Defect detection method for steel pipe column for foundation reinforcement

Abstract

The invention relates to material detection, in particular to a steel pipe column defect detection method for foundation reinforcement, which comprises the following steps: acquiring an echo signal received after a sound wave is transmitted to a steel pipe column; performing period division on the echo signal to obtain multiple sections of periodic signals; determining the abnormal possibility of each section of the periodic signal according to the self waveform characteristic of each section of the periodic signal and the waveform characteristic difference between adjacent periodic signals of the periodic signal; determining a multi-path difference value between each section of the periodic signal and the first section of the periodic signal according to the multi-path characteristic of the signal; determining the distortion degree of each section of the periodic signal according to the abnormal possibility and the multi-path difference value corresponding to each section of the periodic signal; and performing defect judgment on the steel pipe column according to the distortion degree of each periodic signal. By adopting the method, the accuracy of defect detection on the steel pipe column for foundation reinforcement can be improved.

Description

Technical Field

[0001] The invention relates to material detection, and in particular to a method for detecting defects of a steel pipe column used for foundation reinforcement. Background Art

[0002] Steel pipe columns used for foundation reinforcement usually have thicker walls in order to withstand greater loads and pressures. Therefore, they are widely used in construction projects such as high-rise buildings, large spatial structures, and industrial plants to provide additional support and stability. Therefore, the quality of such steel pipe columns is an important factor in determining whether a building has safety hazards. During the production process of steel pipe columns, due to factors such as improper material ratios or poor maintenance conditions, defects such as cavities or cracks may exist inside the steel pipe columns. If these defects are not discovered or detected in time, they will seriously affect the stress of the steel pipe column structure, and then affect the overall performance of the building structure, thereby causing safety hazards. Therefore, it is important and necessary to conduct defect detection on steel pipe columns used for foundation reinforcement.

[0003] Since acoustic wave testing is a non-destructive testing method and can observe the internal structure of steel pipe columns, acoustic wave testing is often used to detect defects in steel pipe columns. In traditional methods, acoustic waves are generally emitted to the steel pipe column, and the amplitude of the echo signal is used to determine whether the steel pipe column has defects. However, since the wall thickness of the steel pipe column used for foundation reinforcement is thicker than that of ordinary steel pipe columns, the acoustic wave signal will be severely attenuated, which in turn causes the amplitude difference between the echo signals of the defective steel pipe column and the non-defective steel pipe column to be greatly reduced. Therefore, the accuracy of determining whether the steel pipe column has defects based on the amplitude is not high, and it is easy to make a misjudgment. Summary of the invention

[0004] In order to solve the technical problem of low accuracy in defect detection of steel pipe columns used for foundation reinforcement, the object of the present invention is to provide a method for defect detection of steel pipe columns used for foundation reinforcement. The technical solution adopted is as follows: Acquire the echo signal received after transmitting the sound wave to the steel pipe column; Dividing the echo signal into periods to obtain multiple periodic signals; Determining the possibility of abnormality of each segment of the periodic signal according to the waveform characteristics of each segment of the periodic signal and the difference in waveform characteristics between adjacent periodic signals of the periodic signal; Determine the multipath difference value between each periodic signal and the first periodic signal according to the multipath characteristics of the signal; Determining the degree of distortion of each segment of the periodic signal according to the abnormal possibility and the multipath difference value corresponding to each segment of the periodic signal; Defects of the steel pipe string are determined according to the degree of distortion of each of the periodic signals.

[0005] In one embodiment, determining the abnormal possibility of each segment of the periodic signal according to the waveform characteristics of each segment of the periodic signal and the difference in waveform characteristics between adjacent periodic signals of the periodic signal includes: Fitting each segment of the periodic signal with a sine wave function according to the waveform characteristics of each segment of the periodic signal to obtain a fitting result of each segment of the periodic signal; Determining the probability that the waveform of each segment of the periodic signal is normal according to the fitting result of each segment of the periodic signal; Taking each segment of the periodic signal as a periodic signal to be analyzed, and determining the waveform feature difference between the adjacent periodic signals according to the fitting results of the adjacent periodic signals of the periodic signal to be analyzed and the normal possibility of the waveform; The possibility of abnormality of the periodic signal to be analyzed is determined according to the possibility of normal waveform of the periodic signal to be analyzed and the difference in waveform characteristics between adjacent periodic signals of the periodic signal to be analyzed.

[0006] In one embodiment, the fitting result includes a correction determination coefficient and a fitting frequency; and determining the possibility of a normal waveform of each segment of the periodic signal according to the fitting result of each segment of the periodic signal includes: For each segment of the periodic signal, determining a first difference between an actual frequency of the periodic signal and a corresponding fitting frequency; The possibility that the waveform of the periodic signal is normal is determined based on the correction determination coefficient corresponding to the periodic signal and the first difference.

[0007] In one embodiment, the fitting result includes fitting parameters; the waveform feature difference between the adjacent periodic signals includes a second difference between the normal possibilities of the waveforms of the adjacent periodic signals and a third difference between the fitting parameters of the adjacent periodic signals; The determining, based on the normal possibility of the waveform of the periodic signal to be analyzed and the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed, the abnormal possibility of the periodic signal to be analyzed comprises: The possibility of abnormality of the periodic signal to be analyzed is determined based on the possibility of normal waveform of the periodic signal to be analyzed and the second difference and the third difference between adjacent periodic signals of the periodic signal to be analyzed.

[0008] In one embodiment, determining the multipath difference value between each segment of the periodic signal and the first segment of the periodic signal according to the multipath characteristics of the signal includes: Each segment of the periodic signal is used as a periodic signal to be analyzed, and each segment of the periodic signal except the first segment of the periodic signal before the periodic signal to be analyzed is divided into a first set and a second set; the propagation time length of the periodic signal in the first set is shorter than the propagation time length of the periodic signal in the second set; The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on the difference between the number and signal characteristics of multipath signals in the first set and the first periodic signal, the multipath signal energy in the first set and the multipath signal energy in the second set.

[0009] In one embodiment, determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal according to the difference between the number and signal characteristics of the multipath signals in the first set and the first periodic signal, the multipath signal energy in the first set, and the multipath signal energy in the second set, comprises: Determine a fourth difference between the number of multipath signals in the first set and the number of multipath signals of the first periodic signal, and a fifth difference between signal characteristics of periodic signals in the first set and the signal characteristics of the first periodic signal; Determining the discrete degree of energy of the periodic signals in the first set and the energy intensity of the periodic signals in the second set; A multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined according to the fourth difference, the fifth difference, the discrete degree and the energy intensity.

[0010] In one embodiment, determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal according to the fourth difference, the fifth difference, the discrete degree and the energy intensity includes: The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined according to the product of the fourth difference, the fifth difference, the discrete degree and the energy intensity.

[0011] In one embodiment, determining the degree of distortion of each segment of the periodic signal according to the abnormal possibility and the multipath difference value corresponding to each segment of the periodic signal includes: Taking each section of the periodic signal as the periodic signal to be analyzed, determining the sum of the abnormal possibility of the first section of the periodic signal and the abnormal possibility of the periodic signal to be analyzed; The degree of distortion of the periodic signal to be analyzed is determined according to the product of the sum of the abnormal possibilities and the multipath difference value of the periodic signal to be analyzed.

[0012] In one embodiment, the step of determining defects of the steel pipe string according to the degree of distortion of each of the periodic signals includes: Determining a maximum distortion degree from the distortion degrees of each of the periodic signals; Determine a mean value of the degree of distortion after removing the maximum degree of distortion from the degrees of distortion of each of the periodic signals; Defects of the steel pipe column are determined based on the difference between the maximum distortion degree and the average distortion degree.

[0013] In one embodiment, the step of determining defects of the steel pipe column according to the difference between the maximum distortion degree and the mean distortion degree includes: determining a sixth difference between the maximum distortion degree and the mean distortion degree; Determining a defect determination parameter value according to a product of the maximum distortion degree and the sixth difference value; If the defect determination parameter value is greater than or equal to a preset value, it is determined that the steel pipe column has a defect; If the defect determination parameter value is less than the preset value, it is determined that the steel pipe column has no defects.

[0014] The present invention has the following beneficial effects: by acquiring the echo signal received after transmitting the sound wave to the steel pipe column, the echo signal is divided into periods to obtain a plurality of periodic signals, and then the abnormal possibility of each periodic signal is determined according to the waveform characteristics of each periodic signal and the waveform characteristic difference between adjacent periodic signals of the periodic signal, and then the multipath difference value between each periodic signal and the first periodic signal is determined according to the multipath characteristics of the signal, and the distortion degree of each periodic signal is determined according to the abnormal possibility and the multipath difference value corresponding to each periodic signal, and finally the steel pipe column is judged for defects according to the distortion degree of each periodic signal, thereby reducing the dependence on the amplitude in the process of determining the signal distortion, thereby avoiding the problem that the sound wave signal will be severely attenuated due to the thicker wall thickness of the steel pipe column used for foundation reinforcement than that of the general steel pipe column, and then the accuracy of judging whether the steel pipe column has defects according to the amplitude is not high, thereby improving the accuracy of defect detection of the steel pipe column used for foundation reinforcement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic flow chart of a method for detecting defects in a steel pipe column for foundation reinforcement provided by an embodiment of the present invention; Figure 2 A waveform diagram of an echo signal provided by an embodiment of the present invention; Figure 3 A schematic diagram of a flow chart for determining the abnormal possibility of a periodic signal to be analyzed provided by an embodiment of the present invention; Figure 4 A schematic diagram of a process for determining a multipath difference value between a periodic signal to be analyzed and a first periodic signal provided by an embodiment of the present invention; Figure 5 A waveform diagram of an echo signal with a distorted first-segment periodic signal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a steel pipe column defect detection method for foundation reinforcement proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] A specific scheme of a steel pipe column defect detection method for foundation reinforcement provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a method flow chart of a steel pipe column defect detection method for foundation reinforcement provided by an embodiment of the present invention, including the following steps 102 to 112. Among them: Step 102, obtaining the echo signal received after transmitting the sound wave to the steel pipe string.

[0021] Among them, steel pipe columns refer to steel pipe columns used for foundation reinforcement. The wall thickness of steel pipe columns used for foundation reinforcement is thicker than that of other steel pipe columns. The echo signal is the signal that is emitted to the steel pipe column, the sound wave penetrates the internal material of the steel pipe column, propagates and refracts inside, and then returns from the steel pipe column.

[0022] In one embodiment, the sound wave transmitting device can transmit sound waves to the steel pipe column, and then the sound wave receiving device can receive the echo signal returned by the steel pipe column. The computer device can execute step 102 to obtain the echo signal, and then execute steps 104 to 112 to realize defect detection of the steel pipe column used for foundation reinforcement.

[0023] In one embodiment, data such as the amplitude, time point, and power of the echo signal received at each moment may be recorded.

[0024] In one embodiment, due to the multipath effect, multiple signals may be received at the same time, and steps 104 to 106 may be performed based on a signal generated by superimposing the multiple signals at the same time.

[0025] Step 104, divide the echo signal into periods to obtain multiple periodic signals.

[0026] The period division refers to dividing the echo signal into multiple segments according to the period, each segment being a period. The periodic signal refers to a signal of one period.

[0027] In one embodiment, the computer device may divide the echo signal into periods by wavelet transform to obtain multiple periodic signals.

[0028] It can be understood that sound waves transmit signals to the outside in the form of waves. The emission intensity of sound wave signals in different bands will be different. Therefore, it is necessary to divide the received echo signal into periods and determine whether there are defects in the steel pipe column by analyzing the distortion of the signal within the period.

[0029] Figure 2 The waveform of the received echo signal is schematically shown, and it can be seen that the echo signal has a period, and the echo signal can be divided into periods.

[0030] Step 106, determining the abnormal possibility of each periodic signal according to the waveform characteristics of each periodic signal and the difference in waveform characteristics between adjacent periodic signals.

[0031] Among them, the inherent waveform characteristics refer to the characteristics related to the waveform of the periodic signal itself. The adjacent periodic signals of a periodic signal include the previous periodic signal and the next periodic signal of a periodic signal. The difference in waveform characteristics between adjacent periodic signals refers to the difference between the waveform characteristics of the previous periodic signal and the next periodic signal of a periodic signal. The abnormal possibility of a periodic signal refers to the possibility that the periodic signal is abnormal at the shape level. The abnormal possibility of a periodic signal is positively correlated with the possibility of defects in the steel pipe column. That is, the greater the abnormal possibility of a periodic signal, the greater the possibility that the steel pipe column is defective; conversely, the smaller the abnormal possibility of a periodic signal, the smaller the possibility that the steel pipe column is defective.

[0032] In one embodiment, the computer device can determine the probability of normal waveform of each periodic signal according to the waveform characteristics of each periodic signal. Then, each periodic signal is used as the periodic signal to be analyzed, and the waveform characteristic difference between adjacent periodic signals is determined according to the waveform characteristics of adjacent periodic signals of the periodic signal to be analyzed. Finally, the probability of abnormal waveform of the periodic signal to be analyzed is determined according to the probability of normal waveform of the periodic signal to be analyzed and the waveform characteristic difference between adjacent periodic signals of the periodic signal to be analyzed.

[0033] In one embodiment, the computer device may fit the periodic signal with a sine wave function according to the waveform characteristics of each periodic signal, and determine the probability of the waveform of each periodic signal being normal according to the fitting result.

[0034] In one embodiment, the abnormal possibility of the periodic signal to be analyzed is positively correlated with the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed, and negatively correlated with the normal possibility of the waveform of the periodic signal to be analyzed.

[0035] In one embodiment, the computer device may determine the abnormal possibility of the periodic signal to be analyzed based on the ratio between the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed and the normal possibility of the waveform of the periodic signal to be analyzed.

[0036] In one embodiment, the computer device can fit each periodic signal with a sinusoidal wave function according to the waveform characteristics of each periodic signal to obtain a fitting result for each periodic signal, and determine the possibility of normal waveform of each periodic signal according to the fitting result of each periodic signal.

[0037] In one embodiment, the waveform feature difference between adjacent periodic signals may include at least one of a difference between fitting results of adjacent periodic signals and a difference (second difference) between waveform normal possibilities.

[0038] In one embodiment, the difference between the fitting results of adjacent periodic signals may include the difference between the fitting parameters of adjacent periodic signals (third difference). In one embodiment, the fitting parameters may be expressed in the form of a vector. That is, the difference between the fitting results of adjacent periodic signals may include the difference between the fitting parameter vectors of adjacent periodic signals.

[0039] Step 108: Determine the multipath difference value between each periodic signal and the first periodic signal according to the multipath characteristics of the signal.

[0040] Among them, the multipath characteristics of the signal are the characteristics of the multipath signals in a periodic signal. It can be understood that due to the multipath effect, multiple signals will be received at the same time. Therefore, there will be multipath signals in the same period, and the characteristics of the multipath signals are the multipath characteristics of the signal. The first periodic signal refers to the first periodic signal divided from the received echo signal, that is, the first periodic signal received. The multipath difference value is used to characterize the difference in the multipath level between a periodic signal and the first periodic signal.

[0041] It can be understood that since normal reflection will occur when sound waves propagate in a steel pipe column without defects, there will also be certain differences in waveform characteristics between adjacent periodic signals, resulting in a greater possibility of abnormal periodic signals. In order to avoid the problem of inaccurate defect detection caused by this situation, further analysis can be conducted in combination with the impact of defective steel pipe columns on signal propagation. Specifically, on the one hand, the presence of holes, cracks and other defects in the steel pipe column will cause obvious reflection and refraction of the sound wave signal at the defect position, thereby showing that the multipath effect of the sound wave signal is more obvious, while the multipath effect of the sound wave signal is not obvious after propagating in a steel pipe column without defects. On the other hand, the first periodic signal received is less affected by reflection and refraction, and is manifested as a direct wave signal, while the subsequent echo signal received is manifested as a superposition of the direct wave signal and the multipath signal of reflection and refraction. As the transmission time of the sound wave signal increases, the subsequent multipath signal remains stable. Therefore, the multipath effect of the echo signal received at each moment can be used to determine the distortion of the signal caused by defects such as cracks and holes in the internal structure of the steel pipe column.

[0042] In one embodiment, the multipath characteristics of the signal may include at least one of the number of multipath signals, signal characteristics, and multipath signal energy.

[0043] In one embodiment, the computer device can take each periodic signal as the periodic signal to be analyzed, and determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal according to the multipath characteristics of the first periodic signal and the multipath characteristics of each periodic signal before the periodic signal to be analyzed except the first periodic signal.

[0044] In one embodiment, the computer device can use each periodic signal as a periodic signal to be analyzed, and divide each periodic signal except the first periodic signal before the periodic signal to be analyzed into a first set and a second set, wherein the propagation time of the periodic signal in the first set is shorter than the propagation time of the periodic signal in the second set. The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on at least one of the difference between the number of multipath signals in the first set and the first periodic signal, the difference between the signal characteristics of the first set and the first periodic signal, the multipath signal energy in the first set and the multipath signal energy in the second set, etc.

[0045] In one embodiment, the computer device can determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on at least one of the fourth difference between the number of multipath signals in the first set and the first periodic signal, the fifth difference between the signal characteristics of the first set and the first periodic signal, the discrete degree of energy of the periodic signals in the first set and the energy intensity of the periodic signals in the second set, etc.

[0046] Step 110, determining the distortion degree of each periodic signal segment according to the abnormal possibility and the multipath difference value corresponding to each periodic signal segment.

[0047] In one embodiment, the computer device can take each periodic signal as the periodic signal to be analyzed, determine the sum of the abnormal possibility of the first periodic signal and the abnormal possibility of the periodic signal to be analyzed, and then determine the degree of distortion of the periodic signal to be analyzed based on the sum of the abnormal possibilities and the multipath difference value of the periodic signal to be analyzed.

[0048] Step 112, determining defects of the steel pipe string according to the degree of distortion of each periodic signal.

[0049] Among them, defect determination is the process of determining whether the steel pipe column has defects.

[0050] In one embodiment, the computer device can determine the defect determination parameter value according to the degree of distortion of each periodic signal, compare the defect determination parameter value with a preset value, and perform defect determination on the steel pipe column according to the comparison result. The defect determination parameter value is used to measure the maximum difference in the degree of distortion.

[0051] In one embodiment, the computer device can determine the maximum distortion degree from the distortion degrees of each periodic signal, determine the average distortion degree after removing the maximum distortion degree from the distortion degrees of each periodic signal, and determine the defect judgment parameter value according to the difference between the maximum distortion degree and the average distortion degree.

[0052] In one embodiment, if the defect determination parameter value is greater than or equal to a preset value, it is determined that the steel pipe column has defects; if the defect determination parameter value is less than the preset value, it is determined that the steel pipe column does not have defects.

[0053] In one embodiment, the above steel pipe column defect detection method for foundation reinforcement can be performed by a signal acquisition module, a feature analysis module and a defect detection model. The signal acquisition module is used to perform step 102, the feature analysis module is used to perform steps 104 to 110, and the defect detection model is used to perform step 112.

[0054] In the above-mentioned defect detection method for steel pipe columns for foundation reinforcement, by acquiring the echo signal received after transmitting sound waves to the steel pipe column, the echo signal is divided into periods to obtain multiple periodic signals, and then the abnormal possibility of each periodic signal is determined according to the waveform characteristics of each periodic signal and the waveform characteristics difference between adjacent periodic signals of the periodic signal, and then the multipath difference value between each periodic signal and the first periodic signal is determined according to the multipath characteristics of the signal, and the distortion degree of each periodic signal is determined according to the abnormal possibility and multipath difference value corresponding to each periodic signal, and finally, the steel pipe column is judged for defects according to the distortion degree of each periodic signal, which reduces the dependence on amplitude in the process of determining signal distortion, thereby avoiding the problem that the wall thickness of the steel pipe column used for foundation reinforcement is thicker than that of the general steel pipe column, resulting in serious attenuation of the sound wave signal, and then the accuracy of judging whether the steel pipe column has defects according to the amplitude is not high, thereby improving the accuracy of defect detection of the steel pipe column used for foundation reinforcement.

[0055] In one embodiment, Figure 3 As shown, step 106 determines the abnormal possibility of each periodic signal according to the waveform characteristics of each periodic signal and the waveform characteristic differences between adjacent periodic signals of the periodic signal, including the following steps 302 to 308. Among them: Step 302 , fitting each periodic signal with a sine wave function according to the waveform characteristics of each periodic signal to obtain a fitting result of each periodic signal.

[0056] It can be understood that when sound waves propagate in the steel pipe column, they encounter defects such as cracks and cavities, which will cause reflection or refraction of the sound waves. The reflection or refraction characteristics at the defects are different from the normal reflection or refraction characteristics in the steel pipe column, which will cause the received echo signal to change. Although the amplitude difference of the periodic signal is greatly reduced due to the influence of the wall thickness of the steel pipe column, the shape characteristics of the periodic signal can reflect the abnormal condition of the signal to a certain extent. For example, due to the defects in the steel pipe column, the reception time of the sound wave signal is different from the reception time of other normal sound waves, resulting in the periodic signal no longer presenting the characteristics of a sine wave. The greater the difference in shape between the periodic signal and the sine wave, the greater the possibility that the periodic signal is abnormal, and the smaller the possibility that the waveform is normal. Therefore, the possibility of the waveform of the periodic signal being normal can be determined by fitting the periodic signal with the sine wave function.

[0057] In one embodiment, each periodic signal may be fitted with a periodic sine wave function.

[0058] In one embodiment, each periodic signal may be fitted with a sinusoidal wave function by using a least square fitting method.

[0059] In one embodiment, the fitting result may include a correction determination coefficient R and a fitting parameter c. The correction determination coefficient is used to characterize the quality of the fitting effect. The larger the correction determination coefficient, the better the fitting effect, that is, the more significant the sinusoidal characteristics of the periodic signal are, and the greater the possibility that the waveform of the periodic signal is normal; the smaller the correction determination coefficient, the worse the fitting effect, that is, the less significant the sinusoidal characteristics of the periodic signal are, and the less likely the waveform of the periodic signal is normal. The fitting parameter is a parameter in the sine wave function formula obtained by fitting the periodic signal with the sine wave function.

[0060] In one embodiment, the fitting parameters may include a fitting frequency, a fitting amplitude, and a fitting offset. The fitting frequency is the frequency of the sine wave function obtained by fitting. The fitting amplitude is the amplitude of the sine wave function obtained by fitting. The fitting offset is the longitudinal axis offset of the sine wave function obtained by fitting.

[0061] In one embodiment, assume that the sine wave function is represented as , then the fitting parameters include the fitting frequency , fitting amplitude A and fitting offset B.

[0062] Step 304: Determine the probability that the waveform of each periodic signal is normal based on the fitting result of each periodic signal.

[0063] Among them, the waveform normal possibility is used to evaluate the normal possibility of the signal from the waveform level of the signal.

[0064] In one embodiment, the computer device can determine the difference between the actual parameters and the fitting parameters of the periodic signal for each periodic signal, and determine the possibility of the waveform of the periodic signal being normal based on the correction determination coefficient corresponding to the periodic signal and the difference between the actual parameters and the fitting parameters.

[0065] In one embodiment, the probability of the waveform of the periodic signal being normal is negatively correlated with the difference between the actual parameter and the fitting parameter, and the probability of the waveform of the periodic signal being normal is positively correlated with the correction determination coefficient.

[0066] In one embodiment, the computer device may determine the likelihood that the waveform of the periodic signal is normal based on the ratio of the correction determination coefficient corresponding to the periodic signal to the difference between the actual parameter and the fitting parameter.

[0067] Step 306 , taking each periodic signal as a periodic signal to be analyzed, and determining the waveform feature difference between adjacent periodic signals according to the fitting results of adjacent periodic signals of the periodic signal to be analyzed and the possibility of the waveform being normal.

[0068] It can be understood that although the normal possibility of the periodic signal can be determined to a certain extent by fitting the periodic signal with the sine wave function, when the acoustic wave signal is transmitted for a period of time, the acoustic waves emitted before and after are shown as passing through the holes, cracks and other defects in the steel pipe column, so that no sudden obvious changes in the signal will occur. The signal of the defect in the steel pipe column also shows a strong sine wave feature, so it is impossible to accurately judge whether the steel pipe column really has defects. However, when the signal is just transmitted, there is no obvious interference of reflection and refraction waves in the received signal, resulting in a large difference between the waveform obtained at the beginning and the waveform after a period of maintenance, that is, there is a large difference between the sine wave fitted by the periodic signal after the acoustic wave is emitted for a period of time and the waveform of the sine wave fitted by the periodic signal when the acoustic wave is just emitted. Therefore, in the acoustic wave signal showing that there are defects in the steel pipe column, there is a significant distortion of the waveform caused by the interference of the defects between the periodic signal just emitted and the periodic signal after a period of emission, which is shown as the abnormal possibility of this section of the acoustic wave signal. Therefore, the waveform feature difference between the adjacent periodic signals before and after the periodic signal can be combined with the normal possibility of the waveform of the periodic signal itself to more accurately determine the abnormal possibility of the periodic signal.

[0069] In one embodiment, the waveform feature difference between adjacent periodic signals includes at least one of a second difference between the waveform normal possibilities of adjacent periodic signals and a third difference between the fitting parameters of adjacent periodic signals. The computer device may subtract the waveform normal possibilities corresponding to the adjacent periodic signals of the periodic signal to be analyzed to obtain the second difference, and subtract the fitting parameters corresponding to the adjacent periodic signals of the periodic signal to be analyzed to obtain the third difference.

[0070] In one embodiment, the fitting parameters can be expressed in the form of vectors, and the computer device can subtract the fitting parameter vectors corresponding to the adjacent periodic signals of the periodic signal to be analyzed to obtain the third difference. The fitting parameter vector can include the fitting frequency, fitting amplitude and fitting offset.

[0071] Step 308 , determining the possibility of abnormality of the periodic signal to be analyzed based on the possibility of normal waveform of the periodic signal to be analyzed and the difference in waveform characteristics between adjacent periodic signals of the periodic signal to be analyzed.

[0072] In one embodiment, the magnitude of the abnormal possibility of the periodic signal to be analyzed is positively correlated with the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed, and negatively correlated with the waveform normal possibility of the periodic signal to be analyzed.

[0073] In one embodiment, the computer device may determine the abnormal possibility of the periodic signal to be analyzed based on the ratio between the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed and the normal possibility of the waveform of the periodic signal to be analyzed.

[0074] In the above embodiment, each periodic signal is fitted with a sine wave function, and the normal waveform possibility of each periodic signal is determined according to the fitting result, so that it can be determined whether the periodic signal is normal from the waveform level, and then each periodic signal is used as a periodic signal to be analyzed, and the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed is determined according to the fitting result and the normal waveform possibility of adjacent periodic signals of the periodic signal to be analyzed. The abnormal possibility of the periodic signal to be analyzed is determined by combining the normal waveform possibility of the periodic signal to be analyzed and the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed. This can avoid the situation that when the sound wave signal is emitted for a period of time, the sound waves emitted before and after both appear as defects such as holes and cracks in the steel pipe column, and no sudden obvious changes in the signal will occur. The signal of the defect in the steel pipe column also shows a strong sinusoidal wave feature, and it is impossible to accurately judge whether the steel pipe column actually has defects. The waveform feature difference between the adjacent periodic signals before and after the periodic signal is combined with the normal waveform possibility of the periodic signal itself to more accurately determine the abnormal possibility of the periodic signal, thereby improving the accuracy of defect detection of steel pipe columns used for foundation reinforcement.

[0075] In one embodiment, the fitting result includes a correction determination coefficient and a fitting frequency; based on the fitting result of each periodic signal, the possibility of the waveform of each periodic signal being normal is determined, including: determining, for each periodic signal, a first difference between the actual frequency of the periodic signal and the corresponding fitting frequency; based on the correction determination coefficient and the first difference corresponding to the periodic signal, determining the possibility of the waveform of the periodic signal being normal.

[0076] In one embodiment, the computer device may determine the actual frequency of the periodic signal according to the inverse of the propagation time of the periodic signal.

[0077] In one embodiment, the probability that the waveform of the periodic signal is normal is positively correlated with the correction determination coefficient of the periodic signal. The probability that the waveform of the periodic signal is normal is negatively correlated with the first difference corresponding to the periodic signal. That is, the larger the correction determination coefficient, the greater the probability that the waveform of the periodic signal is normal; the smaller the correction determination coefficient, the smaller the probability that the waveform of the periodic signal is normal. The larger the first difference between the actual frequency and the fitting frequency, the smaller the probability that the waveform of the periodic signal is normal; the smaller the first difference between the actual frequency and the fitting frequency, the greater the probability that the waveform of the periodic signal is normal.

[0078] In one embodiment, the computer device may determine the probability that the waveform of the periodic signal is normal based on the ratio between the correction determination coefficient corresponding to the periodic signal and the first difference.

[0079] In one embodiment, the probability that the waveform of a periodic signal is normal can be determined according to the following formula: in, Indicates the probability that the waveform is normal. represents the corrected determination coefficient. represents the fitting frequency. Indicates the actual frequency. Indicates taking the absolute value. Represents the first difference between the actual frequency and the fitted frequency.

[0080] In the above embodiment, for each periodic signal, the first difference between the actual frequency of the periodic signal and the corresponding fitting frequency is determined, and the possibility of the waveform of the periodic signal being normal is determined based on the correction determination coefficient and the first difference corresponding to the periodic signal. In this way, the possibility of the waveform of the periodic signal being normal can be accurately determined based on the correction determination coefficient and the fitting parameters in the fitting result, thereby improving the accuracy of defect detection of steel pipe columns used for foundation reinforcement.

[0081] In one embodiment, the fitting result includes fitting parameters; the waveform feature difference between adjacent periodic signals includes a second difference between the waveform normal possibilities of adjacent periodic signals and a third difference between the fitting parameters of adjacent periodic signals; based on the waveform normal possibility of the periodic signal to be analyzed and the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed, determining the abnormal possibility of the periodic signal to be analyzed, including: determining the abnormal possibility of the periodic signal to be analyzed based on the waveform normal possibility of the periodic signal to be analyzed, and the second difference and the third difference between adjacent periodic signals of the periodic signal to be analyzed.

[0082] In one embodiment, the third difference may be a difference between fitting parameter vectors of adjacent periodic signals.

[0083] In one embodiment, the abnormal possibility of the periodic signal to be analyzed is positively correlated with the second difference and the third difference. The abnormal possibility of the periodic signal to be analyzed is negatively correlated with the normal possibility of the waveform. That is, the larger the second difference or the third difference corresponding to the periodic signal to be analyzed, the greater the abnormal possibility of the periodic signal to be analyzed; the smaller the second difference or the third difference corresponding to the periodic signal to be analyzed, the smaller the abnormal possibility of the periodic signal to be analyzed. The greater the normal possibility of the waveform of the periodic signal to be analyzed, the smaller the abnormal possibility of the periodic signal to be analyzed; the smaller the normal possibility of the waveform of the periodic signal to be analyzed, the greater the abnormal possibility of the periodic signal to be analyzed.

[0084] In one embodiment, the computer device can perform modulo operation on the product of the second difference and the third difference to obtain waveform feature difference values ​​of adjacent periodic signals. The possibility of abnormality of the periodic signal to be analyzed is determined based on the ratio of the waveform feature difference values ​​of adjacent periodic signals of the periodic signal to be analyzed to the possibility of normal waveform of the periodic signal to be analyzed.

[0085] In one embodiment, the abnormal possibility of the periodic signal to be analyzed can be determined according to the following formula: Where a represents the abnormal possibility of the periodic signal to be analyzed. Indicates the probability that the waveform of the signal to be analyzed is normal. They respectively represent the probability that the waveforms of the previous adjacent period signal and the next adjacent period signal of the period signal to be analyzed are normal. The second difference between the normal possibilities of the waveforms of adjacent periodic signals is used to characterize the abnormal variation amplitude between adjacent periodic signals of the periodic signal to be analyzed. The fitting parameter vectors respectively represent the previous adjacent periodic signal and the next adjacent periodic signal of the periodic signal to be analyzed, reflecting the shape characteristics of the fitting. The third difference between the fitting parameter vectors representing adjacent periodic signals reflects the difference in shape between the adjacent periodic signals, that is, reflects the distortion of the waveforms before and after the periodic signal to be analyzed. Indicates modulo.

[0086] In the above embodiment, the possibility of the waveform of the periodic signal to be analyzed being normal, and the second difference and the third difference between adjacent periodic signals of the periodic signal to be analyzed are combined to jointly determine the possibility of the abnormality of the periodic signal to be analyzed. This can avoid the situation where, after the acoustic wave signal is emitted for a period of time, the acoustic waves emitted before and after both appear as defects such as holes and cracks in the steel pipe column, and no sudden obvious changes in the signal will occur. The signal indicating that there are defects in the steel pipe column also shows a strong sinusoidal wave feature, and it is impossible to accurately judge whether there are real defects in the steel pipe column. The possibility of the abnormality of the periodic signal can be determined more accurately, thereby improving the accuracy of defect detection of steel pipe columns used for foundation reinforcement.

[0087] In one embodiment, Figure 4 As shown, step 108 determines the multipath difference value between each periodic signal and the first periodic signal according to the multipath characteristics of the signal, including the following steps 402 to 404. Among them: Step 402, taking each periodic signal as a periodic signal to be analyzed, and dividing each periodic signal except the first periodic signal before the periodic signal to be analyzed into a first set and a second set; the propagation time of the periodic signal in the first set is shorter than the propagation time of the periodic signal in the second set.

[0088] It can be understood that the periodic signals to be analyzed selected in this embodiment are the periodic signals of each segment except the first segment of the periodic signal.

[0089] It can be understood that the first periodic signal is a signal that has not been significantly refracted and reflected in the steel pipe column, and is represented as a direct wave signal. In the case where there are no defects inside the steel pipe column, the multipath effect of the subsequent propagation signal after the first periodic signal is not obvious, the signal energy is small, and it will not interfere with the main direct wave signal. Therefore, the periodic signal in the first set has a strong similarity with the first periodic signal, and both are manifested as direct wave signals of sound waves. The energy of the periodic signal in the second set will be relatively small. In the case of defects inside the steel pipe column, there will be a large number of reflections and refractions of sound waves at the defect location, which will interfere with the signals in the first and second sets. The signals will have obvious multipath effects, the number of signals in the first set will increase significantly, the propagation time of the signals in the first set will increase significantly than the propagation time of the first periodic signal, the energy difference between different signals in the first set will be relatively large, and the energy of the signals in the second set will be significantly enhanced. Therefore, the periodic signals except the first periodic signal before the periodic signal to be analyzed can be divided into a first set and a second set according to the length of the propagation time, and then analyzed according to the multipath characteristics of the signals in the first set and the second set, so as to accurately determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal.

[0090] Step 404, determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the difference between the number of multipath signals and signal characteristics between the first set and the first periodic signal, the multipath signal energy in the first set and the multipath signal energy in the second set.

[0091] The number of multipath signals refers to the number of signals received from different paths in the same time period. It can be understood that under the multipath effect, multiple signals will be received at the same time. Signal characteristics are the overall characteristics of the superposition of various signals in the same time period. Multipath signal energy refers to the energy of signals received from different paths in a period of time.

[0092] In one embodiment, the signal characteristics may include the power and propagation duration of the signal.

[0093] In one embodiment, the signal feature may be an average value of features of signals of each path in the same time period.

[0094] In one embodiment, the multipath signal energy may be characterized by the power of the signal on each path.

[0095] In one embodiment, a computer device can determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on at least one of the difference between the number of multipath signals in the first set and the first periodic signal, the difference between the signal characteristics of the first set and the first periodic signal, the discrete degree of the energy of the periodic signal in the first set, and the energy intensity of the periodic signal in the second set.

[0096] In the above embodiment, each periodic signal except the first periodic signal before the periodic signal to be analyzed is divided into a first set and a second set according to the length of the propagation time, and then according to the difference between the number of multipath signals and signal characteristics between the first set and the first periodic signal, the multipath signal energy in the first set and the multipath signal energy in the second set, the multipath difference value between the periodic signal to be analyzed and the first periodic signal can be accurately determined based on the multipath effect, thereby improving the accuracy of defect detection on steel pipe columns used for foundation reinforcement.

[0097] In one embodiment, the multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on the difference between the number of multipath signals and the signal characteristics between the first set and the first periodic signal, the multipath signal energy in the first set and the multipath signal energy in the second set, including: determining a fourth difference between the number of multipath signals in the first set and the number of multipath signals in the first periodic signal, and a fifth difference between the signal characteristics of the periodic signals in the first set and the signal characteristics of the first periodic signal; determining the discrete degree of energy of the periodic signals in the first set and the energy intensity of the periodic signals in the second set; and determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the fourth difference, the fifth difference, the discrete degree and the energy intensity.

[0098] In one embodiment, the degree of dispersion of the energy of the periodic signals in the first set may be measured by any one of the standard deviation, variance or coefficient of variation of the energy of the periodic signals in the first set.

[0099] In one embodiment, the energy intensity of the periodic signals in the second set may be measured by any one of a mean value, a median value, a sum of the energies of the periodic signals in the second set.

[0100] In one embodiment, the multipath difference value is positively correlated with the fourth difference value, the fifth difference value, the discreteness, and the energy intensity. That is, the larger the fourth difference value is, the larger the multipath difference value is; the smaller the fourth difference value is, the smaller the multipath difference value is. The larger the fifth difference value is, the larger the multipath difference value is; the smaller the fifth difference value is, the smaller the multipath difference value is. The larger the discreteness is, the larger the multipath difference value is; the smaller the discreteness is, the smaller the multipath difference value is. The larger the energy intensity is, the larger the multipath difference value is; the smaller the energy intensity is, the smaller the multipath difference value is.

[0101] In the above embodiment, the multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on the fourth difference between the number of multipath signals in the first set and the number of multipath signals of the first periodic signal, the fifth difference between the signal characteristics of the periodic signal in the first set and the signal characteristics of the first periodic signal, the discrete degree of the energy of the periodic signal in the first set and the energy intensity of the periodic signal in the second set. The multipath difference value between the periodic signal to be analyzed and the first periodic signal can be accurately determined based on the multipath effect of the signal, thereby improving the accuracy of defect detection on steel pipe columns used for foundation reinforcement.

[0102] In one embodiment, the multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined according to the fourth difference, the fifth difference, the degree of discreteness and the energy intensity, including: determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal according to the product of the fourth difference, the fifth difference, the degree of discreteness and the energy intensity.

[0103] In one embodiment, the computer device can determine the multipath difference value between the periodic signal to be analyzed and the first periodic signal based on the product of the fourth difference, the fifth difference, the standard deviation of the energy of the periodic signals in the first set, and the mean of the energy of the periodic signals in the second set.

[0104] In one embodiment, the multipath difference value between the periodic signal to be analyzed and the first periodic signal may be determined according to the following formula: in, Indicates the multipath difference between the periodic signal to be analyzed and the first periodic signal. represents the first set, Represents the second set. The energy of any signal in the first set may be represented by power. Represents the standard deviation of the energy of the periodic signal in the first set. The energy of any signal in the second set may be represented by power. represents the mean value of the energy of the periodic signals in the second set. Represents the first set The number of multipath signals in . Indicates the number of multipath signals in the first periodic signal. The fourth difference between the number of multipath signals in the first set and the number of multipath signals in the first periodic signal is represented. Indicates taking the absolute value. The signal characteristic representing the first periodic signal may be a two-dimensional vector. Represents the signal characteristics of the periodic signal in the first set. The fifth difference between the signal characteristics of the periodic signal in the first set and the signal characteristics of the first periodic signal is represented. Indicates modulo.

[0105] In the above embodiment, based on the fourth difference, the fifth difference, the product of the discreteness and the energy intensity, the multipath difference value between the periodic signal to be analyzed and the first periodic signal can be accurately determined through various multipath characteristics, thereby improving the accuracy of defect detection of steel pipe columns used for foundation reinforcement.

[0106] In one embodiment, the degree of distortion of each periodic signal is determined according to the abnormal possibility and multipath difference value corresponding to each periodic signal, including: taking each periodic signal as the periodic signal to be analyzed, determining the sum of the abnormal possibility of the first periodic signal and the abnormal possibility of the periodic signal to be analyzed; and determining the degree of distortion of the periodic signal to be analyzed according to the product of the sum of the abnormal possibilities and the multipath difference value of the periodic signal to be analyzed.

[0107] In one embodiment, the degree of distortion of the periodic signal to be analyzed may be determined according to the following formula: in, Indicates the degree of distortion of the periodic signal to be analyzed. Indicates the possibility of abnormality in the first periodic signal. Indicates the abnormal possibility of the periodic signal to be analyzed. Indicates the multipath difference value of the periodic signal to be analyzed.

[0108] It is understandable that the first period signal may have been affected by defects in the steel pipe column, resulting in signal distortion. For example, Figure 5The acoustic wave signal in the method has the problem of distortion of the first periodic signal, which may easily lead to inaccurate determination of the degree of distortion based directly on the multipath difference value between the periodic signal to be analyzed and the first periodic signal and the abnormal possibility of the periodic signal to be analyzed. Therefore, the abnormal possibility of the first periodic signal, the abnormal possibility of the periodic signal to be analyzed, and the multipath difference value between the periodic signal to be analyzed and the first periodic signal can be combined to determine the degree of distortion of the signal.

[0109] In the above embodiment, the distortion of the first periodic signal is taken into consideration, so that the degree of distortion can be determined more accurately, thereby improving the accuracy of defect detection on the steel pipe column used for foundation reinforcement.

[0110] In one embodiment, defects of the steel pipe column are determined according to the degree of distortion of each periodic signal, including: determining the maximum degree of distortion from the degrees of distortion of each periodic signal; determining the mean value of the degree of distortion after removing the maximum degree of distortion from the degrees of distortion of each periodic signal; and determining defects of the steel pipe column according to the difference between the maximum degree of distortion and the mean value of the degree of distortion.

[0111] The maximum distortion degree refers to the maximum value among the distortion degrees of each periodic signal. It can be understood that each periodic signal obtains a corresponding distortion degree, obtains a distortion degree sequence, and determines the maximum value from the distortion degree sequence as the maximum distortion degree.

[0112] In one embodiment, the greater the difference between the maximum distortion degree and the average distortion degree, the greater the difference is, indicating that there is obvious signal distortion in a period, and the distortion in the period is significantly different from the distortion in other periods.

[0113] In the above embodiment, the maximum distortion degree is determined from the distortion degrees of each periodic signal, and the average distortion degree after removing the maximum distortion degree from the distortion degrees of each periodic signal is determined. Then, based on the difference between the maximum distortion degree and the average distortion degree, the defects of the steel pipe column are judged, so that the defects of the steel pipe column used for foundation reinforcement can be accurately detected.

[0114] In one embodiment, defects of the steel pipe column are determined based on the difference between the maximum distortion degree and the mean distortion degree, including: determining a sixth difference between the maximum distortion degree and the mean distortion degree; determining a defect determination parameter value based on the product between the maximum distortion degree and the sixth difference; if the defect determination parameter value is greater than or equal to a preset value, it is determined that the steel pipe column has defects; if the defect determination parameter value is less than the preset value, it is determined that the steel pipe column does not have defects.

[0115] In one embodiment, the product of the maximum distortion degree and the sixth difference value may be normalized to obtain a defect determination parameter value, and then the defect determination parameter value may be compared with a preset value.

[0116] In one embodiment, the defect determination parameter value may be determined according to the following formula: in, Indicates the defect judgment parameter value. Represents the normalization function. Indicates the maximum distortion degree. I indicates the sequence number of the maximum distortion degree in the distortion degree sequence. It represents the mean value of the distortion degree after removing the maximum distortion degree from the distortion degrees of each periodic signal. Indicates taking the absolute value. Indicates the sixth difference between the maximum distortion level and the mean distortion level.

[0117] In one embodiment, the preset value can be set according to actual needs. For example, the preset value can be 0.9.

[0118] In the above embodiment, a defect judgment parameter value is determined. If the defect judgment parameter value is greater than or equal to a preset value, it is determined that the steel pipe column has defects. If the defect judgment parameter value is less than the preset value, it is determined that the steel pipe column does not have defects. This enables efficient and accurate defect judgment of steel pipe columns used for foundation reinforcement.

[0119] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for detecting defects in steel pipe columns for foundation reinforcement, characterized in that: The method comprises: Acquire the echo signal received after transmitting the sound wave to the steel pipe column; Dividing the echo signal into periods to obtain multiple periodic signals; Determining the abnormal possibility of each segment of the periodic signal according to the waveform characteristics of each segment of the periodic signal and the difference in waveform characteristics between adjacent periodic signals of the periodic signal; Determine the multipath difference value between each segment of the periodic signal and the first segment of the periodic signal according to the multipath characteristics of the signal; Determining the degree of distortion of each segment of the periodic signal according to the abnormal possibility and the multipath difference value corresponding to each segment of the periodic signal; Defects of the steel pipe string are determined according to the degree of distortion of each of the periodic signals.

2. A method for detecting defects in steel pipe columns for foundation reinforcement according to claim 1, characterized in that: The determining the abnormal possibility of each segment of the periodic signal according to the waveform characteristics of each segment of the periodic signal and the waveform characteristic differences between adjacent periodic signals of the periodic signal respectively includes: Fitting each segment of the periodic signal with a sine wave function according to the waveform characteristics of each segment of the periodic signal to obtain a fitting result of each segment of the periodic signal; Determining the probability that the waveform of each segment of the periodic signal is normal according to the fitting result of each segment of the periodic signal; Taking each segment of the periodic signal as a periodic signal to be analyzed, and determining the waveform feature difference between the adjacent periodic signals according to the fitting results of the adjacent periodic signals of the periodic signal to be analyzed and the normal possibility of the waveform; The possibility of abnormality of the periodic signal to be analyzed is determined according to the possibility of normal waveform of the periodic signal to be analyzed and the difference in waveform characteristics between adjacent periodic signals of the periodic signal to be analyzed.

3. A method for detecting defects in steel pipe columns for foundation reinforcement according to claim 2, characterized in that: The fitting result includes a correction determination coefficient and a fitting frequency; and determining the possibility of a normal waveform of each segment of the periodic signal according to the fitting result of each segment of the periodic signal includes: For each segment of the periodic signal, determining a first difference between an actual frequency of the periodic signal and a corresponding fitting frequency; The possibility that the waveform of the periodic signal is normal is determined based on the correction determination coefficient corresponding to the periodic signal and the first difference.

4. A method for detecting defects in steel pipe columns for foundation reinforcement according to claim 2, characterized in that: The fitting result includes fitting parameters; the waveform feature difference between the adjacent periodic signals includes a second difference between the normal possibilities of the waveforms of the adjacent periodic signals and a third difference between the fitting parameters of the adjacent periodic signals; The determining, based on the normal possibility of the waveform of the periodic signal to be analyzed and the waveform feature difference between adjacent periodic signals of the periodic signal to be analyzed, the abnormal possibility of the periodic signal to be analyzed comprises: The possibility of abnormality of the periodic signal to be analyzed is determined based on the possibility of normal waveform of the periodic signal to be analyzed and the second difference and the third difference between adjacent periodic signals of the periodic signal to be analyzed.

5. A method for detecting defects in steel pipe columns for foundation reinforcement according to claim 1, characterized in that: The step of determining the multipath difference value between each segment of the periodic signal and the first segment of the periodic signal according to the multipath characteristics of the signal comprises: Each segment of the periodic signal is used as a periodic signal to be analyzed, and each segment of the periodic signal except the first segment of the periodic signal before the periodic signal to be analyzed is divided into a first set and a second set; the propagation time length of the periodic signal in the first set is shorter than the propagation time length of the periodic signal in the second set; The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined based on the difference between the number and signal characteristics of multipath signals in the first set and the first periodic signal, the multipath signal energy in the first set and the multipath signal energy in the second set.

6. A method for detecting defects in steel pipe columns for foundation reinforcement according to claim 5, characterized in that: The determining, according to the difference between the number and signal characteristics of multipath signals of the first set and the first periodic signal, the multipath signal energy in the first set and the multipath signal energy in the second set, the multipath difference value between the periodic signal to be analyzed and the first periodic signal comprises: Determine a fourth difference between the number of multipath signals in the first set and the number of multipath signals of the first periodic signal, and a fifth difference between signal characteristics of periodic signals in the first set and the signal characteristics of the first periodic signal; Determining the discrete degree of energy of the periodic signals in the first set and the energy intensity of the periodic signals in the second set; A multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined according to the fourth difference, the fifth difference, the discrete degree and the energy intensity.

7. A method for detecting defects in steel pipe columns for foundation reinforcement according to claim 6, characterized in that: The step of determining the multipath difference value between the periodic signal to be analyzed and the first periodic signal according to the fourth difference, the fifth difference, the discrete degree, and the energy intensity includes: The multipath difference value between the periodic signal to be analyzed and the first periodic signal is determined according to the product of the fourth difference, the fifth difference, the discrete degree and the energy intensity.

8. A method for detecting defects in a steel pipe column for foundation reinforcement according to any one of claims 1 to 7, characterized in that: The step of determining the degree of distortion of each segment of the periodic signal according to the abnormal possibility and the multipath difference value corresponding to each segment of the periodic signal comprises: Taking each section of the periodic signal as the periodic signal to be analyzed, determining the sum of the abnormal possibility of the first section of the periodic signal and the abnormal possibility of the periodic signal to be analyzed; The degree of distortion of the periodic signal to be analyzed is determined according to the product of the sum of the abnormal possibilities and the multipath difference value of the periodic signal to be analyzed.

9. A method for detecting defects in a steel pipe column for foundation reinforcement according to any one of claims 1 to 7, characterized in that: The step of determining defects of the steel pipe string according to the degree of distortion of each of the periodic signals comprises: Determining a maximum distortion degree from the distortion degrees of each of the periodic signals; Determine a mean value of the degree of distortion after removing the maximum degree of distortion from the degrees of distortion of each of the periodic signals; Defects of the steel pipe column are determined based on the difference between the maximum distortion degree and the average distortion degree.

10. A method for detecting defects in steel pipe columns for foundation reinforcement according to claim 9, characterized in that: The step of determining defects of the steel pipe column according to the difference between the maximum distortion degree and the average distortion degree includes: determining a sixth difference between the maximum distortion degree and the mean distortion degree; Determining a defect determination parameter value according to a product of the maximum distortion degree and the sixth difference value; If the defect determination parameter value is greater than or equal to a preset value, it is determined that the steel pipe column has a defect; If the defect determination parameter value is less than the preset value, it is determined that the steel pipe column has no defects.

Images