Urban gas network monitoring point optimization layout method suitable for multi-pressure level system

Abstract

The application discloses a town gas pipe network monitoring point optimization layout method suitable for multi-pressure level systems, relates to the technical field of gas pipe network monitoring, and comprises the following steps: collecting pipe network basic information, and selecting candidate monitoring points according to node importance; constructing a monitoring point-leakage event sensing matrix and a monitoring point multi-objective optimization model; solving the monitoring point multi-objective optimization model by using a genetic algorithm to obtain a Pareto optimal solution set of the monitoring point layout scheme; calculating the comprehensive scores of each monitoring point layout scheme according to multi-dimensional evaluation indexes, and selecting the scheme with the highest comprehensive score as the final monitoring point layout scheme of the multi-pressure level system gas pipe network; the method is based on the sensing conditions of the candidate monitoring points on different leakage events, the multi-objective optimization model is solved by using the genetic algorithm, the multi-dimensional evaluation indexes are combined to select the comprehensive optimal monitoring point layout scheme, the global optimization of the monitoring point layout is realized, and the reliability and accuracy of the monitoring of the multi-pressure level system gas pipe network are improved.

Description

Technical Field

[0001] This invention relates to the field of gas pipeline network monitoring technology, and more specifically, to a method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels. Background Technology

[0002] As a core infrastructure for urban energy supply, the safe and stable operation of urban gas pipeline networks is directly related to residents' lives and public safety. With the acceleration of urbanization, gas pipeline networks have gradually formed a complex network structure with multiple pressure levels, including high pressure, sub-high pressure, medium pressure, and low pressure. The expanding network coverage and increasingly prominent aging pipeline problems have led to a rise in the risk of gas leaks. To promptly detect and handle leaks, the scientific placement of monitoring points at key locations within the pipeline network has become an important means of ensuring network safety.

[0003] In existing technologies, the layout methods for gas pipeline monitoring points mainly rely on empirical judgment or simplified models. The core ideas include: selecting key nodes (such as valves or pressure regulating stations) based on the pipeline topology to set monitoring points, as in Chinese patent CN114562684A, which selects nodes with weak correlation based on the pipeline topology; determining the location of monitoring points through a single indicator (such as pipeline flow or pressure fluctuation); and using simple optimization algorithms to layout monitoring points, as in Chinese patent CN116796473A, which uses a greedy algorithm to optimize and solve the monitoring point layout scheme. Such monitoring point layout schemes often ignore the multi-pressure level characteristics of gas pipelines, leading to large monitoring blind spots and monitoring lag problems. Furthermore, the optimization objectives of traditional monitoring point layout optimization methods are mostly focused on maximizing monitoring coverage or minimizing the number of monitoring points. These methods often result in redundant monitoring point layouts, low economic efficiency, or uneven distribution of monitoring points, leading to low monitoring sensitivity, coverage, and / or effectiveness. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized layout method for monitoring points in urban gas pipeline networks with multiple pressure levels, in order to solve the problems mentioned in the background art, such as uneven layout of monitoring points in gas pipeline networks with multiple pressure levels, resulting in low sensitivity to leakage events and large monitoring blind spots.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels includes the following steps:

[0007] Collect basic information on the multi-pressure level gas pipeline network, select characteristic nodes of the pipeline network based on the basic information, calculate the node importance of each characteristic node, and select candidate monitoring points based on the node importance of each characteristic node.

[0008] A leakage event database is established based on the pipeline network basic information. A monitoring point-leakage event perception matrix is ​​constructed based on the leakage event database and the pressure change value of each of the candidate monitoring points for each leakage event.

[0009] Based on the monitoring point-leakage event perception matrix, a multi-objective optimization model for monitoring points of multi-pressure level gas pipeline networks is established with the optimization objectives of minimizing the detectable leak aperture and maximizing the leakage event monitoring coverage.

[0010] Based on the genetic algorithm, using the monitoring point-leakage event perception matrix as the input of the genetic algorithm, the multi-objective optimization model of the multi-pressure level gas pipeline network monitoring points is solved to obtain the Pareto optimal solution set of the monitoring point layout scheme;

[0011] Calculate the multi-dimensional evaluation index of each monitoring point layout scheme in the Pareto optimal solution set of the monitoring point layout scheme, calculate the evaluation weight of each multi-dimensional evaluation index, and calculate the comprehensive score of each monitoring point layout scheme in the Pareto optimal solution set of the monitoring point layout scheme based on the multi-dimensional evaluation index and the evaluation weight of each multi-dimensional evaluation index. Select the monitoring point layout scheme with the highest comprehensive score as the final multi-pressure level gas pipeline network monitoring point layout scheme.

[0012] The principle of this invention, applicable to the optimized layout method of monitoring points in urban gas pipeline networks with multiple pressure levels, is as follows: Based on the basic information of the multi-pressure-level gas pipeline network, characteristic nodes of the pipeline network are selected. Key characteristic nodes are screened as candidate monitoring points through quantification of node importance, reducing the problems of ignoring pressure levels and subjectivity / one-sidedness inherent in experience-based monitoring point selection. A monitoring point-leakage event perception matrix is ​​constructed to quantify the monitoring response of each candidate monitoring point to different leakage scenarios, providing data support for subsequent optimization model solving. A multi-point monitoring system is established with the dual optimization objectives of minimizing the detectable leak aperture and maximizing the leakage event monitoring coverage. The system optimizes the monitoring model to balance monitoring accuracy and coverage, reducing the problems of redundant or unevenly distributed monitoring points caused by a single optimization objective. It uses a genetic algorithm to solve a multi-objective optimization model, rapidly searching for and approximating the Pareto optimal solution among numerous monitoring point layout schemes, forming a Pareto optimal solution set, thus reducing the imbalance in monitoring point layout caused by local optima. Furthermore, it employs multi-dimensional evaluation indicators to weightedly score different alternative monitoring point layout schemes, selecting the overall optimal scheme, balancing monitoring efficiency and implementation cost, and overcoming the problems of low monitoring sensitivity and large blind spots caused by uneven monitoring point distribution in traditional methods.

[0013] Preferably, to enhance the representativeness and uniformity of the candidate monitoring point selection results and reduce monitoring blind spots, the method for selecting the candidate monitoring points specifically includes the following steps:

[0014] Collect the pipeline topology and basic information of the multi-pressure level gas pipeline network. The basic information includes: the length, diameter, age, surrounding environmental information along the pipeline segment, flow rate and pressure of nodes, and location information of pipeline valve wells in the multi-pressure level gas pipeline network.

[0015] Based on the basic pipeline information, characteristic nodes of the pipeline network are selected. The importance of each characteristic node is calculated according to the node importance evaluation method. Pipeline characteristic nodes with a node importance greater than a preset node importance threshold are selected as candidate monitoring points. Each candidate monitoring point is numbered. The characteristic nodes of the pipeline network include: gas pipeline gate stations, regional pressure regulating stations, user connection points, and valve wells.

[0016] Based on the pipeline pressure level system, the multi-pressure level gas pipeline network is divided into regions to obtain the first division region, the second division region, and so on up to the Nth division region, where N is an integer and N≥2. The region boundary and the number of pipe segments in each pipeline division region are obtained respectively.

[0017] The candidate monitoring points, after being numbered, are classified according to their respective regions to obtain a list of candidate monitoring point regional classifications.

[0018] Preferably, to enhance the objectivity and comprehensiveness of the node importance calculation results, the node importance evaluation method specifically includes the following steps:

[0019] Calculate the basic evaluation index of each characteristic node of the pipeline network, which includes: node degree centrality, pipe diameter, node pressure, node flow rate, pipe age, and pipe surrounding environment level.

[0020] A three-level hierarchical association structure is established, including: target layer, dimension layer, and evaluation index layer. The target layer includes the importance of nodes in the gas pipeline network. The dimension layer includes: pipeline network structure dimension, pipeline network hydraulic dimension, and pipeline network risk dimension. The evaluation index layer includes the basic evaluation indexes of the nodes. The basic evaluation indexes of the nodes are classified according to their respective dimensions to obtain the hierarchical association structure diagram of node importance.

[0021] Based on the Analytic Hierarchy Process (AHP), the relative importance of each dimension in the dimension layer is scored according to the FAHP evaluation scale to obtain a first score result. The relative importance of each basic evaluation indicator in the evaluation index layer is scored to obtain a second score result. Based on the first score result, the first weight value of each dimension in the dimension layer is calculated. Based on the second score result, the second weight value of each basic evaluation indicator in the evaluation index layer is calculated. Based on the second weight value of each basic evaluation indicator in the node and the first weight value of its respective dimension, the third weight value of each basic evaluation indicator in the node is calculated.

[0022] The importance of each pipeline feature node is calculated based on its basic evaluation index and the third weight value of each basic evaluation index.

[0023] Preferably, to accurately quantify the coreness of characteristic nodes in the pipeline network topology, the formula for calculating the node degree centrality is as follows:

[0024] ;

[0025] In the formula, For the first Degree centrality of characteristic nodes in a pipeline network For the first The number of edges connecting a network feature node to other network feature nodes. This refers to the total number of characteristic nodes in the pipeline network.

[0026] To comprehensively weigh and quantify the impact of different basic evaluation indicators on node importance, the formula for calculating node importance is as follows:

[0027] ;

[0028] In the formula, For the first The importance of each characteristic node in the pipeline network. , , , and The first The pipe diameter, node pressure, node flow rate, pipe age, and surrounding environmental level of each characteristic node in the pipeline network. , , , , and These are the third weight values ​​for nodal centrality, pipe diameter, nodal pressure, nodal flow, pipe age, and the surrounding environmental level.

[0029] Preferably, to quantify the response capability of each candidate monitoring point to different leakage events, the specific steps for constructing the monitoring point-leakage event perception matrix include:

[0030] Based on the pipeline topology and basic pipeline information, a transient simulation model of a multi-pressure-level gas pipeline network is established.

[0031] Leakage aperture, leakage location, and gas source pressure are taken as key influencing factors of leakage scenarios. Samples are extracted and combined for each of the key influencing factors of leakage scenarios to form a variety of different pipeline leakage scenarios, so as to accurately simulate different leakage scenarios that may occur in real working conditions.

[0032] The dynamic disturbance parameters of the leakage characteristic parameters are obtained by analyzing the change law of leakage characteristic parameters over time when a pipeline leakage event occurs. The leakage characteristic parameters include the leakage orifice diameter.

[0033] The dynamic disturbance parameters are input as dynamic boundary conditions into the transient simulation model of the multi-pressure-level gas pipeline network. The gas flow equation of the unsteady process is solved iteratively according to a preset time step. During the iterative solution process, the pressure and flow parameters of each candidate monitoring point are updated step by step. After the iterative solution is completed, the time series data of the pressure and flow parameters of each candidate monitoring point are obtained. Based on the time series data, the pressure change value of each candidate monitoring point when each pipeline leakage scenario occurs is calculated. Each pipeline leakage scenario is combined with the pressure change value of each candidate monitoring point when the pipeline leakage scenario occurs to obtain a scenario-based leakage event database. The scenario-based leakage event database intuitively reflects the real response characteristics of different candidate monitoring points to different leakage events.

[0034] Calculate the leakage perception threshold, and based on the scenario-based leakage event database and the leakage perception threshold, determine the perception status of each candidate monitoring point for each pipeline leakage scenario, and construct a monitoring point-leakage event perception matrix. This quantifies the abstract perception capability of the monitoring point for the leakage scenario into specific logical values ​​in the monitoring point-leakage event perception matrix, providing a real numerical basis for the subsequent genetic algorithm solution.

[0035] Preferably, to improve the scientific rigor and objectivity of the leak detection threshold setting results, the leak detection threshold is calculated as follows: Historical monitoring data from existing monitoring points in the multi-pressure-level gas pipeline network is obtained; based on this historical monitoring data, the mean and standard deviation of pressure fluctuation values ​​within each pipeline network division area are calculated; and the leak detection threshold for candidate monitoring points within each pipeline network division area is set according to the following expression:

[0036] ;

[0037] In the formula, Let the leakage detection threshold be the threshold value for the candidate monitoring points within the j-th division area of ​​the pipeline network. and Let be the mean and standard deviation of pressure fluctuations within the j-th segment of the pipeline network, respectively. According to the 3σ principle in statistics, under normal operating conditions, random pressure fluctuations in the pipeline network have a 99.73% probability of being concentrated within three standard deviations, with only a very small number of normal fluctuations exceeding this range. Therefore, the leakage detection threshold is set to... It can effectively distinguish between random pressure fluctuations during normal pipeline operation and abnormal pressure changes during leakage events, thereby reducing false alarms and missed alarms of leakage events at monitoring points.

[0038] Preferably, to quantify the optimization objective of minimizing the detectable leak pore size, the optimization objective of minimizing the detectable leak pore size is expressed as follows:

[0039] ;

[0040] In the formula, For monitoring leak pore diameter, unit: mm. The number of regions is the result of dividing the multi-pressure-level gas pipeline network into regions based on the pipeline pressure level system. For the first The total number of pipe segments within the divided area. For the first The first in the divided area Minimum detectable leak diameter of a section of pipeline, in mm;

[0041] To quantify the optimization objective of maximizing leak event monitoring coverage, the expression for the optimization objective of maximizing leak event monitoring coverage is as follows:

[0042] ;

[0043] ;

[0044] In the formula, To improve the coverage of leak event monitoring, For the first The total number of leakage incidents in each region For the first The first division of the region Leakage incident, For the first The total number of candidate monitoring points for the divided areas; To ensure effective monitoring of leakage events, the optimized monitoring point layout scheme guarantees that at least two monitoring points can accurately monitor each type of leakage event, thus preventing missed reporting of leakage events due to the failure of a single monitoring point. Indicates the first The first division of the region Can the candidate monitoring points detect the first [unclear] in this area? Leakage incident, if possible, If not, then ; To constrain the selection of candidate monitoring point zones, this ensures that in the optimized monitoring point layout scheme, each pipeline division zone can be allocated a reasonable number of monitoring points according to its own monitoring needs. This reduces cost waste or decreased monitoring efficiency caused by excessive concentration or insufficient configuration of monitoring points in certain division zones. The first division of the region If one of the alternative monitoring points is selected, then If the first The first division of the region If none of the alternative monitoring points are selected, then .

[0045] Preferably, to accurately and efficiently solve the multi-objective optimization model while reducing the limitation of getting trapped in local optima during the solution process, the genetic algorithm adopts an improved genetic algorithm, specifically including the following steps:

[0046] Chromosomes are constructed using integer encoding, with one candidate monitoring point number as one gene locus and one monitoring point layout scheme as one chromosome. Multiple chromosomes are randomly generated to form the initial population.

[0047] Based on the monitoring point-leakage event perception matrix, chromosomes in the initial population that satisfy the effective monitoring constraints of the leakage event and the partition selection constraints of the candidate monitoring points are selected to form the parent population. The fitness value of each chromosome in the parent population is calculated, and the expression for calculating the fitness value is as follows:

[0048] ;

[0049] In the formula, This is the fitness value; To enable monitoring of leak orifice diameter; To improve the coverage of leak event monitoring

[0050] Based on the fitness value of each chromosome in the parent population, two chromosomes in the parent population are selected as parent chromosomes. Gene sequence crossover is performed on the two parent chromosomes to obtain two initial daughter chromosomes. The gene loci of the two initial daughter chromosomes are randomly mutated according to a preset mutation rate to obtain two first candidate daughter chromosomes. The chromosome that satisfies the effective monitoring constraint of the leakage event and the partition selection constraint of the candidate monitoring point is selected as the second candidate daughter chromosome. The fitness value of the second candidate daughter chromosome is calculated. The second candidate daughter chromosome is introduced into the parent population to obtain the first parent population after the first iteration.

[0051] The Zth parent population is generated iteratively according to the preset number of iterations Z, and the Zth parent population is output as the Pareto optimal solution set of the monitoring point layout scheme.

[0052] Preferably, to ensure the effectiveness and efficiency of the improved genetic algorithm and avoid generating invalid solutions where the same candidate monitoring point is selected multiple times, in the chromosome construction step using integer encoding, the values ​​of each gene position in each constructed chromosome are not equal, and the values ​​of each gene position in the initial offspring chromosome and the first candidate offspring chromosome are not equal.

[0053] Preferably, to establish a multi-dimensional evaluation system that balances monitoring efficiency and practical engineering applications, the multi-dimensional evaluation indicators include: leakage event monitoring coverage, the number of monitoring points corresponding to the monitoring point layout scheme, and the difficulty of modifying the layout scheme. The formula for calculating the difficulty of modifying the layout scheme is as follows:

[0054] ;

[0055] In the formula, Due to the difficulty of modifying the layout plan, This refers to the number of newly added monitoring points in the monitoring point layout plan. The number of existing monitoring points retained in the monitoring point layout plan;

[0056] To quantify the evaluation results of the multi-dimensional evaluation indicators, the comprehensive score of the monitoring point layout scheme is calculated as follows:

[0057] ;

[0058] In the formula, The overall score for the monitoring point layout plan, To improve the coverage of leak event monitoring, The number of monitoring points corresponding to the monitoring point layout plan. , and The scores are weighted by the comprehensive scores of leakage event monitoring coverage, the number of monitoring points corresponding to the monitoring point layout plan, and the difficulty of modifying the layout plan.

[0059] One or more technical solutions provided by this invention have at least the following technical effects or advantages:

[0060] 1. Based on the perception of different leakage events by candidate monitoring points, a multi-objective optimization model is solved by genetic algorithm, and the optimal monitoring point layout scheme is selected by combining multi-dimensional evaluation indicators. This achieves global optimization of the monitoring point layout, improves the reliability and accuracy of leakage monitoring of gas pipelines under multiple pressure levels, and overcomes the problems of low monitoring sensitivity and large monitoring blind spots in traditional monitoring point layout methods.

[0061] 2. Select candidate monitoring points by evaluating the importance of nodes, and improve the adaptability of the selection results to multi-pressure-level pipeline networks by dividing the regions and classifying the candidate monitoring points by region, thereby reducing the problem of uneven distribution of monitoring points in different pressure division regions;

[0062] 3. By establishing a hierarchical relationship structure through the Analytic Hierarchy Process (FAHP), the influence of different evaluation indicators and their respective dimensions on the overall weight is comprehensively considered. This reduces the limitations of using a single evaluation indicator or selecting candidate monitoring points based on experience, and improves the globality, scientificity, and credibility of the node importance evaluation method.

[0063] 4. By clarifying the calculation method of node importance through a weighted formula, the accuracy, operability, and stability of the node importance evaluation method are improved;

[0064] 5. Based on the transient simulation model of multi-pressure level gas pipeline network and diverse leakage scenarios, dynamic leakage simulation is carried out to calculate the response characteristics of different candidate monitoring points to different leakage events, so as to enhance the realism and reliability of the scenario-based leakage event database, thereby improving the objectivity and realism of the monitoring point-leakage event perception matrix constructed based on the scenario-based leakage event database.

[0065] 6. Calculate and set leakage detection thresholds based on statistical patterns of historical pipeline monitoring data to improve the scientificity and objectivity of leakage detection threshold setting, and provide a unified judgment standard that fits the actual operating patterns of monitoring points for the construction of the monitoring point-leakage event perception matrix;

[0066] 7. By constructing a mathematical model that minimizes the detectable leakage aperture and maximizes the coverage of leakage event monitoring, the multi-objective optimization model is made mathematically solvable. By setting dual constraints, the effectiveness of the solution results of the multi-objective optimization model is ensured.

[0067] 8. By constructing an improved genetic algorithm adapted to multi-objective optimization models, the solution speed is increased and the limitation of getting trapped in local optima during the solution process is reduced;

[0068] 9. By limiting the uniqueness of gene loci in chromosomes, invalid solutions are avoided, thereby improving the effectiveness and reliability of the Pareto optimal solution set for monitoring point layout schemes;

[0069] 10. By constructing a weighted scoring formula for multi-dimensional evaluation indicators, the monitoring efficiency of the monitoring point layout scheme and the actual engineering application are taken into account. At the same time, objective values ​​are assigned to different monitoring point layout schemes to ensure the fairness and repeatability of the selection of the best monitoring point layout scheme. Attached Figure Description

[0070] The accompanying drawings, which are provided to further illustrate embodiments of the invention and constitute a part of this invention, are not intended to limit the scope of the invention.

[0071] Figure 1 This is a flowchart illustrating the method for optimizing the layout of monitoring points in urban gas pipeline networks with multiple pressure levels, as described in this invention.

[0072] Figure 2 This is a flowchart illustrating the application of the multi-pressure-level gas pipeline monitoring point optimization layout method in a specific application according to Embodiment 1 of the present invention.

[0073] Figure 3 This is a hierarchical association structure diagram of the node importance of characteristic nodes in the pipeline network in Embodiment 1 of the present invention;

[0074] Figure 4 This is a topology diagram of a multi-pressure-level gas pipeline network in Embodiment 2 of the present invention;

[0075] Figure 5 This is a layout diagram of monitoring points after the optimized layout of a multi-pressure level gas pipeline network in Embodiment 2 of the present invention. Detailed Implementation

[0076] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, where there is no conflict, the embodiments of the present invention and the features thereof can be combined with each other.

[0077] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0078] Example 1

[0079] Please refer to Figure 1-3 This invention provides a method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, comprising the following steps:

[0080] Collect basic information on the gas pipeline network with multiple pressure levels, select characteristic nodes of the pipeline network based on the basic information, calculate the importance of each characteristic node, select candidate monitoring points based on the importance of each characteristic node, and perform preliminary screening of the characteristic nodes of the pipeline network by means of node importance, so as to initially narrow down the layout range of monitoring points and reduce the amount of calculation in subsequent screening work.

[0081] A leakage event database is established based on the pipeline network basic information. A monitoring point-leakage event perception matrix is ​​constructed based on the leakage event database and the pressure change value of each of the candidate monitoring points for each leakage event.

[0082] Based on the monitoring point-leakage event perception matrix, a multi-objective optimization model for monitoring points of gas pipeline networks with multiple pressure levels is established with the optimization objectives of minimizing the detectable leakage aperture and maximizing the leakage event monitoring coverage. By setting two optimization objectives, the contradiction between monitoring accuracy and coverage is resolved and the global optimization of the monitoring point layout is achieved, thereby overcoming the resource waste caused by redundant monitoring point layout due to a single optimization objective, or the false detection and missed detection caused by uneven distribution of monitoring points.

[0083] Based on the genetic algorithm, using the monitoring point-leakage event perception matrix as the input of the genetic algorithm, the multi-objective optimization model of the multi-pressure level gas pipeline network monitoring points is solved to obtain the Pareto optimal solution set of the monitoring point layout scheme;

[0084] Calculate the multi-dimensional evaluation index of each monitoring point layout scheme in the Pareto optimal solution set of the monitoring point layout scheme, calculate the evaluation weight of each multi-dimensional evaluation index, and calculate the comprehensive score of each monitoring point layout scheme in the Pareto optimal solution set of the monitoring point layout scheme based on the multi-dimensional evaluation index and the evaluation weight of each multi-dimensional evaluation index. Select the monitoring point layout scheme with the highest comprehensive score as the final multi-pressure level gas pipeline network monitoring point layout scheme.

[0085] The method for selecting the candidate monitoring points is as follows:

[0086] Collect the pipeline topology and basic information of the multi-pressure level gas pipeline network. The basic information includes: the length, diameter, age, surrounding environment information along the pipeline, flow rate and pressure of nodes, and location information of pipeline valve wells in each pipe section of the multi-pressure level gas pipeline network.

[0087] Based on the basic pipeline information, characteristic nodes of the pipeline network are selected. The importance of each characteristic node is calculated according to the node importance evaluation method. Pipeline characteristic nodes with a node importance greater than a preset node importance threshold are selected as candidate monitoring points. Each candidate monitoring point is numbered. The characteristic nodes of the pipeline network include: gas pipeline gate stations, regional pressure regulating stations, user connection points, and valve wells.

[0088] Based on the pipeline pressure rating system, the multi-pressure-rated gas pipeline network is divided into regions, resulting in a first region, a second region, and so on up to the Nth region, where N is an integer. Obtain the area boundary and the number of pipe segments in each pipe network division area;

[0089] The candidate monitoring points, after being numbered, are classified according to their respective regions to obtain a list of candidate monitoring point regional classifications.

[0090] The node importance evaluation method specifically includes the following steps:

[0091] Calculate the basic evaluation index of each characteristic node of the pipeline network, which includes: node degree centrality, pipe diameter, node pressure, node flow rate, pipe age, and pipe surrounding environment level.

[0092] A three-level hierarchical relationship structure is established, including: a target layer, a dimension layer, and an evaluation index layer. The target layer includes the importance of nodes in the gas pipeline network; the dimension layer includes: pipeline network structure dimension, pipeline network hydraulic dimension, and pipeline network risk dimension; and the evaluation index layer includes the basic evaluation indicators for each node. These basic evaluation indicators are then categorized according to their respective dimensions, resulting in the following: Figure 3 The diagram shows the hierarchical relationship structure of node importance.

[0093] Based on the Analytic Hierarchy Process (AHP), the relative importance of each dimension in the dimension layer is scored according to the FAHP evaluation scale to obtain a first score result. The relative importance of each basic evaluation indicator in the evaluation index layer is scored to obtain a second score result. Based on the first score result, the first weight value of each dimension in the dimension layer is calculated. Based on the second score result, the second weight value of each basic evaluation indicator in the evaluation index layer is calculated. Based on the second weight value of each basic evaluation indicator and the first weight value of its respective dimension, the third weight value of each basic evaluation indicator is calculated, resulting in the node basic evaluation indicator weight table shown in Table 1.

[0094] Table 1. Weights of Basic Evaluation Indicators for Nodes

[0095]

[0096] The importance of each pipeline feature node is calculated based on its basic evaluation index and the third weight value of each basic evaluation index.

[0097] The formula for calculating the degree centrality of a node is as follows:

[0098] ;

[0099] In the formula, For the first Degree centrality of characteristic nodes in a pipeline network For the first The number of edges connecting a network feature node to other network feature nodes. This refers to the total number of characteristic nodes in the pipeline network.

[0100] The values ​​of pipe diameter and pipe age are obtained from the basic information of the pipeline network. The values ​​of node pressure and node flow are calculated from the transient simulation model of the multi-pressure level gas pipeline network. The environmental level around the pipeline is divided into 5 levels based on the information of the surrounding environment along the pipeline, according to the standard GB 50251-2015 "Code for Design of Gas Transmission Pipeline Engineering". The values ​​are assigned to 1, 2, 3, 4 and 5 respectively.

[0101] The formula for calculating the importance of a node is as follows:

[0102] ;

[0103] In the formula, For the first The importance of each characteristic node in the pipeline network. , , , and The first Pipe diameter, node pressure, node flow rate, pipe age, and surrounding environmental level of each characteristic node in the pipeline network.

[0104] The preset node importance threshold is set according to the node importance of the pipeline feature nodes in each division area to ensure that a sufficient number of pipeline feature nodes in each division area are selected as candidate monitoring points.

[0105] The specific steps for constructing the monitoring point-leakage event perception matrix include:

[0106] Based on the pipeline topology and basic pipeline information, a transient simulation model of a multi-pressure-level gas pipeline network is established.

[0107] Leakage aperture, leakage location, and gas source pressure are taken as key influencing factors of leakage scenarios. Samples are extracted and combined for each of the key influencing factors of leakage scenarios to form a variety of different pipeline leakage scenarios.

[0108] The dynamic disturbance parameters of the leakage characteristic parameters are obtained by analyzing the change law of leakage characteristic parameters over time when a pipeline leakage event occurs. The leakage characteristic parameters include the leakage orifice diameter.

[0109] The dynamic disturbance parameters are input as dynamic boundary conditions into the transient simulation model of the multi-pressure-level gas pipeline network. The gas flow equation of the unsteady process is solved iteratively according to a preset time step. During the iterative solution process, the pressure and flow parameters of each candidate monitoring point are updated step by step. After the iterative solution is completed, the time series data of the pressure and flow parameters of each candidate monitoring point are obtained. The pressure change value of each candidate monitoring point when each pipeline leakage scenario occurs is calculated based on the time series data. Each pipeline leakage scenario is combined with the pressure change value of each candidate monitoring point when the pipeline leakage scenario occurs to obtain a scenario-based leakage event database.

[0110] The leakage detection threshold H is calculated as follows: Historical monitoring data from existing monitoring points in the multi-pressure-level gas pipeline network is obtained. Based on this data, the mean and standard deviation of pressure fluctuation values ​​within each pipeline network's defined area are calculated. The leakage detection threshold H for candidate monitoring points within each pipeline network's defined area is set according to the following expression:

[0111] ;

[0112] In the formula, Let the leakage detection threshold be the threshold value for the candidate monitoring points within the j-th division area of ​​the pipeline network. and These are the mean and standard deviation of the pressure fluctuation values ​​within the j-th division area of ​​the pipeline network, respectively;

[0113] Based on the scenario-based leak event database and the leak perception threshold H, the perception status of each candidate monitoring point for each pipeline leak scenario is determined, and a monitoring point-leak event perception matrix E is constructed. Specifically, for a leak event a, if the pressure change value of a candidate monitoring point b exceeds the leak perception threshold H of the area to which candidate monitoring point b belongs, then the E in the monitoring point-leak event perception matrix E is adjusted. ab A value of 1 indicates that candidate monitoring point b can detect leakage event a, while a value of 0 indicates that candidate monitoring point b cannot detect leakage event a.

[0114] The optimization objective expression for minimizing the detectable leakage orifice diameter is as follows:

[0115] ;

[0116] In the formula, For monitoring leak pore diameter, unit: mm. The number of regions is the result of dividing the multi-pressure-level gas pipeline network into regions based on the pipeline pressure level system. For the first The total number of pipe segments within the divided area. For the first The first in the divided area Minimum detectable leak diameter of a section of pipeline, in mm;

[0117] The optimization objective expression for maximizing the coverage of the leakage event monitoring is:

[0118] ;

[0119] ;

[0120] In the formula, To improve the coverage of leak event monitoring, For the first The total number of leakage incidents in each region For the first The first division of the region Leakage incident, For the first The total number of candidate monitoring points for the divided areas; To effectively monitor and constrain leakage incidents, Indicates the first The first division of the region Can the candidate monitoring points detect the first [unclear] in this area? Leakage incident, if possible, If not, then ; For the selection of candidate monitoring points by zone, if the first... The first division of the region If one of the alternative monitoring points is selected, then If the first The first division of the region If none of the alternative monitoring points are selected, then .

[0121] The genetic algorithm mentioned herein is an improved genetic algorithm, specifically including the following steps:

[0122] Chromosomes are constructed using integer encoding, with each candidate monitoring point number representing a gene locus and each monitoring point layout scheme representing a chromosome. Multiple chromosomes are randomly generated to form the initial population. The values ​​of each gene locus in each generated chromosome are distinct to ensure the effectiveness of the generated candidate monitoring point layout schemes.

[0123] Based on the monitoring point-leakage event perception matrix E, chromosomes in the initial population that satisfy the effective monitoring constraints of the leakage event and the partition selection constraints of the candidate monitoring points are selected to form the parent population. The fitness value of each chromosome in the parent population is calculated, and the expression for calculating the fitness value is as follows:

[0124] ;

[0125] In the formula, This is the fitness value; To enable monitoring of leak orifice diameter; To improve the coverage of leak event monitoring

[0126] Based on the fitness value of each chromosome in the parent population, two chromosomes are selected as parent chromosomes. Gene sequence crossover is performed on the two parent chromosomes to obtain two initial daughter chromosomes. The gene loci of each of the two initial daughter chromosomes are randomly mutated according to a preset mutation rate to obtain two first candidate daughter chromosomes. The values ​​of each gene locus in the initial daughter chromosomes and the first candidate daughter chromosomes are not equal to ensure the effectiveness of the generated candidate monitoring point layout scheme. The chromosome among the two first candidate daughter chromosomes that satisfies the effective monitoring constraint of the leakage event and the candidate monitoring point partitioning constraint is selected as the second candidate daughter chromosome. The fitness value of the second candidate daughter chromosome is calculated. The second candidate daughter chromosome is introduced into the parent population, and the chromosome with the lowest fitness value in the parent population after the introduction of the second candidate daughter chromosome is removed to ensure that the total number of chromosomes in the parent population remains unchanged, resulting in the first parent population after the first iteration.

[0127] The Zth parent population is generated iteratively according to the preset number of iterations Z, and the Zth parent population is output as the Pareto optimal solution set of the monitoring point layout scheme.

[0128] The multi-dimensional evaluation indicators include: leakage event monitoring coverage, the number of monitoring points corresponding to the monitoring point layout scheme, and the difficulty of modifying the layout scheme. The formula for calculating the difficulty of modifying the layout scheme is as follows:

[0129] ;

[0130] In the formula, Due to the difficulty of modifying the layout plan, This refers to the number of newly added monitoring points in the monitoring point layout plan. The number of existing monitoring points retained in the monitoring point layout plan;

[0131] The formula for calculating the comprehensive score of the monitoring point layout scheme is as follows:

[0132] ;

[0133] In the formula, The overall score for the monitoring point layout plan, To improve the coverage of leak event monitoring, The number of monitoring points corresponding to the monitoring point layout plan. , and The scores are weighted by the comprehensive scores of leakage event monitoring coverage, the number of monitoring points corresponding to the monitoring point layout plan, and the difficulty of modifying the layout plan.

[0134] Example 2

[0135] Based on Example 1, Example 2 will be described and illustrated with specific implementation cases.

[0136] by Figure 4 The topology diagram of a multi-pressure-level gas pipeline network shown is the optimization target. The specific optimization process is as follows:

[0137] First, the basic information of the multi-pressure-level gas pipeline network was collected. Based on the pressure level, the multi-pressure-level gas pipeline network was divided into three independent regions: the first sub-high pressure region, the second sub-high pressure region, and the third medium pressure region. The divided multi-pressure-level gas pipeline network contains a total of 54 pipeline segments. Based on the node importance evaluation method, pipeline feature nodes with an importance greater than 0.6 were selected as candidate monitoring points. A total of 50 candidate monitoring points were selected and numbered J1 to J50. The node basic evaluation indicators and node importance of the above 50 candidate monitoring points were constructed into the candidate monitoring point basic information list shown in Table 2.

[0138] Table 2 List of basic information of candidate monitoring points

[0139]

[0140] The 50 candidate monitoring points were categorized according to their respective independent regions, resulting in the candidate monitoring point region classification list shown in Table 3:

[0141] Table 3. List of Candidate Monitoring Sites by Region

[0142]

[0143] Based on the collected pipeline network information, a transient simulation model of the multi-pressure-level gas pipeline network was established using Pipeline Studio software. For a pipe segment with a length of L, the Latin hypercube sampling method was used to extract samples from three key influencing factors: leakage orifice diameter (range 10~50mm), leakage location (range 0~L), and gas source pressure (range 1~1.2MPa) and combine them to generate a total of 324 leakage scenarios.

[0144] The variation of leakage orifice diameter over time was quantified to obtain dynamic disturbance parameters. These parameters were then input into the transient simulation model of the multi-pressure-level gas pipeline network. The implicit finite difference method was used to iteratively solve the gas flow equations for the unsteady-state process at a preset time step of 1 second. Time-series data of pressure and flow parameters at each candidate monitoring point were obtained. The pressure change at each candidate monitoring point was calculated for each leakage scenario, resulting in a scenario-based leakage event database. Based on historical monitoring data from existing monitoring points in the multi-pressure-level gas pipeline network, leakage detection thresholds were set for each area. Specifically, the leakage detection threshold of the first region of the sub-high voltage system. Leakage detection threshold in the second region of the sub-high pressure system Leakage detection threshold in the third medium-pressure zone The scenario-based leakage event database was compared with the leakage perception threshold of each region to construct the monitoring point-leakage event perception matrix as shown in Table 4.

[0145] Table 4 Monitoring Point-Leakage Event Sensing Matrix

[0146]

[0147] With the optimization objectives of minimizing the detectable leakage aperture and maximizing the leakage event monitoring coverage, and with dual constraints of candidate monitoring point zoning selection and effective leakage event monitoring, a multi-objective optimization model for monitoring points in a multi-pressure-level gas pipeline network is established. Based on a genetic algorithm, using the aforementioned monitoring point-leakage event perception matrix as input, the multi-objective optimization model for monitoring points in a multi-pressure-level gas pipeline network is solved. The improved genetic algorithm uses the numerical value of a candidate monitoring point as a gene locus and a monitoring point layout scheme as a chromosome, randomly generating 100 initial monitoring point layout schemes as the initial population. The parent chromosome is selected using a roulette wheel selection method, with the fitness value of each chromosome as the selection criterion. The crossover probability of the offspring chromosome is set to 0.9, and the mutation probability is set to 0.1. The maximum number of iterations is set to 80, and the termination condition is reaching the maximum number of iterations. After solving the problem using the improved genetic algorithm, the Pareto optimal solution set of the monitoring point layout schemes is output.

[0148] Calculate the leakage event monitoring coverage, number of monitoring points, and layout modification difficulty for each monitoring point layout scheme in the Pareto optimal solution set of the above monitoring point layout schemes. Then, use the entropy weight method to calculate the comprehensive score weights for the leakage event monitoring coverage, the number of monitoring points corresponding to each layout scheme, and the layout modification difficulty, respectively, to obtain... , , The final comprehensive score of each monitoring point layout scheme is calculated, as shown in Table 5.

[0149] Table 5. Overall scores of each monitoring point layout scheme

[0150]

[0151] Based on the comprehensive scores of each monitoring point layout scheme, Scheme 2, with the highest comprehensive score, is selected as the final monitoring point layout scheme. Figure 5 The diagram shows the optimized layout of the monitoring points.

[0152] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0153] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing the layout of monitoring points in urban gas pipeline networks with multiple pressure levels, characterized in that, Includes the following steps: Collect basic information on the multi-pressure level gas pipeline network, select characteristic nodes of the pipeline network based on the basic information, calculate the node importance of each characteristic node, and select candidate monitoring points based on the node importance of each characteristic node. Based on the pipeline topology and basic information of the multi-pressure-level gas pipeline network, a transient simulation model of the multi-pressure-level gas pipeline network is established. Leakage aperture, leakage location, and gas source pressure are taken as key influencing factors of leakage scenarios. Samples are extracted and combined for each of the key influencing factors of leakage scenarios to form a variety of different pipeline leakage scenarios. The dynamic disturbance parameters of the leakage characteristic parameters are obtained by analyzing the change law of leakage characteristic parameters over time when a pipeline leakage event occurs. The leakage characteristic parameters include the leakage orifice diameter. The dynamic disturbance parameters are input as dynamic boundary conditions into the transient simulation model of the multi-pressure-level gas pipeline network. The gas flow equation of the unsteady process is solved iteratively according to a preset time step. During the iterative solution process, the pressure and flow parameters of each candidate monitoring point are updated step by step. After the iterative solution is completed, the time series data of the pressure and flow parameters of each candidate monitoring point are obtained. The pressure change value of each candidate monitoring point when each pipeline leakage scenario occurs is calculated based on the time series data. Each pipeline leakage scenario is combined with the pressure change value of each candidate monitoring point when the pipeline leakage scenario occurs to obtain a scenario-based leakage event database. Calculate the leakage perception threshold, and based on the scenario-based leakage event database and the leakage perception threshold, determine the perception status of each candidate monitoring point for each pipeline leakage scenario, and construct a monitoring point-leakage event perception matrix; Based on the monitoring point-leakage event perception matrix, a multi-objective optimization model for monitoring points of multi-pressure level gas pipeline networks is established with the optimization objectives of minimizing the detectable leak aperture and maximizing the leakage event monitoring coverage. Based on the genetic algorithm, using the monitoring point-leakage event perception matrix as the input of the genetic algorithm, the multi-objective optimization model of the multi-pressure level gas pipeline network monitoring points is solved to obtain the Pareto optimal solution set of the monitoring point layout scheme; Calculate the multi-dimensional evaluation index of each monitoring point layout scheme in the Pareto optimal solution set of the monitoring point layout scheme, calculate the evaluation weight of each multi-dimensional evaluation index, and calculate the comprehensive score of each monitoring point layout scheme in the Pareto optimal solution set of the monitoring point layout scheme based on the multi-dimensional evaluation index and the evaluation weight of each multi-dimensional evaluation index. Select the monitoring point layout scheme with the highest comprehensive score as the final multi-pressure level gas pipeline network monitoring point layout scheme.

2. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 1, is characterized in that... The method for selecting the candidate monitoring points specifically includes the following steps: Collect the pipeline topology and basic information of the multi-pressure level gas pipeline network. The basic information includes: the length, diameter, age, surrounding environmental information along the pipeline segment, flow rate and pressure of nodes, and location information of pipeline valve wells in the multi-pressure level gas pipeline network. Based on the basic pipeline information, characteristic nodes of the pipeline network are selected. The importance of each characteristic node is calculated according to the node importance evaluation method. Pipeline characteristic nodes with a node importance greater than a preset node importance threshold are selected as candidate monitoring points. Each candidate monitoring point is numbered. The characteristic nodes of the pipeline network include: gas pipeline gate stations, regional pressure regulating stations, user connection points, and valve wells. Based on the pipeline pressure rating system, the multi-pressure-rated gas pipeline network is divided into regions, resulting in a first region, a second region, and so on up to the Nth region, where N is an integer. Obtain the area boundary and the number of pipe segments in each pipe network division area; The candidate monitoring points, after being numbered, are classified according to their respective regions to obtain a list of candidate monitoring point regional classifications.

3. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 2, is characterized in that... The node importance evaluation method specifically includes the following steps: Calculate the basic evaluation index of each characteristic node of the pipeline network, which includes: node degree centrality, pipe diameter, node pressure, node flow rate, pipe age, and pipe surrounding environment level. A three-level hierarchical association structure is established, including: target layer, dimension layer, and evaluation index layer. The target layer includes the importance of nodes in the gas pipeline network. The dimension layer includes: pipeline network structure dimension, pipeline network hydraulic dimension, and pipeline network risk dimension. The evaluation index layer includes the basic evaluation indexes of the nodes. The basic evaluation indexes of the nodes are classified according to their respective dimensions to obtain the hierarchical association structure diagram of node importance. Based on the Analytic Hierarchy Process (AHP), the relative importance of each dimension in the dimension layer is scored according to the FAHP evaluation scale to obtain a first score result. The relative importance of each basic evaluation indicator in the evaluation index layer is scored to obtain a second score result. Based on the first score result, the first weight value of each dimension in the dimension layer is calculated. Based on the second score result, the second weight value of each basic evaluation indicator in the evaluation index layer is calculated. Based on the second weight value of each basic evaluation indicator in the node and the first weight value of its respective dimension, the third weight value of each basic evaluation indicator in the node is calculated. The importance of each pipeline feature node is calculated based on its basic evaluation index and the third weight value of each basic evaluation index.

4. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 3, is characterized in that... The formula for calculating the degree centrality of a node is: ； In the formula, For the first Degree centrality of characteristic nodes in a pipeline network For the first The number of edges connecting a network feature node to other network feature nodes. This refers to the total number of characteristic nodes in the pipeline network. The formula for calculating the importance of a node is as follows: ； In the formula, For the first The importance of each characteristic node in the pipeline network. , , , and The first The pipe diameter, node pressure, node flow rate, pipe age, and surrounding environmental level of each characteristic node in the pipeline network. , , , , and These are the third weight values ​​for nodal centrality, pipe diameter, nodal pressure, nodal flow, pipe age, and the surrounding environmental level.

5. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 1, is characterized in that... The leakage detection threshold is calculated as follows: historical monitoring data of existing monitoring points in the multi-pressure-level gas pipeline network are obtained; based on the historical monitoring data of existing monitoring points in the multi-pressure-level gas pipeline network, the mean and standard deviation of pressure fluctuation values ​​within each pipeline network division area are calculated; and the leakage detection threshold of the candidate monitoring points within each pipeline network division area is set according to the following expression: ； In the formula, Let the leakage detection threshold be the threshold value for the candidate monitoring points within the j-th division area of ​​the pipeline network. and , respectively, are the mean and standard deviation of pressure fluctuation values ​​within the j-th division area of ​​the pipeline network.

6. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 1 or 2, is characterized in that... The optimization objective expression for minimizing the detectable leakage orifice size is: ； In the formula, To enable monitoring of leak orifice diameter, The number of regions is the result of dividing the multi-pressure-level gas pipeline network into regions based on the pipeline pressure level system. For the first The total number of pipe segments within the divided area. For the first The first in the divided area The minimum detectable leak diameter of a section of pipeline; The optimization objective expression for maximizing the coverage of the leakage event monitoring is: ； ； In the formula, To improve the coverage of leak event monitoring, For the first The total number of leakage incidents in each region For the first The first division of the region Leakage incident, For the first The total number of candidate monitoring points for the divided areas; To effectively monitor and constrain leakage incidents, Indicates the first The first division of the region Can the candidate monitoring points detect the first [unclear] in this area? Leakage incident, if possible, If not, then ; For the selection of candidate monitoring points by zone, if the first... The first division of the region If one of the alternative monitoring points is selected, then If the first The first division of the region If none of the alternative monitoring points are selected, then .

7. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 6, is characterized in that... The genetic algorithm used is an improved genetic algorithm, specifically including the following steps: Chromosomes are constructed using integer encoding, with one candidate monitoring point number as one gene locus and one monitoring point layout scheme as one chromosome. Multiple chromosomes are randomly generated to form the initial population. Based on the monitoring point-leakage event perception matrix, chromosomes in the initial population that satisfy the effective monitoring constraints of the leakage event and the partition selection constraints of the candidate monitoring points are selected to form the parent population. The fitness value of each chromosome in the parent population is calculated, and the expression for calculating the fitness value is as follows: ； In the formula, This is the fitness value; To enable monitoring of leak orifice diameter; To improve the coverage of leak event monitoring Based on the fitness value of each chromosome in the parent population, two chromosomes in the parent population are selected as parent chromosomes. Gene sequence crossover is performed on the two parent chromosomes to obtain two initial daughter chromosomes. The gene loci of the two initial daughter chromosomes are randomly mutated according to a preset mutation rate to obtain two first candidate daughter chromosomes. The chromosome that satisfies the effective monitoring constraint of the leakage event and the partition selection constraint of the candidate monitoring point is selected as the second candidate daughter chromosome. The fitness value of the second candidate daughter chromosome is calculated. The second candidate daughter chromosome is introduced into the parent population to obtain the first parent population after the first iteration. The Zth parent population is generated iteratively according to the preset number of iterations Z, and the Zth parent population is output as the Pareto optimal solution set of the monitoring point layout scheme.

8. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 7, is characterized in that... In the step of constructing chromosomes using integer encoding, the values ​​of each gene locus in each constructed chromosome are not equal, and the values ​​of each gene locus in the initial progeny chromosome and the first candidate progeny chromosome are not equal.

9. The method for optimizing the layout of monitoring points in urban gas pipeline networks applicable to multiple pressure levels, as described in claim 1, is characterized in that... The multi-dimensional evaluation indicators include: leakage event monitoring coverage, the number of monitoring points corresponding to the monitoring point layout scheme, and the difficulty of modifying the layout scheme. The formula for calculating the difficulty of modifying the layout scheme is: ； In the formula, Due to the difficulty of modifying the layout plan, This refers to the number of newly added monitoring points in the monitoring point layout plan. The number of existing monitoring points retained in the monitoring point layout plan; The formula for calculating the comprehensive score of the monitoring point layout scheme is as follows: ； In the formula, The overall score for the monitoring point layout plan, To improve the coverage of leak event monitoring, The number of monitoring points corresponding to the monitoring point layout plan. , and The scores are weighted by the comprehensive scores of leakage event monitoring coverage, the number of monitoring points corresponding to the monitoring point layout plan, and the difficulty of modifying the layout plan.

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