Drainage pipe network model simplification method and system based on graph theory, medium and equipment

Abstract

The invention provides a graph theory-based drainage pipe network model simplification method and system, a medium and equipment. Comprising the steps of establishing a simplification target and an initial simplification standard; establishing a directed topology database of the drainage pipe network according to the basic information of the drainage pipe network model; screening the directed topology database by using the initial simplification standard; and reconstructing the drainage pipe network model based on the screened directed topology database, performing trial calculation, outputting the reconstructed drainage pipe network model when a trial calculation result meets a simplification target, otherwise, updating a simplification standard, and continuously screening the screened directed topology database based on the updated simplification standard. The invention aims to develop a drainage pipe network model simplification method oriented to hydrodynamic performance based on a graph theory, reduce computational nodes on the premise of ensuring hydrodynamic precision, and effectively improve computational efficiency; through directed topological association of a graph database, upper and lower association of a pipe network is emphasized, and a simplified drainage pipe network model can still accurately reflect hydraulic characteristics of an original system.

Description

Technical Field

[0001] This application belongs to the field of water supply and drainage engineering technology, specifically relating to a method and system, medium and equipment for simplifying drainage network models based on graph theory. Background Technology

[0002] Against the backdrop of rapid urbanization and increasingly frequent extreme weather events, urban drainage network mechanism models face technical bottlenecks in practical applications such as online real-time forecasting and early warning, and scheduling scheme comparison. These bottlenecks include long simulation calculation times and insufficient response timeliness. Simplifying the network to improve computational efficiency, while ensuring the accuracy of the model simulation and physical realism, has become an effective way to solve these problems.

[0003] However, current simplification methods for mainstream drainage network models still have many shortcomings. On the one hand, a scientific and reasonable standard for judging network simplification has not yet been established, and most methods rely on manual or semi-automatic operation, resulting in low efficiency and high error rate in the simplification process. On the other hand, there is a lack of a simplification iteration mechanism that takes into account both simulation accuracy and computational efficiency, which can easily lead to insufficient or excessive simplification, thereby affecting the practicality and stability of the model.

[0004] Therefore, there is an urgent need to develop efficient, intelligent, and controllable drainage network simplification technologies to better support the digital and intelligent management needs of urban flood control. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, medium, and equipment for simplifying drainage network models based on graph theory, in order to solve the technical problems of low efficiency and high error rate in the simplification process of drainage network models.

[0006] Firstly, this application provides a graph-based method for simplifying drainage network models. The method includes:

[0007] Establish simplification objectives and initial simplification criteria;

[0008] A directed topology database of the drainage network is established based on the basic information of the drainage network model.

[0009] The nodes in the directed topology database are filtered using the initial simplification criteria.

[0010] The drainage network model is reconstructed based on the filtered directed topology database;

[0011] The reconstructed drainage network model is tested, and the reconstructed drainage network model is output when the test result meets the simplification objective. When the test result does not meet the simplification objective, the initial simplification standard is updated, and the directed topology database is further filtered according to the updated simplification standard.

[0012] In one implementation of the first aspect, a directed topology database of the drainage network is established based on the basic information of the drainage network model, including:

[0013] Determine the node types and relationship types based on the basic information of the drainage network model;

[0014] Based on the node type and the relationship type, extract node attribute data and relationship attribute data from the basic information of the drainage network;

[0015] The node type, node attribute data, relationship type, and relationship attribute data are transferred into a node-relationship database file, and the node-relationship database file is transferred into a directed topology database.

[0016] In one implementation of the first aspect, establishing the initial simplification criteria includes:

[0017] Define rules for merging relationships based on structural similarity to determine whether nodes are redundant;

[0018] Set a maximum consecutive simplification threshold to limit the number of consecutive simplifications of redundant nodes in the directed topology database;

[0019] Define boundary node simplification rules to restrict whether nodes with boundary attributes are simplified, and how the related boundaries are adjusted when simplified. Mount the node to upstream or downstream based on these settings. In one implementation of the first aspect, the nodes in the directed topology database are filtered using the initial simplification criteria, including:

[0020] Based on the boundary point simplification rules and the relational structural similarity merging rules, redundant attributes of nodes are established for nodes in the directed topology database.

[0021] The nodes that can be simplified are selected based on the maximum continuous simplification threshold and the redundancy attributes of the nodes.

[0022] In one implementation of the first aspect, the drainage network model is reconstructed based on the filtered directed topology database, including:

[0023] The relational attributes in the filtered directed topology database are reconstructed to obtain reconstructed relational attribute data.

[0024] Modify the corresponding model nodes and pipes in the drainage network model according to the reconstructed relational attribute data to obtain the reconstructed drainage network model.

[0025] In one implementation of the first aspect, the reconstructed relational attribute data obtained through reconstruction calculation includes: the starting node of the relation, the ending node of the relation, the shape of the relation, the length of the relation, the diameter of the relation, the width of the relation, the burial depth data of the relation, and the roughness coefficient of the relation.

[0026] In one implementation of the first aspect, when the trial calculation result does not meet the simplification objective, updating the initial simplification criterion includes:

[0027] Based on the trial calculation results, the initial simplification standard is updated according to a preset priority.

[0028] Secondly, this application provides a graph-based system for simplifying drainage network models, the system comprising:

[0029] The acquisition module is used to acquire the basic information and simplification objectives of the drainage network model to be simplified, and to establish initial simplification criteria.

[0030] The database creation module is used to create a directed topology database of the drainage network based on the basic information of the drainage network model.

[0031] A simplification module is used to filter nodes in the directed topology database using the initial simplification criteria;

[0032] The reconstruction module is used to reconstruct the drainage network model based on the filtered directed topology database;

[0033] An iterative module is used to perform trial calculations on the reconstructed drainage network model, and output the reconstructed drainage network model when the trial calculation results meet the simplification objective. When the trial calculation results do not meet the simplification objective, the initial simplification criteria are updated, and the directed topology database is further filtered according to the updated simplification criteria.

[0034] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the graph theory-based drainage network model simplification method described in any of the first aspects of this application.

[0035] Fourthly, this application provides an electronic device, the electronic device comprising: a processor and a memory, the memory being used to store a computer program; the processor being communicatively connected to the memory, and when the computer program is invoked, executing the graph theory-based drainage network model simplification method described in any of the first aspects of this application.

[0036] As described above, the graph theory-based drainage network model simplification method, system, medium, and equipment described in this application have the following beneficial effects:

[0037] First, this application emphasizes the vertical connection of the pipeline network through the directed topological association of the graph database, and the simplified model can still accurately reflect the hydraulic characteristics of the original system.

[0038] Second, the graph theory-based drainage network model simplification method proposed in this application can flexibly and automatically handle complex urban pipe network scenarios, reduce data maintenance costs, and reduce errors in manual modeling.

[0039] Third, the graph theory-based drainage network model simplification method provided in this application automatically simplifies the network calculation nodes while ensuring high accuracy, improves calculation efficiency, enables rapid evaluation of the network capacity under multiple working conditions over a long period of time, and ensures the model's immediate response under emergency conditions. Attached Figure Description

[0040] Figure 1 The diagram shown illustrates a scenario application of the graph theory-based drainage network model simplification method of this application in one embodiment.

[0041] Figure 2 The diagram shown is a flowchart of an embodiment of the graph theory-based drainage network model simplification method of this application.

[0042] Figures 3a-3f This diagram illustrates the redundancy label determination process in this application.

[0043] Figure 4 The diagram shown is a flowchart of another embodiment of the graph theory-based drainage network model simplification method of this application.

[0044] Figure 5 The diagram shown is a structural schematic of a graph-based drainage network model simplification system according to an embodiment of this application.

[0045] Figure 6 The diagram shown is a structural schematic of the electronic device of this application in one embodiment.

[0046] Component designation explanation

[0047] 11 mobile phones

[0048] 12 tablet computers

[0049] 13 Laptops

[0050] 5. A simplified system for drainage network model based on graph theory

[0051] 51 Input Module

[0052] 52 Database Creation Module

[0053] 53 Simplified Module

[0054] 54 Refactoring Module

[0055] 55 Iteration Module

[0056] 600 electronic devices

[0057] 601 processor

[0058] 602 Memory

[0059] 6021 Operating System

[0060] 6022 Application

[0061] 603 Network Interface

[0062] 604 bus system

[0063] 605 User Interface

[0064] Steps S21 to S25

[0065] Steps S41 to S45 Detailed Implementation

[0066] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0067] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0068] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0069] To address the technical problems of low efficiency and high error rate in the simplification process of drainage pipe network models, this application provides a graph theory-based method, system, medium, and equipment for simplifying drainage pipe network models. By associating the drainage pipe network model to be simplified with the directed topology of the graph database, emphasizing the vertical association of the pipe network, the simplified model can accurately reflect the hydraulic characteristics of the original model. Under the premise of ensuring high accuracy, the calculation nodes of the pipe network are automatically simplified, improving the calculation efficiency, realizing the rapid evaluation of the pipe network capability under multiple working conditions over a long period of time, ensuring the immediate response of the model under emergency conditions, and breaking through the performance bottleneck of detailed models in online real-time rapid calculation.

[0070] The graph theory-based simplification method for drainage network models in this application can be applied to, for example... Figure 1 The electronic devices shown in this application may include mobile phones 11 with wireless charging capabilities, tablet computers 12, laptop computers 13, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. The specific types of electronic devices are not limited in this application embodiment.

[0071] For example, the electronic device can communicate with networks and other devices wirelessly. The aforementioned wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technologies.

[0072] The following will describe in detail, with reference to the accompanying drawings, the principles and implementation methods of the graph theory-based drainage network model simplification method and system, medium and equipment described in the embodiments of this application, so that those skilled in the art can understand the graph theory-based drainage network model simplification method and system, medium and equipment of this embodiment without creative effort.

[0073] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 2 Detailed explanation. For example... Figure 2 As shown in the embodiments of this application, the method for simplifying the drainage network model based on graph theory includes the following steps S21 to S25.

[0074] Step S21: Establish simplification goals and initial simplification criteria.

[0075] In this implementation, the simplification objectives include: under the same model boundary conditions and computer environment configuration, the reduction ratio of the calculation time of the simplified drainage network model relative to the unsimplified drainage network model meets a preset reduction ratio; under the same model boundary conditions, the deviation rate of the calculation accuracy deviation of the simplified drainage network model relative to the unsimplified drainage network model meets a preset threshold.

[0076] It should be noted that the preset reduction ratio and the preset threshold can be set according to actual needs and are not unique fixed values.

[0077] In this embodiment, the initial simplification criteria include: setting a relational structural similarity merging rule to determine whether a node is redundant; setting a maximum continuous simplification threshold to limit the number of consecutive simplifications of redundant nodes in the directed topology database; setting a boundary node simplification rule to limit whether nodes with boundary attributes are simplified, and the adjustment method of the related boundaries when simplified, and mounting them to upstream or downstream according to the settings.

[0078] Specifically, as shown in Table 1 below, for nodes with boundary attributes, the boundary point simplification rules include: determining whether the node with boundary attributes should be added to the simplification queue according to actual needs, whether the boundary of the node with boundary attributes meets the boundary simplification threshold, and the movement method of the relevant boundary of the node with boundary attributes under different circumstances after simplification.

[0079] Table 1 Simplified Rules for Boundary Nodes

[0080]

[0081] Specifically, whether two relationships connected to the same node need to be merged can be determined based on the structural similarity between the two relationships. The default range of the structural similarity index is shown in Table 2 below.

[0082] Table 2. Structural Similarity Thresholds for Pipe Type Relationships

[0083]

[0084] As shown in Table 2, the structural similarity indicators include: flow area difference threshold, slope difference threshold, and burial depth difference threshold. When an adjacent relationship meets the preset threshold conditions, it can be considered structurally similar. It should be noted that the indicators participating in the structural similarity evaluation can be set according to actual needs; not all three need to participate in the evaluation. Indicators not participating in the evaluation will be assessed using the most stringent standard, i.e., a threshold of 0.

[0085] When merging two relationships connected to the same node, the initial value of the structural similarity index is first determined. Preferably, the initial value is set to 0, or it can be set according to actual needs. When the simplification result obtained according to the initial simplification standard fails to meet the simplification goal, the threshold of the structural similarity index is linearly adjusted in the simplification iteration through step S25, based on the completion status of the simplification goal and the priority of updating the simplification standard.

[0086] Specifically, when the initial value of the structural similarity index participating in the evaluation is 0, the structural similarity index in the first round of simplified iteration takes the default value in Table 2, while the threshold index not participating in the evaluation is always 0. During the adjustment of the threshold of the structural similarity index, the scaling of the threshold is limited by the maximum value in Table 2.

[0087] It should be noted that the maximum value and the initial default value in Table 2 can be modified according to actual needs, and are not unique fixed values.

[0088] Step S22: Establish a directed topology database of the drainage network based on the basic information of the drainage network model.

[0089] In this embodiment, establishing a directed topology database of the drainage network based on the basic information of the drainage network includes: determining node types and relationship types based on the basic information of the drainage network model; extracting node attribute data and relationship attribute data from the basic information of the drainage network model based on the node types and relationship types; transferring the node types, node attribute data, relationship types, and relationship attribute data into a node-relationship database file; and transferring the node-relationship database file into a directed topology database.

[0090] Specifically, node and node attribute data that conform to the node type, and relation and relation attribute data that conform to the relation type are extracted from the basic information of the drainage network model. Based on the generalization method of the drainage system in the Storm Water Management Model (SWMM), the extracted node, node attribute data, relation, and relation attribute data are transferred into a node-relation database file and then transferred into a directed topology database.

[0091] Specifically, the basic information of the drainage network model includes: node data and data on the relationships between nodes.

[0092] The nodes include: inspection wells, regulating reservoirs, and discharge outlets; the relationships include: pipelines, gate valves, weirs, and pumping stations.

[0093] For example, Table 3 below is a correspondence table of nodes and relationships established based on the basic information of a drainage network.

[0094] Table 3: Correspondence between Nodes and Relationships

[0095]

[0096] In this embodiment, the raw data of nodes and relationships in the graph database adopts a standardized CSV table structure design, which facilitates efficient import into the graph database. The node table fields include: a point type label for defining the node type and point type attributes for describing the characteristics of each node entity; the relationship table fields include: a start node number and an end node number linked to the node table via foreign keys, a relationship type label for defining the relationship type, and a relationship type attribute for describing the characteristics of each relationship entity.

[0097] In this embodiment, the node data includes: number, bottom elevation, depth, etc.; the relationship data includes: number, starting node number, ending node number, starting burial depth, ending burial depth, shape, diameter, width, length, etc.

[0098] In addition, the basic information of the drainage network model also includes necessary attributes such as the point source boundary related to the inspection well, the area source boundary related to the inspection well, the installation status of monitoring equipment, and the location of control points.

[0099] Therefore, node data also includes: source water volume, associated catchment area number, associated catchment area area, presence or absence of monitoring equipment, presence or absence of control rules, etc.; relationship data also includes: presence or absence of monitoring equipment, presence or absence of control rules, etc.

[0100] As shown in Tables 4 and 5 below, these are the node type labels and attributes for graph databases, and the relation type labels and attributes for graph databases, respectively.

[0101] Table 4 shows the database node type labels and attributes.

[0102]

[0103] Table 5 shows the database relation type labels and attributes.

[0104]

[0105]

[0106] Specifically, the monitoring equipment includes a level gauge and a flow meter, wherein the level gauge is installed in the inspection well and the flow meter is installed in the pipeline.

[0107] Step S23: Filter the nodes in the directed topology database using the initial simplification criteria.

[0108] In this embodiment, the nodes in the directed topology database are filtered using the initial simplification criteria, including: establishing redundancy attributes of nodes in the directed topology database based on the boundary point simplification rules and the relational structural similarity merging rules; and filtering nodes and relations that can be simplified in the final stage according to the maximum continuous simplification threshold and the redundancy attributes of the nodes.

[0109] Specifically, redundant attribute fields are added to all nodes and relationships in the constructed directed topology database, and a redundant attribute allocation system is constructed. After multiple rounds of judgment on the nodes and relationships in the graph database through this allocation system, the nodes and relationships that can be simplified are determined. The specific attribute values ​​and meanings are shown in Table 6.

[0110] Table 6 Redundancy Attribute Allocation System

[0111]

[0112] Specifically, such as Figures 3a-3f As shown in Table 6, based on the redundant attribute allocation system, the nodes and relationships in the graph database are judged to filter out nodes that meet the simplification criteria. The steps are as follows:

[0113] 1. Initialize the redundant attributes of all points and relations to (0).

[0114] 2. Determine the relationship between parallel pipe types and

[0115] The redundant attribute of the parallel pipeline type relationship with no associated control rules and monitoring equipment and the same burial depth is set to (99) and reconstructed. The redundant attribute of the merged pipeline type relationship is set to (0) to avoid the impact on the determination of non-simplifiable multi-relationship nodes.

[0116] 3. Determine nodes and relationships that cannot be simplified (9999)

[0117] a) Set the redundancy attribute of the starting and ending nodes of the relationship between the associated control rules and the pipeline type of the monitoring equipment, as well as the relationship between the gate valve, weir, and pump station types, to (9999).

[0118] b) Set the redundancy attribute of nodes of storage tank and outlet type, multi-relationship nodes containing more than 2 redundancy attributes of (0), and nodes associated with control rules or monitoring equipment to (9999);

[0119] c) For nodes with source water volume and associated catchment areas, determine whether to set the redundant attribute to (9999) according to the boundary node simplification rules in Table 1.

[0120] d) Set the redundancy attribute to (9999) for the remaining non-simplifiable nodes specified by the user.

[0121] 4. Set the redundancy attribute of all root nodes in the graph database with a redundancy attribute value of (0) to (9998).

[0122] 5. Determine which nodes meet the simplification rules (1)

[0123] a) such as Figure 3a As shown, based on the root node with redundancy attribute (9998), through breadth-first traversal, capture the set of shortest paths with the first redundancy attribute (9999) as the endpoint. All nodes whose redundancy attribute is not (9999) or (9998) passed through by this path are empty nodes, and their redundancy attribute is set to (1).

[0124] b) such as Figure 3b As shown, for the remaining nodes with a redundancy attribute of (0), according to the relational structural similarity merging rules in Table 2, it is determined whether the two pipe type relationships connected to them satisfy the relational structural similarity merging rules. If they do, the redundancy attribute of the node is set to (1).

[0125] 6. Determine the redundant node (99) that meets the maximum number of consecutive simplifications; this is the final node that can be simplified.

[0126] a) such as Figure 3c As shown, the redundancy attribute of all nodes with redundancy attribute (1) in case a) of case 5 is set to (99);

[0127] b) such as Figure 3d As shown, for all nodes with redundancy attribute (1) in case b) of case 5, the redundancy attribute is determined based on the maximum continuous simplification threshold in the initial simplification criterion. The redundancy attribute of nodes that meet the condition is set to (99), and the redundancy attribute of nodes that do not meet the condition is set to (11).

[0128] 7. Determine the redundant relation type (99), which is the final mergeable relation.

[0129] like Figures 3e-3f As shown, for a continuous pipeline type relationship that satisfies the redundancy attribute of the two end nodes being non-(99) and the redundancy attribute of the middle node being all (99), its redundancy attribute is set to (99).

[0130] Step S24: Reconstruct the drainage network model based on the filtered directed topology database.

[0131] In this embodiment, the drainage network model is reconstructed based on the filtered directed topology database, including: reconstructing the relational attributes in the filtered directed topology database to obtain reconstructed relational attribute data; and modifying the corresponding model nodes and pipes in the drainage network model according to the reconstructed relational attribute data to obtain the reconstructed drainage network model.

[0132] In this embodiment, the reconstructed relational attribute data obtained through reconstruction calculation includes: the starting node of the relation, the ending node of the relation, the shape of the relation, the length of the relation, the diameter of the relation, the width of the relation, the burial depth data of the relation, and the roughness coefficient of the relation.

[0133] Specifically, for the starting node and ending node of a relationship: the starting node number and ending node number of the relationship are determined based on the filtered directed topology database.

[0134] Regarding the shape of the relationships: If all the relationships to be merged have the same shape attribute, the shape attribute of the merged relationship will remain unchanged. If they are not the same, the shape of the merged relationship will be circular. For example, if all the relationships to be merged have the same shape, the merged relationship will also be circular. If the shapes of the relationships to be merged include both rectangles and circles, the merged relationship will be circular. If all the relationships to be merged have the same shape, the merged relationship will also be rectangular.

[0135] The length of a relation can be determined based on the number of relations to be merged and the length of each individual relation. The length of the merged relation is the sum of the lengths of the relations to be merged.

[0136] Regarding the diameter of the relationships: If the relationships to be merged have the same dimensions, the diameter of the merged relationship remains unchanged. If they do not have the same dimensions, the diameter of the merged relationship is calculated using the volume method. The specific formula is as follows:

[0137]

[0138] Where R represents the diameter of the merged relationship, and V i Let l be the volume of the i-th relation to be merged. i Let be the length of the i-th relation to be merged, and n be the number of relations to be merged.

[0139] Regarding the width of the relationships: if the dimensions of the relationships to be merged are both rectangular, then the width of the merged relationship remains unchanged; otherwise, the width of the merged relationship is 0.

[0140] For the burial depth data of relationships: the starting burial depth is the starting burial depth of the upstream relationship in the continuous mergeable relationships, and the ending burial depth is the ending burial depth of the downstream relationship in the continuous mergeable relationships.

[0141] The coarsening coefficient of a relation is determined using a weighted method based on relation length, with the specific formula as follows:

[0142]

[0143] Where α represents the weight, r i l represents the roughness coefficient of the i-th relation to be merged. i Let represent the length of the i-th relation to be merged, and n be the number of relations to be merged.

[0144] In this embodiment, when obtaining the reconstructed drainage network model, step S24 further includes: when the initial simplification rule determines that a node with source water volume and associated catchment area is a simplifiable point, the boundary of the node needs to be transferred and attached to the non-simplified point after the simplification determination.

[0145] Specifically, the directedness of the graph database can be used to transfer the boundary to the nearest non-simplified node without point source water volume and associated catchment area in the following three ways: migrate to the nearest upstream node, migrate to the nearest downstream node, or migrate to the nearest upstream or downstream node.

[0146] Step S25: Perform trial calculations on the reconstructed drainage network model. Output the reconstructed drainage network model when the trial calculation results meet the simplification objective. If the trial calculation results do not meet the simplification objective, update the initial simplification criteria and continue to filter the directed topology database according to the updated simplification criteria.

[0147] In this embodiment, step S25 includes: performing trial calculations on the reconstructed drainage network model to obtain the calculation accuracy deviation and calculation time, comparing it with the simulation results of the unsimplified model under the same environment; if the calculation accuracy deviation and calculation time meet the initial simplification target, outputting the reconstructed drainage network model; if the calculation accuracy deviation and calculation time do not meet the simplification target, then, given that the degree of simplification of the original reference model is unknown, adopting a progressive simplification strategy, starting with simplification rules with stronger constraints, and dynamically updating the initial simplification standard according to the simplification effect, and then, according to the updated simplification standard, following... Figure 2 Steps S23-S25 shown in the diagram continue to filter the directed topology database after filtering until the simplification objective is met.

[0148] Specifically, the initial simplification standard shown in step S21 is adjusted and updated using a progressive simplification strategy. The specific adjustment method is as follows: if the calculation accuracy deviation meets the simplification target, but the calculation time does not, the simplification standard can be lowered by increasing the threshold for merging rules based on structural similarity of relationships, increasing the maximum continuous simplification threshold, or relaxing the boundary node simplification rules, in order to improve the calculation speed and reduce the calculation time while meeting the calculation accuracy requirements.

[0149] If the calculation accuracy deviation does not meet the simplification target, but the calculation time meets the simplification target, the simplification standard can be improved by lowering the threshold of the relational structural similarity merging rule, reducing the maximum continuous simplification threshold, or tightening the boundary node simplification rule, so as to improve the calculation accuracy while meeting the calculation speed requirement.

[0150] If the calculation accuracy deviation and calculation time do not meet the simplification target, the simplification standard can be reduced by lowering the threshold of the structural similarity merging rule, reducing the maximum continuous simplification threshold, or tightening the boundary node simplification rule, so as to improve the calculation speed while prioritizing the calculation accuracy.

[0151] The parameter modification priority is: maximum continuous simplification threshold > relational structural similarity merging rule > boundary point simplification rule. When lowering the simplification standard, first gradually increase the maximum continuous simplification threshold to the theoretical maximum value. If the final simplification effect still does not meet the simplification target, then gradually linearly increase the relational structural similarity merging rule threshold to the maximum value in Table 2, as follows:

[0152]

[0153] Among them, C j Let f be the index, i be the iteration number, and f be the number of iterations. j This is the scaling factor corresponding to the indicator. This represents the maximum value of the corresponding indicator in Table 2.

[0154] If the simplification objective still cannot be met, then the boundary point simplification restrictions are lifted.

[0155] Please see Figure 4 The diagram shows a flowchart illustrating a graph-based drainage network model simplification method according to another embodiment of this application. The graph-based drainage network model simplification method described in this embodiment includes the following steps S41 to S45.

[0156] Step S41: Establish a directed topology database of the drainage network based on the basic information of the drainage network model to be simplified;

[0157] Step S42: Establish simplification goals and initial simplification criteria;

[0158] Step S43: Perform a simplified judgment based on the initial simplification criteria to filter nodes in the directed topology database;

[0159] Step S44: Reconstruct the simplified drainage network model based on the filtered directed topology database, and adjust the boundaries;

[0160] Step S45: Perform trial calculations on the reconstructed drainage network model and determine whether the calculation results meet the simplification objective;

[0161] If the trial calculation results meet the simplification objective, then output the reconstructed drainage network model;

[0162] If the trial calculation results do not meet the simplification objective, the simplification criteria are updated, and the process jumps to step S43. The simplification judgment is continued according to the updated simplification criteria to further filter the directed topology database. Steps S43 to S45 are repeated until the obtained drainage network model after several reconstructions meets the simplification objective, and then the reconstructed drainage network model is output.

[0163] The detailed execution method of steps S41-S45 in this embodiment is the same as... Figure 2 The embodiments shown are the same, so they will not be described in detail here.

[0164] The method for simplifying drainage network models based on graph theory described in this application is not limited to the order of steps listed in the above embodiments. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0165] On the other hand, this application also provides a system for simplifying drainage network models based on graph theory.

[0166] Please see Figure 5 This is shown as the simplified drainage network model system 5 based on graph theory described in the embodiments of this application, such as... Figure 5 As shown, the simplified drainage network model 5 based on graph theory includes:

[0167] Input module 51 is used to establish simplification goals and initial simplification criteria;

[0168] Database creation module 52 is used to create a directed topology database of the drainage network based on the basic information of the drainage network model;

[0169] Simplification module 53 is used to filter nodes in the directed topology database using the initial simplification criteria;

[0170] Reconstruction module 54 is used to reconstruct the drainage network model based on the filtered directed topology database;

[0171] Iteration module 55 is used to perform trial calculations on the reconstructed drainage network model, so as to output the reconstructed drainage network model when the trial calculation results meet the simplification objective. When the trial calculation results do not meet the simplification objective, the initial simplification standard is updated, and the directed topology database after screening continues to be screened according to the updated simplification standard.

[0172] Since the specific implementation of this embodiment corresponds to the aforementioned method embodiment, the same details will not be repeated here.

[0173] It should be noted that the above division of modules is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, module x can be a separate processing element, or it can be integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and its function can be called and executed by a processing element of the device. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0174] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).

[0175] This application also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the graph theory-based drainage network model simplification method provided in the embodiments of the present invention.

[0176] In this application, any combination of one or more storage media may be used. The storage medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0177] This application also provides an electronic device. Figure 6 This is a schematic block diagram of the electronic device provided in an embodiment of this application. Figure 6 As shown, electronic device 600 includes at least one processor 601, memory 602, at least one network interface 603, and user interface 605. The various components in electronic device 600 are coupled together via bus system 604. It is understood that bus system 604 is used to implement communication between these components. In addition to a data bus, bus system 604 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 6 The general will label all buses as bus systems.

[0178] User interface 605 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touchscreen.

[0179] It is understood that memory 602 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable categories of memory.

[0180] In this embodiment, the memory 602 is used to store various types of data to support the operation of the electronic device 600. Examples of this data include any executable program that operates on the electronic device 600, such as the operating system 6021 and the application 6022. The operating system 6021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application 6022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The graph theory-based drainage network model simplification method provided in this embodiment can be included in the application 6022.

[0181] The methods disclosed in the embodiments of this application can be applied to or implemented by processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 601 or by instructions in software form. The processor 601 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 601 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. General-purpose processor 601 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of this application can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0182] In an exemplary embodiment, the electronic device 600 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to perform the aforementioned method.

[0183] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0184] In summary, the graph-based drainage network model simplification method, system, medium, and equipment described in this application, compared with existing technologies, emphasize the vertical connections between pipe networks through directed topological associations in a graph database. The simplified model still accurately reflects the hydraulic characteristics of the original system. It can flexibly and automatically handle complex urban pipe network scenarios, reducing data maintenance costs and minimizing manual modeling errors. While ensuring high accuracy, it automatically simplifies pipe network calculation nodes, improving computational efficiency and enabling rapid evaluation of long-term, multi-condition pipe network capabilities, ensuring the model's immediate response under emergency conditions. Therefore, this application effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0185] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for simplifying drainage pipe network models based on graph theory, characterized in that, The method includes the following steps: Establish simplification goals and initial simplification criteria; A directed topology database of the drainage network is established based on the basic information of the drainage network model. The nodes in the directed topology database are filtered using the initial simplification criteria. The drainage network model is reconstructed based on the filtered directed topology database; The reconstructed drainage network model is tested, and the reconstructed drainage network model is output when the test result meets the simplification objective. When the test result does not meet the simplification objective, the initial simplification standard is updated, and the directed topology database is further filtered according to the updated simplification standard.

2. The method for simplifying drainage network models based on graph theory according to claim 1, characterized in that, A directed topology database of the drainage network is established based on the basic information of the drainage network model, including: Determine the node types and relationship types based on the basic information of the drainage network model; Based on the node type and the relationship type, extract node attribute data and relationship attribute data from the basic information of the drainage network model; The node type, node attribute data, relationship type, and relationship attribute data are transferred into a node-relationship database file, and the node-relationship database file is transferred into a directed topology database.

3. The method for simplifying drainage pipe network models based on graph theory according to claim 1, characterized in that, Establishing initial simplification criteria includes: Define rules for merging relationships based on structural similarity to determine whether nodes are redundant; Set a maximum consecutive simplification threshold to limit the number of consecutive simplifications of redundant nodes in the directed topology database; Set boundary node simplification rules to restrict whether nodes with boundary attributes are simplified, and how the related boundaries are adjusted when simplified. Mount the node to the upstream or downstream based on the settings.

4. The method for simplifying drainage network models based on graph theory according to claim 4, characterized in that, Filtering nodes in the directed topology database using the initial simplification criteria includes: Based on the boundary point simplification rules and the relational structural similarity merging rules, redundant attributes of nodes are established for nodes in the directed topology database. The nodes that can be simplified are selected based on the maximum continuous simplification threshold and the redundancy attributes of the nodes.

5. The method for simplifying drainage network models based on graph theory according to claim 1, characterized in that, The drainage network model is reconstructed based on the filtered directed topology database, including: The relational attributes in the filtered directed topology database are reconstructed to obtain reconstructed relational attribute data. Modify the corresponding model nodes and pipes in the drainage network model according to the reconstructed relational attribute data to obtain the reconstructed drainage network model.

6. The method for simplifying drainage network models based on graph theory according to claim 5, characterized in that, The reconstructed relational attribute data obtained through reconstruction calculation includes: the starting node of the relation, the ending node of the relation, the shape of the relation, the length of the relation, the diameter of the relation, the width of the relation, the burial depth data of the relation, and the roughness coefficient of the relation.

7. The method for simplifying drainage network models based on graph theory according to claim 1, characterized in that, When the trial calculation result does not meet the simplification objective, the initial simplification criterion is updated, including: Based on the trial calculation results, the initial simplified standard relational structural similarity merging rules are updated according to a preset priority.

8. A simplified drainage network model system based on graph theory, characterized in that, The system includes: The input module is used to establish simplification goals and initial simplification criteria; The database creation module is used to create a directed topology database of the drainage network based on the basic information of the drainage network model. A simplification module is used to filter nodes in the directed topology database using the initial simplification criteria; The reconstruction module is used to reconstruct the drainage network model based on the filtered directed topology database; An iterative module is used to perform trial calculations on the reconstructed drainage network model, and output the reconstructed drainage network model when the trial calculation results meet the simplification objective. When the trial calculation results do not meet the simplification objective, the initial simplification criteria are updated, and the directed topology database is further filtered according to the updated simplification criteria.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the graph theory-based method for simplifying drainage network models as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes: A memory that stores a computer program; The processor, which is communicatively connected to the memory, executes the graph-based drainage network model simplification method according to any one of claims 1 to 7 when calling the computer program.

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