Method and system for detecting internal defects of building concrete

Abstract

The present invention discloses a method and system for detecting internal defects in building concrete, which relates to the field of ultrasonic detection. The method comprises: 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; based on the detection pulse signal, driving a front-end ultrasonic device to scan a building area along a preset trajectory under three views, and constructing a detection trajectory space through echo reception; performing defect qualitative matching based on the defect knowledge base in the detection trajectory space to locate fuzzy defect data; and performing directional detection deployment and defect identification based on the fuzzy defect data to determine the internal defect detection results. The method solves the technical problems of low detection accuracy and difficulty in defect location and identification in existing internal defect detection of building concrete, achieving the technical effect of improving the accuracy of defect type identification and position positioning accuracy.

Description

Technical Field

[0001] The present application relates to the field of ultrasonic detection, and in particular to a method and system for detecting internal defects of concrete for construction. Background Art

[0002] Accurately detecting internal defects in building concrete is crucial for ensuring the safety and durability of building structures. Currently, defect identification is primarily achieved through ultrasonic testing technology combined with empirical analysis or traditional signal processing algorithms (such as time-frequency analysis and threshold segmentation). However, existing methods are prone to misjudgments, missed detections, and location ambiguity due to signal distortion caused by concrete material heterogeneity, insufficient prior knowledge of defect characteristics, and the lack of spatial information caused by the single scanning trajectory.

[0003] In the current related technologies, internal defect detection of building concrete has technical problems such as low detection accuracy and difficulty in defect positioning and identification. Summary of the Invention

[0004] The present application provides a method and system for detecting internal defects of building concrete, which adopts the following technical means: 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 ultrasonic equipment to perform three-view scanning according to a preset trajectory, receiving echo signals and constructing a detection trajectory space, matching and analyzing the scan data based on the knowledge base, identifying fuzzy defect data (position and type), conducting targeted re-inspection of the fuzzy defect area, and finally determining the accurate detection results of the internal defects. The method solves the technical problems of low detection accuracy and difficulty in defect positioning and identification in the existing internal defect detection of building concrete, and achieves the technical effect of improving the accuracy of defect type identification and position positioning accuracy.

[0005] The present application provides a method for detecting internal defects of building concrete, comprising: setting a detection spectrum, determining a detection pulse signal and performing multivariate defect distortion processing, executing time reversal enhancement and random noise addition, constructing a defect knowledge base and embedding it in a defect detection system; based on the detection pulse signal, driving a front-end ultrasonic device to scan a building area along a preset trajectory under three views, 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, wherein the fuzzy defect data includes defect location and defect type; based on the fuzzy defect data, performing directional detection deployment and defect identification to determine internal defect detection results.

[0006] In a possible implementation, the following processing is performed: the three views include main view, side view and top view dimensions; the geometric parameters of the building area are obtained, and the signal scanning trajectory is planned with a preset detection density to determine the perspective scanning trajectory, wherein any at least two perspective dimensions are taken for scanning trajectory planning; and the perspective scanning trajectory is used as the preset trajectory.

[0007] In a possible implementation, based on the detection pulse signal, the front-end ultrasonic device is driven to scan the building area along a preset trajectory under three views, and the following processing is performed: obtaining the deployment array of the front-end ultrasonic device; performing detection allocation on the deployment array according to the preset trajectory and determining the array scanning mode; and performing detection and driving on the front-end ultrasonic device based on the detection pulse signal and the array scanning mode.

[0008] In a possible implementation, a detection pulse signal is determined and multivariate defect distortion processing is performed, and the following processing is performed: concrete process characteristics of the building area are obtained and a defect database is retrieved; the defect database is traversed, clustering processing based on defect types is performed, and multivariate defect distortion features are extracted, wherein each defect type corresponds to a defect distortion feature element; the detection pulse signal is processed according to the multivariate defect distortion features to determine a multivariate defect distortion signal.

[0009] In a possible implementation, time reversal enhancement and random noise addition are performed to construct a defect knowledge base, and the following processing is performed: the multivariate defect distortion signal is subjected to inverse time series enhancement processing with the time series attenuation of the signal to determine the multivariate enhanced signal; random noise is introduced, the multivariate enhanced signal is subjected to noise addition processing to determine the multivariate defect signal; and the multivariate defect signal is integrated to construct the defect knowledge base.

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

[0011] In a possible implementation, based on the fuzzy defect data, directional detection deployment is performed, and the following processing is performed: traverse the fuzzy defect data and configure the directional detection method of each fuzzy defect; deploy directional detection equipment based on the directional detection method; based on the directional detection equipment, perform directional detection with the defect position of each fuzzy defect as the guide 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: preprocessing the directional detection data and performing defect feature identification to determine the defect detection data; based on the defect detection data, data coverage is performed 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: generating defect warning information based on the internal defect detection result; visualizing the internal defect detection result in a pop-up window on the display interface of the defect detection system, and performing defect alarm management based on the defect warning information.

[0014] The present application also provides an internal defect detection system for building concrete, including: a defect knowledge base construction module, which is used to set the detection spectrum, determine the detection pulse signal and perform multivariate defect distortion processing, execute time reversal enhancement and random noise addition, build a defect knowledge base and embed it in the defect detection system; a scanning module, which is used to drive the front-end ultrasonic equipment according to the detection pulse signal, scan the building area with a preset trajectory 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, wherein the fuzzy defect data includes defect location 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.

[0015] The present application proposes a method and system for detecting internal defects in building concrete. First, the detection spectrum is set, the detection pulse signal is determined, and multivariate defect distortion processing is performed. Time reversal enhancement and random noise are performed, and a defect knowledge base is constructed and embedded in the defect detection system. Then, based on the detection pulse signal, the front-end ultrasonic equipment is driven to scan the building area on a preset track under three views. The detection track space is constructed by echo reception. Then, for the detection track space, qualitative defect matching based on the defect knowledge base is performed to locate fuzzy defect data, wherein the fuzzy defect data includes defect location and defect type. Finally, based on the fuzzy defect data, directional detection deployment and defect identification are performed to determine the internal defect detection results. The technical effect of improving the accuracy of defect type identification and position positioning accuracy is achieved. BRIEF 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 are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A schematic flow chart of a method for detecting internal defects in building concrete provided in an embodiment of the present application.

[0018] Figure 2 A schematic structural diagram of an internal defect detection system for building concrete provided in an embodiment of the present application.

[0019] Description of the accompanying drawings: defect knowledge base construction module 10, scanning module 20, defect qualitative matching module 30, defect identification module 40. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments” which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are 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 art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The present application provides a method for detecting internal defects of building concrete. Figure 1 As shown, the method includes:

[0024] In step S100 , a detection spectrum is set, a detection pulse signal is determined, and multi-element defect distortion processing is performed, time reversal enhancement and random noise are performed, and a defect knowledge base is constructed and embedded in the defect detection system.

[0025] Specifically, the detection spectrum refers to the frequency range of the ultrasonic signal used for detection. A signal generator is used to generate an ultrasonic signal within a specific frequency range. For example, an ultrasonic signal within the 50kHz to 200kHz frequency range is effective for detecting internal concrete defects. The spectrum parameters are set using software to ensure that the signal covers the desired detection frequency range.

[0026] The detection pulse signal refers to a short-duration pulse signal used to stimulate the ultrasonic probe to emit ultrasonic waves. A pulse generator generates this short-duration pulse signal. For example, the pulse width is 100 nanoseconds and the repetition frequency is 1 kHz. The detection pulse signal can be rectangular or Gaussian, depending on the detection requirements. Multivariate defect distortion processing analyzes and processes the changes (distortions) of different defect characteristics under different conditions to extract distortion feature elements that represent these changes. A digital signal processor (DSP) optimizes the detection pulse signal to accommodate the distortion of different defect characteristics, making it more suitable for detecting different types of defects and improving detection accuracy. For example, filtering algorithms remove high-frequency noise or low-frequency interference, while adjusting signal parameters such as amplitude and frequency based on the distortion of the defect characteristics.

[0027] Time-reversal enhancement and random noise addition are used to further optimize the detection pulse signal, enhancing its characteristics and improving its anti-interference capabilities. Time-reversal enhancement is a signal processing technique that inverts the received signal and retransmits it to enhance its focus. For example, time-reversal processing can be performed using a software algorithm. Low-level random noise can be added to the signal to improve its anti-interference capabilities and signal-to-noise ratio. For example, a random noise generator can be used to generate white noise and superimpose it on the detection pulse signal.

[0028] Use the optimized detection pulse signal to test the sample to obtain the defect signal, and build 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 for 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 into an embedded system to realize automated detection functions, such as using an ARM processor or FPGA chip. The embedded system can process ultrasonic signals in real time and communicate with the front-end ultrasonic equipment.

[0029] For example, a Tektronix AFG3102 arbitrary function generator was used, with a frequency range of 50kHz to 200kHz and a pulse width of 100 nanoseconds. A TI TMS320C6748 DSP chip was used to perform signal distortion processing and a time reversal algorithm. A development board based on an ARM Cortex-A9 processor was used to deploy the defect knowledge base and detection algorithm into the system.

[0030] In one possible implementation, determining a detection pulse signal and performing multivariate defect distortion processing, step S100 further includes step S110, obtaining the concrete process characteristics of the building area and retrieving a defect database. Specifically, information related to the concrete in the building area is collected through architectural drawings, construction records, or on-site inspections, including construction methods (e.g., pouring, precast), material specifications (e.g., cement grade, aggregate type), etc. For example, the concrete pouring method is recorded as "layered pouring," the cement grade is "C30," and the aggregate type is "crushed stone." Based on the obtained concrete process characteristics, related defect data is retrieved from a preset defect database, which stores defect types and characteristic parameters corresponding to different process characteristics. For example, for "layered pouring" C30 concrete, the defect database contains characteristic parameters of common defects such as cracks and honeycombs. For example, the characteristic parameters of cracks are [maximum amplitude: 0.8, average amplitude: 0.4, arrival time: 10μs, main frequency: 100kHz], and the characteristic parameters of honeycombs are [maximum amplitude: 0.6, average amplitude: 0.3, arrival time: 15μs, main frequency: 80kHz].

[0031] Step S120 traverses the defect database, performs clustering based on defect type, and extracts a multi-dimensional defect distortion feature. Each defect type corresponds to a defect distortion feature element. Specifically, the retrieved defect data is traversed to extract characteristic parameters for each defect type. A clustering algorithm (such as K-Means) is used to classify the defect characteristic parameters, grouping similar features together. Each cluster center represents a distortion feature element for a specific defect type. For example, crack features are clustered to obtain multiple cluster centers, each corresponding to a specific crack distortion feature. Feature parameters are extracted from each cluster center to form a multi-dimensional defect distortion feature.

[0032] Step S130: Process the detection pulse signal based on the multivariate defect distortion feature to determine a multivariate defect distortion signal. Specifically, adjust the detection pulse signal based on the extracted multivariate defect distortion feature. For example, adjust the amplitude, frequency, and other parameters of the detection pulse signal based on 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 capability of honeycomb defects. Generate optimized detection pulse signals for different defect types. For example, the detection pulse signal for cracks is [frequency: 100kHz, pulse width: 100 nanoseconds], and the detection pulse signal for honeycombs is [frequency: 80kHz, pulse width: 120 nanoseconds]. This implementation method can more accurately match defect types by acquiring concrete process characteristics and retrieving the corresponding defect database. Clustering processing and multivariate defect distortion feature extraction enable the detection pulse signal to be optimized for different defect types, thereby improving detection accuracy.

[0033] In one possible implementation, time-reversal enhancement and random noise addition are performed to construct a defect knowledge base. Step S100 further includes step S140, where the multivariate defect distortion signal is subjected to inverse time-series enhancement based on the signal's time-series attenuation to determine a multivariate enhanced signal. Specifically, the time-series characteristics of the multivariate defect distortion signal, particularly its attenuation characteristics, are analyzed. Defects within concrete can cause ultrasonic signals to attenuate during propagation, and this attenuation is related to the type and location of the defect. Signal processing algorithms (such as autocorrelation analysis or wavelet transform) are used to extract the signal's time-series attenuation characteristics. Based on these characteristics, digital signal processing algorithms (such as inverse filtering or adaptive filtering) are then used to perform inverse time-series enhancement on the multivariate defect distortion signal to compensate for signal attenuation during propagation and enhance its characteristics. For example, if the signal attenuates rapidly during propagation, the signal's characteristics can be enhanced by adjusting its amplitude or phase.

[0034] For example, a wavelet transform is performed on a multivariate defect distortion signal to extract its time-frequency characteristics and analyze the signal's amplitude attenuation at different time points. For example, if a signal attenuates by 30% after propagating for 10 centimeters, an inverse filter is designed based on the attenuation characteristics to enhance the signal. For example, if the signal amplitude attenuates by 30% during propagation, the inverse filter can be used to increase the signal amplitude by 30%.

[0035] Step S150 introduces random noise and performs noise processing on the multivariate enhanced signal to determine a multivariate defect signal. Specifically, a random noise generator is used to generate low-level random noise, such as white noise. The random noise is superimposed on the multivariate enhanced signal to simulate the noise interference in an actual detection environment. By adjusting the amplitude and frequency characteristics of the noise, the noise-added signal is ensured to better reflect the noise environment in actual detection. For example, the noise amplitude is set to 10% of the signal amplitude, and the frequency range covers the detection spectrum. This generates the final multivariate defect signal for subsequent testing and defect knowledge base construction.

[0036] Step S160, integrate the multivariate defect signals and construct the defect knowledge base. Specifically, use the multivariate 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 multiple defect features. Each defect type corresponds to one or more feature vectors, which 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 the defect easier to detect. By introducing random noise and performing noise addition processing, the noise interference in the actual detection environment is simulated, and the anti-interference ability of the detection method is improved.

[0037] Step S200 : According to the detection pulse signal, the front-end ultrasonic device is driven to scan the building area along a preset track under three views, and a detection track space is constructed by echo reception.

[0038] Specifically, an ultrasonic transmitting probe converts the detection pulse signal into an ultrasonic signal and transmits it into the concrete. For example, a single-element or dual-element probe is used, with an operating frequency matching the detection pulse signal. A scanning path is designed, encompassing three dimensions: front view, side view, and top view. For example, a building area is scanned from the front, every 10 cm. A building area is scanned from the side, every 15 cm. A building area is scanned from the top, every 10 cm. A mechanical scanning device (such as a stepper motor-driven slide) controls the probe's movement path, ensuring that it scans along a predetermined trajectory in the three dimensions: front view, side view, and top view. An ultrasonic receiving probe receives the echo signal returning from the concrete. The received echo signal is combined with the probe's position information and arranged in space to construct a detection trajectory. For example, a three-dimensional coordinate system is used to record the position and intensity of each echo signal, generating a three-dimensional spatial map of the detection trajectory.

[0039] For example, a Panametrics V104-R probe operating at 100kHz is used. A linear slide driven by a stepper motor with an accuracy of 0.1mm enables precise probe movement. MATLAB software is used to plot a three-dimensional spatial map of the inspection trajectory, visually displaying the distribution of the echo signal.

[0040] In a possible implementation, step S200 further includes step S210, where the three views include main view, side view, and top view dimensions, and the geometric parameters of the building area are obtained. The signal scanning trajectory is planned with a preset detection density to determine the perspective scanning trajectory, wherein at least any two perspective dimensions are taken for scanning trajectory planning, and the perspective scanning trajectory is used as the preset trajectory.

[0041] Specifically, geometric parameters such as the size and shape of the building area are obtained through architectural drawings or on-site measurements. These parameters include the length, width, height, etc. of the building area. The detection density is set according to the geometric parameters of the building area and the detection requirements. For example, for important structural parts, a high detection density is set, that is, a smaller scanning interval. According to the geometric parameters and detection density, the scanning trajectory of the ultrasonic probe is planned, and at least two viewing angles (such as main view and side view, main view and top view, side view and top view) are selected for scanning trajectory planning. The planned scanning trajectory is used as a 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 scanning, and improves detection efficiency by presetting the detection density and planning the scanning trajectory.

[0042] In one possible implementation, based on the detection pulse signal, the front-end ultrasonic device is driven to scan the building area along a preset trajectory under three views. Step S200 further includes step S220, in which a deployment array of the front-end ultrasonic device is obtained. Specifically, the layout and configuration of the front-end ultrasonic device are determined. For example, the ultrasonic device can be deployed in a linear array, a matrix array, or other forms. Specific parameters of the deployment array are obtained, including information such as the number, position, and spacing of the probes. For example, a linear array may include 10 probes with a spacing of 5 cm.

[0043] In step S230, inspections are assigned to the deployed array according to the preset trajectory, and the array scanning mode is determined. Specifically, scanning tasks are assigned to different probes or probe combinations based on the preset scanning trajectories (front view, side view, and top view). For example, front view scanning is assigned to the first five probes in the linear array, with scanning intervals of 10 cm. Side view scanning is assigned to the last five probes in the linear array, with scanning intervals of 15 cm. Top view scanning is assigned to a specific probe combination in the matrix array, with scanning intervals of 10 cm. In this way, each probe or probe combination is ensured to scan according to the preset trajectory, thereby achieving comprehensive inspection of the building area.

[0044] Step S240, the front-end ultrasonic device is driven for detection according to the detection pulse signal and the array scanning mode. Specifically, the front-end ultrasonic device is driven by the optimized detection pulse signal. According to the determined array scanning mode, each probe or probe combination is controlled to scan according to a preset trajectory. In this way, it is ensured that each probe can accurately transmit and receive ultrasonic signals according to its scanning task and detection requirements, thereby achieving efficient and accurate detection. This implementation method can reasonably utilize the detection capability of each probe, reduce unnecessary repeated scanning, and improve detection efficiency by obtaining the deployment array parameters of the front-end ultrasonic device and performing detection allocation according to the preset trajectory.

[0045] Step S300 : performing defect qualitative matching based on the defect knowledge base for the detection trajectory space to locate fuzzy defect data, wherein the fuzzy defect data includes defect location and defect type.

[0046] Specifically, fuzzy defect data refers to the location and type of possible defects that have been initially detected, but for which no definitive information has yet been determined. The system traverses the detection trajectory space and compares the detected echo signals with feature templates in the defect knowledge base. Feature matching algorithms (such as Euclidean distance and correlation coefficient) are used to determine whether the detected signals match known defect signatures. The possible location and type of defects are located, and fuzzy defect data is recorded, including the defect's location coordinates (e.g., X, Y, and Z coordinates) and the possible defect type (e.g., crack, cavity, etc.).

[0047] For example, wavelet transforms are used to decompose the echo signal and extract its time-frequency characteristics. Using the K-nearest neighbor algorithm (K=5), the detected echo signal features are compared with those in the defect knowledge base to determine if they match. The defect location is recorded as (X=50cm, Y=30cm, Z=20cm), and the possible defect type is a crack.

[0048] In one possible implementation, qualitative defect matching based on the defect knowledge base is performed to locate fuzzy defect data. Step S300 further includes step S310, traversing the detection trajectory space, traversing the defect knowledge base to perform signal state matching, and locating the defect distribution space, wherein the defect distribution space is identified with a defect type, and the defect distribution space is used as the fuzzy defect data.

[0049] Specifically, each detection point in the detection trajectory space is analyzed one by one, and the echo signal characteristics of each detection point, such as amplitude, frequency, and phase, are extracted. Each defect feature template in the defect knowledge base is then compared one by one to determine whether the signal characteristics of the detection point match known defect features. A feature matching algorithm (such as Euclidean distance or correlation coefficient) is used to calculate the similarity or distance between the signal characteristics of the detection point and the defect feature template. If the similarity exceeds a preset threshold or the distance is less than a preset threshold, the detection point is considered to have a possible defect. For each successfully matched detection point, its location coordinates (such as X, Y, and Z coordinates) and corresponding defect type are recorded. The location 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 identified with its corresponding defect type, such as crack, cavity, or honeycomb, to intuitively display the distribution and type of the defects. The located defect distribution space is used as fuzzy defect data, providing a basis for further targeted detection and precise positioning. This implementation traverses the inspection trajectory space and defect knowledge base, analyzing and matching each inspection point one by one. This allows for precise location and type of defects, avoiding omissions and misjudgments, and improving detection accuracy. Using the defect distribution space as fuzzy defect data allows for a visual display of the distribution of defects within the building area, helping to quickly understand the overall defect situation.

[0050] Step S400: performing directional detection deployment and defect identification according to the fuzzy defect data to determine the internal defect detection result.

[0051] Specifically, based on the fuzzy defect data, further targeted inspection is performed on each possible defect location. Using optimized inspection equipment and methods, high-precision inspection is performed on the specific location and type of each fuzzy defect. Through targeted inspection, the precise location, type, and size of the defect are ultimately determined. The inspection results are output to a display device or storage device. For example, a detailed inspection report can be generated, including information such as the defect's location, type, and size.

[0052] For example, moving the probe to a location near (X=50cm, Y=30cm, Z=20cm) reduces the scanning range to 5cm×5cm, increasing the scanning accuracy to 0.5mm. Using a correlation analysis algorithm, the correlation coefficient between the echo signal and the defect characteristic model is calculated, determining the defect type to be a crack and its length to be 10cm. A PDF inspection report is generated, detailing the defect's location, type, and size.

[0053] In one possible implementation, directional detection deployment is performed based on the fuzzy defect data. Step S400 further includes step S410, which traverses the fuzzy defect data and configures a directional detection method for each fuzzy defect. Specifically, each located fuzzy defect data is analyzed individually to extract the defect's location, type, and possible characteristics. Based on the type and characteristics of each fuzzy defect, the most appropriate detection spectrum and detection method are selected. For example, for crack detection, high-frequency ultrasonic detection is selected with a frequency range of 150kHz to 200kHz and a pulse width of 50 nanoseconds; for cavity detection, low-frequency ultrasonic detection is selected with a frequency range of 50kHz to 100kHz and a pulse width of 100 nanoseconds; and for honeycomb detection, laser detection combined with low-frequency ultrasonic detection is selected with a frequency range of 80kHz to 120kHz and a pulse width of 80 nanoseconds. By combining fuzzy defect data with directional detection methods, comprehensiveness and efficiency of detection are improved while ensuring detection accuracy. For example, in areas where cracks and cavities coexist, both high-frequency and low-frequency ultrasonic equipment can be deployed simultaneously for detection.

[0054] Step S420, deploying directional detection equipment according to the directional detection method. Specifically, according to the directional detection method configured in step S410, select appropriate directional detection equipment. For example: for high-frequency ultrasonic detection, select the Panametrics V104-R probe with an operating frequency of 150kHz to 200kHz; for low-frequency ultrasonic detection, select the Panametrics V103-R probe with an operating frequency of 50kHz to 100kHz; for laser detection, select a laser scanner and combine it with a low-frequency ultrasonic device for composite detection. Deploy the selected directional detection equipment to the corresponding location of the building area to ensure that the equipment can cover the location of all fuzzy defects. For example, deploy high-frequency ultrasonic equipment in the crack detection area, deploy low-frequency ultrasonic equipment in the cavity detection area, and deploy the laser scanner in the honeycomb detection area.

[0055] In step S430, the directional detection equipment is used to perform directional detection, guided by the location of each fuzzy defect, to determine directional detection data. Specifically, the deployed directional detection equipment is used for high-precision detection based on the specific location of each fuzzy defect. For example, for crack detection, a high-frequency ultrasonic device is used to scan the crack location and record the echo signal; for cavity detection, a low-frequency ultrasonic device is used to scan the cavity location and record the echo signal; for honeycomb detection, a laser scanner combined with a low-frequency ultrasonic device is used to scan the honeycomb location and record the laser reflection signal and the ultrasonic echo signal. This implementation method configures the most appropriate detection spectrum and detection method to perform high-precision detection for different defect types, ensuring the accuracy of the test results.

[0056] In one possible implementation, defect identification is performed to determine internal defect detection results. Step S400 further includes step S440, which preprocesses the directional detection data and performs defect feature identification to determine defect detection data. Specifically, the directional detection data obtained in step S430 is preprocessed, including operations such as noise removal, signal enhancement, and data correction. For example, a digital filter can be used to remove high-frequency noise, and a signal amplifier can be used to enhance weak signals to ensure data accuracy and reliability. The preprocessed data is analyzed to extract defect characteristic parameters, such as defect size, shape, location, and depth. For example, the amplitude, phase, and frequency of ultrasonic signals can be analyzed to identify the length and depth of cracks, while the intensity and distribution of laser scanning signals can be analyzed to identify the size and location of honeycombs. The identified defect characteristic parameters are organized into defect detection data, including defect location coordinates (e.g., X, Y, and Z coordinates), defect type (e.g., crack, cavity, honeycomb, etc.), and defect size (e.g., length, width, depth, etc.). For example, the position of a crack is determined to be (X=50cm, Y=30cm, Z=20cm), the length is 10cm, and the depth is 2cm.

[0057] In step S450, based on the defect detection data, the defect distribution space is overwritten with data as the internal defect detection result. Specifically, the defect detection data determined in step S440 is integrated with the defect distribution space generated in step S310. The specific location, type and size information of each defect is overlaid into the defect distribution space to generate a complete three-dimensional defect distribution map. The three-dimensional defect distribution map finally generated serves as the internal defect detection result, which intuitively displays the location, type and size of all defects in the building area. Based on the internal defect detection results, a detailed inspection report is generated, including detailed information of the defects, distribution maps, recommended repair measures, etc. For example, the location, length, depth of the cracks, and recommended repair methods can be indicated in the report. 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.

[0058] In one possible implementation, after determining the internal defect detection result, the method further includes: generating defect warning information based on the internal defect detection result; visualizing the internal defect detection result in a pop-up window on the display interface of the defect detection system, and performing defect alarm management based on the defect warning information.

[0059] Specifically, the generated internal defect detection results are analyzed to assess the severity and potential risk of the defects. For example, factors such as defect type, size, and location are used to determine whether the defect requires immediate attention. Based on the analysis results, corresponding defect warning information is generated. The warning information includes the specific location, type, size, risk level, and recommended treatment measures. For example, a crack longer than 10 cm will generate a high-risk warning and recommend immediate repair.

[0060] On the defect detection system's display interface, internal defect detection results are displayed in a pop-up window. This pop-up window includes a 3D defect distribution map, detailed information (such as location, type, and size), and warning information. The pop-up window provides user interaction, such as clicking a defect to view detailed information or clicking a warning to view recommended remedial measures. The pop-up window content is updated in real time, ensuring that users always have access to the latest defect detection information.

[0061] Based on the generated defect warning information, the corresponding alarm mechanism is activated. For example, for high-risk defects, an audible or visual alarm is triggered to alert on-site personnel. Alarm information, including the alarm time, defect location, type, and risk level, is recorded in the system. This record is used for subsequent analysis and management. Defect warning information and alarm notifications are sent to relevant management or maintenance personnel via text message, email, or instant messaging tools to ensure timely action.

[0062] For example, if a crack 5 cm deep and 15 cm long is detected, the system generates a high-risk warning message and displays a pop-up window with a 3D crack distribution map and detailed information. Simultaneously, the system triggers an audible alarm and sends a text message to on-site personnel, reminding them to immediately repair the problem. This approach, by generating defect warning information, promptly alerts relevant personnel to potential defects, ensuring that measures can be taken quickly to prevent further deterioration.

[0063] The embodiment of the present application adopts technical means 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 ultrasonic equipment to perform three-view scanning according to a preset trajectory, receiving echo signals and constructing a detection trajectory space, matching and analyzing the scan data based on the knowledge base, identifying fuzzy defect data (position and type), conducting targeted re-inspection of the fuzzy defect area, and finally determining the accurate detection results of internal defects. These technical means solve the technical problems of low detection accuracy and difficulty in defect positioning and identification in the existing internal defect detection of building concrete, and achieve the technical effect of improving the accuracy of defect type identification and position positioning accuracy.

[0064] In the above, refer to Figure 1 A method for detecting internal defects of building concrete according to an embodiment of the present invention is described in detail. Figure 2 A system for detecting internal defects of building concrete according to an embodiment of the present invention is described.

[0065] A system for detecting internal defects in building concrete according to an embodiment of the present invention is designed to address the technical issues of low detection accuracy and difficulty in defect location and identification in existing internal defect detection systems for building concrete, thereby improving the accuracy of defect type identification and location positioning. The system includes a defect knowledge base construction module 10, a scanning module 20, a defect qualitative matching module 30, and a defect identification module 40.

[0066] The defect knowledge base construction module 10 is used to set the detection spectrum, determine the detection pulse signal and perform multivariate defect distortion processing, execute time reversal enhancement and random noise addition, construct a defect knowledge base and embed it in the defect detection system; the scanning module 20 is used to drive the front-end ultrasonic equipment 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, wherein the fuzzy defect data includes defect location and 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 results.

[0067] The specific configuration of the scanning module 20 will be described in detail below. As described above, the scanning module 20 may further include: a signal scanning trajectory planning unit for obtaining geometric parameters of the building area in three viewing dimensions, including the main view, the side view, and the top view, and performing signal scanning trajectory planning based on a preset detection density to determine a viewing angle scanning trajectory, wherein the scanning trajectory planning is performed using at least two viewing angles, and the viewing angle scanning trajectory is used as the preset trajectory.

[0068] Among them, according to the detection pulse signal, the front-end ultrasonic equipment is driven to scan the building area along a preset trajectory 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 equipment; a detection allocation unit for performing detection allocation on the deployment array according to the preset trajectory and determining the array scanning mode; and a detection driving unit for detecting and driving the front-end ultrasonic equipment according to the detection pulse signal and the array scanning mode.

[0069] The specific configuration of the defect knowledge base construction module 10 will be described in detail below. As described above, the detection pulse signal is determined and multi-dimensional defect distortion processing is performed. The defect knowledge base construction module 10 may further include: a defect database retrieval unit for obtaining concrete process characteristics of the building area and retrieving the defect database; a multi-dimensional defect distortion feature extraction unit for traversing the defect database, performing clustering processing based on defect types, and extracting multi-dimensional defect distortion features, wherein each defect type corresponds to a defect distortion feature element; and a multi-dimensional defect distortion signal determination unit for processing the detection pulse signal based on the multi-dimensional defect distortion features to determine a multi-dimensional defect distortion signal.

[0070] Among them, time reversal enhancement and random noise 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 with the time series attenuation of the signal to determine the multivariate enhanced signal; a noise processing unit for introducing random noise, performing noise processing on the multivariate enhanced signal to determine the multivariate defect signal; and an integration unit for integrating the multivariate defect signal to construct the defect knowledge base.

[0071] The specific configuration of the defect qualitative matching module 30 will be described in detail below. As described above, the defect qualitative matching based on the defect knowledge base is performed to locate the fuzzy defect data. The defect qualitative matching module 30 may further include: a signal state matching unit for traversing the detection trajectory space, traversing the defect knowledge base to perform signal state matching, and locating the defect distribution space, wherein the defect distribution space is identified with the defect type and the defect distribution space is used as the fuzzy defect data.

[0072] The specific configuration of the defect identification module 40 will be described in detail below. As described above, based on the fuzzy defect data, directional detection deployment is performed. The defect identification module 40 may further include: a directional detection mode configuration unit for traversing the fuzzy defect data and configuring a directional detection mode for each fuzzy defect; a directional detection device deployment unit for deploying directional detection devices based on the directional detection mode; and a directional detection unit for performing directional detection based on the directional detection devices, guided by the defect location of each fuzzy defect, to determine directional detection data.

[0073] Among them, defect identification is performed to determine the internal defect detection result. The defect identification module 40 may further include: a defect feature identification unit for preprocessing the directional detection data and performing defect feature identification to determine the defect detection data; 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.

[0074] Among them, after determining the internal defect detection results, the system may further include: a defect warning information generation module for generating defect warning information based on the internal defect detection results; a pop-up visualization module for performing pop-up visualization of the internal defect detection results on the display interface of the defect detection system, and performing defect alarm management based on the defect warning information.

[0075] An internal defect detection system for building concrete provided by an embodiment of the present invention can execute an internal defect detection method for building concrete provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0076] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and 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 distinguishing each other and are not used to limit the scope of protection of the present invention.

[0077] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for detecting internal defects of building concrete, characterized in that: The method comprises: Set the detection spectrum, determine the detection pulse signal and perform multi-dimensional defect distortion processing, perform time reversal enhancement and random noise addition, build a defect knowledge base and embed it into the defect detection system; According to the detection pulse signal, the front-end ultrasonic device is driven to scan the building area along a preset track under three views, and a detection track space is constructed by echo reception; For the detection trajectory space, performing defect qualitative matching based on the defect knowledge base to locate fuzzy defect data, wherein the fuzzy defect data includes defect location and defect type; Performing directional detection deployment and defect identification based on the fuzzy defect data to determine internal defect detection results; The method of performing defect qualitative matching based on the defect knowledge base to locate fuzzy defect data includes: Traversing the detection trajectory space, traversing the defect knowledge base to perform signal state matching, and locating the defect distribution space, wherein the defect distribution space is marked with a defect type; Using the defect distribution space as the fuzzy defect data; The step of performing directional detection deployment according to the fuzzy defect data includes: Traversing the fuzzy defect data and configuring a directional detection method for each fuzzy defect; Deploy directional detection equipment according to the directional detection method; According to the directional detection equipment, directional detection is performed with the defect position of each fuzzy defect as a guide to determine directional detection data; Among them, performing defect identification and determining internal defect detection results include: Preprocessing the directional detection data and performing defect feature recognition to determine defect detection data; According to the defect detection data, data coverage is performed on the defect distribution space as the internal defect detection result.

2. A method for detecting internal defects of building concrete according to claim 1, characterized in that: The three perspectives include main perspective, side perspective, and top perspective dimensions; Acquiring geometric parameters of the building area, performing signal scanning trajectory planning based on a preset detection density, and determining a viewing angle scanning trajectory, wherein the scanning trajectory planning is performed using at least two viewing angle dimensions; The viewing angle scanning trajectory is used as the preset trajectory.

3. A method for detecting internal defects of building concrete according to claim 2, characterized in that: According to the detection pulse signal, the front-end ultrasonic device is driven to perform a preset track scan of the building area under three views, including: Obtaining a deployment array of the front-end ultrasound device; According to the preset trajectory, the deployment array is detected and allocated to determine the array scanning mode; The front-end ultrasonic device is detected and driven according to the detection pulse signal and the array scanning mode.

4. The method for detecting internal defects of building concrete according to claim 1, wherein: Determine the detection pulse signal and perform multi-dimensional defect distortion processing, including: Obtain the concrete process characteristics of the construction area and retrieve the defect database; Traversing the defect database, performing clustering processing based on defect types, and extracting multivariate defect distortion features, wherein each defect type corresponds to a defect distortion feature element; The detection pulse signal is processed according to the multi-element defect distortion characteristics to determine a multi-element defect distortion signal.

5. A method for detecting internal defects of building concrete according to claim 4, characterized in that: Perform time-reversal enhancement and random noise addition to build a defect knowledge base, including: The multivariate defect distortion signal is subjected to inverse time series enhancement processing by attenuating the time series of the signal to determine a multivariate enhanced signal: introducing random noise to perform noise processing on the multivariate enhanced signal to determine a multivariate defect signal; The multi-element defect signals are integrated to construct the defect knowledge base.

6. The method for detecting internal defects of building concrete according to claim 1, wherein: After determining the internal defect detection results, including: generating defect warning information according to the internal defect detection result; On the display interface of the defect detection system, the internal defect detection results are visualized in a pop-up window, and defect alarm management is performed based on the defect warning information.

7. A system for detecting internal defects of building concrete, characterized in that: The system is used to implement the method for detecting internal defects of building concrete according to any one of claims 1 to 6, and the system comprises: The defect knowledge base construction module is used to set the detection spectrum, determine the detection pulse signal, perform multi-dimensional defect distortion processing, perform time reversal enhancement and random noise addition, build the defect knowledge base, and embed it in the defect detection system; A scanning module is used to drive the front-end ultrasonic device according to the detection pulse signal to scan the building area along a preset track under three views and construct a detection track space by echo reception; A defect qualitative matching module is used to perform defect qualitative matching based on the defect knowledge base for the detection trajectory space to locate fuzzy defect data, wherein the fuzzy defect data includes defect location and defect type; The defect recognition module is used to perform directional detection deployment and defect recognition according to the fuzzy defect data, and determine the internal defect detection result.

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