A method and device for detecting the flatness of a tempered glass mirror

Abstract

This invention relates to the field of optical inspection technology and discloses a method and apparatus for detecting the flatness of tempered glass mirrors. The method includes: acquiring an image of the tempered glass mirror surface; constructing a complete morphology model; calculating the difference between the coordinates of points in the complete morphology model and the coordinates of reference points to obtain the deformation amount, and calculating the surface curvature value and deformation gradient value to generate a deformation parameter set; acquiring environmental change data, establishing a mapping relationship between the deformation parameter set and the environmental change dataset, and generating a dynamic deformation prediction model; calculating the current temperature gradient and the current stress gradient and inputting them into the dynamic deformation prediction model to obtain the predicted deformation rate; adjusting the point distribution and data sampling frequency according to the prediction results, acquiring surface morphology data, and generating an optimized deformation monitoring dataset; obtaining an updated surface morphology model; and generating a flatness judgment result by combining a preset standard threshold. This method can improve the accuracy of flatness detection of tempered glass mirrors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical detection, and in particular to a tempered glass mirror flatness detection method and device. BACKGROUND

[0002] At present, as high-end optical elements and building decoration materials, the flatness of tempered glass mirrors directly affects the optical performance and use safety, and has key significance in the fields of precision instruments, building curtain walls, etc. Flatness detection is not only the core link of quality control, but also relates to the stability and reliability of products in complex environments.

[0003] In one prior art, single-point or low-density multi-point data collection is performed under constant environmental conditions, and the flatness of the tempered glass mirror is determined based on static measurement data. However, due to the thermal expansion and stress distribution characteristics of glass materials, environmental changes can cause subtle but critical distortions in surface topography, and the detection equipment may not be able to capture micron-level deformations caused by temperature differences in real time.

[0004] In the prior art, data collection of tempered glass mirrors relies on static measurement under a single environment, which is difficult to adapt to complex deformations of glass under different temperature and stress conditions, and the capture precision of subtle distortions is insufficient, resulting in insufficient flatness detection precision of tempered glass mirrors. SUMMARY

[0005] The present application provides a tempered glass mirror flatness detection method and device to solve the problem of insufficient flatness detection precision of tempered glass mirrors.

[0006] In a first aspect, to solve the above technical problems, the present application provides a tempered glass mirror flatness detection method, comprising:

[0007] Obtaining surface images of multiple points of the tempered glass mirror under different temperature and stress conditions;

[0008] Performing linear interpolation processing on the surface images to obtain a set of completed pixel points, and constructing a complete topography model based on the set of completed pixel points;

[0009] Calculating the difference between the coordinates of the points in the complete topography model and the coordinates of the preset reference points to obtain the deformation amount of each point, and calculating the surface curvature value and the deformation gradient value based on the deformation amount of each point to generate a deformation parameter set;

[0010] Obtaining historical temperature distribution and historical stress distribution of the surface of the tempered glass mirror to generate an environmental change data set, establishing a mapping relationship between the deformation parameter set and the environmental change data set, and generating a dynamic deformation prediction model;

[0011] collecting real-time temperature and real-time stress and determining a current temperature gradient and a current stress gradient, inputting the current temperature gradient and the current stress gradient into the dynamic deformation prediction model to obtain a predicted deformation rate;

[0012] adjusting a point distribution and a data sampling frequency according to the predicted deformation rate, acquiring surface topography data, and generating an optimized deformation monitoring data set;

[0013] updating a three-dimensional coordinate system according to the optimized deformation monitoring data set, calculating new deformation height values of each point according to the updated three-dimensional coordinate system, performing linear interpolation processing on the new deformation height values to obtain a point coordinate set, and constructing an updated surface topography model according to the point coordinate set;

[0014] calculating updated surface curvature values and deformation gradient values according to the updated surface topography model, and generating a flatness determination result according to the updated surface curvature values and deformation gradient values and a preset standard threshold.

[0015] In an optional implementation, the linear interpolation processing on the surface image to obtain a completed pixel point set, and the construction of a complete topography model according to the completed pixel point set, include:

[0016] acquiring pixel points from the surface image, extracting a gray value of each pixel point and converting it into a deformation height value;

[0017] when the deformation height value exceeds a preset height threshold, the corresponding pixel point is marked as an abnormal point;

[0018] extracting deformation height values of neighborhood pixel points within a preset radius around the abnormal point, calculating an average value of the deformation height values of the neighborhood pixel points, replacing the deformation height value of the abnormal point, and obtaining a completed pixel point set;

[0019] according to the completed pixel point set, mapping the pixel points to a three-dimensional coordinate system through uniform grid division to obtain the complete topography model.

[0020] In an optional implementation, the calculation of a difference value between a point coordinate in the complete topography model and a preset reference point coordinate to obtain a deformation amount of each point, the calculation of surface curvature values and deformation gradient values in combination with the deformation amounts of the points, and the generation of a deformation parameter set, include:

[0021] acquiring a point coordinate from the complete topography model and performing denoising processing to obtain a point cloud data set;

[0022] projecting the point cloud data set to a two-dimensional plane, performing triangulation grid processing to obtain a triangular network, and then mapping the triangular network to a three-dimensional space to generate a grid model;

[0023] According to the difference between the point coordinate in the grid model and the preset reference point coordinate, a deformation amount of each point is calculated to generate a deformation amount set;

[0024] According to the deformation amount set, a deformation gradient value is calculated by calculating the deformation amount difference of adjacent points, and a surface curvature value is calculated by calculating the normal vector change rate of the grid point;

[0025] According to the surface curvature value and the deformation gradient value, a deformation parameter set is generated.

[0026] In an optional embodiment, the mapping relationship between the deformation parameter set and the environmental change data set is established to generate a dynamic deformation prediction model, including:

[0027] The deformation parameter set and the environmental change data set are subjected to a time alignment operation;

[0028] The data in the time-aligned deformation parameter set and the data in the environmental change data set are subjected to linear fitting to obtain the mapping relationship between the deformation parameter set and the environmental change data set;

[0029] The mapping relationship is subjected to parameter optimization to generate a dynamic deformation prediction model.

[0030] In an optional embodiment, the point distribution and the data sampling frequency are adjusted according to the predicted deformation rate, including:

[0031] According to the predicted deformation rate, a preset deformation rate threshold is combined to determine whether the predicted deformation rate is greater than the preset deformation rate threshold. When it is determined that the predicted deformation rate is greater than the preset deformation rate threshold, the measurement frequency is adjusted to a first measurement frequency. When it is determined that the predicted deformation rate is less than or equal to the preset deformation rate threshold, the default measurement frequency is maintained to obtain a synchronous measurement frequency.

[0032] According to the predicted deformation rate and the current stress gradient, a deformation abnormal region is analyzed, the measurement point density of the deformation abnormal region is improved, and an optimized point distribution is obtained.

[0033] In an optional embodiment, the three-dimensional coordinate system is updated according to the optimized deformation monitoring data set, a new deformation height value of each point is calculated according to the updated three-dimensional coordinate system, a point coordinate set is obtained by performing linear interpolation processing on the new deformation height value, and an updated surface topography model is constructed according to the point coordinate set, including:

[0034] The three-dimensional coordinate system parameters are optimized according to the optimized deformation monitoring data set, and a calibrated three-dimensional coordinate system is updated;

[0035] According to the updated three-dimensional coordinate system, a new deformation height value of each point is calculated, and when the new deformation height value exceeds the preset height threshold, a new abnormal point is marked, an average value of a new deformation height value of a field within the preset radius around the new abnormal point is calculated to replace the new deformation height value of the new abnormal point, and a point coordinate set is obtained;

[0036] According to the point coordinate set, the point coordinate is mapped to the updated three-dimensional coordinate system through uniform grid division, and an updated surface topography model is obtained.

[0037] In an optional embodiment, the updated surface curvature value and deformation gradient value are calculated according to the updated surface topography model, and a flatness determination result is generated according to the updated surface curvature value and deformation gradient value and a preset standard threshold, including:

[0038] According to the updated surface topography model, an updated surface curvature value and a deformation gradient value are calculated;

[0039] In combination with the preset standard threshold and the updated surface curvature value and deformation gradient value, when it is judged that the updated surface curvature value and deformation gradient value are greater than the preset standard threshold, an abnormal point is recorded, and the updated surface curvature value and deformation gradient value are recorded, and when it is judged that the updated surface curvature value and deformation gradient value are less than or equal to the preset standard threshold, a normal point is recorded.

[0040] According to the abnormal point and the normal point, a flatness determination result is generated.

[0041] In a second aspect, the present application provides a tempered glass mirror flatness detection device, comprising:

[0042] An image acquisition module is configured to acquire surface images of multiple points of the tempered glass mirror under different temperature and stress conditions;

[0043] A model construction module is configured to perform linear interpolation processing on the surface images to obtain a completed pixel point set, and construct a complete topography model according to the completed pixel point set;

[0044] A parameter calculation module is configured to calculate a deformation amount of each point by calculating a difference between a point coordinate in the complete topography model and a preset reference point coordinate, and generate a deformation parameter set by calculating a surface curvature value and a deformation gradient value in combination with the deformation amount of each point.

[0045] A prediction modeling module is configured to acquire a historical temperature distribution and a historical stress distribution of the surface of the tempered glass mirror to generate an environmental change data set, establish a mapping relationship between the deformation parameter set and the environmental change data set, and generate a dynamic deformation prediction model.

[0046] a deformation prediction module configured to collect real-time temperature and real-time stress, determine a current temperature gradient and a current stress gradient, input the current temperature gradient and the current stress gradient into the dynamic deformation prediction model, and obtain a predicted deformation rate;

[0047] an optimization monitoring module configured to adjust point position distribution and data sampling frequency according to the predicted deformation rate, acquire surface topography data, and generate an optimized deformation monitoring data set;

[0048] a model updating module configured to update a three-dimensional coordinate system according to the optimized deformation monitoring data set, calculate new deformation height values of each point position according to the updated three-dimensional coordinate system, perform linear interpolation processing on the new deformation height values to obtain a point position coordinate set, and construct an updated surface topography model according to the point position coordinate set;

[0049] a result output module configured to calculate updated surface curvature values and deformation gradient values according to the updated surface topography model, and generate a flatness determination result according to the updated surface curvature values and deformation gradient values and a preset standard threshold value.

[0050] In a third aspect, the present application further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the steel glass mirror flatness detection method according to any one of the above.

[0051] In a fourth aspect, the present application further provides a computer readable storage medium comprising a stored computer program, wherein the computer program controls a device in which the computer readable storage medium is located to execute the steel glass mirror flatness detection method according to any one of the above when the computer program is running.

[0052] Compared with the prior art, the present application has the following beneficial effects:

[0053] (1) The present application can more comprehensively reflect the surface topography of the glass mirror under various environments that the glass mirror may encounter in actual use by acquiring multi-point surface images of the steel glass mirror under different temperature and stress conditions, thereby providing more abundant and more accurate basic data for subsequent flatness calculation, and effectively improving the detection accuracy.

[0054] (2) The present application collects data under various environmental conditions and further constructs a dynamic deformation prediction model based on the collected data. Through the model, the deformation rate of the steel glass mirror can be predicted according to the real-time collected temperature and stress information, thereby realizing dynamic monitoring and prediction of the deformation of the glass mirror, which helps to discover potential flatness problems in advance and take timely measures for adjustment and optimization.

[0055] (3) This invention adjusts the distribution of data points and the data sampling frequency based on the predicted deformation rate to generate an optimized deformation monitoring dataset. Based on this dataset, the three-dimensional coordinate system is updated, new deformation height values ​​are calculated, and an updated surface morphology model is constructed for flatness determination. This dynamic adjustment and optimization of the detection process allows for more efficient use of detection resources, avoids excessive data collection and processing at unnecessary locations or times, improves detection efficiency, and also helps to more accurately reflect the true flatness of the tempered glass mirror.

[0056] (4) This invention fully considers the influence of environmental factors such as temperature and stress on the flatness of glass mirrors. By establishing a mapping relationship between the set of deformation parameters and the set of environmental change data, the detection method can better adapt to environmental changes in different application scenarios, improve the reliability and stability of the detection results, and provide stronger support for ensuring the optical performance and safety of tempered glass mirrors in actual use. Attached Figure Description

[0057] Figure 1 This is a schematic flowchart of a tempered glass mirror flatness detection method provided in the first embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of a tempered glass mirror flatness detection device provided in the second embodiment of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Reference Figure 1 The first embodiment of the present invention provides a method for detecting the flatness of a tempered glass mirror, comprising the following steps:

[0061] S1, acquire surface images of the tempered glass mirror at multiple points under different temperature and stress conditions;

[0062] S2, perform linear interpolation on the surface image to obtain the completed pixel set, and construct a complete morphology model based on the completed pixel set;

[0063] S3, calculate the difference between the coordinates of the points in the complete topography model and the coordinates of the preset reference points to obtain the deformation of each point, and calculate the surface curvature value and deformation gradient value in combination with the deformation of each point to generate a set of deformation parameters.

[0064] S4. Obtain the historical temperature distribution and historical stress distribution of the tempered glass mirror surface to generate an environmental change dataset, establish the mapping relationship between the deformation parameter set and the environmental change dataset, and generate a dynamic deformation prediction model.

[0065] S5, collect real-time temperature and real-time stress and determine the current temperature gradient and current stress gradient, input the current temperature gradient and the current stress gradient into the dynamic deformation prediction model to obtain the predicted deformation rate;

[0066] S6, adjust the point distribution and data sampling frequency according to the predicted deformation rate, obtain surface morphology data, and generate an optimized deformation monitoring dataset;

[0067] S7. Update the three-dimensional coordinate system according to the optimized deformation monitoring dataset, calculate the new deformation height value of each point according to the updated three-dimensional coordinate system, perform linear interpolation on the new deformation height value to obtain the point coordinate set, and construct an updated surface morphology model according to the point coordinate set.

[0068] S8. Calculate the updated surface curvature value and deformation gradient value based on the updated surface morphology model, and generate a flatness determination result based on the updated surface curvature value, deformation gradient value, and a preset standard threshold.

[0069] In step S1, surface images of the tempered glass mirror at multiple points under different temperature and stress conditions are acquired.

[0070] It should be noted that a series of temperature and stress levels will be pre-set, and two-dimensional images of the tempered glass mirror surface under different combinations of conditions will be acquired using a high-resolution industrial camera. Each pixel in the image contains grayscale value and position information, which will be used to construct the complete morphological model. The series of temperature levels covers the expected operating or storage temperature range of the tempered glass mirror, and the series of stress levels covers the typical or extreme conditions that the tempered glass mirror may withstand.

[0071] The platform used for inspecting tempered glass mirrors is pre-set with the surface reference coordinates of an ideal tempered glass mirror. The platform pre-marks reference points and establishes an initial three-dimensional coordinate system based on these reference points. The position information of each pixel is based on the initial three-dimensional coordinate system, including the pixel's position data on the x-axis, y-axis, and z-axis.

[0072] For example, the preset temperature levels are -20℃, 0℃, 20℃, 40℃, 60℃, and 80℃, and the preset stress levels are 0MPa, 5MPa, 10MPa, 15MPa, and 20MPa. Under the combined conditions of a temperature of 20℃ and a stress of 15MPa, a two-dimensional image of the target tempered glass mirror surface is captured by a high-resolution CCD camera. The resolution of the image is 300×300 pixels, where each pixel contains the grayscale value and position information of that pixel.

[0073] In step S2, linear interpolation is performed on the surface image to obtain a completed pixel set, and a complete morphology model is constructed based on the completed pixel set, including:

[0074] Pixels are obtained from the surface image, and the grayscale value of each pixel is extracted and converted into a deformation height value;

[0075] When the deformation height value exceeds the preset height threshold, the corresponding pixel is marked as an abnormal point;

[0076] Extract the deformation height values ​​of neighboring pixels within a preset radius around the abnormal point, calculate the average value of the deformation height values ​​of the neighboring pixels, and replace it with the deformation height value of the abnormal point to obtain the completed pixel set;

[0077] Based on the completed pixel set, the pixels are mapped to a three-dimensional coordinate system through uniform grid division to obtain the complete morphological model.

[0078] It should be noted that the deformation height value reflects the bulges or depressions on the surface of the tempered glass mirror caused by deformation. This is achieved by extracting the grayscale values ​​of pixels in a two-dimensional image and then converting them using a pre-defined grayscale-to-height conversion table. The average deformation height value is calculated based on the deformation height values ​​of neighboring pixels within a preset radius around the anomaly point, and this average value replaces the deformation height value of the anomaly point, thus achieving a smooth transition in the anomaly area and maintaining data continuity.

[0079] Furthermore, a complete topographic model is constructed based on the initial three-dimensional coordinate system. The pixel coordinate calibration is converted into physical dimensions, so that the x and y axes correspond to the image pixel coordinates, and the z axis is the deformation height value. The xy plane is divided into meshes with a fixed physical spacing, and the z-axis coordinate of each mesh vertex is the deformation height value. Connecting the mesh vertices constructs triangular patches, resulting in the complete topographic model.

[0080] For example, under the combined conditions of a temperature of 20°C and a stress of 15 MPa, the two-dimensional image captured by the high-resolution CCD camera is a 10-bit grayscale image with a grayscale range of 0 to 1023 (1024 grayscale levels). The deformation height value ranges from 0 to 100.0 micrometers. A grayscale level of 0 corresponds to a deformation height of 0, a grayscale level of 1 corresponds to a deformation height of 0.1 micrometers, a grayscale level of 2 corresponds to a deformation height of 0.2 micrometers, and so on, until a grayscale level of 1000 corresponds to a deformation height of 100.0 micrometers. When the deformation height value of a pixel exceeds a preset threshold of 80.0 micrometers, the pixel is marked as an anomaly. Assuming the deformation height value of an anomaly is 82.1 micrometers, the deformation height values ​​of its four neighboring pixels are extracted as 50.0 micrometers, 52.0 micrometers, 48.0 micrometers, and 51.0 micrometers, respectively. The average value is calculated and rounded up to 50.3 micrometers.

[0081] Furthermore, the acquired 300×300 pixel surface image is divided into a grid with a resolution of Δx = Δy = 0.5 mm, corresponding to a region size of 150 mm × 150 mm. The deformation height of the grid vertex pixel is used as the z-axis coordinate, where x, y, and z axes are in millimeters. For example, if the coordinate data of a grid vertex is x = 9.5, y = 4.5, and the deformation height value is 0.0503 mm, then the coordinate data of the grid vertex is (9.5, 4.5, 0.0503). Connecting the grid vertices constructs triangular facets, resulting in a complete topographic model.

[0082] In step S3, the difference between the coordinates of points in the complete topography model and the coordinates of preset reference points is calculated to obtain the deformation amount of each point. The surface curvature value and deformation gradient value are then calculated based on the deformation amount of each point to generate a deformation parameter set, including:

[0083] The point coordinates are obtained from the complete topography model, and denoising is performed to obtain a point cloud dataset;

[0084] The point cloud dataset is projected onto a two-dimensional plane, and triangulation meshing is performed to obtain a triangular network. The triangular network is then mapped onto a three-dimensional space to generate a mesh model.

[0085] The deformation of each point is calculated based on the difference between the coordinates of the points in the mesh model and the coordinates of the preset reference points, and a set of deformation values ​​is generated.

[0086] Based on the set of deformation amounts, the deformation gradient value is obtained by calculating the difference in deformation amounts between adjacent points, and the surface curvature value is obtained by calculating the rate of change of the normal vector of the grid points.

[0087] A set of deformation parameters is generated based on the surface curvature value and the deformation gradient value.

[0088] It should be noted that the complete topography model is a three-dimensional geometric model that maps the topographic features of the tempered glass mirror surface being inspected in a three-dimensional coordinate system. Each point in the complete topography model contains the three-dimensional coordinates of the corresponding pixel. The complete topography model can intuitively display the deformation of the tempered glass mirror surface and is used to calculate the surface deformation gradient and surface curvature values. The three-dimensional coordinates of each point are extracted from the complete topography model. Mean filtering is applied to the point cloud data to replace noisy data, generating a point cloud dataset. The point cloud data is projected onto the xy-plane, and Delaunay triangulation is performed to generate a topologically connected triangular network T = {V, E}, where V is the vertex set and E is the edge set. The vertices of the two-dimensional triangular network are mapped back to three-dimensional space, and the normal vector of each triangular facet is calculated. The deformation ΔH is obtained by calculating the difference between the deformation height value of a point in the mesh model and the z-axis coordinate value of the reference point. i The deformation gradient values ​​of adjacent points are calculated based on the deformation amount, and the surface curvature values ​​are calculated based on the rate of change of the vertex normal vector. A set of deformation parameters including temperature, stress, position coordinates, deformation gradient values, and surface curvature values ​​is generated based on the deformation gradient values ​​and surface curvature values.

[0089] The deformation gradient value is calculated using the following formula for adjacent vertices i and j of a shared edge:

[0090]

[0091] in, It is the deformation gradient value of adjacent points i and j, ΔH i It is the deformation at point i, ΔH j d is the deformation at point j, and d is the Euclidean distance between adjacent points i and j. The deformation gradient value can intuitively reflect the degree of local deformation on the surface of the tempered glass mirror.

[0092] The surface curvature value is calculated using the following formula:

[0093]

[0094] Where, k i It is the surface curvature value. It is the normal vector of the triangular facet. It is the mean of the normal vectors of adjacent triangular faces, d avg It is the average distance between adjacent points.

[0095] Furthermore, the mean normal vector of adjacent triangular facets is calculated using the following formula:

[0096]

[0097] in, is the average normal vector of adjacent triangular faces, and N is the number of adjacent vertices of vertex i. It is the normal vector of the adjacent triangular facet.

[0098] Furthermore, the average distance between adjacent points is calculated using the following formula:

[0099]

[0100] Where, d avg It is the average distance between adjacent vertices, N is the number of adjacent vertices of vertex i, and d h It is the Euclidean distance between vertex i and its neighboring point h.

[0101] It should be noted that the mean filtering method in the embodiments of the present invention is not the only filtering method. In other embodiments, the filtering method can also be other methods commonly used in the art.

[0102] In step S4, the historical temperature distribution and historical stress distribution of the tempered glass mirror surface are obtained to generate an environmental change dataset. A mapping relationship is established between the deformation parameter set and the environmental change dataset, and a dynamic deformation prediction model is generated, including:

[0103] Obtain historical temperature and stress distributions on the surface of tempered glass mirrors to generate an environmental change dataset.

[0104] Perform time alignment on the deformation parameter set and the environmental change dataset;

[0105] The data in the time-aligned deformation parameter set are linearly fitted with the data in the environmental change dataset to obtain the mapping relationship between the deformation parameter set and the environmental change dataset.

[0106] The mapping relationship is optimized to generate a dynamic deformation prediction model.

[0107] It should be noted that historical temperature and stress data are acquired through optical sensor arrays. For example, a fiber optic grating sensor array is used to acquire temperature data of the tempered glass mirror surface, and a Raman spectroscopy sensor array is used to acquire stress data of the tempered glass mirror surface. The historical temperature and stress distributions are temperature data (T) and stress data (σ) at corresponding timestamps. These data are recorded by timestamp to generate an environmental change dataset. The data in the deformation parameter set and the data in the environmental change set are aligned according to the same timestamp to generate tables of surface curvature values, deformation gradient values, temperature values, and stress values ​​at the same time. These tables are used to subsequently construct the mapping relationship between the deformation parameter set and the environmental change set.

[0108] Furthermore, we define the input X as an environmental change dataset (temperature T, stress σ), and the output Y as a set of deformation parameters (deformation gradient values). The surface curvature value k). A linear fitting model, Y = AX + b, is obtained using the least squares method. This linear fitting yields the mapping relationship between the deformation parameter set and the environmental change set. The parameters A and b of the mapping relationship are optimized using stochastic gradient descent (SGD) to obtain optimized parameters A' and b'. The mapping relationship is then modified using these optimized parameters to obtain the dynamic deformation prediction model Y = A'X + b'.

[0109] For example, the linear mapping obtained by least squares fitting is Y = 0.001X + 0.5, where A = 0.001 and b = 0.5. Using stochastic gradient descent, with 10,000 measurement points as input data, T ∈ [25, 85]℃, σ ∈ [5, 30]MPa, and containing 5% Gaussian noise, after 100 iterations, the optimized parameters are obtained as A' = 0.0041 and b' = 0.12. Therefore, the dynamic deformation prediction model is Y = 0.0041X + 0.12.

[0110] It should be noted that the stochastic gradient descent method used in the embodiments of the present invention is one way to optimize the parameters of the mapping relationship. In practical applications, commonly used methods in the field, such as iterative weighted squares, can also be used to optimize the parameters of the mapping relationship.

[0111] Furthermore, the mapping relationship is optimized by parameters. This is achieved through Y... 实测 Subtract Y 预测 The residual e is obtained. If the value of e is greater than 0.01, it is marked as an outlier. Gaussian filtering is applied to the outlier to smooth it out, suppress local noise interference, and generate a dynamic deformation prediction model.

[0112] In step S5, real-time temperature and real-time stress are collected, and the current temperature gradient and current stress gradient are determined. The current temperature gradient and the current stress gradient are then input into the dynamic deformation prediction model to obtain the predicted deformation rate.

[0113] It should be noted that real-time temperature and stress data of the tempered glass mirror surface are acquired through an optical sensor array; for example, the sampling frequency is 10Hz. The real-time temperature and stress data are input into a dynamic deformation prediction model to obtain predicted deformation gradient and surface curvature values. The rate of change of the deformation gradient and the rate of change of the surface curvature are calculated based on a time window.

[0114] For example, the deformation gradient value at t=0s obtained by the dynamic deformation prediction model is 0.008, and the surface curvature value is 0.082mm. -1 The deformation gradient at t = 0.1s is 0.007, and the surface curvature is 0.085mm. -1The rate of change of the deformation gradient value is (0.007-0.008) / 0.1 = -0.01s. -1 The rate of change of the surface curvature value is (0.085-0.082) / 0.1 = 0.03 mm. -1 / s.

[0115] In step S6, the point distribution and data sampling frequency are adjusted according to the predicted deformation rate to obtain surface morphology data and generate an optimized deformation monitoring dataset, including:

[0116] Based on the predicted deformation rate and combined with a preset deformation rate threshold, if the predicted deformation rate is greater than the preset deformation rate threshold, the measurement frequency is adjusted to the first measurement frequency; if the predicted deformation rate is less than or equal to the preset deformation rate threshold, the default measurement frequency is maintained to obtain the synchronous measurement frequency.

[0117] Based on the predicted deformation rate and the current stress gradient, the deformation anomaly region is analyzed, and the density of measurement points in the deformation anomaly region is increased to obtain an optimized point distribution.

[0118] Based on the synchronous measurement frequency and the optimized point distribution, the surface morphology data of the tempered glass mirror is obtained, and an optimized deformation monitoring dataset is generated.

[0119] It should be noted that the preset deformation rate threshold includes a preset deformation gradient change rate threshold and a preset surface curvature change rate threshold, wherein the preset deformation gradient change rate threshold is 0.002s. -1 The preset threshold for the rate of change of surface curvature is 0.1 mm. -1 / s, when the rate of change of deformation gradient or the rate of change of surface curvature is detected to be greater than the preset corresponding threshold, the measurement frequency is adjusted to once per second to obtain the synchronous measurement frequency.

[0120] Furthermore, when the current stress gradient is greater than 1.5 MPa / mm, it is identified as a deformation anomaly region. Four detection points are added between every two detection points within this deformation anomaly region to obtain an optimized point distribution. Based on the optimized point distribution and synchronous measurement frequency, an optimized deformation monitoring dataset containing timestamps, three-dimensional coordinates, timestamp-corresponding temperatures, and timestamp-corresponding stresses is obtained.

[0121] In another implementation, if the rate of change of deformation gradient or the rate of change of surface curvature are both less than or equal to the preset corresponding thresholds, then there is no need to adjust the measurement frequency; the default measurement frequency is used as the synchronous measurement frequency. If no abnormal deformation region is detected, the detection point density is not adjusted.

[0122] In step S7, the three-dimensional coordinate system is updated according to the optimized deformation monitoring dataset. The new deformation height value for each point is calculated based on the updated three-dimensional coordinate system. Linear interpolation is performed on the new deformation height value to obtain a point coordinate set. An updated surface topography model is constructed based on the point coordinate set, including:

[0123] Optimize the three-dimensional coordinate system parameters based on the optimized deformation monitoring dataset, and update and calibrate the three-dimensional coordinate system.

[0124] Calculate the new deformation height value of each point according to the updated three-dimensional coordinate system. When the new deformation height value exceeds the preset height threshold, it is marked as a new anomaly point. Calculate the average value of the new deformation height value in the area within the preset radius around the new anomaly point to replace the new deformation height value of the new anomaly point, and obtain the point coordinate set.

[0125] Based on the set of point coordinates, the point coordinates are mapped to the updated three-dimensional coordinate system through uniform grid division to obtain an updated surface morphology model.

[0126] It should be noted that, based on the stress data contained in the optimized deformation monitoring dataset, the stress value of each measurement point is subtracted from the stress value of the previous measurement point to calculate the new stress gradient. The three measurement points with the smallest new stress gradient are selected to fit the plane equation to obtain the new reference plane. The updated three-dimensional coordinate system is then established based on the new reference plane.

[0127] Furthermore, based on the updated 3D coordinate system, a 2D image captured by a high-resolution industrial camera is used. Each pixel in the 2D image contains a grayscale value and position coordinates. The new height values ​​of each point in the optimized point distribution are obtained based on the grayscale values ​​of the 2D image and a grayscale-height conversion table. When the new height value exceeds a preset height threshold, the corresponding pixel is marked as a new anomaly. The average of the new height values ​​of the neighboring pixels within a preset radius around the new anomaly is calculated, and this average value replaces the new height value of the new anomaly, resulting in a point coordinate set. This point coordinate set contains the 3D coordinate data of all points in the updated 3D coordinate system.

[0128] It is important to note that the preset height threshold needs to be determined based on the thickness, material, and intended use of the tempered glass mirror being inspected. The preset radius is determined based on the required inspection accuracy and speed in the application; the preset radius can be either the four neighboring pixels of the new anomaly or the eight neighboring pixels of the new anomaly.

[0129] For example, the stress value at the current measurement point is 12.30 MPa, and the stress value at the previous measurement point is 12.27 MPa, obtained from the optimized deformation monitoring dataset. The distance between the current and previous measurement points is 0.1 mm. Therefore, the new stress gradient at the current measurement point is (12.30 - 12.27) / 0.1 = 0.3 MPa / mm. After calculating the new stress gradients at all measurement points, three measurement points with the smallest new stress gradients are selected, with coordinates (13.5, 7.5, 0.0493), (1.5, 56.5, 0.0503), and (79.5, 34.5, 0.0507), respectively. A new reference plane is fitted based on these three points, and an updated three-dimensional coordinate system is established.

[0130] Furthermore, a 10-bit grayscale image of the tempered glass mirror surface is captured by a high-resolution CCD camera. The grayscale range is 0 to 1023, totaling 1024 grayscale levels. The deformation height range is 0 to 100.0 micrometers. Grayscale level 0 corresponds to a deformation height of 0, grayscale level 1 corresponds to a deformation height of 0.1 micrometers, grayscale level 2 corresponds to a deformation height of 0.2 micrometers, and so on, until grayscale level 1000 corresponds to a deformation height of 100.0 micrometers, thus obtaining a grayscale-height conversion table. If a pixel has a grayscale value of 804, according to the grayscale-height conversion table, its new height value is 80.4 micrometers. If the new height value of this pixel exceeds the preset height threshold of 80.0 micrometers, the pixel is marked as a new anomaly. The new height values ​​of the four neighboring pixels of the new anomaly are extracted as 50.0 μm, 52.0 μm, 48.0 μm, and 51.0 μm, respectively. The average value is calculated and rounded up to 50.3 μm. The new height value of the new anomaly is then replaced with 50.3 μm.

[0131] It should be noted that the updated surface topography model is obtained by mapping the point coordinates to the updated three-dimensional coordinate system through uniform mesh division. It is worth noting that the steps for generating the updated surface topography model are the same as those for generating the complete topography model in S2, and therefore will not be repeated here.

[0132] In step S8, the updated surface curvature value and deformation gradient value are calculated based on the updated surface topography model. Based on the updated surface curvature value and deformation gradient value and a preset standard threshold, a flatness determination result is generated, including:

[0133] Based on the updated surface topography model, calculate the updated surface curvature value and deformation gradient value;

[0134] The system combines a preset standard threshold with the updated surface curvature value and deformation gradient value for judgment. When the updated surface curvature value or deformation gradient value is greater than the preset standard threshold, it is recorded as an abnormal point and the updated surface curvature value and deformation gradient value are recorded. When the updated surface curvature value and deformation gradient value are less than or equal to the preset standard threshold, it is recorded as a normal point.

[0135] Flatness determination results are generated based on the abnormal and normal points.

[0136] It should be noted that the steps for calculating the updated surface curvature and deformation gradient values ​​based on the updated surface topography model are the same as those in S3, and therefore will not be repeated here.

[0137] Furthermore, the preset standard thresholds include a preset surface curvature value standard threshold and a preset deformation gradient value standard threshold, with the preset surface curvature value standard threshold being 0.01 mm. -1 The preset standard threshold for deformation gradient value is 0.001. Based on the preset standard threshold, minor deformations on the surface of tempered glass mirrors can be determined, thereby improving the accuracy of tempered glass mirror flatness detection.

[0138] For example, suppose the surface curvature at a point is 0.02 mm. -1 The deformation gradient value is 0.001, and the surface curvature at this point is greater than the preset standard threshold of 0.01 mm for surface curvature. -1 If the deformation gradient value does not exceed the preset standard threshold for deformation gradient value, the point is recorded as an anomaly point, and the surface curvature value of the anomaly point is recorded as 0.02 mm. -1 The deformation gradient value is 0.001. Assume the surface curvature at a point is 0.013 mm. -1 If the deformation gradient value is 0.0007, and the surface curvature value and deformation gradient value of this point are less than the preset standard threshold, then it is recorded as a normal point.

[0139] In summary, this invention discloses a method for detecting the flatness of tempered glass mirrors, comprising: acquiring surface images of the tempered glass mirror at multiple points under different temperature and stress conditions; performing linear interpolation processing on the surface images to obtain a completed pixel set, and constructing a complete morphology model based on the completed pixel set; calculating the difference between the coordinates of the points in the complete morphology model and the coordinates of preset reference points to obtain the deformation amount at each point, and calculating the surface curvature value and deformation gradient value based on the deformation amount at each point to generate a deformation parameter set; acquiring the historical temperature distribution and historical stress distribution of the tempered glass mirror surface to generate an environmental change dataset, establishing a mapping relationship between the deformation parameter set and the environmental change dataset, and generating a dynamic deformation prediction model; and collecting real-time temperature and real-time stress data. The invention involves determining the current temperature gradient and current stress gradient, inputting these gradients into the dynamic deformation prediction model to obtain the predicted deformation rate; adjusting the point distribution and data sampling frequency based on the predicted deformation rate to acquire surface morphology data and generate an optimized deformation monitoring dataset; updating the three-dimensional coordinate system based on the optimized deformation monitoring dataset, calculating the new deformation height value for each point based on the updated three-dimensional coordinate system, performing linear interpolation on the new deformation height value to obtain a point coordinate set, and constructing an updated surface morphology model based on the point coordinate set; calculating the updated surface curvature value and deformation gradient value based on the updated surface morphology model, and generating a flatness judgment result based on the updated surface curvature value, deformation gradient value, and a preset standard threshold. This invention, by dynamically acquiring tempered glass mirror data, can adapt to the complex deformations of glass under different temperature and stress conditions, thereby improving the flatness detection accuracy of tempered glass mirrors.

[0140] Reference Figure 2 The second embodiment of the present invention provides a tempered glass mirror flatness testing device, comprising:

[0141] The image acquisition module is used to acquire multi-point surface images of the tempered glass mirror under different temperature and stress conditions.

[0142] The model building module is used to perform linear interpolation processing on the surface image to obtain a complete set of pixels, and to build a complete morphology model based on the complete set of pixels.

[0143] The parameter calculation module is used to calculate the difference between the coordinates of the points in the complete topography model and the coordinates of the preset reference points, obtain the deformation of each point, and calculate the surface curvature value and deformation gradient value by combining the deformation of each point to generate a deformation parameter set.

[0144] The predictive modeling module is used to obtain the historical temperature distribution and historical stress distribution of the tempered glass mirror surface to generate an environmental change dataset, establish the mapping relationship between the deformation parameter set and the environmental change dataset, and generate a dynamic deformation prediction model.

[0145] The deformation prediction module is used to collect real-time temperature and real-time stress and determine the current temperature gradient and the current stress gradient. The current temperature gradient and the current stress gradient are input into the dynamic deformation prediction model to obtain the predicted deformation rate.

[0146] The optimized monitoring module is used to adjust the point distribution and data sampling frequency according to the predicted deformation rate, acquire surface morphology data, and generate an optimized deformation monitoring dataset.

[0147] The model update module is used to update the three-dimensional coordinate system according to the optimized deformation monitoring dataset, calculate the new deformation height value of each point according to the updated three-dimensional coordinate system, perform linear interpolation on the new deformation height value to obtain the point coordinate set, and construct an updated surface morphology model according to the point coordinate set.

[0148] The result output module is used to calculate the updated surface curvature value and deformation gradient value based on the updated surface morphology model, and generate a flatness determination result based on the updated surface curvature value and deformation gradient value and a preset standard threshold.

[0149] It should be noted that the tempered glass mirror flatness testing device provided in this embodiment of the invention is used to perform all the process steps of the tempered glass mirror flatness testing method in the above embodiment. The working principle and beneficial effects of the two are one-to-one, so they will not be described again.

[0150] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a result output program. When the processor executes the computer program, it implements the steps in the various embodiments of the tempered glass mirror flatness detection method described above, for example... Figure 1 Step S2 is shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the model update module.

[0151] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0152] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0153] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0154] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0155] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0156] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0157] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for detecting the flatness of tempered glass mirrors, characterized in that, include: Acquire surface images of tempered glass mirrors at multiple points under different temperature and stress conditions; Linear interpolation is performed on the surface image to obtain the completed pixel set, and a complete morphology model is constructed based on the completed pixel set. The difference between the coordinates of the points in the complete topography model and the coordinates of the preset reference points is calculated to obtain the deformation of each point. The surface curvature value and deformation gradient value are calculated by combining the deformation of each point to generate a set of deformation parameters. Historical temperature and stress distributions on the surface of tempered glass mirrors are obtained to generate an environmental change dataset. A mapping relationship between the deformation parameter set and the environmental change dataset is established to generate a dynamic deformation prediction model. Real-time temperature and stress are collected, and the current temperature gradient and current stress gradient are determined. The current temperature gradient and the current stress gradient are then input into the dynamic deformation prediction model to obtain the predicted deformation rate. Adjust the point distribution and data sampling frequency according to the predicted deformation rate, obtain surface morphology data, and generate an optimized deformation monitoring dataset. The three-dimensional coordinate system is updated based on the optimized deformation monitoring dataset. The new deformation height value of each point is calculated based on the updated three-dimensional coordinate system. The new deformation height value is linearly interpolated to obtain the point coordinate set. An updated surface morphology model is constructed based on the point coordinate set. The updated surface curvature value and deformation gradient value are calculated based on the updated surface morphology model. Based on the updated surface curvature value and deformation gradient value and a preset standard threshold, a flatness determination result is generated. The step of establishing the mapping relationship between the deformation parameter set and the environmental change dataset to generate a dynamic deformation prediction model includes: Perform time alignment on the deformation parameter set and the environmental change dataset; The data in the time-aligned deformation parameter set are linearly fitted with the data in the environmental change dataset to obtain the mapping relationship between the deformation parameter set and the environmental change dataset. The mapping relationship is optimized to generate a dynamic deformation prediction model; The step of adjusting the point distribution and data sampling frequency according to the predicted deformation rate includes: Based on the predicted deformation rate and combined with a preset deformation rate threshold, if the predicted deformation rate is greater than the preset deformation rate threshold, the measurement frequency is adjusted to the first measurement frequency; if the predicted deformation rate is less than or equal to the preset deformation rate threshold, the default measurement frequency is maintained to obtain the synchronous measurement frequency. Based on the predicted deformation rate and the current stress gradient, the deformation anomaly region is analyzed, the measurement point density in the deformation anomaly region is increased, and an optimized point distribution is obtained.

2. The method for detecting the flatness of tempered glass mirror according to claim 1, characterized in that, The step of performing linear interpolation on the surface image to obtain a completed pixel set, and constructing a complete morphology model based on the completed pixel set, includes: Pixels are obtained from the surface image, and the grayscale value of each pixel is extracted and converted into a deformation height value; When the deformation height value exceeds the preset height threshold, the corresponding pixel is marked as an abnormal point; Extract the deformation height values ​​of neighboring pixels within a preset radius around the abnormal point, calculate the average value of the deformation height values ​​of the neighboring pixels, and replace it with the deformation height value of the abnormal point to obtain the completed pixel set; Based on the completed pixel set, the pixels are mapped to a three-dimensional coordinate system through uniform grid division to obtain the complete morphological model.

3. The method for detecting the flatness of tempered glass mirror according to claim 1, characterized in that, The method involves calculating the difference between the coordinates of points in the complete topographic model and the coordinates of preset reference points to obtain the deformation amount at each point. Then, by combining the deformation amounts at each point, the surface curvature value and deformation gradient value are calculated to generate a deformation parameter set, including: The point coordinates are obtained from the complete topography model, and denoising is performed to obtain a point cloud dataset; The point cloud dataset is projected onto a two-dimensional plane, and triangulation meshing is performed to obtain a triangular network. The triangular network is then mapped onto a three-dimensional space to generate a mesh model. The deformation of each point is calculated based on the difference between the coordinates of the points in the mesh model and the coordinates of the preset reference points, and a set of deformation values ​​is generated. Based on the set of deformation amounts, the deformation gradient value is obtained by calculating the difference in deformation amounts between adjacent points, and the surface curvature value is obtained by calculating the rate of change of the normal vector of the grid points. A set of deformation parameters is generated based on the surface curvature value and the deformation gradient value.

4. The method for detecting the flatness of tempered glass mirror according to claim 1, characterized in that, The process of updating the three-dimensional coordinate system based on the optimized deformation monitoring dataset, calculating the new deformation height value for each point based on the updated three-dimensional coordinate system, performing linear interpolation on the new deformation height value to obtain a point coordinate set, and constructing an updated surface morphology model based on the point coordinate set includes: Optimize the three-dimensional coordinate system parameters based on the optimized deformation monitoring dataset, and update and calibrate the three-dimensional coordinate system. Calculate the new deformation height value of each point according to the updated three-dimensional coordinate system. When the new deformation height value exceeds the preset height threshold, it is marked as a new anomaly point. Calculate the average value of the new deformation height value in the area within the preset radius around the new anomaly point to replace the new deformation height value of the new anomaly point, and obtain the point coordinate set. Based on the set of point coordinates, the point coordinates are mapped to the updated three-dimensional coordinate system through uniform grid division to obtain an updated surface morphology model.

5. The method for detecting the flatness of tempered glass mirror according to claim 1, characterized in that, The step of calculating the updated surface curvature value and deformation gradient value based on the updated surface topography model, and generating a flatness determination result based on the updated surface curvature value and deformation gradient value and a preset standard threshold, includes: Based on the updated surface topography model, calculate the updated surface curvature value and deformation gradient value; The system combines a preset standard threshold with the updated surface curvature value and deformation gradient value for judgment. When the updated surface curvature value and deformation gradient value are greater than the preset standard threshold, they are recorded as abnormal points, and the updated surface curvature value and deformation gradient value are recorded. When the updated surface curvature value and deformation gradient value are less than or equal to the preset standard threshold, they are recorded as normal points. Flatness determination results are generated based on the abnormal and normal points.

6. A device for detecting the flatness of a tempered glass mirror, characterized in that, A method for detecting the flatness of a tempered glass mirror as described in any one of claims 1 to 5, comprising: The image acquisition module is used to acquire multi-point surface images of the tempered glass mirror under different temperature and stress conditions. The model building module is used to perform linear interpolation processing on the surface image to obtain a complete set of pixels, and to build a complete morphology model based on the complete set of pixels. The parameter calculation module is used to calculate the difference between the coordinates of the points in the complete topography model and the coordinates of the preset reference points, obtain the deformation of each point, and calculate the surface curvature value and deformation gradient value by combining the deformation of each point to generate a deformation parameter set. The predictive modeling module is used to obtain the historical temperature distribution and historical stress distribution of the tempered glass mirror surface to generate an environmental change dataset, establish the mapping relationship between the deformation parameter set and the environmental change dataset, and generate a dynamic deformation prediction model. The deformation prediction module is used to collect real-time temperature and real-time stress and determine the current temperature gradient and the current stress gradient. The current temperature gradient and the current stress gradient are input into the dynamic deformation prediction model to obtain the predicted deformation rate. The optimized monitoring module is used to adjust the point distribution and data sampling frequency according to the predicted deformation rate, acquire surface morphology data, and generate an optimized deformation monitoring dataset. The model update module is used to update the three-dimensional coordinate system according to the optimized deformation monitoring dataset, calculate the new deformation height value of each point according to the updated three-dimensional coordinate system, perform linear interpolation on the new deformation height value to obtain the point coordinate set, and construct an updated surface morphology model according to the point coordinate set. The result output module is used to calculate the updated surface curvature value and deformation gradient value based on the updated surface morphology model, and generate a flatness determination result based on the updated surface curvature value and deformation gradient value and a preset standard threshold.

7. An electronic device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the tempered glass mirror flatness detection method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the tempered glass mirror flatness detection method as described in any one of claims 1 to 5.

Images