A method, device and related equipment for detecting internal corrosion of an oil pipeline

Abstract

The application relates to the technical field of oil and gas pipeline detection, in particular to an oil pipeline internal corrosion detection method and device and related equipment, the method comprising the following steps: collecting ultrasonic signals received by an ultrasonic probe at each position of an oil pipeline and images of the oil pipeline at all corresponding positions currently collected; the ultrasonic signals contain a plurality of echo signals; analyzing ultrasonic signal characteristics at each position of the oil pipeline and distribution of each position in the images, and constructing a wavelet soft threshold function; performing wavelet denoising on the ultrasonic signals at each position by using the wavelet soft threshold function, and performing oil pipeline internal corrosion detection on the denoised ultrasonic signals. The application aims to improve the accuracy of oil pipeline internal corrosion detection, positioning and corrosion degree diagnosis, and realize high-precision detection of oil pipeline internal corrosion.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas pipeline detection, in particular to an oil pipeline internal corrosion detection method, device and related equipment. BACKGROUND

[0002] With the continuous growth of global energy demand, oil pipelines as the key infrastructure for oil transportation, their safety and reliability are particularly important. In the complex natural environment and long-term operation process, oil pipelines are inevitably affected by internal corrosion, which not only threatens the physical integrity of the pipeline, but also can cause leakage, explosion and other serious accidents, posing a huge risk to the ecological environment and public safety. Therefore, developing effective internal corrosion detection technology is crucial to ensure the safety of energy transportation. At present, the industry has adopted a variety of non-destructive detection methods and technologies to monitor and evaluate the health status of the pipeline, such as ultrasonic testing (UT), magnetic flux leakage detection (MFL), intelligent pigging (Pigging), etc.

[0003] In the existing oil pipeline internal corrosion detection, ultrasonic detection has become one of the non-destructive detection methods due to its unique advantages; however, ultrasonic detection is easily disturbed by environmental noise, resulting in certain errors in the detection results, so a denoising algorithm is needed to filter out the noise, and the wavelet threshold denoising is widely used due to its good time-frequency characteristics. In wavelet threshold denoising, the determination of the threshold is the key to the denoising effect, and in the prior art, the determination of the wavelet threshold function only considers the statistical characteristics of the signal, without considering the time-frequency domain changes of the signal and whether the signal meets the characteristics of the internal corrosion of the pipeline, so the threshold function obtained cannot completely adapt to the changes of the signal, and the detection effect of the denoised signal on the internal corrosion defects of the pipeline is also poor. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide an oil pipeline internal corrosion detection method, device and related equipment, and the technical solution adopted is as follows:

[0005] In a first aspect, the present application provides an oil pipeline internal corrosion detection method, which comprises the following steps:

[0006] Step one: collecting the ultrasonic wave signals received by the ultrasonic probe at each position of the oil pipeline and the images of the oil pipeline at all corresponding positions currently collected; the ultrasonic wave signals contain a plurality of echo signals;

[0007] Step two: analyzing the ultrasonic signal characteristics at each position of the oil pipeline and the distribution of each position in the image, and constructing a wavelet soft threshold function;

[0008] S1, calculate the pipeline corrosion coefficient at each position according to the number and peak value distribution trend characteristics of all echo signals in the ultrasonic signal, and screen out suspected internal corrosion positions;

[0009] S2, calculate the spatial corrosion coefficient of the current all ultrasonic probes detecting the oil pipeline according to the curvature distribution characteristics of the edge points around each suspected internal corrosion position in the image, the pipeline corrosion coefficient of each suspected internal corrosion position, and the pipeline corrosion coefficient of the neighboring suspected internal corrosion positions around each suspected internal corrosion position;

[0010] S3, construct a wavelet soft threshold function using the spatial corrosion coefficient;

[0011] Step three: use the wavelet soft threshold function to perform wavelet denoising on the ultrasonic signal at each position, and perform internal corrosion detection on the denoised ultrasonic signal.

[0012] Preferably, the method for calculating the pipeline corrosion coefficient is:

[0013] Let the pipeline corrosion coefficient at the i-th position be A i , In the formula, N i is the number of echoes received by the ultrasonic probe at the i-th position, C i is the mean value of the first-order difference sequence of the echo sequence at the i-th position, is the average number of echoes received by all ultrasonic probes in the neighborhood of the i-th position, D i is the ratio of the number of negative numbers to the number of positive numbers in the derivative of each index position on the fitting curve of the echo sequence at the i-th position, R i is the determination coefficient of the fitting curve of the echo sequence at the i-th position, is the mean value of the first-order difference sequence of all echo sequences in the neighborhood of the i-th position.

[0014] Preferably, the echo sequence is composed of all peak values greater than the average peak value in the ultrasonic signal at the position.

[0015] Preferably, the screening method for suspected internal corrosion positions is:

[0016] Obtain a segmentation threshold value in the pipeline corrosion coefficients at all positions; mark the positions corresponding to all pipeline corrosion coefficients greater than the segmentation threshold value as suspected internal corrosion positions.

[0017] Preferably, the method for calculating the spatial corrosion coefficient of the current all ultrasonic probes detecting the oil pipeline is:

[0018] Let the spatial corrosion coefficient of the current all ultrasonic probes detecting the oil pipeline be B;

[0019] In the formula, P1 is the correlation degree between the curvature sequence and the corrosion variance sequence, P2 is the correlation degree between the curvature sequence and the corrosion coefficient sequence, and σ is the longitudinal distribution characteristic of the internal corrosion defect;

[0020] In the formula, J is the number of suspected internal corrosion positions, σ j1 , and σ j2 are the variances of the first echo sequence and the second echo sequence of the jth suspected internal corrosion position, respectively.

[0021] Preferably, the construction method of the curvature sequence, the corrosion variance sequence, and the corrosion coefficient sequence is as follows:

[0022] All edge points on the edge lines in the image are obtained, and the curvatures of the edge points on the curve after fitting of the edge lines are calculated;

[0023] The average curvature of the first preset number of edge points closest to each suspected internal corrosion position in the image is taken as the curvature of the suspected internal corrosion position;

[0024] The curvatures of all suspected internal corrosion positions are taken to form a curvature sequence;

[0025] The pipeline corrosion coefficients of all suspected internal corrosion positions are taken to form a corrosion coefficient sequence;

[0026] The variances of the pipeline corrosion coefficients of each suspected internal corrosion position and the second preset number of adjacent suspected internal corrosion positions are calculated, and the variances calculated for all suspected internal corrosion positions are taken to form a corrosion variance sequence.

[0027] Preferably, the construction method of the first echo sequence and the second echo sequence is as follows:

[0028] Among all the echoes received by the ultrasonic probe at each position, the first three echoes with the largest peak values are selected;

[0029] According to the acquisition sequence of the three echoes, the signal between the peak values in the first echo and the second echo is taken to form a first echo sequence, and the signal between the peak values in the second echo and the third echo is taken to form a second echo sequence.

[0030] Preferably, the construction method of the wavelet soft threshold function is as follows:

[0031] In the formula, T(ω, x, λ) is the wavelet soft threshold function, x is a wavelet transform coefficient obtained by performing wavelet transform on the ultrasonic signal to be denoised, λ is a preset threshold, ω = 1-Norm(B), Norm() is a normalization function, and B is the spatial corrosion coefficient of the oil pipeline detected by the current ultrasonic probe.

[0032] In a second aspect, the embodiments of the present application provide an oil pipeline internal corrosion detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the oil pipeline internal corrosion detection method described above when executing the computer program.

[0033] In a third aspect, the embodiments of the present application also provide an oil pipeline internal corrosion detection related device, which stores a computer program, and the computer program implements the oil pipeline internal corrosion detection method described above when executed by a processor.

[0034] As can be seen from the above embodiments, the oil pipeline internal corrosion detection method, device and related device provided by the embodiments of the present application have at least the following beneficial effects:

[0035] According to the number and peak value distribution trend characteristics of all echo signals in the ultrasonic signal, the pipeline corrosion coefficient at each position is calculated, which is used to measure whether the ultrasonic signal at the position conforms to the change characteristics of the internal corrosion of the oil pipeline, thereby improving the accuracy of the comparison and quantization of the signal characteristics of each position of the pipeline with the internal corrosion signal characteristics, and enabling the suspected internal corrosion positions to be preliminarily screened out more accurately. Then, according to the curvature distribution characteristics of the edge points around each suspected internal corrosion position in the image, the pipeline corrosion coefficient of each suspected internal corrosion position, and the pipeline corrosion coefficient of the adjacent suspected internal corrosion positions around each suspected internal corrosion position, the spatial corrosion coefficient of the oil pipeline detected by all ultrasonic probes is calculated, thereby further improving the accuracy of the corrosion feature recognition. Finally, the spatial corrosion coefficient is used to construct a wavelet soft threshold function, thereby realizing the adaptive adjustment of the wavelet threshold, enhancing the adaptability of the wavelet denoising and the signal, and helping to retain more signal detail characteristics of the internal corrosion defects. In this way, when the wavelet denoising is performed, not only the statistical characteristics of the signal are considered, but also whether the signal conforms to the internal corrosion defect characteristics is considered, and adaptive adjustment is performed based on this, thereby retaining more internal corrosion defect signal characteristics, reducing the probability of false judgment and missed judgment in subsequent oil pipeline internal corrosion detection, improving the accuracy of internal corrosion detection, positioning and corrosion degree diagnosis, and thereby realizing high-precision detection of the internal corrosion of the oil pipeline. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any inventive labor.

[0037] Figure 1A step flow chart of a method for detecting corrosion in an oil pipeline is provided in the present application.

[0038] Figure 2 A process flow chart of constructing a wavelet soft threshold function is provided in the present application. DETAILED DESCRIPTION

[0039] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of the method, device and related equipment for detecting corrosion in an oil pipeline according to the present application are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined and limited, such as the term "comprise", "include" or any other variant thereof, is intended to cover non-exclusive inclusion, so that the circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. Without more limitation, the element limited by the phrase "comprising one" does not exclude the presence of another identical element in the article or device comprising the element. In addition, the term "and / or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as understood by those skilled in the art of the technology to which the present application belongs.

[0041] The specific scheme of the method, device and related equipment for detecting corrosion in an oil pipeline according to the present application is described in detail below in combination with the accompanying drawings.

[0042] Please refer to Figure 1 which shows a step flow chart of a method for detecting corrosion in an oil pipeline according to one embodiment of the present application, which includes the following steps:

[0043] Step one: collect the ultrasonic signals received by the ultrasonic probe at each position of the oil pipeline and the images of the oil pipeline at all corresponding positions currently collected; the ultrasonic signals contain a plurality of echo signals.

[0044] The multi-probe array ultrasonic detection head comprises 32 probes, forms a set of parallel processing units, the working frequency of the probes is 5 MHz, the bandwidth is 2.5 MHz, the wafer diameter is 20 mm, each ultrasonic probe in the multi-probe array ultrasonic detection head is a transceiving integrated probe, can emit ultrasonic waves, and can receive ultrasonic signals at the same time, wherein the ultrasonic signals contain multiple echo signals, and the received echo signals are reflected echoes.

[0045] The ultrasonic signals at each position of the oil pipeline are collected by axial movement along the oil pipeline, and at least 20% of the overlapping area of adjacent two collection positions is required to avoid missed detection.

[0046] The image of the oil pipeline is collected by the CCD camera, the collected image is taken as input, a median filtering algorithm is used, and a denoised image is output, and the median filtering algorithm is a known technology and will not be described in detail.

[0047] Step two: analyze the ultrasonic signal characteristics at each position of the oil pipeline and the distribution of each position in the image, and construct a wavelet soft threshold function.

[0048] After the ultrasonic signals are collected, the noise signals need to be filtered out by using a denoising algorithm to improve the data quality because the ultrasonic signals are easily disturbed by environmental noise. Wavelet threshold denoising has good time-frequency characteristics and is widely used, and the threshold function has a decisive influence on the denoising effect. The traditional way is to determine the threshold function according to the statistical characteristics of the signal, but when the defect appears in the early stage or the statistical signal characteristics change is not obvious, the denoising effect is poor, and secondly, this way cannot accurately distinguish whether the change of statistical characteristics is caused by too much noise or by the existence of the oil pipeline defect, so that the denoised signal by using the traditional threshold determination method has poor detection effect on the corrosion defect in the oil pipeline.

[0049] In the present application, the process flow chart for constructing the wavelet soft threshold function is shown in the accompanying Figure 2 , and specifically comprises:

[0050] S1, according to the number and peak value distribution trend characteristics of all echo signals in the ultrasonic signal, calculate the pipeline corrosion coefficient at each position, and screen out suspected internal corrosion positions.

[0051] When the ultrasonic signal propagates, it will cause the change of acoustic impedance when encountering the change of medium or discontinuity, thereby generating echo. Therefore, the condition of the pipeline can be determined according to the time of the echo. Under normal circumstances, when the pipeline is defect-free and the surface of the pipeline is relatively smooth, the acoustic impedance of the medium inside the pipeline remains almost constant, and the ultrasonic wave is vertically incident, thereby causing reflection on the inner and outer surfaces of the pipeline. Due to the relatively smooth surface of the pipeline, the degree of scattering is low, the number of echoes obtained is small, the signal loss is small, and the signal loss of adjacent echoes has certain regularity. At the same time, due to the signal loss, the peak values of each echo decrease monotonously in time sequence.

[0052] When the internal corrosion defect occurs in the oil pipeline, the inner wall surface of the pipeline becomes rough, and the ultrasonic wave will scatter when encountering the uneven surface, thereby causing the main echo that should be concentrated to be scattered into multiple small echoes in different directions, and further causing the echo to become more complex and contain more reflection points, i.e. the number of echoes increases. At the same time, scattering will cause large energy loss, thereby causing the peak intensity of a single echo to be low. Secondly, the scattered waves will be detected by the ultrasonic probes near the internal corrosion defect, thereby causing more echoes to be received by these ultrasonic probes. However, the original echo intensity received by these ultrasonic probes at their respective corresponding positions will not decrease, and even a larger peak intensity will appear due to wave superposition. At the same time, the change of the echo received by each ultrasonic probe is more complex, thereby destroying the monotonous decreasing characteristic of each echo peak value in time sequence.

[0053] The mean value of all peak values of the ultrasonic signal collected by an ultrasonic probe at its corresponding position is calculated. The peak values greater than the mean value are arranged in time sequence to form an echo sequence at the position, and a first-order difference sequence of the echo sequence is obtained. Then, the echo sequence is taken as the input, and a fitting curve is obtained by polynomial fitting. The calculation of the first-order difference sequence and the method of polynomial fitting are both known technologies, and will not be described in detail.

[0054] Based on the above analysis, the pipeline corrosion coefficient at each position is calculated to measure whether the ultrasonic signal at the position conforms to the change characteristics of the internal corrosion of the oil pipeline.

[0055] Taking the ultrasonic signal at the i-th position as an example, the pipeline corrosion coefficient A i at the i-th position is calculated, and the expression is as follows:

[0056] In the formula, N i is the number of echoes received by the ultrasonic probe at the i-th position, C i is the mean value of the first-order difference sequence of the echo sequence at the i-th position, and is the average number of echoes received by all ultrasonic probes in the neighborhood of the i-th position, D i is the ratio of the number of negative numbers to the number of positive numbers in the derivative of the fitting curve of the echo sequence at the i-th position, R i is the determination coefficient of the fitting curve of the echo sequence at the i-th position, is the average of the first-order difference sequence of all echo sequences in the neighborhood of the i-th position.

[0057] In this embodiment, the neighborhood of the i-th position includes n adjacent positions before and after the i-th position. In this embodiment, n is 10, which can be set by the implementer.

[0058] It can be understood that when the oil pipeline is normal, the degree of scattering is low due to the smooth surface of the pipeline, the loss of echoes is less, and there is no defect, which also makes the number of echoes less, i.e. N i , C i are small. Secondly, due to the low degree of scattering, the influence on each ultrasonic probe in the surrounding neighborhood is small, and the loss of echoes at the current position is similar, so that is small, is large. At the same time, the echoes change monotonically and decrease, and the loss of echoes has certain regularity, so that the fitting effect of the fitting curve is better, i.e. D i is large, R i is also large. Therefore, the pipeline corrosion coefficient A i is relatively small.

[0059] When there is an internal corrosion defect in the oil pipeline, the surrounding neighborhood will be affected by the signal scattering at the internal corrosion defect, making the echoes received by the corresponding ultrasonic probe more complex, i.e. the number of echoes is more; secondly, due to the superposition of waves, the echoes will be larger, so that the difference in the loss of adjacent echoes is more obvious, i.e. is negative and small. The rest of the features are opposite, so that the pipeline corrosion coefficient A i is relatively large.

[0060] According to the above method, the pipeline corrosion coefficient at each position is calculated, and all pipeline corrosion coefficients at the positions are taken as input. The Otsu threshold segmentation method is used to output the segmentation threshold. The Otsu threshold segmentation is a known technology and will not be described in detail. All positions corresponding to pipeline corrosion coefficients greater than the segmentation threshold are marked as suspected internal corrosion positions.

[0061] S2, according to the curvature distribution characteristics of each suspected internal corrosion position in the image, the pipeline corrosion coefficient of each suspected internal corrosion position, and the pipeline corrosion coefficient of the suspected internal corrosion position adjacent to each suspected internal corrosion position, the spatial corrosion coefficient of the oil pipeline detected by all ultrasonic probes is calculated.

[0062] Since the change of the echo received by the ultrasonic probe is susceptible to the environment and the state of the pipeline, there is certain limitation and error in the analysis according to the change of the echo received by the ultrasonic probe only, and therefore further combination of the spatial change characteristics of the corrosion in the oil pipeline is needed for further judgment.

[0063] The main reason for the internal corrosion of the oil pipeline is electrolytic corrosion, that is, when the water phase layer or the precipitate deposited in the oil pipeline is more, the electrolyte will cause serious corrosion to the oil pipeline. Therefore, under normal circumstances, the water phase layer or the precipitate deposited in each part of the oil pipeline is less or even none, so that the internal corrosion of the corresponding pipeline position is relatively light; when the water phase layer or the precipitate deposited in the oil pipeline is more, the internal corrosion of the corresponding position will be relatively serious, and the deposition of the water phase layer or the precipitate tends to be at the bottom of the pipeline, so the distribution characteristics of the internal corrosion in the longitudinal direction is that the closer to the bottom of the pipeline, the more serious the internal corrosion.

[0064] Secondly, the accumulation of the water phase layer and the precipitate in the pipeline is also related to the flow state of the medium in the pipeline, when the flow state of the medium in the pipeline changes greatly (such as the positions of elbows, valves, tees, etc.), the increase of the turbulent flow will cause the erosion corrosion to be intensified, that is, the internal corrosion appears in a larger area, and multiple ultrasonic probes adjacent to each other will detect similar internal corrosion signals; while at the positions where the flow state of the medium in the pipeline changes less (such as a relatively flat section of the pipeline), the water phase layer or the precipitate will be accumulated locally due to the slow flow speed, so that the downward corrosion is more serious; that is, the internal corrosion area is positively correlated with the change of the flow state of the medium in the pipeline, and the severity of the internal corrosion is negatively correlated with the change of the flow state of the medium in the pipeline. And since the bending degree of the elbows, valves, tees and the like is large, while the bending degree of the relatively flat section of the pipeline is small, the change of the flow state of the medium in the pipeline is positively correlated with the bending degree of the pipeline.

[0065] Accordingly, the image of the oil pipeline after denoising is taken as the input, the Canny edge detection algorithm is used to output the edge line of the oil pipeline and the coordinates of each point on the edge line; the coordinates of each edge point on the edge line are taken as the input, the polynomial fitting method is used to output the fitting curve of the oil pipeline and to obtain the curvature of each edge point on the fitting curve, and the processes of Canny edge detection and polynomial fitting are both known technologies and will not be described in detail.

[0066] The mean value of the curvatures of the first preset number of edge points closest to each suspected internal corrosion position in the image is taken as the curvature of the suspected internal corrosion position; the curvatures of the suspected internal corrosion positions are arranged in a curvature sequence according to the order from left to right and from top to bottom in the image; the pipeline corrosion coefficients of the suspected internal corrosion positions are arranged in a corrosion coefficient sequence according to the same order as the curvature sequence; and the variance of the pipeline corrosion coefficients of each suspected internal corrosion position and the second preset number of adjacent suspected internal corrosion positions is calculated, and the variances obtained by the suspected internal corrosion positions are arranged in a corrosion variance sequence according to the same order as the curvature sequence. In the embodiment, the first preset number is 5, and the second preset number is 10, which can be set by the implementer.

[0067] Due to the most significant change of acoustic impedance at the interface where the medium changes, the echo generated by the interface is the largest. Among the echoes received by the ultrasonic probe at each position, the first three echoes with the largest peak values are selected, and the signal between the peak values in the first echo and the second echo is taken as a first echo sequence, and the signal between the peak values in the second echo and the third echo is taken as a second echo sequence.

[0068] Based on the above analysis, the spatial corrosion coefficient B of the current all ultrasonic probes detecting the oil pipeline is calculated, which is used to measure the characteristics of the internal corrosion in the pipeline in space.

[0069] In the formula, P1 is the correlation degree between the curvature sequence and the corrosion variance sequence, P2 is the correlation degree between the curvature sequence and the corrosion coefficient sequence, and σ is the longitudinal distribution characteristic of the internal corrosion defect. In the embodiment, the correlation degree is calculated by using the Pearson correlation coefficient.

[0070] In the formula, J is the number of suspected internal corrosion positions, σ j1 and σ j2 are the variances of the first echo sequence and the second echo sequence of the jth suspected internal corrosion position, respectively.

[0071] It can be understood that when the internal corrosion defect characteristics are met, the closer the defect is to the bottom of the pipeline, the more serious the internal corrosion is, and the more serious the internal corrosion is, the more complex the corresponding echo is, and the greater the variance of the corresponding echo sequence is. The first echo sequence is the echo condition on the upper side of the inner surface of the pipeline, and the second echo sequence is the echo condition on the lower side of the inner surface of the pipeline, so that σ j1 is less than σ j2That is, σ is small; secondly, the inner corrosion area is positively correlated with the change of the pipeline medium flow state, and the inner corrosion severity is negatively correlated with the change of the pipeline medium flow state, so that P1 is positive and large, and P2 is negative and small; therefore, the space corrosion coefficient B is large. Conversely, when the inner corrosion defect characteristics are not met, the space corrosion coefficient B is small.

[0072] S3, constructing a wavelet soft threshold function by using the space corrosion coefficient.

[0073] In the determination of the traditional wavelet threshold function, the threshold function is constructed only according to the statistical characteristics of the signal, however, the threshold obtained by using this construction method for wavelet denoising may also remove some inner corrosion characteristics in the signal as noise, thereby causing a relatively large error in the detection of the inner corrosion defect of the oil pipeline using the denoised signal.

[0074] When the oil pipeline does not have an inner corrosion defect, the fluctuation of the ultrasonic signal is affected by external noise, at this time, the threshold function is determined according to the statistical characteristics for denoising, which may cause the threshold to be too small, resulting in the retention of part of the noise signal, and the retained noise signal will affect the accuracy of the inner corrosion detection when detecting the inner corrosion of the oil pipeline, thereby causing a false judgment. When the oil pipeline has an inner corrosion defect, the corresponding ultrasonic signal fluctuation is relatively severe, at this time, the threshold function is determined only according to the statistical characteristics, which will determine a larger threshold, thereby improving the denoising effect, however, at this time, the fluctuation is mainly caused by the inner corrosion detection, and these fluctuations reflect the degree of the inner corrosion defect and other information, if directly removed, it will also affect the accuracy of the subsequent inner corrosion detection, and even cause a missed judgment.

[0075] Therefore, in order to improve the accuracy of the subsequent inner corrosion detection, more features related to the inner corrosion defect need to be retained, thereby constructing an adaptive wavelet soft threshold function.

[0076] T(ω, x, λ) is a wavelet soft threshold function, x is a wavelet transform coefficient, λ is a preset threshold, and ω is an adaptive transform coefficient for measuring whether the signal meets the inner corrosion defect characteristics. Wherein, ω = 1-Norm(B), Norm(B) is a normalization function, which normalizes the space corrosion coefficient to [0, 1]. The wavelet transform coefficient is obtained by performing wavelet transform on the corresponding ultrasonic signal to be denoised.

[0077] In one embodiment of the present embodiment, λ is determined by the VisuShrink threshold calculation method, and the calculation process of the VisuShrink threshold is known as a known technology, and will not be described in detail.

[0078] It can be understood that when the oil pipeline has the internal corrosion defect feature, the spatial corrosion coefficient of the oil pipeline is larger, so that the normalized value is larger, and then ω is smaller, and the threshold value obtained by wavelet soft threshold function calculation is smaller. At this time, the details of the signal are retained, the signal is prevented from being excessively smoothed, so that the accuracy of subsequent internal corrosion detection can be improved, and the missed judgment can be reduced. Conversely, when the oil pipeline does not have the internal corrosion defect feature, the obtained threshold value is larger, which is helpful to more thoroughly remove the noise, and can also reduce the misjudgment probability of subsequent internal corrosion detection.

[0079] Step three: using the wavelet soft threshold function to perform wavelet denoising on the ultrasonic signal at each position, and performing oil pipeline internal corrosion detection on the denoised ultrasonic signal.

[0080] When the internal corrosion of the oil pipeline is detected, first, the spatial corrosion coefficient is calculated according to the characteristic change of the ultrasonic signal of the oil pipeline, and the adaptive wavelet threshold function is constructed.

[0081] Then, the ultrasonic signal is taken as input, wavelet threshold denoising is adopted, and the denoised signal is output. Finally, the denoised signal is taken as input, and the ultrasonic guided wave detection technology is adopted to realize the detection, positioning and corrosion degree evaluation of the internal corrosion defect of the oil pipeline. The wavelet threshold denoising and the ultrasonic guided wave detection technology are known technologies, and will not be described in detail.

[0082] In this way, when the wavelet threshold is determined, not only the statistical characteristics of the signal are considered, but also whether the change characteristics of the signal conform to the characteristics of the internal corrosion defect of the oil pipeline is judged, so that more signal detail characteristics of the internal corrosion defect are retained. When the internal corrosion defect is detected subsequently, the misjudgment and the missed judgment of the internal corrosion defect are reduced, the accuracy of the detection, positioning and corrosion degree evaluation of the internal corrosion defect is improved, and the high-precision detection of the internal corrosion of the oil pipeline is realized.

[0083] Based on the same inventive concept as the above method, the embodiments of the present application also provide an oil pipeline internal corrosion detection device, which comprises a memory, a processor and a computer program stored in the memory and running on the processor. The processor implements the steps of the oil pipeline internal corrosion detection method according to any one of the above methods when executing the computer program.

[0084] Based on the same inventive concept as the above method, the embodiments of the present application also provide an oil pipeline internal corrosion detection related equipment, which stores a computer program. The computer program is executed by a processor to implement the steps of the oil pipeline internal corrosion detection method according to any one of the above methods.

[0085] The various embodiments in the application are described in progressive manner, and the same or similar parts among the various embodiments can be mutually referred to, and each embodiment focuses on the difference from other embodiments.

[0086] It should be noted that, unless otherwise specified and limited, terms such as "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that the circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such article or device. Without more limitations, the element defined by the phrase "including a" does not exclude the presence of another same element in the article or device including the element. In addition, the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0087] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the application cover any and all variations of the application that come within the scope of the general concept of the application and that the application include all such variations as fall within the scope of the invention. It is intended that the application encompass all such variations as fall within the scope of the invention.

[0088] It should be understood that the application is not limited to the precise construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof.

Claims

1. A method of detecting internal corrosion in an oil pipeline, characterized by, The method comprises the following steps: Step one: collecting the ultrasonic signals received by the ultrasonic probe at each position of the oil pipeline and the images of the oil pipeline at all corresponding positions currently collected; the ultrasonic signals contain a plurality of echo signals; Step two: analyzing the ultrasonic signal characteristics at each position of the oil pipeline and the distribution of each position in the image, and constructing a wavelet soft threshold function; S1, according to the number and peak value distribution trend characteristics of all echo signals in the ultrasonic signal, calculating the pipeline corrosion coefficient at each position, and screening out suspected internal corrosion positions; S2, according to the curvature distribution characteristics of the edge points around each suspected internal corrosion position in the image, the pipeline corrosion coefficient of each suspected internal corrosion position, and the pipeline corrosion coefficient of the adjacent suspected internal corrosion positions around each suspected internal corrosion position, calculating the spatial corrosion coefficient of the oil pipeline detected by all ultrasonic probes currently; S3, constructing the wavelet soft threshold function by using the spatial corrosion coefficient; Step three: using the wavelet soft threshold function to perform wavelet denoising on the ultrasonic signal at each position, and performing internal corrosion detection on the denoised ultrasonic signal.

2. A method of detecting corrosion in an oil pipeline as claimed in claim 1, wherein, The method for calculating the pipeline corrosion coefficient is: Let the pipeline corrosion coefficient at the ith position be denoted as A i , In the formula, N i is the number of echoes received by the ultrasonic probe at the ith position, C i is the mean value of the first-order difference sequence of the echo sequence at the ith position, is the average number of echoes received by all ultrasonic probes in the neighborhood of the ith position, D i is the ratio of the number of negative numbers to the number of positive numbers in the derivative of each index position on the fitted curve of the echo sequence at the ith position, R i is the determination coefficient of the fitted curve of the echo sequence at the ith position, is the mean value of the first-order difference sequence of all echo sequences in the neighborhood of the ith position.

3. A method of inspecting a pipeline for internal corrosion as defined in claim 2, wherein The echo sequence is composed of all peak values greater than the average peak value in the ultrasonic signal at the position.

4. A method of inspecting a pipeline for internal corrosion as defined in claim 1, wherein The method for screening the suspected internal corrosion position is: Obtain the segmentation threshold in the pipeline corrosion coefficient at all positions; mark the positions corresponding to all pipeline corrosion coefficients greater than the segmentation threshold as suspected internal corrosion positions.

5. A method of inspecting a pipeline for internal corrosion as defined in claim 1, wherein The method for calculating the spatial corrosion coefficient of the oil pipeline detected by all ultrasonic probes currently is: Let the spatial corrosion coefficient of the oil pipeline detected by all ultrasonic probes currently be B; In the formula, P1 is the correlation degree between the curvature sequence and the corrosion variance sequence, P2 is the correlation degree between the curvature sequence and the corrosion coefficient sequence, and σ is the longitudinal distribution characteristic of the internal corrosion defect. where J is the number of suspected internal corrosion locations, σ j1 , σ j2 are the variances of the first echo sequence, the second echo sequence of the jth suspected internal corrosion location, respectively.

6. A method of detecting corrosion in an oil pipeline as defined in claim 5, wherein The construction method of the curvature sequence, the corrosion variance sequence and the corrosion coefficient sequence is: Obtain all edge points on the edge lines in the image, and calculate the curvature of the edge points on the curve after fitting the edge lines; Take the mean curvature of the first preset number of edge points closest to each suspected internal corrosion position in the image as the curvature of the suspected internal corrosion position; Construct a curvature sequence from the curvatures of all suspected internal corrosion positions; Construct a corrosion coefficient sequence from the pipeline corrosion coefficients of all suspected internal corrosion positions; Calculate the variance of the pipeline corrosion coefficients of each suspected internal corrosion position and the second preset number of adjacent suspected internal corrosion positions, and construct a corrosion variance sequence from the variances calculated for all suspected internal corrosion positions.

7. A method of inspecting a pipeline for internal corrosion as defined in claim 5, wherein The construction method of the first echo sequence and the second echo sequence is: Select the first three echoes with the largest peak values from all echoes received by the ultrasonic probe at each position; According to the collection order of the three echoes, the signal between the peak values in the first echo and the second echo forms the first echo sequence, and the signal between the peak values in the second echo and the third echo forms the second echo sequence.

8. A method of inspecting a pipeline for internal corrosion as defined in claim 1, wherein The construction method of the wavelet soft threshold function is: In the formula, T(ω, x, λ) is a wavelet soft threshold function, and x is a wavelet transform coefficient obtained by wavelet transforming a corresponding ultrasonic signal to be denoised. λ is a preset threshold, ω=1-Norm(B), Norm() is a normalization function, and B is the spatial corrosion coefficient of the oil pipeline detected by all ultrasonic probes currently.

9. An apparatus for detecting internal corrosion in an oil pipeline, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The computer program is executed by the processor to implement the method for detecting corrosion in an oil pipeline according to any one of claims 1-8.

10. An oil pipeline internal corrosion detection related apparatus, the related apparatus storing a computer program, characterized by, The computer program is executed by the processor to implement the method for detecting corrosion in an oil pipeline according to any one of claims 1-8.

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