Method and device for constructing digital twin of girth weld fracture of oil and gas pipeline

Abstract

The present application relates to a method and device for constructing a digital twin of a girth weld fracture in an oil and gas pipeline, including: obtaining bending strain data and axial strain data of at least one girth weld segment on a target pipeline; establishing a load database and a defect database of the girth weld segment of the target pipeline according to the bending strain data and the axial strain data of the girth weld segment; simulating the fracture process of the girth weld segment of the target pipeline according to the load database and the defect database of the girth weld segment to obtain a twin mechanism model of the fracture process of the girth weld segment; digitally mapping the twin mechanism model of the fracture process of the girth weld segment through digital twin technology to construct a digital twin of the girth weld segment. The solution provided by the present application can construct a twin for simulating the fracture process of a girth weld segment, and perform co-growth and simulation prediction on the fracture process of the girth weld segment according to predicted data or actual field data to reduce the occurrence of accidents.

Description

Technical Field

[0001] The present application relates to the technical field of digital twins, and in particular to a method and device for constructing a digital twin of a girth weld fracture in an oil and gas pipeline. Background Art

[0002] With the continuous development of the information age, digital twins are becoming a new tool for humans to deconstruct, describe, and understand the physical world. As a new type of digital technology solution, digital twins will bring a new wave to the digital transformation of society and enterprises, and will also push the digital transformation of various organizations to a new level. Currently, digital twins have received attention from more than a dozen industries and have been applied in practice. In the pipeline field, pipeline digital twins have been built for multiple business scenarios.

[0003] In oil and gas pipelines, pipeline girth weld fracture is one of the main causes of failure, especially for long-distance pipelines, where girth welds are subjected to large external loads, which can seriously threaten the safe operation of the pipeline. However, there is currently a lack of relevant digital twins that can predict and manage the fracture process of pipeline girth weld sections. Summary of the invention

[0004] In order to solve or partially solve the problems existing in the related technology, the present application provides a method and device for constructing a digital twin of an oil and gas pipeline girth weld fracture, which can construct a twin for simulating the fracture process of a pipeline girth weld section, and perform co-growth and simulation prediction of the fracture process of the girth weld section based on predicted data or actual field data, thereby reducing the occurrence of girth weld fracture accidents in oil and gas pipelines.

[0005] The first aspect of the present application provides a method for constructing a digital twin of a girth weld fracture of an oil and gas pipeline, comprising:

[0006] Acquire bending strain data and axial strain data of at least one girth weld pipe section on the target pipeline, wherein the bending strain data is used to indicate the degree of deformation of the girth weld pipe section when subjected to bending stress, and the axial strain data is used to indicate the degree of deformation of the girth weld pipe section when subjected to axial stress;

[0007] Establishing a load database and a defect database of the girth weld pipe section of the target pipeline according to the bending strain data and the axial strain data of the girth weld pipe section, wherein the load database includes the axial strain data and the bending strain data of at least one girth weld pipe section on the target pipeline, and the defect database includes the defect data of at least one girth weld pipe section on the target pipeline;

[0008] Simulating the fracture process of the girth weld pipe section of the target pipeline according to the load database and defect database of the girth weld pipe section to obtain a twin mechanism model of the fracture process of the girth weld pipe section, wherein the twin mechanism model of the fracture process of the girth weld pipe section is used to predict and simulate the fracture process of the girth weld pipe section;

[0009] The twin mechanism model of the fracture process of the girth weld pipe section is digitally mapped by digital twin technology to construct a digital twin of the girth weld pipe section.

[0010] In combination with the first aspect, in a possible implementation of the first aspect, the obtaining of the bending strain data of the girth weld section of the target pipeline includes: collecting first motion data of a movable device in the target pipeline; constructing a finite element simulation model, wherein the finite element simulation model includes a digital model of the target pipeline and a digital model of the movable device; inputting at least one set of operating parameter data into the finite element simulation model, performing at least one operating condition simulation on the target pipeline, and collecting at least one set of second motion data of the movable device in the target pipeline; and obtaining the bending strain data of the girth weld section of the target pipeline based on the first motion data and the at least one set of second motion data.

[0011] In combination with the first aspect, in a possible implementation manner of the first aspect, obtaining the bending strain data of the girth weld section of the target pipeline according to the first motion data and the at least one set of second motion data includes: performing interpolation algorithm fitting on the first motion data and the at least one set of the second motion data to obtain a pipeline displacement curve of the target pipeline under different operating parameter data; performing solution processing on the pipeline displacement curve to obtain the bending strain data of the girth weld section of the target pipeline, and the solution processing at least includes integrating the pipeline displacement curve.

[0012] In combination with the first aspect, in a possible implementation of the first aspect, establishing the defect database of the girth weld pipe section according to the bending strain data and the axial strain data includes: based on an image recognition algorithm, performing defect recognition on corresponding images of the finite element simulation model of the target pipeline under different working condition simulations to obtain defect data of the girth weld pipe section of the target pipeline; and establishing the defect database of the girth weld pipe section according to the defect data of the girth weld pipe section of the target pipeline.

[0013] In combination with the first aspect, in a possible implementation of the first aspect, the fracture process of the girth weld segment of the target pipeline is simulated according to the load database and defect database of the girth weld segment to obtain a twin mechanism model of the fracture process of the girth weld segment, including: constructing a fracture process model corresponding to the girth weld segment of the target pipeline according to the first motion data of the movable device, and the fracture process model is used to simulate the fracture process of the girth weld segment; obtaining at least one group of fracture process training data of the girth weld segment based on the load database and defect database of the girth weld segment; training the fracture process model according to at least one group of fracture process training data of the girth weld segment to obtain fracture process result data of the girth weld segment; verifying the fracture process result data of the girth weld segment, and if the verification passes, determining the trained fracture process model as the twin mechanism model of the fracture process of the girth weld segment.

[0014] In combination with the first aspect, in a possible implementation of the first aspect, the fracture process result data of the girth weld pipe segment is verified, and if the verification is passed, the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment, including: obtaining at least one pipeline sample according to at least one group of fracture process training data of the girth weld pipe segment; performing a fracture toughness test on at least one of the pipeline samples to obtain at least one group of fracture test data of the girth weld pipe segment of the pipeline sample; if the fracture test data is consistent with the fracture process result data of the girth weld pipe segment, the fracture process result data of the girth weld pipe segment is verified, and the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment.

[0015] In combination with the first aspect, in a possible implementation manner of the first aspect, it also includes: obtaining predicted bending strain data and predicted axial strain data of the girth weld pipe section of the target pipeline; inputting the predicted bending strain data and the predicted axial strain data into a twin mechanism model of the fracture process of the girth weld pipe section to simulate the fracture process of the girth weld pipe section, and obtaining predicted data of the fracture process of the girth weld pipe section of the target pipeline.

[0016] A second aspect of the present application provides a device for constructing a digital twin of a girth weld fracture of an oil and gas pipeline, comprising an acquisition module for acquiring bending strain data and axial strain data of at least one girth weld pipe segment on a target pipeline, wherein the bending strain data is used to indicate the degree of deformation of the girth weld pipe segment when subjected to bending stress, and the axial strain data is used to indicate the degree of deformation of the girth weld pipe segment when subjected to axial stress;

[0017] A first processing module is used to establish a load database and a defect database of the girth weld pipe section of the target pipeline according to the bending strain data and the axial strain data of the girth weld pipe section, wherein the load database includes the axial strain data and the bending strain data of at least one girth weld pipe section on the target pipeline, and the defect database includes the defect data of at least one girth weld pipe section on the target pipeline;

[0018] A second processing module is used to simulate the fracture process of the girth weld pipe section of the target pipeline according to the load database and the defect database of the girth weld pipe section, and obtain a twin mechanism model of the fracture process of the girth weld pipe section, wherein the girth weld pipe section fracture process model is used to simulate the fracture process of the girth weld pipe section;

[0019] A construction module is used to digitally map the twin mechanism model of the fracture process of the girth weld pipe section through digital twin technology to construct a digital twin of the girth weld pipe section.

[0020] In combination with the second aspect, in a possible implementation of the second aspect, the acquisition module is also used to collect first motion data of the movable device in the target pipeline; construct a finite element simulation model, the finite element simulation model includes a digital model of the target pipeline and a digital model of the movable device; input at least one set of operating parameter data into the finite element simulation model, perform at least one operating simulation on the target pipeline, and collect at least one set of second motion data of the movable device in the target pipeline; based on the first motion data and the at least one set of second motion data, obtain the bending strain data of the girth weld pipe section of the target pipeline.

[0021] In combination with the second aspect, in a possible implementation manner of the second aspect, the processing module is also used to perform interpolation algorithm fitting on the first motion data and at least one set of the second motion data to obtain a pipeline displacement curve of the target pipeline under different operating parameter data; perform solution processing on the pipeline displacement curve to obtain bending strain data of the girth weld pipe section of the target pipeline, and the solution processing at least includes integrating the pipeline displacement curve.

[0022] In combination with the second aspect, in a possible implementation of the second aspect, the processing module is also used to perform defect identification on corresponding images of the finite element simulation model of the target pipeline under different working conditions based on an image recognition algorithm, and obtain defect data of the girth weld section of the target pipeline; and establish a defect database of the girth weld section based on the defect data of the girth weld section of the target pipeline.

[0023] In combination with the second aspect, in a possible implementation of the second aspect, the construction module is also used to construct a fracture process model corresponding to the girth weld segment of the target pipeline according to the first motion data of the movable device, and the fracture process model is used to simulate the fracture process of the girth weld segment; based on the load database and defect database of the girth weld segment, at least one set of fracture process training data of the girth weld segment is obtained; the fracture process model is trained according to at least one set of fracture process training data of the girth weld segment to obtain the fracture process result data of the girth weld segment; the fracture process result data of the girth weld segment is verified, and if the verification passes, the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld segment.

[0024] In combination with the second aspect, in a possible implementation of the second aspect, the construction module is also used to obtain at least one pipeline sample based on at least one group of fracture process training data of the girth weld pipe segment; perform a fracture toughness test on at least one of the pipeline samples to obtain fracture test data of the girth weld pipe segment of at least one group of the pipeline samples; if the fracture test data and the fracture process result data of the girth weld pipe segment are consistent, the fracture process result data of the girth weld pipe segment is verified, and the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment.

[0025] In combination with the second aspect, in a possible implementation of the second aspect, the processing module is also used to obtain predicted bending strain data and predicted axial strain data of the girth weld pipe section of the target pipeline; the predicted bending strain data and the predicted axial strain data are input into the twin mechanism model of the fracture process of the girth weld pipe section to simulate the fracture process of the girth weld pipe section, and obtain the fracture process prediction data of the girth weld pipe section of the target pipeline.

[0026] A third aspect of the present application provides an electronic device, including:

[0027] Processor; and

[0028] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0029] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0030] The technical solution provided by this application may have the following beneficial effects:

[0031] The method for constructing a digital twin of a girth weld fracture in an oil and gas pipeline of the present application includes obtaining bending strain data and axial strain data of at least one girth weld pipe segment on a target pipeline, wherein the bending strain data is used to indicate the degree of deformation of the girth weld pipe segment when subjected to bending stress, and the axial strain data is used to indicate the degree of deformation of the girth weld pipe segment when subjected to axial stress; according to the bending strain data and axial strain data of the girth weld pipe segment, a load database and a defect database of the girth weld pipe segment of the target pipeline are established, wherein the load database includes the axial strain data and bending strain data of at least one girth weld pipe segment on the target pipeline, and the defect database includes the defect database of at least one girth weld pipe segment on the target pipeline. trap data; simulate the fracture process of the girth weld segment of the target pipeline according to the load database and defect database of the girth weld segment, and obtain the twin mechanism model of the fracture process of the girth weld segment. The twin mechanism model of the fracture process of the girth weld segment is used to predict and simulate the fracture process of the girth weld segment. The twin mechanism model of the fracture process of the girth weld segment is digitally mapped through the digital twin technology to construct a digital twin of the girth weld segment. It is possible to construct a twin for simulating the fracture process of the pipeline girth weld, and perform co-growth and simulation prediction on the fracture process of the girth weld segment according to the predicted data or actual field data, so as to reduce the occurrence of girth weld fracture accidents in oil and gas pipelines.

[0032] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0034] Figure 1 It is a flow chart of a method for constructing a digital twin of a girth weld fracture of an oil and gas pipeline shown in an embodiment of the present application;

[0035] Figure 2 It is a structural schematic diagram of a device for constructing a digital twin of a girth weld fracture of an oil and gas pipeline shown in an embodiment of the present application;

[0036] Figure 3 is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application;

[0037] Figure 4 It is a schematic diagram of a fracture process model corresponding to a girth weld pipe segment constructed based on a phase field method, as shown in an embodiment of the present application;

[0038] Figure 5 It is a schematic diagram of the process of the digital twin of the girth weld shown in the embodiment of the present application. DETAILED DESCRIPTION

[0039] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0040] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "a", "an" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0041] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0042] First, some of the terms and related technologies involved in this application are explained to facilitate understanding by those skilled in the art.

[0043] Bending strain is an important parameter for the degree of deformation inside the pipe when the pipe is bent under external load. It is usually used to reflect the bending state of the pipe under external factors such as uneven geological conditions, soil settlement, floods, earthquakes, etc. The calculation of bending strain can be obtained based on the internal detection technology of inertial mapping, which can effectively identify the bending deformation of the pipe and detect the bending strain.

[0044] Axial strain is a physical quantity in material mechanics that describes the degree of deformation of a material along the direction of the force when it is subjected to an axial force. It is a parameter when a pipe is axially deformed under an external load. It is defined as the ratio of the deformation of the material in the axial direction to its original length. Axial strain can be positive or negative. Positive axial strain means that the material is stretched and the length increases; negative axial strain means that the material is compressed and the length decreases. In engineering and materials science, axial strain is an important parameter for evaluating the deformation capacity and elastic modulus (also called Young's modulus) of a material after being subjected to a force. The elastic modulus is defined as the ratio of the stress to the axial strain of the material within the elastic range, reflecting the material's ability to resist deformation.

[0045] Digital Twin refers to the integration of multi-disciplinary, multi-physical, multi-scale, and multi-probability simulation processes by making full use of physical models, sensor updates, and operation history data to complete mapping in virtual space, thereby reflecting the entire life cycle of the corresponding physical equipment. In layman's terms, digital twin refers to the construction of an identical entity in the digital world through digital means for objects in the physical world, thereby achieving understanding, analysis, and optimization of the physical entity. Digital twins can be understood as a digital presentation of the physical world.

[0046] The finite element simulation model is a digital model based on the finite element analysis (FEA) technology. It discretizes the continuous physical structure into a finite number of small units, namely finite elements, to approximate the behavior of the entire structure. This model uses numerical analysis methods to predict the physical properties of the structure, such as stress, deformation, and temperature, by mathematically modeling and calculating these small units. When constructing a finite element simulation model, the following steps need to be performed: select the type of mesh and define the analysis type, add material properties, apply constraints, define loads, and mesh. After the node value is obtained by computer solution, the field function on the unit and even the entire assembly can be determined, and then the stress, strain, etc. of each unit can be calculated.

[0047] In the related technologies, for the safety management of pipeline girth welds, pipeline companies often formulate monitoring and detection plans to regularly monitor the environment and pipeline changes, and analyze and predict the safety status of pipeline girth weld sections based on data analysis. Although the existing monitoring and detection technologies are relatively mature, and to a certain extent make up for the shortcomings of semi-quantitative scoring based on experience in existing engineering practices, due to the discrete monitoring and detection data and the single and unquantified detection object types such as pipeline external load bending strain and axial stress, there are still drawbacks of "sensing but not knowing, sensing but not being smart" in actual applications, which cannot meet the requirements of smart pipelines for "real-time perception, evaluation, and prediction" of pipeline structural safety performance.

[0048] In recent years, digital twins have been more and more widely spread. At the same time, thanks to the development of new generation information technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence, the implementation of digital twins has gradually become possible. New technologies such as digital twins have effectively promoted the development of digitalization, networking, and intelligence in various industries. In the field of oil and gas pipelines, smart pipeline construction based on core technologies such as digitalization, automation, and informatization has enabled pipeline safety to be further developed. At present, digital twin technology is widely used in aerospace, automobile manufacturing, and electric power industries, and has achieved certain results in the intelligent management and control of engine and power grid operation safety. However, in the field of petrochemicals, the application of digital twin technology is still in its infancy, and most research remains in the theoretical stage and lacks practical application. Especially in the field of girth weld sections of oil and gas pipelines, it is impossible to achieve full-cycle and multi-factor simulation of the fracture process of girth weld sections, and it is impossible to obtain the various stress and strain states of girth weld sections of oil and gas pipelines in real time, which is not conducive to observing the real-time state of girth weld sections.

[0049] In response to the above problems, an embodiment of the present application provides a method for constructing a digital twin of a girth weld section fracture of an oil and gas pipeline, which can construct a twin for simulating the fracture process of a girth weld section of a pipeline, and perform co-growth and simulation prediction of the fracture process of the girth weld section based on predicted data or actual field data, thereby reducing the occurrence of girth weld section fracture accidents of oil and gas pipelines.

[0050] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0051] Figure 1 It is a flow chart of a method for constructing a digital twin of an oil and gas pipeline girth weld fracture as shown in an embodiment of the present application.

[0052] See also Figure 1 , a method for constructing a digital twin of an oil and gas pipeline girth weld fracture, comprising:

[0053] S110: Acquire bending strain data and axial strain data of at least one girth weld pipe section on the target pipeline, wherein the bending strain data is used to indicate the deformation degree of the girth weld pipe section when subjected to bending stress, and the axial strain data is used to indicate the deformation degree of the girth weld pipe section when subjected to axial stress.

[0054] Specifically, the target pipeline may be an oil and gas pipeline. The target pipeline is relatively long and there may be multiple girth weld sections on the pipeline. The bending strain data and axial strain data of all girth weld sections on the target pipeline may be obtained through a detection instrument. The degree of deformation of the girth weld section when subjected to bending stress may be obtained based on the bending strain data of the girth weld section. The degree of deformation of the girth weld section when subjected to axial stress may be obtained based on the axial strain data of the girth weld section.

[0055] In another possible implementation, first motion data of the movable device in the target pipeline is collected; a finite element simulation model is constructed, wherein the finite element simulation model includes a digital model of the target pipeline and a digital model of the movable device; at least one set of operating parameter data is input into the finite element simulation model, the operating condition of the target pipeline is simulated at least once, and at least one set of second motion data of the movable device in the target pipeline is collected; and based on the first motion data and the at least one set of second motion data, the bending strain data of the girth weld pipe section of the target pipeline is obtained.

[0056] Specifically, the movable device may be an internal detector in a pipeline inertial mapping (IMU) pull test, the internal detector carries an inertial measurement unit, and in a pipeline inertial mapping pull test on a target pipeline, the internal detector may run in the target pipeline and collect data, the accelerometer in the inertial measurement unit in the internal detector collects the linear motion data of the running process in real time, the gyroscope in the inertial measurement unit collects the angular velocity data of the running process in real time, and collects the mileage data at the same time, and records all the information, the first motion data may be the data collected by the inertial measurement unit of the internal detector, and the bending strain of the girth weld pipe section detected by the target pipeline test may be calculated through the first motion data, for example, the linear motion, angular velocity and mileage information collected by the internal detector may be processed by navigation calculation, filtering and iteration, and the processed data may be integrated to obtain the position, velocity and attitude angle data corresponding to the internal detector, the attitude angle includes the heading angle, pitch angle and roll angle of the target pipeline detected by the internal detector, and the bending strain data detected by the target pipeline test may be calculated through the obtained data of the target pipeline and the data of the internal detector, including the bending strain data of the girth weld pipe section detected by the target pipeline test.

[0057] Specifically, the target pipeline and the movable device can be modeled according to the finite element analysis software. The modeling can be digitally performed in a one-to-one scale according to the structure and material of the target pipeline. The movable device can also be digitally modeled. The movable device can be placed in the target pipeline to construct a finite element simulation model. The operating parameters of the target pipeline during the pulling test and the operating parameters of the in-service pipeline can be obtained. The operating parameters can be environmental parameters such as pipeline geographic information, such as the terrain along the in-service pipeline, spatial coordinates, operating temperature, operating pressure, etc., and can be the external force load applied to the target pipeline during the pulling test, specifically the pressure applied to the target pipeline, the operating temperature, etc. It can be understood that , by inputting multiple groups of working condition parameter data into the finite element simulation model, performing at least one working condition simulation on the target pipeline, that is, by adjusting different working condition parameters, the target pipeline can be subjected to different loads. During each working condition simulation, the movable device can move in the target pipeline and obtain second motion data. Each group of working condition parameters is input into the finite element simulation model to perform working condition simulation to obtain corresponding second motion data. The bending strain data of the target pipeline can be obtained according to the first motion data and the multiple groups of second motion data. The bending strain data obtained by the first motion data and the multiple groups of second motion data are more accurate than the bending strain data detected by the test on the target pipeline, and can better reflect the real bending strain of the target pipeline.

[0058] In another possible implementation, an interpolation algorithm is performed on the first motion data and at least one set of second motion data to obtain a pipeline displacement curve of the target pipeline under different operating parameter data; the pipeline displacement curve is solved to obtain the bending strain data of the girth weld pipe section of the target pipeline, and the solution processing at least includes integrating the pipeline displacement curve.

[0059] Specifically, after obtaining the first motion data and at least one set of second motion data, an interpolation algorithm fitting can be performed. For example, bilinear interpolation, Lagrange interpolation, spline interpolation, nearest neighbor interpolation, bicubic interpolation and other interpolation methods can be used for fitting to obtain the pipeline displacement curve of the target pipeline under different operating parameter data. The pipeline displacement curve is solved, and the solution processing at least includes integration processing of the pipeline displacement curve to obtain the bending strain data of the girth weld pipe section of the target pipeline. The bending strain data obtained by solving the pipeline displacement curve is more accurate than the bending strain data detected by the target pipeline test, and can better reflect the real bending strain of the target pipeline, so as to obtain the bending strain data of the target pipeline.

[0060] In another possible implementation, the target pipeline may be inspected by using a weak magnetic internal detection technology, and the axial strain data of the target pipeline may be obtained.

[0061] S120: establishing a load database and a defect database of the girth weld segment of the target pipeline according to the bending strain data and the axial strain data of the girth weld segment, wherein the load database includes the axial strain data and the bending strain data of at least one girth weld segment on the target pipeline, and the defect database includes the defect data of at least one girth weld segment on the target pipeline.

[0062] Specifically, by acquiring the bending strain data and axial strain data of the entire target pipeline, the bending strain data and axial strain data of all girth weld sections of the target pipeline can be obtained, and the load database and defect database of the girth weld sections of the target pipeline are constructed through the bending strain data and axial strain data of all girth weld sections.

[0063] In another possible implementation, based on an image recognition algorithm, defect recognition is performed on corresponding images of a finite element simulation model of a target pipeline under different working condition simulations to obtain defect data of a girth weld section of the target pipeline; and a defect database of the girth weld section is established based on the defect data of the girth weld section of the target pipeline.

[0064] Specifically, the image recognition algorithm may be a YOLO algorithm, which is a real-time target detection algorithm. The image recognition algorithm is used to perform defect recognition on images corresponding to the finite element simulation model of the target pipeline under different working conditions, and multiple recognition optimizations are performed to obtain defect data of all girth weld sections of the target pipeline, and a defect database of the girth weld sections is constructed based on the defect data of all girth weld sections of the target pipeline.

[0065] S130: simulating the fracture process of the girth weld pipe segment of the target pipeline according to the load database and defect database of the girth weld pipe segment to obtain a twin mechanism model of the fracture process of the girth weld pipe segment. The twin mechanism model of the fracture process of the girth weld pipe segment is used to predict and simulate the fracture process of the girth weld pipe segment.

[0066] Specifically, a fracture process simulation model of the target pipeline can be constructed. For example, a fracture process simulation model of the target pipeline can be constructed according to a phase field method, and the fracture process of the girth weld segment of the target pipeline is simulated using a load database and a defect database of the girth weld segment of the target pipeline, so that a twin mechanism model of the fracture process of the girth weld segment can be obtained.

[0067] See also Figure 4In another possible implementation, a fracture process model corresponding to the girth weld pipe segment of the target pipeline is constructed according to the first motion data of the movable device, and the fracture process model is used to simulate the fracture process of the girth weld pipe segment; based on the load database and defect database of the girth weld pipe segment, at least one set of fracture process training data of the girth weld pipe segment is obtained; the fracture process model is trained according to at least one set of fracture process training data of the girth weld pipe segment to obtain fracture process result data of the girth weld pipe segment; the fracture process result data of the girth weld pipe segment is verified, and if the verification is passed, the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment.

[0068] Specifically, according to the first motion data of the movable device, a fracture process model corresponding to the girth weld pipe section of the target pipeline can be constructed by the phase field method. According to the load database and the defect database of the girth weld pipe section, the fracture process training data of at least one group of girth weld pipe section can be obtained by permutation and combination. The fracture process training data of the girth weld pipe section can be different defect size parameters of the girth weld pipe section, different load data of the girth weld pipe section, such as the crack depth ratio of the defect, the strength matching coefficient of the pipeline, the softening rate of the heat affected zone, the initial hole volume fraction, the yield strength ratio, etc. The girth weld pipe section can be trained according to different fracture process training data. The fracture process of the pipe segment is simulated to obtain the fracture process result data of the girth weld pipe segment. Each time a set of fracture process training data of the girth weld pipe segment is input, the fracture process model can simulate the fracture process of the girth weld pipe segment and output the fracture process result data. The fracture process model is trained simultaneously during the fracture process simulation. After multiple training iterations, the fracture process result data can be verified. If the verification is passed, it means that the trained fracture process model can be used to simulate the fracture process of the girth weld pipe segment, and the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment.

[0069] In another possible implementation, at least one pipeline sample is obtained based on at least one group of fracture process training data of a girth weld pipe segment; a fracture toughness test is performed on at least one pipeline sample to obtain fracture test data of the girth weld pipe segment of at least one group of pipeline samples; if the fracture test data and the fracture process result data of the girth weld pipe segment are consistent, the fracture process result data of the girth weld pipe segment is verified, and the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment.

[0070] Specifically, according to the fracture process training data, a pipeline sample can be constructed, and a fracture toughness test can be performed on the pipeline sample. The fracture toughness test can evaluate the pipeline's ability to resist fracture when it contains cracks, and obtain fracture test data. The fracture test data and the fracture process data of the girth weld pipe section are compared. If the two are consistent, it means that the fracture process model can be verified for the fracture process simulation of the girth weld pipe section of the target pipeline, and the trained fracture process model can be determined as a twin mechanism model of the fracture process of the girth weld pipe section. The twin mechanism model of the fracture process of the girth weld pipe section can be used to simulate and predict the fracture process of the girth weld pipe section of the target pipeline.

[0071] In another possible implementation, the predicted bending strain data and the predicted axial strain data of the girth weld pipe section of the target pipeline are obtained; the predicted bending strain data and the predicted axial strain data are input into the twin mechanism model of the fracture process of the girth weld pipe section to simulate the fracture process of the girth weld pipe section, and the fracture process prediction data of the girth weld pipe section of the target pipeline are obtained.

[0072] Specifically, the load of the target pipeline can be predicted by machine learning intelligent algorithms. For example, gradient boosting, principal component analysis, random forest, decision tree and other machine learning intelligent algorithms can be used for prediction; the predicted bending strain data and predicted axial strain data of the girth weld section of the target pipeline are obtained, and the predicted bending strain data and the predicted axial strain data are input into the twin mechanism model. The fracture process of the girth weld section of the target pipeline can be simulated according to the predicted bending strain data and the predicted axial strain data, and the fracture process prediction data of the girth weld section of the target pipeline can be obtained. The fracture process prediction data of the girth weld section can be used to facilitate real-time maintenance of the girth weld section to avoid fracture of the girth weld section.

[0073] S140: The twin mechanism model of the fracture process of the girth weld pipe section is digitally mapped through digital twin technology to construct the digital twin of the girth weld pipe section.

[0074] Specifically, the digital twin technology can be used to digitally map the twin mechanism model of the fracture process of the girth weld pipe section, and the digital twin of the girth weld pipe section can be constructed. Through the fusion of digital twin and mechanism simulation, the refined simulation and prediction of the production process can be achieved.

[0075] See also Figure 5In a possible implementation method, computer technology, efficient load data wireless transmission technology, and high-speed information network transmission technology can be used to realize real-time information interaction between pipeline girth weld segment entities, twin mechanism models of pipeline girth weld segment fracture processes, and pipeline girth weld segment safety status prediction and warning services, build a real-time data interaction platform, establish a "three-in-one" pipeline girth weld segment fracture process twin framework, and use visualization technology to construct a pipeline girth weld segment fracture process digital twin. Through the integration of digital twins and mechanism simulation, it is possible to achieve refined simulation and prediction of the fracture process and realize comprehensive risk evaluation of pipeline girth weld segments.

[0076] Specifically, the twin mechanism model of the fracture process of the girth weld pipe section can be digitally mapped through computer digital technology, machine learning and other deep fusion algorithms to construct the digital twin of the girth weld pipe section. The twin of the girth weld pipe section includes the physical network layer, the twin network layer and the network application layer. In the physical network layer, various network elements in the physical entity network interact with the network twin through the twin southbound interface to exchange network data and network control information; the twin network layer is the symbol of the digital twin network system, which includes three key subsystems: data sharing warehouse, service mapping model and network twin management; the network application layer inputs requirements to the twin network layer through the twin northbound interface, and deploys services in the twin network layer through modeled instances; in the digital twin of the girth weld pipe section, not only the architecture of the software-defined network SDN is used, but also the digital twin of the girth weld pipe section can simulate the virtual layer to realize the dynamic control and optimization of complex networks that cannot be achieved by the SDN management and control layer.

[0077] The method for constructing a digital twin of a girth weld fracture in an oil and gas pipeline of the present application includes obtaining bending strain data and axial strain data of at least one girth weld pipe segment on a target pipeline, wherein the bending strain data is used to indicate the degree of deformation of the girth weld pipe segment when subjected to bending stress, and the axial strain data is used to indicate the degree of deformation of the girth weld pipe segment when subjected to axial stress; according to the bending strain data and axial strain data of the girth weld pipe segment, a load database and a defect database of the girth weld pipe segment of the target pipeline are established, wherein the load database includes the axial strain data and bending strain data of at least one girth weld pipe segment on the target pipeline, and the defect database includes the defect database of at least one girth weld pipe segment on the target pipeline. trap data; simulate the fracture process of the girth weld segment of the target pipeline according to the load database and defect database of the girth weld segment, and obtain the twin mechanism model of the fracture process of the girth weld segment. The twin mechanism model of the fracture process of the girth weld segment is used to predict and simulate the fracture process of the girth weld segment. The twin mechanism model of the fracture process of the girth weld segment is digitally mapped through the digital twin technology to construct a digital twin of the girth weld segment. It is possible to construct a twin for simulating the fracture process of the pipeline girth weld, and perform co-growth and simulation prediction on the fracture process of the girth weld segment according to the predicted data or actual field data, so as to reduce the occurrence of girth weld fracture accidents in oil and gas pipelines.

[0078] Corresponding to the aforementioned application function realization method embodiment, the present application also provides a twin mechanism model construction device for pipeline girth welds, an electronic device and corresponding embodiments.

[0079] Figure 2 It is a structural schematic diagram of a digital twin construction device for an oil and gas pipeline girth weld fracture shown in an embodiment of the present application.

[0080] See also Figure 2 , a digital twin construction device 200 for oil and gas pipeline girth weld fracture, comprising an acquisition module 210, a first processing module 220, a second processing module 230, and a construction module 240. Among them:

[0081] The acquisition module 210 is used to acquire bending strain data and axial strain data of at least one girth weld pipe section on the target pipeline, the bending strain data is used to indicate the deformation degree of the girth weld pipe section when subjected to bending stress, and the axial strain data is used to indicate the deformation degree of the girth weld pipe section when subjected to axial stress.

[0082] Specifically, the target pipeline may be an oil and gas pipeline. The target pipeline is relatively long and there may be multiple girth weld sections on the pipeline. The bending strain data and axial strain data of all girth weld sections on the target pipeline may be obtained through a detection instrument. The degree of deformation of the girth weld section when subjected to bending stress may be obtained based on the bending strain data of the girth weld section. The degree of deformation of the girth weld section when subjected to axial stress may be obtained based on the axial strain data of the girth weld section.

[0083] In a possible implementation, the acquisition module 210 is also used to collect first motion data of the movable device in the target pipeline; construct a finite element simulation model, the finite element simulation model includes a digital model of the target pipeline and a digital model of the movable device; input at least one set of operating parameter data into the finite element simulation model, perform at least one operating simulation on the target pipeline, and collect at least one set of second motion data of the movable device in the target pipeline; and obtain the bending strain data of the girth weld pipe section of the target pipeline based on the first motion data and at least one set of second motion data.

[0084] The first processing module 220 is used to establish a load database and a defect database of the girth weld pipe segment of the target pipeline according to the bending strain data and the axial strain data of the girth weld pipe segment, wherein the load database includes the axial strain data and the bending strain data of at least one girth weld pipe segment on the target pipeline, and the defect database includes the defect data of at least one girth weld pipe segment on the target pipeline.

[0085] Specifically, by acquiring the bending strain data and axial strain data of the entire target pipeline, the bending strain data and axial strain data of all girth weld sections of the target pipeline can be obtained, and the load database and defect database of the girth weld sections of the target pipeline are constructed through the bending strain data and axial strain data of all girth weld sections.

[0086] In a possible implementation, the first processing module 220 is further used to perform interpolation algorithm fitting on the first motion data and at least one set of second motion data to obtain a pipeline displacement curve of the target pipeline under different operating parameter data; perform solution processing on the pipeline displacement curve to obtain bending strain data of the girth weld pipe section of the target pipeline, and the solution processing at least includes integrating the pipeline displacement curve.

[0087] In a possible implementation, the first processing module 220 is also used to perform defect identification on corresponding images of the finite element simulation model of the target pipeline under different working condition simulations based on an image recognition algorithm, and obtain defect data of the girth weld pipe section of the target pipeline; and establish a defect database of the girth weld pipe section based on the defect data of the girth weld pipe section of the target pipeline.

[0088] In a possible implementation, the first processing module 220 is also used to obtain predicted bending strain data and predicted axial strain data of the girth weld pipe section of the target pipeline; the predicted bending strain data and the predicted axial strain data are input into the twin mechanism model of the fracture process of the girth weld pipe section to simulate the fracture process of the girth weld pipe section, and obtain the fracture process prediction data of the girth weld pipe section of the target pipeline.

[0089] The second processing module 230 is used to simulate the fracture process of the girth weld pipe segment of the target pipeline according to the load database and defect database of the girth weld pipe segment, and obtain a twin mechanism model of the fracture process of the girth weld pipe segment. The girth weld pipe segment fracture process model is used to simulate the fracture process of the girth weld pipe segment.

[0090] Specifically, a fracture process simulation model of the target pipeline can be constructed. For example, a fracture process simulation model of the target pipeline can be constructed according to a phase field method, and the fracture process of the girth weld segment of the target pipeline is simulated using a load database and a defect database of the girth weld segment of the target pipeline, so that a twin mechanism model of the fracture process of the girth weld segment can be obtained.

[0091] In a possible implementation, the second processing module 230 is also used to construct a fracture process model corresponding to the girth weld pipe segment of the target pipeline according to the first motion data of the movable device, and the fracture process model is used to simulate the fracture process of the girth weld pipe segment; based on the load database and defect database of the girth weld pipe segment, at least one set of fracture process training data of the girth weld pipe segment is obtained; the fracture process model is trained according to at least one set of fracture process training data of the girth weld pipe segment to obtain the fracture process result data of the girth weld pipe segment; the fracture process result data of the girth weld pipe segment is verified, and if the verification is passed, the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment.

[0092] In a possible implementation, the second processing module 230 is further used to obtain at least one pipeline sample based on at least one group of fracture process training data of the girth weld pipe segment; perform a fracture toughness test on at least one pipeline sample to obtain fracture test data of the girth weld pipe segment of at least one group of pipeline samples; if the fracture test data and the fracture process result data of the girth weld pipe segment are consistent, the fracture process result data of the girth weld pipe segment is verified, and the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment.

[0093] The construction module 240 is used to digitally map the twin mechanism model of the fracture process of the girth weld pipe section by using the digital twin technology to construct the digital twin of the girth weld pipe section.

[0094] The oil and gas pipeline girth weld fracture digital twin construction device of the present application comprises obtaining bending strain data and axial strain data of at least one girth weld pipe segment on the target pipeline, the bending strain data is used to indicate the deformation degree of the girth weld pipe segment when subjected to bending stress, and the axial strain data is used to indicate the deformation degree of the girth weld pipe segment when subjected to axial stress; according to the bending strain data and axial strain data of the girth weld pipe segment, a load database and a defect database of the girth weld pipe segment of the target pipeline are established, the load database includes the axial strain data and bending strain data of at least one girth weld pipe segment on the target pipeline, and the defect database includes the defect database of at least one girth weld pipe segment on the target pipeline. trap data; simulate the fracture process of the girth weld segment of the target pipeline according to the load database and defect database of the girth weld segment, and obtain the twin mechanism model of the fracture process of the girth weld segment. The twin mechanism model of the fracture process of the girth weld segment is used to predict and simulate the fracture process of the girth weld segment. The twin mechanism model of the fracture process of the girth weld segment is digitally mapped through the digital twin technology to construct a digital twin of the girth weld segment. It is possible to construct a twin for simulating the fracture process of the pipeline girth weld, and perform co-growth and simulation prediction on the fracture process of the girth weld segment according to the predicted data or actual field data, so as to reduce the occurrence of girth weld fracture accidents in oil and gas pipelines.

[0095] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0096] Figure 3 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0097] See also Figure 3 , the electronic device 300 includes a memory 310 and a processor 320.

[0098] The processor 320 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0099] The memory 310 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 320 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 310 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 310 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0100] The memory 310 stores executable codes, and when the executable codes are processed by the processor 320 , the processor 320 can execute part or all of the methods described above.

[0101] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0102] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0103] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for constructing a digital twin of an oil and gas pipeline girth weld fracture, characterized in that: include: Acquire bending strain data and axial strain data of at least one girth weld pipe section on the target pipeline, wherein the bending strain data is used to indicate the degree of deformation of the girth weld pipe section when subjected to bending stress, and the axial strain data is used to indicate the degree of deformation of the girth weld pipe section when subjected to axial stress; Establishing a load database and a defect database of the girth weld pipe section of the target pipeline according to the bending strain data and the axial strain data of the girth weld pipe section, wherein the load database includes the axial strain data and the bending strain data of at least one girth weld pipe section on the target pipeline, and the defect database includes the defect data of at least one girth weld pipe section on the target pipeline; Constructing a fracture process model corresponding to the girth weld pipe section of the target pipeline according to the first motion data collected by the movable device, wherein the fracture process model is used to simulate the fracture process of the girth weld pipe section; Based on the load database and defect database of the girth weld pipe section, obtaining at least one set of fracture process training data of the girth weld pipe section; The fracture process model is trained according to at least one set of fracture process training data of the girth weld pipe section to obtain fracture process result data of the girth weld pipe section; Obtain at least one pipeline sample according to at least one set of fracture process training data of the girth weld pipe section; Performing a fracture toughness test on at least one of the pipeline samples to obtain fracture test data of at least one group of girth weld pipe sections of the pipeline samples; If the fracture test data and the fracture process result data of the girth weld pipe segment are consistent, the fracture process result data of the girth weld pipe segment is verified, and the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment, and the twin mechanism model of the fracture process of the girth weld pipe segment is used to predict and simulate the fracture process of the girth weld pipe segment; The twin mechanism model of the fracture process of the girth weld pipe section is digitally mapped by digital twin technology to construct a digital twin of the girth weld pipe section.

2. The method according to claim 1, characterized in that The step of obtaining the bending strain data of the girth weld pipe section of the target pipeline includes: collecting first movement data of the movable device in the target pipeline; Constructing a finite element simulation model, wherein the finite element simulation model includes a digital model of the target pipeline and a digital model of the movable device; Inputting at least one set of operating parameter data into the finite element simulation model, performing at least one operating simulation on the target pipeline, and collecting at least one set of second motion data of the movable device in the target pipeline; The bending strain data of the girth weld pipe section of the target pipeline is acquired according to the first motion data and the at least one set of second motion data.

3. The method according to claim 2, characterized in that The step of acquiring the bending strain data of the girth weld pipe section of the target pipeline according to the first motion data and the at least one set of second motion data includes: Performing interpolation algorithm fitting on the first motion data and at least one set of the second motion data to obtain a pipeline displacement curve of the target pipeline under different operating parameter data; The pipeline displacement curve is solved to obtain the bending strain data of the girth weld pipe section of the target pipeline, and the solution at least includes integrating the pipeline displacement curve.

4. The method according to claim 2, characterized in that: The step of establishing a defect database of the girth weld pipe section according to the bending strain data and the axial strain data comprises: Based on an image recognition algorithm, defect recognition is performed on corresponding images of the finite element simulation model of the target pipeline under different working condition simulations to obtain defect data of the girth weld pipe section of the target pipeline; A defect database of the girth weld pipe section is established according to the defect data of the girth weld pipe section of the target pipeline.

5. The method according to claim 1, characterized in that Also includes: Acquiring predicted bending strain data and predicted axial strain data of the girth weld pipe section of the target pipeline; The predicted bending strain data and the predicted axial strain data are input into the twin mechanism model of the fracture process of the girth weld pipe section to simulate the fracture process of the girth weld pipe section, and the fracture process prediction data of the girth weld pipe section of the target pipeline are obtained.

6. A digital twin construction device for oil and gas pipeline girth weld fracture, characterized in that: include: An acquisition module, used to acquire bending strain data and axial strain data of at least one girth weld pipe section on a target pipeline, wherein the bending strain data is used to indicate the deformation degree of the girth weld pipe section when subjected to bending stress, and the axial strain data is used to indicate the deformation degree of the girth weld pipe section when subjected to axial stress; A first processing module is used to establish a load database and a defect database of the girth weld pipe section of the target pipeline according to the bending strain data and the axial strain data of the girth weld pipe section, wherein the load database includes the axial strain data and the bending strain data of at least one girth weld pipe section on the target pipeline, and the defect database includes the defect data of at least one girth weld pipe section on the target pipeline; A second processing module is used to construct a fracture process model corresponding to the girth weld pipe section of the target pipeline according to the first motion data collected by the movable device, wherein the fracture process model is used to simulate the fracture process of the girth weld pipe section; Based on the load database and defect database of the girth weld pipe segment, at least one group of fracture process training data of the girth weld pipe segment is obtained; according to at least one group of fracture process training data of the girth weld pipe segment, the fracture process model is trained to obtain the fracture process result data of the girth weld pipe segment; according to at least one group of fracture process training data of the girth weld pipe segment, at least one pipeline sample is obtained; a fracture toughness test is performed on at least one of the pipeline samples to obtain the fracture test data of the girth weld pipe segment of at least one group of the pipeline samples; if the fracture test data is consistent with the fracture process result data of the girth weld pipe segment, the fracture process result data of the girth weld pipe segment is verified, and the trained fracture process model is determined as the twin mechanism model of the fracture process of the girth weld pipe segment, and the fracture process of the girth weld pipe segment is determined as the twin mechanism model of the fracture process of the girth weld pipe segment. The twin mechanism The model is used to simulate the fracture process of the girth weld pipe section; A construction module is used to digitally map the twin mechanism model of the fracture process of the girth weld pipe section through digital twin technology to construct a digital twin of the girth weld pipe section.

7. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as claimed in any one of claims 1 to 5.

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