A method and system for dynamic simulation of a gas network

Abstract

The present application relates to a kind of gas pipe network dynamic simulation method and system, the method includes: comparing field parameter and simulation data to determine current scene;Based on determination, possible determination and repeated determination, change after scene is then made scene switching.The present application can provide dynamic simulation by scene switching, make simulation system and field reach real-time dynamic synchronization with minimum simulation overhead, so as to realize timely and effective and dynamic management to pipe network system after scene sends change, effectively prevent serious consequences caused by gas leakage, guarantee the safe and reliable operation of gas pipe network.

Description

[0001] The present application belongs to the technical field of gas pipe network control, and particularly relates to a gas pipe network dynamic simulation method and system.

[0002] With the increasingly perfect energy supply system of cities, natural gas, as a main representative of clean energy, plays a huge role in the city energy system, and the development of natural gas industry has become the best choice for the world to improve the environment and promote sustainable economic development. The development and utilization of natural gas cannot be separated from the transportation of the pipe network system. With the continuous increase of the scale of natural gas development and use, the natural gas pipe network system is also becoming increasingly large and complex. Natural gas is a flammable and explosive gas, and the safety requirements for its storage and transportation are very high. In order to ensure the safe and efficient operation of the city gas pipe network, effective accident early warning means is necessary. In the face of the increasingly complex networked pipe system, it is particularly important to achieve reliable and safe remote scheduling, monitoring and other goals of the gas under the informationization and intelligentization means.

[0003] For fault discovery of the gas pipe network, the traditional method is to collect actual working condition data and parameters on site, and then perform manual verification. Obviously, such a method is time-consuming and labor-intensive. With the exponential growth of the number of city gas pipe networks, such safety protection method does not have sustainability, and completely relying on manual operation to realize real-time monitoring, adjustment and optimization scheduling of the operation of the gas pipe network secondary high pressure and above level pressure regulating station will cause waste of resources and lag of adjustment, greatly increasing the workload and labor cost of the operating personnel. A more modern method is to use simulation, simulation and optimization of the gas pipe network; dynamic and online simulation is an effective means for pipe network system planning and design, economic evaluation and operation management. At present, with the rapid development of the pipe network system, the system scale is increasingly large and complex, the topology of the pipe network simulation model is increasingly large and complex, and the simulation calculation time is also increasing. People increasingly need fast calculation methods. However, the commonly used technical parameters are mostly empirical values and nominal values, and there is a lack of collection and correction of actual parameters, so that the final simulation optimization result has a high distortion rate and is not meaningful. That is, the synchronization between the simulation system and the actual site is not timely, and only by improving the accuracy of the simulation model can effective monitoring and restoration of the site be realized.

[0004] The present application can provide dynamic simulation through scene switching, achieve real-time dynamic synchronization between the simulation system and the site with the least simulation overhead, so as to realize timely and effective dynamic management of the pipe network system after the scene is changed, effectively prevent serious consequences caused by gas leakage, and ensure safe and reliable operation of the gas pipe network.

[0005] ​​​To address the aforementioned problems in the prior art, this invention proposes a dynamic simulation method and system for gas pipeline networks, the method comprising:

[0006] Step S1: Set up scenarios based on historical data of target parameters on site, so that each scenario corresponds to different target parameter values ​​or target parameter ranges; there are multiple target parameters, which are the target data that need to be achieved for the optimized control of the gas pipeline network;

[0007] Step S2: Establish a gas pipeline network simulation model; change the model parameters of the simulation model and run the simulation model to obtain simulation data, and establish a first lookup table between the simulation data and scene identifiers; based on the simulation data, set a second lookup table between the parameter value changes of specific nodes or specific node combinations and their specific parameter types and scene changes;

[0008] Step S3: Determine the minimum monitoring set based on the second lookup table; specifically: query the second lookup table to obtain the first set of specific parameter types corresponding to each scenario change; select one element from the first set corresponding to each scenario change to form the set with the fewest elements as the minimum monitoring set;

[0009] Step S4: Compare the field parameters with the simulation data to determine the current scene; specifically: collect field parameters, determine whether the scene identifier and field parameter values ​​are consistent with the simulation data, and if so, determine the scene indicated by the scene identifier as the current scene;

[0010] Step S5: Collect field parameter values ​​for each specific parameter type in the minimum monitoring set at a first time interval; determine whether a scene change may occur based on the field parameter values; if so, place the possible scene changes into the set of changes to be determined and proceed to step S6; otherwise, repeat step S5.

[0011] Step S6: Determine the dynamic monitoring set based on the set of changes to be determined, collect the field parameter values ​​in the dynamic monitoring set at dynamic intervals, and determine whether the scene has changed. If it is determined that the scene has changed, proceed to step S7; if it is determined that the scene has not changed, return to step S5; if it is uncertain whether the scene has changed, repeat step S6.

[0012] Step S7: Based on the determined set of changes, repeatedly determine the scene changes that have occurred and the scene after the changes, switch the scene and return to step S5.

[0013] Furthermore, after establishing the real-time simulation model, the simulation model parameters are set and adjusted to synchronize the simulation model with the on-site gas pipeline network system.

[0014] Furthermore, the node includes a user terminal and a pipe segment.

[0015] Furthermore, acquisition devices are set up at each node to collect the measurement values ​​from the acquisition devices at each node.

[0016] Furthermore, gas flow rate, temperature, and / or pressure.

[0017] Furthermore, the gas in question is natural gas.

[0018] A dynamic simulation system for a gas pipeline network for implementing the above method is characterized in that the system is set on a server and stores and acquires simulation data and field data based on the server; the server is also used to deploy and run simulation models.

[0019] It also includes one or more data acquisition devices set at each node, which are used to collect field data and send the collected field data to the server.

[0020] Furthermore, the gas pipeline network is the natural gas pipeline network of the residential community.

[0021] A computer-readable storage medium, characterized in that it includes a program that, when run on a computer, causes the computer to execute the aforementioned dynamic simulation method for gas pipeline networks.

[0022] A cloud server, characterized in that the cloud server is configured to execute the gas pipeline network dynamic simulation method.

[0023] The beneficial effects of this invention include:

[0024] (1) Based on the target parameters, the scene is divided, and the simulation model is used to obtain comprehensive and complete data corresponding to the scene, which provides a data foundation for the creation of the correspondence and the guidance of scene switching;

[0025] (2) By constructing a minimum monitoring set and a dynamic monitoring set through specific nodes or combinations of specific nodes and their specific parameter types, and in conjunction with dynamic time intervals, local data fluctuations that are easy to occur in both simulation and field are avoided, providing a stable basis for scene judgment, and achieving comprehensive dynamic monitoring of the field with minimal overhead.

[0026] (3) By taking three steps—possible occurrence, confirmed occurrence, and repeated confirmation—the complex problem of scene change is simplified, and the efficiency of scene change is improved; by quantifying the probability of scene change, the most accurate direction for scene change is provided. [Attached Image Description]

[0027] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to unduly limit the invention. In the drawings:

[0028] Figure 1This is a schematic diagram of the dynamic simulation method for gas pipeline networks provided by the present invention.

Detailed Implementation Methods

[0029] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions are merely for explaining the present invention and are not intended to limit the scope of the invention.

[0030] This invention proposes a dynamic simulation method and system for gas pipeline networks, the method comprising the following steps:

[0031] Step S1: Set up scenarios based on historical data of target parameters on site, so that each scenario corresponds to different target parameter values ​​or target parameter ranges; there are multiple target parameters, which are target data that need to be achieved for the optimization and control of the gas pipeline network, such as one or more of the following: gas consumption, failure rate, abnormality rate, number of users, number of complaints, etc.

[0032] Preferably, the step further includes setting scene identifiers based on target parameters. The scene is a multi-dimensional scene, with each scene corresponding to a scene identifier. When there are multiple target parameters, the scene identifier is a multi-dimensional scene identifier, where each element identifies the element value of the scene in that dimension. For example, the identifier (VC1, VC2) is a binary scene identifier, where the first element is gas consumption, and the second element is pipeline scale, etc.; (VC1 = Q1, VC2 = SC1) and (VC1 = Q2, VC2 = SC1) can be used to identify two different scenes; each element value is a specific value or a range of values; by using multi-dimensional target parameters to decompose the on-site performance, i.e., the scene, each scene has a certain representativeness; of course, such decomposition can be based on experience or data; therefore, two scenes cannot coexist.

[0033] Data acquisition is very difficult on-site. If data is not acquired with a specific purpose, the amount of data available for analysis is quite limited. However, target parameters are often relatively complete. This invention divides scenarios based on target parameters and acquires comprehensive and complete data corresponding to the scenarios based on simulation models, providing a data foundation for the creation of correspondences and guidance for scenario switching.

[0034] Preferably, the scene is the current scene or another scene; for the scenario, the scene of another scene can be used as the scene basis for the current scene;

[0035] Step S2: Establish a gas pipeline network simulation model; change the model parameters of the simulation model and run the simulation model to obtain simulation data, and establish a first lookup table between the simulation data and scene identifiers; based on the simulation data, set a second lookup table between the parameter value changes of specific nodes or specific node combinations and their specific parameter types and scene changes; wherein: the parameter values ​​of the specific nodes or node combinations are scene-sensitive parameter values; the parameter values ​​of the specific nodes or node combinations are relatively stable when the scene remains unchanged, thus remaining unchanged or relatively unchanged; while they change when a scene change occurs in a specific direction or change from the first interval to the second interval;

[0036] Parameter types are the types of parameters that can be monitored through nodes, such as: user, pipe segment and / or gas source node pressure value, mass flow rate, temperature value, volumetric flow rate, etc.; pipe segment length, pipe diameter, service life, etc.

[0037] Preferred scenarios include parameter value changes from a first value to a second value, deviations from the current value exceeding a preset range, or changes from a first interval to a second interval; the first interval q11 and the second interval q21 are different intervals but may overlap; ideally, any change in parameter value should be considered an indication of a scene change; in this case, it is not necessary to record or consider parameter value changes, only to judge the changes; the following is an example of considering changes, for example: a specific parameter type of a specific node A1 (A1.V1)<q11,q21> A1.V2<q12,q22> ) or a specific parameter type of a specific node combination A1 and A2 (A1-A2.V1)<q13,q23> The parameter is sensitive to changes in scenarios C1->C2, C1->C3, and C1->C7; that is, it is sensitive to changes because when these changes occur in the scenario, the parameter value of this specific parameter type also changes within different ranges, such as between q11 and q21. Correspondingly, there is a record (A1.V1) in the second lookup table.<q11,q211> A1.V2<q12,q22> ): (C1->C2, C1->C3, C2->C7); (A1-A2.V1<q13,q23> ): (C1->C2, C1->C4, C2->C7); where: A1-A2 are examples of node combinations, and only changes in the parameter values ​​of each node in the node combination indicate a scene change;

[0038] Preferred method: After establishing the real-time simulation model, set and adjust the simulation model parameters to synchronize the simulation model with the on-site gas pipeline network system;

[0039] Preferably, the node includes a user terminal and a pipe segment;

[0040] Preferably, a data acquisition device is installed at each node to collect the measured values ​​of the data acquisition device at each node, such as gas flow rate, temperature and / or pressure.

[0041] Step S3: Determine the minimum monitoring set based on the second lookup table; specifically: query the second lookup table to obtain the first set of specific parameter types corresponding to each scenario change; select one element from the first set corresponding to each scenario change to form the set with the fewest elements as the minimum monitoring set; through the minimum monitoring set and the dynamic monitoring set, comprehensive monitoring of the site is achieved with minimal overhead.

[0042] Alternative: Select a specific element from the first set corresponding to each scenario change to form the minimum set with the fewest elements as the minimum monitoring set; the specific element is most sensitive to scenario changes; that is, the parameter value of the specific element changes the most when the scenario changes.

[0043] Step S4: Compare the field parameters and simulation data to determine the current scenario; specifically: collect field parameters, determine whether the scenario identifier and field parameter values ​​are consistent with the simulation data. If they are consistent, determine that the scenario indicated by the scenario identifier is the current scenario; otherwise, determine that they are inconsistent.

[0044] Determining whether the scene identifier and on-site parameter values ​​are consistent with the simulation data involves: determining the on-site scene identifier based on the range into which the target parameter value in the on-site parameters falls; obtaining the simulation data corresponding to the on-site scene identifier in the first lookup table based on the on-site scene identifier; determining whether the simulation data is consistent with the on-site parameter values; if so, determining whether the scene identifier and on-site parameter values ​​are consistent with the simulation data; otherwise, determining that they are inconsistent.

[0045] Preferred option: In case of inconsistency, return to step S2 to improve the simulation model or resynchronize the simulation model and the field.

[0046] Step S5: Collect field parameter values ​​for each specific parameter type in the minimum monitoring set at a first time interval; determine whether a scene change may occur based on the field parameter values; if so, place the possible scene changes into the set of changes to be determined and proceed to step S6; otherwise, repeat step S5.

[0047] The step of determining whether a scene change may occur based on the on-site parameter values ​​specifically involves: determining whether there are on-site parameter values ​​in the minimum detection set that match the parameter value change conditions in the second lookup table; if so, determining that a scene change may occur; and placing the scene change corresponding to the on-site parameter values ​​that match the parameter value change conditions of a specific parameter type in the second lookup table into the set of changes to be determined.

[0048] Preferred approach: When the set of changes to be determined is not empty, it indicates that a scene change may occur. In this case, data is collected for N consecutive first time intervals, that is, step S5 is repeated, and the changes in the set of changes to be determined are monitored. Elements that appear less than M times consecutively in the set of changes to be determined are deleted. Noisy elements are removed through continuous monitoring, thereby improving the stability of scene monitoring and judgment.

[0049] Preferred: M = N;

[0050] Replaceable: M = N-1;

[0051] Step S6: Determine the dynamic monitoring set based on the set of changes to be determined, collect the field parameter values ​​in the dynamic monitoring set at dynamic intervals, and determine whether the scene has changed. If it is determined that the scene has changed, proceed to step S7; if it is determined that the scene has not changed, return to step S5; if it is uncertain whether the scene has changed, repeat step S6.

[0052] Preferred option: If the number of times step S6 is re-executed exceeds a preset number, return to step S4 or provide feedback for manual adjustment;

[0053] Step S6 specifically includes the following steps:

[0054] Step S61: Perform parameter initialization: Specifically: Set the initial value of the dynamic interval to equal the first time interval T1; set the initial consecutive count DN value to equal (1 day / T1) or (1 hour / T1);

[0055] Step S62: Locate records in the second comparison table that contain any scene change element in the set of changes to be determined, and put the specific parameter type in the record into the dynamic monitoring set;

[0056] Step S63: Collect parameter values ​​of all specific parameter types in the dynamic monitoring set for a continuous number of times at a dynamic interval (DN); accumulate the number of times the change of each specific parameter type matches the parameter value change in the second reference table to obtain a statistical value of occurrence count;

[0057] Step S64: Based on the second lookup table, determine the set of specific parameter types {Par} corresponding to each scenario change element involved in the set of changes to be determined. ch_t}; where: ch is the scene change number, t is the specific parameter type number in the specific parameter type set; N ch_t It is a statistical value representing the occurrence count of a specific parameter type;

[0058] Step S65: Calculate the probability value of change for each scenario;

[0059]

[0060] Where: ω ch_t It is the weight value of a specific parameter type ch_t in a specific parameter type set; in fact, each parameter change plays a different role in the process of scene change, that is, sensitivity and stability are the same, and such distinction can be made by setting the weight value; of course, the same parameter type change may occur in multiple scene changes, and the parameter monitoring operation can be reused by using a dynamic monitoring set, thereby improving monitoring efficiency.

[0061] Step S66: If there is a scene change with a change probability value exceeding the high preset value, then it is determined that a scene change has occurred; and the scene changes with change probability values ​​exceeding the preset value are added to the determined change set; proceed to step S7; if the change probability values ​​corresponding to all scene changes are lower than the low preset value, then if it is determined that no scene change has occurred, return to step S5; otherwise, if it is uncertain whether a scene change has occurred, then re-execute step S6.

[0062] Alternative: When it is uncertain whether a scene change has occurred, determine whether the dynamic interval is the minimum dynamic interval. If so, re-execute step S6; otherwise, reduce the dynamic interval, recalculate the consecutive number of times DN = 1 day / T1, and return to step S63.

[0063] Alternative: Determine if the dynamic interval is the minimum dynamic interval. If so, return to step S2 or S5. If data fluctuations still occur during granular monitoring, it may be necessary to resynchronize or rebuild the simulation model. Of course, in non-essential situations, monitoring can be performed again at the coarse-grained interval to avoid the impact of glitch data on the sensitivity of scene switching.

[0064] Step S7: Based on the determined set of changes, repeatedly determine the scene changes that have occurred and the scene after the changes, switch scenes to perform dynamic simulation, and return to step S5;

[0065] Based on a defined set of changes, the process of repeatedly determining the occurrence of scene changes includes the following steps:

[0066] Step S7A1: Obtain an unprocessed scene change (first scene C1 -> second scene C2) from the set of determined changes;

[0067] Step S7A2: Collect field parameters and determine the consistency between the second scene identifier and field parameter values ​​and the simulation data;

[0068] The determination of the consistency between the second scene identifier, the on-site parameter values, and the simulation data specifically involves: obtaining the simulation data corresponding to the second scene identifier based on the second scene identifier; calculating the consistency of parameter types between the simulation data and the on-site parameter values ​​for each parameter type in turn; and then summing and averaging the parameter type consistency to obtain the final consistency.

[0069] The degree of consistency between the parameter types of the simulation data and the field parameter values ​​is specifically calculated as follows: the degree of consistency between the parameter types is calculated based on the average value FV of the simulation data and the average value CV of the field parameter values.

[0070] Preferably, the consistency degree CO of the parameter types is calculated using the following formula;

[0071]

[0072] Step S7A3: When there is more than one scenario with a consistency level greater than the consistency judgment threshold, select the scenario with the highest consistency level as the changed scenario; when there is less than one scenario with a consistency level greater than the consistency judgment threshold, return to step S7A2.

[0073] Preferred: For the same second scenario that needs to be repeated, when the number of scenarios with a consistency level greater than the consistency judgment threshold is less than one and the number of times the repetition judgment is greater than the upper limit, return to step S4;

[0074] Among them: the consistency judgment threshold and the judgment threshold are both preset values;

[0075] Preferred: The consistency threshold is equal to 90%;

[0076] The scene switching process specifically involves: acquiring the pipeline control strategy and simulation strategy that match the changed scene, and executing the pipeline control strategy and simulation strategy.

[0077] Preferably, the simulation strategy includes a set of parameters to be collected and a collection interval, such as a first time interval; the pipeline control strategy includes a valve opening strategy, such as valve opening time, length, and opening degree; it also includes natural gas supply time and gas supply volume, etc.

[0078] Preferably, the pipeline control strategy includes the failure probability of the field pipeline in the current scenario, and the corresponding pipeline control strategy is adopted based on the failure probability;

[0079] Based on the same inventive concept, the present invention also provides a dynamic simulation system for gas pipeline networks, the system being used to implement the above-mentioned dynamic simulation method for gas pipeline networks;

[0080] The system is hosted on a server and stores and retrieves simulation data and field data based on the server; the server is also used to deploy and run simulation models.

[0081] The system also includes one or more data acquisition devices set at each node, which are used to collect field data and send the collected field data to the server.

[0082] Alternatives: The server can be a cloud server or a local server;

[0083] The terms "local server," "cloud server," "server," and "data acquisition device" encompass all kinds of devices, equipment, and machines used for processing data, including, for example, programmable processors, computers, systems-on-a-chip, or a combination thereof. The device may include special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits). In addition to hardware, the device may also include code that creates an execution environment for the computer program, such as code constituting processor firmware, protocol stacks, database management systems, operating systems, cross-platform runtime environments, virtual machines, or combinations thereof. This device and execution environment can implement various computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures.

[0084] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of programming language, including assembly or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but does not necessarily, correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to said program, or in multiple co-located files (e.g., a file storing one or more modules, subroutines, or code portions). A computer program can be deployed to execute on a single computer or on multiple computers located at a single site or distributed across multiple sites and interconnected by a communications network.

[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A dynamic simulation method for gas pipeline networks, characterized in that, The method includes: Step S1: Set up scenarios based on historical data of target parameters on site, so that each scenario corresponds to different target parameter values ​​or target parameter ranges; there are multiple target parameters, which are the target data that need to be achieved for the optimized control of the gas pipeline network; Step S2: Establish a gas pipeline network simulation model; change the model parameters of the simulation model and run the simulation model to obtain simulation data, and establish a first lookup table between the simulation data and scene identifiers; based on the simulation data, set a second lookup table between the parameter value changes of specific nodes or specific node combinations and their specific parameter types and scene changes; Step S3: Determine the minimum monitoring set based on the second lookup table; specifically: query the second lookup table to obtain the first set of specific parameter types corresponding to each scenario change; select one element from the first set corresponding to each scenario change to form the set with the fewest elements as the minimum monitoring set; Step S4: Compare the field parameters with the simulation data to determine the current scenario; Specifically, the process involves: collecting field parameters, determining whether the scene identifier and field parameter values ​​are consistent with the simulation data, and if so, identifying the scene indicated by the scene identifier as the current scene. Step S5: Collect field parameter values ​​for each specific parameter type in the minimum monitoring set at a first time interval; determine whether a scene change may occur based on the field parameter values; if so, place the possible scene changes into the set of changes to be determined and proceed to step S6; otherwise, repeat step S5. Step S6: Determine the dynamic monitoring set based on the set of changes to be determined, collect the field parameter values ​​in the dynamic monitoring set at dynamic intervals, and determine whether the scene has changed. If it is determined that the scene has changed, proceed to step S7. If it is determined that no scene change has occurred, return to step S5; if it is uncertain whether a scene change has occurred, repeat step S6. Step S7: Based on the determined set of changes, repeatedly determine the scene changes that have occurred and the scene after the changes, switch scenes to perform dynamic simulation, and return to step S5; After establishing the real-time simulation model, the simulation model parameters are set and adjusted to synchronize the simulation model with the on-site gas pipeline network system; The scene switching process specifically involves: acquiring the pipeline control strategy and simulation strategy that match the changed scene, and executing the pipeline control strategy and simulation strategy. The simulation strategy includes a set of parameters to be collected and a collection interval, and the pipeline control strategy includes a valve opening strategy. The pipeline control strategy includes the failure probability of the field pipeline in the current scenario, and the corresponding pipeline control strategy is adopted based on the failure probability.

2. The dynamic simulation method for gas pipeline networks according to claim 1, characterized in that, The node includes a user terminal and a pipeline segment.

3. The dynamic simulation method for gas pipeline networks according to claim 2, characterized in that, Data acquisition devices are set up at each node to collect the measurement values ​​from each node.

4. The dynamic simulation method for gas pipeline networks according to claim 3, characterized in that, The measured values ​​are gas flow rate, temperature, and / or pressure.

5. The dynamic simulation method for gas pipeline networks according to claim 4, characterized in that, The gas in question is natural gas.

6. A dynamic simulation system for a gas pipeline network for implementing the method of any one of claims 1-5, characterized in that, The system is hosted on a server and stores and retrieves simulation data and field data based on the server; the server is also used to deploy and run simulation models. It also includes one or more data acquisition devices set at each node, which are used to collect field data and send the collected field data to the server.

7. The dynamic simulation system for gas pipeline networks according to claim 6, characterized in that, The gas pipeline network mentioned is the natural gas pipeline network of the residential community.

8. A computer-readable storage medium, characterized in that, Includes a program that, when run on a computer, causes the computer to perform the dynamic simulation method for gas pipeline networks as described in any one of claims 1-5.

9. A cloud server, characterized in that, The cloud server is configured to execute the dynamic simulation method for gas pipeline networks as described in any one of claims 1-5.

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