Internal defect detection method and system for building concrete

Abstract

The invention discloses an internal defect detection method and system for building concrete, and relates to the related field of ultrasonic detection.The method comprises the steps that a detection frequency spectrum is set, a detection pulse signal is determined, multi-element defect distortion processing is carried out, time reversal enhancement and random noise adding are executed, and a detection result is obtained; constructing a defect knowledge base and deploying the defect knowledge base in a defect detection system in an embedded manner; according to the detection pulse signals, front-end ultrasonic equipment is driven, preset track scanning under three views is carried out on the building area, and a detection track space is constructed through echo receiving; performing defect qualitative matching based on a defect knowledge base for the detection track space, and positioning fuzzy defect data; and executing directional detection deployment and defect identification according to the fuzzy defect data, and determining an internal defect detection result. The method solves the technical problems of low detection precision and difficulty in defect positioning and recognition in the internal defect detection of the existing building concrete, and achieves the technical effects of improving the accuracy of defect type recognition and the accuracy of position positioning.

Description

Technical Field

[0001] This application relates to the field of ultrasonic testing, and in particular to a method and system for detecting internal defects of building concrete. Background Art

[0002] Accurate detection of internal defects in building concrete is crucial for ensuring the safety and durability of building structures. Currently, this problem is mainly achieved by ultrasonic testing technology combined with manual experience analysis or traditional signal processing algorithms (such as time-frequency analysis, threshold segmentation, etc.) for defect identification. However, due to signal distortion interference caused by the non-uniformity of concrete materials, insufficient prior knowledge of defect characteristics, and lack of spatial information caused by the singularity of the scanning trajectory in the existing methods, the detection results are prone to problems such as misjudgment, missed detection, and fuzzy positioning.

[0003] In the current related technologies, there are technical problems of low detection accuracy, difficult defect positioning and identification in the detection of internal defects of building concrete. Summary of the Invention

[0004] This application provides a method and system for detecting internal defects of building concrete. By setting a detection frequency spectrum, generating a pulse signal, performing distortion correction, time-reversal enhancement and random noise addition, establishing a defect knowledge base and embedding it in the detection system, using an ultrasonic device to perform three-view scanning according to a preset trajectory, receiving echo signals and constructing a detection trajectory space, performing matching analysis on the scanned data based on the knowledge base to identify fuzzy defect data (position and type), and performing directional re-inspection on the fuzzy defect area to finally determine the accurate detection result of internal defects, etc., this application solves the technical problems of low detection accuracy, difficult defect positioning and identification in the existing detection of internal defects of building concrete, and achieves the technical effect of improving the accuracy of defect type identification and the position positioning accuracy.

[0005] This application provides a method for detecting internal defects of building concrete, including: setting a detection frequency spectrum, determining a detection pulse signal and performing multi-defect distortion processing, performing time-reversal enhancement and random noise addition, constructing a defect knowledge base and embedding it in the defect detection system; driving a front-end ultrasonic device according to the detection pulse signal to perform a preset trajectory scan in three views of a building area, and constructing a detection trajectory space through echo reception; performing defect qualitative matching based on the defect knowledge base on the detection trajectory space to locate fuzzy defect data, where the fuzzy defect data includes defect position and defect type; performing directional detection deployment and defect identification according to the fuzzy defect data to determine the internal defect detection result.

[0006] In a possible implementation, the following processing is performed: The three views include the front view, side view, and top view dimensions; obtain the geometric parameters of the building area, plan the signal scanning trajectory with a preset detection density, and determine the perspective scanning trajectory, where any at least two perspective dimensions are selected for the scanning trajectory planning; use the perspective scanning trajectory as the preset trajectory.

[0007] In a possible implementation, according to the detection pulse signal, drive the front-end ultrasonic device to perform a preset trajectory scan of the building area in three views, and perform the following processing: obtain the deployment array of the front-end ultrasonic device; perform detection allocation on the deployment array according to the preset trajectory to determine the array scanning method; perform detection drive on the front-end ultrasonic device according to the detection pulse signal and the array scanning method.

[0008] In a possible implementation, determine the detection pulse signal and perform multi-defect distortion processing, and perform the following processing: obtain the concrete process characteristics of the building area and retrieve the defect database; traverse the defect database, perform clustering processing based on the defect type, and extract multi-defect distortion characteristics, where each defect type corresponds to a defect distortion feature element; process the detection pulse signal according to the multi-defect distortion characteristics to determine the multi-defect distortion signal.

[0009] In a possible implementation, perform time reversal enhancement and random noise addition to construct a defect knowledge base, and perform the following processing: perform inverse time series enhancement processing on the multi-defect distortion signal according to the time series attenuation of the signal to determine the multi-enhanced signal; introduce random noise and perform noise addition processing on the multi-enhanced signal to determine the multi-defect signal; integrate the multi-defect signals to construct the defect knowledge base.

[0010] In a possible implementation, perform defect qualitative matching based on the defect knowledge base to locate fuzzy defect data, and perform the following processing: traverse the detection trajectory space, traverse the defect knowledge base for signal state matching, and locate the defect distribution space, where the defect type is marked in the defect distribution space; use the defect distribution space as the fuzzy defect data.

[0011] In a possible implementation, perform directional detection deployment according to the fuzzy defect data, and perform the following processing: traverse the fuzzy defect data, configure the directional detection method for each fuzzy defect; deploy directional detection devices according to the directional detection method; perform directional detection based on the directional detection devices and guided by the defect positions of each fuzzy defect to determine the directional detection data.

[0012] In a possible implementation, defect identification is performed to determine the internal defect detection result, and the following processing is performed: preprocess the directional detection data and perform defect feature identification to determine the defect detection data; according to the defect detection data, perform data coverage on the defect distribution space as the internal defect detection result.

[0013] In a possible implementation, after determining the internal defect detection result, the following processing is performed: generate a defect warning message according to the internal defect detection result; on the display interface of the defect detection system, perform pop-up visualization on the internal defect detection result, and perform defect alarm management according to the defect warning message.

[0014] This application also provides an internal defect detection system for building concrete, including: a defect knowledge base construction module, configured to set a detection spectrum, determine a detection pulse signal and perform multi-source defect distortion processing, perform time reversal enhancement and random noise addition, construct a defect knowledge base and embed it in the defect detection system; a scanning module, configured to drive a front-end ultrasonic device according to the detection pulse signal, perform a preset trajectory scan on a building area under three views, and construct a detection trajectory space through echo reception; a defect qualitative matching module, configured to perform defect qualitative matching based on the defect knowledge base for the detection trajectory space, and locate fuzzy defect data, where the fuzzy defect data includes a defect position and a defect type; a defect identification module, configured to perform directional detection deployment and defect identification according to the fuzzy defect data to determine the internal defect detection result.

[0015] It is intended to propose an internal defect detection method and system for building concrete through this application. First, set a detection spectrum, determine a detection pulse signal and perform multi-source defect distortion processing, perform time reversal enhancement and random noise addition, construct a defect knowledge base and embed it in the defect detection system. Then, drive a front-end ultrasonic device according to the detection pulse signal, perform a preset trajectory scan on a building area under three views, and construct a detection trajectory space through echo reception. Next, perform defect qualitative matching based on the defect knowledge base for the detection trajectory space, and locate fuzzy defect data, where the fuzzy defect data includes a defect position and a defect type. Finally, perform directional detection deployment and defect identification according to the fuzzy defect data to determine the internal defect detection result. The technical effect of improving the accuracy of defect type identification and the positioning accuracy of the position is achieved. Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. In this application, flowcharts are used to illustrate the operations performed by the systems according to the embodiments of this application. It should be understood that the operations before or below do not necessarily need to be precisely executed in sequence. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0017] Figure 1 It is a schematic flowchart of a method for detecting internal defects of concrete for construction provided by an embodiment of this application.

[0018] Figure 2 It is a schematic structural diagram of a system for detecting internal defects of concrete for construction provided by an embodiment of this application.

[0019] Explanation of reference numerals: Defect knowledge base construction module 10, scanning module 20, defect qualitative matching module 30, defect identification module 40. Detailed implementation manners

[0020] The above description is only an overview of the technical solutions of this application. In order to be able to more clearly understand the technical means of this application, it can be implemented in accordance with the content of the description. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the detailed implementation manners of this application.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the following will further describe this application in detail in conjunction with the accompanying drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0022] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0023] An embodiment of this application provides a method for detecting internal defects of building concrete, as Figure 1 shown. The method includes: Step S100, set a detection spectrum, determine a detection pulse signal and perform multi - defect distortion processing, perform time - reversal enhancement and random noise addition, construct a defect knowledge base and deploy it embedded in the defect detection system.

[0024] Specifically, the detection spectrum refers to the frequency range of ultrasonic signals used for detection. A signal generator is used to generate ultrasonic signals within a specific frequency range. For example, ultrasonic signals with a frequency range of 50 kHz to 200 kHz are selected. This frequency band has a better detection effect on internal defects of concrete. The spectrum parameters are set through software to ensure that the signal covers the required detection frequency range.

[0025] The detection pulse signal refers to a short - time pulse signal used to stimulate the ultrasonic probe to emit ultrasonic waves. A pulse generator is used to generate a short - time pulse signal. For example, the pulse width is 100 nanoseconds and the repetition frequency is 1 kHz. The shape of the detection pulse signal can be a rectangular pulse or a Gaussian pulse, and the specific selection depends on the detection requirements. Multi - defect distortion processing refers to analyzing and processing the changes (distortions) of different defect characteristics under different conditions to extract distortion feature elements that can represent these changes. A digital signal processor (DSP) is used to optimize the detection pulse signal to adapt to the distortions of different defect characteristics, making it more suitable for detecting different types of defects and improving the detection accuracy. For example, high - frequency noise or low - frequency interference is removed through a filtering algorithm, and at the same time, parameters such as the amplitude and frequency of the signal are adjusted according to the distortion of the defect characteristics.

[0026] Time - reversal enhancement and random noise addition are used to further optimize the detection pulse signal, enhance the signal characteristics and improve the anti - interference ability. Among them, time - reversal enhancement is a signal processing technology that enhances the focusing effect of the signal by reversing the received signal and re - emitting it. For example, time - reversal processing is performed through a software algorithm. Low - level random noise is added to the signal to improve the anti - interference ability and signal - to - noise ratio of the signal. For example, a random noise generator is used to generate white noise and superimpose it on the detection pulse signal.

[0027] Test the sample using the optimized detection pulse signal to obtain defect signals, and construct a defect knowledge base based on these signals. Collect sample data of known defects, including ultrasonic echo characteristics of different types of defects (such as cracks, voids, honeycombs, etc.). These samples can be obtained through experimental detection. Extract characteristic parameters from each sample signal, such as amplitude characteristics (maximum echo amplitude, average amplitude, etc.), time characteristics (echo arrival time, signal duration, etc.), frequency characteristics (main frequency components in the spectrum, bandwidth, etc.), and store these characteristic parameters as feature vectors in the defect knowledge base. Deploy the constructed defect knowledge base and detection algorithm to an embedded system to achieve an automated detection function, for example, using an ARM processor or an FPGA chip. The embedded system can process ultrasonic signals in real time and communicate with the front-end ultrasonic device.

[0028] For example, use a Tektronix AFG3102 arbitrary function generator to set the frequency range from 50 kHz to 200 kHz and the pulse width to 100 nanoseconds. Use a TI TMS320C6748 DSP chip for signal distortion processing and time reversal algorithm. Use a development board based on an ARM Cortex-A9 processor to deploy the defect knowledge base and detection algorithm to this system.

[0029] In a possible implementation, determine the detection pulse signal and perform multivariate defect distortion processing. Step S100 further includes step S110 of obtaining the concrete process characteristics of the building area and retrieving the defect database. Specifically, collect concrete-related information of the building area through construction drawings, construction records, or on-site inspections, including construction methods (such as pouring, prefabrication), material specifications (such as cement grade, aggregate type), etc. For example, record that the concrete pouring method is "layered pouring", the cement grade is "C30", and the aggregate type is "crushed stone". According to the obtained concrete process characteristics, retrieve the relevant defect data from the preset defect database. The defect database stores the defect types and their characteristic parameters corresponding to different process characteristics. For example, for C30 concrete with "layered pouring", the defect database contains the characteristic parameters of common defects such as cracks and honeycombs. The characteristic parameters of cracks are [maximum amplitude: 0.8, average amplitude: 0.4, arrival time: 10 μs, main frequency: 100 kHz], and the characteristic parameters of honeycombs are [maximum amplitude: 0.6, average amplitude: 0.3, arrival time: 15 μs, main frequency: 80 kHz].

[0030] Step S120: Traverse the defect database, perform clustering processing based on defect types, and extract multivariate defect distortion features. Specifically, for each defect type, there is a corresponding defect distortion feature element. Specifically, traverse the retrieved defect data and extract the characteristic parameters of each defect type. Use a clustering algorithm (such as K-Means) to classify the defect characteristic parameters, group similar features into one category, and each clustering center represents a distortion feature element of a defect type. For example, cluster the crack features to obtain multiple clustering centers, and each clustering center corresponds to a distortion feature of a type of crack. Extract the characteristic parameters from each clustering center to form multivariate defect distortion features.

[0031] Step S130: Process the detection pulse signal according to the multivariate defect distortion features to determine the multivariate defect distortion signal. Specifically, adjust the detection pulse signal according to the extracted multivariate defect distortion features. For example, adjust the amplitude, frequency, etc. of the detection pulse signal according to the distortion feature element of the crack to make it more suitable for crack detection. For honeycomb defects, adjust the frequency and phase of the detection pulse signal to enhance the detection ability for honeycomb defects. Generate optimized detection pulse signals for different defect types. For example, the detection pulse signal for cracks is [frequency: 100 kHz, pulse width: 100 nanoseconds], and the detection pulse signal for honeycombs is [frequency: 80 kHz, pulse width: 120 nanoseconds]. This implementation method can more accurately match the defect types by obtaining the concrete process characteristics and retrieving the corresponding defect database. Clustering processing and extraction of multivariate defect distortion features enable the detection pulse signal to be optimized for different defect types, thereby improving the detection accuracy.

[0032] In a possible implementation, perform time reversal enhancement and random noise addition to construct a defect knowledge base. Step S100 further includes Step S140: Perform inverse time series enhancement processing on the multivariate defect distortion signal according to the time series attenuation of the signal to determine the multivariate enhanced signal. Specifically, analyze the time series characteristics of the multivariate defect distortion signal, especially the attenuation characteristics of the signal. Defects inside the concrete will cause the ultrasonic signal to attenuate during propagation, and this attenuation is related to the type and location of the defect. Extract the time series attenuation characteristics of the signal through signal processing algorithms (such as autocorrelation analysis or wavelet transform). According to the time series attenuation characteristics of the signal, use digital signal processing algorithms (such as inverse filtering or adaptive filtering) to perform inverse time series enhancement processing on the multivariate defect distortion signal to compensate for the attenuation of the signal during propagation and enhance the characteristics of the signal. For example, if the signal attenuates rapidly during propagation, the characteristics of the signal can be enhanced by adjusting the amplitude or phase of the signal.

[0033] For example, perform wavelet transform on the multi - defect distortion signal, extract the time - frequency characteristics of the signal, and analyze the amplitude attenuation of the signal at different time points. For example, the amplitude of the signal attenuates by 30% after propagating 10 centimeters. According to the attenuation characteristics, design an inverse filter to enhance the signal. For example, if the amplitude of the signal attenuates by 30% during propagation, then the amplitude of the signal is increased by 30% through the inverse filter.

[0034] Step S150: Introduce random noise to perform noise - adding processing on the multi - enhanced signal to determine the multi - defect signal. Specifically, use a random noise generator to generate low - level random noise, such as white noise. Superimpose the random noise on the multi - enhanced signal to simulate the noise interference in the actual detection environment. By adjusting the amplitude and frequency characteristics of the noise, ensure that the signal after adding noise can better reflect the noise environment in actual detection. For example, set the amplitude of the noise to 10% of the signal amplitude, and the frequency range covers the detection spectrum. Generate the final multi - defect signal for subsequent testing and defect knowledge - base construction.

[0035] Step S160: Integrate the multi - defect signal to construct the defect knowledge - base. Specifically, use the multi - defect signal to test the sample to obtain the actual defect signal. Extract the characteristic parameters of these defect signals, such as amplitude, frequency, phase, etc., and store them as feature vectors. Store the extracted feature vectors in the defect knowledge - base to form a database containing various defect characteristics. Each defect type corresponds to one or more feature vectors, and these feature vectors are used for defect identification and matching. This implementation method compensates for the attenuation of the signal during propagation through inverse time - series enhancement processing, enhances the characteristics of the signal, and makes defects easier to detect. By introducing random noise and performing noise - adding processing, it simulates the noise interference in the actual detection environment and improves the anti - interference ability of the detection method.

[0036] Step S200: According to the detection pulse signal, drive the front - end ultrasonic device to perform a preset - trajectory scan on the building area under three views, and construct a detection - trajectory space through echo reception.

[0037] Specifically, an ultrasonic transmitting probe is used to convert the detection pulse signal into an ultrasonic signal and transmit it into the concrete. For example, a single crystal or double crystal probe is adopted, and the operating frequency matches the detection pulse signal. A scanning path is designed, including scanning in three dimensions: front view, side view, and top view. For example: Scanning the building area from the front view, scanning once every 10 cm. Scanning the building area from the side view, scanning once every 15 cm. Scanning the building area from the top view, scanning once every 10 cm. A mechanical scanning device (such as a slide rail driven by a stepper motor) is used to control the moving path of the probe, ensuring that the probe can scan in three dimensions of front view, side view, and top view according to the preset trajectory. An ultrasonic receiving probe is used to receive the echo signal returned from inside the concrete. The received echo signal is combined with the position information of the probe and directly arranged in space to construct a detection trajectory space. For example, a three-dimensional coordinate system is used to record the position and intensity of each echo signal, generating a three-dimensional detection trajectory space map.

[0038] For example, the Panametrics V104-R probe is used, with an operating frequency of 100 kHz. A linear slide rail driven by a stepper motor with an accuracy of 0.1 mm is used to achieve precise movement of the probe. MATLAB software is used to draw the three-dimensional detection trajectory space map to visually display the distribution of the echo signals.

[0039] In a possible implementation manner, step S200 further includes step S210. The three views include the front view, side view, and top view dimensions. Geometric parameters of the building area are obtained, and signal scanning trajectory planning is performed with a preset detection density to determine the perspective scanning trajectory. Among them, any at least two perspective dimensions are selected for scanning trajectory planning, and the perspective scanning trajectory is used as the preset trajectory.

[0040] Specifically, geometric parameters such as the size and shape of the building area are obtained through building drawings or on-site measurements. These parameters include the length, width, height, etc. of the building area. According to the geometric parameters of the building area and the detection requirements, the detection density is set. For example, for important structural parts, a high detection density is set, that is, a smaller scanning interval. According to the geometric parameters and the detection density, the scanning trajectory of the ultrasonic probe is planned, and at least two perspective dimensions (such as front view and side view, front view and top view, side view and top view) are selected for scanning trajectory planning. The planned scanning trajectory is used as the preset trajectory to guide the movement of the ultrasonic probe. These trajectories ensure a comprehensive scan of the building area from different perspectives. This implementation method optimizes the scanning path, reduces unnecessary repeated scans, and improves the detection efficiency by presetting the detection density and planning the scanning trajectory.

[0041] In a possible implementation, according to the detected pulse signal, the front-end ultrasonic device is driven to perform a preset trajectory scan under three views on the building area. Step S200 further includes step S220 of obtaining the deployment array of the front-end ultrasonic device. Specifically, determine the layout and configuration of the front-end ultrasonic device. For example, the ultrasonic device can adopt a linear array, a matrix array or other forms of deployment methods. Obtain the specific parameters of the deployment array, including information such as the number, position and spacing of the probes. For example, a linear array may contain 10 probes with a spacing of 5 cm.

[0042] Step S230, according to the preset trajectory, perform detection allocation on the deployment array to determine the array scanning method. Specifically, according to the preset scanning trajectories (front view, side view, top view), allocate the scanning tasks to different probes or probe combinations. For example: The front view scan is allocated to the first 5 probes in the linear array, scanning once every 10 cm. The side view scan is allocated to the last 5 probes in the linear array, scanning once every 15 cm. The top view scan is allocated to a specific probe combination in the matrix array, scanning once every 10 cm. In this way, ensure that each probe or probe combination can scan according to the preset trajectory, so as to achieve a comprehensive detection of the building area.

[0043] Step S240, according to the detected pulse signal and the array scanning method, perform detection drive on the front-end ultrasonic device. Specifically, use the optimized detected pulse signal to drive the front-end ultrasonic device. According to the determined array scanning method, control each probe or probe combination to scan according to the preset trajectory. In this way, ensure that each probe can accurately emit and receive ultrasonic signals according to its scanning task and detection requirements, so as to achieve efficient and accurate detection. This implementation method can rationally utilize the detection capabilities of each probe, reduce unnecessary repeated scans, and improve the detection efficiency by obtaining the deployment array parameters of the front-end ultrasonic device and performing detection allocation according to the preset trajectory.

[0044] Step S300, for the detection trajectory space, perform defect qualitative matching based on the defect knowledge base to locate fuzzy defect data, where the fuzzy defect data includes the defect position and defect type.

[0045] Specifically, the fuzzy defect data refers to the position and type of defects that may exist detected initially but the defect information has not been determined yet. Traverse the detection trajectory space and compare the detected echo signals with the feature templates in the defect knowledge base. Through feature matching algorithms (such as Euclidean distance, correlation coefficient, etc.), judge whether the detected signal matches the known defect features. Locate the possible defect positions and types, and record the fuzzy defect data, including the position coordinates of the defect (such as X, Y, Z coordinates) and the possible defect types (such as cracks, cavities, etc.).

[0046] For example, the echo signal is decomposed using wavelet transform to extract the time-frequency features of the signal. The K-nearest neighbor algorithm (K = 5) is used to compare the features of the detected echo signal with the features in the defect knowledge base to determine if there is a match. The defect location is recorded as (X = 50 cm, Y = 30 cm, Z = 20 cm), and the possible defect type is a crack.

[0047] In a possible implementation, qualitative defect matching based on the defect knowledge base is performed to locate fuzzy defect data. Step S300 further includes step S310 of traversing the detection trajectory space and traversing the defect knowledge base for signal state matching to locate the defect distribution space, where the defect distribution space is marked with defect types, and the defect distribution space is used as the fuzzy defect data.

[0048] Specifically, each detection point in the detection trajectory space is analyzed one by one to extract the echo signal features of each detection point, such as amplitude, frequency, phase, etc. Each defect feature template in the defect knowledge base is compared one by one to determine if the signal features of the detection point match the known defect features. Feature matching algorithms (such as Euclidean distance, correlation coefficient, etc.) are used to calculate the similarity or distance between the signal features of the detection point and the defect feature template. If the similarity is higher than the preset threshold or the distance is less than the preset threshold, it is considered that there may be a defect at the detection point. For each detection point with a successful match, its position coordinates (such as X, Y, Z coordinates) and the corresponding defect type are recorded. The position and defect type information of these detection points are integrated into a three-dimensional space to form a defect distribution space. In the defect distribution space, each detection point is marked with its corresponding defect type, such as cracks, voids, honeycombs, etc., to visually display the distribution and type of defects. The located defect distribution space is used as fuzzy defect data to provide a basis for further directional detection and precise positioning. This implementation method can accurately locate the position and type of defects by traversing the detection trajectory space and the defect knowledge base, analyzing and matching each detection point one by one, avoiding omissions and misjudgments, and improving the accuracy of detection. Using the defect distribution space as fuzzy defect data can visually display the distribution of defects in the building area, helping to quickly understand the overall situation of the defects.

[0049] Step S400, based on the fuzzy defect data, perform directional detection deployment and defect identification to determine the internal defect detection result.

[0050] Specifically, based on the fuzzy defect data, further directional detection is performed on each possible defect location. Using the optimized detection equipment and methods, high-precision detection is carried out for the specific location and type of each fuzzy defect. Through directional detection, the exact location, type, and size of the defect are finally determined. The detection results are output to a display device or a storage device. For example, a detailed detection report is generated, including information such as the location, type, and size of the defect.

[0051] For example, move the probe to the vicinity of (X = 50 cm, Y = 30 cm, Z = 20 cm), narrow the scanning range to 5 cm × 5 cm, and improve the scanning accuracy to 0.5 mm. Use the correlation analysis algorithm to calculate the correlation coefficient between the echo signal and the defect feature model, and determine that the defect type is a crack with a length of 10 cm. Generate a detection report in PDF format, detailing information such as the location, type, and size of the defect.

[0052] In a possible implementation manner, according to the fuzzy defect data, a directional detection deployment is performed. Step S400 further includes step S410 of traversing the fuzzy defect data and configuring the directional detection methods for each fuzzy defect. Specifically, each located fuzzy defect data is analyzed one by one to extract the location, type, and possible features of the defect. According to the type and features of each fuzzy defect, the most suitable detection spectrum and detection method are selected. For example: for crack detection, select high-frequency ultrasonic detection with a frequency range of 150 kHz to 200 kHz and a pulse width of 50 nanoseconds; for void detection, select low-frequency ultrasonic detection with a frequency range of 50 kHz to 100 kHz and a pulse width of 100 nanoseconds; for honeycomb detection, select laser detection combined with low-frequency ultrasonic detection with a frequency range of 80 kHz to 120 kHz and a pulse width of 80 nanoseconds. By combining the fuzzy defect data and the directional detection methods, it is ensured that while ensuring the detection accuracy, the comprehensiveness and efficiency of the detection are improved. For example, for an area where cracks and voids coexist, high-frequency and low-frequency ultrasonic devices can be deployed simultaneously for detection.

[0053] Step S420: Deploy the directional detection device according to the described directional detection method. Specifically, select a suitable directional detection device according to the directional detection method configured in Step S410. For example, for high-frequency ultrasonic detection, select a Panametrics V104-R probe with a working frequency of 150 kHz to 200 kHz; for low-frequency ultrasonic detection, select a Panametrics V103-R probe with a working frequency of 50 kHz to 100 kHz; for laser detection, select a laser scanner for combined detection with a low-frequency ultrasonic device. Deploy the selected directional detection device to the corresponding location in the building area to ensure that the device can cover all positions of the ambiguous defects. For example, deploy the high-frequency ultrasonic device in the crack detection area, deploy the low-frequency ultrasonic device in the void detection area, and deploy the laser scanner in the honeycomb detection area.

[0054] Step S430: Perform directional detection guided by the defect positions of the respective ambiguous defects according to the described directional detection device, and determine the directional detection data. Specifically, perform high-precision detection using the deployed directional detection device according to the specific position of each ambiguous defect. For example, for crack detection, use the high-frequency ultrasonic device to scan at the crack position and record the echo signal; for void detection, use the low-frequency ultrasonic device to scan at the void position and record the echo signal; for honeycomb detection, use the laser scanner combined with the low-frequency ultrasonic device to scan at the honeycomb position and record the laser reflection signal and the ultrasonic echo signal. This implementation method ensures the accuracy of the detection results by configuring the most suitable detection spectrum and detection method for high-precision detection of different types of defects.

[0055] In a possible implementation, for defect identification and determination of the internal defect detection result, Step S400 further includes Step S440: Preprocess the directional detection data and perform defect feature identification to determine the defect detection data. Specifically, preprocess the directional detection data obtained in Step S430, including operations such as noise removal, signal enhancement, and data correction. For example, use a digital filter to remove high-frequency noise, and enhance weak signals through a signal amplifier to ensure the accuracy and reliability of the data. Analyze the preprocessed data to extract the characteristic parameters of the defects, such as the size, shape, position, depth, etc. of the defects. For example, identify the length and depth of a crack by analyzing the amplitude, phase, frequency, etc. of the ultrasonic signal; identify the size and position of a honeycomb by analyzing the intensity and distribution of the laser scan signal. Organize the identified defect characteristic parameters into defect detection data, including the position coordinates of the defect (such as X, Y, Z coordinates), defect type (such as crack, void, honeycomb, etc.), defect size (such as length, width, depth, etc.). For example, determine that the position of a crack is (X = 50 cm, Y = 30 cm, Z = 20 cm), with a length of 10 cm and a depth of 2 cm.

[0056] Step S450: According to the defect detection data, perform data coverage on the defect distribution space as the internal defect detection result. Specifically, integrate the defect detection data determined in step S440 with the defect distribution space generated in step S310. Cover the specific location, type, and size information of each defect into the defect distribution space to generate a complete three-dimensional defect distribution map. The finally generated three-dimensional defect distribution map serves as the internal defect detection result, intuitively showing the locations, types, and sizes of all defects within the building area. Generate a detailed inspection report based on the internal defect detection result, including detailed information about the defects, distribution maps, recommended repair measures, etc. For example, the report can indicate the location, length, and depth of cracks, as well as the recommended repair methods. This implementation method integrates the defect detection data with the defect distribution space to generate a complete three-dimensional defect distribution map, providing comprehensive defect information.

[0057] In a possible implementation manner, after determining the internal defect detection result, the method further includes: generating defect warning information according to the internal defect detection result; performing pop-up visualization of the internal defect detection result on the display interface of the defect detection system, and performing defect alarm management according to the defect warning information.

[0058] Specifically, analyze the generated internal defect detection result to evaluate the severity and potential risks of the defects. For example, judge whether the defects need to be processed immediately according to factors such as the type, size, and location of the defects. Generate corresponding defect warning information according to the analysis result. The warning information includes the specific location, type, size, risk level, and recommended treatment measures of the defects. For example, for a crack with a length exceeding 10 centimeters, generate a high-risk warning information and recommend immediate repair.

[0059] On the display interface of the defect detection system, display the internal defect detection result in the form of a pop-up window. The pop-up window contains the three-dimensional distribution map of the defects, detailed information (such as location, type, size, etc.), and warning information. The pop-up window can provide user interaction functions. For example, clicking on a defect point can view its detailed information, or clicking on the warning information can view the recommended treatment measures. The content of the pop-up window is updated in real time to ensure that users can always obtain the latest defect detection information.

[0060] Based on the generated defect warning information, start the corresponding alarm mechanism. For example, for high-risk defects, trigger a sound alarm or a light alarm to remind the on-site staff to pay attention. Record the alarm information in the system, including the alarm time, defect location, type, risk level, etc. These records are used for subsequent analysis and management. Send the defect warning information and alarm notifications to relevant managers or maintenance personnel via text message, email, or instant messaging tool to ensure timely measures are taken.

[0061] For example, assume a crack with a depth of 5 cm and a length of 15 cm is detected. The system will generate a high-risk warning message and pop up a window on the display interface that includes a three-dimensional distribution map of the crack and detailed information. At the same time, the system will trigger a sound alarm and send a text message notification to the on-site staff to remind them to perform repairs immediately. This implementation method, by generating defect warning information, timely reminds relevant personnel to pay attention to potential defect problems, ensuring that measures can be taken quickly to prevent the defects from deteriorating further.

[0062] The embodiment of this application uses techniques such as setting a detection spectrum, generating a pulse signal, performing distortion correction, time reversal enhancement, and random noise addition, establishing a defect knowledge base and embedding it into the detection system, using an ultrasonic device to perform three-view scanning according to a preset trajectory, receiving echo signals and constructing a detection trajectory space, performing matching analysis on the scanned data based on the knowledge base, identifying fuzzy defect data (location and type), performing targeted re-inspection on the fuzzy defect area, and finally determining the accurate detection result of internal defects, etc., to solve the technical problems of low detection accuracy, difficult defect location and identification existing in the current internal defect detection of building concrete, and achieving the technical effect of improving the accuracy of defect type identification and the position location accuracy.

[0063] In the above text, reference is made to Figure 1 A detailed description was given of a method for detecting internal defects of building concrete according to an embodiment of the present invention. Next, reference will be made to Figure 2 Describe a system for detecting internal defects of building concrete according to an embodiment of the present invention.

[0064] A system for detecting internal defects of building concrete according to an embodiment of the present invention is used to solve the technical problems of low detection accuracy, difficult defect location and identification existing in the current internal defect detection of building concrete, and achieve the technical effect of improving the accuracy of defect type identification and the position location accuracy. A system for detecting internal defects of building concrete includes: a defect knowledge base construction module 10, a scanning module 20, a defect qualitative matching module 30, and a defect identification module 40.

[0065] The defect knowledge base construction module 10 is used to set the detection spectrum, determine the detection pulse signal and perform multi - element defect distortion processing, execute time - reversal enhancement and random noise addition, construct the defect knowledge base and deploy it embedded in the defect detection system; the scanning module 20 is used to drive the front - end ultrasonic device according to the detection pulse signal, perform a preset - trajectory scan of the building area under three views, and construct a detection - trajectory space through echo reception; the defect qualitative matching module 30 is used to perform defect qualitative matching based on the defect knowledge base for the detection - trajectory space, and locate fuzzy defect data, where the fuzzy defect data includes the defect position and the defect type; the defect identification module 40 is used to perform directional detection deployment and defect identification according to the fuzzy defect data, and determine the internal defect detection result.

[0066] Next, the specific configuration of the scanning module 20 will be described in detail. As described above, the scanning module 20 may further include: a signal scanning trajectory planning unit for obtaining the geometric parameters of the building area in three views including the front view, side view, and top - view dimensions, performing signal scanning trajectory planning with a preset detection density, and determining the perspective scanning trajectory, where any at least two perspective dimensions are taken for scanning trajectory planning, and the perspective scanning trajectory is used as the preset trajectory.

[0067] Among them, driving the front - end ultrasonic device according to the detection pulse signal to perform a preset - trajectory scan of the building area under three views, the scanning module 20 may further include: a deployment array acquisition unit for acquiring the deployment array of the front - end ultrasonic device; a detection allocation unit for performing detection allocation on the deployment array according to the preset trajectory to determine the array scanning method; a detection driving unit for driving the front - end ultrasonic device for detection according to the detection pulse signal and the array scanning method.

[0068] Next, the specific configuration of the defect knowledge base construction module 10 will be described in detail. As described above, determining the detection pulse signal and performing multi - element defect distortion processing, the defect knowledge base construction module 10 may further include: a defect database retrieval unit for obtaining the concrete process characteristics of the building area and retrieving the defect database; a multi - element defect distortion feature extraction unit for traversing the defect database, performing clustering processing based on the defect type, and extracting multi - element defect distortion features, where each defect type corresponds to a defect distortion feature element; a multi - element defect distortion signal determination unit for processing the detection pulse signal according to the multi - element defect distortion features to determine the multi - element defect distortion signal.

[0069] Among them, time reversal enhancement and random noise addition are performed to construct a defect knowledge base. The defect knowledge base construction module 10 may further include: an inverse time series enhancement processing unit for performing inverse time series enhancement processing on the multivariate defect distortion signal according to the time series attenuation of the signal to determine a multivariate enhanced signal; a noise addition processing unit for introducing random noise and performing noise addition processing on the multivariate enhanced signal to determine a multivariate defect signal; and an integration unit for integrating the multivariate defect signals to construct the defect knowledge base.

[0070] Next, the specific configuration of the defect qualitative matching module 30 will be described in detail. As described above, defect qualitative matching based on the defect knowledge base is performed to locate fuzzy defect data. The defect qualitative matching module 30 may further include: a signal state matching unit for traversing the detection trajectory space and traversing the defect knowledge base for signal state matching to locate the defect distribution space, where the defect distribution space is marked with defect types, and taking the defect distribution space as the fuzzy defect data.

[0071] Next, the specific configuration of the defect recognition module 40 will be described in detail. As described above, based on the fuzzy defect data, directional detection deployment is performed. The defect recognition module 40 may further include: a directional detection method configuration unit for traversing the fuzzy defect data and configuring the directional detection methods for each fuzzy defect; a directional detection device deployment unit for deploying directional detection devices according to the directional detection methods; and a directional detection unit for performing directional detection based on the directional detection devices, with the defect positions of each fuzzy defect as the guide, to determine directional detection data.

[0072] Among them, when performing defect recognition to determine the internal defect detection result, the defect recognition module 40 may further include: a defect feature recognition unit for preprocessing the directional detection data and performing defect feature recognition to determine defect detection data; and a data coverage unit for performing data coverage on the defect distribution space according to the defect detection data as the internal defect detection result.

[0073] Among them, after determining the internal defect detection result, the system may further include: a defect warning information generation module for generating defect warning information according to the internal defect detection result; and a pop-up window visualization module for performing pop-up window visualization of the internal defect detection result on the display interface of the defect detection system and performing defect alarm management according to the defect warning information.

[0074] The internal defect detection system for building concrete provided by the embodiments of the present invention can execute the internal defect detection method for building concrete provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0075] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be achieved. In addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0076] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for detecting internal defects of building concrete, characterized in that, The method includes: Setting a detection spectrum, determining a detection pulse signal and performing multivariate defect distortion processing, performing time reversal enhancement and random noise addition, constructing a defect knowledge base and embedding it in a defect detection system; According to the detection pulse signal, driving a front-end ultrasonic device to perform a preset trajectory scan under three views of a building area, and constructing a detection trajectory space through echo reception; For the detection trajectory space, performing defect qualitative matching based on the defect knowledge base to locate fuzzy defect data, where the fuzzy defect data includes defect location and defect type; According to the fuzzy defect data, performing directional detection deployment and defect identification to determine the internal defect detection result.

2. The internal defect detection method of a kind of building concrete according to claim 1, characterized in that, The three views include the front view, side view and top view dimensions; Obtaining geometric parameters of the building area, performing signal scan trajectory planning with a preset detection density, and determining a perspective scan trajectory, where any at least two perspective dimensions are taken for scan trajectory planning; Taking the perspective scan trajectory as the preset trajectory.

3. The internal defect detection method of a building concrete according to claim 2, characterized in that According to the detection pulse signal, driving a front-end ultrasonic device to perform a preset trajectory scan under three views of a building area, including: Obtaining the deployment array of the front-end ultrasonic device; According to the preset trajectory, performing detection allocation on the deployment array to determine the array scan mode; According to the detection pulse signal and the array scan mode, performing detection drive on the front-end ultrasonic device.

4. The internal defect detection method of a kind of building concrete according to claim 1, characterized in that Determining a detection pulse signal and performing multivariate defect distortion processing, including: Obtaining the concrete process characteristics of the building area and retrieving the defect database; Traversing the defect database, performing clustering processing based on defect types, and extracting multivariate defect distortion characteristics, where each defect type corresponds to a defect distortion feature element; According to the multivariate defect distortion characteristics, processing the detection pulse signal to determine a multivariate defect distortion signal.

5. The internal defect detection method of a kind of building concrete according to claim 4, characterized in that, Performing time reversal enhancement and random noise addition, and constructing a defect knowledge base, including: Performing inverse time series enhancement processing on the multivariate defect distortion signal with the time series attenuation of the signal to determine a multivariate enhanced signal: Introducing random noise and performing noise addition processing on the multivariate enhanced signal to determine a multivariate defect signal; Integrating the multivariate defect signals to construct the defect knowledge base.

6. The internal defect detection method of a kind of building concrete according to claim 1, characterized in that, Performing defect qualitative matching based on the defect knowledge base to locate fuzzy defect data, including: Traversing the detection trajectory space, traversing the defect knowledge base for signal state matching, and locating the defect distribution space, where the defect distribution space is marked with the defect type; Taking the defect distribution space as the fuzzy defect data.

7. The internal defect detection method of a building concrete according to claim 6, characterized in that, According to the fuzzy defect data, performing directional detection deployment, including: Traversing the fuzzy defect data and configuring the directional detection methods for each fuzzy defect; According to the directional detection methods, deploying directional detection devices; According to the directional detection devices, performing directional detection with the defect positions of each fuzzy defect as the guide to determine directional detection data.

8. The internal defect detection method of a kind of building concrete according to claim 7, characterized in that, Performing defect identification to determine the internal defect detection result, including: Preprocessing the directional detection data and performing defect feature identification to determine defect detection data; Based on the defect detection data, perform data coverage on the defect distribution space as the internal defect detection result.

9. The internal defect detection method of a kind of building concrete according to claim 1, characterized in that, After determining the internal defect detection result, it includes: Generate a defect warning message according to the internal defect detection result; On the display interface of the defect detection system, perform pop-up visualization on the internal defect detection result and perform defect alarm management according to the defect warning message.

10. An internal defect detection system for building concrete, characterized in that, The system is used to implement the internal defect detection method of a kind of building concrete described in any one of claims 1-9. The system includes: A defect knowledge base construction module, which is used to set the detection spectrum, determine the detection pulse signal and perform multi-defect distortion processing, execute time reversal enhancement and random noise addition, construct a defect knowledge base and deploy it embedded in the defect detection system; A scanning module, which is used to drive the front-end ultrasonic device according to the detection pulse signal, perform a preset trajectory scan on the building area under three views, and construct a detection trajectory space through echo reception; A defect qualitative matching module, which is used to perform defect qualitative matching based on the defect knowledge base for the detection trajectory space, and locate fuzzy defect data, where the fuzzy defect data includes defect position and defect type; A defect identification module, which is used to perform directional detection deployment and defect identification according to the fuzzy defect data, and determine the internal defect detection result.

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