Natural gas pipeline network system risk identification method and device

Abstract

The invention provides a natural gas pipeline network system risk identification method and device, and belongs to the technical field of natural gas. The method comprises the steps that characteristic parameters of all system assemblies in the natural gas pipeline network system are obtained in real time, the system assemblies are nodes and connecting edges between the nodes in a topological structure of the natural gas pipeline network system, and each node is an actual unit node in the natural gas pipeline network system; the connecting edge between the nodes is a gas conveying pipe section for connecting each actual unit node in the natural gas pipeline network system; based on the characteristic parameters of all the system components, a preset optimization model is adopted to calculate the actual output quantity of the system components in the natural gas pipeline network system, and optimal result parameters are obtained; and based on the optimal result parameter and the characteristic parameter of each system component, performing risk identification on the natural gas pipeline network system by adopting a preset risk identification model to obtain an identification result. The risk can be dynamically identified, the operation risk of the natural gas pipeline network system can be accurately identified, and the operation safety and reliability of the pipeline network system are improved.

Description

Technical Field

[0001] This invention relates to the field of natural gas technology, and more specifically to a method for identifying risks in a natural gas pipeline network system, a device for identifying risks in a natural gas pipeline network system, a machine-readable storage medium, and an electronic device. Background Technology

[0002] During the operation of natural gas pipeline networks, a series of risks are inevitable due to factors such as pipeline blockage, network node failure, and short-term fluctuations in user gas demand. Analysis of typical risks in natural gas pipeline networks reveals that, in a networked operation environment, once a risk event occurs, if it is not controlled in a timely manner, it may create a cascading effect, causing the risk to evolve into a more significant and widespread incident, impacting the upstream supply side, the midstream storage and transportation side, and the downstream demand side to varying degrees.

[0003] Current research on risks in natural gas pipeline systems largely focuses on preventative measures and incident handling. While preventative measures offer some foresight, incident handling tends to be time-delayed, neglecting real-time aspects in both studies, resulting in low accuracy in identifying risks in natural gas pipeline systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying risks in a natural gas pipeline system, a device for identifying risks in a natural gas pipeline system, a machine-readable storage medium, and an electronic device. This method for identifying risks in a natural gas pipeline system can dynamically identify risks, achieve accurate identification of operational risks in a natural gas pipeline system, and improve the operational safety and reliability of the pipeline system.

[0005] To achieve the above objectives, the first aspect of this application provides a method for risk identification in a natural gas pipeline network system, comprising:

[0006] The system acquires characteristic parameters of each system component in the natural gas pipeline network system in real time. The system component is the node and the edge between the node in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system. The actual unit node includes at least a gas source node, a user node, and an intermediate station node. The edge between the node is the gas transmission pipeline segment connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the user's planned gas demand.

[0007] Based on the characteristic parameters of each system component, the actual output of the system components in the natural gas pipeline network system is calculated using a preset optimization model to obtain the optimal result parameters;

[0008] Based on the optimal result parameters and the characteristic parameters of each system component, a preset risk identification model is used to identify the risks of the natural gas pipeline network system, and the identification results are obtained.

[0009] In this embodiment of the application, the step of calculating the actual output of the system components in the natural gas pipeline network system based on the characteristic parameters of each system component using a preset optimization model to obtain the optimal result parameters includes:

[0010] Substitute the characteristic parameters of each system component into the objective function of the preset optimization model, and calculate the optimal result parameters while simultaneously satisfying the first and second constraints.

[0011] The first constraint is the mass conservation constraint of each system component in the natural gas pipeline network system, and the second constraint is the production constraint of each system component in the natural gas pipeline network system.

[0012] In this embodiment of the application, the objective function of the preset optimization model is:

[0013] maxF total =βF safety +(1-β)F conomy ;

[0014] in:

[0015] F economy =Scale(Income) total -Cost supply -Cost pipe ),

[0016] F safety =α1f1+α2f2,

[0017]

[0018] F total To optimize the objective function value of the model, F safety For the safety target value, F conomy β represents the target benefit value, β is the safety impact factor, and Scale represents the scaling of the total revenue of the natural gas pipeline network system. total Cost represents the total revenue from gas sales of the natural gas pipeline network system. supply Cost represents the total cost of gas source procurement for the natural gas pipeline network system. pipe Let α1 be the total cost of pipeline transportation in the natural gas pipeline network system, f1 be the weight coefficient of the gas transmission pipeline system components, α2 be the weight coefficient of the user system components, and f2 be the sub-objective function of the user system components. This represents the actual positive gas transmission rate of gas pipeline segment n. This represents the upper limit of the positive transmission capacity of gas pipeline segment n. This represents the actual reverse flow rate of gas pipeline segment p. λ represents the upper limit of reverse transmission capacity for gas transmission segment n, where N is the number of gas transmission segment system components in the natural gas pipeline network system; i The gas supply priority for user i, The planned gas demand for user i. Let I be the actual gas intake of user i, and let I be the total number of users in the natural gas pipeline network.

[0019] In this embodiment of the application, the optimal result parameters include the actual gas intake of each user and the actual gas transmission volume of each gas pipeline segment, and the preset risk identification model includes the gas pipeline segment risk identification index model and the user risk identification index model.

[0020] Based on the optimal result parameters and the characteristic parameters of each system component, a pre-set risk identification model is used to identify risks in the natural gas pipeline network system, and the identification results are obtained, including:

[0021] Based on the actual gas intake of each user and the characteristic parameters of each user, the user risk index is calculated using the user risk identification index model, and the user risk assessment result is determined based on the user risk index.

[0022] Based on the actual transmission volume of each gas pipeline segment and the characteristic parameters of each gas pipeline segment, the risk index of the gas pipeline segment is calculated using the risk identification index model of the gas pipeline segment, and the risk assessment result of the gas pipeline segment is determined based on the risk index of the gas pipeline segment.

[0023] Based on the user risk assessment results and the gas pipeline section risk assessment results, the identification results are obtained.

[0024] In this embodiment of the application, the user risk identification indicator model includes any one or more of the following:

[0025]

[0026] Among them, Sat i Sat is the gas supply satisfaction rate for user i. average Var is the average gas supply satisfaction rate for all users in the natural gas pipeline network system. demand This represents the variance of the gas supply satisfaction rate for all users in the natural gas pipeline network system. The planned gas demand for user i. Let I be the actual gas intake of user i, and let I be the total number of users in the natural gas pipeline network.

[0027] In this embodiment of the application, the risk identification index model for the gas pipeline segment includes any one or more of the following:

[0028]

[0029]

[0030] Among them, Load n Load is the load factor of gas pipeline segment n. average Var represents the average load factor of all gas transmission pipeline segments in the natural gas pipeline network system. pipe The variance representing the load rate of the gas pipeline section in the system. This represents the actual positive gas transmission rate of gas pipeline segment n. This represents the upper limit of the positive transmission capacity of gas pipeline segment n. This represents the actual reverse flow rate of gas pipeline segment p. denoted as the upper limit of reverse transmission capacity for gas transmission pipeline segment n, where N is the number of gas transmission pipeline system components in the natural gas pipeline network system.

[0031] In this embodiment of the application, the step of calculating the actual output of the system components in the natural gas pipeline network system based on the characteristic parameters of each system component using a preset optimization model to obtain the optimal result parameters includes:

[0032] The characteristic parameters of each system component are input into the solver, and the solver is used to calculate the optimal solution of the preset optimization model to determine the actual output of the system components in the natural gas pipeline network system and obtain the optimal result parameters.

[0033] The second aspect of this application discloses a risk identification device for a natural gas pipeline network system, comprising:

[0034] The acquisition module is used to acquire the characteristic parameters of each system component in the natural gas pipeline network system in real time. The system component is the node and the edge between the node in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system. The actual unit node includes at least a gas source node, a user node, and an intermediate station node. The edge between the node is the gas transmission pipeline segment connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the user's planned gas demand.

[0035] The calculation module is used to calculate the actual output of the system components in the natural gas pipeline network system based on the characteristic parameters of each system component and using a preset optimization model to obtain the optimal result parameters.

[0036] The identification module is used to identify risks in the natural gas pipeline system based on the optimal result parameters and the characteristic parameters of each system component, using a preset risk identification model, and to obtain the identification results.

[0037] A third aspect of this application provides an electronic device, the electronic device comprising:

[0038] At least one processor;

[0039] A memory connected to the at least one processor;

[0040] The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the above-mentioned natural gas pipeline network system risk identification method by executing the instructions stored in the memory.

[0041] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned natural gas pipeline network system risk identification method.

[0042] The above technical solution involves acquiring the characteristic parameters of each system component in the natural gas pipeline network system in real time. The system components are nodes and inter-node connections in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system, including at least gas source nodes, user nodes, and intermediate station nodes. The inter-node connections are gas transmission pipeline segments connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the planned gas demand of the user. Based on the characteristic parameters of each system component, a pre-set optimization model is used to calculate the actual output of the system components in the natural gas pipeline network system, obtaining the optimal result parameters. Based on the optimal result parameters and the characteristic parameters of each system component, a pre-set risk identification model is used to identify risks in the natural gas pipeline network system, obtaining the identification results. By acquiring the characteristic parameters of each system component in real time, when the characteristic parameters of the system components change, the solution results of the optimization model will also change accordingly, thus affecting the numerical changes of the evaluation indicators. Therefore, risks can be dynamically identified, and the operational risks of the natural gas pipeline network system can be accurately identified, improving the operational safety and reliability of the pipeline network system. This provides scientific guidance for the reliable and safe operation of the natural gas pipeline network system, and provides strong support for reducing the frequency of pipeline network system risks, improving the stability of gas supply to users, and ensuring the orderly operation of society.

[0043] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0044] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0045] Figure 1 The illustration shows a flowchart of a risk identification method for a natural gas pipeline network system according to an embodiment of this application;

[0046] Figure 2 A schematic diagram illustrating the topology of a natural gas pipeline network system according to an embodiment of this application is shown.

[0047] Figure 3 This illustration schematically shows a structural diagram of a natural gas pipeline network system risk identification device according to an embodiment of this application;

[0048] Figure 4 The diagram illustrates the internal structure of a computer device according to an embodiment of this application.

[0049] Explanation of reference numerals in the attached figures

[0050] 410 - Acquisition module; 420 - Calculation module; 430 - Identification module; A01 - Processor; A02 - Network interface; A03 - Internal memory; A04 - Display screen; A05 - Input device; A06 - Non-volatile storage medium; B01 - Operating system; B02 - Computer program. Detailed Implementation

[0051] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0052] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0053] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0054] Figure 1 The illustration shows a flowchart of a risk identification method for a natural gas pipeline network system according to an embodiment of this application. Figure 1 As shown, this embodiment provides a method for risk identification in a natural gas pipeline network system, including the following steps:

[0055] Step 210: Real-time acquisition of characteristic parameters of each system component in the natural gas pipeline network system. The system component is the node and the edge between the nodes in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system. The actual unit node includes at least a gas source node, a user node, and an intermediate station node. The edge between the nodes is the gas transmission pipeline segment connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the user's planned gas demand.

[0056] In this embodiment, to accurately identify risks in the natural gas pipeline network system, the structure of the system can be mathematically abstracted. Specifically, using complex network theory, the natural gas pipeline network system can be represented as a topological model composed of edges connecting nodes. In this topological model, each node represents an actual unit node in the pipeline network system, such as a gas source node, user node, or intermediate station node; the edges connecting nodes represent the gas transmission pipeline segments connecting these actual unit nodes, such as… Figure 2 As shown, Figure 2 The diagram illustrates a topological representation of a natural gas pipeline network system according to an embodiment of this application. After mathematical abstraction, the unit nodes and edges in the topological model of the natural gas pipeline network system possess the following characteristic parameters:

[0057] Degree: Degree is the most fundamental computational metric in complex network structures, typically represented by the number of connecting edges surrounding a node. In natural gas pipeline systems, a higher degree indicates greater importance of a node within the network. The specific formula for calculating degree is as follows:

[0058] Where, d i Represents node v i degree, a ij Represents node v i With node v j The edges connecting the nodes are N, where N represents the number of nodes in the pipeline network system.

[0059] Clustering coefficient: The clustering coefficient represents the degree of clustering of nodes in a natural gas pipeline network system. The larger the clustering coefficient, the stronger the connection between nodes. The clustering coefficient measures the degree of connection between a node's neighboring nodes, and its formula is as follows:

[0060] Among them, C i Represents node v i Clustering coefficient, L i Indicates the relationship with node v i The number of edges connecting all adjacent nodes, where n is the number of edges connected to node v. i Number of adjacent nodes.

[0061] Global clustering coefficient: The global clustering coefficient is the average of the clustering coefficients of all nodes in the entire pipeline network system. It is used to measure the overall clustering degree of the pipeline network, and its formula is as follows:

[0062] Where C is the global clustering coefficient, C i Represents node v i The clustering coefficient is denoted by N, where N represents the total number of nodes in the entire network.

[0063] Vertex betweenness: In a natural gas pipeline network system, the vertex betweenness can be represented by the ratio of the number of vertices to the number of edges traversed in the shortest path. The specific formula is as follows:

[0064] Among them, C B (v) represents the point intermediate number, M sd (v) indicates that the node v has been visited. i The number of shortest paths, M sd This represents the number of shortest paths between gas source s and user d in the pipeline network system.

[0065] Shortest path and average path length: The shortest path is the path from one node to another in a pipeline network system that requires the fewest number of edges. The minimum number of edges between two nodes is called the shortest path length. The average path length of the entire pipeline network system can be calculated by averaging the path lengths between all nodes. The specific calculation formula is as follows:

[0066] Where L is the average path length, l ijThis represents the minimum number of edges traversed from the initial node of the network to any other node, where N represents the number of nodes in the network system.

[0067] Using the above characteristic parameters, a preliminary static analysis of the unit nodes and connections in the pipeline network topology can be performed, and the importance of each unit node and connection can be preliminarily determined based on the value of the characteristic parameters.

[0068] In this embodiment, the actual unit nodes and inter-node connections covered in the natural gas pipeline network system can be collectively referred to as system components. That is, the system components in the natural gas pipeline network system include at least the following four categories:

[0069] 1. Gas Source: The gas source is the upstream link of the natural gas pipeline network system, representing the total amount of natural gas entering the network. In the optimization model, the sum of the gas source values ​​represents the total gas supply of the system. Gas source types include domestically produced gas, imported pipeline gas, offshore LNG, and unconventional gas, etc.

[0070] 2. Gas Pipeline Segment: A gas pipeline segment is a midstream component in the natural gas pipeline network system. It serves as the medium for transporting natural gas resources and refers to a section of the pipeline without an upstream or downstream distribution point. These gas pipeline segments are interconnected to form long-distance gas pipelines, which in turn form a complete gas pipeline network.

[0071] 3. Users: Users are downstream entities in the natural gas pipeline network system, representing the total amount of natural gas consumed by the system. A user's total gas demand represents the total system demand. A particular user's gas demand represents the total gas demand of all actual users connected to the same pipeline distribution point in the system. User types include residential gas, industrial gas, and transportation gas, etc.

[0072] 4. Intermediate stations: Intermediate stations belong to the midstream link in the natural gas pipeline network system. They play the role of connecting various system components and can be connected and work together with the other three types of components to form a complete natural gas pipeline network system.

[0073] Since each system component plays a different role in the natural gas pipeline network system, their functions also vary greatly. Therefore, the characteristic parameters of each system component can be obtained separately. Please refer to Table 1, which is a table of characteristic parameters for natural gas pipeline network system components. In this embodiment, the upper and lower limits of the gas transmission capacity of the gas transmission pipeline section correspond to the upper and lower limits of the gas transmission capacity of the gas transmission pipeline section in Table 1, and the user's planned gas demand corresponds to the user's planned gas demand in Table 1. The above information can be obtained by user input or from other systems; this embodiment does not impose any limitations.

[0074] Table 1 Characteristic parameters of natural gas pipeline network system components

[0075]

[0076]

[0077] Step 220: Based on the characteristic parameters of each system component, calculate the actual output of the system components in the natural gas pipeline network system using a preset optimization model to obtain the optimal result parameters;

[0078] In this embodiment, the pre-set optimization model is constructed based on optimization theory and can consist of an objective function and constraints. The purpose of the optimization model is to obtain the optimal solution based on the set objective function and satisfy the constraints, thereby minimizing the deviation rate between the gas delivery pipeline section and the user's gas supply. This achieves the optimal delivery path and resource allocation with limited resources. By using the optimization model to calculate the actual output of each system component based on its characteristic parameters, the optimal result parameters are obtained.

[0079] In some embodiments, the step of calculating the actual output of the system components in the natural gas pipeline network system and obtaining the optimal result parameters based on the characteristic parameters of each system component using a preset optimization model includes: substituting the characteristic parameters of each system component into the objective function of the preset optimization model, and calculating the optimal result parameters while simultaneously satisfying the first constraint and the second constraint; wherein the first constraint is the mass conservation constraint of each system component in the natural gas pipeline network system, and the second constraint is the production constraint of each system component in the natural gas pipeline network system.

[0080] In this embodiment, whether the output of the optimization model conforms to actual production depends on the rationality of the model's constraints. Therefore, to ensure that the output conforms to actual production, two constraints are set simultaneously: mass conservation constraints during the operation of the natural gas pipeline system and production constraints on each component of the natural gas pipeline system, corresponding to the first and second constraints, respectively. Therefore, when calculating the objective function of the optimization model, only the output that simultaneously satisfies both constraints conforms to actual production, ensuring the validity of the optimal result parameters and thus contributing to the effectiveness of subsequent calculations.

[0081] The purpose of setting mass conservation constraints for natural gas pipeline network system components is to ensure a balance between the gas inflow and outflow of the system components. The mass conservation constraints for each component can be expressed by the following formula:

[0082] 1) Conservation of gas source quality:

[0083]

[0084] 2) User quality conservation:

[0085]

[0086] 3) Mass conservation of gas pipeline segments:

[0087]

[0088] 4) Mass conservation in intermediate stations:

[0089]

[0090] The meanings of the parameters involved in the system component mass conservation constraints are shown in Table 2:

[0091] Table 2 System Component Mass Conservation Constraints

[0092]

[0093]

[0094] The production constraints of each system component can be expressed by the following formula:

[0095] 1) Gas source production constraints:

[0096]

[0097] 2) User production constraints:

[0098]

[0099] 3) Production constraints of gas pipeline sections:

[0100]

[0101] 4) Production constraints at intermediate stations:

[0102]

[0103] The meanings of the parameters involved in the system component quality production constraints are shown in Table 3:

[0104] Table 3 System Component Production Constraints

[0105]

[0106]

[0107] Once the objective function and constraints of the natural gas pipeline network system optimization model are determined, the characteristic parameters of each system component are substituted into the preset objective function of the optimization model to begin solving the optimization model. Due to the large scale of the natural gas pipeline network system, the numerous system components, and the complex constraints, the computational load for solving the optimization model is substantial. Therefore, commercial professional solvers can be used to solve the model.

[0108] In some embodiments, the step of calculating the actual output of the system components in the natural gas pipeline network system based on the characteristic parameters of each system component and using a preset optimization model to obtain the optimal result parameters includes: inputting the characteristic parameters of each system component into a solver, using the solver to calculate the optimal solution of the preset optimization model, thereby determining the actual output of the system components in the natural gas pipeline network system and obtaining the optimal result parameters.

[0109] In this embodiment, the solver can be the CPLEX solver, which can be used to solve problems such as linear programming, integer programming, mixed integer programming, and quadratic programming; the FICO Xpress solver supports various problems such as linear programming, integer programming, mixed integer programming, quadratic programming, and nonlinear programming; the MOSEK solver can be used to solve problems such as linear, quadratic, and mixed integer programming, and has the advantages of high performance and strong scalability. Alternatively, the Gurobi large-scale mathematical solver can be used, which has extremely fast solution speed and accuracy, and can be widely used to solve linear programming, integer programming, and mixed integer programming problems. Using the solver, the optimal solution of the preset optimization model can be calculated quickly and accurately to determine the actual output of the system components in the natural gas pipeline network system and obtain the optimal result parameters.

[0110] For example, to meet the requirements of solving linear programming and mixed-integer linear programming problems in the optimization model, the Gurobi solver can be preferentially used to calculate and solve the optimization model. The output parameters of the optimization model are shown in Table 4.

[0111] Table 4 Output results of the optimization model

[0112]

[0113]

[0114] In some embodiments, the objective function of the preset optimization model is:

[0115] maxF total =βF safety +(1-β)F conomy ;

[0116] in:

[0117] F economy =Scale(Income) total -Cost supply -Cost pipe ),

[0118] F safety =α1f1+α2f2,

[0119]

[0120] F total To optimize the objective function value of the model, F safety For the safety target value, F conomy Let β be the benefit target value, and β be the safety impact factor, which can be set to [0, 1], depending on the actual situation. In F... conomy In this context, "Scale" represents scaling down the total revenue of the natural gas pipeline network system. This is primarily because the output values ​​of the safety and benefit objectives have different orders of magnitude. To ensure the output values ​​of the optimization model are reasonable and meaningful, a scaling down operation can be performed on the total revenue of the natural gas pipeline network system. total Cost represents the total revenue from gas sales of the natural gas pipeline network system, expressed in ten thousand yuan. supply Cost represents the total cost of gas source procurement for the natural gas pipeline network system, expressed in ten thousand yuan. pipe This refers to the total cost of transporting a section of the natural gas pipeline network system, expressed in ten thousand yuan. conomy Several parameters can be obtained once the natural gas pipeline network system is determined. In F safety In this context, α1 represents the weighting coefficient of the gas pipeline system components, which can be set to [0, 1], depending on the specific circumstances. f1 represents the sub-objective function of the gas pipeline system components. 2 f1 represents the weight coefficients of the user system components, which can be set to [0, 1], depending on the specific circumstances. f2 represents the sub-objective function of the user system components. This represents the actual positive gas transmission capacity of pipeline segment n, and the unit can be 10. 4 m 3 / d, This represents the upper limit of positive transmission capacity for gas pipeline segment n, and the unit can be 10. 4 m 3 / d, This represents the actual reverse flow rate of gas pipeline segment p, and the unit can be 10. 4 m 3 / d, This represents the upper limit of the reverse transmission capacity for gas pipeline segment n, and the unit can be 10. 4 m 3 / d, where N is the number of gas transmission pipeline system components in the natural gas pipeline network; λ i The gas supply priority for user i, The planned gas demand for user i, the unit can be 10. 4 m 3 / d, The actual gas intake of user i, the unit can be 10. 4 m 3 / d, where I is the total number of users in the natural gas pipeline network.

[0121] It should be noted that the objective function of the optimization model comprehensively considers system safety and system gas sales revenue. Under different system risk scenarios, the safety impact factor β will change with the severity of the risk scenario. When general risk scenarios such as short-term supply and demand fluctuations and seasonal peak shaving occur, the value of the safety impact factor β can be appropriately reduced. However, when extreme risk scenarios such as natural disasters such as earthquakes and floods occur, the value of the safety impact factor β should be increased as much as possible.

[0122] It should also be noted that all users in the natural gas pipeline network system have a gas supply priority λ. i This attribute, gas supply priority, can be set by the user according to actual conditions. The higher the user's gas supply priority value, the greater their importance in the natural gas pipeline network system. When the natural gas pipeline network system is in a state of "supply falling short of demand," the model will reduce the gas supply of each user by a certain percentage based on the defined gas supply priority values. The higher the gas supply priority value, the lower the reduction percentage for each user. After the reduction process is completed, the natural gas pipeline network system will return to a state of "supply and demand balance."

[0123] By setting an objective function that includes both safety and benefit objectives, the system's safety and sales revenue are comprehensively considered, thus ensuring that the objective function aligns with actual production and helps guarantee the effectiveness of risk identification results.

[0124] Step 230: Based on the optimal result parameters and the characteristic parameters of each system component, a preset risk identification model is used to identify the risks of the natural gas pipeline network system and obtain the identification results.

[0125] In this embodiment, after the optimization model is solved, the output results of the optimization model can be evaluated by the risk identification model in order to accurately identify the key risk points in the natural gas pipeline network system.

[0126] In some embodiments, the optimal result parameters include the actual gas intake of each user and the actual gas delivery volume of each gas pipeline segment, and the preset risk identification model includes a gas pipeline segment risk identification index model and a user risk identification index model; that is, risk identification can be carried out separately from the perspectives of the gas pipeline segment and the user.

[0127] Accordingly, based on the optimal result parameters and the characteristic parameters of each system component, a preset risk identification model is used to identify the risks of the natural gas pipeline network system, and the identification results are obtained, including:

[0128] First, based on the actual gas intake of each user and the characteristic parameters of each user, the user risk index is calculated using the user risk identification index model, and the user risk assessment result is determined based on the user risk index.

[0129] The user risk identification indicator model includes any one or more of the following:

[0130]

[0131] Among them, Sat i The gas supply satisfaction rate for user i is defined as follows: the higher the gas supply satisfaction rate, the closer the user's gas supply is to their planned gas demand, the less gas is reduced, and the lower the risk in the system; Sat average This is the average gas supply satisfaction rate for all users in the natural gas pipeline network system. A higher value indicates a higher overall gas supply satisfaction rate and lower risk for all users. demand This is the variance of the gas supply satisfaction rate for all users in the natural gas pipeline network system. The smaller this value, the smaller the fluctuation in the gas supply satisfaction rate for all users in the system, and the closer it is to the average gas supply satisfaction rate. From the perspective of the system as a whole, the lower the risk for the user segment. The planned gas demand for user i. Let I be the actual gas intake of user i, and let I be the total number of users in the natural gas pipeline network.

[0132] In this embodiment, the user risk identification index model includes calculations of the user's gas supply satisfaction rate, the average of the gas supply satisfaction rates of all users, and the variance of the gas supply satisfaction rates of all users, thereby enabling a more comprehensive evaluation of the user's risk. In specific implementation, the above three values ​​can be calculated simultaneously as user risk indicators, or only one or more of them can be calculated as user risk indicators to obtain the user risk assessment result.

[0133] Then, based on the actual transmission volume of each gas pipeline segment and the characteristic parameters of each gas pipeline segment, the risk index of the gas pipeline segment is calculated using the risk identification index model of the gas pipeline segment, and the risk assessment result of the gas pipeline segment is determined based on the risk index of the gas pipeline segment.

[0134] The risk identification index model for the gas pipeline segment includes any one or more of the following:

[0135]

[0136] Among them, Load n The load factor represents the load rate of gas pipeline segment n. A higher load factor indicates that the actual transmission capacity of gas pipeline segment n is closer to its upper limit of pipe capacity, and therefore, the greater the risk during operation. average Var represents the average load rate of all gas transmission pipeline segments in the natural gas pipeline network system. A higher average load rate indicates a greater overall operational risk for the gas transmission pipeline system components. pipe The variance represents the load rate of the gas transmission pipeline segments in the system. The larger the variance, the more uneven the distribution of the load rate of the gas transmission pipeline segments in the system, the more high-load-rate segments and low-load-rate segments there are, and the greater the overall risk. This represents the actual positive gas transmission rate of gas pipeline segment n. This represents the upper limit of the positive transmission capacity of gas pipeline segment n. This represents the actual reverse flow rate of gas pipeline segment p. denoted as the upper limit of reverse transmission capacity for gas transmission pipeline segment n, where N is the number of gas transmission pipeline system components in the natural gas pipeline network system.

[0137] In this embodiment, the aforementioned gas pipeline segment risk identification index model includes calculations of the gas pipeline segment load rate, the average load rate of all gas pipeline segments, and the variance of the system gas pipeline segment load rate, thereby enabling a more comprehensive evaluation of the risk of the gas pipeline segment. In specific implementation, the above three values ​​can be calculated simultaneously as gas pipeline segment risk indicators, or only one or more of them can be calculated as gas pipeline segment risk indicators to obtain the gas pipeline segment risk assessment result.

[0138] Finally, based on the user risk assessment results and the gas pipeline section risk assessment results, the identification results are obtained.

[0139] In this embodiment, after obtaining the user risk assessment results and the gas pipeline section risk assessment results respectively, the risk of the entire natural gas pipeline network system can be obtained.

[0140] By evaluating the output of the optimization model using risk identification index models for gas pipeline segments and users, the evaluation values ​​for each gas pipeline segment and each gas user can be analyzed. This allows for the identification of potential risk points in the system, enabling the relevant operating companies of the natural gas pipeline network to address these risks promptly. For gas pipeline segments with high load rates, measures such as reducing gas transmission volume and using multiple pipeline transmission routes can be taken to lower their load rates. For users with low gas supply satisfaction rates, measures such as increasing their gas supply priority and increasing the gas supply volume can be implemented. This allows for the accurate identification and quantitative evaluation of risks in the natural gas pipeline network system, indirectly promoting timely risk response and improving the safety, reliability, and stability of the pipeline network system.

[0141] In the above implementation process, the characteristic parameters of each system component in the natural gas pipeline network system are acquired in real time. The system components are nodes and inter-node connections in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system, and the actual unit node includes at least a gas source node, a user node, and an intermediate station node. The inter-node connections are gas transmission pipeline segments connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the planned gas demand of the user. Based on the characteristic parameters of each system component, a preset optimization model is used to calculate the actual output of the system components in the natural gas pipeline network system to obtain the optimal result parameters. Based on the optimal result parameters and the characteristic parameters of each system component, a preset risk identification model is used to identify the risks of the natural gas pipeline network system to obtain the identification results. By acquiring the characteristic parameters of each system component in real time, when the characteristic parameters of the system components change, such as when user needs change, the solution results of the optimization model will also change accordingly, thus affecting the numerical changes of the evaluation indicators. Therefore, risks can be dynamically identified, enabling accurate identification of operational risks in the natural gas pipeline network system and quantitative risk assessment of potential risk points. This improves the operational safety and reliability of the pipeline network system, providing scientific guidance for the reliable and safe operation of the natural gas pipeline network system, reducing the frequency of pipeline network system risks, improving the stability of gas supply to users, and ensuring the orderly operation of society.

[0142] Figure 1 This is a flowchart illustrating the risk identification method for a natural gas pipeline network system in this embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0143] Please refer to Figure 3 , Figure 3 This schematic diagram illustrates the structure of a natural gas pipeline network system risk identification device according to an embodiment of the present application. This embodiment provides a natural gas pipeline network system risk identification device, including an acquisition module 410, a calculation module 420, and an identification module 430, wherein:

[0144] The acquisition module 410 is used to acquire the characteristic parameters of each system component in the natural gas pipeline network system in real time. The system component is the node and the edge between the node in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system. The actual unit node includes at least a gas source node, a user node, and an intermediate station node. The edge between the node is the gas transmission pipeline segment connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the user's planned gas demand.

[0145] The calculation module 420 is used to calculate the actual output of the system components in the natural gas pipeline network system based on the characteristic parameters of each system component and using a preset optimization model to obtain the optimal result parameters.

[0146] The identification module 430 is used to identify risks in the natural gas pipeline system based on the optimal result parameters and the characteristic parameters of each system component, using a preset risk identification model, and to obtain identification results.

[0147] The natural gas pipeline network system risk identification device includes a processor and a memory. The acquisition module 410, calculation module 420 and identification module 430 are all stored in the memory as program units. The processor executes the program units stored in the memory to realize the corresponding functions.

[0148] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and risks can be dynamically identified by adjusting kernel parameters.

[0149] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0150] This invention provides a machine-readable storage medium storing a program that, when executed by a processor, implements the natural gas pipeline network system risk identification method.

[0151] This invention provides a processor for running a program, wherein the program executes the natural gas pipeline network system risk identification method during runtime.

[0152] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a risk identification method for a natural gas pipeline network system. The display screen A04 can be an LCD screen or an e-ink display screen. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0153] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0154] In one embodiment, the natural gas pipeline network system risk identification method and apparatus provided in this application can be implemented as a computer program, which can be implemented in various ways, such as... Figure 4 The computer device shown runs on this device. The computer device's memory can store the various program modules that make up the risk identification method apparatus for the natural gas pipeline network system, for example, Figure 3 The acquisition module 410, calculation module 420, and identification module 430 are shown. The computer program, composed of these modules, causes the processor to execute the steps in the natural gas pipeline network system risk identification method of the various embodiments of this application described in this specification.

[0155] Figure 4 The computer equipment shown can be used as follows Figure 3 The acquisition module 410 in the illustrated natural gas pipeline network system risk identification method device executes step 210. A computer device can execute step 220 via the calculation module 420. A computer device can execute step 230 via the identification module 430.

[0156] This application provides an electronic device comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the aforementioned natural gas pipeline network system risk identification method by executing the instructions stored in the memory. When the processor executes the instructions, it performs the following steps:

[0157] The system acquires characteristic parameters of each system component in the natural gas pipeline network system in real time. The system component is the node and the edge between the node in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system. The actual unit node includes at least a gas source node, a user node, and an intermediate station node. The edge between the node is the gas transmission pipeline segment connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the user's planned gas demand.

[0158] Based on the characteristic parameters of each system component, the actual output of the system components in the natural gas pipeline network system is calculated using a preset optimization model to obtain the optimal result parameters;

[0159] Based on the optimal result parameters and the characteristic parameters of each system component, a preset risk identification model is used to identify the risks of the natural gas pipeline network system, and the identification results are obtained.

[0160] In one embodiment, the step of calculating the actual output of the system components in the natural gas pipeline network system using a preset optimization model based on the characteristic parameters of each system component to obtain the optimal result parameters includes:

[0161] Substitute the characteristic parameters of each system component into the objective function of the preset optimization model, and calculate the optimal result parameters while simultaneously satisfying the first and second constraints.

[0162] The first constraint is the mass conservation constraint of each system component in the natural gas pipeline network system, and the second constraint is the production constraint of each system component in the natural gas pipeline network system.

[0163] In one embodiment, the objective function of the preset optimization model is:

[0164] maxF total =βF safety +(1-β)F conomy ;

[0165] in:

[0166] F economy =Scale(Income) total-Cost supply -Cost pipe ),

[0167] F safety =α1f1+α2f2,

[0168]

[0169] F total To optimize the objective function value of the model, F safety For the safety target value, F conomy β represents the target benefit value, β is the safety impact factor, and Scale represents the scaling of the total revenue of the natural gas pipeline network system. total Cost represents the total revenue from gas sales of the natural gas pipeline network system. supply Cost represents the total cost of gas source procurement for the natural gas pipeline network system. pipe Let α1 be the total cost of pipeline transportation in the natural gas pipeline network system, f1 be the weight coefficient of the gas transmission pipeline system components, α2 be the weight coefficient of the user system components, and f2 be the sub-objective function of the user system components. This represents the actual positive gas transmission rate of gas pipeline segment n. This represents the upper limit of the positive transmission capacity of gas pipeline segment n. This represents the actual reverse flow rate of gas pipeline segment p. λ represents the upper limit of reverse transmission capacity for gas transmission segment n, where N is the number of gas transmission segment system components in the natural gas pipeline network system; i The gas supply priority for user i, The planned gas demand for user i. Let I be the actual gas intake of user i, and let I be the total number of users in the natural gas pipeline network.

[0170] In one embodiment, the optimal result parameters include the actual gas intake of each user and the actual gas transmission volume of each gas pipeline segment, and the preset risk identification model includes a gas pipeline segment risk identification index model and a user risk identification index model.

[0171] Based on the optimal result parameters and the characteristic parameters of each system component, a pre-set risk identification model is used to identify risks in the natural gas pipeline network system, and the identification results are obtained, including:

[0172] Based on the actual gas intake of each user and the characteristic parameters of each user, the user risk index is calculated using the user risk identification index model, and the user risk assessment result is determined based on the user risk index.

[0173] Based on the actual transmission volume of each gas pipeline segment and the characteristic parameters of each gas pipeline segment, the risk index of the gas pipeline segment is calculated using the risk identification index model of the gas pipeline segment, and the risk assessment result of the gas pipeline segment is determined based on the risk index of the gas pipeline segment.

[0174] Based on the user risk assessment results and the gas pipeline section risk assessment results, the identification results are obtained.

[0175] In one embodiment, the user risk identification indicator model includes any one or more of the following:

[0176]

[0177] Among them, Sat i Sat is the gas supply satisfaction rate for user i. average Var is the average gas supply satisfaction rate for all users in the natural gas pipeline network system. demand This represents the variance of the gas supply satisfaction rate for all users in the natural gas pipeline network system. The planned gas demand for user i. Let I be the actual gas intake of user i, and let I be the total number of users in the natural gas pipeline network.

[0178] In one embodiment, the gas pipeline segment risk identification index model includes any one or more of the following:

[0179]

[0180] Among them, Load n Load is the load factor of gas pipeline segment n. average Var represents the average load factor of all gas transmission pipeline segments in the natural gas pipeline network system. pipe The variance representing the load rate of the gas pipeline section in the system. This represents the actual positive gas transmission rate of gas pipeline segment n. This represents the upper limit of the positive transmission capacity of gas pipeline segment n. This represents the actual reverse flow rate of gas pipeline segment p. denoted as the upper limit of reverse transmission capacity for gas transmission pipeline segment n, where N is the number of gas transmission pipeline system components in the natural gas pipeline network system.

[0181] In one embodiment, the step of calculating the actual output of the system components in the natural gas pipeline network system using a preset optimization model based on the characteristic parameters of each system component to obtain the optimal result parameters includes:

[0182] The characteristic parameters of each system component are input into the solver, and the solver is used to calculate the optimal solution of the preset optimization model to determine the actual output of the system components in the natural gas pipeline network system and obtain the optimal result parameters.

[0183] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0184] This application 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0185] 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.

[0186] 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.

[0187] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0188] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0189] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0190] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0191] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for risk identification in a natural gas pipeline network system, characterized in that, include: The system acquires characteristic parameters of each system component in the natural gas pipeline network system in real time. The system component is the node and the edge between the node in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system. The actual unit node includes at least a gas source node, a user node, and an intermediate station node. The edge between the node is the gas transmission pipeline segment connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the user's planned gas demand. Based on the characteristic parameters of each system component, the actual output of the system components in the natural gas pipeline network system is calculated using a preset optimization model to obtain the optimal result parameters; Based on the optimal result parameters and the characteristic parameters of each system component, a preset risk identification model is used to identify the risks of the natural gas pipeline network system, and the identification results are obtained.

2. The risk identification method for natural gas pipeline network systems according to claim 1, characterized in that, Based on the characteristic parameters of each system component, a preset optimization model is used to calculate the actual output of the system components in the natural gas pipeline network system to obtain the optimal result parameters, including: Substitute the characteristic parameters of each system component into the objective function of the preset optimization model, and calculate the optimal result parameters while simultaneously satisfying the first and second constraints. The first constraint is the mass conservation constraint of each system component in the natural gas pipeline network system, and the second constraint is the production constraint of each system component in the natural gas pipeline network system.

3. The method for risk identification of natural gas pipeline network systems according to claim 2, characterized in that, The objective function of the preset optimization model is: max F total =βF safety +(1-β)F conomy ; in: F economy =Scale(Income total -Cost supply -Cost pipe ), F safety =α1f1+α2f2, F total To optimize the objective function value of the model, F safety For the safety target value, F conomy β represents the target benefit value, β is the safety impact factor, and Scale represents the scaling of the total revenue of the natural gas pipeline network system. total Cost represents the total revenue from gas sales of the natural gas pipeline network system. supply Cost represents the total cost of gas source procurement for the natural gas pipeline network system. pipe Let α1 be the total cost of pipeline transportation in the natural gas pipeline network system, f1 be the weight coefficient of the gas transmission pipeline system components, α2 be the weight coefficient of the user system components, and f2 be the sub-objective function of the user system components. This represents the actual positive gas transmission rate of gas pipeline segment n. This represents the upper limit of the positive transmission capacity of gas pipeline segment n. This represents the actual reverse flow rate of gas pipeline segment p. λ represents the upper limit of reverse transmission capacity for gas transmission segment n, where N is the number of gas transmission segment system components in the natural gas pipeline network system; i The gas supply priority for user i, The planned gas demand for user i. Let I be the actual gas intake of user i, and let I be the total number of users in the natural gas pipeline network.

4. The risk identification method for natural gas pipeline network systems according to claim 1, characterized in that, The optimal result parameters include the actual gas intake of each user and the actual gas transmission volume of each gas pipeline segment, and the preset risk identification model includes the gas pipeline segment risk identification index model and the user risk identification index model. Based on the optimal result parameters and the characteristic parameters of each system component, a pre-set risk identification model is used to identify risks in the natural gas pipeline network system, and the identification results are obtained, including: Based on the actual gas intake of each user and the characteristic parameters of each user, the user risk index is calculated using the user risk identification index model, and the user risk assessment result is determined based on the user risk index. Based on the actual transmission volume of each gas pipeline segment and the characteristic parameters of each gas pipeline segment, the risk index of the gas pipeline segment is calculated using the risk identification index model of the gas pipeline segment, and the risk assessment result of the gas pipeline segment is determined based on the risk index of the gas pipeline segment. Based on the user risk assessment results and the gas pipeline section risk assessment results, the identification results are obtained.

5. The natural gas pipeline network system risk identification method according to claim 4, characterized in that, The user risk identification indicator model includes one or more of the following: Among them, Sat i Sat is the gas supply satisfaction rate for user i. average Var is the average gas supply satisfaction rate for all users in the natural gas pipeline network system. demand This represents the variance of the gas supply satisfaction rate for all users in the natural gas pipeline network system. The planned gas demand for user i. Let I be the actual gas intake of user i, and let I be the total number of users in the natural gas pipeline network.

6. The method for risk identification of a natural gas pipeline network system according to claim 4, characterized in that, The risk identification index model for the gas pipeline segment includes one or more of the following: Among them, Load n Load is the load factor of gas pipeline segment n. average Var represents the average load factor of all gas transmission pipeline segments in the natural gas pipeline network system. pipe The variance representing the load rate of the gas pipeline section in the system. This represents the actual positive gas transmission rate of gas pipeline segment n. This represents the upper limit of the positive transmission capacity of gas pipeline segment n. This represents the actual reverse flow rate of gas pipeline segment p. denoted as the upper limit of reverse transmission capacity for gas transmission pipeline segment n, where N is the number of gas transmission pipeline system components in the natural gas pipeline network system.

7. The method for risk identification of natural gas pipeline network systems according to claim 1, characterized in that, Based on the characteristic parameters of each system component, a preset optimization model is used to calculate the actual output of the system components in the natural gas pipeline network system to obtain the optimal result parameters, including: The characteristic parameters of each system component are input into the solver, and the solver is used to calculate the optimal solution of the preset optimization model to determine the actual output of the system components in the natural gas pipeline network system and obtain the optimal result parameters.

8. A risk identification device for a natural gas pipeline network system, characterized in that, include: The acquisition module is used to acquire the characteristic parameters of each system component in the natural gas pipeline network system in real time. The system component is the node and the edge between the node in the topology of the natural gas pipeline network system. Each node is an actual unit node in the natural gas pipeline network system. The actual unit node includes at least a gas source node, a user node, and an intermediate station node. The edge between the node is the gas transmission pipeline segment connecting each actual unit node in the natural gas pipeline network system. The characteristic parameters of each system component include at least the upper and lower limits of the transmission capacity of the gas transmission pipeline segment and the user's planned gas demand. The calculation module is used to calculate the actual output of the system components in the natural gas pipeline network system based on the characteristic parameters of each system component and using a preset optimization model to obtain the optimal result parameters. The identification module is used to identify risks in the natural gas pipeline system based on the optimal result parameters and the characteristic parameters of each system component, using a preset risk identification model, and to obtain the identification results.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; A memory connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the natural gas pipeline network system risk identification method according to any one of claims 1 to 7 by executing the instructions stored in the memory.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the natural gas pipeline network system risk identification method according to any one of claims 1 to 7.

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