Wellbore defect rapid detection method

Abstract

The invention discloses a shaft defect rapid detection method. The method comprises the steps of detection area planning and signal acquisition, signal processing and defect judgment reference determination, space mapping and defect judgment and the like. The shaft defect rapid detection method is based on an air coupling impact echo method and is a nondestructive detection method aiming at shaft detection defects, and rapid data acquisition can be realized through response of an air coupling sensor acquisition structure to an impact echo signal; the intact position and the position with cracks or defects can be clearly distinguished under the condition that the main frequency intervals are overlapped by analyzing the energy ratio of the spectrogram; by drawing the defect cloud picture, the defect information can be visually displayed, and the difficulty of manual judgment can be reduced. According to the shaft defect rapid detection method, the engineering application problems of low detection efficiency, high main frequency misjudgment rate and the like can be solved on the premise of realizing non-contact detection, and a theoretical basis and data support can be provided for performing efficient, high-precision and high-adaptability nondestructive detection evaluation on the shaft.

Description

TECHNICAL FIELD

[0001] The present application relates to a defect detection method, in particular to a shaft defect rapid detection method for mining, energy development and underground engineering, belonging to the technical field of underground engineering. BACKGROUND

[0002] In mining, energy development and underground engineering, the shaft, as the core channel connecting the ground and the underground operation site, is usually a structure of concrete lining + steel support, and its structural integrity is directly related to the safety and efficiency of the project. The shaft is subjected to multiple environmental effects such as stratum pressure, mechanical vibration and chemical corrosion for a long time, which can cause cracks, spalling or even fracture of the concrete lining or steel support structure, while groundwater can seep through the cracks and cause corrosion and steel corrosion, which can further aggravate the structural deterioration; the shaft in soft rock stratum or high stress area is prone to deformation, shrinkage or deflection under non-uniform extrusion, which threatens the stability of equipment operation, while material aging, freeze-thaw cycle and hidden debonding, cavities and other problems can further increase the safety hazards. Therefore, the shaft needs to be detected and maintained regularly to discover and handle potential safety hazards in time.

[0003] The conventional technical means for traditional shaft safety detection relies on manual inspection or local sensors, which is low in efficiency, incomplete in coverage and difficult to capture dynamic defects in real time, especially in complex deep shaft environment, it is difficult to effectively identify micro-cracks, internal delamination and other hidden diseases, which has significant limitations: manual inspection relies on experienced technicians to use visual inspection, knocking and listening or handheld crack width meter to identify surface damage, but it is difficult to cover the full height of the shaft due to the harsh underground environment, high-risk overhead work and light conditions, especially the sensitivity to deep hidden defects is insufficient; the monitoring system based on point sensors (such as strain gauges, displacement meters) is usually laid at preset risk points, which is restricted by installation density and fixed position, and cannot dynamically track the random crack propagation or sudden structural deformation, and the sensors are easily drifting or failing due to long-term exposure to high humidity and high corrosion environment, resulting in a decrease in data reliability.

[0004] In recent years, although non-destructive testing technologies such as ultrasonic flaw detection and ground penetrating radar scanning can partially obtain defect information such as void and cavity inside the well wall, the detection depth and resolution are greatly affected by the wellbore geometry, medium heterogeneity and electromagnetic interference, especially in kilometer deep wells, the signal attenuation is serious, and it is difficult to realize high-precision three-dimensional imaging. Researchers in the industry have tried to introduce distributed optical fiber sensing technology to capture abnormal strain field in wellbore, or through acoustic emission monitoring to capture rock mass fracture signals, however, the spatiotemporal alignment of multi-source heterogeneous data is difficult, the environmental noise suppression algorithm is not mature, and other problems restrict the engineering practicability, and the existing methods are mostly limited to single physical quantity monitoring, such as impact echo detection method for single-channel collection by contact sensor, which not only consumes a lot of manpower and material resources, greatly limits the detection efficiency, and its method of relying on a single dominant frequency to determine the defect position lacks systematic analysis of the structure damage evolution mechanism and multi-factor coupling effect, often leading to risk assessment lagging behind the actual damage development. SUMMARY

[0005] In view of the problems existing in the prior art, the present application provides a wellbore defect rapid detection method, which can realize non-contact detection and solve the problems of low detection efficiency, high dominant frequency misjudgment rate and other engineering application difficulties, and can provide a theoretical basis and data support for efficient, high-precision and highly adaptable non-destructive testing evaluation of wellbores.

[0006] To achieve the above purpose, the wellbore defect rapid detection method specifically includes the following steps: Step 1, detection area planning and signal acquisition: dividing the detection area every certain distance along the circumferential direction of the inner wall of the wellbore, and walking on the inner wall of the wellbore by the detection acquisition device in the detection area for detection and collecting structure response impact echo signal data; Step 2, signal processing and defect determination reference determination: first, the time domain signal collected is subjected to fast Fourier transform to obtain a frequency spectrum, then the signal of each knocking point is subjected to frequency spectrum analysis, the energy proportion of the selected frequency interval and the energy proportion of the effective frequency range , and finally, the known defect position is continuously knocked and the signal is extracted, and the average value of the energy proportion of the signal at this position is set as the defect determination reference value; Step 3, spatial mapping and defect determination: after constructing the spatial coordinate data set, a defect distribution cloud chart is generated and colored, and the position of the defect is obtained by comparing with the reference value.

[0007] Further, Step 2 is specifically as follows: Step 2-1, the time domain signal collected is subjected to fast Fourier transform to obtain a frequency spectrum, and the specific formula is as follows: wherein: is the frequency index corresponding to the actual frequency; is the spectrum amplitude when the frequency is ; is the amplitude of the th sampling point; is the Hanning window function; is the number of FFT points; Step 2-2, perform spectrum analysis on each tapping point signal: Calculate the energy proportion of the selected frequency interval , the specific formula is as follows: , wherein ∈ selected frequency interval wherein: is the amplitude corresponding to the th frequency in the interval; Calculate the energy proportion of the effective frequency range , the specific formula is as follows: , wherein ∈ effective signal frequency interval wherein: is the amplitude corresponding to the th frequency in the interval; Then the damage index is defined as: ; Step 2-3, determine the defect judgment reference: perform continuous tapping at the known defect position and extract the signal, set the average value of the energy proportion of the signal at this position as the reference value, the specific formula is as follows: wherein: is the damage index; is the damage index reference; is the number of continuous taps; represents the th tap; is the quantization degree of the SDI value; is the damage index value of the th tap.

[0008] Further, Step 3 is as follows: Step 3-1, construct the spatial coordinate data set: Construct the horizontal coordinate set: array ; Construct the vertical coordinate set: array ; Step 3-2, generate a defect distribution cloud chart: A three-dimensional cloud chart is generated with the line coordinates as (X, Y) and the damage index SCI value as the Z axis; The pixel color depth is proportional to the damage index SDI value, the area where the defect is located is defined as warm color, and the intact part is defined as cold color, and a wellbore detection cloud chart is obtained; Step 3-3, defect judgment: If , it is determined that there is no defect; if , it is determined that there is a defect.

[0009] Further, in Step 1, the detection and collection device comprises an impact excitation mechanism and a data collection mechanism, the impact excitation mechanism comprises a steel ball for generating mechanical impact waves, and the data collection mechanism comprises an air-coupled sensor for collecting structural response impact echo signals; During data collection, the detection and collection device runs at a constant speed along the wellbore detection area from bottom to top, and the steel ball performs a fixed-frequency knock.

[0010] Further, the detection and collection device further comprises a vehicle body and a battery pack arranged on the vehicle body; The impact excitation mechanism further comprises a driving motor in driving connection with the steel ball, and the driving motor is electrically connected with the battery pack; The data collection mechanism further comprises a data collection card and a data transmission component electrically connected with the battery pack, the air-coupled sensor is arranged corresponding to the position of the steel ball, and the air-coupled sensor is electrically connected with the data collection card for converting analog signals into digital signals, and the data collection card is electrically connected with the data transmission component for bidirectional data transmission.

[0011] Further, the vehicle body is provided with a coded counting wheel for recording the running distance of the detection and collection device in real time.

[0012] Preferably, the air-coupled sensor is a MEMS microphone.

[0013] Preferably, the data transmission component adopts a wireless data transmission mode.

[0014] Further, for a vertical well, the vehicle body of the detection and collection device is lifted by a rope elevator.

[0015] Further, the vehicle body of the detection and collection device is provided with a helical wing in driving connection with the driving motor, and the rotating direction of the helical wing corresponds to the inner wall of the wellbore.

[0016] Compared with the prior art, the wellbore defect rapid detection method based on air-coupled impact echo method is a nondestructive testing method for detecting defects of a wellbore, impact echo signals of structural responses are collected through an air-coupled sensor, the air-coupled sensor does not need to be closely attached to a surface of a structure to be detected, and rapid data collection can be realized; through analysis of an energy proportion of a spectrum diagram, the position of a crack or a defect can be clearly distinguished in the case of coincidence in a main frequency interval; through drawing of a defect cloud diagram, defect information can be directly displayed, and the difficulty of manual distinguishing can be reduced. The wellbore defect rapid detection method is simple and feasible, has relatively strong practicability, has good effects, can realize non-contact detection, and can solve engineering application problems such as low detection efficiency and high main frequency misjudgment rate, can provide a theoretical basis and data support for nondestructive testing and evaluation of a wellbore with high efficiency, high precision and high adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a structural schematic diagram of a detection and collection device of the present application; Figure 2 is a top view structural schematic diagram of a vertical shaft wellbore of an embodiment of the present application; Figure 3 is an actual defect position diagram of a selected region of a wellbore of an embodiment of the present application; Figure 4 is a spectrum diagram obtained when an acceleration sensor is used to collect signals in an embodiment of the present application, wherein (a) is a spectrum diagram of a wellbore intact position, and (b) is a spectrum diagram of a wellbore defect position; Figure 5 is a spectrum diagram obtained when a MEMS microphone is used to collect signals in an embodiment of the present application, wherein (a) is a spectrum diagram of a wellbore intact position, and (b) is a spectrum diagram of a wellbore defect position; Figure 6 is a cloud diagram generated using a main frequency of each knock signal in an embodiment of the present application; Figure 7 is an energy proportion statistical diagram of a specific frequency band of three measuring lines in an embodiment of the present application, wherein (a) is an energy proportion of a specific frequency band of each knock of a first measuring line, (b) is an energy proportion of a specific frequency band of each knock of a second measuring line, and (c) is an energy proportion of a specific frequency band of each knock of a third measuring line; Figure 8 is an energy proportion diagram of a selected frequency band of continuous knocking of a known defect position in an embodiment of the present application; Figure 9 is a wellbore detection cloud diagram obtained using an SDI value in an embodiment of the present application.

[0018] In the figure: 1, rope hoist, 2, shaft inner wall, 3, knocking steel ball, 4, air coupling sensor, 5, driving motor, 6, data acquisition card, 7, data transmission assembly, 8, battery pack, 9, spiral wing, 10, arrow (detecting and collecting device running direction from bottom to top), 11, coded counting wheel. DETAILED DESCRIPTION

[0019] The present shaft defect rapid detection method is based on the air coupling principle, captures the leakage wave signal excited by the stress wave in a non-contact manner, and uses frequency band energy proportion analysis instead of traditional main frequency offset detection to realize the accuracy and anti-interference ability of defect identification.

[0020] The present invention will be further described below with reference to the detection of a certain coal mine shaft, combined with the accompanying drawings.

[0021] The present shaft defect rapid detection method specifically includes the following steps: Step 1, detection area planning and signal collection: dividing the detection area every certain distance along the circumferential direction of the shaft inner wall 2, and collecting the structural response impact echo signal data by the detecting and collecting device walking on the shaft inner wall 2 in the detection area.

[0022] As shown in Figure 1 The detecting and collecting device includes a vehicle body and a battery pack 8, an impact excitation mechanism and a data acquisition mechanism arranged on the vehicle body. The vehicle body can be provided with a coded counting wheel 11. When the detecting and collecting device moves along the shaft inner wall 2 as a whole, the coded counting wheel 11 can record the walking distance of the detecting and collecting device in real time. The impact excitation mechanism includes a knocking steel ball 3 for generating mechanical impact waves and a driving motor 5 in transmission connection with the knocking steel ball 3. The driving motor 5 is electrically connected with the battery pack 8. By controlling the driving motor 5 to act, the knocking steel ball 3 can be knocked to excite the shaft inner wall 2. The knocking frequency of the knocking steel ball 3 is controlled by the driving motor 5. The driving motor 5 can control the number of knocks of the knocking steel ball 3 within a certain time by adjusting the output voltage. The data acquisition mechanism includes an air coupling sensor 4, a data acquisition card 6 and a data transmission assembly 7 electrically connected with the battery pack 8. The air coupling sensor 4 is arranged corresponding to the position of the knocking steel ball 3. The air coupling sensor 4 for collecting the structural response impact echo signal can use a MEMS microphone. The data acquisition card 6 is used to convert the analog signal into a digital signal. The data acquisition card 6 can be built-in with a signal amplifier. The data transmission assembly 7 is used for upward and downward data transmission. The data transmission assembly 7 can use wired or wireless data transmission mode.

[0023] For the vertical shaft, the vehicle body of the detection and collection device can be lifted by the rope hoist 1. In order to enable the code counting wheel 11 to be firmly attached to the inner wall 2 of the shaft, a spiral wing 9 can also be arranged on the vehicle body and in transmission connection with the driving motor 5. The rotation direction of the spiral wing 9 corresponds to the arrangement of the inner wall 2 of the shaft. The driving motor 5 can drive the spiral wing 9 to rotate, thereby generating negative pressure between the vehicle body and the inner wall 2 of the shaft, and thereby enabling the code counting wheel 11 to be firmly attached to the inner wall 2 of the shaft.

[0024] Step2, process the collected signal: Step2-1, perform Fast Fourier Transform (FFT) on the collected time domain signal to obtain a frequency spectrum diagram, and the specific formula is as follows: In the formula: is the frequency index (integer), corresponding to the actual frequency; is the spectrum amplitude when the frequency is ; is the amplitude of the i th sampling point; is the Hanning window function; is the FFT point number.

[0025] Step2-2, perform spectrum analysis on each knocking point signal: Calculate the energy proportion of the selected frequency interval , and the specific formula is as follows: , wherein ∈ selected frequency interval In the formula: is the amplitude corresponding to the i th frequency in the interval.

[0026] Calculate the energy proportion of the effective frequency range , and the specific formula is as follows: , wherein ∈ effective signal frequency interval In the formula: is the amplitude corresponding to the i th frequency in the interval.

[0027] Then the damage index is defined as: .

[0028] Step2-3, determine the defect judgment reference: continuously knock on the known defect position and extract the signal, and set the average value of the energy proportion of the signal at this position as the reference value, and the specific formula is as follows: ​​​ In the formula: is the damage index reference value; is the number of continuous taps; is the number of taps, indicating a single measurement instance; is the discrete degree of quantified SDI value, used to set a dynamic decision interval; is the damage index value of the nth tap.

[0029] If the damage index value of the tap point calculated according to Step 2-2 is less than the value of the damage index reference value, then the tap point is defect-free; if the damage index value of the tap point calculated according to Step 2-2 is greater than the value of the damage index reference value, then the tap point has a defect.

[0030] Step 3, spatial mapping and defect determination: after constructing the spatial coordinate data set, a defect distribution cloud chart is generated and colored, and the location of the defect is obtained by comparison with the reference value. The details are as follows: Step 3-1, constructing the spatial coordinate data set: Constructing the horizontal coordinate set: array .

[0031] Constructing the vertical coordinate set: array .

[0032] Step 3-2, generating the defect distribution cloud chart: Coordinate system: X axis = horizontal coordinate set, Y axis = vertical coordinate set, three-dimensional cloud chart is generated with the measurement line coordinates as (X, Y) and the SCI value as Z axis.

[0033] Coloring rule: the color depth of a pixel point is proportional to the SDI value, wherein warm color (red) represents the area where the defect is located, and the intact part is cold color (green, yellow, blue).

[0034] Step 3-3, defect determination: If , it is determined to be defect-free; if , it is determined to have a defect.

[0035] The embodiment is a shaft structure of a vertical shaft, and the vertical shaft structure is shown as follows: Figure 2 When determining the shaft detection area, the area is divided every 10 cm along the circumference of the inner wall 2 of the shaft, and each area is tested for a distance of 2 m, with a total of three measurement lines. The detection process starts with the rope hoist 1 driving the detection collection device to move vertically in the shaft, and the detection collection device is equipped with a laser distance sensor 3 and a camera 4.​​​​Figure 1 As shown, after the detection and acquisition device enters the well casing, the battery pack 8 powers the device, and the rotating propeller 9 generates radial centrifugal force, ensuring the device remains stably attached to the inner wall 2 of the well casing at a preset detection interval. The cable lift 1 moves the device upwards in the direction of arrow 10, and the drive motor 5 drives the striking steel ball 3 to impact the inner wall 2 of the well casing, generating stress waves. The air coupling sensor 4 simultaneously captures the structural response impact echo signal, which is then digitized by the data acquisition card 6 and uploaded to the ground station by the data transmission component 7, which includes a wireless network card. During data acquisition, after each impact, the cable lift 1 pulls the detection and acquisition device upwards, and the coded counting wheel 11 measures the displacement of the steel cable in real time (accuracy ±0.1mm). Simultaneously, the propeller 9 continues to rotate to maintain a constant pressure of the detection and acquisition device on the inner wall 2 of the well casing. The entire process achieves depth calibration through the displacement feedback of the coded counting wheel 11, forming a closed-loop detection path.

[0036] During signal acquisition, the drive motor 5 is activated while the rope hoist 1 is pulled upwards at a constant speed. The spiral vane 9 keeps the machine pressed tightly against the wall. The drive motor 5 drives the striking steel ball 3 to strike at a fixed frequency. Within a spatial range of 3cm horizontally and 3cm vertically from each striking point, a MEMS microphone is used for signal acquisition. The acquisition range of this sensor is 100Hz~20kHz. An oscilloscope manufactured by PicoTech in the UK is used. The data is then transmitted to a computer via a wireless network card. To verify the engineering applicability of this rapid defect detection method for wellbore, an accelerometer is also used to acquire signals within a spatial range of 3cm horizontally and 3cm vertically from each striking point for comparison.

[0037] The acquired impact echoes were analyzed and processed using MATLAB and Fast Fourier Transform (FFT) on the computer, retaining the frequency spectrum and data of each impact signal. For the data acquired by the MEMS microphone and accelerometer, spectral analysis was performed separately, and the results are as follows: Figure 5 , Figure 4 As shown, the specific frequency range to be selected and the frequency range of the effective signal energy are determined. This is achieved through comparison. Figure 4 , Figure 5 The spectrum diagram shows that when using the accelerometer, the dominant frequencies of the intact and defective locations largely overlap, and through... Figure 6 As can be seen from the main frequency cloud map, the main frequencies presented throughout the entire structure are similar. The actual defect locations in the wellbore of this embodiment are as follows: Figure 3 As shown, the black area represents the region where defects are located, with only a few defects appearing at the bottom. Figure 6The main frequency method cloud chart used shows that the whole plate has defects or cracks, which does not match the actual situation, and cannot accurately determine the defect position and intact position. Therefore, the SDI index of the wellbore defect rapid detection method is used to select the specific frequency range.

[0038] In a knocking signal, the amplitude corresponding to each frequency in the selected specific frequency range is selected, and the sum of the squares is calculated; the frequency range corresponding to the selected total energy is calculated, the amplitude corresponding to each frequency in the frequency range is selected, and the sum of the squares is calculated. The specific frequency range energy ratio chart of the three measuring lines is shown in Figure 7 .

[0039] For a known defect position, 10 continuous knocks are performed and the signal is extracted, and the average value of the signal energy ratio of the position is set as the reference value, then As shown in Figure 8 , the average value calculated is 15%, and 15% is selected as the reference value.

[0040] An array is constructed according to the horizontal direction coordinate x of the knocking position, and an array is constructed according to the vertical direction coordinate y of the knocking position. The x-axis and y-axis are respectively constructed using the array and the array to generate a cloud chart. The color of the cloud chart is selected as the energy percentage of the specific frequency range of each knock, and the SDI value is used for cloud chart imaging as shown in Figure 9 . The overall energy ratio is generally less than 15%, i.e. the yellow, green and blue regions are defect-free regions. The color differentiation can be considered to be caused by the thickness and damage of the concrete. There is a small amount of red region in the area from 1.5m to 2m below, and this part of the concrete has certain voids or cracks, which can accurately identify the location of the cracks and defects.

[0041] The wellbore defect rapid detection method based on air coupling impact echo method is a non-destructive testing method for detecting defects in wellbores, which is simple, feasible, practical, and effective. It can solve the engineering application problems of low detection efficiency and high main frequency misjudgment rate under the premise of non-contact detection, and can provide theoretical basis and data support for efficient, high-precision and highly adaptable non-destructive testing and evaluation of wellbores.

Claims

1. A rapid detection method for wellbore defects, characterized in that, Specifically, the following steps are included: Step 1, Detection area planning and signal acquisition: Divide the detection area along the circumferential direction of the well shaft on the inner wall (2) of the well shaft at set intervals. Within the detection area, use the detection acquisition device to walk on the inner wall (2) of the well shaft to carry out detection and acquire structural response impact echo signal data. Step 2, Signal Processing and Determination of Defect Judgment Criteria: First, perform a Fast Fourier Transform on the acquired time-domain signal to obtain the spectrum. Then, perform spectral analysis on the signal at each impact point to calculate the energy proportion of the selected frequency range. Energy percentage within the effective frequency range Finally, the known defect locations are continuously tapped and the signals are extracted. The average value of the signal energy ratio at that location is set as the defect judgment benchmark value. Step 3, Spatial Mapping and Defect Judgment: After constructing the spatial coordinate dataset, a defect distribution cloud map is generated and colored. By comparing it with the baseline value, the location of the defect is determined.

2. The rapid detection method for wellbore defects according to claim 1, characterized in that, Step 2 is as follows: Step 2-1: Perform a Fast Fourier Transform on the acquired time-domain signal to obtain the spectrum. The specific formula is as follows: In the formula: This is the frequency index corresponding to the actual frequency; The frequency is Spectral amplitude at time; For the first The amplitude of each sampling point; For the Hanning window function; The number of FFT points; Step 2-2: Perform spectrum analysis on the signal at each tap point: Calculate the energy percentage of the selected frequency range The specific formula is as follows: ,in ∈ Selected frequency range In the formula: For the th interval The amplitude corresponding to each frequency; Calculate the energy percentage of the effective frequency range The specific formula is as follows: ,in ∈ effective signal frequency region In the formula: For the th interval The amplitude corresponding to each frequency; Damage indicators Defined as: ; Step 2-3, Determine the defect judgment benchmark: For a known defect location, continuously tap and extract the signal. Set the average percentage of signal energy at that location as the benchmark value. The specific formula is as follows: In the formula: As a damage indicator; As a benchmark for damage indicators; The number of consecutive taps; Indicates the number of times the object was struck; To quantify the dispersion of SDI values; This represents the damage index value for the nth strike.

3. The rapid detection method for wellbore defects according to claim 2, characterized in that, Step 3 is as follows: Step 3-1, Construct a spatial coordinate dataset: Constructing a collection of horizontal coordinates: an array ; Constructing a set of vertical coordinates: array ; Step 3-2, Generate a defect distribution cloud map: A three-dimensional cloud map is generated with the survey line coordinates as (X,Y) and the damage index SCI value as the Z-axis. The color depth of a pixel is proportional to the damage index SDI value. The defective area is defined as a warm color, and the intact part is defined as a cool color, thus obtaining a well shaft inspection cloud map. Step 3-3, Defect Judgment: like If it is, then it is judged to be without defects; if If so, it is determined that there is a defect.

4. The rapid detection method for wellbore defects according to claim 1, characterized in that, In Step 1, the detection and acquisition device includes an impact excitation mechanism and a data acquisition mechanism. The impact excitation mechanism includes a striking steel ball (3) for generating mechanical shock waves, and the data acquisition mechanism includes an air coupling sensor (4) for acquiring structural response impact echo signals. During the data acquisition process, the detection and acquisition device moves at a constant speed from bottom to top along the detection area of ​​the well shaft and strikes the steel ball (3) at a fixed frequency.

5. The rapid detection method for wellbore defects according to claim 4, characterized in that, The detection and acquisition device also includes the vehicle body and the battery pack (8) installed on the vehicle body; The impact excitation mechanism also includes a drive motor (5) that is connected to the striking steel ball (3) and is electrically connected to the battery pack (8); The data acquisition mechanism also includes a data acquisition card (6) and a data transmission component (7) electrically connected to the battery pack (8). The air coupling sensor (4) is set at the position of the striking steel ball (3), and the air coupling sensor (4) is electrically connected to the data acquisition card (6) for converting analog signals into digital signals. The data acquisition card (6) is electrically connected to the data transmission component (7) for bidirectional data transmission.

6. The rapid detection method for wellbore defects according to claim 5, characterized in that, The vehicle body is equipped with a coding counting wheel (11) for real-time recording of the travel distance of the detection and acquisition device.

7. The rapid detection method for wellbore defects according to claim 5, characterized in that, The air-coupled sensor (4) is a MEMS microphone.

8. The rapid detection method for wellbore defects according to claim 5, characterized in that, The data transmission component (7) uses wireless data transmission.

9. The rapid detection method for wellbore defects according to claim 5, characterized in that, For vertical shafts, the vehicle body of the detection and collection device is lifted and pulled by a rope hoist (1).

10. The rapid detection method for wellbore defects according to claim 5, characterized in that, The vehicle body of the detection and acquisition device is equipped with a spiral blade (9) that is connected to the drive motor (5) for transmission, and the rotation direction of the spiral blade (9) is set to correspond to the inner wall (2) of the well barrel.

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