Pipeline circumferential weld evaluation method, device and equipment and storage medium

Abstract

The invention discloses a pipeline circumferential weld evaluation method, device and equipment and a storage medium. Comprising the following steps: acquiring evaluation related parameters and simulation times of a to-be-evaluated pipeline, and determining a probability distribution type of the evaluation related parameters; constructing a limit state equation of the to-be-evaluated pipeline according to the evaluation related parameters; based on the probability distribution type and the simulation times, adopting a Latin hypercube sampling method to carry out random sampling to obtain each sequence; and substituting the sequences into the limit state equation so as to carry out circumferential weld evaluation on the to-be-evaluated pipeline. By acquiring the parameters and determining the probability distribution type, the random characteristics of the parameters can be comprehensively reflected, and a data basis is provided for evaluation. By constructing a limit state equation, the safety state of the circumferential weld is quantified, and a visual judgment standard is provided. The adopted Latin hypercube sampling method has the characteristic of uniform layering, and the sample value of the tail can be obtained under the condition of less sampling, so that the circumferential weld evaluation is more accurate, and theoretical guidance is provided for safe operation of the pipeline.

Description

Technical Field

[0001] The present invention relates to the technical field of safety assessment, and particularly to a method, device, equipment and storage medium for evaluating pipeline girth welds. Background Art

[0002] The transportation of oil and gas pipelines is crucial to the national economy, but pipeline failures can cause serious losses. Due to welding technology and construction quality problems, girth welds are prone to defects, among which crack defects are highly harmful. Therefore, it is of great significance to conduct reliability analysis on pipelines with crack defects to ensure the safe operation of oil and gas pipelines.

[0003] Since the 1970s, reliability engineering has been applied to pipeline assessment technology, and common assessment specifications such as R6, BS7910 and SINTAP have gradually emerged. These specifications are mostly based on fracture mechanics theory and adopt deterministic methods, regarding the factors affecting structural failure as definite values to judge the structural safety.

[0004] However, in engineering applications, the index data for evaluating girth weld stress fluctuates significantly, presenting uncertainty. However, the existing deterministic assessment methods do not consider the uncertainty of these factors, resulting in a large deviation between the assessment results and the actual situation of the components, being unable to accurately evaluate the structural safety level and easily causing potential safety hazards. Summary of the Invention

[0005] The present invention provides a method, device, equipment and storage medium for evaluating pipeline girth welds. The Latin hypercube sampling method adopted has the characteristic of uniform stratification, which can obtain the sample values at the tails with fewer samplings, making the reliability calculation of girth welds more accurate and providing theoretical guidance for the safe operation of oil and gas pipelines.

[0006] According to one aspect of the present invention, there is provided a method for evaluating pipeline girth welds, the method comprising:

[0007] Obtaining the evaluation-related parameters and the number of simulations of the pipeline to be evaluated, and determining the probability distribution type of the evaluation-related parameters;

[0008] Constructing a limit state equation of the pipeline to be evaluated according to the evaluation-related parameters;

[0009] Performing random sampling using the Latin hypercube sampling method based on the probability distribution type and the number of simulations to obtain each sequence;

[0010] Substituting each sequence into the limit state equation to evaluate the girth welds of the pipeline to be evaluated.

[0011] Optionally, determine the probability distribution type of the evaluation-related parameters, including: performing statistical analysis on the evaluation-related parameters to determine basic statistics, frequency histograms, and cumulative frequency diagrams, where the evaluation-related parameters include internal pressure data, actual working condition load data, defect size data, and material property data; using the least squares method to determine the distribution parameters corresponding to the basic statistics; determining the data division intervals according to the frequency histograms, and determining the best-fitting distribution based on the data division intervals and distribution parameters, and taking the best-fitting distribution as the probability distribution type.

[0012] Optionally, construct the limit state equation of the pipeline to be evaluated based on the evaluation-related parameters, including: defining each basic random variable involved in the limit state equation based on the evaluation-related parameters; respectively establishing limit state functions based on stress and strain for the structural characteristics of the pipeline girth weld and crack defect characteristics; combining each basic random variable and each limit state function to generate the limit state equation.

[0013] Optionally, based on the probability distribution type and the number of simulations, use the Latin hypercube sampling method to perform random sampling to obtain each sequence, including: determining the sampling variables that have the most significant impact on the girth weld failure probability according to the cumulative frequency diagram and the probability distribution type; based on the number of simulations, using the Latin hypercube sampling method to perform random sampling on the sampling variables in each basic random variable to obtain each sequence that conforms to the probability distribution type of the basic random variables.

[0014] Optionally, determine the sampling variables that have the most significant impact on the girth weld failure probability according to the cumulative frequency diagram and the probability distribution type, including: calculating the correlation coefficient between each basic random variable and the girth weld failure according to the cumulative frequency diagram and the probability distribution type; when the correlation coefficient is greater than the preset threshold, taking the basic random variable as the sampling variable.

[0015] Optionally, substitute each sequence into the limit state equation to evaluate the girth weld of the pipeline to be evaluated, including: substituting each sequence into the limit state equation respectively to determine each output result; determining the girth weld state corresponding to each output result, where the girth weld state includes a reliable state, a limit state, and a failure state; performing probability statistics on each girth weld state to determine the failure probability of the girth weld, and taking the failure probability as the evaluation result.

[0016] Optionally, determine the girth weld state corresponding to each output result, including: when the output result is greater than 0, determining the girth weld state as a reliable state; when the output result is less than 0, determining the girth weld state as a failure state; when the output result is equal to 0, determining the girth weld state as a limit state.

[0017] According to another aspect of the present invention, there is provided a device for evaluating a pipeline girth weld, the device comprising:

[0018] A relevant parameter determination module, configured to obtain evaluation-related parameters and the number of simulations of a pipeline to be evaluated, and determine the probability distribution type of the evaluation-related parameters;

[0019] A limit state equation construction module, configured to construct a limit state equation of the pipeline to be evaluated according to the evaluation-related parameters;

[0020] A random sampling module, configured to perform random sampling using the Latin hypercube sampling method based on the probability distribution type and the number of simulations to obtain each sequence;

[0021] A girth weld evaluation module, configured to substitute each sequence into the limit state equation to evaluate the girth weld of the pipeline to be evaluated.

[0022] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0023] At least one processor;

[0024] And a memory communicatively connected to the at least one processor;

[0025] Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a pipeline girth weld evaluation method according to any embodiment of the present invention.

[0026] According to another aspect of the present invention, there is provided a computer-readable storage medium, which stores computer instructions for causing a processor to implement a pipeline girth weld evaluation method according to any embodiment of the present invention when executed.

[0027] The technical solution of the embodiment of the present invention can comprehensively reflect the random characteristics of parameters by obtaining parameters and determining the probability distribution type, providing a data basis for evaluation. By constructing a limit state equation, the safety state of the girth weld is quantified, providing an intuitive judgment criterion. The Latin hypercube sampling method adopted has the characteristic of uniform stratification, and can obtain the sample values at the tail with fewer samplings, making the girth weld evaluation more accurate and providing theoretical guidance for the safe operation of the pipeline.

[0028] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0030] Figure 1 It is a flowchart of a pipeline girth weld evaluation method provided in Embodiment 1 of the present invention;

[0031] Figure 2 It is a flowchart of another pipeline girth weld evaluation method provided in Embodiment 2 of the present invention;

[0032] Figure 3 It is a schematic structural diagram of a pipeline girth weld evaluation device provided in Embodiment 3 of the present invention;

[0033] Figure 4 It is a schematic structural diagram of an electronic device for implementing the pipeline girth weld evaluation method of the embodiments of the present invention. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0036] Embodiment 1

[0037] Figure 1The following is a flowchart of a method for evaluating a pipeline girth weld provided in the first embodiment of the present invention. This embodiment is applicable to the scenario of evaluating the reliability of girth welds with crack defects in oil and gas pipelines. This method can be executed by a pipeline girth weld evaluation device, which can be implemented in the form of hardware and / or software, and can be configured in a computer controller.

[0038] As Figure 1 shown, the method includes:

[0039] S110. Obtain the evaluation-related parameters and the number of simulations of the pipeline to be evaluated, and determine the probability distribution type of the evaluation-related parameters.

[0040] Among them, the pipeline girth weld is the weld formed at the annular interface of two sections of pipelines by using a welding process during the connection of oil and gas pipelines. Due to factors such as welding technical conditions and on-site construction quality, welding defects such as cracks, pores, slag inclusions, incomplete penetration, and lack of fusion are likely to occur in the girth weld and the near-weld zone, becoming weak points in pipeline safety. The evaluation-related parameters include internal pressure data, actual working condition load data, defect size data, and material property data. The number of simulations refers to the set number of sampling repetitions when using the Monte Carlo calculation method with Latin hypercube sampling. The probability distribution type refers to the mathematical distribution form used to describe the random variation law of the evaluation-related parameters.

[0041] Optionally, determining the probability distribution type of the evaluation-related parameters includes: performing statistical analysis on the evaluation-related parameters to determine basic statistics, frequency histograms, and cumulative frequency diagrams, where the evaluation-related parameters include internal pressure data, actual working condition load data, defect size data, and material property data; using the least squares method to determine the distribution parameters corresponding to the basic statistics; determining the data division intervals according to the frequency histograms, and determining the best-fitting distribution according to the data division intervals and the distribution parameters, and taking the best-fitting distribution as the probability distribution type.

[0042] Among them, the internal pressure data refers to the pressure value generated by oil and gas flowing inside the pipeline on the pipe wall. The actual working condition load data covers various types, such as thermal loads generated by the thermal expansion and contraction of the pipeline due to environmental temperature changes, or loads caused by geological activities in the pipeline laying area, external mechanical extrusion, etc. The defect size data is used to describe the size of defects such as cracks at the girth weld, such as the length, depth, and width of the crack. The material property data refers to the characteristics of the material that makes up the pipeline. For example, the yield strength is the stress value when the material begins to produce obvious plastic deformation; the tensile strength represents the maximum stress that the material can withstand before tensile fracture; the fracture toughness reflects the ability of the material to resist crack propagation. The material property data determines the bearing capacity and anti-destruction ability of the pipeline girth weld.

[0043] Specifically, the controller will perform preliminary processing on various types of evaluation-related parameter data collected. The controller refers to the computer controller for pipeline girth weld evaluation. Among them, the basic statistics include the mean and variance. The mean represents the average level of the data and can reflect the central tendency of the parameters. Calculating the mean of a set of internal pressure data can help understand the approximate internal pressure of the pipeline during the statistical period. The variance is used to measure the degree of dispersion of the data. The larger the variance, the more dispersed the data. For example, for two sets of defect size data, the set with a larger variance indicates that the difference in defect sizes is more obvious. The frequency histogram divides the data into several intervals, counts the frequency of data occurrences in each interval, and uses the height of the rectangle to represent the frequency. Through the frequency histogram, the distribution of data in different intervals can be intuitively seen. The cumulative frequency graph is used to show the cumulative frequency of data less than or equal to a certain value and can present the distribution trend of the data.

[0044] It can be known that the least squares method is a process of finding the best function match for data by minimizing the sum of the squares of the errors. Taking the example of determining the distribution parameters of the yield strength of a batch of pipeline materials, after collecting a large amount of yield strength data of this material, assuming it may follow a normal distribution, the least squares method is used to fit and calculate these data, adjusting parameters such as the mean and standard deviation in the normal distribution to minimize the sum of the squares of the errors between the theoretical distribution and the actual data. The finally determined mean, standard deviation, etc. are the distribution parameters corresponding to the yield strength data.

[0045] Furthermore, the intervals can be divided by referring to the shape of the frequency histogram and the characteristics of the data distribution, and divided at the positions where the data distribution changes significantly. Finally, based on the divided intervals and the determined distribution parameters, the χ 2 test can be used to determine the best-fitting distribution, which is expressed by the following formula (1):

[0046]

[0047] Among them, D represents the sum of the differences between the frequencies and probabilities of all intervals, n represents the sample size within the interval, and m represents the number of intervals. The principle of the χ 2 test is to calculate the sum of the deviations between the frequencies of the sample data within the interval and the cumulative distribution probabilities. For a sample with a capacity of n, divided into m intervals, the frequency falling into each interval is ni / n, and assuming it follows a certain distribution, the probability of falling into this interval is pi. Then the sum D of the differences between the frequencies and probabilities of all intervals follows the χ 2 distribution. Given a significance level α, there is a corresponding confidence limit Dα. If D > Dα, it means that the data does not follow the assumed distribution; otherwise, it follows. When multiple assumed distributions all meet the compliance conditions, compare their D values. The smaller the D value, the better the fitting degree of the distribution to the data and the higher the confidence level.

[0048] S120. Construct the limit state equation of the pipeline to be evaluated according to the evaluation-related parameters.

[0049] Among them, the limit state equation is a mathematical expression used to describe whether the pipeline reaches the limit state, and the basic random variables involved include the basic parameters of the pipeline, the defect size parameters, and the external environment parameters.

[0050] Optionally, constructing the limit state equation of the pipeline to be evaluated according to the evaluation-related parameters includes: defining each basic random variable involved in the limit state equation based on the evaluation-related parameters; respectively establishing the limit state functions based on stress and strain for the structural characteristics of the pipeline girth weld and the crack defect characteristics; combining each basic random variable and each limit state function to generate the limit state equation.

[0051] Among them, the evaluation-related parameters include internal pressure data, actual working condition load data, defect size data, and material property data. These parameters are uncertain in actual engineering and are thus defined as basic random variables.

[0052] Specifically, when the controller respectively establishes the limit state functions based on stress and strain for the structural characteristics of the pipeline girth weld and the crack defect characteristics, it will consider the stress conditions of the pipeline girth weld under various loads. For the girth weld with crack defects, stress concentration will occur at the crack tip, resulting in a significant increase in local stress. According to fracture mechanics theory, when the stress intensity factor at the crack tip reaches the fracture toughness of the material, the crack will start to expand, leading to pipeline failure. From the perspective of strain, when the strain of the pipeline girth weld exceeds the allowable strain of the material, the pipeline will fail. Finally, the controller will substitute the defined basic random variables into the limit state functions based on stress and strain to obtain the comprehensive limit state equation, which is expressed by the following formula (2):

[0053] Z = g 1 (x 11 , x 12 , ……, x 1k ) × g 2 (x 21 , x 22 , ……, x 2k )(2)

[0054] Among them, g 1 represents the limit state function based on stress, g 2 represents the limit state function based on strain, and x 11 , x 12 , ……, x 1k and x 21 , x 22 , ……, x 2k represent the basic random variables.

[0055] S130. Based on the type of probability distribution and the number of simulations, use the Latin hypercube sampling method to perform random sampling to obtain each sequence of numbers.

[0056] Among them, the Latin hypercube sampling is a stratified sampling method, which can more evenly cover the entire sample space with fewer sampling times and obtain more representative samples. By performing random sampling on the variables that have the most significant influence on the failure probability of the girth weld in the girth weld limit state equation. According to the set number of simulations N, after the sampling operation, N groups of sequences of numbers that conform to the probability distribution type of the basic random variables can be obtained.

[0057] Optionally, based on the type of probability distribution and the number of simulations, use the Latin hypercube sampling method to perform random sampling to obtain each sequence of numbers, including: determining the sampling variables that have the most significant influence on the failure probability of the girth weld according to the cumulative frequency diagram and the type of probability distribution; based on the number of simulations, using the Latin hypercube sampling method to perform random sampling on the sampling variables in each basic random variable to obtain each sequence of numbers that conform to the probability distribution type of the basic random variables.

[0058] It should be noted that the number of simulations is a parameter preset during the sampling process, denoted by N, which determines the scale of sampling and the accuracy of the calculation results. In practical applications, the number of simulations can be reasonably determined according to specific engineering requirements and computing resources.

[0059] In a specific implementation manner, according to the probability distribution of the sampling variables, divide its value range into N non-overlapping sub-intervals, and the probability of each sub-interval is equal, all being 1 / N. Assume that the value range of the internal pressure of the pipeline is [0, 10] MPa and the number of simulations N = 5, then the internal pressure value range is divided into 5 sub-intervals: [0, 2) MPa, [2, 4) MPa, [4, 6) MPa, [6, 8) MPa, [8, 10] MPa. The controller will independently randomly select a sample value within each sub-interval. During sampling, it is necessary to ensure that the selected value conforms to the probability distribution characteristics of the variable. One sample value is selected from each of the N sub-intervals of each sampling variable to form a sequence of numbers that conforms to the probability distribution type of the basic random variables. By repeating the above sampling operation until N groups of such sequences of numbers are obtained. Assume that there are two sampling variables, the crack depth and the internal pressure of the pipeline. One value is respectively selected from the 5 sub-intervals of the crack depth, and then one value is respectively selected from the 5 sub-intervals of the internal pressure of the pipeline. Combine these two values into a set of data, such as (0.52 mm, 3 MPa), and repeat 5 times to obtain 5 groups of sequences of numbers that conform to the probability distribution. The obtained sequences of numbers will be used to substitute into the girth weld limit state equation later to calculate the failure probability of the girth weld, thereby providing data support for evaluating the reliability of the girth weld with crack defects.

[0060] Optionally, determine the sampling variables that have the most significant impact on the failure probability of girth welds based on the cumulative frequency diagram and the type of probability distribution, including: calculating the correlation coefficient between each basic random variable and the failure of the girth weld according to the cumulative frequency diagram and the type of probability distribution; when the correlation coefficient is greater than a preset threshold, use the basic random variable as the sampling variable.

[0061] Among them, when analyzing the influencing factors of the failure probability of girth welds, the cumulative frequency diagram can intuitively present the distribution trend of different evaluation-related parameters within the entire value range. For example, when observing the cumulative frequency diagram of internal pressure data, if it is found that the cumulative frequency changes rapidly within a certain interval of the internal pressure, it indicates that the internal pressure values within this interval appear more frequently and have a greater impact on the overall distribution characteristics of the internal pressure parameter. Combine the cumulative frequency diagram and the type of probability distribution to analyze the influence degree of each parameter on the failure probability of girth welds. Those parameters that change drastically in the cumulative frequency diagram and have a large impact on the overall distribution in the probability distribution often have the most significant impact on the failure probability of girth welds. For example, through a large amount of experimental data and theoretical analysis, it is found that a slight change in the crack depth may cause a significant decrease in the load-bearing capacity of the girth weld. In the cumulative frequency diagram, the cumulative frequency of the crack depth changes significantly within a specific interval, and its probability distribution type also shows that the uncertainty of this parameter has a greater impact on the failure probability of the girth weld. Therefore, the crack depth can be determined as one of the sampling variables that have the most significant impact on the failure probability of the girth weld.

[0062] Specifically, the controller can use statistical analysis methods to calculate the correlation coefficient between each basic random variable and the failure of the girth weld in combination with the cumulative frequency diagram and the type of probability distribution. Taking the calculation of the correlation coefficient between the internal pressure and the failure of the girth weld as an example, organize the internal pressure data under different working conditions according to their probability distribution characteristics, and then combine the information on whether the girth weld fails under the corresponding working conditions, and use the Pearson correlation coefficient formula to calculate the degree of correlation between the two. The finally calculated correlation coefficient reflects the closeness of the relationship between the change of the internal pressure and the possibility of the failure of the girth weld. The preset threshold is determined based on a large amount of experimental data, engineering experience, and theoretical analysis. When the calculated correlation coefficient between a certain basic random variable and the failure of the girth weld is greater than the preset threshold, this basic random variable is determined as the sampling variable. Suppose the preset threshold is 0.6, and the calculated correlation coefficient between the crack length and the failure of the girth weld is 0.75, which is greater than 0.6, then the crack length will be regarded as the sampling variable. Because the larger the correlation coefficient, the more significant the impact of the variable on the failure probability of the girth weld. In subsequent sampling and failure probability calculations, these variables need to be considered key points to improve the accuracy of the evaluation.

[0063] S140. Substitute each sequence into the limit state equation to evaluate the girth weld of the pipeline to be evaluated.

[0064] Specifically, the controller substitutes each sequence obtained through Latin hypercube sampling into the constructed limit state equation in sequence. For each set of parameter values, the value of the performance function Z is calculated. Through multiple simulations, a series of Z values can be obtained. According to the distribution of these Z values, the failure probability of the pipeline girth weld can be evaluated. For example, if the proportion of the number of times Z < 0 in the total number of simulations is p, it can be considered that the failure probability of the pipeline girth weld is approximately p. At the same time, through the analysis of the simulation results, the influence degree of each evaluation-related parameter on the performance of the pipeline girth weld can be understood, providing a reference basis for the design, maintenance, and management of the pipeline.

[0065] The technical solution of the embodiment of the present invention can comprehensively reflect the random characteristics of parameters by obtaining parameters and determining the probability distribution type, providing a data basis for evaluation. By constructing the limit state equation, the safety state of the girth weld is quantified, providing an intuitive judgment criterion. The Latin hypercube sampling method adopted has the characteristic of uniform stratification, and the tail sample values can be obtained with fewer samplings, making the evaluation of the girth weld more accurate and providing theoretical guidance for the safe operation of the pipeline.

[0066] Embodiment 2

[0067] Figure 2 It is a flowchart of a method for evaluating a pipeline girth weld provided by Embodiment 2 of the present invention. In this embodiment, on the basis of Embodiment 1 above, the specific process of substituting each sequence into the limit state equation to evaluate the girth weld of the pipeline to be evaluated is added. Among them, the specific contents of steps S250 - S260 are substantially the same as those of steps S120 - S130 in Embodiment 1, so they will not be elaborated in this embodiment. As Figure 2 shown, the method includes:

[0068] S210. Obtain the evaluation-related parameters and the number of simulations of the pipeline to be evaluated, and determine the probability distribution type of the evaluation-related parameters.

[0069] Optionally, determining the probability distribution type of the evaluation-related parameters includes: performing statistical analysis on the evaluation-related parameters to determine the basic statistics, frequency histogram, and cumulative frequency graph, where the evaluation-related parameters include internal pressure data, actual working condition load data, defect size data, and material property data; using the least squares method to determine the distribution parameters corresponding to the basic statistics; determining the data division interval according to the frequency histogram, and determining the best-fitting distribution according to the data division interval and the distribution parameters, and taking the best-fitting distribution as the probability distribution type.

[0070] S220. Construct the limit state equation of the pipeline to be evaluated according to the evaluation-related parameters.

[0071] Optionally, construct the limit state equation of the pipeline to be evaluated based on the evaluation-related parameters, including: defining each basic random variable involved in the limit state equation based on the evaluation-related parameters; establishing limit state functions based on stress and strain respectively for the structural characteristics of the girth weld and the crack defect characteristics of the pipeline; combining each basic random variable and each limit state function to generate the limit state equation.

[0072] S230. Based on the probability distribution type and the number of simulations, use the Latin hypercube sampling method to perform random sampling to obtain each sequence.

[0073] Optionally, based on the probability distribution type and the number of simulations, use the Latin hypercube sampling method to perform random sampling to obtain each sequence, including: determining the sampling variables that have the most significant impact on the failure probability of the girth weld according to the cumulative frequency diagram and the probability distribution type; based on the number of simulations, using the Latin hypercube sampling method to perform random sampling on the sampling variables in each basic random variable to obtain each sequence that conforms to the probability distribution type of the basic random variable.

[0074] Optionally, determine the sampling variables that have the most significant impact on the failure probability of the girth weld according to the cumulative frequency diagram and the probability distribution type, including: calculating the correlation coefficient between each basic random variable and the failure of the girth weld according to the cumulative frequency diagram and the probability distribution type; when the correlation coefficient is greater than the preset threshold, use the basic random variable as the sampling variable.

[0075] S240. Substitute each sequence into the limit state equation to determine each output result.

[0076] S250. Determine the girth weld state corresponding to each output result, where the girth weld state includes a reliable state, a limit state, and a failure state.

[0077] Optionally, determine the girth weld state corresponding to each output result, including: when the output result is greater than 0, determine the girth weld state as a reliable state; when the output result is less than 0, determine the girth weld state as a failure state; when the output result is equal to 0, determine the girth weld state as a limit state.

[0078] Specifically, when the sequence obtained by Latin hypercube sampling is substituted into the limit state equation, if the calculated output result is greater than 0, it indicates that the girth weld has sufficient load-bearing capacity under the current load conditions, crack defect conditions, and material properties. From a mechanical perspective, at this time, the stress borne by the girth weld is lower than the allowable stress of its material, or its deformation is within the safe range, and it will not fail due to the current working conditions. At this time, the state of the girth weld can be determined to be a reliable state. When the output result of the limit state equation is less than 0, it means that the stress borne by the girth weld has exceeded the load-bearing capacity of the material, or the deformation has exceeded the allowable range. At this time, the state of the girth weld can be determined to be a failure state. In the failure state, the actual stress borne by the girth weld is greater than the allowable stress that the material can bear, and the material may have undergone plastic deformation or even fracture, and the girth weld has lost its normal load-bearing capacity, unable to ensure the safe operation of the pipeline. In this case, the girth weld may fail at any time, such as serious problems like crack propagation and leakage. Therefore, in the failure state, measures must be taken immediately for repair or replacement to prevent safety accidents caused by oil and gas leakage. When the output result of the limit state equation is equal to 0, it means that the girth weld is in a critical state, and the stress it bears just reaches the allowable stress of the material, or the deformation reaches the allowable limit value. At this time, the state of the girth weld can be determined to be a limit state. In the limit state, the girth weld is in an extremely unstable state, and any slight external interference, such as a slight fluctuation in internal pressure or a slight change in temperature, may break this balance and cause the girth weld to change from the limit state to the failure state. In actual engineering, once it is found that the girth weld is in the limit state, it is necessary to closely monitor its state changes and arrange maintenance or repair work as soon as possible to avoid the sudden failure of the girth weld and ensure the safe and stable operation of the pipeline system.

[0079] S260. Conduct probability statistics on the states of each girth weld to determine the failure probability of the girth weld, and use the failure probability as the evaluation result.

[0080] Among them, the failure probability intuitively reflects the likelihood of the girth weld failing under the current working conditions and defect conditions, and is a key indicator for evaluating the safety of the girth weld. The lower the failure probability, the higher the reliability of the girth weld; conversely, the higher the failure probability, the greater the safety risk of the girth weld, and corresponding repair, maintenance, or replacement measures need to be taken to ensure the safe operation of the pipeline.

[0081] Specifically, conduct statistical analysis on the states of all girth welds obtained by substituting the sequence for calculation. Assume that a total of N samplings are carried out and substituted into the limit state equation for calculation, and among them, the calculation results show that the girth weld is in the failure state M times. According to probability statistics theory, the failure probability P of the girth weld f can be obtained through the formula P fIt is calculated as M / N. For example, if 1000 sampling calculations are carried out and 50 results show that the girth weld is in a failure state, then the failure probability is 0.05.

[0082] The technical solution of the embodiment of the present invention can comprehensively reflect the random characteristics of parameters by obtaining parameters and determining the probability distribution type, providing a data basis for evaluation. By constructing a limit state equation, the safety state of the girth weld is quantified, providing an intuitive judgment criterion. The Latin hypercube sampling method adopted has the characteristic of uniform stratification, and can obtain the sample values at the tail with fewer samplings, making the evaluation of the girth weld more accurate and providing theoretical guidance for the safe operation of the pipeline.

[0083] Embodiment III

[0084] Figure 3 It is a structural schematic diagram of a pipeline girth weld evaluation device provided by Embodiment III of the present invention. As Figure 3 shown, the device includes: a relevant parameter determination module 310, configured to obtain evaluation relevant parameters and the number of simulations of the pipeline to be evaluated, and determine the probability distribution type of the evaluation relevant parameters;

[0085] a limit state equation construction module 320, configured to construct a limit state equation of the pipeline to be evaluated according to the evaluation relevant parameters;

[0086] a random sampling module 330, configured to perform random sampling by using the Latin hypercube sampling method based on the probability distribution type and the number of simulations to obtain each sequence;

[0087] a girth weld evaluation module 340, configured to substitute each sequence into the limit state equation to evaluate the girth weld of the pipeline to be evaluated.

[0088] Optionally, the relevant parameter determination module 310 is specifically configured to: perform statistical analysis on the evaluation relevant parameters to determine basic statistics, frequency histograms, and cumulative frequency diagrams, where the evaluation relevant parameters include internal pressure data, actual working condition load data, defect size data, and material property data; use the least squares method to determine the distribution parameters corresponding to the basic statistics; determine the data division intervals according to the frequency histograms, and determine the best fitting distribution according to the data division intervals and the distribution parameters, and use the best fitting distribution as the probability distribution type.

[0089] Optionally, the limit state equation construction module 320 is specifically configured to: define each basic random variable involved in the limit state equation based on the evaluation relevant parameters; respectively establish limit state functions based on stress and strain for the structural characteristics of the pipeline girth weld and the crack defect characteristics; combine each basic random variable and each limit state function to generate a limit state equation.

[0090] Optionally, the random sampling module 330 specifically includes: a sampling variable determination unit, configured to: determine the sampling variables that have the most significant impact on the failure probability of girth welds according to the cumulative frequency diagram and the probability distribution type; and a random sampling unit, configured to: perform random sampling on the sampling variables in each basic random variable based on the number of simulations by using the Latin hypercube sampling method to obtain each sequence that conforms to the probability distribution type of the basic random variables.

[0091] Optionally, the sampling variable determination unit is specifically configured to: calculate the correlation coefficient between each basic random variable and the failure of the girth weld according to the cumulative frequency diagram and the probability distribution type; and when the correlation coefficient is greater than a preset threshold, use the basic random variable as the sampling variable.

[0092] Optionally, the girth weld evaluation module 340 specifically includes: an output result determination unit, configured to: substitute each sequence into the limit state equation to determine each output result; a girth weld state determination unit, configured to: determine the girth weld state corresponding to each output result, where the girth weld state includes a reliable state, a limit state, and a failure state; and an evaluation result determination unit, configured to: perform probability statistics on each girth weld state to determine the failure probability of the girth weld, and use the failure probability as the evaluation result.

[0093] Optionally, the girth weld state determination unit is specifically configured to: when the output result is greater than 0, determine that the girth weld state is a reliable state; when the output result is less than 0, determine that the girth weld state is a failure state; and when the output result is equal to 0, determine that the girth weld state is a limit state.

[0094] The technical solution of the embodiment of the present invention can comprehensively reflect the random characteristics of parameters by obtaining parameters and determining the probability distribution type, providing a data basis for evaluation. By constructing the limit state equation, the safety state of the girth weld is quantified, providing an intuitive judgment criterion. The Latin hypercube sampling method adopted has the characteristic of uniform stratification, and can obtain the sample values at the tail with fewer samplings, making the evaluation of the girth weld more accurate and providing theoretical guidance for the safe operation of the pipeline.

[0095] A pipeline girth weld evaluation device provided by an embodiment of the present invention can execute a pipeline girth weld evaluation method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0096] Embodiment 4

[0097] Figure 4The structural schematic diagram of an electronic device 10 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0098] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0099] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for evaluating the girth weld of a pipeline.

[0101] In some embodiments, a method for evaluating a circumferential weld of a pipeline can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for evaluating a circumferential weld of a pipeline described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform a method for evaluating a circumferential weld of a pipeline by any other suitable means (e.g., by means of firmware).

[0102] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0106] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of the communication network include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0107] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0108] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0109] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A pipeline girth weld evaluation method, characterized in that: include: Obtaining evaluation-related parameters and simulation times of the pipeline to be evaluated, and determining a probability distribution type of the evaluation-related parameters; Constructing a limit state equation of the pipeline to be evaluated according to the evaluation related parameters; Based on the probability distribution type and the number of simulations, a Latin hypercube sampling method is used to perform random sampling to obtain each number series; Substitute each of the number series into the limit state equation to evaluate the girth weld of the pipeline to be evaluated.

2. The method according to claim 1, characterized in that The determining of the probability distribution type of the evaluation-related parameters comprises: Performing statistical analysis on the evaluation-related parameters to determine basic statistics, frequency histograms, and cumulative frequency graphs, wherein the evaluation-related parameters include internal pressure data, actual operating load data, defect size data, and material performance data; Determine the distribution parameter corresponding to the basic statistic by using the least square method; A data partition interval is determined according to the frequency histogram, a best fitting distribution is determined according to the data partition interval and the distribution parameter, and the best fitting distribution is used as the probability distribution type.

3. The method according to claim 2, characterized in that The step of constructing the limit state equation of the pipeline to be evaluated according to the evaluation related parameters includes: Defining basic random variables involved in the limit state equation based on the evaluation related parameters; According to the structural characteristics of pipeline girth weld and crack defect characteristics, the limit state functions based on stress and strain are established respectively; The basic random variables and the limit state functions are combined to generate the limit state equations.

4. The method according to claim 3, characterized in that: Based on the probability distribution type and the number of simulations, Latin hypercube sampling method is used to perform random sampling to obtain various series, including: Determine the sampling variable that has the most significant impact on the probability of failure of the girth weld according to the cumulative frequency graph and the probability distribution type; Based on the number of simulations, a Latin hypercube sampling method is used to randomly sample the sampling variables in each of the basic random variables to obtain each number series that conforms to the probability distribution type of the basic random variable.

5. The method according to claim 4, characterized in that The determining of the sampling variables that have the most significant impact on the failure probability of the girth weld according to the cumulative frequency graph and the probability distribution type comprises: Calculate the correlation coefficient between each of the basic random variables and the girth weld failure according to the cumulative frequency graph and the probability distribution type; When the correlation coefficient is greater than a preset threshold, the basic random variable is used as the sampling variable.

6. The method according to claim 1, characterized in that Substituting each of the number series into the limit state equation to evaluate the girth weld of the pipeline to be evaluated includes: Substituting each of the number series into the limit state equation to determine each output result; Determine the girth weld state corresponding to each of the output results, wherein the girth weld state includes a reliable state, a limit state, and a failure state; Probabilistic statistics are performed on the states of each girth weld to determine the failure probability of the girth weld, and the failure probability is used as an evaluation result.

7. The method according to claim 6, characterized in that Determining the girth weld state corresponding to each of the output results includes: When the output result is greater than 0, it is determined that the state of the girth weld is a reliable state; When the output result is less than 0, the state of the girth weld is determined to be a failure state; When the output result is equal to 0, it is determined that the state of the girth weld is a limit state.

8. A pipeline girth weld evaluation device, characterized in that: include: A relevant parameter determination module is used to obtain evaluation-related parameters and simulation times of the pipeline to be evaluated, and determine the probability distribution type of the evaluation-related parameters; A limit state equation construction module, used to construct a limit state equation of the pipeline to be evaluated according to the evaluation related parameters; A random sampling module, used for performing random sampling by using a Latin hypercube sampling method based on the probability distribution type and the number of simulations to obtain each number series; The girth weld evaluation module is used to substitute each of the number series into the limit state equation to perform girth weld evaluation on the pipeline to be evaluated.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method according to any one of claims 1 to 7 when executed.

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