CN114429034A - Pressure monitoring point moving arrangement method for water supply network hydraulic model water quantity checking - Google Patents
Pressure monitoring point moving arrangement method for water supply network hydraulic model water quantity checking Download PDFInfo
- Publication number
- CN114429034A CN114429034A CN202111610339.XA CN202111610339A CN114429034A CN 114429034 A CN114429034 A CN 114429034A CN 202111610339 A CN202111610339 A CN 202111610339A CN 114429034 A CN114429034 A CN 114429034A
- Authority
- CN
- China
- Prior art keywords
- checking
- sens
- monitoring point
- monitoring
- vector
- 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.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 161
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 122
- 238000000034 method Methods 0.000 title claims abstract description 49
- 239000011159 matrix material Substances 0.000 claims abstract description 33
- 238000005457 optimization Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 239000013598 vector Substances 0.000 claims description 54
- 230000035945 sensitivity Effects 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000010276 construction Methods 0.000 abstract description 2
- 238000012423 maintenance Methods 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000010845 search algorithm Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 241000287127 Passeridae Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/14—Pipes
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computational Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Examining Or Testing Airtightness (AREA)
Abstract
The invention discloses a pressure monitoring point moving arrangement method for water supply network hydraulic model water quantity checking, which comprises the following steps: dividing a checking period according to the total number of the nodes of the pipe network and the number of the pressure monitoring sensors; obtaining a Jacobian matrix of node pressure relative to node water quantity based on an initialized pipe network hydraulic model; solving a monitoring point moving arrangement scheme according to a Jacobian matrix and an improved implicit enumeration optimization method; and checking and calculating the node water quantity parameters of the water supply network hydraulic model according to the monitoring data acquired by the monitoring point position deployment scheme in each checking period, and improving the calculation precision. On the premise of equivalent cost, the invention can multiply the acquisition amount of monitoring data, increase the pressure monitoring space density of the pipe network, acquire more pipe network running state information and is beneficial to improving the model checking precision; on the premise that the model checking precision is equivalent, the method can greatly reduce the hardware cost, the construction cost and the maintenance cost of model parameter checking.
Description
Technical Field
The invention relates to the technical field of urban water supply system modeling, in particular to a pressure monitoring point mobile arrangement method for water supply network hydraulic model water quantity checking.
Background
The urban water supply network system is a life line of a city and is an essential important municipal infrastructure in a modern city. In recent years, with the rapid development of the urbanization process, the scale of an urban water supply pipe network system is continuously enlarged, the complexity is gradually increased, and in addition, the problems of pipe network aging, high leakage rate of the urban water supply pipe network, difficulty in reducing the leakage rate and the like are solved, so that the requirements and the technical difficulty for effectively managing and operating and maintaining the pipe network are higher and higher. The high-efficiency management of urban water affairs by using a modern digital information means is a consensus of domestic and foreign water industry. The establishment of the water supply network hydraulic model is a core component of information management, and is one of core technical means for realizing reasonable planning and modification of a pipe network, system condition diagnosis and real-time operation optimization.
Under the strategic background of rapid development of 'smart cities' and 'smart water affairs' taking internet +, internet of things, big data, artificial intelligence and the like as labels, the modeling of the water supply network is more and more emphasized, and water supply enterprises in some domestic cities such as Beijing, Shanghai, Guangzhou, Foshan and the like successively establish water supply network hydraulic models. The pipe network model is a simulation model which can accurately represent an actual system under a specific modeling aim. At present, the pipe network hydraulic model plays an important role in the aspects of pipe network planning design, pipe network reconstruction and expansion and the like. However, due to the limitation of the accuracy of the pipe network model, the deep application of the pipe network model in recent years suffers from the related technical bottleneck. For example, deep applications such as pipe network leakage identification and control, pipe network abnormal state diagnosis, pipe network water quality simulation, energy conservation optimization and the like based on hydraulic model checking have unsatisfactory application effects due to the accuracy problem of the pipe network model.
In the hydraulic modeling process of the water supply network, the pipe network model is checked by using monitoring data such as pipe network pressure, flow and the like, so that the precision of the pipe network model is improved. Generally speaking, for a water supply pipe network system, the more monitoring points are arranged, the higher the checking precision of the model can be. However, the number of monitoring devices is relatively very limited, limited by economic investment. Partial scholars or engineers propose various monitoring point optimal arrangement methods (such as CN105894130B and CN109930658B) to optimally arrange monitoring points of monitoring equipment in site; however, the number of monitoring devices is limited relatively, even if site selection optimization of monitoring points is performed, the spatial monitoring density of the whole water supply network is still low in a fixed monitoring mode, and insufficient monitoring information of the real-time running state of the pipe network is still a key bottleneck which causes difficulty in improving the accuracy of a hydraulic model of the pipe network; for the checking method, random search algorithms such as genetic and BP neural networks, Particle Swarm Optimization (PSO), sparrow search algorithm and the like are conventionally adopted for checking (CN108898512A, CN112149358A, CN112163301A, CN112733443A and the like), but the random algorithms have low calculation efficiency and are difficult to be applied to large-scale pipe networks and application scenarios with real-time requirements. In conclusion, in the face of the bottleneck problem that the checking precision of the hydraulic model of the pipe network is difficult to improve due to the fact that monitoring equipment is relatively few and monitoring information is insufficient, the invention provides an innovative idea of mobile monitoring, and aims to alternately monitor through mobile equipment, multiply the monitoring data quantity, construct a corresponding model checking method, effectively break through the technical bottleneck and promote the theoretical and technical development of checking of the pipe network model.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a pressure monitoring point moving arrangement method for water supply pipe network hydraulic model water quantity checking, enough pipe network pressure monitoring data can be obtained through a limited number of pressure monitoring sensors, and therefore model checking precision is improved.
The technical scheme of the invention is as follows:
a pressure monitoring point moving arrangement method facing water supply network hydraulic model water quantity checking comprises the following steps:
s1, making a checking plan, and dividing the checking plan into round _ num checking periods, wherein the number of monitoring points of each checking period is sens _ num, and the duration of each checking period is t; the monitoring point is the installation position of the pressure monitoring sensor;
the number of all monitoring points of the check plan is:
length(sens)＝sens_num×round_num
the total duration of the check plan is:
T＝t×round_num
s2, obtaining a Jacobian matrix HQ of the node pressure relative to the node water quantity based on the initialized pipe network hydraulic model;
s3, solving a monitoring point movement scheme sens _ place according to a Jacobian matrix HQ and an improved hidden enumeration optimization method; the monitoring point moving scheme sens _ place refers to: when the current check period is changed to the next check period, the position of the monitoring point needs to be changed from the current position;
the mathematical description method of the monitoring point moving scheme sens _ place comprises the following steps: defining the positions of different monitoring points in each checking period as a vector, namely a monitoring point position deployment scheme sens _ place [ k ], wherein k represents the kth checking period; the sum of the monitoring point position deployment schemes sens _ place [ k ] of all the verification periods is the monitoring point movement scheme sens _ place;
s4, selecting a monitoring point position deployment scheme sens _ place [1] in the first checking period according to the time sequence of the checking plan, and calculating the node water amount of the water supply network hydraulic model through an iterative method;
s5, calculating the optimal parameter estimation value of the water supply network hydraulic model in the checking period, and setting the calculation result as the parameter of the water supply network hydraulic model;
and S6, selecting a monitoring point position deployment scheme sens _ place [2] in the second checking period, repeatedly executing S4 and S5, and gradually improving the calculation accuracy of the water supply network hydraulic model by adjusting parameters until all the checking periods are finished.
Further, step S2 includes the following steps:
s21, setting a node association matrix A of the water supply network:
s22, calculating the partial differential of the head loss to the pipe section by using a Haiche-Williams equation:
wherein: h is head loss, KuD, L, q and c are the pipe diameter (mm), pipe length (m), water quantity (L/s) and Haichi-Williams coefficient of the pipeline;
s23, writing partial differential of head loss to pipe section as diagonal matrix form:
s24, solving a Jacobian matrix HQ of the node pressure relative to the node water quantity according to the following formula:
HQ＝-(ABAT)-1。
further, step S3 includes the following steps:
s31, constructing an objective function according to the Jacobian HQ:
wherein: n is the total number of nodes of the water supply network, and sens is an index of the position of the monitoring point;
s32, solving the objective function f (sens) by using an improved implicit enumeration optimization method to obtain a vector, namely, the total arrangement of the monitor point positions, namely, sendall _ vector; the monitoring point position is always arranged with sendall _ vector to describe a node index set of all monitoring points;
s33, clustering all monitoring points in the sensor _ vector according to the spatial positions by using a kmeans + + method, wherein the number cluster _ num of the classified groups is equal to the number sens _ num of the monitoring points;
s34, sorting the monitoring points in each classification group from large to small according to the 1 norm of the sensitivity vector of the monitoring points to obtain a monitoring point position sequence; the sensitivity vector refers to a certain row in a Jacobian matrix HQ, wherein the sensitivity vector of the monitoring point i is the ith row of the Jacobian matrix HQ; the monitoring point position sequence represents different positions of the same pressure monitoring sensor in each checking period;
if the length of the monitoring point position sequence is smaller than the round _ num of the checking period, supplementing monitoring points to the tail of the sequence in a circulating mode until the length of the sequence is equal to the round _ num of the checking period;
s35, for a first checking period, sequentially selecting first data from the monitoring point position sequences of each classification group to obtain a vector with the dimension equal to the monitoring point quantity sens _ num, wherein the vector is the monitoring point position deployment scheme sens _ place [1] of the first checking period; for a second checking period, sequentially selecting second data from the monitoring point position sequences of each classification group, and similarly obtaining a vector with the dimension equal to the number sens _ num of the monitoring points, wherein the vector is a monitoring point position deployment scheme sens _ place [2] of the second checking period; and in the same way, obtaining the monitoring point position deployment schemes of all the check periods, namely the monitoring point movement scheme sens _ place.
Further, step S4 includes the following steps:
s41, dividing the current check cycle into M time periods, and marking as: time period t1Time period t2…, period tM；
S42, setting a vector Q [ t ] for describing the water quantity arrangement of all nodes; the dimensionality of the vector Q [ t ] is equal to the total number of nodes of the model, and the numerical value of each dimensionality represents the water quantity of the corresponding node respectively;
s43, for a period t1Setting Q [ t]Initial value of Q [ t ]1]Average of model total water:
Q[t1]＝ones(1,n)*Qavg
Qavg＝Qtotal/n
wherein: qtotalIs the total water quantity (L/s), Q of the modelavgThe water quantity average value (L/s) of the model nodes is obtained, and ones (1, n) are row vectors with the length being the total number n of the nodes and the elements being 1;
s44, assuming that the current cycle is the kth check cycle, reading the monitoring point position deployment scheme sens _ place [ k ], and setting the pressure error amount to be eliminated by the target iteration:
dH＝Ho[sens]-Hs[sens]
wherein: hoPressure (m), H measured for pressure monitoring sensorssA corrected pressure (m) for each iteration;
s45, arranging the solution water quantity to be Q [ t1]A Jacobi matrix HQ of the pipe network model;
s46, solving an equation HQ multiplied by dQ ═ dH to obtain Q [ t1]Correction amount dQ of (1);
s47, by formula Q [ t1]n+1＝Q[t1]n+ dQ the water quantity layout Q [ t ] of the next iteration is calculated1]n+1；
S48, stopping iteration when dH is less than the set allowable error threshold, and obtaining the result of time period t1Water quantity arrangement Q [ t ]1]The final result of (a);
s49, mixing Q [ t ]1]As an initial value, the time period t is calculated2Water quantity arrangement Q [ t ]2](ii) a By analogy, S45 to S48 are repeatedly executed until the water amount arrangement Q [ t ] of all the periods is calculated]；
S410, arranging all the obtained water consumption in a single time interval according to a time sequence to obtain a node water consumption mode; the node water consumption mode refers to a coefficient of water consumption of a water consumption node in a pipe network model changing along with time.
Further, the specific steps of the improved implicit enumeration optimization method in step S32 are as follows:
s51, setting a vector sendall _ init which is a node index set of all monitoring points;
s52, randomly arranging all nodes to obtain an access point group Nin(i) Sequentially selecting one access point;
s53, randomly arranging all the initial solution vector elements to obtain a point group Nout(j) Replacing the out point by the in point according to the sequence to obtain a new solution sendall _ new;
s54, substituting the original solution sensall _ init and the new solution sensall _ new into the objective function in the step S31, solving the original solution f (sensall _ init) and the new solution f (sensall _ new), and selecting a more optimal solution to enter next iteration;
s55, repeatedly executing S52, S53 and S54 until the optimal solution cannot be generated; the finally obtained solution is the total arrangement sendall _ vector of the monitoring point positions.
Further, the specific method of step S44 is as follows:
and (3) marking HQ as A, dH as b and dQ as an unknown number x to be solved, and solving the Ax-b by adopting an iterative method in the following steps:
b2＝b-A·dx1
b3＝b2-A·dx2
……
bn+1＝bn-A·dxn
……
after the iteration is finished, if the iteration number is N, the value of x is:
wherein: sum (a, axis ═ 0) is expressed as column addition to matrix a, sum (a, axis ═ 1) is expressed as row addition to matrix a, and abs (a) is expressed as absolute value taking of matrix a.
The beneficial technical effects of the invention are as follows:
(1) on the premise of equivalent cost, the invention can multiply the acquisition amount of monitoring data, increase the monitoring density of the pipe network pressure space, acquire more pipe network operation state information and provide a solid data base for improving the checking precision of the water supply pipe network hydraulic model;
(2) on the premise that the model checking precision is equivalent, the method can greatly reduce the hardware cost, the construction cost and the maintenance cost of model parameter checking; compared with the traditional random search algorithm, the numerical solution provided by the invention can greatly reduce the calculation time, improve the calculation efficiency and have better adaptability to the application scene of real-time checking;
(3) the invention has the capability of accumulating and absorbing the continuous accumulation of the monitoring data, namely, along with the accumulation of the monitoring data in the time dimension and the space dimension, the checking precision of the pipe network is gradually improved on the whole trend, and approaches to the corresponding checking precision when all the nodes are uniformly provided with the monitoring equipment.
The method can be applied to water supply network model parameter checking, operation state diagnosis and the like, and has wide application prospect.
Drawings
FIG. 1 is a pipe network topology diagram of an embodiment;
FIG. 2 is a schematic diagram of a monitoring point movement scheme;
FIG. 3 is a schematic diagram of a monitoring point moving and arranging process of a pipe network according to an embodiment;
FIG. 4 is a graph showing the variation of the total node absolute pressure check error during the mobile monitoring of the pipe network of the embodiment;
FIG. 5 is a graph showing the variation of the total node flow relative checking error in the mobile monitoring process of the pipe network according to the embodiment;
FIG. 6 is a graph showing the variation of the flow rate of the whole pipe section in the process of mobile monitoring of the pipe network according to the embodiment.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and examples. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment is a water supply network model of an engineering case, as shown in the attached figure 1. The pipe network system is provided with 4 water supply sources, time modes are possessed respectively, the total water consumption node number is 484, the node water consumption mode number is 17, the nodes are divided into 17 groups and are endowed with random water consumption modes respectively, the base line water consumption is set randomly, the total number of pipelines is 567, the roughness coefficient of the pipelines is set randomly, and the range is 90-130.
The parameter checking steps are as follows:
s1, making a checking plan, wherein the checking plan is divided into round _ num checking periods, the number of monitoring points in each checking period is sens _ num (namely the number of movable pressure monitoring sensors), and the duration of each checking period is t; the monitoring point is the installation position of the pressure monitoring sensor.
For the example round _ num is 20, sens _ num is 10, and t is 24 hours.
The number of all monitoring points of the check plan is:
length(sens)＝sens_num×round_num＝10×20＝200
the total duration of the check plan is:
t × round _ num 24 × 20 ═ 480 (hour)
And S2, obtaining a Jacobian matrix HQ of the node pressure relative to the node water quantity based on the initialized pipe network hydraulic model. The method comprises the following specific steps:
(2-1) setting a node association matrix A of the water supply network:
(2-2) calculating the partial differential of the head loss to the pipe section by using a Haiche-Williams equation:
wherein: h is head loss, KuD, L, q and c are the pipe diameter (mm), pipe length (m), water quantity (L/s) and Haichi-Williams coefficient of the pipeline;
(2-3) partial differentiation of head loss versus pipe section is written in the form of diagonal matrix:
(2-4) solving a Jacobian matrix HQ of the node pressure relative to the node water quantity according to the following formula:
HQ＝-(ABAT)-1
and S3, solving the monitoring point movement scheme sens _ place according to the Jacobian matrix HQ and an improved hidden enumeration optimization method. The monitoring point moving scheme sens _ place refers to: when the current check period is changed to the next check period, the monitoring point needs to be changed from the current position to which position, as shown in fig. 2.
The mathematical description method of the monitoring point movement scheme sens _ place comprises the following steps: defining the positions of different monitoring points in each checking period as a vector, namely a monitoring point position deployment scheme sens _ place [ k ], wherein k represents the kth checking period; and the sum of the monitoring point position deployment schemes sens _ place [ k ] in all the verification periods is the monitoring point movement scheme sens _ place.
The method comprises the following specific steps:
(3-1) constructing an objective function according to a Jacobian matrix HQ:
wherein: n is the total number of nodes of the water supply network, and sens is the index of the position of the monitoring point.
And (3-2) solving the objective function f (sens) by using an improved implicit enumeration optimization method to obtain a vector, namely the total arrangement of the monitor point positions, sensall _ vector. The total arrangement of monitor point locations sendall _ vector describes the node index set of all monitor points. The method comprises the following specific steps:
(3-2-1) setting a vector sendall _ init which is a node index set of all monitoring points;
(3-2-2) randomly arranging all the nodes to obtain an access point group Nin(i) Sequentially selecting one access point;
(3-2-3) randomly arranging all initial solution vector elements to obtain a point group Nout(j) Replacing the out point by the in point according to the sequence to obtain a new solution sendall _ new;
(3-2-4) substituting the original solution sendall _ init and the new solution sendall _ new into the objective function in the step S31, solving the original solution f (sendall _ init) and the new solution f (sendall _ new), and selecting a more optimal solution to enter the next iteration;
(3-2-5) repeating the execution of S52, S53, S54 until a time when no more optimal solution can be generated; the finally obtained solution is the total arrangement sendall _ vector of the monitoring point positions.
(3-3) clustering all monitoring points in the sensor _ vector according to the spatial positions by using a kmeans + + method, wherein the number of the classified groups cluster _ num is equal to the number of the monitoring points sens _ num.
(3-4) sorting the monitoring points in each classification group from large to small according to the 1 norm of the sensitivity vector of the monitoring points to obtain a monitoring point position sequence; the sensitivity vector refers to a certain row in a Jacobian matrix HQ, wherein the sensitivity vector of a monitoring point i is the ith row of the Jacobian matrix HQ; if the length of the monitoring point position sequence is smaller than the round _ num of the checking period, supplementing monitoring points to the tail of the sequence in a circulating mode until the length of the sequence is equal to the round _ num of the checking period; the sequence of monitor point locations represents different locations of the same pressure monitoring sensor during each calibration cycle.
(3-5) for a first checking period, sequentially selecting first data from the monitoring point position sequences of each classification group to obtain a vector with the dimension equal to the monitoring point quantity sens _ num, wherein the vector is the monitoring point position deployment scheme sens _ place [1] of the first checking period; for a second checking period, sequentially selecting second data from the monitoring point position sequences of each classification group, and similarly obtaining a vector with the dimension equal to the number sens _ num of the monitoring points, wherein the vector is a monitoring point position deployment scheme sens _ place [2] of the second checking period; and in the same way, obtaining the monitoring point position deployment schemes of all the check periods, namely the monitoring point movement scheme sens _ place. The monitoring point moving process of the embodiment is shown in fig. 3.
S4, selecting a monitoring point position deployment scheme sens _ place [1] in the first checking period according to the time sequence of the checking plan, and calculating the node water quantity of the water supply network hydraulic model by an iterative method, wherein the specific steps are as follows:
(4-1) dividing the current check cycle into M time segments, and marking as: time period t1Time period t2…, time period tM；
(4-2) setting a vector Q [ t ] for describing the water quantity arrangement of all the nodes; the dimensionality of the vector Q [ t ] is equal to the total number of nodes of the model, and the numerical value of each dimensionality represents the water quantity of the corresponding node respectively;
(4-3) for time period t1Setting Q [ t]Initial value of Q [ t ]1]Average of model total water:
Q[t1]＝ones(1,n)*Qavg
Qavg＝Qtotal/n
wherein: qtotalIs the total water quantity (L/s), Q of the modelavgThe water quantity average value (L/s) of the model nodes is obtained, and ones (1, n) are row vectors with the length being the total number n of the nodes and the elements being 1;
(4-4) assuming that the current cycle is the kth check cycle, reading the monitoring point position deployment scheme sens _ place [ k ] and setting the pressure error amount to be eliminated by the target iteration:
dH＝Ho[sens]-Hs[sens]
wherein：HoPressure (m), H measured for pressure monitoring sensorssA corrected pressure (m) for each iteration;
(4-5) solving Water quantity arrangement Q [ t ]1]A Jacobian matrix HQ of the pipe network model;
(4-6) solving the equation HQ × dQ ═ dH to obtain Q [ t1]Correction amount dQ of (1);
(4-7) by the formula Q [ t ]1]n+1＝Q[t1]n+ dQ the water quantity layout Q [ t ] of the next iteration is calculated1]n+1；
(4-8) when dH is less than the set allowable error threshold, the iteration is stopped, and the time period t is obtained1Water quantity arrangement Q [ t ]1]The final result of (a);
(4-9) adding Q [ t ]1]As an initial value, the time period t is calculated2Water quantity arrangement Q [ t ]2](ii) a By analogy, S45 to S48 are repeatedly executed until the water amount arrangement Q [ t ] of all the periods is calculated]；
And (4-10) connecting the water consumption in all time periods to obtain a node water consumption mode.
S5, calculating the optimal parameter estimation value of the water supply network hydraulic model in the checking period, and setting the calculation result as the parameter of the water supply network hydraulic model;
and S6, selecting a monitoring point position deployment scheme sens _ place [2] in the second checking period, repeatedly executing S4 and S5, and gradually improving the calculation accuracy of the water supply network hydraulic model by adjusting parameters until all the checking periods are finished.
The parameter checking result is evaluated and calculated by adopting the following indexes:
absolute error of node pressure:
node flow relative error:
relative error of pipe section flow:
wherein Htrue、Qtrue、qtrueReal node pressure, node flow, pipeline flow values, H, set for the case pipe network respectivelys、Qs、qsThe values are respectively the simulated node pressure, the node flow and the pipeline flow after the case pipe network is checked.
The parameter checking and evaluating results of the mobile monitoring process are shown in the attached figures 4-6.
While the embodiments of the present invention have been disclosed above, it is not limited to the applications listed in the description and embodiments, but is fully applicable to various fields suitable for the present invention, and it will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in the embodiments without departing from the principle and spirit of the present invention, and therefore the present invention is not limited to the specific details without departing from the general concept defined in the claims and the scope of equivalents thereof.
Claims (6)
1. A pressure monitoring point moving arrangement method facing water supply network hydraulic model water quantity checking is characterized by comprising the following steps:
s1, making a checking plan, and dividing the checking plan into round _ num checking periods, wherein the number of monitoring points of each checking period is sens _ num, and the duration of each checking period is t; the monitoring point is the installation position of the pressure monitoring sensor;
the number of all monitoring points of the check plan is:
length(sens)＝sens_num×round_num
the total duration of the check plan is:
T＝t×round_num
s2, obtaining a Jacobian matrix HQ of the node pressure relative to the node water quantity based on the initialized pipe network hydraulic model;
s3, solving a monitoring point movement scheme sens _ place according to a Jacobian matrix HQ and an improved implicit enumeration optimization method; the monitoring point moving scheme sens _ place refers to: when the current check period is changed to the next check period, the position of the monitoring point needs to be changed from the current position;
the mathematical description method of the monitoring point moving scheme sens _ place comprises the following steps: defining the positions of different monitoring points in each checking period as a vector, namely a monitoring point position deployment scheme sens _ place [ k ], wherein k represents the kth checking period; the sum of the monitoring point position deployment schemes sens _ place [ k ] of all the verification periods is the monitoring point movement scheme sens _ place;
s4, selecting a monitoring point position deployment scheme sens _ place [1] in the first checking period according to the time sequence of the checking plan, and calculating the node water amount of the water supply network hydraulic model through an iterative method;
s5, calculating the optimal parameter estimation value of the water supply network hydraulic model in the checking period, and setting the calculation result as the parameter of the water supply network hydraulic model;
and S6, selecting a monitoring point position deployment scheme sens _ place [2] in the second checking period, repeatedly executing S4 and S5, and gradually improving the calculation accuracy of the water supply network hydraulic model by adjusting parameters until all the checking periods are finished.
2. The method for moving and arranging the pressure monitoring points for water quantity checking of the hydraulic model of the water supply pipe network as claimed in claim 1, wherein the step S2 comprises the following steps:
s21, setting a node association matrix A of the water supply network:
s22, calculating partial differential of head loss to the pipe section by using a Haiche-Williams equation:
wherein: h is head loss, KuD, L, q and c are the pipe diameter (mm), pipe length (m), water quantity (L/s) and Haichi-Williams coefficient of the pipeline;
s23, writing the partial differential of the head loss to the pipe section in the form of a diagonal matrix:
s24, calculating a Jacobian matrix HQ of the node pressure relative to the node water quantity according to the following formula:
HQ＝-(ABAT)-1。
3. the method for moving and arranging the pressure monitoring points for water quantity checking of the hydraulic model of the water supply pipe network as claimed in claim 1, wherein the step S3 comprises the following steps:
s31, constructing an objective function according to the Jacobian HQ:
wherein: n is the total number of nodes of the water supply network, and sens is an index of the position of the monitoring point;
s32, solving an objective function f (sens) by using an improved implicit enumeration optimization method to obtain a vector, namely a total arrangement sendall _ vector of the monitoring point position; the total arrangement of the sendall _ vector at the monitoring point position describes a node index set of all monitoring points;
s33, clustering all monitoring points in the sensell _ vector according to the spatial positions by using a kmeans + + method, wherein the number of cluster groups is equal to the number of monitoring points sens _ num;
s34, sorting the monitoring points in each classification group from large to small according to the 1 norm of the sensitivity vector of the monitoring points to obtain a monitoring point position sequence; the sensitivity vector refers to a certain row in a Jacobian matrix HQ, wherein the sensitivity vector of the monitoring point i is the ith row of the Jacobian matrix HQ; the monitoring point position sequence represents different positions of the same pressure monitoring sensor in each checking period;
if the length of the monitoring point position sequence is smaller than the round _ num of the checking period, supplementing monitoring points to the tail of the sequence in a circulating mode until the length of the sequence is equal to the round _ num of the checking period;
s35, for a first checking period, sequentially selecting first data from the monitoring point position sequences of each classification group to obtain a vector with the dimension equal to the number sens _ num of the monitoring points, wherein the vector is the monitoring point position deployment scheme sens _ place [1] of the first checking period; for a second checking period, sequentially selecting second data from the monitoring point position sequences of each classification group, and similarly obtaining a vector with the dimension equal to the number sens _ num of the monitoring points, wherein the vector is a monitoring point position deployment scheme sens _ place [2] of the second checking period; and in the same way, obtaining the monitoring point position deployment schemes of all the check periods, namely the monitoring point movement scheme sens _ place.
4. The method for moving and arranging the pressure monitoring points facing the water supply pipe network hydraulic model water quantity checking as claimed in claim 1, wherein the step S4 comprises the following steps:
s41, dividing the current checking cycle into M time periods, and marking as: time period t1Time period t2…, period tM；
S42, setting a vector Q [ t ] for describing the water quantity arrangement of all nodes; the dimensionality of the vector Q [ t ] is equal to the total number of nodes of the model, and the numerical value of each dimensionality represents the water quantity of the corresponding node respectively;
s43, for a period t1Setting Q [ t]Initial value of Q [ t ]1]Average of model total water:
Q[t1]＝ones(1,n)*Qavg
Qavg＝Qtotal/n
wherein: qtotalIs the total water quantity (L/s), Q of the modelavgThe water quantity average value (L/s) of the model nodes is obtained, and ones (1, n) are row vectors with the length being the total number n of the nodes and the elements being 1;
s44, assuming that the current cycle is the kth check cycle, reading the monitoring point position deployment scheme sens _ place [ k ], and setting the pressure error amount to be eliminated by the target iteration:
dH＝Ho[sens]-Hs[sens]
wherein: hoPressure (m), H measured for a pressure monitoring sensorsA corrected pressure (m) for each iteration;
s45, arranging the solution water quantity to be Q [ t1]A Jacobian matrix HQ of the pipe network model;
s46, solving equation HQ multiplied by dQ ═ dH to obtain Q [ t1]Correction amount dQ of (1);
s47, passing formula Q [ t [ ]1]n+1＝Q[t1]n+ dQ the water quantity layout Q [ t ] of the next iteration is calculated1]n+1；
S48, stopping iteration when dH is less than the set allowable error threshold, and obtaining the result of time period t1Water quantity arrangement Q [ t ]1]The final result of (a);
s49, mixing Q [ t1]As an initial value, the time period t is calculated2Water quantity arrangement Q [ t ]2](ii) a By analogy, S45 to S48 are repeatedly executed until the water amount arrangement Q [ t ] of all the periods is calculated]；
S410, arranging all the obtained water consumption in a single time interval according to a time sequence to obtain a node water consumption mode; the node water consumption mode refers to a coefficient of water consumption of a water consumption node in a pipe network model changing along with time.
5. The method for movably arranging the pressure monitoring points facing the water supply pipe network hydraulic model water quantity checking as claimed in claim 3, wherein:
the specific steps of the improved hidden enumeration optimization method in step S32 are as follows:
s51, setting a vector sendall _ init as a node index set of all monitoring points;
s52, randomly arranging all nodes to obtain an access point group Nin(i) Sequentially selecting one access point;
s53, randomly arranging all initial solution vector elements to obtain a point group Nout(j) Replacing the out point by the in point according to the sequence to obtain a new solution sendall _ new;
s54, substituting the original solution sensall _ init and the new solution sensall _ new into the objective function in the step S31, solving the original solution f (sensall _ init) and the new solution f (sensall _ new), and selecting a more optimal solution to enter next iteration;
s55, repeatedly executing S52, S53 and S54 until the optimal solution cannot be generated; the finally obtained solution is the total arrangement sendall _ vector of the monitoring point positions.
6. The method for movably arranging the pressure monitoring points facing the water supply pipe network hydraulic model water quantity checking as claimed in claim 4, wherein the method comprises the following steps:
the specific method of step S44 is as follows:
and (3) marking HQ as A, dH as b and dQ as an unknown number x to be solved, and solving the Ax-b by adopting an iterative method in the following steps:
after the iteration is finished, if the iteration number is N, the value of x is:
wherein: sum (a, axis ═ 0) is expressed by adding columns to the matrix a, sum (a, axis ═ 1) is expressed by adding rows to the matrix a, and abs (a) is expressed by taking absolute values to the matrix a.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111610339.XA CN114429034B (en) | 2021-12-27 | 2021-12-27 | Pressure monitoring point moving arrangement method for checking water quantity of water supply network hydraulic model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111610339.XA CN114429034B (en) | 2021-12-27 | 2021-12-27 | Pressure monitoring point moving arrangement method for checking water quantity of water supply network hydraulic model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114429034A true CN114429034A (en) | 2022-05-03 |
| CN114429034B CN114429034B (en) | 2024-09-06 |
Family
ID=81310460
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111610339.XA Active CN114429034B (en) | 2021-12-27 | 2021-12-27 | Pressure monitoring point moving arrangement method for checking water quantity of water supply network hydraulic model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114429034B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116127671A (en) * | 2023-04-17 | 2023-05-16 | 四川奥凸环保科技有限公司 | Water supply network parameter optimization method, system, equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103839190A (en) * | 2014-02-19 | 2014-06-04 | 清华大学深圳研究生院 | Pipe network node flow measuring and dispatching method based on pressure monitoring |
| CN110986747A (en) * | 2019-12-20 | 2020-04-10 | 桂林电子科技大学 | Landslide displacement combined prediction method and system |
| CN111639838A (en) * | 2020-05-08 | 2020-09-08 | 中国地质大学（武汉） | Water quality monitoring point layout optimization method suitable for water supply pipe network |
| US20200331772A1 (en) * | 2017-12-20 | 2020-10-22 | Intellitect Water Ltd. | A water network monitor, monitoring system and method |
| CN112182984A (en) * | 2020-08-18 | 2021-01-05 | 浙江大学 | Sewage pipe network real-time simulation method based on water supply Internet of things data assimilation |
| CN112733443A (en) * | 2020-12-31 | 2021-04-30 | 北京工业大学 | Water supply network model parameter optimization checking method based on virtual monitoring points |
| CN113139228A (en) * | 2021-04-22 | 2021-07-20 | 南京智慧岩土工程技术研究院有限公司 | Monitoring point arrangement optimization method for large-span foundation pit complex support system structure |
-
2021
- 2021-12-27 CN CN202111610339.XA patent/CN114429034B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103839190A (en) * | 2014-02-19 | 2014-06-04 | 清华大学深圳研究生院 | Pipe network node flow measuring and dispatching method based on pressure monitoring |
| US20200331772A1 (en) * | 2017-12-20 | 2020-10-22 | Intellitect Water Ltd. | A water network monitor, monitoring system and method |
| CN110986747A (en) * | 2019-12-20 | 2020-04-10 | 桂林电子科技大学 | Landslide displacement combined prediction method and system |
| CN111639838A (en) * | 2020-05-08 | 2020-09-08 | 中国地质大学（武汉） | Water quality monitoring point layout optimization method suitable for water supply pipe network |
| CN112182984A (en) * | 2020-08-18 | 2021-01-05 | 浙江大学 | Sewage pipe network real-time simulation method based on water supply Internet of things data assimilation |
| CN112733443A (en) * | 2020-12-31 | 2021-04-30 | 北京工业大学 | Water supply network model parameter optimization checking method based on virtual monitoring points |
| CN113139228A (en) * | 2021-04-22 | 2021-07-20 | 南京智慧岩土工程技术研究院有限公司 | Monitoring point arrangement optimization method for large-span foundation pit complex support system structure |
Non-Patent Citations (2)
| Title |
|---|
| 郭效琛;李萌;史晓雨;杜鹏飞;李志一;: "基于在线监测的排水管网事故预警技术研究与应用", 中国给水排水, no. 19, 1 October 2018 (2018-10-01), pages 139 - 143 * |
| 齐秀峰;: "基于量子神经网络拟合法的矿区地表变形监测", 金属矿山, no. 04, 15 April 2016 (2016-04-15), pages 159 - 162 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116127671A (en) * | 2023-04-17 | 2023-05-16 | 四川奥凸环保科技有限公司 | Water supply network parameter optimization method, system, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114429034B (en) | 2024-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110187290A (en) | A kind of lithium ion battery residual life prediction technique based on pattern of fusion algorithm | |
| CN110939870B (en) | Water supply network pressure monitoring point arrangement method for pipe burst monitoring | |
| Yu et al. | Stochastic optimal generation command dispatch based on improved hierarchical reinforcement learning approach | |
| CN105069240A (en) | Survey station layout intelligent optimization method of spatial measurement positioning system | |
| CN110083125A (en) | A kind of machine tool thermal error modeling method based on deep learning | |
| CN114429034A (en) | Pressure monitoring point moving arrangement method for water supply network hydraulic model water quantity checking | |
| CN105530651A (en) | Optimal base station layout method in wireless positioning system | |
| CN111366291B (en) | Water supply pipeline pressure prediction and roughness calibration method based on entropy and artificial neural network | |
| CN111415068A (en) | Power distribution decision modeling method based on relevance of transformation measures and loss load index | |
| CN114818483A (en) | Electromechanical disturbance positioning and propagation prediction method based on graph neural network | |
| CN114909706B (en) | Two-level network balance regulation and control method based on reinforcement learning algorithm and differential pressure control | |
| CN117289652A (en) | Numerical control machine tool spindle thermal error modeling method based on multi-universe optimization | |
| CN118036477B (en) | Well position and well control parameter optimization method based on space-time diagram neural network | |
| Zheng et al. | Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction | |
| CN114219126B (en) | Small hydropower infiltration area network load supply prediction method based on residual error correction | |
| CN113690930B (en) | NSGA-III algorithm-based medium and long term locating and sizing method for distributed photovoltaic power supply | |
| Decke et al. | DADO–low-cost query strategies for deep active design optimization | |
| CN114221334B (en) | Rapid state estimation method based on graph neural network | |
| CN113705932B (en) | Short-term load prediction method and device | |
| CN116090597A (en) | Method for short-term power grid load prediction model | |
| Ma et al. | Fixture layout optimization based on hybrid algorithm of gaot and rbf-nn for sheet metal parts | |
| CN105117530A (en) | Method for recognizing parameters of steam turbine and speed regulating system thereof through combined thickness and thinness adjustment | |
| CN111695082A (en) | Anti-differential state estimation method for intelligent power distribution network | |
| Tian et al. | Static Voltage Stability Margin Evaluation Considering Stochastic Power Injection Based on Deep Reinforcement Learning Theory | |
| CN118691839B (en) | Neural network model modeling method based on maintenance or fault topology change scene |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |