CN117807907A - Method and system for checking rough coefficient C value of water supply network system model - Google Patents
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Abstract
The invention discloses a method and a system for checking a rough coefficient C value of a water supply network system model, belongs to the technical field of intelligent water management of water supply networks, and aims to effectively and accurately simulate an actual water supply network system, wherein the checked network is consistent with an actual hydraulic calculation result, and the algorithm is high in calculation speed and accurate in calculation result. Different from the traditional optimization method, the method has intelligence, self-organization, self-adaption and learning. The method is not limited by the search space, a plurality of points in the solution space are searched and propagated near the excellent points, and the optimizing process continuously pays attention to the more advantages in the search solution space, so that the defect that the traditional optimizing method only converges on the local optimal solution can be avoided, and the global optimal solution is obtained; although the optimizing process has randomness, the optimizing process does not blindly perform exhaustive searching in the solution space, but rather is heuristic searching, and the optimized searching track only occupies a small part of the solution space, so that the excessive workload can be avoided.
Description
Technical Field
The invention belongs to the technical field of intelligent water management of water supply networks, and particularly relates to a method and a system for checking a rough coefficient C value of a water supply network system model.
Background
The hydraulic model of the water supply network has the functions of estimating the hydraulic state of the water supply network in real time, optimizing water resource allocation, simulating and analyzing network accidents and the like. Currently, it has become an important tool for water supply network design, operation and management. However, as the operation time of the water supply network increases, the rough coefficient of the pipeline is continuously changed, the hydraulic model cannot accurately reflect the actual operation condition of the network, and at the moment, the rough coefficient C value in the model needs to be checked, so that the water supply model can reflect the actual operation condition of the network, and the hydraulic model has practical application value. Therefore, the model checking of the water supply network is an important step in the process of constructing the accurate hydraulic model.
Since the 70 s of the last century, considerable research has been conducted on the method of checking pipe network models, and the obtained checking methods can be divided into three main categories: iterative checking method, display checking method and implicit checking method. The iterative check method has the defects that the pipe network scale is not too large during check, and the check parameters are not too many; in the checking process, the actual pipe network is required to be simplified so as to reduce the dimension of the model and accelerate the convergence rate of the model. In the checking process of the display checking method, the number of the monitoring values limits checking parameters, and the checking problem must be kept positive; the pipe network monitoring error is not considered, namely, the measured data are assumed to be completely correct; the checking parameters cannot be expressed in numerical form. For a large pipe network, the implicit check method cannot ensure that the search result is an optimal solution; the solving result is only optimal in mathematical problem, and has no meaning for practical engineering.
At present, the sum of squares of the differences between the measured values and calculated values of the adjusted variables is used as an objective function, the hydraulic condition and the variable value range are used as constraint conditions, and the optimal methods such as the steepest descent method, the variable rotation method, the composite simplex method and the like are used for solving, but the methods have low convergence speed and are easy to fall into a local optimal solution. Once the pipe network is large in scale, the workload is excessive in operation, time is wasted, and the calculation efficiency is low.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method and a system for checking the rough coefficient C value of a water supply network system model, so as to solve the defects that the traditional checking method is not strong in practicability, low in calculation efficiency and not easy to obtain an optimal solution.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
a method for checking the rough coefficient C value of a water supply network system model comprises the following steps:
grouping the pipelines and determining the upper and lower limits of the C value change of each group;
according to the combination of the upper limit and the lower limit of the change of each group of C values, the square sum of the difference between the measured pressure value of the pressure measuring point of the water supply network under the multi-working condition and the calculated pressure value of the model is minimized to construct an objective function;
and according to the objective function, determining constraint conditions, and calculating the optimal roughness coefficient by using a genetic algorithm.
Preferably, the pipes are grouped according to different pipes, pipe diameters and paving times.
Preferably, the objective function is:
wherein:
s is the sum of squares of the difference between the calculated value and the measured pressure value of the node water pressure at the monitoring point;
m is the checking working condition number;
n is the number of monitoring points;
H i (t) calculating pressure for the model of the ith monitoring point under the nth working condition;
the measured pressure of the ith monitoring point under the nth working condition is obtained.
Preferably, the constraint is: the maximum difference between the calculated model pressure and the measured pressure calculated by all monitoring points is less than or equal to 4 meters.
Preferably, the constraint is:
wherein H is i (t) calculating pressure for the model of the ith monitoring point under the nth working condition;the measured pressure of the ith monitoring point under the nth working condition is obtained.
Preferably, the calculating the optimal coarse coefficient by using the genetic algorithm is to continuously select, cross and mutate the offspring population by using the genetic algorithm, continuously enable the coarse coefficient to change within the range, and calculate the optimal coarse coefficient.
Preferably, the calculation of the optimal roughness coefficient comprises:
selecting available pressure monitoring points as checking monitoring points by using a pressure monitoring system to derive actual measurement pressure data of each working condition of each pressure monitoring pointThe pressure data of the monitoring point after each time of C value change operation of the pipe network is recorded as H i The sum of squares of the difference values of the actually measured pressure data and the calculated pressure data is the target, if the difference values meet constraint conditions, namely the simulation result meets the precision, the pipe network which is most in line with the reality is obtained through iterative calculationC value.
A system for checking a water supply network system model roughness coefficient C value, comprising:
the grouping unit is used for grouping the pipelines and determining the upper limit and the lower limit of the change of the C value of each group;
the objective function construction unit is used for constructing an objective function by combining the upper limit and the lower limit of the change of each group of C values and minimizing the sum of squares of the difference values of the measured pressure values of the pressure measuring points of the water supply network and the calculated pressure values of the model under the multi-working condition;
and the calculating unit is used for calculating the optimal roughness coefficient by utilizing a genetic algorithm according to the objective function and determining constraint conditions.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any one of the methods of checking the roughness coefficient C value of a water supply network system model described above when the computer program is executed.
A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the method of checking a water supply network system model roughness coefficient C value of any one of the above.
Compared with the prior art, the invention has the following beneficial effects:
the checked pipe network is consistent with the actual hydraulic calculation result, the actual water supply pipe network system is effectively and accurately simulated, the algorithm is fast in calculation speed, and the calculation result is accurate. The GA method is different from the traditional optimization method, and has intelligence, self-organization, self-adaption and learning. The method is not limited by the search space, a plurality of points in the solution space are searched and propagated near the excellent points, and the optimizing process continuously pays attention to the more advantages in the search solution space, so that the defect that the traditional optimizing method only converges on the local optimal solution can be avoided, and the global optimal solution is obtained; although the optimizing process has randomness, the optimizing process does not blindly perform exhaustive searching in the solution space, but rather is heuristic searching, and the optimized searching track only occupies a small part of the solution space, so that the excessive workload can be avoided.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a flow chart of a technical scheme of an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the attached drawing figures:
the invention provides a method for checking a rough coefficient C value of a water supply network system model based on a genetic algorithm, which aims to overcome the defects that the traditional checking method is low in practicality and calculation efficiency and is difficult to obtain an optimal solution.
In order to achieve the above purpose, referring to fig. 1, the invention discloses a method for checking the rough coefficient C value of a water supply network system model, which comprises the following steps:
s1: grouping the pipelines and determining the upper and lower limits of the C value change of each group;
s2: according to the combination of the upper limit and the lower limit of the change of each group of C values, the square sum of the difference between the measured pressure value of the pressure measuring point of the water supply network under the multi-working condition and the calculated pressure value of the model is minimized to construct an objective function;
s3: and according to the objective function, determining constraint conditions, and calculating the optimal roughness coefficient by using a genetic algorithm.
In some embodiments, the pipes are grouped according to different pipe, pipe diameters, and age of lay.
In some embodiments, the objective function is:
wherein:
s is the sum of squares of the difference between the calculated value and the measured pressure value of the node water pressure at the monitoring point;
m is the checking working condition number;
n is the number of monitoring points;
H i (t) calculating pressure for the model of the ith monitoring point under the nth working condition;
the measured pressure of the ith monitoring point under the nth working condition is obtained.
In some embodiments, the constraints are: the maximum difference between the calculated model pressure and the measured pressure calculated by all monitoring points is less than or equal to 4 meters.
In some embodiments, the constraints are:
wherein H is i (t) calculating pressure for the model of the ith monitoring point under the nth working condition;the measured pressure of the ith monitoring point under the nth working condition is obtained.
In some embodiments, the calculating the optimal roughness coefficient by using the genetic algorithm is specifically to continuously select, cross and mutate the offspring population by using the genetic algorithm, so that the roughness coefficient continuously varies within a range, and calculate the optimal roughness coefficient.
In some embodiments, the optimal roughness coefficient is calculated, specifically including:
selecting available pressure monitoring points as checking monitoring points by using a pressure monitoring system to derive actual measurement pressure data of each working condition of each pressure monitoring pointThe pressure data of the monitoring point after each time of C value change operation of the pipe network is recorded as H i And if the difference values of the measured pressure data and the calculated pressure data meet constraint conditions, namely the simulation result accords with the precision, carrying out iterative calculation to obtain the pipe network C value which is most in line with reality.
[ example ]
Referring to fig. 2, the invention groups pipes with different pipe diameters and laying ages, confirms the upper and lower limits of the variation of each group of C values according to experience values, combines the EPANET2.2 software and MATLAB tool kit, constructs an objective function by minimizing the sum of the square difference between the measured value and the calculated value of the pressure measuring point of the water supply pipe network under the multi-working condition, uses the difference between the measured pressure value and the calculated pressure value as a constraint condition of less than 4m, transforms the C value check into an optimization problem, and utilizes a genetic algorithm (Genetic Algorithm, GA) to continuously select, cross and mutate the sub-population, continuously enable the roughness coefficient to change within the range, and automatically calculates the optimal roughness coefficient. The specific process is as follows:
selecting available pressure monitoring points as checking monitoring points by using a pressure monitoring system to derive actual measurement pressure data of each working condition of each pressure monitoring pointThe pressure data of the monitoring point after each time of C value change operation of the pipe network is recorded as H i And if the difference values of the measured pressure data and the calculated pressure data meet constraint conditions, namely the simulation result accords with the precision, carrying out iterative calculation to obtain the pipe network C value which is most in line with reality.
The objective function is set as:
wherein:
s is the sum of squares of the difference between the calculated value and the measured pressure value of the node water pressure at the monitoring point;
m is the checking working condition number;
n is the number of monitoring points;
H i (t) calculating pressure for the model of the ith monitoring point under the nth working condition;
the measured pressure of the ith monitoring point under the nth working condition is obtained.
According to the model checking standard recommended in the book of theory and analysis of water supply pipe network system, the difference between the actual measured recorded value and the calculated value of 100% pressure measuring point water pressure is less than or equal to +/-4 m; the difference between the measured value and the calculated value of the 80% pressure measuring point water pressure is less than or equal to +/-2 m; the difference between the measured value and the calculated value of the 50% pressure measuring point water pressure is less than or equal to +/-1, and the model can be considered to accord with the precision, and the current water supply pipe network is successfully simulated.
The constraint conditions are:
namely, the maximum difference between the calculated pressure of the model calculated by all monitoring points and the measured pressure is less than or equal to 4 meters (100 percent is less than or equal to 4).
Firstly, setting cross mutation probability of population, number of individuals and maximum genetic algebra; and inquiring the standard according to the pipe, pipe diameter and pipe age, and setting a preliminary roughness coefficient, namely the initial population. Setting the pipeline grouping of the same rough coefficient and the upper and lower limits of the variation of the rough coefficient, selecting a pressure checking time period, and reading the pressure actual measurement range of the selected monitoring point. And calling an EPANET2.2 toolbox, and executing first hydraulic analysis on the adjusted water supply network system capable of initially and normally running to obtain an initial node calculated pressure value corresponding to the initial roughness coefficient.
Selecting a calculated pressure value of the monitoring points, calculating an objective function of an initial population individual, and outputting an optimal population and the objective function value if the difference value between the actual pressure of all the monitoring points and the calculated pressure of the model is less than or equal to 4; if the difference value between the actual pressure of the monitoring point and the calculated pressure of the model is greater than 4, selecting, intersecting and mutating, wherein the result of each step is used as the input of the next step, continuously optimizing individuals of the population through iteration to obtain a new population, namely changing the C value of each pipeline in the water supply pipeline network, and re-calling the EPANET2.2 toolbox to carry out hydraulic analysis, wherein the process is continuously iterated until the termination condition is met.
By repeating the above processes, the population is continuously optimized, and the optimal result is finally obtained. The end result is an optimal solution that minimizes the objective function, i.e., the optimal pipeline network design and operating parameters. The genetic algorithm simulates the evolution process in nature through continuous selection, crossing and mutation operation, and can effectively search the optimal solution.
At the moment, the checked pipe network is consistent with the actual hydraulic calculation result, the actual water supply pipe network system is effectively and accurately simulated, the algorithm is fast in calculation speed, and the calculation result is accurate. The GA method is different from the traditional optimization method, and has intelligence, self-organization, self-adaption and learning. The method is not limited by the search space, a plurality of points in the solution space are searched and propagated near the excellent points, and the optimizing process continuously pays attention to the more advantages in the search solution space, so that the defect that the traditional optimizing method only converges on the local optimal solution can be avoided, and the global optimal solution is obtained; although the optimizing process has randomness, the optimizing process does not blindly perform exhaustive searching in the solution space, but rather is heuristic searching, and the optimized searching track only occupies a small part of the solution space, so that the excessive workload can be avoided.
The invention also discloses a system for checking the rough coefficient C value of the water supply network system model, which comprises:
the grouping unit is used for grouping the pipelines and determining the upper limit and the lower limit of the change of the C value of each group;
the objective function construction unit is used for constructing an objective function by combining the upper limit and the lower limit of the change of each group of C values and minimizing the sum of squares of the difference values of the measured pressure values of the pressure measuring points of the water supply network and the calculated pressure values of the model under the multi-working condition;
and the calculating unit is used for calculating the optimal roughness coefficient by utilizing a genetic algorithm according to the objective function and determining constraint conditions.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of any one of the methods of checking the roughness coefficient C value of a water supply network system model described above when the computer program is executed.
A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the method of checking a water supply network system model roughness coefficient C value of any one of the above.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The method for checking the rough coefficient C value of the water supply network system model is characterized by comprising the following steps of:
grouping the pipelines and determining the upper and lower limits of the C value change of each group;
according to the combination of the upper limit and the lower limit of the change of each group of C values, the square sum of the difference between the measured pressure value of the pressure measuring point of the water supply network under the multi-working condition and the calculated pressure value of the model is minimized to construct an objective function;
and according to the objective function, determining constraint conditions, and calculating the optimal roughness coefficient by using a genetic algorithm.
2. The method for checking the roughness coefficient C value of a water supply network system model according to claim 1, wherein the pipelines are grouped according to different pipes, pipe diameters and paving times.
3. The method for checking the roughness coefficient C value of a water supply network system model according to claim 1, wherein the objective function is:
wherein:
s is the sum of squares of the difference between the calculated value and the measured pressure value of the node water pressure at the monitoring point;
m is the checking working condition number;
n is the number of monitoring points;
H i (t) calculating pressure for the model of the ith monitoring point under the nth working condition;
the measured pressure of the ith monitoring point under the nth working condition is obtained.
4. The method for checking the roughness coefficient C value of the water supply network system model according to claim 1, wherein the constraint condition is: the maximum difference between the calculated model pressure and the measured pressure calculated by all monitoring points is less than or equal to 4 meters.
5. The method for checking the roughness coefficient C value of a water supply network system model as claimed in claim 4, wherein the constraint condition is:
wherein H is i (t) calculating pressure for the model of the ith monitoring point under the nth working condition;the measured pressure of the ith monitoring point under the nth working condition is obtained.
6. The method for checking the C value of the roughness coefficient of the water supply network system model according to claim 1, wherein the calculating the optimal roughness coefficient by using the genetic algorithm is to select, cross and mutate the offspring population continuously by using the genetic algorithm, so that the roughness coefficient is changed continuously in the range, and the optimal roughness coefficient is calculated.
7. The method for checking the roughness coefficient C value of a water supply network system model as claimed in claim 6, wherein the calculating the optimal roughness coefficient comprises the following steps:
the pressure monitoring system is utilized to select available pressure monitoring points as checking monitoring points, and actual measurement pressure data H of each working condition of each pressure monitoring point is derived i 0 The pressure data of the monitoring point after each time of C value change operation of the pipe network is recorded as H i The sum of squares of the difference values of the measured pressure data and the calculated pressure data is the target, if the difference values meet the constraint condition, namely the simulation result is signAnd (5) combining the precision, and carrying out iterative calculation to obtain a pipe network C value which is most in line with the actual pipe network.
8. A system for checking the C value of a coefficient of roughness of a water supply network system model, comprising:
the grouping unit is used for grouping the pipelines and determining the upper limit and the lower limit of the change of the C value of each group;
the objective function construction unit is used for constructing an objective function by combining the upper limit and the lower limit of the change of each group of C values and minimizing the sum of squares of the difference values of the measured pressure values of the pressure measuring points of the water supply network and the calculated pressure values of the model under the multi-working condition;
and the calculating unit is used for calculating the optimal roughness coefficient by utilizing a genetic algorithm according to the objective function and determining constraint conditions.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of checking the roughness coefficient C value of a water supply network system model as claimed in any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the method of checking the roughness coefficient C value of a water supply network system model as claimed in any one of claims 1 to 7.
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