High-precision ultrasonic sensor for wall cleaning machine, detection system and method

Abstract

The application discloses a high-precision ultrasonic mud skin detection sensor for a wall brushing machine, a detection system and a method, and belongs to the technical field of underground anti-seepage wall construction quality detection. The sensor adopts an arc circumferential pipe matched with an array radial pipe structure, and is matched with the inner concave arc-shaped side wall after the adapter joint pipe is pulled out, and realizes self-adaptive balance of the differential pressure in the super-deep working condition through a valve plug; the detection system guarantees a constant detection distance through the limiting mechanism matched with the arc-shaped wall, and realizes time-sharing collection to avoid acoustic crosstalk; the detection method adopts column coordinate system registration, weighted fusion in the overlapping area and double-interface accurate identification, and realizes blind area-free detection of the mud skin thickness. The application can be operated synchronously with the wall brushing machine, is stable in the super-deep high-pressure environment, has high detection precision, and can realize closed-loop management and control of the wall brushing quality.

Description

Technical Field

[0001] This invention relates to the field of water conservancy foundation treatment technology, and more particularly to the field of construction and testing technology of ultra-deep anti-seepage walls, specifically to a high-precision ultrasonic mud skin detection sensor, detection system and method for wall-brushing machines. Background Technology

[0002] Ultra-deep underground cutoff walls are core water-stopping structures in water conservancy projects, deep foundation pit support, and tailings dam seepage prevention. Their construction quality directly determines the seepage prevention effect and operational safety of the project. Currently, ultra-deep cutoff walls are mostly constructed in sections using the joint pipe method. After the joint pipe is removed, a concave cylindrical arc-shaped sidewall is formed at the end of the section. The mud and sludge residue on the surface of this sidewall directly affects the joint bonding strength of subsequent concrete pouring, easily forming leakage channels at the joints. Therefore, it is necessary to clean the mud and sludge from the sidewall using a wall-cleaning machine. Accurate detection of the mud and sludge thickness is a key step in judging the quality of wall cleaning and ensuring the quality of wall construction.

[0003] In existing technologies, mud cake detection in cutoff walls primarily employs methods such as manual probing, core drilling, and in-hole imaging. Manual probing is only suitable for shallow trenches and cannot be implemented in ultra-deep conditions. Core drilling is a destructive method, inefficient and costly, and cannot achieve full-section detection. In-hole imaging produces extremely poor images in high-turbidity mud environments, making it impossible to accurately identify mud cake thickness. While ultrasonic testing technology, which has emerged in recent years, can achieve non-contact detection in mud environments, it still suffers from the following technical drawbacks: First, existing ultrasonic sensors are mostly planar arrays, which cannot adapt to the concave arc-shaped sidewalls formed after the connector pipe is removed. The sensor beam cannot be perpendicularly incident on the sidewall surface, resulting in a low echo signal-to-noise ratio and large thickness measurement errors. Second, the sealing structure of existing sensors is mostly a fixed rigid seal, which is prone to seal failure and sensor damage due to the high hydrostatic pressure in ultra-deep trenches, making it unsuitable for the construction conditions of ultra-deep cutoff walls reaching hundreds of meters in length. Third, existing multi-probe detection schemes... The lack of standardized overlapping design easily leads to detection blind spots, and there is no targeted fusion processing method for multi-source data in overlapping areas. Data splicing is prone to problems such as jumps and error amplification, making it impossible to achieve accurate identification of mud thickness at the millimeter level. Fourth, most existing detection solutions are offline detection, which cannot be synchronized with the wall brushing machine. The detection and wall brushing process are disconnected, making it impossible to achieve real-time control and closed-loop processing of wall brushing quality, resulting in low construction efficiency. Fifth, the limiting mechanism of the existing detection system cannot be adapted to curved sidewalls, and cannot ensure a constant detection distance between the sensor and the sidewall, further amplifying the detection error.

[0004] In view of the many shortcomings of the existing technology, there is an urgent need to develop a mud skin detection sensor, system and method that can be adapted to the arc sidewall of the connector pipe, work stably in ultra-deep high-pressure environment, can be operated synchronously with the wall brushing machine, and has high detection accuracy. Summary of the Invention

[0005] To address the issue of mud cake detection on the sidewalls of adjacent sections of existing cutoff walls, this application provides a high-precision ultrasonic mud cake detection sensor, detection system, and method for a wall-brushing machine. This system detects mud cake during and / or after cleaning the sidewalls of the preceding cutoff wall section before pouring the current section, preventing joint defects and leaks caused by mud cake adhesion, which could lead to cutoff wall failure.

[0006] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0007] This invention provides a high-precision ultrasonic mud skin detection sensor for a wall scrubber, comprising a frame with a mounting tube for mounting an ultrasonic sensor. The mounting tube includes a hollow, arc-shaped circumferential tube, both ends of which are closed, and at least one end is provided with a valve plug for balancing the pressure difference between the inside and outside of the circumferential tube. The circumferential tube also has a plurality of radial tubes arranged in a circumferential array. Each radial tube includes a tube body that penetrates and is sealed and fixedly connected to the circumferential tube. The tube body is open at one end near the center of the circumferential tube and has a sound-transmitting layer sealed and connected to the tube body at the other end away from the center. The ultrasonic sensor is detachably and sealedly connected to the tube body. The tube body also has a through hole communicating with the circumferential tube.

[0008] Preferably, the ultrasonic sensor is encapsulated with acoustically permeable epoxy potting compound, and the encapsulated size is adapted to the radial tube. The circumferential tube and the radial tube are filled with insulating, acoustically permeable dimethyl silicone oil.

[0009] Preferably, the detection ranges of two adjacent ultrasonic sensors on the surface of the impermeable wall overlap.

[0010] This invention also provides a detection system for real-time detection of the thickness of mud cake adhesion at the full depth of an anti-seepage wall. The system uses the high-precision ultrasonic mud cake detection sensor for wall-brushing machines provided above for detection, and specifically includes:

[0011] The carrier and limiting module include a ground crane and a carrier that moves vertically and vertically at full depth via the ground crane. The detection sensor is installed on the side of the carrier close to the seepage barrier wall. Limiting mechanisms for limiting the minimum distance between the detection sensor and the seepage barrier wall are installed on the same side of the upper and lower ends of the carrier and the detection sensor. The limiting mechanism includes a mounting bracket and a limiting rotary body rotatably mounted on the mounting bracket. The outer edge shape of the limiting rotary body is adapted to the surface of the seepage barrier wall.

[0012] The synchronous signal acquisition module includes a multi-channel synchronous data acquisition card for synchronous triggering and polling acquisition among multiple detection sensors, and a ground terminal connected through an explosion-proof acquisition circuit in the well. The ground terminal is used to record the carrier's descent depth and attitude data in real time.

[0013] This invention also provides a detection method, implemented using the detection system described above, comprising using an ultrasonic sensor to collect ultrasonic echo data of the sidewall of the seepage barrier, and processing the echo data to obtain the mud cake thickness detection result, specifically including the following steps:

[0014] Step S1: System calibration and parameter preset before detection: On-site calibration of the ultrasonic propagation velocity of the mud in the detection environment, completion of beam parameter calibration of a single ultrasonic sensor, and acquisition of the -6dB half-diffusion angle of the ultrasonic sensor; preset the rated normal detection distance between the ultrasonic sensor and the arc-shaped sidewall of the seepage barrier to be 100mm, preset the circumferential overlap coefficient of the detection area of ​​adjacent ultrasonic sensors to be 15%-25%, and simultaneously input the design curvature radius, design arc length and trench design depth parameters of the arc-shaped sidewall under test, and complete the communication debugging of the detection system;

[0015] Step S2: Installation and orientation adaptation of the detection system: Fix the high-precision ultrasonic mud skin detection sensor for the wall brushing machine on the wall brushing machine carrier, so that the arc-shaped circumferential tube of the sensor is coaxially arranged with the arc-shaped sidewall being measured; fit the limiting rotary bodies at the upper and lower ends of the carrier to the surface of the arc-shaped sidewall being measured, so that the beam axis of each ultrasonic sensor coincides with the normal of the corresponding position of the arc-shaped sidewall and points to the curvature center of the arc-shaped sidewall. The limiting rotary bodies constrain the normal distance between all ultrasonic sensors and the arc-shaped sidewall throughout the entire process and stabilize it at the rated detection distance;

[0016] Step S3: Ultra-deep working condition differential pressure adaptive balancing and full-section synchronous data acquisition: The carrier is lowered at a constant speed along the central axis of the arc-shaped sidewall by a ground crane. During the lowering process, the multi-channel synchronous data acquisition card triggers each ultrasonic sensor to emit ultrasonic pulses in sequence and acquire A-scan full wave train echo data in a time-division polling mode. At the same time, the real-time lowering depth data and dual-axis tilt angle attitude data of the carrier are recorded synchronously by the ground terminal to form the original echo dataset with spatial reference.

[0017] Step S4: Preprocessing of raw echo data: Bandpass filtering, time gain compensation, outlier removal and bad channel shielding are performed on the raw echo dataset in sequence to obtain the noise-reduced effective echo dataset.

[0018] Step S5: Establishment of Global Cylindrical Coordinate System and Registration of Arc-shaped Surface Data Space: Establish a global cylindrical coordinate system with the central axis of the measured arc-shaped sidewall as the Z-axis, the depth direction of the trench section as the positive Z-axis, the curvature center of the arc-shaped sidewall as the radial origin, and the circumferential direction of the arc-shaped sidewall as the angular axis; based on the array installation coordinates of each ultrasonic sensor, real-time downlink depth data and tilt angle attitude data, combined with the preset curvature radius of the arc-shaped sidewall, map all echo data in the effective echo dataset to the arc-shaped sidewall surface of the seepage barrier under the global cylindrical coordinate system, complete the coordinate registration from the cylindrical surface coordinates to the two-dimensional unfolded plane, and correct the normal distance deviation between the ultrasonic sensor and the arc-shaped sidewall based on the tilt angle attitude data;

[0019] Step S6: Circumferential detection area division and precise marking of overlapping coverage area: Based on the installation coordinates of each ultrasonic sensor, the calibrated -6dB half-diffusion angle, the rated normal detection distance, and the radius of curvature of the arc-shaped sidewall, the effective beam coverage arc of each ultrasonic sensor is delineated on the curved surface of the arc-shaped sidewall; Boolean operations are performed on the effective beam coverage arcs of adjacent ultrasonic sensors to identify the circumferential overlapping coverage arcs of adjacent ultrasonic sensors, dividing the curved surface of the seepage barrier wall into a single-probe coverage area and a multi-probe overlapping coverage area, and marking the number of ultrasonic sensor coverages at each coordinate point;

[0020] Step S7: Weighted Fusion Processing of Multi-Source Data in Overlapping Areas: For each coordinate point within the overlapping coverage area of ​​multiple probes, the beam center weighting coefficient method is used to perform weighted fusion of multi-source data. Specifically, the normal vertical distance from the coordinate point to the beam axis of each ultrasonic sensor covering the point is calculated, the original weighting coefficients corresponding to each ultrasonic sensor are calculated, the original weighting coefficients are normalized, and the echo data of the coordinate point collected by each ultrasonic sensor is weighted and summed using the normalized weighting coefficients to obtain the fused full-section echo dataset.

[0021] Step S8: Accurate identification of mud skin thickness at both interfaces: For the fused full-section echo dataset, the dual-interface feature peak identification algorithm is used to identify the front interface echo peak corresponding to the mud and mud skin interface and the rear interface echo peak corresponding to the mud skin and concrete wall interface. Based on the ultrasonic propagation velocity of mud and the propagation time difference of the dual interface echo peaks calibrated on site, the mud skin thickness value at each coordinate point of the curved sidewall surface is calculated.

[0022] Step S9: Full-section imaging and wall brushing quality closed-loop control: The mud cake thickness values ​​of all coordinate points in the full section are interpolated by gridding to generate a two-dimensional unfolded imaging map of the mud cake thickness of the full section of the arc-shaped sidewall of the seepage barrier wall. A quantitative inspection report containing the mud cake thickness distribution, the location and size of defects exceeding the standard is output. Simultaneously, the coordinates of the areas with excessive mud cake thickness are fed back to the wall brushing machine control system, which controls the wall brushing machine to perform targeted brushing of the excessive areas. After the brushing is completed, the area is re-inspected, forming a closed-loop control of the entire process including detection, wall brushing and re-inspection.

[0023] Preferably, in step S1, the operating frequency of the ultrasonic sensor is 200kHz-500kHz, preferably 300kHz, the calibrated -6dB half-diffusion angle is preferably 18°, and the deviation of the rated normal detection distance is controlled within ±2mm; the circumferential overlap coefficient is preferably 20%; in step S3, the trigger interval between adjacent ultrasonic sensors in the time-division polling mode is ≥200μs; the longitudinal scanning step of the carrier is less than or equal to the circumferential center arc length distance between adjacent ultrasonic sensors, and a 15%-25% overlap is reserved between adjacent longitudinal scanning segments.

[0024] Preferably, in step S6, the formula for calculating the effective coverage arc length of a single ultrasonic sensor on the curved sidewall surface is:

[0025]

[0026] Where W is the effective coverage arc length of a single ultrasonic sensor, L is the rated normal detection distance, and θ is the -6dB half-diffusion angle calibrated by the ultrasonic sensor.

[0027] When the ratio of the radius of curvature R of the arc-shaped sidewall to the rated normal detection distance L is less than 5, the effective coverage arc length is calculated using the curvature correction formula:

[0028]

[0029] Where R is the design radius of curvature of the arc-shaped sidewall, and the formula for calculating the circumferential center arc length distance between adjacent ultrasonic sensors is:

[0030]

[0031] Where S is the circumferential center arc length distance between adjacent ultrasonic sensors, and α is the circumferential overlap coefficient;

[0032] The number of ultrasonic sensors in a single-sided arc array is N≥B / S, where B is the designed arc length of the arc sidewall being measured, and the calculation result is rounded up.

[0033] Preferably, in step S7, the formula for calculating the original weighting coefficients is:

[0034]

[0035] in, These are the original weighting coefficients for the i-th ultrasonic sensor. Let be the normal vertical distance from the target coordinate point to the beam axis of the i-th ultrasonic sensor. Let be the half-arc length of the effective beam coverage arc of the i-th ultrasonic sensor; the formula for calculating the normalized weighting coefficient is:

[0036]

[0037] in Here are the normalized weighting coefficients, and n is the number of ultrasonic sensors covering the target coordinate points; the formula for calculating the fused echo data is:

[0038]

[0039]

[0040] Where H is the calculated value of the thickness of the fused mud crust. Let be the original value of the mud cake thickness at this point measured by the i-th ultrasonic sensor, and A be the fused echo amplitude. This is the original value of the echo amplitude at this point measured by the i-th ultrasonic sensor.

[0041] Preferably, in step S7, if the original value of the mud skin thickness measured by a certain ultrasonic sensor exceeds the reasonable threshold of 0-50mm, or the echo signal-to-noise ratio is lower than the preset threshold, then the weight coefficient of the ultrasonic sensor is set to 0 and it does not participate in this fusion; if the difference in the normalized weight coefficients of all ultrasonic sensors participating in the fusion within the overlapping area is ≤5%, then the arithmetic mean method is used for data fusion.

[0042] Preferably, in step S8, the formula for calculating the mud cake thickness is:

[0043]

[0044] Where H is the thickness of the mud skin. The acoustic wave propagation time is the peak of the echo at the front interface. denoted as , where is the acoustic propagation time of the echo peak at the rear interface, and c is the ultrasonic propagation velocity of the mud calibrated on-site.

[0045] Beneficial effects:

[0046] 1. This invention employs a structure combining an arc-shaped circumferential tube with a circumferential array of radial tubes, which perfectly adapts to the concave cylindrical arc-shaped sidewall formed after the connector tube is removed. This ensures that the beam axis of all ultrasonic sensors coincides with the normal of the sidewall, achieving perpendicular incidence of sound waves and significantly improving the echo signal-to-noise ratio and detection accuracy. By using a valve plug in conjunction with insulating and sound-permeable dimethyl silicone oil filled inside the tube, passive differential pressure adaptive balance is achieved. This allows the internal and external pressures of the sensor to remain equal in real time without additional power, completely solving the problem of sensor sealing failure and damage under ultra-deep high-pressure environments. It is suitable for the construction conditions of 100-meter-level ultra-deep anti-seepage walls.

[0047] 2. This invention, through a limiting rotating body adapted to the outer edge and the arc-shaped sidewall, and in conjunction with a buffer component, can ensure a constant rated normal detection distance between the sensor and the sidewall throughout the entire process, thus solving the problem of thickness measurement error caused by fluctuations in the detection distance; adopting a time-division polling synchronous acquisition mode, it completely avoids acoustic crosstalk between multiple sensors, and achieves synchronous binding of the timestamps of depth, attitude, and echo data through an intrinsically safe explosion-proof acquisition circuit, providing a reliable spatial reference for subsequent data processing.

[0048] 3. This invention uses a global cylindrical coordinate system for data spatial registration, perfectly adapting to the curved sidewall surface and solving the coordinate deviation problem caused by the mapping of the planar coordinate system; through a standardized circumferential and longitudinal overlapping coverage design, it achieves full-section blind-spot detection of the anti-seepage wall sidewall; the beam center weighting coefficient method is used to weight and fuse multi-source data in the overlapping area, effectively suppressing the random error of a single probe and the interference of mud clutter, and improving the consistency and reliability of the data; through the dual-interface feature peak recognition algorithm, it can accurately identify the interface between mud and mud skin, and between mud skin and concrete wall, achieving millimeter-level mud skin thickness detection.

[0049] 4. The sensor used in this invention can be directly integrated and installed on the wall-brushing machine carrier, and completes full-depth detection synchronously with the wall-brushing machine as it is lowered, eliminating the need for separate detection equipment and significantly improving construction efficiency. Through real-time feedback of detection data, a closed-loop management system for the entire process of "detection, wall brushing, and re-inspection" can be achieved. Targeted brushing can be carried out on areas with excessive mud, completely solving the problem of disconnect between detection and wall brushing in existing technologies, effectively improving the construction quality of the anti-seepage wall joints and reducing the risk of leakage. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 It is an isometric view of the sensor structure;

[0052] Figure 2 yes Figure 1 Full sectional view with the central section symbol AA.

[0053] Figure 3 yes Figure 2 Enlarged view of the structure in area B.

[0054] Figure 4 This is a schematic diagram of the detection sensor in one embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of detection after increasing the number of ultrasonic sensors in another embodiment of the present invention.

[0056] Figure 6 This is a schematic diagram of the detection system performing full-depth detection.

[0057] In the diagram: 1-Frame; 2-Installation tube; 3-Ultrasonic sensor; 21-Circumferential tube; 22-Radial tube; 23-Valve plug; 24-Pressure regulating plug; 221-Tube body; 222-Through hole. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0059] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0060] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0061] In the description of this application, it should be noted that the use of terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" to indicate orientation or positional relationships is based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationships commonly used when the product is in use. These terms are used solely for the convenience of describing this application and for 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, they should not be construed as limitations on this application. Furthermore, the use of terms such as "first" and "second" in the description of this application is only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] Furthermore, the use of terms such as "horizontal" and "vertical" in the description of this application does not imply that the component is required to be absolutely horizontal or suspended, but rather that it may be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal relative to "vertical," and does not mean that the structure must be completely horizontal, but rather that it may be slightly tilted.

[0063] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0064] Example 1:

[0065] like Figures 1-3 As shown, this embodiment provides a high-precision ultrasonic mud skin detection sensor for a wall brushing machine, including a frame 1. The frame 1 has a mounting tube 2 for mounting an ultrasonic sensor 3. The mounting tube 2 includes a hollow, arc-shaped circumferential tube 21. Both ends of the circumferential tube 21 are closed, and at least one end is provided with a valve plug 23 for balancing the pressure difference inside and outside the circumferential tube 21. One end of the circumferential tube 21 can be provided with a detachable pressure regulating plug 24 to facilitate the filling and maintenance of the filling medium inside the tube. A plurality of radial tubes 22 are also arranged in a circumferential array on the circumferential tube 21. The radial tube 22 includes a tube body 221 that passes through and is sealed and fixedly connected to the circumferential tube 21. The tube body 221 is open at one end near the center of the circumferential tube 21, and a sound-transmitting layer is provided at the other end away from the center of the circumferential tube 21 and sealed and connected to the tube body 221. The ultrasonic sensor 3 is detachably and sealedly connected in the tube body 221. The tube body 221 is also provided with a through hole 222 communicating with the circumferential tube 21.

[0066] The ultrasonic sensor 3 is sealed with acoustically permeable epoxy potting compound. The dimensions after sealing are adapted to the inner cavity of the radial tube 22's body 221, ensuring no shaking after installation. The circumferential tube 21 and radial tube 22 are filled with insulating, acoustically permeable dimethyl silicone oil. Through holes 222 on the tube body 221 connect the inner cavities of the circumferential tube 21 and radial tube 22, ensuring uniform pressure distribution of the silicone oil within the tubes. The detection ranges of two adjacent ultrasonic sensors 3 on the surface of the seepage barrier wall overlap; the width of this overlap can be adjusted according to detection requirements.

[0067] Working principle and beneficial effects: The circumferential tube 21 adopts an arc-shaped structure design, which can perfectly fit the concave cylindrical arc-shaped anti-seepage wall sidewall formed after the joint tube is removed. This ensures that the axes of the arrayed radial tubes 22 all point to the curvature center of the arc-shaped sidewall, thereby ensuring that the beam axis of the ultrasonic sensor 3 installed in each radial tube 22 coincides with the normal of the anti-seepage wall sidewall at the corresponding position, realizing the vertical incidence of sound waves, greatly improving the signal-to-noise ratio of the echo signal, and reducing thickness measurement error. The insulating and sound-permeable dimethyl silicone oil filling the circumferential tube 21 and radial tube 22 achieves full cavity connectivity through the through hole 222. Combined with the valve plug 23 at the end of the circumferential tube 21, when the sensor is lowered into the ultra-deep tank section by the wall-cleaning machine, the hydrostatic pressure of the external mud will squeeze the valve plug 23, causing adaptive deformation. This ensures that the pressure of the silicone oil inside the tube is equal to the external mud pressure in real time, thus making the internal and external pressure difference of the ultrasonic sensor 3 approach zero. This completely solves the problems of sensor sealing failure and piezoelectric vibrator damage under ultra-deep and high-pressure environments. Passive pressure difference balance can be achieved without an additional power device, resulting in a simple structure and high reliability. The sound-permeable layer at the end of the tube body 221 can prevent mud from entering the tube, and together with the insulating and sound-permeable dimethyl silicone oil, it ensures lossless ultrasonic signal penetration and avoids signal attenuation. The detection ranges of adjacent ultrasonic sensors 3 are set with overlapping areas, see [reference needed]. Figures 4-5 As shown, this method can completely eliminate detection blind spots between adjacent probes, achieving full cross-sectional coverage of the anti-seepage wall sidewall. The ultrasonic sensor 3 adopts a detachable, sealed connection, facilitating the inspection and replacement of individual sensors and reducing equipment maintenance costs.

[0068] Example 2:

[0069] like Figure 6 As shown, this embodiment provides a detection system for real-time detection of the thickness of mud skin adhesion at the full depth of the anti-seepage wall, including the high-precision ultrasonic mud skin detection sensor for the wall brushing machine described in Embodiment 1, and also including a carrier and limiting module and a synchronous signal acquisition module.

[0070] The carrier and limiting module include a ground crane and a carrier that moves vertically and vertically at full depth via the ground crane. The carrier uses a wall-cleaning machine frame and can be lowered synchronously with the wall-cleaning machine. The detection sensor is fixedly installed on the side of the carrier close to the seepage barrier wall. Limiting mechanisms for limiting the minimum distance between the detection sensor and the seepage barrier wall are installed on the same side of the upper and lower ends of the carrier and the detection sensor. The limiting mechanism includes a mounting bracket and a limiting rotary body rotatably mounted on the mounting bracket. The outer edge shape of the limiting rotary body is adapted to the arc surface of the seepage barrier wall. A compression spring buffer assembly is set between the mounting bracket and the carrier so that the limiting rotary body can adaptively expand and contract according to the concavity and convexity of the seepage barrier wall sidewall and fit the sidewall surface throughout the process.

[0071] The synchronous signal acquisition module includes a multi-channel synchronous data acquisition card for synchronous triggering and polling acquisition among multiple detection sensors, and a ground terminal connected through an underground explosion-proof acquisition circuit. The underground explosion-proof acquisition circuit adopts an intrinsically safe explosion-proof design and has a built-in dual-axis tilt sensor to acquire the carrier's attitude data in real time. The ground terminal is used to record the carrier's descent depth and attitude data in real time, and has a built-in real-time imaging unit that can simultaneously display a two-dimensional unfolded imaging map of the mud skin thickness on the sidewall of the seepage barrier.

[0072] Working principle and beneficial effects: The ground crane controls the carrier to descend at a uniform speed along the depth direction of the anti-seepage wall trench. The limiting rotating bodies of the limiting mechanisms at the upper and lower ends of the carrier roll in contact with the arc-shaped sidewall of the anti-seepage wall. The compression spring buffer assembly between the mounting bracket and the carrier allows the limiting rotating bodies to adaptively expand and contract according to the concavity and convexity of the sidewall, ensuring a tight fit between the limiting rotating bodies and the sidewall throughout the process. This constrains the normal distance between the detection sensor and the sidewall of the anti-seepage wall to remain stable at the preset rated detection distance, completely solving the problem of detection distance fluctuation caused by skewing and shaking during the carrier descent, and reducing thickness measurement errors. The multi-channel synchronous data acquisition card uses a time-division polling mode to trigger each ultrasonic sensor 3 to sequentially emit ultrasonic pulses and collect echo data. The trigger interval between adjacent ultrasonic sensors 3 is ≥200μs, which can completely avoid acoustic crosstalk between multiple ultrasonic sensors 3 and ensure the purity of the echo data. The underground explosion-proof acquisition circuit adopts an intrinsically safe explosion-proof design, adaptable to the explosion-proof construction requirements of ultra-deep underground trench sections. The built-in dual-axis tilt sensor can collect the carrier's attitude data in real time, and synchronously bind the echo data, descent depth data, and attitude data with timestamps, transmitting them synchronously to the ground terminal. This provides a precise spatial reference for subsequent data spatial registration. The real-time imaging unit of the ground terminal can simultaneously display a two-dimensional unfolded image of the mud cake thickness on the sidewall of the seepage barrier, facilitating on-site construction personnel to monitor the wall-brushing quality in real time and adjust construction parameters promptly. This detection system can be directly integrated into the wall-brushing machine, operating synchronously with it, eliminating the need for a separate detection process and significantly improving construction efficiency.

[0073] Example 3:

[0074] like Figures 4-6 As shown, this embodiment provides a method for detecting mud cake on the arc-shaped sidewall of an ultra-deep cutoff wall joint pipe. The method employs the detection system described in Embodiment 2, including using an ultrasonic sensor 3 to collect ultrasonic echo data of the cutoff wall sidewall, processing the echo data to obtain the mud cake thickness detection result. The cutoff wall sidewall is a concave cylindrical arc-shaped sidewall formed after the joint pipe is removed, and the radius of curvature of the arc-shaped sidewall matches the outer diameter of the removed joint pipe. The method includes the following steps:

[0075] S1 System Calibration and Parameter Presetting before Detection: The ultrasonic propagation velocity of the mud in the detection environment is calibrated on-site, and the beam parameters of a single ultrasonic sensor 3 are calibrated to obtain the -6dB half-diffusion angle of the ultrasonic sensor 3. The rated normal detection distance between the ultrasonic sensor 3 and the arc-shaped sidewall of the seepage barrier is preset to 100mm, and the circumferential overlap coefficient of the detection areas of adjacent ultrasonic sensors 3 is preset to 15%-25%. The design radius of curvature, design arc length, and design depth parameters of the arc-shaped sidewall under test are simultaneously recorded, and the communication and debugging of the detection system are completed. The operating frequency of the ultrasonic sensor 3 is 200kHz-500kHz, preferably 300kHz; the calibrated -6dB half-diffusion angle is preferably 18°; the deviation of the rated normal detection distance is controlled within ±2mm; and the circumferential overlap coefficient is preferably 20%.

[0076] S2 Detection System Installation and Attitude Adaptation: The high-precision ultrasonic mud skin detection sensor for the wall brushing machine is fixedly installed on the wall brushing machine carrier, so that the arc-shaped circumferential tube 21 of the sensor is coaxially arranged with the arc-shaped sidewall being measured; the limiting rotary bodies at the upper and lower ends of the carrier are attached to the surface of the arc-shaped sidewall being measured, so that the beam axis of each ultrasonic sensor 3 coincides with the normal of the corresponding position of the arc-shaped sidewall and points to the curvature center of the arc-shaped sidewall. The limiting rotary bodies constrain the normal distance between all ultrasonic sensors 3 and the arc-shaped sidewall throughout the entire process and stabilize it at the rated detection distance.

[0077] S3 Ultra-Deep Working Condition Pressure Differential Adaptive Balancing and Full-Section Synchronous Data Acquisition: The carrier is lowered at a constant speed along the central axis of the arc-shaped sidewall by a ground crane. During the lowering process, the hydrostatic pressure difference of the mud inside and outside the circumferential pipe 21 is balanced by the adaptive deformation of the valve plug 23, so that the pressure of the insulating and sound-permeable dimethyl silicone oil filled in the circumferential pipe 21 and the radial pipe 22 is equal to the external mud pressure in real time, ensuring that the ultrasonic sensor 3 works stably in the ultra-deep and high-pressure environment. During the lowering process, the multi-channel synchronous data acquisition card triggers each ultrasonic sensor 3 to emit ultrasonic pulses in sequence and acquire A-scan full-wavelength echo data in a time-division polling mode. At the same time, the real-time lowering depth data and dual-axis tilt angle attitude data of the carrier are recorded synchronously by the ground terminal to form the original echo dataset with spatial reference.

[0078] S4 raw echo data preprocessing: The raw echo dataset is sequentially subjected to bandpass filtering, time gain compensation, outlier removal and bad channel shielding to obtain the noise-reduced effective echo dataset.

[0079] S5 Global Cylindrical Coordinate System Establishment and Arc-shaped Surface Data Spatial Registration: Establish a global cylindrical coordinate system with the central axis of the measured arc-shaped sidewall as the Z-axis, the depth direction of the trench section as the positive Z-axis, the curvature center of the arc-shaped sidewall as the radial origin, and the circumferential direction of the arc-shaped sidewall as the angular axis; based on the array installation coordinates of each ultrasonic sensor 3, the real-time depth data and tilt angle attitude data, and combined with the preset arc-shaped sidewall curvature radius, map all echo data in the effective echo dataset to the arc-shaped sidewall surface of the seepage prevention wall under the global cylindrical coordinate system.

[0080] S6 Circumferential Detection Area Division and Accurate Marking of Overlapping Coverage Area: Based on the installation coordinates of each ultrasonic sensor 3, the calibrated -6dB half-diffusion angle, the rated normal detection distance, and the radius of curvature of the arc-shaped sidewall, the effective beam coverage arc of each ultrasonic sensor 3 is delineated on the curved surface of the arc-shaped sidewall; Boolean operations are performed on the effective beam coverage arcs of adjacent ultrasonic sensors 3 to identify the circumferential overlapping coverage arcs of adjacent ultrasonic sensors 3, dividing the curved surface of the seepage barrier wall into a single-probe coverage area and a multi-probe overlapping coverage area, and marking the number of times the ultrasonic sensor 3 covers each coordinate point.

[0081] The formula for calculating the effective coverage arc length of a single ultrasonic sensor 3 on the curved sidewall surface is as follows:

[0082]

[0083] In the formula, W is the effective coverage arc length of a single ultrasonic sensor 3, L is the rated normal detection distance, and θ is the -6dB half-diffusion angle calibrated by the ultrasonic sensor 3; when the ratio of the radius of curvature R of the arc-shaped sidewall to the rated normal detection distance L is less than 5, the effective coverage arc length is calculated using the curvature correction formula:

[0084]

[0085] Where R is the design radius of curvature of the arc-shaped sidewall, and the formula for calculating the circumferential center arc length distance between adjacent ultrasonic sensors is:

[0086]

[0087] Where S is the circumferential center arc length distance between adjacent ultrasonic sensors, and α is the circumferential overlap coefficient;

[0088] The number of ultrasonic sensors in a single-sided arc array is N≥B / S, where B is the designed arc length of the arc sidewall being measured, and the calculation result is rounded up.

[0089] S7 Overlapping Area Multi-Source Data Weighted Fusion Processing: For each coordinate point within the overlapping coverage area of ​​multiple probes, the beam center weight coefficient method is used to perform multi-source data weighted fusion. Specifically, the normal vertical distance from the coordinate point to the beam axis of each ultrasonic sensor 3 covering the point is calculated, the original weight coefficients corresponding to each ultrasonic sensor 3 are calculated, the original weight coefficients are normalized, and the echo data of the coordinate point collected by each ultrasonic sensor 3 are weighted and summed using the normalized weight coefficients to obtain the fused full-section echo dataset.

[0090] The formula for calculating the original weight coefficient is as follows:

[0091]

[0092] in, These are the original weighting coefficients for the i-th ultrasonic sensor. Let be the normal vertical distance from the target coordinate point to the beam axis of the i-th ultrasonic sensor. Let be the half-arc length of the effective beam coverage arc of the i-th ultrasonic sensor; the formula for calculating the normalized weighting coefficient is:

[0093]

[0094] in Here are the normalized weighting coefficients, and n is the number of ultrasonic sensors covering the target coordinate points; the formula for calculating the fused echo data is:

[0095]

[0096]

[0097] Where H is the calculated thickness of the fused mud crust. Let be the original value of the mud cake thickness at this point measured by the i-th ultrasonic sensor, and A be the fused echo amplitude. This is the original value of the echo amplitude at this point measured by the i-th ultrasonic sensor.

[0098] S8 Mud Skin Thickness Dual-Interface Accurate Identification: For the fused full-section echo dataset, a dual-interface feature peak identification algorithm is used to identify the front interface echo peak corresponding to the mud-mud skin interface and the rear interface echo peak corresponding to the mud skin-concrete wall interface. Based on the ultrasonic propagation velocity of mud calibrated on site and the propagation time difference of the dual-interface echo peaks, the mud skin thickness value of each coordinate point on the curved sidewall surface is calculated.

[0099] The formula for calculating the thickness of the mud cake is as follows:

[0100]

[0101] Where H is the thickness of the mud skin. The acoustic wave propagation time is the peak of the echo at the front interface. denoted as , where is the acoustic propagation time of the echo peak at the rear interface, and c is the ultrasonic propagation velocity of the mud calibrated on-site.

[0102] This method targets the concave arc-shaped sidewall formed after the joint pipe is removed. It employs a global cylindrical coordinate system for data spatial registration, perfectly adapting to the curved surface characteristics of the arc-shaped sidewall and solving the problems of coordinate misalignment and large thickness measurement errors caused by existing planar coordinate system mapping. Through a standardized circumferential overlap coefficient design, combined with a three-row array of ultrasonic sensors, it achieves blind-zone-free full coverage of the arc-shaped sidewall circumference. The beam center weighting coefficient method is used to weight and fuse multi-source data in the overlapping area, giving higher weight to data with high reliability in the beam center region and lower weight to data in the beam edge region. This effectively suppresses random errors from single probes, mud clutter interference, and data jumps, significantly improving the consistency and accuracy of the detection data. Using a dual-interface feature peak recognition algorithm, it can accurately distinguish between the two interfaces: mud and mud skin, and mud skin and concrete wall. Based on the calibrated mud sound velocity and propagation time difference, the mud skin thickness is calculated, achieving millimeter-level detection accuracy. During the detection process, the sensor's differential pressure adaptive balancing structure ensures stable operation in ultra-deep high-pressure environments. Combined with the constant detection distance guaranteed by the limiting mechanism, this further enhances the stability and reliability of full-depth detection.

[0103] Example 4:

[0104] like Figures 4-6 As shown, this embodiment is a preferred implementation based on Embodiment 3. This embodiment further optimizes the steps of the detection method based on Embodiment 3, as detailed below:

[0105] Step S1 also includes pre-filling the circumferential tube 21 and the radial tube 22 with insulating and sound-permeable dimethyl silicone oil. The pre-filling pressure is equal to the normal pressure at the slot opening. The passive pressure differential adaptive balance during the lowering process is achieved through the valve plug 23. During the pre-filling process, air bubbles in the tube must be completely removed to avoid the air layer blocking the ultrasonic signal.

[0106] In step S3, the trigger interval between adjacent ultrasonic sensors 3 in the time-division polling mode is ≥200μs; the longitudinal scanning step of the carrier is less than or equal to the circumferential center arc length distance between adjacent ultrasonic sensors 3, and 15%-25% overlap is reserved between adjacent longitudinal scanning segments to eliminate the detection blind zone in the depth direction of the groove segment.

[0107] In step S4, the bandpass filter uses a Butterworth bandpass filter of 100kHz-500kHz to filter out high-frequency electrical noise and low-frequency fluid noise in the mud environment; the time gain compensation is to linearly compensate the gain according to the sound wave propagation time to offset the sound wave propagation attenuation in the mud and ensure that the amplitude of the deep echo and the shallow echo are consistent; the outlier removal is to remove invalid echo data that exceeds the preset reasonable threshold of mud skin thickness of 0-50mm; the bad channel shielding is to identify the faulty ultrasonic sensor 3 channels without effective characteristic echoes and shield their corresponding data to avoid the faulty data from interfering with the subsequent processing results.

[0108] Step S5 also includes completing the coordinate registration from cylindrical coordinates to the two-dimensional unfolded plane, and correcting the normal distance deviation between the ultrasonic sensor 3 and the arc-shaped sidewall based on the tilt angle attitude data; wherein, the coordinate registration from cylindrical coordinates to the two-dimensional unfolded plane is specifically as follows: taking the circumferential unfolding direction of the arc-shaped sidewall as the X-axis and the groove depth direction as the Y-axis, the circumferential angle coordinates in the cylindrical coordinate system are converted into linear length coordinates of the X-axis, and the depth coordinates are directly mapped to the Y-axis coordinates, so that all coordinate points on the curved surface of the arc-shaped sidewall correspond to the unique coordinates of the two-dimensional unfolded plane, which facilitates subsequent imaging and defect localization.

[0109] Step S7 also includes anomaly data masking rules: if the original value of mud thickness measured by a certain ultrasonic sensor 3 exceeds the reasonable threshold of 0-50mm, or the echo signal-to-noise ratio is lower than the preset threshold, then the weight coefficient of the ultrasonic sensor 3 is set to 0 and it does not participate in this fusion; if the difference in the normalized weight coefficients of all ultrasonic sensors 3 participating in the fusion within the overlapping area is ≤5%, then the arithmetic mean method is used for data fusion to simplify the computation of the embedded system and improve processing efficiency.

[0110] Following step S8, step S9, full-section imaging and wall-brushing quality closed-loop control, is also included: The mud cake thickness values ​​at all coordinate points of the full section are interpolated using a grid to generate a two-dimensional unfolded imaging map of the mud cake thickness across the entire arc-shaped sidewall of the anti-seepage wall. A quantitative inspection report containing the mud cake thickness distribution, location and size of defects exceeding the standard is output. Simultaneously, the coordinates of areas with excessive mud cake thickness are fed back to the wall-brushing machine control system, which controls the wall-brushing machine to perform targeted brushing of the excessive areas. After brushing is completed, the area is re-inspected, forming a closed-loop control process for detection, wall brushing, and re-inspection. Among these steps, the grid interpolation uses the Kriging interpolation method, the two-dimensional unfolded imaging map is a grayscale map or heat map of mud cake thickness, and the quantitative inspection report marks the areas where the mud cake thickness exceeds the preset threshold.

[0111] Working Principle and Beneficial Effects: This embodiment avoids the blockage of ultrasonic signals by pre-filling with insulating and sound-permeable dimethyl silicone oil and thoroughly venting the air layer inside the tube, thus further improving the sound transmission rate of the ultrasonic signal. By strictly controlling the trigger interval of adjacent ultrasonic sensors 3, acoustic crosstalk is completely avoided. Through the overlapping design of the longitudinal scanning segment, the detection blind zone in the depth direction is eliminated, achieving full-section blind-zone-free detection. Through optimized preprocessing steps, clutter interference is further filtered out, improving the signal-to-noise ratio of the echo data. By outlier removal and bad channel shielding, interference from invalid data on the detection results is avoided. Through registration from cylindrical coordinates to a two-dimensional unfolded plane, planar imaging of the curved sidewall surface is achieved, facilitating on-site personnel to intuitively view the mud distribution. By correcting the normal distance deviation through tilt angle attitude data, the detection error caused by carrier tilt is further reduced. Through outlier data shielding rules, the robustness of the fusion algorithm is improved, so that even if a single sensor fails, it will not affect the overall detection results. Through a closed-loop management system encompassing "inspection-wall brushing-re-inspection," the system enables simultaneous inspection during wall brushing, targeted re-brushing of areas exceeding standards, and re-inspection and confirmation after re-brushing. This completely resolves the issues of disconnect between inspection and wall brushing processes and the inability to control wall brushing quality in real time in existing technologies. It significantly improves the construction quality of joints and seams in ultra-deep anti-seepage walls, reduces the risk of subsequent leakage, and is suitable for the construction requirements of high-standard ultra-deep anti-seepage wall projects such as water conservancy projects and deep foundation pits.

[0112] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A high-precision ultrasonic mud skin detection sensor for a wall scrubber, comprising a frame (1), wherein the frame (1) has a mounting tube (2) for mounting an ultrasonic sensor (3), characterized in that: The mounting tube (2) includes a hollow and arc-shaped circumferential tube (21), both ends of which are closed and at least one end is provided with a valve plug (23) for balancing the pressure difference inside and outside the circumferential tube (21); a plurality of radial tubes (22) are also arranged in a circumferential array on the circumferential tube (21), the radial tube (22) includes a tube body (221) that passes through and is sealed and fixedly connected to the circumferential tube (21), the tube body (221) is open at one end near the center of the circumferential tube (21), and a sound-transmitting layer is provided at the other end away from the center of the circumferential tube (21) and sealed and connected to the tube body (221), the ultrasonic sensor (3) is detachably and sealed and connected in the tube body (221), and the tube body (221) is also provided with a through hole (222) that communicates with the circumferential tube (21).

2. The high-precision ultrasonic mud skin detection sensor for wall brushing machines according to claim 1, characterized in that: The ultrasonic sensor (3) is sealed with acoustically permeable epoxy potting compound, and the size after sealing is adapted to the radial tube (22). The circumferential tube (21) and the radial tube (22) are filled with insulating acoustically permeable dimethyl silicone oil.

3. The high-precision ultrasonic mud skin detection sensor for wall brushing machines according to claim 1, characterized in that: The detection range of two adjacent ultrasonic sensors (3) on the surface of the impermeable wall overlaps.

4. A detection system for real-time detection of the thickness of mud cake adhesion at the full depth of an anti-seepage wall, comprising the high-precision ultrasonic mud cake detection sensor for a wall-brushing machine as described in any one of claims 1-3, characterized in that: Also includes The carrier and limiting module include a ground crane and a carrier that moves vertically and vertically at full depth via the ground crane. The detection sensor is installed on the side of the carrier close to the seepage barrier wall. Limiting mechanisms for limiting the minimum distance between the detection sensor and the seepage barrier wall are installed on the same side of the upper and lower ends of the carrier and the detection sensor. The limiting mechanism includes a mounting bracket and a limiting rotary body rotatably mounted on the mounting bracket. The outer edge shape of the limiting rotary body is adapted to the surface of the seepage barrier wall. The synchronous signal acquisition module includes a multi-channel synchronous data acquisition card for synchronous triggering and polling acquisition among multiple detection sensors, and a ground terminal connected through an explosion-proof acquisition circuit in the well. The ground terminal is used to record the carrier's descent depth and attitude data in real time.

5. A detection method, implemented using the detection system described in claim 4, comprising using an ultrasonic sensor to collect ultrasonic echo data of the sidewall of the seepage barrier, and processing the echo data to obtain the mud cake thickness detection result, characterized in that, Specifically, the following steps are included: Step S1: System calibration and parameter preset before detection: On-site calibration of the ultrasonic propagation velocity of the mud in the detection environment, completion of beam parameter calibration of a single ultrasonic sensor, and acquisition of the -6dB half-diffusion angle of the ultrasonic sensor; preset the rated normal detection distance between the ultrasonic sensor and the arc-shaped sidewall of the seepage barrier to be 100mm, preset the circumferential overlap coefficient of the detection area of ​​adjacent ultrasonic sensors to be 15%-25%, and simultaneously input the design curvature radius, design arc length and trench design depth parameters of the arc-shaped sidewall under test, and complete the communication debugging of the detection system; Step S2: Installation and orientation adaptation of the detection system: Fix the high-precision ultrasonic mud skin detection sensor for the wall brushing machine on the wall brushing machine carrier, so that the arc-shaped circumferential tube of the sensor is coaxially arranged with the arc-shaped sidewall being measured; fit the limiting rotary bodies at the upper and lower ends of the carrier to the surface of the arc-shaped sidewall being measured, so that the beam axis of each ultrasonic sensor coincides with the normal of the corresponding position of the arc-shaped sidewall and points to the curvature center of the arc-shaped sidewall. The limiting rotary bodies constrain the normal distance between all ultrasonic sensors and the arc-shaped sidewall throughout the entire process and stabilize it at the rated detection distance; Step S3: Ultra-deep working condition differential pressure adaptive balancing and full-section synchronous data acquisition: The carrier is lowered at a constant speed along the central axis of the arc-shaped sidewall by a ground crane. During the lowering process, the multi-channel synchronous data acquisition card triggers each ultrasonic sensor to emit ultrasonic pulses in sequence and acquire A-scan full wave train echo data in a time-division polling mode. At the same time, the real-time lowering depth data and dual-axis tilt angle attitude data of the carrier are recorded synchronously by the ground terminal to form the original echo dataset with spatial reference. Step S4: Preprocessing of raw echo data: Bandpass filtering, time gain compensation, outlier removal and bad channel shielding are performed on the raw echo dataset in sequence to obtain the noise-reduced effective echo dataset. Step S5: Establishment of Global Cylindrical Coordinate System and Registration of Arc-shaped Surface Data Space: Establish a global cylindrical coordinate system with the central axis of the measured arc-shaped sidewall as the Z-axis, the depth direction of the trench section as the positive Z-axis, the curvature center of the arc-shaped sidewall as the radial origin, and the circumferential direction of the arc-shaped sidewall as the angular axis; based on the array installation coordinates of each ultrasonic sensor, real-time downlink depth data and tilt angle attitude data, combined with the preset curvature radius of the arc-shaped sidewall, map all echo data in the effective echo dataset to the arc-shaped sidewall surface of the seepage barrier under the global cylindrical coordinate system, complete the coordinate registration from the cylindrical surface coordinates to the two-dimensional unfolded plane, and correct the normal distance deviation between the ultrasonic sensor and the arc-shaped sidewall based on the tilt angle attitude data; Step S6: Circumferential detection area division and precise marking of overlapping coverage area: Based on the installation coordinates of each ultrasonic sensor, the calibrated -6dB half-diffusion angle, the rated normal detection distance, and the radius of curvature of the arc-shaped sidewall, the effective beam coverage arc of each ultrasonic sensor is delineated on the curved surface of the arc-shaped sidewall; Boolean operations are performed on the effective beam coverage arcs of adjacent ultrasonic sensors to identify the circumferential overlapping coverage arcs of adjacent ultrasonic sensors, dividing the curved surface of the seepage barrier wall into a single-probe coverage area and a multi-probe overlapping coverage area, and marking the number of ultrasonic sensor coverages at each coordinate point; Step S7: Weighted Fusion Processing of Multi-Source Data in Overlapping Areas: For each coordinate point within the overlapping coverage area of ​​multiple probes, the beam center weighting coefficient method is used to perform weighted fusion of multi-source data. Specifically, the normal vertical distance from the coordinate point to the beam axis of each ultrasonic sensor covering the point is calculated, the original weighting coefficients corresponding to each ultrasonic sensor are calculated, the original weighting coefficients are normalized, and the echo data of the coordinate point collected by each ultrasonic sensor is weighted and summed using the normalized weighting coefficients to obtain the fused full-section echo dataset. Step S8: Accurate identification of mud skin thickness at both interfaces: For the fused full-section echo dataset, the dual-interface feature peak identification algorithm is used to identify the front interface echo peak corresponding to the mud and mud skin interface and the rear interface echo peak corresponding to the mud skin and concrete wall interface. Based on the ultrasonic propagation velocity of mud and the propagation time difference of the dual interface echo peaks calibrated on site, the mud skin thickness value at each coordinate point of the curved sidewall surface is calculated. Step S9: Full-section imaging and wall brushing quality closed-loop control: The mud cake thickness values ​​of all coordinate points in the full section are interpolated by gridding to generate a two-dimensional unfolded imaging map of the mud cake thickness of the full section of the arc-shaped sidewall of the seepage barrier wall. A quantitative inspection report containing the mud cake thickness distribution, the location and size of defects exceeding the standard is output. Simultaneously, the coordinates of the areas with excessive mud cake thickness are fed back to the wall brushing machine control system, which controls the wall brushing machine to perform targeted brushing of the excessive areas. After the brushing is completed, the area is re-inspected, forming a closed-loop control of the entire process including detection, wall brushing and re-inspection.

6. The detection method according to claim 5, characterized in that, In step S1, the ultrasonic sensor operates at a frequency of 200kHz-500kHz, has a calibrated -6dB half-diffusion angle of 18°, and its rated normal detection distance deviation is controlled within ±2mm; the circumferential overlap coefficient is 20%; in step S3, the trigger interval between adjacent ultrasonic sensors in the time-division polling mode is ≥200μs; the longitudinal scanning step of the carrier is less than or equal to the circumferential center arc length distance between adjacent ultrasonic sensors, and a 15%-25% overlap is reserved between adjacent longitudinal scanning segments.

7. The detection method according to claim 5, characterized in that, In step S6, the formula for calculating the effective coverage arc length of a single ultrasonic sensor on the curved sidewall surface is as follows: Where W is the effective coverage arc length of a single ultrasonic sensor, L is the rated normal detection distance, and θ is the -6dB half-diffusion angle calibrated by the ultrasonic sensor; when the ratio of the radius of curvature R of the arc-shaped sidewall to the rated normal detection distance L is less than 5, the effective coverage arc length is calculated using the curvature correction formula: Where R is the design radius of curvature of the arc-shaped sidewall, and the formula for calculating the circumferential center arc length distance between adjacent ultrasonic sensors is: Where S is the circumferential center arc length distance between adjacent ultrasonic sensors, and α is the circumferential overlap coefficient; the number of ultrasonic sensors in a single-sided arc array N ≥ B / S, where B is the designed arc length of the measured arc sidewall, and the calculation result is rounded up.

8. The detection method according to claim 5, characterized in that, In step S7, the formula for calculating the original weight coefficients is: ;in, These are the original weighting coefficients for the i-th ultrasonic sensor. Let be the normal vertical distance from the target coordinate point to the beam axis of the i-th ultrasonic sensor. Let be the half-arc length of the effective beam coverage arc of the i-th ultrasonic sensor; the formula for calculating the normalized weighting coefficient is: ;in, Here are the normalized weighting coefficients, and n is the number of ultrasonic sensors covering the target coordinate points; the formula for calculating the fused echo data is: , Where H is the calculated thickness of the fused mud crust. Let be the original value of the mud cake thickness at this point measured by the i-th ultrasonic sensor, and A be the fused echo amplitude. This is the original value of the echo amplitude at this point measured by the i-th ultrasonic sensor.

9. The detection method according to claim 5, characterized in that, In step S7, if the original value of mud thickness measured by an ultrasonic sensor exceeds the reasonable threshold of 0-50mm, or the echo signal-to-noise ratio is lower than the preset threshold, the weight coefficient of the ultrasonic sensor is set to 0 and it does not participate in this fusion; if the difference in the normalized weight coefficients of all ultrasonic sensors participating in the fusion within the overlapping area is ≤5%, the arithmetic mean method is used for data fusion.

10. The detection method according to claim 5, characterized in that, In step S8, the formula for calculating the thickness of the mud skin is: Where H is the thickness of the mud skin. The acoustic wave propagation time is the time it takes for the echo peak at the front interface to reach its peak. denoted as , where is the acoustic propagation time of the echo peak at the rear interface, and c is the ultrasonic propagation velocity of the mud calibrated on-site.

Images