Wave velocity testing system and method under concrete compression state based on machine learning

Abstract

The invention discloses a wave velocity testing system and method under concrete pressure state based on machine learning, and relates to the field of wave velocity measurement. The invention determines whether it is necessary to adjust the parameters of the sound wave measuring transducer from the parameter angle of the sound wave measuring transducer by whether the noise level data in the received sound wave waveform image meets the requirements, thereby improving the accuracy of the time data of the ultrasonic wave passing through the concrete; by constructing a waveform image mapping model, a mapping tool is provided for obtaining the waveform image noise level data corresponding to the collected historical received sound wave waveform image data; by constructing a final sound wave waveform noise level mapping equation, a mapping model is provided for subsequent adjustment of the test parameter data of the sound wave test transducer and the sound wave receiving amplification factor data during the current wave velocity test, thereby eliminating the need to perform a wave velocity test experiment after each adjustment, thereby improving operability.

Description

Technical Field

[0001] The present invention belongs to the field of wave velocity measurement, and in particular, relates to a wave velocity testing system and method under concrete compression state based on machine learning. Background Art

[0002] There is a quantitative relationship between the strength of concrete and its acoustic wave velocity value. The velocity test of concrete specimens under no stress can be independently tested by ultrasonic devices. However, concrete structures bear loads and are mainly under pressure. In the early stage of concrete compression, the internal pores become smaller and denser, and the velocity value increases; in the middle stage of compression, the tensile stress inside the concrete gradually increases, cracks begin to appear at the contact point between coarse aggregate and fine aggregate, and the velocity value reaches the maximum value; in the late stage of compression, the cracks inside the concrete gradually expand, forming through cracks, and the velocity value decreases.

[0003] Because the relevant parameters of the wave velocity measurement transducer were not accurately and reasonably set before the experiment, there may be a lot of noise data in the ultrasonic waveform image received during the wave velocity test, which will affect the process of measuring the time data of the ultrasonic wave passing through the concrete, making the measured passage time data inaccurate, and further leading to inaccurate calculation results of the wave velocity in the concrete. Summary of the invention

[0004] In response to the problems in the related technology, the present invention proposes a wave velocity testing system and method for concrete under pressure based on machine learning to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a method for testing wave velocity of concrete under pressure based on machine learning, comprising the following steps:

[0007] S1, collecting multiple sets of transducer experimental parameter data, sound wave receiving magnification data, concrete property parameter data and received sound wave waveform image data during historical wave velocity tests, and obtaining a historical transducer parameter data matrix, a historical sound wave magnification data set, a historical concrete property parameter data matrix and a historical received waveform image data set; collecting multiple sets of existing sound wave waveform image data and corresponding waveform noise level data, and obtaining an existing sound wave waveform image data set and an existing waveform noise level data set;

[0008] S2, using the existing sound wave waveform image data set and the existing waveform noise level data set to construct a waveform image mapping model and map the noise level data corresponding to the historical received waveform image data set to obtain the historical received waveform image noise level data set;

[0009] S3, using the historical transducer parameter data matrix, the historical sound wave amplification data set, the historical concrete property parameter data matrix and the historical received waveform image noise level data set to construct the final sound wave waveform noise level mapping equation;

[0010] S4, adjusting the test parameter data of the acoustic wave test transducer and the acoustic wave receiving amplification factor data during the current wave velocity test in accordance with the final acoustic wave waveform noise level mapping equation, and performing a wave velocity test of the concrete under pressure after the adjustment is completed;

[0011] By collecting multiple sets of transducer experimental parameter data, sound wave receiving amplification data, concrete property parameter data and received sound wave waveform image data during historical wave velocity tests, data support is provided for the subsequent construction of the final sound wave waveform noise level mapping equation; by collecting multiple sets of existing sound wave waveform image data and the corresponding waveform noise degree level data, a data basis is provided for the subsequent construction of a waveform image mapping model; by constructing a waveform image mapping model, a mapping tool is provided for obtaining the waveform image noise level data corresponding to the collected historical received sound wave waveform image data; by mapping the noise level data corresponding to the historical received waveform image data set, label data is provided for the subsequent construction of the final sound wave waveform noise level mapping equation; by constructing the final sound wave waveform noise level mapping equation, a mapping model is provided for the subsequent adjustment of the experimental parameter data of the sound wave test transducer and the sound wave receiving amplification data during the current wave velocity test, so that there is no need to conduct a wave velocity test experiment after each adjustment, thereby improving operability.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11. Set various test parameter types of the acoustic wave test transducer to obtain a transducer experimental parameter type set. , a 1i Indicates the i-th test parameter type of the set acoustic wave test transducer, Represents the total number of test parameter types of the set acoustic wave test transducer; then sets multiple attribute parameters of concrete to obtain a concrete attribute parameter type set , a 2i Indicates the type of the i-th attribute parameter of the set concrete. Indicates the total number of concrete attribute parameter types that are set;

[0014] S12, according to the transducer experimental parameter type set and the concrete property parameter type set, when the wave velocity test of concrete with different property parameters was performed using an acoustic wave test transducer with different parameters set in the past, the transducer experimental parameter data, the acoustic wave receiving amplification factor data, the concrete property parameter data and the received acoustic wave waveform image data of the corresponding acoustic wave test transducer were collected to obtain a historical transducer parameter data matrix , Historical sound wave amplification data set , Historical concrete attribute parameter data matrix And historical receiving waveform image dataset , , They respectively represent the acquired sound wave amplification factor data of the i-th group of historical concrete wave velocity tests and the corresponding received sound wave waveform image data, Indicates the total number of wave velocity tests on concrete in the collected history; , They are as follows:

[0015] ; ;

[0016] in, , They respectively represent the j-th type of transducer experimental parameter data and concrete property parameter data when the i-th group of history is collected to perform wave velocity test on concrete;

[0017] S13, collecting multiple sets of existing sound wave waveform image data and corresponding waveform noise level data to obtain an existing sound wave waveform image data set and existing waveform noise level datasets , b 1i 、b 2i They represent the i-th existing sound wave waveform image data and the corresponding waveform noise level data collected respectively, Indicates the total number of existing sound wave waveform image data collected, and the total number of existing sound wave waveform image data collected is the same as the total number of corresponding waveform noise degree level data;

[0018] The types of transducer experimental parameters include frequency, power, amplitude, working voltage, impedance, coupling coefficient, transmission power, duty cycle and working frequency, etc.; the types of concrete attribute parameters include cement density, water density, sand density, gravel density and compressive strength, etc.; when measuring the wave velocity of internal ultrasonic waves in concrete under different pressure conditions, the time is often measured by collecting ultrasonic signal data, and then the wave velocity data is calculated; therefore, there are certain requirements for the quality of ultrasonic waveform image data; therefore, in this scheme, by collecting multiple sets of existing sound wave waveform image data and corresponding waveform noise level data, data support is provided for the subsequent construction of a waveform image mapping model, thereby providing data support for obtaining the noise level data of each sound wave waveform image.

[0019] Preferably, S2 comprises the following steps:

[0020] S21, constructing a waveform image mapping model using the existing sound wave waveform image dataset and the existing waveform noise level dataset;

[0021] S22, inputting the historical received waveform image data set into the waveform image mapping model for mapping, to obtain a historical received waveform image noise level data set , Represents the noise level data of the received acoustic wave waveform image data when the wave velocity test of concrete was performed in the history;

[0022] By mapping the historical received waveform image data set, the waveform image data is converted into digital data, which reduces the complexity of the data and facilitates the subsequent construction of the final sound wave waveform noise level mapping equation based on the data.

[0023] Preferably, S3 comprises the following steps:

[0024] S31, constructing an initial sound wave waveform noise level mapping equation; as follows,

[0025] ;

[0026] in, is the dependent variable of the initial sound wave waveform noise level mapping equation, representing the received waveform image noise level data; ceil represents the rounding function; is the ith independent variable of the initial sound waveform noise level mapping equation, representing the ith type of transducer parameter data, for The coefficient of the independent variable ; is the first part of the initial sound wave waveform noise level mapping equation independent variables, representing the concrete property parameter data of the i-th type, for The coefficient of the independent variable ; is the first part of the initial sound wave waveform noise level mapping equation An independent variable, representing the sound wave amplification factor data, for The coefficient of the independent variable ; is the offset of the initial sound wave waveform noise level mapping equation;

[0027] S32, substituting the historical transducer parameter data matrix, the historical sound wave amplification data set, and the historical concrete property parameter data matrix into the initial sound wave waveform noise level mapping equation for mapping, to obtain a historical sound wave waveform image noise level mapping data set , c i Indicates historical acoustic waveform image noise level mapping data obtained by inputting the i-th row data in the historical transducer parameter data matrix, the historical concrete property parameter data matrix, and the i-th data in the historical acoustic wave amplification factor data set into the initial acoustic waveform noise level mapping equation;

[0028] S33, setting a mapping error threshold; calculating the Euclidean distance between the historical sound wave waveform image noise level mapping data set and the historical received waveform image noise level data set to obtain noise level mapping error data; when the noise level mapping error data is greater than or equal to the mapping error threshold, optimizing the initial sound wave waveform noise level mapping equation until the noise level mapping error data is less than the mapping error threshold, and obtaining the final sound wave waveform noise level mapping equation; otherwise, there is no need to optimize the initial sound wave waveform noise level mapping equation, and the initial sound wave waveform noise level mapping equation is used as the final sound wave waveform noise level mapping equation;

[0029] By substituting the historical transducer parameter data matrix, the historical sound wave amplification factor data set, and the historical concrete property parameter data matrix into the initial sound wave waveform noise level mapping equation for pre-mapping, it is possible to determine whether the sound wave waveform noise level mapping equation has good mapping accuracy based on the current independent variable coefficients and bias; if the requirements are not met, the independent variable coefficients and bias are adjusted to ensure the mapping accuracy of the mapping equation, and also provide a basis for determining whether the independent variable coefficients and bias of the initial sound wave waveform noise level mapping equation need to be adjusted.

[0030] Preferably, S4 comprises the following steps:

[0031] S41, collecting the test parameter data of the acoustic wave test transducer during the current wave velocity test, the property parameters of the concrete, and the acoustic wave receiving amplification factor data according to the transducer experimental parameter type set and the concrete property parameter type set, to obtain the current transducer parameter data set , Current concrete attribute parameter data set And the current sound wave receiving amplification data ; , They respectively represent the test parameter data of the i-th type of acoustic wave test transducer and the property parameters of concrete during the current wave velocity test;

[0032] S42, inputting the current transducer parameter data set, the current concrete property parameter data set and the current sound wave receiving amplification factor data into the final sound wave waveform noise level mapping equation for mapping, to obtain the current sound wave waveform noise level data e;

[0033] S43, adjusting the current transducer parameter data set and the current sound wave receiving amplification factor data according to the current sound wave waveform noise level data to obtain the current final transducer parameter data set and the current final sound wave receiving amplification factor data;

[0034] S44, using the current final transducer parameter data set and the current final sound wave receiving amplification factor data to set the test parameter data of the sound wave test transducer during the current wave velocity test; after the setting is completed, a set sound wave test transducer is obtained; and the set sound wave test transducer is used to perform a wave velocity test on concrete;

[0035] By adjusting the relevant parameters of the wave velocity measurement transducer and the sound wave receiving amplification data in conjunction with the final sound wave waveform noise level mapping equation constructed previously, the noise of the received waveform image is lower, thereby making the measurement accuracy higher.

[0036] Preferably, the S43 comprises the following steps:

[0037] S431, setting a waveform image noise level threshold;

[0038] S432. When the current sound wave waveform noise level data is greater than or equal to the waveform image noise level threshold, the current transducer parameter data set and the current sound wave reception amplification data are adjusted until the current sound wave waveform noise level data is less than the waveform image noise level threshold, thereby obtaining the current final transducer parameter data set and the current final sound wave reception amplification data; otherwise, the current transducer parameter data set and the current sound wave reception amplification data do not need to be adjusted, and the current transducer parameter data set and the current sound wave reception amplification data are used as the current final transducer parameter data set and the current final sound wave reception amplification data, respectively;

[0039] By setting the waveform image noise level threshold, a quantitative basis is provided for determining whether the current sound wave waveform noise level data meets the test requirements.

[0040] Preferably, adjusting the current transducer parameter data set and the current sound wave receiving amplification factor data in S432 includes the following steps:

[0041] S4321, construct wave speed test parameters to optimize wild horse populations , represents the wave speed test parameter optimization of the i-th horse in the wild horse population, Indicates the size of the wild horse population optimized by the wave speed test parameters: Set the maximum number of iterations of the wave speed test parameters to optimize the wild horse population And the current number of iterations is , respectively, are recorded as the maximum number of iterations of wave speed test parameter optimization and the current number of iterations of wave speed test parameter optimization; the search space dimension of the wave speed test parameter optimization wild horse population is ;

[0042] S4322, setting the value interval of each type of test parameter data of the acoustic wave test transducer and the acoustic wave receiving amplification factor data when performing the current wave velocity test, and obtaining the transducer parameter value interval set And the magnification range [d 41 ,d 42 ]; , They represent the lower limit and upper limit of the experimental parameters of the ith type of the acoustic wave test transducer during the current wave velocity test; d 41 d 42 They respectively represent the lower limit and upper limit of the sound wave receiving amplification factor data during the current wave velocity test;

[0043] The wave velocity test parameters are set according to the transducer parameter value interval set and the amplification multiple value interval to optimize the initial position of each wild horse in the wild horse population, and obtain a second initial position matrix ;as follows,

[0044] ;

[0045] in, represents the position component of the initial position of the kth wild horse in the wild horse population optimized by the wave speed test parameter in the i-th search space dimension; , The calculation formulas are as follows:

[0046] ;

[0047] ;

[0048] In the formula, rand 2ki , Respectively for , Generate a random number between 0 and 1;

[0049] S4323, constructing a wave speed test parameter to optimize the fitness function of the wild horse population according to the current sound wave waveform noise level data e ;as follows,

[0050] ;

[0051] S4324, start iteration, and set the current iteration number of the wave speed test parameter optimization to 1 before iteration; in the first round of iteration, use the wave speed test parameter to optimize the fitness function of the wild horse population to calculate the fitness value of the initial position of each wild horse in the second initial position matrix to obtain a third fitness value set; use the maximum fitness value in the third fitness value set and the corresponding initial position of the wild horse as the third global optimal fitness and the third global optimal position respectively; update the initial position of each wild horse in the second initial position matrix according to the third global optimal fitness and the third global optimal position; after the update is completed, add 1 to the current iteration number of the wave speed test parameter optimization and enter the next round of iteration;

[0052] In each other round of iteration, the fitness function of the wild horse population optimized by the wave speed test parameters is used to calculate the fitness value of the position of each wild horse in the wild horse population optimized by the wave speed test parameters obtained in the previous round of iteration, and a fourth fitness value set is obtained; the maximum fitness value in the fourth fitness value set and the corresponding position of the wild horse are respectively used as the fourth global optimal fitness and the fourth global optimal position; the position of each wild horse in the wild horse population optimized by the wave speed test parameters obtained in the previous round of iteration is updated according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the current iteration number of the wave speed test parameter optimization is increased by 1 and the next round of iteration is entered;

[0053] S4325, when When , stop the iteration and get the second final global optimal position, otherwise, continue to iterate until Substitute each position component of the second final global optimal position and the current concrete attribute parameter data set into the final acoustic waveform noise level mapping equation for mapping to obtain the current optimized acoustic waveform noise level data;

[0054] When the currently optimized sound wave waveform noise level data is less than the waveform image noise level threshold, each corresponding position component in the second final global optimal position is used as the current final transducer parameter data set and the current final sound wave receiving amplification factor data; otherwise, return to S4324 to continue iterating until the currently optimized sound wave waveform noise level data is less than the waveform image noise level threshold;

[0055] In this scheme, the relevant parameters of the wave velocity measurement transducer and the sound wave receiving amplification factor data are iteratively adjusted multiple times by using the Mustang optimization algorithm. At the same time, the noise level of the waveform image corresponding to the relevant parameters of the wave velocity measurement transducer and the sound wave receiving amplification factor data obtained in each round of iteration is used as the fitness function; therefore, as the iteration proceeds, the noise of the waveform image data finally received becomes lower and lower, thereby facilitating the accurate measurement of the time data of ultrasonic waves passing through concrete.

[0056] The wave velocity testing system under concrete compression state based on machine learning includes a wave velocity testing parameter type setting module, a historical wave velocity testing parameter data acquisition module, an existing wave velocity testing parameter data acquisition module, a historical waveform image noise level data mapping module, an acoustic waveform noise level mapping equation construction module, a current wave velocity testing parameter optimization module and a wave velocity testing module.

[0057] The present invention has the following beneficial effects:

[0058] 1. In the present invention, from the perspective of the parameters of the acoustic wave measuring transducer, whether the noise level data in the received acoustic wave waveform image meets the requirements is determined to determine whether the parameters of the acoustic wave measuring transducer need to be adjusted, thereby ensuring that the noise in the received acoustic wave waveform image is less, thereby improving the accuracy of the time data of the ultrasonic wave passing through the concrete; by constructing a waveform image mapping model, a mapping tool is provided for obtaining the waveform image noise level data corresponding to the collected historical received acoustic wave waveform image data; by constructing the final acoustic wave waveform noise level mapping equation, a mapping model is provided for subsequent adjustment of the test parameter data of the acoustic wave test transducer and the acoustic wave receiving amplification factor data during the current wave velocity test, thereby eliminating the need to perform a wave velocity test experiment after each adjustment, thereby improving operability.

[0059] 2. In the present invention, the waveform image data is converted into digital data by mapping the historical received waveform image data set, thereby reducing the complexity of the data, thereby facilitating the subsequent construction of the final sound wave waveform noise level mapping equation based on the data.

[0060] 3. In the present invention, the relevant parameters of the wave velocity measurement transducer and the sound wave receiving amplification factor data are iteratively adjusted for multiple times by using the Mustang optimization algorithm, and the noise level of the waveform image corresponding to the relevant parameters of the wave velocity measurement transducer and the sound wave receiving amplification factor data iteratively obtained in each round of iteration is used as the fitness function; therefore, as the iteration proceeds, the noise of the waveform image data finally received becomes lower and lower, thereby facilitating the accurate measurement of the time data of the ultrasonic wave passing through the concrete.

[0061] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0063] Figure 1 It is a flow chart of a method for testing wave velocity under concrete compression state based on machine learning of the present invention;

[0064] Figure 2 It is a structural schematic diagram of the wave velocity test device under concrete pressure state in the present invention, wherein F represents that the concrete is under pressure. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0066] Embodiment 1

[0067] See also Figure 1 This embodiment is a method for testing wave velocity under concrete pressure based on machine learning, comprising the following steps:

[0068] (1) Adjusting the parameters of the acoustic wave test transducer; comprising the following steps:

[0069] S1, collecting multiple sets of transducer experimental parameter data, sound wave receiving magnification data, concrete property parameter data and received sound wave waveform image data during historical wave velocity tests, and obtaining a historical transducer parameter data matrix, a historical sound wave magnification data set, a historical concrete property parameter data matrix and a historical received waveform image data set; collecting multiple sets of existing sound wave waveform image data and corresponding waveform noise level data, and obtaining an existing sound wave waveform image data set and an existing waveform noise level data set;

[0070] The S1 comprises the following steps:

[0071] S11. Set various test parameter types of the acoustic wave test transducer to obtain a transducer experimental parameter type set. , a 1i Indicates the i-th test parameter type of the set acoustic wave test transducer, Represents the total number of test parameter types of the set acoustic wave test transducer; then sets multiple attribute parameters of concrete to obtain a concrete attribute parameter type set , a 2i Indicates the type of the i-th attribute parameter of the set concrete. Indicates the total number of concrete attribute parameter types that are set;

[0072] S12, according to the transducer experimental parameter type set and the concrete property parameter type set, when the wave velocity test of concrete with different property parameters was performed using an acoustic wave test transducer with different parameters set in the past, the transducer experimental parameter data, the acoustic wave receiving amplification factor data, the concrete property parameter data and the received acoustic wave waveform image data of the corresponding acoustic wave test transducer were collected to obtain a historical transducer parameter data matrix , Historical sound wave amplification data set , Historical concrete attribute parameter data matrix And historical receiving waveform image dataset , , They respectively represent the acquired sound wave amplification factor data of the i-th group of historical concrete wave velocity tests and the corresponding received sound wave waveform image data, Indicates the total number of wave velocity tests on concrete in the collected history; , They are as follows:

[0073] ; ;

[0074] in, , They respectively represent the j-th type of transducer experimental parameter data and concrete property parameter data when the i-th group of history is collected to perform wave velocity test on concrete;

[0075] S13, collecting multiple sets of existing sound wave waveform image data and corresponding waveform noise level data to obtain an existing sound wave waveform image data set and existing waveform noise level datasets , b 1i 、b 2i They represent the i-th existing sound wave waveform image data and the corresponding waveform noise level data collected respectively, Indicates the total number of existing sound wave waveform image data collected, and the total number of existing sound wave waveform image data collected is the same as the total number of corresponding waveform noise degree level data;

[0076] S2, using the existing sound wave waveform image data set and the existing waveform noise level data set to construct a waveform image mapping model and map the noise level data corresponding to the historical received waveform image data set to obtain the historical received waveform image noise level data set;

[0077] The S2 comprises the following steps:

[0078] S21, constructing a waveform image mapping model using the existing sound wave waveform image dataset and the existing waveform noise level dataset;

[0079] The S21 comprises the following steps:

[0080] S211, constructing an initial CNN classification model and setting a first training data ratio; dividing the existing sound wave waveform image data set and the existing waveform noise level data set according to the first training data ratio to obtain an existing sound wave waveform image training data set, an existing waveform noise level training data set, an existing sound wave waveform image test data set, and an existing waveform noise level test data set;

[0081] S212, setting a first training error threshold; inputting the existing acoustic waveform image training data set and the existing waveform noise level training data set as training data and training labels respectively into the initial CNN classification model for training; during the training process, when the training error is less than the first training error threshold, stopping the training to obtain a trained CNN classification model; otherwise, continuing the training until the training error is less than the first training error threshold;

[0082] S213, setting a first test accuracy threshold; inputting the existing sound wave waveform image test data set and the existing waveform noise level test data set as test data and test labels respectively into the trained CNN classification model for testing; after the test is completed, obtaining a first test accuracy; when the first test accuracy is greater than or equal to the first test accuracy threshold, using the trained CNN classification model as a waveform image mapping model; otherwise, returning to S212 to continue training the trained CNN classification model until the first test accuracy is greater than or equal to the first test accuracy threshold, and obtaining a waveform image mapping model;

[0083] S22, inputting the historical received waveform image data set into the waveform image mapping model for mapping, to obtain a historical received waveform image noise level data set , Represents the noise level data of the received acoustic wave waveform image data when the wave velocity test of concrete was performed in the history;

[0084] S3, using the historical transducer parameter data matrix, the historical sound wave amplification data set, the historical concrete property parameter data matrix and the historical received waveform image noise level data set to construct the final sound wave waveform noise level mapping equation;

[0085] The S3 comprises the following steps:

[0086] S31, constructing an initial sound wave waveform noise level mapping equation; as follows,

[0087] ;

[0088] in, is the dependent variable of the initial sound wave waveform noise level mapping equation, representing the received waveform image noise level data; ceil represents the rounding function; is the ith independent variable of the initial sound waveform noise level mapping equation, representing the ith type of transducer parameter data, for The coefficient of the independent variable ; is the first part of the initial sound wave waveform noise level mapping equation independent variables, representing the concrete property parameter data of the i-th type, for The coefficient of the independent variable ; is the first part of the initial sound wave waveform noise level mapping equation An independent variable, representing the sound wave amplification factor data, for The coefficient of the independent variable ; is the offset of the initial sound wave waveform noise level mapping equation;

[0089] S32, substituting the historical transducer parameter data matrix, the historical sound wave amplification data set, and the historical concrete property parameter data matrix into the initial sound wave waveform noise level mapping equation for mapping, to obtain a historical sound wave waveform image noise level mapping data set , c i Indicates historical acoustic waveform image noise level mapping data obtained by inputting the i-th row data in the historical transducer parameter data matrix, the historical concrete property parameter data matrix, and the i-th data in the historical acoustic wave amplification factor data set into the initial acoustic waveform noise level mapping equation;

[0090] S33, setting a mapping error threshold; calculating the Euclidean distance between the historical sound wave waveform image noise level mapping data set and the historical received waveform image noise level data set to obtain noise level mapping error data; when the noise level mapping error data is greater than or equal to the mapping error threshold, optimizing the initial sound wave waveform noise level mapping equation until the noise level mapping error data is less than the mapping error threshold, and obtaining the final sound wave waveform noise level mapping equation; otherwise, there is no need to optimize the initial sound wave waveform noise level mapping equation, and the initial sound wave waveform noise level mapping equation is used as the final sound wave waveform noise level mapping equation;

[0091] The optimization of the initial sound wave waveform noise level mapping equation in S33 comprises the following steps:

[0092] S331, Constructing noise level mapping equation to optimize wild horse population , Denotes that the noise level mapping equation optimizes the i-th horse in the wild horse population, The noise level mapping equation optimizes the size of the wild horse population: The maximum number of iterations of the noise level mapping equation to optimize the wild horse population is set to And the current iteration number is , respectively, are the maximum number of iterations of noise level mapping equation optimization and the current number of iterations of noise level mapping equation optimization; the search space dimension of the noise level mapping equation optimization of wild horse population is ;

[0093] S332, setting the value interval of each independent variable coefficient and the offset in the initial sound wave waveform noise level mapping equation, and obtaining the independent variable coefficient value interval matrix d1 and the offset value interval [d 21 ,d 22 ];d 21 d 22 They respectively represent the lower limit and upper limit of the bias value in the initial sound waveform noise level mapping equation; d1 is as follows,

[0094] ;

[0095] in, , respectively represent the lower limit and upper limit of the independent variable coefficient corresponding to the ith independent variable in the initial sound waveform noise level mapping equation; , They represent the first The lower limit and upper limit of the value of the independent variable coefficient corresponding to the independent variable;

[0096] The noise level mapping equation is set according to the independent variable coefficient value interval matrix and the bias value interval to optimize the initial position of each wild horse in the wild horse population, and the first initial position matrix set is obtained. , The noise level mapping equation optimizes the initial position matrix of the kth wild horse in the wild horse population; as follows,

[0097] ;

[0098] in, , They represent the initial position of the kth wild horse in the wild horse population optimized by the noise level mapping equation in the initial sound waveform noise level mapping equation, and the i-th independent variable and the i-th independent variable in the initial sound waveform noise level mapping equation. The position component on the dimension of the independent variable coefficient corresponding to the independent variable; The noise level mapping equation represents the position component of the initial position of the kth wild horse in the wild horse population in the offset dimension in the initial sound wave waveform noise level mapping equation; , , The calculation formulas are as follows:

[0099] ;

[0100] ;

[0101] ;

[0102] In the formula, rand 1k1i 、rand 1k2i 、rand 1k3 Respectively for , , Generate a random number between 0 and 1;

[0103] S333: construct a noise level mapping equation based on the historical sound wave waveform image noise level mapping data set and the historical received waveform image noise level data set to optimize the fitness function of the wild horse population ;as follows,

[0104] ;

[0105] S334, start iteration, before iteration, set the current iteration number of the noise level mapping equation optimization to 1; in the first round of iteration, use the noise level mapping equation to optimize the fitness function of the wild horse population to calculate the fitness value of the initial position matrix of each wild horse in the first initial position matrix set, and obtain the first fitness value set; use the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the wild horse as the first global optimal fitness and the first global optimal position respectively; update the initial position matrix of each wild horse in the first initial position matrix set according to the first global optimal fitness and the first global optimal position; after the update is completed, add 1 to the current iteration number of the noise level mapping equation optimization and enter the next round of iteration;

[0106] In each other round of iteration, the fitness function of the wild horse population optimized by the noise level mapping equation is calculated by optimizing the fitness value of the position matrix of each wild horse in the wild horse population optimized by the noise level mapping equation obtained in the previous round of iteration, and a second fitness value set is obtained; the maximum fitness value in the second fitness value set and the corresponding wild horse position matrix are respectively used as the second global optimal fitness and the second global optimal position; the position matrix of each wild horse in the wild horse population optimized by the noise level mapping equation obtained in the previous round of iteration is updated according to the second global optimal fitness and the second global optimal position; after the update is completed, the current iteration number of the noise level mapping equation optimization is increased by 1 and the next iteration is entered;

[0107] S335, when When , stop the iteration and get the first final global optimal position, otherwise, continue to iterate until time; substitute each position component of the first final global optimal position into the initial sound wave waveform noise level mapping equation to obtain the optimized sound wave waveform noise level mapping equation; substitute the historical transducer parameter data matrix, the historical sound wave amplification data set, and the historical concrete attribute parameter data matrix into the optimized sound wave waveform noise level mapping equation for mapping to obtain the optimized historical sound wave waveform image noise level mapping data set; calculate the Euclidean distance between the optimized historical sound wave waveform image noise level mapping data set and the historical received waveform image noise level data set to obtain the optimized noise level mapping error data;

[0108] When the optimized noise level mapping error data is less than the mapping error threshold, the optimized sound waveform noise level mapping equation is used as the final sound waveform noise level mapping equation; otherwise, return to S324 and continue to iterate until the optimized noise level mapping error data is less than the mapping error threshold;

[0109] The Mustang optimization algorithm can effectively search for the global optimal solution of the problem and has a strong optimization ability; it can maintain high accuracy during the search process and avoid falling into the local optimal solution; it can quickly converge to the optimal solution during the search process, which improves the efficiency of the algorithm; it is insensitive to the diversity of the initial population and has good robustness; based on the above advantages, the Mustang optimization algorithm is used in this scheme to perform multiple iterations on each independent variable coefficient and bias of the initial sound waveform noise level mapping equation, wherein the error between the mapping data of the initial sound waveform noise level mapping equation and the actual data is used as the fitness function; therefore, as the iteration proceeds, the mapping accuracy of the initial sound waveform noise level mapping equation becomes higher and higher, and finally the mapping requirements are met;

[0110] S4, adjusting the test parameter data of the acoustic wave test transducer and the acoustic wave receiving amplification factor data during the current wave velocity test in accordance with the final acoustic wave waveform noise level mapping equation, and performing a wave velocity test of the concrete under pressure after the adjustment is completed;

[0111] The S4 comprises the following steps:

[0112] S41, collecting the test parameter data of the acoustic wave test transducer during the current wave velocity test, the property parameters of the concrete, and the acoustic wave receiving amplification factor data according to the transducer experimental parameter type set and the concrete property parameter type set, to obtain the current transducer parameter data set , Current concrete attribute parameter data set And the current sound wave receiving amplification data ; , They respectively represent the test parameter data of the i-th type of acoustic wave test transducer and the property parameters of concrete during the current wave velocity test;

[0113] S42, inputting the current transducer parameter data set, the current concrete property parameter data set and the current sound wave receiving amplification factor data into the final sound wave waveform noise level mapping equation for mapping, to obtain the current sound wave waveform noise level data e;

[0114] S43, adjusting the current transducer parameter data set and the current sound wave receiving amplification factor data according to the current sound wave waveform noise level data to obtain the current final transducer parameter data set and the current final sound wave receiving amplification factor data;

[0115] The S43 comprises the following steps:

[0116] S431, setting a waveform image noise level threshold;

[0117] S432. When the current sound wave waveform noise level data is greater than or equal to the waveform image noise level threshold, the current transducer parameter data set and the current sound wave reception amplification data are adjusted until the current sound wave waveform noise level data is less than the waveform image noise level threshold, thereby obtaining the current final transducer parameter data set and the current final sound wave reception amplification data; otherwise, the current transducer parameter data set and the current sound wave reception amplification data do not need to be adjusted, and the current transducer parameter data set and the current sound wave reception amplification data are used as the current final transducer parameter data set and the current final sound wave reception amplification data, respectively;

[0118] The step of adjusting the current transducer parameter data set and the current sound wave receiving amplification factor data in S432 includes the following steps:

[0119] S4321, construct wave speed test parameters to optimize wild horse populations , represents the wave speed test parameter optimization of the i-th horse in the wild horse population, Indicates the size of the wild horse population optimized by the wave speed test parameters: Set the maximum number of iterations of the wave speed test parameters to optimize the wild horse population And the current iteration number is , respectively, are recorded as the maximum number of iterations of wave speed test parameter optimization and the current number of iterations of wave speed test parameter optimization; the search space dimension of the wave speed test parameter optimization wild horse population is ;

[0120] S4322, setting the value interval of each type of test parameter data of the acoustic wave test transducer and the acoustic wave receiving amplification factor data when performing the current wave velocity test, and obtaining the transducer parameter value interval set And the magnification range [d 41 ,d 42 ]; , They represent the lower limit and upper limit of the experimental parameters of the ith type of the acoustic wave test transducer during the current wave velocity test; d 41 d 42 They respectively represent the lower limit and upper limit of the sound wave receiving amplification factor data during the current wave velocity test;

[0121] The wave velocity test parameters are set according to the transducer parameter value interval set and the amplification multiple value interval to optimize the initial position of each wild horse in the wild horse population, and obtain a second initial position matrix ;as follows,

[0122] ;

[0123] in, represents the position component of the initial position of the kth wild horse in the wild horse population optimized by the wave speed test parameter in the i-th search space dimension; , The calculation formulas are as follows:

[0124] ;

[0125] ;

[0126] In the formula, rand 2ki , Respectively for , Generate a random number between 0 and 1;

[0127] S4323, constructing a wave speed test parameter to optimize the fitness function of the wild horse population according to the current sound wave waveform noise level data e ;as follows,

[0128] ;

[0129] S4324, start iteration, and set the current iteration number of the wave speed test parameter optimization to 1 before iteration; in the first round of iteration, use the wave speed test parameter to optimize the fitness function of the wild horse population to calculate the fitness value of the initial position of each wild horse in the second initial position matrix to obtain a third fitness value set; use the maximum fitness value in the third fitness value set and the corresponding initial position of the wild horse as the third global optimal fitness and the third global optimal position respectively; update the initial position of each wild horse in the second initial position matrix according to the third global optimal fitness and the third global optimal position; after the update is completed, add 1 to the current iteration number of the wave speed test parameter optimization and enter the next round of iteration;

[0130] In each other round of iteration, the fitness function of the wild horse population optimized by the wave speed test parameters is used to calculate the fitness value of the position of each wild horse in the wild horse population optimized by the wave speed test parameters obtained in the previous round of iteration, and a fourth fitness value set is obtained; the maximum fitness value in the fourth fitness value set and the corresponding position of the wild horse are respectively used as the fourth global optimal fitness and the fourth global optimal position; the position of each wild horse in the wild horse population optimized by the wave speed test parameters obtained in the previous round of iteration is updated according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the current iteration number of the wave speed test parameter optimization is increased by 1 and the next round of iteration is entered;

[0131] S4325, when When , stop the iteration and get the second final global optimal position, otherwise, continue to iterate until Substitute each position component of the second final global optimal position and the current concrete attribute parameter data set into the final acoustic waveform noise level mapping equation for mapping to obtain the current optimized acoustic waveform noise level data;

[0132] When the currently optimized sound wave waveform noise level data is less than the waveform image noise level threshold, each corresponding position component in the second final global optimal position is used as the current final transducer parameter data set and the current final sound wave receiving amplification factor data; otherwise, return to S4324 to continue iterating until the currently optimized sound wave waveform noise level data is less than the waveform image noise level threshold;

[0133] S44, using the current final transducer parameter data set and the current final sound wave receiving amplification factor data to set the test parameter data of the sound wave test transducer during the current wave velocity test; after the setting is completed, a set sound wave test transducer is obtained; and the set sound wave test transducer is used to perform a wave velocity test on concrete;

[0134] (2) After the parameters of the acoustic wave test transducer are adjusted, the test surface of the acoustic wave test transducer is coated with coupling agent butter, and the transducer is placed in the test box 2. The transducer is rotated to ensure that the space between the test surface of the transducer and the test box 2 is filled with butter; the test box 1 is covered, and the spring is in close contact with the back of the transducer, which presses against the transducer to ensure that the test surface of the transducer and the test box 2 are always in close contact;

[0135] (3) Apply butter on the outer surface of test box 2 and install the first test box;

[0136] (4) Repeat (2) and (3) to make a second test box. Put the first test box and the second test box in close contact with each other with the butter-coated surface, start the wave speed test, and obtain the time t0;

[0137] (5) Separate the test box, test the upper and lower surface dimensions of the concrete test block, and input the data into the pressure testing machine control computer. Apply butter on the upper and lower surfaces of the concrete test block and place it between the test boxes. Install the resistance displacement meter and place the test box and the concrete test block as a whole in the pressure testing machine, and make contact with the upper and lower pressure plates of the testing machine.

[0138] (6) Power-on test; First, we get, when F=0, the wave speed test time t1, the distance between the test boxes l1; as the test starts, t i At this moment, the press records the pressure F i , wave speed test time t i , distance l i , and t is obtained according to the following formula i Moment, Pressure F i The concrete wave velocity value is:

[0139] ;

[0140] Where: Indicates t i The pressure at the moment i The concrete wave velocity value.

[0141] Embodiment 2

[0142] This embodiment discloses a wave velocity testing system under concrete pressure state based on machine learning, and the system can implement the method of the above embodiment, including a wave velocity testing parameter type setting module, a historical wave velocity testing parameter data acquisition module, an existing wave velocity testing parameter data acquisition module, a historical waveform image noise level data mapping module, an acoustic waveform noise level mapping equation construction module, a current wave velocity testing parameter optimization module and a wave velocity testing module;

[0143] The wave velocity test parameter type setting module is used to set various test parameter types of the acoustic wave test transducer and various property parameters of concrete, and obtain a transducer experimental parameter type set and a concrete property parameter type set;

[0144] The historical wave velocity test parameter data acquisition module is used to collect multiple groups of transducer experimental parameter data, sound wave receiving amplification data, concrete property parameter data and received sound wave waveform image data during historical wave velocity tests according to the transducer experimental parameter type set and the concrete property parameter type set, and obtain a historical transducer parameter data matrix, a historical sound wave amplification data set, a historical concrete property parameter data matrix and a historical received waveform image data set;

[0145] The existing wave velocity test parameter data acquisition module is used to collect multiple groups of existing sound wave waveform image data and corresponding waveform noise level data to obtain an existing sound wave waveform image data set and an existing waveform noise level data set;

[0146] The historical waveform image noise level data mapping module is used to construct a waveform image mapping model using the existing sound wave waveform image data set and the existing waveform noise level data set and map the noise level data corresponding to the historical received waveform image data set to obtain the historical received waveform image noise level data set;

[0147] The acoustic waveform noise level mapping equation construction module is used to construct a final acoustic waveform noise level mapping equation using a historical transducer parameter data matrix, a historical acoustic amplification factor data set, a historical concrete property parameter data matrix, and a historical received waveform image noise level data set;

[0148] The current wave velocity test parameter optimization module is used to adjust the test parameter data of the sound wave test transducer and the sound wave receiving amplification factor data during the current wave velocity test in coordination with the final sound wave waveform noise level mapping equation to obtain the current final transducer parameter data set and the current final sound wave receiving amplification factor data;

[0149] The wave velocity test module is used to perform a wave velocity test of concrete under pressure in cooperation with the current final transducer parameter data set and the current final sound wave receiving amplification factor data.

[0150] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0151] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A wave velocity testing method for concrete under pressure based on machine learning, characterized in that: The following steps are involved: S1, collecting multiple sets of transducer experimental parameter data, sound wave receiving magnification data, concrete property parameter data and received sound wave waveform image data during historical wave velocity tests, and obtaining a historical transducer parameter data matrix, a historical sound wave magnification data set, a historical concrete property parameter data matrix and a historical received waveform image data set; collecting multiple sets of existing sound wave waveform image data and corresponding waveform noise level data, and obtaining an existing sound wave waveform image data set and an existing waveform noise level data set; S2, using the existing sound wave waveform image data set and the existing waveform noise level data set to construct a waveform image mapping model and map the noise level data corresponding to the historical received waveform image data set to obtain the historical received waveform image noise level data set; S3, using the historical transducer parameter data matrix, the historical sound wave amplification data set, the historical concrete property parameter data matrix and the historical received waveform image noise level data set to construct the final sound wave waveform noise level mapping equation; S4. Adjust the test parameter data of the acoustic wave test transducer and the acoustic wave receiving amplification factor data during the current wave velocity test in accordance with the final acoustic wave waveform noise level mapping equation. After the adjustment is completed, perform a wave velocity test of the concrete under pressure.

2. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 1 is characterized in that: The S1 comprises the following steps: S11, setting a plurality of test parameter types of the acoustic wave test transducer to obtain a transducer test parameter type set; and then setting a plurality of property parameters of concrete to obtain a concrete property parameter type set; S12, according to the transducer experimental parameter type set and the concrete property parameter type set, when the wave velocity test was performed on concrete with different property parameters using an acoustic wave test transducer with different parameters set in history, the transducer experimental parameter data of the transmitted acoustic wave, the acoustic wave receiving amplification factor data, the concrete property parameter data and the received acoustic wave waveform image data of the corresponding acoustic wave test transducer were collected to obtain a historical transducer parameter data matrix, a historical acoustic wave amplification factor data set, a historical concrete property parameter data matrix and a historical received waveform image data set; S13, collecting multiple groups of existing sound wave waveform image data and corresponding waveform noise level data to obtain an existing sound wave waveform image data set and an existing waveform noise level data set.

3. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 2 is characterized in that: The S2 comprises the following steps: S21, constructing a waveform image mapping model using the existing sound wave waveform image dataset and the existing waveform noise level dataset; S22: Input the historical received waveform image data set into a waveform image mapping model for mapping, to obtain a historical received waveform image noise level data set.

4. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 3 is characterized in that: The waveform image mapping model adopts a CNN neural network model.

5. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 4 is characterized in that: The S3 comprises the following steps: S31, constructing an initial sound wave waveform noise level mapping equation; S32, substituting the historical transducer parameter data matrix, the historical sound wave amplification data set, and the historical concrete property parameter data matrix into the initial sound wave waveform noise level mapping equation for mapping, to obtain a historical sound wave waveform image noise level mapping data set; S33, setting a mapping error threshold; calculating the Euclidean distance between the historical sound wave waveform image noise level mapping data set and the historical received waveform image noise level data set to obtain noise level mapping error data; when the noise level mapping error data is greater than or equal to the mapping error threshold, optimizing the initial sound wave waveform noise level mapping equation until the noise level mapping error data is less than the mapping error threshold, to obtain the final sound wave waveform noise level mapping equation; otherwise, there is no need to optimize the initial sound wave waveform noise level mapping equation, and the initial sound wave waveform noise level mapping equation is used as the final sound wave waveform noise level mapping equation.

6. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 5 is characterized in that: In S33, the initial sound wave waveform noise level mapping equation is optimized using the Mustang optimization algorithm.

7. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 6 is characterized in that: The S4 comprises the following steps: S41, collecting test parameter data of the acoustic wave test transducer during the current wave velocity test, property parameters of concrete, and acoustic wave receiving amplification factor data according to the transducer experimental parameter type set and the concrete property parameter type set, to obtain a current transducer parameter data set, a current concrete property parameter data set, and a current acoustic wave receiving amplification factor data; S42, inputting the current transducer parameter data set, the current concrete property parameter data set and the current sound wave receiving amplification factor data into the final sound wave waveform noise level mapping equation for mapping, to obtain the current sound wave waveform noise level data; S43, adjusting the current transducer parameter data set and the current sound wave receiving amplification factor data according to the current sound wave waveform noise level data to obtain the current final transducer parameter data set and the current final sound wave receiving amplification factor data; S44, using the current final transducer parameter data set and the current final sound wave receiving amplification factor data to set the test parameter data of the sound wave test transducer during the current wave velocity test; after the setting is completed, a set sound wave test transducer is obtained; and the set sound wave test transducer is used to perform a wave velocity test on concrete.

8. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 7 is characterized in that: The S43 comprises the following steps: S431, setting a waveform image noise level threshold; S432. When the current sound wave waveform noise level data is greater than or equal to the waveform image noise level threshold, the current transducer parameter data set and the current sound wave reception amplification data are adjusted until the current sound wave waveform noise level data is less than the waveform image noise level threshold, and the current final transducer parameter data set and the current final sound wave reception amplification data are obtained; otherwise, there is no need to adjust the current transducer parameter data set and the current sound wave reception amplification data, and the current transducer parameter data set and the current sound wave reception amplification data are used as the current final transducer parameter data set and the current final sound wave reception amplification data, respectively.

9. The method for testing wave velocity of concrete under pressure based on machine learning according to claim 8 is characterized in that: The step of adjusting the current transducer parameter data set and the current sound wave receiving amplification factor data in S432 includes the following steps: S4321, constructing a wild horse population for wave speed test parameter optimization: setting the maximum number of iterations for the wave speed test parameter optimization wild horse population to And the current number of iterations is , respectively recorded as the maximum number of iterations for wave speed test parameter optimization and the current number of iterations for wave speed test parameter optimization; S4322, setting the value intervals of each type of test parameter data and the sound wave receiving amplification factor data of the sound wave test transducer when performing the current wave velocity test, and obtaining a transducer parameter value interval set and amplification factor value interval; The wave velocity test parameters are set according to the transducer parameter value interval set and the amplification factor value interval to optimize the initial position of each wild horse in the wild horse population, and obtain a second initial position matrix; S4323, constructing a wave speed test parameter to optimize the fitness function of the wild horse population according to the current sound wave waveform noise level data; S4324, start iteration; in the first round of iteration, use the wave speed test parameter to optimize the fitness function of the wild horse population to calculate the fitness value of the initial position of each wild horse in the second initial position matrix, and update the initial position of each wild horse in the second initial position matrix; in each other round of iteration, use the wave speed test parameter to optimize the fitness function of the wild horse population to calculate the fitness value of the position of each wild horse in the wild horse population optimized by the wave speed test parameter updated in the previous round of iteration, and update the position of each wild horse in the wild horse population optimized by the wave speed test parameter updated in the previous round of iteration; S4325, when When , stop the iteration and get the second final global optimal position, otherwise, continue to iterate until until the time; each position component of the second final global optimal position and the current concrete attribute parameter data set are substituted into the final sound wave waveform noise level mapping equation for mapping to obtain the current optimized sound wave waveform noise level data; when the current optimized sound wave waveform noise level data is less than the waveform image noise level threshold, each corresponding position component in the second final global optimal position is used as the current final transducer parameter data set and the current final sound wave receiving amplification data; otherwise, return to S4324 to continue iterating until the current optimized sound wave waveform noise level data is less than the waveform image noise level threshold.

10. A system for implementing the method for testing wave velocity of concrete under pressure based on machine learning as described in any one of claims 1 to 9, characterized in that: It includes a wave speed test parameter type setting module, a historical wave speed test parameter data acquisition module, an existing wave speed test parameter data acquisition module, a historical waveform image noise level data mapping module, an acoustic waveform noise level mapping equation construction module, a current wave speed test parameter optimization module and a wave speed test module; The wave velocity test parameter type setting module is used to set various test parameter types of the acoustic wave test transducer and various property parameters of concrete, and obtain a transducer experimental parameter type set and a concrete property parameter type set; The historical wave velocity test parameter data acquisition module is used to collect multiple groups of transducer experimental parameter data, sound wave receiving amplification data, concrete property parameter data and received sound wave waveform image data during historical wave velocity tests according to the transducer experimental parameter type set and the concrete property parameter type set, and obtain a historical transducer parameter data matrix, a historical sound wave amplification data set, a historical concrete property parameter data matrix and a historical received waveform image data set; The existing wave velocity test parameter data acquisition module is used to collect multiple groups of existing sound wave waveform image data and corresponding waveform noise level data to obtain an existing sound wave waveform image data set and an existing waveform noise level data set; The historical waveform image noise level data mapping module is used to construct a waveform image mapping model using the existing sound wave waveform image data set and the existing waveform noise level data set and map the noise level data corresponding to the historical received waveform image data set to obtain the historical received waveform image noise level data set; The acoustic waveform noise level mapping equation construction module is used to construct a final acoustic waveform noise level mapping equation using a historical transducer parameter data matrix, a historical acoustic amplification factor data set, a historical concrete property parameter data matrix, and a historical received waveform image noise level data set; The current wave velocity test parameter optimization module is used to adjust the test parameter data of the sound wave test transducer and the sound wave receiving amplification factor data during the current wave velocity test in coordination with the final sound wave waveform noise level mapping equation to obtain the current final transducer parameter data set and the current final sound wave receiving amplification factor data; The wave velocity test module is used to perform a wave velocity test of concrete under pressure in cooperation with the current final transducer parameter data set and the current final sound wave receiving amplification factor data.

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