Pipeline tensile bearing capacity assessment method, equipment, medium and product

Abstract

The invention relates to the technical field of pipeline safety, and discloses a pipeline tensile bearing capacity evaluation method and device, a medium and a product, and the method comprises the steps: obtaining a plurality of influence factors of the tensile bearing capacity of pipeline corrosion in a defective pipeline; determining coupling difficulty information of the plurality of influence factors and influence degree information of the plurality of influence factors on the tensile bearing capacity; dividing the plurality of influence factors into a plurality of types of influence factor sets based on the coupling difficulty information; determining an influence rule corresponding to each influence factor based on finite element simulation and the influence degree information of the plurality of influence factors; generating a preliminary data set according to at least one type of influence factor set and a corresponding influence rule through finite element simulation; and generating an evaluation model according to machine learning and the preliminary data set. According to the method, the evaluation accuracy of the tensile bearing capacity of the pipeline is improved.

Description

Technical Field

[0001] The present invention relates to the field of pipeline safety technology, and in particular to a pipeline tensile bearing capacity assessment method, equipment, medium and product. Background Art

[0002] Metal loss defects often occur in areas where the pipeline's anti-corrosion coating is damaged. External corrosion causes localized thinning of the pipeline, resulting in a significant decrease in strain capacity. Traditional methods for assessing and maintaining these metal loss defects were developed under the assumption that hoop stress is significantly greater than axial stress, assuming that hoop stress is the primary driver of potential failure. However, in geological disaster areas, soil movements such as landslides, geological subsidence, and freeze-thaw settlement can cause oil and gas pipelines to experience large axial stresses and strains. Under such external conditions, traditional stress-based assessment methods are generally no longer applicable, requiring strain-based assessments. It is necessary to study the tensile bearing capacity of large-diameter, high-grade steel pipelines containing metal loss defects under axial strain conditions. Therefore, improving the accuracy of pipeline tensile bearing capacity assessment is a pressing technical issue. Summary of the Invention

[0003] The purpose of the present invention is to provide a pipeline tensile bearing capacity assessment method, equipment, medium and product, which can improve the accuracy of pipeline bearing capacity assessment.

[0004] In a first aspect, the present invention provides a method for evaluating the tensile bearing capacity of a pipeline, comprising: obtaining multiple influencing factors of the tensile bearing capacity of a defective pipeline for pipeline corrosion; Determining coupling difficulty information of the multiple influencing factors and information on the degree of influence on the tensile bearing capacity; Dividing the plurality of influencing factors into a plurality of types of influencing factor sets based on the coupling difficulty information; different types of influencing factor sets correspond to different coupling difficulties; Based on finite element simulation and the influence degree information of multiple influencing factors, determine the influence law corresponding to each influencing factor; generating a preliminary data set according to at least one type of influencing factor set and corresponding influencing laws through finite element simulation; An evaluation model is generated based on machine learning and the preliminary data set, and the evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated.

[0005] In a possible implementation, the multiple influencing factors include at least one of the following: defect length, defect width, defect depth, yield strength ratio, internal pressure, pipe diameter, and wall thickness.

[0006] In a possible implementation, generating a preliminary data set according to at least one type of influencing factor set and corresponding influencing rules through finite element simulation includes: The preliminary data set is generated through finite element simulation based on the at least one type of influencing factor set, the influencing law corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set.

[0007] In a possible implementation, the multiple types of influencing factor sets include a first type of influencing factor set, and the coupling difficulty of the first type of influencing factor set is greater than the coupling difficulty of the other types of influencing factor sets; when the at least one type of influencing factor set includes the first type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: At least one influencing factor in the first type of influencing factor set is selected in turn as a variable, the other influencing factors in the first type of influencing factor set are kept unchanged, and the preliminary data set is generated based on machine learning and the influence law of the at least one influencing factor.

[0008] In a possible implementation, the multiple types of influencing factor sets include a second type of influencing factor set. In a case where the at least one type of influencing factor set includes the second type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: The preliminary data set is generated based on a normalization method and an influence rule of the at least one influencing factor.

[0009] In a possible implementation, the multiple types of influencing factor sets include a third type of influencing factor set. In a case where the at least one type of influencing factor set includes the third type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: The influence rule of the at least one influencing factor is fitted into a bilinear distribution formula, and the preliminary data set is generated based on the bilinear distribution formula and the third type of influencing factor set.

[0010] In a possible implementation, the multiple types of influencing factor sets include a fourth type of influencing factor set. In a case where the at least one type of influencing factor set includes the fourth type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: Based on the fourth type of influencing factor set and the fourth type of influencing rules, the values of the influencing factors in the fourth type of influencing factor set are determined as fixed values to obtain the preliminary data set.

[0011] In a second aspect, the present invention provides a pipeline tensile bearing capacity assessment device, comprising: an acquisition unit, a determination unit, and a processing unit; An acquisition unit, used to acquire multiple influencing factors of the tensile bearing capacity of the defective pipeline for pipeline corrosion; a determining unit, configured to determine coupling difficulty information of the plurality of influencing factors and information on the degree of influence on the tensile bearing capacity; A processing unit, configured to classify the plurality of influencing factors into a plurality of types of influencing factor sets based on the coupling difficulty information; different types of influencing factor sets correspond to different coupling difficulties; The determination unit is further used to determine the influence law corresponding to each influencing factor based on the finite element simulation and the influence degree information of the multiple influencing factors; The processing unit is further configured to generate a preliminary data set according to at least one type of influencing factor set and corresponding influencing rules through finite element simulation; The processing unit is further configured to generate an evaluation model based on machine learning and the preliminary data set, wherein the evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated.

[0012] In a possible implementation, the multiple influencing factors include at least one of the following: defect length, defect width, defect depth, yield strength ratio, internal pressure, pipe diameter, and wall thickness.

[0013] In a possible implementation, the processing unit is specifically configured to: The preliminary data set is generated through finite element simulation based on the at least one type of influencing factor set, the influencing law corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set.

[0014] In a possible implementation, the processing unit is specifically configured to: At least one influencing factor in the first type of influencing factor set is selected in turn as a variable, the other influencing factors in the first type of influencing factor set are kept unchanged, and the preliminary data set is generated based on machine learning and the influence law of the at least one influencing factor.

[0015] In a possible implementation, the processing unit is specifically configured to: The preliminary data set is generated based on a normalization method and an influence rule of the at least one influencing factor.

[0016] In a possible implementation, the processing unit is specifically configured to: The influence rule of the at least one influencing factor is fitted into a bilinear distribution formula, and the preliminary data set is generated based on the bilinear distribution formula and the third type of influencing factor set.

[0017] In a possible implementation, the processing unit is specifically configured to: Based on the fourth type of influencing factor set and the fourth type of influencing rules, the values of the influencing factors in the fourth type of influencing factor set are determined as fixed values to obtain the preliminary data set.

[0018] In a third aspect, an electronic device is provided, comprising a memory and a processor; the memory is used to store computer-executable instructions, and the processor is connected to the memory via a bus; when the electronic device is running, the processor executes the computer-executable instructions stored in the memory, so that the electronic device executes the pipeline tensile bearing capacity assessment method described in the first aspect.

[0019] The electronic device may be a network device or a component within the network device, such as a chip system within the network device. The chip system is configured to support the network device in implementing the functions described in the first aspect and any possible implementation thereof, such as acquiring, determining, and transmitting data and / or information involved in the aforementioned pipeline tensile bearing capacity assessment method. The chip system includes a chip and may also include other discrete components or circuit structures.

[0020] In a fourth aspect, a computer-readable storage medium is provided, the computer-readable storage medium comprising computer-executable instructions. When the computer-executable instructions are executed on a computer, the computer is caused to execute the pipeline tensile bearing capacity assessment method described in the first aspect.

[0021] In a fifth aspect, a computer program product is also provided, which includes computer instructions. When the computer instructions are executed on a pipeline tensile bearing capacity assessment device, the pipeline tensile bearing capacity assessment device performs the pipeline tensile bearing capacity assessment method as described in the first aspect above.

[0022] It should be noted that the aforementioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the pipeline tensile bearing capacity assessment device, or may be packaged separately from the processor of the pipeline tensile bearing capacity assessment device, and this is not limited in this embodiment of the present application.

[0023] The description of the second, third, fourth and fifth aspects of this application can refer to the detailed description of the first aspect.

[0024] In the embodiments of this application, the name of the aforementioned pipeline tensile bearing capacity assessment device does not limit the device or functional modules themselves. In actual implementation, these devices or functional modules may appear by other names. For example, the receiving unit may also be called a receiving module, a receiver, etc. As long as the functions of each device or functional module are similar to those of this application, they are within the scope of the claims of this application and their equivalents.

[0025] Compared with the prior art, the beneficial effect of the present invention is that multiple influencing factors on the tensile bearing capacity of pipeline corrosion in defective pipelines can be obtained. These influencing factors will affect the tensile bearing capacity of the pipeline against corrosion. Afterwards, the coupling difficulty information and the degree of influence information on the tensile bearing capacity of the multiple influencing factors can be determined, and the multiple influencing factors can be divided into multiple types of influencing factor sets based on the coupling difficulty information. Among them, different types of influencing factor sets correspond to different coupling difficulties. Then, based on finite element simulation and the degree of influence information of multiple influencing factors, the influence law corresponding to each influencing factor can be determined. Subsequently, a preliminary data set can be generated according to at least one type of influencing factor set and the corresponding influence law through finite element simulation, and an evaluation model can be generated according to machine learning and the preliminary data set. The evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated; different types of influencing factor sets correspond to different prediction methods.

[0026] Based on the coupling difficulty, multiple influencing factors can be divided into sets of influencing factors with varying coupling difficulty. A preliminary dataset can be generated based on the influence patterns and sets of influencing factors. Subsequently, machine learning can be used to predict the tensile bearing capacity of the pipeline to be evaluated. This allows the tensile bearing capacity to be evaluated based on the coupling of multiple influencing factors, resulting in a more accurate evaluation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0028] Figure 1 This is a block diagram of the architecture of a pipeline tensile bearing capacity assessment system provided by the present invention; Figure 2 1 is a schematic diagram of the hardware structure of an electronic device 101 provided by the present invention; Figure 3 It is a flow chart of a pipeline tensile bearing capacity evaluation method provided by the present invention; Figure 4 It is a flow chart of another pipeline tensile bearing capacity evaluation method provided by the present invention; Figure 5 It is a flow chart of another pipeline tensile bearing capacity evaluation method provided by the present invention; Figure 6 It is a flow chart of another pipeline tensile bearing capacity evaluation method provided by the present invention; Figure 7 It is a flow chart of another pipeline tensile bearing capacity evaluation method provided by the present invention; Figure 8 It is a flow chart of another pipeline tensile bearing capacity evaluation method provided by the present invention; Figure 9 It is a structural schematic diagram of a pipeline tensile bearing capacity evaluation device provided by the present invention. DETAILED DESCRIPTION

[0029] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features described in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0030] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0031] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order.

[0032] Metal loss defects often occur in areas where the pipeline's anti-corrosion coating is damaged. External corrosion causes localized thinning of the pipeline, resulting in a significant decrease in strain capacity. Traditional methods for assessing and maintaining these metal loss defects were developed under the assumption that hoop stress is significantly greater than axial stress, assuming that hoop stress is the primary driver of potential failure. However, in geological disaster areas, soil movements such as landslides, geological subsidence, and freeze-thaw settlement can cause oil and gas pipelines to experience large axial stresses and strains. Under such external conditions, traditional stress-based assessment methods are generally no longer applicable, requiring strain-based assessments. It is necessary to study the tensile bearing capacity of large-diameter, high-grade steel pipelines containing metal loss defects under axial strain conditions. Therefore, improving the accuracy of pipeline tensile bearing capacity assessment is a pressing technical issue.

[0033] In this regard, the embodiment of the present application can obtain multiple influencing factors on the tensile bearing capacity of the pipeline corrosion in the defective pipeline. These influencing factors will affect the tensile bearing capacity of the pipeline against corrosion. Afterwards, the coupling difficulty information and the degree of influence information on the tensile bearing capacity of the multiple influencing factors can be determined, and the multiple influencing factors can be divided into multiple types of influencing factor sets based on the coupling difficulty information. Among them, different types of influencing factor sets correspond to different coupling difficulties. Then, based on finite element simulation and the degree of influence information of multiple influencing factors, the influence law corresponding to each influencing factor can be determined. Subsequently, a preliminary data set can be generated according to at least one type of influencing factor set and the corresponding influence law through finite element simulation, and an evaluation model can be generated according to machine learning and the preliminary data set. The evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated; different types of influencing factor sets correspond to different prediction methods.

[0034] Based on the coupling difficulty, multiple influencing factors can be divided into sets of influencing factors with varying coupling difficulty. A preliminary dataset can be generated based on the influence patterns and sets of influencing factors. Subsequently, machine learning can be used to predict the tensile bearing capacity of the pipeline to be evaluated. This allows the tensile bearing capacity to be evaluated based on the coupling of multiple influencing factors, resulting in a more accurate evaluation model.

[0035] Figure 1 This is a block diagram of the pipeline tensile bearing capacity assessment system provided by the present invention. Figure 1 As shown, the pipeline tensile bearing capacity evaluation system includes: an electronic device 101 and a data storage device 102.

[0036] The electronic device 101 is communicatively connected to the data storage device 102. Optionally, the electronic device 101 and the data storage device 102 can be the same device or different devices, which is not limited here.

[0037] In an embodiment of the present application, the data storage device 102 can store data of defective pipes, such as multiple influencing factors. The electronic device 101 can obtain multiple influencing factors from the data storage device 102. Afterwards, the electronic device 101 can determine the coupling difficulty information and the degree of influence information on the tensile bearing capacity of the multiple influencing factors, and divide the multiple influencing factors into multiple types of influencing factor sets based on the coupling difficulty information. Among them, different types of influencing factor sets correspond to different coupling difficulties. Then, the electronic device 101 can determine the influence law corresponding to each influencing factor based on finite element simulation and the degree of influence information of multiple influencing factors. Subsequently, the electronic device 101 can generate a preliminary data set based on at least one type of influencing factor set and the corresponding influence law through finite element simulation, and generate an evaluation model based on machine learning and the preliminary data set. The evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated.

[0038] In a possible implementation, the electronic device 101 may be a terminal, a server, or other electronic devices, which is not limited in the embodiment of the present application.

[0039] Optionally, the terminal may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. The terminal may communicate with one or more core networks via a radio access network (RAN). The terminal may be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal, or a portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile device that exchanges voice and / or data with a radio access network, such as a mobile phone, tablet computer, laptop computer, netbook, or personal digital assistant (PDA).

[0040] Optionally, the above-mentioned server can be a server in a server cluster (consisting of multiple servers), or a chip in the server, or a system on a chip in the server, or can be implemented by a virtual machine (VM) deployed on a physical machine, which is not limited in this embodiment of the present application.

[0041] Figure 2 1 is a hardware structure diagram of an electronic device 101 provided by the present invention. The electronic device 101 may include a processor 202, and the processor 202 is used to execute application code to implement the pipeline tensile bearing capacity assessment method of the present application.

[0042] The processor 202 may be a central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.

[0043] like Figure 2 As shown, the electronic device 101 may further include a memory 203. The memory 203 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 202.

[0044] The memory 203 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 203 may be independent and connected to the processor 202 via the bus 204. The memory 203 may also be integrated with the processor 202.

[0045] like Figure 2 As shown, the electronic device 101 may further include a communication interface 201, wherein the communication interface 201, the processor 202, and the memory 203 may be coupled to each other, for example, via a bus 204. The communication interface 201 is used to exchange information with other devices, for example, to support information exchange between multiple modules and other devices.

[0046] It should be pointed out that Figure 2 The structure of the electronic device 101 shown in the figure does not constitute a limitation on the electronic device 101. Figure 2 In addition to the components shown, the module may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0047] In actual implementation, the functions implemented by the electronic device 101 can be Figure 2 The processor 202 shown calls the program code in the memory 203 to implement it.

[0048] The pipeline tensile bearing capacity evaluation method provided in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0049] like Figure 3 As shown in FIG, the pipeline tensile bearing capacity assessment method includes: S301. Obtain multiple factors affecting the tensile bearing capacity of a defective pipeline due to pipeline corrosion.

[0050] S302: Determine coupling difficulty information of multiple influencing factors and information on the degree of influence on tensile bearing capacity.

[0051] S303 . Divide the multiple influencing factors into multiple types of influencing factor sets based on the coupling difficulty information through finite element simulation.

[0052] Among them, different types of influencing factor sets correspond to different coupling difficulties.

[0053] S304: Determine the influence rule corresponding to each influencing factor based on the finite element simulation and the influence degree information of the multiple influencing factors.

[0054] S305: Generate a preliminary data set according to at least one type of influencing factor set and corresponding influencing rules.

[0055] S306. Generate an evaluation model based on machine learning and the preliminary data set.

[0056] Among them, the evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated.

[0057] In an embodiment of the present application, the defective pipeline may be a pipeline containing a volumetric defect, for example, a defect volume within the pipeline greater than a threshold. Because pipeline defects affect the corrosion tensile bearing capacity, the electronic device can obtain data corresponding to multiple influencing factors of the defect. The types of the multiple influencing factors can be pre-set. The electronic device can then determine the coupling difficulty between the multiple influencing factors and the degree of influence of each influencing factor on the tensile bearing capacity. For example, a defect with a low correlation between length, width, and depth, for example, may have a high coupling difficulty and a high degree of influence on the bearing capacity (i.e., tensile bearing capacity).

[0058] Subsequently, the electronic device can classify the multiple influencing factors into multiple types of influencing factor sets based on the coupling difficulty information, that is, into influencing factor sets with different coupling difficulties, where each influencing factor set includes one or more influencing factors. In this way, influencing factors in influencing factor sets with a higher coupling difficulty can be coupled using methods such as machine learning, while influencing factors in influencing factor sets with a relatively easy coupling difficulty can be coupled using simpler methods.

[0059] Furthermore, the electronic device can determine the influence pattern corresponding to each influencing factor based on the finite element simulation and the influence degree information of each influencing factor. For example, multiple sets of simulation data can be generated using the finite element simulation and data corresponding to multiple influencing factors. The influence pattern corresponding to each influencing factor can then be determined based on these multiple sets of simulation data. For example, the smaller the pipe wall, the smaller the tensile load-bearing capacity.

[0060] The electronic device then generates a preliminary data set based on at least one type of influencing factor set and its corresponding influencing patterns. Subsequently, the electronic device can determine an evaluation model based on this preliminary data set. This allows the evaluation model to be used to generate the load-bearing capacity of the pipeline being evaluated during subsequent pipeline assessments.

[0061] In this way, the evaluation model is established by comprehensively considering multiple influencing factors and the difficulty of coupling between them. Since the coupling difficulty varies for different sets of influencing factors, the evaluation model can be generated based on different prediction methods. In this way, the tensile bearing capacity can be evaluated based on the combined coupling of multiple influencing factors, resulting in a more accurate evaluation model.

[0062] For example, a batch mesh model is modeled using finite element analysis software (such as ABAQUS). The pipe size is 1422 mm, the pipe wall thickness is 32.1 mm, the pipe length is 10,000 mm, and the defect is located at the midpoint of the pipe length. The defect size includes defect length, defect width, and defect depth. The modeling range of the defect length is 32.1-720 mm, the modeling range of the defect width is 72-960 mm, and the modeling range of the defect depth is 3-16 mm.

[0063] The material properties were set using finite element analysis software. The materials used were X80 pipeline steel materials with four different yield ratios, namely 0.83, 0.88, 0.92, and 0.93.

[0064] The boundary conditions were set using finite element analysis software. Internal pressure was applied to the inner surface of the pipe, a rigid constraint was applied to one end of the pipe, and a displacement load of 60 mm was applied to the other end of the pipe.

[0065] The maximum load criterion was used to determine the tensile bearing capacity failure criterion for defective pipelines. The time-load relationship at the pipeline end was extracted. When the load reached its maximum value, the pipeline was considered to have failed, and the time at this point was defined as the failure moment. The time-strain curve at a location three times the pipe diameter from the defect was also extracted, and the strain value corresponding to the failure moment was defined as the tensile strain capacity.

[0066] Based on the above pre-processing settings and mesh modeling, the other influencing factors are kept constant, and the three sizes of volumetric defects are used as variables. 500 sets of finite element simulations are performed to analyze the influence of defect size and form a preliminary data set.

[0067] It should be noted that the order of S303 and S304 is not limited.

[0068] In some embodiments, combined Figure 3 ,like Figure 4As shown, in the above S305, generating a preliminary data set according to at least one type of influencing factor set and the corresponding influencing rules specifically includes: S401 . Generate a preliminary data set through finite element simulation based on at least one type of influencing factor set, an influencing law corresponding to each influencing factor, and a data generation method corresponding to each type of influencing factor set.

[0069] Different sets of influencing factors correspond to different data generation methods. For example, sets of influencing factors that are difficult to couple can be coupled using data generation methods such as machine learning. This allows multiple difficult-to-couple influencing factors to be coupled together to create an evaluation model, thereby improving evaluation accuracy.

[0070] In some other embodiments, combined Figure 4 ,like Figure 5 As shown, the multiple types of influencing factor sets include a first type of influencing factor set, and the coupling difficulty of the first type of influencing factor set is greater than the coupling difficulty of the other types of influencing factor sets; when at least one type of influencing factor set includes the first type of influencing factor set, in the above S401, generating the preliminary data set based on the at least one type of influencing factor set, the influence law corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set specifically includes: S501. Select at least one influencing factor in the first type of influencing factor set as a variable in turn, keep the other influencing factors in the first type of influencing factor set unchanged, and generate a preliminary data set based on machine learning and the influence rule of at least one influencing factor.

[0071] The first type of influencing factor set is the most difficult to couple among the multiple types of influencing factor sets. For example, the first type of influencing factor set may include defect length, defect width, and defect depth. Electronic devices can use defect length as a variable, while keeping defect width and defect depth constant. In this way, finite element simulation can generate multiple data sets with varying defect lengths. Similarly, defect width or defect depth can be used as a variable to generate corresponding data, thus obtaining a preliminary data set.

[0072] Furthermore, due to the difficulty of coupling defect length, depth, and width, traditional coupling methods are incapable of achieving this. Therefore, machine learning can be used to couple the data corresponding to these three influencing factors. This results in a richer preliminary dataset and stronger correlations between the various influencing factors, thereby improving assessment accuracy.

[0073] In some embodiments, combined Figure 4 ,like Figure 6As shown, the multiple types of influencing factor sets include the second type of influencing factor set. In the case where at least one type of influencing factor set includes the second type of influencing factor set, in the above S401, generating the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set specifically includes: S601: Generate a preliminary data set based on a normalization method and an influence rule of at least one influencing factor.

[0074] The coupling difficulty in the second type of influencing factor set is lower than that of the first type of influencing factors. For example, the second type of influencing factor set may include yield ratio, pipe diameter, wall thickness, and internal pressure. Since the coupling difficulty of the influencing factors in the second type of influencing factor set meets the current coupling, that is, the formulated coupling. Therefore, the electronic device can keep the yield ratio and internal pressure unchanged, and use the pipe diameter and wall thickness as variables to generate a preliminary data set using a normalized method. In this way, the preliminary data set can contain data corresponding to both the first type of influencing factor set and the second type of influencing factor set, thereby making the preliminary data set richer and the resulting evaluation model more accurate.

[0075] For example, keeping the yield ratio, pipe diameter, wall thickness and internal pressure unchanged, finite element numerical simulation studies are conducted on the specifications of 1422mm pipe diameter-32.1mm wall thickness, 1219mm-pipe diameter-18.4mm wall thickness and 1422mm pipe diameter-17.8mm wall thickness respectively. After the numerical simulation is completed, post-processing analysis is performed. According to the maximum load criterion, a node at a 90° position of the pipeline that is more than three times the pipe diameter away from the defect size is selected, and the strain corresponding to the maximum failure moment of the node is extracted. The strain is defined as the tensile strain capacity, and the influence of pipe diameter and wall thickness is analyzed. The normalization method is used to couple the two influencing factors of pipe diameter and wall thickness into the generation process of the evaluation model.

[0076] In some embodiments, combined Figure 4 ,like Figure 7 As shown, the multiple types of influencing factor sets include a third type of influencing factor set. When at least one type of influencing factor set includes the third type of influencing factor set, in the above S401, generating a preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set specifically includes: S701: Fit the influence rule of at least one influencing factor into a bilinear distribution formula, and generate a preliminary data set based on the bilinear distribution formula and a set of influencing factors of the third type.

[0077] The coupling difficulty of the third type of influencing factor set is lower than that of the first type of influencing factors. For example, the third type of influencing factor set may include yield strength ratio, pipe diameter, wall thickness, and internal pressure. Since the coupling difficulty of the influencing factors in the third type of influencing factor set meets the current coupling, that is, the formulated coupling, the electronic device can keep the defect size, internal pressure, pipe diameter, and wall thickness unchanged, and use the yield strength ratio as a variable to generate a preliminary data set in a normalized manner. In this way, the preliminary data set can contain data corresponding to both the first type of influencing factor set and the third type of influencing factor set, thereby enriching the preliminary data set and making the resulting evaluation model more accurate.

[0078] For example, keeping the defect size, pipe diameter, wall thickness, and internal pressure unchanged, the effects of the material properties of X80 pipeline steel with four different yield ratios on the tensile bearing capacity are studied in the numerical simulation of finite element analysis software. The yield ratios of the materials are 0.83, 0.88, 0.92, and 0.93, respectively, and finite element simulation calculations are carried out. After the numerical simulation is completed, post-processing analysis is performed. According to the maximum stress criterion, a node at a position 90° of the pipeline that is more than three times the pipe diameter away from the defect size is selected, and the strain corresponding to the moment of failure of the node is extracted. The strain is defined as the tensile strain capacity. The influence of the material yield ratio on the tensile bearing capacity is analyzed, the law is summarized, and a bilinear distribution formula is fitted, thereby coupling the yield ratio into the determination process of the evaluation model.

[0079] In some embodiments, combined Figure 4 ,like Figure 8 As shown, the multiple types of influencing factor sets include a fourth type of influencing factor set. When at least one type of influencing factor set includes the fourth type of influencing factor set, in the above S401, generating a preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: S801 : Based on the fourth type of influencing factor set and the fourth type of influencing rules, determine the values of the influencing factors in the fourth type of influencing factor set as fixed values to obtain a preliminary data set.

[0080] The fourth type of influencing factor set presents a relatively low level of coupling difficulty, including, for example, internal pressure. The electronic device can maintain the defect size, pipe diameter, wall thickness, and yield strength ratio constant, perform finite element simulations, and analyze the effects of internal pressure on tensile bearing capacity. Following the numerical simulation, post-processing analysis is performed. Based on the maximum load criterion, a node at a 90-degree angle to the pipe, at least three pipe diameters away from the defect, is selected. The strain corresponding to the failure moment at this node is extracted, and this strain is defined as the tensile strain capacity. The rules are summarized, and the boundary values under the operating conditions are determined. The internal pressure is fixed, thus obtaining a preliminary data set.

[0081] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily appreciate that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0082] In the embodiments of the present application, the pipeline tensile bearing capacity assessment device can be divided into functional modules based on the above-described method examples. For example, each functional module can be divided into corresponding functional modules, or two or more functions can be integrated into a single processing module. The above-mentioned integrated modules can be implemented as hardware or software functional modules. Optionally, the module division in the embodiments of the present application is illustrative and merely represents a logical functional division. In actual implementation, other division methods may be used.

[0083] like Figure 9 , which is a schematic structural diagram of a pipeline tensile bearing capacity evaluation device provided by the present invention. Figure 9 The pipeline tensile bearing capacity evaluation device shown includes: an acquisition unit 901 , a determination unit 902 , and a processing unit 903 .

[0084] An acquisition unit 901 is used to acquire multiple factors affecting the tensile bearing capacity of a defective pipeline for pipeline corrosion; A determining unit 902 is configured to determine coupling difficulty information of the plurality of influencing factors and information on the degree of influence on the tensile bearing capacity; A processing unit 903 is configured to classify the plurality of influencing factors into a plurality of types of influencing factor sets based on the coupling difficulty information; different types of influencing factor sets correspond to different coupling difficulties; The determining unit 902 is further configured to determine an influence rule corresponding to each influencing factor based on the finite element simulation and the influence degree information of the multiple influencing factors; The processing unit 903 is further configured to generate a preliminary data set according to at least one type of influencing factor set and corresponding influencing rules through finite element simulation; The processing unit 903 is further configured to generate an evaluation model based on machine learning and the preliminary data set, wherein the evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated.

[0085] In a possible implementation, the multiple influencing factors include at least one of the following: defect length, defect width, defect depth, yield strength ratio, internal pressure, pipe diameter, and wall thickness.

[0086] In a possible implementation, the processing unit 903 is specifically configured to: The preliminary data set is generated through finite element simulation based on the at least one type of influencing factor set, the influencing law corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set.

[0087] In a possible implementation, the processing unit 903 is specifically configured to: At least one influencing factor in the first type of influencing factor set is selected in turn as a variable, the other influencing factors in the first type of influencing factor set are kept unchanged, and the preliminary data set is generated based on machine learning and the influence law of the at least one influencing factor.

[0088] In a possible implementation, the processing unit 903 is specifically configured to: The preliminary data set is generated based on a normalization method and an influence rule of the at least one influencing factor.

[0089] In a possible implementation, the processing unit 903 is specifically configured to: The influence rule of the at least one influencing factor is fitted into a bilinear distribution formula, and the preliminary data set is generated based on the bilinear distribution formula and the third type of influencing factor set.

[0090] In a possible implementation, the processing unit 903 is specifically configured to: Based on the fourth type of influencing factor set and the fourth type of influencing rules, the values of the influencing factors in the fourth type of influencing factor set are determined as fixed values to obtain the preliminary data set.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

[0092] The system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiment can be combined into one module or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps and are not to be regarded as improper limitations of the present invention.

[0093] Those skilled in the art should be aware that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. In order to clearly illustrate the interchangeability of electronic hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Claims

1. A method for evaluating the tensile bearing capacity of a pipeline, characterized in that: include: Obtain multiple factors affecting the tensile bearing capacity of pipeline corrosion in defective pipelines; Determining coupling difficulty information of the multiple influencing factors and information on the degree of influence on the tensile bearing capacity; Dividing the plurality of influencing factors into a plurality of types of influencing factor sets based on the coupling difficulty information; different types of influencing factor sets correspond to different coupling difficulties; Based on finite element simulation and the influence degree information of multiple influencing factors, determine the influence law corresponding to each influencing factor; generating a preliminary data set according to at least one type of influencing factor set and corresponding influencing laws through finite element simulation; An evaluation model is generated based on machine learning and the preliminary data set, and the evaluation model is used to predict the tensile bearing capacity of the pipeline to be evaluated.

2. The method according to claim 1, characterized in that The multiple influencing factors include at least one of the following: defect length, defect width, defect depth, yield strength ratio, internal pressure, pipe diameter, and wall thickness.

3. The method according to claim 1, characterized in that The generating of a preliminary data set according to at least one type of influencing factor set and corresponding influencing rules through finite element simulation includes: The preliminary data set is generated through finite element simulation based on the at least one type of influencing factor set, the influencing law corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set.

4. The method according to claim 3, characterized in that The multiple types of influencing factor sets include a first type of influencing factor set, the coupling difficulty of the first type of influencing factor set is greater than the coupling difficulty of the other types of influencing factor sets; in the case where the at least one type of influencing factor set includes the first type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: At least one influencing factor in the first type of influencing factor set is selected in turn as a variable, the other influencing factors in the first type of influencing factor set are kept unchanged, and the preliminary data set is generated based on machine learning and the influence law of the at least one influencing factor.

5. The method according to claim 4, characterized in that The plurality of types of influencing factor sets include a second type of influencing factor set, and in a case where the at least one type of influencing factor set includes the second type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: The preliminary data set is generated based on a normalization method and an influence rule of the at least one influencing factor.

6. The method according to claim 4, characterized in that The plurality of types of influencing factor sets include a third type of influencing factor set, and in a case where the at least one type of influencing factor set includes the third type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: The influence rule of the at least one influencing factor is fitted into a bilinear distribution formula, and the preliminary data set is generated based on the bilinear distribution formula and the third type of influencing factor set.

7. The method according to claim 4, characterized in that The plurality of types of influencing factor sets include a fourth type of influencing factor set, and in a case where the at least one type of influencing factor set includes the fourth type of influencing factor set, The generating of the preliminary data set based on the at least one type of influencing factor set, the influencing rule corresponding to each influencing factor, and the data generation method corresponding to each type of influencing factor set includes: Based on the fourth type of influencing factor set and the fourth type of influencing rules, the values of the influencing factors in the fourth type of influencing factor set are determined as fixed values to obtain the preliminary data set.

8. An electronic device, characterized in that: include: memory and processor; Memory and processor coupling; The memory is used to store instructions executable by the processor; When the processor executes the instructions, the method according to any one of claims 1 to 7 is performed.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product comprises computer program instructions, and when the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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