Pipeline magnetic flux leakage detection data wall thickness quantification method

Abstract

The present application belongs to the technical field of oil and gas pipeline internal detection, and particularly relates to a pipeline magnetic flux leakage detection data wall thickness quantification method, aiming at solving the problem that the prior art cannot quantize the pipeline wall thickness with high precision in the pipeline magnetic flux leakage internal detection. The method comprises: obtaining simulation data of oil and gas pipeline magnetic flux leakage detection as a base value; only considering the relationship between the base value and the wall thickness, constructing a univariate model of the pipeline wall thickness and the base value of different diameters, then modeling and fitting the mathematical relationship between the wall thickness and the base value, determining the mathematical model of the wall thickness and the base value based on the fitting result as a first model; introducing a speed influence factor in the first model to establish a wall thickness quantification model with the speed factor, as a second model, that is, the final mathematical model of the wall thickness and the base value; obtaining the base value data, and obtaining the wall thickness quantification result through the final mathematical model of the wall thickness and the base value. The present application realizes high-precision quantification of the pipeline magnetic flux leakage detection wall thickness.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas pipeline internal detection technology, specifically relating to a method, system, equipment, and storage medium for quantifying pipeline wall thickness using magnetic flux leakage detection data. Background Technology

[0002] In nondestructive testing, magnetic flux leakage (MF) testing is the most widely used online inspection method for natural gas pipelines. Currently, the most common MF testing method involves first saturating the pipe wall detection zone with a permanent magnet, then using a sensor array to collect MF signals to detect defects.

[0003] Data processing of detection signals is a crucial step in pipeline magnetic flux leakage (MFL) internal inspection. The main purpose of interpreting internal inspection data is to identify and accurately quantify pipeline defects, enabling subsequent accurate integrity assessments and the implementation of appropriate maintenance and repair measures. Pipeline wall thickness is critical in defect quantification. Therefore, determining the wall thickness grade and value of the pipe section where the defect is located directly impacts the accuracy of subsequent defect quantification and the overall integrity assessment results.

[0004] With the widespread implementation of pipeline internal inspection, the amount of magnetic flux leakage detection data has surged, and intelligent analysis of internal inspection data has gradually become a top priority for the inspection industry to improve work efficiency and accuracy.

[0005] During the investigation of the existing automatic wall thickness measurement model in the pipeline inspection data analysis system, it was found that the original model was a univariate power function of wall thickness with respect to the base value, which not only had low analytical accuracy but also lacked universality. Therefore, the confirmation of wall thickness information often required extensive manual judgment based on wall thickness variation data signals and human experience after obtaining construction materials such as weld manuals from the owner. If the owner's data was missing or inaccurate, coupled with omissions and misjudgments during manual verification, it would adversely affect the subsequent measurement of defect dimensions. Based on this, this invention proposes a method for quantifying wall thickness in pipeline magnetic flux leakage detection data. The aim is to achieve high-precision quantification of wall thickness in pipeline magnetic flux leakage detection data, improve the accuracy of defect quantification, and thus improve the level of pipeline integrity assessment. Summary of the Invention

[0006] To address the aforementioned problems in the prior art, specifically the inability of existing technologies to accurately quantify pipe wall thickness in pipe magnetic flux leakage detection, the present invention, in its first aspect, proposes a method for quantifying pipe wall thickness based on magnetic flux leakage detection data. This method includes:

[0007] S10, acquire simulation data of magnetic flux leakage detection for oil and gas pipelines as the baseline value;

[0008] S20, using the base value, considering only the relationship between the base value and the wall thickness, construct a univariate model of the wall thickness base value of pipes of different diameters and perform error analysis to obtain the first error analysis result;

[0009] Based on the first error analysis results, a mathematical relationship between wall thickness and base value is modeled and fitted using multiple mathematical models. Based on the fitting results, a mathematical model for wall thickness and base value is determined as the first model.

[0010] S30, Introduce a velocity influence factor into the first model and establish a wall thickness quantification model with velocity factor as the second model, that is, the final mathematical model of wall thickness and base value.

[0011] S40, acquire base value data, and obtain the wall thickness quantification result through the mathematical model of the final wall thickness and base value.

[0012] In some preferred embodiments, the various mathematical models include linear models, logarithmic models, inverse models, quadratic models, cubic models, composite models, power models, S-models, growth models, exponential models, and logistic models.

[0013] In some preferred embodiments, the first model is:

[0014]

[0015] Among them, w t B represents the wall thickness, B represents the baseline value, and a, b, and c are the fitting parameters.

[0016] In some preferred embodiments, the second model is:

[0017]

[0018] Where v represents velocity and d is the fitting parameter.

[0019] A second aspect of the present invention provides a system for quantifying the wall thickness of pipeline magnetic flux leakage detection data, the system comprising:

[0020] The data acquisition module is configured to acquire simulation data of the axial data of the outer wall of the oil and gas pipeline as the base value;

[0021] The error analysis module is configured to use the base value, considering only the relationship between the base value and the wall thickness, to construct a univariate model of the wall thickness base value of pipes of different diameters and perform error analysis to obtain the first error analysis result;

[0022] Based on the first error analysis results, a mathematical relationship between wall thickness and base value is modeled and fitted using multiple mathematical models. Based on the fitting results, a mathematical model for wall thickness and base value is determined as the first model.

[0023] The model building module is configured to introduce a velocity influence factor into the first model and establish a wall thickness quantification model with the velocity factor as the second model, i.e., the final mathematical model of wall thickness and base value.

[0024] The wall thickness quantization module is configured to acquire base value data and obtain the wall thickness quantization result through the mathematical model of the final wall thickness and the base value.

[0025] A third aspect of the present invention provides an electronic device comprising:

[0026] At least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor to implement the above-described method for quantifying the wall thickness of pipeline magnetic flux leakage detection data.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for execution by a computer to implement the above-described method for quantifying the wall thickness of pipeline magnetic flux leakage detection data.

[0028] The beneficial effects of this invention are:

[0029] This invention enables high-precision quantification of pipe wall thickness based on magnetic flux leakage detection data, improves the accuracy of defect quantification, and thus enhances the level of pipe integrity assessment.

[0030] This invention introduces a wall thickness quantification model algorithm based on a velocity factor. While accurately quantifying wall thickness values, it also improves the accuracy of defect size quantification, leading to more accurate estimates of maintenance ratios, remaining pipeline service life, and re-inspection cycle evaluations, while reducing unnecessary excavation costs. This invention's method can comprehensively quantify the wall thickness accuracy level of magnetic flux leakage detection data, effectively improving the scientific rigor and precision of the quantification. Attached Figure Description

[0031] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0032] Figure 1 This is a schematic flowchart of a method for quantifying the wall thickness of pipeline magnetic flux leakage detection data according to an embodiment of the present invention;

[0033] Figure 2 This is a detailed flowchart illustrating a method for quantifying the wall thickness of pipeline magnetic flux leakage detection data according to an embodiment of the present invention.

[0034] Figure 3 This is a schematic diagram of the accuracy parameters of various wall thickness quantization models according to an embodiment of the present invention;

[0035] Figure 4 This is a flowchart of an automatic outlier correction algorithm according to an embodiment of the present invention;

[0036] Figure 5 This is a flowchart of wall thickness parameter adjustment according to an embodiment of the present invention;

[0037] Figure 6 This is a schematic diagram illustrating the quantification error of the φ325 wall thickness according to an embodiment of the present invention;

[0038] Figure 7 This is a schematic diagram illustrating the quantification error of the φ508 wall thickness according to an embodiment of the present invention;

[0039] Figure 8 This is a schematic diagram illustrating the quantification error of the φ1016 wall thickness according to an embodiment of the present invention;

[0040] Figure 9 This is a partial velocity diagram of an actual engineering project according to an embodiment of the present invention;

[0041] Figure 10 This is a schematic diagram of a new wall thickness quantization model for a project according to an embodiment of the present invention - wall thickness quantization error;

[0042] Figure 11 This is a schematic diagram of a metal loss excavation point 1 according to an embodiment of the present invention;

[0043] Figure 12 This is a schematic diagram of a metal loss excavation point 2 according to an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0045] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0047] A method for quantifying the wall thickness of pipeline magnetic flux leakage detection data according to the first embodiment of the present invention, such as... Figure 1 As shown, it includes the following steps:

[0048] S10, acquire simulation data of magnetic flux leakage detection data of oil and gas pipelines as the base value;

[0049] S20, using the base value, considering only the relationship between the base value and the wall thickness, construct a univariate model of the wall thickness base value of pipes of different diameters and perform error analysis to obtain the first error analysis result;

[0050] Based on the first error analysis results, a mathematical relationship between wall thickness and base value is modeled and fitted using multiple mathematical models. Based on the fitting results, a mathematical model for wall thickness and base value is determined as the first model.

[0051] S30, Introduce a velocity influence factor into the first model and establish a wall thickness quantification model with velocity factor as the second model, that is, the final mathematical model of wall thickness and base value.

[0052] S40, acquire base value data, and obtain the wall thickness quantification result through the mathematical model of the final wall thickness and base value.

[0053] To more clearly explain the method for quantifying pipe wall thickness using magnetic flux leakage detection data according to the present invention, the following description is in conjunction with the appendix. Figure 2 The steps of one embodiment of the method of the present invention will be described in detail below.

[0054] During the investigation of the existing automatic wall thickness measurement model in the internal inspection data analysis software, it was found that the original model was a univariate power function of wall thickness with respect to the base value, which not only had low analytical accuracy but also lacked universality. Therefore, the confirmation of wall thickness information often required extensive manual judgment based on wall thickness change data signals and human experience after obtaining construction materials such as weld manuals from the owner. If the owner's data was missing or inaccurate, coupled with omissions and misjudgments during manual verification, it would adversely affect the subsequent measurement of defect dimensions. This invention aims to achieve high-precision quantification of wall thickness in pipeline magnetic flux leakage detection data, improve the accuracy of defect quantification, and thus improve the level of pipeline integrity assessment. It proposes a method for quantifying wall thickness in pipeline magnetic flux leakage detection data, including: establishing an intelligent wall thickness quantification model that incorporates a velocity influence factor, and determining the exponential model used. For the revised wall thickness quantification model, we independently developed new wall thickness identification algorithms, outlier automatic correction algorithms, and wall thickness adaptation algorithms. We also recompiled the automatic wall thickness analysis and processing module of the internal inspection data analysis software, making the analyzed wall thickness values ​​more consistent with the actual pipeline wall thickness information. The quantification error is ≤±0.8mm at 90% confidence level. This improves the accuracy of defect size quantification, making the estimated maintenance ratio, remaining pipeline service life, and re-inspection cycle evaluation more accurate, thus reducing the owner's maintenance costs. Details are as follows:

[0055] S10, obtain simulation data of the axial data of the outer wall of the oil and gas pipeline as the base value;

[0056] In this implementation, theoretical data simulated by electromagnetic field simulation software is used for modeling. By analyzing the principle of magnetic circuit simulation, it is decided to use the baseline value of pipeline leakage magnetic field detection data for theoretical modeling.

[0057] S20, using the base value, considering only the relationship between the base value and the wall thickness, construct a univariate model of the wall thickness base value of pipes of different diameters and perform error analysis to obtain the first error analysis result;

[0058] Based on the first error analysis results, a mathematical relationship between wall thickness and base value is modeled and fitted using multiple mathematical models. Based on the fitting results, a mathematical model for wall thickness and base value is determined as the first model.

[0059] In this implementation, 11 mathematical models were selected for fitting the single-factor model between the wall thickness and the baseline value for the three calibers at 1 m / s. Considering both model accuracy and universality, the final mathematical model of wall thickness and baseline value was obtained (the model with the smallest error was selected as the final logarithmic (S-model) model). This invention is preferred when the velocity is 1 (i.e., the influence of the velocity factor is not considered), and the expression is as follows:

[0060]

[0061] Among them, w t Let B represent the wall thickness, B represent the baseline value, and a, b, and c represent the fitting parameters, all of which are constants.

[0062] The correlation coefficients and model errors of linear models for small-diameter (preferably φ325), medium-diameter (preferably φ508), and large-diameter (preferably φ1016) pipes were analyzed to determine the strength of the linear relationship and the fitting effect. The correlation coefficients of the linear models for the three pipe diameters are all close to 1, indicating a strong linear relationship. From the error analysis, the model errors for all three pipe diameters are very small, achieving a good fitting effect (i.e., for the three pipe diameter data, 11 mathematical models were used to fit the data, and the best-fitting model was found. The three pipe diameters represent typical values ​​within the range of pipe diameter variations during pipeline inspection, and the 11 mathematical models represent trying as many mathematical algorithms as possible for fitting).

[0063] S30, Introduce a velocity influence factor into the first model to establish a wall thickness quantification model with velocity factor as the second model; collect tension data of pipes of different diameters and substitute them into the second model for error analysis to obtain the second error analysis results;

[0064] In this implementation, tensile test data and actual engineering data were used as sample data. Considering the effect of velocity, it was found that as the velocity increases, the base value for the same wall thickness gradually decreases. Following the first model, the velocity factor was added to the exponential factor, as shown in the following expression:

[0065]

[0066] Among them, w t Let B represent the wall thickness, B represent the baseline value, v represent the velocity, and a, b, c, and d represent the fitting parameters, all of which are constants.

[0067] Magnetic flux leakage (MFL) detection involves transient electromagnetic problems of moving conductors. Current research largely relies on simulations of static magnetic fields, neglecting the impact of detection speed. Analysis of extensive MFL detection data reveals that during static detection, the material magnetization time is sufficient, and the magnetic saturation process is negligible. However, during online detection, increased detection speed shortens the effective magnetization time of the tested pipe fitting, leading to unsaturated magnetization in high-speed, thick-walled pipes. The pipe section wall thickness is closely related to the pipe section baseline and the detector speed, exhibiting a complex relationship. Therefore, in intelligent wall thickness quantification research, regression analysis based on MFL physical quantities is used to establish an exponential quantification model, which can improve the accuracy of wall thickness quantification.

[0068] S40, acquire base value data, and obtain the wall thickness quantification result through the mathematical model of the final wall thickness and base value.

[0069] In this implementation, the final mathematical model, after modeling and optimization, is updated to the original algorithm position in the magnetic flux leakage detection data analysis software to achieve high-precision analysis of wall thickness values. Specifically:

[0070] First, remove outliers from the base values ​​to adjust the messy base value data, such as... Figure 4 As shown, the specific steps are as follows: Obtain all pipe segment data and store it in a two-dimensional array; traverse all numerical points in the two-dimensional array and remove points that are higher than the upper limit UL and the lower limit LL; take the mode M of the remaining meaningful points: M = L + [fb / (fa + fb)] × i; M = U - [fb / (fa + fb)] × i, where L represents the lower limit of the group containing the mode, U represents the upper limit of the group containing the mode, fb represents the difference between the frequency of the group containing the mode and the frequency of the neighboring group of the lower limit, fa represents the difference between the frequency of the group containing the mode and the frequency of the neighboring group of the upper limit, and i represents the group interval of the group containing the mode.

[0071] Parameters are adjusted according to different data types and different base values, such as... Figure 5 As shown, input the corresponding parameters into the software parameter adjustment dialog box (i.e., the mathematical model of the final wall thickness and base value) to automatically measure the wall thickness.

[0072] Taking tensile data as an example, the wall thickness quantification model was used to quantify the wall thickness of tensile data for three diameters: φ325, φ508, and φ1016. The accuracy is as follows: for φ325, the wall thickness quantification error is ≤ ±0.53mm at a 90% confidence level. Figure 6 As shown, the wall thickness quantification error of φ508 is ≤ ±0.13mm with a 90% confidence level. Figure 7 As shown, the quantification error of the φ1016 wall thickness at a 90% confidence level is ≤ ±0.45mm. Figure 8 As shown. The test results prove that the wall thickness quantification error meets the requirement of ≤±0.8mm with a 90% confidence level.

[0073] In addition, the invention's effectiveness is demonstrated using a 2021 intelligent pipeline inspection project as an example. The pipeline traverses complex terrain, resulting in significant impact on the detector during operation. Data revealed that when the detector approached a bend, its speed dropped to 0.02 m / s immediately upon entering the bend, but instantly reached 10.1 m / s after passing the bend. The speed curve for the portion of the detector's journey after passing the bend is shown below. Figure 9 As shown, the wall thickness measurement results show a significant deviation after passing through this bend.

[0074] The detector's operating speed decreased from a high level to a normal range as it passed through the bend. Although the problem of abnormal wall thickness signal measurement was reduced, a large error still existed, which did not match the actual excavated wall thickness information.

[0075] Analysis of these phenomena revealed that the original wall thickness measurement model that does not consider speed is no longer suitable for the requirements of wall thickness data measurement under special working conditions and urgently needs to be upgraded.

[0076] First, the original wall thickness quantification model was used to analyze the pipe wall thickness over the entire mileage to obtain all pipe wall thickness information. Then, based on the wall thickness, metal loss points were analyzed and their dimensions were measured, resulting in 9269 metal loss points (MLs), of which 16 had a metal loss point depth ≥ 50%wt. Based on the defect length and depth dimensions, the ASME B31G method was used to evaluate the remaining strength of the 9269 metal losses found in this inspection, resulting in 41 recommended excavation points.

[0077] A new wall thickness quantization model was used to obtain all pipe wall thickness information, such as... Figure 10 As shown in Table 1, a metal loss point analysis was then performed based on the wall thickness, and the dimensions were remeasured. A total of 9856 ML points were identified, of which 29 had a metal loss point depth ≥ 50%wt. A residual strength evaluation of the pipeline integrity metal loss was then conducted, resulting in 11 recommended excavation points.

[0078] Table 1

[0079] Excavation verification was conducted using the recommended excavation points provided in the inspection report. The verification results showed that the pipe wall thickness information provided in the inspection report met the wall thickness quantification accuracy of ≤±0.8mm, and 13 more dangerous defects were found compared to the original model parameters. After evaluation, 30 unnecessary excavation points were avoided, saving over 300,000 yuan. Example excavation points are shown in Tables 2 and 3 (see details for specific examples). Figure 11 , 12 ):

[0080] Table 2

[0081] Table 3

[0082] In summary, this embodiment, using common magnetic flux leakage (MF) internal detection as an example, demonstrates a wall thickness quantification model algorithm that incorporates a velocity factor. This accurately quantifies wall thickness values ​​while improving the accuracy of defect size quantification, leading to more accurate estimates of maintenance ratios, remaining pipeline lifespan, and re-inspection cycle evaluations, while reducing unnecessary excavation costs. This method can comprehensively quantify the wall thickness accuracy level of MF detection data, effectively improving the scientific rigor and precision of the quantification.

[0083] A second embodiment of the present invention provides a pipe magnetic flux leakage detection data wall thickness quantification system, the system comprising:

[0084] The data acquisition module is configured to acquire simulation data of the axial data of the outer wall of the oil and gas pipeline as the base value;

[0085] The error analysis module is configured to use the base value, considering only the relationship between the base value and the wall thickness, to construct a univariate model of the wall thickness base value of pipes of different diameters and perform error analysis to obtain the first error analysis result;

[0086] Based on the first error analysis results, the mathematical relationship between the wall thickness and the base value under low speed conditions is modeled and fitted with multiple mathematical models. Based on the fitting results, the mathematical model between the wall thickness and the base value is determined as the first model.

[0087] The model building module is configured to introduce a velocity influence factor into the first model and establish a wall thickness quantification model with the velocity factor as the second model, i.e., the final mathematical model of wall thickness and base value.

[0088] The wall thickness quantization module is configured to acquire base value data and obtain the wall thickness quantization result through the mathematical model of the final wall thickness and the base value.

[0089] It should be noted that the pipe leakage magnetic flux detection data wall thickness quantification system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0090] An electronic device according to a third embodiment of the present invention includes: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement the above-described method for quantifying the wall thickness of pipeline magnetic flux leakage detection data.

[0091] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described method for quantifying the wall thickness of pipeline magnetic flux leakage detection data.

[0092] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the above-described electronic device and computer-readable storage medium can be referred to the corresponding process in the foregoing method examples, and will not be repeated here.

[0093] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0094] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0095] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0096] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for quantifying the wall thickness of pipeline magnetic flux leakage detection data, characterized in that, The method includes the following steps: S10, acquire simulation data of magnetic flux leakage detection for oil and gas pipelines as the baseline value; S20, using the base value, considering only the relationship between the base value and the wall thickness, construct a univariate model of the wall thickness base value of pipes of different diameters and perform error analysis to obtain the first error analysis result; Based on the first error analysis results, a mathematical relationship between wall thickness and base value is modeled and fitted using multiple mathematical models. Based on the fitting results, a mathematical model for wall thickness and base value is determined as the first model. S30, Introduce a velocity influence factor into the first model and establish a wall thickness quantification model with velocity factor as the second model, that is, the final mathematical model of wall thickness and base value. S40, acquire base value data, and obtain the wall thickness quantification result through the mathematical model of the final wall thickness and base value.

2. The method for quantifying the wall thickness of pipeline magnetic flux leakage detection data according to claim 1, characterized in that, The various mathematical models include linear models, logarithmic models, inverse models, quadratic models, cubic models, composite models, power models, S-models, growth models, exponential models, and logistic models.

3. The method for quantifying the wall thickness of pipeline magnetic flux leakage detection data according to claim 2, characterized in that, The first model is: Among them, w t B represents the wall thickness, B represents the baseline value, and a, b, and c are the fitting parameters.

4. The method for quantifying the wall thickness of pipeline magnetic flux leakage detection data according to claim 3, characterized in that, The second model is: Where v represents velocity and d is the fitting parameter.

5. A system for quantifying the wall thickness of pipeline magnetic flux leakage detection data, characterized in that, The system includes: The data acquisition module is configured to acquire simulation data of the axial data of the outer wall of the oil and gas pipeline as the base value; The error analysis module is configured to use the base value, considering only the relationship between the base value and the wall thickness, to construct a univariate model of the wall thickness base value of pipes of different diameters and perform error analysis to obtain the first error analysis result; Based on the first error analysis results, a mathematical relationship between wall thickness and base value is modeled and fitted using multiple mathematical models. Based on the fitting results, a mathematical model for wall thickness and base value is determined as the first model. The model building module is configured to introduce a velocity influence factor into the first model and establish a wall thickness quantification model with the velocity factor as the second model, i.e., the final mathematical model of wall thickness and base value. The wall thickness quantization module is configured to acquire base value data and obtain the wall thickness quantization result through the mathematical model of the final wall thickness and the base value.

6. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor to implement a method for quantifying the wall thickness of pipeline magnetic flux leakage detection data as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are executed by the computer to implement the pipe magnetic flux leakage detection data wall thickness quantification method according to any one of claims 1-4.

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