CN117214461A - Suspension wing type flow velocity meter calibration method based on fusion algorithm - Google Patents
Suspension wing type flow velocity meter calibration method based on fusion algorithm Download PDFInfo
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Abstract
The invention discloses a suspension-wing type flow rate meter calibration method based on a fusion algorithm, which comprises the steps of firstly, performing a median method anti-shake filtering on angle S data actually measured by a suspension-wing type flow rate meter; then, the relation between the flow velocity V data measured by the standard flow velocity meter is analyzed and error calculation is carried out; finally, performing data fitting by adopting a nonlinear least square method to obtain a calibration model corresponding to the S-V curve relation expression between the data; and adjusting the flow velocity of the test point and correcting errors through a calibration model corresponding to the S-V curve relation expression. According to the invention, a calibration model is designed by taking a median method and a nonlinear least square method as fusion algorithms, so that the calibration with higher precision and smaller error on the errors of test point data is realized.
Description
Technical Field
The invention belongs to the technical field of water flow measurement, and particularly relates to a suspension wing type flow meter calibration method based on a fusion algorithm.
Background
With the continuous updating of river flowmeters, higher requirements are also put on the calibration and correction of the measurement data of the river flowmeters. The most common handheld flow meter generally calculates the flow rate based on the number of revolutions of the rotor driven by the water flow. However, in the actual use process, the handheld flow rate meter is not suitable for long-time work due to the complex environment in river water, so that the suspended wing type flow rate meter appears, and in the actual measurement, certain parameter calibration is usually carried out on the measured parameters, namely, a plurality of datum reference points are introduced in the measurement use process.
In order to calibrate the measurement parameters, the traditional calibration method is to perform segmentation data processing on the measured data points (including angle values) and the actual flow rate, solve the slope and offset constants of each adjacent measurement parameter point, analyze and screen according to the slope values of the adjacent measurement parameters, and select a more appropriate reference data point. The traditional calibration method is based on a measuring parameter change rate model, so that the method is only suitable for the working condition that the fluctuation of measured data points is not very large, the angle value and the flow velocity value basically meet the linear relation, the working condition that the fluctuation of the measured data points is very large can not be approximated to the linear relation, the error obtained by calculating the error of the reference point selected after the processing of the method is very large, and the method can not realize automatic correction of the larger error, so that the error correction process becomes complex and complicated, and the method is difficult to directly popularize and apply.
Disclosure of Invention
The invention aims to: in order to overcome the defects in the prior art, the invention provides a suspension wing type flow velocity meter calibration method based on a fusion algorithm, which uses a median method and a nonlinear least square method as a fusion algorithm to design a calibration model, so as to realize the calibration of higher precision and smaller error of test point data.
The technical scheme is as follows: in order to achieve the above purpose, the invention provides a suspension wing type flowmeter calibration method based on a fusion algorithm, wherein the fusion algorithm is composed of a median method and a nonlinear least square method, and the specific steps are as follows:
step S1: firstly, performing a median method jitter elimination filtering on the angle S data actually measured by the suspended wing type flow velocity meter;
step S2: then, the relation between the flow velocity V data measured by the standard flow velocity meter is analyzed and error calculation is carried out;
step S3: finally, performing data fitting by adopting a nonlinear least square method to obtain a calibration model corresponding to the S-V curve relation expression between the data;
step S4: and adjusting the flow velocity of the test point and correcting errors through a calibration model corresponding to the S-V curve relation expression.
Further, the data source based on the flow velocity meter is composed of a plurality of groups of standard data point sets actually measured by the standard flow velocity meter, therefore, all standard data point sets measured by the suspended wing type flow velocity meter are respectively composed of a flow velocity V and an angle S, and a linear relation is satisfied between each adjacent standard data point set:
V real world ＝K i ×S Angle of +C
Wherein: k represents a linear relationship coefficient, K is according to S Angle of Determining the numerical range of (2); k (K) i Representing the slopes of two adjacent standard data point sets; c represents the offset of the overall linear relation formed by a plurality of groups of standard data point sets and is an offset constant; v (V) Real world Representing a flow velocity value actually measured according to the flow velocity meter; s is S Angle of Indicating the rotor angle.
Further, K i The expression of (2) is as follows:
wherein: (S) i ，V i ) Representing sets of standard data points measured by the flow meter, i=1, 2,3, … …, n.
Further, for any group of test point sets, the angle values S in a period of time are sequenced and then median values are removed, then a nonlinear least square method is adopted for fitting, and a final curve function M-degree polynomial is set:
wherein: x is a univariate input, ω 0 ，ω 1 ，…，ω M Is M+1 parameters;
the least squares method using square as the loss function includes:
for omega in the formula j Solving the bias guide and making the bias guide be 0:
the method can obtain:
fitting polynomial coefficientsSolving a system of linear equations:
and (3) calculating:
substituting the calculation result into a linear equation set for solving, and obtaining the S-V fitting relation.
The beneficial effects are that: according to the invention, a calibration model is designed by taking a median method and a nonlinear least square method as fusion algorithms, so that the calibration with higher precision and smaller error on the errors of test point data is realized; in addition, the method of the invention not only can automatically correct errors, but also can automatically output the datum point fitting curve, meets the error requirement of the test point data set, and has good application value.
Drawings
FIG. 1 is a schematic diagram of a standard data point set curve fitted by a linear fitting method and a least square fitting method;
FIG. 2 is a schematic diagram comparing respective curves of a linear fit and a least squares fit to a flow line.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
A suspension wing type flowmeter calibration method based on a fusion algorithm comprises a median method and a nonlinear least square method, and the method comprises the following specific steps:
step S1: firstly, performing a median method jitter elimination filtering on the angle S data actually measured by the suspended wing type flow velocity meter;
step S2: then, the relation between the flow velocity V data measured by the standard flow velocity meter is analyzed and error calculation is carried out;
step S3: finally, performing data fitting by adopting a nonlinear least square method to obtain a calibration model corresponding to the S-V curve relation expression between the data;
step S4: and adjusting the flow velocity of the test point and correcting errors through a calibration model corresponding to the S-V curve relation expression.
The data source based on the flow velocity meter is composed of a plurality of groups of standard data point sets actually measured by the standard flow velocity meter, therefore, all standard data point sets measured by the suspended wing type flow velocity meter are respectively composed of a flow velocity V and an angle S, and the linear relation is satisfied between every two adjacent standard data point sets:
V real world ＝K i ×S Angle of +C
Wherein: k represents linearityRelation coefficient, K is according to S Angle of Determining the numerical range of (2); k (K) i Representing the slopes of two adjacent standard data point sets; c represents the offset of the overall linear relation formed by a plurality of groups of standard data point sets and is an offset constant; v (V) Real world Representing a flow velocity value actually measured according to the flow velocity meter; s is S Angle of Indicating the rotor angle.
K i The expression of (2) is as follows:
wherein: (S) i ，V i ) Representing sets of standard data points measured by the flow meter, i=1, 2,3, … …, n.
Because the relation between the flow velocity V and the angle S is nonlinear, and the values of V and S are discrete point sets, for any group of test point sets, the angle values S in a period of time are sequenced and then the median value is removed, then a nonlinear least square method is adopted for fitting, and a final curve function M-degree polynomial is set:
wherein: x is a univariate input, ω 0 ，ω 1 ，…，ω M Is M+1 parameters;
the least squares method using square as the loss function includes:
for omega in the formula j Solving the bias guide and making the bias guide be 0:
the method can obtain:
fitting polynomial coefficientsSolving a system of linear equations:
and (3) calculating:
substituting the calculation result into a linear equation set for solving, and obtaining the S-V fitting relation.
According to the invention, a calibration model is designed by taking a median method and a nonlinear least square method as fusion algorithms, so that the calibration with higher precision and smaller error on the errors of test point data is realized; in addition, the method of the invention not only can automatically correct errors, but also can automatically output the datum point fitting curve, meets the error requirement of the test point data set, and has good application value.
The equation for the least squares linear fit is: y=a×x+b. The principle of the nonlinear fitting of the least square method is as follows: in the nonlinear least squares fitting, the actual meaning is to find a function y=f (x) that is closest to all data points under a certain criterion, and to apply a certain basis function form, such as: y=a0+a1/x, where y is the coefficient a= (a) 0 ，a 1 ，a 2 ...a n-1 ，a n ) T For the above-mentioned basis function model, the nonlinear function of (a)Substituting the model, the model can become +>Calculate +.f. from the transform of the raw data (xi, yi) (i=1, 2,3, …, n)>(i=1, 2,3, …, n), a0 and a1 can be obtained by linear fitting, thereby obtaining a nonlinear fitting relation of y=a0+a1/x.
In order to verify the feasibility based on the median method and the least square method, the invention utilizes standard flow velocity equipment and collects the angle data of the suspended wing type flow velocity meter, and the method comprises the following steps:
and carrying out linear processing on the data point set to obtain a standard data point set S-V fitting relation formula: y=0.01485363x+0.06462, as shown in curve B of fig. 1, it can be found that at an actual flow rate of 0.698m/s, the calculated flow rate is 0.8m/s, and the error is 14.6% at the maximum.
And performing least square fitting on the data point set to obtain a standard data point set S-V fitting relation formula: y=6.9×10 -8 x 4 +4.452×10 -7 x 3 -5.02646×10 -4 x 2 +2.868×10 -2 x+0.0272, substituting the angle into the relation to obtain a C curve as shown in FIG. 1, the C curve is more consistent with the actual flow rate than the linear processing, and when the maximum error point is the flow rate of 0.6m/s, the actual flow rate is 0.572m/s, and the error is 4.7%.
In the practical application process, 681 angle data collected by the suspended wing type flow rate meter are processed by a median method, and then flow rate calculation is performed by using a relation formula fitted by a least square method, as shown in fig. 2: by comparison, the flow velocity mutation and the amplitude are obviously reduced after the median method is combined with the least square nonlinear fitting flow velocity.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (4)
1. A suspension wing type flow velocity meter calibration method based on a fusion algorithm is characterized in that: the fusion algorithm consists of a median method and a nonlinear least square method, and comprises the following specific steps:
step S1: firstly, performing a median method jitter elimination filtering on the angle S data actually measured by the suspended wing type flow velocity meter;
step S2: then, the relation between the flow velocity V data measured by the standard flow velocity meter is analyzed and error calculation is carried out;
step S3: finally, performing data fitting by adopting a nonlinear least square method to obtain a calibration model corresponding to the S-V curve relation expression between the data;
step S4: and adjusting the flow velocity of the test point and correcting errors through a calibration model corresponding to the S-V curve relation expression.
2. The fusion algorithm-based suspension wing type flow meter calibration method as set forth in claim 1, wherein the method comprises the following steps: the data source based on the flow velocity meter is composed of a plurality of groups of standard data point sets actually measured by the standard flow velocity meter, therefore, all standard data point sets measured by the suspended wing type flow velocity meter are respectively composed of a flow velocity V and an angle S, and the linear relation is satisfied between every two adjacent standard data point sets:
V real world ＝K i ×S Angle of +C
Wherein: k represents a linear relationship coefficient, K is according to S Angle of Determining the numerical range of (2); k (K) i Representing the slopes of two adjacent standard data point sets; c represents the offset of the overall linear relation formed by a plurality of groups of standard data point sets and is an offset constant; v (V) Real world Representing a flow velocity value actually measured according to the flow velocity meter; s is S Angle of Indicating the rotor angle.
3. The fusion algorithm-based suspension wing type flow meter calibration method as set forth in claim 2, wherein the method comprises the following steps: k (K) i The expression of (2) is as follows:
wherein: (S) i ，V i ) Represents a plurality of sets of standard data points measured by the flow meter, i=1, 2, 3.
4. A method for calibrating a suspended wing type flow rate meter based on a fusion algorithm according to claim 3, wherein the method comprises the following steps: for any group of test point sets, angle values S in a period of time are sequenced and then median values are removed, then a nonlinear least square method is adopted for fitting, and a final curve function M-degree polynomial is set:
wherein: x is a univariate input, ω 0 ，ω 1 ，...，ω M Is M+1 parameters;
the least squares method using square as the loss function includes:
for omega in the formula j Solving the bias guide and making the bias guide be 0:
the method can obtain:
fitting polynomial coefficientsSolving a system of linear equations:
and (3) calculating:
substituting the calculation result into a linear equation set for solving, and obtaining the S-V fitting relation.
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