Tunnel lining quality automatic radar detection device and detection method thereof

Abstract

This invention belongs to the field of tunnel concrete lining quality inspection technology, and discloses an automated radar inspection device and method for tunnel lining quality. The method includes: acquiring tunnel point cloud data using a 3D laser scanning device mounted on a mobile vehicle platform; a control and management server generating an inspection operation path and marking obstacle locations based on the point cloud data; a UAV flight platform carrying a ground-penetrating radar inspection device flying to the operation starting point; in obstacle-free sections, activating a fixed adsorption rotor to adsorb the ground-penetrating radar inspection device onto the lining surface for inspection in a wall-hugging walking mode; when encountering obstacles, switching to flight mode to fly over the obstacles and then resuming the wall-hugging walking mode. This invention employs a dual-mode operation combining wall-hugging and flight modes, achieving fully automated tunnel lining quality inspection without requiring personnel to operate at height, significantly improving inspection safety, operational efficiency, and data quality.

Description

Technical Field

[0001] This invention relates to the field of tunnel concrete lining quality inspection technology, and in particular to an automated radar inspection device and method for tunnel lining quality. Background Technology

[0002] Tunnel engineering is an important component of modern transportation infrastructure construction, encompassing various forms such as highway tunnels, railway tunnels, subway tunnels, and urban integrated utility tunnels. As a crucial component of the support structure, the construction quality of tunnel lining directly affects operational safety during construction and traffic safety during operation. Therefore, accurate and efficient testing of lining quality is of paramount importance.

[0003] Currently, ground-penetrating radar (GPR) has become one of the mainstream methods for tunnel lining quality inspection due to its advantages of being non-destructive, efficient, and having high resolution. However, in actual operation, due to the limited space and clearance height inside the tunnel, traditional radar inspection operations usually require the use of specialized platforms such as inspection trolleys, boom lifts, or dump trucks to lift the inspection personnel and radar antenna to high places such as the tunnel arch and sidewalls for operation. This operation mode has the following significant drawbacks: First, high-altitude operations pose high safety risks. Inspection personnel are on high-altitude mobile platforms for extended periods, making them susceptible to factors such as dim lighting inside the tunnel, uneven road surfaces, and platform swaying, resulting in safety hazards such as falls from heights. Second, inspection efficiency and quality are difficult to guarantee. The operation process heavily relies on the skill level of the platform operators and the coordination between them and the inspection personnel. The stability, continuity, and fit of the survey line movement are difficult to control precisely, and manual operation can easily lead to uneven data collection and missed measurements. Third, the operation organization and coordination are complex. Large inspection platforms often occupy a large space inside the tunnel, which may interfere with other processes, and the entry, exit, and relocation of equipment are not flexible enough.

[0004] With the rapid development of drone technology, a new approach has emerged in recent years: using drones equipped with radar antennas for tunnel inspection. This approach utilizes the drone's flight capabilities to carry the radar antenna to a high position within the tunnel, thus addressing safety concerns related to personnel working at heights. However, relying solely on drone flight for inspection presents several technical bottlenecks: First, radar antennas typically have considerable weight, and prolonged hovering or slow-flying drones place extremely high demands on endurance, making it difficult to meet the requirements of continuous long-distance tunnel inspection. Second, when flying close to the tunnel lining surface, the airflow generated by the drone's rotors is easily affected by the wall, leading to unstable flight attitude and making it difficult to maintain stable contact between the antenna and the lining surface. Third, the tunnel's inner surface contains obstacles such as misalignments, anchor bolt heads, accumulated water, and debris, making it difficult to achieve unobstructed passage entirely by simply relying on adsorption. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the purpose of this invention is to provide an automated radar detection device and method for tunnel lining quality, which combines flight maneuverability with wall-hugging stability, so as to realize an automated detection operation mode in which the ground radar detection device can not only walk stably close to the lining surface to collect data, but also flexibly fly over obstacles.

[0006] To achieve this objective, the present invention adopts the following technical solution: On one hand, the present invention provides an automated radar detection method for tunnel lining quality, comprising the following steps: S1. Scan the interior of the tunnel using a mobile vehicle-mounted platform equipped with a 3D laser scanning device to generate tunnel point cloud data; S2. The control and management server generates a detection operation path based on the tunnel point cloud data and marks the location of obstacles in the path; S3. Control the UAV flight platform carrying the ground-penetrating radar detection device to fly to the starting point of the detection operation path; S4. In unobstructed sections, activate the fixed adsorption rotor of the UAV flight platform to adsorb the ground radar detection device onto the tunnel lining surface, move along the detection operation path in a wall-walking mode and collect radar detection data. S5. When an obstacle is detected ahead, control the drone flight platform to switch from wall-hugging mode to flight mode, and adjust the flight attitude through the rotatable power rotor of the drone flight platform to fly over the obstacle. S6. After flying over the obstacle, control the drone flight platform to switch back to the wall-hugging mode and continue the inspection operation; S7. After the inspection is completed, retrieve the drone flight platform to the mobile vehicle platform and export the inspection data.

[0007] As a preferred embodiment of the automated radar detection method for tunnel lining quality, step S2 specifically includes: Step S2 specifically includes: S21. Perform noise reduction, filtering, and thinning on the original point cloud; S22. Take sections along the tunnel direction at preset intervals and determine the normal direction of each section; S23. On each cross section, the design shape of the tunnel cross section is fitted using the least squares method to obtain the center point of each cross section; S24. Connect the center points of each section in sequence and smooth them out to form the longitudinal centerline trajectory of the tunnel. S25. Based on the longitudinal centerline trajectory of the tunnel, generate the detection operation path of the radar detection survey line according to the tunnel cross-section design shape and detection specification requirements; S26. Using 3D laser point cloud and lidar echo intensity map, semantic segmentation of obstacles is performed using a data model, and category labels of obstacles are output. S27. Separate the effective point set of obstacles from the tunnel point cloud data, fit a geometric bounding volume based on the point set, and output the geometric dimensions of the obstacles. S28. Mark obstacles whose height exceeds a preset threshold in the geometry as a trigger condition for switching flight modes.

[0008] As a preferred embodiment of the automated radar detection method for tunnel lining quality, the radar detection survey lines generated in step S25 include longitudinal survey lines and circumferential survey lines. The method for generating the longitudinal survey lines includes: defining the top longitudinal line of the tunnel as the survey line at a preset height position where the upper part of the tunnel centerline forms a 90° angle with the horizontal plane; and laying other longitudinal survey lines at equal intervals based on the top longitudinal line and the arc length calculated according to the tunnel diameter. The method for generating the circumferential survey line includes: calculating based on mileage, and generating circumferential scanning survey lines along the tunnel cross section at preset intervals within a preset angle range with respect to the horizontal plane.

[0009] As a preferred embodiment of the automated radar detection method for tunnel lining quality, step S4 specifically includes: S41. In the unobstructed section, activate the fixed adsorption rotor to adsorb the ground radar detection device onto the tunnel lining surface. By adjusting the rotation speed of each rotatable power rotor, the UAV flight platform generates a thrust component parallel to the tunnel lining surface, driving the walking wheels of the ground radar detection device to roll along the lining surface and move along the detection operation path in a wall-hugging walking mode. S42. Real-time detection of the angle between the UAV flight platform and the horizontal line using a horizontal angle sensor. The pressure sensor detects the contact pressure between the ground-penetrating radar detection device and the tunnel lining surface in real time. S43. The UAV flight platform dynamically adjusts the adsorption force of the fixed adsorption rotor according to the following adsorption pressure calculation formula to keep the adsorption pressure within a preset range: Where F is the target fitting pressure, For the gravity of the ground-penetrating radar detection device, For the gravity of the drone flight platform, denoted as , where is the angle between the drone flight platform and the horizontal line, and k is a preset safety factor.

[0010] S44. Record the walking distance and location information in the wall-hugging walking mode using the encoder set on the drone flight platform.

[0011] As a preferred embodiment of the automated radar detection method for tunnel lining quality, step S44 specifically includes: S441. The encoder's code disk is mounted on the motor shaft of the walking wheel and rotates synchronously with the walking wheel. The light source shines through the gap of the code disk onto the photosensitive element to generate a periodically changing light signal and converts the light signal into an electrical pulse signal. S442: The encoder outputs two pulse signals with a 90° phase difference. By judging the phase lead or lag relationship between the two pulse signals, the rotation direction of the walking wheel is determined. S443. Based on the number of pulses received per unit time. Calculate the wheel speed based on the number of pulses P per encoder revolution. Calculate the linear velocity based on the wheel diameter D. Then calculate the driving distance. This enables real-time recording of the detection location.

[0012] As a preferred solution for the automated radar detection method for tunnel lining quality, in steps S3 to S6, the mobile vehicle-mounted platform follows the UAV flight platform through a wireless positioning module. The UAV flight platform transmits wireless signals, the mobile vehicle-mounted platform receives the signals and calculates the distance based on the received signal strength, tracks the UAV flight platform in real time, and provides power to the UAV flight platform to achieve long-term continuous operation.

[0013] As a preferred option for an automated radar detection method for tunnel lining quality, in steps S4 to S6, when collecting radar detection data, the mobile vehicle-mounted platform adopts different following methods depending on the detection path type: when performing longitudinal survey line detection, the mobile vehicle-mounted platform follows the UAV flight platform autonomously at a fixed distance; when performing circumferential survey line detection, the mobile vehicle-mounted platform and the UAV flight platform work together in a mileage synchronization manner.

[0014] As a preferred embodiment of the automated radar detection method for tunnel lining quality, step S5 specifically includes: S51. Detects obstacles ahead using a lidar mounted on the drone flight platform; When the detected obstacle matches the location of the obstacle marked in step S2, and the obstacle height is ≥0.03m or the height of the obstacle from the tunnel lining surface is ≤ the overall height of the UAV flight platform, the lidar issues an alarm signal. S52. Based on the alarm signal from the lidar, the UAV flight platform adjusts the speed of each rotatable rotor to adjust the attitude of the UAV flight platform and gradually establish a stable hovering state, preparing for detachment from the wall. S53. After the hovering state is stable, the fixed adsorption rotor is turned off to release the adsorption force; at the same time, by adjusting the speed of each rotatable power rotor, the fuselage of the UAV flight platform is tilted appropriately, thereby obtaining a thrust component away from the tunnel lining surface, and controlling the UAV flight platform to fly smoothly away from the lining surface. S54. The distance between the UAV flight platform and the obstacle is monitored in real time by lidar, and the UAV flight platform is controlled to maintain a safe distance of 0.5m from the obstacle. The ground-penetrating radar detection device is switched to time-triggered mode, and attitude control is achieved by adjusting the speed of each rotatable power rotor to smoothly cross the obstacle.

[0015] As a preferred embodiment of the automated radar detection method for tunnel lining quality, step S6 specifically includes: S61. After crossing the obstacle, the UAV flight platform is controlled by the lidar to return to a stable hovering state by adjusting the speed of each rotatable power rotor at 0.5m. The attitude of the aircraft is adjusted so that the ground radar detection device is re-aligned with the tunnel lining surface. The fixed adsorption rotor is activated to control the UAV flight platform to approach the tunnel lining surface smoothly at a speed of 0.01m / s. S62. When the bonding pressure detected by the pressure sensor reaches the preset target value, the speed of each rotatable power rotor is adjusted to make the UAV flight platform generate a thrust component parallel to the tunnel lining surface, driving the walking wheels to roll along the lining surface and restore the wall-hugging walking mode.

[0016] On the other hand, the present invention provides an automated radar detection device for tunnel lining quality, used to implement the above-mentioned automated radar detection method for tunnel lining quality, comprising: Ground-penetrating radar (GPR) detection equipment is used to perform tunnel lining inspection operations; Unmanned aerial vehicle (UAV) flight platform, used to carry ground-penetrating radar detection devices; A mobile vehicle-mounted platform is installed inside the tunnel and can move along the tunnel's direction of travel. It is equipped with a power supply and is connected to the UAV flight platform to provide external power to the UAV flight platform. A three-dimensional laser scanning device, installed on the mobile vehicle platform, is used to scan the interior of the tunnel and generate tunnel point cloud data; The walking wheels are mounted on the ground-penetrating radar detection device and are in direct contact with the tunnel lining surface. They are used to move the ground-penetrating radar detection device in the wall-hugging walking mode. A fixed adsorption rotor is installed at the bottom of the UAV flight platform to generate adsorption force in the wall-hugging walking mode, so that the walking wheels on the ground radar detection device are in close contact with the tunnel lining surface. Rotatable powered rotors are arranged around the UAV flight platform to provide walking power in wall-hugging walking mode and flight power and attitude adjustment in flight mode. A control and management server is communicatively connected to both the mobile vehicle platform and the UAV flight platform. It is used to generate detection operation paths based on the tunnel point cloud data of the mobile vehicle platform and to control the UAV flight platform to switch between wall-hugging walking mode and flight mode.

[0017] As a preferred option for automated radar inspection equipment for tunnel lining quality, it also includes: A pressure sensor is installed between the ground-penetrating radar detection device and the UAV flight platform to detect the contact pressure between the ground-penetrating radar detection device and the tunnel lining surface. A horizontal angle sensor, mounted on the UAV flight platform, is used to detect the angle between the UAV flight platform and the horizontal line in real time. ; An encoder module is mounted on the motor shaft of the walking wheel and is used to record walking distance and position information in wall-hugging walking mode; A lidar unit, mounted on the UAV's flight platform, is used to detect obstacles ahead. A wireless positioning module is installed on the mobile vehicle platform and the drone flight platform to track the drone's position in real time using wireless signal strength.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention uses a ground-penetrating radar detection device mounted on an unmanned aerial vehicle (UAV) flight platform to conduct tunnel lining inspection, which completely changes the traditional manual high-altitude operation mode that relies on platforms such as inspection trolleys and articulated boom trucks, and avoids the safety hazards of inspection personnel working in high-risk areas such as tunnel arches for a long time. The entire inspection process is autonomously controlled by the control and management server, without the need for personnel to operate at heights, thus fundamentally ensuring the personal safety of the operators.

[0019] (2) This invention adopts a two-stage operation mode of scanning first and then inspection. The tunnel point cloud data is quickly acquired through a three-dimensional laser scanning system, and the control management server automatically generates the optimal inspection operation path, avoiding the blindness and repetitive work of manual path planning. The UAV flight platform performs inspection in a wall-hugging walking mode, with fast and continuous walking speed, which greatly improves efficiency compared with traditional manually controlled platforms. At the same time, the mobile vehicle platform provides external power to the UAV through a power cable, solving the problem of insufficient UAV endurance and enabling continuous inspection of long-distance tunnels.

[0020] (3) This invention uses a pressure sensor to detect the contact pressure between the ground-penetrating radar detection device and the tunnel lining surface in real time, and dynamically adjusts the adsorption force of the fixed adsorption rotor according to the attitude angle to keep the contact pressure within a preset range. This ensures that the radar antenna is in close contact with the lining surface, eliminates the attitude instability caused by airflow interference during flight, and guarantees the coupling effect of radar waves and the quality of data acquisition. At the same time, the encoder accurately records the walking distance and position information, realizing precise positioning of the detection location and providing a reliable position reference for subsequent data analysis.

[0021] (4) The present invention adopts a dual-mode working method that combines the wall-walking mode and the flight mode. In the barrier-free section, the wall-walking mode is used for stable detection. When encountering obstacles such as misalignment, anchor head, water accumulation, or floating scum, it automatically switches to the flight mode to fly over the obstacles. After passing the obstacles, it automatically returns to the wall-walking mode, realizing continuous autonomous detection operation in complex environments. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments 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.

[0023] Figure 1 This is a flowchart illustrating the automated radar detection method for tunnel lining quality as described in Embodiment 1 of the present invention.

[0024] Figure 2 This is a schematic diagram of the structure of the automated radar detection equipment for tunnel lining quality described in Embodiment 2 of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of the UAV flight platform equipped with a ground-penetrating radar detection device according to Embodiment 2 of the present invention.

[0026] Figure 4 This is a structural schematic diagram from another perspective of the UAV flight platform equipped with a ground-penetrating radar detection device described in Embodiment 2 of the present invention.

[0027] Explanation of reference numerals in the attached figures: 1. Ground-penetrating radar detection device; 2. Unmanned aerial vehicle (UAV) flight platform; 3. Mobile vehicle-mounted platform; 4. Walking wheels; 5. Fixed adsorption rotor; 6. Rotatable powered rotor; 7. Control and management server; 8. Horizontal angle sensor; 9. LiDAR. Detailed Implementation

[0028] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0029] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this patent. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0030] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0031] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0032] Example 1: like Figure 1 As shown, this embodiment provides an automated radar detection method for tunnel lining quality, including the following steps: Step S1: Tunnel Scanning and Point Cloud Data Acquisition The mobile vehicle-mounted platform 3, equipped with 3D laser scanning equipment, performs a full-section scan of the tunnel interior along the tunnel's forward direction, generating tunnel point cloud data. As a ground support system, the mobile vehicle-mounted platform 3 can autonomously move along the tunnel's tracks or road surface, providing a stable mobile platform for the scanning operation.

[0033] Step S2: Detect the operation path generation and obstacle marking The control and management server 7 generates a detection operation path based on the tunnel point cloud data obtained in step S1, and marks the locations of obstacles in the path. Specifically, this includes the following sub-steps: Step S21: Denoise, filter and thin out the original point cloud to remove noise points and redundant data, reduce the amount of data and improve the efficiency of subsequent processing. Step S22: Cut sections along the tunnel direction at preset intervals and determine the normal direction of each section. This normal direction is the local direction of the tunnel. Step S23: On each cross section, the design shape of the tunnel cross section is fitted using the least squares method to obtain the center point of each cross section; it should be noted that the design shape includes one or more of the following: circle, ellipse, and horseshoe shape.

[0034] Step S24: Connect the center points of each section in sequence and smooth them to form a smooth longitudinal centerline trajectory of the tunnel. Step S25: Based on the longitudinal centerline trajectory of the tunnel, generate the detection operation path of the radar detection survey line according to the tunnel cross-section design shape and detection specification requirements.

[0035] It should be noted that the radar detection survey lines generated in step S25 include longitudinal survey lines and circumferential survey lines; The method for generating longitudinal survey lines includes: defining the top longitudinal line of the tunnel as the survey line at a preset height position where the upper part of the tunnel centerline forms a 90° angle with the horizontal plane; other longitudinal survey lines are laid out at equal intervals based on the top longitudinal line and the arc length is calculated according to the tunnel diameter. The method for generating circumferential survey lines includes: calculating by mileage, generating circumferential scanning survey lines along the tunnel cross section at preset intervals within a preset angle range with respect to the horizontal plane; The control and management server 7 optimizes the process based on tunnel mileage, inspection specifications, and operational efficiency, arranging the generated longitudinal and circumferential survey lines into an ordered queue of inspection tasks, and assigning a unique execution sequence number and inspection parameters to each survey line.

[0036] Step S26: Using the 3D laser point cloud and the LiDAR 9 echo intensity map, semantic segmentation of obstacles is performed using a data model, and category labels of obstacles are output. It should be noted that the category labels include one or more of the following: misalignment, anchor head, water accumulation reflective area, and scum clump. Step S27: Separate the effective point set of the obstacle from the tunnel point cloud data, fit the geometric bounding volume based on the point set, and output the geometric dimensions of the obstacle, including length, height and width; Step S28: Mark obstacles whose height exceeds a preset threshold in the geometry as trigger conditions for flight mode switching.

[0037] The preset threshold is dynamically adjusted based on the radius of curvature of the tunnel cross-section: when the radius of curvature of the tunnel cross-section is less than the preset radius threshold, the height trigger threshold is increased. In this embodiment, the preset radius threshold is preferably 5m, and the height trigger threshold under normal circumstances is preferably 0.03m. When the radius of curvature R of the tunnel cross-section is less than 5m, the height trigger threshold is increased to 0.04m to avoid false triggering caused by false height difference due to curvature amplification.

[0038] Step S3: The drone flies to the starting point of the operation. The unmanned aerial vehicle (UAV) flight platform 2, equipped with a ground-penetrating radar detection device 1, flies to the starting point of the detection operation path generated in step S2. The mobile vehicle-mounted platform 3 carries a power supply and provides external power to the UAV flight platform 2 via a power cable, enabling continuous operation over extended periods. Simultaneously, real-time positioning and tracking are achieved through a wireless positioning module located between the mobile vehicle-mounted platform 3 and the UAV flight platform 2. Specifically, the UAV flight platform 2 transmits a wireless signal, the mobile vehicle-mounted platform 3 receives the signal, calculates the distance based on the received signal strength, and tracks the UAV's position in real time.

[0039] Step S4: Detecting the wall-hugging walking mode In unobstructed sections, the fixed adsorption rotor 5 of the UAV flight platform 2 is activated, allowing the ground-penetrating radar detection device 1 to adhere to the tunnel lining surface. It then moves along the detection path in a wall-hugging mode to collect radar detection data. Specifically, the following sub-steps are included: Step S41: In the unobstructed section, activate the fixed adsorption rotor 5 to adsorb the ground radar detection device 1 onto the tunnel lining surface. By adjusting the rotation speed of each rotatable power rotor 6, the UAV flight platform 2 generates a thrust component parallel to the tunnel lining surface, driving the walking wheels 4 of the ground radar detection device 1 to roll along the lining surface and move along the detection operation path in a wall-hugging walking mode. Step S42: Detect the angle between the UAV flight platform 2 and the horizontal line in real time using the horizontal angle sensor 8. The pressure sensor detects the contact pressure between the ground radar detection device 1 and the tunnel lining surface in real time. Step S43: The UAV flight platform 2 dynamically adjusts the adsorption force of the fixed adsorption rotor 5 according to the following adsorption pressure calculation formula to keep the adsorption pressure within the preset range: Where F is the target fitting pressure, For the gravity of the ground-penetrating radar detection device 1, For the unmanned aerial vehicle (UAV) flight platform, 2 gravity is the angle between the UAV flight platform 2 and the horizontal line, and k is the preset safety factor.

[0040] It should be noted that the safety factor k in this embodiment is preferably 1.2 to ensure sufficient adsorption force so that the antenna is tightly attached to the lining surface, while avoiding excessive pressure that could damage the equipment or cause excessive walking resistance.

[0041] Step S44: Record the walking distance and position information in the wall-hugging walking mode using the encoder set on the UAV flight platform 2. This includes the following sub-steps: Step S441: The encoder's code disk is mounted on the motor shaft of the walking wheel 4 and rotates synchronously with the walking wheel 4. The light source shines through the gap of the code disk onto the photosensitive element to generate a periodically changing light signal and converts the light signal into an electrical pulse signal. Step S442: The encoder outputs two pulse signals with a 90° phase difference. By judging the phase lead or lag relationship between the two pulse signals, the rotation direction of the walking wheel 4 is determined. Step S443: Based on the number of pulses received per unit time Calculate the rotational speed of the four traveling wheels based on the number of pulses P per revolution of the encoder. Calculate the linear velocity based on the wheel diameter D. Then calculate the driving distance. This enables real-time recording of the detection location.

[0042] Step S5, Flight Mode Switching and Obstacle Clearing When an obstacle is detected ahead, the control drone flight platform 2 switches from wall-hugging mode to flight mode, and adjusts its flight attitude using the rotatable powered rotor 6 to fly over the obstacle. This includes the following sub-steps: Step S51: Detect obstacles ahead using the lidar 9 installed on the UAV flight platform 2; When the detected obstacle matches the location of the obstacle marked in step S2, and the obstacle height is ≥0.03m or the height of the obstacle from the tunnel lining surface is ≤ the overall height of the UAV flight platform 2, the lidar 9 issues an alarm signal. Step S52: Based on the alarm signal from the lidar 9, the drone flight platform 2 adjusts the rotation speed of each rotatable power rotor 6 to adjust the attitude of the drone flight platform 2 and gradually establish a stable hovering state, preparing for detachment from the wall. Step S53: After the hovering state is stable, the fixed adsorption rotor 5 is turned off to release the adsorption force; at the same time, by adjusting the rotation speed of each rotatable power rotor 6, the fuselage of the UAV flight platform 2 is tilted appropriately, thereby obtaining a thrust component away from the tunnel lining surface, and controlling the UAV flight platform 2 to fly away from the lining surface smoothly. Step S54: Monitor the distance between the UAV flight platform 2 and the obstacle in real time using the lidar 9, maintain a safe distance of 0.5m between the UAV flight platform 2 and the obstacle, switch the ground-penetrating radar detection device 1 to time-triggered mode, and achieve attitude control by adjusting the rotation speed of each rotatable power rotor 6 to smoothly cross the obstacle. It should be noted that the time-triggered mode described in this embodiment refers to the situation where, after the UAV flight platform 2 switches to flight mode and detaches from the tunnel lining surface, the encoder can no longer provide distance pulse signals because the walking wheels 4 have separated from the lining surface. At this time, the ground-penetrating radar detection device 1 automatically switches to a data acquisition mode with a fixed time interval as the trigger signal. In time-triggered mode, the system periodically triggers radar wave transmission and echo signal reception according to a preset time interval, ensuring uninterrupted data detection during obstacle crossing. After the UAV flight platform 2 resumes wall-hugging walking mode, the ground-penetrating radar detection device 1 automatically switches back to distance-triggered mode, resuming the data acquisition mode with encoder pulse signals as the trigger source. The combined use of time-triggered mode and distance-triggered mode ensures the continuity and integrity of data acquisition by the UAV flight platform 2 throughout the entire detection operation, avoiding data loss caused by obstacle crossing.

[0043] Step S6: Restore wall-hugging walking mode After clearing the obstacle, the control drone flight platform 2 switches back to wall-hugging mode and continues to collect radar detection data along the detection operation path. This includes the following sub-steps: Step S61: After crossing the obstacle, the UAV flight platform 2 is controlled by the lidar 9 to return to a stable hovering state at 0.5m. The rotation speed of each rotatable power rotor 6 is adjusted. The attitude of the aircraft is adjusted so that the ground radar detection device 1 is re-aligned with the tunnel lining surface. The fixed adsorption rotor 5 is activated to control the UAV flight platform 2 to approach the tunnel lining surface smoothly at a speed of 0.01m / s. Step S62: When the bonding pressure detected by the pressure sensor reaches the preset target value, the rotation speed of each rotatable power rotor 6 is adjusted to make the UAV flight platform 2 generate a thrust component parallel to the tunnel lining surface, driving the walking wheels 4 to roll along the lining surface and restore the wall-hugging walking mode.

[0044] Step S7: Work Completion and Equipment Recovery After the inspection is completed, the drone flight platform 2 is retrieved to the mobile vehicle platform 3, and the inspection data is exported. Throughout steps S3 to S6, the mobile vehicle platform 3 follows the drone flight platform 2 via a wireless positioning module and provides continuous power to the drone flight platform 2.

[0045] During the detection process in steps S4 to S6, the mobile vehicle platform 3 adopts different following methods according to the detection path type: when performing longitudinal survey line detection, the mobile vehicle platform 3 follows the UAV flight platform 2 autonomously at a fixed distance; when performing circumferential survey line detection, the UAV flight platform 2 is equipped with a ground-penetrating radar detection device 1 to perform circumferential scanning at a fixed mileage section. At this time, the mobile vehicle platform 3 stops at the corresponding mileage position. The mobile vehicle platform 3 and the UAV flight platform 2 work together in a mileage synchronization manner to ensure that the power supply cable is always kept in a moderate tension state to avoid tangling or pulling.

[0046] This embodiment adopts a dual-mode working method that combines wall-walking mode and flight mode. In obstacle-free sections, it uses wall-walking mode for stable detection. When encountering obstacles such as misaligned platforms, anchor heads, water accumulation, or floating debris, it automatically switches to flight mode to fly over the obstacles. After passing the obstacles, it automatically resumes wall-walking mode, realizing continuous autonomous detection operations in complex environments.

[0047] Example 2: like Figures 2 to 4 As shown, this embodiment provides an automated radar detection device for tunnel lining quality, used to implement the automated radar detection method for tunnel lining quality in Embodiment 1, including: Ground-penetrating radar detection device 1 is used to perform tunnel lining detection operations; Unmanned aerial vehicle (UAV) platform 2 is used to carry ground-penetrating radar detection device 1; The mobile vehicle platform 3 is set up inside the tunnel and can move along the tunnel's direction of travel. It is equipped with a power supply and is connected to the UAV flight platform 2 via a power cable to provide external power to the UAV flight platform 2. The three-dimensional laser scanning equipment, installed on the mobile vehicle platform 3, is used to scan the interior of the tunnel and generate tunnel point cloud data; The walking wheel 4 is movably mounted on the ground radar detection device 1 and directly contacts the tunnel lining surface. It is used to drive the ground radar detection device 1 to move in the wall-hugging walking mode. The fixed adsorption rotor 5 is set at the bottom of the UAV flight platform 2 and is used to generate adsorption force in the wall-hugging walking mode so that the walking wheels 4 on the ground radar detection device 1 are in close contact with the tunnel lining surface. Rotatable powered rotors 6 are set around the UAV flight platform 2 to provide walking power in wall-hugging walking mode and flight power and attitude adjustment in flight mode; The control and management server 7 is located in the mobile vehicle platform 3 and is connected to both the mobile vehicle platform 3 and the UAV flight platform 2. It is used to generate detection operation paths based on the tunnel point cloud data of the mobile vehicle platform 3 and to control the UAV flight platform 2 to switch between wall-hugging walking mode and flight mode. A pressure sensor is installed between the ground-penetrating radar detection device 1 and the UAV flight platform 2 to detect the contact pressure between the ground-penetrating radar detection device 1 and the tunnel lining surface. The horizontal angle sensor 8 is installed on the UAV flight platform 2 and is used to detect the angle between the UAV flight platform 2 and the horizontal line in real time. ; The encoder module is mounted on the motor shaft of the walking wheel 4 and is used to record walking distance and position information in the wall-hugging walking mode; LiDAR 9 is mounted on the UAV flight platform 2 and is used to detect obstacles in front; The wireless positioning module is installed on the mobile vehicle platform 3 and the drone flight platform 2, and is used to track the drone's position in real time by means of wireless signal strength.

[0048] The specific structure and working principle of the ground-penetrating radar detection device 1 described in this embodiment can be achieved by using commercially available ground-penetrating radar equipment that is mature in this field. The specific detection methods and technical parameters can be implemented in accordance with relevant industry standards such as the "Code for Non-destructive Testing of Railway Tunnel Lining Quality" (TB10223-2004).

[0049] In this embodiment, four traveling wheels 4 are preferably provided, movably disposed around the top surface of the ground-penetrating radar detection device 1. The traveling wheels 4 can rotate freely to adapt to the curvature changes and unevenness of the tunnel lining surface. The four traveling wheels 4 are distributed in a rectangular or square layout at the four corners of the ground-penetrating radar detection device 1. Under the adsorption force provided by the fixed adsorption rotor 5, the four traveling wheels 4 are evenly stressed, enabling the ground-penetrating radar detection device 1 to stably conform to the tunnel lining surface.

[0050] In this embodiment, the horizontal angle sensor 8 is preferably disposed on both sides of the UAV flight platform 2. In the wall-hugging walking mode, due to the different positions of the tunnel lining surface such as the arch, sidewalls, and bottom, the attitude angle of the UAV flight platform 2 is affected. It will change accordingly. The horizontal angle sensor 8 will detect... The value is transmitted to the control and management server 7 in real time for the calculation of the target adhesion pressure. This allows the control and management server 7 to dynamically adjust the adhesion force of the fixed adsorption rotor 5 according to the actual position of the UAV, ensuring that the walking wheel 4 can adhere to the tunnel lining surface with constant pressure in different postures, thereby ensuring the stability and reliability of radar detection data.

[0051] The wireless positioning module described in this embodiment can specifically be a Bluetooth positioning module. The drone flight platform 2 transmits Bluetooth signals, and the Bluetooth receiving module on the mobile vehicle platform 3 calculates the distance in real time based on the received signal strength, thus achieving position tracking of the drone flight platform 2. In tunnel environments without GPS signals, Bluetooth positioning offers advantages such as low power consumption, simple deployment, and moderate cost.

[0052] In addition, as an alternative implementation, the wireless positioning module may also employ one or more of ultra-wideband positioning modules, Wi-Fi positioning modules, or ZigBee positioning modules to achieve higher accuracy or longer-distance positioning requirements. Those skilled in the art can select appropriate wireless positioning technologies based on the actual tunnel length, accuracy requirements, and cost budget. The specific implementation principles and algorithms are mature technologies in this field and will not be elaborated upon here.

[0053] It is understood that the mobile vehicle-mounted platform 3 described in this embodiment is prior art, and its specific structure includes, but is not limited to, conventional components such as a vehicle body, running gear, power system, steering system, and braking system. As a ground support carrier, the mobile vehicle-mounted platform 3 is mainly used to carry modules such as 3D laser scanning equipment and power supply, and can move autonomously along the tunnel track or road surface. This invention does not involve improvements to the mechanical structure or drive control of the mobile vehicle-mounted platform 3 itself, but rather utilizes it as a movable ground platform to cooperate with the UAV flight platform 2 to complete tunnel lining quality inspection operations. Any conventional vehicle-mounted platform capable of moving along the tunnel and carrying the required functional modules can be used as the mobile vehicle-mounted platform 3 described in this embodiment. Those skilled in the art can select a suitable platform model and make adaptive modifications according to the actual working conditions of the tunnel.

[0054] Similarly, the control management server 7 described in this embodiment belongs to the prior art, and its hardware structure includes, but is not limited to, conventional components such as industrial control computers, embedded processors, data storage units, and communication interface modules. The data processing, path planning, command sending, and status monitoring functions implemented by the control management server 7 are all conventional control logics that can be implemented by those skilled in the art based on common knowledge and mature technologies. This invention does not involve improvements to the hardware structure or basic software of the control management server 7 itself, but rather utilizes the existing control management server 7 as the control center of an automated detection system to realize the automated detection method for tunnel lining quality described in the preceding steps. Therefore, any general-purpose or special-purpose control device capable of executing the method described in this invention can be used as the control management server 7 described in this embodiment.

[0055] This embodiment utilizes a UAV flight platform 2 equipped with a ground-penetrating radar detection device 1 for tunnel lining inspection, completely changing the traditional manual high-altitude operation mode that relies on platforms such as inspection trolleys and articulated boom lifts. This avoids the safety hazards of inspection personnel working in high-risk areas such as tunnel arches for extended periods. The entire inspection process is autonomously controlled by the control and management server 7, eliminating the need for personnel to operate at heights and fundamentally ensuring the personal safety of the workers.

[0056] It should be stated that the above-described specific embodiments are merely preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that various modifications, equivalent substitutions, and variations can be made to the present invention. However, such variations, as long as they do not depart from the spirit of the present invention, should be within the scope of protection of the present invention. Furthermore, some terminology used in this specification and claims is not limiting, but merely for ease of description.

Claims

1. An automated radar detection method for tunnel lining quality, characterized in that, Includes the following steps: S1. Scan the interior of the tunnel using a mobile vehicle-mounted platform equipped with a 3D laser scanning device to generate tunnel point cloud data; S2. The control and management server generates a detection operation path based on the tunnel point cloud data and marks the location of obstacles in the path; S3. Control the UAV flight platform carrying the ground-penetrating radar detection device to fly to the starting point of the detection operation path; S4. In unobstructed sections, activate the fixed adsorption rotor of the UAV flight platform to adsorb the ground radar detection device onto the tunnel lining surface, move along the detection operation path in a wall-walking mode and collect radar detection data. S5. When an obstacle is detected ahead, control the drone flight platform to switch from wall-hugging mode to flight mode, and adjust the flight attitude through the rotatable power rotor of the drone flight platform to fly over the obstacle. S6. After flying over the obstacle, control the drone flight platform to switch back to the wall-hugging mode and continue the inspection operation; S7. After the inspection is completed, retrieve the drone flight platform to the mobile vehicle platform and export the inspection data.

2. The automated radar detection method for tunnel lining quality according to claim 1, characterized in that, Step S2 specifically includes: S21. Perform noise reduction, filtering, and thinning on the original point cloud; S22. Take sections along the tunnel direction at preset intervals and determine the normal direction of each section; S23. On each cross section, the design shape of the tunnel cross section is fitted using the least squares method to obtain the center point of each cross section; S24. Connect the center points of each section in sequence and smooth them out to form the longitudinal centerline trajectory of the tunnel. S25. Based on the longitudinal centerline trajectory of the tunnel, generate the detection operation path of the radar detection survey line according to the tunnel cross-section design shape and detection specification requirements; S26. Using 3D laser point cloud and lidar echo intensity map, semantic segmentation of obstacles is performed using a data model, and category labels of obstacles are output. S27. Separate the effective point set of obstacles from the tunnel point cloud data, fit a geometric bounding volume based on the point set, and output the geometric dimensions of the obstacles. S28. Mark obstacles whose height exceeds a preset threshold in the geometry as a trigger condition for switching flight modes.

3. The automated radar detection method for tunnel lining quality according to claim 2, characterized in that, The radar detection survey lines generated in step S25 include longitudinal survey lines and circumferential survey lines; The method for generating the longitudinal survey lines includes: defining the top longitudinal line of the tunnel as the survey line at a preset height position where the upper part of the tunnel centerline forms a 90° angle with the horizontal plane; and laying other longitudinal survey lines at equal intervals based on the top longitudinal line and the arc length calculated according to the tunnel diameter. The method for generating the circumferential survey line includes: calculating based on mileage, and generating circumferential scanning survey lines along the tunnel cross section at preset intervals within a preset angle range with respect to the horizontal plane.

4. The automated radar detection method for tunnel lining quality according to claim 1, characterized in that, Step S4 specifically includes: S41. In the unobstructed section, activate the fixed adsorption rotor to adsorb the ground radar detection device onto the tunnel lining surface. By adjusting the rotation speed of each rotatable power rotor, the UAV flight platform generates a thrust component parallel to the tunnel lining surface, driving the walking wheels of the ground radar detection device to roll along the lining surface and move along the detection operation path in a wall-hugging walking mode. S42. Real-time detection of the angle between the UAV flight platform and the horizontal line using a horizontal angle sensor. The pressure sensor detects the contact pressure between the ground-penetrating radar detection device and the tunnel lining surface in real time. S43. The UAV flight platform dynamically adjusts the adsorption force of the fixed adsorption rotor according to the following adsorption pressure calculation formula to keep the adsorption pressure within a preset range: Where F is the target fitting pressure, For the gravity of the ground-penetrating radar detection device, For the gravity of the drone flight platform, is the angle between the drone flight platform and the horizontal line, and k is the preset safety factor; S44. Record the walking distance and location information in the wall-hugging walking mode using the encoder set on the drone flight platform.

5. The automated radar detection method for tunnel lining quality according to claim 4, characterized in that, Step S44 specifically includes: S441. The encoder's code disk is mounted on the motor shaft of the walking wheel and rotates synchronously with the walking wheel. The light source shines through the gap of the code disk onto the photosensitive element to generate a periodically changing light signal and converts the light signal into an electrical pulse signal. S442: The encoder outputs two pulse signals with a 90° phase difference. By judging the phase lead or lag relationship between the two pulse signals, the rotation direction of the walking wheel is determined. S443. Based on the number of pulses received per unit time. Calculate the wheel speed based on the number of pulses P per encoder revolution. Calculate the linear velocity based on the wheel diameter D. Then calculate the driving distance. This enables real-time recording of the detection location.

6. The automated radar detection method for tunnel lining quality according to claim 1, characterized in that, In steps S3 to S6, the mobile vehicle platform follows the UAV flight platform through the wireless positioning module. The UAV flight platform transmits wireless signals, the mobile vehicle platform receives the signals and calculates the distance based on the received signal strength, tracks the UAV flight platform in real time, and provides power to the UAV flight platform to achieve long-term continuous operation.

7. The automated radar detection method for tunnel lining quality according to claim 1, characterized in that, Step S5 specifically includes: S51. Detects obstacles ahead using a lidar mounted on the drone flight platform; When the detected obstacle matches the location of the obstacle marked in step S2, and the obstacle height is ≥0.03m or the height of the obstacle from the tunnel lining surface is ≤ the overall height of the UAV flight platform, the lidar issues an alarm signal. S52. Based on the alarm signal from the lidar, the UAV flight platform adjusts the speed of each rotatable rotor to adjust the attitude of the UAV flight platform and gradually establish a stable hovering state, preparing for detachment from the wall. S53. After the hovering state is stable, the fixed adsorption rotor is turned off to release the adsorption force; at the same time, by adjusting the speed of each rotatable power rotor, the fuselage of the UAV flight platform is tilted appropriately, thereby obtaining a thrust component away from the tunnel lining surface, and controlling the UAV flight platform to fly smoothly away from the lining surface. S54. The distance between the UAV flight platform and the obstacle is monitored in real time by lidar, and the UAV flight platform is controlled to maintain a safe distance of 0.5m from the obstacle. The ground-penetrating radar detection device is switched to time-triggered mode, and attitude control is achieved by adjusting the speed of each rotatable power rotor to smoothly cross the obstacle.

8. The automated radar detection method for tunnel lining quality according to claim 7, characterized in that, Step S6 specifically includes: S61. After crossing the obstacle, the UAV flight platform is controlled by the lidar to return to a stable hovering state by adjusting the speed of each rotatable power rotor at 0.5m. The attitude of the aircraft is adjusted so that the ground radar detection device is re-aligned with the tunnel lining surface. The fixed adsorption rotor is activated to control the UAV flight platform to approach the tunnel lining surface smoothly at a speed of 0.01m / s. S62. When the bonding pressure detected by the pressure sensor reaches the preset target value, the speed of each rotatable power rotor is adjusted to make the UAV flight platform generate a thrust component parallel to the tunnel lining surface, driving the walking wheels to roll along the lining surface and restore the wall-hugging walking mode.

9. An automated radar detection device for tunnel lining quality, used to implement the automated radar detection method for tunnel lining quality as described in any one of claims 1-8, characterized in that, include: Ground-penetrating radar (GPR) detection equipment is used to perform tunnel lining inspection operations; Unmanned aerial vehicle (UAV) flight platform, used to carry ground-penetrating radar detection devices; A mobile vehicle-mounted platform is installed inside the tunnel and can move along the tunnel's direction of travel. It is equipped with a power supply and is connected to the UAV flight platform to provide external power to the UAV flight platform. A three-dimensional laser scanning device, installed on the mobile vehicle platform, is used to scan the interior of the tunnel and generate tunnel point cloud data; The walking wheels are mounted on the ground-penetrating radar detection device and are in direct contact with the tunnel lining surface. They are used to move the ground-penetrating radar detection device in the wall-hugging walking mode. A fixed adsorption rotor is installed at the bottom of the UAV flight platform to generate adsorption force in the wall-hugging walking mode, so that the walking wheels on the ground radar detection device are in close contact with the tunnel lining surface. Rotatable powered rotors are arranged around the UAV flight platform to provide walking power in wall-hugging walking mode and flight power and attitude adjustment in flight mode. A control and management server is communicatively connected to both the mobile vehicle platform and the UAV flight platform. It is used to generate detection operation paths based on the tunnel point cloud data of the mobile vehicle platform and to control the UAV flight platform to switch between wall-hugging walking mode and flight mode.

10. The automated radar detection equipment for tunnel lining quality according to claim 9, characterized in that, Also includes: A pressure sensor is installed between the ground-penetrating radar detection device and the UAV flight platform to detect the contact pressure between the ground-penetrating radar detection device and the tunnel lining surface. A horizontal angle sensor, mounted on the UAV flight platform, is used to detect the angle between the UAV flight platform and the horizontal line in real time. ; An encoder module is mounted on the motor shaft of the walking wheel and is used to record walking distance and position information in wall-hugging walking mode; A lidar unit, mounted on the UAV's flight platform, is used to detect obstacles ahead. A wireless positioning module is installed on the mobile vehicle platform and the drone flight platform to track the drone's position in real time using wireless signal strength.

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