A device for predicting the state of a natural gas pipeline

Abstract

The application discloses a natural gas pipeline state prediction device, relates to the technical field of data processing, and can improve the prediction efficiency of the natural gas pipeline state. The natural gas pipeline state prediction device comprises an edge computing unit, an SCADA system communication unit and a result output unit. The SCADA system communication unit is used for being connected with an SCADA system. The result output unit is used for being connected with a field terminal. The SCADA system communication unit is used for receiving production data of the natural gas pipeline from the SCADA system and sending the production data to the edge computing unit. The edge computing unit is used for receiving the production data from the SCADA system communication unit, inputting the production data into a built-in simulation machine learning simulation model, predicting the running parameter state of the natural gas pipeline, obtaining a prediction result of the running parameter state, and sending the prediction result to the result output unit. The result output unit is used for sending the prediction result to the field terminal, so that the field terminal displays the prediction result.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a device for predicting the status of a natural gas pipeline. Background Technology

[0002] With the rapid development of pipeline transportation technology, the transportation of natural gas often requires the use of a large number of natural gas pipelines. These pipelines are prone to damage, leading to production equipment shutdowns. Without appropriate natural gas pipeline condition prediction devices, potential pipeline damage cannot be prevented, which could then result in damage to production equipment.

[0003] Currently, traditional natural gas pipeline condition prediction devices require the construction of complex mathematical or physical models, and then use these models to simulate the natural gas pipeline. Because constructing such mathematical or physical models is time-consuming, traditional natural gas pipeline condition prediction devices suffer from low prediction efficiency. Utility Model Content

[0004] The purpose of this application is to provide a natural gas pipeline condition prediction device, which aims to solve the problem of low prediction efficiency of natural gas pipeline conditions in traditional natural gas pipeline condition prediction devices.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a natural gas pipeline status prediction device, comprising: an edge computing unit, a data acquisition and monitoring control (SCADA) system communication unit, and a result output unit; the SCADA system communication unit and the edge computing unit are connected; the result output unit and the edge computing unit are connected; the SCADA system communication unit is used to connect to the SCADA system; the SCADA system is used to acquire production data of the natural gas pipeline from the production site; the result output unit is used to connect to a field terminal; the SCADA system communication unit is used to receive the production data of the natural gas pipeline from the SCADA system and send the production data to the edge computing unit, the production data including at least one of the following: outlet pressure, outlet temperature, gas flow rate, and valve status; the edge computing unit is used to receive the production data from the SCADA system communication unit, input the production data into a built-in simulation machine learning model, predict the operating parameter status of the natural gas pipeline, obtain the prediction result of the operating parameter status, and send the prediction result to the result output unit, the operating parameter status including at least one of the following: pressure distribution status, temperature change status, downstream key point status, and distribution point status; the result output unit is used to send the prediction result to the field terminal so that the field terminal displays the prediction result.

[0007] In one possible design, the edge computing unit is connected to the communication unit of the SCADA system for data acquisition and monitoring control via the Open Platform Unified Architecture (OPC UA) communication interface; the edge computing unit is also connected to the result output unit via a high-definition multimedia (HDMI) interface.

[0008] In one possible design, the natural gas pipeline status prediction device also includes a cloud training platform communication unit; the cloud training platform communication unit is used to connect to the cloud training platform; the cloud training platform communication unit is used to connect to the edge computing unit via the Modicon protocol Modbus interface; the cloud training platform communication unit is used to receive simulated machine learning simulation models from the cloud training platform and send simulated machine learning simulation models to the edge computing unit.

[0009] In one possible design, the edge computing unit is also used to send prediction results to the cloud training platform communication unit; the cloud training platform communication unit is also used to send prediction results to the cloud training platform and receive a simulated machine learning model optimized by the cloud training platform based on the prediction results.

[0010] In one possible design, the edge computing unit further includes: a processor, memory, and a motherboard; the memory and processor are located on the motherboard; the processor is connected to the memory; the processor is connected to the SCADA system communication unit; and the processor is connected to the result output unit. The memory is used to store the simulated machine learning model, production data, and prediction results; the processor is used to predict the operating parameters of the natural gas pipeline based on the simulated machine learning model.

[0011] In one possible design, the states between the motherboard and the SCADA system communication unit include: a connected state and a disconnected state; when the state between the motherboard and the SCADA system communication unit is connected, the connection between the motherboard and the SCADA system communication unit is fixed by a preset fixing method.

[0012] In one possible design, the motherboard is equipped with an embedded controller; the preset fixing method includes fixing the motherboard and the SCADA system communication unit through the embedded controller.

[0013] In one possible design, the natural gas pipeline status prediction device further includes: a wide-temperature heat dissipation enclosure; the edge computing unit, the SCADA system communication unit, and the result output unit are located inside the wide-temperature heat dissipation enclosure; the wide-temperature heat dissipation enclosure is used to dissipate heat from the edge computing unit, the SCADA system communication unit, and the result output unit so that the edge computing unit, the SCADA system communication unit, and the result output unit are within a preset temperature range.

[0014] In one possible design, the processor and the cloud training platform communication unit are connected.

[0015] In one possible design, memory is used to store the operating system, machine learning model libraries, local runtime logs, and cached data.

[0016] Based on the above technical solution, the natural gas pipeline condition prediction device provided in this application predicts the parameter state of the natural gas pipeline by inputting the production data of the natural gas pipeline into a pre-trained pipeline operating condition simulation machine learning model. The natural gas pipeline condition prediction device has a built-in pre-trained machine learning simulation model with high computing speed, which can quickly respond to production data and predict the parameter state of the natural gas pipeline. Therefore, the device provided in this application shortens the calculation time of natural gas pipeline simulation technology, thereby improving the prediction efficiency of natural gas pipeline condition. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the structure of a natural gas pipeline status prediction system provided in an embodiment of this application;

[0019] Figure 2 A schematic diagram of the structure of a natural gas pipeline status prediction device provided in an embodiment of this application;

[0020] Figure 3 A schematic diagram of another natural gas pipeline status prediction device provided in an embodiment of this application;

[0021] Figure 4 A schematic diagram of another natural gas pipeline status prediction device provided in an embodiment of this application;

[0022] Figure 5 A schematic diagram of another natural gas pipeline status prediction device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In the description of this application, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or relative positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and for simplification, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Unless otherwise specified, the above-mentioned orientational descriptions can be flexibly set in practical applications, provided that the relative positional relationships shown in the accompanying drawings are satisfied.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0026] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "communication" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection. They can refer to a direct connection or an indirect connection through an intermediate medium, or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0027] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0028] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0029] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0030] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0031] Currently, traditional natural gas pipeline condition prediction devices require the construction of complex mathematical or physical models, and then use these models to simulate the natural gas pipeline. Because constructing such mathematical or physical models is time-consuming, traditional natural gas pipeline condition prediction devices suffer from low efficiency in predicting the condition of natural gas pipelines.

[0032] To address the aforementioned technical problems, this application provides a natural gas pipeline condition prediction device. This device predicts the parameter state of the natural gas pipeline by inputting the production data of the natural gas pipeline into a pre-trained pipeline operating condition simulation machine learning model. The natural gas pipeline condition prediction device has a built-in pre-trained machine learning simulation model with high computing speed, which can quickly respond to production data and predict the parameter state of the natural gas pipeline. Therefore, the device provided by this application shortens the calculation time of natural gas pipeline simulation technology, thereby improving the prediction efficiency of natural gas pipeline condition.

[0033] The implementation environment of the embodiments of this application is described below.

[0034] like Figure 1 As shown in the illustration, a natural gas pipeline status prediction system is provided in this application embodiment. The system includes: a natural gas pipeline status prediction device 101, a supervisory control and data acquisition (SCADA) system 102, a field terminal 103, and a cloud training platform 104. The natural gas pipeline status prediction device 101 is connected to the SCADA system 102, the field terminal 103, and the cloud training platform 104.

[0035] The natural gas pipeline status prediction device 101 is used to predict the status of the natural gas pipeline based on production data from the SCADA system 102, obtain the prediction results of the natural gas pipeline, and send the prediction results to the field terminal 103.

[0036] The SCADA system 102 is used to acquire production data of the natural gas pipeline and send the production data of the natural gas pipeline to the natural gas pipeline status prediction device 101.

[0037] In one possible design, the natural gas pipeline status prediction device 101 includes a SCADA system communication unit, and the natural gas pipeline status prediction device 101 is connected to the SCADA system 102 through the SCADA system communication unit.

[0038] The field terminal 103 is used to receive the prediction results from the natural gas pipeline status prediction device 101 and display the prediction results.

[0039] In one possible design, the field terminal 103 can also generate potential risks and optimization schemes for the natural gas pipeline based on the prediction results. Potential risks could include overpressure risks, flow alarms, and leakage warnings. Optimization schemes could include increasing the natural gas transport speed to improve natural gas transport efficiency without incurring overpressure risks.

[0040] The cloud training platform 104 is used to generate a simulated machine learning model and send the simulated machine learning model to the natural gas pipeline status prediction device 101.

[0041] For example, the cloud training platform 104 can be deployed on an enterprise private cloud infrastructure. The cloud training platform 104 can be a high-performance distributed computing cluster, whose core components include at least one of the following: massively parallel computing nodes, distributed storage systems, and security interface gateways.

[0042] In one possible design, the natural gas pipeline status prediction device 101 includes a result output unit, which is connected to the field terminal 103.

[0043] In one possible design, the field terminal 103 is an all-in-one device for receiving and displaying data. This all-in-one device is equipped with a receiving unit that is communicatively connected to the natural gas pipeline status prediction device 101. The receiving unit is used to receive the prediction results and display them on the field terminal 103.

[0044] The following is a detailed description of the natural gas pipeline status prediction device 101 with reference to the accompanying drawings.

[0045] like Figure 2The image shows a natural gas pipeline status prediction device provided in an embodiment of this application. The natural gas pipeline status prediction device 101 includes: a SCADA system communication unit 201, an edge computing unit 202, and a result output unit 203; the SCADA system communication unit 201 and the edge computing unit 202 are connected; the result output unit 203 is connected to the edge computing unit 202; the SCADA system communication unit 201 is used to connect to the SCADA system 102. The SCADA system 102 is used to acquire production data of the natural gas pipeline from the production site. The result output unit 203 is used to connect to a field terminal.

[0046] In this embodiment, the SCADA system communication unit 201 is used to receive production data from the natural gas pipeline from the SCADA system 102 and send the production data to the edge computing unit 202. The edge computing unit 202 is used to receive the production data from the SCADA system communication unit 201, input the production data into a built-in simulation machine learning model, predict the operating parameter status of the natural gas pipeline, obtain the prediction result of the operating parameter status, and send the prediction result to the result output unit. The result output unit 203 is used to send the prediction result to the field terminal so that the field terminal can display the prediction result.

[0047] It should be noted that production data includes at least one of the following: outlet pressure, outlet temperature, gas flow rate, and valve status. Operating parameter status includes at least one of the following: pressure distribution status, temperature change status, downstream key point status, and distribution point status.

[0048] In one possible design, the edge computing unit 202 is connected to the SCADA system communication unit 201 via an OPC UA communication interface; the edge computing unit 202 is connected to the result output unit 203 via an HDMI interface.

[0049] In one possible design, the edge computing unit 202 can be an edge computing module built around a Raspberry Pi 4B+.

[0050] Based on the above technical solutions, the natural gas pipeline condition prediction device provided in this application predicts the parameter status of the natural gas pipeline by inputting the production data of the natural gas pipeline into a pre-trained pipeline operating condition simulation machine learning model. The natural gas pipeline condition prediction device has a built-in pre-trained machine learning simulation model with high computing speed, which can quickly respond to production data and predict the parameter status of the natural gas pipeline. Therefore, the device provided in this application shortens the calculation time of natural gas pipeline simulation technology, thereby improving the prediction efficiency of natural gas pipeline condition.

[0051] In one possible design, such as Figure 3 As shown, the natural gas pipeline status prediction device 101 also includes a cloud training platform communication unit 301. The cloud training platform communication unit 301 can be used to connect to the cloud training platform 104 (not shown in the figure). The cloud training platform communication unit 301 is used to connect to the edge computing unit 202 via a Modbus interface. The cloud training platform communication unit 301 is used to receive simulated machine learning models from the cloud training platform 104 and send simulated machine learning models to the edge computing unit 202.

[0052] For example, such as Figure 3 As shown, a cloud training platform communication unit 301 can be deployed in the natural gas pipeline status prediction device 101. The cloud training platform communication unit 301 can be connected to the edge computing unit 202 via a Modbus interface.

[0053] In one possible design, the edge computing unit 202 is also used to send prediction results to the cloud training platform communication unit 301. The cloud training platform communication unit 301 is also used to send prediction results to the cloud training platform 104 and receive a simulated machine learning model optimized based on the prediction results from the cloud training platform 104.

[0054] In one possible design, the edge computing unit 202 further includes a processor, memory, and a motherboard. The memory and processor are located on the motherboard. The processor is connected to the memory. The processor is connected to the SCADA system communication unit 201. The processor is connected to the result output unit 203. The memory is used to store the simulated machine learning model, production data, and prediction results. The processor is used to predict the operating parameters of the natural gas pipeline based on the simulated machine learning model.

[0055] For example, such as Figure 4 As shown, the edge computing unit 202 may include a processor 401, memory 402, and a motherboard 403. The memory 402 is connected to the processor 401. The processor 401 is connected to the SCADA system communication unit 201 via the motherboard 403 and an OPC UA interface. The processor 401 is connected to the result output unit 203 via the motherboard 403 and an HDMI interface. The processor 401 is connected to the edge computing unit 202 via the motherboard 403 and a Modbus interface.

[0056] For example, memory 402 can be a high-capacity solid-state storage unit, and can also be used to store operating systems, machine learning model libraries, local runtime logs, and cached data.

[0057] In one possible design, the states between the motherboard 403 and the SCADA system communication unit 201 include: a connected state and a disconnected state. When the state between the motherboard 403 and the SCADA system communication unit 201 is connected, the connection between the motherboard and the SCADA system communication unit 201 is fixed by a preset fixing method.

[0058] In one possible design, the motherboard 403 is equipped with an embedded controller. A preset mounting method includes mounting the motherboard 403 and the SCADA system communication unit 201 via the embedded controller.

[0059] It is understandable that the result output unit 203 and the cloud training platform communication unit 301 can both be fixed to the motherboard 402 through the above-mentioned preset fixing method.

[0060] For example, the SCADA system communication unit 201, the result output unit 203, and the cloud training platform communication unit 301 can be integrated into a communication board or a conversion gateway. When the SCADA system communication unit 201, the result output unit 203, and the cloud training platform communication unit 301 are integrated into a communication board, the communication board can be connected to the motherboard 402 via an embedded controller in an onboard configuration. When the SCADA system communication unit 201, the result output unit 203, and the cloud training platform communication unit 301 are integrated into a conversion gateway, the conversion gateway can be connected to the motherboard 402 via a plug-in connection.

[0061] In one possible design, the natural gas pipeline status prediction device 101 further includes a wide-temperature heat dissipation enclosure. The edge computing unit 202, the SCADA system communication unit 201, and the result output unit 203 are located within the wide-temperature heat dissipation enclosure; the enclosure is used to dissipate heat from the edge computing unit 202, the SCADA system communication unit 201, and the result output unit 203, so that the edge computing unit 202, the SCADA system communication unit 201, and the result output unit 203 are within a preset temperature range.

[0062] For example, such as Figure 5 As shown, the edge computing unit 202, the SCADA system communication unit 201, and the result output unit 203 are located inside the wide-temperature heat dissipation enclosure 501. The wide-temperature heat dissipation enclosure 501 has an ingress protection rating (IP) of IP67 and an operating temperature range of [-30℃, 75℃], meaning that the natural gas pipeline status prediction device 101 can operate normally in an ambient temperature range of [-30℃, 75℃].

[0063] Understandably, a wide-temperature cooling enclosure can prevent dust from entering the natural gas pipeline status prediction device and can prevent the device from malfunctioning due to short-term immersion, enabling the natural gas pipeline status prediction device to operate normally in most environments and improving the stability of the natural gas pipeline status prediction device.

[0064] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0065] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A device for predicting the condition of a natural gas pipeline, characterized in that, The natural gas pipeline status prediction device includes an edge computing unit, a data acquisition and monitoring control SCADA system communication unit, and a result output unit; the SCADA system communication unit and the edge computing unit are connected; the result output unit and the edge computing unit are connected. The SCADA system communication unit is used to connect to the SCADA system; the SCADA system is used to acquire production data of the natural gas pipeline from the production site; the result output unit is used to connect to the field terminal. The SCADA system communication unit is used to receive production data from the natural gas pipeline of the SCADA system and send the production data to the edge computing unit. The production data includes at least one of the following: outlet pressure, outlet temperature, gas flow rate, and valve status. The edge computing unit is used to receive production data from the SCADA system communication unit, input the production data into a built-in simulation machine learning model, predict the operating parameter status of the natural gas pipeline, obtain the prediction result of the operating parameter status, and send the prediction result to the result output unit. The operating parameter status includes at least one of the following: pressure distribution status, temperature change status, downstream key point status, and distribution point status. The result output unit is used to send the prediction result to the field terminal so that the field terminal can display the prediction result.

2. The natural gas pipeline status prediction device according to claim 1, characterized in that, The edge computing unit is connected to the communication unit of the SCADA system for data acquisition and monitoring control via the Open Platform Unified Architecture (OPC UA) communication interface; the edge computing unit is also connected to the result output unit via a high-definition multimedia (HDMI) interface.

3. The natural gas pipeline status prediction device according to claim 1, characterized in that, The natural gas pipeline status prediction device also includes a cloud training platform communication unit; the cloud training platform communication unit is used to connect to the cloud training platform; the cloud training platform communication unit is used to connect to the edge computing unit through the Modbus interface of the Modicon protocol; The cloud training platform communication unit is used to receive the simulated machine learning model from the cloud training platform and send the simulated machine learning model to the edge computing unit.

4. The natural gas pipeline status prediction device according to claim 3, characterized in that, The edge computing unit is also used to send the prediction result to the cloud training platform communication unit; The cloud training platform communication unit is also used to send the prediction results to the cloud training platform and receive the simulated machine learning simulation model optimized by the cloud training platform based on the prediction results.

5. The natural gas pipeline status prediction device according to claim 1, characterized in that, The edge computing unit further includes: a processor, memory, and a motherboard; the memory and the processor are located on the motherboard; the processor is connected to the memory; the processor is connected to the SCADA system communication unit; and the processor is connected to the result output unit. The memory is used to store the simulated machine learning model, the production data, and the prediction results; The processor is used to predict the operating parameter status of the natural gas pipeline based on the simulated machine learning model.

6. The natural gas pipeline status prediction device according to claim 5, characterized in that, The states between the motherboard and the SCADA system communication unit include: a connected state and a disconnected state; when the state between the motherboard and the SCADA system communication unit is the connected state, the connection between the motherboard and the SCADA system communication unit is fixed by a preset fixing method.

7. The natural gas pipeline status prediction device according to claim 6, characterized in that, The motherboard is equipped with an embedded controller; The preset fixing method includes fixing the motherboard and the SCADA system communication unit through the embedded controller.

8. The natural gas pipeline status prediction device according to claim 1, characterized in that, The natural gas pipeline status prediction device further includes: a wide-temperature heat dissipation chassis; the edge computing unit, the SCADA system communication unit, and the result output unit are located inside the wide-temperature heat dissipation chassis; The wide-temperature heat dissipation chassis is used to dissipate heat from the edge computing unit, the SCADA system communication unit, and the result output unit, so that the edge computing unit, the SCADA system communication unit, and the result output unit are within a preset temperature range.

9. The natural gas pipeline status prediction device according to claim 5, characterized in that, The processor and the cloud training platform are connected to a communication unit.

10. The natural gas pipeline status prediction device according to claim 5, characterized in that, The memory is used to store the operating system, machine learning model library, local runtime logs, and cached data.

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