Pipeline welding seam detection method and device based on magnetic memory

Abstract

The invention provides a pipeline welding seam detection method and device based on magnetic memory. The method comprises the steps that magnetic memory signals of a tangential component and a normal component of a pipeline welding seam area are collected, and first magnetic charge density distribution caused by metal phase change and second magnetic charge density distribution caused by residual stress are calculated respectively based on amplitude distribution characteristics of the magnetic memory signals; performing coupling superposition on the distribution of the first magnetic charge density and the distribution of the second magnetic charge density to obtain total magnetic charge density distribution representing stress and phase change coupling; according to the total magnetic charge density distribution, calculating predicted magnetic signal distribution of the welding seam area through integration; and comparing and analyzing the predicted magnetic signal distribution and the magnetic memory signal, and when an inner peak and an outer peak appear in a normal component in a comparison result and an extreme value appears in a tangential component at a corresponding position, determining that a crack defect exists in a welding seam area, and generating a welding seam detection result. According to the method, the multi-parameter coupling effect can be comprehensively considered, the weld crack defect is accurately identified through comparative analysis, and the detection precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline non-destructive testing, and in particular to a pipeline weld detection method and device based on magnetic memory. BACKGROUND

[0002] In existing pipeline weld detection applications, the magnetic memory detection method usually relies on the acquisition and analysis of magnetic field signals in the weld area, and identifies abnormal changes in the magnetic signals to determine the integrity state of the weld, thereby achieving the evaluation of the safety of the pipeline structure. However, the weld area of the pipeline has complex physical characteristics, and during the welding process, phase changes in the metal, residual stress distribution, and various defect forms such as micro-cracks may occur. These different physical phenomena produce mutually superimposed magnetic signal responses in the magnetic memory detection, and each has different signal performance characteristics.

[0003] Since the existing magnetic memory detection method mainly uses the traditional force-magnetic coupling analysis method, only the influence of stress factors on the magnetic signal is considered, and the comprehensive action mechanism of various physical factors in the weld area is not fully considered. This analysis method has limitations in dealing with complex weld magnetic signals, resulting in deviations between the detection results and the actual weld state. When there are phase change regions, high residual stress regions, and potential cracks in the weld, the existing method cannot accurately distinguish the magnetic signal contributions from these different sources, and may easily misjudge the signals, miss defects, or over-detect. This not only reduces the accuracy and reliability of the detection, but also may produce incorrect judgments when identifying critical defects, thereby affecting the effectiveness and consistency of the pipeline safety evaluation. SUMMARY

[0004] The main purpose of the present application is to solve the technical problem that the existing pipeline weld magnetic memory detection method fails to analyze the coupling influence of multiple physical factors, resulting in inconsistencies between the detection results and the actual weld defect state.

[0005] The first aspect of the present application provides a pipeline weld detection method based on magnetic memory, which comprises: acquiring the magnetic memory signals of the tangential component and the normal component of the pipeline weld area, and based on the amplitude distribution characteristics of the magnetic memory signals, calculating the first magnetic charge density distribution caused by the metal phase change and the second magnetic charge density distribution caused by the residual stress, respectively; coupling and superimposing the distribution of the first magnetic charge density and the second magnetic charge density distribution to obtain the total magnetic charge density distribution representing the coupling of stress and phase change; According to the total magnetic charge density distribution, the predicted magnetic signal distribution of the weld area is calculated by integration; The predicted magnetic signal distribution is compared with the magnetic memory signal, and when the comparison result is that the normal component appears double peaks inside and outside and the tangential component appears extreme value at the corresponding position, it is determined that the weld area has crack defects, and a weld detection result is generated.

[0006] The second aspect of the present application provides a pipeline weld detection device based on magnetic memory, which comprises: A magnetic charge calculation unit is configured to collect magnetic memory signals of tangential components and normal components of a pipeline weld area, and calculate a first magnetic charge density distribution caused by metal phase change and a second magnetic charge density distribution caused by residual stress based on amplitude distribution characteristics of the magnetic memory signals. A coupling superposition unit is configured to couple and superimpose the first magnetic charge density distribution and the second magnetic charge density distribution to obtain a total magnetic charge density distribution representing stress and phase change coupling. A prediction calculation unit is configured to calculate a predicted magnetic signal distribution of the weld area by integration according to the total magnetic charge density distribution. A defect identification unit is configured to compare the predicted magnetic signal distribution with the magnetic memory signal, and when the comparison result is that the normal component appears double peaks inside and outside and the tangential component appears extreme value at the corresponding position, it is determined that the weld area has crack defects, and a weld detection result is generated.

[0007] The pipeline weld detection method and device based on magnetic memory described above, by collecting magnetic memory signals of tangential components and normal components of a pipeline weld area, and calculating a first magnetic charge density distribution caused by metal phase change and a second magnetic charge density distribution caused by residual stress based on amplitude distribution characteristics of the magnetic memory signals, coupling and superimposing the first magnetic charge density distribution and the second magnetic charge density distribution to obtain a total magnetic charge density distribution representing stress and phase change coupling, calculating a predicted magnetic signal distribution of the weld area by integration according to the total magnetic charge density distribution, and comparing the predicted magnetic signal distribution with the magnetic memory signal, when the comparison result is that the normal component appears double peaks inside and outside and the tangential component appears extreme value at the corresponding position, it is determined that the weld area has crack defects, and a weld detection result is generated. The present application can comprehensively consider the multi-parameter coupling effect of metal phase change and residual stress in the magnetic memory detection process, realize accurate identification of weld crack defects through the comparison and analysis mechanism of the predicted signal and the measured signal, and thus improve the precision and reliability of pipeline weld detection.

[0008] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structure particularly pointed out in the description, claims, and drawings.

[0009] In order to make the above objectives, characteristics and advantages of the present application more apparent, more comprehensible, the following preferred embodiments are specifically described in detail below, together with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 Figure 1 is a schematic diagram of a first embodiment of a pipeline weld detection method based on magnetic memory according to an embodiment of the present application; Figure 2 Figure 2 is a schematic diagram of a second embodiment of a pipeline weld detection method based on magnetic memory according to an embodiment of the present application; Figure 3 Figure 3 is a schematic diagram of an embodiment of a pipeline weld detection device based on magnetic memory according to an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to make the objectives, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0012] The terms "comprise" and "have" and any variations thereof mentioned in the embodiments of the present application are intended to cover the inclusions without exclusivity. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but can optionally further comprise other steps or units not listed, or can optionally further comprise other steps or units inherent to the process, method, product or device.

[0013] In order to facilitate the understanding of the embodiments, first, a pipeline weld detection method based on magnetic memory disclosed by the embodiments of the present application is described in detail. As shown in Figure 1 the method comprises the following steps: 101, collecting magnetic memory signals of tangential components and normal components of a pipeline weld area, and based on the amplitude distribution characteristics of the magnetic memory signals, respectively calculating a first magnetic charge density distribution caused by metal phase change and a second magnetic charge density distribution caused by residual stress; In this embodiment, the magnetic memory signal refers to the leakage magnetic field signal formed by the spontaneous magnetization phenomenon of ferromagnetic materials under the action of the geomagnetic field due to stress concentration, wherein the tangential component refers to the magnetic field intensity component along the axial direction of the pipeline, and the normal component refers to the magnetic field intensity component perpendicular to the pipeline surface and outward. The amplitude distribution characteristics include quantifiable spatial distribution parameters such as signal peak size, gradient change rate, signal polarity conversion position, etc. The first magnetic charge density distribution refers to the spatial distribution density of equivalent magnetic charge in the weld area caused by the change of magnetic permeability of the material due to metal phase transition during welding, and the second magnetic charge density distribution refers to the spatial distribution density of equivalent magnetic charge in the weld area caused by the movement of magnetic domain wall blocked under the action of residual stress.

[0014] Specifically, a three-component magnetic sensor is used for continuous scanning measurement along the axial direction of the weld, and the sensor lift-off height is controlled within the range of 2-8 millimeters, which is based on the balance between signal strength and spatial resolution. Below 2 millimeters, it is easy to be affected by surface roughness, and above 8 millimeters, the signal decays too quickly. The scanning speed is 5-15 millimeters per second, and the data sampling frequency is set to more than 200 Hz. The environmental magnetic field strength is maintained within the range of 27.9-51.8 kiloamperes per meter (equivalent to 0.35-0.65 oersted) during measurement to avoid the influence of strong electromagnetic interference sources. The collected raw magnetic memory signal data is filtered by a band-pass filter to remove high-frequency noise and low-frequency drift, and the filter band is set to 2-50 Hz. Based on the purified magnetic memory signal data, by analyzing the magnetization characteristic information and gradient characteristic information at different positions of the weld area, combining the linear relationship between the magnetization intensity and the volume fraction of martensite of the material and the stress magnetization coupling theory, the first magnetic charge density distribution caused by metal phase transition and the second magnetic charge density distribution caused by residual stress are calculated respectively. Among them, the calculation of phase transition magnetic charge density needs to consider the proportion of austenite to martensite transformation in the welding heat affected zone and the corresponding change of saturation magnetization intensity, and the calculation of stress magnetic charge density needs to combine the parameters such as elastic modulus and domain wall flexibility coefficient of the material for inversion analysis.

[0015] For example, in the X70 pipeline steel girth weld detection with a diameter of 1219 mm and a wall thickness of 18.4 mm, the TSC-2M-8 magnetic memory detector is used to scan 150 mm along the weld centerline, and the tangential and normal magnetic memory signal data of 75 effective measurement points are obtained. The signal amplitude range is usually between-200 and +200 amperes per meter, and the corresponding magnetic induction intensity is about-25.1 to +25.1 microtesla. This step provides a reliable data basis for subsequent accurate differentiation of magnetic field contributions of different physical factors by separating the acquisition of two orthogonal magnetic field components and extracting the quantitative amplitude distribution characteristics. Compared with the traditional method of only collecting a single component, more complete weld magnetic field information can be obtained. In addition, in other embodiments, this step can also use a Hall sensor array for multi-point synchronous measurement, and the complete magnetic field distribution data of the weld area can be obtained at one time through spatial gridding point arrangement, and the same signal characterization effect can be achieved.

[0016] 102. coupling and superimposing the distribution of the first magnetic charge density and the distribution of the second magnetic charge density to obtain a total magnetic charge density distribution representing the stress and phase change coupling; In this embodiment, the coupling and superimposing the distribution of the first magnetic charge density and the distribution of the second magnetic charge density to obtain a total magnetic charge density distribution representing the stress and phase change coupling comprises: unifying the spatial coordinates of the first magnetic charge density distribution and the second magnetic charge density distribution to obtain a coordinate-consistent magnetic charge density distribution; calculating the peak amplitudes of the first magnetic charge density distribution and the second magnetic charge density distribution respectively, and determining the contribution weights of the corresponding phase change factors and stress factors according to the peak amplitude ratio to obtain coupling weight coefficients; based on the coupling weight coefficients, performing weighted superposition operation on the coordinate-consistent magnetic charge density distribution to obtain a coupled magnetic charge density distribution; and smoothing and correcting the coupled magnetic charge density distribution to obtain a total magnetic charge density distribution representing the stress and phase change coupling.

[0017] It should be noted that the spatial coordinate unification refers to the process of aligning the magnetic charge density distribution data of two different sources in the same spatial reference system. The coordinate consistent magnetic charge density distribution refers to the magnetic charge density data with the same spatial grid and sampling point position after spatial registration. Specifically, a rectangular coordinate system is established with the weld centerline as the X-axis zero point and the pipe radial as the Y-axis. The grid spacing is uniformly set to 2 mm x 2 mm. For data points in the first magnetic charge density distribution and the second magnetic charge density distribution that are not on the standard grid nodes, a nearest neighbor interpolation method is used for resampling. The operation steps are: calculate the Euclidean distance of each original data point to the nearest grid node, assign the magnetic charge density value of the data point to the nearest grid node, and take the average value if multiple original data points correspond to the same grid node. For example, in the girth weld detection of a pipe with a diameter of 1219 mm, the first magnetic charge density distribution originally has 150 irregularly distributed sampling points, and the second magnetic charge density distribution originally has 120 sampling points. After spatial coordinate unification, they are both resampled to 15 x 12 regular grid nodes, a total of 180 data. This unification process eliminates the differences in spatial positioning of the two magnetic charge distributions, providing a unified calculation basis for subsequent numerical operations, and ultimately obtaining a coordinate consistent magnetic charge density distribution. In addition, in other embodiments, a bilinear interpolation algorithm can be used to obtain smoother resampling results.

[0018] It should be noted that the peak amplitude refers to the maximum absolute value in the magnetic charge density distribution data. The contribution weight refers to the numerical proportion coefficient of the influence degree of different physical factors on the total magnetic charge density. The coupling weight coefficient refers to the normalization coefficient used for weighted superposition calculation. Specifically, traverse the values of all 180 grid nodes in the coordinate consistent first magnetic charge density distribution, calculate the absolute value of each value, and take the maximum value as the peak amplitude of the first magnetic charge density distribution. The same method is used to calculate the peak amplitude of the second magnetic charge density distribution. Then, according to the normalization principle, the contribution weight is calculated: the phase change factor contribution weight is equal to the peak amplitude of the first magnetic charge density distribution divided by the sum of the two peak amplitudes, and the stress factor contribution weight is equal to the peak amplitude of the second magnetic charge density distribution divided by the sum of the two peak amplitudes, ensuring that the sum of the two weight values is equal to 1. For example, when the peak amplitude of the first magnetic charge density distribution is 2.5 x 10 to the power of -6 coulombs per cubic meter and the peak amplitude of the second magnetic charge density distribution is 1.8 x 10 to the power of -6 coulombs per cubic meter, the calculated phase change factor contribution weight is 0.58 and the stress factor contribution weight is 0.42. This calculation method is based on the physical principle that the peak amplitude reflects the influence strength of each factor, and quantifies the relative importance of different physical factors through numerical values, avoiding the problem that the dominant factor may be diluted by simple averaging, and ultimately obtaining the coupling weight coefficient.

[0019] Specifically, the weighted superposition operation is a mathematical operation process of linear combination of two coordinate-consistent magnetic charge density distributions according to coupling weight coefficients. The operation steps are: processing 180 grid nodes one by one, for each node position, multiplying the value of the first magnetic charge density distribution at the node by the phase change factor contribution weight 0.58, multiplying the value of the second magnetic charge density distribution at the node by the stress factor contribution weight 0.42, and then adding the two weighted results to obtain the coupling magnetic charge density value of the node. The mathematical expression is: the coupling magnetic charge density at node i is equal to the first magnetic charge density at node i multiplied by 0.58 plus the second magnetic charge density at node i multiplied by 0.42. For example, at the 50th grid node, the first magnetic charge density is 1.2*10-6 coulomb per cubic meter, the second magnetic charge density is 0.8*10-6 coulomb per cubic meter, and the calculated coupling magnetic charge density is 1.03*10-6 coulomb per cubic meter. The weighted superposition process realizes the quantitative fusion of the magnetic field effects of the two physical factors of phase change and stress by mathematical means, can reasonably match according to the actual influence degree of each factor compared with the traditional simple addition method, avoids the problem that weak signals are covered by strong signals, and finally obtains the coupled magnetic charge density distribution.

[0020] It should be noted that the smoothing correction refers to a filtering technology for continuous processing of discrete data to eliminate numerical mutation and calculation noise. Specifically, a 5-point moving average filtering algorithm is used to smooth and correct the coupled magnetic charge density distribution. The operation steps are: for each internal grid node, the arithmetic mean value of 5 data of the node and its adjacent 4 nodes above, below, left and right is taken as the correction value of the node. For the boundary nodes, 3-point or 4-point average is used according to the actual number of adjacent nodes. The selection of 5-point moving average window is based on the consideration of the physical feature size of about 10 millimeters of the weld, which can effectively smooth the numerical oscillation and will not excessively blur the real physical boundary characteristics. The filtering process is carried out row by row in the order from left to right and from top to bottom, ensuring that all nodes are processed. For example, for the grid node at position 6*8, the correction value is equal to the original value of the 6*8 node plus one fifth of the sum of the values of the 5*8, 7*8, 6*7 and 6*9 nodes. The smoothing correction eliminates the numerical discontinuity generated in the coupling superposition operation, makes the magnetic charge density distribution meet the physical continuity requirement, improves the numerical stability of the subsequent magnetic field calculation, and finally obtains the total magnetic charge density distribution representing the stress and phase change coupling.

[0021] 103、According to the total magnetic charge density distribution, the predicted magnetic signal distribution of the weld area is calculated by integration; In the embodiment, the calculating the predicted magnetic signal distribution of the weld area by integral according to the total magnetic charge density distribution comprises: determining the sensing detection positions corresponding to the collected magnetic memory signals, extracting the density peak value area as the magnetic charge source point according to the total magnetic charge density distribution, and establishing the spatial geometric relationship between the magnetic charge source point and the sensing detection position; based on the spatial geometric relationship, performing weighted integral operation on the total magnetic charge density distribution according to the spatial distance from the magnetic charge source point to the sensing detection position, to obtain the magnetic field intensity distribution; and decomposing the magnetic field intensity distribution to obtain the predicted magnetic signal distribution.

[0022] It should be noted that the sensing detection position refers to the specific spatial coordinates of the sensor when the magnetic memory detection device collects signals. The density peak value area refers to the grid area in the total magnetic charge density distribution whose absolute value exceeds a set threshold. The magnetic charge source point refers to a representative spatial coordinate point selected from the density peak value area for magnetic field calculation. The spatial geometric relationship refers to the linear distance and spatial angle relationship between each magnetic charge source point and the sensing detection position. Specifically, according to the actual collection process of the magnetic memory signal, a sensing detection position is set every 2 mm along the weld axis, and the sensor lifting height is fixed at 5 mm, forming a detection position coordinate sequence. The 180 grid nodes of the total magnetic charge density distribution are screened, the arithmetic mean of the absolute values of the magnetic charge density of all nodes is calculated, and the nodes with absolute values greater than 1.5 times the average value are identified as the density peak value area. A grid node is selected as a magnetic charge source point every 6 mm from the density peak value area according to the principle of uniform spatial distribution. The three-dimensional Euclidean distance between the coordinates of each magnetic charge source point and the coordinates of each sensing detection position is calculated, and the distance value and the directional cosine value of the connecting line are recorded. For example, in a 150 mm long weld detection, 75 sensing detection positions are set, 28 density peak value areas are screened, 16 magnetic charge source points are extracted, and 1200 distance-angle data pairs are formed. This step provides a quantifiable spatial reference for magnetic field integral calculation by clearly defining the main contribution area of the magnetic charge distribution and establishing an accurate geometric positioning relationship, avoiding the interference of weak signal source points on the calculation results compared with the traditional method, and finally establishing the spatial geometric relationship between the magnetic charge source point and the sensing detection position.

[0023] Specifically, the weighted integral operation is based on the physical principle that the electric field strength is inversely proportional to the square of the distance in Coulomb's law to calculate the magnetic field. The operation steps are: for each sensing detection position, the magnetic field strength contribution of all 16 magnetic charge source points to the position is calculated one by one. The magnetic field strength contribution of a single source point is equal to the magnetic charge density value of the source point divided by the square of the distance from the source point to the detection position, and then divided by 4π times the magnetic permeability constant. The magnetic permeability constant is 4π×10 to the negative 7 power henry per meter. The magnetic field strength contributions from all source points are algebraically summed to obtain the total magnetic field strength at the detection position. The magnetic field strength of all 75 detection positions is calculated in the same way to form a magnetic field strength distribution data sequence. The meaning of the inverse square distance weighting is that the influence of the near distance magnetic charge source point on the detection position is much greater than that of the far distance source point, which conforms to the physical law of magnetic field propagation. For example, the source point with a distance of 6 mm from the detection position and a magnetic charge density of 1.8×10 to the negative 6 power coulomb per cubic meter has a magnetic field contribution of 1.59×10 to the negative 8 power tesla, while the same density source point with a distance of 12 mm has a contribution of only 3.98×10 to the negative 9 power tesla, and the distance has a significant effect. This integral operation can accurately calculate the superimposed magnetic field generated by the distributed magnetic charge source in space by strictly following the mathematical expression of electromagnetic field theory, which is more in line with the physical reality than the simple linear superposition, and finally obtains the magnetic field strength distribution.

[0024] It should be noted that the magnetic field component decomposition refers to the vector operation of projecting and decomposing the magnetic field strength vector along a specific direction. The predicted magnetic signal distribution refers to the numerical sequence of the tangential and normal magnetic field components at each detection position obtained by theoretical calculation. Specifically, a coordinate system with the weld axial direction as the X axis and the pipe radial direction as the Y axis is established, and the tangential direction is defined as the positive direction of the X axis and the normal direction is defined as the positive direction of the Y axis. For the magnetic field strength vector of each detection position, the component projections in the tangential and normal directions are calculated. The tangential component is equal to the dot product of the magnetic field strength vector and the tangential unit vector, and the normal component is equal to the dot product of the magnetic field strength vector and the normal unit vector. The dot product operation is realized by multiplying the corresponding components of the vectors and then summing them. After processing all 75 detection positions, 75 tangential component values and 75 normal component values are obtained, which form the tangential component distribution sequence and the normal component distribution sequence, respectively. For example, at the 40th detection position, the magnetic field strength vector is (-42, +31, -8) ampere per meter, and after decomposition, the tangential component is -42 ampere per meter and the normal component is +31 ampere per meter. This decomposition process is based on the basic operations of vector algebra, which converts three-dimensional magnetic field information into two-dimensional component form directly corresponding to the measured magnetic memory signal, making the subsequent comparative analysis have a clear mathematical basis and physical meaning, and finally obtaining the predicted magnetic signal distribution. In addition, this step can also use the radial and tangential decomposition method in the polar coordinate system to adapt to the geometric characteristics of the curved pipeline.

[0025] 104、the predicted magnetic signal distribution is compared with the magnetic memory signal, when the comparison result shows that the normal component has inner and outer double peaks and the tangential component has extreme values at the corresponding positions, it is determined that the weld area has crack defects, and a weld detection result is generated.

[0026] In the embodiment, the comparison analysis of the predicted magnetic signal distribution and the magnetic memory signal includes: based on the spatial geometric relationship, the predicted magnetic signal distribution and the magnetic memory signal are point-by-point difference calculated at the same sensing detection position, to obtain a signal deviation distribution; peak and valley features of the signal deviation distribution are extracted to identify the double peak structure of the normal component and the extreme value position of the tangential component; when the normal component shows the inner and outer double peak structure and the tangential component has extreme values at the same spatial position, it is determined that the weld area has crack defects. The generation of the weld detection result includes: according to the spatial coordinates of the inner double peak of the normal component and the spatial coordinates of the extreme value of the tangential component, the defect position information of the weld area is determined, and the tangential component gradient and the normal component gradient of the signal deviation distribution are calculated; the tangential component gradient and the normal component gradient are squared and summed to obtain a gradient vector sum, and the gradient vector sum is subjected to a negative exponential function transformation to obtain a damage level index; according to the damage level index, the welding damage level is determined, and based on the defect position information and the welding damage level, the weld detection result is generated.

[0027] It should be noted that the signal deviation distribution refers to the distribution of the numerical difference between the predicted magnetic signal distribution and the actually measured magnetic memory signal at each detection position. Specifically, a one-to-one data point matching method is used to ensure that the predicted magnetic signal distribution and the magnetic memory signal both contain 75 detection points at the same coordinate positions. The difference value is calculated in sequence according to the detection position number from 1 to 75. At the ith position, the tangential component deviation is equal to the measured tangential component value minus the predicted tangential component value, and the normal component deviation is equal to the measured normal component value minus the predicted normal component value. The calculation process uses simple subtraction operation and does not perform any data transformation or filtering processing. After the calculation, 75 tangential component deviation values and 75 normal component deviation values are obtained. For example, at the 25th detection position, the measured tangential component is -62 A / m, and the predicted tangential component is -18 A / m. The calculated tangential component deviation is -44 A / m. The measured normal component is +35 A / m, and the predicted normal component is +12 A / m. The calculated normal component deviation is +23 A / m. This point-by-point difference calculation quantifies the deviation between the theoretical prediction and the actual measurement by direct numerical subtraction, and the physical meaning of the deviation is the magnetic field anomaly that the prediction cannot explain. These anomalies mainly come from the additional magnetic field contribution of defects such as cracks. Compared with directly analyzing the original signal, the defect characteristics can be more clearly highlighted, and finally the signal deviation distribution is obtained. In other embodiments, this step can also use relative deviation calculation, which normalizes the absolute deviation by dividing it by the measured signal amplitude.

[0028] It should be noted that the peak-valley feature extraction refers to a data processing process of identifying local maximum and minimum values in a signal by mathematical algorithms. The double-peak structure refers to a waveform feature in which two independent peaks appear in the signal distribution and meet specific interval and amplitude conditions. The extreme value position refers to the coordinate of the detection point where the signal reaches the local maximum or minimum. Specifically, 75 data points of the normal component deviation are scanned point by point, and the size relationship of each data point with its adjacent points before and after it is calculated. When the value of a certain point is greater than that of the previous point and the next point at the same time, it is identified as a peak point, and when the value of a certain point is less than that of the previous point and the next point at the same time, it is identified as a valley point. The amplitude threshold of the effective peak value is set to 1.5 times the standard deviation of the signal and not less than 15 amperes per meter, and the tangential component extreme threshold is set to 2.0 times the standard deviation of the signal and not less than 20 amperes per meter. Only the peak points whose absolute values exceed the threshold are retained. The quantitative determination standard of the double-peak structure is that there are two effective peaks in the normal component deviation, the spatial distance between the two peaks is within the range of 10-25 millimeters, and the ratio of the amplitude of the inner peak to that of the outer peak is between 0.5 and 1.2. The inner peak is defined as the peak closer to the weld center line, and the outer peak is defined as the peak farther from the weld center line. The same adjacent point comparison method is used for the tangential component extreme value recognition, and the extreme value threshold is set to 20 amperes per meter. For example, in a certain detection, the normal component deviation appears a peak of +28 amperes per meter at the 18th position and a peak of +19 amperes per meter at the 32nd position, the interval between the two peaks is 28 millimeters, and the amplitude ratio is 0.68, which meets all the quantitative conditions of the double-peak structure. This peak-valley feature extraction converts the subjective waveform recognition into objective mathematical judgment through explicit numerical criteria, avoiding the influence of human factors on the results, and has higher consistency and reproducibility compared with the traditional visual interpretation method. Finally, the double-peak structure of the normal component and the extreme value position of the tangential component are identified. In other embodiments, this step can also use a sliding window algorithm for local peak search.

[0029] Specifically, the crack defect determination needs to satisfy two necessary conditions simultaneously, i.e., existence of bimodal structure and spatial position correspondence. The bimodal structure refers to two peaks in the deviation of the normal component, which are divided according to the distance from the weld centerline. The spatial position correspondence refers to the coordinate difference between the extreme point of the tangential component and the inner peak point of the normal component being within the allowable range. The determination process includes three sequential steps: the first step confirms the existence of a bimodal structure in the deviation distribution of the normal component that meets the quantification standard, and records the detection position number where the inner peak is located; the second step confirms the existence of an extreme point in the deviation distribution of the tangential component whose absolute value exceeds 20 amperes per meter, and records the detection position number where the extreme point is located; the third step calculates the difference between the inner peak position and the extreme position, and determines the spatial position correspondence when the absolute value of the difference is less than or equal to the distance between two detection positions (i.e., 4 mm). Only when all three conditions are met can it be determined that there is a crack defect. For example, when the inner peak of the normal component is located at the 18th detection position, the extreme point of the tangential component is located at the 19th detection position, and the distance between them is 2 mm, which satisfies the spatial correspondence condition, combined with the existence of the bimodal structure, it is determined that there is a crack defect in the region. This dual condition determination is based on the physical characteristics of the crack defect, the bimodal of the normal component reflects the disturbance of the magnetic flux distribution on both sides of the crack, and the extreme point of the tangential component reflects the concentration of the magnetic flux at the crack, the spatial coincidence of the two characteristics provides reliable evidence for the existence of the defect, which significantly reduces the false positive rate compared to single feature determination, and finally determines that there is a crack defect in the weld region. In other embodiments, this step can also be combined with auxiliary features such as peak width and signal symmetry for comprehensive determination.

[0030] It should be noted that the defect position information includes the axial coordinate, radial range and influence length and other geometric parameters of the crack defect. Specifically, the detection position corresponding to the peak of the normal component inside is taken as the main position identification of the defect, and the axial coordinate of the position is the axial position of the defect. The starting position and the ending position of the double-peak structure are recorded, and the distance between the two positions is taken as the length estimation of the defect influence area. Numerical differentiation calculation is performed on the signal deviation distribution, and the tangential component gradient calculation formula is: the gradient value of the i-th position is equal to the deviation value of the i+1-th position minus the deviation value of the i-1-th position, and then divided by the distance interval of 4 mm. The normal component gradient adopts the same central difference calculation method. The boundary position is calculated by forward difference or backward difference. The gradient calculation covers all detection positions in the defect area, forming a tangential component gradient array and a normal component gradient array. For example, at the 20th detection position, the tangential deviation of the previous position is-50 amperes per meter, and the next position is-38 amperes per meter, and the tangential component gradient is calculated to be 3 amperes per meter per millimeter; the previous position normal deviation is +27 amperes per meter, and the next position is +19 amperes per meter, and the normal component gradient is calculated to be-2 amperes per meter per millimeter. The position determination and gradient calculation convert the spatial distribution characteristics of the defect into quantifiable geometric parameters and intensity parameters through accurate coordinate recording and standard numerical differentiation method, provide accurate input data for damage degree evaluation, and finally determine the defect position information of the weld area and calculate the gradient of the signal deviation distribution.

[0031] Specifically, the gradient vector sum is a scalar parameter representing the signal change intensity, which is calculated by the vector module length. The damage grade index is a dimensionless damage evaluation index based on the gradient vector sum. The calculation steps are as follows: for each detection position in the defect area, the square of the tangential component gradient of the position is added to the square of the normal component gradient, and then the square root of the sum is taken to obtain the gradient vector sum of the position. The maximum value of the gradient vector sum in the defect area is selected as the characteristic value for damage grade calculation. The damage grade index calculation formula is: F equals the negative 0.05 times the maximum value of the gradient vector sum of e, where e is the natural constant 2.718, and 0.05 is an empirical correction coefficient. The damage grade classification adopts a three-level division: the F value greater than 0.6 is a low-risk grade, which needs regular inspection; the F value between 0.2 and 0.6 is a medium-risk grade, which needs planned maintenance; and the F value less than 0.2 is a high-risk grade, which needs immediate repair. For example, the maximum value of the gradient vector sum of a certain defect area is 8.5 ampere per meter per millimeter, and the calculated F value is 0.33, corresponding to a medium-risk grade, and it is recommended to arrange planned maintenance. The grade index converts the complex gradient information into a standardized risk evaluation result through mathematical transformation, and the exponential function transformation ensures that the value is monotonously distributed in the 0-1 interval, which is convenient for engineering decision-making and risk management. Compared with qualitative evaluation, it provides an objective quantitative standard, and finally determines the welding damage grade according to the damage grade index and generates a complete detection result containing position information and risk grade.

[0032] In the embodiment of the present application, the tangential component and normal component magnetic memory signals of the pipeline weld area are collected, and based on the amplitude distribution characteristics of the magnetic memory signals, the first magnetic charge density distribution caused by metal phase change and the second magnetic charge density distribution caused by residual stress are calculated respectively; the first magnetic charge density distribution and the second magnetic charge density distribution are coupled and superimposed to obtain the total magnetic charge density distribution representing the coupling of stress and phase change; according to the total magnetic charge density distribution, the predicted magnetic signal distribution of the weld area is calculated by integration; the predicted magnetic signal distribution and the magnetic memory signal are compared and analyzed, when the comparison result shows that the normal component appears double peaks inside and outside and the tangential component appears extreme value at the corresponding position, it is determined that the weld area has crack defects, and the weld detection result is generated. The present application can comprehensively consider the multi-parameter coupling effect of metal phase change and residual stress in the magnetic memory detection process, and realize accurate identification of weld crack defects through comparison and analysis mechanism of predicted signal and measured signal, thereby improving the accuracy and reliability of pipeline weld detection.

[0033] Please refer to Figure 2 Another embodiment of the pipeline weld detection method based on magnetic memory in the embodiment of the present application includes: 201, low-pass filter preprocessing is performed on the magnetic memory signal to obtain purified magnetic memory signal data; In this embodiment, after acquiring the magnetic memory signals of the tangential and normal components of the pipeline weld area, the acquired raw magnetic memory signals are preprocessed using a Butterworth low-pass digital filter. The filter cutoff frequency is set to 50 Hz, and the filter order is selected as 4th. The filtering process is achieved through point-by-point convolution, where each sampling point is weighted and summed with the filter coefficients. The processing procedure is as follows: the raw signal data sequence in the magnetic memory signal is read, input into the filter point by point in chronological order, and the filtered data is output. For example, in the X70 pipeline weld inspection, the raw signal amplitude ranges from -250 to +180 amperes per meter. After low-pass filtering, the high-frequency noise amplitude is reduced from the original 20-30 amperes per meter to 3-5 amperes per meter. This preprocessing step eliminates electrical noise and random interference during the measurement process, ensuring that subsequent calculations are based on the real magnetic field change signal, avoiding misjudging noise as physical defect features, and finally obtaining purified magnetic memory signal data.

[0034] 202. Based on the amplitude distribution characteristics of the purified magnetic memory signal data, the phase transformation magnetic charge distribution in the weld region is calculated using the martensite volume fraction to obtain the first magnetic charge density distribution; In this embodiment, the amplitude distribution features include magnetization feature information and gradient feature information. The step of calculating the phase transition magnetic charge distribution of the weld region based on the amplitude distribution features of the purified magnetic memory signal data and the martensite volume fraction to obtain the first magnetic charge density distribution includes: dividing the weld region into multiple welding influence regions and extracting the magnetization feature information of the purified magnetic memory signal data in each welding influence region; calculating the change in metal structure in each welding influence region based on the magnetization feature information through phase transition magnetization inversion; and performing magnetic charge density conversion calculation on the change in metal structure based on the material magnetization characteristics corresponding to the weld region to obtain the first magnetic charge density distribution.

[0035] In this embodiment, the magnetization characteristic information refers to a quantitative parameter reflecting the magnetization degree of the material, including the signal average value, peak amplitude and saturation. The gradient characteristic information refers to a parameter reflecting the spatial variation rate of the magnetic field, including the first derivative value and the second derivative value. Specifically, according to the welding heat conduction theory, the weld area is divided into a fusion zone, a heat affected zone and a base material zone. The fusion zone is located within 3 millimeters on both sides of the weld centerline, the heat affected zone is located within 3-12 millimeters outside the fusion zone, and the base material zone is located outside the heat affected zone. The average amplitude, the difference between the maximum value and the minimum value in each region are calculated as the magnetization characteristic information of the cleaned magnetic memory signal data. The calculation method is: the signal amplitudes of all measurement points in the region are added and divided by the total number of measurement points to obtain the average value. For example, in the girth weld detection of a 1219 millimeter diameter pipeline, the fusion zone usually contains 15-25 measurement points, and the magnetization characteristic information values of each welding affected zone can be obtained by statistical calculation. This division method is based on the actual temperature distribution in the welding physical process and can accurately correspond to different metal organization regions, and finally the magnetization characteristic information of each welding affected zone is extracted. In other embodiments, this step can also use an adaptive region boundary recognition method based on signal gradient mutation points.

[0036] Specifically, the phase change magnetization inversion calculation is based on the linear relationship between the saturation magnetization and the martensite volume fraction. The calculation process is: first, determine the reference value of the saturation magnetization of the base material region, which is about 217 electromagnetic units per gram (equivalent to 217*10^-3 ampere square meter per kilogram) for X70 pipeline steel, and this value is based on the standard material characteristics of X70 pipeline steel, in which the saturation magnetization of ferrite is about 2.15 tesla; then, according to the magnetization characteristic information of each welding affected zone, combined with the excitation condition of the geomagnetic field intensity of 0.5 oersted, the actual saturation magnetization of the region is calculated; finally, the martensite volume fraction is calculated through the linear relationship. The specific calculation formula is: the martensite volume fraction is equal to the bracketed base material saturation magnetization minus the region saturation magnetization divided by the unit martensite magnetization difference, where the unit martensite magnetization difference is 2.9*10^-3 ampere square meter per kilogram per percent, which is determined based on the difference between the magnetization characteristics of martensite and ferrite. For example, when the actual saturation magnetization of a certain heat affected zone is 205 electromagnetic units per gram, the calculated martensite volume fraction of the region is about 41 percent. This inversion calculation can accurately evaluate the influence of welding thermal cycle on the phase change degree of the material by quantifying the microstructure change, and finally obtain the metal organization change quantity of each welding affected zone. In other embodiments, this step can also be calibrated combined with X-ray diffraction measurement results to improve the accuracy of the calculation of the martensite content.

[0037] Specifically, the magnetic charge density conversion calculation converts the metal organization change amount into a spatial magnetic charge distribution. The material magnetization characteristic mainly refers to the permeability parameter. The relative permeability of the X70 pipeline steel base material is about 80, and the relative permeability of the fully martensitic structure is about 45. The conversion calculation is based on the physical relationship that the magnetic charge density is equal to the permeability gradient multiplied by the magnetic field strength. The calculation steps are as follows: according to the metal organization change amount of each region, the corresponding permeability value is determined, the permeability difference between adjacent regions is calculated, the permeability gradient is obtained by dividing the region spacing, and the magnetic charge density is obtained by multiplying the geomagnetic field strength of 0.5 Oersted. For example, when the martensitic volume fraction of the fusion zone is 60 percent, the relative permeability is 55, the permeability difference with the base material region is 25, and the region spacing is 6 mm, the calculated magnetic charge density is about 1.8 x 10-6 coulomb per cubic meter. The conversion process establishes a quantitative correspondence between the microstructure parameters and the macroscopic magnetic field distribution, so that the contribution of phase transition to the magnetic memory signal can be accurately predicted, and finally the first magnetic charge density distribution is obtained. In other embodiments, this step can also use an interpolation algorithm to continuously process the magnetic charge density between discrete regions.

[0038] 203、According to the amplitude distribution characteristics of the purified magnetic memory signal data, the stress magnetic charge distribution of the weld region is calculated through stress magnetization coupling, to obtain a second magnetic charge density distribution; In this embodiment, the second magnetic charge density distribution obtained by calculating the stress magnetic charge distribution of the weld region according to the amplitude distribution characteristics of the purified magnetic memory signal data includes: dividing the stress distribution region of the weld region, and extracting the gradient characteristic information of the purified magnetic memory signal data in each stress distribution region; based on the gradient characteristic information, the residual stress distribution of each stress distribution region is calculated through stress magnetization inversion; based on the material magnetization characteristic corresponding to the weld region, the residual stress distribution is subjected to magnetic charge density conversion calculation to obtain the second magnetic charge density distribution.

[0039] Specifically, the stress distribution region division is based on the typical distribution pattern of welding residual stress. The division method is as follows: taking the weld centerline as the reference, a stress analysis unit is set every 4 mm to both sides, and each unit is 4 mm wide. The first-order gradient value of the magnetic memory signal in each unit is calculated, that is, the signal difference of adjacent measuring points divided by the measuring point spacing of 2 mm. At the same time, the gradient direction angle is calculated, that is, the angle between the gradient vector and the horizontal direction. For example, in the girth weld detection, the high stress region is usually located within 8 mm on both sides of the weld centerline, and the gradient characteristic information value of this region is usually 25-45 amperes per meter per millimeter, while the gradient value of the low stress region is usually less than 10 amperes per meter per millimeter. This division method can accurately locate the spatial position and intensity distribution of the stress concentration region by quantifying the magnetic field gradient distribution, and finally extract the gradient characteristic information of each stress distribution region. In other embodiments, this step can also use an adaptive region division method based on the gradient amplitude threshold.

[0040] Specifically, the stress magnetization inversion calculation is based on the inverse solution of the magneto-elastic coupling theory. The calculation process needs to input the elastic modulus of the material 210 gigapascals and the domain wall flexibility coefficient 1.2 x 10 negative 11th power pascal negative 1st power. The inversion calculation steps are as follows: the gradient characteristic information of each stress distribution region is substituted into the stress magnetization coupling equation to obtain the equivalent stress value of the region. The coupling equation is: the change in magnetization intensity is equal to the domain wall flexibility coefficient multiplied by the stress value divided by the elastic modulus. Through inverse solution, it is obtained that: the stress value is equal to the change in magnetization intensity multiplied by the elastic modulus divided by the domain wall flexibility coefficient. For example, when the magnetic memory signal gradient of a certain region is 35 amperes per meter per millimeter, the corresponding change in magnetization intensity is about 8.5 x 10 negative 4th power amperes per meter, and the inversion calculation obtains that the residual stress of the region is about 180 megapascals. This inversion calculation can accurately evaluate the stress distribution state inside the weld by quantifying the physical relationship between stress and magnetic signal, and finally obtain the residual stress distribution of each stress distribution region. In other embodiments, this step can also be calibrated and verified in combination with the X-ray stress measurement results.

[0041] Specifically, when converting the residual stress distribution into the magnetic charge density, the mechanism of changing the domain structure based on the stress and then generating the equivalent magnetic charge is considered. The conversion calculation considers the geometric relationship between the stress direction and the magnetic field direction, and converts the stress value into the magnetic charge density through the stress magnetization coupling coefficient. The stress magnetization coupling coefficient for X70 pipeline steel is about 6.8×10-9 C / m3 / Mpa. The calculation steps are: multiplying the residual stress value of each stress distribution area by the stress magnetization coupling coefficient to obtain the corresponding magnetic charge density value. When the stress is tensile stress, the magnetic charge density is positive, and when the stress is compressive stress, the magnetic charge density is negative. For example, when the residual stress of a certain area is 200 MPa tensile stress, the corresponding magnetic charge density is about 1.36×10-6 C / m3. This conversion calculation establishes a quantitative bridge between the macro stress state and the micro magnetic charge distribution, so that the contribution of residual stress to the magnetic memory signal can be accurately separated and quantified, and finally the second magnetic charge density distribution is obtained. In other embodiments, this step can also consider the comprehensive effect of multi-axial stress state, and more accurate conversion calculation can be performed through stress tensor decomposition.

[0042] 204、coupling and superimposing the distribution of the first magnetic charge density and the second magnetic charge density distribution to obtain a total magnetic charge density distribution representing stress and phase change coupling; 205、According to the total magnetic charge density distribution, the predicted magnetic signal distribution of the weld area is calculated by integration; 206、Comparative analysis of the predicted magnetic signal distribution and the magnetic memory signal, when the normal component appears double peaks inside and outside and the tangential component appears extreme value at the corresponding position in the comparison result, it is determined that the weld area exists crack defect, and the weld detection result is generated.

[0043] In this embodiment, steps 204-206 are similar to steps 102-104 in the first embodiment, and will not be described here.

[0044] In the embodiment, the tangential component and the normal component of the magnetic memory signals of the pipeline weld area are collected, and based on the amplitude distribution characteristics of the magnetic memory signals, a first magnetic charge density distribution caused by metal phase change and a second magnetic charge density distribution caused by residual stress are calculated respectively; the first magnetic charge density distribution and the second magnetic charge density distribution are coupled and superimposed to obtain a total magnetic charge density distribution representing stress and phase change coupling; according to the total magnetic charge density distribution, the predicted magnetic signal distribution of the weld area is calculated by integration; the predicted magnetic signal distribution and the magnetic memory signals are compared and analyzed, when the normal component appears double peaks inside and outside and the tangential component appears extreme value at the corresponding position in the comparison result, it is determined that the weld area has crack defects, and a weld detection result is generated. The present application can comprehensively consider the multi-parameter coupling effect of metal phase change and residual stress in the magnetic memory detection process, realize the accurate identification of the weld crack defects through the comparison and analysis mechanism of the predicted signal and the measured signal, and thus improve the precision and reliability of the pipeline weld detection.

[0045] The above describes the pipeline weld detection method based on magnetic memory in the embodiment of the present application, and the following describes the pipeline weld detection device based on magnetic memory in the embodiment of the present application, please refer to Figure 3 An embodiment of the pipeline weld detection device based on magnetic memory in the embodiment of the present application includes: The magnetic charge calculation unit 301 is used for collecting the tangential component and the normal component of the magnetic memory signals of the pipeline weld area, and based on the amplitude distribution characteristics of the magnetic memory signals, calculating the first magnetic charge density distribution caused by metal phase change and the second magnetic charge density distribution caused by residual stress respectively; The coupling superposition unit 302 is used for coupling and superimposing the first magnetic charge density distribution and the second magnetic charge density distribution to obtain a total magnetic charge density distribution representing stress and phase change coupling; The prediction calculation unit 303 is used for calculating the predicted magnetic signal distribution of the weld area by integration according to the total magnetic charge density distribution; The defect identification unit 304 is used for comparing and analyzing the predicted magnetic signal distribution and the magnetic memory signals, when the normal component appears double peaks inside and outside and the tangential component appears extreme value at the corresponding position in the comparison result, it is determined that the weld area has crack defects, and a weld detection result is generated.

[0046] In the embodiment of the present application, the tangential component and the normal component of the magnetic memory signals of the pipeline weld area are collected, and based on the amplitude distribution characteristics of the magnetic memory signals, a first magnetic charge density distribution caused by metal phase change and a second magnetic charge density distribution caused by residual stress are calculated respectively; the first magnetic charge density distribution and the second magnetic charge density distribution are coupled and superimposed to obtain a total magnetic charge density distribution representing stress and phase change coupling; according to the total magnetic charge density distribution, the predicted magnetic signal distribution of the weld area is calculated by integration; the predicted magnetic signal distribution and the magnetic memory signals are compared and analyzed, when the comparison result shows that the normal component has double peaks inside and outside and the tangential component has extreme value at the corresponding position, it is determined that the weld area has crack defects, and a weld detection result is generated. The present application can comprehensively consider the multi-parameter coupling effect of metal phase change and residual stress in the magnetic memory detection process, and through the comparison and analysis mechanism of the predicted signal and the measured signal, the accurate identification of the weld crack defects is realized, so as to improve the precision and reliability of the pipeline weld detection.

[0047] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0048] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A magnetic memory-based method for detecting a weld of a pipe, characterized by, The pipeline weld detection method based on magnetic memory comprises: Collecting magnetic memory signals of tangential components and normal components of a pipeline weld area, and calculating a first magnetic charge density distribution caused by metal phase change and a second magnetic charge density distribution caused by residual stress based on amplitude distribution characteristics of the magnetic memory signals; Coupling and superimposing the first magnetic charge density distribution and the second magnetic charge density distribution to obtain a total magnetic charge density distribution representing stress and phase change coupling; According to the total magnetic charge density distribution, calculating a predicted magnetic signal distribution of the weld area by integration; Comparing and analyzing the predicted magnetic signal distribution and the magnetic memory signals, and determining that the weld area has a crack defect when inner and outer double peaks appear in the normal component and an extreme value appears in the tangential component in the comparison result, and generating a weld detection result.

2. The magnetic memory-based pipe weld inspection method of claim 1, wherein, The calculating of the first magnetic charge density distribution caused by metal phase change and the second magnetic charge density distribution caused by residual stress based on the amplitude distribution characteristics of the magnetic memory signals comprises: Performing low-pass filtering preprocessing on the magnetic memory signals to obtain purified magnetic memory signal data; According to the amplitude distribution characteristics of the purified magnetic memory signal data, calculating a phase change magnetic charge distribution of the weld area by martensite volume fraction to obtain the first magnetic charge density distribution; According to the amplitude distribution characteristics of the purified magnetic memory signal data, calculating a stress magnetic charge distribution of the weld area by stress magnetization coupling to obtain the second magnetic charge density distribution.

3. The magnetic memory-based pipe weld inspection method of claim 2, wherein, The amplitude distribution characteristics include magnetization characteristic information and gradient characteristic information, and the calculating of the phase change magnetic charge distribution of the weld area by martensite volume fraction to obtain the first magnetic charge density distribution based on the amplitude distribution characteristics of the purified magnetic memory signal data comprises: Dividing the weld area into multiple welding influence areas, and extracting magnetization characteristic information of the purified magnetic memory signal data in each welding influence area; Based on the magnetization characteristic information, calculating a metal organization change amount of each welding influence area by phase change magnetization inversion; Based on the material magnetization characteristics corresponding to the weld area, performing magnetic charge density conversion calculation on the metal organization change amount to obtain the first magnetic charge density distribution.

4. The magnetic memory-based pipe weld inspection method of claim 3, wherein, The calculating of the stress magnetic charge distribution of the weld area by stress magnetization coupling to obtain the second magnetic charge density distribution based on the amplitude distribution characteristics of the purified magnetic memory signal data comprises: Dividing the weld area into stress distribution areas, and extracting gradient characteristic information of the purified magnetic memory signal data in each stress distribution area; Based on the gradient characteristic information, calculating a residual stress distribution of each stress distribution area by stress magnetization inversion; Based on the material magnetization characteristics corresponding to the weld area, performing magnetic charge density conversion calculation on the residual stress distribution to obtain the second magnetic charge density distribution.

5. The magnetic memory-based pipe weld inspection method of claim 1, wherein, The coupling and superimposing of the first magnetic charge density distribution and the second magnetic charge density distribution to obtain the total magnetic charge density distribution representing stress and phase change coupling comprises: Uniformizing spatial coordinates of the first magnetic charge density distribution and the second magnetic charge density distribution to obtain magnetic charge density distributions with consistent coordinates; Peak amplitudes of the first magnetic charge density distribution and the second magnetic charge density distribution are calculated respectively, and contribution weights of corresponding phase change factors and stress factors are determined according to a peak amplitude ratio, to obtain a coupling weight coefficient; Based on the coupling weight coefficient, the magnetic charge density distributions with the same coordinates are subjected to weighted superposition operation, to obtain a coupled magnetic charge density distribution; The coupled magnetic charge density distribution is subjected to smoothing correction, to obtain a total magnetic charge density distribution representing stress and phase change coupling.

6. The magnetic memory-based pipe weld inspection method of claim 1, wherein, The integral calculation of the predicted magnetic signal distribution of the weld area from the total magnetic charge density distribution includes: The sensing detection position corresponding to the magnetic memory signal is determined, and the density peak area is extracted as a magnetic charge source point according to the total magnetic charge density distribution, and a spatial geometric relationship between the magnetic charge source point and the sensing detection position is established; Based on the spatial geometric relationship, the total magnetic charge density distribution is subjected to weighted integral operation according to the spatial distance from the magnetic charge source point to the sensing detection position, to obtain a magnetic field intensity distribution; The magnetic field intensity distribution is subjected to magnetic field component decomposition, to obtain a predicted magnetic signal distribution.

7. The magnetic memory-based pipe weld inspection method of claim 6, wherein, The comparison and analysis of the predicted magnetic signal distribution and the magnetic memory signal includes: Based on the spatial geometric relationship, the predicted magnetic signal distribution and the magnetic memory signal are subjected to point-by-point difference calculation at the same sensing detection position, to obtain a signal deviation distribution; The signal deviation distribution is subjected to peak-valley feature extraction, to identify the double-peak structure of the normal component and the extreme value position of the tangential component; When the normal component presents the double-peak structure and the tangential component presents the extreme value at the same spatial position, it is determined that the weld area has a crack defect.

8. The magnetic memory-based pipe weld inspection method of claim 7, wherein, The generation of the weld detection result includes: The defect position information of the weld area is determined according to the spatial coordinates of the inner double-peak of the normal component and the spatial coordinates of the extreme value of the tangential component, and the tangential component gradient and the normal component gradient of the signal deviation distribution are calculated; The tangential component gradient and the normal component gradient are subjected to square summation and square root operation, to obtain a gradient vector sum, and the gradient vector sum is subjected to negative exponential function transformation, to obtain a damage level index; The welding damage level is determined according to the damage level index, and the weld detection result is generated based on the defect position information and the welding damage level.

9. A magnetic memory-based pipe weld inspection apparatus, characterized by, The pipeline weld detection device based on magnetic memory includes: A magnetic charge calculation unit is configured to collect magnetic memory signals of tangential components and normal components of a pipeline weld area, and calculate a first magnetic charge density distribution caused by metal phase change and a second magnetic charge density distribution caused by residual stress based on amplitude distribution characteristics of the magnetic memory signals; A coupling superposition unit is configured to couple and superimpose the first magnetic charge density distribution and the second magnetic charge density distribution, to obtain a total magnetic charge density distribution representing stress and phase change coupling; A prediction calculation unit is configured to calculate a predicted magnetic signal distribution of the weld area by integral calculation from the total magnetic charge density distribution. The defect identification unit is configured to compare and analyze the predicted magnetic signal distribution and the magnetic memory signal, and determine that the weld area has a crack defect when the normal component appears as double peaks inside and outside and the tangential component appears as an extreme value at the corresponding position in the comparison result, and generate a weld detection result.

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