Building outer wall defect detection method and system based on acousto-optic thermal multi-physical field coupling

Abstract

The application provides a building outer wall defect detection method and system based on acousto-optic-thermal multi-physical field coupling, and relates to the technical field of intelligent nondestructive testing. The method comprises the following steps: according to the comparison result between the optical indexes of each region in the building outer wall image and the index conditions, candidate regions with defects are selected from multiple regions of the building outer wall image; a temperature change sequence of each pixel point collected during the application of sound excitation and thermal excitation to the outer wall region corresponding to the candidate region is obtained; time-frequency transformation and phase-locked analysis are performed on the temperature change sequence of each pixel point to obtain the thermal-acoustic coupling parameter and resonance intensity parameter of each pixel point; the defect type to which each pixel point belongs is determined; for multiple pixel points with the defect type of hollowing in the candidate region, based on the preset calibration relationship between the resonance peak feature and the hollowing depth and the bonding stiffness, the hollowing depth and the bonding stiffness are obtained by inversion according to the resonance peak feature of the multiple pixel points.

Description

Technical Field

[0001] This invention relates to the field of intelligent nondestructive testing technology, and more specifically, to a method and system for detecting defects in building exterior walls based on the coupling of multiple physical fields of sound, light, and heat. Background Technology

[0002] Building exterior wall defects are a key factor affecting building safety and service life. Currently, building exterior wall defect detection technologies rely on infrared thermal imaging and acoustic excitation. Infrared thermal imaging indirectly captures abnormal temperature differences in the internal structure by receiving thermal radiation signals from the wall, enabling the identification and judgment of potential defects. However, on the one hand, passive infrared imaging relies solely on natural temperature differences, resulting in extremely poor detection performance at night or in low-temperature environments. The system is prone to misjudging background hot spots as hollow defects. On the other hand, surface temperature is easily affected by factors such as solar radiation, shadows, wind speed, and humidity. Especially in unstable thermal environments, the acquired thermal images are prone to numerous "background hot spots," reducing the temperature contrast between defective and normal areas and affecting accuracy. Furthermore, the effective depth of thermal imaging detection is typically only a few millimeters to 1 centimeter below the surface layer. When defects are located deeper below the finishing layer or mortar layer, the thermal response signal is significantly weakened.

[0003] Acoustic excitation technology applies excitation to the wall surface, such as by tapping or emitting sound waves, and analyzes the reflected information to determine the presence of defects. Theoretically, this method can detect hollow areas located beneath the finish layer; however, traditional manual tapping relies on human experience, resulting in slow detection speeds and high subjectivity. Using acoustic sensors requires close contact with the wall surface to obtain effective signals, and these signals are easily affected by background noise, wind noise, equipment vibration, material density, thickness, and surface roughness.

[0004] In summary, single-modal detection solutions are insufficient to meet the requirements for accurate identification and in-depth quantification of defects in exterior walls. Therefore, it is urgent to develop a multimodal fusion detection technology solution to overcome the technical bottleneck of single-modal detection through the collaborative utilization of information. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for detecting defects in building exterior walls based on the coupling of multiple physical fields of sound, light, and heat.

[0006] One aspect of the present invention provides a method for detecting defects in building exterior walls based on acoustic-optical-thermal multiphysics coupling, comprising: screening candidate regions with defects from multiple regions of a building exterior wall image based on comparison results between optical indicators and indicator conditions of each region in the building exterior wall image, wherein the building exterior wall image is acquired by an optical acquisition module; acquiring a temperature change sequence of each pixel point acquired during the application of acoustic excitation and thermal excitation to the exterior wall region corresponding to the candidate region; performing time-frequency transformation and phase-locked analysis on the temperature change sequence of each pixel point to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel point; determining the defect type of each pixel point based on the thermo-acoustic coupling parameters, resonance intensity parameters, and geometric reference parameters of each pixel point determined based on the building exterior wall image; and for multiple pixels in the candidate region whose defect type is hollow, inverting the hollow depth and bonding stiffness based on the resonance peak features of the multiple pixels based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness.

[0007] According to an embodiment of the present invention, performing time-frequency transformation and phase-locked analysis on the temperature change sequence of each pixel to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel includes: performing a discrete Fourier transform on the temperature change sequence of each pixel to obtain a frequency domain representation of the temperature change sequence; performing a digital phase-locked operation on the temperature change sequence of each pixel to obtain the temperature spectrum amplitude of each pixel; determining the first noise power of the symmetrical neighborhood of the acoustic excitation based on the half-width of the symmetrical neighborhood and the total power of the acoustic excitation frequency band determined based on the frequency domain representation; determining the thermo-acoustic coupling parameters using the first noise power and the temperature spectrum amplitude; and determining the temperature spectrum amplitude corresponding to the resonance peak frequency during the acoustic excitation process according to a preset frequency range as the resonance intensity parameter.

[0008] According to an embodiment of the present invention, determining thermoacoustic coupling parameters using a first noise power and a temperature spectrum amplitude includes: determining the ratio of the temperature spectrum amplitude to the square root of the first noise power as the thermoacoustic coupling parameter.

[0009] According to an embodiment of the present invention, the defect type of each pixel is determined based on the thermoacoustic coupling parameters, resonance intensity parameters, and geometric reference parameters of each pixel determined based on the building exterior wall image. This includes: for each pixel that passes the signal reliability test: determining the corresponding adaptive weights based on the signal-to-noise ratios of the thermoacoustic coupling parameters, resonance intensity parameters, and geometric reference parameters; weighting and summing the normalized thermoacoustic coupling parameters, normalized resonance intensity parameters, and normalized geometric reference parameters with their respective adaptive weights to obtain a comprehensive defect confidence level; and determining the defect type of each pixel based on a first comparison relationship, a second comparison relationship, a third comparison relationship, and / or a fourth comparison relationship, wherein the first comparison relationship, the second comparison relationship, the third comparison relationship, and the fourth comparison relationship are comparison relationships between the comprehensive defect confidence level, the thermoacoustic coupling parameters, the resonance intensity parameters, the geometric reference parameters, and their respective corresponding thresholds, respectively.

[0010] According to an embodiment of the present invention, determining the corresponding adaptive weights based on the signal-to-noise ratios (SNRs) of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter includes: summing the SNRs of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter to obtain a comprehensive SNR; and determining the ratios of the SNRs of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter to the comprehensive SNR as the corresponding adaptive weights for the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter.

[0011] According to an embodiment of the present invention, the signal-to-noise ratio of the thermoacoustic coupling parameter is the ratio of the square of the temperature spectrum amplitude to the first noise power; the signal-to-noise ratio of the resonance intensity parameter is the ratio of the square of the temperature spectrum amplitude to the second noise power in the symmetrical neighborhood of the resonance peak frequency, wherein the second noise power is the ratio of the total power of the acoustic excitation frequency band to the total number of frequencies in the preset frequency range; and the signal-to-noise ratio of the geometric reference parameter is the ratio of the geometric reference parameter to the geometric reference parameter distribution of a non-defective exterior wall of the same material.

[0012] According to an embodiment of the present invention, determining the defect type of each pixel based on a first comparison relationship, a second comparison relationship, a third comparison relationship, and / or a fourth comparison relationship includes: for each pixel that passes the signal reliability detection: if the defect comprehensive confidence level is greater than or equal to the defect judgment threshold, the thermo-acoustic coupling parameter is greater than or equal to the thermo-acoustic coupling threshold, and the resonance intensity parameter is greater than or equal to the resonance intensity threshold, the defect type of the pixel is determined to be a hollow; if the geometric reference parameter is greater than or equal to the geometric reference threshold, the thermo-acoustic coupling parameter is less than the thermo-acoustic coupling threshold, and the resonance intensity parameter is less than the resonance intensity threshold, the defect type of the pixel is determined to be a surface crack.

[0013] According to an embodiment of the present invention, for pixels in a candidate region whose defect type is hollow, the hollow depth and bonding stiffness are obtained by inverting the resonance peak features of the pixels based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness, respectively. This includes: determining at least one hollow region based on multiple pixels with the defect type of hollow using a connected component algorithm; determining the average value of the resonance peak quality parameters of each hollow region based on the resonance peak quality parameters of multiple pixels in each hollow region, wherein the resonance peak quality parameters are determined based on the resonance peak features of the resonance peak frequency of each pixel and the half-power bandwidth of the resonance peak; selecting hollow regions to be inverted from the at least one hollow region based on the average value of the resonance peak quality parameters of each hollow region and the area of ​​the hollow region; and for the hollow regions to be inverted, inverting the hollow depth and bonding stiffness of each hollow region based on the resonance peak features of each pixel in the hollow region to be inverted, based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness, respectively.

[0014] According to an embodiment of the present invention, the method for detecting defects in building exterior walls based on acoustic-optical-thermal multiphysics coupling further includes: generating a visualized defect category image and a hollow depth map for the hollow area to be inverted, based on the defect type of each pixel and the building exterior wall image; and / or generating a structured detection report based on intermediate calculation parameters for each pixel, wherein the intermediate calculation parameters include at least one of the following: bonding stiffness, resonance peak quality parameter, and thermo-acoustic coupling parameter.

[0015] Another aspect of the present invention provides a building exterior wall defect detection system based on acoustic-optical-thermal multiphysics coupling, comprising: a drone platform configured to fly to a position at a predetermined distance from the building exterior wall to be detected in response to a control command; the drone platform includes an optical acquisition module, an acoustic excitation module, a thermal excitation module, an infrared imaging module, and an edge computing module; the optical acquisition module is configured to project structured light onto the building exterior wall and acquire an image of the building exterior wall after the structured light is projected; the acoustic excitation module is configured to apply acoustic excitation to the exterior wall region corresponding to a candidate region in the building exterior wall image, wherein the candidate region is selected by the edge computing module from multiple regions of the building exterior wall image based on the comparison results between optical indicators and indicator conditions of each region in the building exterior wall image; and the thermal excitation module is configured to apply acoustic excitation to the exterior wall... A thermal excitation is applied to the region; an infrared imaging module is configured to: acquire the temperature change sequence of each pixel in the candidate region during the application of acoustic and thermal excitation to the corresponding exterior wall region; an edge computing module is configured to: perform time-frequency transformation and phase-locked analysis on the temperature change sequence of each pixel to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel; determine the defect type of each pixel based on the thermo-acoustic coupling parameters, resonance intensity parameters, and geometric reference parameters of each pixel determined based on the building exterior wall image, including voids; for multiple pixels in the candidate region with voids as the defect type, the void depth and bonding stiffness are obtained by inverting the resonance peak features of multiple pixels based on the preset calibration relationship between resonance peak features and void depth and bonding stiffness.

[0016] In embodiments of the present invention, optical images are first used to preliminarily screen candidate regions with defects. This allows for the targeted identification of defective candidate regions from a large area of ​​building exterior walls, facilitating subsequent improvements in detection efficiency. For each candidate region, acoustic and thermal excitations are simultaneously applied to induce a specific response from the defect in the coupled field. By collecting and analyzing the temperature change sequence of each pixel under acoustic and thermal excitations, the thermoacoustic coupling parameters and resonance intensity parameters under the coupled field are obtained. Subsequently, using the thermoacoustic coupling parameters, resonance intensity parameters, and the geometric reference parameters obtained from the optical analysis above, multi-physics field coupling defect detection based on acoustic-optical-thermal coupling can be achieved. This avoids the problem of incomplete defect characterization by a single physical field, enabling accurate identification of defects in building exterior walls and significantly reducing the probability of false positives. Furthermore, through specific hollow types, defect parameters can be quantified based on the resonance peak characteristics obtained from the acoustic-thermal coupling field. Attached Figure Description

[0017] The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0018] Figure 1 A flowchart of a method for detecting defects in building exterior walls based on the coupling of multiple physical fields (acoustic, optical, and thermal) according to an embodiment of the present invention is shown.

[0019] Figure 2 A schematic diagram illustrating the principle of temperature change sequence acquisition for a building exterior wall defect detection method based on acoustic-optical-thermal multi-physics field coupling according to an embodiment of the present invention is shown.

[0020] Figure 3 A schematic diagram illustrating the principle of obtaining the first noise power in a building exterior wall defect detection method based on acoustic-optical-thermal multiphysics coupling according to an embodiment of the present invention is shown.

[0021] Figure 4 A schematic diagram of a building exterior wall defect detection system based on acoustic-optical-thermal multiphysics field coupling according to an embodiment of the present invention is shown.

[0022] Figure 5 A schematic diagram of an electronic device suitable for implementing a method for detecting defects in building exterior walls based on the coupling of multiple physical fields (acoustic, optical, and thermal fields) according to an embodiment of the present invention is shown. Detailed Implementation

[0023] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0026] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0027] In recent years, some studies have attempted to combine infrared thermal imaging with acoustic detection to improve detection accuracy. Most of these studies employ a parallel acquisition of data from two modalities combined with static weighted analysis. However, this approach only weights the data at the result level, lacking modeling and utilization of the physical coupling mechanism between acoustic excitation and thermal response, thus failing to fully leverage the complementary advantages of multimodal information. Furthermore, static weighted fusion does not consider the real-time signal-to-noise ratio of each modal signal, leading to a predominant mode bias problem in complex environments, affecting fusion accuracy.

[0028] To address the aforementioned issues, this invention provides a method and system for detecting defects in building exterior walls based on the coupling of multiple physical fields (acoustic, optical, and thermal). This method breaks through the traditional approach of simply weighting and fusing multimodal data. It simultaneously applies acoustic and thermal excitations to candidate regions, utilizes the inherent physical coupling law of acoustic and thermal excitations, amplifies the specific response of defect regions through the synergistic effect of multiple physical fields, and combines optical image features to construct a defect detection strategy under multi-dimensional parameters, thereby achieving accurate detection of exterior wall defects under the coupling of multiple physical fields.

[0029] Figure 1 A flowchart illustrating a method for detecting defects in building exterior walls based on the coupling of multiple physics fields (acoustic, optical, and thermal fields) according to an embodiment of the present invention is shown. Figure 1 As shown, the process of the building exterior wall defect detection method based on the coupling of multiple physical fields of sound, light, and heat includes S110~S150.

[0030] In S110, based on the comparison results between the optical indicators and indicator conditions of each region in the building exterior wall image, candidate regions with defects are screened from multiple regions of the building exterior wall image, wherein the building exterior wall image is acquired by an optical acquisition module.

[0031] In S120, the temperature change sequence of each pixel point acquired during the application of acoustic and thermal excitation to the outer wall area corresponding to the candidate area is obtained.

[0032] In S130, time-frequency transformation and phase-locked analysis are performed on the temperature change sequence of each pixel to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel.

[0033] In S140, the defect type of each pixel is determined based on the thermoacoustic coupling parameters, resonance intensity parameters, and geometric reference parameters of each pixel determined based on the building exterior wall image.

[0034] In S150, for multiple pixels in the candidate region with the defect type of hollow, the hollow depth and bonding stiffness are obtained by inverting the resonance peak features of multiple pixels based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness.

[0035] In order to improve the efficiency and accuracy of the detection results when performing defect detection on building exterior walls, potential defect areas in the wall structure can be screened first, that is, candidate areas that may have defects can be identified from the overall exterior wall structure.

[0036] First, images of the building's exterior walls are acquired using an optical acquisition module, which then divides the acquired images into multiple regions. The optical acquisition module may include a structured light camera and / or an RGB (Red, Green, Blue) camera. For each region, the imaged region can be processed into corresponding point cloud information and texture information. Point cloud information is a collection of discrete points in three-dimensional space containing three-dimensional coordinates and optional additional attributes, comprehensively reproducing the geometric shape and spatial characteristics of the real object. Texture information refers to the grayscale or color space distribution patterns of pixel regions within the image and the correlation characteristics between pixel neighborhoods, serving as an important reference for characterizing the surface morphology of the detection area. For each region, the curvature, unevenness, and other geometric indicators of the exterior wall corresponding to each region can be determined based on the texture information and point cloud information, and candidate regions can be selected based on this. Furthermore, for the aforementioned building exterior wall images, a geometric reference map can be generated based on the point cloud information of the entire building exterior wall image. This geometric reference map can serve as the geometric reference parameters for the entire building exterior wall image. Candidate regions in the building exterior wall image can be marked on the aforementioned geometric reference map. For example, the geometric reference map of the candidate region is... .

[0037] For example, optical parameters such as curvature and unevenness of different regions in a building exterior wall image can be compared with their corresponding parameter conditions. For instance, if one or more optical parameters of a region meet the corresponding parameter conditions, that region can be designated as a candidate region. For example, if the curvature and unevenness of region 1 exceed the curvature threshold and unevenness threshold respectively, region 1 is marked as a candidate region for subsequent acoustic-thermal excitation. It should be noted that the parameter conditions can be set based on actual data measurements of sample exterior walls of the same material and under the same working conditions, according to the relationship between each parameter and the defect patterns of the exterior wall.

[0038] By analyzing optical indicators, candidate areas with potential defects can be identified from large building exterior walls. This allows for targeted acoustic and thermal excitation. For example, for the candidate areas identified in the above process, a directional acoustic excitation module is activated to perform a linear frequency sweep of the acoustic excitation. At the start of acoustic excitation... After a certain period, thermal excitation is applied to the exterior wall area corresponding to the area where acoustic excitation was performed via the thermal excitation module. Simultaneously, the infrared imaging module continuously acquires a sequence of thermal images of the exterior wall area. For the thermal image sequence, the candidate area can be divided according to pixels, and the temperature change sequence of each pixel over time can be extracted. Where n is the temperature change sequence. The time sequence index of the nth point out of the total number of N sampling points in the time domain.

[0039] In this embodiment, acoustic excitation can be applied using multiple frequencies from the acoustic excitation sweep set F, such as the acoustic excitation sweep set F including multiple frequencies used for linear sweeping. When using the acoustic excitation sweep set F for acoustic excitation, the building exterior wall can resonate at a certain frequency, which is the resonance peak frequency. This can be determined by analyzing the temperature change sequence. The frequency of the resonance peak is obtained by identifying the resonance peak through spectral analysis. The resonance peak characteristics can be the resonance peak frequency.

[0040] Temperature change sequence for each pixel Perform time-frequency transformation and phase-locked loop analysis to extract... The corresponding frequency domain characteristics are used to determine the noise power. Then, based on the noise power and frequency domain amplitude characteristics, thermoacoustic coupling parameters can be further obtained. Furthermore, for the acoustic excitation sweep frequency range... Furthermore, it can identify the frequencies corresponding to the resonance peaks and determine the resonance intensity parameters accordingly. It should be noted that the thermoacoustic coupling parameters are used to characterize the correlation between the thermal response characteristics of the candidate region and the acoustic-thermal coupling field. The resonance intensity parameters are used to characterize the resonance characteristics of the candidate region under acoustic excitation.

[0041] Based on the thermo-acoustic coupling parameters and resonance intensity parameters corresponding to each pixel, and through the geometric reference map The geometric reference parameters are weighted and fused to obtain the overall defect confidence score. By combining the overall defect confidence score, thermo-acoustic coupling parameters, resonance intensity parameters, and geometric reference parameters with their respective thresholds, the defect type of the pixel is determined. Each threshold can be calibrated based on measured sample data.

[0042] If the defect type of the candidate area is determined to be hollow, in order to further quantify the defect characteristics, the hollow depth and bonding stiffness can be further quantitatively analyzed based on the preset calibration relationship between the hollow depth and the resonance peak characteristics.

[0043] In embodiments of the present invention, optical images are first used to preliminarily screen candidate regions with defects. This allows for the targeted identification of defective candidate regions from a large area of ​​building exterior walls, facilitating subsequent improvements in detection efficiency. For each candidate region, acoustic and thermal excitations are simultaneously applied to induce a specific response from the defect in the coupled field. By collecting and analyzing the temperature change sequence of each pixel under acoustic and thermal excitations, the thermoacoustic coupling parameters and resonance intensity parameters under the coupled field are obtained. Subsequently, using the thermoacoustic coupling parameters, resonance intensity parameters, and the geometric reference parameters obtained from the optical analysis above, multi-physics field coupling defect detection based on acoustic-optical-thermal coupling can be achieved. This avoids the problem of incomplete defect characterization by a single physical field, enabling accurate identification of defects in building exterior walls and significantly reducing the probability of false positives. Furthermore, through specific hollow types, defect parameters can be quantified based on the resonance peak characteristics obtained from the acoustic-thermal coupling field.

[0044] It should be noted that the steps for obtaining the preset calibration relationship between the hollow depth and the resonance peak characteristics include the following operations. First, for a standard component with the same material and working conditions as the building exterior wall to be tested mentioned above, whose hollow depth is known (e.g., a standard component with a material of ceramic tile-mortar-base layer), using this standard component as the experimental carrier, the hollow depth can be calibrated by collecting a large amount of experimental data. With resonance peak characteristics By establishing empirical relationships and error bands, a preset calibration relationship is obtained. Based on the resonance peak characteristics of the candidate region and this preset calibration relationship, the void depth can be inverted. Simultaneously, based on experimental data for standard parts, an equivalent mass-spring model can be established, and the ranges of equivalent bond stiffness and equivalent mass of the capping layer in the model can be determined to ensure accurate inversion of bond stiffness in subsequent processes.

[0045] In addition, the geometric reference map of the candidate region in operation S110 It can be determined by the following formula (1):

[0046] (1)

[0047] In the formula, M is the set of pixels included in the candidate region. Let M be the number of pixels in the pixel set, and p be any point within the pixel set. This represents the height of the outer wall point corresponding to pixel p from the ground. Let M be the average height of the outer wall points corresponding to all pixels in the pixel set M.

[0048] Figure 2A schematic diagram illustrating the principle of temperature change sequence acquisition in a building exterior wall defect detection method based on acoustic-optical-thermal multiphysics coupling according to an embodiment of the present invention is shown. Figure 2 As shown, for the candidate region, the directional acoustic excitation module is activated to perform a linear frequency sweep of 100-2000Hz acoustic excitation, with a duration t1 of approximately 2 seconds and a scan step size of [missing information]. The conditions shown in formula (2) must be met:

[0049] (2)

[0050] In the formula, The scanning cycle of the infrared imaging module. This is the scan step size.

[0051] To facilitate understanding of the complementary characteristics of acoustic-thermal coupling, the following analysis first quantifies the modulation effect of acoustic excitation on the thermal response of the defect interface in the candidate region through a principle-based analysis. For example, the candidate region can be equivalently represented by an equivalent heat capacity... The first-order lumped-parameter thermal system is composed of the interfacial thermal resistance R[n]. Based on energy conservation, its temperature response formula is shown in formula (3):

[0052] (3)

[0053] in, , Temperature change sequence for each pixel Baseline temperature rise relative to baseline temperature T0, baseline temperature Also known as steady-state background temperature, This refers to the heat flux input per unit area.

[0054] Under acoustic excitation, interfacial thermal resistance A small modulation is generated near the average value, and its expression is shown in formula (4):

[0055] , (4)

[0056] in, R0 is the proportionality coefficient, and R0 is the average steady-state thermal resistance at the interface before acoustic excitation. Its value can be obtained by pre-calibrating the relationship between heat flow and temperature by measuring a standard part under steady-state heating conditions.

[0057] Under small perturbation conditions, linearizing the temperature response formula and expressing it in the frequency domain yields the temperature-to-acoustic excitation heat flux transfer function as shown in formula (5). :

[0058] = (5)

[0059] Where ω is the angular frequency, For the Fourier transform of heat flux, For the Fourier transform of the relative baseline temperature rise, Here, j is the equivalent heat capacity, j is the imaginary unit, and R0 is the average steady-state thermal resistance at the interface before acoustic excitation. Equation (5) shows that the candidate region behaves as a first-order low-pass system in the frequency domain, and the high-frequency thermal excitation will be significantly attenuated during transmission, demonstrating natural noise resistance characteristics.

[0060] Furthermore, consider sound waves at a single frequency The defect interface acting on the candidate region is equivalent to the heat flux input per unit area. The expression is shown in formula (6):

[0061] (6)

[0062] in, Let α be the initial heat flux, and α be the acoustic modulation coefficient. , It is related to the acoustic excitation sound pressure amplitude, and cos is represented by a function.

[0063] Substituting this excitation into the transfer function in formula (5), at the acoustic excitation angular frequency... By taking the modulus at the given location, the temperature-modulated amplitude can be obtained as shown in formula (7). :

[0064] (7)

[0065] In the formula Let be the characteristic temperature scale under acoustic excitation. As can be seen from formula (7), when other parameters are constant, the acoustic excitation frequency... The higher the temperature, the greater the amplitude modulation. The smaller the value, the stronger the system's suppression of high-frequency components, thereby improving the selectivity for low-frequency acoustic modulation signals. This demonstrates that acoustic excitation modulates the thermal response of the defect interface in the candidate region, and that a coupling field exists between the two.

[0066] According to an embodiment of the present invention, performing time-frequency transformation and phase-locked analysis on the temperature change sequence of each pixel to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel includes: performing a discrete Fourier transform on the temperature change sequence of each pixel to obtain a frequency domain representation of the temperature change sequence; performing a digital phase-locked operation on the temperature change sequence of each pixel to obtain the temperature spectrum amplitude of each pixel; determining the first noise power of the symmetrical neighborhood of the acoustic excitation based on the half-width of the symmetrical neighborhood and the total power of the acoustic excitation frequency band determined based on the frequency domain representation; determining the thermo-acoustic coupling parameters using the first noise power and the temperature spectrum amplitude; and determining the temperature spectrum amplitude corresponding to the resonance peak frequency during the acoustic excitation process according to a preset frequency range as the resonance intensity parameter.

[0067] First, for the temperature change sequence of each pixel Performing the discrete Fourier transform as shown in equation (8), we obtain Frequency domain representation :

[0068] (8)

[0069] In the formula, n is the temperature change sequence. The time index of the nth point in the total number of sampling points N contained in the time domain, where k is the frequency domain index and j is the imaginary unit.

[0070] based on The total power of the acoustic excitation frequency band can be obtained. 2 .

[0071] Then, for the temperature change sequence of each pixel Digital phase-locked loop (PLL) operation is performed to obtain the in-phase component amplitude X at the reference frequency ω as shown in formula (9) and the quadrature component amplitude Y at the reference frequency ω as shown in formula (10):

[0072] (9)

[0073] (10)

[0074] The temperature spectrum amplitude of each pixel is obtained based on X and Y. Its expression is shown in formula (11):

[0075] (11)

[0076] It should be noted that the above reference frequencies are all taken at the acoustic excitation frequency, and the window needs to cover the period before and after the acoustic excitation pulse to ensure that phase-locked sampling can be performed at the acoustic excitation frequency.

[0077] Figure 3A schematic diagram illustrating the principle of a method for detecting defects in building exterior walls based on multi-physics coupling of acoustic, optical, and thermal fields, according to an embodiment of the present invention, is shown, illustrating the acquisition of the first noise power. Figure 3 As shown, at the acoustic excitation frequency At this point, a symmetric neighborhood centered at this frequency is constructed, based on the half-width B of the symmetric neighborhood and the total power of the acoustic excitation band. 2 Determine the first noise power in the symmetrical neighborhood of the dominant frequency. Its expression is shown in formula (12):

[0078] (12)

[0079] In the formula, k is the frequency domain index. It should be noted that the neighborhood is defined as the symmetrical frequency band excluding the acoustic excitation frequency (dominant frequency). Therefore, the first noise power corresponds to the noise power in the symmetrical neighborhood excluding the dominant frequency (i.e., not including the dominant frequency).

[0080] Based on the first noise power and temperature spectrum amplitude, the thermoacoustic coupling parameter k1 can be obtained.

[0081] Temperature variation sequence under acoustic excitation sweep set F Spectral analysis was performed to identify the resonance frequencies corresponding to the resonance peaks. Additionally, the temperature change sequence was analyzed. The largest temperature spectral amplitude corresponds to the resonance peak frequency, which can be used as a characteristic of the resonance peak. Its expression is shown in formula (13):

[0082] (13)

[0083] Where f is any frequency in the acoustic excitation sweep set F.

[0084] The resonance intensity parameter R is the amplitude of the temperature spectrum corresponding to the resonance peak frequency, and its expression is shown in formula (14):

[0085] (14)

[0086] According to an embodiment of the present invention, utilizing a first noise power and temperature spectrum amplitude The thermoacoustic coupling parameters are determined by: the ratio of the temperature spectrum amplitude to the square root of the first noise power, which is determined as the thermoacoustic coupling parameter k1.

[0087] The expression for the thermoacoustic coupling parameter k1 is shown in formula (15):

[0088] (15)

[0089] The above process actively modulates the thermal response characteristics of the defect interface by using the vibration coupling between the acoustic signal and the defect interface in the candidate region through acoustic excitation. This allows the real defect signal, which was originally masked by background thermal noise, to generate an identifiable temperature spectrum amplitude at a specific acoustic excitation frequency. Combined with Fourier transform and lock-in amplification algorithms to extract the signal, the background thermal noise can be effectively suppressed.

[0090] According to an embodiment of the present invention, the defect type of each pixel is determined based on the thermoacoustic coupling parameters, resonance intensity parameters, and geometric reference parameters of each pixel determined based on the building exterior wall image. This includes: for each pixel that passes the signal reliability test: determining the corresponding adaptive weights based on the signal-to-noise ratios of the thermoacoustic coupling parameters, resonance intensity parameters, and geometric reference parameters; weighting and summing the normalized thermoacoustic coupling parameters, normalized resonance intensity parameters, and normalized geometric reference parameters with their respective adaptive weights to obtain a comprehensive defect confidence level; and determining the defect type of each pixel based on a first comparison relationship, a second comparison relationship, a third comparison relationship, and / or a fourth comparison relationship, wherein the first comparison relationship, the second comparison relationship, the third comparison relationship, and the fourth comparison relationship are comparison relationships between the comprehensive defect confidence level, the thermoacoustic coupling parameters, the resonance intensity parameters, the geometric reference parameters, and their respective corresponding thresholds, respectively.

[0091] For example, the first comparison relationship, the second comparison relationship, the third comparison relationship, and the fourth comparison relationship are the relationships between the comprehensive confidence level of the defect and the defect judgment threshold, the thermo-acoustic coupling parameter and the thermo-acoustic coupling threshold, the resonance intensity parameter and the resonance intensity threshold, and the geometric reference parameter and the geometric reference threshold, respectively.

[0092] After performing acoustic and thermal excitation and signal acquisition on each candidate region that may have defects, the reliability of its pixels is further tested to determine whether the pixels corresponding to the candidate region represent real defects or noise points formed by noise interference.

[0093] First, the temperature spectrum distribution is statistically analyzed within the neighborhood of the acoustic excitation frequency. Based on this, the signal-to-noise ratio (SNR) at the acoustic excitation frequency is calculated. 2 ( If the thermal acoustic response of a pixel is significant, it is considered a reliable defect signal through signal reliability detection, and the pixel proceeds to the next process; otherwise, it is considered noise and automatically removed.

[0094] For pixels that pass the signal reliability test, further detection of their specific defect types is required. Based on the signal-to-noise ratio (SNR) characteristics of the thermoacoustic coupling parameters, resonance intensity parameters, and geometric reference parameters, an adaptive weight is dynamically determined for each parameter. The weight allocation logic is positively correlated with the signal reliability reflected by the parameter's SNR; that is, the higher the parameter's SNR, the more its adaptive weight reflects the parameter's contribution priority in defect identification.

[0095] Geometric reference parameter Graw, thermoacoustic coupling parameter k1, and resonance intensity parameter Normalization to Furthermore, a modality-based approach is adopted. Adaptive weights ,in, Calculate the overall confidence level C of the defect, where the expression for C is shown in formula (16):

[0096] (16)

[0097] Finally, based on the first comparison relationship between the comprehensive confidence level of the defect and the preset corresponding threshold, the second comparison relationship between the thermoacoustic coupling parameter and the preset corresponding threshold, the third comparison relationship between the resonance intensity parameter and the preset corresponding threshold, and the fourth comparison relationship between the geometric reference parameter and the preset corresponding threshold, the defect type of each pixel is determined.

[0098] According to an embodiment of the present invention, determining the defect type of each pixel based on a first comparison relationship, a second comparison relationship, a third comparison relationship, and / or a fourth comparison relationship includes: for each pixel that passes the signal reliability detection: if the defect comprehensive confidence level is greater than or equal to the defect judgment threshold, the thermo-acoustic coupling parameter is greater than or equal to the thermo-acoustic coupling threshold, and the resonance intensity parameter is greater than or equal to the resonance intensity threshold, the defect type of the pixel is determined to be a hollow; if the geometric reference parameter is greater than or equal to the geometric reference threshold, the thermo-acoustic coupling parameter is less than the thermo-acoustic coupling threshold, and the resonance intensity parameter is less than the resonance intensity threshold, the defect type of the pixel is determined to be a surface crack.

[0099] When the overall confidence level of the defect, C, is greater than or equal to the defect judgment threshold. ( If the thermo-acoustic coupling parameter k1 is greater than or equal to the thermo-acoustic coupling threshold and the resonance intensity parameter R is greater than or equal to the resonance intensity threshold, then the defect type of the pixel is determined to be a hollow area. If the geometric reference parameter Graw is greater than or equal to the geometric reference threshold, the thermo-acoustic coupling parameter k1 is less than the thermo-acoustic coupling threshold, and the resonance intensity parameter R is less than the resonance intensity threshold, then the defect type of the pixel is determined to be a surface crack. In other cases, the defect type can be determined as "no defect".

[0100] According to an embodiment of the present invention, determining the corresponding adaptive weights based on the signal-to-noise ratios (SNRs) of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter includes: summing the SNRs of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter to obtain a comprehensive SNR; and determining the ratios of the SNRs of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter to the comprehensive SNR as the corresponding adaptive weights for the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter.

[0101] When allocating weights, first consider the thermo-acoustic coupling parameter k1 and the resonance intensity parameter. The overall signal-to-noise ratio (SNR) is obtained by summing the SNR of the geometric reference parameter Graw and the individual SNR of Graw. This summates the adaptive weights for each parameter. This is the ratio of the signal-to-noise ratio of each parameter to the overall signal-to-noise ratio, and its expression is shown in formula (17):

[0102] (17)

[0103] According to an embodiment of the present invention, the signal-to-noise ratio of the thermoacoustic coupling parameter is the ratio of the square of the temperature spectrum amplitude to the first noise power; the signal-to-noise ratio of the resonance intensity parameter is the ratio of the square of the temperature spectrum amplitude to the second noise power in the symmetrical neighborhood of the resonance peak frequency, wherein the second noise power is the ratio of the total power of the acoustic excitation frequency band to the total number of frequencies in the preset frequency range; and the signal-to-noise ratio of the geometric reference parameter is the ratio of the geometric reference parameter to the geometric reference parameter distribution of a non-defective exterior wall of the same material.

[0104] Statistically analyze the temperature spectrum distribution in the neighborhood of the acoustic excitation frequency. For the signal-to-noise ratio of the thermo-acoustic coupling parameter k1, where i is k1, then... , Defined as the ratio of the square of the temperature spectrum amplitude at the acoustic excitation frequency to the first noise power, its expression is shown in formula (18):

[0105] (18)

[0106] in, The amplitude of the temperature spectrum. This is the first noise power.

[0107] Regarding resonance intensity parameters The signal-to-noise ratio, where i is R, then , Defined as the ratio of the square of the temperature spectrum amplitude at the acoustic excitation frequency to the second noise power, its expression is shown in formula (19):

[0108] (19)

[0109] in, The second noise power is the total power of the acoustic excitation frequency band. 2 The total number of frequencies within the preset frequency range The ratio of is expressed as shown in formula (20):

[0110] (20)

[0111] For the geometric datum parameter Graw, i is Graw. , The ratio of the standard deviation or average value of the geometric reference parameter Graw to the geometric reference parameter values ​​calculated on a intact, flat wall surface of the same material is given by formula (21):

[0112] (twenty one)

[0113] in, The standard deviation or average value of the geometric reference parameter values ​​calculated on a wall surface of the same material that is intact and flat.

[0114] This invention constructs a defect comprehensive confidence calculation mechanism adapted to multi-feature fusion scenarios, evaluates the confidence of three types of feature parameters, and ultimately can accurately distinguish between surface cracks, hollow areas and other defect types as well as defect-free areas in a single detection task, significantly improving the efficiency and classification accuracy of defect detection and possessing strong scenario adaptability.

[0115] According to an embodiment of the present invention, for pixels in a candidate region whose defect type is hollow, the hollow depth and bonding stiffness are obtained by inverting the resonance peak features of the pixels based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness, respectively. This includes: determining at least one hollow region based on multiple pixels with the defect type of hollow using a connected component algorithm; determining the average value of the resonance peak quality parameters of each hollow region based on the resonance peak quality parameters of multiple pixels in each hollow region, wherein the resonance peak quality parameters are determined based on the resonance peak features of the resonance peak frequency of each pixel and the half-power bandwidth of the resonance peak; selecting hollow regions to be inverted from the at least one hollow region based on the average value of the resonance peak quality parameters of each hollow region and the area of ​​the hollow region; and for the hollow regions to be inverted, inverting the hollow depth and bonding stiffness of each hollow region based on the resonance peak features of each pixel in the hollow region to be inverted, based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness, respectively.

[0116] If the defect type of a pixel is determined to be a hollow area, its hollow area depth and bonding stiffness are further quantified. First, at least one connected component is constructed based on the pixel with the defect type of hollow area. This area is considered a void region. For this void region, its quality factor Q is calculated, which serves as the basis for determining whether to perform subsequent void depth inversion. The expression for Q is shown in formula (22):

[0117] (twenty two)

[0118] in, Resonance peak characteristics, This is the half-power bandwidth of the resonance peak (i.e., the half-width of the symmetric neighborhood, B). It only exists in the hollow region. quality factors Greater than or equal to the preset quality factor Threshold and area of ​​hollow region Greater than or equal to the preset area threshold At that time, the hollow area is considered to be a hollow area to be inverted, and subsequent calculation steps are performed on it.

[0119] For the selected hollow areas to be inverted, the resonant peak characteristics of each pixel within the hollow area are analyzed. Calculate the frequency of the region. Its expression is shown in formula (23):

[0120] (twenty three)

[0121] in, To adjust the parameters, the amplitude of the resonance peak temperature spectrum can be adjusted. Thermoacoustic coupling parameters at resonant peak frequency One of the two.

[0122] Uncertainty The expression is shown in formula (24):

[0123] (twenty four).

[0124] Based on the preset calibration relationship g, the hollow depth is obtained. Its expression is shown in formula (25):

[0125] (25)

[0126] in, To calibrate the residuals, ⊕ denotes partial derivatives, and ⊕ denotes XOR calculation.

[0127] Based on the equivalent mass-spring model, the bonding stiffness can be obtained according to formula (26). :

[0128] (26)

[0129] in, The equivalent mass can be obtained based on the material density and thickness.

[0130] Uncertainty of bond stiffness The expression is shown in formula (27):

[0131] (27)

[0132] Finally, based on the above results, a hollow depth map based on both pixel and region levels is output. Bond strength diagram With uncertainty diagram .

[0133] According to an embodiment of the present invention, a visualized defect category image and a hollow depth map for the hollow area to be inverted are generated based on the defect type of each pixel and the building exterior wall image; and / or, a structured inspection report is generated based on the intermediate calculation parameters of each pixel, wherein the intermediate calculation parameters include at least one of the following: bond stiffness, resonance peak quality parameter, and thermoacoustic coupling parameter.

[0134] For exterior wall defect detection tasks, after determining the defect category for each pixel, a visualized defect category image can be generated by overlaying it onto visible light or orthophotos (such as the building exterior wall images collected above). For example, red represents hollow areas, and yellow represents cracks. Simultaneously, the system outputs a hollow depth map D(x, y) for the area to be inverted, as well as a defect comprehensive confidence level C map.

[0135] Furthermore, it can generate structured inspection reports that include at least one intermediate calculated parameter from among bond stiffness, resonance peak quality parameters, and thermoacoustic coupling parameters, providing technical support for subsequent defect assessment and repair decisions. Simultaneously, it records and collects metadata such as wind speed, pose jitter RMS, and equipment gain.

[0136] When the average signal-to-noise ratio falls below the threshold or the pose jitter exceeds the limit, a "re-inspection recommended" label is given for the corresponding area, and the quantitative results such as center coordinates, area, and average depth are listed in the table. Finally, the key intermediate calculation parameters and parameter versions used for defect judgment are saved to ensure reproducibility during retesting or review.

[0137] Figure 4 A schematic diagram of a building exterior wall defect detection system based on acoustic-optical-thermal multiphysics coupling according to an embodiment of the present invention is shown, such as... Figure 4As shown, the system includes: a UAV platform 10, configured to fly to a position at a predetermined distance from the exterior wall of the building to be detected in response to a control command; the UAV platform 10 includes an optical acquisition module (not shown in the figure), an acoustic excitation module 20, a thermal excitation module 30, an infrared imaging module 40, and an edge computing module 50; the optical acquisition module is configured to project structured light onto the exterior wall of the building and acquire an image of the exterior wall after the structured light is projected; the acoustic excitation module 20 is configured to apply acoustic excitation to the exterior wall area corresponding to a candidate area in the exterior wall image, the candidate area being selected by the edge computing module from multiple areas of the exterior wall image based on the comparison results between the optical indicators and indicator conditions of each area in the exterior wall image; the thermal excitation module 30 is configured to apply thermal excitation to the exterior wall area. The infrared imaging module 40 is configured to: acquire the temperature change sequence of each pixel in the candidate area during the application of acoustic and thermal excitation to the exterior wall area corresponding to the candidate area; the edge computing module 50 is configured to: perform time-frequency transformation and phase-locked analysis on the temperature change sequence of each pixel to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel; determine the defect type of each pixel based on the thermo-acoustic coupling parameters, resonance intensity parameters, and geometric reference parameters of each pixel determined based on the building exterior wall image, including voids; for multiple pixels in the candidate area with voids as the defect type, the void depth and bonding stiffness are obtained by inverting the resonance peak features of multiple pixels based on the preset calibration relationship between resonance peak features and void depth and bonding stiffness.

[0138] This invention utilizes an unmanned aerial vehicle (UAV) platform 10 equipped with an optical acquisition module to acquire optical indicators of various areas of a building's exterior wall and screen candidate areas that may have defects. Then, directional acoustic excitation and transient thermal excitation are applied to these candidate areas via an acoustic excitation module 20 to modulate the contact thermal resistance at the defect interface. An infrared imaging module 40 is used to acquire the temperature change sequence of each pixel in the candidate area under acoustic and thermal excitation. An edge computing module 50 performs time-frequency transformation and phase-locked loop analysis on the temperature change sequence of each pixel, extracting parameters such as temperature spectrum amplitude and power noise at the acoustic excitation frequency, and then calculating the thermoacoustic coupling parameters and resonance intensity parameters of each pixel. The thermoacoustic coupling parameters and resonance intensity parameters are then adaptively weighted and fused with geometric reference parameters to identify typical defects such as hollow areas and surface cracks. Based on a pre-established "resonance frequency—hollow area depth / bonding stiffness" calibration relationship, the defect depth and bonding performance can be further quantitatively inverted, enabling non-contact, highly robust, and quantifiable rapid inspection of the exterior wall.

[0139] The aforementioned edge computing module can be a chip capable of detecting building exterior wall defects based on the coupling of multiple physical fields such as sound, light, and heat.

[0140] Alternatively, the aforementioned edge computing module can also communicate with electronic devices to transmit the collected information to the electronic devices, which can then perform the aforementioned building exterior wall defect detection based on the coupling of multiple physical fields of sound, light, and heat.

[0141] Figure 5 A schematic diagram of an electronic device suitable for implementing a method for detecting defects in building exterior walls based on the coupling of multiple physical fields (acoustic, optical, and thermal fields) according to an embodiment of the present invention is shown. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0142] like Figure 5 As shown, an electronic device 500 according to an embodiment of the present invention includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0143] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.

[0144] According to an embodiment of the present invention, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0145] According to embodiments of the present invention, the method flow according to embodiments of the present invention can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of the embodiments of the present invention. According to embodiments of the present invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0146] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0147] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0148] For example, according to embodiments of the present invention, a computer-readable storage medium may include the ROM 502 and / or RAM 503 described above and / or one or more memories other than ROM 502 and RAM 503.

[0149] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of the present invention. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the methods provided in the embodiments of the present invention.

[0150] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this embodiment of the invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0151] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0152] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0153] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or pairings fall within the scope of this invention.

[0154] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A method for detecting defects in building exterior walls based on multi-physics field coupling of sound, light, and heat, characterized in that, The method includes: Based on the comparison results between the optical indicators and indicator conditions of each region in the building exterior wall image, candidate regions with defects are screened from multiple regions of the building exterior wall image, wherein the building exterior wall image is acquired by an optical acquisition module; Obtain the temperature change sequence of each pixel collected during the application of acoustic and thermal excitation to the outer wall region corresponding to the candidate region; Time-frequency transformation and phase-locked analysis are performed on the temperature change sequence of each pixel to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel. The defect type of each pixel is determined based on the thermoacoustic coupling parameters, the resonance intensity parameters, and the geometric reference parameters of each pixel determined based on the building exterior wall image. For multiple pixels in the candidate region whose defect type is hollow, the hollow depth and bonding stiffness are obtained by inverting the resonance peak features of the multiple pixels based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness. The step of performing time-frequency transformation and phase-locked analysis on the temperature change sequence of each pixel to obtain the thermo-acoustic coupling parameters and resonance intensity parameters of each pixel includes: Perform a discrete Fourier transform on the temperature change sequence of each pixel to obtain the frequency domain representation of the temperature change sequence; Perform digital phase-locked loop operation on the temperature change sequence of each pixel to obtain the temperature spectrum amplitude of each pixel; The first noise power of the symmetrical neighborhood of the acoustic excitation is determined based on the symmetrical neighborhood half-width of the acoustic excitation and the total power of the acoustic excitation frequency band determined based on the frequency domain representation. The thermoacoustic coupling parameters are determined using the first noise power and the temperature spectrum amplitude; and The temperature spectrum amplitude corresponding to the resonance peak frequency during the application of the acoustic excitation within the preset frequency range is determined as the resonance intensity parameter.

2. The method according to claim 1, characterized in that, The step of determining the thermoacoustic coupling parameters using the first noise power and the temperature spectrum amplitude includes: The ratio of the temperature spectrum amplitude to the square root of the first noise power is determined as the thermoacoustic coupling parameter.

3. The method according to claim 1, characterized in that, The step of determining the defect type of each pixel based on the thermoacoustic coupling parameters, the resonance intensity parameters, and the geometric reference parameters of each pixel determined based on the building exterior wall image includes: For each of the aforementioned pixels that passed the signal reliability test: Based on the signal-to-noise ratios of the thermoacoustic coupling parameters, the resonance intensity parameters, and the geometric reference parameters, their respective adaptive weights are determined. The defect comprehensive confidence level is obtained by weighting and summing the normalized thermoacoustic coupling parameters, the normalized resonance intensity parameters, and the normalized geometric reference parameters with their respective adaptive weights; and Based on the first comparison relationship, the second comparison relationship, the third comparison relationship, and / or the fourth comparison relationship, the defect type of each pixel is determined, wherein the first comparison relationship, the second comparison relationship, the third comparison relationship, and the fourth comparison relationship are respectively the comparison relationships between the defect comprehensive confidence level, the thermo-acoustic coupling parameter, the resonance intensity parameter, the geometric reference parameter, and their respective thresholds.

4. The method according to claim 3, characterized in that, The step of determining the adaptive weights corresponding to the thermoacoustic coupling parameters, the resonance intensity parameters, and the geometric reference parameters based on their respective signal-to-noise ratios includes: The signal-to-noise ratio of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter is summed to obtain the overall signal-to-noise ratio. The ratio of the signal-to-noise ratio of each of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter to the overall signal-to-noise ratio is determined as the adaptive weight corresponding to each of the thermoacoustic coupling parameter, the resonance intensity parameter, and the geometric reference parameter.

5. The method according to claim 4, characterized in that, The signal-to-noise ratio of the thermoacoustic coupling parameter is the ratio of the square of the temperature spectrum amplitude to the first noise power. The signal-to-noise ratio of the resonance intensity parameter is: the ratio of the square of the temperature spectrum amplitude to the second noise power in the symmetrical neighborhood of the resonance peak frequency, where the second noise power is: the ratio of the total power of the acoustic excitation frequency band to the total number of frequencies in the preset frequency range. The signal-to-noise ratio of the geometric reference parameter is the ratio of the geometric reference parameter to the geometric reference parameter distribution of a non-defective exterior wall of the same material.

6. The method according to claim 3, characterized in that, The step of determining the defect type of each pixel based on the first comparison relationship, the second comparison relationship, the third comparison relationship, and / or the fourth comparison relationship includes: For each of the aforementioned pixels that passed the signal reliability test: If the overall confidence level of the defect is greater than or equal to the defect judgment threshold, the thermo-acoustic coupling parameter is greater than or equal to the thermo-acoustic coupling threshold, and the resonance intensity parameter is greater than or equal to the resonance intensity threshold, the defect type of the pixel is determined to be hollow. If the geometric reference parameter is greater than or equal to the geometric reference threshold, the thermo-acoustic coupling parameter is less than the thermo-acoustic coupling threshold, and the resonance intensity parameter is less than the resonance intensity threshold, the defect type of the pixel is determined to be a surface crack.

7. The method according to claim 1, characterized in that, For pixels in the candidate region with a defect type of hollow, the hollow depth and bonding stiffness are obtained by inverting the resonance peak features of the pixels based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness, including: Based on the connected component algorithm, at least one hollow region is determined according to multiple pixels with the defect type of hollow. The average value of the resonant peak quality parameters of each hollow region is determined based on the resonant peak quality parameters of the multiple pixels in each hollow region. The resonant peak quality parameters are determined based on the resonant peak characteristics of the resonant peak frequency and the half-power bandwidth of the resonant peak of each pixel. Based on the average value of the resonance peak quality parameter of each hollow region and the area of ​​the hollow region, a hollow region to be inverted is selected from at least one of the hollow regions; For the hollow area to be inverted, based on the preset calibration relationship between the resonance peak features and the hollow depth and bonding stiffness, the hollow depth and bonding stiffness of each hollow area to be inverted are obtained according to the resonance peak features of each pixel in the hollow area to be inverted.

8. The method according to claim 7, characterized in that, The method further includes: Based on the defect type of each pixel and the building exterior wall image, generate a visualized defect category image and a hollow depth map for the hollow area to be inverted; and / or, A structured detection report is generated based on the intermediate calculation parameters of each pixel, wherein the intermediate calculation parameters include at least one of the following: the bonding stiffness, the resonance peak quality parameter, and the thermoacoustic coupling parameter.

9. A building exterior wall defect detection system based on acoustic-optical-thermal multiphysics field coupling, characterized in that, The system includes: The drone platform is configured to fly to a position at a predetermined distance from the exterior wall of the building to be inspected in response to a control command; the drone platform includes an optical acquisition module, an acoustic excitation module, a thermal excitation module, an infrared imaging module, and an edge computing module; The optical acquisition module is configured to: project structured light onto the building exterior wall and acquire an image of the building exterior wall after the structured light has been projected; The acoustic excitation module is configured to apply acoustic excitation to the exterior wall region corresponding to the candidate region in the building exterior wall image, wherein the candidate region is selected by the edge computing module from multiple regions of the building exterior wall image based on the comparison results between the optical indicators and indicator conditions of each region in the building exterior wall image; The thermal excitation module is configured to apply thermal excitation to the exterior wall area; The infrared imaging module is configured to: acquire the temperature change sequence of each pixel in the candidate region during the application of acoustic and thermal excitation to the outer wall region corresponding to the candidate region; The edge computing module is configured to: perform time-frequency transformation and phase-locked analysis on the temperature change sequence of each pixel to obtain the thermoacoustic coupling parameters and resonance intensity parameters of each pixel; determine the defect type of each pixel based on the thermoacoustic coupling parameters, the resonance intensity parameters, and the geometric reference parameters of each pixel determined based on the building exterior wall image, wherein the defect type includes hollowness; and for multiple pixels in the candidate region whose defect type is hollowness, obtain the hollowness depth and bonding stiffness by inverting the resonance peak features of the multiple pixels based on the preset calibration relationship between resonance peak features and hollowness depth and bonding stiffness. The thermoacoustic coupling parameters and the resonance intensity parameters are determined in the following manner: Perform a discrete Fourier transform on the temperature change sequence of each pixel to obtain the frequency domain representation of the temperature change sequence; Perform digital phase-locked loop operation on the temperature change sequence of each pixel to obtain the temperature spectrum amplitude of each pixel; The first noise power of the symmetrical neighborhood of the acoustic excitation is determined based on the symmetrical neighborhood half-width of the acoustic excitation and the total power of the acoustic excitation frequency band determined based on the frequency domain representation. The thermoacoustic coupling parameters are determined using the first noise power and the temperature spectrum amplitude. The temperature spectrum amplitude corresponding to the resonance peak frequency during the application of the acoustic excitation within the preset frequency range is determined as the resonance intensity parameter.

Images