Model construction method, device and equipment for oil and gas pipe network and medium

Abstract

The invention relates to the technical field of oil and gas pipe networks, and discloses a model construction method, device and equipment for an oil and gas pipe network and a medium, and the method comprises the following steps: determining a target and a range of a to-be-constructed model; performing data collection and data processing according to the target and the range of the to-be-constructed model; constructing a model based on the collected and processed data; extracting the oil and gas pipe network actual data in the oil and gas pipe network data as characteristic parameters of a to-be-constructed model; establishing a deep learning model; and training the deep learning model to obtain a trained deep learning model, inputting the feature parameters into the trained deep learning model, and outputting a result. According to the method, the oil and gas pipe network model is constructed through deep learning, and based on the data characteristics of the oil and gas pipe network, the efficiency of model construction is improved, the cost of model construction is reduced, and the accuracy of prediction and diagnosis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas pipeline networks, and in particular to a model building method, device, equipment and medium for oil and gas pipeline networks. Background Art

[0002] The oil and gas pipeline network model refers to the mathematical modeling of the oil and gas pipeline network system to describe its operating rules and characteristics. The model can be used to simulate the operation of the pipeline system, predict the load changes of the pipeline, and provide decision support for the design, operation and management of the oil and gas pipeline network. The oil and gas pipeline network model usually includes pipeline models, node models and fluid models. Among them, the pipeline model describes the geometric characteristics of the pipeline and the fluid dynamics characteristics of the pipeline; the node model describes the connection relationship between the pipeline and nodes such as storage tanks and pump stations and the flow rate, pressure and other parameters of the nodes; the fluid model describes the physical properties and flow characteristics of oil and gas. The establishment of the oil and gas pipeline network model needs to consider many factors, such as the material, design pressure, and operating pressure of the pipeline, and at the same time, it is necessary to comprehensively consider the impact of various factors on the model. The established oil and gas pipeline network model can be simulated and calculated by computer to obtain a deeper understanding and knowledge to provide support for decision-making. The application of the oil and gas pipeline network model is very extensive and can be applied to various links such as oil and gas transportation, storage and processing, such as pipeline design, operation management, safety monitoring, etc. At the same time, the oil and gas pipeline network model is also one of the important tools for studying the oil and gas energy system, which can be used to evaluate and optimize the performance and benefits of the oil and gas energy system. The construction of the oil and gas pipeline network model has the following problems: Difficulty in data collection and processing: Data collection and processing of the oil and gas pipeline network is a complex task, which requires the collection of a large amount of data, and screening, cleaning and sorting to ensure the accuracy and reliability of the model; Difficulty in mathematical model establishment: The operation rules and characteristics of the oil and gas pipeline network are relatively complex. Establishing an appropriate mathematical model requires professional knowledge and skills, and a lot of verification and debugging are required to ensure that the model can accurately reflect the actual situation; Difficulty in model verification and debugging: Model verification and debugging of the oil and gas pipeline network is a complex task, which requires comparison of actual data and simulation results, and continuous adjustment and optimization of the model to ensure the accuracy and reliability of the model; Large amount of calculation and long time consumption: The construction of the oil and gas pipeline network model requires a lot of calculation and analysis, which consumes a lot of time and computing resources; Multiple factors need to be considered: The construction of the oil and gas pipeline network model needs to consider multiple factors, such as the material, design pressure, operating pressure of the pipeline, etc., and the impact of multiple factors on the model needs to be considered comprehensively. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a model building method, device, equipment and medium for oil and gas pipeline networks.

[0004] In order to achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for constructing a model for an oil and gas pipeline network, comprising the following steps: Step S1, determining the target and scope of the model to be constructed; Step S2, collecting and processing data according to the objectives and scope of the model to be constructed; Step S3, building a model based on the data collected and processed in step S2.

[0005] Furthermore, the data collection and data processing in step S2 specifically include the following steps: Step S2.1, collecting oil and gas pipeline network data; Step S2.2, denoising the oil and gas pipeline network data collected in step S2.1.

[0006] Furthermore, the oil and gas pipeline network data in step S2.1 includes oil and gas pipeline network modeling data and oil and gas pipeline network actual data.

[0007] Furthermore, the model is constructed in step S3, specifically including the following steps: Step S3.1, extracting the actual data of the oil and gas pipeline network from the oil and gas pipeline network data as characteristic parameters of the model to be constructed; Step S3.2, establishing a deep learning model; Step S3.3, train the deep learning model to obtain a trained deep learning model, and input the feature parameters obtained in step S3.1 into the trained deep learning model to output the result.

[0008] Furthermore, the actual data of the oil and gas pipeline network includes real-time data of the oil and gas pipeline network and corresponding operation results of the oil and gas pipeline network.

[0009] Furthermore, the real-time data of the oil and gas pipeline network includes pipeline parameters, fluid parameters, operation parameters, environmental parameters, equipment parameters and safety parameters.

[0010] Furthermore, after obtaining the trained deep model, step S3.3 also includes model verification adjustment, which is used to iteratively update the deep learning model by comparing the difference between the deep learning model results and the actual data of the oil and gas pipeline network, so as to obtain an accurate and stable deep learning model.

[0011] Furthermore, the deep learning model is one of a convolutional neural network model, a recursive neural network model, and a long short-term memory model.

[0012] Furthermore, the oil and gas pipeline network operation results corresponding to the real-time data of the oil and gas pipeline network include: the transportation performance of the oil and gas pipeline, the safety performance of the oil and gas pipeline, the flow characteristics of the fluid, the quality characteristics of the fluid, the transportation efficiency of the oil and gas pipeline, the operating environment of the oil and gas pipeline, and the environmental protection performance of the oil and gas pipeline.

[0013] Furthermore, after obtaining the trained deep model in step S3.3, it also includes: performing simulation analysis on the deep learning model for predictive diagnosis of the operation of the oil and gas pipeline network.

[0014] In a second aspect, in order to solve the above technical problems, the present invention also provides a model building device for oil and gas pipeline network, comprising: The determination module is used to determine the objectives and scope of the model to be built; The data collection and processing module is used to collect and process data according to the objectives and scope of the model to be constructed; the model construction module is used to construct a model based on the collected and processed data.

[0015] In a third aspect, in order to solve the above-mentioned technical problem, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the method of the present application is implemented when the processor executes the computer program.

[0016] In a fourth aspect, in order to solve the above-mentioned technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program implements the method of the present application when executed by a processor.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention constructs an oil and gas pipeline network model through deep learning. Based on the characteristics of oil and gas pipeline network data, the efficiency of model construction is improved, the cost of model construction is reduced, and the accuracy of prediction and diagnosis is improved.

[0018] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments of the present invention are briefly introduced below.

[0020] Figure 1 A flow chart of a model building method for an oil and gas pipeline network provided in Example 1 of the present invention; Figure 2A flow chart of data collection and data processing in a model building method for an oil and gas pipeline network provided in Example 1 of the present invention; Figure 3 A flow chart of constructing a model in a model construction method for an oil and gas pipeline network provided in Example 1 of the present invention; Figure 4 A schematic diagram of a structure of a model building device for an oil and gas pipeline network provided by one embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0023] The solution provided by the embodiment of the present invention can be applied to any application scenario where a model of an oil and gas pipeline network needs to be constructed. The solution provided by the embodiment of the present invention can be executed by any electronic device, for example, a user's terminal device. The embodiment of the present invention provides a possible implementation method, such as Figure 1 As shown, a flowchart of a method for building a model for an oil and gas pipeline network is provided. The solution can be executed by any electronic device, for example, a terminal device, or a terminal device and a server. For the convenience of description, the method provided by the embodiment of the present invention will be described below using the terminal device as an example of the execution subject. Figure 1 The method may include the following steps: Step S1, determine the goal and scope of the model to be constructed.

[0024] According to the actual needs of the oil and gas pipeline network, determine the objectives and scope of the model to be built, such as: simulating the operation of the pipeline system, predicting the load changes of the pipeline, etc.

[0025] Step S2, collecting and processing data according to the objective and scope of the model to be constructed.

[0026] Collect data related to the oil and gas pipeline network through sensor equipment or oil and gas pipeline network data management system. The collected data includes the length, diameter, pressure, grade, geographical location, etc. of the pipeline, and perform data processing and cleaning to ensure the accuracy and reliability of the model; Figure 2 This is a flow chart of data collection and data processing in a model building method for an oil and gas pipeline network provided in Example 1 of the present invention. Figure 2 , specifically including the following steps: Step S2.1, collecting oil and gas pipeline network data; The SCADA system is used to collect oil and gas pipeline network data, among which the oil and gas pipeline network data includes oil and gas pipeline network modeling data and oil and gas pipeline network actual data. The oil and gas pipeline network modeling data refers to the basic data required for building the model. These data usually include the geometric characteristics, topological structure, pipeline material properties, pipeline strength, pipeline life, etc. of the pipeline. These data can be collected and obtained through various sensors, inspection equipment and GIS technical means; the oil and gas pipeline network actual data refers to various data obtained from the actual operation of the oil and gas pipeline network. The oil and gas pipeline network actual data includes the oil and gas pipeline network real-time data and the corresponding oil and gas pipeline network operation results. Specifically, the oil and gas pipeline network real-time data may include pipeline parameters, fluid parameters, operation parameters, environmental parameters, equipment parameters and safety parameters. Among them, pipeline parameters directly affect the pipeline's transportation performance and safety performance, including the pipeline's material, diameter, length, wall thickness, anti-corrosion layer, etc.; operating parameters directly affect the flow characteristics and quality characteristics of oil and gas, including the density, viscosity, compressibility, water content, sulfur content, etc. of oil and gas; operating parameters directly affect the pipeline's transportation efficiency and safety performance, including the pipeline's operating pressure, temperature, flow rate, etc.; environmental parameters directly affect the pipeline's operating environment and safety performance, including the meteorological, geological, hydrological and other conditions around the pipeline; equipment parameters directly affect the pipeline's transportation efficiency and safety performance, including the performance parameters of equipment such as pump stations, storage tanks, and valves; safety parameters directly affect the pipeline's safety and environmental performance, including safety indicators such as pipeline stress, deformation, and leakage.

[0027] Step S2.2, performing denoising processing on the oil and gas pipeline network data collected in step S2.1; The oil and gas pipeline network data collected by S2.1 can be denoised using the data cleaning method commonly used in the prior art; Furthermore, deep learning and wavelet transform can be combined for joint denoising. Specifically, the wavelet function, decomposition level and threshold of the wavelet transform are obtained through the deep learning model, and the oil and gas pipeline network data are denoised according to the wavelet function, decomposition level and threshold obtained by the deep learning model.

[0028] Step S3, building a model based on the data collected and processed in step S2.

[0029] Figure 3 A flowchart of constructing a model in a model construction method for an oil and gas pipeline network provided in Example 1 of the present invention. Figure 3 , specifically including the following steps: Step S3.1, extracting the actual data of the oil and gas pipeline network from the oil and gas pipeline network data as characteristic parameters of the model to be constructed; Specifically, the pipeline parameters, fluid parameters, operating parameters, environmental parameters, equipment parameters and safety parameters of the actual data of the oil and gas pipeline network and their corresponding oil and gas pipeline network operation results are used as feature parameters of the model to be constructed. Deep learning is used to analyze and learn the above data, and automatically extract features related to the state of the oil and gas pipeline network, such as pipeline pressure, oil and gas density, temperature, flow rate and other parameters, as well as geological and meteorological factors around the pipeline, to provide comprehensive feature support for subsequent model construction.

[0030] Step S3.2, establishing a deep learning model; The deep learning model can learn a large amount of data and automatically fit the operation rules and characteristics of the oil and gas pipeline. The deep learning model can be one of: a convolutional neural network model, a recursive neural network model, and a long short-term memory model; Step S3.3, train the deep learning model to obtain a trained deep learning model, and input the feature parameters obtained in step S3.1 into the trained deep learning model, and output the result (model result).

[0031] After the training is completed, the trained deep learning model is obtained, the characteristic parameters of the deep learning model are obtained as input data, input into the trained deep learning model, and the model results are output.

[0032] After obtaining the trained deep model, step S3.3 also includes model verification adjustment, which is used to compare the difference between the deep learning model results and the actual data of the oil and gas pipeline network, and iteratively update the deep learning model based on the difference to obtain an accurate and stable deep learning model.

[0033] The model results are compared with the actual data of the oil and gas pipeline network. If there is a data difference between the two, the output results of the deep learning model need to be continuously iteratively updated to achieve adjustment and optimization until the output results of the deep learning model are the same as the actual data of the oil and gas pipeline network, that is, an accurate and stable deep learning model is finally obtained; adjustment and optimization of the deep learning model improves the generalization and robustness of the model, and can also improve the accuracy and predictive ability of the model.

[0034] Furthermore, the deep learning model adjusts and optimizes the output results, specifically including: continuously iterating and updating the output results of the deep learning model through the back propagation algorithm to obtain the error model, and setting a stop condition during the training of the deep learning model. When the iteration reaches the maximum number of iterations or the error data reaches the iteration threshold, the iteration can be stopped; the maximum number of iterations and the iteration condition threshold are parameters that need to be set during the iteration process. The maximum number of iterations specifies the maximum number of iterations, and the iteration condition threshold refers to stopping the iteration when a certain accuracy is reached; therefore, when selecting these two parameters, it is necessary to select according to the specific situation: for the case where the maximum number of iterations is large, the selection of the iteration condition threshold can be considered. The iteration condition threshold is usually a small value, and the iteration is stopped when the value is reached. Under normal circumstances, the iteration condition threshold should be as small as possible, so that the iteration can obtain more accurate results. But at the same time, the iteration condition threshold cannot be too small, otherwise it will cause the iteration process to be too complicated, consuming a lot of time and computing resources; the iteration condition threshold can be adjusted according to actual needs; if more accurate results are required, the iteration condition threshold can be reduced; if the iteration speed is too slow, it can be appropriately increased.

[0035] In short, when choosing the maximum number of iterations and the iteration condition threshold, it is necessary to comprehensively consider the accuracy and efficiency of the algorithm. Selecting appropriate parameters can make the algorithm obtain more accurate and faster results.

[0036] Optionally, after obtaining the trained deep model in step S3.3, the step further includes: performing simulation analysis on the deep learning model for predictive diagnosis of the operation of the oil and gas pipeline network.

[0037] Specifically, according to actual needs, the model is applied for simulation and analysis, for example, simulating the operation of the pipeline system, predicting the load changes of the pipeline, etc. Through simulation and analysis, more in-depth data can be obtained to support decision-making and optimization; among them, according to the real-time monitoring of the deep learning model, when the output results of the model are constantly changing, it proves that the oil and gas pipeline network has a fault. The deep learning model can learn and extract features from a large amount of data, and use these features to predict future values. According to the abnormal values ​​output by the model, the prediction results of the deep learning model are combined to predict and analyze possible faults in the oil and gas pipeline network; according to the prediction results, corresponding fault handling measures are taken in time, such as maintenance, replacement of parts, etc., and the cause of the fault is analyzed in depth to avoid similar faults from happening again.

[0038] Based on Figure 1 Based on the same principle as the method shown in , the embodiment of the present invention also provides a model building device 20 for oil and gas pipeline network, such as Figure 4As shown in , the model building device 20 for oil and gas pipeline network may include a determination module 210, a data collection and processing module 220 and a model building module 230, wherein: A determination module 210 is used to determine the target and scope of the model to be constructed; The data collection and processing module 220 is used to collect and process data according to the target and scope of the model to be constructed; the model construction module 230 is used to construct a model based on the collected and processed data.

[0039] Optionally, the data collection and processing module 220 is specifically used for: Collect oil and gas pipeline network data; The collected oil and gas pipeline network data is denoised.

[0040] Optionally, the oil and gas pipeline network data includes oil and gas pipeline network modeling data and oil and gas pipeline network actual data.

[0041] Optionally, the model building module 230 is specifically used to: Extracting actual data of the oil and gas pipeline network from the oil and gas pipeline network data as characteristic parameters of the model to be constructed; Building deep learning models; The deep learning model is trained to obtain a trained deep learning model, and the acquired feature parameters are input into the trained deep learning model to output the result.

[0042] Optionally, the actual data of the oil and gas pipeline network includes real-time data of the oil and gas pipeline network and corresponding operation results of the oil and gas pipeline network.

[0043] Optionally, the real-time data of the oil and gas pipeline network includes pipeline parameters, fluid parameters, operation parameters, environmental parameters, equipment parameters and safety parameters.

[0044] Optionally, after obtaining the trained deep model, it also includes: The model verification and adjustment module is used to compare the differences between the deep learning model results and the actual data of the oil and gas pipeline network, iteratively update the deep learning model, and obtain an accurate and stable deep learning model.

[0045] The model building device for oil and gas pipeline networks in the embodiments of the present invention can execute the model building method for oil and gas pipeline networks provided in the embodiments of the present invention. The implementation principles are similar. The actions performed by the modules and units in the model building device for oil and gas pipeline networks in the embodiments of the present invention correspond to the steps in the model building method for oil and gas pipeline networks in the embodiments of the present invention. For the detailed functional description of each module in the model building device for oil and gas pipeline networks, please refer to the description in the corresponding model building method for oil and gas pipeline networks shown in the previous text, which will not be repeated here.

[0046] Among them, the above-mentioned model building device for oil and gas pipeline networks can be a computer program (including program code) running in a computer device, for example, the model building device for oil and gas pipeline networks is an application software; the device can be used to execute the corresponding steps in the method provided in an embodiment of the present invention.

[0047] In some embodiments, the model building device for the oil and gas pipeline network provided by the embodiment of the present invention can be implemented in a combination of software and hardware. As an example, the model building device for the oil and gas pipeline network provided by the embodiment of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the model building method for the oil and gas pipeline network provided by the embodiment of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.

[0048] In other embodiments, the model building device for oil and gas pipeline network provided by the embodiment of the present invention can be implemented in software. Figure 4 A model building device for an oil and gas pipeline network stored in a memory is shown, which may be software in the form of a program and a plug-in, and includes a series of modules, including a determination module 210, a data collection and processing module 220 and a model building module 230, for implementing the model building method for an oil and gas pipeline network provided in an embodiment of the present invention.

[0049] The modules involved in the embodiments of the present invention may be implemented by software or hardware, wherein the name of a module does not limit the module itself in some cases.

[0050] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention, which may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0051] In an alternative embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0052] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0053] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0054] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0055] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0056] The electronic device may also be a terminal device. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0057] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0058] According to another aspect of the present invention, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the implementation modes of the above-mentioned various embodiments.

[0059] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0060] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0061] The computer-readable storage medium provided by the embodiment of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0062] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0063] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

Claims

1. A method for constructing a model for an oil and gas pipeline network, characterized in that: The following steps are involved: Step S1, determining the target and scope of the model to be constructed; Step S2, collecting and processing data according to the objectives and scope of the model to be constructed; Step S3, building a model based on the data collected and processed in step S2.

2. A model building method for oil and gas pipeline network according to claim 1, characterized in that: The data collection and data processing in step S2 specifically include the following steps: Step S2.1, collecting oil and gas pipeline network data; Step S2.2, denoising the oil and gas pipeline network data collected in step S2.

1.

3. A model building method for oil and gas pipeline network according to claim 2, characterized in that: The oil and gas pipeline network data in step S2.1 includes oil and gas pipeline network modeling data and oil and gas pipeline network actual data.

4. A method for constructing a model for an oil and gas pipeline network according to claim 1, characterized in that: The model is constructed in step S3, which specifically includes the following steps: Step S3.1, extracting the actual data of the oil and gas pipeline network from the oil and gas pipeline network data as characteristic parameters of the model to be constructed; Step S3.2, establishing a deep learning model; Step S3.3, train the deep learning model to obtain a trained deep learning model, and input the feature parameters obtained in step S3.1 into the trained deep learning model to output the result.

5. A method for constructing a model for an oil and gas pipeline network according to claim 3, characterized in that: The actual data of the oil and gas pipeline network includes real-time data of the oil and gas pipeline network and corresponding operation results of the oil and gas pipeline network.

6. A method for constructing a model for an oil and gas pipeline network according to claim 5, characterized in that: The real-time data of the oil and gas pipeline network includes pipeline parameters, fluid parameters, operation parameters, environmental parameters, equipment parameters and safety parameters.

7. A method for constructing a model for an oil and gas pipeline network according to claim 4, characterized in that: After obtaining the trained deep model, step S3.3 also includes model verification adjustment, which is used to compare the differences between the deep learning model results and the actual data of the oil and gas pipeline network, iteratively update the deep learning model, and obtain an accurate and stable deep learning model.

8. A model building device for oil and gas pipeline network, characterized in that: include: The determination module is used to determine the objectives and scope of the model to be built; A data collection and processing module, used for collecting and processing data according to the objectives and scope of the model to be constructed; Model building module, used to build models based on collected and processed data.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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