CN111680374B - Pipe network topology relation checking and repairing method - Google Patents
Pipe network topology relation checking and repairing method Download PDFInfo
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Abstract
The invention discloses a method for checking and repairing a pipe network topological relation, which comprises the steps of selecting a pipe network path with a known flow direction, and measuring initial inner bottom elevation values of all nodes of the pipe network according to the front-back sequence of the flow direction; sequentially traversing all pipe sections along the flow direction of the pipe network path, and calculating the gradient of each pipe section; checking whether the gradient data has a negative value; removing negative value data, composing data set by residual non-negative gradient data, identifying abnormal gradient data, counting normal gradient data, obtaining interval range of normal gradient [ s ] min ,s max ]The method comprises the steps of carrying out a first treatment on the surface of the Identifying the gradient data as a negative gradient and the starting and stopping nodes of the pipe section corresponding to the abnormal gradient data to form an abnormal node set, adjusting the inner bottom elevation value of the abnormal nodes in the abnormal node set, and wherein the abnormal nodes with abnormal inner bottom elevations exist in the abnormal node set. The method has the advantages of realizing automatic identification, automatic modification and optimization of the elevation of the inner bottom of the abnormal node of the pipe network, along with convenience, rapidness, high efficiency and accuracy.
Description
Technical Field
The invention relates to the technical field of drainage pipe networks, in particular to a method for checking and repairing a pipe network topological relation.
Background
Urban underground drainage pipe network has characteristics such as system huge, disguise are strong and safety problem is outstanding, in recent years, urban managers attach more and more importance to the general investigation work of urban underground drainage pipe network basic information to solve the problems that management department is incomplete, unclear etc. to underground drainage pipe network information grasp, provide guarantee for urban drainage system safe operation and upgrading and efficiency enhancement work. The general investigation of the basic information of the drainage pipe network mainly comprises general investigation of the basic information of the position, connection, elevation, burial depth, length and the like of drainage facilities such as drainage pipelines, underdrain, inspection wells, rain water combs and the like. The elevation data of pipe network nodes such as inspection wells, inspection wells and the like, including ground elevation and inner bottom elevation, are used as one of key basic information of the drainage pipe network, the accuracy of the elevation data is very critical, and the hydraulic working condition analysis, operation scheduling and later-stage reconstruction planning of a manager on the drainage pipe network are directly affected.
The number of nodes such as inspection wells, inspection wells and the like of the urban underground drainage pipe network is huge, and partial abnormal data such as abnormal elevation in the bottom of the pipe network nodes generally exist in general investigation results due to the problems of precision of measuring tools, negligence of measuring staff and the like in the pipe network detection process, so that the pipe network has a counter slope problem in topological relation and has serious influence on pipe network hydraulic modeling and pipe network operation scheduling, and therefore, after the general investigation of the drainage pipe network is finished, the topology of the drainage pipe network needs to be inspected, repaired or re-measured. In 2012, in the 'urban pipe network space data quality inspection System design and implementation' of the 'Yanyuyao' paper, the like takes urban pipe network space data as a main research object, researches a urban pipe network space data quality model of the system according to specifications and standards of a large amount of space data, analyzes a rule model and a method for urban pipe network space data quality inspection in detail, and obtains an urban pipe network space data quality inspection rule base and an evaluation software result based on the rule base, but does not specifically provide a pipe network data automatic inspection method. At present, topology inspection and modification of a drainage pipe network mainly rely on manual inspection of all pipe sections and all nodes section by section and point by point, and whether a reverse slope problem exists is judged by combining upstream and downstream node data, and if the reverse slope problem exists, the elevation of the inner bottom of the node is manually modified by referring to the gradient of the upstream and downstream pipe sections. The method for manually checking and modifying the pipe network topology has the advantages of huge workload, low efficiency and high time cost.
Disclosure of Invention
The invention aims to solve the technical problems of huge workload, low efficiency and high time cost in pipe network inspection in the prior art.
The invention solves the technical problems by the following technical means: a pipe network topology relation checking and repairing method comprises the following steps:
s1, selecting a pipe network path with a known flow direction from a pipe network to be inspected, marking n nodes according to the front-back sequence of the flow direction, measuring initial inner bottom elevation values of all nodes of the pipe network, and ensuring that the initial inner bottom elevation values of the nodes at the initial positions are accurate;
the nodes comprise inspection wells and water outlets according to the flow direction of the pipe network to be inspected;
s2, forming a pipe section by the pipelines between adjacent nodes, traversing all the pipe sections in sequence along the flow direction of the pipe network path, and calculating the gradient of each pipe section to obtain gradient data of the pipe section;
s3, checking whether the gradient data has a negative value or not; removing negative value data, forming a data set by residual non-negative gradient data, carrying out statistical analysis on the data set, identifying abnormal gradient data in the data set, and carrying out statistics on normal gradient data to obtain a range [ s ] of normal gradient min ,s max ]；
S4, identifying the starting and stopping nodes of the pipe section corresponding to the gradient data with negative gradient and abnormal gradient data to form an abnormal node set J; while other nodes (i.e. node numbers
Is a normal node having a normal insole elevation.
S5, adjusting the initial inner bottom elevation value of the abnormal nodes in the abnormal node set, wherein the abnormal nodes with abnormal inner bottom elevations exist in the abnormal node set.
According to the method, for a pipe network path capable of definitely judging the flow direction, firstly, the gradient of all pipe sections is calculated, then, abnormal pipe section gradient data, and corresponding starting and ending nodes of the abnormal pipe sections are automatically identified through a box diagram algorithm, and finally, the inner bottom elevation of the abnormal node of the abnormal pipe section in the abnormal pipe section is automatically modified and optimized through establishment of a fitness function and constraint conditions. The intelligent inspection method for the drainage pipe network topology realizes automatic identification, automatic modification and optimization of the elevation of the inner bottom of the abnormal node of the pipe network, and is convenient, quick, efficient and accurate.
Preferably, the gradient data of the pipe section in S2 is obtained by using the following model:
wherein i is the ith node of the pipe network path, n nodes are altogether, i=1 is the node of the initial position of the pipe network path from the upstream, L i,i+1 Representing the length of the pipe segment between a start node i and a stop node i +1,
an initial insole elevation value representing an ith node, the following table i represents the ith node, superscript 0 represents an initial value,/->
Representing the initial insole elevation value of the i+1th node.
Preferably, in the step S3, statistical analysis is performed on the data set by adopting a method of identifying abnormal gradient data based on a box diagram algorithm;
s31, forming a data set by residual non-negative gradient data, and arranging data set elements in a descending order;
s32, calculating the first quartile Q of all non-negative gradient data in the descending data set 1 ＝min{[1+(k-1)·0.25]，(k+1)·0.25}；
Calculating the third quartile Q of all non-negative slope data in the descending data set 3 ＝max{[1+(k-1)·0.75]，(k+1)·0.75}；
Calculating the quartile range iqr=q 3 -Q 1 ；
k is the number of all non-negative grade data;
s33, calculating an outlier cutoff point (Q) 3 +1.5 IQR) and (Q) 1 -1.5 IQR), greater than (Q) in non-negative grade data 3 +1.5 IQR) or less (Q) 1 -1.5 IQR) is abnormal gradient data.
Preferably, in the step S5, the method adjusts the insole elevation values of the abnormal nodes in the abnormal node set in the following manner;
s51, taking the inner bottom elevation values of all nodes on the pipe network path as variables to be optimized, constructing an objective function according to the initial inner bottom elevation values of all the nodes, and taking the inverse of the objective function as an fitness function;
the construction objective function is as follows:
the fitness function is constructed as follows:
E i the adjusted height value of the insole of the ith node;
the following conditions were taken as constraints of the fitness function:
(a) The gradient data of the pipe section calculated by the inner bottom elevation value of the adjusted node is in the interval range of the normal gradient:
(b) The adjusted insole elevation value of the node is set to a depth below the ground:
(c) A node having a normal initial insole elevation value,
the height value of the inner bottom is directly taken as the initial height value of the inner bottom without modification:
setting an abnormal node set as a set J, and setting other nodes as normal nodes with normal inner bottom elevation values;
the constraint conditions are respectively converted into a matrix form as follows:
wherein E is 1 Representing the adjusted insole elevation value of node 1, E 2 Representing the adjusted insole elevation value of node 2, E n Representing the adjusted insole elevation value, L, of the nth node 1-2 Representing the length of the pipe section between the starting node being 1 and the ending node being 2, L 2-3 Representing the length of the pipe section between the starting node being 2 and the ending node being 3, L n-1-n Indicating that the starting node is n-1 and the ending node is nLength of the pipe section between s min Interval range s of normal grade min ,s max ]Lower limit of s max Interval range s of normal grade min ,s max ]Is set at the upper limit of (c),
ground elevation for node 1, +.>
Ground elevation for node 2, +.>
For the ground elevation of the nth node, deep is the minimum depth of the manhole specified by engineering design specifications, +.>
An adjusted insole elevation value representing nodes outside the abnormal node set J, +.>
Representing the initial insole elevation values of nodes outside the abnormal node set J.
S52, coding the inner bottom elevation variable of each node by using a floating point number coding mode, wherein the coding length is n, and the number of the coding length is the same as that of the variable;
s53, floating point numbers in n specified ranges are arranged into an individual, and a plurality of individuals are randomly generated to serve as an initial population;
s54, calculating the fitness function value f of each individual of the initial population i Calculating probability P of each individual being selected into next generation population i The method comprises the steps of carrying out a first treatment on the surface of the In the individual selection process, selecting two individuals each time according to a roulette selection mechanism, wherein the individuals enter the next generation population with high fitness;
f herein i Specifically, the fitness function value of the ith individual in the initial population is represented; p (P) i Representing a probability that the ith individual is selected to enter the next generation population;
s55, for the generation of selection operationThe new population is paired with each other randomly, and the cross probability P of each pairing is calculated c Non-uniform arithmetic crossover is carried out on the two paired individuals according to the crossover probability; wherein the crossover probability P c Dynamic changes with individual fitness:
s56, according to the variation probability P m Performing basic position variation operation on the population individuals subjected to the cross operation; wherein the variation probability P m Dynamic changes with individual fitness:
s57, obtaining a child population after mutation operation, calculating fitness function values of individuals of the child population, and outputting individuals with highest fitness, namely optimal fitness individuals; comparing the optimal fitness individuals with the optimal fitness individuals of the parent population, and taking whether Euclidean distance between the two individuals is smaller than a convergence condition allowing residual errors to be used as population evolution;
s58, if the convergence condition is met, the optimal fitness individual is the optimal individual, the individual gene value is the optimal pipe network node inner bottom elevation value, and the adjusted pipe network node inner bottom elevation value is obtained; if the convergence condition is not satisfied, returning to S54, and continuing the selection, crossing and mutation operations of the offspring population until the offspring population satisfies the convergence condition;
or setting the maximum evolution algebra of the population, stopping calculating when the maximum evolution algebra is reached, and outputting the individual with the optimal fitness as the optimal individual.
Preferably, in the S54
Preferably, in the S55
f max 、f avg Respectively representing the maximum fitness and the average fitness, k, of a new population generated by the selection operation 1 、k 2 Is constant, k 1 <k 2 。
Preferably, in S55, the genes to be crossed by two individuals are set as x and y, and the new genes formed after crossing are x ', y':
wherein alpha is a random number which accords with uniform probability distribution in the range of (0, r), 0<r is less than or equal to 1, and r varies with evolution algebra.
Preferably, in the S56
f’ max 、f’ avg Respectively represent the maximum fitness and the average fitness, k of the new population generated by the crossover operation 3 、k 4 Is constant, k 3 <k 4 。
Preferably, in the step S56, the gene to be mutated in the individual is set as z, and the mutated new gene z' is:
z'＝z+(R-L)·γ
and gamma is a random number conforming to uniform probability distribution in the range of [0,1], L and R are the left boundary and the right boundary of the corresponding gene value range respectively, L is not less than z and not more than R, and L and R are respectively calculated according to constraint conditions of the fitness function.
Preferably, the euclidean distance between the two individuals in S57 is:
The invention has the advantages that: according to the method, for a pipe network path capable of definitely judging the flow direction, firstly, the gradient of all pipe sections is calculated, then, abnormal pipe section gradient data, and corresponding starting and ending nodes of the abnormal pipe sections are automatically identified through a box diagram algorithm, and finally, the inner bottom elevation of the abnormal node of the abnormal pipe section in the abnormal pipe section is automatically modified and optimized through establishment of a fitness function and constraint conditions. The intelligent inspection method for the drainage pipe network topology realizes automatic identification, automatic modification and optimization of the elevation of the inner bottom of the abnormal node of the pipe network, and is convenient, quick, efficient and accurate.
Drawings
FIG. 1 is a flow chart of a method for checking and repairing the topology relation of a pipe network in the embodiment 1 of the invention;
FIG. 2 is a flow chart of adjusting initial insole elevation values of abnormal nodes in the abnormal node set according to embodiment 1 of the present invention;
FIG. 3 is a schematic diagram of a drainage network topology according to embodiment 2 of the present invention;
wherein the arrows in the figure represent the flow direction.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
A pipe network topology relation checking and repairing method comprises the following steps:
s1, selecting a pipe network path with a known flow direction from a pipe network to be inspected, marking n nodes according to the front-back sequence of the flow direction, measuring initial inner bottom elevation values of all nodes of the pipe network, and ensuring that the initial inner bottom elevation values of the nodes at the initial positions are accurate;
s2, forming a pipe section by the pipelines between adjacent nodes, traversing all the pipe sections in sequence along the flow direction of the pipe network path, and calculating the gradient of each pipe section to obtain gradient data of the pipe section;
the gradient data of the pipe section in the step S2 are obtained by adopting the following model:
wherein i is the ith node of the pipe network path, n nodes are altogether, i=1 is the node of the initial position of the pipe network path from the upstream, L i,i+1 Representing the length of the pipe segment between a start node i and a stop node i +1,
an initial insole elevation value representing an ith node, the following table i represents the ith node, superscript 0 represents an initial value,/->
Representing the initial insole elevation value of the i+1th node.
S3, checking whether the gradient data has a negative value or not; removing negative value data, forming a data set by residual non-negative gradient data, carrying out statistical analysis on the data set, identifying abnormal gradient data in the data set, and carrying out statistics on normal gradient data to obtain a range [ s ] of normal gradient min ,s max ]；
The statistical analysis of the data set is realized in the S3 in the following way;
s31, forming a data set by residual non-negative gradient data, and arranging data set elements in a descending order;
s32, calculating the first quartile Q of all non-negative gradient data in the descending data set 1 ＝min{[1+(k-1)·0.25]，(k+1)·0.25}；
Calculating the third quartile Q of all non-negative slope data in the descending data set 3 ＝max{[1+(k-1)·0.75]，(k+1)·0.75}；
Calculating the quartile range iqr=q 3 -Q 1 ；
k is the number of all non-negative grade data;
s33, calculating an outlier cutoff point (Q) 3 +1.5 IQR) and (Q) 1 -1.5 IQR), greater than (Q) in non-negative grade data 3 +1.5 IQR) or less (Q) 1 -1.5 IQR) is abnormal gradient data.
S4, identifying the starting and stopping nodes of the pipe section corresponding to the gradient data with negative gradient and abnormal gradient data to form an abnormal node set J;
s5, adjusting the initial inner bottom elevation value of the abnormal nodes in the abnormal node set, wherein the abnormal nodes with abnormal inner bottom elevations exist in the abnormal node set.
The method comprises the following steps of adjusting the elevation values of the inner bottoms of abnormal nodes in the abnormal node set;
s51, taking the inner bottom elevation values of all nodes on the pipe network path as variables to be optimized, constructing an objective function according to the initial inner bottom elevation values of all the nodes, and taking the inverse of the objective function as an fitness function;
the construction objective function is as follows:
the fitness function is constructed as follows:
E i the adjusted height value of the insole of the ith node;
the following conditions were taken as constraints of the fitness function:
(a) The gradient data of the pipe section calculated by the inner bottom elevation value of the adjusted node is in the interval range of the normal gradient:
(b) The adjusted insole elevation value of the node is set to a depth below the ground:
(c) A node having a normal initial insole elevation value,
the height value of the inner bottom is directly taken as the initial height value of the inner bottom without modification:
setting an abnormal node set as a set J, and setting other nodes as normal nodes with normal inner bottom elevation values;
the constraint conditions are respectively converted into a matrix form as follows:
wherein E is 1 Representing the adjusted insole elevation value of node 1, E 2 Representing the adjusted insole elevation value of node 2, E n Representing the adjusted insole elevation value, L, of the nth node 1-2 Representing the length of the pipe section between the starting node being 1 and the ending node being 2, L 2-3 Representing the length of the pipe section between the starting node being 2 and the ending node being 3, L n-1-n Representing the length of the pipe section between the starting node of n-1 and the ending node of n, s min Interval range s of normal grade min ,s max ]Lower limit of s max Interval range s of normal grade min ,s max ]Is set at the upper limit of (c),
ground elevation for node 1, +.>
Ground elevation for node 2, +.>
For the ground elevation of the nth node, deep is the minimum depth of the manhole specified by engineering design specifications, +.>
An adjusted insole elevation value representing nodes outside the abnormal node set J, +.>
Representing the initial insole elevation values of nodes outside the abnormal node set J.
S52, coding the inner bottom elevation variable of each node by using a floating point number coding mode, wherein the coding length is n, and the number of the coding length is the same as that of the variable;
s53, floating point numbers in n specified ranges are arranged into an individual, and a plurality of individuals are randomly generated to serve as an initial population;
s54, meterCalculating the fitness function value f of each individual of the initial population i Calculating probability P of each individual being selected into next generation population i The method comprises the steps of carrying out a first treatment on the surface of the In the individual selection process, selecting two individuals each time according to a roulette selection mechanism, wherein the individuals enter the next generation population with high fitness;
s55, for the new population generated by the selection operation, pairing the individuals of the new population in pairs randomly, and calculating the cross probability P of each pairing c Non-uniform arithmetic crossover is carried out on the two paired individuals according to the crossover probability; wherein the crossover probability P c Dynamic changes with individual fitness:
setting the genes to be crossed of two individuals as x and y, and forming new genes as x ', y':
wherein alpha is a random number which accords with uniform probability distribution within the range of (0, r), 0<r is less than or equal to 1, and r varies along with evolution algebra;
s56, according to the variation probability P m Performing basic position variation operation on the population individuals subjected to the cross operation; wherein the variation probability P m Dynamic changes with individual fitness:
s57, obtaining a child population after mutation operation, calculating fitness function values of individuals of the child population, and outputting individuals with highest fitness, namely optimal fitness individuals; comparing the optimal fitness individuals with the optimal fitness individuals of the parent population, and taking whether Euclidean distance between the two individuals is smaller than a convergence condition allowing residual errors to be used as population evolution;
s58, if the convergence condition is met, the optimal fitness individual is the optimal individual, and the individual gene value is the optimal pipe network node inner bottom elevation value; if the convergence condition is not satisfied, returning to S54, and continuing the selection, crossing and mutation operations of the offspring population until the offspring population satisfies the convergence condition;
or setting the maximum evolution algebra of the population, stopping calculating when the maximum evolution algebra is reached, and outputting the individual with the optimal fitness as the optimal individual.
In said S54
In the S55
f max 、f avg Respectively representing the maximum fitness and the average fitness, k, of a new population generated by the selection operation 1 、k 2 Is constant, k 1 <k 2 。
f’ max 、f’ avg Respectively represent the maximum fitness and the average fitness, k of the new population generated by the crossover operation 3 、k 4 Is constant, k 3 <k 4 。
In S56, the gene to be mutated in the individual is set as z, and the mutated new gene z' is:
z'＝z+(R-L)·γ
and gamma is a random number conforming to uniform probability distribution in the range of [0,1], L and R are the left boundary and the right boundary of the corresponding gene value range respectively, L is not less than z and not more than R, and L and R are respectively calculated according to constraint conditions of the fitness function.
Preferably, the euclidean distance between the two individuals in S57 is:
Example 2
The embodiment discloses a method for checking and repairing the topological relation of the drainage pipe network by adopting the method for checking and repairing the topological relation of the pipe network.
As shown in fig. 3, the topology of the drainage network is shown, wherein the reference numerals of the "MH" prefix denote manhole nodes, the reference numerals of the "O" prefix denote water outlet nodes, and the reference numerals of the "CO" prefix denote pipe segments. According to the position of the water outlet of the pipe network, the urban terrain elevation and the water system characteristics, the water flow direction of the pipe network can be determined, and the water flow direction is shown by an arrow in the figure. Table 1 is the parameters related to the manhole and water outlet nodes, and table 2 is the parameters of each pipe section. Knowing that the insole elevation values of the upstream starting nodes MH-1, MH-13, MH-19 and MH-25 on the pipe network are accurate values, the abnormal node insole elevation is found and modified by using the intelligent inspection method of the drainage pipe network topology.
TABLE 1 node parameters
TABLE 2 parameters of pipe sections
As shown in fig. 3, pipe network path i (MH-1_mh-2_mh-3_ … _mh-12_o-1), path ii (MH-13_mh-14_ … _mh-4_ … _mh-7), path iii (MH-19_mh-20_ … _mh-7_ … _mh-10), and path iv (MH-25_mh-26_ … _mh-10_ … _o-1) were selected for inspection, respectively, and table 3 shows the automatic inspection and modification results of the elevation of the insole in the node.
Table 3 node inspection results
In summary, for a pipe network path capable of definitely judging the flow direction, the gradient of all pipe sections is calculated firstly, then abnormal pipe section gradient data and corresponding starting and ending nodes of the abnormal pipe sections are automatically identified through a box diagram algorithm, and finally, the abnormal node inner bottom elevation of the abnormal pipe section in the abnormal pipe section is automatically modified and optimized through a genetic algorithm by establishing a fitness function and constraint conditions. The intelligent inspection method for the drainage pipe network topology realizes automatic identification, automatic modification and optimization of the elevation of the inner bottom of the abnormal node of the pipe network, and is convenient, quick, efficient and accurate.
It is noted that, in this document, relational terms such as first and second, and the like, if any, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for checking and repairing a pipe network topological relation is characterized by comprising the following steps:
s1, selecting a pipe network path with a known flow direction from a pipe network to be inspected, marking n nodes according to the front-back sequence of the flow direction, and measuring initial inner bottom elevation values of all nodes of the pipe network;
s2, forming a pipe section by the pipelines between adjacent nodes, traversing all the pipe sections in sequence along the flow direction of the pipe network path, and calculating the gradient of each pipe section to obtain gradient data of the pipe section;
s3, checking whether the gradient data has a negative value or not; removing negative value data, forming a data set by residual non-negative gradient data, carrying out statistical analysis on the data set, identifying abnormal gradient data in the data set, and carrying out statistics on normal gradient data to obtain a range [ s ] of normal gradient min ,s max ]；
S4, identifying the starting and stopping nodes of the pipe section corresponding to the gradient data with negative gradient and abnormal gradient data to form an abnormal node set J;
s5, adjusting the initial inner bottom elevation value of the abnormal nodes in the abnormal node set.
2. The method for checking and repairing the topological relation of the pipe network according to claim 1, wherein the gradient data of the pipe section in the step S2 is obtained by adopting the following model:
wherein i is the ith node of the pipe network path, n nodes are altogether, i=1 is the node of the initial position of the pipe network path from the upstream, L i,i+1 Representing the length of the pipe segment between a start node i and a stop node i +1,
an initial insole elevation value representing an ith node, the following table i represents the ith node, superscript 0 represents an initial value,/->
Representing the initial insole elevation value of the i+1th node.
3. The method for checking and repairing the topological relation of the pipe network according to claim 1, wherein the statistical analysis of the data set is realized in the following manner in the step S3;
s31, forming a data set by residual non-negative gradient data, and arranging data set elements in a descending order;
s32, calculating the first quartile Q of all non-negative gradient data in the descending data set 1 ＝min{[1+(k-1)·0.25]，(k+1)·0.25}；
Calculating the third quartile Q of all non-negative slope data in the descending data set 3 ＝max{[1+(k-1)·0.75]，(k+1)·0.75}；
Calculating the quartile range iqr=q 3 -Q 1 ；
k is the number of all non-negative grade data;
s33, calculating an outlier cutoff point (Q) 3 +1.5 IQR) and (Q) 1 -1.5 IQR), greater than (Q) in non-negative grade data 3 +1.5 IQR) or less (Q) 1 -1.5 IQR) is abnormal gradient data.
4. The method for checking and repairing the topological relation of the pipe network according to claim 1, wherein in the step S5, the inner bottom elevation value of the abnormal nodes in the abnormal node set is adjusted in the following manner;
s51, taking the inner bottom elevation values of all nodes on the pipe network path as variables to be optimized, constructing an objective function according to the initial inner bottom elevation values of all the nodes, and taking the inverse of the objective function as an fitness function;
the construction objective function is as follows:
the fitness function is constructed as follows:
E i the adjusted height value of the insole of the ith node;
the following conditions were taken as constraints of the fitness function:
(a) The gradient data of the pipe section calculated by the inner bottom elevation value of the adjusted node is in the interval range of the normal gradient:
(b) The adjusted insole elevation value of the node is set to a depth below the ground:
(c) A node having a normal initial insole elevation value,
the insole elevation value is directly taken as the initial insole elevation value:
setting an abnormal node set as a set J, and setting other nodes as normal nodes with normal inner bottom elevation values;
the constraint conditions are respectively converted into a matrix form as follows:
wherein E is 1 Representing the adjusted insole elevation value of node 1, E 2 Representing the adjusted insole elevation value of node 2, E n Representing the adjusted insole elevation value, L, of the nth node 1-2 Representing the length of the pipe section between the starting node being 1 and the ending node being 2, L 2-3 Representing the length of the pipe section between the starting node being 2 and the ending node being 3, L n-1-n Representing the length of the pipe section between the starting node of n-1 and the ending node of n, s min Interval range s of normal grade min ,s max ]Lower limit of s max Interval range s of normal grade min ,s max ]Is set at the upper limit of (c),
ground elevation for node 1, +.>
Ground elevation for node 2，/>
For the ground elevation of the nth node, deep is the minimum depth of the manhole specified by engineering design specifications, +.>
An adjusted insole elevation value representing nodes outside the abnormal node set J, +.>
An initial insole elevation value representing nodes other than the abnormal node set J;
s52, coding the inner bottom elevation variable of each node by using a floating point number coding mode, wherein the coding length is n;
s53, floating point numbers in n specified ranges are arranged into an individual, and a plurality of individuals are randomly generated to serve as an initial population;
s54, calculating the fitness function value f of each individual of the initial population i Calculating probability P of each individual being selected into next generation population i The method comprises the steps of carrying out a first treatment on the surface of the In the individual selection process, selecting two individuals each time according to a roulette selection mechanism, wherein the individuals enter the next generation population with high fitness;
s55, for the new population generated by the selection operation, pairing the individuals of the new population in pairs randomly, and calculating the cross probability P of each pairing c Non-uniform arithmetic crossover is carried out on the two paired individuals according to the crossover probability; wherein the crossover probability P c Dynamic changes with individual fitness:
s56, according to the variation probability P m Performing basic position variation operation on the population individuals subjected to the cross operation; wherein the variation probability P m Dynamic changes with individual fitness:
s57, obtaining a child population after mutation operation, calculating fitness function values of individuals of the child population, and outputting individuals with highest fitness, namely optimal fitness individuals; comparing the optimal fitness individuals with the optimal fitness individuals of the parent population, and taking whether Euclidean distance between the two individuals is smaller than a convergence condition allowing residual errors to be used as population evolution;
s58, if the convergence condition is met, the optimal fitness individual is the optimal individual, and the individual gene value is the optimal pipe network node inner bottom elevation value; if the convergence condition is not satisfied, returning to S54, and continuing the selection, crossing and mutation operations of the offspring population until the offspring population satisfies the convergence condition;
or setting the maximum evolution algebra of the population, stopping calculating when the maximum evolution algebra is reached, and outputting the individual with the optimal fitness as the optimal individual.
5. The method for checking and repairing topological relation of pipe network according to claim 4, wherein in S54
6. The method for checking and repairing the topological relation of the pipe network according to claim 4, wherein in the step S55
f max 、f avg Respectively representing the maximum fitness and the average fitness, k, of a new population generated by the selection operation 1 、k 2 Is constant, k 1 <k 2 。
7. The method for checking and repairing a topological relation of a pipe network according to claim 4, wherein in S55, two genes to be crossed of the individual are set as x and y, and new genes formed after crossing are x ', y':
wherein alpha is a random number which accords with uniform probability distribution in the range of (0, r), 0<r is less than or equal to 1, and r varies with evolution algebra.
8. The method for checking and repairing the topological relation of the pipe network according to claim 4, wherein in the step S56
f' max 、f' avg Respectively represent the maximum fitness and the average fitness, k of the new population generated by the crossover operation 3 、k 4 Is constant, k 3 <k 4 。
9. The method for checking and repairing a topological relation of a pipe network according to claim 4, wherein in S56, the gene to be mutated in the individual is set as z, and the mutated new gene z' is:
z'＝z+(R-L)·γ
gamma is a random number conforming to uniform probability distribution in the range of 0,1, L and R are the left boundary and the right boundary of the corresponding gene value range respectively, and L is not less than z and not more than R.
10. The method for checking and repairing the topological relation of the pipe network according to claim 4, wherein the euclidean distance between two individuals in the step S57 is as follows:
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