Intelligent gas pipeline transportation performance determination method and internet of things system

Abstract

The embodiment of the specification provides a smart gas pipeline transportation performance determination method and an Internet of Things system. The method is executed by a smart gas pipeline network equipment management sub-platform. The method comprises the following steps: obtaining running information of a target gas pipeline section in a first time period through a smart gas data center; determining a first performance parameter of the target gas pipeline section at least one time in the first time period based on the running information, wherein the first performance parameter at least comprises transportation performance of the target gas pipeline section in the first time period; wherein the transportation performance of the target gas pipeline section in the first time period is determined by the following steps: determining a gas pipeline graph corresponding to the target gas pipeline section through the smart gas data center; determining the transportation performance of the target gas pipeline section in the first time period based on the gas pipeline graph through a transportation performance determination model, wherein the transportation performance determination model is a graph neural network model.

Description

[0001] Case Analysis

[0002] This application is a divisional application of Chinese application filed on October 14, 2022, with application number 202211256470.5, entitled "Smart Gas Pipeline Life Prediction Method, Internet of Things System, Device and Medium". Technical Field

[0003] This specification relates to the field of gas pipeline safety, and in particular to a method for determining the transportation performance of intelligent gas pipelines and an Internet of Things system. Background Technology

[0004] Gas pipeline transmission has become the preferred method of gas transportation due to its advantages such as low cost, safety and airtightness, large transport capacity, and ease of control and management. However, gas is flammable and explosive, and pipeline leaks can cause a series of disasters, including explosions, fires, poisoning, and environmental pollution. If a leak occurs in a densely populated residential area, the damage can be even more severe. The main causes of gas pipeline leaks include external damage, corrosion and aging of materials and equipment, improper operation, and natural disasters.

[0005] Gas pipelines may experience leaks during transport, and pipeline maintenance is a demanding process. Therefore, it is essential to screen and replace high-risk gas pipeline sections before leaks occur. Consequently, a smart method, IoT system, device, and medium are needed to determine the transport performance of gas pipelines. Summary of the Invention

[0006] This specification provides one or more embodiments of a method for determining the transportation performance of a smart gas pipeline. The method is executed by a smart gas pipeline network equipment management sub-platform. The method includes: acquiring operational information of a target gas pipeline segment within a first time period through a smart gas data center; determining a first performance parameter of the target gas pipeline segment at at least one moment within the first time period based on the operational information, wherein the first performance parameter includes at least the transportation performance of the target gas pipeline segment within the first time period; wherein determining the transportation performance of the target gas pipeline segment within the first time period includes: determining a gas pipeline map corresponding to the target gas pipeline segment through the smart gas data center, wherein the gas pipeline map is a map showing the connection relationships of multiple gas pipeline segments within a preset range of the target gas pipeline segment, wherein the nodes of the gas pipeline map include segmentation points between gas pipeline segments, gas storage points, gate stations, and pipeline bends, and the edges of the gas pipeline map include gas pipeline segments; the node features of the gas pipeline map include whether gas is processed, and the edge features of the gas pipeline map include pipeline parameters and pipeline maintenance status; and determining the transportation performance of the target gas pipeline segment within the first time period through a transportation performance determination model based on the gas pipeline map, wherein the transportation performance determination model is a graph neural network model.

[0007] This specification provides one or more embodiments of an IoT system for determining the transportation performance of a smart gas pipeline. The system includes a smart gas pipeline network equipment management sub-platform and a smart gas data center. The smart gas pipeline network equipment management sub-platform is used to: acquire operational information of a target gas pipeline segment within a first time period through the smart gas data center; and, based on the operational information, determine a first performance parameter of the target gas pipeline segment at at least one moment within the first time period, wherein the first performance parameter includes at least the transportation performance of the target gas pipeline segment within the first time period. Determining the transportation performance of the target gas pipeline segment within the first time period includes: acquiring operational information of a target gas pipeline segment within a first time period through the smart gas data center. A gas pipeline map corresponding to the target gas pipeline segment is defined. The gas pipeline map is a map showing the connection relationship of multiple gas pipeline segments within a preset range of the target gas pipeline segment. The nodes of the gas pipeline map include the division points between gas pipeline segments, gas storage points, gate stations, and pipeline bends. The edges of the gas pipeline map include gas pipeline segments. The node features of the gas pipeline map include whether the gas is processed. The edge features of the gas pipeline map include pipeline parameters and pipeline maintenance status. Based on the gas pipeline map, the transportation performance of the target gas pipeline segment within the first time period is determined by a transportation performance determination model, which is a graph neural network model.

[0008] This specification provides one or more embodiments of a smart gas pipeline transportation performance determination device, the device including at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least a portion of the computer instructions to implement the method.

[0009] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the intelligent gas pipeline transportation performance determination method. Attached Figure Description

[0010] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0011] Figure 1 This is a schematic diagram illustrating an application scenario of an intelligent gas pipeline life prediction IoT system according to some embodiments of this specification;

[0012] Figure 2 This is an exemplary block diagram of an IoT system for predicting the lifespan of smart gas pipelines, as shown in some embodiments of this specification.

[0013] Figure 3 This is an exemplary flowchart of a smart gas pipeline life prediction method according to some embodiments of this specification;

[0014] Figure 4 This is a schematic diagram illustrating the determination of pipe integrity according to some embodiments of this specification;

[0015] Figure 5A This is a schematic diagram of a gas pipeline diagram according to some embodiments of this specification;

[0016] Figure 5B This is a schematic diagram illustrating the determination of the transport performance of a target gas pipeline segment according to some embodiments of this specification;

[0017] Figure 6 This is an exemplary structural diagram of a performance parameter prediction model shown in some embodiments of this specification;

[0018] Figure 7 This is an exemplary flowchart illustrating the determination of a second performance parameter sequence according to some embodiments of this specification. Detailed Implementation

[0019] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0020] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0021] As indicated in this specification, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0022] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0023] Figure 1 This is a schematic diagram illustrating an application scenario of an intelligent gas pipeline life prediction IoT system based on some embodiments of this specification.

[0024] like Figure 1 As shown, application scenario 100 may include server 110, network 120, terminal device 130, monitoring device 140, storage device 150 and target gas pipeline section 160.

[0025] In some embodiments, application scenario 100 can determine the remaining lifespan of a target gas pipeline segment by implementing the smart gas pipeline lifespan prediction method and / or IoT system disclosed in this specification. For example, in a typical application scenario, the smart gas pipeline lifespan prediction IoT system can acquire the operating information of the target gas pipeline segment 160 within a first time period through monitoring device 140; based on the operating information, the server 110 determines a first performance parameter of the target gas pipeline segment 160 at least at one moment within the first time period, wherein the first performance parameter includes at least the transport performance of the target gas pipeline segment 160 within the first time period; based on the first performance parameter at at least one moment, the server 110 determines a first performance parameter sequence of the target gas pipeline segment 160 within the first time period, wherein the first performance parameter sequence is a sequence obtained by arranging the first performance parameters at at least one moment in chronological order; based on the first performance parameter sequence, the server 110 determines the remaining lifespan of the target gas pipeline segment 160. For more information on operating information, first performance parameters, first time points, first performance parameter sequences, and remaining lifespan, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0026] Server 110 and terminal device 130 can be connected via network 120, and server 110 can be connected to storage device 150 via network 120. Server 110 may include a processing device, which can be used to execute the intelligent gas pipeline life prediction method described in some embodiments of this specification. Network 120 can connect the various components of application scenario 100 and / or connect the system to external resources. Storage device 150 can be used to store data and / or instructions; for example, storage device 150 can store operating information, first performance parameters, a sequence of first performance parameters, second performance parameters, a sequence of second performance parameters, and remaining life. Storage device 150 can be directly connected to server 110 or located inside server 110. Terminal device 130 refers to one or more terminal devices or software. In some embodiments, terminal device 130 can receive the remaining life of a target gas pipeline segment sent by the processing device and display it to the user. Exemplarily, terminal device 130 may include one or any combination of mobile device 130-1, tablet computer 130-2, laptop computer 130-3, or other devices with input and / or output functions. Monitoring device 140 can be used to acquire operational information of the target gas pipeline segment within a first time period. Exemplary monitoring device 140 may include a camera, a crawling robot, etc. The target gas pipeline segment 160 may be a gas transport pipeline or segment for which gas pipeline life prediction is required. For example, a natural gas pipeline segment, a carbon dioxide pipeline segment, etc.

[0027] It should be noted that application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can make various modifications or variations based on the description in this specification. For example, application scenario 100 may also include a database. Furthermore, application scenario 100 may be implemented on other devices to achieve similar or different functions. However, these changes and modifications will not depart from the scope of this specification.

[0028] An Internet of Things (IoT) system is an information processing system that includes some or all of the following platforms: a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The user platform is the functional platform for acquiring user-sensed information and generating control information. The service platform connects the management platform and the user platform, providing communication services for both sensing and control information. The management platform coordinates and manages the connections and collaboration between the various functional platforms (such as the user platform and the service platform). The management platform aggregates information from the IoT operating system and provides sensing and control management functions. The service platform connects the management platform and the object platform, providing communication services for both sensing and control information. The user platform is the functional platform for acquiring user-sensed information and generating control information.

[0029] Information processing in an IoT system can be divided into user-perceived information processing and control information processing. Control information can be generated based on user-perceived information. In some embodiments, control information may include user-demand control information, and user-perceived information may include user query information. The processing of perception information involves the object platform acquiring the perception information and transmitting it to the management platform via the sensor network platform. User-demand control information is transmitted from the management platform to the user platform via the service platform, thereby controlling the sending of prompt information.

[0030] Figure 2 This is an exemplary block diagram of an IoT system for predicting the lifespan of smart gas pipelines, as shown in some embodiments of this specification.

[0031] like Figure 2 As shown, the intelligent gas pipeline life prediction Internet of Things system 200 (hereinafter referred to as system 200) may include an intelligent gas user platform 210, an intelligent gas service platform 220, an intelligent gas safety management platform 230, an intelligent gas sensor network platform 240, and an intelligent gas pipeline equipment object platform 250. In some embodiments, system 200 may be part of a server or implemented by a server.

[0032] In some embodiments, system 200 can be applied to various scenarios for predicting the lifespan of gas pipelines. In some embodiments, system 200 can obtain a query instruction based on a gas user's query request for gas pipeline lifespan, and obtain query results according to the query instruction. In some embodiments, system 200 can obtain the operating information of a target gas pipeline segment within a first time period through a smart gas data center; based on the operating information, determine a first performance parameter of the target gas pipeline segment at least at one moment within the first time period, wherein the first performance parameter includes at least the transportation performance of the target gas pipeline segment within the first time period; based on the first performance parameter at least at one moment, determine a sequence of first performance parameters of the target gas pipeline segment within the first time period, wherein the sequence of first performance parameters is a sequence obtained by arranging the first performance parameters at least at one moment in chronological order; and based on the sequence of first performance parameters, determine the remaining lifespan of the target gas pipeline segment.

[0033] The various scenarios in which System 200 can be used include gas pipeline maintenance, gas pipeline leak detection, and gas pipeline laying. It should be noted that the above scenarios are merely examples and do not limit the specific application scenarios of System 200. Those skilled in the art can apply System 200 to any other suitable scenario based on the content disclosed in this embodiment.

[0034] The smart gas user platform 210 can be user-centric, including a platform for acquiring user needs and providing information feedback to users. In some embodiments, the smart gas user platform 210 can be configured as a terminal device, such as a mobile phone, computer, or other smart device.

[0035] In some embodiments, the smart gas user platform 210 may include a gas user sub-platform and a regulatory user sub-platform. Gas users can receive reminders related to the remaining lifespan of a target gas pipeline segment from the smart gas service platform 220 through the gas user sub-platform; regulatory users can send a query command regarding the remaining lifespan of a target gas pipeline segment to the smart gas service platform 220 through the regulatory user sub-platform. Gas users can be users of the target gas pipeline segment, and regulatory users can be managers or government officials of the target gas pipeline segment. In some embodiments, the smart gas user platform 210 can obtain user input commands through a terminal device to query information related to the remaining lifespan of the target gas pipeline segment. For example, the smart gas user platform 210 can also provide feedback on the remaining lifespan of the target gas pipeline segment to the user.

[0036] The smart gas service platform 220 can be a platform that provides information / data transmission and interaction.

[0037] In some embodiments, the smart gas service platform 220 can be used for information and / or data interaction between the smart gas safety management platform 230 and the smart gas user platform 210. For example, the smart gas service platform 220 can receive a query command for the remaining life of a target gas pipeline segment sent by the smart gas user platform 210, process the data, and then send it to the smart gas safety management platform 230; and it can also obtain information related to the remaining life of the target gas pipeline segment from the smart gas safety management platform 230, process the data, and then send it to the smart gas user platform 210.

[0038] In some embodiments, the smart gas service platform 220 may include a smart gas consumption service sub-platform and a smart monitoring service sub-platform. In some embodiments, the smart gas consumption service sub-platform may be used to receive reminder information related to the remaining lifespan of a target gas pipeline segment sent by the smart gas safety management platform 230, and send it to the gas user sub-platform. In some embodiments, the smart monitoring service sub-platform may be used to receive a query instruction for the remaining lifespan of a target gas pipeline segment sent by the monitoring user sub-platform, and send it to the smart gas safety management platform 230.

[0039] The intelligent gas safety management platform 230 can refer to an Internet of Things platform that coordinates and integrates the connections and collaborations between various functional platforms, providing perception management and control management.

[0040] In some embodiments, the intelligent gas safety management platform 230 can be used for information and / or data processing. For example, the intelligent gas safety management platform 230 can be used for pipeline inspection safety management, pipeline gas leak monitoring, and pipeline equipment safety monitoring.

[0041] In some embodiments, the intelligent gas safety management platform 230 can also be used for information and / or data interaction between the intelligent gas service platform 220 and the intelligent gas sensor network platform 240. For example, the intelligent gas safety management platform 230 can receive a target gas pipeline segment remaining life query instruction sent by the intelligent gas service platform 220 (such as the intelligent regulatory service sub-platform), process the storage, and then send it to the intelligent gas sensor network platform 240; and it can also obtain operating information from the intelligent gas sensor network platform 240, process the storage, and then send it to the intelligent gas service platform 220.

[0042] In some embodiments, the intelligent gas safety management platform 230 may include an intelligent gas pipeline equipment management sub-platform and an intelligent gas data center.

[0043] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can be used to obtain the operating information of a target gas pipeline segment within a first time period through an intelligent gas data center; based on the operating information, determine a first performance parameter of the target gas pipeline segment at at least one moment within the first time period, wherein the first performance parameter includes at least the transportation performance of the target gas pipeline segment within the first time period; based on the first performance parameter at the at least one moment, determine a first performance parameter sequence of the target gas pipeline segment within the first time period, wherein the first performance parameter sequence is a sequence obtained by arranging the first performance parameters at the at least one moment in chronological order; and based on the first performance parameter sequence, determine the remaining lifespan of the target gas pipeline segment.

[0044] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can be further used to: acquire an image of the target gas pipeline segment using a crawling robot; and determine the pipeline integrity of the target gas pipeline segment based on the image using an image recognition model, wherein the image recognition model is a machine learning model.

[0045] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can be further used to: determine the gas pipeline map corresponding to the target gas pipeline segment through the intelligent gas data center, wherein the gas pipeline map is a map of the connection relationship of multiple gas pipeline segments within a preset range of the target gas pipeline segment; and determine the transportation performance of the target gas pipeline segment in the first time period based on the gas pipeline map through a transportation performance determination model, wherein the transportation performance determination model is a graph neural network model.

[0046] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can be further used to: predict a second performance parameter sequence of the target gas pipeline segment in a second time period based on the first performance parameter sequence, wherein the second performance parameter sequence includes at least one performance parameter for a future time; and determine the remaining lifespan of the target gas pipeline segment based on the first performance parameter sequence and / or the second performance parameter sequence.

[0047] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can be further used to: acquire multiple performance degradation models; for each of the multiple performance degradation models, determine the goodness of fit between the first performance parameter sequence and the performance degradation model; based on the goodness of fit between the first performance parameter sequence and each performance degradation model, determine a target performance degradation model from the multiple performance degradation models; and based on the target performance degradation model, determine the second performance parameter sequence.

[0048] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can be further used to: determine the second performance parameter sequence of the target gas pipeline segment in the second time period based on the first performance parameter sequence and through a performance parameter prediction model, wherein the performance parameter prediction model is a time series prediction model.

[0049] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can be further used to: process the first performance parameter sequence and the environmental feature sequence through the performance parameter prediction model to determine the second performance parameter sequence of the target gas pipeline segment in the second time period, wherein the environmental feature sequence includes multiple weather data of the environment in which the target gas pipeline segment is located in the first time period and / or the second time period.

[0050] A smart gas data center can be a data management sub-platform for storing, retrieving, and transferring data. The smart gas data center can store historical data, such as historical gas pipeline maps, historical first performance parameters, first performance parameter sequences, historical operating information, and historical remaining lifespan. This data can be obtained through manual input or by executing this method historically. In some embodiments, the smart gas data center can be used to send the remaining lifespan of the target gas pipeline segment to the smart gas service platform 220.

[0051] For more information about the Smart Gas Safety Management Platform 230, please refer to [link / reference]. Figure 3 , Figure 4 , Figure 5A , Figure 5B , Figure 6 , Figure 7 And its related descriptions.

[0052] The intelligent gas sensor network platform 240 can refer to a platform that uniformly manages sensor communication between various platforms in system 200. In some embodiments, the intelligent gas sensor network platform 240 can be configured as a communication network and gateway. The intelligent gas sensor network platform 240 can employ multiple gateway servers or multiple intelligent routers, without further limitation.

[0053] In some embodiments, the smart gas sensor network platform 240 can be used for network management, protocol management, command management, and data parsing. In some embodiments, the smart gas sensor network platform 240 can be used to send the remaining lifespan of a target gas pipeline segment to a smart gas data center.

[0054] The intelligent gas pipeline network equipment object platform 250 can be a functional device for monitoring and transmitting data on target pipeline sections. In some embodiments, the intelligent gas pipeline network equipment object platform 250 can be configured as a monitoring device, such as a monitoring device, camera, or crawling robot. In some embodiments, the intelligent gas pipeline network equipment object platform 250 can send the acquired operational information to the intelligent gas safety management platform 230 via the intelligent gas sensor network platform 240.

[0055] In some embodiments of this specification, the gas pipeline life prediction system based on the smart gas Internet of Things can ensure the opposition between different types of data, ensure data classification and transmission, traceability, and classification and processing of instructions, making the Internet of Things structure and data processing clear and controllable, and facilitating the management and control of the Internet of Things and data processing.

[0056] Figure 3 This is an exemplary flowchart of a smart gas pipeline life prediction method according to some embodiments of this specification. In some embodiments, process 300 can be executed by a smart gas pipeline network equipment management sub-platform. Figure 3 As shown, process 300 includes the following steps:

[0057] Step 310: The intelligent gas pipeline network equipment management sub-platform obtains the operating information of the target gas pipeline segment in the first time period through the intelligent gas data center.

[0058] The first time period can be a specific historical period. For example, the first time period could be a three-month historical period.

[0059] Operational information can be information relevant to the use of the target gas pipeline section. For example, operational information may include the pressure, temperature, and gas flow rate within the pipeline section during its use.

[0060] Step 320: Based on the operation information, the intelligent gas pipeline network equipment management sub-platform determines the first performance parameter of the target gas pipeline segment at least at one moment within the first time period.

[0061] The first performance parameter may be a parameter characterizing the operational performance of the target gas pipeline segment at a certain moment. In some embodiments, the first performance parameter may also include the transportation performance of the target gas pipeline segment during a first time period. For a detailed description of transportation performance, see [link to relevant documentation]. Figure 5B And related descriptions. In some embodiments, the first performance parameter may further include the pipeline integrity of the target gas pipeline segment during a first time period. For a detailed explanation of pipeline integrity, see [link to relevant documentation]. Figure 4And related descriptions. In some embodiments, the first performance parameter may also include other content. For example, the first performance parameter may also include one or more of the following: load capacity, corrosion resistance, mechanical strength, etc., of the target gas pipeline section at a certain moment. The first performance parameter can be represented numerically, for example, the stress value of the maximum load, the stress value of the mechanical strength, etc. In some embodiments, the first performance parameter can be determined by mathematical fitting, artificial intelligence, etc.

[0062] Step 330: The intelligent gas pipeline network equipment management sub-platform determines the sequence of first performance parameters of the target gas pipeline segment within a first time period based on the first performance parameters at least at one moment.

[0063] In some embodiments, the first performance parameter sequence is a sequence obtained by arranging the first performance parameters at at least one moment in chronological order. The chronological order can be either ascending or descending chronological order.

[0064] Step 340: The intelligent gas pipeline network equipment management sub-platform determines the remaining lifespan of the target gas pipeline section based on the first performance parameter sequence.

[0065] The remaining lifespan can be the remaining service life of the target gas pipeline segment. For example, the remaining lifespan of the target gas pipeline segment could be 9 months, 3 years, etc. In some embodiments, the remaining lifespan of the target gas pipeline segment can be obtained by processing the first performance parameter sequence through mathematical fitting, artificial intelligence processing, etc. For specific instructions on determining the remaining lifespan of the target gas pipeline segment, please refer to [link to documentation]. Figure 6 And its related descriptions.

[0066] In some embodiments, process 300 may further include step 350, in which the intelligent gas pipeline network equipment management sub-platform sends the remaining lifespan of the target gas pipeline segment to the intelligent gas data center.

[0067] In some embodiments, a smart gas data center can be used to send the remaining lifespan of a target gas pipeline segment to a smart gas service platform, the smart gas service platform can be used to send the remaining lifespan of the target gas pipeline segment to a smart gas user platform, and the smart gas user platform can be used to allow users to query the remaining lifespan of the target gas pipeline segment.

[0068] The intelligent gas pipeline life prediction method described in some embodiments of this specification can predict the remaining service life of a target gas pipeline segment, estimate the remaining service life of each pipeline segment in advance, facilitate pipeline network inspection, and promptly handle high-risk pipeline segments to avoid potential leakage risks.

[0069] Figure 4 This is a schematic diagram illustrating the determination of pipe integrity according to some embodiments of this specification.

[0070] In some embodiments, the first performance parameter further includes the pipeline integrity of the target gas pipeline segment during a first time period. For example... Figure 4 As shown, the intelligent gas pipeline network equipment management sub-platform can be further used to acquire images 420 of the target gas pipeline segment through a crawling robot 410; based on the image 420 of the target gas pipeline segment, the pipeline integrity 440 of the target gas pipeline segment is determined by an image recognition model 430.

[0071] The crawling robot 410 can be an intelligent device for monitoring a target gas pipeline segment. In some embodiments, the crawling robot has the function of crawling on the inner wall of the target gas pipeline segment. For example, the crawling robot may include structures such as suction cups and magnetic materials for adhering to the inner wall of the pipeline and crawling. In some embodiments, the crawling robot can acquire information related to the target gas pipeline segment through preset sensors. For example, the crawling robot may include an infrared device and a camera for acquiring an image 420 of the target gas pipeline segment and corrosion information of the inner wall; the crawling robot may include a temperature sensor for acquiring temperature information within the target gas pipeline segment; the crawling robot may include a pressure sensor for acquiring pressure information within the target gas pipeline segment; the crawling robot may include a flow rate sensor for acquiring airflow velocity information within the target gas pipeline segment; the crawling robot may include a humidity sensor for acquiring humidity information within the target gas pipeline segment. In some embodiments, the crawling robot can crawl and monitor remotely or crawl and monitor autonomously by preset parameters. In some embodiments, the crawling robot can crawl on the outer wall of the target gas pipeline segment to acquire the above-mentioned information on the outer wall of the target gas pipeline segment.

[0072] In some embodiments, the crawling robot can acquire images of a target gas pipeline segment at a target frequency. The target frequency can be the frequency at which the crawling robot acquires information about the target gas pipeline segment. In some embodiments, the target frequency can be related to the difference rate between the target gas pipeline segment and other gas pipeline segments. For example, a target frequency corresponding to each difference rate can be determined using a preset relationship table between the difference rate and the target frequency. The difference rate can characterize the difference in the internal physical environment (such as temperature, pressure, humidity, etc.) between the target gas pipeline segment and a reference gas pipeline segment. For example, it can be obtained by retrieving historical parameters such as temperature, pressure, and humidity from a smart gas data center as parameters for the reference gas pipeline segment and comparing them with the corresponding parameters of the target gas pipeline segment. The difference rate can be expressed as a percentage, such as 10%. In some embodiments, when the difference rate between the target gas pipeline segment and other gas pipeline segments is greater than a difference rate threshold, it indicates that the internal physical environment of the target gas pipeline segment may have changed due to factors such as leakage or corrosion aging, and the target gas pipeline segment may be an abnormal pipeline segment with a leakage risk. The difference rate threshold can be manually set and determined.

[0073] Image recognition model 430 can be a machine learning model, for example, image recognition model 430 can be a convolutional neural network model. The input of image recognition model 430 can include an image 420 of the target gas pipeline segment, and the output can include pipeline integrity 440.

[0074] Pipeline integrity can be a parameter characterizing the degree of integrity of a pipeline. Pipeline integrity can be a specific value within 100. In some embodiments, pipeline integrity can be determined by the degree of cracks, corrosion, bulges, and dents within a target gas pipeline section. The more severe the cracks, corrosion, bulges, and dents within the pipeline, the lower the pipeline integrity can be.

[0075] In some embodiments, the image recognition model 430 can be trained using a large number of labeled training samples. Specifically, multiple sets of labeled training samples are input into the initial image recognition model. A loss function is constructed based on the output of the initial image recognition model and the labels. The parameters of the initial image recognition model are iteratively updated based on the loss function. In some embodiments, training can be performed using various methods based on the training samples. For example, training can be performed using gradient descent. Training ends when a preset condition is met, and a trained image recognition model is obtained. The preset condition can be the convergence of the loss function. In some embodiments, the training samples can include images of a historical gas pipeline segment, and the labels can be the pipeline integrity corresponding to the images of that historical gas pipeline segment. The labels can be obtained through manual annotation.

[0076] In some embodiments of this specification, the use of crawling robots to acquire images and other information of the target gas pipeline section can reduce the waste of human resources caused by manual measurement. The use of crawling robots can avoid affecting the normal operation of the gas pipeline. In addition, further processing of the images through image recognition models can improve the intelligence level of pipeline integrity determination and avoid the subjective influence of manual determination.

[0077] Figure 5A This is a schematic diagram of a gas pipeline diagram according to some embodiments of this specification; Figure 5B This is a schematic diagram illustrating the determination of the transport performance of a target gas pipeline segment according to some embodiments of this specification.

[0078] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can determine the gas pipeline map corresponding to the target gas pipeline segment through the intelligent gas data center. For example, the intelligent gas data center retrieves historical gas pipeline segment information from storage devices and databases to generate the gas pipeline map.

[0079] A gas pipeline map can be a map reflecting the connection relationships of multiple gas pipeline segments within a preset range of a target gas pipeline segment. Nodes in the gas pipeline map can be junctions between gas pipeline segments, gas storage points, gate stations, or pipeline bends, while edges can be individual gas pipeline segments. For example... Figure 5A As shown, A, B, C, D, and E can be nodes, line segments AB, BC, CD, and CE can be edges, and line segment CD can be a target gas pipeline segment. In some embodiments, the node features of the gas pipeline map can include the docking deviation between various gas pipeline segments. The docking deviation can reflect the angular deviation and assembly error / tolerance of the docking structure between various gas pipeline segments. For example, the angular error of node D is ±0.025°. It is understood that the larger the docking deviation, the greater the possibility of leakage in the gas pipeline segment. In some embodiments, the docking deviation can be determined by manual input or by measurement by a crawling robot.

[0080] In some embodiments, the node features of a gas pipeline map may include whether the gas is processed. For example, the node feature of node D may include pressurizing the gas; the node feature of node A may include depressurizing the gas. In some embodiments, whether the gas is processed can be represented by feature values ​​or vectors. For example, if the node feature of node D is pressurizing the gas by 100 Pa, the feature value of node D may be +100. In some embodiments, whether the gas is processed can be determined by manual input. In some embodiments, the edge features of a gas pipeline map may include pipeline parameters and pipeline maintenance status. Pipeline parameters may include information such as pipeline length, pipeline inner diameter, pipe wall thickness, and pipeline material. Pipeline maintenance status may include pipeline maintenance cycle, last maintenance time, etc. In some embodiments, pipeline parameters and pipeline maintenance status can be determined by manual input. In some embodiments, the edge features of a gas pipeline map may include pipeline integrity. Pipeline integrity can characterize the condition of a pipeline segment. In some embodiments, pipeline integrity can be represented by a value within 100.

[0081] like Figure 5B As shown, the intelligent gas pipeline network equipment management sub-platform can determine the transportation performance of the target gas pipeline segment in the first time period based on the gas pipeline map 510 and the transportation performance determination model 520.

[0082] The transport performance 530 can be a parameter characterizing the gas-carrying capacity of a target gas pipeline segment. Transport performance can be expressed as a value within 100. It is understood that the better the transport performance of a target gas pipeline segment at a given moment, the greater its remaining lifespan is likely to be at that moment.

[0083] The transportation performance determination model 520 can be a machine learning model, such as a graph neural network model. The input to the transportation performance determination model 520 can include a gas pipeline map 510, and the output can include transportation performance 530.

[0084] In some embodiments, the input to the transport performance determination model 520 may further include a difference rate 540 between multiple gas pipeline segments. The difference rate 540 may be used as a standalone input or as a node feature input to a gas pipeline map. See [link to documentation] for an explanation of the difference rate. Figure 4 And its related descriptions.

[0085] In some embodiments, the transportation performance determination model 520 can be trained using a large number of labeled training samples. Specifically, the labeled training samples are input into the transportation performance determination model, and the parameters of the model are updated through training. In some embodiments, training can be performed based on the training samples using various methods. For example, training can be performed using gradient descent. Training ends when a preset condition is met. The preset condition is the convergence of the loss function. In some embodiments, the training samples may include historical gas pipeline maps and historical difference rates, and the labels may be the transportation performance corresponding to each node in the historical gas pipeline map. The historical gas pipeline map includes historical nodes, historical edges, historical node features, and historical edge features. The labels can be obtained through manual annotation.

[0086] The gas pipeline diagrams described in some embodiments of this specification can visualize the pipeline network corresponding to the target gas pipeline segment, which is beneficial for analyzing the transport capacity at the pipeline connection points. At the same time, by introducing losses such as temperature, pressure, and gas velocity, performance prediction deviations can be avoided due to inherent transmission losses in pipeline length. In addition, by using a transport capacity determination model, intelligent determination of the transport capacity of pipeline segments can be achieved, reducing errors caused by human judgment.

[0087] Figure 6 This is a schematic diagram of a performance parameter prediction model based on some embodiments of this specification.

[0088] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can predict the second performance parameter sequence of the target gas pipeline segment in a second time period based on the first performance parameter sequence.

[0089] The second time period can be a future time period. For example, the second time period can be a period of two months in the future. In some embodiments, the second time period can be a period of time following the first time period.

[0090] The second performance parameter can be the performance parameter of the target gas pipeline section within a second time period. The second performance parameter sequence is a sequence obtained by arranging the second performance parameters at least once within the second time period in chronological order. The chronological order can be ascending or descending. For example, the second performance parameter sequence may include second performance parameter 1, second performance parameter 2, ..., second performance parameter N, etc., for a future time period. In some embodiments, the second performance parameter sequence includes performance parameters at least once in the future. In some embodiments, the second performance parameter sequence can be obtained by processing the first performance parameter sequence using mathematical fitting, artificial intelligence, or other methods. In some embodiments, the second performance parameter sequence can be determined by a target performance degradation model. For a detailed explanation of the target performance degradation model, see [link to relevant documentation]. Figure 6 And its related descriptions.

[0091] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can determine the second performance parameter sequence 640 of the target gas pipeline segment in the second time period based on the first performance parameter sequence 610 and the performance parameter prediction model 630.

[0092] The performance parameter prediction model 630 can be a machine learning model, such as a time series prediction model. The input of the performance parameter prediction model 630 can include a first performance parameter sequence 610, and the output can include a second performance parameter sequence 640.

[0093] In some embodiments, the input to the performance parameter prediction model 630 may further include an environmental feature sequence 620. The intelligent gas pipeline network equipment management sub-platform can process the first performance parameter sequence 610 and the environmental feature sequence 620 through the performance parameter prediction model 630 to determine the second performance parameter sequence 640 of the target gas pipeline segment in the second time period.

[0094] The environmental feature sequence 620 may be a plurality of environmental features arranged in chronological order. In some embodiments, the environmental feature sequence 620 may include weather data at multiple points in time of the environment in which the target gas pipeline segment is located during a first time period and / or a second time period. For example, environmental feature 1, environmental feature 2, ..., environmental feature N, etc. In some embodiments, the environmental feature sequence 620 may be acquired via a network or via sensors, crawling robots, etc.

[0095] In some embodiments, the performance parameter prediction model 630 may include a first prediction layer 631 and a second prediction layer 634. The input to the first prediction layer 631 may include a first performance parameter sequence 610, and the output may include a second performance parameter sequence 632 before correction. The input to the second prediction layer may include the second performance parameter sequence 632 before correction, a confidence level 633, the first performance parameter sequence 610, and an environmental feature sequence 620, and the output may include a second performance parameter sequence 640.

[0096] The confidence level can be a parameter reflecting the reliability of the second performance parameter sequence before correction. In some embodiments, the confidence level can be related to the number of performance parameters in the second performance parameter sequence before correction output by the first prediction layer, and the complexity of the gas pipeline map. For example, the more performance parameters in the second performance parameter sequence before correction, and the greater the complexity of the gas pipeline map, the more complete the data of the second performance parameter sequence before correction is, and the higher its confidence level can be. In some embodiments, the confidence level can be determined by a preset correlation table between confidence level and the number of performance parameters, or between confidence level and complexity.

[0097] In some embodiments, the first prediction layer and the second prediction layer can be obtained through joint training. For example, an initial first prediction layer and an initial second prediction layer can be trained based on a large number of labeled training samples. The training samples may include historical first performance parameter sequences, historical environmental feature sequences, and the confidence scores of historical first performance parameter sequences. The labels may be the corresponding second performance parameter sequences. The labels can be manually annotated. The historical first performance parameter sequences from the training samples are input into the first prediction layer; the output of the first prediction layer, the confidence scores of the historical first performance parameter sequences, the historical first performance parameter sequences, and the historical environmental feature sequences are input into the second prediction layer to obtain the second performance parameter sequence output by the second prediction layer. A loss function is constructed based on the labeled second performance parameter sequence and the second performance parameter sequence output by the second prediction layer, and the parameters of the first and second prediction layers are updated synchronously. Through parameter updates, the trained first and second prediction layers are obtained.

[0098] The performance parameter prediction model described in some embodiments of this specification can realize intelligent prediction of the second performance parameter sequence; in addition, by introducing environmental feature sequences and confidence levels to correct the predicted second performance parameter sequence, a second performance parameter sequence that is more consistent with the real-time environmental conditions can be obtained.

[0099] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can determine the remaining lifespan 650 of a target gas pipeline segment based on a first performance parameter sequence and / or a second performance parameter sequence. In some embodiments, the remaining lifespan 650 of the target gas pipeline segment can be determined based on the first performance parameter sequence and / or the second performance parameter sequence through a preset functional relationship. For example, the first performance parameter sequence and / or the second performance parameter sequence can have a linear functional relationship with the remaining lifespan of the target gas pipeline segment; that is, the smaller each of the first and / or second performance parameters is, the smaller the remaining lifespan decreases according to a linear functional relationship. It is understood that the preset functional relationship can also include other relationships such as exponential functions, power functions, etc. Those skilled in the art can determine the specific preset functional relationship based on different application scenarios, which will not be elaborated here.

[0100] Figure 7 This is an exemplary flowchart illustrating the determination of a second performance parameter sequence according to some embodiments of this specification. In some embodiments, process 700 may be executed by a smart gas pipeline network equipment management sub-platform. Figure 7 As shown, process 700 includes the following steps:

[0101] Step 710: The intelligent gas pipeline network equipment management sub-platform obtains multiple performance degradation models.

[0102] A performance degradation model can be a model that reflects the change of performance parameters of a target gas pipeline section over time. For example, performance parameter degradation models can include linear degradation models, nonlinear degradation models (such as power-law degradation models, exponential degradation models, logarithmic degradation models, etc.). In some embodiments, performance degradation models can be obtained through a network or by retrieving them from storage devices, databases, etc. The intelligent gas pipeline network equipment management sub-platform can obtain multiple performance degradation models simultaneously.

[0103] Step 720: For each of the multiple performance degradation models, the intelligent gas pipeline network equipment management sub-platform determines the goodness of fit between the first performance parameter sequence and the performance degradation model.

[0104] Goodness of fit characterizes the degree of fit between the first performance parameter sequence and the performance degradation model. For example, the better the regression curve of the performance degradation model fits the observed values ​​of the first performance parameter sequence, the greater the goodness of fit can be.

[0105] Step 730: The intelligent gas pipeline network equipment management sub-platform determines the target performance degradation model from multiple performance degradation models based on the goodness of fit between the first performance parameter sequence and each performance degradation model.

[0106] The target performance degradation model can be the model to be used in the end. In some embodiments, the intelligent gas pipeline network equipment management sub-platform can determine the performance degradation model with the highest goodness of fit among multiple performance degradation models as the target performance degradation model.

[0107] Step 740: The intelligent gas pipeline network equipment management sub-platform determines the second performance parameter sequence based on the target performance degradation model.

[0108] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can input a first performance parameter sequence into a target performance degradation model and determine a second performance parameter sequence through model fitting.

[0109] In some embodiments, the intelligent gas pipeline network equipment management sub-platform can determine the remaining lifespan of the target gas pipeline segment based on the second performance parameter sequence obtained in the above manner. For detailed instructions on determining the remaining lifespan of the target gas pipeline segment, please refer to [link to relevant documentation]. Figure 6 And its related descriptions.

[0110] In some embodiments of this specification, the second performance parameter sequence is determined by model fitting regression. The theoretical value of the second performance parameter sequence can be obtained through mathematical calculation, providing a theoretical reference for the subsequent determination of the actual remaining life.

[0111] This specification provides a smart gas pipeline life prediction device, which includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least some of the computer instructions to implement the aforementioned smart gas pipeline life prediction method.

[0112] This specification provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the aforementioned intelligent gas pipeline life prediction method.

[0113] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0114] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0115] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0116] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0117] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0118] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0119] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for determining the performance of a smart gas pipeline, characterized in that, The method is executed by the intelligent gas pipeline network equipment management sub-platform, and the method includes: The operation information of the target gas pipeline segment within the first time period is obtained through the intelligent gas data center. Based on the operational information, a first performance parameter of the target gas pipeline segment is determined at least at one moment within the first time period. The first performance parameter includes the pipeline integrity and transport performance of the target gas pipeline segment within the first time period. The pipeline integrity is a parameter characterizing the integrity of the pipeline, and the transport performance is a parameter characterizing the gas carrying capacity of the target gas pipeline segment. Based on the operational information, the determination of a first performance parameter for the target gas pipeline segment at at least one moment within the first time period, wherein the first performance parameter includes the pipeline integrity and transportation performance of the target gas pipeline segment within the first time period, including: Images of a target gas pipeline segment are acquired by a crawling robot at a target frequency, the target frequency being related to the difference rate between the target gas pipeline segment and other gas pipeline segments, the difference rate representing the difference in the internal physical environment between the target gas pipeline segment and a reference gas pipeline segment; Based on the image of the target gas pipeline segment, the pipeline integrity of the target gas pipeline segment is determined by an image recognition model; The smart gas data center determines the gas pipeline map corresponding to the target gas pipeline segment. The gas pipeline map is a map showing the connection relationship of multiple gas pipeline segments within a preset range of the target gas pipeline segment. The nodes of the gas pipeline map include the segmentation points between gas pipeline segments, gas storage points, gate stations, and pipeline bends. The edges of the gas pipeline map include gas pipeline segments. The node features of the gas pipeline map include whether the gas has been processed, the docking deviation, and the difference rate. The docking deviation reflects the angular deviation, assembly error, or tolerance of the docking structure between each gas pipeline segment. The edge features of the gas pipeline map include pipeline parameters, pipeline integrity, and pipeline maintenance status. Based on the gas pipeline map, the transportation performance of the target gas pipeline segment during the first time period is determined by a transportation performance determination model, which is a graph neural network model.

2. The method of claim 1, wherein, The method further includes: Based on the first performance parameter at at least one moment, a first performance parameter sequence of the target gas pipeline segment within the first time period is determined, wherein the first performance parameter sequence is a sequence obtained by arranging the first performance parameters at at least one moment in chronological order. Based on the first performance parameter sequence, the remaining lifespan of the target gas pipeline section is determined.

3. The method of claim 2, wherein, The method further includes: The remaining lifespan of the target gas pipeline segment is sent to the smart gas data center, which then sends the remaining lifespan of the target gas pipeline segment to the smart gas service platform. The smart gas service platform then sends the remaining lifespan of the target gas pipeline segment to the smart gas user platform, which allows users to query the remaining lifespan of the target gas pipeline segment.

4. A smart gas pipeline transportation performance determination Internet of Things system, characterized in that, The system includes a smart gas safety management platform, which comprises a smart gas pipeline equipment management sub-platform and a smart gas data center. The smart gas pipeline equipment management sub-platform is used for: The operation information of the target gas pipeline segment within the first time period is obtained through the intelligent gas data center. Based on the operational information, a first performance parameter of the target gas pipeline segment is determined at least at one moment within the first time period. The first performance parameter includes the pipeline integrity and transport performance of the target gas pipeline segment within the first time period. The pipeline integrity is a parameter characterizing the integrity of the pipeline, and the transport performance is a parameter characterizing the gas carrying capacity of the target gas pipeline segment. Based on the operational information, the determination of a first performance parameter for the target gas pipeline segment at at least one moment within the first time period, wherein the first performance parameter includes the pipeline integrity and transportation performance of the target gas pipeline segment within the first time period, including: Images of a target gas pipeline segment are acquired by a crawling robot at a target frequency, the target frequency being related to the difference rate between the target gas pipeline segment and other gas pipeline segments, the difference rate representing the difference in the internal physical environment between the target gas pipeline segment and a reference gas pipeline segment; Based on the image of the target gas pipeline segment, the pipeline integrity of the target gas pipeline segment is determined by an image recognition model; The smart gas data center determines the gas pipeline map corresponding to the target gas pipeline segment. The gas pipeline map is a map showing the connection relationship of multiple gas pipeline segments within a preset range of the target gas pipeline segment. The nodes of the gas pipeline map include the segmentation points between gas pipeline segments, gas storage points, gate stations, and pipeline bends. The edges of the gas pipeline map include gas pipeline segments. The node features of the gas pipeline map include whether the gas has been processed, the docking deviation, and the difference rate. The docking deviation reflects the angular deviation, assembly error, or tolerance of the docking structure between each gas pipeline segment. The edge features of the gas pipeline map include pipeline parameters, pipeline integrity, and pipeline maintenance status. Based on the gas pipeline map, the transportation performance of the target gas pipeline segment during the first time period is determined by a transportation performance determination model, which is a graph neural network model.

5. A smart gas pipeline transportation performance determination device, characterized in that, The apparatus includes at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least a portion of the computer instructions to implement the method as claimed in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the method as described in any one of claims 1 to 3.

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