Tunnel defect positioning method and system based on ultrasonic detection

Abstract

The invention discloses a tunnel defect positioning method and system based on ultrasonic detection, and relates to the technical field of ultrasonic defect detection.The method comprises the steps that firstly, a digital twin model of a tunnel is built, probes are tested in the model, the probes for shallow layer detection, middle layer detection and deep layer detection are selected respectively, and an ultrasonic detection robot is formed for defect detection; meanwhile, a coupling test is carried out in the model, an optimal coupling agent is selected for the tunnel, and the movement speed and the coupling effect are adjusted during detection according to the detection result and the signal characteristics in the detection process, so that the comprehensiveness and the accuracy of defect detection are ensured, the probe damage is reduced, the probe detection sensitivity is ensured, and the detection efficiency is improved. In addition, ultrasonic detection robot self-inspection is conducted regularly, normal operation of ultrasonic detection robot assemblies is guaranteed, the accuracy of defect positioning detection is guaranteed, and meanwhile the detection efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic defect detection technology, and specifically to a method and system for locating tunnel defects based on ultrasonic detection. Background Technology

[0002] Tunnel defects may include cracks, spalling, cavities, etc. Ultrasonic testing is one of the commonly used non-destructive testing methods. Different ultrasonic probes are suitable for different detection depths, and the curvature of the tunnel wall has a certain influence on probe coupling. Therefore, when performing ultrasonic testing on tunnel defects, it is necessary to select a suitable combination of ultrasonic probes and perform appropriate coupling compensation to improve the accuracy of the test.

[0003] Existing technologies, such as the invention patent application CN118299039A, disclose a three-dimensional imaging and interaction method for an ultrasound detector, including the following steps: obtaining a fitted probe shape; determining probe pressure through feedback information; obtaining multi-layer, multi-angle ultrasonic signals; evaluating the stability and clarity of each signal based on the multi-frequency ultrasonic signals; using adaptive filtering and beamforming processing to obtain optimized ultrasonic signals; using inverse signal processing and time-frequency analysis to obtain the geometric and material properties of the target area, and adjusting the ultrasonic emission parameters to achieve dynamic focusing; reconstructing the structure and obtaining a three-dimensional image based on the dynamic focusing parameters and the optimized ultrasonic signals; identifying key features in the generated three-dimensional image; and obtaining targeted diagnostic auxiliary information based on the marked key features in the three-dimensional image. This three-dimensional imaging and interaction method for ultrasound detectors can automatically identify lesions, provide auxiliary diagnosis, and enhance the user experience.

[0004] Existing technologies, such as the invention patent application CN116359340A, disclose a method and system for grid-based precision inspection of tunnel lining based on a rolling ultrasonic device. This method combines a rolling ultrasonic testing device with a grid-based precision inspection trolley as a precision inspection scanning mechanism. Based on the grid-structured tunnel lining space, a predetermined scanning method is used to achieve full-coverage inspection of the lining surface, collecting internal lining data. The detection data is uploaded in real-time to the testing host and a remote server system. Based on the spatial position of the grid, the internal lining data of each grid unit is reconstructed in three dimensions, thereby optimizing the signal-to-noise ratio and resolution of the data to form high-quality ultrasonic testing data and identify internal lining defects. The use of a rolling ultrasonic testing device achieves continuous, full-coverage inspection of the tunnel lining, overcoming the problems of insufficient accuracy and low work efficiency in existing lining inspection technologies. It offers high detection accuracy, low time consumption, low operator requirements, and strong practicality.

[0005] The above scheme discloses the detection process and signal processing of the ultrasonic testing equipment. However, for tunnels, due to the diversity of construction materials and the depth of defects, the ultrasonic testing equipment has powerful detection capabilities, but the above scheme does not select the best ultrasonic probes for the detection of defects in shallow, middle and deep layers according to the thickness of the layer to form an ultrasonic probe combination for tunnel detection. This results in limited ultrasonic detection accuracy. The lack of layered probe selection requires repeated parameter adjustments and detection, which reduces detection efficiency.

[0006] Furthermore, in tunnel inspection, the curvature of the inner wall significantly affects the coupling effect of the ultrasonic probe. However, the above-mentioned scheme lacks analysis of coupling compensation during the inspection process. Simply adjusting the coupling effect by changing the pressure causes the probe to be subjected to excessive pressure for a long time, which may lead to microcracks in the probe due to fatigue, decreased sensitivity, or even failure of the probe. Moreover, after the probe is deformed by pressure, the directivity of the sound beam shifts, which increases the defect location error and reduces the accuracy of defect location detection. Summary of the Invention

[0007] To address the aforementioned technical shortcomings, the present invention aims to provide a method and system for locating tunnel defects based on ultrasonic testing.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In the first aspect, the present invention provides a tunnel defect location method based on ultrasonic detection, including the following steps: S1, acquiring tunnel inner wall data, setting up an ultrasonic detection robot for tunnel defect location, installing a retractable track in the tunnel, mounting the ultrasonic detection robot, and equipping the ultrasonic detection robot with a multi-degree-of-freedom robotic arm; at the same time, using the tunnel inner wall data, selecting the optimal coupling agent for the tunnel.

[0009] S2. The ultrasonic inspection robot performs defect detection at a preset initial moving speed, while simultaneously acquiring the signal characteristics of the ultrasonic inspection robot and adjusting its moving speed. During defect detection, the stability of the coupling state is acquired, and when the stability of the coupling state is insufficient, coupling compensation is performed based on the optimal coupling agent.

[0010] S3. Obtain the movement speed control record and coupling compensation record of the ultrasonic inspection robot, perform regular self-inspection on the ultrasonic inspection robot, and provide self-inspection feedback. At the same time, obtain the defect detection data of the ultrasonic inspection robot and mark the location of each defect in the tunnel digital twin model.

[0011] Secondly, the present invention provides a tunnel defect location system based on ultrasonic testing, comprising: a configuration analysis module for acquiring tunnel inner wall data, setting up an ultrasonic testing robot for tunnel defect location, installing a retractable track in the tunnel to carry the ultrasonic testing robot, and equipping the ultrasonic testing robot with a multi-degree-of-freedom robotic arm; and simultaneously using the tunnel inner wall data to select the optimal coupling agent for the tunnel.

[0012] The detection and control module is used to perform defect detection by the ultrasonic inspection robot at a preset initial moving speed, while simultaneously acquiring and monitoring the signal characteristics of the ultrasonic inspection robot and controlling its moving speed. At the same time, during defect detection, it acquires the stability of the coupling state, and when the stability of the coupling state is insufficient, it performs coupling compensation based on the optimal coupling agent.

[0013] The detection feedback module is used to acquire the movement speed control record and coupling compensation record of the ultrasonic inspection robot, perform regular self-inspection of the ultrasonic inspection robot, and provide self-inspection feedback. At the same time, it acquires the defect detection data of the ultrasonic inspection robot and marks the location of each defect in the tunnel digital twin model.

[0014] The beneficial effects of this invention are as follows: This invention provides a tunnel defect localization method and system based on ultrasonic testing. First, a digital twin model of the tunnel is constructed, and probes are tested in the model. Probes for shallow, medium, and deep detection are selected respectively to form an ultrasonic testing robot for defect detection. At the same time, coupling tests are performed in the model to select the optimal coupling agent for the tunnel. During the detection process, the moving speed and coupling effect are adjusted according to the detection results and signal characteristics to ensure the comprehensiveness and accuracy of defect detection, while reducing probe damage and ensuring probe detection sensitivity. In addition, the ultrasonic testing robot is periodically self-tested to ensure the normal operation of the ultrasonic testing robot components, ensuring the accuracy of defect localization and detection, and improving detection efficiency. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention.

[0017] Figure 2 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: See Figure 1 As shown, a tunnel defect location method based on ultrasonic testing includes the following steps: S1. Obtain the inner wall data of the tunnel, set up an ultrasonic inspection robot for tunnel defect location, install a retractable track in the tunnel, carry the ultrasonic inspection robot, and equip the ultrasonic inspection robot with a multi-degree-of-freedom robotic arm; at the same time, use the inner wall data of the tunnel to select the best coupling agent for the tunnel.

[0020] In a specific embodiment, the specific process of S1 is as follows: S11, obtain the geometric data of the tunnel through three-dimensional laser scanning and UAV mapping, and obtain the material data of the tunnel from the database. The geometric data and material data constitute the inner wall data, and a digital twin model of the tunnel is constructed. Ultrasonic probe selection test is performed in the digital twin model of the tunnel, the best ultrasonic probe combination is selected, and the ultrasonic probe combination is installed in the ultrasonic testing robot.

[0021] It should be noted that the geometric data includes the actual dimensions of the tunnel, the thickness of the lining, and the curvature of the surface, while the material data assigns accurate acoustic parameters to the concrete, steel, and other structures, such as sound velocity, density, and attenuation coefficient.

[0022] The construction of digital twin models is an existing technology and will not be elaborated here.

[0023] Preferably, the process of selecting and testing the ultrasonic probe in the digital twin model of the tunnel is as follows: S11-1, the tunnel is divided into shallow, middle and deep layers according to its thickness, and several defects are set in the shallow, middle and deep layers of the digital twin model of the tunnel respectively.

[0024] The location of each defect is defined by the testing personnel. The thickness ranges corresponding to the shallow, middle, and deep layers are determined by professionals based on geological conditions and the lining structure; no specific numerical limits are imposed here.

[0025] S11-2. Simulate the current tunnel environment conditions in the digital twin model of the tunnel, and use various ultrasonic probes in the digital twin model of the tunnel to detect defects in the tunnel under the current tunnel environment conditions, and obtain defect detection data of various ultrasonic probes.

[0026] It should be noted that the current environmental data of the tunnel is acquired through an environmental acquisition device and input into the digital twin model of the tunnel to simulate the current tunnel environmental conditions. The environmental acquisition device includes temperature sensors and humidity sensors, and the environmental data includes ambient temperature and humidity.

[0027] The defect detection data includes the location of each defect.

[0028] S11-3. Using the defect detection data of various ultrasonic probes, analyze the defect detection level of each ultrasonic probe in each layer. Based on the defect detection level of each ultrasonic probe in each layer, select the target ultrasonic probe for each layer and combine the target ultrasonic probes in each layer into the optimal ultrasonic probe combination.

[0029] In the above, the detection locations of each defect are obtained from the defect detection data of various ultrasonic probes, and the detection locations of each defect in each layer by various ultrasonic probes are obtained and marked as follows. The layers are divided into shallow, medium, and deep layers; the location of each defect in each layer is obtained from the digital twin model and marked as follows. Where q represents the number of each type of ultrasonic probe, m represents the number of each layer, m=1,2,3, where 1,2,3 represent the shallow layer, the middle layer and the deep layer respectively, and f represents the number of each defect. Both q and f are positive integers.

[0030] Using the calculation formula: The offset rate of the f-th defect in the m-th layer detected by the q-th type of ultrasonic probe is obtained. The average offset rate of each defect in each layer detected by various ultrasonic probes is calculated by averaging the offset rates of each defect in each layer detected by various ultrasonic probes.

[0031] The average offset rate of defects in each layer detected by various ultrasonic probes is compared with the offset rate range corresponding to each preset defect detection level. If the average offset rate of a defect in a certain layer detected by a certain type of ultrasonic probe is within the offset rate range corresponding to a certain defect detection level, then the defect detection level is taken as the defect detection level of that type of ultrasonic probe in that layer, thereby obtaining the defect detection level of various ultrasonic probes in each layer.

[0032] It should be noted that the offset rate range corresponding to each defect detection level is set by professionals according to the needs of tunnel defect location, and no specific numerical limit is imposed here.

[0033] Based on the defect detection levels of various ultrasonic probes in each layer, the defect detection levels of various ultrasonic probes in each layer are statistically analyzed, and the ultrasonic probe with the highest defect detection level in each layer is selected as the target ultrasonic probe for each layer.

[0034] If the number of ultrasonic probe types with the highest defect detection level is greater than 1, then the ultrasonic probe with the smallest offset rate among those with the highest defect detection level is selected as the target ultrasonic probe.

[0035] The maximum defect detection level of the ultrasonic probe in each layer is used as the reference defect detection level for the ultrasonic inspection robot in each layer.

[0036] It should be noted that during the testing process, the frequency and resolution settings of various ultrasonic probes were set to their optimal operating settings, which can be obtained from the probe's instruction manual.

[0037] S12. Install a retractable track in the tunnel, carry an ultrasonic inspection robot, embed the ultrasonic probe assembly into the ultrasonic inspection robot, and equip the ultrasonic inspection robot with a multi-degree-of-freedom robotic arm to adapt to the tunnel's arched surface.

[0038] S13. Conduct coupling tests on the ultrasonic inspection robot in the digital twin model of the tunnel, select the best coupling agent for the ultrasonic inspection robot, analyze the optimal thickness of the best coupling agent and the signal attenuation benchmark value of the ultrasonic inspection robot, and construct the correspondence between signal attenuation and energy compensation.

[0039] Preferably, the specific process of S13 is as follows: S13-1, Simulate the current environmental conditions of the tunnel in the digital twin model of the tunnel, and set several defects in each layer. In the digital twin model of the tunnel, place the ultrasonic inspection robot on the inner wall of the tunnel under the current environmental conditions.

[0040] S13-2. Sort the types of coupling agents to be tested, and perform coupling tests on each type of coupling agent in order of sorting. Apply a preset amount of each type of coupling agent for the first time. After application, use the best combination of ultrasonic probes to collect tunnel defect detection data and obtain the coupling agent thickness after application. Then, based on the initial application, increase the amount of each type of coupling agent in turn. After each application, use an ultrasonic testing robot to collect tunnel defect detection data and signal characteristics, and obtain the coupling agent thickness after each application, until the coupling agent thickness reaches the preset maximum thickness, and then stop applying.

[0041] It should be noted that the thickness of the coupling agent after application is obtained from the digital twin model, and the signal characteristics of the ultrasonic testing robot in the digital twin model are received at the same time. The signal characteristics include signal-to-noise ratio, energy transfer efficiency, waveform distortion rate, pulse amplitude and waveform shape, etc.

[0042] The preset maximum thickness is the maximum allowable thickness of the coupling agent set by professionals based on experience and actual tunnel conditions; no specific numerical limit is imposed here.

[0043] S13-3 After the coating is completed, obtain tunnel defect detection data and signal characteristics of the ultrasonic inspection robot when using different types and thicknesses of coupling agents. Analyze the optimal coupling agent for the ultrasonic inspection robot, as well as the optimal thickness of the optimal coupling agent and the signal attenuation benchmark value of the ultrasonic inspection robot. At the same time, construct the correspondence between signal attenuation and energy compensation.

[0044] In the above, the tunnel defect detection data of the ultrasonic inspection robot using various types of coupling agents and thicknesses are analyzed according to the defect detection level of various ultrasonic probes in each layer, and the defect detection level of the ultrasonic inspection robot using various types of coupling agents and thicknesses in each layer is obtained.

[0045] Signal-to-noise ratio, energy transfer efficiency, and waveform distortion rate were obtained from the signal characteristics of ultrasonic testing robots using various coupling agent types and thicknesses, and then normalized. The processed values ​​are denoted as follows: , and Where g represents the number of each coupling agent type, p represents the number of each thickness, and both g and p are positive integers. The calculation formula is as follows: The signal coefficients of the ultrasonic testing robot when using the g-th type of coupling agent at the p-th thickness are obtained. .

[0046] Extract the reference defect detection level of the ultrasonic inspection robot at each layer and label it as follows: The defect detection level of the ultrasonic inspection robot at each layer when using different types and thicknesses of coupling agent is marked as follows: Using the calculation formula: The detection comprehensive coefficient is obtained when the ultrasonic testing robot uses the g-th type of coupling agent at the p-th thickness. .

[0047] The type of coupling agent with the highest comprehensive coefficient is selected as the best coupling agent, and the thickness of the coupling agent with the highest comprehensive coefficient is selected as the optimal thickness.

[0048] In a digital twin model of the tunnel, an ultrasonic inspection robot is coupled using the optimal coupling agent and thickness. Several defect detection tests are then performed using the ultrasonic inspection robot, and the amplitude of the transmitted and received signals are recorded for each test. The results are then calculated using the following formula: The signal attenuation value for each test is calculated, and then the average value is calculated to obtain the signal attenuation baseline value.

[0049] The specific process for establishing the correspondence between signal attenuation and energy compensation is as follows: An ultrasonic testing robot is coupled using the optimal coupling agent and thickness in the digital twin model of the tunnel. Then, the ultrasonic testing robot performs several defect detection tests, obtaining the signal attenuation value for each test. If the signal attenuation value in a certain test is greater than the signal attenuation reference value, capability compensation is adjusted, adjusting the energy compensation value until the signal attenuation value is the same as the signal attenuation reference value. The difference between the signal attenuation value and the signal attenuation reference value is taken as the signal attenuation difference. The signal attenuation difference and energy compensation value for each capability compensation adjustment are recorded. The energy compensation values ​​corresponding to each signal attenuation difference are statistically analyzed. The average of the energy compensation values ​​corresponding to each signal attenuation difference is calculated to obtain the energy compensation value corresponding to each signal attenuation difference, thus obtaining the correspondence between signal attenuation and energy compensation.

[0050] S2. The ultrasonic inspection robot performs defect detection at a preset initial moving speed, while simultaneously acquiring the signal characteristics of the ultrasonic inspection robot and adjusting its moving speed. During defect detection, the stability of the coupling state is acquired, and when the stability of the coupling state is insufficient, coupling compensation is performed based on the optimal coupling agent.

[0051] In a specific embodiment, the specific process of S2 is as follows: S21, firstly, the ultrasonic inspection robot performs defect detection according to the preset initial moving speed, obtains the defect detection data and signal characteristics of the ultrasonic inspection robot, analyzes the monitoring status of the ultrasonic inspection robot, and then adjusts it.

[0052] Preferably, the process of analyzing the monitoring status of the ultrasonic inspection robot is as follows: obtaining the detection location data of each defect from the defect detection data of the ultrasonic inspection robot, dividing the tunnel into grids, obtaining the number of defects in each grid based on the detection location data of each defect, and analyzing the defect level in each grid.

[0053] In the above process, the number of defects in each grid is compared with the number of defects corresponding to each preset defect level. If the number of defects in a certain grid is the same as the number of defects in a certain defect level, then the defect level is the defect level in that grid, thus obtaining the defect level in each grid.

[0054] The range of defect quantity corresponding to each defect level is set and adjusted by professionals according to the testing requirements, and no specific numerical limit is set here.

[0055] The received signal amplitudes at each grid are obtained from the signal characteristics of the ultrasonic inspection robot. The stability level of the coupling state in each grid is analyzed. Based on the defect level and the stability level of the coupling state in each grid, the movement speed control value of the ultrasonic inspection robot in each grid is determined and corresponding control is performed.

[0056] In the above, the calculation formula is used: The signal fluctuation value in the r-th grid is obtained. In the formula, r represents the number of each grid cell, and r is a positive integer. , , These represent the maximum received signal amplitude, minimum received signal amplitude, and average received signal amplitude at the r-th grid, respectively. The average received signal amplitude across all grids is calculated by averaging the received signal amplitudes at each grid.

[0057] The signal fluctuation value in each grid is compared with the signal fluctuation value range corresponding to each preset coupling state stability level. If the signal fluctuation value in a certain grid is within the signal fluctuation value range corresponding to a certain coupling state stability level, then the coupling state stability level is the coupling state stability level of that grid, thereby obtaining the coupling state stability level in each grid.

[0058] It should be noted that the preset signal fluctuation value ranges corresponding to the stability levels of each coupling state are set and adjusted by professionals according to the testing requirements, and no specific numerical limits are imposed here.

[0059] The defect level and coupling stability level in each grid are normalized, and the processed values ​​are denoted as follows: and Using the calculation formula: The detection state value in the r-th grid is obtained. .

[0060] The movement speed control value corresponding to each detection state value is obtained from the database, and the movement speed control value in each grid is obtained as the movement speed control value of the ultrasonic inspection robot in each grid.

[0061] It should be noted that the analysis process for the moving speed control value corresponding to each detection state value and the energy compensation value corresponding to each signal attenuation difference is the same, and will not be repeated here.

[0062] S22. Based on the monitoring status of the ultrasonic testing robot, obtain the coupling state stability of the ultrasonic testing robot. When the coupling state stability is insufficient, perform coupling compensation on the optimal coupling agent according to the optimal thickness of the optimal coupling agent, the signal attenuation reference value of the ultrasonic testing robot, and the correspondence between signal attenuation and energy compensation.

[0063] Preferably, the coupling compensation of the optimal coupling agent is specifically performed as follows: obtaining the amplitude of each transmitted signal and the amplitude of each received signal from the signal characteristics of the ultrasonic testing robot, calculating the signal attenuation value of the ultrasonic testing robot, and simultaneously collecting the actual coupling agent thickness when the coupling stability is insufficient.

[0064] If the actual coupling agent thickness differs from the optimal thickness, thickness coupling compensation is performed; if the signal attenuation value is greater than the signal attenuation reference value, energy coupling compensation is performed.

[0065] Thickness coupling compensation: The difference between the optimal thickness and the actual coupling agent thickness is the compensation thickness. The coupling agent is applied in batches until the applied thickness is the same as the compensation thickness.

[0066] Energy coupling compensation: The difference between the signal attenuation value and the signal attenuation reference value is the signal attenuation difference. Based on the correspondence between signal attenuation and energy compensation, the energy compensation value corresponding to the signal attenuation difference is obtained and adjusted accordingly.

[0067] It should be noted that energy compensation includes transmit voltage and receive gain, among other things.

[0068] S3. Obtain the movement speed control record and coupling compensation record of the ultrasonic inspection robot, perform regular self-inspection on the ultrasonic inspection robot, and provide self-inspection feedback. At the same time, obtain the defect detection data of the ultrasonic inspection robot and mark the location of each defect in the tunnel digital twin model.

[0069] In a specific embodiment, the specific process of S3 is as follows: S31, obtain the movement control speed and speed change data duration corresponding to each control from the movement speed control record of the ultrasonic testing robot, obtain the coupling agent characteristic data after each thickness coupling compensation from the coupling compensation record, and analyze the state coefficient of the ultrasonic testing robot.

[0070] It should be noted that the ultrasonic testing robot is equipped with a viscometer and a humidity sensor to collect the viscosity and humidity of the coupling agent. The coupling agent characteristic data includes the viscosity and humidity of the coupling agent at various times.

[0071] Preferably, the analysis process of the state coefficients of the ultrasonic testing robot is as follows: divide the movement control speed corresponding to each control by the duration of the speed change data to obtain the acceleration corresponding to each control, and then... The calculation method yields the acceleration fluctuation value, and the viscosity and humidity of the coupling agent at each moment are obtained from the coupling agent characteristic data after each thickness coupling compensation. The calculation method involves calculating the viscosity and humidity fluctuations of the coupling agent, normalizing these fluctuations, and denoting the normalized values ​​as U1, U2, and U3, respectively. The calculation formula is then used. The state coefficient U of the ultrasonic testing robot is obtained.

[0072] S32. Based on the state coefficient of the ultrasonic testing robot, set the self-testing frequency of the ultrasonic testing robot, control the ultrasonic testing robot to perform self-testing according to the self-testing frequency, obtain the test data of each self-test, analyze the quality test results of the ultrasonic testing robot, and provide self-testing feedback.

[0073] Preferably, the state coefficients of the ultrasonic testing robot are compared with the state coefficient ranges corresponding to the self-testing frequencies in the database. If the state coefficients of the ultrasonic testing robot are within the state coefficient range corresponding to a certain self-testing frequency, it indicates that the self-testing frequency is the self-testing frequency of the ultrasonic testing robot.

[0074] It should be noted that the state coefficient range corresponding to each inspection frequency is set and adjusted by professionals based on historical robot maintenance records and tunnel conditions, and no specific numerical limits are imposed here.

[0075] The self-inspection of the ultrasonic testing robot mentioned above includes mechanical motion self-inspection, ultrasonic testing self-inspection, and data processing self-inspection.

[0076] Mechanical motion self-inspection: A laser tracker and a current sensor are installed in the ultrasonic inspection robot. The laser tracker measures the positional error of the robotic arm end in a standard space (i.e., a 1m×1m×1m cube). The current sensor collects the motor current waveform and obtains the current fluctuation value.

[0077] Among them, the positional error and The analysis method is the same, so it will not be repeated here. The current fluctuation value and The analysis method is the same, so it will not be repeated here.

[0078] Ultrasonic testing self-test: A spectrum analyzer and a flow sensor are installed in the ultrasonic testing robot. The spectrum analyzer collects the actual probe frequency and obtains the set probe frequency from the control terminal to obtain the frequency deviation. The flow sensor monitors the actual supply of coupling agent in real time and obtains the set supply amount from the control terminal to obtain the deviation rate between the actual supply flow rate and the set supply amount.

[0079] The frequency offset is calculated as follows: (actual probe frequency - set probe frequency) ÷ set probe frequency. The calculation method for the supply deviation rate is the same as that for the frequency offset, and will not be repeated here.

[0080] Data processing self-test: The inherent noise signal generated when there is no signal input is collected by a spectrum analyzer, i.e., the background noise; the error between the transmitted pulse and the acquisition clock is collected by an oscilloscope in the ultrasonic testing robot.

[0081] The detection data includes point error, current fluctuation value, frequency offset, supply deviation rate, background noise, and error between the transmitted pulse and the acquisition clock.

[0082] The detection data of each self-inspection is compared with the detection data threshold. If the detection data of a certain self-inspection is greater than the detection data threshold, the quality inspection result of the ultrasonic inspection robot is determined to be unqualified, and the unqualified prompt is fed back to the display terminal.

[0083] The detection data thresholds are set by professionals based on industry and testing requirements, and no specific numerical limits are set here.

[0084] Example 2: See Figure 2 As shown, a tunnel defect location system based on ultrasonic testing includes: a configuration analysis module for acquiring tunnel inner wall data; setting up an ultrasonic testing robot for tunnel defect location; installing a retractable track in the tunnel to carry the ultrasonic testing robot; and equipping the ultrasonic testing robot with a multi-degree-of-freedom robotic arm; and simultaneously using the tunnel inner wall data to select the optimal coupling agent for the tunnel.

[0085] The detection and control module is used to perform defect detection by the ultrasonic inspection robot at a preset initial moving speed, while simultaneously acquiring and monitoring the signal characteristics of the ultrasonic inspection robot and controlling its moving speed. At the same time, during defect detection, it acquires the stability of the coupling state, and when the stability of the coupling state is insufficient, it performs coupling compensation based on the optimal coupling agent.

[0086] The detection feedback module is used to acquire the movement speed control record and coupling compensation record of the ultrasonic inspection robot, perform regular self-inspection of the ultrasonic inspection robot, and provide self-inspection feedback. At the same time, it acquires the defect detection data of the ultrasonic inspection robot and marks the location of each defect in the tunnel digital twin model.

[0087] The database is used to store tunnel material data, moving speed control values ​​corresponding to each detection state value, and state coefficient ranges corresponding to each detection frequency.

[0088] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for locating tunnel defects based on ultrasonic testing, characterized in that, Includes the following steps: S1. Obtain the inner wall data of the tunnel, set up an ultrasonic inspection robot for tunnel defect location, install a retractable track in the tunnel to carry the ultrasonic inspection robot, and equip the ultrasonic inspection robot with a multi-degree-of-freedom robotic arm; at the same time, use the inner wall data of the tunnel to select the best coupling agent for the tunnel. S2. The ultrasonic inspection robot performs defect detection at a preset initial moving speed, while simultaneously acquiring the signal characteristics of the ultrasonic inspection robot and adjusting its moving speed. At the same time, during defect detection, the stability of the coupling state is acquired, and when the stability of the coupling state is insufficient, coupling compensation is performed based on the optimal coupling agent. S3. Obtain the movement speed control record and coupling compensation record of the ultrasonic inspection robot, perform regular self-inspection on the ultrasonic inspection robot, and provide self-inspection feedback. At the same time, obtain the defect detection data of the ultrasonic inspection robot and mark the location of each defect in the tunnel digital twin model.

2. The tunnel defect location method based on ultrasonic testing according to claim 1, characterized in that, The specific process of S1 is as follows: S11. Obtain the geometric data of the tunnel through three-dimensional laser scanning and UAV mapping, and obtain the material data of the tunnel from the database. The geometric data and material data constitute the inner wall data, and a digital twin model of the tunnel is constructed. Ultrasonic probe selection test is carried out in the digital twin model of the tunnel, the best ultrasonic probe combination is selected, and the ultrasonic probe combination is installed in the ultrasonic testing robot. S12. Install a retractable track in the tunnel, carry an ultrasonic inspection robot, embed the ultrasonic probe assembly into the ultrasonic inspection robot, and equip the ultrasonic inspection robot with a multi-degree-of-freedom robotic arm to adapt to the tunnel's arched surface. S13. Conduct coupling tests on the ultrasonic inspection robot in the digital twin model of the tunnel, select the best coupling agent for the ultrasonic inspection robot, analyze the optimal thickness of the best coupling agent and the signal attenuation benchmark value of the ultrasonic inspection robot, and construct the correspondence between signal attenuation and energy compensation.

3. The tunnel defect location method based on ultrasonic detection according to claim 2, characterized in that, The specific process for selecting and testing ultrasonic probes in the digital twin model of the tunnel is as follows: S11-1. Divide the tunnel into shallow, middle and deep layers according to its thickness, and set several defects in the shallow, middle and deep layers of the tunnel's digital twin model respectively. S11-2. Simulate the current tunnel environment conditions in the digital twin model of the tunnel, and use various ultrasonic probes in the digital twin model of the tunnel to detect defects in the tunnel under the current tunnel environment conditions, and obtain defect detection data of various ultrasonic probes. S11-3. Using the defect detection data of various ultrasonic probes, analyze the defect detection level of each ultrasonic probe in each layer. Based on the defect detection level of each ultrasonic probe in each layer, select the target ultrasonic probe for each layer and combine the target ultrasonic probes in each layer into the optimal ultrasonic probe combination.

4. The tunnel defect location method based on ultrasonic detection according to claim 3, characterized in that, The specific process of S13 is as follows: S13-1. Simulate the current environmental conditions of the tunnel in the digital twin model of the tunnel, and set several defects in each layer. In the digital twin model of the tunnel, place the ultrasonic inspection robot on the inner wall of the tunnel under the current environmental conditions. S13-2. Sort the types of coupling agents to be tested, and perform coupling tests on each type of coupling agent in order of sorting. Apply a preset amount of each type of coupling agent for the first time. After application, use an ultrasonic testing robot to collect tunnel defect detection data and signal characteristics, and obtain the coupling agent thickness after application. Then, based on the initial application, increase the amount of each type of coupling agent in turn. After each application, use an ultrasonic testing robot to collect tunnel defect detection data and obtain the coupling agent thickness after each application, until the coupling agent thickness reaches the preset maximum thickness, and then stop applying. S13-3 After the coating is completed, obtain tunnel defect detection data and signal characteristics of the ultrasonic inspection robot when using different types and thicknesses of coupling agents. Analyze the optimal coupling agent for the ultrasonic inspection robot, as well as the optimal thickness of the optimal coupling agent and the signal attenuation benchmark value of the ultrasonic inspection robot. At the same time, construct the correspondence between signal attenuation and energy compensation.

5. The tunnel defect location method based on ultrasonic detection according to claim 4, characterized in that, The specific process of S2 is as follows: S21. First, the ultrasonic inspection robot performs defect detection at a preset initial moving speed, acquires defect detection data and signal characteristics of the ultrasonic inspection robot, analyzes the monitoring status of the ultrasonic inspection robot, and then adjusts it. S22. Based on the monitoring status of the ultrasonic testing robot, obtain the coupling state stability of the ultrasonic testing robot. When the coupling state stability is insufficient, perform coupling compensation on the optimal coupling agent according to the optimal thickness of the optimal coupling agent, the signal attenuation reference value of the ultrasonic testing robot, and the correspondence between signal attenuation and energy compensation.

6. The tunnel defect location method based on ultrasonic detection according to claim 5, characterized in that, The specific process for analyzing the monitoring status of the ultrasonic testing robot is as follows: The detection location data of each defect is obtained from the defect detection data of the ultrasonic inspection robot. The tunnel is divided into grids. Based on the detection location data of each defect, the number of defects in each grid is obtained, and the defect level in each grid is analyzed. The received signal amplitudes at each grid are obtained from the signal characteristics of the ultrasonic inspection robot. The stability level of the coupling state in each grid is analyzed. Based on the defect level and the stability level of the coupling state in each grid, the movement speed control value of the ultrasonic inspection robot in each grid is determined and corresponding control is performed.

7. The tunnel defect location method based on ultrasonic detection according to claim 6, characterized in that, The specific process for coupling compensation using the optimal coupling agent is as follows: The amplitudes of each transmitted signal and each received signal are obtained from the signal characteristics of the ultrasonic testing robot. The signal attenuation value of the ultrasonic testing robot is calculated. At the same time, when the coupling stability is insufficient, the actual coupling agent thickness is collected. If the actual coupling agent thickness differs from the optimal thickness, thickness coupling compensation is performed; if the signal attenuation value is greater than the signal attenuation reference value, energy coupling compensation is performed. Thickness coupling compensation: The difference between the optimal thickness and the actual coupling agent thickness is the compensation thickness. The coupling agent is applied in batches until the applied thickness is the same as the compensation thickness. Energy coupling compensation: The difference between the signal attenuation value and the signal attenuation reference value is the signal attenuation difference. Based on the correspondence between signal attenuation and energy compensation, the energy compensation value corresponding to the signal attenuation difference is obtained and adjusted accordingly.

8. The tunnel defect location method based on ultrasonic testing according to claim 1, characterized in that, The specific process of S3 is as follows: S31. Obtain the movement control speed and speed change data duration corresponding to each control from the movement speed control record of the ultrasonic testing robot, obtain the coupling agent characteristic data after each thickness coupling compensation from the coupling compensation record, and analyze the state coefficient of the ultrasonic testing robot. S32. Based on the state coefficient of the ultrasonic testing robot, set the self-testing frequency of the ultrasonic testing robot, control the ultrasonic testing robot to perform self-testing according to the self-testing frequency, obtain the test data of each self-test, analyze the quality test results of the ultrasonic testing robot, and provide self-testing feedback.

9. A tunnel defect location method based on ultrasonic testing according to claim 8, characterized in that, The analysis process of the state coefficients of the ultrasonic testing robot is as follows: Divide the movement speed corresponding to each adjustment by the duration of the speed change data to obtain the acceleration corresponding to each adjustment. Calculate the acceleration fluctuation value. Obtain the viscosity and humidity of the coupling agent at each moment from the coupling agent characteristic data after each thickness coupling compensation, and calculate the viscosity and humidity fluctuation values ​​of the coupling agent. Normalize the acceleration fluctuation value, viscosity fluctuation value, and humidity fluctuation value of the coupling agent. The normalized values ​​are denoted as U1, U2, and U3, respectively. Use the calculation formula: The state coefficient U of the ultrasonic testing robot is obtained.

10. A tunnel defect location system implementing the tunnel defect location method based on ultrasonic detection according to any one of claims 1-9, characterized in that, include: The configuration analysis module is used to acquire tunnel inner wall data, set up an ultrasonic inspection robot for tunnel defect location, install a retractable track in the tunnel to carry the ultrasonic inspection robot, and equip the ultrasonic inspection robot with a multi-degree-of-freedom robotic arm; at the same time, the optimal coupling agent for the tunnel is selected using the tunnel inner wall data; The detection and control module is used to perform defect detection by the ultrasonic inspection robot at a preset initial moving speed, while simultaneously acquiring the signal characteristics of the ultrasonic inspection robot and controlling its moving speed; at the same time, during defect detection, it acquires the stability of the coupling state, and when the stability of the coupling state is insufficient, it performs coupling compensation based on the optimal coupling agent. The detection feedback module is used to acquire the movement speed control record and coupling compensation record of the ultrasonic inspection robot, perform regular self-inspection of the ultrasonic inspection robot, and provide self-inspection feedback. At the same time, it acquires the defect detection data of the ultrasonic inspection robot and marks the location of each defect in the tunnel digital twin model.

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