CN113094652A - Water meter water quantity metering loss determination method and system - Google Patents

Water meter water quantity metering loss determination method and system Download PDF

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CN113094652A
CN113094652A CN202110340303.8A CN202110340303A CN113094652A CN 113094652 A CN113094652 A CN 113094652A CN 202110340303 A CN202110340303 A CN 202110340303A CN 113094652 A CN113094652 A CN 113094652A
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徐强
张佳欣
强志民
孔祥达
杨雪铮
王海波
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Beijing Waterworks Group Co ltd
Research Center for Eco Environmental Sciences of CAS
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Abstract

The invention relates to a method and a system for determining water meter water metering loss. The method comprises the following steps: continuously monitoring the water consumption of the residential users at set monitoring time intervals, and determining the cumulative frequency of the daily water consumption; building a random water consumption mode simulation model of residents according to the accumulated frequency, and determining a flow matrix of a random water consumption mode of the residents of multiple households; randomly selecting a plurality of water meters with the same model, the same caliber and the same service life, and determining water meter parameters; determining a plurality of test points according to water meter parameters, and calculating the metering error of each water meter under the test points; calculating the average value and the standard deviation of the metering errors of the test points according to the metering errors, and drawing a random metering error curve; for any flow in the multi-household resident random water use mode flow matrix, determining a random metering error corresponding to any flow according to a random metering error curve; and determining the daily water metering loss of the water meter according to the random metering error. The invention can reduce the water quantity metering loss error of the water meter and improve the metering precision.

Description

Water meter water quantity metering loss determination method and system
Technical Field
The invention relates to the field of water meter water amount monitoring, in particular to a method and a system for determining water meter water amount metering loss.
Background
The metering loss of water in a pipe network is an important component in the leakage of the pipe network, and in order to effectively control the metering loss of the pipe network, the proportion of the metering loss of water needs to be determined, and a targeted control scheme is formulated. And two groups of information are necessary for calculating the loss water quantity: the water meter comprises a random metering error curve of the water meter and a water consumption mode of a user, wherein the random metering error curve can reflect the metering characteristic of the water meter, the water consumption mode of the user can reflect the water consumption rule of the user, and the random metering error curve and the water consumption mode of the user can form a metering loss water amount calculation method based on multi-parameter coupling of meter metering performance, running state and the water consumption mode of the user, so that a foundation is laid for analyzing the transfer rule of metering errors and effectively controlling the metering loss water amount, but the random metering error curve of the water meter and the water consumption mode of the user have certain uncertainty.
The existing method for determining the water quantity metering loss of the water meter comprises the following steps:
(1) determining a water demand mode:
the users are classified firstly according to simple standards, and 2-3 categories are generally enough to achieve better effect. The method comprises the steps of selecting a C-level flowmeter with accurate record as a testing device to measure water consumption of a user, wherein the flowmeter is provided with a pulse emitter and a data recorder at the same time, the volume of a single pulse is not higher than 0.1L, flow distortion is avoided, the data recorder stores each pulse in time, and the testing time is not less than one week. The resulting water usage patterns are expressed as fractional flow rates and as a percentage of the fractional flow rates, as shown in table 1.
(2) Obtaining an error curve:
testing water meters at a large number of different flow rates is not possible and therefore requires reconstruction of an error curve from error data for several different flow rates, experience has shown that measuring errors at small flow rates is more important and the error curve must be well defined at these flow rates. To properly reconstruct the error curve in this range, the pick-up flow is known or approximated. It is also proposed to test the meter at a minimum flow rate and at another flow rate between the minimum flow rate and the demarcation flow rate. Considering that the water consumption rarely exceeds 1500L/h and that the error of medium to high flow is almost the same, it is sufficient to test at 750L/h and 1500L/h, and it can be considered that the error from this flow is kept constant up to the maximum flow. The error for all other flows can be obtained by linear interpolation between the available points. The random metrology error curve constructed in the manner described above is shown in FIG. 1.
(3) Calculating the metering error of the water meter:
knowing the user's water usage pattern and reconstructing the error curve for the meters, the percentage of actual water usage recorded by the meters can be determined. The weighted errors of figure 1 and user type i are calculated to obtain the numbers shown in table 1.
The total volume measured on the water meter was 4.7% (. 100-.
The water meter weight error is 90.60% -100% — 9.40%.
TABLE 1 Water gauge weighted error analysis table
Figure BDA0002999294900000021
The disadvantages of the above scheme are:
(1) the randomness of the water usage pattern and metering error is not considered. In the case that the user water consumption mode has randomness and the water meter metering error curve also has randomness, the calculated water meter weighted error is also random, and the result of the scheme possibly represents the error condition of most water meters, but does not show the randomness and the difference of the error.
(2) The determination method of the water demand mode is too coarse, the flow is divided according to different intervals, the subjectivity is high, and the influence of water consumption on the water meter weighting error every time is not fully considered; any flow interval after segmentation only corresponds to one error value, and the metering volume calculated by the error value is unreasonable.
Disclosure of Invention
The invention aims to provide a method and a system for determining water meter water metering loss, which aim to solve the problems that the traditional water meter water metering loss calculation method does not reflect the randomness and the difference of water meter metering errors, flow is divided according to different intervals, the method has strong subjectivity, and the influence of water consumption on the water meter weighted error each time is not fully considered, so that the water meter water metering loss error is large and the metering precision is low.
In order to achieve the purpose, the invention provides the following scheme:
a method for determining water loss of a water meter comprises the following steps:
continuously monitoring the water consumption of the residential users at set monitoring time intervals, and determining the cumulative frequency of the daily water consumption;
constructing a flow matrix of a multi-family resident random water consumption mode according to the accumulated frequency;
randomly selecting a plurality of water meters with the same model, the same caliber and the same service life, and determining water meter parameters; the water meter parameters comprise minimum flow, demarcation flow, common flow and overload flow;
determining a plurality of test points according to the water meter parameters, and calculating the metering error of each water meter under the test points;
calculating the average value and the standard deviation of the metering error of each test point according to the metering error, and drawing a random metering error curve;
for any flow in the multi-household resident random water consumption mode flow matrix, determining a random metering error corresponding to any flow according to the random metering error curve;
and determining the metering loss of the water meter for the whole day according to the random metering error.
Optionally, determining a plurality of test points according to the water meter parameters, and calculating a metering error of each water meter under the plurality of test points specifically includes:
according to the formula
Figure BDA0002999294900000031
Calculating the metering error of each water meter under a plurality of test points; wherein E iss(p)The relative error of the s water meter under the test flow of the P test point, P is the total number of the test points, Vs(p)Indicating volume, VA, of the s water meter flowing through in unit time under the p test points(p)The true volume of the flow of the s water meter per unit time under the p test point is shown.
Optionally, the calculating a mean value and a standard deviation of the metering error of each test point according to the metering error, and drawing a random metering error curve specifically includes:
using formulas
Figure BDA0002999294900000041
Calculating the average value of the metering error of each test point; wherein,
Figure BDA0002999294900000042
the average value of the metering errors is obtained; n is the total number of the water meters;
using formulas
Figure BDA0002999294900000043
Calculating the standard deviation of the metering error of each test point; wherein σpIs the standard deviation of the measurement error;
and drawing a random metering error curve according to the metering error average value and the metering error standard deviation.
Optionally, the determining, according to the random metering error curve, a random metering error corresponding to any flow rate in the multiple-household-resident random water usage pattern flow rate matrix specifically includes:
for any flow in the multi-family resident random water usage pattern flow matrix, a formula is utilized
Figure BDA0002999294900000044
Determining a random metering error corresponding to any flow; wherein E israndom(k)For any one time flow IkA corresponding random metering error; i ispTo locate the flow I on the random metering error curvekThe flow of the previous test point; i isp+1To locate the flow I on the random metering error curvekThe flow of the latter test point; erandom(p)Is the flow rate IpA corresponding random metering error; erandom(p+1)Is the flow rate Ip+1Corresponding random metrology error.
Optionally, the determining the measurement loss of the water meter for the whole day according to the random measurement error specifically includes:
according to the random metering error, aiming at each time of water consumption, utilizing a formula
Figure BDA0002999294900000045
Calculating the actual water consumption; wherein Q iskFor the actual water consumption, DkIs the flow rate IkCorresponding water using time length; k is any water consumption number, and K is the water consumption times of the resident users per day;
by using
Figure BDA0002999294900000046
Determining the measurement loss of the water meter for the whole day; eGeneral assemblyThe water loss of the water meter is measured all day long.
A water meter loss of water metering system comprising:
the cumulative frequency determining module is used for continuously monitoring the water consumption of the resident users at set monitoring time intervals and determining the cumulative frequency of the daily water consumption;
the resident random water consumption mode simulation model building module is used for building a multi-family resident random water consumption mode flow matrix according to the accumulated frequency;
the water meter parameter determining module is used for randomly selecting a plurality of water meters with the same model, the same caliber and the same service life and determining water meter parameters; the water meter parameters comprise minimum flow, demarcation flow, common flow and overload flow;
the metering error testing module is used for determining a plurality of testing points according to the water meter parameters and calculating the metering error of each water meter under the plurality of testing points;
the random metering error curve drawing module is used for calculating the metering error average value and the standard deviation of each test point according to the metering error and drawing a random metering error curve;
the random metering error determining module is used for determining a random metering error corresponding to any flow according to the random metering error curve for any flow in the multi-family resident random water consumption mode flow matrix;
and the measurement loss determining module is used for determining the measurement loss of the water meter for the whole day according to the random measurement error.
Optionally, the metering error testing module specifically includes:
a measurement error test unit for testing the measurement error according to the formula
Figure BDA0002999294900000051
Calculating the metering error of each water meter under a plurality of test points; wherein E iss(p)The relative error of the s water meter under the test flow of the P test point, P is the total number of the test points, Vs(p)Indicating volume, VA, of the s water meter flowing through in unit time under the p test points(p)The true volume of the flow of the s water meter per unit time under the p test point is shown.
Optionally, the module for drawing a random metering error curve specifically includes:
a calculation unit of average value of measurement error for using formula
Figure BDA0002999294900000052
Calculating the average value of the metering error of each test point; wherein,
Figure BDA0002999294900000053
the average value of the metering errors is obtained; n is the total number of the water meters;
a standard deviation calculation unit of measurement error for using the formula
Figure BDA0002999294900000054
Calculating the standard deviation of the metering error of each test point; wherein σpIs the standard deviation of the measurement error;
and the random metering error curve drawing unit is used for drawing a random metering error curve according to the metering error average value and the metering error standard deviation.
Optionally, the random metering error determining module specifically includes:
a random metering error determining unit for utilizing a formula for any one flow in the multi-family resident random water usage pattern flow matrix
Figure BDA0002999294900000061
Determining a random metering error corresponding to any flow; wherein E israndom(k)For any one time flow IkA corresponding random metering error; i ispTo locate the flow I on the random metering error curvekThe flow of the previous test point; i isp+1To locate the flow I on the random metering error curvekThe flow of the latter test point; erandom(p)Is the flow rate IpA corresponding random metering error; erandom(p+1)Is the flow rate Ip+1Corresponding random metrology error.
Optionally, the metering loss determining module specifically includes:
the actual water consumption calculating unit is used for utilizing a formula for each time of water consumption according to the random metering error
Figure BDA0002999294900000062
Calculating the actual water consumption; wherein Q iskFor the actual water consumption, DkIs the flow rate IkCorresponding water using time length; k is any water consumption number, and K is the water consumption times of the resident users per day;
a measurement loss determination unit for utilizing
Figure BDA0002999294900000063
Determining the measurement loss of the water meter for the whole day; eGeneral assemblyThe water loss of the water meter is measured all day long.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a method and a system for determining water meter water metering loss, which are used for constructing a resident random water consumption mode simulation model, determining the random water consumption of each time based on the constructed resident random water consumption mode simulation model, drawing a random metering error curve, obtaining the metering error of each time of water consumption according to the random water consumption and combining the random metering error curve, and further calculating the metering loss of the water meter for one day.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a graph of error reconstruction using pickup flow and linear interpolation;
FIG. 2 is a flow chart of a method for determining water loss of a water meter according to the present invention;
fig. 3 is a flow chart of a method for determining water meter water metering loss in practical application provided by the present invention;
FIG. 4 is a block diagram of a water meter loss determination system according to the present invention;
FIG. 5 is a diagram of a random water usage pattern of 1 household residents according to the present invention;
FIG. 6 is a diagram of a random water usage pattern for 1000 households according to the present invention;
FIG. 7 is a diagram of genetic algebra versus objective function provided by the present invention;
FIG. 8 is a graph of time versus water usage provided by the present invention;
FIG. 9 is a graph of time versus absolute error provided by the present invention;
FIG. 10 is a graph of time versus relative error provided by the present invention;
FIG. 11 is a graph of the metering error of a DN15-4 year water meter provided by the present invention;
FIG. 12 is a graph of a 1-family-resident random water usage pattern for parameter simulation after calibration according to the present invention;
fig. 13 is a graph of the random error of DN15-4 year water meter provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 invention aims to provide a method and a system for determining water meter water metering loss, which can reduce water meter water metering loss errors and improve metering precision.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 2 is a flowchart of a method for determining water meter water metering loss according to the present invention, and as shown in fig. 2, a method for determining water meter water metering loss includes:
step 201: the water consumption of the resident user is continuously monitored at set monitoring time intervals, and the cumulative frequency of the daily water consumption is determined. Wherein, the set monitoring time interval can be 5min, 7min, 10min or other time intervals.
Step 202: and constructing a flow matrix of the multi-family resident random water consumption mode according to the accumulated frequency.
Step 203: randomly selecting a plurality of water meters with the same model, the same caliber and the same service life, and determining water meter parameters; the water meter parameters include minimum flow, demarcation flow, common flow and overload flow.
Step 204: and determining a plurality of test points according to the water meter parameters, and calculating the metering error of each water meter under the test points.
The step 204 specifically includes: according to the formula
Figure BDA0002999294900000081
Calculating the metering error of each water meter under a plurality of test points; wherein E iss(p)The relative error of the s water meter under the test flow of the P test point, P is the total number of the test points, Vs(p)Indicating volume, VA, of the s water meter flowing through in unit time under the p test points(p)The true volume of the flow of the s water meter per unit time under the p test point is shown.
Step 205: and calculating the average value and the standard deviation of the metering error of each test point according to the metering error, and drawing a random metering error curve.
The step 205 specifically includes: using formulas
Figure BDA0002999294900000082
Calculating the average value of the metering error of each test point; wherein,
Figure BDA0002999294900000083
the average value of the metering errors is obtained; n is the total number of the water meters; using formulas
Figure BDA0002999294900000084
Calculating the standard deviation of the metering error of each test point; wherein σpIs the standard deviation of the measurement error; and drawing a random metering error curve according to the metering error average value and the metering error standard deviation.
Step 206: and for any flow in the multi-household resident random water consumption mode flow matrix, determining a random metering error corresponding to any flow according to the random metering error curve.
The 206 specifically includes: for any flow in the multi-family resident random water usage pattern flow matrix, a formula is utilized
Figure BDA0002999294900000085
Determining a random metering error corresponding to any flow; wherein E israndom(k)For any one time flow IkA corresponding random metering error; i ispTo locate the flow I on the random metering error curvekThe flow of the previous test point; i isp+1To locate the flow I on the random metering error curvekThe flow of the latter test point; erandom(p)Is the flow rate IpA corresponding random metering error; erandom(p+1)Is the flow rate Ip+1Corresponding random metrology error.
Step 207: and determining the metering loss of the water meter for the whole day according to the random metering error.
Step 207 specifically includes: according to the random metering error, aiming at each time of water consumption, utilizing a formula
Figure BDA0002999294900000091
Calculating the actual water consumption; wherein Q iskFor the actual water consumption, DkIs the flow rate IkCorresponding water using time length; k is any water consumption number, and K is the water consumption times of the resident users per day; by using
Figure BDA0002999294900000092
Determining the measurement loss of the water meter for the whole day; eGeneral assemblyThe water loss of the water meter is measured all day long.
Based on the method for determining the water meter water quantity metering loss provided by the invention, the steps are further explained as follows:
the technical scheme of the invention is as follows: selecting a certain number of residential users to monitor and calculate the average water consumption of the residential users within every 5min, constructing a random water consumption mode simulation model, and determining five parameters in the model by utilizing a genetic algorithm rate so as to simulate daily water consumption of the users; selecting water meters with the same model, the same caliber and the same service life to test the metering error of the water meters, and drawing a metering error curve; and calculating the metering error of the water meter according to the water consumption mode of the user and the metering error curve, and fig. 3 is a flow chart of the method for determining the water metering loss of the water meter in practical application provided by the invention.
The specific implementation steps are as follows:
1. random water use pattern generation method
(1) Selecting a certain number of resident users (preferably more than 500 users), continuously monitoring the water consumption of the users for several days, preferably monitoring the water consumption of the users for 5min, recording and calculating the time period z, wherein the number of monitoring time periods in one day is 288, andi(i 1, 2.., 288) average water usage q for all usersi(i 1, 2.., 288), and then calculating ziFrequency f of water consumption in time intervalsiAnd cumulative frequency F of daily water consumptioniThe formula is as follows:
Figure BDA0002999294900000093
Figure BDA0002999294900000094
(2) and constructing a random water use mode simulation model. Simulating the water consumption times K of the resident users with the parameter lambda per day according to the Poisson distribution, generating K random numbers which accord with (0,1) uniform distribution, and counting (F)i-1,Fi](i 1, 2.., 288) the number n of random numbers in the intervali(i 1, 2.., 288), as a random number of water uses over the period, generating z in turniN within a time intervaliEach of the combinations (300 × (i-1),300 × i](300 seconds in 5 min) are uniformly distributed random integers and are arranged according to an ascending order as the random water use generation time t of the residential userk(K1, 2.. K.) K, K parameters of μ are generated from a lognormal distribution1、σ1(μ1、σ1Is a parameter of lognormal distribution, and respectively represents a flow pulse IkMean and standard deviation of) flow pulses Ik(K ═ 1,2,. K), K parameters μ2、σ2(μ2、σ2Is a lognormal distribution parameter which respectively represents the water consumption time length DkMean and standard deviation of) water usage period Dk(K1, 2.., K), decomposing the whole day into 86400 times in seconds, and sequentially decomposing D into DkIndividual flow rate IkFill in to tkAt the initial moment, obtaining a flow matrix I of the random water use mode of the single-family residents1×86400；
(3) Repeating the step (2) m times, and constructing a plurality of single-family resident random water use pattern flow matrixes Im×86400Summing up the m times of simulation results at the same time to obtain a flow matrix A of the multi-family resident random water consumption pattern1×86400At 5min intervals, for A1×86400The flow in (1) is summed, and the water consumption in each time interval is qAi(i 1, 2.., 288), obtaining a water quantity matrix Q of the m-family resident random water consumption modeA＝[qA1,qA2,...,qA288]；
(4) Utilizing genetic algorithm to calibrate parameters lambda and mu1、σ1、μ2、σ2To obtain an objective function
Figure BDA0002999294900000101
The suggested value range of each parameter is more than or equal to 120 and less than or equal to 300, and more than or equal to 1 and less than or equal to mu1≤2、0.3≤σ1≤0.8、2≤μ2≤3、0.9≤σ2Less than or equal to 1.4, when the evolution algebra reaches a certain number (such as 20) and the fitness value is not changed for a plurality of continuous generations (such as 20), terminating the evolution and outputting the optimal parameters;
2. water meter metering error curve generation method
(1) Randomly selecting N (more than 20) water meters with the same model, the same diameter and the same service life, and numbering Ms(s is 1,2, …, N), and determining the minimum flow Q of the water meter1Dividing flow rate Q2Flow rate Q in common use3And overload flow Q4。
(2) P test points are designed for each water meter, and in the invention, 13 test point positions are taken as an example, as shown in table 2.
Table 2 distribution table for 13 test point positions designed for each water meter
Figure BDA0002999294900000102
Figure BDA0002999294900000111
Wherein Q isaThe initial flow of the water meter needs to be obtained through testing in an experiment.
(3) Selecting a volume method to test and record the indicating volume V flowing through each water meter in unit time under each test points(p)(the indicated volume of the flow of the s water meter in unit time under the p test point) and the real volume VAs(p)(the true volume of the flow through the s meter per unit time at the p test point), the relative change in flow rate during each test should not exceed: Qa-Q2(excluding Q)2)：±2.5％；Q2(including Q)2) To Q4: plus or minus 5 percent; each test point can be tested one or more times, and if the test is carried out for multiple times, the indication volume and the real volume are respectively taken as the average value of the multiple tests.
(4) Calculating the metering error of each water meter at each test point according to the formula (3):
Figure BDA0002999294900000112
in the formula: es(p)The relative error of the s water meter under the test flow of the p test point is expressed by percentage;
v-test period tdVolume added or subtracted in the internal indicating means in cubic meters (m)3)；
VA-test period tdThe reference volume of internal flow is in cubic meters (m)3)。
(5) Calculating the average value of the metering error of each test point
Figure BDA0002999294900000115
And its standard deviation σpThe formulas are shown in (4) and (5), and a metering error curve is drawn by using the metering error data.
Figure BDA0002999294900000113
Figure BDA0002999294900000114
3. Calculating the metering error of the water meter
(1) The parameters determined by the utilization rate generate a corresponding flow matrix of the resident random water use mode according to the caliber of the selected water meter, for example, if the water meter with the caliber of DN15 is selected by testing, a flow matrix I of the single-family resident random water use mode is generated1×86400。
(2) From a normal distribution, mean values are generated respectively
Figure BDA0002999294900000121
And standard deviation of σp(P ═ 1,2,.., P) for a watermeter random metering error curve.
(3) For each flow I in the water usage pattern matrixk(with a corresponding duration of Dk) Finding the flow I of the front and the rear test points on the random metering error curvep、Ip+1And a random metering error Erandom(p)、Erandom(p+1)Calculating the flow I according to the formula (6) by interpolationkCorresponding random metering error Erandom(k)：
Figure BDA0002999294900000122
(4) For each water use (flow rate I)kDuration of Dk) According toThe actual water consumption is calculated by equation (7):
Figure BDA0002999294900000123
(5) calculating the error of the water meter in the water amount measured in one day:
Figure BDA0002999294900000124
fig. 4 is a structural diagram of a water meter water metering loss determining system provided by the present invention, and as shown in fig. 4, a water meter water metering loss determining system includes:
the cumulative frequency determination module 401 is configured to continuously monitor the water consumption of the residential user at the set monitoring time interval, and determine the cumulative frequency of the daily water consumption.
And the random residential water use pattern simulation model building module 402 is used for building a flow matrix of a random residential water use pattern of multiple households according to the accumulated frequency.
A water meter parameter determining module 403, configured to randomly select multiple water meters of the same model, the same caliber, and the same service life, and determine water meter parameters; the water meter parameters include minimum flow, demarcation flow, common flow and overload flow.
And a metering error testing module 404, configured to determine multiple test points according to the water meter parameters, and calculate a metering error of each water meter at the multiple test points.
The metering error testing module 404 specifically includes: a measurement error test unit for testing the measurement error according to the formula
Figure BDA0002999294900000125
Calculating the metering error of each water meter under a plurality of test points; wherein E iss(p)For the relative error, V, of the s water meter under the test flow of the p test points(p)The indicated volume of the flow of the s water meter in unit time under the P test point is shown, P is the total number of the test points, VAs(p)For the s water meter in a unit under the p test pointThe true volume flowing through in time.
And a random metering error curve drawing module 405, configured to calculate a metering error average value and a standard deviation of each test point according to the metering error, and draw a random metering error curve.
The random metering error curve drawing module 405 specifically includes: a calculation unit of average value of measurement error for using formula
Figure BDA0002999294900000131
Calculating the average value of the metering error of each test point; wherein,
Figure BDA0002999294900000132
the average value of the metering errors is obtained; n is the total number of the water meters; a standard deviation calculation unit of measurement error for using the formula
Figure BDA0002999294900000133
Calculating the standard deviation of the metering error of each test point; wherein σpIs the standard deviation of the measurement error; and the random metering error curve drawing unit is used for drawing a random metering error curve according to the metering error average value and the metering error standard deviation.
And a random metering error determining module 406, configured to determine, according to the random metering error curve, a random metering error corresponding to any one flow rate in the multiple-household-resident random water usage pattern flow rate matrix.
The random metering error determining module 406 specifically includes: a random metering error determining unit for utilizing a formula for any one flow in the multi-family resident random water usage pattern flow matrix
Figure BDA0002999294900000134
Determining a random metering error corresponding to any flow; wherein E israndom(k)For any one time flow IkA corresponding random metering error; i ispTo locate the flow I on the random metering error curvekThe flow of the previous test point; i isp+1Is at homeThe random metering error curve is positioned at the flow IkThe flow of the latter test point; erandom(p)Is the flow rate IpA corresponding random metering error; erandom(p+1)Is the flow rate Ip+1Corresponding random metrology error.
And the metering loss determining module 407 is configured to determine the metering loss of the water meter for the whole day according to the random metering error.
The measurement loss determining module 407 specifically includes: the actual water consumption calculating unit is used for utilizing a formula for each time of water consumption according to the random metering error
Figure BDA0002999294900000135
Calculating the actual water consumption; wherein Q iskFor the actual water consumption, DkIs the flow rate IkCorresponding water using time length; k is any water consumption number, and K is the water consumption times of the resident users per day; a measurement loss determination unit for utilizing
Figure BDA0002999294900000141
Determining the measurement loss of the water meter for the whole day; eGeneral assemblyThe water loss of the water meter is measured all day long.
The practical case is as follows:
1. random water use pattern generation method
(1) 776 resident users have been selected in a certain district in the north, and every user's water consumption has been monitored for 22 consecutive days, and the monitoring time interval is 5min, then divide into 288 periods throughout the day, calculate the user average water consumption q of every period:
q＝[0.5427 0.4228 0.4387 0.3934 0.3523 0.3893 0.3721 0.3527 0.3205 0.2919 0.263 0.2882 0.3013 0.2753 0.2275 0.2152 0.2213 0.1834 0.1879 0.1603 0.1594 0.1648 0.1479 0.1422 0.1459 0.1863 0.2135 0.148 0.1531 0.1331 0.1283 0.1374 0.172 0.1538 0.1385 0.1147 0.1353 0.1219 0.1219 0.1501 0.1515 0.1494 0.1078 0.1201 0.1229 0.1226 0.1285 0.1397 0.2205 0.299 0.2084 0.2544 0.2292 0.222 0.2033 0.2111 0.1958 0.2034 0.1941 0.1825 0.185 0.2191 0.2286 0.2613 0.2588 0.2755 0.2783 0.3028 0.3766 0.4 0.4823 0.4646 0.4631 0.5232 0.5962 0.6128 0.6839 0.6401 0.7076 0.744 0.8108 0.8463 0.8761 0.9078 0.9562 1.0378 1.1368 1.1132 1.1969 1.1936 1.2581 1.2454 1.2725 1.3535 1.386 1.3773 1.4569 1.5368 1.4582 1.3358 1.4976 1.4586 1.3586 1.5094 1.4057 1.3816 1.3925 1.3831 1.2918 1.2601 1.2426 1.3796 1.1924 1.202 1.2841 1.2075 1.1537 1.1613 1.2134 1.1467 1.2378 1.2509 1.1327 1.1475 1.2296 1.1631 1.1202 1.1024 1.1456 1.1434 1.1596 1.1274 1.2182 1.2239 1.2296 1.2126 1.1861 1.2364 1.2872 1.317 1.3109 1.2325 1.2507 1.3049 1.3697 1.4573 1.396 1.2848 1.4037 1.3406 1.273 1.2765 1.2128 1.2275 1.3131 1.2007 1.1834 1.1856 1.2353 1.1071 1.0847 0.9613 1.019 0.9943 1.0455 1.0466 0.938 0.9682 0.8883 0.868 0.8735 0.8225 0.9021 0.805 0.8597 0.8163 0.8378 0.8402 0.9232 0.8621 0.8848 0.9529 0.903 0.8759 0.8544 0.9116 0.8666 0.8523 0.8248 0.8444 0.9431 0.9541 0.9932 0.9775 1.0341 0.9834 0.9562 1.0136 1.0941 1.0657 1.0539 1.0532 1.0751 1.1138 1.1121 1.1791 1.2106 1.1659 1.2139 1.1817 1.2925 1.3238 1.2839 1.2914 1.3027 1.387 1.3343 1.3258 1.3345 1.3827 1.4207 1.4159 1.3067 1.3814 1.3435 1.4026 1.331 1.3757 1.3286 1.3722 1.31 1.2837 1.368 1.3159 1.3054 1.3266 1.3253 1.2874 1.1994 1.2535 1.2824 1.2296 1.1445 1.1666 1.2384 1.3881 1.3564 1.2406 1.2756 1.197 1.1767 1.2172 1.1835 1.2734 1.3349 1.4455 1.4258 1.414 1.3095 1.3455 1.2988 1.2375 1.2246 1.2883 1.2229 1.2493 1.1933 1.1815 1.1158 1.1425 1.1598 1.0644 1.0484 0.9963 0.9781 0.9643 0.9063 0.9083 0.9405 0.8257 0.7517 0.7588 0.7331 0.6585 0.6508 0.56 0.5558 0.522]；
using formulas
Figure BDA0002999294900000151
Calculating the frequency of water consumption in each time interval, and further calculating the cumulative frequency F:
F＝[0.0021 0.0037 0.0054 0.0069 0.0082 0.0097 0.0111 0.0125 0.0137 0.01480.01580.01690.018 0.01910.02 0.0208 0.0216 0.0223 0.023 0.0237 0.0243 0.0249 0.0255 0.026 0.0266 0.0273 0.0281 0.0286 0.0292 0.0297 0.0302 0.0308 0.0314 0.032 0.0325 0.033 0.0335 0.0339 0.0344 0.035 0.0356 0.0361 0.0365 0.037 0.0375 0.0379 0.0384 0.039 0.0398 0.0409 0.0417 0.0427 0.0436 0.0444 0.0452 0.046 0.0468 0.0475 0.0483 0.049 0.0497 0.0505 0.0514 0.0524 0.0534 0.0544 0.0555 0.0566 0.0581 0.0596 0.0614 0.0632 0.065 0.067 0.0693 0.0716 0.0742 0.0766 0.0794 0.0822 0.0853 0.0885 0.0919 0.0953 0.099 0.1029 0.1073 0.1115 0.1161 0.1206 0.1254 0.1302 0.135 0.1402 0.1455 0.1508 0.1563 0.1622 0.1677 0.1728 0.1786 0.1841 0.1893 0.1951 0.2004 0.2057 0.211 0.2163 0.2212 0.226 0.2308 0.236 0.2406 0.2452 0.2501 0.2547 0.2591 0.2635 0.2682 0.2725 0.2773 0.282 0.2863 0.2907 0.2954 0.2999 0.3041 0.3083 0.3127 0.3171 0.3215 0.3258 0.3305 0.3351 0.3398 0.3444 0.349 0.3537 0.3586 0.3636 0.3686 0.3733 0.3781 0.3831 0.3883 0.3939 0.3992 0.4041 0.4095 0.4146 0.4194 0.4243 0.4289 0.4336 0.4386 0.4432 0.4477 0.4522 0.457 0.4612 0.4653 0.469 0.4729 0.4767 0.4807 0.4847 0.4882 0.4919 0.4953 0.4986 0.502 0.5051 0.5085 0.5116 0.5149 0.518 0.5212 0.5244 0.5279 0.5312 0.5346 0.5382 0.5417 0.545 0.5483 0.5518 0.5551 0.5583 0.5615 0.5647 0.5683 0.5719 0.5757 0.5795 0.5834 0.5872 0.5908 0.5947 0.5989 0.6029 0.6069 0.611 0.6151 0.6193 0.6236 0.6281 0.6327 0.6371 0.6418 0.6463 0.6512 0.6563 0.6612 0.6661 0.6711 0.6763 0.6814 0.6865 0.6916 0.6969 0.7023 0.7077 0.7127 0.7179 0.7231 0.7284 0.7335 0.7388 0.7438 0.7491 0.7541 0.759 0.7642 0.7692 0.7742 0.7792 0.7843 0.7892 0.7938 0.7986 0.8035 0.8082 0.8125 0.817 0.8217 0.827 0.8322 0.8369 0.8418 0.8464 0.8508 0.8555 0.86 0.8649 0.87 0.8755 0.8809 0.8863 0.8913 0.8964 0.9014 0.9061 0.9108 0.9157 0.9204 0.9251 0.9297 0.9342 0.9385 0.9428 0.9473 0.9513 0.9553 0.9591 0.9628 0.9665 0.97 0.9735 0.977 0.9802 0.9831 0.986 0.9888 0.9913 0.9937 0.9959 0.998 1]；
(2) and constructing a random water use mode simulation model. Simulating the number of times of water consumption per day of a resident user with a parameter λ 165 (the value of λ is automatically generated by the genetic algorithm, and 165 is taken as an example) according to the poisson distribution, wherein the result is 141 times; generating 141 random numbers uniformly distributed according to (0,1), and counting (F)i-1,Fi](i 1, 2.., 288), as the number of times of random water use n per period:
n＝[1 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 1 2 0 0 0 2 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0 2 1 0 0 2 0 3 1 2 0 0 1 1 0 0 1 0 1 1 0 2 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 0 1 2 2 0 1 0 3 1 1 0 1 2 1 1 1 1 0 1 0 1 1 2 2 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 1 1 0 0 1 1 1 1 3 1 0 0 0 1 0 3 1 4 1 1 1 0 1 0 2 0 1 0 0 1 1 0 3 0 1 1 0 1 1 1 2 0 1 0 0 1 1 1 0 0 0 1 0 0 1 0 2 0 1 0 0 1 0 0 1 3 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0]；
sequentially generating n in each time intervaliRandom integers which are uniformly distributed according to (300 x (i-1),300 x i) are arranged in ascending order, and the random water use generation time t of the residential user is obtained:
t＝[841197 1348 2283 2452 5071 11032 12439 15379 15662 16830 19641 20915 21402 22406 22639 23317 23524 23692 24671 24847 25682 26321 27675 28148 29001 29584 29738 30807 30813 31168 31970 32012 32553 32566 32695 32781 33041 33245 34027 34471 35391 35829 36102 36787 36863 37064 37282 38046 39079 40015 40372 41635 42590 43077 43213 43467 43634 43749 44142 44809 44967 44998 45187 45582 45961 46214 46231 46652 47031 47199 47666 48113 48666 48905 49411 49429 49575 49607 50448 51867 57364 61334 61443 61540 62106 62603 63427 63616 64138 64474 64585 64658 64735 65062 66078 66641 66767 66820 67069 67359 67410 67472 67493 67562 68046 68266 68981 69317 69410 69984 70884 71311 71710 71752 71879 72512 72656 73471 73651 74091 74296 74367 74998 75792 76011 76291 77477 78375 79087 79131 79563 80524 81322 81673 81701 81810 83165 83652 85264 85825](ii) a Generating 141 parameters of mu according to lognormal distribution1＝1.52、σ1＝0.49(μ1、σ1Is automatically generated by a genetic algorithm, here by way of example only) of a flow pulse I (L/min):
I＝[6.707 13.8117 2.3567 3.7271 2.6775 4.4412 3.2194 2.5582 3.2373 16.4748 3.2904 5.5216 4.3063 7.051 7.4701 1.3814 8.0781 7.7094 4.8174 3.655 3.3818 2.8962 3.2042 5.0174 7.6112 9.4538 3.428 3.6924 9.5244 4.3002 3.0037 12.5657 8.6235 2.8914 4.3669 3.2924 4.748 4.4734 1.8669 4.4601 3.374 2.7108 6.074 5.4531 7.8815 4.9888 6.6296 2.7367 3.8371 7.3841 4.5253 2.7122 7.7742 2.2066 5.289 16.2037 5.2883 3.3487 6.146 3.5102 3.5747 3.6948 3.3858 6.5146 4.6417 4.8662 2.0831 3.0707 3.5461 7.1898 11.5529 3.3031 8.204 5.0972 3.8923 5.7828 4.5819 3.8763 5.1509 3.1086 4.3484 4.7839 6.5265 4.0735 2.7684 4.0126 9.6987 6.2378 3.1418 5.7269 10.2544 9.3551 2.2239 4.6447 2.7629 8.6202 7.9903 3.3551 4.4439 7.2593 4.1835 9.1055 7.4037 5.446 7.169 5.9206 2.1044 4.8533 8.3211 8.5644 3.4511 4.1509 4.0573 3.5735 4.0359 4.8616 7.0386 3.5688 7.0371 2.8686 12.8526 4.8455 8.1809 3.8278 4.1633 7.7329 8.1167 3.7773 2.7142 3.6391 4.3699 2.6866 2.615 1.5392 4.6031 3 8.5628 5.543 2.8942 5.255 5.8279]；
generating 141 parameters of mu according to lognormal distribution2＝2.31、σ2＝1.08(μ2、σ2The value of (d) is automatically generated by a genetic algorithm, here by way of example only) for an integer water usage duration d(s):
D＝[14 24 7 32 3 60 14 15 76 8 51 541 3 13 17 33 38 159 14 1 19 15 5 20 12 18 7 22 20 2 9 6 10 7 3 8 6 9 9 9 4 1 5 10 7 2 280 6 6 65 3 2 9 5 21 24 19 13 4 11 11 10 10 2 43 5 44 5 5 15 2 8 16 10 5 11 18 6 76 8 27 5 23 25 14 4 9 6 11 8 15 6 3 6 16 3 46 1 11 12 77 26 15 10 28 2 8 4 9 4 4 5 3 44 3 14 12 13 6 5 13 2 71 2 1 35 46 9 3 12 3 5 32 5 14 10 9 40 31 8]；
decomposing the whole day into 86400 times in units of seconds, and sequentially dividing 141 flow rates IkFill in to tkAt the time of initiation, each flow has a duration DkObtaining a flow matrix I of the single-family resident random water usage pattern1×86400As shown in fig. 5.
(3) Repeating the step (2)1000 times, and constructing 1000 random water consumption pattern flow moments of single-family residentsArray I1000×86400Summing up 1000 times of simulation results at the same time to obtain a flow matrix A of the multi-family resident random water consumption pattern1×86400As shown in fig. 6.
At 5min intervals, for A1×86400The flow summation in the process of the two steps is carried out to obtain a water quantity matrix q of a 1000-family resident random water consumption modeA(m3)：
qA＝[0.4648 0.3661 0.3896 0.3364 0.2268 0.3706 0.352 0.4396 0.2594 0.2871 0.2352 0.2553 0.2934 0.282 0.2686 0.2256 0.1699 0.1654 0.1455 0.1285 0.0983 0.1244 0.1454 0.1285 0.1765 0.1627 0.2246 0.1556 0.1707 0.1398 0.1141 0.1299 0.1911 0.087 0.1266 0.0751 0.1281 0.1031 0.1129 0.1269 0.1434 0.1479 0.0921 0.1523 0.0897 0.1057 0.098 0.134 0.1901 0.2188 0.1936 0.2439 0.2016 0.1913 0.2537 0.2349 0.147 0.1835 0.1593 0.2012 0.2067 0.2311 0.2769 0.3017 0.2733 0.2957 0.3036 0.3466 0.3755 0.3448 0.3798 0.4485 0.5276 0.5998 0.6868 0.6021 0.6434 0.5686 0.7465 0.6617 0.7075 0.8099 0.8423 0.93 0.7504 1.062 1.1576 1.0975 1.054 1.1599 1.2053 1.068 1.1374 1.283 1.432 1.4796 1.5336 1.5141 1.4415 1.2236 1.4972 1.4319 1.3938 1.4917 1.4164 1.4551 1.33 1.3829 1.4908 1.3061 1.0921 1.3452 1.1164 1.169 1.3083 1.1348 1.1153 1.0656 1.2161 1.1265 1.504 1.2469 1.1074 1.0155 1.1743 1.1761 1.2124 1.0746 1.3145 1.219 1.1901 1.1059 1.2107 1.2151 1.0354 1.221 1.0832 1.2777 1.2668 1.1874 1.1937 1.1402 1.2751 1.3867 1.3187 1.4154 1.3856 1.3801 1.3806 1.1237 1.3506 1.3676 1.2317 1.2111 1.2554 1.2703 1.121 1.1094 1.1098 1.1188 1.0512 0.942 0.9151 0.9084 0.9012 0.9944 0.8819 0.9519 0.9248 0.8572 0.7438 0.7055 0.8715 0.6313 0.8564 0.8156 0.796 0.7768 0.8458 0.8072 0.8498 0.873 0.7674 0.8869 0.781 0.8525 0.8331 0.8775 0.9649 0.8861 0.9031 0.9803 1.0079 0.9074 1.0291 0.9577 0.925 0.9505 1.0486 1.0621 0.9104 1.1201 1.0445 1.2073 1.2595 1.03 1.2327 1.1455 1.1517 1.1591 1.2587 1.2934 1.1948 1.1798 1.1754 1.3883 1.3306 1.3475 1.1883 1.3273 1.3897 1.3032 1.2907 1.2672 1.3639 1.426 1.1564 1.3906 1.267 1.4901 1.275 1.104 1.4752 1.3226 1.1093 1.3164 1.2365 1.3265 1.2453 1.247 1.1789 1.1601 1.206 1.0933 1.1228 1.3587 1.4218 1.1497 1.2709 1.3579 1.1425 1.2076 1.2124 1.0795 1.2308 1.4831 1.4457 1.2349 1.2964 1.3976 1.2806 1.145 1.3154 1.2915 1.241 1.1387 1.1306 1.1145 1.0491 1.0455 1.1199 0.9317 1.0039 0.9946 0.9194 0.9095 0.8714 0.9214 0.979 0.8143 0.7723 0.6444 0.6721 0.7254 0.6545 0.5608 0.6095 0.4989](ii) a (4) To be provided with
Figure BDA0002999294900000191
The parameters lambda and mu are calibrated by genetic algorithm as target function1、σ1、μ2、σ2The parameter ranges are that lambda is more than or equal to 120 and less than or equal to 300 and 1 and less than or equal to mu1≤2、0.3≤σ1≤0.8、2≤μ2≤3、0.9≤σ2The population size is less than or equal to 1.4, the selection, crossing and mutation probabilities are respectively 0.9, 0.7 and 0.01, the evolution termination condition is that the evolution algebra is more than or equal to 20 and SS is not changed for 20 continuous generations, and the calibration result is as follows: λ 168, μ1＝1.5686、σ1＝0.5581、μ2＝2.307、σ21.0093 as shown in fig. 7-10. 2. Water meter metering error curve generation method
(1) Randomly selecting 20 water meters with the caliber of DN15 and the service life of 4 years, and numbering M in sequences(s-1, 2, …,20), determining the minimum flow rate Q of the water meter1＝0.031m3H, boundary flow Q2＝0.05m3H, common flow rate Q3＝2.5m3H and overload flow Q4＝3.125m3/h。
(2) The 13 test points of this type of meter are shown in table 3.
Table 2 distribution of 13 test point positions and flow meter for each water meter
Figure BDA0002999294900000201
Since the flow rates of the 4 th and 5 th test points are close, 0.5 (Q) is used1+Q2) Instead.
(3) Selecting volume method to test and record each water meter in each testIndicating volume V flowing through unit time under test points(p)And true volume VAs(p). In the research, each test point is tested twice repeatedly, and the indicating value volumes of the two tests are V respectivelys(p)1、Vs(p)2The real volume is VAs(p)1、VAs(p)2Calculating the mean value of the indicated volume and the mean value of the real volume of the two tests, and respectively recording as Vs(p)And VAs(p)。
(4) And (4) calculating the metering error of each water meter at each test point according to the formula (3), as shown in table 4.
Table 4 metering error meter for each water meter at each test point
Figure BDA0002999294900000202
Figure BDA0002999294900000211
Figure BDA0002999294900000221
(5) Calculating the average value of the metering error of each test point according to the formulas (4) and (5)
Figure BDA0002999294900000222
And standard deviation σpAs shown in table 5.
TABLE 5 mean value of the measurement error for each test point
Figure BDA0002999294900000223
And standard deviation σpWatch (A)
Figure BDA0002999294900000224
The resulting error curve is shown in fig. 11.
3. Calculating the metering error of the water meter
(1) Substituting the parameters determined by the 1 st subsection rate into the 1 st subsection step (2) and (3), generating a corresponding resident random water use mode flow matrix according to the caliber of the selected water meter, and generating a single-household resident random water use mode flow matrix I due to the fact that the water meter with the caliber of DN15 is selected1×86400As shown in fig. 12.
(2) According to the error mean value of each flow test point
Figure BDA0002999294900000225
And standard deviation σpAnd generating a water meter random metering error corresponding to the flow by utilizing normal distribution, as shown in table 6.
Random metering error meter for meter 6 water meter
Figure BDA0002999294900000226
A random metering error curve for the DN15-4 year water meter was created as shown in fig. 13.
(3) For each flow I in the water usage pattern matrixk(with a corresponding duration of Dk) Finding the flow I of the front and the rear test points on the random metering error curvep、Ip+1And a random metering error Erandom(p)、Erandom(p+1)Calculating the flow I according to the formula (6) by interpolationkCorresponding random metering error Erandom(k)：
Taking a certain primary water as an example, the flow rate of the primary water is 0.487m3H, duration of 18s, flow rate of 0.467m3H and 0.842m3And h, calculating the corresponding random metering error by interpolation as follows:
Figure BDA0002999294900000231
(4) for each water use (flow rate I)kDuration of Dk) The actual water consumption is calculated according to the formula (7).
Figure BDA0002999294900000232
(5) And (4) calculating the error of the water meter for measuring the water quantity per day according to a formula (8).
Figure BDA0002999294900000233
In summary, in practical applications, the method for determining the water meter water metering loss provided by the present invention specifically comprises the following steps:
(1) and (3) solving the average water consumption of the residents within a monitoring interval of 5min by monitoring the daily water consumption of the residents to obtain the cumulative frequency distribution of the daily water consumption.
(2) When building a simulation model of the random water consumption pattern of residents, counting after generating the water consumption times K per day (F)i-1,Fi]The number n of random numbers in the interval (i 1, 2.. 288) which are uniformly distributed according to (0,1)i(i 1, 2.., 288), as the number of random water uses over the period of time; generating ziN within a time intervaliRandom integers which are uniformly distributed according to the (300 x (i-1),300 x i) and are arranged in ascending order as the random water use generation time t of the residential userk(k＝1,2,...,K)。
(3) To be provided with
Figure BDA0002999294900000234
And (4) an objective function, and utilizing a genetic algorithm to calibrate the optimal parameters of the model when the objective function is minimum.
(4) Randomly selecting N (more than 20 general) water meters with the same model, the same diameter and the same service life, and determining the minimum flow Q of the water meters1Dividing flow rate Q2Flow rate Q in common use3And overload flow Q4And 13 test points are designed for each water meter.
(5) The method comprises the following steps of selecting a volume method to test and record an indication volume and a real volume of each water meter flowing through in unit time under each designed flow point, wherein each test point can test one to many times; calculating the average value of the metering error of N water meters at each test point
Figure BDA0002999294900000235
And standard deviation σpAnd drawing a metering error curve with an error line.
(6) According to the average value of the metering error of each test point
Figure BDA0002999294900000236
And standard deviation σpAnd generating a water meter random metering error corresponding to the flow by utilizing normal distribution, and generating a random metering error curve.
(7) The parameters determined by the utilization rate generate a corresponding resident random water consumption mode flow matrix according to the caliber of the selected water meter, and for each flow I in the water consumption mode matrixk(with a corresponding duration of Dk) Finding flow I by interpolation method on random metering error curvekCorresponding random metering error Erandom(k)。
The invention has the advantages that:
(1) the randomness of the water consumption mode and the metering error is reflected. The method calculates the weighted error of the water meter based on the user random water consumption mode and the water meter random metering error curve, and reflects the randomness and the difference of the error.
(2) The water usage pattern is more refined. The user water consumption mode simulation method provided by the invention can accurately describe the flow and the time of each time of random water consumption of the user, further calculate the metering error of each time of water consumption by combining the random metering error curve, and finally obtain the weighted error of the water meter in one day.
In practical applications, the time interval of water monitoring by the user is not necessarily limited to 5min in the present invention, and may be other time intervals, when other time intervals are adopted, the number of the monitoring periods and the subscripts of the statistics are adjusted to corresponding values, the "300" of "(300 × (i-1),300 × i)" is replaced by the number of seconds corresponding to other time intervals, and the "5 min" is replaced by the other time intervals adopted. For example, when the time interval is 15min, the daily monitoring time interval is 96, the maximum value of the subscript of each corresponding statistic is 96, random numbers uniformly distributed among (900 × (i-1),900 × i) are generated, and the flow rates are summed according to the 15min interval.
When the parameters are calibrated by using the genetic algorithm, the range of the parameters can be modified according to the investigation result of other documents, and the termination condition of the genetic algorithm evolution can be that the objective function does not change after the evolution is carried out for multiple generations, such as: and taking the target function smaller than a set value as a termination condition.
The number of the water meter test points and the test flow can be properly adjusted according to the characteristic flow, but a metering error curve is reproduced as completely as possible.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A method for determining water meter water metering loss is characterized by comprising the following steps:
continuously monitoring the water consumption of the residential users at set monitoring time intervals, and determining the cumulative frequency of the daily water consumption;
constructing a flow matrix of a multi-family resident random water consumption mode according to the accumulated frequency;
randomly selecting a plurality of water meters with the same model, the same caliber and the same service life, and determining water meter parameters; the water meter parameters comprise minimum flow, demarcation flow, common flow and overload flow;
determining a plurality of test points according to the water meter parameters, and calculating the metering error of each water meter under the test points;
calculating the average value and the standard deviation of the metering error of each test point according to the metering error, and drawing a random metering error curve;
for any flow in the multi-household resident random water consumption mode flow matrix, determining a random metering error corresponding to any flow according to the random metering error curve;
and determining the metering loss of the water meter for the whole day according to the random metering error.
2. The method for determining water meter water metering loss according to claim 1, wherein the steps of determining a plurality of test points according to the water meter parameters and calculating the metering error of each water meter under the plurality of test points comprise:
according to the formula
Figure FDA0002999294890000011
Calculating the metering error of each water meter under a plurality of test points; wherein E iss(p)The relative error of the s water meter under the test flow of the P test point, P is the total number of the test points, Vs(p)Indicating volume, VA, of the s water meter flowing through in unit time under the p test points(p)The true volume of the flow of the s water meter per unit time under the p test point is shown.
3. The method for determining water meter water metering loss according to claim 2, wherein the step of calculating the average value and the standard deviation of the metering error of each test point according to the metering error and drawing a random metering error curve specifically comprises the steps of:
using formulas
Figure FDA0002999294890000012
Calculating the average value of the metering error of each test point; wherein,
Figure FDA0002999294890000013
to measureError average value; n is the total number of the water meters;
using formulas
Figure FDA0002999294890000021
Calculating the standard deviation of the metering error of each test point; wherein σpIs the standard deviation of the measurement error;
and drawing a random metering error curve according to the metering error average value and the metering error standard deviation.
4. The method for determining water meter water consumption metering loss according to claim 3, wherein for any flow in the multi-family resident random water usage pattern flow matrix, determining a random metering error corresponding to any flow according to the random metering error curve specifically comprises:
for any flow in the multi-family resident random water usage pattern flow matrix, a formula is utilized
Figure FDA0002999294890000022
Determining a random metering error corresponding to any flow; wherein E israndom(k)For any one time flow IkA corresponding random metering error; i ispTo locate the flow I on the random metering error curvekThe flow of the previous test point; i isp+1To locate the flow I on the random metering error curvekThe flow of the latter test point; erandom(p)Is the flow rate IpA corresponding random metering error; erandom(p+1)Is the flow rate Ip+1Corresponding random metrology error.
5. The method for determining water meter water metering loss according to claim 4, wherein the determining the water meter water metering loss for the whole day according to the random metering error specifically comprises:
according to the random metering error, aiming at each time of water consumption, utilizing a formula
Figure FDA0002999294890000023
Calculating the actual water consumption; wherein Q iskFor the actual water consumption, DkIs the flow rate IkCorresponding water using time length; k is any water consumption number, and K is the water consumption times of the resident users per day;
by using
Figure FDA0002999294890000024
Determining the measurement loss of the water meter for the whole day; eGeneral assemblyThe water loss of the water meter is measured all day long.
6. A water meter loss of water metering system, comprising:
the cumulative frequency determining module is used for continuously monitoring the water consumption of the resident users at set monitoring time intervals and determining the cumulative frequency of the daily water consumption;
the resident random water consumption mode simulation model building module is used for building a multi-family resident random water consumption mode flow matrix according to the accumulated frequency;
the water meter parameter determining module is used for randomly selecting a plurality of water meters with the same model, the same caliber and the same service life and determining water meter parameters; the water meter parameters comprise minimum flow, demarcation flow, common flow and overload flow;
the metering error testing module is used for determining a plurality of testing points according to the water meter parameters and calculating the metering error of each water meter under the plurality of testing points;
the random metering error curve drawing module is used for calculating the metering error average value and the standard deviation of each test point according to the metering error and drawing a random metering error curve;
the random metering error determining module is used for determining a random metering error corresponding to any flow according to the random metering error curve for any flow in the multi-family resident random water consumption mode flow matrix;
and the measurement loss determining module is used for determining the measurement loss of the water meter for the whole day according to the random measurement error.
7. The water meter water metering loss determining system of claim 6, wherein the metering error testing module specifically comprises:
a measurement error test unit for testing the measurement error according to the formula
Figure FDA0002999294890000031
Calculating the metering error of each water meter under a plurality of test points; wherein E iss(p)The relative error of the s water meter under the test flow of the P test point, P is the total number of the test points, Vs(p)Indicating volume, VA, of the s water meter flowing through in unit time under the p test points(p)The true volume of the flow of the s water meter per unit time under the p test point is shown.
8. The water meter water metering loss determining system of claim 7, wherein the random metering error curve drawing module specifically comprises:
a calculation unit of average value of measurement error for using formula
Figure FDA0002999294890000032
Calculating the average value of the metering error of each test point; wherein,
Figure FDA0002999294890000033
the average value of the metering errors is obtained; n is the total number of the water meters;
a standard deviation calculation unit of measurement error for using the formula
Figure FDA0002999294890000034
Calculating the standard deviation of the metering error of each test point; wherein σpIs the standard deviation of the measurement error;
and the random metering error curve drawing unit is used for drawing a random metering error curve according to the metering error average value and the metering error standard deviation.
9. The water meter water metering loss determining system of claim 8, wherein the random metering error determining module specifically comprises:
a random metering error determining unit for utilizing a formula for any one flow in the multi-family resident random water usage pattern flow matrix
Figure FDA0002999294890000041
Determining a random metering error corresponding to any flow; wherein E israndom(k)For any one time flow IkA corresponding random metering error; i ispTo locate the flow I on the random metering error curvekThe flow of the previous test point; i isp+1To locate the flow I on the random metering error curvekThe flow of the latter test point; erandom(p)Is the flow rate IpA corresponding random metering error; erandom(p+1)Flow rate Ip+1Corresponding random metrology error.
10. The water meter water metering loss determining system of claim 9, wherein the metering loss determining module specifically comprises:
the actual water consumption calculating unit is used for utilizing a formula for each time of water consumption according to the random metering error
Figure FDA0002999294890000042
Calculating the actual water consumption; wherein Q iskFor the actual water consumption, DkIs the flow rate IkCorresponding water using time length; k is any water consumption number, and K is the water consumption times of the resident users per day;
a measurement loss determination unit for utilizing
Figure FDA0002999294890000043
Determining the measurement loss of the water meter for the whole day; eGeneral assemblyThe water loss of the water meter is measured all day long.
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Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104050513A (en) * 2014-04-15 2014-09-17 中国科学院遥感与数字地球研究所 Space sampling scheme optimizing method for crop planting area monitoring
CN105651361A (en) * 2016-03-10 2016-06-08 深圳市阿美特科技有限公司 Automatic pressure regulation, detection and calibration device and method for ultrasonic water meter
CN105753289A (en) * 2016-04-07 2016-07-13 中国石油化工股份有限公司 Oil field sludge multi-component extraction device and method for recycling water in dryer thereof
TW201625925A (en) * 2014-09-29 2016-07-16 濟奧美股份有限公司 Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing
CN107621621A (en) * 2017-09-05 2018-01-23 天津市电力科技发展有限公司 A kind of intelligent electric energy meter is in Line synthesis error calibration method
CN107655552A (en) * 2017-11-14 2018-02-02 南京林洋电力科技有限公司 A kind of Ultrasonic water meter flow velocity modification method based on the non-measured section of propagation time difference and drift error
CN107657083A (en) * 2017-09-05 2018-02-02 天津市电力科技发展有限公司 A kind of intellectual water meter is in Line synthesis error calibration method
CN107870312A (en) * 2017-09-05 2018-04-03 天津市电力科技发展有限公司 A kind of intelligent electric energy meter kinematic error remote calibration method
CN109444791A (en) * 2018-11-09 2019-03-08 华中科技大学 A kind of capacitance type potential transformer error state appraisal procedure and system
CN110244254A (en) * 2019-06-21 2019-09-17 上海市质子重离子医院有限公司 A kind of multistage electric energy meter estimation error and method for diagnosing faults based on ratio
CN111537938A (en) * 2020-03-31 2020-08-14 国网江西省电力有限公司电力科学研究院 Error short-time prediction method for electronic transformer based on intelligent algorithm

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104050513A (en) * 2014-04-15 2014-09-17 中国科学院遥感与数字地球研究所 Space sampling scheme optimizing method for crop planting area monitoring
TW201625925A (en) * 2014-09-29 2016-07-16 濟奧美股份有限公司 Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing
CN105651361A (en) * 2016-03-10 2016-06-08 深圳市阿美特科技有限公司 Automatic pressure regulation, detection and calibration device and method for ultrasonic water meter
CN105753289A (en) * 2016-04-07 2016-07-13 中国石油化工股份有限公司 Oil field sludge multi-component extraction device and method for recycling water in dryer thereof
CN107621621A (en) * 2017-09-05 2018-01-23 天津市电力科技发展有限公司 A kind of intelligent electric energy meter is in Line synthesis error calibration method
CN107657083A (en) * 2017-09-05 2018-02-02 天津市电力科技发展有限公司 A kind of intellectual water meter is in Line synthesis error calibration method
CN107870312A (en) * 2017-09-05 2018-04-03 天津市电力科技发展有限公司 A kind of intelligent electric energy meter kinematic error remote calibration method
CN107655552A (en) * 2017-11-14 2018-02-02 南京林洋电力科技有限公司 A kind of Ultrasonic water meter flow velocity modification method based on the non-measured section of propagation time difference and drift error
CN109444791A (en) * 2018-11-09 2019-03-08 华中科技大学 A kind of capacitance type potential transformer error state appraisal procedure and system
CN110244254A (en) * 2019-06-21 2019-09-17 上海市质子重离子医院有限公司 A kind of multistage electric energy meter estimation error and method for diagnosing faults based on ratio
CN111537938A (en) * 2020-03-31 2020-08-14 国网江西省电力有限公司电力科学研究院 Error short-time prediction method for electronic transformer based on intelligent algorithm

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
丁水龙;李乐;李帆;方炯;: "工商用单声道超声波燃气表计量特性测试", 城市燃气 *
兰丁滋校;朱建文;罗倩华;毛哲凯;汪东升;沈国伟;骆杉杉;信昆仑;: "供水管网水量整体计量误差分析方法研究", 给水排水 *
周颖;: "冷水水表示值误差测量结果的不确定度评定", 计量与测试技术 *
张佳欣 等: "DMA数据质量评估与错误数据识别方法" *
徐强 等: "智慧水务背景下的供水管网漏损控制研究进展" *
王辉;: "居民小口径水表计量误差案例研究", 黑龙江科技信息 *
董梅;: "TI超声波MCU为智能水表提供更高精度计量", 世界电子元器件 *
赵玲爽: "多点数据集随机生成方法", 水利水电技术 *

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