An early warning system and method for the aging degree of aluminum single panels for curtain walls

Abstract

The present invention discloses an early warning system and method for the aging degree of curtain wall aluminum veneers, relating to the technical field of image analysis, including: acquiring an image of the aluminum veneer to be detected and dividing the image of the aluminum veneer to be detected into multiple pixel windows; acquiring non-aged aluminum veneers with the same specifications as the aluminum veneer to be detected and various aged characteristic aluminum veneers, and establishing an influence model of each aging characteristic on the aging of the aluminum veneer; according to the influence model, obtaining the influence weights corresponding to each aging characteristic, and further screening out the first aging window and the second aging window; merging each aging window to obtain the first aging area and the second aging area in the aluminum veneer to be detected, and further performing early warning prompts and processing on the aluminum veneer to be detected. By analyzing the images of various aluminum veneers, the present invention obtains different aging areas, improves the detection accuracy, predicts the aging trend of the aluminum veneer, makes early treatment, and ensures the maintenance safety and overall aesthetics of the building curtain wall.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis, and specifically to an early warning system and method for the aging degree of curtain wall aluminum single plates. Background Art

[0002] Curtain wall aluminum single plates, also known as exterior wall aluminum single plates, are single-piece aluminum plates made of aluminum alloy through processing techniques such as cutting, bending, and spraying. They mainly consist of a panel, stiffeners, and corner codes. Curtain wall aluminum single plates are widely used in buildings, including the external decoration of various buildings such as commercial buildings, office buildings, hotels, exhibition halls, and theaters. By carefully designing and selecting aluminum single plates with different colors and textures, various styles and effects can be created to meet the overall design requirements of the building. However, due to seasonal changes, day-night temperature differences, and long-term exposure to sunlight, etc., the aging phenomenon of curtain wall aluminum single plates will occur. The aging phenomenon is reflected in characteristics such as fading, deformation, and corrosion. Aging will lead to problems such as potential safety hazards and impaired functions, causing significant direct or indirect losses, and endangering personal safety.

[0003] The existing technologies for detecting the aging phenomenon include visual inspection and ultrasonic detection methods. Although the above methods have their own advantages in terms of measurement speed and measurement methods, etc., there are also some deficiencies, such as large subjectivity and poor detection accuracy. Therefore, according to the influence degree of each aging characteristic on the aging degree, comprehensive analysis is carried out to obtain the aging degree of the curtain wall aluminum single plate, and different early warnings and treatments are carried out, which improves the detection accuracy, predicts the aging trend of the aluminum single plate, and makes early treatments, which is of extremely crucial significance for the maintenance, safety guarantee, and overall aesthetics of the building curtain wall. Summary of the Invention

[0004] The purpose of the present invention is to provide an early warning system and method for the aging degree of curtain wall aluminum single plates to solve the problems put forward in the existing technologies.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] An early warning method for the aging degree of curtain wall aluminum single plates includes the following steps:

[0007] Step S100: Obtain the image of the aluminum single plate to be detected and perform preprocessing. According to each pixel point in the image of the aluminum single plate to be detected, divide the image of the aluminum single plate to be detected into multiple pixel windows;

[0008] Step S200: Obtain non-aged aluminum single plates with the same specifications as the aluminum single plate to be detected, and aluminum single plates with each aging characteristic, and perform performance tests respectively; and analyze the pre-taken images of non-aged aluminum single plates and images of aluminum single plates with each aging characteristic, and establish an influence model of each aging characteristic on the aging of aluminum single plates;

[0009] Step S300: According to the influence model, obtain the influence weights corresponding to each aging characteristic, acquire the pixel points in each pixel window, and based on the influence weights, obtain the aging value of each pixel window, and filter out the first aging window and the second aging window;

[0010] Step S400: According to the first aging window and the second aging window, merge each aging window to obtain the first aging area and the second aging area in the aluminum single panel to be detected, and then give an early warning prompt and processing to the aluminum single panel to be detected.

[0011] Further, step S100 includes:

[0012] Step S110: Take an image of the aluminum single panel to be detected and convert it into a square grayscale image. Taking the lower left corner of the grayscale image as the origin, establish a plane coordinate system of the grayscale image; a pixel window is composed of several pixels, and set the minimum number of pixels of the pixel window to M 1 *M 1 , the maximum number of pixels is M 2 *M 2 , set the size of the feature window to be equal to M 0 *M 0 , 1 < M 1 < M 0 < M 2 , and the lower left corner position corresponds to the origin in the plane coordinate system. Take the upper right corner of the feature window as the target point, and set the initial size of the variable window to M 0 *M 0 , and the lower left corner position corresponds to the target point;

[0013] Among them, if the length and width of the aluminum single panel are not much different, it can be converted into a square by means of cutting or stretching, etc. However, when the length and width of the aluminum single panel are quite different, the aluminum single panel can be divided into multiple pieces, and each divided piece is converted into a square, which is specifically determined according to the actual situation.

[0014] Step S120: Degrade the grayscale value of each pixel in the grayscale image to obtain Q-level grayscale levels; according to each pixel in the feature window, record the total number of pixels with the grayscale level of q-level as V q , obtain the probability p(q) of the grayscale level of q-level = V q / M 0 *M 0 , and then obtain the entropy value of the feature window Among them, log 2 is the logarithm with base 2. Similarly, obtain the entropy value H(X) of the variable window 2 ; if H(X) 1 < H(X) 2, the size to be changed of the window to be changed is obtained as max() is to find the maximum value, is to round up. If H(X) 1 >H(X) 2 , the size to be changed is When H(X) 2 = 0, M = M 2 , is to round down. If H(X) 1 = H(X) 2 , M = M 0 , and then the actual size of the window to be changed is obtained as M * M;

[0015] The larger the entropy value, the higher the degree of disorder of the system and the greater the chaos. In information theory, entropy is a measure of uncertainty. The greater the amount of information, the smaller the uncertainty and the smaller the entropy; the smaller the amount of information, the greater the uncertainty and the greater the entropy. If the entropy values of three windows a, b, and c are H(a) = 1.271, H(b) = 1.843, and H(c) = 1 respectively; taking a as the feature window, if b is the window to be changed, since the entropy value of a is smaller, it means that the gray value distribution on a is more uniform, while b is not as uniform as a in distribution, and the window situation of b is more complex. To make a more detailed judgment on window b, the window of b should not be set too large, so the size of b should be relatively small; similarly, if c is the window to be changed, since the entropy value of c is smaller, it means that the gray value distribution on c is more uniform, while a is not as uniform as c in distribution, and the window situation of c is more uniform than a, so the window of c should be set larger;

[0016] Step S130: Denote the upper right corner of the window to be changed as the target point P. If the number of pixels in the upper right of the target point P in the grayscale image is less than M 2 *M 2 , then all the pixels in the upper right are aggregated into a window to be changed. If the number of pixels in the upper right is not less than M 2 *M 2 , another window to be changed is obtained again according to this step S100, and the entropy values of the two windows to be changed are obtained, and the actual size of the other window to be changed is obtained. This is cycled in turn to obtain several windows to be changed. The four sides of each window to be changed are extended to the edge of the grayscale image, and the smallest area enclosed by the extended lines is used as the pixel window, and then all pixel windows are obtained.

[0017] Furthermore, step S200 includes:

[0018] Step S210: Perform pressure tests on the non-aged aluminum single plates and each aluminum single plate with aging characteristics. The part of the aluminum single plate where the pressure test is performed is used as the test part. The pressure test is used to determine the maximum pressure value that the aluminum single plate can withstand when bearing the pressure perpendicular to its surface. Take the maximum pressure value of the non-aged aluminum single plate as the target pressure value, and the maximum pressure value of the aluminum single plate with aging characteristics as the aging pressure value. Take the difference between the target pressure value and the aging pressure value as the pressure difference, and perform normalization;

[0019] Step S220: Obtain the RGB images, grayscale images, and depth images of the non-aged aluminum single plate and each aluminum single plate with aging characteristics. The aging characteristics include fading, corrosion, and deformation; Randomly obtain F pixels from the test part of a certain aluminum single plate with aging characteristics AL as aging pixels, and randomly obtain a certain pixel a from the non-aged aluminum single plate. If the aging characteristic of the aluminum single plate AL is fading, according to the RGB value (R ^ , G ^ , B ^ ) of a certain pixel a, obtain the characteristic value of the test part where e is the natural logarithm, R f , G f , and B f are the R, G, and B values of the f-th aging pixel respectively; If the aging characteristic is corrosion, according to the grayscale value V a of a certain pixel a, obtain the characteristic value V f is the grayscale value of the f-th aging pixel; If the aging characteristic is deformation, according to the depth value d a of a certain pixel a, obtain the characteristic value d f is the depth value of the f-th aging pixel; Furthermore, according to the aging characteristics of the aluminum single plate AL, obtain the aging characteristic difference of the aluminum single plate AL: n represents the type of aging characteristics, n = 1, 2, or 3, h 1 is the fading characteristic coefficient, h 2 is the corrosion characteristic coefficient, h 3 is the deformation characteristic coefficient;

[0020] Step S230: Establish a two-dimensional coordinate system with the aging characteristic difference as the abscissa and the pressure difference as the ordinate; According to all the aluminum single plates with a certain aging characteristic, the corresponding aging characteristic differences and pressure differences, obtain the coordinates corresponding to each aluminum single plate in the two-dimensional coordinate system, and according to the least squares method, obtain the influence model of a certain aging characteristic as Y(n) = k n *T + b n , where n represents the type of aging characteristics, Y(n) is the pressure difference, T is the aging characteristic difference, k n is the slope, b n is the intercept, and then obtain the influence model of each aging characteristic.

[0021] Further, step S300 includes: obtaining the influence weight of the nth aging characteristic according to the slope in each influence model where k n is the slope of the nth influence model, and N is the total number of influence models; then, according to the average RGB value, average grayscale value, and average depth value of the pixels in a certain pixel window, and based on step S200, the difference of each aging characteristic corresponding to a certain pixel window and the corresponding pressure difference are obtained, and normalization is performed. Furthermore, the aging value of a certain pixel window is obtained as follows: Y n is the nth pressure difference; a first aging degree threshold D 1 and a second aging degree threshold D 2 are set, 0 < D 1 < D 2 If D 1 < D ≤ D 2 , it is determined that a certain pixel window is a first aging window. If D 2 < D, it is determined that a certain pixel window is a second aging window.

[0022] It should be noted that the slope of the model is used here to represent the weight of each aging characteristic. This is only because the slope of the model here represents the influence degree of the change of the aging characteristic on the pressure value. Moreover, when the slope is larger, it means that the corresponding aging characteristic has a greater influence on the pressure value, and the pressure value is a specific manifestation directly reflecting the aging degree. Therefore, the slope of the model is used in this solution to represent the weight of each aging characteristic. In this solution, the first aging window is a pixel window with a smaller aging degree, and the second aging window is a pixel window with a larger aging degree. The type distinction of the aging window is mainly for the rationality of merging regions in the following steps.

[0023] Further, step S400 includes:

[0024] Step S410: Establish a two-dimensional coordinate system for the image of the aluminum single plate to be detected, and obtain the central coordinates of each pixel window therein; randomly obtain an aging window, the type of the aging window is L, and obtain all pixel windows in the eight neighborhoods around the aging window, which are used as eight-neighborhood windows. If the number of eight-neighborhood windows of type L is greater than the preset number threshold, the window types of all eight-neighborhood windows are set as L, and the aging value remains unchanged, and the aging window and all eight-neighborhood windows are marked; if the number is not greater than the number threshold, randomly obtain another aging window, and repeat step S410;

[0025] In this solution, the eight - neighborhood window refers to eight adjacent pixel windows around a pixel window in image processing, which is a conventional term in the prior art, that is, the pixel windows in the eight directions of adjacent up, down, left, right, upper - left, lower - left, upper - right, and lower - right. The quantity threshold is 6. Because when the quantity threshold is 6, any direction of up, down, left, or right of the pixel window can be fully considered. And if the aging degree in any of the up, down, left, or right directions is L, for the convenience of merging, it is determined at this time that the types of all pixel windows around it are L. Therefore, the types of all pixel windows in its adjacent eight - neighborhood windows are L;

[0026] Step S420: Take the center coordinate of a certain aging window as P 0 , with the aging value being D 0 , and take the center coordinate of the g - th eight - neighborhood window as P g , with the aging value being D g , if D 0 < D g , starting from P 0 , pointing in the direction of P g , with the magnitude being |D g - D 0 |, to obtain a vector If D 0 > D g , starting from P 0 , pointing in the opposite direction of P g , with the magnitude being |D g - D 0 |, to obtain a vector Furthermore, according to each eight - neighborhood window, all vectors are obtained, and the vectors are added together to obtain the pointing vector V corresponding to a certain pixel window 0 ; furthermore, obtain the center coordinate closest to the end point of the distance pointing vector V 0 , and the corresponding pixel window WIN; if the pixel window WIN is not an aging window, according to step S400, randomly obtain an unmarked aging window and its surrounding eight - neighborhood windows. If the pixel window WIN is an aging window, obtain the surrounding eight - neighborhood windows of the pixel window WIN, and then until all aging windows are marked, and the final multiple adjacent and same - type aging windows are used as an aging area.

[0027] This solution does not make an overall judgment on the curtain wall aluminum single - panel, but only judges the local aging degree, and divides the aging degree into mild and severe degrees. The mild degree is the first aging area, and the severe degree is the second aging area. Subsequently, it is hoped to achieve different treatments for different aging areas, such as cleaning and waxing maintenance for the mild aging area, and coating repair for the severe area, to ensure its adhesion and durability, extend the service life of the curtain wall aluminum single - panel, and ensure the safety and beauty of the building.

[0028] An early warning system for the aging degree of aluminum single panels for curtain walls, comprising a pixel window division module, an influence model establishment module, an aging window screening module, and an aging window merging module;

[0029] The pixel window division module: used to obtain the image of the aluminum single panel to be detected, perform preprocessing, and divide the image of the aluminum single panel to be detected into multiple pixel windows according to each pixel point in the image of the aluminum single panel to be detected;

[0030] The influence model establishment module: used to obtain non-aging aluminum single panels with the same specifications as the aluminum single panel to be detected, as well as aluminum single panels with various aging characteristics, and perform performance tests respectively; and analyze the images of non-aging aluminum single panels and aluminum single panels with various aging characteristics taken in advance, and establish an influence model of various aging characteristics on the aging of aluminum single panels;

[0031] The aging window screening module: used to obtain the influence weights corresponding to various aging characteristics according to the influence model, obtain the pixel points in each pixel window, obtain the aging value of each pixel window according to the influence weights, and screen out the first aging window and the second aging window;

[0032] The aging window merging module: used to merge each aging window according to the first aging window and the second aging window to obtain the first aging area and the second aging area in the aluminum single panel to be detected, and further perform early warning prompts and processing on the aluminum single panel to be detected.

[0033] Furthermore, the pixel window division module includes a pixel window setting unit, a to-be-changed window determination unit, and a pixel window division unit;

[0034] The pixel window setting unit: used to take the image of the aluminum single panel to be detected and convert it into a square grayscale image. The pixel window is composed of several pixels, and the smallest pixel window, the largest pixel window, as well as the feature window and the to-be-changed window are set;

[0035] The to-be-changed window determination unit: used to perform degradation processing on the grayscale value of each pixel in the grayscale image, thereby obtaining the entropy value of the feature window and the entropy value of the to-be-changed window, and further obtaining the actual size of the to-be-changed window;

[0036] The pixel window division unit: used to obtain all the to-be-changed windows, extend the four sides of each to-be-changed window to the edge of the grayscale image, and take the smallest area surrounded by the extended lines as the pixel window, thereby obtaining all pixel windows.

[0037] Furthermore, the aging window merging module includes an eight-neighborhood window obtaining unit and an aging window merging unit;

[0038] Eight - field window acquisition unit: It is used to establish a two - dimensional coordinate system of the aluminum veneer image to be detected, obtain the center coordinates of each pixel window therein; randomly acquire a certain aging window and all the surrounding eight - field windows; and mark the pixel windows.

[0039] Aging window merging unit: It is used to obtain the vector corresponding to a certain aging window according to a certain aging window and the eight - field windows; further obtain the pixel window WIN and make a judgment until all the aging windows are marked.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides an early warning system and method for the aging degree of curtain wall aluminum veneers. It acquires the image of the aluminum veneer to be detected, divides the image of the aluminum veneer to be detected into multiple pixel windows; acquires non - aging aluminum veneers with the same specifications as the aluminum veneer to be detected and various aging - characteristic aluminum veneers, and establishes an influence model of each aging characteristic on the aging of the aluminum veneer; according to the influence model, obtains the influence weights corresponding to each aging characteristic, and further screens out the first aging window and the second aging window; merges each aging window to obtain the first aging area and the second aging area in the aluminum veneer to be detected, and then gives an early warning prompt and processing for the aluminum veneer to be detected. By analyzing the images of various aluminum veneers, the present invention obtains different aging areas, improves the detection accuracy, predicts the aging trend of the aluminum veneer, makes early treatment, and makes corresponding treatment methods for different aging degrees, ensuring the maintenance safety and overall aesthetics of the building curtain wall. Brief Description of the Drawings

[0041] Figure 1 It is a schematic flow chart of an early warning method for the aging degree of curtain wall aluminum veneers of the present invention;

[0042] Figure 2 It is a structural diagram of an early warning system for the aging degree of curtain wall aluminum veneers of the present invention. Detailed Embodiments

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0044] Embodiment: As Figure 1 shown, the present invention provides a technical solution for an early warning system and method for the aging degree of curtain wall aluminum veneers, including the following steps:

[0045] Step S100: Obtain the image of the aluminum veneer to be detected, perform preprocessing, and divide the image of the aluminum veneer to be detected into multiple pixel windows according to each pixel point in the image of the aluminum veneer to be detected.

[0046] Step S110: Take the image of the aluminum veneer to be detected and convert it into a square grayscale image. Establish a plane coordinate system for the grayscale image with the lower left corner of the grayscale image as the origin; a pixel window is composed of several pixels. Set the minimum number of pixels in the pixel window to M 1 *M 1 , and the maximum number of pixels to M 2 *M 2 , set the size of the feature window to be equal to M 0 *M 0 , 1 < M 1 < M 0 < M 2 , and the lower left corner position corresponds to the origin in the plane coordinate system. Take the upper right corner of the feature window as the target point, and set the initial size of the variable window to M 0 *M 0 , and the lower left corner position corresponds to the target point.

[0047] Step S120: Degrade the grayscale value of each pixel in the grayscale image to obtain Q grayscale levels; according to each pixel in the feature window, record the total number of pixels with the grayscale level of q as V q , and obtain the probability p(q) of the grayscale level of q as p(q) = V q / M 0 *M 0 , and then obtain the entropy value of the feature window where, log 2 is the logarithm with base 2. Similarly, obtain the entropy value H(X) of the variable window 2 ; if H(X) 1 < H(X) 2 , obtain the variable size of the variable window as max() is to find the maximum value, is to round up. If H(X) 1 > H(X) 2 , the variable size is When H(X) 2 = 0, M = M 2 , is to round down. If H(X) 1 = H(X) 2 , M = M 0 , and then obtain the actual size of the variable window as M * M.

[0048] The larger the entropy value, the higher the degree of disorder and the greater the chaos of the system. In information theory, entropy is a measure of uncertainty. The greater the amount of information, the smaller the uncertainty and the smaller the entropy; the smaller the amount of information, the greater the uncertainty and the greater the entropy. To better illustrate, the following embodiments are given: In this embodiment, the gray level is reduced to Q = 8 levels. Since the original gray value is 0-255, the original 256 gray values are now divided into 8 intervals, that is, the gray value is now represented by 0-7; if there are three pixel windows a, b, and c, and the window size is 5*5 for each, and the number of pixels with gray values from 0-7 on each window is a = (15, 8, 2, 0, 0, 0, 0, 0), b = (9, 8, 6, 2, 0, 0, 0, 0), and c = (13, 12, 0, 0, 0, 0, 0, 0), then according to the formula The entropy values of these three windows are obtained as H(a) = 1.271, H(b) = 1.843, and H(c) = 1 respectively; taking a as the feature window, if b is the window to be changed, since the entropy value of a is smaller, it means that the gray value distribution on a is more uniform, while b is not as uniform as a in distribution, and the window situation of b is more complex. To make a more detailed judgment on window b, the window of b should not be set too large, so the size of b should be relatively small; similarly, if c is the window to be changed, since the entropy value of c is smaller, it means that the gray value distribution on c is more uniform, while a is not as uniform as c in distribution, and the window situation of c is more uniform than a, so the window of c should be set larger.

[0049] Step S130: Mark the upper right corner of the window to be changed as the target point P. If the number of pixels in the upper right of the target point P in the grayscale image is less than M 2 *M 2 , then all the pixels in the upper right are gathered into a window to be changed. If the number of pixels in the upper right is not less than M 2 *M 2 , another window to be changed is obtained again according to this step S100, and the entropy values of the two windows to be changed are obtained, and the actual size of the other window to be changed is obtained. This is cycled in turn to obtain several windows to be changed. The four sides of each window to be changed are extended to the edge of the grayscale image, and the smallest area surrounded by the extended lines is used as the pixel window, and then all pixel windows are obtained.

[0050] Step S200: Obtain non-aged aluminum veneers of the same specifications as the aluminum veneer to be detected, as well as aluminum veneers with various aging characteristics, and perform performance tests on them respectively; and analyze the pre-shot images of non-aged aluminum veneers and aluminum veneers with various aging characteristics, and establish an influence model of various aging characteristics on the aging of aluminum veneers.

[0051] Step S210: Perform pressure tests on the non-aging aluminum veneer and the aluminum veneers with aging characteristics, and use the part of the aluminum veneer where the pressure test is performed as the test part. The pressure test is used to determine the maximum pressure value that the aluminum veneer can withstand when it is subjected to a pressure perpendicular to its surface. The maximum pressure value of the non-aging aluminum veneer is used as the target pressure value, the maximum pressure value of the aluminum veneer with aging characteristics is used as the aging pressure value, and the difference between the target pressure value and the aging pressure value is used as the pressure difference value, which will be normalized.

[0052] Step S220: Obtain RGB images, grayscale images, and depth images of the non-aging aluminum veneer and each aging characteristic aluminum veneer, where the aging characteristics include fading, corrosion, and deformation; randomly obtain F pixels from the test part of the aluminum veneer AL with a certain aging characteristic as aging pixels, and randomly obtain a certain pixel a from the non-aging aluminum veneer. If the aging characteristic of the aluminum veneer AL is fading, according to the RGB value (R ^ ,G ^ ,B ^ ), and obtain the characteristic value of the test part Where e is the natural logarithm, R f , G f and B f are the R, G and B values ​​of the fth aged pixel respectively; if the aging characteristic is corrosion, according to the gray value V of a pixel a a , get the characteristic value V f is the gray value of the fth aged pixel; if the aging characteristic is deformation, according to the depth value d of a certain pixel a a , get the characteristic value d f is the depth value of the f-th aged pixel; and then according to the aging characteristics of the aluminum veneer AL, the aging characteristic difference of the aluminum veneer AL is obtained: n represents the type of aging characteristics, n = 1, 2 or 3, h 1 is the fading characteristic coefficient, h 2 is the corrosion characteristic coefficient, h 3 is the deformation characteristic coefficient.

[0053] In this solution, only three common types of fading, corrosion and deformation are given, and these three types can basically cover most of the aging phenomena. In actual scenarios, they may not be limited to these three types, but their basic ideas and methods are relatively similar. For example, characteristic values ​​can be obtained through variance and RGB values. For specific implementation, please refer to this solution and will not be described in detail here.

[0054] Step S230: Establish a two-dimensional coordinate system with the aging characteristic difference as the abscissa and the pressure difference as the ordinate; according to all the aluminum veneers with a certain aging characteristic, the corresponding aging characteristic differences and pressure differences, obtain the coordinates corresponding to each aluminum veneer in the two-dimensional coordinate system, and according to the least squares method, obtain the influence model of a certain aging characteristic as Y(n) = k n *T + b n , where n represents the type of aging characteristic, Y(n) is the pressure difference, T is the aging characteristic difference, k n is the slope, and b n is the intercept, and then obtain the influence model of each aging characteristic.

[0055] Step S300: According to the influence model, obtain the influence weights corresponding to each aging characteristic, obtain the pixel points in each pixel window, and according to the influence weights, obtain the aging value of each pixel window, and screen out the first aging window and the second aging window.

[0056] According to the slope in each influence model, obtain the influence weight of the nth aging characteristic where k n is the slope of the nth influence model, and N is the total number of influence models; then according to the average RGB value, average gray value and average depth value of the pixels in a certain pixel window, based on Step S200, obtain the corresponding aging characteristic difference and corresponding pressure difference of each aging characteristic of a certain pixel window, and perform normalization, and then obtain the aging value of a certain pixel window as: Y n is the nth pressure difference; set the first aging degree threshold D 1 and the second aging degree threshold D 2 , 0 < D 1 < D 2 , if D 1 < D ≤ D 2 , determine that a certain pixel window is the first aging window, if D 2 < D, determine that a certain pixel window is the second aging window.

[0057] It should be noted that here the slope of the model is used to characterize the weights of each aging characteristic, only because the slope of the model here represents the degree of influence of the change of the aging characteristic on the pressure value, and when the slope is larger, it means that the corresponding aging characteristic has a greater influence on the pressure value, and the pressure value is a direct manifestation of the aging degree. Therefore, in this solution, the slope of the model is used to characterize the weights of each aging characteristic. In this solution, the first aging window is the pixel window with a smaller aging degree, and the second aging window is the pixel window with a larger aging degree. The type distinction of the aging window is mainly for the rationality of the following steps of merging regions.

[0058] Step S400: According to the first aging window and the second aging window, merge each aging window to obtain the first aging region and the second aging region in the aluminum single panel to be detected, and then give an early warning prompt and processing to the aluminum single panel to be detected.

[0059] Step S410: Establish a two-dimensional coordinate system for the image of the aluminum single panel to be detected to obtain the central coordinates of each pixel window therein; randomly obtain a certain aging window, the type of the aging window is L, obtain all pixel windows in the eight neighborhoods around a certain aging window, and all of them are used as eight-neighborhood windows. If the number of eight-neighborhood windows of window type L is greater than the preset quantity threshold, set the window types of all eight-neighborhood windows as L, and the aging value remains unchanged, and mark a certain aging window and all eight-neighborhood windows; if the quantity is not greater than the quantity threshold, randomly obtain another aging window, and repeat this step S410.

[0060] In this solution, the eight-neighborhood window is eight adjacent pixel windows around a pixel window in image processing, which is a conventional statement in the prior art, that is, the pixel windows in the eight directions of adjacent up, down, left, right, upper left, lower left, upper right, and lower right. The quantity threshold is 6. Because when the quantity threshold is 6, any direction of up, down, left, and right of the pixel window can be fully considered. And if the aging degree in any direction of up, down, left, and right is L, for the convenience of merging, it is determined at this time that the types of all pixel windows around it are L. Therefore, the types of all pixel windows in its adjacent eight-neighborhood windows are L.

[0061] Step S420: Take the central coordinate of a certain aging window as P 0 , and the aging value is D 0 , and take the central coordinate of the g-th eight-neighborhood window as P g , and the aging value is D g , if D 0 < D g , with P 0 as the starting point, pointing to the direction of P g , and the magnitude is |D g - D 0 |, to obtain the vector If D 0 > D g , with P 0 as the starting point, pointing to the opposite direction of P g , and the magnitude is |D g - D 0 |, to obtain the vector Furthermore, according to each eight-neighborhood window, obtain all vectors, and add the vectors to obtain the pointing vector V corresponding to a certain pixel window 0 ; furthermore, obtain the distance pointing vector V 0The central coordinates closest to the end point, and the corresponding pixel window WIN; if the pixel window WIN is not an aging window, according to step S400, randomly obtain an unmarked aging window and the surrounding eight-neighborhood windows. If the pixel window WIN is an aging window, obtain the surrounding eight-neighborhood windows of the pixel window WIN, and then until all aging windows are marked, and take the final multiple adjacent and same-type aging windows as an aging area.

[0062] This solution does not make an overall judgment on the curtain wall aluminum veneer, but only judges the local aging degree, and divides the aging degree into mild and severe degrees. The mild degree is the first aging area, and the severe degree is the second aging area. What is hoped to be achieved in the future is to implement different treatments for different aging areas, such as cleaning and waxing maintenance for the mild aging area, and coating repair for the severe area, to ensure its adhesion and durability, extend the service life of the curtain wall aluminum veneer, and ensure the safety and beauty of the building.

[0063] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A warning method for the aging degree of curtain wall aluminum veneer, characterized in that: It includes the following steps: Step S100: Obtain the aluminum veneer image to be detected, and perform preprocessing. According to each pixel point in the aluminum veneer image to be detected, divide the aluminum veneer image to be detected into multiple pixel windows; Step S200: Obtain non-aged aluminum veneers with the same specifications as the aluminum veneer to be detected, as well as aluminum veneers with various aging characteristics, and perform performance tests respectively; and analyze the pre-shot non-aged aluminum veneer images and aluminum veneer images with various aging characteristics, and establish an influence model of various aging characteristics on the aging of aluminum veneers; Step S300: According to the influence model, obtain the influence weights corresponding to various aging characteristics, obtain the pixel points in each pixel window, and according to the influence weights, obtain the aging value of each pixel window, and screen out the first aging window and the second aging window; Step S400: According to the first aging window and the second aging window, merge each aging window to obtain the first aging area and the second aging area in the aluminum veneer to be detected, and then give an early warning prompt and processing to the aluminum veneer to be detected.

2. The early warning method for aging degree of curtain wall aluminum veneer according to claim 1 is characterized in that: Step S100 includes: Step S110: Shoot the aluminum veneer image to be detected and convert it into a square grayscale image. Taking the lower left corner of the grayscale image as the origin, establish the plane coordinate system of the grayscale image; the pixel window is composed of several pixels. Set the minimum pixel number of the pixel window to M1*M1, the maximum pixel number to M2*M2, set the size of the feature window to be equal to M0*M0, 1<M1<M0<M2, and the lower left corner position corresponds to the origin in the plane coordinate system. Take the upper right corner of the feature window as the target point, set the initial size of the variable window to M0*M0, and the lower left corner position corresponds to the target point; Step S120: Degrade the gray value of each pixel in the grayscale image to obtain Q-level gray levels; for each pixel in the feature window, record the total number of pixels with the gray level of q-level as V q , and obtain the probability p(q) of the gray level of q-level as p(q) = V q / M0*M0, and then obtain the entropy value of the feature window where, log2 is the logarithm with base 2. Similarly, obtain the entropy value H(X)2 of the window to be changed; if H(X)1 < H(X)2, obtain the size to be changed of the window to be changed as max() is to find the maximum value, is to round up. If H(X)1 > H(X)2, the size to be changed is When H(X)2 = 0, M = M2, is to round down. If H(X)1 = H(X)2, M = M0, and then obtain the actual size of the window to be changed as M*M; Step S130: Denote the upper right corner of the variable window as the target point P. If the number of pixels in the upper right of the target point P in the grayscale image is less than M2*M2, converge all the pixels in the upper right into a variable window. If the number of pixels in the upper right is not less than M2*M2, obtain another variable window according to this step S100 again, and obtain the entropy values of the two variable windows, obtain the actual size of another variable window, and loop in turn to obtain several variable windows. Extend the four sides of each variable window to the edge of the grayscale image, and take the smallest area surrounded by the extended lines as the pixel window, and then obtain all pixel windows.

3. The early warning method for aging degree of curtain wall aluminum veneer according to claim 1 is characterized in that: Step S200 includes: Step S210: Perform pressure tests on both the non-aged aluminum veneer and the aluminum veneers with various aging characteristics. Take the part of the aluminum veneer where the pressure test is performed as the test part. The pressure test is used to determine the maximum pressure value that the aluminum veneer can withstand when承受垂直于其表面的压力时能够承受的最大压力值, take the maximum pressure value of the non-aged aluminum veneer as the target pressure value, the maximum pressure value of the aging characteristic aluminum veneer as the aging pressure value, take the difference between the target pressure value and the aging pressure value as the pressure difference, and normalize it; Step S220: Obtain RGB images, grayscale images, and depth images of the non-aging aluminum veneer and each aging characteristic aluminum veneer, where the aging characteristics include fading, corrosion, and deformation; randomly obtain F pixels from the test part of the aluminum veneer AL with a certain aging characteristic as aging pixels, and randomly obtain a certain pixel a from the non-aging aluminum veneer. If the aging characteristic of the aluminum veneer AL is fading, according to the RGB value (R ^ ,G ^ ,B ^ ), and obtain the characteristic value of the test part Where, e is the natural logarithm, R f , G f and B f are the R, G and B values ​​of the fth aged pixel respectively; if the aging characteristic is corrosion, according to the gray value V of a pixel a a , get the characteristic value V f is the gray value of the fth aged pixel; if the aging characteristic is deformation, according to the depth value d of a certain pixel a a , get the characteristic value d f is the depth value of the f-th aged pixel; and then according to the aging characteristics of the aluminum veneer AL, the aging characteristic difference of the aluminum veneer AL is obtained: n represents the type of aging characteristics, n = 1, 2 or 3, h1 is the fading characteristic coefficient, h2 is the corrosion characteristic coefficient, and h3 is the deformation characteristic coefficient; Step S230: Establish a two-dimensional coordinate system with the aging characteristic difference as the horizontal coordinate and the pressure difference as the vertical coordinate; obtain the corresponding coordinates of each aluminum single plate in the two-dimensional coordinate system according to the aging characteristic difference and pressure difference of all aluminum single plates with a certain aging characteristic, and obtain the influence model of the certain aging characteristic as Y(n)=k according to the least square method. n *T+b n , where n represents the type of aging characteristics, Y(n) is the pressure difference, T is the aging characteristic difference, and k n is the slope, b n is the intercept, and then the influence model of each aging characteristic is obtained.

4. The early warning method for the aging degree of curtain wall aluminum veneer according to claim 3 is characterized in that: Step S300 includes: obtaining the influence weight of the nth aging characteristic according to the slope in each influence model where k n is the slope of the nth influence model, and N is the total number of influence models; furthermore, according to the average RGB value, average grayscale value, and average depth value of the pixels in a certain pixel window, and based on step S200, the difference of each aging characteristic corresponding to the certain pixel window and the corresponding pressure difference of each are obtained, and normalization is performed. Furthermore, the aging value of the certain pixel window is obtained as follows: Y n is the nth pressure difference; a first aging degree threshold D1 and a second aging degree threshold D2 are set, 0 < D1 < D2. If D1 < D ≤ D2, it is determined that a certain pixel window is a first aging window. If D2 < D, it is determined that a certain pixel window is a second aging window.

5. The early warning method for aging degree of curtain wall aluminum veneer according to claim 1 is characterized in that: Step S400 includes: Step S410: Establish a two-dimensional coordinate system of the aluminum veneer image to be detected, and obtain the center coordinates of each pixel window therein; randomly obtain a certain aging window, the aging window type is L, obtain all pixel windows in the eight fields around the certain aging window, and use them as eight-field windows. If the number of eight-field windows with the window type of L is greater than a preset number threshold, the window type of all eight-field windows is L, and the aging value remains unchanged, and the certain aging window and all eight-field windows are marked; if the number is not greater than the number threshold, randomly obtain another aging window, and repeat this step S410; Step S420: The center coordinate of the aging window is taken as P0, the aging value is D0, and the center coordinate of the g-th eight-domain window is taken as P g , the aging value is D g , if D0<D g , starting from P0 and pointing to P g Direction, size |D g -D0|, get the vector If D0>D g , starting from P0 and pointing to P g The opposite direction of |D g -D0|, get the vector Then, according to each eight-domain window, all vectors are obtained, and the vectors are added to obtain the pointing vector V0 corresponding to a certain pixel window; then the center coordinates closest to the end point of the pointing vector V0, the corresponding pixel window WIN, are obtained; if the pixel window WIN is not an aging window, according to step S400, an unmarked aging window and the surrounding eight-domain windows are randomly obtained; if the pixel window WIN is an aging window, the eight-domain windows around the pixel window WIN are obtained, and then all the aging windows are marked, and the final multiple adjacent aging windows of the same type are regarded as an aging area.

6. An early warning system for the aging degree of aluminum veneer for curtain wall, used to execute the early warning method for the aging degree of aluminum veneer for curtain wall as claimed in any one of claims 1 to 5, characterized in that: The system includes a pixel window division module, an impact model establishment module, an aging window screening module and an aging window merging module; Pixel window division module: used to obtain the aluminum veneer image to be detected, and perform preprocessing, and divide the aluminum veneer image to be detected into multiple pixel windows according to each pixel point in the aluminum veneer image to be detected; Impact model building module: used to obtain non-aging aluminum veneers of the same specifications as the aluminum veneers to be tested, as well as aluminum veneers with various aging characteristics, and perform performance tests on them respectively; and analyze the pre-shot images of non-aging aluminum veneers and images of aluminum veneers with various aging characteristics, and establish an impact model of various aging characteristics on the aging of aluminum veneers; Aging window screening module: used to obtain the influence weight corresponding to each aging characteristic according to the influence model, obtain the pixel points in each pixel window, obtain the aging value of each pixel window according to the influence weight, and screen out the first aging window and the second aging window; Aging window merging module: used to merge the aging windows according to the first aging window and the second aging window, obtain the first aging area and the second aging area in the aluminum veneer to be detected, and then provide early warning prompts and processing for the aluminum veneer to be detected.

7. The early warning system for aging degree of aluminum veneer for curtain wall according to claim 6 is characterized in that: The pixel window division module includes a pixel window setting unit, a window to be changed determination unit and a pixel window division unit; Pixel window setting unit: used to capture the image of the aluminum veneer to be inspected and convert it into a square grayscale image. The pixel window is composed of several pixels. The minimum pixel window, the maximum pixel window, the characteristic window and the window to be changed are set. The window to be changed determining unit is used to perform a degradation process on the grayscale value of each pixel in the grayscale image, thereby obtaining the entropy value of the feature window and the entropy value of the window to be changed, thereby obtaining the actual size of the window to be changed; Pixel window division unit: used to obtain all the windows to be changed, extend the four sides of each window to be changed to the edge of the grayscale image, and use the minimum area surrounded by the extension lines as the pixel window, thereby obtaining all the pixel windows.

8. The early warning system for aging degree of aluminum veneer for curtain wall according to claim 6 is characterized in that: The aging window merging module includes an eight-domain window acquisition unit and an aging window merging unit; Eight-domain window acquisition unit: used to establish a two-dimensional coordinate system of the aluminum veneer image to be detected, and obtain the center coordinates of each pixel window therein; randomly obtain a certain aging window and all the surrounding eight-domain windows; And mark the pixel window; Aging window merging unit: used to obtain a vector corresponding to a certain aging window according to a certain aging window and an eight-domain window; then obtain the pixel window WIN, and make judgments until all aging windows are marked.

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