CN113094652B - Water meter water quantity metering loss determining method and system - Google Patents
Water meter water quantity metering loss determining method and system Download PDFInfo
- Publication number
- CN113094652B CN113094652B CN202110340303.8A CN202110340303A CN113094652B CN 113094652 B CN113094652 B CN 113094652B CN 202110340303 A CN202110340303 A CN 202110340303A CN 113094652 B CN113094652 B CN 113094652B
- Authority
- CN
- China
- Prior art keywords
- water
- random
- metering
- flow
- error
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/17—Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
Abstract
The invention relates to a method and a system for determining water metering loss of a water meter. The method comprises the following steps: continuously monitoring the water consumption of resident users at set monitoring time intervals to determine the accumulated frequency of the water consumption; constructing a resident random water use mode simulation model according to the accumulated frequency, and determining a multi-user resident random water use mode flow matrix; 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 the parameters of the water meters, and calculating the metering error of each water meter under the test points; calculating a metering error average value and a standard deviation of a test point according to the metering error, and drawing a random metering error curve; for any one flow in the multi-user resident random water use mode flow matrix, determining a random metering error corresponding to the any one flow according to a random metering error curve; and determining the metering loss of the water meter for all days according to the random metering error. The invention can reduce the water metering loss error of the water meter and improve the metering precision.
Description
Technical Field
The invention relates to the field of water meter water quantity monitoring, in particular to a method and a system for determining water meter water quantity metering loss.
Background
The pipe network metering loss water quantity is an important component in pipe network leakage, and in order to effectively control the pipe network metering loss water quantity, the proportion of the pipe network metering loss water quantity needs to be definitely metered, so that a targeted control scheme is formulated. While calculating the amount of water lost to metering, two sets of information are necessary: the random metering error curve of the water meter and the water consumption mode of the user 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, the random metering error curve of the water meter and the water consumption mode of the user can form a metering loss water quantity calculation method based on multi-parameter coupling of the metering performance, the running state and the water consumption mode of the user, a foundation is laid for analyzing the delivery rule of the metering error and effectively controlling the metering loss water quantity, and however, the random metering error curve of the water meter and the water consumption mode of the user have certain uncertainty.
The existing water meter water quantity metering loss determining method comprises the following steps:
(1) Determining a water demand mode:
firstly, classifying users according to simple standards, and generally classifying the users into 2-3 categories is enough to obtain better effects. The C-level flowmeter with accurate record is selected as test equipment for measuring water consumption of a user, the flowmeter is simultaneously provided with a pulse emitter and a data recorder, the single pulse volume is not higher than 0.1L, flow distortion is avoided, the data recorder is used for timely storing each pulse, and the test duration is not lower than one week. The final water usage pattern was expressed in terms of the fractional flow rates and the percentages of the fractional flow rates of the respective segments, as shown in table 1.
(2) Obtaining an error curve:
testing a meter at a large number of different flows is not possible and therefore it is necessary to reconstruct an error curve from the error data of several different flows, experience has shown that at small flows the measurement error is more important and that the error curve must be well defined at these flows. To reconstruct the error curve correctly within this range, the pick-up flow is known or approximated. It is further suggested 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 errors for medium and high flows are almost the same, testing at flow rates of 750L/h and 1500L/h is sufficient, it is considered that the error from this flow rate remains constant up to the maximum flow rate. The error of all other flows can be obtained by linear interpolation between the available points. The random metering error curve constructed by the method described above is shown in figure 1.
(3) Calculating the metering error of the water meter:
knowing the water usage pattern of the user and reconstructing the error curve of the water meter, the percentage of the actual water usage recorded by the water meters can be determined. The weighted errors of fig. 1 and user type i were calculated to obtain the numbers shown in table 1.
Total volume of water meter
＝4.7％*(100-100)％+2.3％*(100-100)％+0.5％*(100-68)％+1.9％*(100-52)％+4.3％*(100-11)％+8.5％*(100-0)％+75.7％*(100-0.8)％+1.9％*(100-0.8)％+0.2％*(100-0.8)％＝90.60％。
Water meter weighting error = 90.60% -100% = -9.40%.
Table 1 water meter weighted error analysis table
The disadvantages of the above solution are:
(1) The randomness of the water usage pattern and metering error is not considered. In the case that the user uses the water pattern and the water meter measurement error curve has randomness, the calculated water meter weighting error is random, and the result of the scheme possibly represents the error condition of most water meters, but the randomness and the difference of the errors are not reflected.
(2) The method for determining the water demand mode is too coarse, the flow is divided according to different intervals, the subjectivity is very strong, and the influence of water consumption on the water meter weighting error in each time is not fully considered; any flow interval after segmentation corresponds to only one error value, and thus the calculated metering volume is unreasonable.
Disclosure of Invention
The invention aims to provide a method and a system for determining water meter water metering loss, which are used for solving the problems that the traditional method for calculating the water meter water metering loss does not show randomness and difference of water meter metering errors, the flow is divided according to different intervals, the subjectivity is very strong, the influence of water consumption on the water meter weighing error each time is not fully considered, the water meter water metering loss error is large, and the metering precision is low.
In order to achieve the above object, the present invention provides the following solutions:
a method for determining water meter water metering loss, comprising:
continuously monitoring the water consumption of resident users at set monitoring time intervals to determine the accumulated frequency of the water consumption;
constructing a multi-user resident random water use mode flow matrix 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 plurality of test points;
calculating a metering error average value and a standard deviation of each test point according to the metering error, and drawing a random metering error curve;
for any one flow in the multi-user resident random water use mode flow matrix, determining a random metering error corresponding to any one flow according to the random metering error curve;
and determining the metering loss of the water meter for all days 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
Calculating the metering error of each water meter under a plurality of test points; wherein E is s(p) For the relative error of the s-th water meter under the test flow of the P-th test point, P is the total number of test points, V s(p) For the volume of the indication value of the flow of the s-th water meter in unit time under the p-th test point, VA s(p) The real volume of the s-th water meter flowing in unit time under the p-th test point.
Optionally, calculating a measurement error average value and a standard deviation of each test point according to the measurement error, and drawing a random measurement error curve, including:
using the formula
Calculating the average value of the metering errors of each test point; wherein (1)>
Is the average value of the metering errors; n is the total number of the water meters;
using the formula
Calculating the standard deviation of the measurement error of each test point; wherein sigma p Is 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, for any one of the flows in the random water usage pattern flow matrix of the multi-user resident, determining a random metering error corresponding to the any one of the flows according to the random metering error curve specifically includes:
for any one flow in the multi-user resident random water use mode flow matrix, a formula is utilized
Determining random metering errors corresponding to any flow; wherein E is random(k) For any flow I k Corresponding random metering errors; i p To be located at the flow I on the random metering error curve k The flow of the previous test point; i p+1 To be located at the flow I on the random metering error curve k The flow of the latter test point; e (E) random(p) For flow I p Corresponding random metering errors; e (E) random(p+1) For flow I p+1 Corresponding random metering errors.
Optionally, the determining the metering loss of the water meter for all days according to the random metering error specifically includes:
according to the random metering error, for each water use, a formula is utilized
Calculating the actual water consumption; wherein Q is k For the actual water consumption, D k For flow I k Corresponding water use time length; k is any water number, and K is the number of times of water consumption of a resident user per day;
by means of
Determining the metering loss of water used by the water meter all day; e (E) Total (S) The water meter is used for measuring the water consumption all day.
A water meter water metering loss determination system comprising:
the accumulated frequency determining module is used for continuously monitoring the water consumption of the resident user at set monitoring time intervals to determine the accumulated frequency of the water consumption;
the resident random water use mode simulation model construction module is used for constructing a multi-user resident random water use 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 to determine 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 test points according to the water meter parameters and calculating the metering error of each water meter under the plurality of test points;
the random measurement error curve drawing module is used for calculating the measurement error average value and standard deviation of each test point according to the measurement error and drawing a random measurement error curve;
the random metering error determining module is used for determining random metering errors corresponding to any one flow according to the random metering error curve for any one flow in the multi-user resident random water use mode flow matrix;
and the metering loss determining module is used for determining the metering loss of the water meter for all days according to the random metering error.
Optionally, the metering error testing module specifically includes:
a metering error testing unit for testing the metering error according to the formula
Calculating the metering error of each water meter under a plurality of test points; wherein E is s(p) For the relative error of the s-th water meter under the test flow of the p-th test pointP is the total number of test points, V s(p) For the volume of the indication value of the flow of the s-th water meter in unit time under the p-th test point, VA s(p) The real volume of the s-th water meter flowing in unit time under the p-th test point.
Optionally, the random measurement error curve drawing module specifically includes:
a metering error average calculating unit for using the formula
Calculating the average value of the metering errors of each test point; wherein (1)>
Is the average value of the metering errors; n is the total number of the water meters;
a standard deviation calculation unit for calculating standard deviation of measurement error by using formula
Calculating the standard deviation of the measurement error of each test point; wherein sigma p Is the standard deviation of the measurement error;
and the random measurement error curve drawing unit is used for drawing a random measurement error curve according to the measurement error average value and the measurement error standard deviation.
Optionally, the random measurement error determining module specifically includes:
a random metering error determining unit, configured to utilize a formula for any one of the flows in the random water usage pattern flow matrix for the residents of multiple households
Determining random metering errors corresponding to any flow; wherein E is random(k) For any flow I k Corresponding random metering errors; i p To be located at the flow I on the random metering error curve k The flow of the previous test point; i p+1 To be located at the flow I on the random metering error curve k The flow of the latter test point; e (E) random(p) For flow I p Corresponding random metering errors; e (E) random(p+1) For flow I p+1 Corresponding random metering errors.
Optionally, the metering loss determining module specifically includes:
the actual water consumption calculating unit is used for utilizing a formula for each water consumption according to the random metering error
Calculating the actual water consumption; wherein Q is k For the actual water consumption, D k For flow I k Corresponding water use time length; k is any water number, and K is the number of times of water consumption of a resident user per day;
a metering loss determination unit for utilizing
Determining the metering loss of water used by the water meter all day; e (E) Total (S) The water meter is used for measuring the water consumption all day.
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 metering loss of a water meter, which are used for constructing a resident random water consumption mode simulation model, determining random water consumption flow of each time based on the constructed resident random water consumption mode simulation model, drawing a random metering error curve, combining the random metering error curve according to the random water consumption flow to obtain metering error of each time of water consumption, and further calculating the metering loss of one day of water consumption of a water meter.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are 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 other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
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 the water metering loss of a water meter;
FIG. 3 is a flow chart of a method for determining the metering loss of the water amount of the water meter in actual application;
FIG. 4 is a diagram of a water meter water metering loss determination system according to the present invention;
FIG. 5 is a diagram of a 1-family resident random water consumption pattern provided by the invention;
FIG. 6 is a diagram of a random water consumption pattern for 1000 residents provided by the invention;
FIG. 7 is a graph of the relation between the genetic algebra and the objective function provided by the present invention;
FIG. 8 is a graph showing the relationship between time and water consumption 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 measured 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 the rated parameter simulation provided by the invention;
fig. 13 is a graph of random error for a DN15-4 year meter provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for determining water meter water metering loss, which can reduce the water meter water metering loss error and improve the metering precision.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 2 is a flow chart of a method for determining water metering loss of a water meter, as shown in fig. 2, the method for determining water metering loss of the water meter comprises the following steps:
step 201: and continuously monitoring the water consumption of the resident user at set monitoring time intervals to determine the accumulated frequency of the water consumption. The set monitoring time interval may be 5min, 7min, 10min, or other time intervals.
Step 202: and constructing a multi-user resident random water use mode flow matrix 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 comprise 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 plurality of test points.
The step 204 specifically includes: according to the formula
Calculating the metering error of each water meter under a plurality of test points; wherein E is s(p) For the relative error of the s-th water meter under the test flow of the P-th test point, P is the total number of test points, V s(p) For the volume of the indication value of the flow of the s-th water meter in unit time under the p-th test point, VA s(p) The real volume of the s-th water meter flowing in unit time under the p-th test point.
Step 205: calculating the average value and 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 the formula
Calculating the average value of the metering errors of each test point; wherein (1)>
Is the average value of the metering errors; n is the total number of the water meters; using the formula->
Calculating the standard deviation of the measurement error of each test point; wherein sigma p Is 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 one flow in the multi-user resident random water use mode flow matrix, determining a random metering error corresponding to any one flow according to the random metering error curve.
The 206 specifically includes: for any one flow in the multi-user resident random water use mode flow matrix, a formula is utilized
Determining random metering errors corresponding to any flow; wherein E is random(k) For any flow I k Corresponding random metering errors; i p To be located at the flow I on the random metering error curve k The flow of the previous test point; i p+1 To be located at the flow I on the random metering error curve k The flow of the latter test point; e (E) random(p) For flow I p Corresponding random metering errors; e (E) random(p+1) For flow I p+1 Corresponding random metering errors.
Step 207: and determining the metering loss of the water meter for all days according to the random metering error.
The step 207 specifically includes: according to the random metering error, for each water use, a formula is utilized
Calculating the actual water consumption; wherein Q is k For the actual water consumption, D k For flow I k Corresponding water use time length; k is any one of water braidingThe number K is the number of times of water consumption of a resident user every day; by->
Determining the metering loss of water used by the water meter all day; e (E) Total (S) The water meter is used for measuring the water consumption all day.
Based on the method for determining the water metering loss of the water meter, which is provided by the invention, the steps are further described:
the technical scheme of the invention is as follows: selecting a certain number of resident users to monitor and calculate average water consumption in every 5min, constructing a random water consumption mode simulation model, determining five parameters in the model by utilizing a genetic algorithm rate, and further simulating the daily water consumption of the users; measuring the metering error of the water meters with the same model, the same caliber and the same service life, and drawing a metering error curve; FIG. 3 is a flowchart of a method for determining the metering loss of the water in the actual application provided by the invention.
The specific implementation steps are as follows:
1. random water 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 a plurality of days, preferably 5 minutes at monitoring time intervals, wherein the number of monitoring time periods in one day is 288, and recording and calculating the time period z i (i=1, 2,., 288) user-average water usage q of all users within (i=1, 2,) i (i=1, 2,.,. 288), and then calculate z i Frequency f of water consumption in time period i And cumulative frequency F of daily water consumption i The formula is as follows:
(2) And constructing a random water use mode simulation model. Modeling parameters according to poisson distributionGenerating K random numbers which are consistent with (0, 1) uniform distribution by using the number K of times of water consumption of resident users with lambda every day, and counting (F i-1 ,F i ](i=1, 2,., 288.) number of random numbers n in interval i (i=1, 2,.,. 288), as a random number of water usage times during the period, generating z in turn i N in time period i The number corresponds to (300× (i-1), 300×i](5 min is 300 seconds) random integers which are uniformly distributed and are arranged in ascending order as random water use occurrence time t of resident users k (k=1, 2,) K, K) generating K parameters μ from the lognormal distribution 1 、σ 1 (μ 1 、σ 1 Is a parameter of lognormal distribution and respectively represents flow pulse I k Mean and standard deviation) flow pulse I k (k=1, 2,., K) K parameters are μ 2 、σ 2 (μ 2 、σ 2 Is a parameter of lognormal distribution and respectively represents the water use time length D k Mean and standard deviation) water use period D k (k=1, 2,., K) the whole day was decomposed into 86400 moments in seconds, D being sequentially taken out k Individual flow I k Fill to t k At the initial moment, a random water use mode flow matrix I of the residents of the single household is obtained 1×86400 ；
(3) Repeating the step (2) for m times to construct a plurality of individual resident random water use mode flow matrixes I m×86400 Summing m simulation results at the same moment to obtain a multi-user resident random water use mode flow matrix A 1×86400 At intervals of 5min, for A 1×86400 The flow summation in each period is q Ai (i=1, 2,.,. 288) to obtain a matrix Q of m-family resident random water use pattern water quantity A ＝[q A1 ,q A2 ,...,q A288 ]；
(4) The parameters lambda, mu are defined by genetic algorithm 1 、σ 1 、μ 2 、σ 2 Is to find the optimum value of the target function
The recommended value range of each parameter is 120-300 lambda and 1-mu 1 ≤2、0.3≤σ 1 ≤0.8、2≤μ 2 ≤3、0.9≤σ 2 The number of evolution algebra reaches a certain number (such as 20) and the fitness value is continuous for multiple generations (such as 20) without change, the evolution is stopped, and the optimal parameters are output;
2. water meter measurement error curve generation method
(1) Randomly selecting N (generally more than 20) water meters with the same model, the same caliber and the same service life, and the serial numbers are M in sequence s (s=1, 2, …, N), determining the minimum flow Q of the water meter of this type 1 Demarcation flow Q 2 Flow rate Q in common use 3 And overload flow rate Q 4 。
(2) Each meter was designed with P test points, in the present invention, 13 test point positions are taken as an example, as shown in table 2.
Table 2 13 test point location distribution table per meter design
Wherein Q is a The initial flow of the water meter is obtained by testing in an experiment.
(3) The volume method is selected to test and record the indication volume V of each water meter flowing in unit time under each test point s(p) (the volume of the indicator value flowing through the s-th water meter in unit time under the p-th test point) and the actual volume VA s(p) (actual volume of flow per unit time for the s-th meter under the p-th test point), the relative change in flow during each test should not exceed: qa-Q 2 (not including Q 2 )：±2.5％；Q 2 (including Q 2 ) To Q 4 : 5%; each test point can be tested for one or more times, and if the test is performed for multiple times, the indication volume and the true volume are respectively averaged for multiple tests.
(4) Calculating the metering error of each water meter at each test point according to the formula (3):
wherein: e (E) s(p) -the relative error of the s-th water meter under the test flow of the p-th test point is expressed as a percentage;
v-test period t d The volume added or subtracted in the internal indicating means is in cubic meters (m 3 )；
VA-test period t d The reference volume of the internal flow is given in cubic meters (m 3 )。
(5) Calculating the average value of the metering errors of each test point
And standard deviation sigma of p The formula is shown as (4) (5), and the metering error data is used for drawing a metering error curve.
3. Calculating the metering error of a water meter
(1) The parameters of the utilization ratio are determined, and a corresponding resident random water use mode flow matrix is generated according to the caliber of the selected water meter, for example, if the water meter with the caliber DN15 is selected through testing, a single resident random water use mode flow matrix I is generated 1×86400 。
(2) The average values generated according to the normal distribution are respectively
And standard deviation is sigma respectively p (p=1, 2,., P).
(3) For each flow I in the water pattern matrix k (its pair ofThe duration of the response is D k ) Finding the flow I of the front test point and the rear test point on a random metering error curve p 、I p+1 And random metering error E random(p) 、E random(p+1) Calculating the flow I according to the formula (6) by utilizing an interpolation method k Corresponding random measurement error E random(k) ：
(4) For each water usage (flow is I k Duration of D k ) Calculating the actual water consumption according to the formula (7):
(5) Calculating the error of the daily metering water quantity of the water meter:
fig. 4 is a block diagram of a water meter water metering loss determining system according to the present invention, as shown in fig. 4, a water meter water metering loss determining system includes:
the cumulative frequency determining module 401 is configured to continuously monitor the water consumption of the residential user at a set monitoring time interval, and determine the cumulative frequency of the water consumption.
The resident random water usage pattern simulation model construction module 402 is configured to construct a multi-resident random water usage pattern flow matrix according to the cumulative frequency.
The water meter parameter determining module 403 is configured to randomly select a plurality of water meters with the same model, the same caliber and the same service life, and determine water meter parameters; the water meter parameters comprise minimum flow, demarcation flow, common flow and overload flow.
And the metering error testing module 404 is used for 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.
The metering error testing module 404 specifically includes: a metering error testing unit for testing the metering error according to the formula
Calculating the metering error of each water meter under a plurality of test points; wherein E is s(p) For the relative error of the s-th water meter under the test flow of the p-th test point, V s(p) The volume of the indicating value of the s-th water meter flowing in unit time under the P-th test point is that P is the total number of the test points, VA s(p) The real volume of the s-th water meter flowing in unit time under the p-th test point.
And the random measurement error curve drawing module 405 is configured to calculate a measurement error average value and a standard deviation of each test point according to the measurement error, and draw a random measurement error curve.
The random measurement error curve drawing module 405 specifically includes: a metering error average calculating unit for using the formula
Calculating the average value of the metering errors of each test point; wherein (1)>
Is the average value of the metering errors; n is the total number of the water meters; a standard deviation calculation unit for calculating standard deviation of measurement error by using formula
Calculating the standard deviation of the measurement error of each test point; wherein sigma p Is the standard deviation of the measurement error; and the random measurement error curve drawing unit is used for drawing a random measurement error curve according to the measurement error average value and the measurement error standard deviation.
The random measurement error determining module 406 is configured to determine, for any one of the flows in the random water usage pattern flow matrix of the multi-user resident, a random measurement error corresponding to the any one of the flows according to the random measurement error curve.
The random measurement error determining module 406 specifically includes: a random metering error determining unit, configured to utilize a formula for any one of the flows in the random water usage pattern flow matrix for the residents of multiple households
Determining random metering errors corresponding to any flow; wherein E is random(k) For any flow I k Corresponding random metering errors; i p To be located at the flow I on the random metering error curve k The flow of the previous test point; i p+1 To be located at the flow I on the random metering error curve k The flow of the latter test point; e (E) random(p) For flow I p Corresponding random metering errors; e (E) random(p+1) For flow I p+1 Corresponding random metering errors.
The metering loss determining module 407 is configured to determine the metering loss of the water meter for all days according to the random metering error.
The metering loss determining module 407 specifically includes: the actual water consumption calculating unit is used for utilizing a formula for each water consumption according to the random metering error
Calculating the actual water consumption; wherein Q is k For the actual water consumption, D k For flow I k Corresponding water use time length; k is any water number, and K is the number of times of water consumption of a resident user per day; a metering loss determination unit for utilizing +.>
Determining the metering loss of water used by the water meter all day; e (E) Total (S) The water meter is used for measuring the water consumption all day.
Actual case:
1. random water pattern generation method
(1) 776 resident users are selected in a certain district in the north, the water consumption of each user is monitored for 22 consecutive days, the monitoring time interval is 5min, the whole day is divided into 288 time periods, and the user average water consumption q of each time period is calculated:
q＝[0.5427 0.42280.43870.39340.35230.38930.37210.3527
0.32050.29190.263 0.28820.30130.27530.22750.21520.2213
0.18340.18790.16030.15940.16480.14790.14220.14590.1863
0.21350.148 0.15310.13310.12830.13740.172 0.15380.1385
0.11470.13530.12190.12190.15010.15150.14940.10780.1201
0.12290.12260.12850.13970.22050.299 0.20840.25440.2292
0.222 0.20330.21110.19580.20340.19410.18250.185 0.2191
0.22860.26130.25880.27550.27830.30280.37660.4 0.4823
0.46460.46310.52320.59620.61280.68390.64010.70760.744
0.81080.84630.87610.90780.95621.03781.13681.11321.1969
1.19361.25811.24541.27251.35351.386 1.37731.45691.5368
1.45821.33581.49761.45861.35861.50941.40571.38161.3925
1.38311.29181.26011.24261.37961.19241.202 1.28411.2075
1.15371.16131.21341.14671.23781.25091.13271.14751.2296
1.16311.12021.10241.14561.14341.15961.12741.21821.2239
1.22961.21261.18611.23641.28721.317 1.31091.23251.2507
1.30491.36971.45731.396 1.28481.40371.34061.273 1.2765
1.21281.22751.31311.20071.18341.18561.23531.10711.0847
0.96131.019 0.99431.04551.04660.938 0.96820.88830.868
0.87350.82250.90210.805 0.85970.81630.83780.84020.9232
0.86210.88480.95290.903 0.87590.85440.91160.86660.8523
0.82480.84440.94310.95410.99320.97751.03410.98340.9562
1.01361.09411.06571.05391.05321.07511.11381.11211.1791
1.21061.16591.21391.18171.29251.32381.28391.29141.3027
1.387 1.33431.32581.33451.38271.42071.41591.30671.3814
1.34351.40261.331 1.37571.32861.37221.31 1.28371.368
1.31591.30541.32661.32531.28741.19941.25351.28241.2296
1.14451.16661.23841.38811.35641.24061.27561.197 1.1767
1.21721.18351.27341.33491.44551.42581.414 1.30951.3455
1.29881.23751.22461.28831.22291.24931.19331.18151.1158
1.14251.15981.06441.04840.99630.97810.96430.90630.9083
0.94050.82570.75170.75880.73310.65850.65080.56 0.5558
0.522]；
using the formula
Calculating the frequency of the water consumption in each period, and further calculating the cumulative frequency F:
F＝[0.0021 0.00370.00540.00690.00820.00970.01110.0125
0.01370.01480.01580.01690.018 0.01910.02 0.02080.0216
0.02230.023 0.02370.02430.02490.02550.026 0.02660.0273
0.02810.02860.02920.02970.03020.03080.03140.032 0.0325
0.033 0.03350.03390.03440.035 0.03560.03610.03650.037
0.03750.03790.03840.039 0.03980.04090.04170.04270.0436
0.04440.04520.046 0.04680.04750.04830.049 0.04970.0505
0.05140.05240.05340.05440.05550.05660.05810.05960.0614
0.06320.065 0.067 0.06930.07160.07420.07660.07940.0822
0.08530.08850.09190.09530.0990.10290.10730.11150.1161
0.12060.12540.13020.1350.14020.14550.15080.15630.1622
0.16770.17280.17860.18410.18930.19510.20040.20570.211
0.21630.22120.2260.23080.2360.24060.24520.25010.2547
0.25910.26350.26820.27250.27730.2820.28630.29070.2954
0.29990.30410.30830.31270.31710.32150.32580.33050.3351
0.33980.34440.3490.35370.35860.36360.36860.37330.3781
0.38310.38830.39390.39920.40410.40950.41460.41940.4243
0.42890.43360.43860.44320.44770.45220.4570.46120.4653
0.4690.47290.47670.48070.48470.48820.49190.49530.4986
0.5020.50510.50850.51160.51490.5180.52120.52440.5279
0.53120.53460.53820.54170.5450.54830.55180.55510.5583
0.56150.56470.56830.57190.57570.57950.58340.58720.5908
0.59470.59890.60290.60690.611 0.61510.61930.62360.6281
0.63270.63710.64180.64630.65120.65630.66120.66610.6711
0.67630.68140.68650.69160.69690.70230.70770.71270.7179
0.72310.72840.73350.73880.74380.74910.75410.7590.7642
0.76920.77420.77920.78430.78920.79380.79860.80350.8082
0.81250.8170.82170.8270.83220.83690.84180.84640.8508
0.85550.86 0.86490.87 0.87550.88090.88630.89130.8964
0.90140.90610.91080.91570.92040.92510.92970.93420.9385
0.94280.94730.95130.95530.95910.96280.96650.97 0.9735
0.9770.98020.98310.9860.98880.99130.99370.99590.998 1]；
(2) And constructing a random water use mode simulation model. Simulating the number of times of water use per day of resident users with the parameter of lambda=165 according to poisson distribution (the value of lambda is automatically generated by the genetic algorithm, 165 is taken as an example here), and the result is 141 times; generating 141 random numbers which are consistent with (0, 1) uniform distribution, and counting (F i-1 ,F i ]Number of random numbers between (i=1, 2,., 288) as random number of water usage n per period:
n＝[10 0 1 1 0 0 1 1 0 0 0 0 0 0 0 1 00 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]；
generating n in each time period in turn i The random integers which are in accordance with (300 x (i-1), 300 x i) and are uniformly distributed are arranged in ascending order, and the random water consumption occurrence time t of the residential users 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
693176941069984708847131171710717527187972512
726567347173651740917429674367749987579276011
762917747778375790877913179563805248132281673
817018181083165836528526485825]；
generating 141 parameters mu from lognormal distribution 1 ＝1.52、σ 1 ＝0.49(μ 1 、σ 1 Is automatically generated by a genetic algorithm, here by way of example only) flow pulse I (L/min):
I＝[6.70713.81172.35673.72712.67754.44123.21942.5582
3.2373 16.4748 3.29045.52164.30637.051 7.47011.3814
8.07817.70944.81743.655 3.38182.89623.20425.01747.6112
9.45383.428 3.69249.52444.30023.003712.5657 8.6235
2.89144.36693.29244.7484.47341.86694.46013.3742.7108
6.074 5.45317.88154.98886.62962.73673.83717.38414.5253
2.71227.77422.20665.289 16.2037 5.28833.34876.146
3.51023.57473.69483.38586.51464.64174.86622.08313.0707
3.54617.189811.5529 3.30318.204 5.09723.89235.7828
4.58193.87635.15093.10864.34844.78396.52654.07352.7684
4.01269.69876.23783.14185.726910.2544 9.35512.2239
4.64472.76298.62027.99033.35514.44397.25934.18359.1055
7.40375.446 7.169 5.92062.10444.85338.32118.56443.4511
4.15094.05733.57354.03594.86167.03863.56887.03712.8686
12.8526 4.84558.18093.82784.16337.73298.11673.7773
2.71423.63914.36992.68662.615 1.53924.60313 8.56285.543
2.89425.255 5.8279]；
generating 141 parameters mu from lognormal distribution 2 ＝2.31、σ 2 ＝1.08(μ 2 、σ 2 The value of (a) is automatically generated by a genetic algorithm, here by way of example only) an integer water use duration D(s):
D＝[14 247 323 601415768 51541 3 131733
38159 141 19155 2012187 22202 9 6 107 3 8 6 9 9 9 4 1 5 10 7 2 280 6 6 65 3 2 9 5 212419134 11 11 10 10 2 43 5 44 5 5 15 2 8 16 105 11 186 76 8 27 5 23 25 14 4 9 6 11 8 15 6 3 6 16 3 461 11 12 77 26 15 10 28 2 8 4 9 4 4 5 3 443 14 12 13 6 5 132 712 1 35 46 9 3 123 5 32 5 14 10 9 40 31 8]；
the whole day is decomposed into 86400 moments in units of seconds, and 141 flow rates I are sequentially carried out k Fill to t k At the moment of start, the duration of each flow is D k Obtaining a random water use mode flow matrix I of single-family residents 1×86400 As shown in fig. 5.
(3) Repeating the step (2) 1000 times to construct 1000 individual resident random water use mode flow matrixes I 1000×86400 Summing up 1000 simulation results at the same moment to obtain a multi-user resident random water use mode flow matrix A 1×86400 As shown in fig. 6.
At intervals of 5min, for A 1×86400 The flow of the water is summed to obtain a water matrix q of a 1000-family resident random water use mode A (m 3 )：
q A ＝[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]；
(4) To be used for
The parameters lambda, mu are rated for the objective function by utilizing a genetic algorithm 1 、σ 1 、μ 2 、σ 2 The parameter range is 120-300 and 1-mu 1 ≤2、0.3≤σ 1 ≤0.8、2≤μ 2 ≤3、0.9≤σ 2 The population size is less than or equal to 1.4, the selection, crossover 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 continuous for 20 generations without change, and the rated result is: λ=168, μ 1 ＝1.5686、σ 1 ＝0.5581、μ 2 ＝2.307、σ 2 =1.0093, as shown in fig. 7-10.
2. Water meter measurement error curve generation method
(1) Randomly selecting 20 water meters with apertures DN15 and service life of 4 years, and numbering M in sequence s (s=1, 2, …, 20), determining the minimum flow Q of the water meter of this type 1 ＝0.031m 3 /h, demarcation flow Q 2 ＝0.05m 3 /h, the usual flow rate Q 3 ＝2.5m 3 /h and overload flow Q 4 ＝3.125m 3 /h。
(2) 13 test points of the water meter are shown in table 3.
Table 3 13 test point position distribution and flow meter per meter design
Since the flow rates of the 4 th test point and the 5 th test point are close, the flow rate of the test point is 0.5 (Q 1 +Q 2 ) Instead of.
(3) The volume method is selected to test and record the indication volume V of each water meter flowing in unit time under each test point s(p) And true volume VA s(p) . In the study, each test point is repeatedly tested twice, and the indication volumes of the two tests are V respectively s(p)1 、V s(p)2 The true volumes are respectively VA s(p)1 、VA s(p)2 Calculating the indicating value volume average value and the real volume average value of the two tests, respectively recorded as V s(p) And VA (VA) s(p) 。
(4) The measurement error of each meter at each test point was calculated according to equation (3) as shown in table 4.
Table 4 metering error table of each water meter at each test point
(5) Calculated according to the formula (4) (5)Average value of measurement error of each test point
And standard deviation sigma p As shown in table 5.
TABLE 5 average value of measurement errors for each test point
And standard deviation sigma p Watch (watch)
The resulting metering error curve is shown in FIG. 11.
3. Calculating the metering error of a water meter
(1) utilizing the parameters determined by the 1 st section rate, carrying the steps (2) and (3) of the section 1, generating a corresponding random water use mode flow matrix of residents according to the caliber of the selected water meter, and generating a random water use mode flow matrix I of individual residents due to the water meter with the caliber of DN15 1×86400 As shown in fig. 12.
(2) According to the error mean value of each flow test point
And standard deviation sigma p The random metering error of the water meter corresponding to the flow is generated by using the normal distribution, as shown in table 6. />
Table 6 random metering error meter for water meter
A random metering error curve for DN15-4 year meters was established as shown in fig. 13.
(3) For use ofEach flow I in the water pattern matrix k (corresponding to the duration of D k ) Finding the flow I of the front test point and the rear test point on a random metering error curve p 、I p+1 And random metering error E random(p) 、E random(p+1) Calculating the flow I according to the formula (6) by utilizing an interpolation method k Corresponding random measurement error E random(k) ：
Taking a certain water use as an example, the flow rate of the water use is 0.487m 3 And/h, duration of 18s, flow rate of 0.467m 3 /h and 0.842m 3 And (3) interpolating to calculate the corresponding random metering error as follows:
(4) For each water usage (flow is I k Duration of D k ) The actual water usage is calculated according to equation (7).
(5) And (3) calculating the error of the daily metering water quantity of the water meter according to the formula (8).
In summary, in practical application, the method for determining the water metering loss of the water meter provided by the invention comprises the following specific steps:
(1) And (5) solving the average household water consumption in a 5-minute monitoring interval through resident household water consumption monitoring to obtain the cumulative frequency distribution of the household water consumption.
(2) When a model for simulating a random water usage pattern of residents is constructed, the number of times of daily water usage K is generated, and then statistics (F i-1 ,F i ](i=1, 2,.,. 288.) number n of random numbers in the interval corresponding to (0, 1) uniform distribution i (i=1, 2,.,. 288) as the number of random water usage times in the period; generating z i Time period ofInner n i The random integers are distributed uniformly according with (300 x (i-1), 300 x i) and are arranged in ascending order as random water use generation time t of resident users k (k＝1,2,...,K)。
(3) To be used for
And (3) the objective function, and determining the optimal parameters of the model when the objective function is minimum by utilizing a genetic algorithm.
(4) Randomly selecting N (generally more than 20) water meters with the same model, the same caliber and the same service life, and determining the minimum flow Q of the water meters 1 Demarcation flow Q 2 Flow rate Q in common use 3 And overload flow rate Q 4 13 test points are designed for each water meter.
(5) The method is characterized in that a volume method is selected to test and record the indicating value volume and the true volume of each water meter flowing in unit time under each designed flow point, and each test point can be tested one to a plurality of times; calculating the average value of the metering errors of N water meters at each test point
And standard deviation sigma p And drawing a metering error curve with an error line.
(6) Based on the average value of the measurement errors of each test point
And standard deviation sigma p And generating a random metering error of the water meter corresponding to the flow by using normal distribution, and generating a random metering error curve. />
(7) The parameters of the utilization ratio are determined, a corresponding resident random water use mode flow matrix is generated according to the caliber of the selected water meter, and each flow I in the water use mode matrix k (corresponding to the duration of D k ) The flow I is found by interpolation method on a random metering error curve k Corresponding random measurement error E random(k) 。
The invention has the advantages that:
(1) The randomness of the water consumption mode and the metering error is reflected. The invention calculates the weighting error of the water meter based on the random water consumption mode of the user and the random metering error curve of the water meter, and shows the randomness and the difference of the errors.
(2) The water use pattern is finer. The user water consumption mode simulation method provided by the invention can accurately describe the flow and the time length of each random water consumption of the user, further calculate the metering error of each water consumption by combining a random metering error curve, and finally obtain the weighting error of the water meter in one day.
In practical applications, the time interval of user water consumption monitoring is not necessarily limited to 5min, but may be other time intervals, when other time intervals are adopted, the number of monitoring periods and the subscripts of each statistic are required to be adjusted to be corresponding values, and "300" of "(300× (i-1), 300×i)" is replaced by seconds corresponding to other time intervals, and "5min" is replaced by other adopted time intervals. For example, when the time interval is 15min, 96 monitoring periods are obtained every day, the subscript maximum value of each corresponding statistic is 96, random numbers uniformly distributed among (900× (i-1), 900×i) are generated, and the flow is summed at 15min intervals.
When the genetic algorithm is utilized to rate the parameters, the range of the parameters can be modified according to the research results of other documents, and the termination condition of the genetic algorithm evolution can be not that the objective function is not changed after the evolution for a plurality of generations, for example: and taking the target function smaller than a certain 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 the metering error curve should be reproduced as completely as possible.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (2)
1. A method for determining water meter water metering loss, comprising:
continuously monitoring the water consumption of resident users at set monitoring time intervals to determine the accumulated frequency of the water consumption;
constructing a multi-user resident random water use mode flow matrix 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 plurality of test points, wherein the method specifically comprises the following steps:
according to the formula
Calculating the metering error of each water meter under a plurality of test points; wherein E is s(p) For the relative error of the s-th water meter under the test flow of the P-th test point, P is the total number of test points, V s(p) For the volume of the indication value of the flow of the s-th water meter in unit time under the p-th test point, VA s(p) The real volume of the s-th water meter flowing through in unit time under the p-th test point is set;
calculating a metering error average value and a standard deviation of each test point according to the metering error, and drawing a random metering error curve, wherein the method specifically comprises the following steps of:
using the formula
Calculating the average value of the metering errors of each test point; wherein the method comprises the steps of，
Is the average value of the metering errors; n is the total number of the water meters;
using the formula
Calculating the standard deviation of the measurement error of each test point; wherein sigma p Is the standard deviation of the measurement error;
drawing a random metering error curve according to the metering error average value and the metering error standard deviation;
for any one flow in the multi-user resident random water use mode flow matrix, determining a random metering error corresponding to any one flow according to the random metering error curve, and specifically comprising the following steps:
for any one flow in the multi-user resident random water use mode flow matrix, a formula is utilized
Determining random metering errors corresponding to any flow; wherein Erandom (k) is any one flow I k Corresponding random metering errors; i p To be located at the flow I on the random metering error curve k The flow of the previous test point; i p+1 To be located at the flow I on the random metering error curve k The flow of the latter test point; e (E) random(p) For flow I p Corresponding random metering errors; e (E) random(p+1) For flow I p+1 Corresponding random metering errors;
determining the metering loss of the water meter for all days according to the random metering error, and specifically comprising the following steps:
according to the random metering error, for each water use, a formula is utilized
(k=1, 2,., K) calculating the actual water usage; wherein Q is k For the actual water consumption, D k The water consumption time length corresponding to the flow Ik; k is any water number, and K is the number of times of water consumption of a resident user per day;
by means of
Determining the metering loss of water used by the water meter all day; e (E) Total (S) The water meter is used for measuring the water consumption all day.
2. A water meter water metering loss determination system, comprising:
the accumulated frequency determining module is used for continuously monitoring the water consumption of the resident user at set monitoring time intervals to determine the accumulated frequency of the water consumption;
the resident random water use mode simulation model construction module is used for constructing a multi-user resident random water use 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 to determine 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 test points according to the water meter parameters and calculating the metering error of each water meter under the plurality of test points; the metering error testing module specifically comprises:
a metering error testing unit for testing the metering error according to the formula
Calculating the metering error of each water meter under a plurality of test points; wherein E is s(p) For the relative error of the s-th water meter under the test flow of the P-th test point, P is the total number of test points, V s(p) For the volume of the indication value of the flow of the s-th water meter in unit time under the p-th test point, VA s(p) The real volume of the s-th water meter flowing through in unit time under the p-th test point is set;
the random measurement error curve drawing module is used for calculating the measurement error average value and standard deviation of each test point according to the measurement error and drawing a random measurement error curve; the random metering error curve drawing module specifically comprises:
a metering error average calculating unit for using the formula
Calculating the average value of the metering errors of each test point; wherein (1)>
Is the average value of the metering errors; n is the total number of the water meters;
a standard deviation calculation unit for calculating standard deviation of measurement error by using formula
Calculating the standard deviation of the measurement error of each test point; wherein sigma p Is the standard deviation of the measurement error;
the random measurement error curve drawing unit is used for drawing a random measurement error curve according to the measurement error average value and the measurement error standard deviation;
the random metering error determining module is used for determining random metering errors corresponding to any one flow according to the random metering error curve for any one flow in the multi-user resident random water use mode flow matrix; the random metering error determining module specifically comprises:
a random metering error determining unit, configured to utilize a formula for any one of the flows in the random water usage pattern flow matrix for the residents of multiple households
Determining random metering errors corresponding to any flow; wherein Erandom (k) is any one flow I k Corresponding random metering errors; i p To be located at the flow I on the random metering error curve k The flow of the previous test point; i p+1 To be located at the flow I on the random metering error curve k The flow of the latter test point; e (E) random(p) For flow I p Corresponding random metering errors; e (E) random(p+1) For flow I p+1 Corresponding random metering errors;
the metering loss determining module is used for determining the metering loss of the water meter for all days according to the random metering error; the metering loss determining module specifically comprises:
the actual water consumption calculating unit is used for utilizing a formula for each water consumption according to the random metering error
(k=1, 2,., K) calculating the actual water usage; wherein Q is k For the actual water consumption, D k The water consumption time length corresponding to the flow Ik; k is any water number, and K is the number of times of water consumption of a resident user per day;
a metering loss determination unit for utilizing
Determining the metering loss of water used by the water meter all day; e (E) Total (S) The water meter is used for measuring the water consumption all day. />
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110340303.8A CN113094652B (en) | 2021-03-30 | 2021-03-30 | Water meter water quantity metering loss determining method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110340303.8A CN113094652B (en) | 2021-03-30 | 2021-03-30 | Water meter water quantity metering loss determining method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113094652A CN113094652A (en) | 2021-07-09 |
| CN113094652B true CN113094652B (en) | 2023-04-25 |
Family
ID=76671204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110340303.8A Active CN113094652B (en) | 2021-03-30 | 2021-03-30 | Water meter water quantity metering loss determining method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113094652B (en) |
Citations (11)
| 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 |
| 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 |
| 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 |
| 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 |
-
2021
- 2021-03-30 CN CN202110340303.8A patent/CN113094652B/en active Active
Patent Citations (11)
| 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)
| Title |
|---|
| TI超声波MCU为智能水表提供更高精度计量;董梅;;世界电子元器件(第10期);20-22 * |
| 供水管网水量整体计量误差分析方法研究;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;;给水排水(第07期);125-131 * |
| 冷水水表示值误差测量结果的不确定度评定;周颖;;计量与测试技术(第07期);86-87 * |
| 多点数据集随机生成方法;赵玲爽;水利水电技术(第10期);2-6 * |
| 居民小口径水表计量误差案例研究;王辉;;黑龙江科技信息(第16期);73 * |
| 工商用单声道超声波燃气表计量特性测试;丁水龙;李乐;李帆;方炯;;城市燃气(第12期);13-18 * |
| 张佳欣 等.DMA数据质量评估与错误数据识别方法.《给水排水》.2020,第第56卷卷(第第03期期),134-138. * |
| 徐强 等.智慧水务背景下的供水管网漏损控制研究进展.《环境科学学报》.2020,第第40卷卷(第第12期期),4234-4239. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113094652A (en) | 2021-07-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Arregui et al. | Reducing apparent losses caused by meters inaccuracies | |
| CN106598918B (en) | Nonuniformity methods for calculating designed flood based on quantile estimate | |
| CN112130109A (en) | Method for detecting metering performance abnormity of intelligent electric energy meter | |
| CN115854999B (en) | H-ADCP section average flow velocity self-correction method based on scene self-adaption | |
| CN112001576A (en) | Accounting method for electric power consumption of renewable energy source | |
| CN110503471A (en) | A kind of user's maximum demand estimation method | |
| CN115640881A (en) | Reservoir water level correction method of reservoir warehousing runoff forecasting model based on LSTM | |
| CN113094652B (en) | Water meter water quantity metering loss determining method and system | |
| CN113626959B (en) | Sewage pipe network hydraulic model building method based on three-dimensional geographic information | |
| CN116050595A (en) | Attention mechanism and decomposition mechanism coupled runoff amount prediction method | |
| CN103900666B (en) | The measuring accuracy control method of diaphragm gas meter longtime running | |
| CN114819322A (en) | Method for forecasting lake inflow flow of lake | |
| CN112487667B (en) | Plate corrosion degradation life prediction method based on internal and external field test information fusion | |
| CN114357737A (en) | Agent optimization calibration method for time-varying parameters of large-scale hydrological model | |
| CN113094984B (en) | Resident random water consumption mode simulation method and system based on genetic algorithm | |
| Ferreol | How to measure and reduce water meter park inefficiency | |
| Ma et al. | Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator approach | |
| CN115186960A (en) | Accounting method and device for effective collection and treatment capacity of urban sewage | |
| CN109327026B (en) | Low-voltage distribution network interval state estimation method | |
| Carpenter | A methodology for assessing the utility of distributed model forecast applications in an operational environment | |
| CN111047352A (en) | Progressive electric power spot market operation efficiency measuring method and system | |
| CN117350180B (en) | Method for constructing flood rapid prediction model set under complex working condition | |
| CN116668455B (en) | Multi-block-chain-oriented platform node data management method and system | |
| CN113094876B (en) | Reservoir flood control risk calculation method and system based on ARIMA model under future situation | |
| CN113781276B (en) | Sewage pipe network flow uncertainty analysis method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |