A method for detecting strength of wall hanging panels

Abstract

The present application relates to the technical field of wall panel strength detection, and specifically to a wall panel strength detection method, the method comprising: determining the signal fluctuation disorder of each potential abnormal point according to the change trend of the fitting curve of each potential abnormal point, combined with the change trend and fluctuation degree of all elements between the first rising edge end point and the first falling edge start point on the fitting curve of each potential abnormal point; determining the signal defect confidence of each potential abnormal point based on the energy difference between all transmitted pulse signals and all received pulse signals of each potential abnormal point, and combining the difference in signal fluctuation disorder between potential abnormal points adjacent to each potential abnormal point, and detecting the strength of the wall panel. The present application improves the accuracy of wall panel strength detection by analyzing the difference in internal defect areas within the aggregate area of ​​the wall panel.

Description

Technical Field

[0001] The present application relates to the technical field of wall hanging board strength detection, and in particular to a wall hanging board strength detection method. Background Art

[0002] Wall panels are a kind of decorative material used for building exterior walls. They are hung on the outside of the wall through dry hanging and other construction methods to achieve beautification, fire prevention and heat preservation effects. Therefore, high requirements are placed on the strength of wall panels. In order to meet the required functions, wall panels are usually made of insulation core material, surface layer and lightweight concrete poured around, and a certain amount of steel bar layers are configured. External defects and internal defects of wall panels will affect the wall panels, and external defects can be detected by ordinary visual inspection. For internal defects, ultrasonic detection is currently the main method of detection.

[0003] When using the ultrasonic measurement method to detect internal defects of wall panels, it is mainly assumed that the wall panels are continuous entities, so the energy attenuation and sound speed of the ultrasonic wave during the propagation process are fixed. When the ultrasonic wave passes through internal defects such as looseness or voids, the acoustic parameters change significantly compared to normal wall panels. The main reason is that the ultrasonic wave propagates slowly in the cavity, so usually only the sound speed is used as the main parameter for determining internal defects. However, in actual measurements, since the internal medium of the wall panel is not uniform, especially the internal insulation core material and aggregate steel bars, they will also affect the acoustic parameters of ultrasonic wave propagation, resulting in deviations in the strength detection of the wall panel, reducing the accuracy of the strength detection of the wall panel. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a wall hanging panel strength detection method to solve the existing problems.

[0005] A wall hanging board strength detection method of the present application adopts the following technical solution:

[0006] An embodiment of the present application provides a wall hanging plate strength detection method, the method comprising the following steps:

[0007] Obtain the transmitted pulse signal, received pulse signal and sound velocity value of each preset scanning point on the wall hanging plate to be detected at all sampling times within a preset time length, and record the average of the sound velocity values ​​of each scanning point at all sampling times as the sound velocity data of each scanning point;

[0008] The scanning points whose sound velocity data are less than or equal to the preset first threshold value are recorded as potential abnormal points, and a baseline correction algorithm is used based on all received pulse signals of each potential abnormal point to determine all signals to be analyzed at each potential abnormal point, and all signals to be analyzed are fitted to obtain fitting curves of each potential abnormal point, and the signal fluctuation value of each potential abnormal point is determined according to the change trend of the fitting curve of each potential abnormal point;

[0009] Based on the change trend of all elements from the first rising edge end point to the first falling edge start point on the fitting curve of each potential abnormal point, the average trend strength of each potential abnormal point is determined, and the signal distortion degree of each potential abnormal point is determined in combination with the fluctuation degree of all the elements;

[0010] Based on the signal distortion and the signal fluctuation value, determining the signal fluctuation disorder degree of each potential abnormal point;

[0011] Based on the energy difference between all transmitted pulse signals and all received pulse signals of each potential abnormal point, the signal attenuation of each potential abnormal point is determined, and the signal defect confidence of each potential abnormal point is determined in combination with the difference in signal fluctuation disorder between potential abnormal points adjacent to each potential abnormal point;

[0012] Based on the signal defect confidence and the sound velocity data, the corrected sound velocity value of each potential abnormal point is determined, and the strength of the wall hanging board is tested.

[0013] Preferably, the method for determining all signals to be analyzed of each potential abnormal point is:

[0014] All pulse signals of each potential abnormal point are used as input of the baseline correction algorithm to obtain the baseline of each potential abnormal point, and the received pulse signal with a peak value greater than the maximum value on the baseline is used as the signal to be analyzed.

[0015] Preferably, the expression of the signal fluctuation value of each potential abnormal point is: ; In the formula, Represents the signal fluctuation value of potential abnormal point i; , They respectively represent the angle between the line connecting the first rising edge end point and the first data point and the baseline, and the angle between the line connecting the first falling edge start point and the last data point and the baseline on the fitting curve of potential outlier point i; cos() represents the cosine function.

[0016] Preferably, the method for determining the average trend strength of each potential outlier point is:

[0017] For each potential abnormal point, the distance from each element between the first rising edge end point and the first falling edge start point on the fitting curve to the line connecting the first rising edge end point and the first falling edge start point is recorded as the high level distance;

[0018] All high-level distances between the first high-level distance to the maximum high-level distance of each potential anomaly point, and all high-level distances between the maximum high-level distance to the last high-level distance are taken as input of the time series decomposition algorithm to obtain two trend strength values ​​of each potential anomaly point. The average trend strength of each potential anomaly point is the average of the two trend strengths of each potential anomaly point.

[0019] Preferably, the signal distortion degree of each potential abnormal point is a result of merging the average trend strength of each potential abnormal point with the discrete degree of all the high-level distances.

[0020] Preferably, the signal fluctuation disorder degree of each potential abnormal point is the result of integrating the signal fluctuation value of each potential abnormal point with the signal distortion degree.

[0021] Preferably, the signal attenuation of each potential abnormal point is the difference between the total energy of all transmitted pulse signals of each potential abnormal point in the frequency domain and the total energy of all received pulse signals in the frequency domain.

[0022] Preferably, the expression of the signal defect confidence of each potential abnormal point is: ; In the formula, Indicates the signal defect confidence of potential abnormal point i; Indicates the signal fluctuation disorder of potential abnormal point i; , They respectively represent the sum of the signal fluctuation disorder of the potential abnormal points adjacent to the potential abnormal point i on the left and right, and the sum of the signal fluctuation disorder of the potential abnormal points adjacent to the potential abnormal point i above and below; represents the signal attenuation of potential outlier point i; min() represents the minimum function; norm() represents the normalization function.

[0023] Preferably, the expression of the corrected sound velocity value of each potential abnormal point is: ; In the formula, represents the corrected sound velocity value of potential abnormal point i; represents the corrected sound velocity data of potential abnormal point i; Represents the sound velocity data of potential abnormal point i; Indicates a preset value.

[0024] Preferably, the testing of the strength of the wall hanging board includes:

[0025] The corrected sound velocity values ​​of all potential abnormal points are used as the input of the threshold segmentation algorithm to obtain the segmentation threshold, and the absolute value of the difference between the first two results of the difference between all the corrected sound velocity values ​​and the segmentation threshold arranged in ascending order is calculated. If the ratio of the absolute value to the extreme difference of the corrected sound velocity values ​​of all potential abnormal points is greater than the preset second threshold, the wall hanging board strength is unqualified, otherwise, the wall hanging board strength is qualified.

[0026] This application has at least the following beneficial effects:

[0027] The present application determines the signal fluctuation value of each potential abnormal point according to the changing trend of the fitting curve of each potential abnormal point; determines the signal fluctuation disorder of each potential abnormal point based on the changing trend and fluctuation degree of all elements between the end point of the first rising edge and the starting point of the first falling edge on the fitting curve of each potential abnormal point, and the signal fluctuation value, which has the beneficial effect of distinguishing the influence of wall hanging board defects and filling materials on the signal, and more accurately judging whether there are defects inside the wall hanging board; based on the energy difference between all transmitted pulse signals and all received pulse signals of each potential abnormal point, and combined with the difference in signal fluctuation disorder between potential abnormal points adjacent to each potential abnormal point, determines the signal defect confidence of each potential abnormal point, which has the beneficial effect of reducing the interference degree of the aggregate area of ​​the wall hanging board on the internal defect detection; based on the signal defect confidence and the sound velocity data, the present application determines the corrected sound velocity value of each potential abnormal point, and detects the strength of the wall hanging board, which has the beneficial effect of improving the accuracy of internal defect detection. The present application analyzes the differences in internal defect areas within the aggregate area of ​​the wall hanging board, eliminates the interference of the aggregate area on the internal defect detection, and improves the accuracy of the wall hanging board strength detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 A flowchart of a wall hanging board strength detection method provided in one embodiment of the present application;

[0030] Figure 2 A schematic diagram of the structure of the wall hanging board production and detection process provided by one embodiment of the present application;

[0031] Figure 3 A flowchart of the steps for obtaining the average trend strength provided by one embodiment of the present application;

[0032] Figure 4 A schematic diagram of a signal defect confidence extraction process provided for one embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the wall hanging board strength detection method proposed in the present application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0034] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0035] The specific scheme of a wall hanging panel strength detection method provided by the present application is described in detail below with reference to the accompanying drawings.

[0036] An embodiment of the present application provides a wall hanging board strength detection method. Specifically, the following wall hanging board strength detection method is provided. Figure 1 , the method comprises the following steps:

[0037] Step S1: Obtain the transmitted pulse signal, received pulse signal and sound velocity value of each preset scanning point on the wall panel to be detected at all sampling moments within a preset time length.

[0038] In order to deploy a digital ultrasonic detector on a wall hanging board production line to detect the produced hanging boards, during the production process of the wall hanging boards, the wall hanging boards are placed horizontally, and W scanning points are evenly set on the horizontal plane of the wall hanging boards to be detected. The sending port of the digital ultrasonic detector generates a pulse signal, which is emitted to all scanning points on the wall hanging boards to be detected through the signal transmission line and the transducer. The pulse signal penetrates the hanging board and is received by the receiving transducer and transmitted to the receiving end of the digital ultrasonic detector. Preferably, the structural schematic diagram of the wall hanging board production detection process provided in this embodiment is as follows: Figure 2 As shown in the figure, 1 represents a digital ultrasonic detector, 2 represents a device sending port, 3 represents a device receiving port, 4 represents a signal transmission line, 5 represents a transmitting end transducer, 6 represents a receiving end transducer, and 7 represents a wall hanging panel to be detected.

[0039] The transmitting port of the digital ultrasonic detector transmits a pulse signal to the wall panel to be detected. The transmitted pulse signal is a rectangular pulse signal with a duration of , stop transmitting after transmitting, and obtain the duration of each scanning point on the wall hanging plate to be detected The transmitted pulse signal at all times.

[0040] Furthermore, the receiving end of the digital ultrasonic detector collects signals at a sampling frequency of f from the start of transmission, and sets the preset time length to , that is, the receiving time of the receiving end After that, the signal is no longer collected, so the scanning points on the wall hanging board to be detected are obtained within the preset time. The received pulse signals at all sampling moments are obtained by scanning the wall panels to be detected using point-line scanning to obtain the preset duration of W scanning points on the panels. The sound velocity values ​​at all sampling moments within the scanning point are recorded as the sound velocity data of each scanning point.

[0041] It should be noted that the launch duration , sampling frequency f, preset duration The values ​​of the number of scanning points W are all set manually. In this embodiment, the emission duration The value is 2μs, the sampling frequency f is 200MHz, and the preset duration The value of is 2s, and the value of the number of scanning points W is 400. The preset time should be longer than the launch duration. The implementer can set it according to the specific situation. , sampling frequency f, preset duration The setting of the value of the number of scanning points W is not particularly limited in this embodiment.

[0042] Step S2: Determine the signal fluctuation value of each potential abnormal point according to the change trend of the fitting curve of each potential abnormal point.

[0043] During the production process of wall hanging panels, various materials may be squeezed loosely, which may cause defects such as voids inside. Unlike external defects that are easy to detect, internal defects are not easy to detect and will affect the strength of the wall hanging panels. Therefore, it is necessary to use ultrasonic testing to detect internal defects of wall hanging panels.

[0044] The ultrasonic measurement method mainly uses the ultrasonic pulse signal to propagate in the object. When encountering internal defects such as cavities or loose areas, the ultrasonic wave propagates at a slower speed, which forms a significant difference with the scanning points at other normal positions, thereby screening out the positions corresponding to the internal defects. However, in order to improve the performance of the wall hanging board, the insulation core material and steel bar aggregates will be filled in the hanging board, and these aggregate positions will also interfere with the ultrasonic pulse signal.

[0045] Therefore, in order to distinguish the internal defects of the wall hanging board and the interference of the filling material on the ultrasonic pulse signal, the potential abnormal points are preliminarily screened out through analysis, and the received pulse signals of the potential abnormal points are analyzed, so as to more accurately determine the location of the internal defects. The specific process is as follows:

[0046] The scanning points whose sound velocity data is less than or equal to the preset first threshold are recorded as potential abnormal points. The value of the preset first threshold is artificially set. In this embodiment, the sound velocity values ​​of all scanning points are arranged, and the first quartile in the arrangement result is used as the threshold value. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0047] Potential abnormal points of the wall panels to be inspected may include normal position scanning points, aggregate position scanning points and internal defect scanning points. For internal defect scanning points, they correspond to the positions of loose or hollow inside the panels. Since the shapes of loose or hollow are random and messy, the ultrasonic signal is easily disturbed by scattering and refraction during transmission at the scanning points, resulting in a large distortion of the waveform of the received pulse signal, which is specifically manifested as a slow change trend at the rising and falling edges.

[0048] Therefore, by analyzing the changing trend of the rising and falling edges of the pulse signal at the potential scanning point, as well as the changing trend and fluctuation degree of the high level between the rising and falling edges, the location of the internal defect can be identified. The specific process is as follows:

[0049] (1) In the wall hanging panel to be detected, all pulse signals of each potential abnormal point are used as the input of the baseline correction algorithm to obtain the baseline of each potential abnormal point, and the received pulse signal with a peak value greater than the maximum value on the baseline is used as the signal to be analyzed.

[0050] (2) Further, all the signals to be analyzed at each potential abnormal point are fitted to obtain a fitting curve of each potential abnormal point.

[0051] It should be noted that there are many commonly used curve fitting methods. In this embodiment, the least squares method is used to fit all the signals to be analyzed of the potential outliers. The implementer may also use other fitting methods such as polynomial regression. This embodiment does not impose any special restrictions on the choice of fitting method.

[0052] Among them, the least square method is a well-known technology, and the specific process of fitting the data is not described in detail.

[0053] (3) Further, according to the changing trend of the fitting curve of each potential abnormal point, the signal fluctuation value of each potential abnormal point is determined, so as to judge the speed of the changing trend of the upper and lower edges of the pulse signal, specifically:

[0054] The expression of the signal fluctuation value of potential abnormal point i is: ; In the formula, , They respectively represent the angle between the line connecting the first rising edge end point and the first data point and the baseline, and the angle between the line connecting the first falling edge start point and the last data point and the baseline on the fitting curve of potential outlier point i; cos() represents the cosine function.

[0055] Furthermore, according to the signal fluctuation value of each potential abnormal point, it can be understood that if the potential abnormal point is a scanning point corresponding to an internal defect, the distortion of the received pulse signal is more serious due to the influence of looseness or voids. At this time, the signal jump edge (rising edge and falling edge) will be an inclined curve. At this time, the angle between the end point of the rising edge and the starting point of the falling edge and the line connecting the first and tail endpoints and the baseline will become smaller, thereby obtaining a larger signal fluctuation value of the potential abnormal point; conversely, if the potential abnormal point is a normal or aggregate scanning point, the jump edge (rising edge and falling edge) of the received pulse signal will be a nearly vertical straight line. At this time, the angle between the end point of the rising edge and the starting point of the falling edge and the line connecting the first and tail endpoints and the baseline will become larger, thereby obtaining a smaller signal fluctuation value of the potential abnormal point.

[0056] Step S3: Based on the change trend of all elements between the first pair of rising and falling edge starting points on the fitting curve of each potential abnormal point, determine the average trend strength of each potential abnormal point, and determine the signal distortion of each potential abnormal point in combination with the fluctuation degree of all the elements.

[0057] The signal intensity variation of the high-level interval of the received pulse signal received at the scanning point where the internal defect of the wall hanging plate is located is similar to the vault distribution. However, the plates corresponding to the normal position scanning point and the aggregate position scanning point are at least dense and continuous, so the interference in the ultrasonic propagation process is relatively small, making the received pulse signal closer to the rectangular pulse waveform during transmission.

[0058] Therefore, in order to determine whether the received pulse signal of the potential abnormal point is distorted, and thus determine whether there are defects inside the wall hanging board, the average trend strength of each potential abnormal point is determined based on the change trend of all elements between the starting points of the first pair of rising and falling edges on the fitting curve of each potential abnormal point, and the signal distortion degree of each potential abnormal point is determined in combination with the fluctuation degree of all the elements, specifically:

[0059] (1) Analyze the distance between each element between the first rising edge end point and the first falling edge start point on the fitting curve of each potential abnormal point and the line connecting the first rising edge end point and the first falling edge start point, and record it as the high level distance;

[0060] (2) Furthermore, all high-level distances between the first high-level distance to the maximum high-level distance of each potential anomaly point and all high-level distances between the maximum high-level distance to the last high-level distance are used as inputs of the time series decomposition algorithm to obtain two trend intensities of each potential anomaly point. For the sake of ease of expression, they are respectively recorded as the first trend intensity and the second trend intensity.

[0061] It should be noted that there are many commonly used time series decomposition algorithms. In this embodiment, the seasonal trend decomposition algorithm (STL) is used to obtain the first trend intensity and the second trend intensity. The implementer may also use other time series decomposition algorithms such as the SEATS decomposition algorithm. This embodiment does not impose any special restrictions on the selection of the time series decomposition algorithm.

[0062] Among them, the seasonal trend decomposition algorithm is a well-known technology, and the specific process of calculating the trend intensity will not be repeated here.

[0063] (3) Furthermore, the average of the first trend strength and the second trend strength of each potential outlier point is recorded as the average trend strength of each potential outlier point.

[0064] (4) Based on the average trend strength and in combination with the degree of dispersion of all high-level distances of each potential abnormal point, the signal distortion degree of each potential abnormal point is determined. The signal distortion degree of each potential abnormal point is the result of the fusion of the average trend strength of each potential abnormal point and the degree of dispersion.

[0065] It should be noted that there are many methods for measuring the degree of discreteness. In this embodiment, the degree of discreteness of the high-level distance is measured by calculating the variance of all high-level distances of each potential abnormal point, thereby measuring the degree of distortion of the pulse signal. The implementer may also use other methods that can measure the degree of discreteness, such as mean difference or dispersion coefficient. This embodiment does not impose any special restrictions on the selection of the method for measuring the degree of discreteness.

[0066] In addition, it should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0067] Preferably, the expression of the signal distortion degree of the potential abnormal point i in this embodiment is: ; In the formula, Indicates the degree of dispersion of all high-level distances of potential abnormal point i; Indicates the average trend strength of potential abnormal points. In practical applications, as another implementation, the signal distortion degree of each potential abnormal point is an exponential function value with a natural constant as the base and the sum of the discrete degree of all high-level distances of each potential abnormal point and the average trend strength as the independent variable.

[0068] Preferably, the average trend strength acquisition step flow chart provided in this embodiment is as follows: Figure 3 shown.

[0069] Furthermore, according to the signal distortion of each potential abnormal point, it can be understood that if the potential abnormal point is a scanning point corresponding to an internal defect, the looseness or void will cause the high-level interval of the originally flat rectangular pulse to be distorted into an arch shape. At this time, the signal strength of the high-level interval first increases and then decreases, thereby obtaining a large degree of discreteness of the high-level distance, and the more severe the signal distortion, the greater the average trend strength, and finally the larger the signal distortion value; conversely, if the potential abnormal point is a scanning point corresponding to a normal or aggregate, the signal strength of the high-level interval is close to a horizontal straight line, thereby obtaining a small degree of discreteness of the high-level distance, and the smaller the signal distortion, the smaller the average trend strength, and finally the smaller the signal distortion.

[0070] Step S4: Based on the signal distortion and the signal fluctuation value, determine the signal fluctuation disorder of each potential abnormal point.

[0071] Based on step S2 and step S3, the signal fluctuation value and signal distortion can be obtained, so as to distinguish the influence of the wall hanging board defects and the filling material on the signal, so as to more accurately judge whether there are defects inside the wall hanging board. Therefore, in order to more accurately identify the internal defects of the wall hanging board, the slowness of the change trend of the upper and lower edges of the pulse signal and the fluctuation degree of the high-level interval are combined to jointly determine the signal fluctuation disorder to identify the internal defects in the wall hanging board. The specific process is as follows:

[0072] Based on the signal fluctuation value and signal distortion of each potential abnormal point, the signal fluctuation disorder degree of each potential abnormal point is determined. The signal fluctuation disorder degree of each potential abnormal point is the result of the fusion of the signal fluctuation value and signal distortion degree of each potential abnormal point.

[0073] Preferably, the expression of the signal fluctuation disorder degree of the potential abnormal point i in this embodiment is: ; In the formula, Represents the signal fluctuation value of potential abnormal point i; Indicates the signal distortion of potential abnormal point i. In practical application, as another implementation, the signal fluctuation disorder of each potential abnormal point is an exponential function value with a natural constant as the base and the sum of the signal fluctuation value and the signal distortion of each potential abnormal point as the independent variable.

[0074] Furthermore, according to the signal fluctuation disorder of each potential abnormal point, it can be understood that for a normal or aggregate scanning point, the distortion of the received pulse signal is relatively small. At this time, the end point of the first rising edge corresponds to the end point of the rising edge of the rectangular pulse signal, and the starting point of the first falling edge corresponds to the starting point of the falling edge of the rectangular pulse signal. Therefore, the closer the angle between the rising edge and the falling edge of the pulse signal and the baseline is to 90°, the smaller the signal fluctuation value is; and the line connecting the end point of the rising edge and the starting point of the falling edge is basically the high level interval of the rectangular pulse signal. Since the signal distortion is small, the distance from the signal strength to the connecting line in the high level interval is basically the same, and there is no changing trend, so the signal distortion is small; therefore, if the signal fluctuation value is smaller, The smaller the signal distortion, the smaller the value of the signal fluctuation disorder of the potential abnormal point, indicating that the possibility that the potential abnormal point is an internal defect scanning point is smaller; conversely, if the angle between the rising edge and the falling edge in the pulse signal and the baseline is close to 90°, the possibility is smaller, and the signal fluctuation value is larger; and the line connecting the end point of the rising edge and the starting point of the falling edge is basically the high level interval of the rectangular pulse signal. Due to the large signal distortion, the distance from the signal strength to the connecting line in the high level interval is more inconsistent, and the degree of fluctuation is greater, thereby obtaining a greater signal distortion; therefore, if the signal fluctuation value is larger, the signal distortion is larger, and the value of the signal fluctuation disorder of the potential abnormal point is larger, indicating that the possibility that the potential abnormal point is an internal defect scanning point is larger.

[0075] Step S5: Based on the energy difference between all transmitted pulse signals and all received pulse signals of each potential abnormal point, the signal attenuation of each potential abnormal point is determined, and combined with the difference in signal fluctuation disorder between potential abnormal points adjacent to each potential abnormal point, the signal defect confidence of each potential abnormal point is determined.

[0076] Through step S4, the waveform distortion of the received pulse signal of the potential abnormal point can be obtained, thereby determining the possibility of internal defects at each scanning point in the potential abnormal point set. However, since the sampling environment is uncontrollable, different scanning points are affected differently by environmental noise when sampling, which may cause the waveform of some aggregate position scanning points to be distorted, which may lead to misjudgment of them as abnormal points, so further analysis is required in combination with the situation of the scanning points.

[0077] In the wall hanging board, the abnormal scanning point corresponds to the loose or hollow area, so the ultrasonic pulse signal needs to pass through a part of the air, so the energy attenuation of the ultrasonic pulse signal is small. For the aggregate scanning point corresponding to the insulation core material or steel bar in the hanging board, although the material has changed, the whole is solid, so the signal attenuation is more serious.

[0078] Based on the above analysis, in order to eliminate the interference of environmental influences during sampling on internal defect detection, the attenuation degree from signal emission to signal reception in potential scanning points is compared to identify whether there are internal defects in the wall hanging board. Specifically:

[0079] S501: Determine the signal attenuation of each potential abnormal point based on the energy difference between all transmitted pulse signals and all received pulse signals of each potential abnormal point.

[0080] The signal attenuation of each potential abnormal point is the difference between the total energy of all transmitted pulse signals in the frequency domain and the cumulative sum of the total energy of all received pulse signals in the frequency domain of each potential abnormal point. The total energy of all transmitted frequency domain signals in the frequency domain refers to the cumulative sum of the energy of all transmitted pulse signals in the frequency domain, and the total energy of all received pulse signals in the frequency domain refers to the cumulative sum of the energy of all received pulse signals in the frequency domain.

[0081] The calculation process of the energy of the signal at each frequency is a well-known technology, and the specific calculation process will not be described in detail.

[0082] It should be noted that there are many ways to measure the difference. In this embodiment, the difference between the total energy of the transmitted pulse signal and the total energy of the received pulse signal is measured by calculating the difference between the total energy of all transmitted pulse signals in the frequency domain of each potential abnormal point and the total energy of all received pulse signals in the frequency domain, thereby measuring the degree of attenuation of the signal energy. The implementer may also use other methods to measure the difference, such as ratio. Regarding the choice of the method for measuring the difference, this embodiment does not impose any special restrictions.

[0083] Furthermore, according to the signal attenuation of each potential abnormal point, it can be understood that for the abnormal scanning point, since the ultrasonic signal penetrates the loose and void air areas, the signal attenuation is small, and the difference between the energy at the reflection end and the energy at the receiving end is small, that is, the signal attenuation is small; on the contrary, for the aggregate scanning point, since the ultrasonic pulse signal penetrates the average dense area, the signal attenuation intensity is large, resulting in a large signal attenuation.

[0084] S502: Determine the signal defect confidence of each potential abnormal point based on the signal attenuation of each potential abnormal point and in combination with the difference in signal fluctuation disorder between potential abnormal points adjacent to each potential abnormal point.

[0085] There are also differences in the spatial distribution of abnormal scanning points and aggregate scanning points. The areas corresponding to looseness and voids are often smaller. Therefore, if a scanning point is an abnormal scanning point, it is more different from the surrounding scanning points. For the aggregate area, the structural composition of the hanging board is the same in at least one direction, that is, one scanning point corresponds to the steel bar area. Usually, the steel bars are distributed horizontally and vertically, and the wall hanging board structure corresponding to the scanning points adjacent to the left and right or above and below the scanning point is similar. Therefore, based on the difference in the signal fluctuation disorder of the potential scanning point between the horizontal direction and the vertical direction of each potential abnormal point, and the signal attenuation, the signal defect confidence of each potential abnormal point is determined, specifically:

[0086] The expression of signal defect confidence of potential abnormal point i is: ; In the formula, Indicates the signal fluctuation disorder of potential abnormal point i; , They respectively represent the sum of the signal fluctuation disorder of the potential abnormal points adjacent to the potential abnormal point i on the left and right, and the sum of the signal fluctuation disorder of the potential abnormal points adjacent to the potential abnormal point i above and below; represents the signal attenuation of potential outlier point i; min() represents the minimum function; norm() represents the normalization function.

[0087] Furthermore, according to the signal defect confidence of each potential abnormal point, it can be understood that if the potential abnormal point is a real defect scanning point, the greater the signal fluctuation disorder in the horizontal and vertical directions of the scanning point, and the smaller the signal attenuation, the greater the signal defect confidence of the potential abnormal point. On the contrary, if the potential abnormal point is an aggregate scanning point, the signal fluctuation disorder in at least one of the horizontal and vertical directions is small, and the signal attenuation value is large, the signal defect confidence value of the potential abnormal point is small.

[0088] Preferably, the signal defect confidence extraction process schematic diagram provided in this embodiment is as follows Figure 4 shown.

[0089] Step S6: Based on the signal defect confidence of each potential abnormal point and in combination with the sound velocity data, the corrected sound velocity value of each potential abnormal point is determined to detect the strength of the wall hanging board.

[0090] Based on the sound velocity data of the potential abnormal point and the signal defect confidence of the potential abnormal point obtained in step S3, the sound velocity value of the potential abnormal point is corrected, specifically:

[0091] The expression of the corrected sound velocity value of potential abnormal point i is: ; In the formula, represents the corrected sound velocity data of potential abnormal point i; Represents the sound velocity data of potential abnormal point i; Indicates a preset value.

[0092] It should be noted that The value of is set manually. The value of is 2, which is used to adjust the influence of signal defect confidence on the corrected sound velocity value. The value of is directly related to the threshold of signal defect confidence. When the value of is 2, it means that the threshold for correcting the sound velocity data is 0.5. When the signal defect confidence is greater than 0.5, the potential abnormal point is determined to be the point where the real defect is located, thereby increasing the value of the denominator to reduce the sound velocity data. On the contrary, if the signal defect confidence value of the potential abnormal point is less than 0.5, the sound velocity data is amplified to distinguish the real defect from the aggregate area. In order to avoid over-correction, If the value is too small, the correction effect will not be obvious; if it is too large, the corrected sound velocity value will be distorted, affecting the accuracy of subsequent analysis. The value of is generally set to [2, 5], and the implementer can set it according to the specific situation. This embodiment does not impose any special restrictions.

[0093] If the potential abnormal point is the scanning point where the real defect is located, the signal defect confidence value is large, so the sound velocity data of the potential abnormal point is reduced. On the contrary, if the signal defect confidence value of the potential abnormal point is small, the sound velocity data of the potential abnormal point is amplified to distinguish it from the scanning point where the real defect is located.

[0094] Furthermore, the corrected sound velocity values ​​of all potential abnormal points are used as input of the threshold segmentation algorithm to obtain the segmentation threshold, and the absolute value of the difference between the first two results of the difference between all the corrected sound velocity values ​​and the segmentation threshold arranged in ascending order is calculated. If the ratio of the absolute value to the extreme difference of the corrected sound velocity values ​​of all potential abnormal points is greater than a preset second threshold, it indicates that the wall hanging board to be detected has internal defects and the strength of the wall hanging board is unqualified; conversely, if the ratio of the difference between the first two results to the extreme difference of the corrected sound velocity values ​​of all potential abnormal points is less than or equal to the preset second threshold, it indicates that the interior of the wall hanging board to be detected is intact, there are no internal defects, and the strength of the wall hanging board is qualified.

[0095] It should be noted that the value of the preset second threshold is artificially set. In this embodiment, the value of the preset second threshold is 0.2. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0096] At this point, this embodiment uses an ultrasonic detector to obtain ultrasonic data and propagation sound velocity at each scanning point on the wall panel to be detected, and obtains the signal fluctuation disorder degree based on the waveform distortion of the received pulse signal at the transition edge and high level interval of the potential abnormal point, reflecting the degree to which the received pulse signal is affected by the internal defects of the wall panel, and obtains the signal defect confidence according to the attenuation and spatial position offset of the potential abnormal point to avoid the interference of noise on the internal defects, and uses the signal defect confidence and the original sound velocity data to correct the sound velocity value, and uses the corrected sound velocity value to detect the strength of the wall panel to be detected. This embodiment improves the accuracy of wall panel strength detection by analyzing the difference between the aggregate area and the internal defects in the wall panel, and the interference of the aggregate area on the internal defect detection.

[0097] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0099] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A wall hanging board strength detection method, characterized in that: The method comprises the following steps: Obtain the transmitted pulse signal, received pulse signal and sound velocity value of each preset scanning point on the wall hanging plate to be detected at all sampling times within a preset time length, and record the average of the sound velocity values ​​of each scanning point at all sampling times as the sound velocity data of each scanning point; The scanning points whose sound velocity data are less than or equal to the preset first threshold value are recorded as potential abnormal points, and a baseline correction algorithm is used based on all received pulse signals of each potential abnormal point to determine all signals to be analyzed at each potential abnormal point, and all signals to be analyzed are fitted to obtain fitting curves of each potential abnormal point, and the signal fluctuation value of each potential abnormal point is determined according to the change trend of the fitting curve of each potential abnormal point; Based on the change trend of all elements from the first rising edge end point to the first falling edge start point on the fitting curve of each potential abnormal point, the average trend strength of each potential abnormal point is determined, and the signal distortion degree of each potential abnormal point is determined in combination with the fluctuation degree of all the elements; Based on the signal distortion and the signal fluctuation value, determining the signal fluctuation disorder degree of each potential abnormal point; Based on the energy difference between all transmitted pulse signals and all received pulse signals of each potential abnormal point, the signal attenuation of each potential abnormal point is determined, and the signal defect confidence of each potential abnormal point is determined in combination with the difference in signal fluctuation disorder between potential abnormal points adjacent to each potential abnormal point; Based on the signal defect confidence and the sound velocity data, the corrected sound velocity value of each potential abnormal point is determined to detect the strength of the wall hanging board; The method for determining the average trend strength of each potential abnormal point is: For each potential abnormal point, the distance from each element between the first rising edge end point and the first falling edge start point on the fitting curve to the line connecting the first rising edge end point and the first falling edge start point is recorded as the high level distance; All high-level distances between the first high-level distance and the maximum high-level distance of each potential abnormal point, and all high-level distances between the maximum high-level distance and the last high-level distance are respectively used as inputs of the time series decomposition algorithm to obtain two trend strength values ​​of each potential abnormal point. The average trend strength of each potential abnormal point is the average of the two trend strengths of each potential abnormal point. The expression of the signal defect confidence of each potential abnormal point is: ; In the formula, Indicates the signal defect confidence of potential abnormal point i; Indicates the signal fluctuation disorder of potential abnormal point i; , They respectively represent the sum of the signal fluctuation disorder of the potential abnormal points adjacent to the potential abnormal point i on the left and right, and the sum of the signal fluctuation disorder of the potential abnormal points adjacent to the potential abnormal point i above and below; represents the signal attenuation of potential outlier point i; min() represents the minimum function; norm() represents the normalization function; The expression of the corrected sound velocity value of each potential abnormal point is: ; In the formula, represents the corrected sound velocity value of potential abnormal point i; represents the corrected sound velocity data of potential abnormal point i; Represents the sound velocity data of potential abnormal point i; Indicates a preset value; The testing of the strength of the wall hanging plate includes: The corrected sound velocity values ​​of all potential abnormal points are used as the input of the threshold segmentation algorithm to obtain the segmentation threshold, and the absolute value of the difference between the first two results of the difference between all the corrected sound velocity values ​​and the segmentation threshold arranged in ascending order is calculated. If the ratio of the absolute value to the extreme difference of the corrected sound velocity values ​​of all potential abnormal points is greater than the preset second threshold, the wall hanging board strength is unqualified, otherwise, the wall hanging board strength is qualified.

2. A wall hanging board strength detection method as claimed in claim 1, characterized in that: The method for determining all the signals to be analyzed at each potential abnormal point is: All pulse signals of each potential abnormal point are used as input of the baseline correction algorithm to obtain the baseline of each potential abnormal point, and the received pulse signal with a peak value greater than the maximum value on the baseline is used as the signal to be analyzed.

3. A wall hanging board strength detection method as claimed in claim 2, characterized in that: The expression of the signal fluctuation value of each potential abnormal point is: ; In the formula, Represents the signal fluctuation value of potential abnormal point i; , Respectively represent the angle between the line connecting the first rising edge end point and the first data point and the baseline, and the angle between the line connecting the first falling edge start point and the last data point and the baseline on the fitting curve of the potential abnormal point i; cos() represents the cosine function.

4. A wall hanging board strength detection method as claimed in claim 1, characterized in that: The signal distortion degree of each potential abnormal point is the result of the fusion of the average trend strength of each potential abnormal point and the discrete degree of all the high level distances.

5. A wall hanging board strength detection method as claimed in claim 1, characterized in that: The signal fluctuation disorder degree of each potential abnormal point is the result of integrating the signal fluctuation value of each potential abnormal point with the signal distortion degree.

6. A wall hanging board strength detection method as claimed in claim 1, characterized in that: The signal attenuation of each potential abnormal point is the difference between the total energy of all transmitted pulse signals of each potential abnormal point in the frequency domain and the total energy of all received pulse signals in the frequency domain.

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