Concrete multidimensional detection robot and detection method thereof

Abstract

The application discloses a concrete multidimensional detection robot and a detection method thereof, and comprises a moving module, a detection module, a control and analysis module and a power supply module; wherein the power supply module is in power connection with the moving module, the detection module and the control and analysis module, and provides working power supply for each module; the moving module is used for adjusting the posture according to the space posture requirements of different detection scenes and generating a planning path; the detection module is arranged on the moving module and is used for acquiring multidimensional detection data of concrete and transmitting the multidimensional detection data to the control and analysis module; the control and analysis module is in bidirectional communication connection with the moving module, the detection module and the power supply module, and is used for analyzing the multidimensional detection data to obtain a detection result. The application realizes integrated and automatic operation of concrete structure multidimensional detection, improves detection efficiency and data precision, reduces manual intervention, and meets the fine requirement of engineering structure safety evaluation.

Description

Technical Field

[0001] This invention relates to the field of concrete structure testing equipment and automated testing technology, and in particular to a multi-dimensional concrete testing robot and its testing method. Background Technology

[0002] Concrete structures are widely used in construction, bridges, tunnels, and other engineering fields. During their service life, surface defects (such as cracks, damage, steel corrosion, water seepage and whitening), strength defects, and internal defects (such as voids and looseness) directly affect structural safety and durability. Currently, the field of concrete testing faces the following technical challenges: 1) Fragmented testing functions: In existing technologies, surface defect detection largely relies on manual visual inspection or single image acquisition equipment; strength testing relies on handheld devices such as rebound hammers and core drills; and internal defect detection requires the separate use of ultrasonic detectors, ground-penetrating radar, impact echo, and array ultrasonic equipment. These three types of equipment lack integrated design, resulting in cumbersome testing processes, reduced efficiency, and difficulty in correlating and analyzing data from different devices. 2) Low automation: Traditional testing methods rely on manual operation, which is not only labor-intensive but also susceptible to the influence of operator experience and subjective judgment, leading to poor testing accuracy and data consistency. Especially in high-altitude or hazardous conditions such as the bottom of bridge main beams, the sides of concrete main towers, and tunnel sidewalls, manual testing poses safety hazards. 3) Insufficient quantitative analysis: Existing integrated testing equipment can only achieve qualitative / semi-quantitative detection of apparent defects and strength. It is difficult to accurately quantify key parameters such as the size, location, and morphology of internal defects, which cannot meet the needs of engineering for refined assessment of structural defects. 4) Poor data synergy: Data from each testing module is stored and processed independently, lacking a unified control and data fusion platform. This results in the inability to perform real-time linked analysis of test results, affecting the accuracy and timeliness of defect diagnosis.

[0003] Therefore, developing an integrated, automated, and high-precision multi-parameter concrete testing device is key to solving the above problems. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the aforementioned technologies. Therefore, the purpose of this invention is to propose a multi-dimensional concrete inspection robot and its inspection method, realizing integrated and automated operation of multi-dimensional concrete structure inspection, improving inspection efficiency and data accuracy, reducing manual intervention, and meeting the refined requirements of engineering structure safety assessment.

[0005] To achieve the above objectives, this invention proposes a multi-dimensional concrete inspection robot, comprising: a movement module, an inspection module, a control and analysis module, and a power supply module; wherein, The power supply module establishes a power connection with the mobile module, detection module, and control and analysis module, providing power to each module. The movement module is used to adjust the attitude to meet the spatial attitude requirements of different detection scenarios and generate a planned path; The detection module, located on the mobile module, is used to acquire multi-dimensional detection data of concrete and transmit it to the control and analysis module. The control and analysis module establishes a two-way communication connection with the movement module, detection module, and power supply module to analyze multi-dimensional detection data and obtain detection results.

[0006] According to some embodiments of the present invention, a moving module includes: The mobile body is used to adapt to concrete surfaces via a multi-degree-of-freedom tracked wheel set or a negative pressure adsorption chassis. The attitude adjustment module is used to collect three-axis attitude tilt data of the concrete surface based on the IMU inertial measurement unit, and to dynamically level the moving body according to the three-axis attitude tilt data. The path planning module is used to acquire point cloud data of the moving scene based on LiDAR and image data of the moving scene based on the image acquisition module. It then performs obstacle recognition based on the point cloud data and image data to generate a planned path.

[0007] According to some embodiments of the present invention, the detection module includes: The modeling module is used to construct a 3D model of the detection scene, and then construct a local spatial coordinate system; The surface defect detection module is used to acquire images of concrete surfaces and identify the type, size and location of surface defects to obtain surface defect detection data. The strength testing module is used to measure the compressive strength data of concrete using the ultrasonic-rebound combined method. The first internal defect detection module is used to perform preliminary screening and investigation of internal defects in concrete using array impact echo technology, determine whether there are defects inside the concrete, mark the planar location and approximate depth of the defect area, and use it as the first internal defect detection data. The second internal defect detection module is used to quantitatively detect and accurately scan internal defects in concrete using three-dimensional ultrasonic technology. It performs three-dimensional ultrasonic detection on the defect area marked by the first internal defect detection module to obtain the three-dimensional size, volume and spatial shape of the defect as the second internal defect detection data. The determination module is used to determine multi-dimensional detection data based on apparent defect detection data, compressive strength data, first internal defect detection data, and second internal defect detection data, and then transmit the data to the control and analysis module.

[0008] According to some embodiments of the present invention, a modeling module includes: 3D LiDAR is used to collect 3D point cloud data of the scene being inspected. Visual sensors are used to acquire environmental images of the detection scene; The IMU (Inertial Measurement Unit) is used to collect attitude information of the moving module. The construction module is used to build a 3D model of the detection scene based on 3D point cloud data, environmental images and pose information using a map synchronous construction algorithm, and then establish a local spatial coordinate system.

[0009] According to some embodiments of the present invention, an apparent disease detection module includes: The supplementary lighting module is used to acquire the ambient light intensity of the detection scene and adaptively adjust the lighting parameters; A camera module is used to acquire images of the concrete surface based on adjusted lighting parameters; The recognition module is used to identify concrete surface images, determine the type, size, and location of apparent defects, and obtain apparent defect detection data.

[0010] According to some embodiments of the present invention, the strength detection module includes: Several rebound hammers, arranged in a rectangular shape, are used to receive synchronous trigger commands from the control and analysis module, synchronously trigger the acquisition of concrete rebound values, and transmit them to the first signal processing module. An ultrasonic transducer is used to receive a transmission command sent by a first signal processing module and transmit the received ultrasonic signal to the first signal processing module. The first signal processing module is used to correct the coupling state of the ultrasonic signal to obtain corrected data; based on the rebound value, corrected data and preset technical specifications, the compressive strength data of concrete is measured through a preset segmented calibration model.

[0011] According to some embodiments of the present invention, a first internal defect detection module includes: The array impact source module includes an array composed of several electromagnetic excitation units; the array impact source sequentially excites the target by a preset delay, so that the stress waves generated by each electromagnetic excitation unit are superimposed at the target depth / position to form a focused beam. The array receiver module includes an array of piezoelectric accelerometers corresponding to several electromagnetic excitation units, used to collect vibration signals and simultaneously collect reflected wave signals at different locations, and record the arrival time, amplitude and phase difference of the reflected wave signals. The second signal processing module incorporates a synthetic aperture imaging reconstruction method. It performs time-space domain mapping on the acquired multi-channel signals using synthetic aperture focusing technology and inverse time migration algorithm to reconstruct the image information of the defects. It is also used to perform Fourier transform and power spectrum analysis on the vibration signals acquired by the array receiver module to extract wave velocity information corresponding to the characteristic frequencies. Based on the image information and wave velocity information, it determines the planar location and approximate depth of the internal defects in the concrete, thus obtaining the first internal defect detection data.

[0012] According to some embodiments of the present invention, a second internal defect detection module includes: The transducer array includes M×N independent ultrasonic transducers arranged in a two-dimensional matrix. The ultrasonic probe of each ultrasonic transducer can independently excite / receive ultrasonic waves for three-dimensional beam focusing and deflection. The FPGA control module receives the detection start command from the control and analysis module. It uses a time-division multiplexing mechanism to control one group of transducers to emit narrow pulse signals, while the other groups of transducers synchronously receive the reflected signals. The reflected signals are then processed by a synthetic aperture focusing algorithm to perform time-delay superposition, calculate the three-dimensional coordinates of the reflection interface, and quantitatively output the three-dimensional size, volume, and spatial morphology of the defect as the second internal defect detection data.

[0013] According to some embodiments of the present invention, a transducer array adjustment module is further included, for: Based on the first internal defect detection data sent by the first internal defect detection module and the three-dimensional point cloud data determined by the modeling module, the target attitude of the motorized gimbal where the transducer array is placed is determined. The current attitude of the motorized gimbal is obtained, and attitude adjustment is performed when it is determined that the current attitude is inconsistent with the target attitude.

[0014] According to some embodiments of the present invention, the detection method of the concrete multi-dimensional detection robot as described above includes: The mobile module adjusts its attitude to meet the spatial attitude requirements of different detection scenarios and generates a planned path. During the execution of the planned path, a three-dimensional model of the detection scene is constructed based on the modeling module included in the detection module, thereby establishing a local spatial coordinate system for the detection area; Based on the appearance defect detection module included in the detection module, images of the concrete surface are acquired and appearance defect types, sizes and locations are identified to obtain appearance defect detection data; The strength testing module included in the testing module measures the compressive strength data of concrete using the ultrasonic-rebound combined method. When determining the apparent defects based on the apparent defects detection data and / or the compressive strength detection anomalies based on the compressive strength data, the first internal defect detection module included in the detection module uses array impact echo technology to perform preliminary screening and preliminary investigation of internal defects in concrete, determine whether there are defects inside the concrete, mark the planar location and approximate depth of the defect area, and use it as the first internal defect detection data. When an internal defect detection anomaly is determined based on the first internal defect detection data, the second internal defect detection module included in the detection module uses three-dimensional ultrasonic technology to quantitatively detect and accurately scan the internal defects of the concrete. Three-dimensional ultrasonic detection is performed on the defect area marked by the first internal defect detection module to obtain the three-dimensional size, volume and spatial morphology of the defect and determine the detection result.

[0015] This invention proposes a multi-dimensional concrete inspection robot and its inspection method, which realizes a multi-module integrated design, breaks through the pain point of the dispersed functions of traditional inspection equipment, and realizes full-dimensional inspection of concrete from appearance to interior, from strength to damage, significantly improving inspection efficiency and accuracy. Based on the control and analysis module, the multi-dimensional inspection data is analyzed to realize multi-dimensional data joint analysis, thereby improving the accuracy and timeliness of diagnosis.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a block diagram of a concrete multi-dimensional inspection robot according to an embodiment of the present invention; Figure 2 This is a block diagram of a mobile module according to an embodiment of the present invention; Figure 3 This is a bottom view of a concrete multi-dimensional inspection robot according to an embodiment of the present invention; Figure 4 This is a front view of a concrete multi-dimensional inspection robot according to an embodiment of the present invention; Figure 5 This is a flowchart of a detection method for a concrete multi-dimensional detection robot according to an embodiment of the present invention.

[0019] Figure label: Module 1, Module 12, Module 2, Module 2, Module 21, Module 22, Module 23, Module 24, Module 25, Module 26, Module 27, Module 28, Module 29, Module 20 ...0, Module 21, Module 22, Module 23, Module 24, Module 25, Module 20, Module 21, Module 22, Module 23, Module 24, Module 25, Module 26, Module 27, Module 28, Module 29, Module 20, Module 20, Module 20, Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] like Figure 1 As shown in the figure, this embodiment of the invention proposes a multi-dimensional concrete inspection robot, including: a movement module 1, an inspection module 2, a control and analysis module 3, and a power supply module 5; wherein, Power supply module 5 establishes a power connection with mobile module 1, detection module 2, and control and analysis module 3 to provide working power to each module; The mobile module 1 is used to adjust the attitude to meet the spatial attitude requirements of different detection scenarios and generate a planned path. Detection module 2, installed on the moving module 1, is used to acquire multi-dimensional detection data of concrete and transmit it to the control and analysis module 3; The control and analysis module 3 establishes a two-way communication connection with the movement module 1, the detection module 2, and the power supply module 5 to analyze multi-dimensional detection data and obtain detection results.

[0022] The working principle of the above technical solution is as follows: Power supply module 5 establishes a power connection with moving module 1, detection module 2, and control and analysis module 3, providing a stable power supply. It addresses the spatial posture requirements of different detection scenarios, such as roughness and tilting. Posture adjustment is achieved through moving module 1, enabling the detection module mounted on it to accurately acquire relevant detection data. Simultaneously, obstacle recognition and path planning are completed based on LiDAR point cloud data and image data. Detection module 2 integrates functions such as local spatial coordinate system construction, surface defect detection, strength testing, and dual internal defect detection. It is used to acquire multi-dimensional detection data of concrete and transmit it to control and analysis module 3. Control and analysis module 3 analyzes the multi-dimensional detection data to obtain the detection results.

[0023] The beneficial effects of the above technical solution are: to realize multi-module integrated design, break the pain point of the dispersed functions of traditional testing equipment, realize full-dimensional testing of concrete, greatly improve testing efficiency and accuracy, and analyze multi-dimensional testing data based on the control and analysis module 3 to realize multi-dimensional data joint analysis, thereby improving the accuracy and timeliness of diagnosis.

[0024] like Figure 2 As shown, according to some embodiments of the present invention, the moving module 1 includes: The mobile body is used to adapt to concrete surfaces via a multi-degree-of-freedom tracked wheel set or a negative pressure adsorption chassis. The attitude adjustment module 12 is used to collect three-axis attitude tilt data of the concrete surface based on the IMU inertial measurement unit, and to dynamically level the moving body according to the three-axis attitude tilt data. The path planning module is used to acquire point cloud data of the moving scene based on LiDAR and image data of the moving scene based on the image acquisition module. It then performs obstacle recognition based on the point cloud data and image data to generate a planned path.

[0025] The working principle of the above technical solution is as follows: A multi-degree-of-freedom tracked wheel assembly adapts to rough, uneven concrete surfaces (such as building floor slabs, the interior of box girders, and other concave and convex areas). A negative pressure adsorption chassis meets the stable attachment requirements for high-altitude inverted scenarios (such as the top and side walls of tunnels, the bottom of bridge beams, and the sides of main towers). Based on an IMU (Inertial Measurement Unit), three-axis attitude tilt data of the concrete surface is collected. An IMU is a device that measures the three-axis attitude angles (or angular rates) and acceleration of an object. One IMU contains three single-axis accelerometers and three single-axis gyroscopes. The accelerometers detect the acceleration signals of the object along three independent axes in the carrier coordinate system, while the gyroscopes detect the angular velocity signals of the carrier relative to the navigation coordinate system. Measuring the angular velocity and acceleration of the object in three-dimensional space allows for the calculation of the object's attitude. Based on the three-axis attitude tilt data, the moving body is dynamically leveled to ensure that the detection module is always perpendicular to the detection surface. Obstacle recognition is performed based on point cloud data and image data, generating a planned path to achieve autonomous obstacle avoidance and dynamic correction of the planned path.

[0026] The beneficial effects of the above technical solution are as follows: It improves the adaptability of concrete testing applications; the attitude adjustment module 12 uses data-driven dynamic leveling to ensure that the moving body and the mounted detection module remain perpendicular to the detection surface, avoiding measurement errors caused by the angle deviation between the detection module and the detection surface due to surface tilt, thus improving the accuracy of multi-dimensional detection data. It also accurately locates the spatial position and size of obstacles using point cloud data, clarifies obstacle attributes using image data, and generates a planned path using a path planning algorithm, thereby improving the safety of movement.

[0027] According to some embodiments of the present invention, the detection module 2 includes: Modeling module 21 is used to construct a 3D model of the detection scene and then establish a local spatial coordinate system; The surface defect detection module 22 is used to acquire concrete surface images and identify the type, size and location of surface defects to obtain surface defect detection data. The strength testing module 23 is used to measure the compressive strength data of concrete using the ultrasonic-rebound combined method; The first internal defect detection module 24 is used to perform preliminary screening and investigation of internal defects in concrete using array impact echo technology, determine whether there are defects inside the concrete, mark the planar location and approximate depth of the defect area, and use it as the first internal defect detection data. The second internal defect detection module 25 is used to quantitatively detect and accurately scan internal defects in concrete using three-dimensional ultrasonic technology. It performs three-dimensional ultrasonic detection on the defect area marked by the first internal defect detection module 24 to obtain the three-dimensional size, volume and spatial shape of the defect as the second internal defect detection data. The determination module is used to determine multi-dimensional detection data based on the apparent defect detection data, compressive strength data, first internal defect detection data, and second internal defect detection data, and then transmit the data to the control and analysis module 3.

[0028] The working principle of the above technical solution is as follows: Modeling module 21 constructs a three-dimensional model of the detection scene, and then establishes a local spatial coordinate system to provide a spatial reference for the entire detection process, realize robot self-positioning and obstacle recognition, provide environmental data support for path planning, and provide coordinate reference for the spatial positioning of apparent defects and internal defects. Apparent defect detection module 22 collects concrete surface images and identifies the type, size and location of apparent defects to obtain apparent defect detection data; Strength detection module 23 measures the compressive strength data of concrete through ultrasonic-rebound combined method; First internal defect detection module 24 is used to perform preliminary screening and preliminary investigation of internal defects of concrete through array impact echo technology (stress wave), determine whether there are defects inside the concrete, mark the planar position and approximate depth of the defect area as the first internal defect detection data; wherein, the defect area includes voids, cavities, and cracks; Based on the first internal defect detection module 24, more than 90% of the defect-free areas are quickly eliminated. The second internal defect detection module 25 uses three-dimensional ultrasonic technology to quantitatively detect and precisely scan internal defects in concrete. It performs three-dimensional ultrasonic detection on the defect area marked by the first internal defect detection module 24 to obtain the three-dimensional dimensions (length × width × height), volume and spatial shape of the defect, which are used as the second internal defect detection data. Based on the apparent defect detection data, compressive strength data, first internal defect detection data and second internal defect detection data, it determines multi-dimensional detection data and transmits it to the control and analysis module 3.

[0029] The beneficial effects of the above technical solution are: integrating surface defect detection, strength detection and defect detection to achieve multi-dimensional integrated detection, accurately determining surface defect detection data, compressive strength data, first internal defect detection data and second internal defect detection data, and improving data synergy.

[0030] According to some embodiments of the present invention, a modeling module includes: 3D LiDAR is used to collect 3D point cloud data of the scene being inspected. Visual sensors are used to acquire environmental images of the detection scene; The IMU (Inertial Measurement Unit) is used to collect attitude information of the moving module. The construction module is used to build a 3D model of the detection scene based on 3D point cloud data, environmental images and pose information using a map synchronous construction algorithm, and then establish a local spatial coordinate system.

[0031] The working principle and beneficial effects of the above technical solution are as follows: The 3D LiDAR has 16 or more lines of parameters. The 3D LiDAR emits a laser beam, which reflects off the concrete in the detection scene. The 3D LiDAR receives the reflected laser signal, records the laser propagation time, angle, and distance information, and then converts this information into high-density 3D point cloud data of the detection scene. This facilitates the complete capture of the scene's spatial contours, surface details, and obstacle distribution information. The visual sensor is a camera, which captures environmental images of the detection scene and extracts feature points from these images. This facilitates frame matching of the 3D point cloud data, solving the problem of insufficient features in textureless environments and further improving positioning stability. The IMU (Inertial Measurement Unit) module measures the acceleration and angular velocity of the moving module to determine its attitude information and short-term position. The map synchronization algorithm is the LVI-SLAM (LiDAR-Visual-Inertial SLAM) algorithm, used to integrate 3D point cloud data, environmental images, and attitude information to accurately construct a 3D model of the detection scene and establish a local spatial coordinate system.

[0032] In one embodiment, the modeling module includes: 3D LiDAR is used to collect 3D point cloud data of the scene being inspected. The IMU (Inertial Measurement Unit) is used to collect attitude information of the moving module. The module is used to construct a 3D model of the detection scene based on 3D point cloud data and pose information using the LIO-SAM algorithm, and then establish a local spatial coordinate system.

[0033] The working principle and beneficial effects of the above technical solution are as follows: The 3D point cloud data of the detection scene accurately captures the microscopic geometric features of the concrete surface; attitude information is used to correct the robot's pose deviation; the LIO-SAM (LiDAR-Inertial Odometry via Smoothing and Mapping) algorithm adopts a factor graph block optimization design, which can simultaneously complete data fusion and modeling during the movement of the mobile module, improving modeling accuracy and efficiency. In terms of accuracy, the IMU compensates for the attitude error of the LiDAR sampling interval, and the block optimization fusion with multiple constraints reduces the impact of sensor noise. In terms of efficiency, the parallel architecture of the factor graph block optimization algorithm reduces the optimization time per frame, avoids the delay of traditional serial processes, adapts to a wide range of high-dynamic scenes, and improves operational efficiency and adaptability.

[0034] According to some embodiments of the present invention, the apparent disease detection module 22 includes: The supplementary lighting module is used to acquire the ambient light intensity of the detection scene and adaptively adjust the lighting parameters; A camera module is used to acquire images of the concrete surface based on adjusted lighting parameters; The recognition module is used to identify concrete surface images, determine the type, size, and location of apparent defects, and obtain apparent defect detection data.

[0035] The working principle of the above technical solution is as follows: The supplementary lighting module has a built-in ambient light sensor to collect ambient light intensity data of the detection scene. The control and analysis module 3 sends adjustment commands to the supplementary lighting module through pulse width modulation according to the preset illumination threshold, so as to provide a uniform and stable illumination environment for image acquisition. The camera module acquires images of the concrete surface based on the adjusted illumination parameters; the recognition module performs feature matching and classification recognition on the concrete surface images based on the YOLOv11 algorithm, automatically determines the type of defects, calculates the quantitative parameters of defects such as length and width through pixel calibration, and combines the timestamp and three-dimensional spatial coordinate label attached to the concrete surface image to locate the three-dimensional position information of the defects, thereby obtaining the surface defect detection data.

[0036] The beneficial effects of the above technical solution are: by sensing changes in illumination in real time through an ambient light sensor and performing adaptive adjustment of illumination, accurate images of the concrete surface can be obtained, thereby obtaining accurate data for detecting apparent defects.

[0037] According to some embodiments of the present invention, the strength detection module 23 includes: Several rebound hammers are arranged in a rectangular shape to receive synchronous trigger commands from the control and analysis module 3, and synchronously trigger the acquisition of the rebound value of concrete, which is then transmitted to the first signal processing module. An ultrasonic transducer is used to receive a transmission command sent by a first signal processing module and transmit the received ultrasonic signal to the first signal processing module. The first signal processing module is used to correct the coupling state of the ultrasonic signal to obtain corrected data; based on the rebound value, corrected data and preset technical specifications, the compressive strength data of concrete is measured through a preset segmented calibration model.

[0038] The working principle of the above technical solution is as follows: There are four rebound hammers, all conforming to the GB / T 50107-2010 standard. When the moving module 1 moves to the preset detection area, the control and analysis module 3 synchronously sends trigger commands to the rebound hammers and ultrasonic transducers. The four rebound hammers respond to the commands by simultaneously impacting the concrete surface, collecting the rebound value corresponding to the surface hardness of the concrete, and transmitting it to the first signal processing module. The ultrasonic transducer receives the transmission command from the first signal processing module, outputs a high-frequency ultrasonic signal, and after penetrating the concrete, it returns via reflection / transmission. The reflected signal is received and amplified, and the processed ultrasonic signal is transmitted to the first signal processing module, while simultaneously recording the signal propagation time. By analyzing the amplitude and phase changes of the ultrasonic signal, the coupling state between the ultrasonic transducer and the concrete surface is determined, the ultrasonic propagation time is corrected, and the actual ultrasonic velocity is calculated as correction data. The first signal processing module calls the preset technical specification standard (T / CECS 02-2020 "Technical Specification for Testing the Compressive Strength of Concrete by Ultrasonic Rebound Comprehensive Method"), loads a segmented calibration model divided according to the concrete strength grade, covering C15-C80, a total of 6 calibration intervals; inputs the rebound value and correction data into the calibration model of the corresponding strength grade, obtains the concrete compressive strength data through multivariate regression calculation, and attaches the three-dimensional spatial coordinate label of the test area to determine the concrete compressive strength data.

[0039] The beneficial effects of the above technical solution are: correction of the coupling state of ultrasonic signals, adaptation to coupling stability fluctuations under dry coupling conditions, and improved accuracy of data acquisition. It also allows for accurate measurement of concrete compressive strength data based on preset technical specifications and standards and a pre-defined segmented calibration model.

[0040] According to some embodiments of the present invention, the first internal defect detection module 24 includes: The array impact source module includes an array composed of several electromagnetic excitation units; the array impact source sequentially excites the target by a preset delay, so that the stress waves generated by each electromagnetic excitation unit are superimposed at the target depth / position to form a focused beam. The array receiver module includes an array of piezoelectric accelerometers corresponding to several electromagnetic excitation units, used to collect vibration signals and simultaneously collect reflected wave signals at different locations, and record the arrival time, amplitude and phase difference of the reflected wave signals. The second signal processing module incorporates a synthetic aperture imaging reconstruction method. It performs time-space domain mapping on the acquired multi-channel signals using synthetic aperture focusing technology and inverse time migration algorithm to reconstruct the image information of the defects. It is also used to perform Fourier transform and power spectrum analysis on the vibration signals acquired by the array receiver module to extract wave velocity information corresponding to the characteristic frequencies. Based on the image information and wave velocity information, it determines the planar location and approximate depth of the internal defects in the concrete, thus obtaining the first internal defect detection data.

[0041] The working principle of the above technical solution is as follows: The array impact source module consists of several electromagnetic excitation units. The control unit triggers each excitation unit sequentially according to a preset delay command. During the propagation process, the stress waves generated by each unit form coherent superposition at the target depth / position, and finally converge into a focused beam with concentrated energy, which can accurately act on the area to be detected, improving the penetration ability and efficiency of stress waves to deep defects. The array receiver module uses a piezoelectric accelerometer array corresponding to each electromagnetic excitation unit to simultaneously complete the acquisition of two types of signals: one is the vibration signal of the concrete surface and interior after excitation, and the other is the reflected wave signal reflected after the stress wave encounters the defect interface or the interface between the concrete and other media. During the acquisition process, the arrival time, amplitude, and phase difference of the reflected wave are recorded simultaneously. The second signal processing module uses synthetic aperture focusing technology (SAFT) and reverse time migration algorithm (RTM) to perform time-space domain mapping transformation on the original signals acquired by multiple channels, reconstructing the two-dimensional / three-dimensional image information of the defect, and intuitively presenting the morphological characteristics of the defect; Fourier transform and power spectrum analysis are performed on the vibration signal to extract the characteristic frequencies related to the defect, and the corresponding wave velocity information is calculated in combination with the characteristic frequencies. Finally, the module integrates the reconstructed image information and wave velocity data, calculates the planar location and approximate depth of the defect through a geometric positioning model, and outputs accurate first internal defect detection data.

[0042] The beneficial effects of the above technical solution are as follows: Based on the second signal processing module, the multi-channel signals acquired are mapped in the time-space domain using synthetic aperture focusing technology and inverse time migration algorithm to reconstruct the image information of the defects; it is also used to perform Fourier transform and power spectrum analysis on the vibration signals acquired by the array receiver module to extract the wave velocity information corresponding to the characteristic frequencies; based on the image information and wave velocity information, the planar position and approximate depth of the internal defects in the concrete are determined to obtain the first internal defect detection data, which facilitates the improvement of detection accuracy and defect identification capability.

[0043] According to some embodiments of the present invention, the second internal defect detection module 25 includes: The transducer array includes M×N independent ultrasonic transducers arranged in a two-dimensional matrix. The ultrasonic probe of each ultrasonic transducer can independently excite / receive ultrasonic waves for three-dimensional beam focusing and deflection. The FPGA control module receives the detection start command from the control and analysis module 3. It uses a time-division multiplexing mechanism to control one group of transducers to emit narrow pulse signals, while the other groups of transducers synchronously receive the reflected signals. The reflected signals are then processed by a synthetic aperture focusing algorithm to perform time-delay superposition, calculate the three-dimensional coordinates of the reflection interface, and quantitatively output the three-dimensional size, volume, and spatial morphology of the defect as the second internal defect detection data.

[0044] The working principle of the above technical solution is as follows: The transducer array consists of a two-dimensional matrix structure composed of M×N independent ultrasonic transducers. Each transducer's ultrasonic probe can independently realize excitation and reception functions. The FPGA control module adopts a time-division multiplexing mechanism, sequentially controlling one group of transducers to emit narrow-pulse ultrasonic signals according to a preset grouping. At the same time, the remaining non-emitting transducer groups synchronously switch to receiving mode, accurately capturing the reflected signals generated by the ultrasonic signal encountering the defect interface when propagating inside the concrete. The transducer array can achieve focusing and deflection of the three-dimensional beam by independently controlling the excitation / reception timing of each probe, ensuring that the ultrasonic signal can accurately cover the detection area and improving the ability to capture hidden defects. The FPGA control module incorporates a synthetic aperture focusing algorithm to uniformly process the reflected signals acquired by each receiving channel. By delaying and superimposing the reflected signals received by transducers at different locations, it cancels out diffusion and interference during signal propagation, thereby improving the signal-to-noise ratio of the reflected signals. Based on the processed signal data and combined with the spatial coordinate parameters of the transducer array, it reversely calculates the three-dimensional coordinates of the reflection interface (i.e., the defect interface). Furthermore, through a three-dimensional reconstruction algorithm, it quantifies and outputs the core parameters such as the three-dimensional size, volume, and spatial morphology of the defect, accurately determining the second internal defect detection data.

[0045] The beneficial effects of the above technical solution are as follows: By using an M×N two-dimensional matrix transducer array and stereo beam control technology, combined with a synthetic aperture focusing algorithm, the three-dimensional size, volume, and spatial morphology of defects can be directly output, improving the efficiency and accuracy of three-dimensional quantitative detection. The use of a time-division multiplexing mechanism significantly shortens the detection cycle and improves detection efficiency; simultaneously, through array-coordinated reception and delay superposition processing, noise caused by environmental interference and signal diffusion can be effectively filtered, significantly improving the signal-to-noise ratio of the reflected signal and reducing the missed detection rate of minute defects.

[0046] In one embodiment, a transducer array adjustment module is further included, for: Based on the first internal defect detection data sent by the first internal defect detection module and the three-dimensional point cloud data determined by the modeling module, the target attitude of the motorized gimbal where the transducer array is placed is determined. The current attitude of the motorized gimbal is obtained, and attitude adjustment is performed when it is determined that the current attitude is inconsistent with the target attitude.

[0047] The working principle and beneficial effects of the above technical solution are as follows: Based on the first internal defect detection data sent by the first internal defect detection module and the three-dimensional point cloud data determined by the modeling module, the target posture of the motorized pan-tilt unit where the transducer array is placed is determined; the current posture of the motorized pan-tilt unit is acquired, and posture adjustment is performed when the current posture is inconsistent with the target posture. This ensures that the central axis of the transducer array is consistent with the surface normal vector of the defect area, the ultrasonic beam is perpendicular to the surface of the defect area, and the scanning is performed on the defect area and a small effective area around it, eliminating invalid blank areas and improving scanning efficiency and positioning accuracy.

[0048] Based on the first internal defect detection data sent by the first internal defect detection module and the three-dimensional point cloud data determined by the modeling module, the target attitude of the motorized gimbal where the transducer array is placed is determined, including: Based on the iterative nearest point algorithm, coordinate transformation is performed on the first internal defect detection data, transforming the first internal defect detection data in the local detection coordinate system to the global world coordinate system corresponding to the 3D point cloud data; Based on the unified global world coordinate system, the center coordinates of the defect area marked by the first internal defect detection data are used as the center, and the horizontal and vertical lengths are extended by a preset length, and the depth direction is extended by a preset depth. The local three-dimensional model is determined by dividing the space based on the octree space partitioning algorithm. Calculate the covariance matrix of the point cloud within the node based on the local 3D model; ; in, It is the covariance matrix; The number of point clouds within a node; Let be the coordinates of the i-th point cloud; The mean coordinates of the point cloud within the node; The covariance matrix is ​​decomposed into eigenvalues, and the eigenvector corresponding to the smallest eigenvalue is used as the normal vector of the node surface. The target attitude of the motorized gimbal with transducer array is ( ); ; ; in, The component of the normal vector along the X-axis in the global world coordinate system; The component of the normal vector along the Y-axis in the global world coordinate system; The component of the normal vector along the Z-axis in the global world coordinate system; The projection of the normal vector onto the XY plane is the angle between the projection vector and the positive X-axis. The angle between the normal vector and the Z-axis is denoted as .

[0049] The working principle and beneficial effects of the above technical solution are as follows: An iterative nearest-point algorithm is used to transform the coordinates of the two sets of data, mapping the local defect data to the global coordinate system, ensuring that the position of the defect area is completely aligned with the spatial position of the 3D point cloud. An octree spatial partitioning algorithm is used to model the local point cloud in blocks, facilitating the focusing of the defect area and reducing computational consumption. For the node point cloud of the local 3D model, the covariance matrix (quantifying the dispersion of the point cloud in the X / Y / Z directions) is calculated, and then eigenvalue decomposition is performed on the covariance matrix to accurately extract the vertical orientation of the defect area surface, thereby facilitating the accurate calculation of the target attitude of the motorized gimbal where the transducer array is placed.

[0050] According to some embodiments of the present invention, the attitude adjustment module 12 includes a ball screw type lifting adjustment assembly; The detection module is connected to the moving module 1 via a ball screw type lifting adjustment assembly; The ball screw type lifting adjustment component generates adjustment commands for the stepper motor driver based on the three-axis attitude tilt data, and performs dynamic leveling of the moving body.

[0051] The working principle and beneficial effects of the above technical solution: The ball screw type lifting adjustment component generates adjustment commands for the stepper motor driver based on the three-axis attitude tilt data, performs dynamic leveling of the moving body, improves the leveling accuracy and stability, and avoids detection path deviation caused by attitude tilt.

[0052] According to some embodiments of the present invention, the control and analysis module 3 includes: The registration module is used to perform spatiotemporal registration on multi-dimensional detection data to obtain registration data. Analysis module, used for: Noise filtering and feature fusion are performed on the registered data using a multi-source heterogeneous data association algorithm to obtain fused data; The system calls upon national standard data from the built-in disease database, and matches the fused data with standard grading indicators based on the fuzzy comprehensive evaluation method to generate an assessment report containing graded assessment results, disease quantification parameters, risk levels, and maintenance recommendations, which serves as the test result.

[0053] The working principle of the above technical solution is as follows: Based on the timestamps attached to the multi-dimensional detection data, a time alignment algorithm is used to map the raw data of different detection modules to a unified time axis for spatiotemporal registration, resulting in registered data. A multi-source heterogeneous data association algorithm is used to filter noise from the registered data, extracting core features of each dimension, such as defect size thresholds, strength compliance rates, and defect volume ratios. A weighted fusion algorithm is then used to map these multi-source features to the same analytical dimension, resulting in fused data. The built-in defect database stores grading indicators from national standards such as the "Technical Condition Assessment Standard for Highway Bridges" (JTG / TH21-2011) and the "Technical Standard for Highway Maintenance" (JTG 5110—2023), including concrete strength grade thresholds, defect volume safety limits, and apparent defect grading standards. Based on the fuzzy comprehensive evaluation method, the system performs fuzzy matching between the fused data and the standard grading indicators to determine the structural health level of each inspection area. Simultaneously, it combines the quantitative parameters of the fused data to determine the risk level and matches a pre-set maintenance plan library according to the defect type and severity. Finally, it generates an assessment report containing the graded assessment results, defect quantitative parameters, risk level, and maintenance recommendations as the inspection result.

[0054] The beneficial effects of the above technical solution are as follows: by calling national standard data from the built-in disease database, the fused data is matched with the standard grading indicators based on the fuzzy comprehensive evaluation method, thereby improving the accuracy of the obtained test results.

[0055] In one embodiment, the system further includes a data storage and transmission module 4, comprising a local storage unit (SD card, storage capacity ≥1TB) and a wireless transmission unit (4G / 5G / WiFi module) to enable local backup of multi-dimensional detection data and remote real-time transmission to terminal devices (computers, mobile phones).

[0056] In one embodiment, four rebound hammers are located at the four corners of the detection module to detect the concrete strength of the detection area. The average value of the detection results is taken to give the average concrete strength of the area.

[0057] In one embodiment, a lifting adjustment component 6 is also included. The detection module is connected to the moving module 1 through the lifting adjustment component 6, which can realize adaptive adjustment of the detection distance (adjustment range 50-200mm) to adapt to concrete surfaces with different flatness.

[0058] like Figure 3 and Figure 4As shown, a first internal defect detection module 24 is arranged on the outermost layer of the moving module 1, a second internal defect detection module 25 is arranged in the middle, and strength detection modules 23 are arranged at the four corner points with the second internal defect detection module 25 as the center. The data storage and transmission module 4 is connected to the control and analysis module 3. The apparent defect detection module 22 is arranged in front and on the side of the moving module 1 to expand the detection range. For example, four apparent defect detection modules 22 are arranged in front of the moving module 1, and one apparent defect detection module 22 is arranged on each of the two sides of the moving module. The control and analysis module 3 and the data storage and transmission module 4 are arranged on the other side of the first internal defect detection module 25. The modeling module 21 is arranged in front of the moving module 1. The detection module 2 is connected to the moving module 1 through the lifting adjustment component 6.

[0059] like Figure 5 As shown, in one embodiment, a detection method for a concrete multi-dimensional detection robot includes steps S1-S6: S1. Adjust the attitude based on the spatial attitude requirements of different detection scenarios according to the mobile module 1, and generate a planned path; S2. During the execution of the planned path, a three-dimensional model of the detection scene is constructed based on the modeling module included in the detection module 2, thereby establishing a local spatial coordinate system for the detection area; S3. Based on the appearance defect detection module 22 included in the detection module 2, the concrete surface image is collected and the appearance defect type, size and location are identified to obtain appearance defect detection data. S4. The strength detection module 23 included in the detection module 2 measures the compressive strength data of concrete using the ultrasonic-rebound combined method. S5. When the apparent defect detection is determined based on the apparent defect detection data and / or the compressive strength detection is determined based on the compressive strength data, the first internal defect detection module 24 included in the detection module 2 uses array impact echo technology to perform preliminary screening and preliminary investigation of internal defects in concrete, determine whether there are defects inside the concrete, mark the planar position and approximate depth of the defect area, and use it as the first internal defect detection data. S6. When an internal defect detection anomaly is determined based on the first internal defect detection data, the second internal defect detection module 25 included in the detection module 2 performs quantitative detection and precise scanning of the internal defects of the concrete using three-dimensional ultrasonic technology. Three-dimensional ultrasonic detection is performed on the defect area marked by the first internal defect detection module 24 to obtain the three-dimensional size, volume and spatial shape of the defect and determine the detection result.

[0060] The working principle and beneficial effects of the above technical solution are as follows: First, the mobile module 1 adapts to the spatial posture requirements of different detection scenarios and automatically completes posture calibration; during the movement of the concrete multi-dimensional detection robot along the planned path, the detection module simultaneously starts the basic detection process: based on the modeling module included in the detection module, a three-dimensional model of the detection scenario is constructed, and then a local spatial coordinate system of the detection area is established; on the one hand, the surface defect detection module 22 acquires high-definition images of the concrete surface, and through image recognition algorithms (analyzing image features, accurately identifying the types of surface defects such as cracks, spalling, honeycombing, and pitting), while quantifying their dimensions (such as crack length and width). The system outputs surface defect detection data based on the spatial location of the concrete. The strength detection module 23 uses an ultrasonic-rebound combined method, emitting ultrasonic signals through an ultrasonic probe and collecting rebound values ​​through a rebound device. Combined with the concrete strength conversion formula, it measures and outputs the concrete's compressive strength data. If the surface defect detection data shows severe surface defects (such as large cracks or large-area spalling), and / or the compressive strength data is lower than the preset standard value (i.e., abnormal compressive strength detection), the first internal defect detection module 24 is automatically triggered. The first internal defect detection module 24 uses array impact echo technology, which uses an array impact source to excite stress waves and an array receiver to collect the reflected values. The transmitted signal, after signal processing, performs a preliminary screening of internal defects in the concrete, determining whether defects exist and marking the planar location and approximate depth of the defect area, forming the first internal defect detection data. If the first internal defect detection data shows the presence of internal defects in the concrete (i.e., abnormal internal defect detection), the second internal defect detection module 25 is further activated to conduct targeted and precise detection. The second internal defect detection module 25 uses three-dimensional ultrasonic technology to focus on the defect area marked by the first internal defect detection module 24 for precise scanning. It transmits / receives ultrasonic signals through a two-dimensional matrix transducer array, combined with algorithms such as synthetic aperture focusing to analyze the signal. The system processes the defects to obtain their three-dimensional dimensions, volume, and spatial morphology, resulting in a complete and accurate inspection report. Integrating three core inspection dimensions—appearance defects, compressive strength, and internal defects (preliminary + precise)—it forms a complete inspection chain from surface to interior, from qualitative to quantitative analysis, providing a more comprehensive reflection of the concrete's health status. It employs a layered logic of basic inspection → anomaly-triggered preliminary internal inspection → further triggered precise internal inspection, avoiding ineffective in-depth inspections of areas without anomalies and significantly improving overall inspection efficiency. Simultaneously, the precise inspection stage focuses on marking defect areas, reducing redundant inspection ranges and further enhancing the accuracy of defect detection.

[0061] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-dimensional concrete inspection robot, characterized in that, include: The module comprises a mobile module, a detection module, a control and analysis module, and a power supply module; among which, The power supply module establishes a power connection with the mobile module, detection module, and control and analysis module, providing power to each module. The movement module is used to adjust the attitude to meet the spatial attitude requirements of different detection scenarios and generate a planned path; The detection module, located on the mobile module, is used to acquire multi-dimensional detection data of concrete and transmit it to the control and analysis module. The control and analysis module establishes a two-way communication connection with the movement module, detection module, and power supply module to analyze multi-dimensional detection data and obtain detection results. The detection module includes: The modeling module is used to construct a 3D model of the detection scene and then establish a local spatial coordinate system; The surface defect detection module is used to acquire images of concrete surfaces and identify the type, size and location of surface defects to obtain surface defect detection data. The strength testing module is used to measure the compressive strength data of concrete using the ultrasonic-rebound combined method. The first internal defect detection module is used to perform preliminary screening and investigation of internal defects in concrete using array impact echo technology, determine whether there are defects inside the concrete, mark the planar location and approximate depth of the defect area, and use it as the first internal defect detection data. The second internal defect detection module is used to quantitatively detect and accurately scan internal defects in concrete using three-dimensional ultrasonic technology. It performs three-dimensional ultrasonic detection on the defect area marked by the first internal defect detection module to obtain the three-dimensional size, volume and spatial shape of the defect as the second internal defect detection data. The determination module is used to determine multi-dimensional detection data based on the apparent disease detection data, compressive strength data, first internal defect detection data, and second internal defect detection data, and then transmit the data to the control and analysis module. The first internal defect detection module includes: The array impact source module includes an array composed of several electromagnetic excitation units; the array impact source sequentially excites the target by a preset delay, so that the stress waves generated by each electromagnetic excitation unit are superimposed at the target depth / position to form a focused beam. The array receiver module includes an array of piezoelectric accelerometers corresponding to several electromagnetic excitation units, used to collect vibration signals and simultaneously collect reflected wave signals at different locations, and record the arrival time, amplitude and phase difference of the reflected wave signals. The second signal processing module incorporates a synthetic aperture imaging reconstruction method. It performs time-space domain mapping on the acquired multi-channel signals using synthetic aperture focusing technology and inverse time-shifting algorithm to reconstruct the image information of the defects. It is also used to perform Fourier transform and power spectrum analysis on the vibration signals acquired by the array receiver module to extract wave velocity information corresponding to the characteristic frequencies. Based on the image information and wave velocity information, it determines the planar location and approximate depth of the internal defects in the concrete, thus obtaining the first internal defect detection data. The second internal defect detection module includes: The transducer array includes M×N independent ultrasonic transducers arranged in a two-dimensional matrix. The ultrasonic probe of each ultrasonic transducer can independently excite / receive ultrasonic waves for three-dimensional beam focusing and deflection. The FPGA control module receives the detection start command from the control and analysis module. It uses a time-division multiplexing mechanism to control one group of transducers to emit narrow pulse signals, while the other groups of transducers synchronously receive the reflected signals. The reflected signals are then processed by a synthetic aperture focusing algorithm to perform time-delay superposition, calculate the three-dimensional coordinates of the reflection interface, and quantitatively output the three-dimensional size, volume, and spatial morphology of the defect as the second internal defect detection data. It also includes a transducer array adjustment module for: Based on the first internal defect detection data sent by the first internal defect detection module and the three-dimensional point cloud data determined by the modeling module, the target attitude of the motorized gimbal where the transducer array is placed is determined. The current attitude of the motorized gimbal is obtained, and attitude adjustment is performed when it is determined that the current attitude is inconsistent with the target attitude. Based on the first internal defect detection data sent by the first internal defect detection module and the three-dimensional point cloud data determined by the modeling module, the target attitude of the motorized gimbal where the transducer array is placed is determined, including: Based on the iterative nearest point algorithm, coordinate transformation is performed on the first internal defect detection data, transforming the first internal defect detection data in the local detection coordinate system to the global world coordinate system corresponding to the 3D point cloud data; Based on the unified global world coordinate system, the center coordinates of the defect area marked by the first internal defect detection data are used as the center, and the horizontal and vertical lengths are extended by a preset length, and the depth direction is extended by a preset depth. The local three-dimensional model is determined by dividing the space based on the octree space partitioning algorithm. Calculate the covariance matrix of the point cloud within the node based on the local 3D model; ； in, It is the covariance matrix; The number of point clouds within a node; Let be the coordinates of the i-th point cloud; The mean coordinates of the point cloud within the node; The covariance matrix is ​​decomposed into eigenvalues, and the eigenvector corresponding to the smallest eigenvalue is used as the normal vector of the node surface. The target attitude of the motorized gimbal with transducer array is ( ); ； ； in, The component of the normal vector along the X-axis in the global world coordinate system; The component of the normal vector along the Y-axis in the global world coordinate system; The component of the normal vector along the Z-axis in the global world coordinate system; The projection of the normal vector onto the XY plane is the angle between the projection vector and the positive X-axis. The angle between the normal vector and the Z-axis is denoted as .

2. The concrete multi-dimensional inspection robot as described in claim 1, characterized in that, The mobile module includes: The mobile body is used to adapt to concrete surfaces via a multi-degree-of-freedom tracked wheel set or a negative pressure adsorption chassis. The attitude adjustment module is used to collect three-axis attitude tilt data of the concrete surface based on the IMU inertial measurement unit, and to dynamically level the moving body according to the three-axis attitude tilt data. The path planning module is used to acquire point cloud data of the moving scene based on LiDAR and image data of the moving scene based on the image acquisition module. It then performs obstacle recognition based on the point cloud data and image data to generate a planned path.

3. The concrete multi-dimensional inspection robot as described in claim 1, characterized in that, The modeling module includes: 3D LiDAR is used to collect 3D point cloud data of the scene being inspected. Visual sensors are used to acquire environmental images of the detection scene; The IMU (Inertial Measurement Unit) is used to collect attitude information of the moving module. The construction module is used to build a 3D model of the detection scene based on 3D point cloud data, environmental images and attitude information using a simultaneous localization and mapping algorithm, and then establish a local spatial coordinate system.

4. The concrete multi-dimensional inspection robot as described in claim 1, characterized in that, The apparent disease detection module includes: The supplementary lighting module is used to acquire the ambient light intensity of the detection scene and adaptively adjust the lighting parameters; A camera module is used to acquire images of the concrete surface based on adjusted lighting parameters; The recognition module is used to identify concrete surface images, determine the type, size, and location of apparent defects, and obtain apparent defect detection data.

5. The concrete multi-dimensional inspection robot as described in claim 1, characterized in that, The strength detection module includes: Several rebound hammers, arranged in a rectangular shape, are used to receive synchronous trigger commands from the control and analysis module, synchronously trigger the acquisition of concrete rebound values, and transmit them to the first signal processing module. An ultrasonic transducer is used to receive a transmission command sent by a first signal processing module and transmit the received ultrasonic signal to the first signal processing module. The first signal processing module is used to correct the coupling state of the ultrasonic signal to obtain corrected data; based on the rebound value, corrected data and preset technical specifications, the compressive strength data of concrete is measured through a preset segmented calibration model.

6. The detection method of the concrete multi-dimensional detection robot as described in any one of claims 1-5, characterized in that, include: The mobile module adjusts its attitude to meet the spatial attitude requirements of different detection scenarios and generates a planned path. During the execution of the planned path, a three-dimensional model of the detection scene is constructed based on the modeling module included in the detection module, thereby establishing a local spatial coordinate system for the detection area; Based on the appearance defect detection module included in the detection module, images of the concrete surface are acquired and appearance defect types, sizes and locations are identified to obtain appearance defect detection data; The strength testing module included in the testing module measures the compressive strength data of concrete using the ultrasonic-rebound combined method. When determining the apparent defects based on the apparent defects detection data and / or the compressive strength detection anomalies based on the compressive strength data, the first internal defect detection module included in the detection module uses array impact echo technology to perform preliminary screening and preliminary investigation of internal defects in concrete, determine whether there are defects inside the concrete, mark the planar location and approximate depth of the defect area, and use it as the first internal defect detection data. When an internal defect detection anomaly is determined based on the first internal defect detection data, the second internal defect detection module included in the detection module uses three-dimensional ultrasonic technology to quantitatively detect and accurately scan the internal defects of the concrete. Three-dimensional ultrasonic detection is performed on the defect area marked by the first internal defect detection module to obtain the three-dimensional size, volume and spatial morphology of the defect and determine the detection result.

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