A method and system for testing building engineering materials

Abstract

The present invention relates to the technical field of material testing, and particularly to a method and system for testing building engineering materials. The method includes the following steps: performing multi-region scanning on the wall coating of building decoration through an ultrasonic scanner to obtain a wall coating acoustic wave data set, and identifying the non-uniform distribution of coating texture. Then, based on the coating texture data, calculating the density distribution difference between the inner and outer coatings between layers, and further performing a quantitative simulation of water resistance penetration loss to obtain water resistance penetration loss data. Subsequently, based on the water resistance penetration loss data, inferring the periodic aging process of the penetration mechanism of the coating, and combining the aging data of the penetration mechanism to detect the tolerance performance of the coating material to obtain material tolerance performance data, so as to comprehensively evaluate the quality and durability of the coating. The present invention makes the testing technology more perfect through the optimization of the testing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of material detection, and particularly to a method and system for detecting building engineering materials. Background Art

[0002] With the development of building engineering, the quality and performance requirements of building materials are getting higher and higher. Especially in the aspect of coating materials, durability and long-term use performance have become one of the important criteria for evaluating building quality. As an indispensable part of building engineering, coatings are widely used in various parts such as walls, floors, and roofs. They not only play a role in beautifying and decorating, but also play a key role in waterproofing, anti-corrosion, anti-aging, etc. Therefore, how to scientifically and accurately detect the quality and performance of coating materials, especially their water resistance, durability, and aging conditions, has become an important issue in building engineering quality control. However, there is a problem in a traditional method for detecting building engineering materials that the analysis of the waterproof performance of the coating is inaccurate, resulting in a large error in the detection of the aging of the coating material. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for detecting building engineering materials to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for detecting building engineering materials, the method includes the following steps:

[0005] Step S1: Scanning the coating on the building decoration wall in multiple areas by an ultrasonic scanner to obtain a wall coating acoustic wave data set; identifying the non-uniform distribution of the coating texture based on the wall coating acoustic wave data set to obtain non-uniform distribution data of the coating texture;

[0006] Step S2: Calculating the density distribution difference between the inner and outer coatings of the layer according to the non-uniform distribution data of the coating texture to obtain density distribution difference data between the inner and outer coatings; quantitatively simulating the water resistance penetration loss according to the difference data of the interlayer adhesion compactness to obtain quantitatively simulated water resistance penetration loss data;

[0007] Step S3: Inferring the periodic aging of the penetration mechanism based on the quantitatively simulated water resistance penetration loss data to obtain periodic aging data of the penetration mechanism; detecting the tolerance performance of the coating material based on the periodic aging data of the penetration mechanism to obtain tolerance performance data of the coating material.

[0008] Preferably, step S1 includes the following steps:

[0009] Step S11: Scanning the coating on the building decoration wall in multiple areas by an ultrasonic scanner to obtain a wall coating acoustic wave data set;

[0010] Step S12: Denoise the acoustic wave dataset of the wall coating to obtain a denoised acoustic wave dataset of the wall coating;

[0011] Step S13: Analyze the internal and external microtopography structure of the coating based on the denoised acoustic wave dataset of the wall coating to obtain data on the internal and external topography structure of the coating;

[0012] Step S14: Identify the non-uniform distribution of the coating texture based on the data of the internal and external topography structure of the coating to obtain data on the non-uniform distribution of the coating texture.

[0013] Preferably, step S2 includes the following steps:

[0014] Step S21: Obtain data on the properties of the coating raw materials; Analyze the internal and external layer structure of the coating based on the acoustic wave dataset of the wall coating for the data of the internal and external topography structure of the coating to obtain data on the internal and external layer structure of the coating;

[0015] Step S22: Calculate the density distribution difference between the internal and external coatings of the layer based on the data of the non-uniform distribution of the coating texture and the acoustic wave dataset of the wall coating for the data of the internal and external layer structure of the coating to obtain data on the density distribution difference between the internal and external coatings;

[0016] Step S23: Analyze the difference in the interlayer adhesion density based on the data of the density distribution difference between the internal and external coatings and the data of the internal and external layer structure of the coating to obtain data on the difference in the interlayer adhesion density;

[0017] Step S24: Perform a prediction simulation on the depth expansion of the solar radiation cracks for the data on the difference in the interlayer adhesion density to obtain data on the predicted crack depth expansion;

[0018] Step S25: Perform a quantitative simulation on the water resistance penetration loss based on the data on the difference in the interlayer adhesion density, the data on the predicted crack depth expansion, and the data on the properties of the coating raw materials to obtain data on the quantitative water resistance penetration loss.

[0019] Preferably, step S24 includes the following steps:

[0020] Step S241: Obtain all-weather solar radiation data; Evaluate the ultraviolet thermal radiation intensity for the all-weather solar radiation data to obtain data on the ultraviolet thermal radiation intensity;

[0021] Step S242: Analyze the critical interval of heat decomposition resistance for the data on the properties of the coating raw materials to obtain the critical interval of heat decomposition resistance of the raw materials;

[0022] Step S243: Fit the non-linear relationship of thermal expansion between the interlayer adhesion density difference data and the critical interval of heat decomposition resistance of the raw materials based on the ultraviolet thermal radiation intensity data to obtain the non-linear relationship of thermal expansion between the coating layers;

[0023] Step S244: Based on the non-linear relationship of thermal expansion between coating layers and the data of the difference in interlayer adhesion density, perform decomposition processing on the difference in thermal expansion stress distribution to obtain the data of the difference in thermal expansion stress distribution;

[0024] Step S245: According to the non-linear relationship of thermal expansion between coating layers and the data of the difference in thermal expansion stress distribution, perform a prediction simulation on the depth expansion of solar radiation cracks to obtain the predicted data of crack depth expansion.

[0025] Preferably, step S25 includes the following steps:

[0026] Step S251: Analyze the trend of the crack depth expansion prediction data to obtain the data of the crack expansion trend; based on the data of the crack expansion trend, estimate the vertical depth variance between the trends of the crack depth expansion prediction data to obtain the vertical depth variance of the trend difference;

[0027] Step S252: According to the vertical depth variance of the trend difference, estimate the weakening of the interlayer adhesion density data to obtain the data of the weakening of the interlayer adhesion density;

[0028] Step S253: Based on the vertical depth variance of the trend difference and the data of the weakening of the interlayer adhesion density, quantify the loss of the dense structure of the coating raw material properties to obtain the data of the loss of the dense structure of the coating;

[0029] Step S254: According to the vertical depth variance of the trend difference, the data of the weakening of the interlayer adhesion density, and the data of the loss of the dense structure of the coating, estimate the loss intensity of the water vapor penetration barrier to obtain the data of the loss intensity of the water vapor penetration barrier;

[0030] Step S255: Based on the data of the loss intensity of the water vapor penetration barrier, perform a quantification simulation of the water resistance penetration loss to obtain the quantified data of the water resistance penetration loss.

[0031] Preferably, step S3 includes the following steps:

[0032] Step S31: Normalize the quantified data of the water resistance penetration loss to obtain the normalized data of the water resistance penetration loss;

[0033] Step S32: Infer the periodic aging of the penetration mechanism for the normalized data of the water resistance penetration loss to obtain the data of the periodic aging of the penetration mechanism;

[0034] Step S33: Based on the data of the periodic aging of the penetration mechanism, perform a performance test on the tolerance of the coating material to obtain the data of the tolerance performance of the coating material.

[0035] Preferably, step S32 includes the following steps:

[0036] Step S321: Conduct an assessment of the progressive intensification of the penetration time series on the normalized data of the water resistance penetration loss to obtain the progressive intensification data of the penetration time series;

[0037] Step S322: Perform iterative fitting on the progressive intensification data of the penetration time series to obtain the iterative data of the progressive intensification of the penetration time series;

[0038] Step S323: Conduct a non - linear regression analysis on the iterative data of the progressive intensification of the penetration time series to obtain the regression data of the iterative intensification of the penetration;

[0039] Step S324: Infer the periodic aging of the penetration mechanism based on the regression data of the iterative intensification of the penetration to obtain the periodic aging data of the penetration mechanism.

[0040] Preferably, for implementing the building engineering material detection method described above, the building engineering material detection system includes:

[0041] A texture non - uniformity distribution recognition module, which is used to perform multi - area scanning on the building decoration wall coating through an ultrasonic scanner to obtain the wall coating acoustic wave data set; based on the wall coating acoustic wave data set, conduct the recognition of the non - uniformity distribution of the coating texture to obtain the non - uniformity distribution data of the coating texture;

[0042] A water resistance penetration loss quantification module, which is used to calculate the density distribution difference between the inner and outer coatings according to the non - uniformity distribution data of the coating texture to obtain the density distribution difference data between the inner and outer coatings; perform a simulation of the water resistance penetration loss quantification according to the difference data of the inter - layer adhesion density to obtain the water resistance penetration loss quantification data;

[0043] A tolerance performance detection module, which is used to infer the periodic aging of the penetration mechanism on the water resistance penetration loss quantification data to obtain the periodic aging data of the penetration mechanism; based on the periodic aging data of the penetration mechanism, conduct the tolerance performance detection of the coating material to obtain the tolerance performance data of the coating material.

[0044] The beneficial effects of the present invention are as follows. By using an ultrasonic scanner to perform multi-region scanning on the wall coating of building decoration, a sound wave dataset of the wall coating can be obtained. This process can accurately reflect the non-uniform distribution of the texture of the coating, providing detailed basic data for subsequent analysis. This multi-region scanning technology can identify the differences in texture and thickness in each region of the coating, thereby providing a scientific basis for the repair and maintenance of building coatings. Especially when detecting coating aging and potential problems, it helps to detect coating defects at an early stage, improving the efficiency and quality of maintenance. After obtaining the data on the non-uniform distribution of the coating texture, further calculating the density distribution differences between the inner and outer coatings can clearly distinguish the density differences between the inner and outer coatings. This calculation is crucial for evaluating the overall structure and hierarchical stability of the coating, especially in aspects such as waterproofing and heat insulation. Through density difference analysis, the differences in coating adhesion and compactness can be quantified, further guiding the simulation of water resistance penetration loss, and thus providing important data support for the watertightness and anti-seepage performance of the building, ensuring the durability and service life of the coating. The analysis of the differences in interlayer adhesion and compactness in the steps provides accurate basic data for the quantitative simulation of water resistance penetration loss. Through this simulation, the water resistance performance of the coating under different environments can be quantified, especially the protection effect under special conditions such as moisture or heavy rain. This process provides quantitative performance data for coating design and material selection, enabling the prediction and optimization of the use effect of building coatings. Through scientific simulation, the deficiencies of previous empirical judgments are avoided, unnecessary resource waste is reduced, and the safety and comfort of the building are improved. Finally, based on the quantitative data of water resistance penetration loss, inferring the periodic aging of the penetration mechanism is to study the performance degradation law of the coating during long-term use. By systematically inferring the aging process of the coating, the durability of the coating material under different service years and climatic conditions can be predicted. This aging inference can not only guide the selection of coating materials and the maintenance cycle, but also provide data support for the research and development of coating materials, improving the overall performance of coating materials and ensuring that the building maintains good protection performance for a long time. Therefore, the present invention is an optimized treatment of a traditional building engineering material detection method, solving the problems of inaccurate analysis of the waterproof performance of the coating and large errors in the detection of coating material aging in the traditional building engineering material detection method, improving the accuracy of the analysis of the waterproof performance of the coating, and reducing the error in the detection of coating material aging. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic flow chart of the steps of a building engineering material detection method;

[0046] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in

[0047] Figure 3 isFigure 1 Schematic diagram of the detailed implementation steps of step S3 in Specific implementation manner

[0048] Please refer to Figures 1 to 3 , a method for detecting building engineering materials, the method comprising the following steps:

[0049] Step S1: Perform multi-region scanning on the wall coating of building decoration through an ultrasonic scanner to obtain a wall coating acoustic wave data set; based on the wall coating acoustic wave data set, perform non-uniform distribution recognition of the coating texture to obtain non-uniform distribution data of the coating texture;

[0050] Step S2: Calculate the density distribution difference between the inner and outer coatings of the layer according to the non-uniform distribution data of the coating texture to obtain the density distribution difference data between the inner and outer coatings; perform a quantitative simulation of the water resistance penetration loss according to the difference data of the interlayer adhesion density to obtain the quantitative data of the water resistance penetration loss;

[0051] Step S3: Infer the periodic aging of the penetration mechanism from the quantitative data of the water resistance penetration loss to obtain the periodic aging data of the penetration mechanism; perform a tolerance performance test on the coating material based on the periodic aging data of the penetration mechanism to obtain the tolerance performance data of the coating material.

[0052] In an embodiment of the present invention, referring to Figure 1 as described, it is a schematic diagram of the step flow of a method for detecting building engineering materials according to the present invention. In this example, the method for detecting building engineering materials comprises the following steps:

[0053] Step S1: Perform multi-region scanning on the wall coating of building decoration through an ultrasonic scanner to obtain a wall coating acoustic wave data set; based on the wall coating acoustic wave data set, perform non-uniform distribution recognition of the coating texture to obtain non-uniform distribution data of the coating texture;

[0054] In the embodiments of the present invention, when a multi - area scan is performed on the wall coating of building decoration by an ultrasonic scanner, the scanner emits ultrasonic signals at a fixed frequency, enabling them to penetrate the coating and generate reflections at different material interfaces. By receiving the reflected signals and measuring the time difference of signal propagation and the amplitude attenuation, the structural information inside the coating is obtained. During the scanning process, the frequency of the ultrasonic wave should be controlled between 1 MHz and 10 MHz to adapt to coating materials with different thicknesses and densities, avoiding excessive attenuation of high - frequency signals or insufficient resolution of low - frequency signals. By scanning multiple areas, a complete acoustic wave dataset of the wall coating is obtained. When extracting features from this dataset, the wavelet transform method is used to decompose the signal into different frequency bands to identify the texture change features therein, and a threshold - based method is used to remove noise and enhance the effective information of the signal. Subsequently, texture feature extraction is performed on the denoised signal, and the gray - level co - occurrence matrix analysis method is used to calculate parameters such as energy, contrast, and correlation to quantify the non - uniformity distribution characteristics on the surface and inside of the coating, thereby obtaining the non - uniformity distribution data of the coating texture.

[0055] Step S2: Calculate the density distribution difference between the inner and outer coatings according to the non - uniformity distribution data of the coating texture to obtain the density distribution difference data between the inner and outer coatings; perform a quantitative simulation of the water - resistance penetration loss based on the difference data of the inter - layer adhesion compactness to obtain the quantitative data of the water - resistance penetration loss;

[0056] In the embodiments of the present invention, based on the obtained non - uniformity distribution data of the coating texture, a statistical analysis method is used to calculate the density distribution difference between the inner and outer layers of the coating. By measuring the attenuation degree of ultrasonic signals in different areas and using the reference data of comparing with the same - type standard materials, the change range of the coating density is calculated. When calculating the difference in inter - layer adhesion compactness, the echo amplitude comparison analysis method is adopted to compare the reflectivity of ultrasonic signals at different interfaces, analyze the bonding situation of the coating interfaces, and combine with the density distribution data to evaluate the change trend of the adhesion compactness. To quantify the water - resistance penetration loss, based on the difference data of the adhesion compactness, the water penetration rate is measured by a penetration experiment, and the corresponding relationship between the penetration depth and time is established. Through interpolation fitting of multiple measurement results, the quantitative data of the water penetration loss is obtained.

[0057] Step S3: Infer the periodic aging of the penetration mechanism based on the quantitative data of the water - resistance penetration loss to obtain the periodic aging data of the penetration mechanism; perform a performance detection of the coating material tolerance based on the periodic aging data of the penetration mechanism to obtain the performance data of the coating material tolerance.

[0058] In the embodiments of the present invention, for the quantified data of water resistance penetration loss, based on the data record of the long-term exposure environment, the change trend of the penetration amount over time is analyzed, and a time series analysis method is used to establish an aging model of the penetration mechanism. By measuring the change of the penetration rate at different time points and using the curve fitting method to predict the long-term aging effect, the periodic aging data of the penetration mechanism are obtained. Combining the aging data, the tolerance performance of the coating material is tested, and the fatigue test method of the material is used to evaluate the durability of the material under different penetration depth conditions. By applying periodic stress, the endurance limit value of the material is recorded and its change trend is analyzed, and finally the tolerance performance data of the coating material are obtained.

[0059] Preferably, step S1 includes the following steps:

[0060] Step S11: Perform multi-region scanning on the building decoration wall coating through an ultrasonic scanner to obtain a wall coating acoustic wave data set;

[0061] Step S12: Perform noise reduction processing on the wall coating acoustic wave data set to obtain a wall coating noise-reduced acoustic wave data set;

[0062] Step S13: Based on the wall coating noise-reduced acoustic wave data set, analyze the internal and external micro-topography structures of the coating to obtain the internal and external topography structure data of the coating;

[0063] Step S14: Based on the internal and external topography structure data of the coating, identify the non-uniform distribution of the coating texture to obtain the non-uniform distribution data of the coating texture.

[0064] In the embodiments of the present invention, during the multi-region scanning process of the ultrasonic scanner on the wall coating in building decoration, it is first necessary to set an appropriate scanning frequency range so that ultrasonic waves can effectively penetrate the coating and form reflection signals at the material interface. Generally, the frequency of the ultrasonic probe is selected between 1 MHz and 10 MHz, where the low frequency is suitable for thicker or denser coatings, while the high frequency is suitable for thinner or more delicate surface structures. When the probe contacts the wall coating, a coupling agent should be used to fill the gap between the probe and the coating to reduce the energy loss of the sound wave and improve the signal transmission efficiency. The scanning is carried out in a grid pattern, setting a fixed step size (such as 1 mm to 5 mm) to sequentially perform a coverage detection on the wall to ensure data integrity. During the scanning process, after the ultrasonic emission signal enters the coating, it will reflect when encountering internal defects, interfaces or material density changes in the coating. The receiving probe captures the reflection signal and records its propagation time, amplitude attenuation and frequency change conditions to generate an acoustic wave dataset of the wall coating. The obtained acoustic wave dataset of the wall coating contains useful signals and environmental noise, and the data needs to be denoised to ensure the accuracy of the signals. The denoising process first performs band-pass filtering on the collected acoustic wave signals, setting the filtering frequency band to suppress noise interference components (such as mechanical vibrations, environmental noise, etc.). Subsequently, the wavelet transform method is applied to the filtered signal to decompose it into sub-signals of different frequency bands, and the threshold denoising method is used to remove high-frequency noise interference. After signal reconstruction, time-domain and frequency-domain analyses are performed on the denoised data, and the power spectral density of the signal is calculated using the Fourier transform to detect whether there are still sudden noise or discrete noise points. If there are still interference signals, the adaptive filtering method is used to further optimize the data. After the denoising process is completed, a denoised acoustic wave dataset of the wall coating is obtained, providing a clear signal input for subsequent analysis. Using the denoised acoustic wave dataset, the microscopic morphology structures inside and outside the coating are analyzed. First, based on the time delay and attenuation of the reflection signal, the thickness distribution of the coating is calculated, and by comparing the data of multiple scanning regions, the uniformity of the coating thickness is judged. Subsequently, the ultrasonic echo spectrum analysis method is used to analyze the spectral characteristics of the signal and extract the microscopic structure information of the coating material. If there are holes, cracks or delamination phenomena inside the coating, the spectrum of its echo signal will show characteristic distortions, which can be discriminated by comparing with the reference signal of the normal coating. To further analyze the bonding situation between the inner and outer layers of the coating, the ultrasonic phase difference method is used to measure the phase change of the echo signal at different depths, calculate the bonding strength of the coating interface, and obtain the morphological structure data inside and outside the coating. Based on the morphological structure data inside and outside the coating, the non-uniform distribution of the coating texture is identified. First, texture features are extracted from the ultrasonic reflection signals of different scanning regions, and parameters such as energy, contrast, and correlation are calculated using the gray-level co-occurrence matrix analysis method, and a coating texture feature map is drawn in the two-dimensional space. Subsequently, the edge detection algorithm is used to segment the texture structures on the surface and inside the coating to identify regions with local density non-uniformity, microscopic cracks or particle accumulation.If the coating material has uneven particle distribution, the entropy value will increase significantly in the calculation of texture feature parameters. If there are local cracks on the coating surface, the contrast parameter will show abnormal fluctuations. Finally, by comprehensively analyzing the texture parameters, the non-uniform distribution data of the coating texture can be obtained, and further combined with other physical detection means to verify the true structural characteristics of the non-uniform distribution area.

[0065] Preferably, step S2 includes the following steps:

[0066] Step S21: Obtain the property data of the coating raw materials; perform interlayer structure analysis on the internal and external morphology structure data of the coating according to the acoustic wave dataset of the wall coating to obtain the interlayer structure data of the coating;

[0067] Step S22: Calculate the density distribution difference between the inner and outer coatings of the coating interlayer structure data according to the non-uniform distribution data of the coating texture and the acoustic wave dataset of the wall coating to obtain the density distribution difference data between the inner and outer coatings;

[0068] Step S23: Perform analysis on the difference in interlayer adhesion density based on the density distribution difference data between the inner and outer coatings and the interlayer structure data of the coating to obtain the difference data in interlayer adhesion density;

[0069] Step S24: Perform a prediction simulation on the depth extension of the sunlight crack on the difference data in interlayer adhesion density to obtain the prediction data on the depth extension of the crack;

[0070] Step S25: Perform a quantitative simulation on the water resistance penetration loss according to the difference data in interlayer adhesion density, the prediction data on the depth extension of the crack, and the property data of the coating raw materials to obtain the quantitative data on the water resistance penetration loss.

[0071] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0072] Step S21: Obtain the property data of the coating raw materials; perform interlayer structure analysis on the internal and external morphology structure data of the coating according to the acoustic wave dataset of the wall coating to obtain the interlayer structure data of the coating;

[0073] In the embodiments of the present invention, when obtaining the property data of the coating raw materials, first, a raw material sample of the wall coating is collected, a representative coating area is selected for analysis, a professional device such as a scanning electron microscope (SEM) is used to collect high-resolution images, and the microscopic morphology of the coating is observed. At the same time, an X-ray diffraction (XRD) instrument is used to qualitatively and quantitatively analyze the phase of the coating raw materials, obtain the mineral composition and its relative content of the coating material. Then, thermogravimetric analysis (TGA) is used to measure the thermal stability of the coating raw materials, and differential scanning calorimetry (DSC) is combined for thermal performance analysis, so as to obtain physical property data such as the thermal expansion coefficient and melting point of the coating raw materials. These data provide basic data for subsequent interlayer structure analysis. At the same time, by comparing the raw material characteristics of different coatings, the performance and reliability differences of each coating under environmental changes can be obtained. When analyzing the interlayer structure data of the inner and outer layers of the coating, by installing multi-point acoustic sensors, acoustic wave propagation tests are carried out on the wall coating at different positions to obtain the time and frequency data of acoustic wave propagation. Through these acoustic wave data, the interface structure between the inner and outer layers of the coating is analyzed by the acoustic wave reflection method. Combining parameters such as the thickness, density, and elastic modulus of the coating, the structural distribution of the inner and outer layers of the coating is deduced by using the change in the acoustic wave propagation speed, and then the interlayer structure difference of the coating is obtained. Combining the texture distribution of the coating, the interface condition between the inner and outer layers of the coating can be further refined. For example, by comparing the acoustic wave propagation time delays in different regions, it can be analyzed whether there are interface defects between the inner and outer layers of the coating, or physical property differences caused by non-uniform coating.

[0074] Step S22: Calculate the density distribution difference between the inner and outer coatings of the interlayer structure data of the coating based on the non-uniform texture distribution data of the coating and the acoustic wave data set of the wall coating, and obtain the density distribution difference data between the inner and outer coatings.

[0075] In the embodiments of the present invention, based on the non-uniform texture distribution data of the coating and the acoustic wave data set of the wall coating, the interlayer structure data of the coating is analyzed to calculate the density distribution difference of each layer of the coating. First, time-frequency analysis is performed on the acoustic wave data of the wall coating, ultrasonic reflection signals at different positions are extracted, and the acoustic impedance of each layer of the coating is calculated in combination with the thickness data of each layer of the coating. Subsequently, the density distribution of different regions of the coating is calculated according to the acoustic impedance. If the density of a certain region is significantly lower than that of other regions, it indicates that there are microcracks or pores in this region. To further accurately calculate the density distribution difference between the inner and outer coatings of the interlayer, X-ray photoelectron spectroscopy is used to determine the chemical composition on the surface of the coating to judge whether there is material composition segregation caused by density non-uniformity. Finally, based on the above experimental data, the density change amplitude between the inner and outer layers of the coating is calculated, and the result is stored in the database for subsequent analysis.

[0076] In another embodiment, based on the non-uniform distribution data of the coating texture and the acoustic wave dataset of the wall coating, the density distribution difference between the inner and outer layers of the coating is calculated for the interlayer structure data of the coating. First, using the echo intensity and propagation time in the ultrasonic scan data, the acoustic impedance of different regions of the coating is calculated. The change in acoustic impedance reflects the distribution difference of the coating density. The acoustic impedance calculation is based on the relationship between the material density and the ultrasonic propagation speed. The signal attenuation rate at different depths is extracted, and combined with the ultrasonic tomography (UTI) technology, the coating density distribution map is reconstructed. The image segmentation method is used to divide the coating into multiple regions, and the density mean and standard deviation of each region are calculated to evaluate the uniformity of the density. Combining with X-ray transmission imaging, the compactness inside the coating is analyzed, and the change trend of the coating density distribution is calculated using the gray value gradient. Through the three-dimensional reconstruction technology, a coating density distribution model is established, the density gradient and change rate between different layers are calculated, and the density distribution difference data between the inner and outer coatings are obtained.

[0077] Step S23: Analyze the difference in interlayer adhesion compactness based on the density distribution difference data between the inner and outer coatings and the interlayer structure data of the coating to obtain the difference data of interlayer adhesion compactness;

[0078] In the embodiment of the present invention, based on the density distribution difference data of the coating and the interlayer structure data of the coating, the interlayer adhesion compactness is analyzed. First, the nano-indentation test method is used to measure the hardness and elastic modulus of each layer of the coating, and the change of the hardness gradient is analyzed to judge the bonding strength of the coating interface. Subsequently, the interface shear experiment is carried out, the shear stress is loaded on the coating in different regions, and the failure mode of the coating interface is recorded. If brittle fracture occurs at the interface, it indicates that the adhesion compactness is low. Combining with the X-ray tomography technology, the three-dimensional reconstruction of the microcracks inside the coating is carried out, and the relationship between the crack propagation path and the interlayer bonding strength is calculated. Finally, through comprehensive analysis of the experimental data, the difference in the interlayer adhesion compactness of the coating is calculated, and the adhesion strength distribution of each region is recorded in the database.

[0079] In another embodiment, based on the data of the density distribution difference between the inner and outer coatings and the data of the structure between the inner and outer layers of the coating, the analysis of the difference in the interlayer adhesion density is carried out. The nano-indentation test technology is adopted to conduct mechanical property tests on the coating areas at different depths, measure the hardness and elastic modulus of the coating, and evaluate the mechanical property differences inside the coating. The scanning electron microscope (SEM) combined with energy dispersive spectroscopy (EDS) is used to observe the bonding state of the coating interface, analyze the chemical composition distribution at the interface, and determine whether there are interface defects or unevenly distributed binders. The ultrasonic shear wave test method is adopted to measure the shear wave propagation speed at the coating interface, and calculate the changes in the shear modulus and adhesion strength. Through the micro-tensile test, the adhesion force between different layers is measured, and the peeling energy of the interface layer is calculated. Combining with thermomechanical analysis (TMA), the expansion coefficient of the coating at different temperatures is measured, and the influence of temperature on the interlayer adhesion density is analyzed. The finite element analysis method is used to simulate the stress distribution of the coating under external forces, identify the weak areas of the adhesion density, and obtain the data of the difference in the interlayer adhesion density.

[0080] Step S24: Perform a prediction simulation on the depth expansion of the sunshine crack based on the data of the difference in the interlayer adhesion density, and obtain the predicted data of the crack depth expansion;

[0081] In the embodiment of the present invention, based on the data of the difference in the interlayer adhesion density, the depth expansion of the sunshine crack is predicted. First, the climate data of the building area are obtained, including the sunshine intensity, the temperature change range and the humidity fluctuation condition, and the temperature stress distribution of the coating is calculated in combination with the thermal expansion coefficient of the coating. Subsequently, a thermal stress model of the coating is established by using the finite element analysis method, and the temperature load under the actual climate conditions is applied to simulate the expansion trend of the crack under long-term sunlight exposure. Combining with the laboratory ultraviolet aging test data, the influence of ultraviolet rays on the degradation rate of the coating surface is analyzed, and the aging parameters in the crack expansion model are adjusted. Finally, the crack depth change curve of the coating is obtained through simulation calculation, and the periodic change law of the crack expansion is recorded for subsequent analysis.

[0082] In another embodiment, a prediction simulation of the depth expansion of sunlight cracks is performed on the data of the difference in interlayer adhesion density. First, obtain the annual sunlight data of the area where the building is located, including parameters such as solar radiation intensity, ultraviolet index, and daily maximum and minimum temperatures. Use the ultraviolet thermal radiation intensity evaluation model to calculate the change in the thermal load of the coating at different time periods, and combine the data of the critical interval of the coating's heat-resistant decomposition to analyze the deterioration trend of the coating under long-term sunlight. Adopt the method of fitting the non-linear relationship of thermal expansion to analyze the expansion behavior of the coating under different temperature conditions and calculate the change in interlayer stress. Use the data of the difference in thermal expansion stress distribution to simulate the crack propagation rate at the coating interface, and adopt the fracture mechanics method to calculate the propagation path and depth change of the crack under different stress states. Combine the accelerated aging experiment data to establish a time-dependent model of crack propagation, predict the depth change trend of cracks during the long-term use of the coating, and obtain the prediction data of crack depth expansion.

[0083] Step S25: Perform a quantitative simulation of the water resistance penetration loss based on the data of the difference in interlayer adhesion density, the prediction data of crack depth expansion, and the data of the properties of the coating raw materials, and obtain the quantitative data of the water resistance penetration loss.

[0084] In the embodiment of the present invention, the water resistance penetration loss is quantified according to the data of the difference in interlayer adhesion density, the prediction data of crack depth expansion, and the data of the properties of the coating raw materials. First, use the water vapor transmission experiment to measure the water vapor barrier performance of the coating, and calculate the water vapor penetration rate in combination with the porosity of the coating. Subsequently, based on the crack propagation data, calculate the influence of cracks on the water vapor penetration ability. If the crack propagates to the coating interface, the water vapor penetration rate will increase significantly. Combine the water absorption data of the coating to calculate the diffusion rate of water in the coating and simulate the change trend of the water resistance of the coating under a long-term humidity environment. Finally, use the experimental data and simulation results to quantify the water resistance penetration loss of the coating and form a complete water resistance performance database for subsequent detection and analysis.

[0085] In another embodiment, a water resistance penetration loss quantification simulation is performed based on the interlayer adhesion density difference data, crack depth expansion prediction data, and coating raw material property data. First, based on the crack propagation pattern direction data, the spatial distribution characteristics of the cracks are analyzed, and a statistical analysis method of the crack propagation direction is used to calculate the uniformity distribution index of the cracks. A vertical depth variance estimation of the direction differences of the crack depth expansion prediction data is performed to calculate the depth change range of the cracks in different regions. Combining the crack propagation data, an interlayer adhesion density weakening estimation of the interlayer adhesion density difference data is performed to analyze the strength change of the coating under long-term penetration. Through the coating densification structure loss quantification method, the porosity change and water vapor penetration ability inside the coating are calculated. Based on the water vapor penetration barrier loss strength estimation method, the water vapor diffusion coefficient of the coating is calculated, and combined with the accelerated water resistance experiment data, a water resistance penetration loss quantification model is established to obtain the water resistance penetration loss quantification data.

[0086] Preferably, step S24 includes the following steps:

[0087] Step S241: Obtain all-weather sunshine data; evaluate the ultraviolet thermal radiation intensity of the all-weather sunshine data to obtain ultraviolet thermal radiation intensity data;

[0088] Step S242: Perform a heat decomposition critical interval analysis on the coating raw material property data to obtain the raw material heat decomposition critical interval;

[0089] Step S243: Fit the non-linear relationship of thermal expansion between the interlayer adhesion density difference data and the raw material heat decomposition critical interval according to the ultraviolet thermal radiation intensity data to obtain the non-linear relationship of thermal expansion between the coating layers;

[0090] Step S244: Perform a decomposition process on the difference in thermal expansion stress distribution based on the non-linear relationship of thermal expansion between the coating layers and the interlayer adhesion density difference data to obtain the difference data of thermal expansion stress distribution;

[0091] Step S245: Perform a prediction simulation of the crack depth expansion under sunshine according to the non-linear relationship of thermal expansion between the coating layers and the difference data of thermal expansion stress distribution to obtain the crack depth expansion prediction data.

[0092] In the embodiments of the present invention, all-weather sunlight data of the area where the building is located is obtained through meteorological data acquisition equipment, including parameters such as total solar radiation, ultraviolet index, daily maximum and minimum temperatures, solar altitude angle changes, cloud cover conditions, etc. A spectroradiometer is used to measure the ultraviolet radiation intensity, collect ultraviolet radiation data in the 280nm - 400nm band, and record the radiation intensity changes at different time periods. Using time series analysis methods, long-term trend analysis is performed on the sunlight data to determine the variation laws of ultraviolet radiation in different seasons. Based on Fourier transform technology, periodic analysis is carried out on the data to extract the main frequency characteristics of sunlight intensity, and identify the radiation intensity change patterns of annual and daily cycles. The numerical integration method is used to cumulatively calculate the all-weather ultraviolet radiation data to obtain the daily, monthly, and annual cumulative ultraviolet radiation intensities. Combining the surface orientation and tilt angle of the building, the effective radiation received by the coating surface is calculated, and the actual radiation intensity is adjusted using the angle correction coefficient. Based on the above analysis results, an ultraviolet radiation intensity database is established to provide basic data for subsequent coating aging analysis. A thermogravimetric analyzer (TGA) is used to test the thermal stability of the coating raw materials. Under a nitrogen atmosphere, the sample is heated at a heating rate of 10°C / min, and the mass loss is recorded to analyze the initial decomposition temperature, maximum decomposition rate temperature, and residual mass of the coating material. A differential scanning calorimeter (DSC) is used to determine the glass transition temperature (Tg), melting temperature (Tm), and thermal decomposition temperature (Td) of the coating material, and analyze the thermal performance changes of the coating under different temperature conditions. Combining Fourier transform infrared spectroscopy (FTIR) tests, the functional group changes of the coating at different temperatures are monitored, the chemical bond breakage is identified, and its thermal degradation mechanism is judged. X-ray photoelectron spectroscopy (XPS) is used to analyze the surface element composition of the coating material after high-temperature treatment to determine whether there is element loss caused by oxidation or thermal decomposition. Based on the above experimental data, the heat-resistant decomposition critical interval of the coating material is determined, that is, the highest temperature range that the material can withstand in a stable state, and the decomposition rate and mass loss rate in different temperature intervals are recorded. Based on a thermomechanical analyzer (TMA), the linear expansion coefficient of the coating sample at different temperatures is measured, the thermal expansion rate in different temperature intervals is recorded, and the thermal expansion coefficient of the coating material is calculated. A dynamic thermomechanical analyzer (DMA) is used to measure the storage modulus, loss modulus, and loss factor of the coating at different temperatures, and analyze the influence of thermal expansion on the mechanical properties of the coating. Combining the ultraviolet thermal radiation intensity data, the surface temperature change of the coating under actual sunlight conditions is calculated, and a curve of the coating temperature changing with time is established. Using the nonlinear regression method, the thermal expansion coefficient of the coating is fitted with the temperature change data to obtain the nonlinear thermal expansion relationship between the coating layers. Through the finite element analysis method, the influence of temperature change on the interlayer stress distribution of the coating is simulated, the thermal stress changes under different temperature gradients are calculated, and the stress concentration areas generated at the coating interface are identified.Combined with the data on the difference in interlayer adhesion density, analyze the strain distribution at the interface during the thermal expansion of the coating, and obtain the non-linear relationship of interlayer thermal expansion of the coating. According to the non-linear relationship of interlayer thermal expansion of the coating, calculate the thermal stress distribution of the coating material under different temperature conditions. Use the finite element method to mesh the coating structure, define the material properties of different layers, and apply temperature loads to simulate the thermal expansion behavior of the coating under sunlight changes. Use the thermal stress analysis method to calculate the thermal stress distribution of the coating under different temperature gradients, decompose the stress distribution difference, and extract the stress concentration in the key area. Use the fracture mechanics method to calculate the thermal stress concentration factor at the coating interface and analyze the crack propagation trend caused by thermal expansion. Combined with the data on the difference in interlayer adhesion density, analyze the stress transfer mechanism between different layers during the thermal expansion of the coating, and determine the positions of stress relaxation or stress concentration between layers. Based on the above analysis, construct a database of thermal expansion stress distribution differences to provide data support for the subsequent prediction of crack depth expansion. Use digital image correlation (DIC) technology to measure the surface strain of the coated sample after sunlight heating, and record the crack propagation rate and direction. Use infrared thermal imaging technology to monitor the surface temperature field of the coating, analyze the temperature gradient change around the crack, and determine the thermal stress concentration area. Combined with the data on the difference in thermal expansion stress distribution, calculate the crack propagation depth and rate under different temperature cycles. Use the fracture mechanics method to calculate the energy release rate of crack propagation and analyze the crack propagation trend under different stress states. Through fatigue tests, perform high and low temperature cycle tests on the coated samples, record the time dependence of crack propagation, and establish a time evolution model of crack propagation. Based on long-term meteorological data, simulate the crack propagation rate of the coating in different seasons and calculate the cumulative propagation depth of the crack under long-term exposure conditions. Combine the experimental data and numerical calculation results to establish a crack propagation prediction database and obtain the crack depth expansion prediction data.

[0093] Preferably, step S25 includes the following steps:

[0094] Step S251: Analyze the propagation morphology trend of the crack depth expansion prediction data to obtain the crack propagation morphology trend data; based on the crack propagation morphology trend data, estimate the vertical depth variance between the trend differences of the crack depth expansion prediction data to obtain the vertical depth variance of the trend difference;

[0095] Step S252: Estimate the weakening of the interlayer adhesion density of the interlayer adhesion density difference data according to the vertical depth variance of the trend difference to obtain the interlayer adhesion density weakening data;

[0096] Step S253: Quantify the loss of the coating dense structure of the coating raw material properties data based on the vertical depth variance of the trend difference and the interlayer adhesion density weakening data to obtain the coating dense structure loss data;

[0097] Step S254: Estimate the water vapor penetration barrier loss intensity based on the vertical depth variance of the orientation difference, the data of the weakening of the interlayer adhesion density, and the data of the loss of the coating dense structure, and obtain the water vapor penetration barrier loss intensity data;

[0098] Step S255: Conduct a quantitative simulation of the water resistance penetration loss based on the water vapor penetration barrier loss intensity data, and obtain the quantitative data of the water resistance penetration loss.

[0099] In the embodiment of the present invention, the crack depth extension prediction data is obtained by step S24, and includes information such as the depth, position and extension direction of the crack inside the coating. In order to further analyze the extension morphology trend of the crack, the crack depth extension prediction data is processed by image processing technology. First, the crack depth extension prediction data is converted into a two-dimensional image form, in which the position and depth of the crack are represented by different gray levels of pixel values. The image is processed using an edge detection algorithm (such as a Canny operator) to extract the edge contour of the crack. Then, the crack direction is analyzed by a morphological analysis method (such as a skeleton extraction algorithm) to obtain the main direction and branch direction of the crack. For example, assuming that the crack extends from the coating surface to the inside, the main direction is vertically downward, and the branch direction is the oblique extension on the left and right sides. The main direction and branch direction information of these cracks are the crack extension morphology trend data. Next, based on the crack extension morphology trend data, the vertical depth variance between the trend differences is estimated for the crack depth extension prediction data. Specifically, the crack is divided into multiple sub-areas according to its extension direction, and each sub-area corresponds to a specific crack direction. In each sub-region, the variance of the crack depth is calculated to evaluate the degree of change of the crack depth in different directions. For example, for cracks with a main direction of vertical downward, the depth variance in the vertical direction is calculated; for cracks with an oblique branch direction, the depth variance in the oblique direction is calculated. By comparing the crack depth variances of different directions, the vertical depth variance of the strike difference is obtained. This parameter reflects the difference in the depth change of the crack in different directions, and provides an important basis for subsequent analysis. The vertical depth variance of the strike difference obtained in step S251 is an important feature of the crack depth change, which is closely related to the interlayer adhesion density inside the coating. The interlayer adhesion density difference data is obtained through step S23, which includes the distribution of the adhesion density between the inner and outer layers of the coating. In order to estimate the degree of weakening of the interlayer adhesion density, a method based on weight distribution is adopted. First, the vertical depth variance of the strike difference is spatially aligned with the interlayer adhesion density difference data to ensure that the corresponding positions of the two inside the coating are consistent. Then, according to the size of the vertical depth variance of the strike difference, a weight value is assigned to the interlayer adhesion density at each location. The calculation of the weight value is based on the empirical relationship between the crack depth variance and the adhesion density. For example, assuming that the larger the crack depth variance is, the more drastic the crack depth change at that location is, and the more easily the interlayer adhesion density is damaged, so the assigned weight value is also larger. Specifically, for each tiny area inside the coating, the corresponding vertical depth variance value of the strike difference is calculated, and the weight value is calculated according to the preset empirical formula. Then, the weight value is multiplied by the original interlayer adhesion density value of the area to obtain the estimated value of the weakened interlayer adhesion density of the area. By weakening the interlayer adhesion density of all areas inside the coating, the interlayer adhesion density weakening data is obtained.This data reflects the decrease in the interlayer adhesion density inside the coating due to crack propagation, providing an important basis for subsequent analysis. The coating raw material property data is obtained through step S21 and includes information such as the chemical composition, molecular structure, and physical properties of the coating raw materials. These data are the basis for evaluating the loss of the dense structure of the coating. The variance of the vertical depth of the strike difference and the data of the weakening of the interlayer adhesion density provide specific information on the internal structure change of the coating. To quantify the loss of the dense structure of the coating, a method based on multi-parameter fusion is adopted. First, the variance of the vertical depth of the strike difference and the data of the weakening of the interlayer adhesion density are normalized so that their value ranges are between 0 and 1, facilitating comparison and fusion with the coating raw material property data. Then, according to the correlation between the key parameters (such as porosity, elastic modulus, etc.) in the coating raw material property data and the variance of the vertical depth of the strike difference and the data of the weakening of the interlayer adhesion density, a quantification model is established. Specifically, for each tiny area inside the coating, calculate its corresponding normalized variance value of the vertical depth of the strike difference and the weakening value of the interlayer adhesion density, and combine the coating raw material property parameters (such as porosity) of this area to calculate the loss value of the dense structure of the coating in this area through a preset quantification formula. For example, assume that the higher the porosity, the larger the variance of the vertical depth of the strike difference, and the more severe the weakening of the interlayer adhesion density, the greater the loss value of the dense structure of the coating. By calculating and summarizing the loss of the dense structure of the coating in all areas inside the coating, the loss data of the dense structure of the coating is obtained. This data reflects the loss of the dense structure inside the coating due to crack propagation and the decrease in the interlayer adhesion density, providing an important basis for subsequent analysis. The water vapor permeation barrier loss intensity is an important indicator for evaluating the water resistance of the coating. The variance of the vertical depth of the strike difference, the data of the weakening of the interlayer adhesion density, and the loss data of the dense structure of the coating are all closely related to the water vapor permeation barrier performance. To estimate the water vapor permeation barrier loss intensity, an analysis method based on a physical model is adopted. First, according to the variance of the vertical depth of the strike difference and the data of the weakening of the interlayer adhesion density, calculate the change in the water vapor permeation path inside the coating. The larger the variance of the crack depth, the weaker the interlayer adhesion density, the more complex the water vapor permeation path, and the smaller the permeation resistance. Then, combined with the loss data of the dense structure of the coating, evaluate the change in the porosity and pore connectivity inside the coating. The greater the loss of the dense structure, the higher the porosity, and the better the pore connectivity, the easier the water vapor permeation. Specifically, by establishing a water vapor permeation model, the variance of the vertical depth of the strike difference, the data of the weakening of the interlayer adhesion density, and the loss data of the dense structure of the coating are used as input parameters. The model is based on Fick's diffusion law and considers the porosity, pore connectivity, and crack distribution inside the coating to calculate the permeation flux of water vapor inside the coating.For example, assume that the porosity inside the coating increases from 0.1 to 0.2, the variance of crack depth increases from 0.01 to 0.05, and the interlayer adhesion density decreases from 0.9 to 0.7. Then, the water vapor permeation flux will increase significantly. By calculating and summarizing the water vapor permeation fluxes in different regions inside the coating, the water vapor permeation barrier loss intensity data is obtained. This data reflects the change in the water vapor permeation performance of the coating under crack propagation and structural loss conditions, providing an important basis for subsequent analysis. The quantitative simulation of water resistance permeation loss is a key step in evaluating the water resistance performance of the coating. The water vapor permeation barrier loss intensity data is obtained through step S254 and includes the change in the water vapor permeation performance inside the coating. To conduct the quantitative simulation of water resistance permeation loss, a method based on time series analysis is adopted. First, based on the water vapor permeation barrier loss intensity data, a water resistance permeation model of the coating is established. The model assumes that the water vapor permeation process is a dynamic process that changes over time, and the permeation loss intensity gradually increases over time. Then, the model is solved by numerical simulation methods to calculate the water resistance permeation loss of the coating at different time points. Specifically, the coating is divided into multiple tiny regions, and the water vapor permeation barrier loss intensity of each region is used as the initial condition. During the simulation process, according to the preset time step (such as every day), the water vapor permeation amount of each region is gradually calculated. For example, assume that the water vapor permeation barrier loss intensity of the coating at the initial moment is 0.1. As time goes by, the crack propagation and structural loss intensify, and the water vapor permeation barrier loss intensity gradually increases to 0.3. Through simulation calculations, the water vapor permeation amounts of the coating at different time points are recorded, and finally the quantitative data of water resistance permeation loss is obtained. This data reflects the decrease in water resistance of the coating due to crack propagation and structural loss during actual use, providing an important basis for the quality evaluation and improvement of building engineering materials.

[0100] Preferably, step S254 includes the following steps:

[0101] Perform the strengthening analysis of the sparse structure inside the coating based on the vertical depth variance of the strike difference, the weakening data of the interlayer adhesion density, and the dense structure loss data of the coating to obtain the strengthening data of the sparse structure inside the coating;

[0102] Perform the numerical integration of the weakening of the crosslinking and curing inside and outside the coating on the strengthening data of the sparse structure inside the coating, the weakening data of the interlayer adhesion density, and the dense structure loss data of the coating to obtain the numerical value of the weakening of the crosslinking and curing inside and outside the coating;

[0103] Perform the simulation of the increment of the water vapor interface diffusion density based on the strengthening data of the sparse structure inside the coating and the numerical value of the weakening of the crosslinking and curing inside and outside the coating to obtain the increment data of the water vapor interface diffusion density;

[0104] Estimate the water vapor permeation barrier loss intensity based on the water vapor interface diffusion density increment data to obtain the water vapor permeation barrier loss intensity data.

[0105] In the embodiments of the present invention, first, in the initial stage of step S254, the strengthening analysis of the sparse structure inside the coating is carried out. Specifically, based on the acoustic wave dataset of the wall coating (this dataset reflects information such as the thickness, material density, and surface morphology of the coating), the sparse structure of the coating can be evaluated by calculating the vertical depth variance of the direction difference of the coating and the weakening data of the interlayer adhesion density. The vertical depth variance of the direction difference can be deduced from the difference in the acoustic wave propagation speed in different regions of the coating, where the change in the acoustic wave propagation speed reflects the compactness and structural uniformity of the coating material. Based on this difference, the strengthening data of the sparse structure inside the coating is obtained through algorithm analysis. Next, the numerical integration of the weakening of the crosslinking and curing inside and outside the coating is carried out on the strengthening data of the sparse structure inside the coating, the weakening data of the interlayer adhesion density, and the loss data of the dense structure of the coating. In this process, first, the preprocessing of each data item is required. The weakening data of the interlayer adhesion density and the loss data of the dense structure of the coating are weighted and fused, and the comprehensive influence of each parameter is calculated through the weighted average method. This processing aims to reveal the weakening of the microstructure inside the coating, especially the aging caused by long-term use or the influence of external loads. On this basis, through the numerical integration method, the weakening value of the crosslinking and curing inside and outside the coating is deduced, so as to obtain the overall structural weakening trend of the coating under different environments. Based on the strengthening data of the sparse structure inside the coating and the weakening value of the crosslinking and curing inside and outside the coating, the simulation of the increment of the diffusion density at the water vapor interface is further carried out. The core task of this step is to simulate the diffusion behavior of water vapor in the coating. Using the water vapor diffusion model, based on the previously derived structural data, the diffusion process of water vapor molecules at the coating interface is simulated. The diffusion model takes into account factors such as the porosity of the coating, the interfacial tension, and the compactness of the interlayer structure, and these factors will affect the permeability of water vapor in the coating. Through repeated iteration, the increment data of the diffusion density at the water vapor interface is finally obtained, and this data indicates the degree of water vapor diffusion on the coating surface and its underlying structure within a given time. Finally, the estimation of the loss intensity of the water vapor penetration barrier is carried out according to the increment data of the diffusion density at the water vapor interface. The loss intensity of the water vapor penetration barrier is closely related to the waterproof performance of the coating. Therefore, based on the simulation results of water vapor diffusion, the waterproof ability of the coating can be evaluated. This process estimates the loss intensity of the water vapor penetration barrier ability of the coating under specific conditions by analyzing the increment data of the diffusion density of water vapor and combining the penetration resistance characteristics of the coating material. This loss intensity can reflect the aging or performance attenuation caused by water vapor penetration during the actual use of the coating. Finally, this loss intensity data provides a key reference for evaluating the waterproof effect and maintenance cycle of the coating. This implementation process specifically and elaborately describes how to deduce the microscopic structural characteristics of the coating according to the acoustic wave dataset, calculate the structural weakening through data integration, and then simulate the water vapor diffusion and its influence on the coating performance.

[0106] Preferably, step S3 includes the following steps:

[0107] Step S31: Normalize the water resistance penetration loss quantification data to obtain the normalized water resistance penetration loss data;

[0108] Step S32: Infer the periodic aging of the penetration mechanism from the normalized water resistance penetration loss data to obtain the periodic aging data of the penetration mechanism;

[0109] Step S33: Detect the tolerance performance of the coating material based on the periodic aging data of the penetration mechanism to obtain the tolerance performance data of the coating material.

[0110] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0111] Step S31: Normalize the water resistance penetration loss quantification data to obtain the normalized water resistance penetration loss data;

[0112] In the embodiment of the present invention, when normalizing the water resistance penetration loss quantification data, first, based on the water vapor penetration barrier loss intensity data, the data is standardized according to the maximum-minimum normalization method, and the extreme value normalization method is used to perform a linear transformation on the water resistance penetration loss data, mapping all data to between 0 and 1 to avoid the influence of different data scales on subsequent analysis. After obtaining the maximum and minimum values of the data, by calculating the difference between each data point and the minimum value and dividing it by the difference between the maximum value and the minimum value, a normalized data set is obtained. The range of the normalized data is limited to between 0 and 1, making the test data of different batches comparable and ensuring the accuracy of subsequent analysis. During the normalization process, outliers are removed. The box plot method is used to detect outliers, and the data is screened according to the quartile distribution, removing the outlier points that exceed 1.5 times the interquartile range to improve the reliability of the data. At the same time, the kernel density estimation method is used to analyze the distribution of the data to ensure the uniformity of the normalized data, and finally the normalized water resistance penetration loss data is obtained.

[0113] Step S32: Infer the periodic aging of the penetration mechanism from the normalized water resistance penetration loss data to obtain the periodic aging data of the penetration mechanism;

[0114] In the embodiments of the present invention, for the periodic aging inference of the water resistance penetration loss normalization data, first, based on the normalized data, according to the time series analysis method, the trend analysis of the water resistance penetration loss data changing with time is carried out. The autoregressive integrated moving average model (ARIMA model) is used to predict the future penetration loss trend. The sliding window technology is adopted to smooth the data to eliminate the influence of short-term fluctuations and improve the stability of the prediction. By calculating the change rate of the water resistance penetration loss in different time periods and combining with the periodic aging experimental data, the durability change of the coating under the long-term water vapor erosion is analyzed. The Fourier transform method is used to perform frequency domain analysis on the penetration loss data, extract the periodic change characteristics, and calculate the main aging period. According to the change period of the data, a periodic aging prediction model is established. The exponential decay function is used to fit the aging trend, and the penetration loss rate at different time nodes is calculated. Through data regression analysis, the coating damage rate at different aging stages is obtained. Based on the multiple regression analysis method, a coating penetration mechanism aging equation is established, and the model is optimized in combination with the experimental data. Finally, the periodic aging data of the penetration mechanism are obtained.

[0115] Step S33: Perform the tolerance performance detection of the coating material based on the periodic aging data of the penetration mechanism to obtain the tolerance performance data of the coating material.

[0116] In the embodiments of the present invention, for the performance detection of the tolerance of the coating material based on the periodically aged data of the penetration mechanism, first, according to the periodically aged data, a stress accelerated aging test (Stress Accelerated Aging Test) is used to simulate the coating aging process under long-term use environment. By controlling the environmental humidity, temperature, and ultraviolet irradiation intensity, the durability change of the coating at different aging cycles is detected. A dynamic contact angle tester (Dynamic Contact Angle Tester) is used to measure the water vapor adsorption capacity of the coating surface, and the change of the coating tolerance with the aging degree is analyzed. A gas permeation test (Gas Permeation Test) is used to measure the water vapor permeation rate of the coating at different aging stages. Combined with a scanning electron microscope (Scanning Electron Microscope), the change of the coating microstructure is analyzed. Energy dispersive spectroscopy (Energy Dispersive Spectroscopy) technology is used to detect the change of the chemical composition of the coating during the aging process, and the loss rate of the main components of the coating is obtained. Fourier transform infrared spectroscopy (Fourier Transform Infrared Spectroscopy) is used to detect the change of the coating functional groups and analyze the chemical bond breakage situation. A nanoindentation test (Nanoindentation Test) is used to measure the change of the coating hardness with the aging time. Combined with the periodically aged data, the attenuation rate of the coating tolerance is calculated, and finally the performance data of the coating material tolerance is obtained.

[0117] Preferably, step S32 includes the following steps:

[0118] Step S321: Evaluate the penetration time-series progressive aggravation of the water resistance penetration loss normalized data to obtain the penetration time-series progressive aggravation data;

[0119] Step S322: Perform iterative fitting on the penetration time-series progressive aggravation data to obtain the penetration time-series aggravation iterative data;

[0120] Step S323: Perform non-linear regression analysis on the penetration time-series aggravation iterative data to obtain the penetration iterative aggravation regression data;

[0121] Step S324: Infer the periodic aging of the penetration mechanism based on the penetration iterative aggravation regression data to obtain the periodic aging data of the penetration mechanism.

[0122] In the embodiments of the present invention, for the evaluation of the progressive aggravation of the penetration time series of the water resistance penetration loss normalization data, first, based on the time series analysis method, the normalized water resistance penetration loss data are sorted according to the time axis to construct a time series data set. The moving average method is used to smooth the data to reduce the influence of short-term fluctuations on the long-term trend. The penetration loss increment within different time windows is calculated, and the growth trend of the penetration loss is obtained through the cumulative increment analysis method. The differencing method is used to eliminate the trend influence and improve the data stationarity. Based on the growth rate of the data, the exponential smoothing method is used to predict the penetration loss trend at future time points. Combining with the growth curve of the penetration rate, the data piecewise regression analysis method is used to calculate the change rate of the penetration rate in different stages. The local regression weighted scatter smoothing (LOESS) method is used to further optimize the penetration time series trend curve to obtain the progressive aggravation data of the penetration time series. For the iterative fitting of the progressive aggravation data of the penetration time series, first, based on the obtained progressive aggravation data of the penetration time series, the least squares method is used to preliminarily fit the data, and the residual of the fitting curve is calculated to judge the data fitting degree. If the fitting error is large, the gradient descent method is used to optimize the fitting parameters and adjust the shape of the fitting curve to make it more conform to the actual data change trend. During the optimization process, the loss function of each iteration is calculated to ensure that the loss value gradually decreases. The polynomial regression method is used to perform high-order fitting on the data to improve the fitting accuracy. By adjusting the polynomial order, the best fitting curve is found. If the data has large non-linear characteristics, the spline interpolation method is used to optimize the fitting result, and a more smooth fitting curve is constructed using piecewise functions to ensure the continuity and differentiability of the data. Finally, the iterative data of the penetration time series aggravation are obtained. For the non-linear regression analysis of the iterative data of the penetration time series aggravation, first, based on the fitted data, the non-linear least squares method is used to solve the optimal regression parameters. The power function and the exponential function are used to perform curve fitting on the data, and the determination coefficient (R²) of the regression curve is calculated to measure the regression effect. If the R² value is low, the regression model is adjusted and the logarithmic regression is tried.Data fitting is performed using logarithmic regression or logistic regression. By iteratively optimizing the regression parameters, the data fitting error is minimized. During the regression analysis process, the Bayesian Information Criterion and the Akaike Information Criterion are used for model selection to screen out the regression model that best fits the data trend. The robustness of the regression model is evaluated using the Monte Carlo Simulation method to ensure that the model can accurately predict the seepage loss trend at different time periods. Finally, seepage iteration intensification regression data is obtained. Based on the seepage iteration intensification regression data, periodic aging of the seepage mechanism is inferred. First, based on the regression data, the seepage loss growth rate within different time periods is calculated. The spectral characteristics of the data are analyzed through Fourier Transform to identify the main periodic components of the seepage loss. The autoregressive moving average model (ARMA) is used to predict the future seepage trend, and the seepage loss rate at different cycle stages is calculated. Combining with long-term aging test data, the seepage loss data is decomposed into three parts: long-term trend, seasonal fluctuation, and random perturbation using the Time Series Decomposition method and analyzed separately. The seepage loss rate is fitted using the Exponential Decay Model to obtain the long-term aging trend. Based on the staged regression method, the change rate of the loss rate at each aging stage is calculated. Combining with the data on the change of the coating microstructure, the physical and chemical change characteristics of the material during the aging process are analyzed. Finally, the periodic aging data of the seepage mechanism is obtained.,

[0123] Preferably, the present invention also provides a building engineering material detection system for performing the building engineering material detection method described above. The building engineering material detection system includes:

[0124] A texture non-uniformity distribution recognition module for multi-region scanning of the building decoration wall coating using an ultrasonic scanner to obtain a wall coating acoustic wave data set; performing coating texture non-uniformity distribution recognition based on the wall coating acoustic wave data set to obtain coating texture non-uniformity distribution data;

[0125] The water resistance penetration loss quantification module is used to calculate the density distribution difference between the inner and outer coatings based on the non-uniform distribution data of the coating texture, so as to obtain the density distribution difference data between the inner and outer coatings; and to perform water resistance penetration loss quantification simulation based on the difference data of the interlayer adhesion compactness, so as to obtain the water resistance penetration loss quantification data.

[0126] The tolerance performance detection module is used to infer the periodic aging of the penetration mechanism based on the water resistance penetration loss quantification data, so as to obtain the periodic aging data of the penetration mechanism; and to detect the tolerance performance of the coating material based on the periodic aging data of the penetration mechanism, so as to obtain the tolerance performance data of the coating material.

[0127] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A construction material detection method, characterized in that: The following steps are involved: Step S1: Scanning a multi-region wall coating of a building decoration by an ultrasonic scanner to obtain a wall coating acoustic wave data set; Based on the wall coating acoustic wave data set, the coating texture non-uniform distribution is identified to obtain the coating texture non-uniform distribution data; Step S2: Calculate the density distribution difference between inner and outer coatings according to the coating texture non-uniformity distribution data to obtain the density distribution difference data between inner and outer coatings; According to the interlayer adhesion density difference data, a quantitative simulation of water resistance penetration loss is performed to obtain quantitative data of water resistance penetration loss; wherein step S2 includes: Step S21: Acquire coating raw material property data; perform coating inner and outer layer structure analysis on the coating inner and outer surface structure data according to the wall coating acoustic wave data set to obtain coating inner and outer layer structure data; Step S22: Calculate the density distribution difference between the inner and outer coatings according to the coating texture non-uniformity distribution data and the wall coating acoustic wave data set, and obtain the density distribution difference data between the inner and outer coatings; Step S23: performing interlayer adhesion density difference analysis based on the inner and outer coating density distribution difference data and the inner and outer layer structure data of the coating to obtain interlayer adhesion density difference data; Step S24: performing a sunlight crack depth extension prediction simulation on the interlayer adhesion density difference data to obtain crack depth extension prediction data; Step S25: performing a quantitative simulation of water resistance penetration loss according to the interlayer adhesion density difference data, the crack depth extension prediction data and the coating material property data to obtain quantitative data of water resistance penetration loss; Step S3: Perform periodic aging inference on the water resistance penetration loss quantification data to obtain periodic aging data on the penetration mechanism; perform coating material tolerance performance testing based on the periodic aging data on the penetration mechanism to obtain coating material tolerance performance data.

2. The construction material detection method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Scanning the wall coating of the building decoration in multiple regions by using an ultrasonic scanner to obtain a wall coating acoustic wave data set; Step S12: performing noise reduction processing on the wall coating sound wave dataset to obtain a wall coating noise reduction sound wave dataset; Step S13: performing microscopic morphology analysis of the inside and outside of the coating based on the wall coating noise reduction wave data set to obtain the inside and outside morphology data of the coating; Step S14: Identify the coating texture non-uniformity distribution based on the coating internal and external appearance structure data to obtain the coating texture non-uniformity distribution data.

3. The construction material detection method according to claim 1, characterized in that: Step S24 includes the following steps: Step S241: Acquire all-weather sunshine data; evaluate the ultraviolet thermal radiation intensity of the all-weather sunshine data to obtain ultraviolet thermal radiation intensity data; Step S242: Analyze the critical range of heat decomposition resistance of the coating raw material property data to obtain the critical range of heat decomposition resistance of the raw material; Step S243: fitting the nonlinear relationship of thermal expansion of the interlayer adhesion density difference data and the critical range of heat-resistant decomposition of the raw material according to the ultraviolet thermal radiation intensity data to obtain the nonlinear relationship of thermal expansion of the coating layer; Step S244: performing thermal expansion stress distribution difference decomposition processing based on the nonlinear relationship of thermal expansion between coating layers and the interlayer adhesion density difference data to obtain thermal expansion stress distribution difference data; Step S245: Perform a sunlight crack depth propagation prediction simulation based on the nonlinear relationship of thermal expansion between coating layers and the thermal expansion stress distribution difference data to obtain crack depth propagation prediction data.

4. The construction material detection method according to claim 1, characterized in that: Step S25 includes the following steps: Step S251: performing an expansion morphology trend analysis on the crack depth extension prediction data to obtain crack extension morphology trend data; estimating the vertical depth variance between the trend differences of the crack depth extension prediction data based on the crack extension morphology trend data to obtain the vertical depth variance of the trend difference; Step S252: performing interlayer adhesion density weakening estimation on the interlayer adhesion density difference data according to the vertical depth variance of the strike difference to obtain interlayer adhesion density weakening data; Step S253: quantifying the coating density structure loss of the coating raw material property data based on the vertical depth variance of the strike difference and the interlayer adhesion density weakening data to obtain the coating density structure loss data; Step S254: estimating the water vapor penetration barrier loss strength according to the vertical depth variance of the strike difference, the interlayer adhesion density weakening data and the coating density structure loss data, and obtaining the water vapor penetration barrier loss strength data; Step S255: Perform a quantitative simulation of water penetration resistance loss based on the water vapor penetration barrier loss strength data to obtain quantitative data of water penetration resistance loss.

5. The construction material detection method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: normalizing the quantified data of water resistance penetration loss to obtain normalized data of water resistance penetration loss; Step S32: performing permeation mechanism periodic aging inference on the normalized data of water resistance permeation loss to obtain permeation mechanism periodic aging data; Step S33: Perform coating material tolerance performance testing based on the penetration mechanism periodic aging data to obtain coating material tolerance performance data.

6. The construction material detection method according to claim 5, characterized in that: Step S32 includes the following steps: Step S321: performing a permeation time series progressive aggravation evaluation on the normalized water resistance permeation loss data to obtain permeation time series progressive aggravation data; Step S322: performing iterative fitting on the penetration time series progressive intensification data to obtain penetration time series intensification iterative data; Step S323: performing nonlinear regression analysis on the infiltration time series intensified iteration data to obtain infiltration iterative intensified regression data; Step S324: inferring the periodic aging of the permeability mechanism according to the permeability iterative aggravation regression data to obtain the periodic aging data of the permeability mechanism.

7. A construction material detection system, characterized in that: Used to perform the construction material detection method according to claim 1, the construction material detection system comprises: The texture non-uniform distribution recognition module is used to perform multi-area scanning of the building decoration wall coating through an ultrasonic scanner to obtain a wall coating acoustic wave data set; based on the wall coating acoustic wave data set, the coating texture non-uniform distribution is recognized to obtain coating texture non-uniform distribution data; The water resistance penetration loss quantification module is used to calculate the density distribution difference between the inner and outer coatings between the layers according to the coating texture non-uniformity distribution data, and obtain the density distribution difference data of the inner and outer coatings; to perform a quantitative simulation of the water resistance penetration loss according to the interlayer adhesion density difference data, and obtain the quantitative data of the water resistance penetration loss; the water resistance penetration loss quantification module is used to: Acquire the coating raw material property data; perform internal and external interlayer structure analysis on the coating internal and external appearance structure data according to the wall coating acoustic wave data set to obtain the coating internal and external interlayer structure data; According to the coating texture non-uniform distribution data and the wall coating acoustic wave data set, the density distribution difference between the inner and outer layers of the coating is calculated to obtain the density distribution difference data of the inner and outer coatings; Based on the density distribution difference data of the inner and outer coatings and the structure data of the inner and outer layers of the coatings, the interlayer adhesion density difference analysis is performed to obtain the interlayer adhesion density difference data; The interlayer adhesion density difference data is used to simulate the sunshine crack depth propagation prediction to obtain the crack depth propagation prediction data; According to the inter-layer adhesion density difference data, crack depth extension prediction data and coating raw material property data, a quantitative simulation of water resistance penetration loss is performed to obtain quantitative data of water resistance penetration loss; The tolerance performance detection module is used to infer the periodic aging of the penetration mechanism based on the quantitative data of water resistance penetration loss to obtain the periodic aging data of the penetration mechanism; based on the periodic aging data of the penetration mechanism, the tolerance performance of the coating material is detected to obtain the tolerance performance data of the coating material.

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