CN108255786A - The interference compensation computational methods and system of a kind of weighing results - Google Patents
The interference compensation computational methods and system of a kind of weighing results Download PDFInfo
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- CN108255786A CN108255786A CN201711211062.7A CN201711211062A CN108255786A CN 108255786 A CN108255786 A CN 108255786A CN 201711211062 A CN201711211062 A CN 201711211062A CN 108255786 A CN108255786 A CN 108255786A
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- weighing
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- 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
- G06F17/15—Correlation function computation including computation of convolution operations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G23/00—Auxiliary devices for weighing apparatus
Abstract
The invention discloses the interference compensation computational methods and system of a kind of weighing results, and by establishing models coupling multiple sensors data, precision caused by solving the problems, such as single-sensor measurement data is insufficient；The interference of weighing sensor is handled particular for the differential pressure that air generates, the accuracy of weighing results in production process can be improved.
Description
Technical field
The present invention relates to signal detection and process field, the interference compensation computational methods of particularly a kind of weighing results and
System.
Background technology
In technical field of automatic control, in order to ensure to reach the requirement of industry, accurate measure is to ensure control essence
The premise of degree.During modern industrial production especially automated production, production is monitored and controls with various sensors
Parameters in the process make equipment be operated in normal condition or optimum state, and product are made to reach best quality.Sensing
Important foundation of the device as modern information technologies is obtained from the main path and means of the information in right field, from tested pair
The a certain attribute of elephant obtains input signal, and is converted into the other signals that can be readily detected simultaneously according to certain rule
Output, generally electric signal.So in a sense, sensor is also a kind of control system, has control system
Structure and property.
In industry measurement and control system, due to the complexity of working environment so that the measurement essence of the sensor in system operation
Degree is affected, not always accurate so as to cause the measurement data of sensor.With the development of information technology, continuous
Seek the material of high-quality, during designing better anti-jamming circuit, start constantly to consider to establish mould in a manner of mathematics
Type, by the data-optimized calculating of multiple sensors so as to obtain more accurate measured value, from a variety of or multiple sensings
The information and data of device carry out integrated treatment, obtain more accurately and reliably measuring technique, can in information processing so as to reduce
The error that can occur.Obvious this method is at low cost, and with more extensive adaptability.
Weighing sensor is widely used in various production processes, in the application scenario for requiring high-precision weighing result,
Due to the influence of environmental factor, such as wind pressure, vibration, it can so that the signal that weighing sensor exports is done by environmental factor
It disturbs and influences measurement accuracy.Exploitation removes the technology of such interference, is the important channel for improving accuracy of weighing.
All it is mostly non-linear relation between the different physical quantitys that different sensors obtains in nature, so building
Vertical majorized function need to choose during solving it is a kind of it is reliable, convergence rate is very fast and it is given non-thread to strictly observe
The optimization algorithm of property restrictive condition.Sequential quadratic programming (SQP) method is that a kind of highly effective solution nonlinear constrained optimization is asked
The algorithm of topic, and with global convergence.
Invention content
The technical problems to be solved by the invention are, in view of the shortcomings of the prior art, the interference for providing a kind of weighing results is mended
Computational methods and system are repaid, precision caused by solving the problems, such as single-sensor measurement data is insufficient.
In order to solve the above technical problems, the technical solution adopted in the present invention is：A kind of interference compensation meter of weighing results
Calculation method, includes the following steps：
1) little differential pressure sensor is installed by weighing sensor, two input ports of little differential pressure sensor connect respectively
Hard modeling pipe, one of port are placed on weighing sensor platform position, another port is placed on normal atmosphere
In the closed environment of pressure；
2) determine the sampling period, measured article enters after weighing platform, weighing sensor and little differential pressure sensor simultaneously with
Fixed cycle starts gathered data, records and preserves weighing data and differential pressure data；
3) weighing data that step 2) obtains and differential pressure data are established into state-space model, passes through Kalman filtering
Algorithm and sequential quadratic programming method solve the optimal problem of nonlinear function of Problem with Some Constrained Conditions, symmetrical with differential pressure data
Weight result compensates and corrects, to obtain the actual weight of testee.
In step 3), the state-space model expression formula is as follows：
Wherein,
tn=tn-1+ξn,ξn~N (0, τ2)；N be sampled data total number, ynIt is n-th of number of weighing
According to tnIt is n-th of filtered weighing results；PnIt is to describe data of the differential pressure signal to measurement result influencing characterisitic n-th,
pn-iIt is the n-th-i differential pressure data, aiFor pn-iRegression coefficient, m is the order of setting model；unRepresent that n-th group measures number
According to the random fluctuation signal included；α1≥0、α2>=0 is proportionality coefficient, and meet inequality condition α1+α2<1, it represents respectively
(n-1)th Singular variance item and (n-1)th squared proportion, α in n-th of Singular variance item0＞ 0 is constant；wnIt is
Random signal obeys standardized normal distribution；It is Singular variance；ξnIt is random error, obedience desired value is 0, standard deviation τ2's
Normal distribution；~represent to obey certain regularity of distribution.
The nonlinear function of Problem with Some Constrained Conditions is solved by following Kalman filtering and sequential quadratic programming method
Optimization problem, to obtain the actual weight of state-space model parameter, variable initial value and filtered testee：
s.t.α0＞ 0, α1≥0,α2≥0,α1+α2<1
Wherein, Xn|n-1It is status predication value, Xn|nIt is state filtering value, Vn|n-1It is status predication error covariance, Vn|nIt is
State estimation error covariance, τ are constant.γnIt is prediction error, n values are from 1 to N, ψnIt is prediction error variance,It is different
Variance, KnIt is Kalman filtering gain.
Correspondingly, the present invention also provides a kind of interference compensation computing system of weighing results, including：
Little differential pressure sensor, two input ports of the little differential pressure sensor connect hard modeling pipe, one of end respectively
Mouth is placed on weighing sensor platform position, another port is placed in the closed environment with standard atmospheric pressure；
Weighing sensor for starting gathered data simultaneously with the fixed cycle with the little differential pressure sensor, is recorded and is protected
Deposit weighing data and differential pressure data；
Processing unit for the weighing data of acquisition and differential pressure data to be established state-space model, passes through Kalman
Filtering algorithm and sequential quadratic programming method solve the optimal problem of nonlinear function of Problem with Some Constrained Conditions, with differential pressure value pair
Weighing results compensate and correct, to obtain the actual weight of testee.
The processing unit is embedded microprocessor or industrial personal computer.
Compared with prior art, the advantageous effect of present invention is that：The present invention is by establishing a variety of biographies of models coupling
Sensor data, precision caused by solving the problems, such as single-sensor measurement data is insufficient；Particular for empty around sensor
The differential pressure that gas generates handles the interference of weighing sensor, can significantly improve the accuracy of measurement of weighing sensor.
Description of the drawings
Fig. 1 is the front and rear result of weighing-up wave filtering；Wherein, (a) is the weighing-up wave before filtering；(b) it is weighing-up wave
Filtered result.Realization process of the present invention includes the following steps：
1) weighing sensor and little differential pressure sensor have identical sampling time ts, while testee is recorded to scale
Have N=T/t on platform in T time altogethersA data form row vector y, and little differential pressure sensor data form vector p；
2) optimal solution of the above-mentioned optimization problem of matlab program solutions is worked out, first sets the solving condition of the optimization problem,
Including model order m, three proportionality coefficient α0、α1And α2.The max calculation number MaxFunEvals of majorized function is set, most
Big iterations MaxIter, the end condition TolX of Optimal Parameters, the end condition TolFun of majorized function；
3) the mean value t of weighing data is calculated0And as quantity of state tnInitial value, set the initial values of Optimal ParametersWhereinFor m+1 dimensional vectors.
4) inequality constraints condition is establishedWherein b0, lb, ub be with X have same dimension row
Vector, A0Square formation for m+5 ranks.The value of the first dimension of lb, ub is chosen according to actual conditions.Following order is performed in matlab
Enable rowThen bound lb (2), ub (2) are reset；Lb (3), ub (3)；lb
(4), ub (4)；It is performed simultaneously matlab orders A0=zeros (length (X0),length(X0)) complete to square formation A0Just
Beginningization, then A is set0(4,3), A0(4,4)；b0(4)；
5) majorized function Iter_bottle is established, (m+2) * N-dimensional matrix xm and xs is established, is to its preceding m row initializationN-m row below need to be iterated operation continuous renewal, so being first all initialized as 0.Perform matlab instructions γ
=zeros (1, N)；N-dimensional coefficient row vector γ is initialized as 0 vector, while establishes N-dimensional row vector σ2, by the institute in the vector
There is the variance that element is initialized as weighing data.
6) N-m calculating is recycled.It is asked by the Kalman filtering algorithm and sequential quadratic programming method established before
The optimal problem of nonlinear function of Problem with Some Constrained Conditions is solved, data is imported, the SQP Optimized Iteratives function in matlab is called to calculate
Obtain result.
Specific embodiment
Weighing sensor and little differential pressure sensor have identical sampling time ts, while testee is recorded to weighing platform
Have N=T/t in upper T time altogethersA data form row vector y, and little differential pressure sensor data form vector p；
The optimal solution of the above-mentioned optimization problem of matlab program solutions is worked out, first sets the solving condition of the optimization problem, packet
Include model order m, three proportionality coefficient α0、α1And α2.Matlab instruction optimset setting parameters are performed, set function
Max calculation number MaxFunEvals is 300, and maximum iteration MaxIter is 100, the termination difference item of Optimal Parameters
Part TolX is 1e-175, the termination difference condition TolFun of majorized function is 1e-75。
Calculate the mean value t of weighing data0And as quantity of state tnInitial value, set the initial values of Optimal ParametersIts
InFor m+1 dimensional vectors.
Establish inequality constraints conditionWherein b0, lb, ub be with X have same dimension row to
Amount, A0Square formation for m+5 ranks.The value of the first dimension of lb, ub is chosen according to actual conditions, if for example, the weight of testee exists
Near 8g, lb (1)=6.5, ub (1)=9.5 can be set.Following order line is performed in matlabThen bound is reset, enables lb (2)=0.00001, ub (2)=0.2；lb
(3)=0, ub (3)=0.94；Lb (4)=0, ub (4)=0.9999；It is performed simultaneously matlab orders A0=zeros (length
(X0),length(X0)) complete to square formation A0Initialization, set A0(4,3)=1, A0(4,4)=1；b0(4)=0.9999；
Iteration function Iter_bottle is established, (m+2) * N-dimensional matrix xm and xs is established, is to preceding m row initializations
N-m row below need to be iterated operation continuous renewal, so being first all initialized as 0.Execution matlab instructions γ=
zeros(1,N)；N-dimensional coefficient row vector γ is initialized as 0 vector, while establishes N dimension row vectors σ2, will be all in the vector
Element is initialized as the variance of weighing data.
N-m calculating of cycle.It is solved by the Kalman filtering algorithm and sequential quadratic programming method established before
The optimal problem of nonlinear function of Problem with Some Constrained Conditions：
s.t.α0＞ 0, α1≥0,α2≥0,α1+α2<1
Import the SQP Optimized Iterative functions in data, calling matlab
[X]=fmincon (' Iter_bottle', X0, A0, b0, [], [], lb, ub, [], options, y, p, m)；
To result.Since data volume is larger, partial test data are only listed as reference, as shown in table 1.
1 test data of table
| 8.744 | 8.75 | 8.737 | 8.684 | 8.668 | 8.656 |
| 8.694 | 8.732 | 8.751 | 8.734 | 8.723 | 8.713 |
| 8.723 | 8.662 | 8.63 | 8.726 | 8.746 | 8.764 |
| 8.748 | 8.782 | 8.811 | 8.688 | 8.661 | … |
It is 8.7124 to finally obtain optimization result of calculation.
Claims (5)
1. the interference compensation computational methods of a kind of weighing results, which is characterized in that include the following steps：
1) little differential pressure sensor is installed by weighing sensor, two input ports of little differential pressure sensor connect hard modeling respectively
Pipe, one of port are placed on weighing sensor platform position, another port is placed on the close of standard atmospheric pressure
In closed loop border；
2) sampling period is determined, measured article enters after weighing platform, and weighing sensor and little differential pressure sensor are simultaneously with fixation
Period starts gathered data, records and preserves weighing data and differential pressure data；
3) state-space model is established for the weighing data that step 2) obtains and differential pressure data, is calculated by Kalman filtering
Method and sequential quadratic programming method solve the optimal problem of nonlinear function of Problem with Some Constrained Conditions, with differential pressure data to weighing
As a result it compensates and corrects, to obtain the actual weight of testee.
2. the interference compensation computational methods of weighing results according to claim 1, which is characterized in that described in step 3)
State-space model expression formula is as follows：
Xn=[tn a0 … am]T
Cn=[1 pn … pn-m]
G=[1 0 ... 0]T
Wherein
tn=tn-1+ξn,ξn~N (0, τ2)；N be sampled data total number, ynIt is n-th of weighing data, tnAfter being n-th of filtering
Weighing results；PnIt is to describe data of the differential pressure signal to measurement result influencing characterisitic, p n-thn-iIt is the n-th-i differential pressures
Data, aiFor pn-iRegression coefficient, m is the order of setting model；unRepresent the random fluctuation letter that n-th group measurement data is included
Number；α1≥0、α2>=0 is proportionality coefficient, and meet inequality condition α1+α2<1, represent respectively (n-1)th Singular variance item and n-th-
1 squared proportion, α in n-th of Singular variance item0＞ 0 is constant；wnIt is random signal, obeys standard normal point
Cloth；It is Singular variance；ξnIt is random error, obedience desired value is 0, standard deviation τ2Normal distribution；~represent to obey certain
The regularity of distribution.
3. the interference compensation computational methods of weighing results according to claim 1, which is characterized in that utilize claim 2
The state-space model solves belt restraining item by following Kalman filtering algorithm and sequential quadratic programming method
The optimal problem of nonlinear function of part, to obtain state-space model parameter, variable initial value and filtered measured object
The actual weight of body：
s.t.α0＞ 0, α1≥0,α2≥0,α1+α2<1
Wherein, Xn|n-1It is status predication value, Xn|nIt is state filtering value, Vn|n-1It is status predication error covariance, Vn|nIt is state
Evaluated error covariance, τ are constant.γnIt is prediction error, n values are from 1 to N, ψnIt is prediction error variance,It is Singular variance,
KnIt is Kalman filtering gain.
4. a kind of interference compensation computing system of weighing results, which is characterized in that including：
Little differential pressure sensor, two input ports of the little differential pressure sensor connect hard modeling pipe respectively, and one of port is put
It puts in weighing sensor weighting platform position, another port is placed in the closed environment with standard atmospheric pressure；
Weighing sensor for starting gathered data simultaneously with the fixed cycle with the little differential pressure sensor, records and preserves title
Tuple evidence and differential pressure data；
Processing unit for the weighing data of acquisition and differential pressure data to be established state-space model, passes through Kalman filtering
Algorithm and sequential quadratic programming method solve the optimal problem of nonlinear function of Problem with Some Constrained Conditions, with differential pressure value to weighing
As a result it compensates and corrects, to obtain the actual weight of testee.
5. system according to claim 4, which is characterized in that the processing unit is embedded microprocessor or industry control
Machine.
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| CN110823337A (en) * | 2018-08-10 | 2020-02-21 | 河南工业大学 | Granary state detection method and system based on bottom surface single-ring pressure sensor |
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| CN113959549A (en) * | 2021-09-16 | 2022-01-21 | 三一汽车制造有限公司 | Weighing data processing method and device and storage medium |
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| CN111896087A (en) * | 2020-08-12 | 2020-11-06 | 无锡跃进科技有限公司 | Dynamic metering method for hopper scale |
| CN113188642A (en) * | 2021-03-24 | 2021-07-30 | 中交第二航务工程局有限公司 | Self-diagnosis device for material weighing and control method thereof |
| CN113188642B (en) * | 2021-03-24 | 2023-05-09 | 中交第二航务工程局有限公司 | Self-diagnosis device for weighing materials and control method thereof |
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