CN114235111B - Ultrasonic water meter flow calibration method based on model optimization - Google Patents
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Abstract
The invention relates to the technical field of intelligent water affairs and discloses an ultrasonic water meter flow calibration method based on model optimization, which comprises the following steps: s1, controlling the flow rate to obtain a standard group and a test group; s2, performing nonlinear fitting on the flow characteristic curve based on the standard group data, and calculating an initial mathematical model; s3, evaluating the mathematical model based on the test panel data; s4, optimizing model parameters based on the test set data; s5, obtaining the optimal sectional calibration coefficient of a single water meter through a differential evolution algorithm based on a mathematical model; and s6, repeating the steps from s3 to s5, and completing the flow calibration of all the water meters. The method directly uses the original data to solve the calibration coefficient, is simple and efficient, avoids accuracy errors caused by unreasonable manual selection of segmentation points, corrects the calibration coefficient from the source, optimizes an independent model for each water meter, eliminates the influence caused by processing or installation errors, controls the accuracy within a required range, and realizes the high consistency of water meters in the same batch or model.
Description
Technical Field
The invention relates to the technical field of intelligent water affairs, in particular to an ultrasonic water meter flow calibration method based on model optimization.
Background
The ultrasonic water meter is widely applied to the fields of civilian use, industry and the like due to the advantages of high metering precision, wide range ratio, small pressure loss and the like, the flow rate is calculated by calculating the propagation time difference of upstream and downstream ultrasonic signals, and the average flow rate in a pipeline is obtained based on a calibration coefficient. At present, in the flow calibration process, the flow segmentation calibration is generally carried out by referring to a theoretical formula of fluid mechanics and according to manual experience, the process is iterated repeatedly, and the optimal solution is difficult to ensure. In addition, time cost and economic cost are considered, when the calibration coefficients of a plurality of water meters of the same type are secondarily corrected, the utilized measurement data are often less, and the accuracy of the water meters in a full-flow interval is difficult to guarantee through a traditional secondary correction method.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an ultrasonic water meter flow calibration method based on model optimization. The method directly uses the original measurement data to solve the calibration coefficient, and avoids the precision error caused by unreasonable manual selection of the segmentation points. In addition, the calibration coefficient is corrected from the source, independent mathematical model parameters are optimized for each water meter on the basis of the initial mathematical model, the influence caused by machining or installation errors is eliminated, and the precision is controlled within a required range.
The purpose of the invention can be realized by the following technical scheme:
a model optimization-based ultrasonic water meter flow calibration method is characterized by comprising the following steps:
s 1: using flow data measured by one calibrated ultrasonic water meter in a full flow interval as a standard group, and using flow data measured by all ultrasonic water meters to be calibrated in the full flow interval as a test group;
s 2: drawing a flow characteristic curve based on the standard group data, and carrying out nonlinear fitting on the flow characteristic curve to obtain an initial mathematical model;
s 3: calculating relative error based on test group dataRDAccording to relative errorRDEvaluating the mathematical model to determine whether to skip step s 4;
s 4: optimizing mathematical model parameters through a differential evolution algorithm based on the test group data;
s 5: based on a mathematical model, obtaining an optimal sectional calibration coefficient of a single ultrasonic water meter to be calibrated through a differential evolution algorithm;
s 6: and (5) repeating the steps s3 to s5 to finish the flow calibration of all the ultrasonic water meters to be calibrated.
Further, the data set in step s1 includes the average flow velocity measured by each ultrasonic water meter and the average value of the propagation time difference between the upstream and downstream received signals
Wherein the average flow velocity
The calculation formula of (c) is as follows:
in the formula (I), the compound is shown in the specification,T j is as followsjThe duration of the secondary measurement is long,m j is as followsjDuration of time of secondary measurementT j The mass of the water flowing into the tank,
is as followsjThe density of the water at the time of the secondary measurement,Sthe sectional area of the pipeline is measured.
Further, the upstream and downstream received signal propagation time difference is calculated by a time difference method with the upstream and downstream received signals as input.
Further, the flow characteristic curve in the step s2 is a curve of the calibration coefficient varying with the average value of the propagation time difference of the upstream and downstream received signals, wherein the calibration coefficientKThe calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
is at the firstjAt the time of secondary measurementiUpstream and downstream of the group the propagation time difference of the received signals, NjIs as followsjThe total number of differences in propagation time between the upstream and downstream received signals at the time of the secondary measurement,
the average flow rate for the standard set obtained for the calibrated ultrasonic water meter for the jth measurement,
is as followsjThe average of the upstream and downstream received signal propagation time differences of the secondary measurements.
Further, the non-linear fitting in the step s2 is a least square fitting, and an initial mathematical model is obtained
Wherein
Are model parameters.
Further, the relative error in the step s3RDThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,
for the test grouplThe average flow rate of the group is,Kfor the calibration coefficients calculated by the mathematical model,
is the first in the test grouplAn average of the upstream and downstream received signal propagation time differences of the group;
the mathematical model evaluation principle is as follows:
if it isRD l If the error values are less than or equal to one half of the maximum allowable error, the mathematical model is considered to be consistent with the test set data, and the step s4 is skipped;
if present, isRD l If the maximum allowable error is greater than one half of the maximum allowable error and less than or equal to the maximum allowable error, the mathematical model is considered not to be consistent with the test group data, and the step s4 is required to optimize the parameters of the mathematical model;
if present, isRD l If the error is larger than the maximum allowable error, the mathematical model is considered not to meet the requirement, and the ultrasonic water meter is analyzed independently by departing from the process.
Further, the step s4 includes the following specific steps:
s 4.1: average flow rate values based on test group data
And (a)
) Define an objective function by the Euclidean distance between the two, and use a mathematical model
Middle parameter
The preset allowable range of the model is a constraint condition, and variables are designed based on a mathematical model parameter theta;
s 4.2: setting a search range of a differential evolution algorithm on a mathematical model, and initializing the number of population individuals, the maximum iteration times, cross factors and variation factors;
s 4.3: randomly generating a population as a parent population in the search range of the mathematical model parameter theta, adding 1 to the iteration times, and calculating the fitness of individuals in the parent population according to the target function and the constraint condition;
s 4.4: randomly selecting individuals from the parent population to perform crossing and mutation based on the crossing factors and the mutation factors to generate a test population;
s 4.5: calculating the fitness of individuals in the test population, comparing the fitness of individuals in the parent population with the fitness of individuals in the test population, and generating a child population according to a comparison result;
s 4.6: performing non-dominant sorting of individuals in the offspring population;
s 4.7: calculating crowding distances of individuals in the offspring population, and eliminating individuals with relatively small crowding distances to keep the number of the individuals in the offspring population consistent with the number of the individuals in the initial population;
s 4.8: judging whether the existing iteration times reach the requirement of the maximum iteration times, if so, finishing the parameter optimization of the mathematical model, and ending the process; otherwise jump to step s 4.3.
Further, the step s5 specifically includes the following steps:
s 5.1: average flow rate values based on test group data
And (a)
) Defining an objective function by the Euclidean distance between the two, randomly generating a segmentation point position beta and a segmentation calibration coefficient alpha, taking the allowable range of the segmentation point position beta and the segmentation calibration coefficient alpha as a constraint condition, and taking parameters
Designing variables;
s 5.2: setting a search range of a differential evolution algorithm for the parameter gamma, and initializing the number of population individuals, the maximum iteration times, cross factors and variation factors;
s 5.3: randomly generating a population as a parent population in the search range of the parameter gamma, adding 1 to the iteration number, and calculating the fitness of individuals in the parent population according to the target function and the constraint condition;
s 5.4: randomly selecting individuals from the parent population to perform crossing and mutation based on the crossing factors and the mutation factors to generate a test population;
s 5.5: calculating the fitness of individuals in the test population, comparing the fitness of individuals in the parent population with the fitness of individuals in the test population, and generating a child population according to a comparison result;
s 5.6: performing non-dominated sorting of individuals in the progeny population;
s 5.7: calculating crowding distances of individuals in the offspring population, and eliminating individuals with relatively small crowding distances to keep the number of the individuals in the offspring population consistent with the number of the individuals in the initial population;
s 5.8: judging whether the existing iteration times reach the requirement of the maximum iteration times, if so, completing parameter optimization, and ending the process; otherwise jump to step s 5.3.
The invention has the beneficial technical effects that: the method has the advantages that the calibration coefficient is solved by directly using the original measurement data, simplicity and high efficiency are realized, and the precision error caused by unreasonable manual selection of the segmentation points is avoided. In addition, the calibration coefficient is corrected from the source, independent mathematical model parameters are optimized for each water meter on the basis of the initial mathematical model, the influence caused by machining or installation errors is eliminated, and the precision is controlled within a required range, so that the high consistency of water meters in the same batch or model is realized.
Drawings
FIG. 1 is a general flow diagram of the present invention.
Fig. 2 is a flow characteristic curve of the ultrasonic water meter according to the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Example (b):
as shown in fig. 1, a method for calibrating flow of an ultrasonic water meter based on model optimization includes the following steps:
s 1: and adjusting the flow control device, and after the flow is stable, using the flow data measured by one calibrated ultrasonic water meter in the full-flow interval as a standard group, and using the flow data measured by all the calibrated ultrasonic water meters in the full-flow interval as a test group. The data set comprises the average flow speed measured by each ultrasonic water meter and the average value of the propagation time difference of the upstream and downstream received signals
Wherein the average flow velocity
The calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,T j is a firstjThe duration of the secondary measurement is long,m j is a firstjDuration of time of secondary measurementT j The mass of water flowing into the tank,
is a firstjThe density of the water at the time of the secondary measurement,Sthe sectional area of the pipeline at the section is measured.
The propagation time difference of the upstream and downstream received signals is calculated by a time difference method by taking the upstream and downstream received signals as input.
s 2: and drawing a flow characteristic curve based on the standard group data, and carrying out nonlinear fitting on the flow characteristic curve to obtain an initial mathematical model. The flow characteristic curve is a variation curve of a calibration coefficient along with the average value of the propagation time difference of upstream and downstream received signals, wherein the calibration coefficientKThe calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
is at the firstjAt the time of secondary measurementiUpstream and downstream of the group, the difference in propagation time of the received signals, NjIs a firstjThe total number of differences in propagation time between the upstream and downstream received signals at the time of the secondary measurement,
the average flow rate for the standard set obtained for the calibrated ultrasonic water meter for the jth measurement,
is as followsjThe average of the upstream and downstream received signal propagation time differences of the secondary measurements.
The nonlinear fitting is least square fitting to obtain an initial mathematical model
. The mathematical model expression is related to the ultrasonic water meter model, and the parameters in the mathematical model are related to the water meter pipeline structure and the sound path design parameters.
As shown in fig. 2, which is a flow characteristic curve of a certain model DN15 ultrasonic water meter, the calibration coefficient changes with the change of the average value of the propagation time difference of the upstream and downstream received signals. After least squares fitting based on the data of fig. 2, the expression of the initial mathematical model is obtained as follows:
in the formula, a, b, c and d are all adjustable parameters in the mathematical model, and the values are related to the model of the ultrasonic water meter. The value range of the parameter a is [0.02016, 0.02107], the value range of the parameter b is [ -0.0001136, 0.0003671], the value range of the parameter c is [ -0.009227, -0.008075], and the value range of the parameter d is [ -0.2465, -0.1726 ].
s 3: calculating relative error based on test group dataRDAccording to the relative errorRDThe mathematical model is evaluated to determine whether to skip step s4 as a result of the evaluation. Relative errorRDThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,
to test grouplThe average flow rate of the group is,Kfor the calibration coefficients calculated by the mathematical model,
is the first in the test grouplThe average of the differences in propagation time of the received signals upstream and downstream of the group.
The mathematical model evaluation principle is as follows:
if it isRD l If the error values are less than or equal to one half of the maximum allowable error, the mathematical model is considered to be consistent with the test group data, and the step s4 is skipped;
if present, isRD l If the maximum allowable error is more than one half of the maximum allowable error and less than or equal to the maximum allowable error, the mathematical model is considered not to be in accordance with the test group data, and the step s4 needs to be carried out to optimize the parameters of the mathematical model;
if present, isRD l If the error is larger than the maximum allowable error, the mathematical model is considered not to meet the requirement, and the ultrasonic water meter is analyzed independently without the process.
In the embodiment, a certain model DN15 ultrasonic water meter is a 2-level meter, and the set maximum allowable error is 2-level precision required by national standards.
s 4: and optimizing the mathematical model parameters through a differential evolution algorithm based on the test group data. The method comprises the following specific steps:
s 4.1: average flow rate value based on test group data
And (a) and
) Define an objective function by the Euclidean distance between the two, and use a mathematical model
Middle parameter
The preset allowable range is a constraint condition, and variables are designed based on a mathematical model parameter theta;
s 4.2: setting a search range of a differential evolution algorithm on a mathematical model, and initializing the number of population individuals, the maximum iteration times, cross factors and variation factors;
s 4.3: randomly generating a population as a parent population in the search range of the mathematical model parameter theta, adding 1 to the iteration number, and calculating the fitness of individuals in the parent population according to the target function and the constraint condition;
s 4.4: randomly selecting individuals from the parent population for crossing and mutation based on the crossing factors and the mutation factors to generate a test population;
s 4.5: calculating the fitness of individuals in the test population, comparing the fitness of individuals in the parent population with the fitness of individuals in the test population, and generating a child population according to a comparison result;
s 4.6: performing non-dominant sorting of individuals in the offspring population;
s 4.7: calculating crowding distances of individuals in the offspring population, and eliminating individuals with relatively small crowding distances to keep the number of the individuals in the offspring population consistent with the number of the individuals in the initial population;
s 4.8: judging whether the existing iteration times reach the requirement of the maximum iteration times, if so, finishing the parameter optimization of the mathematical model, and ending the process; otherwise jump to step s 4.3.
s 5: and based on the mathematical model, obtaining the optimal sectional calibration coefficient of the single ultrasonic water meter to be calibrated through a differential evolution algorithm. Sectional schoolQuasi-coefficients are logarithmic models
The specific steps of s5 are as follows:
s 5.1: average flow rate value based on test group data
And (a) and
) The Euclidean distance between the two points defines an objective function, a segment point position beta and a segment calibration coefficient alpha are randomly generated, the allowable range of the segment point position beta and the segment calibration coefficient alpha is used as a constraint condition, and parameters are used
Designing variables;
s 5.2: setting a search range of a differential evolution algorithm for the parameter gamma, and initializing the number of population individuals, the maximum iteration times, cross factors and variation factors;
s 5.3: randomly generating a population as a parent population in the search range of the parameter gamma, adding 1 to the iteration number, and calculating the fitness of individuals in the parent population according to the target function and the constraint condition;
s 5.4: randomly selecting individuals from the parent population for crossing and mutation based on the crossing factors and the mutation factors to generate a test population;
s 5.5: calculating the fitness of individuals in the test population, comparing the fitness of individuals in the parent population with the fitness of individuals in the test population, and generating a child population according to a comparison result;
s 5.6: performing non-dominant sorting of individuals in the offspring population;
s 5.7: calculating crowding distances of individuals in the offspring population, and eliminating individuals with relatively small crowding distances to keep the number of the individuals in the offspring population consistent with the number of the individuals in the initial population;
s 5.8: judging whether the existing iteration times reach the requirement of the maximum iteration times, if so, completing parameter optimization, and ending the process; otherwise jump to step s 5.3.
s 6: and (5) repeating the steps s3 to s5 to finish the flow calibration of all the ultrasonic water meters to be calibrated.
The above-mentioned embodiments are illustrative of the specific embodiments of the present invention, and not restrictive, and those skilled in the relevant art can make various changes and modifications to the invention without departing from the spirit and scope of the invention, so that all equivalent technical solutions should fall within the scope of the present invention.
Claims (3)
1. A model optimization-based ultrasonic water meter flow calibration method is characterized by comprising the following steps:
s 1: the method comprises the steps that flow data measured by one calibrated ultrasonic water meter in a full-flow interval are used as a standard group, and flow data measured by all to-be-calibrated ultrasonic water meters in the full-flow interval are used as a test group; the finally obtained data set comprises the average flow speed measured by each ultrasonic water meter and the average value of the propagation time difference of the upstream and downstream received signals
Wherein the average flow velocity
The calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,T j is a firstjThe duration of the secondary measurement is long,m j is a firstjDuration of time of secondary measurementT j The mass of water flowing into the tank,
is a firstjSecond measurementThe density of water at the time of measurement,Smeasuring the sectional area of the pipeline at the section;
s 2: drawing a flow characteristic curve based on the standard group data, and carrying out nonlinear fitting on the flow characteristic curve to obtain an initial mathematical model; wherein the flow characteristic curve is a curve in which a calibration coefficient varies with an average value of propagation time differences of upstream and downstream received signals, wherein the calibration coefficientKThe calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
is at the firstjAt the time of secondary measurementiUpstream and downstream of the group, the difference in propagation time of the received signals, NjIs as followsjThe total number of differences in propagation time between the upstream and downstream received signals at the time of the secondary measurement,
the average flow rate for the standard set obtained for the calibrated ultrasonic water meter for the jth measurement,
is as followsjAn average value of the upstream and downstream received signal propagation time differences of the secondary measurements;
s 3: calculating relative error based on test set dataRDAccording to relative errorRDEvaluating the mathematical model to determine whether to skip step s 4; wherein the relative errorRDThe calculation formula of (c) is as follows:
in the formula (I), the compound is shown in the specification,
to test grouplThe average flow rate of the group is,Kfor the calibration coefficients calculated by the mathematical model,
is the first in the test grouplAn average of the upstream and downstream received signal propagation time differences of the group;
the mathematical model evaluation principle is as follows:
if it isRD l If the error values are less than or equal to one half of the maximum allowable error, the mathematical model is considered to be consistent with the test group data, and the step s4 is skipped;
if present, isRD l If the maximum allowable error is greater than one half of the maximum allowable error and less than or equal to the maximum allowable error, the mathematical model is considered not to be consistent with the test group data, and the step s4 is required to optimize the parameters of the mathematical model;
if present, isRD l If the error is larger than the maximum allowable error, the mathematical model is considered not to meet the requirement, the process needs to be separated, and the ultrasonic water meter is analyzed independently;
s 4: optimizing mathematical model parameters by a differential evolution algorithm based on test group data; the method comprises the following specific steps:
s 4.1: average flow rate value based on test group data
And (a) and
) Define an objective function by the Euclidean distance between the two, and use a mathematical model
Middle parameter
The preset allowable range is a constraint condition, and variables are designed based on a mathematical model parameter theta;
s 4.2: setting a search range of a differential evolution algorithm on a mathematical model, and initializing the number of population individuals, the maximum iteration times, cross factors and variation factors;
s 4.3: randomly generating a population as a parent population in the search range of the mathematical model parameter theta, adding 1 to the iteration number, and calculating the fitness of individuals in the parent population according to the target function and the constraint condition;
s 4.4: randomly selecting individuals from the parent population to perform crossing and mutation based on the crossing factors and the mutation factors to generate a test population;
s 4.5: calculating the fitness of individuals in the test population, comparing the fitness of individuals in the parent population with the fitness of individuals in the test population, and generating a child population according to a comparison result;
s 4.6: performing non-dominated sorting of individuals in the progeny population;
s 4.7: calculating crowding distances of individuals in the offspring population, and eliminating individuals with relatively small crowding distances to keep the number of the individuals in the offspring population consistent with the number of the individuals in the initial population;
s 4.8: judging whether the existing iteration times reach the requirement of the maximum iteration times, if so, finishing the parameter optimization of the mathematical model, and ending the process; otherwise, jumping to the step s 4.3;
s 5: based on a mathematical model, obtaining an optimal sectional calibration coefficient of a single ultrasonic water meter to be calibrated through a differential evolution algorithm; the method comprises the following specific steps:
s 5.1: average flow rate value based on test group data
And (a)
) Defining an objective function by the Euclidean distance between the two points, randomly generating a segmentation point position beta and a segmentation calibration coefficient alpha, and using the segmentation point position beta and the segmentation calibration coefficientThe allowable range of alpha is used as a constraint condition, and the parameter is used as
Designing variables;
s 5.2: setting a search range of a differential evolution algorithm for the parameter gamma, and initializing the number of population individuals, the maximum iteration times, cross factors and variation factors;
s 5.3: randomly generating a population as a parent population in the search range of the parameter gamma, adding 1 to the iteration times, and calculating the fitness of individuals in the parent population according to the target function and the constraint condition;
s 5.4: randomly selecting individuals from the parent population to perform crossing and mutation based on the crossing factors and the mutation factors to generate a test population;
s 5.5: calculating the fitness of individuals in the test population, comparing the fitness of individuals in the parent population with the fitness of individuals in the test population, and generating a child population according to a comparison result;
s 5.6: performing non-dominant sorting of individuals in the offspring population;
s 5.7: calculating crowding distances of individuals in the offspring population, and eliminating individuals with relatively small crowding distances to keep the number of the individuals in the offspring population consistent with the number of the individuals in the initial population;
s 5.8: judging whether the existing iteration times reach the maximum iteration time requirement, if so, completing parameter optimization, and ending the process; otherwise, jumping to the step s 5.3;
s 6: and (5) repeating the steps s3 to s5 to finish the flow calibration of all the ultrasonic water meters to be calibrated.
2. The method of claim 1, wherein the difference in propagation time between the upstream and downstream received signals is calculated by a time difference method using the upstream and downstream received signals as inputs.
3. The method for calibrating flow rate of an ultrasonic water meter based on model optimization of claim 1, wherein the step s is performed in a manner of2, obtaining an initial mathematical model by using least square fitting as the nonlinear fitting
Wherein
Are model parameters.
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| CN116793462B (en) * | 2023-08-21 | 2023-11-03 | 成都千嘉科技股份有限公司 | Calibration method and calibration device based on ultrasonic gas meter |
| CN117268512B (en) * | 2023-11-23 | 2024-02-09 | 青岛鼎信通讯科技有限公司 | Consistency optimization method suitable for ultrasonic water meter |
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| A Novel Mathematical Model for Transit-time Ultrasonic Flow Measurement;Lei Kang;《2019 IEEE International Ultrasonics Symposium (IUS)》;20191209;全文 * |
| Neural-network-based error reduction in calibrating utility ultrasonic flow meters;B. Yazdanshenasshad;《Flow Measurement and Instrumentation》;20181011;全文 * |
| Ultrasonic flow detection and research on regression analysis calibration method;Ming-liang Liu;《2012 International Conference on Systems and Informatics (ICSAI2012)》;20120625;全文 * |
| 基于回波峰值拟合的气体超声流量计信号处理;田雷;《电子测量与仪器学报》;20170731;第31卷(第7期);全文 * |
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