CN108255786B - Method and system for calculating interference compensation of weighing result - Google Patents

Method and system for calculating interference compensation of weighing result Download PDF

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CN108255786B
CN108255786B CN201711211062.7A CN201711211062A CN108255786B CN 108255786 B CN108255786 B CN 108255786B CN 201711211062 A CN201711211062 A CN 201711211062A CN 108255786 B CN108255786 B CN 108255786B
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weighing
differential pressure
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sensor
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CN108255786A (en
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彭辉
程琢
杨晗
张超男
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Central South University
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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G23/00—Auxiliary devices for weighing apparatus

Abstract

The invention discloses a method and a system for calculating interference compensation of a weighing result, which solve the problem of insufficient precision caused by measuring data by a single sensor by establishing a model and combining various sensor data; the method is particularly suitable for the interference treatment of the micro-differential pressure symmetrical retransmission sensor generated by air, and can improve the accuracy of the weighing result in the production process.

Description

Method and system for calculating interference compensation of weighing result
Technical Field
The invention relates to the field of signal detection and processing, in particular to a method and a system for calculating interference compensation of a weighing result.
Background
In the technical field of automation control, in order to ensure that industrial requirements are met, accurate measurement is a precondition for ensuring control precision. In modern industrial, especially automated, processes, various sensors are used to monitor and control various parameters of the process, to operate the equipment in a normal or optimal state, and to maximize the quality of the product. As an important basis of modern information technology, sensors are the main ways and means for acquiring information in the natural field, and obtain an input signal from a certain attribute of a measured object, and convert the input signal into other signals which can be easily detected according to a certain rule and output the signals, generally electrical signals. Thus, the sensor is also a control system in a sense that it has the structure and nature of the control system.
In an industrial measurement and control system, due to the complexity of a working environment, the measurement accuracy of a sensor in the operation of the system is affected, so that the measurement data of the sensor is not always accurate. With the development of information technology, in the process of continuously seeking high-quality materials and designing better anti-interference circuits, models are continuously established in a mathematical mode, more accurate measurement values are obtained through data optimization calculation of various sensors, information and data from various or multiple sensors are comprehensively processed, a more accurate and reliable measurement technology is obtained, and errors possibly occurring in information processing are reduced. Obviously, the method has low cost and wider adaptability.
The weighing sensor is widely applied to various production processes, and in application occasions requiring high-precision weighing results, due to the influence of environmental factors, such as wind pressure, vibration and the like, signals output by the weighing sensor are interfered by the environmental factors to influence the measurement precision. The development of techniques to remove such disturbances is an important approach to improve weighing accuracy.
Different physical quantities acquired by different sensors in nature are mostly in a nonlinear relation, so that a reliable optimization algorithm which has a high convergence rate and strictly complies with given nonlinear limiting conditions needs to be selected in the process of establishing the optimization function solution. The Sequential Quadratic Programming (SQP) method is a very effective algorithm for solving the nonlinear constraint optimization problem and has global convergence.
Disclosure of Invention
The invention aims to solve the technical problem that the accuracy is insufficient due to the fact that a single sensor measures data by providing a method and a system for calculating interference compensation of a weighing result aiming at the defects of the prior art.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a method for calculating interference compensation of weighing results comprises the following steps:
1) installing a micro differential pressure sensor beside the weighing sensor, wherein two input ports of the micro differential pressure sensor are respectively connected with a hard plastic pipe, one port of the micro differential pressure sensor is placed at the weighing platform of the weighing sensor, and the other port of the micro differential pressure sensor is placed in a closed environment with standard atmospheric pressure;
2) determining a sampling period, and after the measured object enters a weighing platform, starting to acquire data by the weighing sensor and the micro differential pressure sensor at a fixed period at the same time, and recording and storing the weighing data and the micro differential pressure data;
3) establishing a state space model for the weighing data and the micro differential pressure data acquired in the step 2), solving a nonlinear function optimization problem with constraint conditions through a Kalman filtering algorithm and a sequence quadratic programming method, and performing compensation correction on a weighing result by using the micro differential pressure data to obtain the actual weight of the measured object.
In step 3), the expression of the state space model is as follows:
Figure GDA0003070874100000021
wherein the content of the first and second substances,
Figure GDA0003070874100000022
Figure GDA0003070874100000031
tn＝tn-1+ξn,ξn～N(0,τ2) (ii) a N is the total number of the sampled data, ynIs the nth weighing data, tnIs the nth filtered weighing result; pnIs the nth data describing the influence of the differential pressure signal on the measurement result, pn-iIs the n-i micro differential pressure data, aiIs pn-iM is the order of the set model; u. ofnRepresenting a random fluctuation signal contained in the nth set of measurement data; alpha is alpha1≥0、α2Not less than 0 is a proportionality coefficient and satisfies an inequality condition alpha1+α2<1, respectively represent the n-1 th heteroscedastic term and the thThe proportion of n-1 error square terms in the nth heteroscedastic term, alpha0> 0 is a constant; w is anIs a random signal, obeying a standard normal distribution;
Figure GDA0003070874100000032
is the variance; xinIs a random error, obeys an expectation value of 0 and a standard deviation of tau2Normal distribution of (2); the representation obeys some distribution law.
Solving the nonlinear function optimization problem with constraint conditions through the following Kalman filtering and sequence quadratic programming methods to obtain state space model parameters, variable initial values and the actual weight of the measured object after filtering:
Figure GDA0003070874100000033
Figure GDA0003070874100000034
Figure GDA0003070874100000035
s.t.α0＞0,α1≥0,α2≥0,α1+α2<1
wherein, Xn|n-1Is the state prediction value, Xn|nIs a state filtered value, Vn|n-1Is the state prediction error covariance, Vn|nIs the state estimation error covariance, τ is a constant. Gamma raynIs the prediction error, N takes a value from 1 to N, psinIs the variance of the prediction error and is,
Figure GDA0003070874100000036
is variance, KnIs the kalman filter gain.
Correspondingly, the invention also provides a system for calculating the interference compensation of the weighing result, which comprises the following components:
the two input ports of the micro differential pressure sensor are respectively connected with a hard plastic pipe, one port of the micro differential pressure sensor is placed at the weighing platform of the weighing sensor, and the other port of the micro differential pressure sensor is placed in a closed environment with standard atmospheric pressure;
the weighing sensor is used for starting to acquire data at a fixed period simultaneously with the micro differential pressure sensor and recording and storing the weighing data and the micro differential pressure data;
and the processing unit is used for establishing a state space model for the acquired weighing data and micro-differential pressure data, solving the nonlinear function optimization problem with constraint conditions through a Kalman filtering algorithm and a sequence quadratic programming method, and compensating and correcting the weighing result by using the micro-differential pressure value to obtain the actual weight of the measured object.
The processing unit is an embedded microprocessor or an industrial personal computer.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the problem of insufficient precision caused by measuring data by a single sensor is solved by establishing a model and combining multiple sensor data; the interference processing of the micro differential pressure symmetrical retransmission sensor generated by air around the sensor is particularly aimed at, and the measurement accuracy of the weighing sensor can be remarkably improved.
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FIG. 1 shows the results before and after filtering of the weighing signal; wherein, (a) is a weighing signal before filtering; (b) is the result of weighing signal filtering.
Detailed Description
The implementation process of the invention comprises the following steps:
1) the weighing sensor and the micro differential pressure sensor have the same sampling time tsSimultaneously recording the T time of the measured object on the weighing platform, wherein the total time N is T/TsThe data form a row vector y, and the data of the micro differential pressure sensor form a vector p;
2) the matlab program is compiled to solve the optimal solution of the optimization problem, and the solving conditions of the optimization problem are set firstly, wherein the solving conditions comprise a model order m and three proportionality coefficients alpha0、α1And alpha2. Setting maximum number of computations for optimization functionThe number maxfenevals, the maximum iteration number MaxIter, the termination condition TolX of the optimization parameter, and the termination condition TolFun of the optimization function;
3) calculating the mean value t of the weighing data0And as the state quantity tnSetting the initial value of the optimization parameter
Figure GDA0003070874100000051
Wherein
Figure GDA0003070874100000052
Is an m +1 dimensional column vector.
4) Establishing inequality constraint conditions
Figure GDA0003070874100000053
Wherein b is0Lb, ub are column vectors of the same dimension as X, A0Is a square matrix of m +5 orders. And selecting the values of the first dimension of lb and ub according to actual conditions. The following command lines are executed in matlab
Figure GDA0003070874100000054
Then resetting upper and lower limits lb (2) and ub (2); lb (3), ub (3); lb (4), ub (4); simultaneous execution of matlab command A0＝zeros(length(X0),length(X0) Complete a pairwise matrix A0Initialization of (A) and resetting of0(4,3)，A0(4,4)；b0(4)；
5) Establishing an optimization function Iter _ button, establishing (m +2) × N dimensional matrixes xm and xs, and initializing the first m columns of the matrixes to
Figure GDA0003070874100000055
The next N-m columns need to be updated continuously by iterative operation, so all are initialized to 0. Executing matlab command gamma zero (1, N); initializing N-dimensional coefficient column vector gamma as 0 vector and simultaneously establishing N-dimensional column vector sigma2All elements in the vector are initialized to the variance of the weighing data.
6) And circulating N-m times of calculation. Solving the nonlinear function optimization problem with constraint conditions through a previously established Kalman filtering algorithm and a sequence quadratic programming method, importing data, and calling an SQP optimization iteration function in matlab to calculate to obtain a result.
The weighing sensor and the micro differential pressure sensor have the same sampling time tsSimultaneously recording the T time of the measured object on the weighing platform, wherein the total time N is T/TsThe data form a row vector y, and the data of the micro differential pressure sensor form a vector p;
the matlab program is compiled to solve the optimal solution of the optimization problem, and the solving conditions of the optimization problem are set firstly, wherein the solving conditions comprise a model order m and three proportionality coefficients alpha0、α1And alpha2. Executing matlab instruction optimest to set various parameters, setting the maximum calculation times MaxFunEvals of the function to be 300, the maximum iteration times MaxIter to be 100, and setting the condition of the termination difference value TolX of the optimized parameters to be 1e-175The terminal difference condition TolFun of the optimization function is 1e-75。
Calculating the mean value t of the weighing data0And as the state quantity tnSetting the initial value of the optimization parameter
Figure GDA0003070874100000061
Wherein
Figure GDA0003070874100000062
Is an m +1 dimensional column vector.
Establishing inequality constraint conditions
Figure GDA0003070874100000063
Wherein b is0Lb, ub are column vectors of the same dimension as X, A0Is a square matrix of m +5 orders. The first dimension of lb and ub is selected according to actual conditions, and for example, if the weight of the object to be measured is around 8g, lb (1) may be 6.5 and ub (1) may be 9.5. The following command lines are executed in matlab
Figure GDA0003070874100000064
Then resetting the upper limit and the lower limit, and enabling lb (2) to be 0.00001 and ub (2) to be 0.2; lb (3) ═ 0, ub (3) ═ 0.94; lb (4) ═ 0, ub (4) ═ 0.9999; simultaneous execution of matlab command A0＝zeros(length(X0),length(X0) Complete a pairwise matrix A0Initialization of, setting A0(4,3)＝1，A0(4,4)＝1；b0(4)＝0.9999；
Establishing an iterative function Iter _ bottle, establishing (m +2) × N dimensional matrixes xm and xs, and initializing the first m columns to be
Figure GDA0003070874100000065
The next N-m columns need to be updated continuously by iterative operation, so all are initialized to 0. Executing matlab command gamma zero (1, N); initializing N-dimensional coefficient column vector gamma as 0 vector and simultaneously establishing N-dimensional column vector sigma2All elements in the vector are initialized to the variance of the weighing data.
And circulating N-m times of calculation. Solving the nonlinear function optimization problem with constraint conditions through the previously established Kalman filtering algorithm and a sequential quadratic programming method:
Figure GDA0003070874100000071
Figure GDA0003070874100000072
Figure GDA0003070874100000073
s.t.α0＞0,α1≥0,α2≥0,α1+α2<1
importing data and calling SQP (sequence query procedure) optimization iteration function in matlab
[ X ] ═ fmincon ('Iter _ bottle', X0, a0, b0, [ ], [ ], lb, ub, [ ], options, y, p, m); the results were obtained. Due to the large amount of data, only a portion of the test data is listed as a reference, as shown in table 1.
TABLE 1 test data
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 …
The final optimization calculation result is 8.7124.

Claims (4)

1. A method for calculating interference compensation of weighing results is characterized by comprising the following steps:
1) installing a micro differential pressure sensor beside the weighing sensor, wherein two input ports of the micro differential pressure sensor are respectively connected with a hard plastic pipe, one port of the micro differential pressure sensor is placed at the weighing platform of the weighing sensor, and the other port of the micro differential pressure sensor is placed in a closed environment with standard atmospheric pressure;
2) determining a sampling period, and after the measured object enters a weighing platform, starting to acquire data by the weighing sensor and the micro differential pressure sensor at a fixed period at the same time, and recording and storing the weighing data and the micro differential pressure data;
3) establishing a state space model aiming at the weighing data and micro-differential pressure data acquired in the step 2), solving a nonlinear function optimization problem with constraint conditions through a Kalman filtering algorithm and a sequence quadratic programming method, and performing compensation correction on a weighing result by using the micro-differential pressure data to obtain the actual weight of a measured object;
the state space model expression is as follows:
Figure FDA0003034403300000011
Xn＝[tn a0 … am]T
Figure FDA0003034403300000012
Cn＝[1 pn … pn-m]
G＝[1 0 … 0]T
wherein the content of the first and second substances,
Figure FDA0003034403300000013
tn＝tn-1+ξn,ξn～N(0,τ2) (ii) a N is the total number of the sampled data, ynIs the nth weighing data, tnIs the nth filtered weighing result; pnIs the nth data describing the influence of the differential pressure signal on the measurement result, pn-iIs the n-i micro differential pressure data, aiIs pn-iM is the order of the set model; u. ofnRepresenting a random fluctuation signal contained in the nth set of measurement data; alpha is alpha1≥0、α2Not less than 0 is a proportionality coefficient and satisfies an inequality condition alpha1+α2<1, respectively representing the proportion of the n-1 th heteroscedastic term and the n-1 th error square term in the n-th heteroscedastic term, alpha0> 0 is a constant; w is anIs a random signal, obeying a standard normal distribution;
Figure FDA0003034403300000021
is the variance; xinIs a random error, obeys an expectation value of 0 and a standard deviation of tau2Normal distribution of (2); the representation obeys some distribution law.
2. The method of claim 1, wherein the state space model is used to solve the nonlinear function optimization problem with constraint conditions by the following kalman filter algorithm and sequential quadratic programming method to obtain the state space model parameters, initial values of variables, and the actual weight of the filtered object:
Figure FDA0003034403300000022
Figure FDA0003034403300000023
Figure FDA0003034403300000024
s.t.α0＞0,α1≥0,α2≥0,α1+α2<1
wherein, Xn|n-1Is the state prediction value, Xn|nIs a state filtered value, Vn|n-1Is the state prediction error covariance, Vn|nIs the state estimation error covariance, τ is a constant, γnIs the prediction error, N takes a value from 1 to N, psinIs the variance of the prediction error and is,
Figure FDA0003034403300000025
is variance, KnIs the kalman filter gain.
3. A system for calculating disturbance compensation of weighing results, comprising:
the two input ports of the micro differential pressure sensor are respectively connected with a hard plastic pipe, one port of the micro differential pressure sensor is placed at the position of a weighing sensor weighing platform, and the other port of the micro differential pressure sensor is placed in a closed environment with standard atmospheric pressure;
the weighing sensor is used for starting to acquire data at a fixed period simultaneously with the micro differential pressure sensor and recording and storing the weighing data and the micro differential pressure data;
the processing unit is used for establishing a state space model for the acquired weighing data and micro-differential pressure data, solving a nonlinear function optimization problem with constraint conditions through a Kalman filtering algorithm and a sequence quadratic programming method, and compensating and correcting a weighing result by using the micro-differential pressure value to obtain the actual weight of a measured object;
the state space model expression is as follows:
Figure FDA0003034403300000031
Xn＝[tn a0 … am]T
Figure FDA0003034403300000032
Cn＝[1 pn … pn-m]
G＝[1 0 … 0]T
wherein the content of the first and second substances,
Figure FDA0003034403300000033
tn＝tn-1+ξn,ξn～N(0,τ2) (ii) a N is the total number of the sampling data, yn is the nth weighing data, tnIs the nth filtered weighing result; pnIs the nth data describing the influence of the differential pressure signal on the measurement result, pn-iIs the n-i micro differential pressure data, aiIs pn-iM is the order of the set model; u. ofnRepresenting a random fluctuation signal contained in the nth set of measurement data; alpha is alpha1≥0、α2Not less than 0 is a proportionality coefficient and satisfies an inequality condition alpha1+α2<1, respectively representing the proportion of the n-1 th heteroscedastic term and the n-1 th error square term in the n-th heteroscedastic term, alpha0> 0 is a constant; w is anIs a random signal, obeying a standard normal distribution;
Figure FDA0003034403300000034
is the variance; xinIs a random error, obeys an expectation value of 0 and a standard deviation of tau2Normal distribution of (2); the representation obeys some distribution law.
4. The system of claim 3, wherein the processing unit is an embedded microprocessor or an industrial personal computer.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110823337B (en) * 2018-08-10 2021-05-18 河南工业大学 Granary state detection method and system based on bottom surface single-ring pressure sensor
CN113588062B (en) * 2020-04-30 2024-02-02 梅特勒-托利多(常州)测量技术有限公司 Method and system for measuring interference of detection equipment
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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
CN113959549B (en) * 2021-09-16 2023-07-21 三一汽车制造有限公司 Weighing data processing method, device and storage medium

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101625256A (en) * 2008-07-10 2010-01-13 中冶赛迪工程技术股份有限公司 Intelligent correction method for under-pressure weighing
CN102141423A (en) * 2010-01-29 2011-08-03 通用电气公司 System and method for measuring solid mass flow in solid-gas mixture in real time
CN102506983A (en) * 2011-10-31 2012-06-20 湖南师范大学 Weighing error automatic compensation method of vehicle scale
CN102758277A (en) * 2012-07-02 2012-10-31 无锡市灵特电子仪器设备有限公司 Cotton-carding autoleveler and control method thereof
CN103278813A (en) * 2013-05-02 2013-09-04 哈尔滨工程大学 State estimation method based on high-order unscented Kalman filtering
JP2014041547A (en) * 2012-08-23 2014-03-06 Nippon Telegr & Teleph Corp <Ntt> Time series data analysis device, method and program
CN105652665A (en) * 2016-03-03 2016-06-08 东南大学 Coordinated control method of cooling-heating-power cogeneration system of micro gas turbine
CN106441531A (en) * 2016-12-08 2017-02-22 重庆市华驰交通科技有限公司 Dynamic weighing method and system on condition of uniform motion of vehicle
CN206514927U (en) * 2016-09-20 2017-09-22 上海睿丰自动化系统有限公司 A kind of high precision dynamic weighing system
CN107367319A (en) * 2017-02-28 2017-11-21 淮阴师范学院 The Wavelet Neural Network Method of capacitance weighing sensor nonlinear compensation

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101625256A (en) * 2008-07-10 2010-01-13 中冶赛迪工程技术股份有限公司 Intelligent correction method for under-pressure weighing
CN102141423A (en) * 2010-01-29 2011-08-03 通用电气公司 System and method for measuring solid mass flow in solid-gas mixture in real time
CN102506983A (en) * 2011-10-31 2012-06-20 湖南师范大学 Weighing error automatic compensation method of vehicle scale
CN102758277A (en) * 2012-07-02 2012-10-31 无锡市灵特电子仪器设备有限公司 Cotton-carding autoleveler and control method thereof
JP2014041547A (en) * 2012-08-23 2014-03-06 Nippon Telegr & Teleph Corp <Ntt> Time series data analysis device, method and program
CN103278813A (en) * 2013-05-02 2013-09-04 哈尔滨工程大学 State estimation method based on high-order unscented Kalman filtering
CN105652665A (en) * 2016-03-03 2016-06-08 东南大学 Coordinated control method of cooling-heating-power cogeneration system of micro gas turbine
CN206514927U (en) * 2016-09-20 2017-09-22 上海睿丰自动化系统有限公司 A kind of high precision dynamic weighing system
CN106441531A (en) * 2016-12-08 2017-02-22 重庆市华驰交通科技有限公司 Dynamic weighing method and system on condition of uniform motion of vehicle
CN107367319A (en) * 2017-02-28 2017-11-21 淮阴师范学院 The Wavelet Neural Network Method of capacitance weighing sensor nonlinear compensation

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
A self-organizing state space type microstructure model for financial asset allocation;Gan Min 等;《IEEE Access》;20161109;第4卷;8035-8043 *
Kalman filter for dynamic weighing system;Halimic M. 等;《1995 Proceedings of the IEEE International Symposium on Industrial Electronics》;19950714;第2卷;786-791 *
基于自适应卡尔曼滤波的动态称重算法的研究;杨军 等;《自动化与仪表》;20140815(第08期);5-8 *
基于高斯和粒子滤波的动态称重数据处理;张丽妹 等;《计测技术》;20130228;第33卷(第01期);14-17 *

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