CN111353202B - Partitioning method for underground pipe network general investigation in municipal administration - Google Patents

Partitioning method for underground pipe network general investigation in municipal administration Download PDF

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CN111353202B
CN111353202B CN202010400805.0A CN202010400805A CN111353202B CN 111353202 B CN111353202 B CN 111353202B CN 202010400805 A CN202010400805 A CN 202010400805A CN 111353202 B CN111353202 B CN 111353202B
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pipe
pipe network
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CN111353202A (en
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朱少楠
邵家琦
李猛
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Nanjing University of Posts and Telecommunications
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a partitioning method facing to underground pipe network general survey in municipal administration, which comprises the following steps of (1) collecting pipe network data of an area to be general surveyed, obtaining pipe point data and pipe section data according to the pipe network information of the area to be general surveyed, and constructing a topology structure chart of a pipe network; (2) extracting a main network of the underground pipe network based on a cutting algorithm; (3) based on a community discovery algorithm, dividing an underground pipe network into a plurality of minimum partition units by taking the length of the pipe network as a convergence condition; (4) inputting the number of appointed general investigation partitions, and aggregating the minimum partition units of each pipe network by taking the length and the area of the minimum partition unit as constraint conditions; (5) and outputting pipe network partition results, including pipe network vector data and statistical information such as pipe network length, area and the like of each partition. The invention overcomes the defects that the traditional regular grid partitioning method damages the continuity of the pipe network, the workload of general survey is uneven and the like in the general survey work of urban underground pipe network management.

Description

Partitioning method for underground pipe network general investigation in municipal administration
Technical Field
The invention relates to a partitioning method for an urban water supply pipe network, in particular to a partitioning method for general investigation of an urban underground water supply pipe network in municipal management, and belongs to the technical field of municipal engineering management.
Background
Underground pipelines are an important infrastructure of cities, called city "lifelines". With the continuous expansion of the urban scale, the old and new underground pipelines are mixed, and the network structure is increasingly complicated. Due to the lack of comprehensive pipeline information, especially accurate spatial position information, various safety accidents often occur, even resulting in significant economic loss. While strengthening scientific planning and informatization management of pipelines, the general investigation and detection work of the existing urban underground pipelines is more and more emphasized. In the traditional pipe network general survey, a general survey area is generally defined manually or partitioned in a regular grid mode based on map sheets. Not only the overall topological connectivity of the pipe network is damaged, but also the discontinuity of the general survey pipe section is caused. Meanwhile, the structure of a pipe network in the region is ignored, and the distribution of the partitioned general survey workload is unbalanced. Therefore, when the underground pipe network is generally checked, how to scientifically and effectively divide the area of the pipe network is a key problem in the specific operation of the general pipe network check.
At present, the pipe network partitioning technology mainly aims at the research of various problems of pipe network design, analysis and the like, is usually established on the basis of pipe network simplification for reducing the scale and complexity of the pipe network, focuses on discussing the trunk condition of the pipe network, and is applied to the planning of newly-increased pipe networks in cities and the direction of pressure partition optimization and the like aiming at the existing pipe networks. However, from the present general survey of pipe networks, the analysis and modeling of the urban underground complex pipe networks are carried out, and the pipe network partition research is rarely related. The integrity and topological structure characteristics of a pipe network are considered, so that the requirement that a certain working area number exists in a general investigation partition is met, and the targeted research is lacked.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a partitioning method for underground pipe network general investigation in municipal management, which realizes approximately equal total length and area of pipe network partitioning general investigation and meets the requirement of reasonable distribution of the general investigation workload. The method is suitable for the majority of pipe network general survey partition problems. The physical property of the pipe network is fully considered, the good connectivity of the pipe network in the subareas is kept, and the fracture of the pipe sections after the subareas is avoided.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a partition method facing underground pipe network general investigation in municipal administration comprises the following steps:
step 1, collecting pipe network information of an area to be generally surveyed, and obtaining pipe point data and pipe section data according to the pipe network information of the area to be generally surveyed. Pipe network equipment in general investigation for underground pipe networks has important characteristics, and pipe network equipment (such as tee joints, valve wells, air leakage and other facility equipment) and relevant information (such as materials, pipe diameters and the like) of pipe sections are focused. The method needs to abstract the pipe network equipment into vector points and pipe sections into vector lines for the first time, and integrates the pipe network data and the pipe section data to form pipe network vector data. And in the second abstraction, the pipe sections of the pipe points are used as uniform space entity objects, and the integral pipe sections are projected from the Euclidean space to the topological space. And abstracting the pipe nodes into nodes according to the pipe network vector data, abstracting the pipe sections into edges connecting the nodes, and constructing a pipe network topology undirected graph by taking the lengths of the pipe sections as the weights of the edges.
And 2, the pipe network topological undirected graph obtained in the step 1 is an actual pipe network structure and is not simplified. And (4) carrying out structural division on the pipe network by using a specific cutting algorithm. The division result comprises a main skeleton network structure and a branch network structure, wherein the main skeleton network structure is the core of the whole pipe network and mainly comprises a main pipe network and a part of branch pipe networks related to key topological positions. The branch network structure has obvious predecessor and successor relations, and a hierarchical tree structure is formed, wherein only one predecessor node is arranged, and leaf nodes are arranged without successor nodes. And extracting a main network structure and a branch network structure of the water supply network in the pipe network topology undirected graph. In the pipe network topology undirected graph, a plurality of nodes with the degree of 1 exist, new nodes with the degree of 1 appear after the nodes are deleted, and after iteration, the remaining topological structure is a backbone skeleton network structure. And recording the sequence of deleting the nodes, and outputting in the reverse order to construct a tree structure.
And 3, dividing the water supply network into nc units according to the obtained main network structure and branch network structure.
Step 31, regarding each node in the backbone network structure as an independent unit, where the number of the initial units is the same as the number of the nodes. And the weight of each element includes all the length weights of the tree structure mounted at that node. Because the workload of the pipe network general survey is mainly the length of the pipe network, the length attribute of the pipe section is set as the weight.
Step 32, for each nodeiTry to get the nodeiAnd distributing the data to the unit where the adjacent node is positioned, calculating the modularity difference value delta Q between the data before distribution and the data after distribution, and recording the adjacent node with the largest modularity difference value delta Q. If maximum Δ Q>0, then nodeiAnd allocating the unit where the adjacent node with the maximum delta Q is located, and otherwise, abandoning the division. The calculation formula of the modularity difference value is as follows:
Figure DEST_PATH_IMAGE001
Figure DEST_PATH_IMAGE002
wherein, the Delta Q represents the modularity difference,TopologySimindicating the topological similarity of two adjacent nodes,i,nis the number of the node to which the node is connected,iis shown asiThe number of the nodes is one,nis composed ofiThe adjacent node of (a) is,
Figure DEST_PATH_IMAGE003
is the sum of the weights within the cell C,
Figure DEST_PATH_IMAGE004
the internal weight of the cell C is represented,
Figure DEST_PATH_IMAGE005
is the sum of the weights of the external connections of the connection unit C,
Figure DEST_PATH_IMAGE006
represents the external weight of the connection unit C, C represents the adjacent nodenThe unit in which the device is located,
Figure DEST_PATH_IMAGE007
is a connecting nodeiThe sum of the weights of the external connections of (2),
Figure DEST_PATH_IMAGE008
is a nodeiThe sum added to the connection weights of the internal nodes,
Figure DEST_PATH_IMAGE009
is the sum of the weights of the entire network.
And step 33, repeating the step 32 until the affiliated units of all the nodes are not changed any more.
And step 34, regarding the node with the same attribution obtained in the step 33 as a new node, and reconstructing the subgraph, wherein the weight between the two new nodes is the sum of the weights of all edges between the two corresponding units.
And step 35, giving a minimum community sub-group resolution parameter, taking the sub-graph obtained in the step 34 as an input, and re-executing the step 31 to the step 34 until the given minimum sub-group resolution parameter is met to obtain a minimum community sub-group and a community sub-group.
And step 36, outputting the minimum community sub-group and the connection relation of the community sub-groups, and taking the minimum community sub-group as a minimum partition unit of the pipe network.
And 4, on the basis of the connection relation of the subgroups, according to the number of the specified partitions, the lengths are equivalent and the areas are secondary convergence conditions, the minimum partition unit aggregation of the pipe network is realized, and the specified number of the partitions is reached. Possibility to treat new node as community sub-group, nodeiThe probability of being selected as a community subgroup is calculated by the following specific formula:
Figure DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE011
representing nodesiThe probability of being selected as a community sub-group,D（i) Is defined as a nodeiThe distance to the centroid of the neighbor's subgroup euclidean space,
Figure DEST_PATH_IMAGE012
representing nodesiAll adjacent nodes ofnTo the nodeiThe sum of the squares of the distances of (a). In addition, unlike the convergence condition of the general community discovery algorithm, the method also defines the convergence condition on the space area from the aspect of area allocation:
Figure DEST_PATH_IMAGE013
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE014
the minimum value of the area of each partition is represented,
Figure DEST_PATH_IMAGE015
the number of the partitions is represented by,
Figure DEST_PATH_IMAGE016
are the nodes of the candidate nodes, and are the nodes,
Figure DEST_PATH_IMAGE017
is the first
Figure DEST_PATH_IMAGE018
The number of the sub-areas is equal to that of the sub-areas,m i is the centroid of the adjoining nodes. Based on different convergence numbers, selecting
Figure 44936DEST_PATH_IMAGE014
And taking the value at the extreme value point as an optimal sub-cluster distribution mode.
And step 41, giving the weight of the branched network structure to the node of the main network structure mounted by the branched network structure, and giving the length of the pipe section of the branched network structure to the unit to which the node belongs according to the unit to which the node belongs.
And 42, selecting the node with the minimum weight in the community sub-group, combining the node with the minimum weight in the adjacent nodes as a new node, and adding the sum of the edge weights between the original node and the original node to obtain the new node weight. When the two nodes with the minimum weight do not have direct topological correlation, the area of the outsourcing rectangle when the two nodes join the neighbor is respectively calculated, and a combination scheme with large area change is selected.
And 43, repeating the step 42 until all the nodes are combined, and obtaining each partition under different partition schemes.
And 44, respectively calculating the variance of the length of each partition under different partition schemes, and selecting the partition scheme with the minimum variance. When the length variance is equal, the area of the bounding rectangle is used as a judgment condition.
And 5, outputting a pipe network partitioning result, including vector data and partitioned pipe network length information.
Preferably: the method for constructing the topological undirected graph of the pipe network in the step 1 comprises the following steps:
step 11, collecting pipe network equipment (such as facilities and equipment such as a tee joint, a valve well, air leakage and the like) and relevant information (such as material, pipe diameter and the like) of a pipe section, wherein the object modeling of the pipe network needs to abstract the concept of pipe network vector data (including pipe point data and pipe section data). Firstly, checking the pipe point data and the pipe section data to ensure that each pipe section is a connecting line of two pipe points and no redundant pipe point equipment exists in the pipe section. And correcting the pipe sections and pipe points which do not meet the requirements, deleting the pipe sections lacking the end point pipe points, and breaking the pipe sections with other pipe points inside. On the basis of finishing the inspection, abstracting the pipe points into nodes, abstracting the pipe sections into edges connecting the nodes, and constructing a topological graph of the pipe network by taking the lengths of the pipe sections as the weights of the edges.
Step 12, analyzing each node in turn, finding the adjacent node sharing the same edge with the node, constructing the adjacent table of the node, if the node is
Figure DEST_PATH_IMAGE019
And node
Figure DEST_PATH_IMAGE020
If connected, add a record in the adjacency list
Figure DEST_PATH_IMAGE021
，
Figure DEST_PATH_IMAGE022
Is the number of the node to which the node is connected,
Figure 316255DEST_PATH_IMAGE019
、
Figure 122537DEST_PATH_IMAGE020
respectively represent
Figure 421800DEST_PATH_IMAGE022
And (4) each node.
And step 13, because an undirected graph is constructed for the pipe network, the pipe points do not distinguish the degree of entry and the degree of exit, and therefore repeated node adjacency information is deleted according to the node number size rule, and a pipe network topology undirected graph G is formed according to the adjacency list.
Preferably: the method for extracting the main network structure and the branch network structure of the water supply network in the pipe network topology undirected graph in the step 2 comprises the following steps:
and step 21, traversing each node in the pipe network topology undirected graph, recording the node as a discrete value if the node has no adjacent node, and deleting the node.
Step 22, traversing each edge in the topological undirected graph of the pipe network
Figure DEST_PATH_IMAGE023
，
Figure DEST_PATH_IMAGE024
，
Figure DEST_PATH_IMAGE025
Is the number of the edge or edges,
Figure 136727DEST_PATH_IMAGE023
is shown as
Figure 255862DEST_PATH_IMAGE025
The number of the edges is one,
Figure DEST_PATH_IMAGE026
is the number of the node to which the node is connected,
Figure DEST_PATH_IMAGE027
is shown as
Figure 115102DEST_PATH_IMAGE026
A node, if node
Figure DEST_PATH_IMAGE028
Has more adjacent nodes than nodes
Figure DEST_PATH_IMAGE029
Number of adjacent nodes of, and node
Figure 924664DEST_PATH_IMAGE028
Only sum node
Figure 369681DEST_PATH_IMAGE029
Adjacent to each other, then
Figure 851478DEST_PATH_IMAGE028
Is composed of
Figure 389776DEST_PATH_IMAGE029
The parent node updates the parent-child relationship table and deletes the edge
Figure 273418DEST_PATH_IMAGE023
And node
Figure 12704DEST_PATH_IMAGE029
。
And 23, repeating the step 21 and the step 22 until no node can be simplified, wherein the rest structure in the pipe network topological undirected graph is the main skeleton network structure R of the pipe network.
Step 24, according to the parent-child relationship table, for any node in the parent-child relationship tableV g1 Find its predecessor nodeV j1 If, ifV j1 If the data is still in the parent-child relationship table, the search is continuedV j1 Is a precursor nodeV k1 And the analogy is repeated until the precursor node does not exist or is a point in the backbone framework network structure, a tree structure T is constructed according to the rule, the tree structure T is a branch network structure,g1、j1、k1is a node number, each representingg1、j1、k1And (4) each node.
Preferably: the method for outputting the pipe network partition result in the step 5 comprises the following steps:
and step 51, assigning the partition number to the minimum partition unit and all nodes in the unit to obtain a partition number table of the management point.
And step 52, associating the partition number table with the vector data according to the identification field of the management point.
And 53, counting the lengths of the pipe sections of different partitions, and recording the length of the pipe section into the length of the partition if two pipe points of one pipe section are in the same partition. Otherwise the length of the pipe section is ignored.
Step 54, outputting the vector data associated with the partition table and the length statistics of each partition.
Preferably: in the step 3, a pipe network structure diagram G = G (V, E), V is a node abstracted by pipe points, E is an edge abstracted by pipe sections, and nc units are determined in the pipe network structure diagram G by community discovery, wherein nc is greater than or equal to 1, so that a node set of each unit forms a coverage of the node V.
Compared with the prior art, the invention has the following beneficial effects:
(1) the process of dividing the region is a 'discrete-aggregation' process, a community discovery algorithm is utilized to find the minimum partition unit maintaining connectivity, and the partition generation is carried out by considering the constraint conditions of length, area and the like. Therefore, the total length of each partitioned pipe network, namely the general survey workload, is basically kept equivalent, the general survey working area is optimized, and the reasonable distribution of the workload during the general survey is facilitated.
(2) The connection relation of pipeline entities is considered, the integrity of a pipe network in the region is guaranteed, pipe sections are split by traditional methods such as grid partition and the like, and the redundancy of general investigation work is increased.
(3) The traditional pipe network partitioning method is characterized in that pipe networks are connected end to end, and a ring pipe section is extracted to serve as a backbone network. The cutting algorithm of the invention can overcome the defect and obtain the main pipe section and the secondary pipe section of the pipe network topological structure.
(4) The number of divided regions can be flexibly set according to users, and the method has strong flexibility, and the traditional grid dividing method can only adopt even number.
Drawings
Fig. 1 is a schematic diagram of a backbone network structure of a pipe network.
Fig. 2 is a schematic diagram of a tree structure of a pipe network.
FIG. 3 is a schematic view of a polymerization process of a minimum unit.
FIG. 4 is a backbone network of a water supply network in a city.
Fig. 5 is a schematic diagram of a city pipe network divided into 2 partitions.
Fig. 6 is a schematic diagram of a city pipe network divided into 4 partitions.
Fig. 7 is a schematic diagram of a city pipe network divided into 5 partitions.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
A partition method facing underground pipe network general investigation in municipal administration comprises the following steps:
step 1, collecting pipe network equipment (such as tee joints, valve wells, air leakage and other facility equipment) and relevant information (such as materials, pipe diameters and the like) of pipe sections. The method needs to abstract the pipe network equipment into vector points and pipe sections into vector lines for the first time, and pipe network vector data are formed according to the integrated pipe point data and the pipe section data. And in the second abstraction, the pipe sections of the pipe points are used as uniform space entity objects, and the integral pipe sections are projected from the Euclidean space to the topological space. And abstracting the pipe nodes into nodes according to the pipe network vector data, abstracting the pipe sections into edges connecting the nodes, and constructing a pipe network topology undirected graph by taking the lengths of the pipe sections as the weights of the edges.
The method for constructing the topological undirected graph of the pipe network comprises the following steps:
step 11, the pipe network vector data (shape format) includes pipe section data and pipe section data. And checking the pipe point data and the pipe section data to ensure that each pipe section is a connecting line of two pipe points and no pipe point exists in the pipe section. And correcting the pipe sections and pipe points which do not meet the requirements, deleting the pipe sections lacking the end point pipe points, and breaking the pipe sections with other pipe points inside. On the basis of finishing the inspection, abstracting the pipe points into nodes, abstracting the pipe sections into edges connecting the nodes, and constructing a topological graph of the pipe network by taking the lengths of the pipe sections as the weights of the edges.
Step (ii) of12, analyzing each node in turn, finding the adjacent node sharing the same edge with the node, constructing the adjacent table of the node, if the node is
Figure 106431DEST_PATH_IMAGE019
And node
Figure 58206DEST_PATH_IMAGE020
If connected, add a record in the adjacency list
Figure 186568DEST_PATH_IMAGE021
，
Figure 96755DEST_PATH_IMAGE022
Is the number of the node to which the node is connected,
Figure 412199DEST_PATH_IMAGE019
、
Figure 167665DEST_PATH_IMAGE020
respectively represent
Figure 625235DEST_PATH_IMAGE022
And (4) each node.
Step 13, according to the node number size rule, deleting the repeated node adjacency information, such as two records in the adjacency list
Figure 100002_DEST_PATH_IMAGE030
If, if
Figure DEST_PATH_IMAGE031
If the node number is large, deleting the adjacency list
Figure DEST_PATH_IMAGE032
And forming a pipe network topological undirected graph G according to the adjacency list.
And 2, the pipe network topology undirected graph obtained in the step 1 comprises a main network structure and a branch network structure, wherein the main skeleton network structure is the core of the whole pipe network and mainly comprises a main pipe network and a part of branch pipe networks related to key topological positions. The branch network structure has obvious predecessor and successor relations, and a hierarchical tree structure is formed, wherein only one predecessor node is arranged, and leaf nodes are arranged without successor nodes. As shown in fig. 1 and 2, the pipe network can be divided into a backbone network structure and a tree structure in terms of spatial structure. The main pipe sections carry the main load functions of the entire network, as shown in figure 1. When the pipeline is laid into a residential area or a newly built area, the pipeline often has a tree structure, as shown in fig. 2. By utilizing the characteristic, a cutting algorithm is executed, leaf nodes are deleted in an iterative mode, and a main network structure and a branch network structure of the water supply network in the pipe network topology undirected graph can be extracted.
The method for extracting the main network structure and the branch network structure of the water supply pipe network in the pipe network topology undirected graph comprises the following steps:
and step 21, traversing each node in the pipe network topology undirected graph, recording the node as a discrete value if the node has no adjacent node, and deleting the node.
Step 22, traversing each edge in the topological undirected graph of the pipe network
Figure 283487DEST_PATH_IMAGE023
，
Figure 351806DEST_PATH_IMAGE024
，
Figure 910963DEST_PATH_IMAGE025
Is the number of the edge or edges,
Figure 748338DEST_PATH_IMAGE023
is shown as
Figure 469169DEST_PATH_IMAGE025
The number of the edges is one,
Figure 24785DEST_PATH_IMAGE026
is the number of the node to which the node is connected,
Figure 116194DEST_PATH_IMAGE027
is shown as
Figure 214600DEST_PATH_IMAGE026
A node, if node
Figure 965387DEST_PATH_IMAGE028
Has more adjacent nodes than nodes
Figure 742719DEST_PATH_IMAGE029
Number of adjacent nodes of, and node
Figure 502734DEST_PATH_IMAGE028
Only sum node
Figure 517963DEST_PATH_IMAGE029
Adjacent to each other, then
Figure 846177DEST_PATH_IMAGE028
Is composed of
Figure 845225DEST_PATH_IMAGE029
The parent node updates the parent-child relationship table and deletes the edge
Figure 408931DEST_PATH_IMAGE023
And node
Figure 685191DEST_PATH_IMAGE029
Figure 49220DEST_PATH_IMAGE029
。
And 23, repeating the step 21 and the step 22 until no node can be deleted, wherein the rest structure in the topological undirected graph of the pipe network is the main skeleton structure R of the pipe network, the structure is the core of the whole pipe network and mainly comprises a main line pipe network and part of branch pipe networks related to key topological positions, the structure is a structure behind the branch pipe networks which can be deleted, the main lines with partial edges are not important in the topological structure, and conversely, some branch pipes have important topological structure positions.
Step 24, according to the parent-child relationship table, for any node in the parent-child relationship tableV g1 Can find its predecessor nodeV j1 If, ifV j1 If the data is still in the parent-child relationship table, the search is continuedV j1 Is a precursor nodeV k1 And the analogy is repeated until the precursor node does not exist or is a point in the backbone skeleton structure, a tree structure T is constructed according to the rule, the tree structure T is a branched network structure,g1、j1、k1is a node number, each representingg1、j1、k1And (4) each node.
And 3, dividing the water supply network into nc units according to the obtained main network structure and branch network structure.
The method comprises the following steps that a pipe network structure diagram G = G (V, E), V is a node abstracted by pipe points, E is an edge abstracted by pipe sections, and nc units are determined in the pipe network structure diagram G through community discovery, wherein nc is larger than or equal to 1, so that a node set of each unit forms one coverage of the node V.
The method for dividing a water supply network into nc units is as follows:
step 31, regarding each node in the backbone network structure as an independent unit, where the number of the initial units is the same as the number of the nodes.
Step 32, for each nodeiTry to get the nodeiAnd distributing the data to the unit where the adjacent node is positioned, calculating the modularity difference value delta Q between the data before distribution and the data after distribution, and recording the adjacent node with the largest modularity difference value delta Q. If maximum Δ Q>0, then nodeiAnd allocating the unit where the adjacent node with the maximum delta Q is located, and otherwise, abandoning the division. The calculation formula of the modularity difference value is as follows:
Figure 269985DEST_PATH_IMAGE001
Figure 778327DEST_PATH_IMAGE002
where deltaq represents the modularity difference,TopologySimindicating the topological similarity of two adjacent nodes,i,nis the number of the node to which the node is connected,iis shown asiThe number of the nodes is one,nis composed ofiThe adjacent node of (a) is,
Figure DEST_PATH_IMAGE033
is the sum of the weights within the cell C,
Figure 830466DEST_PATH_IMAGE004
the internal weight of the cell C is represented,
Figure 890694DEST_PATH_IMAGE005
is the sum of the weights of the external connections of the connection unit C,
Figure 5281DEST_PATH_IMAGE006
represents the external weight of the connection unit C, C represents the adjacent nodenThe unit in which the device is located,
Figure 51734DEST_PATH_IMAGE008
is a nodeiThe sum added to the connection weights of the internal nodes,
Figure 161642DEST_PATH_IMAGE007
is a connecting nodeiThe sum of the weights of the external connections of (2),
Figure 127193DEST_PATH_IMAGE009
is the sum of the weights of the entire network.
And step 33, repeating the step 32 until the affiliated units of all the nodes are not changed any more.
And step 34, regarding the node with the same attribution obtained in the step 33 as a new node, and reconstructing the subgraph, wherein the weight between the two new nodes is the sum of the weights of all edges between the two corresponding units.
And 35, giving a minimum community sub-group resolution parameter, taking the sub-image obtained in the step 34 as an input, and re-executing the steps 31 to 34 until the given minimum community sub-group resolution parameter is reached to obtain a minimum community sub-group and a community sub-group.
And step 36, taking the minimum community sub-group as a minimum partition unit of the pipe network, and outputting the minimum community sub-group and the connection relation between the minimum community sub-group and the community sub-group.
And 4, according to the number of the specified partitions, the lengths are equivalent and used as convergence conditions, and the minimum partition unit aggregation (dynamic aggregation) of the pipe network is realized. When the adjacent nodes with smaller weight are multiple, the area size of the outsourcing rectangle when the node is respectively added into the neighbor is calculated, and the node belongs to the adjacent node with smaller area. And finally determining the attribution condition of the cliques through binary judgment of the length and the area. The specific formula is as follows:
Figure 559153DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 268352DEST_PATH_IMAGE011
representing nodesiThe probability of being selected as a community sub-group,D（i) Is defined as a nodeiThe distance to the centroid of the neighbor's subgroup euclidean space,
Figure 639291DEST_PATH_IMAGE012
representing nodesiAll adjacent nodes ofnTo the nodeiThe sum of the squares of the distances of (a). In addition, unlike the convergence condition of the general community discovery algorithm, the method also defines the convergence condition on the space area from the aspect of area allocation:
Figure 775743DEST_PATH_IMAGE013
wherein, among others,
Figure 599342DEST_PATH_IMAGE014
the minimum value of the area of each partition is represented,
Figure 112232DEST_PATH_IMAGE015
the number of the partitions is represented by,
Figure 337677DEST_PATH_IMAGE016
are the nodes of the candidate nodes, and are the nodes,
Figure 645031DEST_PATH_IMAGE017
is the first
Figure 221505DEST_PATH_IMAGE018
The number of the sub-areas is equal to that of the sub-areas,m i is the centroid of the adjoining nodes. Based on different convergence numbers, selecting
Figure 272507DEST_PATH_IMAGE014
And taking the value at the extreme value point as an optimal sub-cluster distribution mode.
The polymerization process of the smallest unit is as follows:
and step 41, giving the weight of the branched network structure to the node of the main network structure mounted by the branched network structure, and giving the length of the pipe section of the branched network structure to the unit to which the node belongs according to the unit to which the node belongs.
And 42, selecting the node with the minimum weight in the community sub-group, combining the node with the minimum weight in the adjacent nodes as a new node, and adding the sum of the edge weights between the original node and the original node to obtain the new node weight.
And 43, repeating the step 42 until all the nodes are combined, and obtaining each partition under different partition schemes.
And 44, respectively calculating the variance of the length of each partition under different partition schemes, and selecting the partition scheme with the minimum variance. When the length variance is equal, the area of the bounding rectangle is used as a judgment condition.
As shown in fig. 3, a topological network formed by one unit has 8 small units a to h in total, and each node level is determined successively according to a defined rule.
And (3) for the first iteration, selecting nodes with leaf nodes hung on the backbone skeleton nodes, and merging the units according to a cutting mode, so that c belongs to b, g and h belongs to e, and the whole backbone skeleton network is divided into four parts.
The second iteration selects the smallest node a, which belongs to the lowest weighted neighboring node b or d, this time a triple net solution. And by analogy, combining d and e to form a bipartite scheme of the pipe network.
And finally, all the small units converge to a node representing the whole pipe network.
And 5, outputting a pipe network partition result, wherein the pipe network partition result comprises vector data, pipe network length information of partitions and area information of outsourcing rectangles.
The method for outputting the pipe network partition result comprises the following steps:
and step 51, assigning the partition number to the minimum partition unit and all nodes in the unit to obtain a partition number table of the management point.
And step 52, associating the partition number table with the vector data according to the identification field of the management point.
And 53, counting the lengths of the pipe sections of different partitions, and recording the length of the pipe section into the length of the partition if two pipe points of one pipe section are in the same partition. Otherwise the length of the pipe section is ignored.
Step 54, outputting the vector data associated with the partition table and the length statistics of each partition.
Examples of the invention
Taking an example water supply network data as an example, the coverage area of the water supply network data is about 10.2 square kilometers, the network data comprises 12807 pipe sections, 15114 pipe sections and 102.78km in total length. And respectively carrying out 2-minute, 4-minute and 5-minute division schemes on the pipe network. The pipe network structure is shown in fig. 4.
As shown in fig. 5, when the pipe network is divided into 2 partitions: partition 1 contained a total of 6676 pipe points, 7917 pipe sections, with a total length of 58.172 km. The subarea 2 comprises 6131 pipe points, 7197 pipe sections, the total length of the pipe sections is 44.608km, and the area of the pipe sections is 4.04km respectively2And 3.98km2。
As shown in fig. 6, when the pipe network is divided into 4 partitions: the 1 to 4 partitions respectively contain 3488, 2833, 3145 and 3341 tube points. 3644 strips, 3698 strips, 3759 strips and 4034 tube segments. The total lengths of the pipe sections are respectively 24.39km, 25.61km, 23.34km and 30.66 km. The area is 2.01km respectively2、1.99km2、2.15km2And 1.99km2。
As shown in fig. 7, when the pipe network is divided into 5 partitions: the 1-5 partitions respectively comprise 2760, 2253, 3328, 2106 and 2360 pipe points. 2704, 2890, 3892, 2874 and 2934 pipe sections. The total lengths of the pipe sections are 25.04km, 25.26km, 25.21km and 25.81km respectively. The areas are respectively 1.24km2、1.16km2、1.33km2、1.40km2And 1.52km2。
The method overcomes the defects that the traditional regular grid partitioning method damages the continuity of the pipe network, the workload of general survey is uneven and the like in the general survey work of the urban underground pipe network, and realizes reasonable pipe network partitioning by taking the number of the general survey kilometers as an index.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (5)

1. A partitioning method facing underground pipe network general survey in municipal administration is characterized by comprising the following steps:
step 1, collecting pipe network information of an area to be generally surveyed, obtaining pipe network data and pipe section data according to the pipe network information of the area to be generally surveyed, abstracting twice for pipe network equipment in underground pipe network general survey, wherein the pipe network equipment is abstracted into vector points for the first time, the pipe sections are abstracted into vector lines, and the pipe network vector data are formed by integrating the pipe network data and the pipe section data; the pipe section with pipe points is used as a uniform space entity object in the second abstraction, the pipe section with the pipe points is projected to a topological space from an Euclidean space as a whole, the pipe points are abstracted into nodes according to pipe network vector data, the pipe section is abstracted into edges connecting the nodes, the length of the pipe section is used as the weight of the edges, and a pipe network topological undirected graph is constructed;
step 2, the pipe network topological undirected graph obtained in the step 1 is an actual pipe network structure, is not simplified, and is divided by using a cutting algorithm; the division result comprises a main skeleton network structure and a branch network structure, wherein the main skeleton network structure is the core of the whole pipe network and mainly comprises a main pipe network and a part of branch pipe networks related to key topological positions; the branch network structure has obvious predecessor and successor relations to form a hierarchical tree structure, wherein only one predecessor node is provided, and leaf nodes are provided without successor nodes; extracting a main network structure and a branch network structure of a water supply network in a pipe network topology undirected graph; in the pipe network topology undirected graph, a plurality of nodes with the degree of 1 exist, new nodes with the degree of 1 appear after the nodes are deleted, and after iteration, the remaining topological structure is a backbone skeleton network structure; recording the sequence of deleting nodes, and outputting in a reverse order to construct a tree structure;
step 3, dividing the water supply network into nc units according to the obtained main network structure and branch network structure;
step 31, regarding each node in the backbone network structure as an independent unit, wherein the number of the initial units is the same as the number of the nodes;
step 32, for each nodeiTry to get the nodeiDistributing the data to a unit where the adjacent node is located, calculating a modularity difference value delta Q between the data before distribution and the data after distribution, and recording the adjacent node with the largest modularity difference value delta Q; if maximum Δ Q>0, then nodeiDistributing the unit where the adjacent node with the maximum delta Q is located, and otherwise, abandoning the division; the calculation formula of the modularity difference value is as follows:
Figure 885130DEST_PATH_IMAGE001
Figure 313706DEST_PATH_IMAGE002
wherein, the Delta Q represents the modularity difference,TopologySimindicating the topological similarity of two adjacent nodes,i,nis the number of the node to which the node is connected,iis shown asiThe number of the nodes is one,nis composed ofiThe adjacent node of (a) is,
Figure 778710DEST_PATH_IMAGE003
is the sum of the weights within the cell C,
Figure 609132DEST_PATH_IMAGE004
the internal weight of the cell C is represented,
Figure 803353DEST_PATH_IMAGE005
is the sum of the weights of the external connections of the connection unit C,
Figure 391329DEST_PATH_IMAGE006
represents the external weight of the connection unit C, C represents the adjacent nodenThe unit in which the device is located,
Figure 332128DEST_PATH_IMAGE007
is a nodeiThe sum added to the connection weights of the internal nodes,
Figure 954739DEST_PATH_IMAGE008
is a connecting nodeiThe sum of the weights of the external connections of (2),
Figure 444495DEST_PATH_IMAGE009
is the sum of the weights of the entire network;
step 33, repeating step 32 until all the belonged units of the nodes are not changed;
step 34, regarding the node with the same attribution obtained in the step 33 as a new node, and reconstructing a subgraph, wherein the weight between two new nodes is the sum of the weights of all edges between two corresponding units;
step 35, giving a minimum community sub-group resolution parameter, taking the sub-image obtained in the step 34 as an input, and re-executing the steps 31 to 34 until the given minimum community sub-group resolution parameter is reached to obtain a minimum community sub-group and a community sub-group;
step 36, outputting the minimum community sub-group and the connection relation of the community sub-groups, and taking the minimum community sub-group as a minimum partition unit of the pipe network;
step 4, according to the number of the specified partitionsThe length is taken as a main convergence condition, the area is taken as a secondary convergence condition, and the minimum partition unit aggregation of the pipe network is realized to reach the specified partition number; possibility to treat new node as community sub-group, nodeiThe probability of being selected as a community subgroup is calculated by the following specific formula:
Figure 788276DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 936361DEST_PATH_IMAGE011
representing nodesiThe probability of being selected as a community sub-group,D（i) Is defined as a nodeiThe distance to the centroid of the neighbor's subgroup euclidean space,
Figure 944637DEST_PATH_IMAGE012
representing nodesiAll adjacent nodes ofnTo the nodeiAlso from the area allocation point of view, defines the convergence condition on the spatial area:
Figure 277398DEST_PATH_IMAGE013
wherein the content of the first and second substances,
Figure 980912DEST_PATH_IMAGE014
the minimum value of the area of each partition is represented,
Figure 782953DEST_PATH_IMAGE015
the number of the partitions is represented by,
Figure 645736DEST_PATH_IMAGE016
are the nodes of the candidate nodes, and are the nodes,
Figure 149398DEST_PATH_IMAGE017
is the first
Figure 340208DEST_PATH_IMAGE018
The number of the sub-areas is equal to that of the sub-areas,m i selecting the centroids of adjacent nodes according to different convergence numbers
Figure 954729DEST_PATH_IMAGE014
Taking the numerical value at the extreme value point as an optimal sub-cluster distribution mode;
step 41, giving the weight of the branched network structure to the node of the main network structure mounted by the branched network structure, and giving the length of the pipe section of the branched network structure to the unit to which the node belongs according to the unit to which the node belongs;
step 42, selecting the node with the minimum weight in the community sub-group, combining the node with the minimum weight in the adjacent nodes as a new node, wherein the new node weight is obtained by adding the sum of the edge weights between the original node and the original node; when the two nodes with the minimum weight do not have direct topological correlation, the area of the outsourcing rectangle when the two nodes are added into the neighbor is respectively calculated, and a combination scheme with large area change is selected;
43, repeating the step 42 until all the nodes are combined to obtain each partition under different partition schemes;
step 44, respectively calculating the variance of the length of each partition under different partition schemes, selecting the partition scheme with the minimum variance, and using the area of the outsourcing rectangle as a judgment condition when the length variances are equal;
and 5, outputting pipe network partition results, wherein the pipe network partition results comprise vector data, pipe network length to be generally searched in the general search partition and partition area information.
2. The zoning method facing underground pipe network census in municipal management according to claim 1, wherein: the method for constructing the topological undirected graph of the pipe network in the step 1 comprises the following steps:
step 11, the pipe network vector data comprises pipe section data and pipe section data; checking the pipe point data and the pipe section data to ensure that each pipe section is a connecting line of two pipe points and no pipe point exists in the pipe section; correcting the pipe sections and pipe points which do not meet the requirements, deleting the pipe sections lacking the end point pipe points, and breaking the pipe sections with other pipe points inside; on the basis of finishing the inspection, abstracting a pipe point into a node, abstracting a pipe section into an edge connecting the node, and constructing a topological graph of the pipe network by taking the length of the pipe section as the weight of the edge;
step 12, analyzing each node in turn, finding the adjacent node sharing the same edge with the node, constructing the adjacent table of the node, if the node is
Figure 143789DEST_PATH_IMAGE019
And node
Figure 818353DEST_PATH_IMAGE020
If connected, add a record in the adjacency list
Figure 355514DEST_PATH_IMAGE021
，
Figure 508147DEST_PATH_IMAGE022
Is the number of the node to which the node is connected,
Figure 82872DEST_PATH_IMAGE019
、
Figure 538124DEST_PATH_IMAGE020
respectively represent
Figure 828160DEST_PATH_IMAGE022
A node;
and step 13, deleting repeated node adjacency information according to the node number size rule, and forming a pipe network topology undirected graph G according to the adjacency list.
3. The zoning method facing to underground pipe network general investigation in municipal management according to claim 2, characterized in that: the method for extracting the main network structure and the branch network structure of the water supply network in the pipe network topology undirected graph in the step 2 comprises the following steps:
step 21, traversing each node in the topological undirected graph of the pipe network, if the node has no adjacent node, recording the node as a discrete value, and deleting the node;
step 22, traversing each edge in the topological undirected graph of the pipe network
Figure 518904DEST_PATH_IMAGE023
，
Figure 948136DEST_PATH_IMAGE024
，
Figure 433344DEST_PATH_IMAGE025
Is the number of the edge or edges,
Figure 945097DEST_PATH_IMAGE023
is shown as
Figure 439532DEST_PATH_IMAGE025
The number of the edges is one,
Figure 469410DEST_PATH_IMAGE026
is the number of the node to which the node is connected,
Figure 656678DEST_PATH_IMAGE027
is shown as
Figure 655726DEST_PATH_IMAGE026
A node, if node
Figure 94798DEST_PATH_IMAGE028
Has more adjacent nodes than nodes
Figure DEST_PATH_IMAGE030
Number of adjacent nodes of, and node
Figure 623256DEST_PATH_IMAGE028
Only sum node
Figure 981425DEST_PATH_IMAGE030
Adjacent to each other, then
Figure 608715DEST_PATH_IMAGE028
Is composed of
Figure 710532DEST_PATH_IMAGE030
The parent node updates the parent-child relationship table and deletes the edge
Figure 968863DEST_PATH_IMAGE023
And node
Figure 497933DEST_PATH_IMAGE030
；
Step 23, repeating the step 21 and the step 22 until no node can be deleted, wherein the rest structure in the topological undirected graph of the pipe network is a main skeleton network structure R of the pipe network, and the structure is the core of the whole pipe network and mainly comprises a main pipe network and a part of branch pipe networks related to key topological positions;
step 24, according to the parent-child relationship table, for any node in the parent-child relationship tableV g1 Find its predecessor nodeV j1 If, ifV j1 If the data is still in the parent-child relationship table, the search is continuedV j1 Is a precursor nodeV k1 And the analogy is repeated until the precursor node does not exist or is a point in the backbone framework network structure, a tree structure T is constructed according to the rule, the tree structure T is a branch network structure,g1、j1、k1is a node number, each representingg1、j1、k1And (4) each node.
4. The zoning method facing underground pipe network census in municipal management according to claim 3, wherein: the method for realizing the polymerization of the minimum partition unit of the pipe network in the step 4 comprises the following steps:
step 41, giving the weight of the branched network structure to the node of the main network structure mounted by the branched network structure, and giving the length of the pipe section of the branched network structure to the unit to which the node belongs according to the unit to which the node belongs;
step 42, selecting the node with the minimum weight in the graph, combining the node with the minimum weight in the adjacent nodes as a new node, wherein the weight of the new node is obtained by adding the sum of the edge weights between the original node and the original node;
43, repeating the step 42 until all the nodes are combined to obtain each partition under different partition schemes;
and 44, respectively calculating the variance of the length of each partition under different partition schemes, and selecting the partition scheme with the minimum variance.
5. The zoning method facing underground pipe network census in municipal management according to claim 4, wherein: the method for outputting the pipe network partition result in the step 5 comprises the following steps:
step 51, assigning the partition number to the minimum partition unit and all nodes in the unit to obtain a partition number table of the management point;
step 52, associating the partition numbering table with the vector data according to the identification field of the control point;
step 53, counting the lengths of the pipe sections of different partitions, and recording the length of the pipe section into the length of the partition if two pipe points of one pipe section are in the same partition; otherwise, neglecting the length of the pipe section;
and step 54, outputting the vector data related to the partition table and the statistical result of the length and the occupied area of each partition.
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