CN115270619A - Sensitivity-based water supply network pipeline grouping and parameter checking method and device - Google Patents
Sensitivity-based water supply network pipeline grouping and parameter checking method and device Download PDFInfo
- Publication number
- CN115270619A CN115270619A CN202210863034.8A CN202210863034A CN115270619A CN 115270619 A CN115270619 A CN 115270619A CN 202210863034 A CN202210863034 A CN 202210863034A CN 115270619 A CN115270619 A CN 115270619A
- Authority
- CN
- China
- Prior art keywords
- pipeline
- sensitivity
- grouping
- pipe
- pipe network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 71
- 230000035945 sensitivity Effects 0.000 title claims abstract description 43
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 42
- 238000012544 monitoring process Methods 0.000 claims abstract description 83
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 29
- 238000010206 sensitivity analysis Methods 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 11
- 238000007726 management method Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 2
- 230000008901 benefit Effects 0.000 description 7
- 230000002068 genetic effect Effects 0.000 description 6
- 239000000463 material Substances 0.000 description 6
- 238000011160 research Methods 0.000 description 5
- 238000009826 distribution Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 229910001141 Ductile iron Inorganic materials 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000009530 blood pressure measurement Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Business, Economics & Management (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Computational Mathematics (AREA)
- Economics (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Computer Networks & Wireless Communication (AREA)
- Algebra (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Medical Informatics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Pipeline Systems (AREA)
Abstract
The invention relates to a method and a device for grouping water supply pipe networks and checking parameters based on sensitivity, wherein the method comprises the following steps: collecting and processing pipe network pressure and flow monitoring data; (2) counting the pipeline attribute information in the pipe network; (3) carrying out sensitivity analysis on the pipeline to be checked in the pipe network; (4) Weighting and clustering the high-sensitivity pipelines according to the pipeline attribute information, and calculating the pipeline attribute clustering center of each group in the high-sensitivity pipeline clustering result; (5) Classifying the rest low-sensitivity pipelines into the grouping of the high-sensitivity pipelines to complete the pipeline grouping; (6) And carrying out rough coefficient check under the grouping, and outputting a pipeline rough coefficient check result and a monitoring point error. Compared with the prior art, the method adopts a two-step clustering method, and aims at the respective clustering of the high-sensitivity pipeline and the low-sensitivity pipeline in the pipe network, so that the automatic grouping of the parameters to be checked can be realized in the pipe network model checking, and the stability and the accuracy of a subsequent checking algorithm can be obviously improved.
Description
Technical Field
The invention relates to the technical field of urban water supply network analysis, in particular to a method and a device for grouping water supply network pipelines and checking parameters based on sensitivity.
Background
The urban water supply network is an important component of an urban water supply system, and the establishment of a pipe network model is very important for scientific and effective management of the complicated water supply network. In order to enable the established pipe network model to be as close to the real state of the water supply pipe network as possible, the established model needs to be checked, namely, the difference value between the pressure or flow value obtained by simulation and the measured value is within an allowable range by adjusting model parameters (such as pipeline roughness coefficient and node water demand), so that the parameters are determined, and the operation condition of the water supply system is obtained through the pipe network model. Because the pipe network model has a plurality of parameters to be checked and the number of monitoring points is limited, in order to make the determinacy or overdetermination of the checking problem and the like often involve node flow aggregation and pipeline grouping, the grouping quality has great influence on the performance of the checking algorithm. At present, in the research, the pipelines are mostly grouped according to information such as pipes, pipe ages and the like by depending on manual experience, and the pipelines in the same group are assumed to have the same roughness coefficient. The method has certain rationality, but for a large-scale pipe network, manual grouping consumes time and labor, the attribute characteristics of parameters and hydraulic characteristics of the pipe network cannot be comprehensively considered, and the grouping effectiveness and the accuracy of checking results cannot be guaranteed. Therefore, the automatic grouping method for the pipe network model parameters has important significance for developing the potential of various checking algorithms and realizing the checking of the large-scale pipe network model.
The method for grouping the checking parameters of the pipe network model at home and abroad has less research, mainly comprises a clustering algorithm, a genetic algorithm and a non-grouping checking method, and the following representative researches comprise:
1) Clustering algorithm
As in the literature:
[1]:Kumar S M,Narasimhan R,Bhallamudi S M.Parameter Estimation in Water Distribution Networks[J].Water Resources Management,2010,24(6):1251-1272.
the method adopts a clustering algorithm, such as a K-means clustering method, to cluster the characteristics of the pipe diameter, the pipe age and the like of the pipeline, the clustering result is influenced by an initial clustering center, and an optimal clustering result can be obtained after repeated clustering.
The advantages and disadvantages are as follows: the method has the advantages that the method is popular and easy to understand, the physical significance is clear, namely the rough coefficient is related to the pipeline attributes such as pipes and pipe ages, and the pipelines with similar rough coefficients can be divided into a group according to the clustering of the pipeline attributes. But has the disadvantages that: (1) The method depends heavily on the accuracy of the clustering algorithm, and the clustering algorithm needs to be executed for multiple times to select the optimal packet; (2) The grouping method is independent of the checking process, the convergence of the checking algorithm under the grouping result and the accuracy of the checking result cannot be guaranteed, and the effect is not good when the grouping method is applied to a large-scale pipe network; (3) The influence of each attribute of the pipeline and the hydraulic characteristics of the pipeline network on the grouping result is not comprehensively considered. Therefore, the method is not widely applied to large-scale pipe network check at present.
2) Genetic algorithm
As in the literature:
[2]:Jung D,Choi Y H,Kim J H.Optimal Node Grouping for Water Distribution System Demand Estimation[J].Water,2016,8(4):160.
the method adopts the following main technical measures: and (3) directly optimizing grouping of pipelines or nodes by adopting a genetic algorithm, wherein the target function is the minimum checking error of the monitoring point or the minimum error of the pipe network parameters.
The advantages and disadvantages are as follows: such methods have the advantage that the optimization goal is unambiguous, i.e. the checking error is minimized, theoretically encompassing all the solution space. However, in such methods, a random method is mostly adopted to generate an initial solution of the genetic algorithm, and for a large-scale pipe network, the search space is too large, and the randomly generated initial solution cannot ensure the convergence of the algorithm. At present, the method is only verified in a small-scale pipe network, and has no practical application in a large-scale annular water supply pipe network.
3) Non-grouping checking method
As in the literature:
[4]:Cheng W,He Z.Calibration of Nodal Demand in Water Distribution Systems[J].Journal of Water Resources Planning and Management,2010,137(1):31-40.
[5]:Letting L,Hamam Y,Abu-Mahfouz A.Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization[J].Water,2017,9(8):593.
the method adopts the following main technical measures: and (3) checking pipe network parameters by adopting a method of solving a minimum norm solution by adopting a singular value decomposition method or carrying out a genetic algorithm and a particle swarm algorithm for multiple times to obtain an average value.
The advantages and disadvantages are as follows: the method has the advantages that the manual grouping of the pipe networks is not needed, the labor is saved, but the method has some defects: (1) The checking result may have a relatively large difference from the actual situation, and the result uncertainty is relatively large; (2) The time consumption for a large pipe network is long by executing the genetic algorithm for multiple times and the like, and the real-time checking requirement cannot be met.
In summary, although some researches on the grouping method of the checking parameters of the pipe network model have been made, most of the researches do not combine the hydraulic characteristics of the pipe network and the attributes of the pipes, and no better automatic grouping method replaces manual grouping, so that the method has a great influence on the checking of the model parameters.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method and a device for grouping water supply pipe networks and checking parameters based on sensitivity.
The purpose of the invention can be realized by the following technical scheme:
a sensitivity-based water supply network pipeline grouping and parameter checking method comprises the following steps:
(1) Collecting and processing pipe network pressure and flow monitoring data;
(2) Counting pipeline attribute information in a pipe network;
(3) Carrying out sensitivity analysis on a pipeline to be checked in a pipe network;
(4) According to the pipeline attribute information, carrying out weighted clustering on the high-sensitivity pipelines, and calculating the pipeline attribute clustering center of each group in the high-sensitivity pipeline clustering result;
(5) The rest low-sensitivity pipelines are classified into the groups of the high-sensitivity pipelines to complete the grouping of the pipelines;
(6) And carrying out rough coefficient check under the grouping, and outputting a pipeline rough coefficient check result and a monitoring point error.
Preferably, step (1) specifically comprises: and screening the monitoring data of the pressure and the flow of the pipe network, eliminating abnormal data and estimating monitoring errors of monitoring points.
Preferably, the step (2) specifically comprises:
(21) Collecting pipeline attribute information in a pipe network, wherein the pipeline attribute information comprises m types of pipeline attribute data;
(22) Generating a pipe network pipe information matrix P, wherein the matrix P comprises the pipe attribute data of all pipes in the pipe network;
(23) Normalizing each type of pipeline attribute data in the pipeline information matrix P of the network management to obtain xkp′，xkp' is normalized data of the k property p of the pipeline, k =1,2, \ 8230;, n, p =1,2, \ 8230;, m, where n is the number of pipelines in the pipe network.
Preferably, the pipeline attribute information includes pipe material, age of pipe and pipe diameter.
Preferably, step (3) specifically comprises:
(31) Acquiring a sensitivity matrix J of the pipeline roughness coefficient to the monitoring point:
JHk，kHifor the sensitivity of the ith pressure monitoring point in the pipe network to the k th pipeline roughness coefficient, jqk，kqjK =1, 2.., n, i =1,2 for sensitivity of the jth flow monitoring point in the pipe network to the kth pipe roughness coefficient,the number of pressure monitoring points and the number of flow monitoring points in a pipe network are respectively increased, wherein n, j =1,2,. Cndot, n, kHn and kqn;
(32) Calculating a weight matrix of a pipe network monitoring point:
for pressure monitoring points, the weight matrix is:
WkH＝(wkH1 wkH2 … wkHn)
wkHiis the weight of the ith pressure monitoring point,
in the formula, σHError variance, w, for pressure monitoring pointsHChecking the weight of the pressure monitoring data in the rough coefficient;
for the traffic monitoring points, the weight matrix is:
Wkq＝(wkq1 wkq2 … wkqn)
wherein, wkqjIs the weight of the jth traffic monitoring point,
in the formula, σkqjIs the error variance, w, of the flow monitoring point jqChecking the weight of the flow monitoring data in the rough coefficient;
(33) Calculating the weighted sensitivity of each pipeline to all monitoring points:
Sk＝∑(wkHi×JHk，kHi)+∑(wkqj×Jqk，kqj)
Skis the weighted sensitivity of the kth pipe.
Preferably, the step (4) specifically comprises:
(41) Weighted sensitivity to a pipe SkSorting, selecting high-sensitivity pipeline and recording as G1；
(42) For G1Using weighted clustering calculations according to attribute characteristicsThe method is divided into groups according to the method,
weighted distance d (x) between pipeline and cluster center in each group in weighted clustering algorithmk-c) is calculated according to the following formula:
xkp' standardized data for the k-attribute p of the pipeline, cp' is normalized data for attribute p in the set of cluster centers; w is apIs an attribute weight of an attribute p, p =1, 2.. And m is a number of pipe attributes,
continuously updating the clustering center to obtain a high-sensitivity pipeline grouping result;
(43) Standardized clustering center c for calculating properties of each group of high-sensitivity pipelinesp′：
In the formula, NgIs the g group number of pipelines.
Preferably, step (5) specifically comprises:
(51) And performing importance evaluation on the factors influencing the rough coefficient of the pipeline of the pipe network according to background data and known information of the pipe network, and giving corresponding variation ranges to the weights of the attributes of the pipeline according to importance sorting results.
(52) Calculating the weighted distance between the residual low-sensitivity pipelines and each group of clustering centers by adopting the weighted distance calculation formula in the step (42);
(53) And (4) classifying the low-sensitivity pipelines into the high-sensitivity pipeline group with the closest distance, updating each group of clustering centers, and repeating for multiple times until the clustering centers are not changed any more, thereby finishing the pipeline grouping.
Preferably, step (6) specifically comprises: and checking the rough coefficient of the pipeline by adopting methods such as Gauss-Newton iteration and the like under the current grouping, and outputting a final checking result and a monitoring point error.
An apparatus comprising a memory having a computer program and a processor that when executed implements the sensitivity-based water supply network pipe grouping and parameter verification method.
A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the sensitivity-based method for water supply network pipe grouping and parameter verification.
Compared with the prior art, the invention has the following advantages:
(1) The method adopts a two-step clustering method, and respectively clusters high-sensitivity pipelines and low-sensitivity pipelines in a pipe network, wherein the first step of clustering is performed on the high-sensitivity pipelines, so that the high-sensitivity pipelines are relatively uniformly distributed in each group, and further the convergence and stability of a checking algorithm are ensured, and the second step of clustering is performed on the low-sensitivity pipelines, so that the pipelines with similar rough coefficients are divided into one group, and the grouping accuracy and the reliability of a checking result are improved.
(2) The invention can realize the automatic grouping of the parameters of the pipe network model, replaces the traditional manual grouping method, saves manpower and material resources and improves the possibility of grouping and checking the parameters of the large pipe network model;
(3) According to the method, grouping of the parameters to be checked of the pipe network is realized through a two-step clustering algorithm, and meanwhile, the hydraulic characteristics of a pipe network model and the attribute characteristics of the pipeline are considered, so that the convergence of a subsequent checking algorithm can be improved; meanwhile, the pipelines with similar rough coefficients are divided into a group as much as possible, so that the grouping accuracy is improved, and the method has a relatively definite physical significance.
Drawings
Fig. 1 is a flow chart of a method for grouping water supply pipes and checking parameters based on sensitivity according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
The embodiment provides a sensitivity-based water supply network pipeline grouping and parameter checking method, which comprises the following steps:
(1) Collecting and processing pipe network pressure and flow monitoring data;
(2) Counting pipeline attribute information in a pipe network;
(3) Carrying out sensitivity analysis on a pipeline to be checked in the pipe network;
(4) Weighting and clustering the high-sensitivity pipelines according to the pipeline attribute information, and calculating the pipeline attribute clustering center of each group in the high-sensitivity pipeline clustering result;
(5) The rest low-sensitivity pipelines are classified into the groups of the high-sensitivity pipelines to complete the grouping of the pipelines;
(6) And carrying out rough coefficient check under the grouping, and outputting a pipeline rough coefficient check result and a monitoring point error.
The method adopts a two-step clustering method, and aims at respectively clustering high-sensitivity pipelines and low-sensitivity pipelines in a pipe network, wherein the first step of clustering aims at the high-sensitivity pipelines to ensure that the high-sensitivity pipelines are relatively and uniformly distributed in each group, so that the convergence and the stability of a checking algorithm are ensured, and the second step of clustering aims at the low-sensitivity pipelines to divide the pipelines with similar rough coefficients into one group, so that the grouping accuracy is improved, and the reliability of a checking result is improved.
The step (1) specifically comprises the following steps: and screening the monitoring data of the pipe network pressure and flow, eliminating abnormal data and estimating monitoring errors of monitoring points.
The step (2) specifically comprises the following steps:
(21) Collecting pipeline attribute information in a pipe network, wherein the pipeline attribute information comprises m types of pipeline attribute data, and the pipeline attribute information in the embodiment comprises pipes, pipe ages and pipe diameters, so that m =3;
(22) Generating a pipe network pipeline information matrix P, wherein the matrix P comprises pipeline attribute data of all pipelines in a pipe network, and in this embodiment, P represents:
wherein n is the number of pipelines in the pipe network; mk，agek，dk(k =1,2,. And n) are respectively the pipe material, age (year) and pipe diameter (mm) of the pipeline k;
(23) Normalizing each type of pipeline attribute data in the network management pipeline information matrix P to obtain xkp′，xkp' is normalized data of the k attribute P of the pipeline, where k =1,2,.. Ang, n, P =1,2,.. Ang, m, where n is the number of pipelines in the pipe network, and in this embodiment, the data in the matrix P is normalized in the following manner:
wherein x iskpAnd xkpRespectively representing the values before and after normalization of the attribute p of the pipeline k, wherein min (p) is the minimum value of all the pipeline attributes p; max (p) is the maximum value of all pipe properties p.
The step (3) specifically comprises the following steps:
(31) Acquiring a sensitivity matrix J of the pipeline roughness coefficient to the monitoring point:
JHk，kHisensitivity of ith pressure monitoring point to kth pipeline roughness coefficient in pipe network, jqk，kqjFor the sensitivity of a jth flow monitoring point in a pipe network to a kth pipeline roughness coefficient, k =1, 2.. Multidot.n, i =1, 2.. Multidot.n, j =1, 2.. Multidot.n, kHn and kqn respectively represent the number of pressure monitoring points and flow monitoring points in the pipe network;
the sensitivity matrix can be obtained through software simulation test, hydraulic simulation is executed after disturbance is added to the rough coefficient of each pipeline in sequence, the change condition of monitoring data of the pipe network pressure and flow monitoring points after disturbance is added is recorded, and then the sensitivity matrix of the pipe network is obtained;
(32) Calculating a weight matrix of a pipe network monitoring point:
for pressure monitoring points, the weight matrix is:
WkH＝(wkH1 wkH2 … wkHn)
wkHiis the weight of the ith pressure monitoring point,
in the formula, σHError variance, w, for pressure monitoring pointsHChecking the weight of the pressure monitoring data in the rough coefficient;
for the traffic monitoring points, the weight matrix is:
Wkq＝(wkq1 wkq2 … wkqn)
wherein, wkqjIs the weight of the jth traffic monitoring point,
in the formula, σkqjIs the error variance, w, of the flow monitoring point jqChecking the weight of the medium flow monitoring data for the rough coefficient;
(33) Calculating the weighted sensitivity of each pipeline to all monitoring points:
Sk＝∑(wkHi×JHk，kHi)+∑(wkqj×Jqk，kqj)
Skweighted sensitivity for the kth pipe.
The step (4) specifically comprises the following steps:
(41) Weighted sensitivity to a pipe SkSorting, selecting high-sensitivity pipeline and marking as G1；
(42) For G1Grouping by adopting a weighted clustering algorithm according to the attribute characteristics,
weighted distance d (x) between pipeline and cluster center of each group in weighted clustering algorithmk-c) is calculated according to the formula：
xkp' standardized data for the k-attribute p of the pipeline, cp' is normalized data for an attribute p in the set of cluster centers; w is apIs an attribute weight of an attribute p, p =1, 2.. And m is a number of pipe attributes,
continuously updating the clustering center to obtain a high-sensitivity pipeline grouping result;
(43) Standardized clustering center c for calculating pipeline attributes of each group of high-sensitivity pipelinesp′：
In the formula, NgIs the number of the group g pipeline.
The step (5) specifically comprises the following steps:
(51) And performing importance evaluation on the factors influencing the rough coefficient of the pipeline of the pipe network according to background data and known information of the pipe network, and giving corresponding variation ranges to the weights of the attributes of the pipeline according to importance sorting results.
(52) Calculating the weighted distance between the residual low-sensitivity pipelines and each group of clustering centers by adopting the weighted distance calculation formula in the step (42);
(53) And (4) classifying the low-sensitivity pipelines into the high-sensitivity pipeline group with the closest distance, updating each group of clustering centers, and repeating for multiple times until the clustering centers are not changed any more, thereby finishing the pipeline grouping.
The step (6) specifically comprises the following steps: and checking the rough coefficient of the pipeline by adopting methods such as Gaussian-Newton iteration and the like under the current grouping, and outputting a final checking result and a monitoring point error.
In this embodiment, rough coefficient checking of a certain water supply network is taken as an example, and the implementation process of the water supply network model checking parameter grouping method based on sensitivity analysis is further described.
(1) Collecting and processing pipe network pressure and flow monitoring data;
31 pressure monitoring points and 26 flow monitoring points are arranged in the water supply pipe network, the pressure monitoring data vector is recorded as kH, and the flow monitoring data vector is recorded as kq.
(2) Counting pipe network pipeline information including pipes, pipe age and pipe diameter;
the pipe network has 567 pipelines in total, namely 567 rough coefficients to be checked. The pipeline attribute matrix is generated as follows:
wherein, MkThe roughness coefficient of the new pipe is used to express the roughness coefficient of the pipe kkIndicates the age (year), d, of the pipe kkDenotes the pipe diameter (mm) of the pipe k. If the pipeline 34 is a ductile cast iron pipe, the roughness coefficient of the new pipeline of the pipe is 110, the age of the pipe is 40 years, the pipe diameter is 200mm, and the maximum value and the minimum value of the pipe, the age of the pipe and the pipe diameter in the pipe network are shown in table 1:
TABLE 1 certain water supply network pipeline attributes
The pipe attribute vector for the corresponding pipe 34 is therefore: (130, 40, 200), the normalized pipe attribute vector is: (0.5,1.0,0.143).
(3) Carrying out sensitivity analysis on the rough coefficient to be checked in the pipe network;
the error of the flow monitoring point of the pipe network is 2%, the standard deviation of the error of the pressure monitoring point is 0.1m, the pipeline roughness coefficient is checked by adopting Gaussian-Newton iteration, the weight of pressure monitoring data in the checking process is 1, the weight of the flow monitoring data is 0.001, the weight of each monitoring point is calculated, and the weight of the pressure monitoring point i is as follows:
the weight of the flow monitoring point j is as follows:
in the formula, mukqjFor a monitor value of flow monitor j, e.g., 1.69L/s for flow monitor 504, the monitor weight is:
and calculating and sequencing weighted sensitivity sum of each pipeline.
(4) Clustering the high-sensitivity pipelines according to the sensitivity characteristics;
selecting pipelines with the sensitivity ranking of the first 70% as high-sensitivity pipeline groups, and adopting a weighted clustering algorithm to group the high-sensitivity pipeline groups into 10 groups. And calculating the attribute clustering center of each group of high-sensitivity pipelines.
(5) And classifying the residual low-sensitivity pipelines into a high-sensitivity pipeline group to complete pipeline grouping.
In the case, the influence of the pipe material and the pipe age on the roughness coefficient of the pipeline is large, and the influence of the pipe diameter is small, so that the attribute of the pipe material and the pipe age is given a large weight, and the pipe diameter is given a small weight. And calculating the weighted distance between the low-sensitivity pipeline and each group of clustering centers, dividing the pipeline into the groups with the minimum distance, updating each group of clustering centers, and iterating for multiple times until the clustering centers are not changed any more, thereby finishing the grouping of the pipelines.
(6) And checking the rough coefficient of the pipe network under the grouping, and outputting a final checking result and a monitoring point error.
The final grouping result, the rough coefficient check error and the detail data of the monitoring point simulation error are shown in tables 2-4.
Table 2 illustrates the checking results of pipe network roughness coefficients
TABLE 3 example pipe network pressure measurement point check result error
Table 4 example pipe network flow point check result error
By integrating the above steps, the automatic grouping of the pipe network model parameters can be realized, the traditional manual grouping method is replaced, the manpower and material resources are saved, and the possibility of grouping and checking the large pipe network model parameters is improved; according to the method, grouping of parameters to be checked of the pipe network is achieved through a two-step clustering algorithm, hydraulic characteristics of a pipe network model and attribute characteristics of a pipeline are considered, and convergence of a subsequent checking algorithm can be improved; meanwhile, the pipelines with similar rough coefficients are divided into a group as much as possible, so that the grouping accuracy is improved, and the method has a relatively definite physical significance.
Example 2
This embodiment provides an apparatus, including a memory and a processor, where the memory has a computer program, and the processor implements the method for grouping water supply network pipes and checking parameters based on sensitivity in embodiment 1 when executing the computer program, where the method is described in detail in embodiment 1, and is not described again in this embodiment.
Example 3
This embodiment provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for grouping water supply network pipelines and checking parameters based on sensitivity in embodiment 1 is implemented, which is described in detail in embodiment 1, and is not described again in this embodiment.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the scope of the technical idea of the present invention.
Claims (10)
1. A sensitivity-based water supply network pipeline grouping and parameter checking method is characterized by comprising the following steps:
(1) Collecting and processing pipe network pressure and flow monitoring data;
(2) Counting pipeline attribute information in a pipe network;
(3) Carrying out sensitivity analysis on a pipeline to be checked in the pipe network;
(4) Weighting and clustering the high-sensitivity pipelines according to the pipeline attribute information, and calculating the pipeline attribute clustering center of each group in the high-sensitivity pipeline clustering result;
(5) Classifying the rest low-sensitivity pipelines into the grouping of the high-sensitivity pipelines to complete the pipeline grouping;
(6) And carrying out rough coefficient check under the grouping, and outputting a pipeline rough coefficient check result and a monitoring point error.
2. The sensitivity-based water supply network pipeline grouping and parameter checking method according to claim 1, wherein the step (1) specifically comprises: and screening the monitoring data of the pressure and the flow of the pipe network, eliminating abnormal data and estimating monitoring errors of monitoring points.
3. The method for grouping and checking parameters of the water supply pipe network based on the sensitivity as claimed in claim 1, wherein the step (2) comprises:
(21) Collecting pipeline attribute information in a pipe network, wherein the pipeline attribute information comprises m types of pipeline attribute data;
(22) Generating a pipe network pipe information matrix P, wherein the matrix P comprises the pipe attribute data of all pipes in the pipe network;
(23) Normalizing each type of pipeline attribute data in the pipeline information matrix P of the network management to obtain xkp′，xkp' is normalized data of the k property p of the pipeline, k =1,2, \ 8230;, n, p =1,2, \ 8230;, m, where n is the number of pipelines in the pipe network.
4. The method as claimed in claim 1, wherein the pipeline attribute information includes pipe length, pipe age, and pipe diameter.
5. The sensitivity-based water supply network pipeline grouping and parameter checking method according to claim 1, wherein the step (3) specifically comprises:
(31) Acquiring a sensitivity matrix J of the pipeline roughness coefficient to the monitoring points:
JHk,kHifor the sensitivity of the ith pressure monitoring point in the pipe network to the k th pipeline roughness coefficient, jqk,kqjThe sensitivity of the jth flow monitoring point in the pipe network to the kth pipe roughness coefficient is K =1,2, \8230, n, i =1,2, \8230, n, j =1,2, \8230, n, kHn and kqn are respectively the number of pressure monitoring points and flow monitoring points in the pipe network;
(32) Calculating a weight matrix of a pipe network monitoring point:
for pressure monitoring points, the weight matrix is:
WkH＝(wkH1 wkH2 … wkHn)
wkHiis the weight of the ith pressure monitoring point,
in the formula, σHError variance, w, for pressure monitoring pointsHChecking the weight of the medium pressure monitoring data for the rough coefficient;
for the traffic monitoring points, the weight matrix is:
Wkq＝(wkq1 wkq2 … wkqn)
wherein, wkqjIs the weight of the jth traffic monitoring point,
in the formula, σkqjIs the error variance, w, of the flow monitoring point jqChecking the weight of the medium flow monitoring data for the rough coefficient;
(33) Calculating the weighted sensitivity of each pipeline to all monitoring points:
Sk＝∑(wkHi×JHk,kHi)+∑(wkqj×Jqk,kqj)
Skis the weighted sensitivity of the kth pipe.
6. The sensitivity-based water supply network pipeline grouping and parameter checking method according to claim 5, wherein the step (4) specifically comprises:
(41) Weighted sensitivity to the pipe SkSorting, selecting high-sensitivity pipeline and marking as G1；
(42) For G1Grouping by adopting a weighted clustering algorithm according to the attribute characteristics,
weighted distance d (x) between pipeline and cluster center of each group in weighted clustering algorithmk-c) is calculated according to the following formula:
xkp' standardized data for the k-attribute p of the pipeline, cp' is normalized data for attribute p in the set of cluster centers; w is apIs the attribute weight of attribute p, p =1,2, \ 8230;, m, m is the number of pipe attributes,
continuously updating the clustering center to obtain a high-sensitivity pipeline grouping result;
(43) Calculating outStandardized clustering center c of each group of pipeline attributes of high-sensitivity pipelinesp′：
In the formula, NgIs the g group number of pipelines.
7. The method for grouping and checking parameters of the water supply pipe network based on the sensitivity as claimed in claim 6, wherein the step (5) comprises:
(51) And performing importance evaluation on the factors influencing the rough coefficient of the pipeline network according to background information and known information of the pipeline network, and endowing corresponding change ranges to the weight of each attribute of the pipeline according to the importance sorting result.
(52) Calculating the weighted distance between the residual low-sensitivity pipelines and each group of clustering centers by adopting the weighted distance calculation formula in the step (42);
(53) And (4) classifying the low-sensitivity pipelines into the high-sensitivity pipeline group with the closest distance, updating each group of clustering centers, and repeating for multiple times until the clustering centers are not changed any more, thereby finishing the pipeline grouping.
8. The sensitivity-based water supply network pipeline grouping and parameter checking method according to claim 1, wherein the step (6) specifically comprises: and checking the rough coefficient of the pipeline by adopting methods such as Gaussian-Newton iteration and the like under the current grouping, and outputting a final checking result and a monitoring point error.
9. An apparatus comprising a memory and a processor, said memory having a computer program, wherein said processor when executing said computer program implements the sensitivity-based water supply network tube grouping and parameter verification method of any one of claims 1 to 8.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the sensitivity-based water supply network tube grouping and parameter verification method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210863034.8A CN115270619A (en) | 2022-07-21 | 2022-07-21 | Sensitivity-based water supply network pipeline grouping and parameter checking method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210863034.8A CN115270619A (en) | 2022-07-21 | 2022-07-21 | Sensitivity-based water supply network pipeline grouping and parameter checking method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115270619A true CN115270619A (en) | 2022-11-01 |
Family
ID=83767656
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210863034.8A Pending CN115270619A (en) | 2022-07-21 | 2022-07-21 | Sensitivity-based water supply network pipeline grouping and parameter checking method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115270619A (en) |
-
2022
- 2022-07-21 CN CN202210863034.8A patent/CN115270619A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112382352B (en) | Method for quickly evaluating structural characteristics of metal organic framework material based on machine learning | |
| CN108038300B (en) | Optical fiber state evaluation method based on improved membership function combined with neural network | |
| TWI711983B (en) | Method and device for judging business district | |
| CN108197744B (en) | Method and system for determining photovoltaic power generation power | |
| TW201926204A (en) | Method and device for identifying corporations with abnormal transactions | |
| CN106022954B (en) | Multiple BP neural network load prediction method based on grey correlation degree | |
| CN108898275B (en) | Cloud model-based cow breeding environment comfort evaluation method and system | |
| CN108052387B (en) | Resource allocation prediction method and system in mobile cloud computing | |
| CN110348665A (en) | A kind of low-voltage platform area electric power system data quality evaluating method and device | |
| CN112990627B (en) | Power quality evaluation method | |
| CN110782131A (en) | Performance evaluation method for water cooperation organization of farmers | |
| CN111294841A (en) | Method and device for processing wireless network problem and storage medium | |
| CN116306910A (en) | Fair privacy calculation method based on federal node contribution | |
| CN116008671A (en) | Lightning positioning method based on time difference and clustering | |
| CN117150416B (en) | Method, system, medium and equipment for detecting abnormal nodes of industrial Internet | |
| CN110163507A (en) | A kind of processing method of Line Loss of Distribution Network System | |
| CN107423319B (en) | Junk web page detection method | |
| CN117745119A (en) | Power transmission and transformation project construction bearing capacity early warning method and device | |
| CN109039797A (en) | Big stream detection method based on intensified learning | |
| CN112446168A (en) | Effluent BOD concentration soft measurement method based on MIC and RBFNN | |
| CN109190749A (en) | A kind of prediction technique and device for the intelligent electric meter service life | |
| CN117170980A (en) | Early warning method, device, equipment and storage medium for server hardware abnormality | |
| CN115270619A (en) | Sensitivity-based water supply network pipeline grouping and parameter checking method and device | |
| CN116680969A (en) | Filler evaluation parameter prediction method and device for PSO-BP algorithm | |
| CN114722606B (en) | Hydrological model parameter estimation method and equipment based on remote sensing soil humidity data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |