A laser scanning and big data analysis device for epoxy floor coating thickness

Abstract

This invention relates to the field of epoxy flooring quality testing technology, specifically to a laser scanning and big data analysis device for epoxy flooring coating thickness. The device includes: acquiring laser resonance spectrum scanning data and stress wave scanning data of the epoxy flooring surface to be tested; calculating a first vibration mode frequency set of the coating surface based on the laser resonance spectrum scanning data, and calculating a second vibration mode frequency set of the coating-substrate interface based on the stress wave scanning data; determining the current thickness distribution of the coating by querying a preset thickness-frequency correlation spectrum database based on the first and second vibration mode frequency sets; and performing a matching degree analysis based on the current thickness distribution and historical construction data in the thickness-frequency correlation spectrum database to determine the uniformity level and potential defect areas of the coating. Potential defects are accurately identified by matching data stored in the thickness-frequency correlation spectrum database.

Description

Technical Field

[0001] This invention relates to the field of epoxy flooring quality testing technology, specifically to an epoxy flooring coating thickness laser scanning and big data analysis device. Background Technology

[0002] With the booming development of modern industrial and commercial environments, the selection of flooring materials has become increasingly important. Epoxy flooring, with its excellent wear resistance, corrosion resistance, aesthetics, and easy cleaning properties, has been widely used in many places such as electronics factories, pharmaceutical workshops, food processing plants, parking lots, and shopping malls. However, the performance of epoxy flooring largely depends on the uniformity of the coating thickness. Precisely controlling the coating thickness has become one of the key factors in ensuring the quality and service life of epoxy flooring.

[0003] Existing detection methods have limitations and lack multi-dimensional vibration mode collaborative analysis. Traditional detection methods mostly use sampling methods, which are destructive and can damage the integrity of the floor. In addition, the sampling points are limited and cannot reflect the overall thickness distribution. In non-destructive testing, ultrasonic thickness gauges can only detect a single thickness value at the interface between the coating and the substrate, and cannot capture the micro-vibration characteristics of the coating surface. The detection accuracy for thin coatings is insufficient, and it cannot identify potential defect areas. Moreover, the thickness information of epoxy floor coatings is not only related to the propagation characteristics of interfacial stress waves, but also closely related to the micro-vibration modes of the coating surface. Existing technologies rely on only a single stress wave or a single laser vibration signal, resulting in large deviations in thickness calculation, especially for areas with local stress concentration, where the thickness cannot be accurately inverted. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a laser scanning and big data analysis device for epoxy floor coating thickness.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A laser scanning and big data analysis device for epoxy floor coating thickness, comprising:

[0006] Data acquisition module: Acquires laser resonance spectrum scanning data and stress wave scanning data of the epoxy flooring surface to be tested;

[0007] Vibration signal acquisition module: Based on laser resonance spectrum scanning data, calculate the first vibration mode frequency set of the coating surface, and based on stress wave scanning data, calculate the second vibration mode frequency set of the coating-substrate interface;

[0008] Thickness matching module: Based on the first vibration mode frequency set and the second vibration mode frequency set, the current thickness distribution of the coating is determined by querying a preset thickness-frequency correlation spectrum database;

[0009] Defect Analysis Module: Based on the current thickness distribution and historical construction data in the thickness and frequency correlation spectrum database, a matching degree analysis is performed to determine the uniformity level of the coating and potential defect areas.

[0010] In a preferred embodiment, the data acquisition module emits a modulated laser with a first preset frequency range onto the epoxy flooring surface to be tested via a laser exciter to induce microscopic surface vibration. The laser exciter does not output a constant laser, but rather a light wave whose frequency is sinusoidally or swept according to the first preset frequency range. When the modulated laser irradiates the coating surface, its periodically changing photopressure effect applies a periodic excitation force to the coating surface. Under the action of the excitation force, the epoxy flooring coating surface will generate microscopic elastic vibration. The frequency components of this vibration are directly related to the modulation frequency and the thickness, elastic modulus, and density of the coating itself.

[0011] A laser interferometric vibrometer is used to collect the phase change information of the reflected laser light generated by the vibration of the microscopic surface and generate laser resonance spectrum scanning data. The laser interferometric vibrometer emits a stable probe laser beam to the excited coating surface. After the probe laser is reflected by the vibrating surface, the phase of the reflected light will be modulated by the surface vibration velocity due to the Doppler effect. The laser interferometric vibrometer demodulates the phase difference change information between the reflected light and the reference light through the principle of optical interference. This phase difference change information directly corresponds to the instantaneous vibration displacement and velocity of the coating surface. The evolution of this phase change information over time is recorded and analyzed as a time-domain displacement signal.

[0012] A stress wave pulse with a second preset frequency range is emitted to the epoxy floor surface to be tested. The stress wave pulse is coupled into the interior of the epoxy floor coating in the form of an elastic wave and mainly propagates in the interior of the coating and the interface area between it and the concrete substrate. During the propagation process, the stress wave will be reflected, refracted and mode-converted when it encounters abrupt changes in material properties.

[0013] A laser Doppler vibrometer is used to collect the surface vibration response generated after stress waves propagate in the coating and the body, and to generate stress wave scanning data. When the emitted stress wave pulse propagates in the coating structure and reflects back to the surface, it will cause instantaneous micro-vibrations at specific points on the surface. The laser Doppler vibrometer directly records the waveform of the vibration velocity changing with time by detecting the frequency offset of the reflected laser at specific points. This waveform is the stress wave scanning data, which contains information about the coating thickness, internal structural integrity and interface bonding state carried by the stress wave during propagation.

[0014] In a preferred embodiment, the vibration signal acquisition module converts the time-domain displacement signal acquired by the laser interferometer to the frequency domain using a fast Fourier transform algorithm, thereby obtaining an amplitude spectrum diagram. The specific calculation formula is as follows:

[0015]

[0016] in, Represents the complex amplitude in the frequency domain. This represents the time-domain displacement signal acquired by the laser interferometer, where N represents the number of sampling points. denoted by the rotation factor, the decomposition of the time-domain signal into the frequency domain is realized. j represents the imaginary unit. In the spectrum diagram, the frequencies corresponding to several main peaks with amplitudes significantly higher than the background noise are automatically identified and extracted. These peak frequencies correspond to the multi-order dominant resonance frequencies generated on the coating surface after being excited by the modulated laser. The set of multi-order dominant resonance frequencies is defined as the first vibration mode frequency set, which directly characterizes the inherent dynamic characteristics of the coating itself.

[0017] The vibration signal acquisition module processes stress wave scanning data in parallel. Based on the transient vibration velocity time-domain signal acquired by the laser Doppler vibrometer, it performs spectral analysis on the velocity time-domain signal to extract its characteristic frequency components, combined with the wave velocity of the stress wave propagating in the double-layer medium: ,frequency Inverse calculations were performed using the thickness theory model to decouple and extract characteristic frequencies that reflect the vibration state of the coating-substrate interface. The set of these characteristic frequencies was defined as the second vibration mode frequency set. Indicates the propagation speed of stress waves. The 2d represents the round-trip propagation time, d represents the coating thickness, and 2d represents the round-trip path length of the stress wave. Represents characteristic frequency, Indicates the propagation speed of stress waves. Indicates the characteristic wavelength.

[0018] In a preferred embodiment, the thickness matching module realizes real-time interaction between the first vibration modal frequency set, the second vibration modal frequency set, and the thickness-frequency correlation spectrum database via a data bus. The preset mapping relationship model of the thickness-frequency correlation spectrum database is a three-dimensional nonlinear model of coating thickness and the first and second vibration modal frequency sets established based on a large amount of sample data. The mapping relationship model includes matching degree calculation and thickness inversion calculation, constructing an input vector F from the characteristic frequency data of the current scanning point:

[0019]

[0020] in, - This represents the m-th frequency value in the first vibration mode frequency set. - Representing the nm frequency values ​​of the second vibration mode frequency set, calculate the input vector F and the k-th sample data vector in the thickness and frequency correlation spectrum database. Matching degree The specific calculation formula is as follows:

[0021]

[0022] in, This represents the i-th feature frequency component of the input vector F. Represents sample vector The i-th characteristic frequency component is used as the input vector, which is the frequency set of the first vibration mode and the frequency set of the second vibration mode at the current scanning point. This input vector is substituted into the mapping model. The mapping model compares the input vector with the correspondence between the frequency set and thickness in the sample database, and outputs the coating thickness value at the scanning point. If the matching degree between the input vector and the sample data is lower than a preset matching threshold, the model's difference calculation function is activated. The top P samples with the highest matching degree are selected from the database, where P is a preset value, and weights are assigned to these P samples. The weights and their specific calculation formulas are as follows:

[0023]

[0024] Where T represents a preset temperature parameter used to control the smoothness of the weight distribution, and the final output thickness value is the weighted average of the thicknesses of these P samples. The specific calculation formula is as follows:

[0025]

[0026] in, This represents the calculated coating thickness at the current scan point, which is the final output of the mapping model. This represents the known coating thickness value corresponding to the kth closest sample;

[0027] The scanning support moves along a preset path and at a preset scanning step size to complete one scan. The X and Y coordinates of each scanning point are stored along with the corresponding thickness value to form a two-dimensional thickness matrix. The two-dimensional coordinates of each scanning point and its corresponding thickness value are stored in a matrix H, which can be specifically represented as follows:

[0028]

[0029] The thickness matrix is ​​transformed into a visual thickness distribution map using a color mapping algorithm. Each pixel in the thickness distribution map corresponds to a preset actual area size, ensuring that the thickness gradient of the edge area and the uniformity of the middle area can be clearly presented.

[0030] In a preferred embodiment, the defect analysis module retrieves thickness distribution data from a thickness-frequency correlation spectrum database of historical construction projects with the same construction and environmental parameters for the currently tested floor. This data is used as a matching benchmark for evaluating the uniformity of the current coating. The statistical deviation between the current thickness distribution and the matching benchmark thickness distribution is calculated using a root mean square error algorithm. The specific calculation formula is as follows:

[0031]

[0032] in, This represents the root mean square error, where M and N represent the number of rows and columns of the thickness matrix. This represents the thickness value at point (i,j) in the current matrix. This represents the thickness value at the corresponding point in the reference matrix;

[0033] Based on the calculated statistical deviation value, automatic classification is performed according to the preset uniformity judgment logic. If the deviation value is less than or equal to the first preset uniformity threshold, the current coating uniformity level is determined to be excellent uniformity. If the deviation value is greater than the first preset uniformity threshold but less than or equal to the second preset uniformity threshold, it is determined to be qualified uniformity. If the deviation value is greater than the second preset uniformity threshold, it is determined to be unqualified uniformity, and a defect scan is performed. The current thickness distribution map is analyzed to identify whether there are continuous regions where the thickness value changes abruptly. The judgment criterion is whether the thickness difference between adjacent scan points exceeds the preset defect threshold. All continuous regions that meet the judgment criteria are marked as potential defect regions.

[0034] The beneficial effects of this invention are as follows: This invention adopts laser excitation and laser vibration acquisition technology, which eliminates the need for drilling samples or contact damage to the coating, enabling non-destructive testing of the entire area of ​​epoxy floor coatings. This avoids damage to the floor caused by traditional sampling methods. By combining dual-dimensional vibration signals, first and second vibration mode frequency sets are generated. Thickness is inverted through a three-dimensional mapping relationship model, improving detection accuracy. By using sample data and historical construction data stored in the thickness-frequency correlation spectrum database, an objective matching benchmark is established, avoiding subjective judgment based on human experience and accurately identifying potential defects. Attached Figure Description

[0035] Figure 1 This is a flowchart of the present invention;

[0036] Figure 2 This is a block diagram of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0039] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0040] like Figure 1 This embodiment provides: a laser scanning and big data analysis device for epoxy floor coating thickness, comprising:

[0041] Data acquisition module: Acquires laser resonance spectrum scanning data and stress wave scanning data of the epoxy flooring surface to be tested;

[0042] In this embodiment of the invention, the data acquisition module needs to be specifically described. The data acquisition module emits a modulated laser with a first preset frequency range to the epoxy floor surface to be tested through a laser exciter to induce microscopic surface vibration. The laser exciter does not output a constant laser, but a light wave whose frequency is sinusoidally or swept according to the first preset frequency range. When the modulated laser irradiates the coating surface, its periodically changing photopressure effect will apply a periodic excitation force to the coating surface. Under the action of the excitation force, the epoxy floor coating surface will generate microscopic elastic vibration. The frequency components of this vibration are directly related to the modulation frequency and the thickness, elastic modulus and density of the coating itself.

[0043] A laser interferometric vibrometer is used to collect the phase change information of the reflected laser light generated by the vibration of the microscopic surface and generate laser resonance spectrum scanning data. The laser interferometric vibrometer emits a stable probe laser beam to the excited coating surface. After the probe laser is reflected by the vibrating surface, the phase of the reflected light will be modulated by the surface vibration velocity due to the Doppler effect. The laser interferometric vibrometer demodulates the phase difference change information between the reflected light and the reference light through the principle of optical interference. This phase difference change information directly corresponds to the instantaneous vibration displacement and velocity of the coating surface. The evolution of this phase change information over time is recorded and analyzed as a time-domain displacement signal.

[0044] A stress wave pulse with a second preset frequency range is emitted to the epoxy floor surface to be tested. The stress wave pulse is coupled into the interior of the epoxy floor coating in the form of an elastic wave and mainly propagates in the interior of the coating and the interface area between it and the concrete substrate. During the propagation process, the stress wave will be reflected, refracted and mode-converted when it encounters abrupt changes in material properties.

[0045] A laser Doppler vibrometer is used to collect the surface vibration response generated after stress waves propagate in the coating and the body, and to generate stress wave scanning data. When the emitted stress wave pulse propagates in the coating structure and reflects back to the surface, it will cause instantaneous micro-vibrations at specific points on the surface. The laser Doppler vibrometer directly records the waveform of the vibration velocity changing with time by detecting the frequency offset of the reflected laser at specific points. This waveform is the stress wave scanning data, which contains information about the coating thickness, internal structural integrity and interface bonding state carried by the stress wave during propagation.

[0046] Vibration signal acquisition module: Based on laser resonance spectrum scanning data, calculate the first vibration mode frequency set of the coating surface, and based on stress wave scanning data, calculate the second vibration mode frequency set of the coating-substrate interface;

[0047] In this embodiment of the invention, the vibration signal acquisition module needs to be specifically described. This module converts the time-domain displacement signal acquired by the laser interferometer to the frequency domain using a fast Fourier transform algorithm, thereby obtaining an amplitude spectrum. The specific calculation formula is as follows:

[0048]

[0049] in, Represents the complex amplitude in the frequency domain. This represents the time-domain displacement signal acquired by the laser interferometer, where N represents the number of sampling points. denoted by the rotation factor, the decomposition of the time-domain signal into the frequency domain is realized. j represents the imaginary unit. In the spectrum diagram, the frequencies corresponding to several main peaks with amplitudes significantly higher than the background noise are automatically identified and extracted. These peak frequencies correspond to the multi-order dominant resonance frequencies generated on the coating surface after being excited by the modulated laser. The set of multi-order dominant resonance frequencies is defined as the first vibration mode frequency set, which directly characterizes the inherent dynamic characteristics of the coating itself.

[0050] The vibration signal acquisition module processes stress wave scanning data in parallel. Based on the transient vibration velocity time-domain signal acquired by the laser Doppler vibrometer, it performs spectral analysis on the velocity time-domain signal to extract its characteristic frequency components, combined with the wave velocity of the stress wave propagating in the double-layer medium: ,frequency Inverse calculations were performed using the thickness theory model to decouple and extract characteristic frequencies that reflect the vibration state of the coating-substrate interface. The set of these characteristic frequencies was defined as the second vibration mode frequency set. Indicates the propagation speed of stress waves. The 2d represents the round-trip propagation time, d represents the coating thickness, and 2d represents the round-trip path length of the stress wave. Represents characteristic frequency, Indicates the propagation speed of stress waves. Indicates the characteristic wavelength;

[0051] It should be noted that the core of the thickness theoretical model lies in establishing a physical and mathematical model of stress wave propagation in the specific structure of epoxy floor coating-substrate, and using numerical methods to solve for the coating thickness. The principle is as follows:

[0052] A physical model was constructed and governing equations were established. This model abstracts the actual epoxy flooring system into an ideal two-layer medium structure, with the upper layer being the epoxy resin coating to be tested, having a thickness of h. This layer of material has its inherent density. and the Lamé constant and (in That is, shear modulus The elastic properties are defined as follows: the lower layer is a concrete matrix, which is considered as a semi-infinite space, and its material properties are determined by density. and the Lamé constant and The propagation dynamics of stress waves in this structure are described, and are governed by the elastic dynamics wave equation.

[0053] The dispersion equation is derived as the core model. For the propagation problem of surface waves such as Rayleigh waves in such layered media, the solution of the wave equation must strictly satisfy a series of boundary conditions: the stress is zero on the free surface of the coating, the displacement and stress must be continuous at the interface between the coating and the substrate, and the displacement is zero at infinite depth in the substrate.

[0054] Finally, the inversion calculation process is implemented, which consists of three main steps: the first step is forward modeling, which involves creating a theoretical dispersion curve database and pre-setting a set of thickness values. (i=1, 2, N), and combined with known material parameters, for each thickness By solving the above dispersion equation using numerical methods, a theoretical dispersion curve is obtained, which consists of a series of frequencies f and corresponding wave velocities. The data pairs describe the relationship between wave velocity and frequency at a specific thickness, and the set of curves corresponding to all thicknesses constitutes the theoretical database.

[0055] The second step involves measuring the time-domain signal of the surface vibration response after stress wave propagation using a laser Doppler vibrometer, and then extracting the dispersion curve obtained from the experiment through signal processing, which is a series of measured data pairs.

[0056] The third step involves matching the experimental dispersion curve with each curve in the theoretical database. The optimal match is found by minimizing an objective function (e.g., calculating the root mean square error between the experimental and theoretical wave velocities at all frequency points), resulting in the theoretical thickness that minimizes this error function value. That is, the final coating thickness estimate calculated by the system is determined.

[0057] Thickness matching module: Based on the first vibration mode frequency set and the second vibration mode frequency set, the current thickness distribution of the coating is determined by querying a preset thickness-frequency correlation spectrum database;

[0058] In this embodiment of the invention, the thickness matching module needs to be specifically described. This module enables real-time interaction between the first vibration modal frequency set, the second vibration modal frequency set, and the thickness-frequency correlation spectrum database via a data bus. The preset mapping relationship model of the thickness-frequency correlation spectrum database is a three-dimensional nonlinear model of the coating thickness and the first and second vibration modal frequency sets, established based on a large amount of sample data. The mapping relationship model includes matching degree calculation and thickness inversion calculation, constructing an input vector F from the characteristic frequency data of the current scanning point.

[0059]

[0060] in, - This represents the m-th frequency value in the first vibration mode frequency set. - Representing the nm frequency values ​​of the second vibration mode frequency set, calculate the input vector F and the k-th sample data vector in the thickness and frequency correlation spectrum database. Matching degree The specific calculation formula is as follows:

[0061]

[0062] in, This represents the i-th feature frequency component of the input vector F. Represents sample vector The i-th characteristic frequency component is used as the input vector, which is the frequency set of the first vibration mode and the frequency set of the second vibration mode at the current scanning point. This input vector is substituted into the mapping model. The mapping model compares the input vector with the correspondence between the frequency set and thickness in the sample database, and outputs the coating thickness value at the scanning point. If the matching degree between the input vector and the sample data is lower than a preset matching threshold, the model's difference calculation function is activated. The top P samples with the highest matching degree are selected from the database, where P is a preset value, and weights are assigned to these P samples. The weights and their specific calculation formulas are as follows:

[0063]

[0064] Where T represents a preset temperature parameter used to control the smoothness of the weight distribution, and the final output thickness value is the weighted average of the thicknesses of these P samples. The specific calculation formula is as follows:

[0065]

[0066] in, This represents the calculated coating thickness at the current scan point, which is the final output of the mapping model. This represents the known coating thickness value corresponding to the kth closest sample;

[0067] The scanning support moves along a preset path and at a preset scanning step size to complete one scan. The X and Y coordinates of each scanning point are stored along with the corresponding thickness value to form a two-dimensional thickness matrix. The two-dimensional coordinates of each scanning point and its corresponding thickness value are stored in a matrix H, which can be specifically represented as follows:

[0068]

[0069] Among them, the thickness matrix is ​​transformed into a visual thickness distribution map through a color mapping algorithm. Each pixel in the thickness distribution map corresponds to a preset actual area size, ensuring that the thickness gradient of the edge area and the uniformity of the middle area can be clearly presented.

[0070] Defect Analysis Module: Based on the current thickness distribution and historical construction data in the thickness and frequency correlation spectrum database, a matching degree analysis is performed to determine the uniformity level of the coating and potential defect areas;

[0071] In this embodiment of the invention, the defect analysis module needs to be specifically described. This module retrieves thickness distribution data from a thickness-frequency correlation spectrum database of historical construction projects with the same construction and environmental parameters for the currently tested floor. This data is used as a matching benchmark for evaluating the uniformity of the current coating. The module then calculates the statistical deviation between the current thickness distribution and the matching benchmark thickness distribution using a root mean square error algorithm. The specific calculation formula is as follows:

[0072]

[0073] in, This represents the root mean square error, where M and N represent the number of rows and columns of the thickness matrix. This represents the thickness value at point (i,j) in the current matrix. This represents the thickness value at the corresponding point in the reference matrix;

[0074] Based on the calculated statistical deviation value, automatic classification is performed according to the preset uniformity judgment logic. If the deviation value is ≤ the first preset uniformity threshold, the current coating uniformity level is determined to be excellent uniformity. If the deviation value is > the first preset uniformity threshold but ≤ the second preset uniformity threshold, it is determined to be qualified uniformity. If the deviation value is > the second preset uniformity threshold, it is determined to be unqualified uniformity, and defect scanning is performed. It analyzes the current thickness distribution map to identify whether there are continuous regions where the thickness value changes abruptly. The judgment criterion is whether the thickness difference between adjacent scan points exceeds the preset defect threshold. All continuous regions that meet the judgment criteria are marked as potential defect regions. By analyzing the thickness distribution map and based on the preset defect judgment threshold, such as a thickness change ratio threshold, the system automatically searches for and locates all scan points that meet the condition that the ratio of the absolute value of the thickness difference between adjacent scan points to the thickness of that point exceeds the preset defect judgment threshold. All such points that are spatially continuous are clustered into an independent continuous region. Each such continuous region is marked as a potential defect region, such as a local overthin area, a material accumulation area, or an area that may be accompanied by interface voids.

[0075] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0076] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0081] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A laser scanning and big data analysis device for epoxy floor coating thickness, characterized in that, include: Data acquisition module: Acquires laser resonance spectrum scanning data and stress wave scanning data of the epoxy flooring surface to be tested; Vibration signal acquisition module: Based on laser resonance spectrum scanning data, calculate the first vibration mode frequency set of the coating surface, and based on stress wave scanning data, calculate the second vibration mode frequency set of the coating-substrate interface; Thickness matching module: Based on the first vibration mode frequency set and the second vibration mode frequency set, the current thickness distribution of the coating is determined by querying a preset thickness-frequency correlation spectrum database; Defect Analysis Module: Based on the current thickness distribution and historical construction data in the thickness and frequency correlation spectrum database, a matching degree analysis is performed to determine the uniformity level of the coating and potential defect areas; The thickness matching module enables real-time interaction between the first vibration modal frequency set, the second vibration modal frequency set, and the thickness-frequency correlation spectrum database via a data bus. The preset mapping relationship model of the thickness-frequency correlation spectrum database is a three-dimensional nonlinear model of coating thickness and the first and second vibration modal frequency sets, established based on a large amount of sample data. The mapping relationship model includes matching degree calculation and thickness inversion calculation, constructing an input vector F from the characteristic frequency data of the current scanning point: in, - This represents the m-th frequency value in the first vibration mode frequency set. - Representing the nm frequency values ​​of the second vibration mode frequency set, calculate the input vector F and the k-th sample data vector in the thickness and frequency correlation spectrum database. Matching degree The specific calculation formula is as follows: in, This represents the i-th feature frequency component of the input vector F. Represents sample vector The i-th characteristic frequency component; The defect analysis module retrieves thickness distribution data from the thickness and frequency correlation spectrum database of historical construction projects with the same construction and environmental parameters for the currently tested floor. This data is used as a matching benchmark to evaluate the uniformity of the current coating. The root mean square error algorithm is used to calculate the statistical deviation between the current thickness distribution and the matching benchmark thickness distribution. The specific calculation formula is as follows: in, This represents the root mean square error, where M and N represent the number of rows and columns of the thickness matrix. This represents the thickness value at point (i,j) in the current matrix. This represents the thickness value of the corresponding point in the reference matrix.

2. The epoxy floor coating thickness laser scanning and big data analysis device according to claim 1, characterized in that, The data acquisition module emits a modulated laser with a first preset frequency range onto the epoxy flooring surface to be tested via a laser exciter to induce microscopic surface vibration. The laser exciter does not output a constant laser, but rather a light wave whose frequency is sinusoidally or swept according to the first preset frequency range. When the modulated laser irradiates the coating surface, its periodically changing photopressure effect will apply a periodic excitation force to the coating surface. Under the action of the excitation force, the epoxy flooring coating surface will generate microscopic elastic vibration. The frequency components of this vibration are directly related to the modulation frequency and the thickness, elastic modulus, and density of the coating itself. A laser interferometric vibrometer is used to collect the phase change information of the reflected laser light generated by the vibration of the microscopic surface and generate laser resonance spectrum scanning data. The laser interferometric vibrometer emits a stable probe laser beam to the excited coating surface. After the probe laser is reflected by the vibrating surface, the phase of the reflected light will be modulated by the surface vibration velocity due to the Doppler effect. The laser interferometric vibrometer demodulates the phase difference change information between the reflected light and the reference light through the principle of optical interference. This phase difference change information directly corresponds to the instantaneous vibration displacement and velocity of the coating surface. The evolution of this phase change information over time is recorded and analyzed as a time-domain displacement signal. A stress wave pulse with a second preset frequency range is emitted to the epoxy floor surface to be tested. The stress wave pulse is coupled into the interior of the epoxy floor coating in the form of an elastic wave and mainly propagates in the interior of the coating and the interface area between it and the concrete substrate. During the propagation process, the stress wave will be reflected, refracted and mode-converted when it encounters abrupt changes in material properties. A laser Doppler vibrometer is used to collect the surface vibration response generated after stress waves propagate in the coating and the body, and to generate stress wave scanning data. When the emitted stress wave pulse propagates in the coating structure and reflects back to the surface, it will cause instantaneous micro-vibrations at specific points on the surface. The laser Doppler vibrometer directly records the waveform of the vibration velocity changing with time by detecting the frequency offset of the reflected laser at specific points. This waveform is the stress wave scanning data, which contains information about the coating thickness, internal structural integrity and interface bonding state carried by the stress wave during propagation.

3. The epoxy floor coating thickness laser scanning and big data analysis device according to claim 1, characterized in that, The vibration signal acquisition module converts the time-domain displacement signal acquired by the laser interferometric vibrometer to the frequency domain using a fast Fourier transform algorithm, thereby obtaining the amplitude spectrum. The specific calculation formula is as follows: in, Represents the complex amplitude in the frequency domain. This represents the time-domain displacement signal acquired by the laser interferometer, where N represents the number of sampling points. The rotation factor represents the decomposition of the time-domain signal into the frequency domain, and j represents the imaginary unit. In the spectrum, the frequencies corresponding to several major peaks with amplitudes significantly higher than the background noise are automatically identified and extracted. These peak frequencies correspond to the multi-order dominant resonance frequencies generated on the coating surface after being excited by modulated laser. The set of multi-order dominant resonance frequencies is defined as the first vibration mode frequency set, which directly characterizes the inherent dynamic characteristics of the coating itself.

4. The epoxy floor coating thickness laser scanning and big data analysis device according to claim 1, characterized in that, The vibration signal acquisition module processes stress wave scanning data in parallel. Based on the transient vibration velocity time-domain signal acquired by the laser Doppler vibrometer, it performs spectral analysis on the velocity time-domain signal to extract its characteristic frequency components, combined with the wave velocity of the stress wave propagating in the double-layer medium: ,frequency Inverse calculations were performed using a thickness theory model to decouple and extract characteristic frequencies that reflect the vibrational state of the coating-substrate interface. The set of its characteristic frequencies is defined as the second vibration mode frequency set, where, Indicates the propagation speed of stress waves. The 2d represents the round-trip propagation time, d represents the coating thickness, and 2d represents the round-trip path length of the stress wave. Represents characteristic frequency, Indicates the propagation speed of stress waves. Indicates the characteristic wavelength.

5. The epoxy floor coating thickness laser scanning and big data analysis device according to claim 1, characterized in that, The first and second vibration mode frequency sets of the current scanning point are used as input vectors and substituted into the mapping model. The mapping model compares the input vector with the correspondence between frequency sets and thicknesses in the sample database, and outputs the coating thickness value at the scanning point. If the matching degree between the input vector and the sample data is lower than a preset matching threshold, the model's difference calculation function is activated, and the top P samples with the highest matching degree (P is a preset value) are selected from the database, and weights are assigned to these P samples. The weights and their specific calculation formulas are as follows: Where T represents a preset temperature parameter used to control the smoothness of the weight distribution, and the final output thickness value is the weighted average of the thicknesses of these P samples. The specific calculation formula is as follows: in, This represents the calculated coating thickness at the current scan point, which is the final output of the mapping model. This represents the known coating thickness value corresponding to the kth closest sample; The scanning support moves along a preset path and at a preset scanning step size to complete one scan. The X and Y coordinates of each scanning point are stored along with the corresponding thickness value to form a two-dimensional thickness matrix. The two-dimensional coordinates of each scanning point and its corresponding thickness value are stored in a matrix H, which can be specifically represented as follows: The thickness matrix is ​​transformed into a visual thickness distribution map using a color mapping algorithm. Each pixel in the thickness distribution map corresponds to a preset actual area size, ensuring that the thickness gradient of the edge area and the uniformity of the middle area can be clearly presented.

6. The epoxy floor coating thickness laser scanning and big data analysis device according to claim 1, characterized in that, Based on the calculated statistical deviation value, automatic classification is performed according to the preset uniformity judgment logic. If the deviation value is less than or equal to the first preset uniformity threshold, the current coating uniformity level is determined to be excellent uniformity. If the deviation value is greater than the first preset uniformity threshold but less than or equal to the second preset uniformity threshold, it is determined to be qualified uniformity. If the deviation value is greater than the second preset uniformity threshold, it is determined to be unqualified uniformity, and a defect scan is performed to analyze the current thickness distribution map and identify whether there are continuous regions where the thickness value changes abruptly. The judgment criterion is whether the thickness difference between adjacent scan points exceeds the preset defect threshold. All continuous regions that meet the judgment criteria are marked as potential defect regions.

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