A pipeline network distribution bottleneck analysis method based on search algorithm

Abstract

A method for analyzing bottlenecks in pipe network distribution based on a search algorithm belongs to the technical field of pipe network pipeline monitoring. For a pipe network with problems in the pipe network, the flow and pressure of each pipe section in the pipe network with problems are obtained; for the pipe network with problems, a list of algorithm elements required by a connectivity search algorithm is constructed, and the strongly connected unit components in the algorithm element list are solved. The connectivity search algorithm is used to diagnose the connectivity relationship of the strongly connected unit components, and the edges connecting the strongly connected unit components are the weak pipe sections in the pipe network; for the weak pipe sections, the bottleneck pipe section number and sorting are obtained in combination with the flow and pressure information of each pipe section. The present invention is based on the current status of the pipe network with stable operation and maintenance, and analyzes from the connectivity connection relationship and hydraulic conditions, which can quickly discover the bottleneck problem of the pipe network, greatly simplify the difficulty of analysis, reduce the threshold for finding the bottleneck of the pipe network, greatly save costs, and effectively guarantee the operation of the pipe network.

Description

Technical Field

[0001] The invention belongs to the technical field of pipe network pipeline monitoring, and in particular relates to a pipe network transmission and distribution bottleneck analysis method based on a search algorithm. Background Art

[0002] From design to daily operation and maintenance, urban gas pipeline networks often have large discrepancies between the designed pipeline operation parameters and the actual operation and maintenance due to the transformation of the pipeline network and the development of the user scale. In particular, there may be load matching problems between new pipelines and old pipelines. The pipeline network in the old city is relatively fixed, and the building forms such as street width and burial depth seriously restrict the transformation and laying of the pipeline network. During the peak period of winter gas supply and demand, the pipeline network results seriously restrict the gas supply. In order to solve the problem of increasing demand for pipeline gas supply during actual operation and maintenance, some pipelines ensure gas supply balance by adding new pipelines or replacing larger diameter pipelines at the original location. Therefore, how to quickly find the location of bottleneck pipelines in the complex urban pipeline network is the key to optimizing the operation of the pipeline network.

[0003] At present, some large gas companies have purchased foreign pipeline simulation software such as Synergi Gas and Winflow, and have connected monitoring data for simulation calculations, simulated the transmission and distribution capacity of the pipeline network, and verified the transmission and distribution plan. However, this method has problems such as the cost of purchasing software and the high cost of operation and maintenance. In addition, the pipeline network is often thousands of kilometers long, and the pipeline network also includes various types of valves, tees, pressure regulating stations, and pressure regulating boxes. It is necessary to rely on simulation software to reproduce the operating conditions of the pipeline network and find the bottleneck pipeline. It also requires accurate pipeline modeling and solid and accurate basic data. For gas companies in second- and third-tier cities, they generally do not have so many professional simulation engineers, and the cost of transformation is also high. Therefore, it is very important to find the bottleneck pipeline quickly, accurately, and at low cost.

[0004] The safety and reliability of urban gas transmission and distribution pipelines are mainly evaluated by subjective methods such as Kent method, fault tree method, fuzzy comprehensive evaluation method, etc. Taking reliability as the research object, there are also analysis angles such as structural reliability, connectivity reliability, hydraulic conditions, and gas supply reliability. The basic research work on gas supply reliability mainly focuses on the statistics of the probability distribution of daily urban gas demand and the construction of a reliability calculation model.

[0005] Chinese patent publication CN116680868A discloses a method, device, terminal and storage medium for analyzing bottlenecks in a gas pipeline network. First, the target pipeline network is simplified to obtain suspected bottleneck pipelines, and the pipeline network path with the shortest distance between each suspected bottleneck pipeline and the pressure regulating station is calculated to determine the bottleneck pipeline in the suspected bottleneck pipeline. The condition for determining the suspected bottleneck pipeline in this method is that the pipeline diameter is smaller than the diameter of the upstream pipeline and smaller than the diameter of the downstream pipeline of the pipeline. However, the physical size of the pipe section used in the article as the determination condition for the suspected bottleneck pipeline may not be consistent with the actual bottleneck pipeline.

[0006] Chinese patent publication CN115935569A discloses a method for analyzing bottlenecks in urban drainage pipes. The drainage pipe network model established based on the SWMM model simulates the result data, and then uses the entropy weight method to use the simulation result data as the input feature index data to determine the index weight for evaluating the pipeline bottleneck. The comprehensive index method is then used to comprehensively calculate the index weight to obtain the pipeline bottleneck index. In this method, the drainage pipe network is generally pressure-free and non-full flow, and the local pipes are full flow during the rainstorm period; while the gas medium in the gas pipe network determines that the pipe is always pressurized and full flow, so this method is not suitable for the gas pipe network.

[0007] Southwest Petroleum University's master's thesis "Reliability Research on Urban Gas Pipeline Network System" (Ma Liang) proposed a connection reliability index to calculate and analyze the reliability of the pipeline network, namely the reliability of the connection between a designated user and at least one gas source, the reliability of the connection between all users and at least one gas source, the reliability of the connection between a designated gas source and all gas users, and the reliability of the connection between all gas users and all gas sources; and proposed a connection reliability calculation method based on the minimum path set matrix. However, the research method in this paper also has certain defects: first, this urban gas pipeline network connection reliability is for the pipeline network in the planning stage, which depends on the topological structure of the pipe section and does not depend on the actual operating parameters of the pipeline network; second, the two-terminal problem based on the minimum path set essentially depends on the probability of going from one node to another, provided that the probability of normal operation of each pipeline is known, and this statistical probability is difficult to obtain; third, the Bayesian network analysis method proposed in the paper is for the fault maintenance stage, and the main characteristic indicators involved are failure probability, posterior probability, and joint valve probability.

[0008] In the master's thesis of China University of Petroleum, "Research on the Reliability of my country's Natural Gas Pipeline Network Based on Graph Theory" (Liu Wenchao), the connectivity reliability of the pipeline network is analyzed. From the perspectives of city gate stations, gas transmission first stations, and gas transmission terminal stations, the connectivity reliability evaluation index of the natural gas pipeline system is established, and the connectivity reliability is expressed through logical events. The connectivity importance indexes are probabilistic connectivity and connectivity based on the minimum path set. The former is based on the probability of normal and failure states, and is solved using composite functions. The calculation rules and matrix calculations are large; the latter has high computational complexity and ignores the attenuation of the transportation function caused by pipeline aging, blockage, leakage, etc. in real scenarios. It does not consider the capacity weight of the pipeline, but only considers whether the pipeline exists or not, and has poor adaptability to the dynamics of the pipeline structure. The number of minimum paths between each gas consumption node and the gas source point is often very large. At this time, the solution method of the connectivity reliability of the gas consumption node of a simple gas pipeline network is often difficult to complete, and the so-called NP (Non-deterministic Polynomia) problem may occur.

[0009] In the analysis of the reliability of the topological structure of the gas pipeline network, the single-source gas pipeline network is taken as an example to analyze the reliability of the connectivity of the gas-using nodes and the reliability of the pipeline network system. From the perspective of the reliability of the connectivity of the gas-using nodes: for the complex ring network structure, the Monte-Carlo method is used to randomly sample a large number of nodes (the sampling results are 0 or 1), and then the accessibility matrix is ​​calculated to obtain the connectivity probability of each gas-using node. From the perspective of the reliability of the connectivity of the pipeline network system: starting from the perspective of the gas source and the entire pipeline network, the connectivity reliability of the gas source point and all gas-using nodes is studied, which is to find the product of the weight factor of the gas-using node j and the connectivity reliability of the gas-using node j, and then accumulate and sum the values ​​within the range of all gas-using nodes. This method uses Monte-Carlo to obtain the probability of connectivity of each pipe segment (a random number between 0 and 1) as a quantitative evaluation indicator, ignoring the potential bottleneck factors caused by the structure of the pipeline network itself.

[0010] The transmission and distribution capacity of urban pipeline networks is not only related to the structural safety of pipelines, but also closely related to the topological connection of pipeline networks. Urban transmission and distribution pipeline networks are generally composed of gas source points, terminal gas consumption points and intermediate transmission pipelines. Urban pipeline networks are often connected in a branched, ringed, or branched-ringed manner. The main purpose of the ring network is to ensure that when a certain pipe section fails, the gas source can reach the user point through other paths. The current urban gas pipeline network is usually composed of several, dozens or even hundreds of ring networks. Since the number of pipeline paths and the scale of the pipeline network (the number of nodes, pipelines and rings) grow nonlinearly, the number of minimum paths between each gas consumption node and the gas source point is often very large. In the current urban gas planning and design, pipeline renewal, maintenance and renovation, there is still a lack of quantitative evaluation methods for the overall topology of the pipeline network. Summary of the invention

[0011] In view of the problems existing in the prior art, the present invention analyzes the hydraulic working conditions and node connectivity of the pipe network connectivity based on graph theory search related algorithms, and comprehensively analyzes the analysis results of the hydraulic working conditions and node connectivity to find the pipe network distribution bottleneck. Considering the uncertainty of the flow direction of some pipelines, the pipe network with a ring pipe network structure is focused on, and the directed graph under certain typical working conditions is analyzed; the branch structure part is not analyzed in detail.

[0012] The present invention provides a pipeline network transmission and distribution bottleneck analysis method based on a search algorithm, comprising the following steps:

[0013] For the problematic pipe network in the pipe network, obtaining the flow and pressure of each pipe section in the problematic pipe network;

[0014] For the problematic pipe network, construct an algorithm element list required by a connectivity search algorithm, and solve the strongly connected unit components in the algorithm element list;

[0015] If there are multiple strongly connected unit components, the edges connecting the strongly connected unit components are physically weak pipe sections in the pipe network;

[0016] If there is only one strongly connected unit, find the common nodes in the adjacency matrix, obtain the connection relationship between the node in the strongly connected unit and other nodes, and the connection pipeline number, and find the important nodes;

[0017] If no strongly connected unit is found, focus on the connectivity of the pipe network under accident conditions;

[0018] For the weak pipe sections, the bottleneck pipe section number and ranking are obtained by combining the flow and pressure information of each pipe section;

[0019] For the important nodes, the flow and pressure of the important nodes are monitored, and the monitoring data of the important nodes are used to analyze whether the important nodes can meet the needs of the downstream pipeline network of the important nodes.

[0020] The flow rate and pressure of each pipe section in the problematic pipe network are calculated based on the simulation model;

[0021] Based on the monitoring information of the SCADA system, the flow and pressure information of all user points in the problematic pipeline area, the pipeline attribute information, and the steady-state simulation analysis topology structure established according to the pipeline connection relationship, a pipeline simulation model is established and simulation calculations are performed to obtain the flow and pressure of each pipe section in the problematic pipeline.

[0022] The flow and pressure of each pipe section in the problematic pipeline network are measured using the DMA regional metering zoning method to gradually identify or narrow the area of ​​unbalanced gas supply, and further install flow meters and pressure gauges in certain suspected bottleneck pipe sections for real-time recording to obtain the flow and pressure of the suspected bottleneck pipe sections.

[0023] The vertices in the algorithm element list include pipeline connection points, user terminals, pressure regulating stations, and gate stations in the problematic pipeline network.

[0024] The connection relationships between different strongly connected unit components are marked with different colors, and the colors in each strongly connected unit component are consistent; through the color difference, the pipe section connected by different strongly connected unit components is quickly found, and the pipe section is the weak pipe section in the pipe network.

[0025] For the weak pipe section, determine the bottleneck pipe section according to the pressure or flow;

[0026] When judging based on pressure, if the average pressure of the pipe network connected to the rear end of the weak pipe section is greater than the pressure of the weak pipe section, the weak pipe section is determined to be a bottleneck pipe section;

[0027] When judging based on flow rate, if the flow rate required per hour by the pipe network connected to the rear end of the weak pipe section is greater than the flow rate passing through the weak pipe section per hour, the weak pipe section is judged to be a bottleneck pipe section.

[0028] Determine the degree of the bottleneck based on the percentage between the flow rate passing through the bottleneck pipe section per hour and the flow rate required by the back end;

[0029] It is a slight bottleneck.

[0030] It is a medium bottleneck level;

[0031] It is a serious bottleneck level;

[0032] Among them, Q k represents the flow rate in the bottleneck pipe section; Q x It represents the flow rate of any pipe section downstream of the suspected pipe section, k+1≤x≤n.

[0033] If no strongly connected unit is found, the loop pipeline in the algorithm element list is set to an undirected graph, and the branch pipeline is set to a directed graph. The algorithm element list is traversed by a depth-first search algorithm to obtain all paths in each loop in the pipeline network, thereby realizing backup path gas supply under accident conditions.

[0034] The existing network connectivity analysis often needs to consider the connection relationship between the pipe sections, the connection relationship between the gas source and the user, the probability and distribution of accidents in the pipe sections, which has many influencing factors and is very complicated to calculate. The present invention is based on the current stable operation and maintenance of the pipeline network, and only analyzes the connectivity connection relationship and hydraulic conditions, which can quickly find the bottleneck problem of the pipeline network, greatly simplifying the analysis difficulty, lowering the threshold for finding the bottleneck of the pipeline network, greatly saving costs, and effectively ensuring the operation of the pipeline network. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] For a more complete understanding of the present invention, reference will now be made to the following description taken in conjunction with the accompanying drawings, in which:

[0036] Figure 1 It is a flow chart of a pipeline network transmission and distribution bottleneck analysis method based on a search algorithm of the present invention;

[0037] Figure 2 It is a simplified directed graph diagram of a single gas source point pipeline network;

[0038] Figure 3 This is a simplified schematic diagram of a pipe network;

[0039] Figure 4 It is a schematic diagram of a directed graph with only one strongly connected unit;

[0040] Figure 5 is a schematic diagram of the adjacency matrix;

[0041] Figure 6 It is a simplified directed graph diagram of a dual gas source point pipeline network;

[0042] Figure 7 It is a schematic diagram of two strongly connected unit components searched out according to the strongly connected unit algorithm;

[0043] Figure 8 This is a schematic diagram of a graph to be searched constructed based on the connection information of the third embodiment. DETAILED DESCRIPTION

[0044] In order to clearly explain the purpose, technical details and effective application of the present invention and facilitate the understanding and implementation of ordinary technicians in the field, the following will be further described in detail in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the implementation examples described here are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0045] The present invention provides a pipeline network distribution bottleneck analysis method based on a search algorithm. The present invention analyzes the hydraulic working conditions and node connectivity of the pipeline network based on a graph theory search-related algorithm. Considering the uncertainty of the flow direction of some pipelines, the pipeline network with a ring-shaped pipeline structure is focused on, and the analysis is performed according to a directed graph under certain typical working conditions; while the branch structure is not analyzed in detail.

[0046] For the above typical working conditions, since the pipe network parameters change dynamically over time, if the pipe network status is completely calculated by simulation and then checked whether there is a local imbalance problem, a very large amount of calculation and accurate pipe network modeling are required, and the operability is poor. Therefore, the present invention uses typical data of the pipe network during peak hours as the typical working condition and as the input condition of the pipe network simulation model, which greatly reduces the amount of calculation.

[0047] Since the flow direction in the pipe network is not immutable, for example, when a certain point leaks, an alternative path is needed to supply gas, and the flow direction of the gas in the pipe section of the relevant path may change, so the tree structure will also change, and it is necessary to re-establish the model and search and calculate. However, the change in the flow direction of the pipe network is a special case, which is unpredictable and irregular, and therefore is not used as the working condition of the present invention. The present invention assumes that after the pipe network working condition reaches a stable condition, the parameters of the working condition are used as the input conditions for the simulation modeling calculation.

[0048] Specifically, see the attached drawings of the specification Figure 1 , the method of the present invention comprises the following steps:

[0049] Step S01: Establish a pipeline network simulation model based on the pipeline network connectivity structure and monitoring data.

[0050] Step S101, determine the macro model of the pipeline network to be analyzed, and find the pipeline network with problems in gas supply uniformity based on the macro data.

[0051] The pipeline network macro model to be analyzed includes the pipe diameter, burial depth, material, gas composition, flow rate, pressure, valves, etc. of the pipeline network. The macro model is based on macro variables such as the total hourly gas consumption of the city, the gas supply pressure and flow rate of the gas source point (gate station), the inlet (or outlet) gas pressure and flow rate of the storage and distribution station, and the pressure of the control point. Through statistical analysis of actual operation data, the regression equation between these macro variables is established.

[0052] The macro model does not need to predict the flow of each node in the pipeline network, but only needs to predict the total gas supply of the entire pipeline network and pre-allocate the gas supply to each gate station and storage and distribution station.

[0053] For the macro model with SCADA system monitoring information, the load is reflected in each gas source point (gate station), storage and distribution station, regional gas supply pressure and flow through the macro model, and important control points and regional monitoring and analysis are carried out to find the pipeline network with problems in gas supply uniformity.

[0054] Step S102: Perform pipe network simulation model calculation on the pipe network with problems to obtain the flow rate and pressure of each pipe section in the pipe network with problems.

[0055] A steady-state simulation analysis topology structure of the pipeline network is established for the pipeline connection relationship in the problematic pipeline network. Steady-state offline calculation does not require real-time data or online deployment, thus reducing the amount of calculation.

[0056] Based on the monitoring information of the SCADA system, the flow and pressure information of all points of users in the problematic pipe network area, the pipe network attribute information and the steady-state simulation analysis topology structure, a pipe network simulation model is established and simulation calculation is performed. The pipe network simulation model includes basic pipeline attributes, which are divided into a node set and a pipe segment set. Among them, the node is represented as a pipe segment connection point, such as a tee, a gas supply user, and a gas source; the pipe segment set includes simplified branches and trunks in the pipe segment.

[0057] The flow and pressure data during the peak period of the pipeline network are used as input conditions, and the pipeline network simulation model is used to solve the problem. The flow and pressure data are extracted from the result file obtained by the solution for use in the subsequent search algorithm. Among them, the pressure data includes the average pressure information of the pipeline, that is, the average of the pressure at the starting point and the end point of the pipeline section.

[0058] Step S02: construct a list of algorithm elements required for the connectivity search algorithm, use the Tarjan algorithm to find the strongly connected unit components in the list of algorithm elements, and find the physically weak pipe sections and weak nodes in the pipe network.

[0059] Step S201: construct an algorithm element list, that is, a graph to be searched.

[0060] Among them, vertices (also called nodes) are the basic part of the graph; the vertices are numbered as v1, v2, v3, etc. Edges are another basic part of the graph; two vertices are connected by an edge, indicating that there is a connection relationship between the two vertices, which is represented by w1, w2, w3, etc. n .

[0061] During the operation of the pipeline network, the media in the pipeline network, such as gas, water, etc., all flow in one direction, so the pipeline network can be simplified into a directed graph. Furthermore, the edges can be weighted to represent the cost from one vertex to another; the cost can be represented by the flow rate or average pressure.

[0062] In the pipe network, the attachments in the pipe section are ignored and simplified, and the vertices in the algorithm element list include pipeline connection points (such as tees), user terminals, pressure regulating stations, and gate stations.

[0063] As shown in the attached figure of the instruction manual Figure 2 The figure shows a simplified directed graph of a single gas source point pipeline network.

[0064] Step S202: for the constructed algorithm element list, the strongly connected unit components in the algorithm element list are obtained by using the Tarjan algorithm.

[0065] Considering the uncertainty of the flow direction of some pipes in the pipe network, the search algorithm mainly targets the structure containing the ring pipe network, and the branch structure is not included in the search scope. Figure 3 The simplified schematic diagram of a pipe network is shown in FIG. Among them, the edges 2-4, 4-5, 3-6, and 3-7 are all branch structures. In the present invention, they are not regarded as bottleneck pipe sections, but the bottleneck pipe section is found in the ring structure 1-2-3. The goal of this step is to find the ring structure in the pipe network.

[0066] With respect to the constructed algorithm element list, the strongly connected unit components in the pipe network can be found through the Tarjan algorithm of the strongly connected unit.

[0067] The searched strong connected unit component set is called strong connected unit component set 1, and the possible range of the searched strong connected unit component nodes is [1, N], where N is the total number of nodes. Since there is no path loop in the branched pipe network, each node is a strong connected unit component, and the number of nodes of this type of strong connected unit component is equal to 1. Therefore, in order to better evaluate the connection information of complex ring networks and branched pipe networks, the nodes of the strong connected unit components of the evaluation object are set to be greater than 1, so as to find the strong connected unit components with the number of strong connected unit component nodes greater than 1. The preliminarily screened strong connected unit component set is called strong connected unit component set 2.

[0068] Step S203: If there are multiple strongly connected unit components obtained in step S202, the edges connecting the strongly connected unit components are the physically weak pipe sections in the pipe network.

[0069] The connection relationships between the strongly connected unit components in the strongly connected unit component set 2 are marked with different colors, and the colors in each strongly connected unit component are consistent. Through the color difference, the pipe segment connected by different strongly connected unit components can be quickly found, and the pipe segment is the weaker pipe segment.

[0070] The color marking can also use the four-color theorem to color each strongly connected component, and mark the weaker pipe sections between the strongly connected components with "mixed colors". The color value calculation formula after RGB color mixing is:

[0071] Color A - (Color A - Color B) * (1 - the percentage of Color A)

[0072] If the colors of two strongly connected units are RGB green (192,229,112) 70% and white (255,255,255) 30%, then:

[0073] R:192-(192-255)*(1-0.7)=210.9

[0074] G:229-(229-255)*(1-0.7)=234.8

[0075] B:112-(112-255)*(1-0.7)=154.9

[0076] After rounding, the mixed color value is: (211,235,155).

[0077] Step S204: If there is only one strongly connected unit obtained in step S202, find the common nodes in the adjacency matrix, obtain the connection relationship between the node in the strongly connected unit and other nodes, and the connection pipeline number, and find the important nodes.

[0078] If there is only one strongly connected unit obtained in step S202, as shown in the accompanying drawings of the specification Figure 4 As shown, other pipelines constitute loops or branches. Then, in combination with the strongly connected unit components obtained by the Tarjan algorithm in step S202, the connection relationship between the nodes in the strongly connected unit and other nodes, as well as the connection pipeline number, are obtained by finding the common nodes in the adjacency matrix to find the important nodes.

[0079] The adjacency matrix is ​​a two-dimensional array representing the vertex connection relationship in a directed graph, and the elements in the matrix can be weights, edge numbers, etc.

[0080] With the attached drawings Figure 4 Taking the graph shown in as an example, its adjacency matrix is ​​shown in the attached figure of the specification Figure 5 shown. Figure 5 The elements in the matrix are the edge numbers.

[0081] from Figure 5 It can be seen that the nodes connected from node 1 include 2, 3, 4, and 6, and the corresponding connecting pipes are numbered 2, 4, 5, and 8 respectively. Compared with the connections starting from other nodes, node 1 obviously connects more nodes, so it is an important node.

[0082] Step S205: If no strongly connected unit is found in step S202, focus on the connectivity of the pipeline network under the accident condition.

[0083] If no strongly connected unit is found in the pipe network, it means that the pipe network structure itself has poor connectivity. In this case, it is hoped that by searching all the paths in each loop in the pipe network, an alternative path for gas supply can be found when an accident occurs in a certain pipe section.

[0084] At this time, the loop pipeline is set as an undirected graph, and the branch pipeline is set as a directed graph. By traversing the pipeline network topology map through the depth first search (DFS) algorithm, all paths in each loop in the pipeline network can be found, thereby realizing backup path gas supply under accident conditions.

[0085] In order to facilitate understanding of the above method of the present invention, the specific steps of the above method of the present invention are described in detail below by taking several specific implementation modes as examples.

[0086] Embodiment 1

[0087] Reference Instructions Figure 6 , which shows a simplified directed graph of a dual gas source point pipeline network.

[0088] According to the strongly connected unit algorithm Figure 4 By searching the dual gas source points shown in , two strongly connected unit components can be found, namely, strongly connected unit component 1 (including nodes B, E, G, C, F) and strongly connected unit component 2 (including nodes H, J, I), as shown in the accompanying drawings of the specification Figure 7 As shown. Figure 6 The branch pipe network in is not considered as a bottleneck pipe section. It can be seen that the paths CD and DH between the two trees are suspected bottleneck pipe sections.

[0089] Embodiment 2

[0090] The connection information of a pipe network is as follows:

[0091] Pipeline Number Starting point number End point number 1 1 2 2 2 3 3 3 1 4 2 4 5 2 5 6 2 7 7 4 6 8 5 6

[0092] Based on the above connection information, a graph to be searched is constructed, as shown in the attached figure of the specification. Figure 4 shown.

[0093] The strongly connected units searched by Tarjan algorithm are 5, 3, 4, 6, (0, 1, 2), respectively, which reflects that pipe sections 1, 2, and 3 have very good connectivity, that is, only one strongly connected unit is found.

[0094] On the other hand, nodes 1, 3, 4, and 5 form a loop from the perspective of the pipe network structure. Figure 4 As shown, when pipeline 5 is damaged and loses its gas supply function, the gas at node 1 can still reach node 4 through pipe sections 4, 7, and 6. This situation can meet the temporary gas demand under leakage conditions.

[0095] At this time, node 1 is a strongly connected unit and a common node in the undirected graph. The operating parameters of this node are extremely important and need to be monitored intensively.

[0096] Embodiment 3

[0097] The connection information of a pipe network is as follows:

[0098] Pipeline Number Starting point number End point number 1 1 3 2 2 5 3 5 6 4 4 8 5 3 7 6 3 4 7 4 5 8 8 6 9 7 8 10 7 9 11 8 10 12 6 11

[0099] Based on the above connection information, a graph to be searched is constructed, as shown in the attached figure of the specification. Figure 8 shown.

[0100] No strongly connected units were found using the Tarjan algorithm.

[0101] For city gas ring network, gas can be supplied through other paths when an accident occurs. Figure 8 As shown. Among them, nodes ① and ② are gas source nodes, responsible for supplying gas to other nodes. When pipeline 5 is damaged and loses its gas supply function, node ① can still reach node ⑦ through pipe sections 1, 6, 4, and 9. This situation can meet the temporary gas demand under leakage conditions.

[0102] For the pipe network structure diagnosis under abnormal working conditions, since the flow in the loop is variable, the ring pipe network topology is simplified into an undirected graph, and the DFS algorithm can be used to find the corresponding loop.

[0103] Since no strongly connected unit is found in this case, under normal circumstances, the pipe network connection structure under this flow direction determination is not ideal; it is possible to consider adding pipes to the existing pipe network to form a strongly connected unit containing multiple nodes to improve the connectivity of the pipe network.

[0104] Step S03: Based on the weak pipe sections or key nodes found in step S02 and in combination with the pipe network flow and pressure information obtained in step S01, output the bottleneck pipe section number and ranking.

[0105] For the weak pipe section obtained in step S02, according to the pressure judgment, if the average pressure of the pipe network connected to the rear end of the weak pipe section is greater than the pressure of the weak pipe section, the weak pipe section is determined to be a bottleneck pipeline.

[0106] Specifically, it is determined according to the following formula:

[0107]

[0108] Where k is the pipe segment number; represents the average pressure of pipe section k; It represents the average pressure of any pipe section downstream of the suspected pipe section, k+1≦x≦n.

[0109] Or according to the flow rate, if the flow rate (m 3 / h) is less than the flow rate required by the back end, it is determined to be a bottleneck pipe section.

[0110] Specifically, it is determined according to the following formula:

[0111]

[0112] Among them, Q k represents the flow rate in the bottleneck pipe section; Q x It represents the flow rate of any pipe section downstream of the suspected pipe section, k+1≤x≤n.

[0113] Furthermore, the degree of the bottleneck can be determined based on the percentage between the flow rate passing through the bottleneck pipe section per hour and the flow rate required by the rear end.

[0114] if

[0115] It is a slight bottleneck.

[0116] It is a medium bottleneck level;

[0117] This is a serious bottleneck level.

[0118] Furthermore, if there are multiple bottleneck pipe sections, the priority of the pipeline bottlenecks is sorted based on the set of suspected bottleneck pipes. The sorting is based on the percentage of the bottleneck pipe section flow in the backend flow during peak hours, the percentage of the backend flow in the total flow of the pipeline network; or the greater the difference between the pressure bottleneck and the expected pressure, the higher the priority.

[0119] If one or more bottleneck pipelines are found, this information can be fed back to the interface interaction end for the company's reference.

[0120] For the key node obtained in step S02, the flow rate, pressure and other parameters of the point are monitored, and the monitoring data of the point is used to analyze whether the node can meet the needs of the downstream pipeline network of the node.

[0121] In another embodiment, instead of relying on the pipe network simulation model calculation in the above step S01, a DMA regional metering zoning method can be used to gradually find out / reduce the area scope of the gas supply imbalance, and further install flow meters and pressure gauges in certain suspected bottleneck pipe sections for real-time recording.

[0122] The DMA (District Metering Area) district metering zoning refers to the formation of virtual or actual independent areas by closing valves or installing flow meters. By measuring the amount of water entering or flowing out of this area, the flow rate of each area can be monitored.

[0123] The DMA regional metering zoning method establishes multiple zones for management and control based on the pipeline network relationship. The first-level zone uses the administrative area boundary as a reference, the second-level zone uses 1,000 households in the street as the scope, and the third-level zone is divided according to abnormal areas and key areas. Flow meters are set up in each unit area for metering. Combined with the flow and pressure information monitored by the SCADA system, the suspected range can be narrowed through layer-by-layer investigation.

[0124] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, no matter from which aspect, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the description of the above embodiments, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A pipeline network distribution bottleneck analysis method based on a search algorithm, comprising the following steps: For the problematic pipe network in the pipe network, obtaining the flow and pressure of each pipe section in the problematic pipe network; For the problematic pipe network, a list of algorithm elements required for a connectivity search algorithm is constructed, and a Tarjan algorithm is used to solve the strongly connected unit components of the structural part containing the ring pipe network in the list of algorithm elements; The vertices in the algorithm element list include the pipeline connection point, user terminal, pressure regulating station, and gate station in the problematic pipeline network; two vertices are connected by an edge, indicating that there is a connection relationship between the two vertices; If there are multiple strongly connected unit components, the edges connecting the strongly connected unit components are physically weak pipe sections in the pipe network; If there is only one strongly connected unit, find the common nodes in the adjacency matrix, obtain the connection relationship between the node in the strongly connected unit and other nodes, and the connection pipeline number, and find the important nodes; If no strongly connected unit is found, focus on the connectivity of the pipe network under accident conditions; For the weak pipe sections, the bottleneck pipe section number and ranking are obtained by combining the flow and pressure information of each pipe section; For the weak pipe section, determine the bottleneck pipe section according to the pressure or flow; When judging based on pressure, if the average pressure of the pipe network connected to the rear end of the weak pipe section is greater than the pressure of the weak pipe section, the weak pipe section is determined to be a bottleneck pipe section; When judging based on flow rate, if the flow rate required per hour by the pipe network connected to the rear end of the weak pipe section is greater than the flow rate passing through the weak pipe section per hour, the weak pipe section is judged to be a bottleneck pipe section; The degree of the bottleneck is determined based on the percentage between the flow rate passing through the bottleneck pipe section per hour and the flow rate required by the back end; or the greater the difference between the pressure bottleneck and the expected pressure, the higher the priority; For the important nodes, the flow and pressure of the important nodes are monitored, and the monitoring data of the important nodes are used to analyze whether the important nodes can meet the needs of the downstream pipeline network of the important nodes.

2. The method according to claim 1, characterized in that The flow rate and pressure of each pipe section in the problematic pipe network are calculated based on the simulation model; Based on the monitoring information of the SCADA system, the flow and pressure information of all user points in the problematic pipeline area, the pipeline attribute information, and the steady-state simulation analysis topology structure established according to the pipeline connection relationship, a pipeline simulation model is established and simulation calculations are performed to obtain the flow and pressure of each pipe section in the problematic pipeline.

3. The method according to claim 1, characterized in that The flow and pressure of each pipe section in the problematic pipeline network are measured using the DMA regional metering zoning method to gradually identify or narrow the area of ​​unbalanced gas supply, and further install flow meters and pressure gauges in certain suspected bottleneck pipe sections for real-time recording to obtain the flow and pressure of the suspected bottleneck pipe sections.

4. The method according to claim 1, characterized in that: Marking the connection relationships between different strongly connected unit components with different colors respectively, and the colors in each strongly connected unit component are consistent; Through the color difference, the pipe section connected by different strongly connected unit components can be quickly found, and the pipe section is the weak pipe section in the pipe network.

5. The method according to claim 1, characterized in that It is a slight bottleneck. It is a medium bottleneck level; It is a serious bottleneck level; Among them, Q k represents the flow rate in the bottleneck pipe section; Q x It represents the flow rate of any pipe section downstream of the suspected pipe section, k+1≤x≤n, and n is the total number of pipe sections.

6. The method according to claim 1, characterized in that If no strongly connected unit is found, the loop pipeline in the algorithm element list is set to an undirected graph, and the branch pipeline is set to a directed graph. The algorithm element list is traversed by a depth-first search algorithm to obtain all paths in each loop in the pipeline network, thereby realizing backup path gas supply under accident conditions.

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