A tunnel secondary lining void detection system and method

Abstract

The present invention relates to a tunnel secondary lining void detection system and method, comprising: an excitation device, an acoustic wave signal acquisition device and a chip; the acoustic wave signal acquisition device is used to: when the excitation device excites the secondary lining surface to be tested of the tunnel, collect the first acoustic wave signal generated by the secondary lining surface to be tested; the acoustic wave signal acquisition device is also used to: perform analog-to-digital conversion on the first acoustic wave signal, obtain and send the second acoustic wave signal to the chip; the chip is used to: obtain the void detection result of the secondary lining surface to be tested according to the second acoustic wave signal. The technical solution of this embodiment adopts hardware and software coordination, in which the computer undertakes the main data processing and calculation, with low cost and strong portability. The technical solution of this embodiment adopts an automated design, without the need to manually build a support frame for high-altitude operations, which reduces labor costs and workload, and also improves the safety of tunnel secondary lining void detection.

Description

Technical Field

[0001] The invention relates to the field of tunnel lining detection, and in particular to a tunnel secondary lining void detection system and method. Background Art

[0002] As a support and safety reserve, the secondary lining structure of the tunnel often has defects such as insufficient thickness, hollowing, and non-compactness. Among them, the hollowing of the secondary lining accounts for the largest proportion among all kinds of diseases. At present, non-destructive testing technology has been increasingly used in the lining quality inspection of newly built tunnels and the normal maintenance of operating tunnels. Non-destructive testing will not damage the tunnel lining structure. It can be used to check the surface and internal defects of the lining structure based on sound, light, electricity and magnetism. According to TB10223-2004 "Regulations for Non-destructive Testing of Railway Tunnel Lining Quality", it is known that the geological radar method and the sound wave method are proposed to be used for detecting the lining quality. However, when the geological radar detects the hollowing of the secondary lining of the tunnel, it often causes inaccurate detection results due to the shielding effect of the steel grid in the secondary lining structure on the ultrasonic wave emitted by the radar, which affects the judgment of the hollowing. Since the percussion method (sound wave method) detection is not affected by the interference caused by the steel bar shielding in the secondary lining structure, the present invention uses the sound wave method to detect and judge the hollowing of the secondary lining. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a tunnel secondary lining void detection system and method.

[0004] The technical solution of a tunnel secondary lining void detection system of the present invention is as follows:

[0005] Including: a vibration device, an acoustic wave signal collection device and a chip;

[0006] The acoustic wave signal collection device is used to collect the first acoustic wave signal generated by the secondary lining surface to be measured when the vibration device excites the secondary lining surface to be measured of the tunnel;

[0007] The acoustic wave signal acquisition device is also used to: perform analog-to-digital conversion on the first acoustic wave signal to obtain and send a second acoustic wave signal to the chip;

[0008] The chip is used to obtain a void detection result of the secondary lining surface to be tested according to the second sound wave signal.

[0009] The beneficial effects of the tunnel secondary lining void detection system of the present invention are as follows:

[0010] The system of the present invention adopts hardware and software coordination, wherein the computer undertakes the main data processing and calculation, and has low cost and strong portability. The system of the present invention adopts an automated design, and does not require manual construction of a support frame for high-altitude operations. While reducing labor costs and workload, it also improves the safety of tunnel secondary lining de-airing detection.

[0011] On the basis of the above scheme, the tunnel secondary lining void detection system of the present invention can also be improved as follows.

[0012] Further, the vibration excitation device includes: a track inspection vehicle and an impact cylinder;

[0013] The acoustic wave signal acquisition device is used to be arranged on the track inspection vehicle, and the impact cylinder is arranged at the front end of the track inspection vehicle to excite the secondary lining surface to be tested.

[0014] Furthermore, the sound wave signal collecting device is: a microphone.

[0015] Furthermore, the chip is specifically used for:

[0016] Performing Fourier transform and wavelet analysis on the second sound wave signal to obtain a target sound wave signal;

[0017] Using a preset detection algorithm, the frequency spectrum corresponding to the target acoustic wave signal is calculated to obtain a target void index of the secondary lining surface to be tested;

[0018] According to the numerical value of the target void index, the void detection result of the secondary lining surface to be tested is obtained; wherein the preset detection algorithm is: α is the target emptying index, T1 is the excellence cycle, T2 is the center of gravity cycle, C1 is the first emptying threshold corresponding to the excellence cycle, C2 is the second emptying threshold corresponding to the center of gravity cycle, C1=T1(1+1.65×F1), C2=T2(1+1.65×F2), F1 is the dispersion coefficient of the predominant period, F2 is the dispersion coefficient of the centroid period, m is the lining structure quality of the secondary lining surface to be tested, and F m is the dispersion coefficient of m, R is the front end curvature radius of the excitation device (110), and F R is the dispersion coefficient of R, V0 is the excitation speed of the excitation device (110), is the dispersion coefficient of V0, E is the dynamic elastic modulus of the tunnel lining structure, F E is the coefficient of dispersion of E.

[0019] Furthermore, the chip is also used for:

[0020] Preprocessing all first training sound wave signals to obtain multiple second training sound wave signals;

[0021] Performing Fourier transform and wavelet analysis on the time domain graph corresponding to each second training sound wave signal to obtain and extracting the eigenvalues ​​of the frequency domain graph corresponding to each second training sound wave signal to obtain all training eigenvalues;

[0022] Training is performed according to all training feature values ​​to obtain the preset detection algorithm.

[0023] The technical solution of a tunnel secondary lining void detection method of the present invention is as follows:

[0024] When the vibration device vibrates the secondary lining surface to be measured of the tunnel, the acoustic wave signal collection device collects the first acoustic wave signal generated by the secondary lining surface to be measured;

[0025] The sound wave signal acquisition device performs analog-to-digital conversion on the first sound wave signal to obtain and send a second sound wave signal to the chip;

[0026] The chip obtains a void detection result of the secondary lining surface to be tested according to the second sound wave signal.

[0027] The beneficial effects of the tunnel secondary lining void detection method of the present invention are as follows:

[0028] The method of the present invention adopts hardware and software cooperation, wherein the computer undertakes the main data processing and calculation, and the cost is low and the portability is strong. The method of the present invention adopts an automated design, and there is no need to manually build a support frame for high-altitude operations. While reducing labor costs and workload, it also improves the safety of tunnel secondary lining de-airing detection.

[0029] On the basis of the above scheme, the tunnel secondary lining void detection method of the present invention can also be improved as follows.

[0030] Further, the vibration excitation device includes: a track inspection vehicle and an impact cylinder;

[0031] The acoustic wave signal acquisition device is arranged on the track inspection vehicle, and the impact cylinder is arranged at the front end of the track inspection vehicle, and is used to excite the surface of the secondary lining to be inspected.

[0032] Furthermore, the sound wave signal collecting device is: a microphone.

[0033] Further, the chip obtains the hollowing detection result of the secondary lining surface to be tested according to the second sound wave signal, including:

[0034] The chip performs Fourier transform and wavelet analysis on the second sound wave signal to obtain a target sound wave signal;

[0035] The chip uses a preset detection algorithm to calculate the frequency spectrum corresponding to the target acoustic wave signal to obtain the target void index of the secondary lining surface to be tested;

[0036] The chip obtains the void detection result of the secondary lining surface to be tested according to the numerical value of the target void index; wherein the preset detection algorithm is: α is the target emptying index, T1 is the excellence cycle, T2 is the center of gravity cycle, C1 is the first emptying threshold corresponding to the excellence cycle, C2 is the second emptying threshold corresponding to the center of gravity cycle, C1=T1(1+1.65×F1), C2=T2(1+1.65×F2), F1 is the dispersion coefficient of the predominant period, F2 is the dispersion coefficient of the centroid period, m is the lining structure quality of the secondary lining surface to be tested, and F m is the dispersion coefficient of m, R is the front end curvature radius of the excitation device (110), and F R is the dispersion coefficient of R, V0 is the excitation speed of the excitation device (110), is the dispersion coefficient of V0, E is the dynamic elastic modulus of the tunnel lining structure, F E is the coefficient of dispersion of E.

[0037] Furthermore, it also includes:

[0038] The chip pre-processes all first training sound wave signals to obtain multiple second training sound wave signals;

[0039] The chip performs Fourier transform and wavelet analysis on the time domain graph corresponding to each second training sound wave signal, obtains and extracts the characteristic value of the frequency domain graph corresponding to each second training sound wave signal, and obtains all training characteristic values;

[0040] The chip is trained according to all training feature values ​​to obtain the preset detection algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the structure of a tunnel secondary lining void detection system according to an embodiment of the present invention;

[0042] Figure 2 This is a structural diagram of the working principle of a vibration excitation device in a tunnel secondary lining void detection system according to an embodiment of the present invention;

[0043] Figure 3The present invention is a schematic flow chart of a method for detecting voids in a secondary lining of a tunnel according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] like Figure 1 As shown, a tunnel secondary lining void detection system 100 according to an embodiment of the present invention comprises: a vibration excitation device 110, an acoustic wave signal acquisition device 120 and a chip 130;

[0045] The acoustic wave signal collecting device 120 is used to collect the first acoustic wave signal generated by the secondary lining surface to be measured when the vibration excitation device 110 excites the secondary lining surface to be measured of the tunnel.

[0046] The excitation method used in this embodiment is the sonic method.

[0047] The first sound wave signal is an analog signal of the sound wave collected by the sound wave signal collection device 120 .

[0048] The sound wave signal acquisition device 120 is further used to perform analog-to-digital conversion on the first sound wave signal to obtain and send a second sound wave signal to the chip 130 .

[0049] The sound wave signal acquisition device 120 is also provided with an AD data acquisition card for performing analog-to-digital conversion on the analog signal to obtain a second sound wave signal; the second sound wave signal is a digital signal of the acquired sound wave.

[0050] The chip 130 is used to obtain a void detection result of the secondary lining surface to be tested according to the second sound wave signal.

[0051] Among them, the chip 130 is equivalent to a host computer, which can be used to process and calculate the collected digital signals.

[0052] Preferably, the vibration excitation device 110 includes: a track inspection vehicle 111 and an impact cylinder 112 .

[0053] Among them, Figure 2 As shown, the vibration excitation device 110 of this embodiment is composed of a track inspection vehicle 111 and an impact cylinder 112 dedicated to the track.

[0054] The acoustic wave signal acquisition device 120 is used to be arranged on the track inspection vehicle 111, and the impact cylinder 112 is arranged at the front end of the track inspection vehicle 111 to excite the secondary lining surface to be tested.

[0055] Specifically, the main features of the excitation device 110 are as follows: (1) The track inspection vehicle 111 has low noise during self-operation. It is equipped with a drive motor 113 for driving the track inspection vehicle 111 forward (backward), and the track inspection vehicle is equipped with a sound wave signal acquisition device 120; (2) The track inspection vehicle is equipped with an odometer and a video recording device to record the inspection process and the actual situation on site; (3) The track inspection vehicle is equipped with a foldable telescopic frame 114, and the front end of the telescopic frame 114 is equipped with an impact cylinder 112. The maximum extension length of the telescopic frame 114 can reach the lining surface; (4) At least two ranging modules 115 (infrared ranging modules) are loaded at the front end of the telescopic frame 114, which can automatically detect obstacles and avoid them (contact network and positioning piles in the tunnel); (5) The internal hammer head of the impact cylinder 112 is made of iron or low-carbon steel, and there is no repeated impact in a single excitation, and the noise generated during excitation is small.

[0056] Preferably, the sound wave signal collecting device 120 is: a microphone.

[0057] The sound wave signal acquisition device 120 of this embodiment uses a high-sensitivity directional microphone to collect a first sound wave signal, and after performing analog-to-digital conversion on the first sound wave signal, obtain a second sound wave signal, and can send the converted second sound wave signal to the chip 130.

[0058] It should be noted that the sound wave signal collection device 120 of this example may also be other sound wave signal collection devices, as long as they can implement the technical solution of the present invention, and there is no limitation here.

[0059] Preferably, the chip 130 is specifically used for:

[0060] Perform Fourier transform and wavelet analysis on the second sound wave signal to obtain a target sound wave signal.

[0061] The process of performing Fourier transform and wavelet analysis on the second sound wave signal is a prior art in the art and will not be described in detail herein. The second sound wave signal corresponds to a time domain graph, and the target sound wave signal corresponds to a frequency domain graph.

[0062] A preset detection algorithm is used to calculate the frequency spectrum corresponding to the target acoustic wave signal to obtain the target void index of the secondary lining surface to be tested.

[0063] Among them, the corresponding dominant period and center of gravity period can be obtained according to the spectrum diagram corresponding to the target sound wave signal. The first void threshold and the second void threshold are only a material characteristic value for the tunnel lining to be tested, and participate in the calculation of the final void index. Specifically, when calculating the void threshold, it is necessary to first obtain the contact time T0 between the excitation device 110 and the secondary lining surface, and then calculate the vibration dominant period T1 = T0 / 1.25. According to the error propagation law, the parameters required to calculate the void threshold are: the lining structure mass of the secondary lining surface to be tested, the front end curvature radius of the excitation device, the excitation speed of the excitation device, and the discrete coefficient of the dynamic elastic modulus of the tunnel lining structure.

[0064] Wherein, the preset detection algorithm is: α is the target emptying index, T1 is the excellence cycle, T2 is the center of gravity cycle, C1 is the first emptying threshold corresponding to the excellence cycle, C2 is the second emptying threshold corresponding to the center of gravity cycle, C1=T1(1+1.65×F1), C2=T2(1+1.65×F2), F1 is the dispersion coefficient of the predominant period, F2 is the dispersion coefficient of the centroid period, m is the lining structure quality of the secondary lining surface to be tested, and F m is the dispersion coefficient of m, R is the front end curvature radius of the excitation device (110), and F R is the dispersion coefficient of R, V0 is the excitation speed of the excitation device (110), is the dispersion coefficient of V0, E is the dynamic elastic modulus of the tunnel lining structure, F E is the coefficient of dispersion of E.

[0065] It should be noted that the predominant period refers to the period of the most significant harmonic component that causes the vibration of the secondary lining of the tunnel, which is related to the lining thickness and material properties of the lining. The centroid period is a concept introduced to reduce the influence of the predominant period on the determination of the void threshold due to factors such as noise and induced modes. It is the same as the coefficient of variation of the predominant period and is calculated from the predominant period.

[0066] According to the numerical value of the target void index, the void detection result of the secondary lining surface to be tested is obtained.

[0067] Among them, the judgment range of the target void index is based on data calculation and experimental verification. The verification method is to knock around the void and collect sound wave data for calculation, and analyze the difference in the void index calculated by the two data when knocking at the center of the void and knocking at a place without a void. Therefore, the larger the target void index, the greater the probability of the existence of voids inside. By knocking from the center of the void to the surrounding area and calculating the results of the void index at different distances, the judgment range can be obtained. Because the specific conditions of each place are different, the judgment of the void index calculation results may be different. Combined with the control experiment, the judgment range of the void index of each tunnel is determined. Assume that the judgment range obtained at this time stipulates: when the target void index ≥1.5, the void detection result of the secondary lining surface to be tested is a defect; when 1≤target void index<1.5, the void detection result of the secondary lining surface to be tested is a suspected defect; when the target void index<1, the void detection result of the secondary lining surface to be tested is concrete compactness. If the target void index is 1.8 at this time, the void detection result of the secondary lining surface to be tested is judged to be a defect.

[0068] Preferably, the chip 130 is also used for:

[0069] All first training sound wave signals are preprocessed to obtain a plurality of second training sound wave signals.

[0070] Among them, the first training sound wave signal is the training data. After receiving the first training sound wave signal, the chip 130 plots the signal to obtain the original sound wave waveform (time domain diagram), observes the collected original sound wave waveform, and observes the elimination of abnormal or poor quality signal samples. The signal is intercepted, and the percussion vibration components with large amplitude and obvious changes are retained to improve the signal-to-noise ratio and refine the signal feature research; therefore, the preprocessed signal is the second training sound wave signal.

[0071] Fourier transform and wavelet analysis are performed on the time domain graph corresponding to each second training sound wave signal to obtain and extract the eigenvalue of the frequency domain graph corresponding to each second training sound wave signal to obtain all training eigenvalues.

[0072] Among them, there will be great differences between the voids and density in the frequency domain diagram obtained through Fourier transform and wavelet analysis, which is mainly manifested as follows: when the dense part is excited, the high-frequency component of the sound wave is much larger than the low-frequency component; when the void is excited, the low-frequency component of the sound wave is larger than the high-frequency component; the intuitive manifestation is that the main frequency domain in the frequency domain diagram when the dense part is excited is high frequency.

[0073] Among them, the training feature value is to explore the frequency domain graph characteristics shown by the frequency domain graph under different excitation conditions by exciting different materials (such as concrete materials of different thicknesses and different strength grades) and using different excitation devices and other experimental conditions in this embodiment.

[0074] Among them, the training data of this embodiment is obtained by continuously exciting the same point three or more times, taking a control experiment, exciting the dense tunnel lining inside, and then exciting the tunnel lining with certain voids inside. After the experiment, the difference in frequency domain graphs is compared to verify whether the conclusion is correct.

[0075] Among them, the coefficient of dispersion of each index in this embodiment is obtained through preprocessed training data. It should be noted that the coefficient of dispersion is also called the coefficient of variation. The coefficient of dispersion is a relative statistic that measures the degree of dispersion of data, and is mainly used to compare the degree of dispersion of different sample data. A large coefficient of dispersion indicates a large degree of dispersion of the data; a small coefficient of dispersion indicates a small degree of dispersion of the data. Its calculation formula is: c = standard deviation / mean value. The standard deviation and mean value are calculated from each data.

[0076] Training is performed according to all training feature values ​​to obtain the preset detection algorithm.

[0077] Among them, by collecting all training feature values, analyzing the characteristics of frequency domain graphs under different situations, and repeating verification, a preset detection algorithm is obtained.

[0078] The technical solution of this embodiment adopts the combination of hardware and software, in which the computer undertakes the main data processing and calculation, which is low-cost and highly portable. The technical solution of this embodiment adopts an automated design, which does not require manual construction of a support frame for high-altitude operations. While reducing labor costs and workload, it also improves the safety of tunnel secondary lining de-airing detection.

[0079] like Figure 3 As shown, a tunnel secondary lining void detection method of this embodiment includes the following steps:

[0080] S1. When the vibration excitation device 110 excites the secondary lining surface to be tested of the tunnel, the acoustic wave signal collection device 120 collects the first acoustic wave signal generated by the secondary lining surface to be tested;

[0081] S2, the sound wave signal acquisition device 120 performs analog-to-digital conversion on the first sound wave signal to obtain and send a second sound wave signal to the chip 130;

[0082] S3. The chip 130 obtains a void detection result of the secondary lining surface to be tested according to the second sound wave signal.

[0083] Preferably, the vibration excitation device 110 includes: a track inspection vehicle 111 and an impact cylinder 112;

[0084] The acoustic wave signal acquisition device 110 is used to be arranged on the track inspection vehicle 111, and the impact cylinder 112 is arranged at the front end of the track inspection vehicle to excite the secondary lining surface to be tested.

[0085] Preferably, the sound wave signal collecting device 120 is: a microphone.

[0086] Preferably, the S3 includes:

[0087] S31, the chip 130 performs Fourier transform and wavelet analysis on the second sound wave signal to obtain a target sound wave signal;

[0088] S32, the chip 130 uses a preset detection algorithm to calculate the frequency spectrum corresponding to the target acoustic wave signal to obtain the target void index of the secondary lining surface to be tested;

[0089] S33, the chip 130 obtains the void detection result of the secondary lining surface to be tested according to the numerical value of the target void index;

[0090] Wherein, the preset detection algorithm is: α is the target emptying index, T1 is the excellence cycle, T2 is the center of gravity cycle, C1 is the first emptying threshold corresponding to the excellence cycle, C2 is the second emptying threshold corresponding to the center of gravity cycle, C1=T1(1+1.65×F1), C2=T2(1+1.65×F2), F1 is the dispersion coefficient of the predominant period, F2 is the dispersion coefficient of the centroid period, m is the lining structure quality of the secondary lining surface to be tested, and F m is the dispersion coefficient of m, R is the front end curvature radius of the excitation device (110), and F R is the dispersion coefficient of R, V0 is the excitation speed of the excitation device (110), is the dispersion coefficient of V0, E is the dynamic elastic modulus of the tunnel lining structure, F E is the coefficient of dispersion of E.

[0091] Preferably, it also includes:

[0092] The chip 130 pre-processes all the first training sound wave signals to obtain a plurality of second training sound wave signals;

[0093] The chip 130 performs Fourier transform and wavelet analysis on the time domain graph corresponding to each second training sound wave signal, obtains and extracts the characteristic value of the frequency domain graph corresponding to each second training sound wave signal, and obtains all training characteristic values;

[0094] The chip 130 is trained according to all the training feature values ​​to obtain the preset detection algorithm.

[0095] The technical solution of this embodiment adopts the combination of hardware and software, in which the computer undertakes the main data processing and calculation, which is low-cost and highly portable. The technical solution of this embodiment adopts an automated design, which does not require manual construction of a support frame for high-altitude operations. While reducing labor costs and workload, it also improves the safety of tunnel secondary lining de-airing detection.

[0096] The process of realizing corresponding functions of various parameters and steps in the above-mentioned method for detecting voids in a secondary tunnel lining of this embodiment can be referred to the various parameters and modules in the above-mentioned embodiment of a system 100 for detecting voids in a secondary tunnel lining, which will not be elaborated here.

[0097] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present invention are not directed to any particular programming language.

[0098] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself is a separate embodiment of the present invention.

[0099] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A tunnel secondary lining void detection system, characterized in that: include: An excitation device (110), an acoustic wave signal collection device (120) and a chip (130); The acoustic wave signal collection device (120) is used to collect a first acoustic wave signal generated by the secondary lining surface to be tested when the vibration excitation device (110) excites the secondary lining surface to be tested of the tunnel; The sound wave signal collection device (120) is also used to: perform analog-to-digital conversion on the first sound wave signal to obtain and send a second sound wave signal to the chip (130); The chip (130) is used to obtain, according to the second sound wave signal, a void detection result of the secondary lining surface to be tested; The chip (130) is specifically used for: Performing Fourier transform and wavelet analysis on the second sound wave signal to obtain a target sound wave signal; Using a preset detection algorithm, the frequency spectrum corresponding to the target acoustic wave signal is calculated to obtain a target void index of the secondary lining surface to be tested; According to the numerical value of the target void index, the void detection result of the secondary lining surface to be tested is obtained; wherein the preset detection algorithm is: α is the target emptying index, T1 is the excellence cycle, T2 is the center of gravity cycle, C1 is the first emptying threshold corresponding to the excellence cycle, C2 is the second emptying threshold corresponding to the center of gravity cycle, C1=T1(1+1.65×F1), C2=T2(1+1.65×F2), F1 is the dispersion coefficient of the predominant period, F2 is the dispersion coefficient of the centroid period, m is the lining structure quality of the secondary lining surface to be tested, and F m is the dispersion coefficient of m, R is the front end curvature radius of the excitation device (110), and F R is the dispersion coefficient of R, V0 is the excitation speed of the excitation device (110), is the dispersion coefficient of V0, E is the dynamic elastic modulus of the tunnel lining structure, F E is the coefficient of dispersion of E; The chip (130) is also used for: Preprocessing all first training sound wave signals to obtain multiple second training sound wave signals; Performing Fourier transform and wavelet analysis on the time domain graph corresponding to each second training sound wave signal to obtain and extracting the eigenvalues ​​of the frequency domain graph corresponding to each second training sound wave signal to obtain all training eigenvalues; Training is performed according to all training feature values ​​to obtain the preset detection algorithm.

2. A tunnel secondary lining void detection system according to claim 1, characterized in that: The vibration excitation device (110) comprises: a track inspection vehicle (111) and an impact cylinder (112); The acoustic wave signal collection device (120) is arranged on the track inspection vehicle (111), and the impact cylinder (112) is arranged at the front end of the track inspection vehicle (111) and is used to excite the secondary lining surface to be inspected.

3. A tunnel secondary lining void detection system according to claim 1, characterized in that: The sound wave signal collection device (120) is a microphone.

4. A method for detecting voids in a tunnel secondary lining, characterized in that: include: When the vibration excitation device (110) excites the secondary lining surface to be tested of the tunnel, the sound wave signal collection device (120) collects the first sound wave signal generated by the secondary lining surface to be tested; The sound wave signal collection device (120) performs analog-to-digital conversion on the first sound wave signal to obtain and send a second sound wave signal to a chip (130); The chip (130) obtains a void detection result of the secondary lining surface to be tested according to the second sound wave signal; The chip (130) obtains a void detection result of the secondary lining surface to be tested according to the second sound wave signal, including: The chip (130) performs Fourier transform and wavelet analysis on the second sound wave signal to obtain a target sound wave signal; The chip (130) uses a preset detection algorithm to calculate the frequency spectrum corresponding to the target sound wave signal to obtain a target void index of the secondary lining surface to be tested; The chip (130) obtains the void detection result of the secondary lining surface to be tested according to the numerical value of the target void index; wherein the preset detection algorithm is: α is the target emptying index, T1 is the excellence cycle, T2 is the center of gravity cycle, C1 is the first emptying threshold corresponding to the excellence cycle, C2 is the second emptying threshold corresponding to the center of gravity cycle, C1=T1(1+1.65×F1), C2=T2(1+1.65×F2), F1 is the dispersion coefficient of the predominant period, F2 is the dispersion coefficient of the centroid period, m is the lining structure quality of the secondary lining surface to be tested, and F m is the dispersion coefficient of m, R is the front end curvature radius of the excitation device (110), and F R is the dispersion coefficient of R, V0 is the excitation speed of the excitation device (110), is the dispersion coefficient of V0, E is the dynamic elastic modulus of the tunnel lining structure, F E is the coefficient of dispersion of E; Also includes: The chip (130) pre-processes all first training sound wave signals to obtain a plurality of second training sound wave signals; The chip (130) performs Fourier transform and wavelet analysis on the time domain graph corresponding to each second training sound wave signal, obtains and extracts the characteristic value of the frequency domain graph corresponding to each second training sound wave signal, and obtains all training characteristic values; The chip (130) is trained according to all training feature values ​​to obtain the preset detection algorithm.

5. A method for detecting voids in secondary lining of a tunnel according to claim 4, characterized in that: The vibration excitation device (110) comprises: a track inspection vehicle (111) and an impact cylinder (112); The acoustic wave signal collection device (120) is arranged on the track inspection vehicle (111), and the impact cylinder (112) is arranged at the front end of the track inspection vehicle (111) and is used to excite the secondary lining surface to be inspected.

6. A method for detecting voids in a tunnel secondary lining according to claim 4, characterized in that: The sound wave signal collection device (120) is a microphone.

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