High-precision concrete missing measurement data processing method and processing terminal

Abstract

The invention relates to the technical field of concrete defect detection and data processing, in particular to a high-precision concrete defect detection data processing method and terminal, and the method comprises the steps: collecting the original data of a detected work area along a detection line; establishing a wavelet model based on the original data, and calculating a received signal through a reflection coefficient and a wavelet; preprocessing the received signal to obtain a preprocessed signal; converting the pre-processed signal into a time-frequency domain signal through improved S transformation, extracting a main frequency signal, and performing interpolation processing on the main frequency signal along a measuring line; converting a time sequence of the original data into a depth sequence through time-depth conversion, and generating a depth detection map through mapping of the depth sequence and the dominant frequency signal; according to the invention, the time-frequency analysis method is adopted to obtain the time-frequency spectrum of the collected signal, more abundant defect detection information is provided, and the problems that the traditional impact echo processing method only judges defects through dominant frequency translation, seriously depends on the working experience of field personnel and is not strong in underlying logic are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete defect detection and data processing, and in particular to a high-precision concrete defect detection data processing method and a processing terminal. Background Art

[0002] At present, the commonly used non-destructive testing technologies for concrete structures on the market are mainly divided into three categories. The first category is the detection method based on electromagnetic waves, represented by the ground penetrating radar method; the second category is the detection method based on elastic wave theory, such as the impact echo method, ultrasonic pulse method, etc.; the third category is the method using penetrating rays as the detection medium, represented by the infrared thermal imaging method. Comprehensively comparing the above three methods, the ground penetrating radar method and the impact echo method are relatively easy to use for large-volume concrete problems in engineering, among which:

[0003] The ground penetrating radar method has the advantages of high sampling frequency and no damage to the structure, and has gradually replaced the commonly used coring detection method in concrete structures. At present, the application of ground penetrating radar in civil engineering includes the detection of defects such as tunnel lining thickness, distribution of steel bars in concrete structures, looseness, cracks and peeling layers. However, the ground penetrating radar method still has certain shortcomings in engineering non-destructive testing. First of all, in terms of principle, the propagation process of electromagnetic waves in concrete media and its defects is the propagation of electromagnetic wave wave equations. The ground penetrating radars used by current engineering industry inspection companies are basically several fixed processing software, which cannot perform good attenuation compensation processing on the defects at the rear of the steel layer. At the same time, the professional ability of many inspection companies is also limited, and the understanding of wave equations is not deep, and the professional ability does not meet the requirements, which leads to great differences in the interpretation of the profile of the ground penetrating radar detection results. Secondly, metal materials such as steel bars have a strong shielding effect on electromagnetic waves. It is difficult for high-frequency electromagnetic waves to penetrate metal media, and it is difficult for low-frequency electromagnetic waves to have a good effect at the rear of metal media. Therefore, this method is not suitable for the detection of defects at the rear of the steel layer of reinforced concrete structures.

[0004] The impact echo method is a non-destructive testing method that uses a small steel ball to tap the concrete surface. The elastic wave generated propagates into the concrete in the form of a spherical wave array. During the propagation of the stress wave, if it encounters a defect or interface (concrete / air), it will be reflected. The sensor placed near the impact point receives the vertical displacement or acceleration of the concrete surface caused by these reflected waves. The traditional impact echo processing method basically converts the time domain signal received by the sensor into a frequency domain signal through fast Fourier transform (FFT). It is believed that the depth of the internal defects of the concrete structure and the thickness of the structure itself can be calculated through the different frequency peaks in the spectrum diagram. In addition, when the thickness of the structure is known, this method can also test the propagation speed of the stress wave inside the concrete. At present, the application scope of the impact echo method is mainly in the detection of the thickness of the concrete structure, the depth of the surface cracks and the internal defects of the concrete. The advantage of this detection method is that the elastic wave (P wave) used is developed based on the elastic wave theory. Compared with the ground penetrating radar method, it is not shielded by metal materials such as steel bars and has a strong penetration ability. From the perspective of the composition of the collected original data, the original data collected by this method can be used to deal with the defects in the rear part of the steel bar layer of the reinforced concrete structure, and there is room for further improvement in the future. After comparing many instruments and equipment on the market, and taking into full consideration the actual defect detection requirements of large-volume concrete, a non-destructive testing method is needed to quickly and accurately identify defects inside concrete and other building materials, such as cracks, voids, and material stratification, without affecting the structural integrity. It is particularly suitable for rapid structural safety assessment in emergency situations and routine maintenance inspections, so that problems can be discovered in a timely manner and appropriate repair measures can be taken.

[0005] In summary, the current nondestructive testing technology for concrete structures has the following problems:

[0006] (1) The nondestructive testing technology based on the ground penetrating radar method cannot perform good attenuation compensation for defects in the rear part of the steel bar layer, and relies heavily on the experience of the interpreter, resulting in great differences in the interpretation of the profile of the ground penetrating radar test results;

[0007] (2) Metal materials such as steel bars have a strong shielding effect on electromagnetic waves. It is difficult for high-frequency electromagnetic waves to penetrate metal media, and it is also difficult for low-frequency electromagnetic waves to have a good effect behind metal media. The non-destructive testing technology based on ground penetrating radar is suitable for the detection of defects behind the steel bar layer of reinforced concrete structures.

[0008] (3) Traditional impact echo processing methods basically use fast Fourier transform (FFT) to convert the time domain signal received by the sensor into a frequency domain signal, and calculate the internal defect depth of the concrete structure and the thickness of the structure itself through the different frequency peaks in the spectrum diagram. This ignores the time domain information of the echo signal and relies heavily on the work experience of the on-site personnel, which is prone to misjudgment of defects.

[0009] (4) Conventional time-frequency analysis methods have insufficient time-frequency resolution, which is not conducive to defect identification. Summary of the Invention

[0010] To solve the above problems, the present invention provides a high-precision concrete defect detection data processing method and a processing terminal, which solve the problem that the traditional impact echo processing method judges defects only by the main frequency translation, seriously relies on the work experience of on-site personnel, and has a weak underlying logic. The interface obtained by the layer interface concrete defect detection processing method based on the improved wave number domain non-scale S transform is clear and meets the high-precision defect detection requirements of concrete structure non-destructive testing.

[0011] The present invention is realized through the following technical solutions:

[0012] A high-precision concrete defect detection data processing method, comprising:

[0013] Collect the original data of the measured work area along the measuring line;

[0014] Based on the original data, establish a wavelet model, and calculate the received signal through the reflection coefficient and the wavelet;

[0015] Perform preprocessing on the received signal to obtain a preprocessed signal;

[0016] Perform redundancy judgment on the preprocessed signal. The redundancy judgment method includes:

[0017] Determine the longitudinal wave velocity V of the measured work area p , the estimated depth H of the measured work area, and the total propagation time T of the signal;

[0018] If H ≤ V p ·T / 2 ≤ 2H, it is judged that the preprocessed signal has no redundant data;

[0019] If V p ·T / 2 < H, it is judged that the detection depth is insufficient;

[0020] If V p ·T / 2 > 2H, it is judged that the preprocessed signal has redundant data, and the data exceeding 2H is intercepted in time.

[0021] Convert the preprocessed signal into a time-frequency domain signal through an improved S transform, extract the main frequency signal, and perform interpolation processing on the main frequency signal along the measuring line;

[0022] Convert the time series of the original data into a depth series through time-depth conversion, and generate a depth detection map through the mapping of the depth series and the main frequency signal.

[0023] Optionally, perform impact echo detection on the measured work area through an impact echo device, and obtain the original data through the impact echo device.

[0024] Optionally, the wavelet model uses a zero-phase Ricker wavelet p, and the reflection coefficient Wherein, a1 is the acoustic impedance of the medium on one side of the defect interface, and a2 is the acoustic impedance of the medium on the other side of the defect interface;

[0025] analog signal Where p(τ) is the value of the zero-phase Ricker wavelet at time point τ, t is the current time, and s(t-τ) is the reflection coefficient of the defect interface in the time period between t and τ.

[0026] Optionally, the method of preprocessing the received signal includes bandpass filtering and gain;

[0027] Transfer function of bandpass filter Where s is a complex frequency variable, ω L is the low frequency cutoff angular frequency, ω H is the high frequency cutoff angular frequency, ω0 is the center frequency, and Q is the quality factor;

[0028] The received signal r(t) is bandpass filtered by the transfer function of the bandpass filter to obtain the filtered signal r f (t);

[0029] Perform gain processing on the filtered signal to obtain a signal s(t)=r after gain processing f (t)·e γt , where γ is the gain factor, t is the time, and e is a natural constant.

[0030] Optionally, the low frequency cutoff angular frequency ω L =2πf L , high frequency cutoff angular frequency ω H =2πf H , center frequency Quality Factors Among them, f L is the low frequency cutoff frequency, f H is the low frequency cutoff frequency.

[0031] Specifically, the method for extracting the main frequency information includes:

[0032] Determine the time spectrum Where s(t) is the preprocessed signal, A is the slope parameter, B is the intercept parameter, η is the time parameter, f is the frequency parameter, i is the imaginary unit, and e is the natural constant;

[0033] Wherein, △t1 and △t2 are the time sampling rates at frequency f1 and frequency f2;

[0034] For each time point, the frequency corresponding to its maximum value in the time-frequency spectrum is obtained, and this frequency is used as the main frequency signal.

[0035] Optionally, the calculation formula for converting a time series to a depth series is: Among them, H t is the signal reflection depth, V p is the longitudinal wave velocity of the measured work area, Δt is the sampling time interval, and n is the number of sampling points in the time series;

[0036] A mapping relationship between the signal reflection depth and the main frequency signal is established to generate a depth detection map.

[0037] Furthermore, the depth detection map is compared with the design blueprint to determine the concrete defects.

[0038] A high-precision concrete defect measurement data processing terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the high-precision concrete defect measurement data processing method as described above is implemented.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0040] The present invention adopts a time-frequency analysis method to obtain the time-frequency spectrum of the collected signal, provides richer defect detection information, and solves the problem that the traditional impact echo processing method judges defects only through main frequency shift, seriously relies on the work experience of on-site personnel, and has weak underlying logic; at the same time, the improved S transform time-frequency analysis method proposed solves the problem that the frequency information in the time-frequency spectrum deviates from the true position due to the traditional S transform algorithm, thereby improving the accuracy of concrete defect detection; finally, the time series is converted into a depth series through time-depth conversion, and accurate mapping of depth information is achieved, which can well display the relative distance relationship between interfaces for complex objects of half-space interfaces, thereby making better quantitative judgments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, are used to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention, and the accompanying drawings are included in and constitute a part of this specification and do not constitute a limitation of the embodiments of the present invention.

[0042] Figure 1 It is a flow chart of the high-precision concrete defect detection data processing method according to the present invention.

[0043] Figure 2 It is part of the data of the high-precision concrete defect detection data processing method according to the present invention.

[0044] Figure 3 It is a depth detection map obtained by using the high-precision concrete defect detection data processing method according to the present invention. DETAILED DESCRIPTION

[0045] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation methods. It is understood that the specific implementation methods described herein are only used to explain the relevant content, rather than to limit the present invention.

[0046] It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings.

[0047] In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0048] The high-precision concrete defect detection data processing method of the present invention is based on the impact echo method, combined with the improved S transform and time-depth conversion, and realizes accurate detection of internal defects of concrete by extracting the main frequency signal and mapping the depth information. The principles include:

[0049] Signal acquisition: The original reflection signal of the measured work area is obtained through the impact echo method.

[0050] Signal processing: Use the wavelet model and reflection coefficient to calculate the simulated signal and the received signal, select the optimal processing parameters, and pre-process the signal through bandpass filtering and gain processing.

[0051] Time-frequency analysis: The preprocessed signal is converted from the time domain to the time-frequency domain through the improved S transform, and the main frequency component signal is extracted.

[0052] Time-to-depth conversion: Convert the main frequency component signal from a time series to a depth sequence to form a depth map of the signal.

[0053] Generate detection map: Based on the mapping relationship between the main frequency signal and the depth information, an intuitive depth detection map is generated to identify internal defects of concrete.

[0054] The present invention conducts a comprehensive analysis on the work area to be tested to understand the actual situation and detection targets. According to the specific conditions of the work area, the survey lines are reasonably arranged to cover the entire area to be tested to complete data collection; when the impact echo device is used for collection, because the depth of the target object is often much greater than the distance between the impactor and the sensor, it is equivalent to a self-excited and self-collected track set. A single sub-wave model is established according to the actual collected data, and the optimal processing parameters are selected; the collected data is pre-processed, including bandpass filtering and gain. A bandpass filter is used to remove high-frequency and low-frequency interference signals, and the signals within the frequency range useful for analysis are retained; according to the set sampling rate and the target depth of the work area to be tested, it is determined whether the acquired time domain signal has redundancy. If it is redundant, the data collected at each measuring point needs to be time-intercepted; based on the improved S transform, the intercepted data is subjected to time-frequency analysis to convert the time domain signal into a time-frequency domain signal.

[0055] Reveal the intensity of each frequency component contained in the signal, so as to help identify frequency anomalies caused by material inhomogeneity, cracks or other structural defects, and quickly identify the main vibration frequency, i.e., the main frequency; after obtaining the main frequency, extract the single-frequency data of each measuring point along the survey line, and perform interpolation processing to enhance the continuity of the image and the integrity of the data, connect the main values ​​of the time values ​​corresponding to frequencies of different scales to obtain the corresponding time depth of each research object, and convert the time series into a depth series through time-to-depth conversion, finally realizing the accurate mapping of depth information, comparing the interface of the design blueprint, and judging defects.

[0056] Embodiment 1

[0057] like Figure 1 As shown, this embodiment provides a high-precision concrete defect detection data processing method, comprising:

[0058] S1. Collecting original data of the measured work area along the measuring line; performing impact echo detection on the measured work area through the impact echo device, and obtaining original data through the impact echo device.

[0059] Conduct a comprehensive analysis of the work area to be tested to understand the actual situation and test objectives, including but not limited to: project overview, structural component design and construction; test basis, purpose and client requirements; test personnel and equipment; test area division, test line layout; test methods, steps, quantity, location and progress; and other supporting work.

[0060] For the division of the area, it is advisable to arrange the long distance of the measuring line for the target object according to the measuring area. The measuring area should be larger than the area of ​​estimated defects, and there should be normal concrete parts with the same conditions for comparison (or the measuring line is long enough to include normal concrete parts with the same conditions), and mark their respective numbers and positions, ensuring that the measuring lines are arranged around the inspected building, and the length of a single measuring line must be greater than the target. In this way, the measuring lines can be reasonably arranged to cover the entire area to be measured according to the specific conditions of the work area.

[0061] Then, a shock echo device (such as a small steel ball impact device) is used to generate shock waves on the concrete surface. The shock waves propagate along the interior of the concrete and are reflected when they encounter material interfaces or defects. The sensor receives these reflected signals and generates raw time domain data.

[0062] S2, establish a wavelet model based on the original data, and calculate the received signal through the reflection coefficient and the wavelet;

[0063] Because the depth of the target object is often much greater than the distance between the impactor and the sensor, which is equivalent to a self-excited and self-collected gather, a single wavelet model is established based on the actual acquisition data, and the optimal processing parameters are selected. In order to conduct effective forward analysis, seismic data is predicted based on the known geological model, and the acquisition parameters are set according to the synthetic records: sampling interval, recording time, seismic wavelet, etc.

[0064] The corresponding seismic response is obtained according to the known (or designed) mathematical and physical models or geological models. When the impact echo equipment is used for acquisition, the depth of the target object is often much greater than the distance between the impactor and the sensor, which is equivalent to the self-excited and self-collected gather. Therefore, the synthetic seismic record method is used in the simulation. When the sub-wave propagates in the medium, it will encounter different interfaces, such as the boundaries of different materials or cracks inside the medium. At each interface, according to the difference in acoustic impedance of the medium, the wave will be partially reflected and partially transmitted.

[0065] The reflection coefficient describes the ratio of the amplitude of the reflected wave to the amplitude of the incident wave at a specific interface. The reflected wave is the convolution of the sub-wave generated by the strike and the reflection characteristics of the medium. The convolution process describes how the sub-wave is modified by the reflection coefficient of each layer of the medium to obtain the final received signal.

[0066] To perform effective forward modeling, i.e. predicting seismic data based on a known geological model, the following key acquisition parameters need to be carefully adjusted and set:

[0067] Sampling interval (Δt): The sampling interval determines the temporal resolution of the data. A smaller sampling interval can capture higher frequency signals, but it also means an increase in the amount of data and a longer processing time. When setting the sampling interval, it is necessary to balance the data resolution and the actual processing capacity to ensure that the key information in the seismic signal can be effectively recorded while avoiding unnecessary data redundancy. Generally, the choice of sampling interval depends on the depth and complexity of the target formation.

[0068] Recording duration (T): The recording duration determines the total time window of seismic data recording, which affects the maximum depth that can be detected. Longer recording durations can cover deeper formations, but also increase the burden of data processing. Setting the appropriate recording duration requires considering the speed of seismic waves propagating in the underground medium and the expected depth of the target formation.

[0069] Zero-phase Ricker wavelet: In seismic exploration, the selection of wavelets is crucial. An ideal wavelet should have the following characteristics: wide bandwidth to ensure the detection of different geological features from low frequency to high frequency. Short pulse to ensure high resolution so that the reflection signals of adjacent strata can be clearly distinguished.

[0070] In practical applications, zero-phase Ricker wavelets are often used as seismic sources because their symmetrical waveforms help simplify waveform interpretation and make the location of reflection events more accurate. The dominant frequency of the wavelet should be selected based on the detection depth and resolution requirements. Generally, deep detection requires a lower frequency so that the wave can penetrate deeper into the ground.

[0071] Reflection coefficient: In the forward model, a detailed formation model needs to be constructed and the reflection coefficient of each interface needs to be calculated. These coefficients are calculated based on the difference in acoustic impedance between adjacent formations.

[0072] Through the convolution operation, the propagation of seismic waves in multi-layer underground structures can be simulated, reflecting the reflection and transmission characteristics of each interface. Ultimately, this synthetic seismic record contains detailed information about the underground structure, which can be used for further layered analysis and interpretation. Through the analysis of synthetic seismic records, the relevant optimal parameters can be determined. Some of the collected records are as follows Figure 2 shown.

[0073] S3. Preprocess the received signal to obtain a preprocessed signal.

[0074] Preprocessing includes bandpass filtering and gain, etc. Bandpass filters are used to remove high-frequency and low-frequency interference signals and retain signals within the frequency range useful for analysis. Gain processing is applied to deep signals to compensate for energy attenuation caused by increased distance and ensure that the signals of deep defects are clearly visible.

[0075] S4. After obtaining the preprocessed signal, perform redundancy judgment on the preprocessed signal. Determine whether the acquired time domain signal has redundancy based on the set sampling rate and the target depth of the measured work area. If redundancy exists, time interception is required for the data collected at each measuring point.

[0076] S5. The preprocessed signal is converted into a time-frequency domain signal through the improved S transform, and the main frequency signal is extracted and interpolated along the survey line. The interpolation process is used to enhance the continuity of the image and the integrity of the data.

[0077] The intensity of each frequency component contained in the main frequency signal helps to identify frequency anomalies caused by material inhomogeneities, cracks or other structural defects. The frequency distribution of the signal at a specific point in time provides a detailed view of the signal changing over time. Tracking transient phenomena in the signal (such as scattered waves when cracks occur) allows us to quickly identify the main vibration frequencies.

[0078] S6. The time series of the original data is converted into a depth series through time-to-depth conversion, and a depth detection map is generated by mapping the depth sequence and the main frequency signal. The detection map indicates the strength of the signal through color or grayscale, which helps to intuitively identify the defect location inside the concrete.

[0079] S7. Compare the depth detection image with the design blueprint to determine the concrete defects. The generated depth detection image is compared with the design blueprint to identify possible defects or abnormal areas inside the concrete. For example, brighter areas may indicate defects, cracks or voids.

[0080] Embodiment 2

[0081] The specific method for redundancy judgment provided in this embodiment includes:

[0082] Determine the longitudinal wave velocity V in the measured area p , usually obtained through experimental determination or reference to existing geological data. The estimated depth H of the work area to be tested is determined before actual testing based on the geological conditions of the work area, target objects and other information. And the total propagation time T of the signal, that is, the time difference from the signal being sent to the signal being received, is usually recorded by the detection equipment.

[0083] V p T / 2 represents the actual depth at which the signal propagates to a certain reflection interface and returns.

[0084] If H≤V p If T / 2≤2H, it is judged that there is no redundant data in the preprocessed signal, which means that the propagation range of the signal is within a reasonable measurement area, and the data is valid and contains the required depth information.

[0085] If V p·If T / 2 < H, it is determined that the detection depth is insufficient; this indicates that the propagation depth of the signal is not enough and may not reach the predetermined detection depth of the work area.

[0086] If V p ·If T / 2 > 2H, it is determined that there is redundant data in the preprocessed signal, and time truncation is performed on the data exceeding 2H; this shows that the propagation of the signal exceeds the target depth of the work area, and there may be redundant data, that is, the invalid signal is reflected from a deeper area. At this time, it is necessary to truncate the data to remove the unnecessary part.

[0087] Example 3

[0088] In practical applications, a zero-phase Ricker wavelet is commonly used as the seismic source because its symmetric waveform helps to simplify the interpretation of the waveform and makes the positioning of reflection events more accurate. The main frequency of the wavelet should be selected according to the detection depth and resolution requirements. Generally, lower frequencies are required for deep detection to facilitate the penetration of wave energy into the ground.

[0089] Therefore, the constructed wavelet model uses a zero-phase Ricker wavelet p, and the reflection coefficient where a1 is the acoustic impedance of the medium on one side of the defect interface, and a2 is the acoustic impedance of the medium on the other side of the defect interface; the reflection coefficient s describes the amplitude ratio between the incident wave and the reflected wave, that is, it determines the reflection intensity of the wave at the interface of different media.

[0090] Received signal where p(τ) is the waveform generated by the zero-phase Ricker wavelet at time point τ. The shape, frequency, and duration of the wavelet will all affect the propagation and reflection characteristics of the signal. t is the current time, indicating the moment when the wavelet has propagated and been received. s(t - τ) is the reflection coefficient of the defect interface within the time period from t to τ, representing the reflection coefficient varying with time, depending on the structure of the medium and taking effect within the time period t - τ. By comparing the simulated signal and the received signal, the simulated signal is made as close as possible to the received signal, thereby determining the optimal parameters (including the sampling interval, the main frequency of the wavelet, etc.).

[0091] Indicates the convolution operation.

[0092] Example 4

[0093] In preprocessing, the methods for preprocessing the received signal include band-pass filtering and gain;

[0094] Band-pass filtering is a processing method used to eliminate unwanted high-frequency and low-frequency noises while retaining the useful frequency range in the signal. The transfer function of the band-pass filtering in the present invention where s is the complex frequency variable, ω L is the low-frequency cut-off angular frequency, ωH is the high frequency cutoff angular frequency, ω0 is the center frequency, and Q is the quality factor. The transfer function is used to describe how the filter processes the different frequency components of the input signal. The frequency domain of the input signal is represented as a complex frequency variable.

[0095] The received signal r(t) is bandpass filtered by the transfer function of the bandpass filter to obtain the filtered signal r f (t); The bandpass filter filters the signal through the transfer function, retains only the frequency components within the bandpass range, and outputs the bandpass filtered signal.

[0096] Gain processing is used to compensate for the energy attenuation of the signal due to distance or other factors during the propagation process. Gain processing is performed on the filtered signal to obtain the signal s(t)=r after gain processing. f (t)·e γt , where γ is the gain factor, t is the time, and e is a natural constant.

[0097] Low frequency cutoff frequency ω L =2πf L , high frequency cutoff angular frequency ω H =2πf H , center frequency Quality Factors Among them, f L is the low frequency cutoff frequency, f H is the low frequency cutoff frequency.

[0098] Embodiment 5

[0099] The improved S transform is a tool used to convert time domain signals into time-frequency domain signals. The S transform can combine the frequency information of the signal with the time position to generate a time-frequency spectrum. Thus, the signal originally only in the time domain is converted into a time-frequency domain signal containing both time and frequency information.

[0100] Methods for extracting main frequency information include:

[0101] Determine the time spectrum Where, s(t) is the preprocessed signal, i.e., the time domain signal. A is the slope parameter, B is the intercept parameter, η is the time parameter, which indicates the components of the signal at different time positions. f is the frequency parameter, which indicates the components of the signal at different frequencies. i is the imaginary unit, and e is the natural constant;

[0102] Wherein, △t1 and △t2 are the time sampling rates at frequency f1 and frequency f2;

[0103] For each time point, the frequency corresponding to the maximum value in the time-frequency spectrum is obtained, and this frequency is used as the main frequency signal. The main frequency signal refers to the main frequency component of the signal in the time-frequency domain, which is usually the frequency with the most concentrated energy in the signal. It reflects the most significant vibration or fluctuation frequency in the signal, which is usually related to concrete defects or material interface characteristics.

[0104] Through the improved S transform, the time-frequency spectrum of the signal at different times and frequencies is obtained. In the time-frequency spectrum, the main frequency often corresponds to the position with the maximum energy (amplitude) at the frequency at a specific time. Therefore, the extraction of the main frequency can be achieved by finding the energy peak of the frequency component at each time point in the time-frequency graph.

[0105] Embodiment 6

[0106] The calculation formula for converting a time series to a depth series is: Among them, H t is the signal reflection depth, V p is the longitudinal wave velocity of the measured work area, Δt is the sampling time interval, and n is the number of sampling points in the time series; the propagation time of each sample point is calculated by receiving the reflected wave of the signal, V p ·Δt·n is the total distance the signal travels, and it is divided by 2 because the propagation of waves is a round trip process. Through time-to-depth conversion, the time series can be mapped to depth information to determine the reflection position and depth of the signal in the concrete.

[0107] A mapping relationship between the signal reflection depth and the main frequency signal is established to generate a depth detection map.

[0108] After time-to-depth conversion, the depth corresponding to each sampling point is obtained. Then the signal energy or frequency information of each depth point is associated with the depth, that is, the main frequency signal obtained by time-frequency analysis is associated with the corresponding depth.

[0109] like Figure 3 As shown, the X-axis represents the position of the survey line, and the Y-axis represents the depth value after time-depth conversion. Color (such as a heat map) is used to represent the strength of the signal. The brighter or redder the color, the higher the signal strength, which may represent a defect or interface. Finally, software tools (such as Matplotlib in MATLAB, Python, etc.) are used to visualize the signal energy or main frequency information (color or grayscale) of the survey line position (X-axis) and depth (Y-axis) to form a two-dimensional image.

[0110] Two-dimensional images can intuitively show abnormalities inside the structure, such as the specific location and size of voids, cracks or other defects. Through this high-resolution imaging technology, it is not only possible to locate defects, but also to estimate the severity of the defects.

[0111] Embodiment 7

[0112] A high-precision concrete defect measurement data processing terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the high-precision concrete defect measurement data processing method as described above is implemented.

[0113] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing of the terminal by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an execution program required for at least one function, etc.

[0114] The data storage area can store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0115] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0116] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0117] It should be understood by those skilled in the art that the above embodiments are only for the purpose of clearly illustrating the present invention, and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or modifications may be made based on the above invention, and these changes or modifications are still within the scope of the present invention.

Claims

1. A high-precision concrete defect detection data processing method, characterized in that: include: Collect the original data of the measured work area along the survey line; A wavelet model is established based on the original data, and the received signal is calculated through the reflection coefficient and the wavelet; Preprocessing the received signal to obtain a preprocessed signal; The preprocessed signal is converted into a time-frequency domain signal through an improved S transform, and the main frequency signal is extracted and interpolated along the survey line. The time series of the original data is converted into a depth sequence through time-to-depth conversion, and a depth detection map is generated by mapping the depth sequence and the main frequency signal.

2. A high-precision concrete defect detection data processing method according to claim 1, characterized in that: Before performing the S transform, the preprocessed signal is subjected to redundancy judgment, and the redundancy judgment method includes: Determine the longitudinal wave velocity V in the measured area p , the estimated depth H of the measured work area, and the total propagation time T of the signal; If H≤V p If T / 2≤2H, it is determined that there is no redundant data in the preprocessing signal; If V p ·T / 2 < H, it is determined that the detection depth is insufficient; If V p If T / 2>2H, it is determined that there is redundant data in the preprocessed signal, and the data exceeding 2H is time-intercepted.

3. A high-precision concrete defect detection data processing method according to claim 1, characterized in that: The impact echo device is used to perform impact echo detection on the work area to be tested, and the original data is obtained through the impact echo device.

4. A high-precision concrete defect detection data processing method according to claim 1, characterized in that: The wavelet model uses a zero-phase Ricker wavelet p, and the reflection coefficient Wherein, a1 is the acoustic impedance of the medium on one side of the defect interface, and a2 is the acoustic impedance of the medium on the other side of the defect interface; analog signal Among them, p(τ) is the value of the zero-phase Ricker wavelet at time point τ, t is the current time, s(t-τ) is the reflection coefficient of the defect interface in the time period between t and τ, and the optimal parameters are determined by comparing the simulated signal and the received signal.

5. The high-precision concrete defect detection data processing method according to claim 1 is characterized in that: Methods for preprocessing the received signal include bandpass filtering and gain; Transfer function of bandpass filter Where s is a complex frequency variable, ω L is the low frequency cutoff angular frequency, ω H is the high frequency cutoff angular frequency, ω0 is the center frequency, and Q is the quality factor; The received signal r(t) is bandpass filtered by the transfer function of the bandpass filter to obtain the filtered signal r f (t); Perform gain processing on the filtered signal to obtain a signal s(t)=r after gain processing f (t)·e γt , where γ is the gain factor, t is the time, and e is a natural constant.

6. A high-precision concrete defect detection data processing method according to claim 5, characterized in that: Low frequency cutoff frequency ω L =2πf L , high frequency cutoff angular frequency ω H =2πf H , center frequency Quality Factors Among them, f L is the low frequency cutoff frequency, f H is the low frequency cutoff frequency.

7. A high-precision concrete defect detection data processing method according to claim 1, characterized in that: Methods for extracting main frequency information include: Determine the time spectrum Where s(t) is the preprocessed signal, A is the slope parameter, B is the intercept parameter, η is the time parameter, f is the frequency parameter, i is the imaginary unit, and e is the natural constant; Wherein, △t1 and △t2 are the time sampling rates at frequency f1 and frequency f2; For each time point, the frequency corresponding to its maximum value in the time-frequency spectrum is obtained, and this frequency is used as the main frequency signal.

8. The high-precision concrete defect detection data processing method according to claim 1 is characterized in that: The calculation formula for converting a time series to a depth series is: Among them, H t is the signal reflection depth, V p is the longitudinal wave velocity of the measured work area, Δt is the sampling time interval, and n is the number of sampling points in the time series; A mapping relationship between the signal reflection depth and the main frequency signal is established to generate a depth detection map.

9. A high-precision concrete defect detection data processing method according to claim 1, characterized in that: Compare the depth test map with the design blueprint to identify concrete defects.

10. A high-precision concrete defect detection data processing terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a high-precision concrete defect detection data processing method as described in any one of claims 1 to 9 is implemented.

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