CN103575351A - Measuring method and measuring system of primary air volume of power station boiler - Google Patents

Abstract

The invention discloses a measuring method and a measuring system of the primary air volume of a power station boiler. The measuring method comprises the following steps of acquiring an auxiliary variable, filtering the errors of the auxiliary variable, establishing a measurement model of the primary air volume based on a support vector regression algorithm, substituting the auxiliary variable into the measurement model for computing, and obtaining the primary air volume. According to the technical scheme, in the measuring method and the measuring system, the auxiliary variable is acquired first, then the errors of the auxiliary variable volume are filtered, at last the measurement model of the primary air volume is established based on the support vector regression algorithm, the auxiliary variable is substituted into the measurement model for computing, and the primary air volume is obtained. A measuring device is not used for directly measuring the primary air volume, so that the fact that a measuring result is seriously drifted along with variation of time due to high-speed scouring of primary air containing dust is avoided. Thus, the problem that the difference between the measuring result obtained by the fact that the measuring device is used for directly measuring the primary air volume and an actual value is large is solved.

Description

Measuring method and the measuring system of a wind air quantity of a kind of station boiler

Technical field

The application relates to thermal power generating technology field, more particularly, relates to measuring method and the measuring system of a wind air quantity of a kind of station boiler.

Background technology

The Measurement accuracy of a wind air quantity of station boiler is the key factor of determining rational coal-air ratio and then improving burning efficiency, is also the prerequisite of combustion control system stable operation, so measurement result requires accurate, reliable.At present, a wind air measuring mainly adopts the measurement mechanisms such as Venturi tube, differential pressure flowmeter, wing type flowmeter directly to measure a wind.The dust content of a wind of station boiler is generally all many.Measurement mechanism is subject to wash away at a high speed containing a wind of dust for a long time, serious wear, cause the measurement result can be along with changing working time and seriously drift, make to use between measurement result that measurement mechanism directly measures wind air quantity and actual value deviation larger.

Summary of the invention

In view of this, the application provides the measuring method of a wind air quantity of a kind of station boiler, to solve the larger problem of deviation between measurement result that measurement mechanism directly measures wind air quantity and actual value of using.

To achieve these goals, the existing scheme proposing is as follows:

A measuring method for a wind air quantity of station boiler, comprises the steps:

Obtain auxiliary variable;

The error of auxiliary variable described in filtering;

Based on support vector regression algorithm, set up the measurement model of a wind air quantity, described auxiliary variable substitution measurement model is calculated, obtain a described wind air quantity.

Preferably, described in, obtaining auxiliary variable comprises:

Obtain coal pulverizer primary air pressure, separator for coal mill pressure, adjustment doors aperture, primary air fan electric current, obtain coal pulverizer electric current, that wind of air preheater is imported and exported differential pressure, instantaneous coal-supplying amount and a wind-warm syndrome is poor.

Preferably, the error of auxiliary variable described in described filtering, comprising:

Adopt margining amplitude technique to reject the gross error of described auxiliary variable;

Adopt filtering algorithm to reduce the stochastic error of described auxiliary variable.

Preferably, the described measurement model of setting up a wind air quantity based on support vector regression algorithm, calculates described auxiliary variable substitution measurement model, obtains a described wind air quantity, comprising:

Obtain training set;

Structure kernel function;

Construct and solve quadratic programming problem, solve Lagrangian coefficient;

Choose the solution that meets described quadratic programming problem;

Construct described measurement model;

Described auxiliary variable substitution measurement model is calculated, obtain a described wind air quantity.

Preferably, also comprise:

Parameter to described measurement model is revised online.

A measuring system for a wind air quantity of station boiler, comprising:

Acquisition device, for obtaining auxiliary variable;

Filtration unit, for the error of auxiliary variable described in filtering;

Calculation element, for setting up wind air quantity model one time, and calculates described auxiliary variable, obtains and export a described wind air quantity.

Preferably, described auxiliary variable comprises:

Coal pulverizer primary air pressure, separator for coal mill pressure, adjustment doors aperture, primary air fan electric current, obtaining wind of coal pulverizer electric current, air preheater, to import and export differential pressure, instantaneous coal-supplying amount and a wind-warm syndrome poor.

Preferably, described filtration unit comprises:

The first filtering module, for adopting margining amplitude technique to reject the gross error of described auxiliary variable;

The second filtering module, for adopting filtering algorithm to reduce the stochastic error of described auxiliary variable.

Preferably, described calculation element comprises:

Load module, for obtaining training set;

The first computing module, for constructing kernel function;

The second computing module, for constructing and solve quadratic programming problem, solves Lagrangian coefficient;

Select module, for choosing the solution that meets described quadratic programming problem;

Constructing module, for constructing described measurement model;

The 3rd computing module, for measurement model described in described auxiliary variable substitution is calculated, and exports a described wind air quantity.

Preferably, also comprise:

Correcting device, revises online for the parameter to described calculation element.

From above-mentioned technical scheme, can find out, in the measuring method of a wind air quantity of the disclosed station boiler of the application, first obtain auxiliary variable, then the error in filtering auxiliary variable, finally based on support vector regression algorithm, set up the measurement model of a wind air quantity, described auxiliary variable substitution measurement model is calculated, obtain wind air quantity one time, rather than with measurement mechanism, a wind air quantity is directly measured, therefore the measurement result that can not wash away at a high speed because of a wind containing dust and cause is along with the time changes and seriously drift, with this, solved the larger problem of deviation between measurement result that measurement mechanism directly measures wind air quantity and actual value of using.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiment of the application, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.

Fig. 1 is the process flow diagram of the measuring method of a wind air quantity of the disclosed a kind of station boiler of the embodiment of the present application;

Fig. 2 is the process flow diagram of the measuring method of a wind air quantity of the disclosed a kind of station boiler of another embodiment of the application;

Fig. 3 is the process flow diagram of the measuring method of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application;

Fig. 4 is the structural drawing of the measuring system of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application;

Fig. 5 is the structural drawing of the measuring system of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application;

Fig. 6 is the structural drawing of the measuring system of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present application, the technical scheme in the embodiment of the present application is clearly and completely described, obviously, described embodiment is only the application's part embodiment, rather than whole embodiment.Embodiment based in the application, those of ordinary skills are not making the every other embodiment obtaining under creative work prerequisite, all belong to the scope of the application's protection.

Embodiment mono-

Fig. 1 is the process flow diagram of the detection method of a wind air quantity of the disclosed a kind of station boiler of the embodiment of the present application one.

As shown in Figure 1, the detection method of a wind air quantity of the disclosed station boiler of the present embodiment comprises the steps:

S101: obtain auxiliary variable;

S102: the error of filtering auxiliary variable;

S103: set up the measurement model of a wind air quantity based on support vector regression algorithm, auxiliary variable substitution measurement model is calculated, obtain wind air quantity one time.

From technique scheme, can find out, in the detection method of a wind air quantity of the disclosed station boiler of the present embodiment, first obtain auxiliary variable, then the error in filtering auxiliary variable, finally based on support vector regression algorithm, set up the measurement model of a wind air quantity, auxiliary variable substitution measurement model is calculated, obtain wind air quantity one time, rather than with measurement mechanism, a wind air quantity is directly measured, therefore can not wash away at a high speed because of a wind containing dust and cause measurement result along with the time changes and seriously drift, with this, solved the larger problem of deviation between measurement result that measurement mechanism directly measures wind air quantity and actual value of using.

Embodiment bis-

Fig. 2 is the process flow diagram of the measuring method of a wind air quantity of the disclosed a kind of station boiler of another embodiment of the application.

As shown in Figure 2, the measuring method of a wind air quantity of the disclosed station boiler of the present embodiment comprises the steps:

S201: obtain auxiliary variable;

First select the auxiliary variable that will obtain.In the present embodiment, be that the method combining according to mechanism and pivot analysis is chosen auxiliary variable.

So-called mechanism, is according to fluid mechanics principle, analyzes, to determine the impact of each factor on air force on affecting the factor of a wind air quantity.So-called pivot analysis, is to find one group of variable, in the situation that variable number is less, carries as much as possible the useful information of former variable, as adopted pivot contribution rate of accumulative total method to determine pivot number.

According to the result of above analysis, it is poor that the auxiliary variable of choosing in the present embodiment comprises coal pulverizer primary air pressure, separator for coal mill pressure, adjustment doors aperture, primary air fan electric current, obtains coal pulverizer electric current, wind of air preheater is imported and exported differential pressure, instantaneous coal-supplying amount and a wind-warm syndrome.

S202: adopt margining amplitude technique to reject the gross error of auxiliary variable;

S203: adopt filtering algorithm to reduce the stochastic error of auxiliary variable;

S204: obtain training set;

Given training set { (p ₁, y ₁) ..., (p _k, y _k) wherein input p _i∈ R ⁿ, output y _i∈ R ^k, i=1,2 ... k; Training set is for from carrying out a part for the sample data that on-the-spot test obtains, wherein p to certain particular rack _i∈ R ⁿfor the auxiliary variable of selecting, y _i∈ R ^ktake variable (being primary air flow herein) as the leading factor.

Training set comprises training sample and test sample book, chooses 2000 groups of data as training sample from domestic certain power plant the DCS historical data providing, and therefrom chooses in addition 200 groups of data as test sample book.Use normalized function that training sample and test sample book are normalized.

S205: structure kernel function;

Construct suitable kernel function, the preferred radial basis kernel function of the present embodiment Kernel Function (RBF), and select suitable precision ε >0 and punish parameters C >0, the default value of precision and the general direct employing model algorithm of punishment parameter.

S206: construct and solve quadratic programming problem, solve Lagrangian coefficient;

$\tilde{a}={({\tilde{a}}_1,...,{\tilde{a}}_k)}^T,{\tilde{a}}^{*}={({{\tilde{a}}_1}^{*},...,{{\tilde{a}}_k}^{*})}^T$

$\underset{a,a^{*}\∈R^k}{\min }=\frac{1}{2}\underset{i,j=1}{\overset{k}{\mathrm{\Σ}}}(a_i^{*}-a_i)(a_j^{*}-a_j)K(p_i,p_j)+\mathrm{\ϵ}\underset{i=1}{\overset{k}{\mathrm{\Σ}}}(a_i^{*}+a_i)-\underset{i=1}{\overset{k}{\mathrm{\Σ}}}{y}_i(a_i^{*}-a_i)$

$s.t.\underset{i=1}{\overset{k}{\mathrm{\Σ}}}(a_i-{a_i}^{*})=\mathrm{0,0}\≤a_i,{a_i}^{*}\≤C,$

i,j=1,2,...,k

the Lagrangian constant of introducing while solving quadratic programming problem (being Constrained and Unconstrained Optimization) for use Lagrangian method, kernel function adopts K (p _i, p)=exp (γ * | p _i-p| ²), in formula, γ is constant, can given initial value arbitrarily or use the initial value of acquiescence, during training, can obtain being more reasonably worth by automatic optimal, wherein p _ithe support vector obtaining during for training, the vector forming after the auxiliary variable normalization of p for actual measurement,

for the constant term of the linear model after training, the training, the parameter that then adopt training sample to carry out model are selected and modelling verification;

S207: choose the solution that meets quadratic programming problem;

Choose the solution that meets above formula constraint condition

or

calculate

$\tilde{b}=y_j-\underset{i=1}{\overset{k}{\mathrm{\Σ}}}({{\tilde{a}}_i}^{*}-{\tilde{a}}_i)K(p_i,p_j)+\mathrm{\ϵ}$ Or $\tilde{b}=y_t-\underset{i=1}{\overset{k}{\mathrm{\Σ}}}({{\tilde{a}}_i}^{*}-{\tilde{a}}_i)K(p_i,p_t)-\mathrm{\ϵ}$

S208: structure measurement model;

$y=g\left(p\right)=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}({{\tilde{a}}_i}^{*}-{\tilde{a}}_i)K(p_i,p)+\tilde{b}$

Wherein Y is a wind air quantity.

S209: auxiliary variable substitution measurement model is calculated, obtain wind air quantity one time.

Embodiment tri-

Fig. 3 is the process flow diagram of the measuring method of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application.

As shown in Figure 3, the measuring method of a wind air quantity of the disclosed station boiler of the present embodiment comprises the steps:

S301: obtain auxiliary variable;

S302: adopt margining amplitude technique to reject the gross error of auxiliary variable;

S303: adopt filtering algorithm to reduce the stochastic error of auxiliary variable.

S304: obtain training set;

S305: structure kernel function;

S306: construct and solve quadratic programming problem, solve Lagrangian coefficient;

S307: choose the solution that meets quadratic programming problem;

S308: structure measurement model

S309: auxiliary variable substitution measurement model is calculated, obtain wind air quantity one time.

S310: the parameter to measurement model is revised online.

By the great amount of samples data that obtain in on-line operation, the parameter of measurement model is revised.

From technique scheme, can find out, the present embodiment is than being to have increased step S310 on the basis of a upper embodiment, for the parameter of measurement model is revised online, so that the wind air quantity obtaining closing to reality air quantity more can improve the precision of measurement.

Embodiment tetra-

Fig. 4 is the structural drawing of the measuring system of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application, comprises acquisition device 10, filtration unit 20 and calculation element 30.

Acquisition device 10 is for obtaining auxiliary variable.

Filtration unit 20 is for the error of filtering auxiliary variable.

Calculation element 30 is for setting up wind air measuring model one time, and auxiliary variable is calculated, and obtains and export one time wind air quantity.

From technique scheme, can find out, in the measuring system of a wind air quantity of the disclosed station boiler of the present embodiment, first obtain auxiliary variable, then the error in filtering auxiliary variable, finally based on support vector regression algorithm, set up the measurement model of a wind air quantity, auxiliary variable substitution measurement model is calculated, obtain wind air quantity one time, rather than with measurement mechanism, a wind air quantity is directly measured, therefore can not wash away at a high speed because of a wind containing dust and cause measurement result along with the time changes and seriously drift, with this, solved the larger problem of deviation between measurement result that measurement mechanism directly measures wind air quantity and actual value of using.

Embodiment five

Fig. 5 is the structural drawing of the measuring system of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application.

As shown in Figure 5, the measuring system of a wind air quantity of the disclosed station boiler of the present embodiment comprises: acquisition device 10, filtration unit 20 and calculation element 30, and wherein filtration unit 20 comprises the first filtering module 21 and the second filtering module 22; Calculation element 30 comprises load module 31, the first computing module 32, the second computing module 33, selects module 34, constructing module 35 and the 3rd computing module 36.Wherein:

Acquisition device 10, for obtaining auxiliary variable, is first selected the auxiliary variable that will obtain in obtaining variable process.In the present embodiment, be that the method combining according to mechanism and pivot analysis is chosen auxiliary variable.

So-called mechanism, is according to fluid mechanics principle, analyzes, to determine the impact of each factor on air force on affecting the factor of a wind air quantity.So-called pivot analysis, is to find one group of variable, in the situation that variable number is less, carries as much as possible the useful information of former variable, as adopted pivot contribution rate of accumulative total method to determine pivot number.

According to the result of above analysis, it is poor that the auxiliary variable of choosing in the present embodiment comprises coal pulverizer primary air pressure, separator for coal mill pressure, adjustment doors aperture, primary air fan electric current, obtains coal pulverizer electric current, wind of air preheater is imported and exported differential pressure, instantaneous coal-supplying amount and a wind-warm syndrome.

The first filtration unit 21 is for receiving auxiliary variable, and employing margining amplitude technique is rejected the gross error of auxiliary variable.

The second filtration unit 22 is for receiving through rejecting the auxiliary variable after gross error, and adopts filtering algorithm to reduce the stochastic error of auxiliary variable.

Load module 30 is for obtaining training set;

Training set { (p ₁, y ₁) ..., (p _k, y _k) wherein input p _i∈ R ⁿ, output y _i∈ R ^k, i=1,2 ... k; For from certain particular rack being carried out to a part for the sample data that on-the-spot test obtains, wherein p _i∈ R ⁿfor the auxiliary variable of selecting, y _i∈ R ^ktake variable (being primary air flow herein) as the leading factor.

Training set comprises training sample and test sample book, chooses 2000 groups of data as training sample from domestic certain power plant the DCS historical data providing, and therefrom chooses in addition 200 groups of data as test sample book.Use normalized function that training sample and test sample book are normalized.

The first computing module 32 is for constructing kernel function;

Construct suitable kernel function, the preferred radial basis kernel function of the present embodiment Kernel Function (RBF), and select suitable precision ε >0 and punish parameters C >0, the default value of precision and the general direct employing model algorithm of punishment parameter.

The second computing module 33, for constructing and solve quadratic programming problem, solves Lagrangian coefficient;

$\tilde{a}={({\tilde{a}}_1,...,{\tilde{a}}_k)}^T,{\tilde{a}}^{*}={({{\tilde{a}}_1}^{*},...,{{\tilde{a}}_k}^{*})}^T$

$\underset{a,a^{*}\∈R^k}{\min }=\frac{1}{2}\underset{i,j=1}{\overset{k}{\mathrm{\Σ}}}(a_i^{*}-a_i)(a_j^{*}-a_j)K(p_i,p_j)+\mathrm{\ϵ}\underset{i=1}{\overset{k}{\mathrm{\Σ}}}(a_i^{*}+a_i)-\underset{i=1}{\overset{k}{\mathrm{\Σ}}}{y}_i(a_i^{*}-a_i)$

$s.t.\underset{i=1}{\overset{k}{\mathrm{\Σ}}}(a_i-{a_i}^{*})=\mathrm{0,0}\≤a_i,{a_i}^{*}\≤C,$

i,j=1,2,...,k

the Lagrangian constant of introducing while solving quadratic programming problem (being Constrained and Unconstrained Optimization) for use Lagrangian method, kernel function adopts K (p _i, p)=exp (γ * | p _i-p| ²), in formula, γ is constant, can given initial value arbitrarily or use the initial value of acquiescence, during training, can obtain being more reasonably worth by automatic optimal, wherein p _ithe support vector obtaining during for training, the vector forming after the auxiliary variable normalization of p for actual measurement,

for the constant term of the linear model after training, the training, the parameter that then adopt training sample to carry out model are selected and modelling verification;

Select module 34 for choosing the solution that meets quadratic programming problem;

Choose the solution that meets above formula constraint condition

or calculate

$\tilde{b}=y_j-\underset{i=1}{\overset{k}{\mathrm{\Σ}}}({{\tilde{a}}_i}^{*}-{\tilde{a}}_i)K(p_i,p_j)+\mathrm{\ϵ}$ Or $\tilde{b}=y_t-\underset{i=1}{\overset{k}{\mathrm{\Σ}}}({{\tilde{a}}_i}^{*}-{\tilde{a}}_i)K(p_i,p_t)-\mathrm{\ϵ}$

Constructing module 35 is for constructing measurement model.

$y=g\left(p\right)=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}({{\tilde{a}}_i}^{*}-{\tilde{a}}_i)K(p_i,p)+\tilde{b}$

Wherein y is a wind air quantity.

The 3rd computing module 36, for auxiliary variable substitution measurement model is calculated, obtains and exports one time wind air quantity.

Embodiment six

Fig. 6 is the structural drawing of the measuring system of a wind air quantity of the disclosed a kind of station boiler of the another embodiment of the application.

As shown in Figure 6, the measuring system of a wind air quantity of the disclosed station boiler of the present embodiment is on the basis of a upper embodiment, to have set up correcting device 40, and correcting device 40 is connected with calculation element 30.

Correcting device 40 is revised the parameter of measurement model by the great amount of samples data that obtain in on-line operation.

From technique scheme, can find out, the present embodiment is on the basis of a upper embodiment, to have set up correcting device 40, for the parameter of measurement model is revised online, so that the wind air quantity obtaining closing to reality air quantity more can improve the precision of measurement.

Finally, also it should be noted that, in this article, relational terms such as the first and second grades is only used for an entity or operation to separate with another entity or operational zone, and not necessarily requires or imply and between these entities or operation, have the relation of any this reality or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby the process, method, article or the equipment that make to comprise a series of key elements not only comprise those key elements, but also comprise other key elements of clearly not listing, or be also included as the intrinsic key element of this process, method, article or equipment.The in the situation that of more restrictions not, the key element being limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment that comprises described key element and also have other identical element.

In this instructions, each embodiment adopts the mode of going forward one by one to describe, and each embodiment stresses is the difference with other embodiment, between each embodiment identical similar part mutually referring to.

Above-mentioned explanation to the disclosed embodiments, makes professional and technical personnel in the field can realize or use the application.To the multiple modification of these embodiment, will be apparent for those skilled in the art, General Principle as defined herein can be in the situation that do not depart from the application's spirit or scope, realization in other embodiments.Therefore, the application will can not be restricted to these embodiment shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.

Claims (10)

1. a measuring method for a wind air quantity of station boiler, is characterized in that, comprises the steps:

Obtain auxiliary variable;

The error of auxiliary variable described in filtering;

Based on support vector regression algorithm, set up the measurement model of a wind air quantity, described auxiliary variable substitution measurement model is calculated, obtain a described wind air quantity.

2. measuring method as claimed in claim 1, is characterized in that, described in obtain auxiliary variable and comprise:

Obtain coal pulverizer primary air pressure, separator for coal mill pressure, adjustment doors aperture, primary air fan electric current, obtain coal pulverizer electric current, that wind of air preheater is imported and exported differential pressure, instantaneous coal-supplying amount and a wind-warm syndrome is poor.

3. measuring method as claimed in claim 1, is characterized in that, the error of auxiliary variable described in described filtering, comprising:

Adopt margining amplitude technique to reject the gross error of described auxiliary variable;

Adopt filtering algorithm to reduce the stochastic error of described auxiliary variable.

4. measuring method as claimed in claim 1, is characterized in that, the described measurement model of setting up a wind air quantity based on support vector regression algorithm calculates described auxiliary variable substitution measurement model, obtains a described wind air quantity, comprising:

Obtain training set;

Structure kernel function;

Construct and solve quadratic programming problem, solve Lagrangian coefficient;

Choose the solution that meets described quadratic programming problem;

Construct described measurement model;

Described auxiliary variable substitution measurement model is calculated, obtain a described wind air quantity.

5. the measuring method as described in claim 1～4 any one, is characterized in that, also comprises:

Parameter to described measurement model is revised online.

6. a measuring system for a wind air quantity of station boiler, is characterized in that, comprising:

Acquisition device, for obtaining auxiliary variable;

Filtration unit, for the error of auxiliary variable described in filtering;

Calculation element, for setting up wind air quantity model one time, and calculates described auxiliary variable, obtains and export a described wind air quantity.

7. measuring system as claimed in claim 6, is characterized in that, described auxiliary variable comprises:

Coal pulverizer primary air pressure, separator for coal mill pressure, adjustment doors aperture, primary air fan electric current, obtaining wind of coal pulverizer electric current, air preheater, to import and export differential pressure, instantaneous coal-supplying amount and a wind-warm syndrome poor.

8. measuring system as claimed in claim 6, is characterized in that, described filtration unit comprises:

The first filtering module, for adopting margining amplitude technique to reject the gross error of described auxiliary variable;

The second filtering module, for adopting filtering algorithm to reduce the stochastic error of described auxiliary variable.

9. measuring system as claimed in claim 6, is characterized in that, described calculation element comprises:

Load module, for obtaining training set;

The first computing module, for constructing kernel function;

The second computing module, for constructing and solve quadratic programming problem, solves Lagrangian coefficient;

Select module, for choosing the solution that meets described quadratic programming problem;

Constructing module, for constructing described measurement model;

The 3rd computing module, for measurement model described in described auxiliary variable substitution is calculated, and exports a described wind air quantity.

10. the measuring system as described in claim 6～9, is characterized in that, also comprises:

Correcting device, revises online for the parameter to described calculation element.

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