, and determining the temperature influence coefficient corresponding to the coating temperature characteristic according to the reference temperature difference and the test temperature difference;

[0029] The temperature influence coefficients corresponding to other coating temperature characteristics in the coating temperature characteristic sequence are determined in the same manner.

[0030] In combination with the first aspect, in certain implementations of the first aspect, a friction wheel is used to apply different test pressures to the optical coating product, and then the test torques of the friction wheel under different test pressures are respectively collected to obtain the test torque sequence.

[0031] In a second aspect, the present application provides a wear resistance performance testing system for an optical coating product, the wear resistance performance testing system comprising:

[0032] A test data acquisition unit is used to obtain the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures, and obtain a test torque sequence and a coating temperature characteristic sequence;

[0033] A test data processing unit, used for performing autoregressive analysis on the test torque sequence to obtain a test torque autoregressive model of the optical coating product;

[0034] The test data processing unit is further used to perform wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic for any coating temperature characteristic in the coating temperature characteristic sequence, and obtain the wear anomaly degree corresponding to each coating temperature characteristic;

[0035] The test data processing unit is further used for extracting the temperature influence coefficients corresponding to the respective coating temperature characteristics in the coating temperature characteristic sequence when the wear anomaly of all coating temperature characteristics is lower than a preset threshold value, obtaining the test pressure labels corresponding to the respective coating temperature characteristics, fitting the temperature influence coefficients, and obtaining the temperature influence function of the optical coating product;

[0036] The test result output unit is used to perform segmented correction on the test torque autoregressive model according to the temperature influence function, obtain the corrected friction coefficient of the optical coating product under different test pressures, and obtain the wear resistance test result according to the corrected friction coefficient under different test pressures.

[0037] In a third aspect, the present application provides a computer terminal device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned method for testing the wear resistance of an optical coating product.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned method for testing the wear resistance of an optical coating product.

[0039] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0040] In a wear resistance testing method and system for an optical coating product provided in the present application, the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures are first obtained to obtain a test torque sequence and a coating temperature characteristic sequence; an autoregressive analysis is performed on the test torque sequence to obtain a test torque autoregressive model of the optical coating product; for any coating temperature feature in the coating temperature characteristic sequence, wear anomaly detection is performed on the regional temperature characteristic image in the coating temperature feature to obtain the wear anomaly degree corresponding to each coating temperature feature; when the wear anomaly degree of all coating temperature features is lower than a preset threshold, the temperature influence coefficient corresponding to each coating temperature feature in the coating temperature characteristic sequence is extracted, and the test pressure label corresponding to each coating temperature feature is obtained to fit the temperature influence coefficient to obtain the temperature influence function of the optical coating product; the test torque autoregressive model is segmentedly corrected based on the temperature influence function to obtain the corrected friction coefficient of the optical coating product under different test pressures, and the wear resistance test result is obtained according to the corrected friction coefficient under different test pressures.

[0041] Therefore, it can be seen that the present application considers that the high temperature generated during the friction process will have a significant impact on the performance of the coating material, such as the dynamic friction coefficient. Therefore, by real-time monitoring of the coating temperature characteristics of the optical coating product, the thermal effect generated by friction can be accurately captured, and by performing wear anomaly detection on the regional temperature characteristic image, the local temperature abnormal heat transfer caused by wear can be detected to obtain the wear anomaly degree of the coating surface. When the wear anomaly degree is lower than the preset threshold, it indicates that the main source of temperature change is friction heat, rather than significant wear damage, thereby ensuring the basic data quality for subsequent analysis, and then extracting the temperature influence coefficient, and fitting it in combination with the test pressure label to establish a temperature influence function. The temperature influence function provides a quantitative correction basis for the high temperature effect. The test torque autoregressive model is corrected in sections and dynamically adjusts the friction coefficient under different test pressures in combination with the temperature influence function to ensure that the prediction results in the high temperature range are corrected, and the predicted value of the test torque autoregressive model is decoupled from the high temperature effect of temperature, thereby eliminating the interference of friction and high temperature on the wear resistance test results, making the test data closer to the actual performance under normal temperature conditions, and improving the accuracy of the wear resistance test of optical coating products.

[0042] In summary, the present application reduces the influence of high friction temperature on the wear resistance test results by performing segmented correction on the test torque autoregressive model through the temperature influence function, thereby improving the accuracy of the wear resistance test of optical coating products. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is an exemplary flow chart of a method for testing the wear resistance of an optical coating product according to some embodiments of the present application;

[0044] Figure 2 is a schematic structural diagram of a wear resistance testing system according to some embodiments of the present application;

[0045] Figure 3 It is a structural schematic diagram of a computer terminal device for implementing a method for testing the wear resistance of an optical coating product according to some embodiments of the present application. DETAILED DESCRIPTION

[0046] The present application obtains the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures to obtain the test torque sequence and the coating temperature characteristic sequence; performs autoregression analysis on the test torque sequence to obtain the test torque autoregression model of the optical coating product; for any coating temperature characteristic in the coating temperature characteristic sequence, performs wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic, so as to obtain the wear anomaly degree corresponding to each coating temperature characteristic; when the wear anomaly degree of all coating temperature characteristics is lower than a preset threshold, after extracting the temperature influence coefficient corresponding to each coating temperature characteristic in the coating temperature characteristic sequence, obtain the test pressure label corresponding to each coating temperature characteristic to fit the temperature influence coefficient, and obtain the temperature influence function of the optical coating product; based on the temperature influence function, the test torque autoregression model is segmentedly corrected to obtain the corrected friction coefficient of the optical coating product under different test pressures, and the wear resistance test result is obtained according to the corrected friction coefficient under different test pressures, thereby reducing the influence of high friction temperature on the wear resistance test result and improving the accuracy of the wear resistance test of the optical coating product.

[0047] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a method for testing the wear resistance of an optical coating product according to some embodiments of the present application. The method 100 for testing the wear resistance of an optical coating product mainly includes the following steps:

[0048] In step S101, the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures are obtained to obtain a test torque sequence and a coating temperature characteristic sequence.

[0049] Optionally, in some embodiments, obtaining the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures to obtain the test torque sequence and the coating temperature characteristic sequence specifically includes: testing the test torque of the optical coating product under different test pressures respectively to obtain the test torque sequence, and collecting the coating temperature characteristics on the surface of the optical coating product after each test to obtain the coating temperature characteristic sequence.

[0050] Optionally, in some embodiments, a friction wheel may be used to apply different test pressures to the optical coating product, and then the test torques of the friction wheel under different test pressures are respectively collected to obtain the test torque sequence.

[0051] Optionally, in some embodiments, the coating temperature characteristics include: the test environment temperature and the surface infrared image of the optical coating product. Each time after the friction wheel applies different test pressures to the optical coating product, the surface infrared images corresponding to the different test pressures can be immediately collected by an infrared camera, and the test environment temperature can be collected by a temperature sensor to obtain the coating temperature characteristic sequence. It should be noted that the high temperature generated during the friction process will have a significant impact on the performance of the coating material (such as the dynamic friction coefficient). By real-time monitoring of the coating temperature characteristics of the optical coating product, the thermal effect generated by friction can be accurately captured.

[0052] In step S102, an autoregressive analysis is performed on the test torque sequence to obtain a test torque autoregressive model of the optical coating product.

[0053] It should be noted that the test torque autoregressive model described in the present application is a test torque value function that changes with the test pressure value, which is obtained by performing autoregressive analysis based on the historical test torque sequence, thereby reducing the number of tests and shortening the time for the wear resistance test of the optical coating product. Optionally, in some embodiments, the test torque sequence is subjected to autoregressive analysis to obtain the test torque autoregressive model of the optical coating product, which specifically includes:

[0054] Obtaining a preset autoregressive period, obtaining a historical test moment sequence, and then determining an autocorrelation coefficient diagram and a partial autocorrelation coefficient diagram of the test moment sequence based on the historical test moment sequence;

[0055] A moving average autoregressive model of the historical test moment sequence is established according to the autocorrelation coefficient diagram and the partial autocorrelation coefficient diagram as the test moment autoregressive model.

[0056] A preferred embodiment of obtaining the test torque autoregressive model in the present application is given below: First, since the sequence label of the test torque sequence is the test pressure value and the preset autoregressive period is 5N, the test torque values ​​within the initial 5N range can be recorded separately to obtain a historical test torque sequence. In other embodiments, the maintenance time period can also be preset to other time lengths; and then a time series graph of the historical test torque sequence can be drawn. The difference is that the horizontal axis of the time series graph corresponds to different test pressures rather than moment values, and then the time series graph of the historical test torque sequence can be exponentially transformed to eliminate the trend of the variance in the time series graph changing with the test pressure.

[0057] Secondly, according to the time series diagram of the historical test moment sequence, the autocorrelation coefficient diagram of the test moment is drawn, wherein the horizontal axis of the autocorrelation coefficient diagram is the number of lags and the vertical axis is the value of the autocorrelation coefficient. The partial autocorrelation coefficient diagram of the test moment is drawn, wherein the horizontal axis of the partial autocorrelation coefficient diagram is the number of lags and the vertical axis is the value of the partial autocorrelation coefficient.

[0058] According to the characteristics of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph, the order of the model and the range of coefficient values ​​can be preliminarily determined. For example, the autocorrelation coefficient graph can be drawn to observe whether the autocorrelation coefficient shows truncation characteristics after a certain order. If the autocorrelation coefficient drops sharply after a certain order and remains near 0, the order of the autoregressive model can be preliminarily determined; the partial autocorrelation coefficient graph can be drawn to observe whether the partial autocorrelation coefficient shows truncation characteristics after a certain order. If the partial autocorrelation coefficient drops sharply after a certain order and remains near 0, the order of the moving average model can be preliminarily determined.

[0059] In specific implementation, the last significant autocorrelation coefficient can be found according to the autocorrelation coefficient graph, which is the order of the autocorrelation model. For example, if the last significant autocorrelation coefficient in the autocorrelation coefficient graph is at the third order, the order of the autocorrelation model is 3; then, according to the partial autocorrelation coefficient graph, the last significant partial autocorrelation coefficient can be found, which is the order of the moving average model. For example, if the last significant partial autocorrelation coefficient in the partial autocorrelation coefficient graph is at the second order, the order of the moving average model is 2; finally, according to the autocorrelation coefficient graph and the partial autocorrelation coefficient graph, the order (p, q) of the autoregressive moving average model is determined. For example, if the autocorrelation coefficient graph and the partial autocorrelation coefficient graph both decay to zero after the third order, the order of the autoregressive moving average model is (3, 3); then, according to the order of the autoregressive moving average model, appropriate parameters are selected to establish an autoregressive moving average model of the historical test moment sequence, the autoregressive moving average model is used as the test moment autoregressive model, and the historical test moment sequence is brought into the test moment autoregressive model, so that subsequent test moments can be predicted.

[0060] It should be noted that the present application performs autoregressive analysis on the test torque sequence to obtain a test torque autoregressive model, thereby enhancing the trend of the test torque changing with the test pressure and reducing the impact of random errors on the test torque results, thereby improving the accuracy of the wear resistance test of optical coating products, and can predict subsequent test results based on the test torque autoregressive model, thereby reducing the number of tests required for wear resistance testing and shortening the time period required for wear resistance testing.

[0061] In step S103, for any coating temperature feature in the coating temperature feature sequence, wear anomaly detection is performed on the regional temperature feature image in the coating temperature feature to obtain the wear anomaly degree corresponding to each coating temperature feature.

[0062] Optionally, in some embodiments, before performing wear abnormality detection on the regional temperature characteristic image in the coating temperature characteristic, the method further includes: acquiring the surface infrared image in the coating temperature characteristic and performing regional segmentation to obtain the regional temperature characteristic image in the coating temperature characteristic.

[0063] Preferably, in some embodiments, obtaining the surface infrared image in the coating temperature feature for regional segmentation to obtain the regional temperature feature image in the coating temperature feature specifically includes:

[0064] Acquire a surface infrared image in the coating temperature feature, perform color space mapping on the surface infrared image, and obtain a grayscale image converted from the surface infrared image;

[0065] Threshold segmentation is performed based on the grayscale image converted from the surface infrared image to determine the regional temperature characteristic image of the coating temperature characteristic.

[0066] It should be noted that the wear abnormality described in the present application is a quantitative indicator to measure whether there is abnormal wear of the optical coating product during the wear resistance test. This indicator determines whether there is local wear caused by microcracks or damage by analyzing the temperature changes of the optical coating product during the friction test, especially the temperature uniformity in the friction area. During the wear resistance test of the optical coating product, the test friction will cause the surface of the optical coating product to have a temperature rise. Usually, the temperature rise has a certain uniformity and regularity in the friction area. However, when the optical coating product is worn due to the test pressure, the microcracks or damage caused by the wear will cause local abnormal heat transfer, resulting in significant non-uniformity of the local temperature. Therefore, the regional temperature characteristic image in the coating temperature characteristic can be used for wear abnormality detection, that is, the temperature uniformity of different detection windows inside it can be used to determine whether the optical coating product has abnormal wear.

[0067] Optionally, in some embodiments, performing wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic to obtain the wear anomaly degree corresponding to each coating temperature characteristic specifically includes:

[0068] Acquire a regional temperature characteristic image of the coating temperature characteristic;

[0069] Obtaining the test environment temperature in the coating temperature characteristic;

[0070] Determine the detection window scale based on the grayscale mean of the regional temperature characteristic image of the coating temperature characteristic and the test environment temperature, and segment the regional temperature characteristic image according to the detection window scale to obtain a plurality of detection windows;

[0071] Performing local temperature uniformity determination in each detection window to obtain the local temperature uniformity coefficient corresponding to each detection window;

[0072] Determining the wear abnormality of the coating temperature characteristic based on the local temperature uniformity coefficient corresponding to each detection window;

[0073] The wear anomaly degrees corresponding to other coating temperature characteristics in the coating temperature characteristic sequence are obtained in the same manner.

[0074] In specific implementation, the grayscale mean of the regional temperature characteristic image of the coating temperature characteristic and the threshold interval of the test environment temperature can be mapped through a mapping table to obtain the corresponding detection window scale. The mapping table is calibrated based on historical experience. Generally speaking, the smaller the grayscale mean of the regional temperature characteristic image and the larger the test environment temperature, the smaller the recognizable scale of the temperature characteristic, and therefore the smaller the corresponding mapped detection window scale. The mapping table can be calibrated based on multiple tests, which is not elaborated in this application.

[0075] Optionally, in some embodiments, since the grayscale of the pixel value in the detection window is obtained by grayscale conversion of the infrared image, its grayscale value reflects the surface temperature of the optical coating product at the pixel position. Therefore, the overall standard deviation of the pixel grayscale in the detection window can be used as the local temperature uniformity coefficient corresponding to the detection window, and then the mean of the local temperature uniformity coefficients corresponding to all the detection windows can be used as the wear abnormality of the coating temperature characteristic.

[0076] It should be noted that wear is usually accompanied by local temperature non-uniformity (such as abnormal heat transfer caused by microcracks or damage). By performing wear anomaly detection on the regional temperature characteristic image, the wear anomaly of the coating surface can be quantified; when the wear anomaly is lower than the preset threshold, it indicates that the main source of temperature change is frictional heat rather than significant wear damage, thereby ensuring the basic data quality for subsequent analysis.

[0077] In step S104, when the wear abnormality of all coating temperature characteristics is lower than a preset threshold, after extracting the temperature influence coefficient corresponding to each coating temperature characteristic in the coating temperature characteristic sequence, the test pressure label corresponding to each coating temperature characteristic is obtained to fit the temperature influence coefficient to obtain the temperature influence function of the optical coating product.

[0078] Optionally, in some embodiments, the preset threshold is calibrated as a constant based on experience.

[0079] It should be noted that when the wear abnormality of all coating temperature characteristics is lower than the preset threshold, it indicates that under the test pressure conditions, the temperature change on the surface of the optical coating product conforms to the normal law, the temperature distribution is relatively uniform, and there is no non-uniform temperature rise caused by wear, microcracks or other local damage. At the same time, under the current test pressure, the wear resistance of the optical coating product is relatively stable, and there is no abnormal wear caused by local wear. At this time, the test torque autoregressive model can be segmentedly corrected according to the temperature influence function to obtain the corrected friction coefficient of the optical coating product under different test pressures, so as to further test the wear resistance of the optical coating product.

[0080] Optionally, in some embodiments, when the wear abnormality of the coating temperature characteristic is higher than a preset threshold, the wear resistance test result of the optical coating product is determined to be unqualified.

[0081] It should be noted that the temperature influence coefficient is a quantitative value of the influence of friction temperature rise on the optical coating product during testing. Preferably, in some embodiments, extracting the temperature influence coefficients corresponding to each coating temperature feature in the coating temperature feature sequence specifically includes:

[0082] For any coating temperature feature in the coating temperature feature sequence, obtaining the test environment temperature and regional temperature feature image corresponding to the coating temperature feature;

[0083] Extract the test temperature difference between the test environment temperature and the regional temperature characteristic image, obtain a preset reference temperature difference, and determine the temperature influence coefficient corresponding to the coating temperature characteristic according to the reference temperature difference and the test temperature difference; in specific implementation, the image grayscale mean of the regional temperature characteristic image can be obtained, and the temperature value corresponding to the grayscale mean in the infrared image can be determined, and then the difference between the temperature value and the test environment temperature is used as the test temperature difference;

[0084] The temperature influence coefficients corresponding to other coating temperature characteristics in the coating temperature characteristic sequence are determined in the same manner.

[0085] In specific implementation, the ratio of the test temperature difference to the reference temperature difference can be used as the temperature influence coefficient corresponding to the coating temperature characteristic. Optionally, in some embodiments, the test pressure labels corresponding to each coating temperature characteristic are obtained to fit the temperature influence coefficient. In the process of obtaining the temperature influence function of the optical coating product, the temperature influence coefficient can be fitted according to the test pressure labels corresponding to each coating temperature characteristic using the Lagrange interpolation method to obtain the temperature influence function of the optical coating product, wherein the independent variable of the temperature influence function is the test pressure value, and the dependent variable is the temperature influence coefficient of the optical coating product.

[0086] It should be noted that the temperature influence function plays a key role in the segmented correction. It can decouple the predicted value of the test torque autoregression model from the high temperature effect of temperature. By determining the friction coefficient of the optical coating product through the corrected model, the interference of high temperature on the wear resistance test results can be eliminated, making the test data closer to the actual performance under normal temperature conditions.

[0087] In step S105, the test torque autoregressive model is segmentedly corrected based on the temperature influence function to obtain the corrected friction coefficient of the optical coating product under different test pressures, and the wear resistance test results are obtained according to the corrected friction coefficient under different test pressures.

[0088] Optionally, in some embodiments, the test torque autoregressive model is segmentedly corrected based on the temperature influence function to obtain the corrected friction coefficient of the optical coating product under different test pressures, specifically including:

[0089] Get the preset calibration interval length;

[0090] Determine a first calibration interval according to a preset calibration interval length, determine the temperature correlation within the first calibration interval according to the temperature influence function and the test moment autoregressive model, and then continue to determine other calibration intervals, and determine the temperature correlation within the other calibration intervals in the same manner;

[0091] The test torque autoregressive model is segmentedly corrected based on the temperature correlation of each correction interval as a correction coefficient to obtain a corrected test torque autoregressive model, and the corrected friction coefficient of the optical coating product under different test pressures is determined based on the corrected test torque autoregressive model.

[0092] It should be noted that the length of the correction interval can be determined based on the distribution of test data, the working characteristics of the optical coating product and the test pressure range. In some embodiments, the length of the correction interval can also be preset as a constant, usually a fixed test pressure range length. It should be noted that the temperature correlation is a quantitative value of the influence of temperature changes in the correction interval on the test torque. In specific implementation, the absolute value of the Pearson correlation coefficient between the temperature influence function and the test torque autoregressive model in the correction interval is used as the temperature correlation corresponding to the correction interval, and then the temperature correlation is used as the correction coefficient to dynamically adjust the autoregressive coefficients of different correction intervals in the test torque autoregressive model, so that the predicted value of the test torque autoregressive model is decoupled from the high temperature effect of temperature, thereby ensuring that the corrected test torque autoregressive model can better fit the torque data under different pressures.

[0093] Optionally, in some embodiments, the ratio between the temperature correlation and the preset correlation can be used as a correction coefficient, and then the autoregressive coefficient in the test torque autoregressive model can be proportionally corrected according to the correction coefficient, so that the test torque value of the previous correction interval can be used as the historical test torque, and the test torque value of the next correction interval can be predicted according to the corrected autoregressive coefficient, thereby eliminating the interference of high temperature on the wear resistance test results, making the test data closer to the actual performance under normal temperature conditions, ensuring that the corrected test torque autoregressive model can better fit the torque data under different pressures, and improving the accuracy of the wear resistance test.

[0094] Optionally, in some embodiments, determining the corrected friction coefficient of the optical coating product under different test pressures based on the corrected test torque autoregressive model specifically includes:

[0095] Obtain different test pressure labels, obtain corresponding test torques in the corrected test torque autoregressive model based on the different test pressure labels, and determine the corrected friction coefficients under different test pressures according to the different test pressure labels and the corresponding test torques. In specific implementation, the ratio between the test torque and the test pressure can be used as the corresponding corrected friction coefficient, which reflects the friction coefficient of the optical coating product when it is in dynamic friction with the friction wheel under the test pressure and without being disturbed by the friction temperature.

[0096] Optionally, in some embodiments, obtaining the wear resistance performance test result according to the corrected friction coefficient under different test pressures specifically includes: performing machine learning based on the corrected friction coefficient under different test pressures to obtain a wear resistance performance score.

[0097] In specific implementation, a linear regression model can be used as a machine learning model, and the test pressure, corrected friction coefficient, and the artificial wear resistance performance score of the corresponding part of the test pressure are used as model input data. The linear regression model divides the data set into a training set and a test set, and the input data is standardized and averaged weighted to score, so as to predict the final wear resistance performance score of the optical coating product.

[0098] In addition, in another aspect of the present application, in some embodiments, the present application provides a wear resistance testing system for optical coating products, referring to Figure 2 , which is a schematic diagram of the structure of a wear resistance test system according to some embodiments of the present application, the wear resistance test system 200 includes: a test data acquisition unit 201, a test data processing unit 202 and a test result output unit 203, which are respectively described as follows:

[0099] A test data acquisition unit 201, wherein the test data acquisition unit 201 is used to obtain the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures, and obtain a test torque sequence and a coating temperature characteristic sequence;

[0100] A test data processing unit 202, the test data processing unit 202 is used to perform autoregression analysis on the test moment sequence to obtain a test moment autoregression model of the optical coating product;

[0101] The test data processing unit 202 is also used to perform wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic for any coating temperature characteristic in the coating temperature characteristic sequence, and obtain the wear anomaly degree corresponding to each coating temperature characteristic;

[0102] The test data processing unit 202 is also used for extracting the temperature influence coefficients corresponding to the respective coating temperature characteristics in the coating temperature characteristic sequence when the wear anomaly of all coating temperature characteristics is lower than a preset threshold, obtaining the test pressure labels corresponding to the respective coating temperature characteristics, fitting the temperature influence coefficients, and obtaining the temperature influence function of the optical coating product;

[0103] The test result output unit 203 is used to perform segmented correction on the test torque autoregressive model according to the temperature influence function, obtain the corrected friction coefficient of the optical coating product under different test pressures, and obtain the wear resistance test result according to the corrected friction coefficient under different test pressures.

[0104] The above describes in detail an example of a method and system for testing the wear resistance of an optical coating product provided in an embodiment of the present application. It can be understood that in order to achieve the above functions, the corresponding device includes a hardware structure and / or software module corresponding to executing each function.

[0105] Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed in this document, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in hardware or in a computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Therefore, professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0106] In addition, the present application also provides a computer terminal device, which includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned method for testing the wear resistance of an optical coating product.

[0107] In some embodiments, reference Figure 3 , which is a schematic diagram of the structure of a computer terminal device for implementing a method for testing the wear resistance of an optical coating product according to some embodiments of the present application. A method for testing the wear resistance of an optical coating product in the above embodiment can be performed by Figure 3 The computer terminal device 300 shown in the figure is implemented, and the computer terminal device 300 includes at least one communication bus 301, a communication interface 302, a processor 303 and a memory 304.

[0108] The processor 303 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of a wear resistance test method for an optical coating product in the present application.

[0109] The communication bus 301 may include a path for transmitting information between the above-mentioned components.

[0110] The memory 304 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 304 may exist independently and be connected to the processor 303 via the communication bus 301. The memory 304 may also be integrated with the processor 303.

[0111] The memory 304 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 303. The processor 303 is used to execute the program code stored in the memory 304. The program code may include one or more software modules. The determination of the wear abnormality in the above embodiment can be implemented by the processor 303 and one or more software modules in the program code in the memory 304.

[0112] The communication interface 302 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0113] Optionally, the computer terminal device 300 may further include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0114] In a specific implementation, as an embodiment, a computer terminal device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0115] The above-mentioned computer terminal device may be a general-purpose computer terminal device or a dedicated computer terminal device. In a specific implementation, the computer terminal device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer terminal device.

[0116] In addition, in other aspects of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned method for testing the wear resistance of an optical coating product.

[0117] In summary, in a method and system for testing the wear resistance of an optical coating product disclosed in an embodiment of the present application, firstly, a test torque sequence and a coating temperature characteristic sequence are obtained by obtaining the test torque and the corresponding coating temperature characteristics of the optical coating product under different test pressures; an autoregressive analysis is performed on the test torque sequence to obtain a test torque autoregressive model of the optical coating product; for any coating temperature characteristic in the coating temperature characteristic sequence, wear anomaly detection is performed on the regional temperature characteristic image in the coating temperature characteristic, so as to obtain the wear anomaly degree corresponding to each coating temperature characteristic; when the wear anomaly degrees of all coating temperature characteristics are the same, the wear anomaly degree of each coating temperature characteristic is obtained; When it is lower than a preset threshold, after extracting the temperature influence coefficients corresponding to each coating temperature feature in the coating temperature feature sequence, the test pressure labels corresponding to each coating temperature feature are obtained to fit the temperature influence coefficients to obtain the temperature influence function of the optical coating product; based on the temperature influence function, the test torque autoregressive model is piecewise corrected to obtain the corrected friction coefficients of the optical coating product under different test pressures, and the wear resistance test results are obtained according to the corrected friction coefficients under different test pressures, thereby reducing the influence of high friction temperature on the wear resistance test results and improving the accuracy of the wear resistance test of the optical coating product.

[0118] The above is only an embodiment of the present application, and the common knowledge such as the specific technical scheme or characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical scheme of the present application, several modifications and improvements can be made, which should also be regarded as the scope of protection of the present application, and these will not affect the effect of the implementation of the present application and the practicality of the patent.

[0119] The scope of protection claimed by this application shall be based on the content of its claims. The specific implementation methods and other records in the specification can be used to interpret the content of the claims. Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include these modifications and variations.

Claims

1. A method for testing the wear resistance of an optical coating product, characterized in that: include: Obtain the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures, and obtain the test torque sequence and coating temperature characteristic sequence; Performing autoregressive analysis on the test torque sequence to obtain a test torque autoregressive model of the optical coating product; For any coating temperature feature in the coating temperature feature sequence, performing wear anomaly detection on the regional temperature feature image in the coating temperature feature to obtain the wear anomaly degree corresponding to each coating temperature feature; When the wear anomaly of all coating temperature characteristics is lower than a preset threshold, after extracting the temperature influence coefficient corresponding to each coating temperature characteristic in the coating temperature characteristic sequence, the test pressure label corresponding to each coating temperature characteristic is obtained to fit the temperature influence coefficient to obtain the temperature influence function of the optical coating product; Based on the temperature influence function, the test torque autoregressive model is segmentedly corrected to obtain the corrected friction coefficient of the optical coating product under different test pressures, and the wear resistance test results are obtained according to the corrected friction coefficient under different test pressures; Among them, performing wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic to obtain the wear anomaly degree corresponding to each coating temperature characteristic specifically includes: Acquire a regional temperature characteristic image of the coating temperature characteristic; Obtaining the test environment temperature in the coating temperature characteristic; Determine the detection window scale based on the grayscale mean of the regional temperature characteristic image of the coating temperature characteristic and the test environment temperature, and segment the regional temperature characteristic image according to the detection window scale to obtain a plurality of detection windows; Performing local temperature uniformity determination in each detection window to obtain the local temperature uniformity coefficient corresponding to each detection window; Determining the wear abnormality of the coating temperature characteristic based on the local temperature uniformity coefficient corresponding to each detection window; The wear anomaly degrees corresponding to other coating temperature characteristics in the coating temperature characteristic sequence are obtained in the same manner.

2. The method according to claim 1, characterized in that Performing autoregressive analysis on the test moment sequence to obtain the test moment autoregressive model of the optical coating product specifically includes: Obtaining a preset autoregressive period, obtaining a historical test moment sequence, and then determining an autocorrelation coefficient diagram and a partial autocorrelation coefficient diagram of the test moment sequence based on the historical test moment sequence; A moving average autoregressive model of the historical test moment sequence is established according to the autocorrelation coefficient diagram and the partial autocorrelation coefficient diagram as the test moment autoregressive model.

3. The method according to claim 1, characterized in that Before performing wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic, the method further includes: Acquire a surface infrared image in the coating temperature feature, perform color space mapping on the surface infrared image, and obtain a grayscale image converted from the surface infrared image; Threshold segmentation is performed based on the grayscale image converted from the surface infrared image to determine the regional temperature characteristic image of the coating temperature characteristic.

4. The method according to claim 1, characterized in that When the wear abnormality of the coating temperature characteristic is higher than a preset threshold, the wear resistance test result of the optical coating product is determined to be unqualified.

5. The method according to claim 1, characterized in that Extracting the temperature influence coefficients corresponding to the respective coating temperature features in the coating temperature feature sequence specifically includes: For any coating temperature feature in the coating temperature feature sequence, obtaining the test environment temperature and regional temperature feature image corresponding to the coating temperature feature; Extracting the test temperature difference between the test environment temperature and the regional temperature characteristic image, obtaining a preset reference temperature difference, and determining the temperature influence coefficient corresponding to the coating temperature characteristic according to the reference temperature difference and the test temperature difference; The temperature influence coefficients corresponding to other coating temperature characteristics in the coating temperature characteristic sequence are determined in the same manner.

6. The method according to claim 1, characterized in that A friction wheel is used to apply different test pressures to the optical coating product, and then the test torques of the friction wheel under different test pressures are respectively collected to obtain the test torque sequence.

7. A wear resistance testing system for optical coating products, which uses the method described in any one of claims 1 to 6 to test the wear resistance of optical coating products, characterized in that: The wear resistance testing system comprises: A test data acquisition unit is used to obtain the test torque and corresponding coating temperature characteristics of the optical coating product under different test pressures, and obtain a test torque sequence and a coating temperature characteristic sequence; A test data processing unit, used for performing autoregressive analysis on the test torque sequence to obtain a test torque autoregressive model of the optical coating product; The test data processing unit is further used to perform wear anomaly detection on the regional temperature characteristic image in the coating temperature characteristic for any coating temperature characteristic in the coating temperature characteristic sequence, and obtain the wear anomaly degree corresponding to each coating temperature characteristic; The test data processing unit is further used for extracting the temperature influence coefficients corresponding to the respective coating temperature characteristics in the coating temperature characteristic sequence when the wear anomaly of all coating temperature characteristics is lower than a preset threshold value, obtaining the test pressure labels corresponding to the respective coating temperature characteristics, fitting the temperature influence coefficients, and obtaining the temperature influence function of the optical coating product; The test result output unit is used to perform segmented correction on the test torque autoregressive model according to the temperature influence function, obtain the corrected friction coefficient of the optical coating product under different test pressures, and obtain the wear resistance test result according to the corrected friction coefficient under different test pressures.

8. A computer terminal device, characterized in that: The computer terminal device includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute a method for testing the wear resistance of an optical coating product as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing at least one computer program, characterized in that: The computer program is loaded and executed by a processor to implement the operations performed by the method for testing the wear resistance of an optical coating product as described in any one of claims 1 to 6.

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