Multi-frequency sound wave analysis method based on reinforced concrete damage detection

Abstract

The invention discloses a multi-frequency sound wave analysis method based on reinforced concrete damage detection, and particularly relates to the technical field of multi-frequency sound wave analysis, and the method specifically comprises the steps: collecting multi-frequency emission information according to emitted multi-frequency sound waves, obtaining a frequency vacancy coefficient according to the multi-frequency emission information, collecting multi-frequency receiving information according to the received multi-frequency sound waves, and obtaining a frequency vacancy coefficient according to the frequency vacancy coefficient; a multi-frequency fusion coefficient is obtained according to the multi-frequency receiving information, a loss model is constructed according to the multi-frequency fusion coefficient and a frequency vacancy coefficient, a space loss index of the loss model is calculated, the space loss index is compared with a preset space loss threshold value, and an identification signal is generated according to a comparison result; an anomaly detection model is established according to the generated identification signal, classification evaluation is performed by using space loss index data of an abnormal state, and an intervention strategy is proposed according to a classification evaluation result, so that the problem of abnormal reinforced concrete damage detection fluctuation caused by frequency-by-frequency emission of multi-frequency signals is effectively solved; and the effectiveness and credibility of reinforced concrete damage detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-frequency sound wave analysis, and more specifically, to a multi-frequency sound wave analysis method based on reinforced concrete damage detection. Background Art

[0002] Multi-frequency acoustic wave analysis based on reinforced concrete damage detection is a method of identifying internal concrete damage by emitting and receiving acoustic waves of different frequencies. The propagation characteristics of acoustic waves in concrete materials are used. Damage affects the propagation speed, attenuation and spectrum characteristics of acoustic waves, and the health status of the material is evaluated by analyzing the reflection and transmission characteristics of acoustic waves.

[0003] For sound wave signals of different frequencies, the method based on broadband signals has high requirements for signal processing algorithms, and multi-frequency signals are stacked on each other. The denoising of external interference signals also relies on efficient signal processing algorithms. The frequency-by-frequency signal method has low processing difficulty, but the transmission delay and reception of signals of different frequencies may conflict. The detection efficiency of the frequency-by-frequency signal is limited by the influence of the transmission timing of the multi-frequency signals, and it is difficult to efficiently detect reinforced concrete damage. In addition, there are obvious differences in the attenuation characteristics of high-frequency signals and low-frequency signals. The impact of fluctuations in the frequency-by-frequency signal during the detection process and the verification of the credibility of the detection results have become urgent issues to be resolved.

[0004] In order to solve the above defects, a technical solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-frequency acoustic wave analysis method based on reinforced concrete damage detection to solve the problems in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a multi-frequency acoustic wave analysis method based on reinforced concrete damage detection, the specific steps include:

[0007] Collecting multi-frequency transmission information according to the transmitted multi-frequency sound waves, and obtaining a frequency vacancy coefficient according to the multi-frequency transmission information;

[0008] Collecting multi-frequency receiving information according to the received multi-frequency sound waves, and obtaining a multi-frequency fusion coefficient according to the multi-frequency receiving information;

[0009] The churn model is constructed based on the multi-frequency fusion coefficient and the frequency vacancy coefficient, and the spatial churn index of the churn model is calculated;

[0010] Comparing the space loss index with a preset space loss threshold, and generating an identification signal according to the comparison result;

[0011] An anomaly detection model is established based on the generated identification signal, and classification evaluation is performed using the spatial loss index data of the abnormal state. An intervention strategy is proposed based on the classification evaluation results.

[0012] Preferably, the method for acquiring the multi-frequency transmission information is:

[0013] The optimal range of the frequency-by-frequency response time for transmitting multi-frequency sound waves is set to [Cb min ,Cb max ];

[0014] Collect the frequency-by-frequency emission response time of the multi-frequency sound wave within the time period T, and mark the frequency-by-frequency emission response time of the multi-frequency sound wave within the time period T as Cb v , where v = {1, 2, 3, ..., n}, and n is a positive integer, Cb v It represents the frequency-by-frequency transmission response time of the vth multi-frequency sound wave within the time period T. The frequency-by-frequency transmission response time of the multi-frequency sound wave within the time period T is the multi-frequency transmission information.

[0015] Preferably, the method for obtaining the frequency vacancy coefficient according to the multi-frequency reception information is:

[0016] Collect the frequency-by-frequency transmission response time data that exceeds the optimal range of the frequency-by-frequency transmission response time within the time period T as the idle frequency-by-frequency transmission response time, and mark the idle frequency-by-frequency transmission response time as Cz x , where x = {1, 2, 3, ..., m}, and m is a positive integer, Cz x Indicates the idle frequency-by-frequency transmission response time of the xth multi-frequency sound wave within the time period T;

[0017] Calculate the frequency vacancy coefficient, the calculation expression is

[0018] Preferably, the method for obtaining multi-frequency reception information is:

[0019] Obtain the receiving efficiency coefficient of detecting multi-frequency sound waves within a period time T, and integrate the receiving efficiency coefficients of detecting multi-frequency sound waves within several period times T into a data set, and mark the data set as R ec = {ms w}, where w = {1, 2, 3, ..., q}, and q is a positive integer, ms w It represents the receiving efficiency coefficient of the multi-frequency sound wave detection within the wth cycle time T. The calculation expression of the receiving efficiency coefficient is: Among them, t a is the sound wave emission time, t s is the sound wave stopping time, S wa is the wavelength of the received sound wave, P r is the number of sonic probes;

[0020] Calculate the standard deviation of the receiving efficiency coefficient for detecting multi-frequency sound waves within a certain period of time T. The calculation expression is: In the formula, gne is the average value of the receiving efficiency coefficient of detecting multi-frequency sound waves within several cycles of time T, and the calculation expression is: S re To receive information on multiple frequencies.

[0021] Preferably, the method for obtaining the multi-frequency fusion coefficient according to the multi-frequency reception information is:

[0022] Take multiple consecutive multi-frequency received information as eigenvalues, obtain the local outlier factor of each eigenvalue, and mark the local outlier factor as L u , where u is the index of the local outlier factor, u = {1, 2, 3, ..., e} and e is a positive integer. When a damaged signal is detected, the characteristic value of the multi-frequency reception information at the time of damaged signal detection is marked, and the local outlier factor of the characteristic value of the multi-frequency reception information at the time of damaged signal detection is obtained and marked as L y , where y is the index of the local outlier factor of the eigenvalue of the multi-frequency received information at the time of the damaged signal detection, y = {1, 2, 3, ..., r} and r is a positive integer. The multi-frequency fusion coefficient is calculated as follows:

[0023] Preferably, the method for constructing the churn model is:

[0024] The maximum frequency of the frequency-by-frequency signal is λ max , the minimum frequency of the frequency-by-frequency signal is λ min , then the churn model expression is In the formula, C hu is the space loss index.

[0025] Preferably, the logic for generating the identification signal according to the comparison result is:

[0026] Preset space loss threshold C th , the calculated space loss index C hu and space loss threshold C th Compare, if the space loss index C hu Greater than or equal to the space loss threshold C th , a red identification signal is generated for the damage detection result;

[0027] If the space loss index C hu Less than the space loss threshold C th , a blue identification signal is generated for the damage detection result.

[0028] Preferably, an anomaly detection model is established for classification evaluation, and the logic of the intervention strategy is proposed according to the classification evaluation results:

[0029] When the damage detection result is a red identification signal, a data set is generated by integrating several consecutive space loss index data within T time after the red identification signal is generated, and the space loss index in the data set is calibrated as Da h , where h is the number of the space loss index, and h = {1, 2, 3...d}, where d is a positive integer;

[0030] Calculate the standard deviation of several space loss indexes in the data set, and calibrate the standard deviation of the space loss index as Vk. Compare the space loss index standard deviation Vk with the preset space loss index standard deviation threshold Pb, and perform intervention according to the comparison results. The intervention strategy is as follows:

[0031] If Vk is greater than or equal to Pb, the operator is prompted that the consistency of damage detection fluctuates too much, and the detection data is recorded and marked as abnormal;

[0032] If Vk is less than Pb, the operator is prompted that the consistency fluctuation of damage detection is stable, and the detection data is recorded.

[0033] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0034] The present application integrates and limits the frequency-by-frequency emission response time of multi-frequency sound waves by acquiring multi-frequency emission information, evaluates the emission response time of each different frequency in the frequency-by-frequency signal by acquiring the frequency vacancy coefficient, evaluates the efficiency of frequency-by-frequency signal detection, and evaluates the detection capability of multi-frequency sound waves by acquiring multi-frequency reception information, thereby effectively improving the ability to distinguish between reinforced concrete damage judgment in damage detection, and discretely analyzes the distribution of damaged and non-damaged states by using an outlier-based anomaly detection method, while measuring the fluctuation level of the damage state. By establishing a loss model and calculating the spatial loss index, combined with the frequency range of the multi-frequency sound wave signal, the scattering and absorption loss degree of the multi-frequency signal during the emission and reception process is effectively evaluated, the abnormal fluctuation level of damage detection is evaluated, and the accuracy and reliability of damage detection are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example 1: Please refer to Figure 1 As shown, the present invention is a multi-frequency acoustic wave analysis method based on reinforced concrete damage detection, and the specific steps include:

[0039] Collecting multi-frequency transmission information according to the transmitted multi-frequency sound waves, and obtaining a frequency vacancy coefficient according to the multi-frequency transmission information;

[0040] Collecting multi-frequency receiving information according to the received multi-frequency sound waves, and obtaining a multi-frequency fusion coefficient according to the multi-frequency receiving information;

[0041] The churn model is constructed based on the multi-frequency fusion coefficient and the frequency vacancy coefficient, and the spatial churn index of the churn model is calculated;

[0042] Comparing the space loss index with a preset space loss threshold, and generating an identification signal according to the comparison result;

[0043] An anomaly detection model is established based on the generated identification signal, and classification evaluation is performed using the spatial loss index data of the abnormal state. An intervention strategy is proposed based on the classification evaluation results.

[0044] Multi-frequency acoustic wave analysis based on reinforced concrete damage detection is a method of identifying internal concrete damage by emitting and receiving acoustic waves of different frequencies. It uses the propagation characteristics of acoustic waves in concrete materials. Damage affects the propagation speed, attenuation and spectrum characteristics of acoustic waves, and thus evaluates the health status of the material by analyzing the reflection and transmission characteristics of acoustic waves.

[0045] Collect multi-frequency transmission information according to the transmitted multi-frequency sound waves, and obtain multi-frequency fusion coefficients according to the multi-frequency transmission information;

[0046] The emitted multi-frequency sound waves contain multiple frequencies. By transmitting sound wave signals of different frequencies at intervals, the interference between signals of different frequencies can be reduced, so that the signal of each frequency can be analyzed more clearly. At the same time, it is helpful to analyze the response of each frequency to concrete damage separately, effectively improve the accuracy of signal processing, and reduce the complexity of signal processing;

[0047] The response delay time of the frequency-by-frequency signal is the delay time for the frequency-by-frequency signal to respond and emit sound waves one by one. If the response delay time of the frequency-by-frequency signal fluctuates abnormally, the following adverse consequences will occur:

[0048] Abnormal delay time fluctuations lead to misidentification or misjudgment of internal concrete damage, thereby ignoring real damage or mislabeling non-damaged areas as damaged areas;

[0049] The instability of delay time increases the complexity of signal processing, making data analysis more difficult, thus affecting the accuracy of the overall detection;

[0050] Maintenance and repair decisions based on inaccurate latency data can lead to unnecessary cost expenditures or overlook potential safety hazards;

[0051] Large fluctuations in response delays require more repeated testing and data verification, which increases the total time and resource consumption of testing;

[0052] Abnormal fluctuations in delay time may be a signal of hidden dangers within the structure. If not discovered in time, it may lead to more serious structural safety problems and increase the risk of accidents;

[0053] If the delay time fluctuates abnormally, the consistency and reliability of the data will decrease, affecting the validity of the overall data set;

[0054] In the damage identification algorithm based on delay time, abnormal fluctuations lead to the degradation of model performance and the inability to accurately predict damage.

[0055] The method for obtaining multi-frequency transmission information is:

[0056] The optimal range of the frequency-by-frequency response time for transmitting multi-frequency sound waves is set to [Cb min ,Cb max ];

[0057] Collect the frequency-by-frequency emission response time of the multi-frequency sound wave within the time period T, and mark the frequency-by-frequency emission response time of the multi-frequency sound wave within the time period T as Cb v , where v = {1, 2, 3, ..., n}, and n is a positive integer, Cb v It represents the frequency-by-frequency emission response time of the vth multi-frequency sound wave within the time period T, and the frequency-by-frequency emission response time of the multi-frequency sound wave within the time period T is the multi-frequency emission information;

[0058] The method for obtaining the frequency vacancy coefficient based on multi-frequency reception information is:

[0059] Collect the frequency-by-frequency transmission response time data that exceeds the optimal range of the frequency-by-frequency transmission response time within the time period T as the idle frequency-by-frequency transmission response time, and mark the idle frequency-by-frequency transmission response time as Cz x , where x = {1, 2, 3, ..., m}, and m is a positive integer, Cz x Indicates the idle frequency-by-frequency transmission response time of the xth multi-frequency sound wave within the time period T;

[0060] Calculate the frequency vacancy coefficient, the calculation expression is

[0061] By analyzing abnormal data with long response delay time, identifying and eliminating erroneous signals caused by noise or other interference, the accuracy of detection can be effectively improved. The increase in response delay time indicates that there are defects inside the material. Common defects include cracks, honeycombs, voids, etc. Defects affect the propagation speed of sound waves and make it difficult to further shorten the response delay of frequency-by-frequency transmission, so as to improve the efficiency of defect detection. The change in delay time is used to assess the severity of damage and assist in judging the health status of the structure. After identifying the abnormal response delay time, more detailed follow-up detection or analysis of the abnormal area will help optimize the overall detection strategy.

[0062] The advantage of frequency-by-frequency signal detection is that the detection signal analysis is concise and the detection accuracy is higher. However, the time required for the complete transmission of the frequency-by-frequency signal is longer than that of the broadband signal. The response delay time of the frequency-by-frequency signal transmission effectively represents the detection efficiency and measures the process status of the reinforced concrete damage detection process.

[0063] Collecting multi-frequency receiving information according to the received multi-frequency sound waves, and obtaining a multi-frequency fusion coefficient according to the multi-frequency receiving information;

[0064] If the ability to detect the smallest physical area fluctuates abnormally during the test, the following adverse consequences may occur:

[0065] Fluctuations in the ability to detect the smallest physical area lead to inaccurate identification of internal damage, missing small damage or misidentifying it as damage, thus affecting the assessment results;

[0066] If the detection capability is unstable, it will lead to misjudgment of the concrete material state, affecting the assessment of the material health status;

[0067] Fluctuations in detection capabilities require more repeated testing to confirm the reliability of the results, increasing testing time and costs;

[0068] Inconsistent detection capabilities lead to differences in the quality of the collected data, affecting subsequent data analysis and interpretation of results;

[0069] Maintenance and repair decisions based on inaccurate or inconsistent data lead to unnecessary expenses or failure to detect potential safety hazards;

[0070] In models and algorithms based on detection capabilities, fluctuations lead to a decrease in the predictive power of the model, reducing its effectiveness in practical applications;

[0071] If the detection capability is insufficient to identify key damage features, it will lead to serious structural safety risks and increase the likelihood of accidents.

[0072] The method for obtaining multi-frequency reception information is as follows:

[0073] Obtain the receiving efficiency coefficient of detecting multi-frequency sound waves within a period time T, and integrate the receiving efficiency coefficients of detecting multi-frequency sound waves within several period times T into a data set, and mark the data set as R ec = {ms w}, where w = {1, 2, 3, ..., q}, and q is a positive integer, ms w It represents the receiving efficiency coefficient of the multi-frequency sound wave detection within the wth cycle time T. The calculation expression of the receiving efficiency coefficient is: Among them, t a is the sound wave emission time, t s is the sound wave stopping time, S wa is the wavelength of the received sound wave, P r is the number of sonic probes;

[0074] Calculate the standard deviation of the receiving efficiency coefficient for detecting multi-frequency sound waves within a certain period of time T. The calculation expression is: Where gen is the average value of the receiving efficiency coefficient of detecting multi-frequency sound waves within several cycles T, and the calculation expression is: S re To receive information on multiple frequencies.

[0075] The method for obtaining the multi-frequency fusion coefficient according to the multi-frequency reception information is:

[0076] Take multiple consecutive multi-frequency received information as eigenvalues, obtain the local outlier factor of each eigenvalue, and mark the local outlier factor as L u , where u is the index of the local outlier factor, u = {1, 2, 3, ..., e} and e is a positive integer. When a damaged signal is detected, the characteristic value of the multi-frequency reception information at the time of damaged signal detection is marked, and the local outlier factor of the characteristic value of the multi-frequency reception information at the time of damaged signal detection is obtained and marked as L y , where y is the index of the local outlier factor of the eigenvalue of the multi-frequency received information at the time of the damaged signal detection, y = {1, 2, 3, ..., r} and r is a positive integer. The multi-frequency fusion coefficient is calculated as follows:

[0077] By obtaining multi-frequency sound wave detection through multi-frequency receiving information, the detection capability of the emitted multi-frequency sound waves is evaluated in combination with the sound wave emission time, the sound wave stop time, the wavelength of the received sound waves and the number of probes. The damage state and the normal state are refined according to the signal strength of the emitted sound waves, and the signal difference between the damage state and the normal state is expanded. The distribution of damage and non-damage states is discretely analyzed by using an outlier-based anomaly detection method, which effectively improves the judgment and discrimination capability of damage detection and improves the damage detection efficiency of frequency-by-frequency signal detection.

[0078] The churn model is constructed based on the multi-frequency fusion coefficient and the frequency vacancy coefficient, and the spatial churn index of the churn model is calculated;

[0079] The method for constructing the churn model is:

[0080] The maximum frequency of the frequency-by-frequency signal is λ max , the minimum frequency of the frequency-by-frequency signal is λ min , then the churn model expression is In the formula, C hu is the space loss index.

[0081] In the frequency-by-frequency signal, high-frequency sound waves decay faster than low-frequency sound waves in the same medium. The wavelength of high-frequency sound waves is shorter and they are more easily scattered by small particles and defects in reinforced concrete. At the same time, the absorption effect is also more obvious. That is, the degree of frequency difference in the frequency-by-frequency signal significantly affects the consistency of the multi-frequency signal detection capability. For signals of different frequencies, there are significant differences in their minimum detection physical spaces. By introducing the frequency range difference in the multi-frequency signal into the loss model, the degree of scattering and absorption loss of the multi-frequency signal during the transmission and reception process is effectively evaluated. At the same time, the multi-frequency fusion coefficient and the frequency vacancy coefficient are combined to evaluate the abnormal fluctuation level of damage detection, thereby improving the accuracy and reliability of damage detection.

[0082] Comparing the space loss index with a preset space loss threshold, and generating an identification signal according to the comparison result;

[0083] Preset space loss threshold C th , the calculated space loss index C hu and space loss threshold C th Compare, if the space loss index C hu Greater than or equal to the space loss threshold C th , a red identification signal is generated for the damage detection result;

[0084] If the space loss index C hu Less than the space loss threshold C th , a blue identification signal is generated for the damage detection result.

[0085] An anomaly detection model is established based on the generated identification signal, and the spatial loss index data of the abnormal state is used for classification evaluation, and an intervention strategy is proposed based on the classification evaluation results;

[0086] When the damage detection result is a red identification signal, a data set is generated by integrating several consecutive space loss index data within T time after the red identification signal is generated, and the space loss index in the data set is calibrated as Da h , where h is the number of the space loss index, and h = {1, 2, 3...d}, where d is a positive integer;

[0087] Calculate the standard deviation of several space loss indexes in the data set, and calibrate the standard deviation of the space loss index as Vk. Compare the space loss index standard deviation Vk with the preset space loss index standard deviation threshold Pb, and perform intervention according to the comparison results. The intervention strategy is as follows:

[0088] If Vk is greater than or equal to Pb, the operator is prompted that the consistency of damage detection fluctuates too much, and the detection data is recorded and marked as abnormal;

[0089] If Vk is less than Pb, the operator is prompted that the consistency fluctuation of damage detection is stable, and the detection data is recorded.

[0090] The present application integrates and limits the frequency-by-frequency emission response time of multi-frequency sound waves by acquiring multi-frequency emission information, evaluates the emission response time of each different frequency in the frequency-by-frequency signal by acquiring the frequency vacancy coefficient, evaluates the efficiency of frequency-by-frequency signal detection, and evaluates the detection capability of multi-frequency sound waves by acquiring multi-frequency reception information, thereby effectively improving the ability to distinguish between reinforced concrete damage judgment in damage detection, and discretely analyzes the distribution of damaged and non-damaged states by using an outlier-based anomaly detection method, while measuring the fluctuation level of the damage state. By establishing a loss model and calculating the spatial loss index, combined with the frequency range of the multi-frequency sound wave signal, the scattering and absorption loss degree of the multi-frequency signal during the emission and reception process is effectively evaluated, the abnormal fluctuation level of damage detection is evaluated, and the accuracy and reliability of damage detection are improved.

[0091] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0092] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of computer program goods. The computer program goods include one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0093] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the method described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

Claims

1. A multi-frequency acoustic wave analysis method based on reinforced concrete damage detection, characterized in that: The specific steps include: Collecting multi-frequency transmission information according to the transmitted multi-frequency sound waves, and obtaining a frequency vacancy coefficient according to the multi-frequency transmission information; Collecting multi-frequency receiving information according to the received multi-frequency sound waves, and obtaining a multi-frequency fusion coefficient according to the multi-frequency receiving information; The churn model is constructed based on the multi-frequency fusion coefficient and the frequency vacancy coefficient, and the spatial churn index of the churn model is calculated; Comparing the space loss index with a preset space loss threshold, and generating an identification signal according to the comparison result; An anomaly detection model is established based on the generated identification signal, and classification evaluation is performed using the spatial loss index data of the abnormal state. An intervention strategy is proposed based on the classification evaluation results.

2. The multi-frequency acoustic wave analysis method based on reinforced concrete damage detection according to claim 1 is characterized in that: The method for obtaining multi-frequency transmission information is: The optimal range of the frequency-by-frequency response time for transmitting multi-frequency sound waves is set to [Cb min ,Cb max ]; Collect the frequency-by-frequency emission response time of the multi-frequency sound wave within the time period T, and mark the frequency-by-frequency emission response time of the multi-frequency sound wave within the time period T as Cb v , where v = {1, 2, 3, ..., n}, and n is a positive integer, Cb v It represents the frequency-by-frequency transmission response time of the vth multi-frequency sound wave within the time period T. The frequency-by-frequency transmission response time of the multi-frequency sound wave within the time period T is the multi-frequency transmission information.

3. The multi-frequency acoustic wave analysis method based on reinforced concrete damage detection according to claim 2 is characterized in that: The method for obtaining the frequency vacancy coefficient based on multi-frequency reception information is: Collect the frequency-by-frequency transmission response time data that exceeds the optimal range of the frequency-by-frequency transmission response time within the time period T as the idle frequency-by-frequency transmission response time, and mark the idle frequency-by-frequency transmission response time as Cz x , where x = {1, 2, 3, ..., m}, and m is a positive integer, Cz x Indicates the idle frequency-by-frequency transmission response time of the xth multi-frequency sound wave within the time period T; Calculate the frequency vacancy coefficient, the calculation expression is 4. The multi-frequency acoustic wave analysis method based on reinforced concrete damage detection according to claim 3 is characterized in that: The method for obtaining multi-frequency reception information is as follows: Obtain the receiving efficiency coefficient of detecting multi-frequency sound waves within a period time T, and integrate the receiving efficiency coefficients of detecting multi-frequency sound waves within several period times T into a data set, and mark the data set as R ec = {ms w }, where w = {1, 2, 3, ..., q}, and q is a positive integer, ms w It represents the receiving efficiency coefficient of the multi-frequency sound wave detection within the wth cycle time T. The calculation expression of the receiving efficiency coefficient is: Among them, t a is the sound wave emission time, t s is the sound wave stopping time, S wa is the wavelength of the received sound wave, P r is the number of sonic probes; Calculate the standard deviation of the receiving efficiency coefficient for detecting multi-frequency sound waves within a certain period of time T. The calculation expression is: Where gen is the average value of the receiving efficiency coefficient of detecting multi-frequency sound waves within several cycles T, and the calculation expression is: S re To receive information on multiple frequencies.

5. The multi-frequency acoustic wave analysis method based on reinforced concrete damage detection according to claim 1 is characterized in that: The method for obtaining the multi-frequency fusion coefficient according to the multi-frequency reception information is: Take multiple consecutive multi-frequency received information as eigenvalues, obtain the local outlier factor of each eigenvalue, and mark the local outlier factor as L u , where u is the index of the local outlier factor, u = {1, 2, 3, ..., e} and e is a positive integer. When a damaged signal is detected, the characteristic value of the multi-frequency reception information at the time of damaged signal detection is marked, and the local outlier factor of the characteristic value of the multi-frequency reception information at the time of damaged signal detection is obtained and marked as L y , where y is the index of the local outlier factor of the eigenvalue of the multi-frequency received information at the time of the damaged signal detection, y = {1, 2, 3, ..., r} and r is a positive integer. The multi-frequency fusion coefficient is calculated as follows:

6. The multi-frequency acoustic wave analysis method based on reinforced concrete damage detection according to claim 5 is characterized in that: The method for constructing the churn model is: The maximum frequency of the frequency-by-frequency signal is λ max , the minimum frequency of the frequency-by-frequency signal is λ min , then the churn model expression is In the formula, C hu is the space loss index.

7. The multi-frequency acoustic wave analysis method based on reinforced concrete damage detection according to claim 6 is characterized in that: The logic of generating the identification signal according to the comparison result is: Preset space loss threshold C th , the calculated space loss index C hu and space loss threshold C th Compare, if the space loss index C hu Greater than or equal to the space loss threshold C th , a red identification signal is generated for the damage detection result; If the space loss index C hu Less than the space loss threshold C th , a blue identification signal is generated for the damage detection result.

8. The multi-frequency acoustic wave analysis method based on reinforced concrete damage detection according to claim 7 is characterized in that: An anomaly detection model is established for classification evaluation. The logic of the intervention strategy proposed based on the classification evaluation results is as follows: When the damage detection result is a red identification signal, a data set is generated by integrating several consecutive space loss index data within T time after the red identification signal is generated, and the space loss index in the data set is calibrated as Da h , where h is the number of the space loss index, and h = {1, 2, 3...d}, where d is a positive integer; Calculate the standard deviation of several space loss indexes in the data set, and calibrate the standard deviation of the space loss index as Vk. Compare the space loss index standard deviation Vk with the preset space loss index standard deviation threshold Pb, and perform intervention according to the comparison results. The intervention strategy is as follows: If Vk is greater than or equal to Pb, the operator is prompted that the consistency of damage detection fluctuates too much, and the detection data is recorded and marked as abnormal; If Vk is less than Pb, the operator is prompted that the consistency fluctuation of damage detection is stable, and the detection data is recorded.

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