A method for constructing a sewer network system model

Abstract

The present application belongs to the technical field of water supply and drainage system data analysis, and provides a water supply and drainage pipe network system model construction method. The present application firstly judges the trunk pipe bias degree of different pipes according to the attribute value size performance of different pipes in the water supply and drainage pipe network system and the access conditions of other pipes, and performs clustering operation on the pipes. Then, the pipes are merged according to the trunk pipe bias performance of each pipe in the edge cluster after clustering of the water supply and drainage pipe network and the unified degree of use. The pipes are also merged according to the distribution performance of the trunk pipes and branch pipes in the regional convergence cluster after clustering of the water supply and drainage pipe network. Finally, the simplified model is reasonably judged according to the simplification degree of the simplified model obtained after each pipe merging and the verification effect feedback, and the final simple model is obtained. The simplified model obtained by the present application not only can reduce the calculation amount, but also can effectively retain the characteristics of the urban pipe network.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology for water supply and drainage systems, and in particular to a method for constructing a model of a water supply and drainage network system. Background Technology

[0002] Simplifying the distribution of urban water supply and drainage pipe network systems based on their location characteristics can significantly reduce the complexity and computational cost of such models, improve simulation efficiency, and ensure the reliability of the model in macro-planning, risk identification, and operation scheduling, since the main pipe network and key nodes are generally retained first when simplifying the distribution of urban water supply and drainage pipe network systems. This provides a clear and efficient basis for decision-making in scientific management.

[0003] Traditional methods for simplifying the distribution of urban water supply and drainage pipe network systems only merge pipe segments at the edge of the network based on the distribution of pipe diameter, length, and location of different pipe segments. They do not take into account the complex connections between different pipes, nor can they perform reasonable merging operations based on the importance of pipes in drainage within the network. As a result, the simplified model is not accurate enough. Summary of the Invention

[0004] To address the above technical problems, this invention provides a method for constructing a water supply and drainage network system model.

[0005] According to the present invention, a method for constructing a water supply and drainage network system model includes:

[0006] Obtain the network layout and attribute values ​​of each pipe in the water supply and drainage network system;

[0007] Based on the magnitude of the attribute values ​​of different pipelines and the connection status of other pipelines in the pipeline network layout, the degree of deviation of the main pipeline of each pipeline segment is obtained.

[0008] Based on the degree of deviation of the main pipeline, clustering operations are performed on all pipelines in the water supply and drainage network to obtain several clusters, including edge clusters and regional convergence clusters.

[0009] For the edge cluster, based on the degree of deviation of the main pipe, the degree of merging deviation of each pipe is analyzed, and the pipes in the edge cluster are merged.

[0010] For the regional convergence cluster, based on the degree of deviation of the main pipe, the distribution of the main pipe and branch pipe in the pipeline is analyzed, and then combined with the degree of merging deviation, the proportion of merged pipeline segments in the regional convergence cluster is obtained, and the pipelines in the regional convergence cluster are merged.

[0011] Each pipeline merge yields a simplified model. The simplification level and verification effect of the simplified model are analyzed to determine the simplification rationality and finalize the simplified model.

[0012] In some embodiments of the present invention, the degree of deviation of the main pipe of each segment is obtained based on the magnitude of the attribute values ​​of different pipes and the access status of other pipes in the pipe network layout, including:

[0013] The pipes in the water supply and drainage network are divided into segments to obtain several pipe segments.

[0014] From the attribute values ​​of the pipeline, obtain the length and diameter of each pipeline segment to obtain the water supply and drainage capacity of each pipeline segment;

[0015] Based on the pipeline network layout, count the number of other pipelines connected to each segment of the pipeline;

[0016] Based on the water supply and drainage capacity and the number of connections, the degree of deviation of the main pipe in each pipeline segment is obtained.

[0017] In some embodiments of the present invention, the degree of deviation of the main pipe of each section of the pipeline is obtained based on the water supply and drainage capacity and the number of connections, including:

[0018] Obtain the maximum water supply and drainage capacity of all pipe segments in the water supply and drainage network, and compare the water supply and drainage capacity of each pipe segment with the maximum water supply and drainage capacity.

[0019] Obtain the maximum number of other pipes connected to each segment of the pipeline in the water supply and drainage network, and compare the ratio of the number of connections corresponding to each segment of the pipeline to the maximum number of connections.

[0020] Based on the magnitude relationship and the ratio relationship, the degree of deviation of the main pipe of each section of the pipeline is obtained.

[0021] In some embodiments of the present invention, based on the degree of deviation of the main pipe, a clustering operation is performed on all pipes in the water supply and drainage network to obtain several clusters, including:

[0022] Using the pipe segments as edges, with the direction of the edges following the direction of water flow, and the connection points between the pipe segments as nodes, a water supply and drainage network diagram is obtained;

[0023] Using the degree of deviation of the main pipe corresponding to each pipeline segment as the weight of each side, the drainage network map is clustered using spectral clustering to obtain several clusters.

[0024] In some embodiments of the present invention, for the edge cluster, based on the degree of deviation of the main pipe, the degree of merging deviation of each pipe is analyzed, including:

[0025] Nodes within the edge cluster that have only incoming edges and no outgoing edges are denoted as endpoints, and multiple endpoints in the edge cluster are obtained.

[0026] Calculate the average degree of deviation of the main pipe for all pipe segments connected to the endpoint to obtain the average degree of deviation of the main pipe;

[0027] Analyze the difference between the main pipe deviation degree of each pipeline segment connected to the terminal point and the average main pipe deviation degree, and combine the average main pipe deviation degree to obtain the combined deviation degree of each pipeline segment connected to the terminal point.

[0028] In some embodiments of the present invention, merging the pipes in the edge cluster includes:

[0029] Preset merging threshold;

[0030] Determine whether the degree of merging bias is greater than the merging threshold;

[0031] If so, all pipe segments connected to the endpoint will be merged to obtain a merged pipe segment.

[0032] In some embodiments of the present invention, for the regional convergence cluster, based on the degree of deviation of the main pipe, the distribution of the main pipe and branch pipes in the pipeline is analyzed, including:

[0033] Preset bias threshold;

[0034] The first number of pipeline segments in the regional convergence cluster whose deviation degree of the main pipe is less than the deviation degree threshold is counted, and the first ratio of the first number to the first total number of pipeline segments in the regional convergence cluster is calculated to obtain the quantity distribution of the main pipe and branch pipe in the pipeline.

[0035] Obtain the convex hull area of ​​all pipe segments in the region cluster, and combine it with the first total number to obtain the pipe distribution density;

[0036] Based on the quantity distribution and distribution density, combined with the degree of deviation of the main pipeline, the degree to which pipelines in the regional convergence cluster can be merged is obtained.

[0037] In some embodiments of the present invention, the proportion of merged pipeline segments of the regional convergence cluster is obtained by combining the degree of merging bias, including:

[0038] Nodes within the region cluster that have only incoming edges and no outgoing edges are denoted as endpoints, and multiple endpoints in the region cluster are obtained.

[0039] The second number of pipe segments connected to the endpoint within the area of ​​the convergence cluster is counted, and the second total number of pipe segments connected to the endpoint is counted. The second ratio of the second number to the second total number is calculated.

[0040] By combining the degree of merging bias, the degree of merging capability, and the second ratio, the proportion of merged pipeline segments in the regional convergence cluster is obtained.

[0041] In some embodiments of the present invention, merging the pipes in the regional convergence cluster includes:

[0042] Based on the proportion of the merged pipeline segments and the second total number, the number of mergeable pipelines in the regional convergence cluster is obtained;

[0043] All pipe segments connected to the endpoint in the regional convergence cluster are sorted in ascending order of the degree of deviation of the main pipe, and the first number of pipe segments that can be merged in the sequence are merged.

[0044] In some embodiments of the present invention, a simplified model is obtained for each pipeline merge, the verification effect and simplification degree of the simplified model are analyzed, the simplification rationality of the simplified model is obtained, and the final simplified model is determined, including:

[0045] Each pipeline merge yields a simplified model;

[0046] By comparing the simplified model and the original model under the same input conditions, the output differences of key performance indicators are obtained to determine the verification effect of the simplified model.

[0047] Calculate the ratio of the simplified length of all pipes in the simplified model to the original length of the pipes in the original model, and combine this with the third total number of pipe segments in the simplified model to obtain the degree of simplification of the simplified model;

[0048] Based on the verification results and the degree of simplification, the simplification rationality of the simplified model is obtained;

[0049] A preset reasonableness threshold is set, and the simplified model corresponding to the first time the simplified reasonableness exceeds the reasonableness threshold is determined as the final simplified model.

[0050] As can be seen from the above embodiments, the water supply and drainage network system model construction method provided by the present invention has the following beneficial effects:

[0051] This invention first determines the degree of deviation of the main pipes in the water supply and drainage network based on the attribute values ​​of different pipes and the connection status of other pipes, and then performs clustering operations on all pipes in the water supply and drainage network. Next, based on the deviation of the main pipes and the uniformity of their functions in the edge clusters after clustering, the pipes in the edge clusters are merged. Then, based on the distribution of main and branch pipes in the regional convergence clusters after clustering, the proportion of mergeable pipes in a single area is obtained, and the pipes in the regional convergence clusters are merged. Finally, based on the simplification degree of the simplified model obtained after each pipe merging and the feedback on the verification effect, the reasonableness of the simplified model is judged, and a suitable simplified model is selected. This invention not only considers the distribution of pipe diameter, length, and location of different pipe segments, but also the complex connections between different pipes and their drainage function in the pipes, merging pipe segments at the edge of the network. This results in a simplified model that effectively retains the characteristics of the urban pipe network while reducing computational load.

[0052] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0053] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic diagram of the basic process of a water supply and drainage network system model construction method provided in an embodiment of the present invention;

[0055] Figure 2 An example diagram of an edge cluster in a drainage network diagram provided by an embodiment of the present invention;

[0056] Figure 3 An example diagram of a regional convergence cluster in a drainage network diagram provided by an embodiment of the present invention;

[0057] Figure 4 This is an example diagram illustrating a comparison between the original model and the simplified model provided in an embodiment of the present invention. Detailed Implementation

[0058] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a water supply and drainage network system model construction method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes that element. Relational terms such as “first” and “second” are used merely to distinguish one entity or operation from another and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0060] The following will describe in detail, with reference to the accompanying drawings, a method for constructing a water supply and drainage network system model provided in this embodiment.

[0061] Please see Figure 1 This illustrates the basic flow of a water supply and drainage network system model construction method provided by an embodiment of the present invention.

[0062] like Figure 1 As shown, an embodiment of the present invention provides a method for constructing a water supply and drainage network system model, which specifically includes the following steps:

[0063] S100: Obtain the network layout and attribute values ​​of each pipe in the water supply and drainage network system.

[0064] The process involves acquiring the network layout and attribute values ​​of each pipe in the water supply and drainage pipeline system. The network layout includes the connection relationships between pipes (such as the number of other pipes connected to each pipe), and attribute values ​​include pipe type (water supply / drainage), pipe diameter, and length. Specifically, firstly, the network layout and direction of the drainage pipeline system are initially obtained using water supply and drainage engineering planning drawings from the city's overall planning map. Then, the scanned water supply and drainage engineering planning drawings are aligned with the correct geographic coordinates in GIS (Geographic Information System) software, and each pipe on the planning drawings is manually traced and converted into vector data. In critical road sections where water supply and drainage engineering planning drawings are severely lacking, ground-penetrating radar (GPR) and other equipment are used for rapid scanning to outline the approximate direction and burial depth of each pipe. Data from different sources is converted to a unified coordinate system and file format. Furthermore, the digitized pipelines are overlaid and matched with the road network to correct significant spatial discrepancies. Finally, the pipeline network topology (connectivity) is established in the GIS software to ensure that the pipelines are logically connected. Finally, add core attributes such as pipe type (water supply / drainage), pipe diameter, and length to each pipe.

[0065] S200: Based on the attribute values ​​of different pipes and the connection status of other pipes in the pipeline network layout, obtain the degree of deviation of the main pipe of each pipe segment.

[0066] Effective model simplification of urban water supply and drainage pipe network systems can significantly reduce model complexity and computational costs. To achieve reasonable simplification, the main pipes and branch pipes should be distinguished first. Main pipes in the water supply and drainage network system are large intercepting or transmitting mains that collect flow from multiple areas, characterized by deep burial, large diameter, and handling the total flow. Branch pipes, on the other hand, connect to storm drains and sewage inspection wells within neighborhoods, characterized by shallow burial, small diameter, and handling source collection. Therefore, main pipes and branch pipes can be distinguished by their pipe diameter, length, and the number of other pipes they connect to.

[0067] Based on the above analysis, in the embodiments of the present invention, the degree of deviation of the main pipe of each pipe segment is obtained according to the magnitude of the attribute values ​​of different pipes and the connection status of other pipes in the pipe network layout. Further, it includes:

[0068] First, the pipes in the water supply and drainage network are segmented to obtain several pipe segments. Specifically, the pipes in the water supply and drainage network are segmented using nodes such as inspection wells, outlets, pumping stations, and treatment plants to obtain several pipe segments.

[0069] Then, from the pipe's attribute values, obtain the length and diameter of each pipe segment to get the water supply and drainage capacity of each pipe segment, and obtain the maximum water supply and drainage capacity of all pipe segments in the water supply and drainage network.

[0070] In addition, based on the pipeline network layout, the number of other pipelines connected to each pipeline segment is counted, and the maximum number of other pipelines connected to all pipeline segments in the water supply and drainage network is obtained.

[0071] Finally, based on the water supply and drainage capacity and the number of connections, the degree of deviation of the main pipe for each pipeline segment is obtained. Specifically, by comparing the water supply and drainage capacity of each pipeline segment with the maximum water supply and drainage capacity, and comparing the ratio of the number of connections for each pipeline segment to the maximum number of connections, the degree of deviation of the main pipe for each pipeline segment is obtained based on these relationships. The formula for calculating the degree of deviation of the main pipe in a segmented pipeline is as follows:

[0072]

[0073] In the formula: Indicates the first The degree of deviation of the main pipeline in each section of the pipeline; This indicates the maximum water supply and drainage capacity of all sections of the pipeline in the water supply and drainage network. Indicates the first The length of each segment of the pipeline; Indicates the first The diameter of each section of the pipeline; Indicates the first The number of other pipelines connected to a section of the pipeline; This indicates the maximum number of other pipes that can be connected to any section of the water supply and drainage network. This represents the minimum value greater than 0. This is to prevent the denominator from being 0, so that it is set ; This represents the minimum value greater than 0. In order to avoid When it is 0 =0 (in) When it is 0, the first Pipeline sections may also be main pipelines, and are set up as follows: ; This represents the maximum and minimum normalization function, with a range of [0,1].

[0074] Indicates the first The water supply and drainage capacity of the segmented pipeline. Indicates the first The relationship between the water supply and drainage capacity of each section of the pipeline and its maximum water supply and drainage capacity. Indicates the first The ratio of the number of connections corresponding to each pipeline segment to the maximum number of connections; when the first segment... Drainage capacity of segmented pipelines The drainage capacity corresponding to the pipe section with the largest drainage capacity The gap The smaller, and the first Number of connecting pipes in a segmented pipeline With maximum number of accesses ratio When it is larger, the first The more a segment of a pipeline matches the characteristics of a main pipeline—larger diameter, higher flow rate, and convergence of flow from multiple areas—the more suitable it is to be judged as a main pipeline. This indicates that the segment... The greater the deviation of the main pipe in the segmented pipeline.

[0075] S300: Based on the degree of deviation of the main pipeline, clustering operation is performed on all pipelines in the water supply and drainage network to obtain several clusters, including edge clusters and regional convergence clusters.

[0076] Based on the degree of deviation of the main pipeline, clustering is performed on all pipelines in the water supply and drainage network to obtain several clusters, including edge clusters and regional convergence clusters. Specifically, firstly, pipeline segments are used as edges, with the direction of the edges following the direction of water flow. Connection points between pipeline segments are used as nodes, i.e., manholes, outlets, pumping stations, treatment plants, etc., to obtain the water supply and drainage network map. Then, using the degree of deviation of the main pipeline corresponding to each pipeline segment as the weight of each edge (each pipeline segment) in the water supply and drainage network map, Spectral Clustering (SC) is used to cluster the drainage network map to obtain several clusters. These clusters include edge clusters and regional convergence clusters. In the drainage network map, clusters containing source nodes (nodes with no incoming edges, only outgoing edges) are designated as edge clusters, such as... Figure 2 As shown; clusters containing the average deviation of each edge (each pipeline segment) from the main pipeline with a deviation less than 0.7, and which are not edge clusters, are designated as regional convergence clusters, such as... Figure 3 As shown.

[0077] S400: For edge clusters, based on the degree of deviation of the main pipe, analyze the degree of merging deviation of each pipe and merge the pipes in the edge cluster.

[0078] In the edge clusters after water supply and drainage pipe network clustering, when multiple pipe segments converge to the same pipe segment, and the pipe diameters of each pipe segment are small and the deviation of the main pipe is small and similar, the more likely each pipe segment is to undertake similar and very limited local water supply and drainage tasks, the more it can be simplified and merged with the connecting pipe (the pipe segment to which multiple pipe segments converge) with a larger deviation of the main pipe.

[0079] Based on the above analysis, in the embodiments of the present invention, for the edge cluster, based on the degree of deviation of the main pipe, the degree of merging deviation of each pipe is analyzed, and the pipes in the edge cluster are merged. Wherein:

[0080] For edge clusters, based on the degree of main pipe bias, the merging bias degree of each pipe is analyzed. This further includes: First, nodes within the edge cluster that have only incoming edges and no outgoing edges are designated as endpoints, obtaining multiple endpoints in the edge cluster; then, the average main pipe bias degree of all pipe segments connected to the endpoints is calculated to obtain the average main pipe bias degree; finally, the difference between the main pipe bias degree of each pipe segment connected to the endpoint and the average main pipe bias degree is analyzed, and combined with the average main pipe bias degree, the merging bias degree of each pipe segment connected to the endpoint is obtained. Specifically, for edge clusters... A single endpoint For example, calculation and endpoint The average deviation of the main pipe from all connected edges (all pipe segments) is denoted as: Then calculate the endpoint. The difference in the degree of deviation of the main pipe on each connected side (each pipe segment) is as follows: ( For edge clusters Midpoint and Endpoint The number of connected pipe segments.

[0081] When the edge cluster It does not exist in When the edge is, the edge cluster endpoint in Differences in the orientation of the main pipes across all connected sides (pipe segments) The smaller the value, and the greater the average degree of main pipe bias. The smaller the size, the smaller the pipe diameter and the smaller the flow rate of each pipe segment in this edge cluster, with the endpoint... The more the connected pipe sections conform to the characteristics of peripheral branch pipes, and the more consistent the degree of deviation of the main pipe, the more likely they are to undertake similar, very limited local water supply and drainage tasks, and the more likely they are to be connected to the termination point. Connected pipe sections and termination points The pipe segments corresponding to outgoing edges are merged and simplified. This leads to the construction of an edge cluster. Midpoint and Endpoint The formula for calculating the degree of merging bias of connected pipe sections is as follows:

[0082]

[0083] In the formula, Represents edge clusters Midpoint and Endpoint The degree of merging bias of connected pipe sections; Represents edge clusters Midpoint and Endpoint The connected first The degree of deviation of the main pipeline in each section of the pipeline; Represents edge clusters Midpoint and Endpoint The average main pipe deviation of all connected pipe segments; Represents edge clusters Midpoint and Endpoint The number of connected pipe segments; represents taking the absolute value; represents the maximum and minimum normalization function, with a range of [0,1].

[0084] Merging pipelines within edge clusters further includes: first, setting a preset merging threshold, which can be 0.6; second, determining whether the merging bias exceeds the merging threshold; if so, i.e. If the endpoint is reached, all pipe segments connected to the endpoint will be merged to form a merged pipe segment; otherwise, i.e. In this case, the pipe segments connected to the endpoint will not be merged. Specifically, based on edge clusters... endpoint in For example, At that time, the edge cluster Midpoint and Endpoint Connected pipe sections and termination points The pipe segments corresponding to the outgoing edges are merged to complete the edge cluster. Midpoint and Endpoint The merging of connected pipe sections.

[0085] Similarly, complete the edge cluster. The merging of pipe segments connected to each endpoint.

[0086] S500: For regional convergence clusters, based on the degree of deviation of the main pipe, analyze the distribution of the main pipe and branch pipe in the pipeline, and then combine the degree of merging deviation to obtain the proportion of merged pipeline segments in the regional convergence cluster, and merge the pipelines in the regional convergence cluster.

[0087] To simplify the water supply and drainage network as much as possible, a certain degree of pipe merging can be carried out for regional convergence clusters. Since some important trunk pipelines may exist in regional convergence clusters, pipelines that can be merged should be screened based on factors such as the proportion of different types of pipelines in the regional convergence cluster.

[0088] Based on the above analysis, in the embodiments of the present invention, for regional convergence clusters, the distribution of main pipes and branch pipes in the pipeline is analyzed based on the degree of main pipe deviation, and then combined with the degree of merging deviation, the proportion of merged pipeline segments in the regional convergence cluster is obtained, and the pipelines in the regional convergence cluster are merged. Wherein:

[0089] For regional convergence clusters, based on the degree of main pipe deviation, the distribution of main and branch pipes in the pipeline is analyzed, further including:

[0090] First, a predefined bias threshold is set, which can be 0.5. Then, the first number of pipe segments in the regional convergence cluster whose bias is less than the bias threshold is counted, and the first ratio of this first number to the first total number of pipe segments in the regional convergence cluster is calculated to obtain the distribution of the number of main and branch pipes in the pipeline. Specifically, taking the regional convergence cluster as an example... For example, statistical regional clusters middle ( Indicates regional clusters The Middle The first number of pipeline segments corresponding to the degree of deviation of the main pipeline segment (the main pipeline deviance) is denoted as... and statistical regional clusters The first total number of all pipeline segments in the pipeline is denoted as . Calculate the first quantity With the first total quantity The first ratio is .

[0091] Furthermore, the convex hull area of ​​all pipe segments in the regional aggregation cluster is obtained, and combined with the first total number, the pipe distribution density is calculated. Specifically, taking the regional aggregation cluster as an example... For example, to obtain regional aggregation clusters The convex hull area of ​​all pipe segments (including regional convergence clusters) The area of ​​the smallest convex polygon among all pipe segments, i.e., the region convergence cluster. The area of ​​the convex polygon formed by connecting the outermost nodes is denoted as... Then the distribution density of the pipeline is .

[0092] Then, based on the quantity distribution and distribution density, combined with the degree of main pipeline bias, the degree of merging potential of pipelines in the regional convergence cluster is obtained. Specifically, taking the regional convergence cluster as an example... For example, when regional clusters converge... Dense distribution of pipeline segments within the area Larger, regional clusters The average degree of deviation of the main pipe corresponding to all included edges (pipe segments). The smaller the ratio When the size is larger, the regional clusters converge. Within the defined pipeline segmentation area, the main pipeline exhibits a relatively small overall deviation and a high distribution density, making it more suitable for extensive pipeline merging. This leads to the formation of regional convergence clusters. The degree to which the sections of the central pipeline can be merged is as follows:

[0093]

[0094] In the formula, Indicates regional clusters The degree to which sections of the pipeline can be merged; Indicates regional clusters The first total number of all pipeline segments in the pipeline; Indicates regional clusters The area of ​​the convex hull of all pipe segments in the middle; Indicates regional clusters The mean of the main pipe deviation corresponding to all edges (pipe segments) contained therein; Indicates regional clusters middle The first number of the corresponding pipeline segments; This represents the maximum and minimum normalization function, with a range of [0,1].

[0095] Based on the degree of merging bias, the proportion of merged pipeline segments in the regional convergence cluster is obtained, further including:

[0096] First, nodes within the region aggregation cluster that have only incoming edges and no outgoing edges are denoted as endpoints, and multiple endpoints in the region aggregation cluster are obtained.

[0097] Then, the second number of pipe segments connected to the endpoint within the regional convergence cluster is counted, and the second total number of pipe segments connected to the endpoint is counted. A second ratio is then calculated between the second number and the second total number. Specifically, this ratio is based on the regional convergence cluster. endpoint in For example, statistical regional clusters Within range and endpoint The second number of connected pipe segments is denoted as... and the endpoint The second total number of connected pipe segments (including those not belonging to regional clusters) Pipeline segments within the scope), denoted as ; Calculate the second quantity With the second total quantity The second ratio is .

[0098] Combining the degree of merging bias, the degree of merging potential, and the second ratio, the proportion of merged pipeline segments in regional convergence clusters is obtained. Specifically, based on regional convergence clusters... endpoint in For example, computing region clustering clusters Midpoint and Endpoint The degree of merging bias of the connected pipe sections is denoted as... When regional clusters converge Mergeability of pipeline segments The larger it is, and the closer it is to the endpoint The degree of merging bias of connected pipeline segments The larger, the second quantity With the second total quantity The second ratio When the size is larger, the regional clusters converge. The more branch pipes in a pipeline segment, and the more they are connected to the endpoint... The more branch pipes exist within a connected pipeline segment, the more suitable it is for pipeline merging, and the larger the proportion of pipeline segments to be merged should be. This leads to the concept of regional convergence clusters. The proportion of the merged pipeline segments is as follows:

[0099]

[0100] In the formula, Indicates regional clusters The proportion of merged pipeline segments; Indicates regional clusters The degree to which sections of the pipeline can be merged; Indicates regional clusters Midpoint and Endpoint The degree of merging bias of each connected pipe segment (determined in the same way as the method described above for obtaining the degree of merging bias of each pipe segment connected to the endpoint in the edge cluster). Indicates regional clusters Within range and endpoint The second number of connected pipe segments; Indicates connection to the endpoint The second total number of pipeline segments; This represents the maximum and minimum normalization function, with a range of [0,1].

[0101] Merging pipelines within a regional convergence cluster further includes:

[0102] First, based on the proportion of merged pipeline segments and the second total number, the number of mergeable pipelines in the regional aggregation cluster is obtained. Specifically, the regional aggregation cluster is constructed. Midpoint and Endpoint The formula for calculating the number of connected, mergeable pipe segments is:

[0103]

[0104] In the formula, Indicates regional clusters Midpoint and Endpoint The number of connected, mergeable pipe segments; Indicates regional clusters Within range and endpoint The second number of connected pipe segments Indicates regional clusters The proportion of merged pipeline segments; This represents the floor function.

[0105] Then, cluster the regions together. Midpoint and Endpoint All connected pipes are divided into sections according to the degree of deviation from the main pipe. Sort the sequence from smallest to largest, and count the number of mergeable pipes in the first few positions. The pipeline segments are merged to complete the regional convergence cluster. Midpoint and Endpoint The merging of connected pipe sections.

[0106] Similarly, complete the regional aggregation cluster The merging of pipe segments connected to each endpoint.

[0107] S600: For each pipeline merge, a simplified model is obtained, the simplification degree and verification effect of the simplified model are analyzed, the simplification rationality of the simplified model is obtained, and the final simplified model is determined.

[0108] Oversimplification of the model may result in a large amount of flow being injected into the main pipe instantaneously at a single point, overestimating the drainage capacity of the main pipe and failing to accurately assess the actual load on each cross-section of the main pipe. Therefore, each time pipes are merged, the drainage pipe network model is simplified. The drainage effect is then verified using the simplified model. When the verification results are good and the pipe model is sufficiently simple, it better meets the simplification requirements, ultimately leading to a suitable simplified model.

[0109] Based on the above analysis, in the embodiments of the present invention, a simplified model is obtained for each pipeline merge, and the simplification degree and verification effect of the simplified model are analyzed to obtain the simplification rationality of the simplified model, and the final simplified model is determined. Further steps include:

[0110] First, a simplified model is obtained for each pipeline merging. Specifically, the "simplification" tool in modern hydraulic modeling software (such as InfoWate) is used to intelligently merge the pipeline network based on the pipeline segments to be merged obtained in the above steps, resulting in a corresponding simplified model. It is required that a simplified model be obtained for each merging analysis.

[0111] Then, with the same input conditions, the simplified model and the original model are compared to obtain the differences in the output of key performance indicators, thus validating the simplified model. Specifically, the same conditions such as precipitation events, infiltration / inflow models, and simulation duration are input into both the simplified and original models. The two models output corresponding key performance indicators such as total water volume deviation, peak flow error, Nash efficiency coefficient (NSE), and percentage deviation (PBIAS). The degree of difference between the corresponding key performance indicators output by the two models is obtained. The formula for calculating the degree of difference can be:

[0112]

[0113] In the formula, Key performance indicators (KPIs) of the simplified model and the original model output. The degree of difference between them; Key performance indicators representing the output of the original model The value; Key performance metrics representing simplified model output The value; This indicates taking the absolute value.

[0114] Based on the degree of influence of key performance indicators (KPIs) on the construction of the water supply and drainage network system model, for example, the total water volume deviation has a significant impact on the model construction, so its corresponding weight can be set to 0.5, while the percentage deviation has a smaller impact, so its corresponding weight can be set to 0.1 (the specific weight values ​​are set based on experience). Weights are then applied to the differences between the simplified model and the original model output for all corresponding KPIs, resulting in a weighted comprehensive score indicating whether the simplified model meets the requirements. This process is used to construct the simplified model. The formula for quantifying the comprehensive score that meets the requirements is:

[0115]

[0116] In the formula, Representing a simplified model A comprehensive score that meets the requirements; Indicates key performance indicators The weights of all key performance indicators The sum of the corresponding weights is 1; Key performance indicators (KPIs) of the simplified model and the original model output. The degree of difference between them; This indicates the number of key performance indicators.

[0117] In addition, the ratio of the simplified length of all pipes in the simplified model to the original length of the pipes in the original model is calculated. Combined with the third total number of pipe segments in the simplified model, the degree of simplification of the simplified model is obtained. Specifically, using the simplified model... For example, calculate the pipeline network model. The length of all pipes in the middle The length of the pipe in the original model without simplification ratio and statistical simplification models The number of pipe sections in the pipeline is denoted as When the ratio The smaller the number of pipe segments The smaller the size, the better the pipeline model. The greater the degree of simplification, the better. Therefore, constructing a simplified model... The formula for calculating the degree of simplification is:

[0118]

[0119] In the formula, Representing a simplified model The degree of simplification; This represents the original length of the pipe in the original model; Represents the original model Simplified length of the central pipe; Representing a simplified model The third total number of pipeline segments.

[0120] Furthermore, by combining the verification results and the degree of simplification, the simplification rationality of the simplified model is obtained. Specifically, the simplified model... For example, after the previous simplification, the simplified model... simplification The larger the score, the better. The larger the value, the better the current simplified model meets the requirements; therefore, constructing a simplified model... The simplified formula for calculating rationality is:

[0121]

[0122] In the formula, Representing a simplified model The degree of simplification and rationality; Representing a simplified model The degree of simplification; Representing a simplified model A comprehensive score that meets the requirements; This represents the maximum and minimum normalization function, with a range of [0,1].

[0123] Finally, a preset reasonableness threshold is set (the value can be 0.9). When the simplification reasonableness is greater than the reasonableness threshold, i.e. This indicates that the current simplified model has met the requirements, therefore the simplification reasonableness is increased to the reasonableness threshold for the first time. The simplified model corresponding to the given time is used as the final simplified model.

[0124] The above methods are used to simplify the pipe network at different locations in the water supply and drainage network system within the processor, and the final simplified model of the water supply and drainage network is obtained through evaluation. The locations and diameters of different pipes in the simplified model are then transferred to a database for storage. By retrieving data from the simplified model and the original model from the database, a visual representation of the water supply and drainage network model before and after simplification is displayed on the operator's screen. Figure 4 As shown.

[0125] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for constructing a model of a water supply and drainage pipe network system, characterized in that, The method includes: Obtain the network layout and attribute values ​​of each pipe in the water supply and drainage network system; Based on the magnitude of the attribute values ​​of different pipelines and the connection status of other pipelines in the pipeline network layout, the degree of deviation of the main pipeline of each pipeline segment is obtained. Based on the degree of deviation of the main pipeline, clustering operations are performed on all pipelines in the water supply and drainage network to obtain several clusters, including edge clusters and regional convergence clusters. For the edge cluster, based on the degree of deviation of the main pipe, the degree of merging deviation of each pipe is analyzed, and the pipes in the edge cluster are merged. For the regional convergence cluster, based on the degree of deviation of the main pipe, the distribution of the main pipe and branch pipe in the pipeline is analyzed, and then combined with the degree of merging deviation, the proportion of merged pipeline segments in the regional convergence cluster is obtained, and the pipelines in the regional convergence cluster are merged. Each pipeline merge yields a simplified model. The simplification level and verification effect of the simplified model are analyzed to determine the simplification rationality and finalize the simplified model.

2. The method for constructing a water supply and drainage network system model according to claim 1, characterized in that, Based on the attribute values ​​of different pipelines and the connection status of other pipelines in the pipeline network layout, the degree of deviation of the main pipeline for each segment is obtained, including: The pipes in the water supply and drainage network are segmented to obtain several pipe segments; From the attribute values ​​of the pipeline, obtain the length and diameter of each pipeline segment to obtain the water supply and drainage capacity of each pipeline segment; Based on the pipeline network layout, count the number of other pipelines connected to each segment of the pipeline; Based on the water supply and drainage capacity and the number of connections, the degree of deviation of the main pipe in each pipeline segment is obtained.

3. The method for constructing a water supply and drainage network system model according to claim 2, characterized in that, Based on the water supply and drainage capacity and the number of connections, the degree of deviation of the main pipe for each section of the pipeline is obtained, including: Obtain the maximum water supply and drainage capacity of all pipe segments in the water supply and drainage network, and compare the water supply and drainage capacity of each pipe segment with the maximum water supply and drainage capacity. Obtain the maximum number of other pipes connected to all segments of the water supply and drainage network, and compare the ratio of the number of connections to each segment of the pipeline to the maximum number of connections. Based on the magnitude relationship and the ratio relationship, the degree of deviation of the main pipe of each section of the pipeline is obtained.

4. The method for constructing a water supply and drainage network system model according to claim 2, characterized in that, Based on the degree of deviation of the main pipeline, clustering is performed on all pipelines in the water supply and drainage network to obtain several clusters, including: Using the pipe segments as edges, with the direction of the edges following the direction of water flow, and the connection points between the pipe segments as nodes, a water supply and drainage network diagram is obtained. Using the degree of deviation of the main pipe corresponding to each pipeline segment as the weight of each side, the drainage network map is clustered using the spectral clustering method to obtain several clusters.

5. The method for constructing a water supply and drainage network system model according to claim 4, characterized in that, For the edge cluster, based on the deviation degree of the main pipe, the merging deviation degree of each pipe is analyzed, including: Nodes within the edge cluster that have only incoming edges and no outgoing edges are denoted as endpoints, and multiple endpoints in the edge cluster are obtained. Calculate the average degree of deviation of the main pipe for all pipe segments connected to the endpoint to obtain the average degree of deviation of the main pipe; Analyze the difference between the main pipe deviation degree of each pipeline segment connected to the terminal point and the average main pipe deviation degree, and combine the average main pipe deviation degree to obtain the combined deviation degree of each pipeline segment connected to the terminal point.

6. The method for constructing a water supply and drainage network system model according to claim 5, characterized in that, Merging the pipes in the edge cluster includes: Preset merging threshold; Determine whether the degree of merging bias is greater than the merging threshold; If so, all pipe segments connected to the endpoint will be merged to obtain a merged pipe segment.

7. The method for constructing a water supply and drainage network system model according to claim 1, characterized in that, For the aforementioned regional convergence cluster, based on the degree of deviation of the main pipe, the distribution of the main pipe and branch pipes in the pipeline is analyzed, including: Preset bias threshold; The first number of pipeline segments in the regional convergence cluster whose deviation degree of the main pipeline is less than the deviation degree threshold is counted, and the first ratio of the first number to the first total number of pipeline segments in the regional convergence cluster is calculated to obtain the quantity distribution of the main pipeline and branch pipeline in the pipeline. Obtain the convex hull area of ​​all pipe segments in the region cluster, and combine it with the first total number to obtain the pipe distribution density; Based on the quantity distribution and distribution density, combined with the degree of deviation of the main pipeline, the degree to which pipelines in the regional convergence cluster can be merged is obtained.

8. The method for constructing a water supply and drainage network system model according to claim 7, characterized in that, Based on the degree of merging bias, the proportion of merged pipeline segments in the regional convergence cluster is obtained, including: Nodes within the region cluster that have only incoming edges and no outgoing edges are denoted as endpoints, and multiple endpoints in the region cluster are obtained. The second number of pipe segments connected to the endpoint within the area of ​​the convergence cluster is counted, and the second total number of pipe segments connected to the endpoint is counted. The second ratio of the second number to the second total number is calculated. By combining the degree of merging bias, the degree of merging capability, and the second ratio, the proportion of merged pipeline segments in the regional convergence cluster is obtained.

9. The method for constructing a water supply and drainage network system model according to claim 8, characterized in that, Merging pipelines within the aforementioned regional convergence cluster includes: Based on the proportion of the merged pipeline segments and the second total number, the number of mergeable pipelines in the regional convergence cluster is obtained; All pipe segments connected to the endpoint in the regional convergence cluster are sorted in ascending order of the degree of deviation of the main pipe, and the first number of pipe segments that can be merged in the sequence are merged.

10. The method for constructing a water supply and drainage network system model according to claim 1, characterized in that, Each pipeline merge yields a simplified model. The validation effect and simplification degree of the simplified model are analyzed to determine the simplification rationality and finalize the simplified model, including: Each pipeline merge yields a simplified model; By comparing the simplified model and the original model under the same input conditions, the output differences of key performance indicators are obtained to determine the verification effect of the simplified model. Calculate the ratio of the simplified length of all pipes in the simplified model to the original length of the pipes in the original model, and combine this with the third total number of pipe segments in the simplified model to obtain the degree of simplification of the simplified model; Based on the verification results and the degree of simplification, the simplification rationality of the simplified model is obtained; A preset reasonableness threshold is set, and the simplified model corresponding to the first time the simplified reasonableness exceeds the reasonableness threshold is determined as the final simplified model.

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