CN103092815B - To the method transferring function by calibration in monitoring device - Google Patents

Abstract

The invention discloses a kind of calibrating installation and to the method transferring function by calibration in monitoring device, described method includes: m parameter of transmission function is set to the parameter value of the 1st iteration；Carry out iterative process at least one times；Wherein in iteration j process, if according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter value, the error evaluation value s calculated less than assessment desired value, it is determined that the parameter value of iteration j is calibration result；Otherwise, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, continue iterative process next time；Rxp₁~Rxp_nN the predetermined value for monitored target；AD₁~AD_nFor n sampled value.Owing to adopting the parameter of contraction algorithm iterative computation transmission function, it is to avoid fall the process solved an equation, it is possible to be applied to nonlinear transmission function and the occasion of non-minimum square law error evaluation function, thus the calibration steps of the present invention has more is widely applied occasion.

Description

To the method transferring function by calibration in monitoring device

Technical field

The present invention relates to collimation technique, particularly relate to calibrating installation and to the method transferring function by calibration in monitoring device.

Background technology

At engineering field, for instance, fields of measurement, control field, the communications field etc. it is frequently necessary to some physical quantity is monitored；The monitoring process of monitoring device is usual as shown in Figure 1: be input in estimating device by the sampled value of sampling apparatus collection, the sampled value of input being estimated by certain transmission function pre-set by estimating device, the estimated value obtained is as the value of monitored target.Sampling apparatus generally can include AD(simulation numeral) transducer；Estimating device can be generally single-chip microcomputer or the device such as microprocessor or CPU.

Before estimating device is applied to monitoring process, it is necessary to the parameter in transmission function is calibrated；And the parameter after calibrating will directly affect estimating device when being applied to monitoring process, the accuracy of the value of obtained monitored target.

At present, the flow process parameter in transmission function being calibrated generally includes following steps as shown in Figure 2:

S201: according to one group of sampled value and expected value, uses transmission function and error evaluation function, calculates error evaluation result.The value of m parameter of the transmission function in error evaluation result is unknown number.

For calculating the transmission function of estimated value, generally it is set to the functional expression fixed according to practical situation by technical staff；Such as, for linear system, once linear function can be adopted as transmission function；More complex system, can adopt many order polynomial function as transmission function；Additionally, also can adopt exponential function or logarithmic function, or piecewise function is as transmission function.The functional expression of transmission function is to arrange in advance, transmits the parameter used in function and then requires over calibration process and determine.

The transmission function of such as quadratic polynomial RxpMon=a × AD × AD+b × AD+c；Wherein, a, b, c is the parameter of transmission function；AD represents the sampled value in Rxp situation, and RxpMon represents the estimated value calculated by transferometer.

The parameter of transmission function is at least one；In most cases transmit the parameter more than one of function.In theory, if the parameter of transmission function is m (m is natural number), then at least needs m sampled value and the corresponding expected value method by solving an equation, calculate the parameter of transmission function.

In practical application, owing to sampling has the reasons such as noise, it is necessary to the number m of the n nuisance parameter of counting of sampled value, being calculated the parameter of transmission function, counting of sampled value is more many, and the parameter of the transmission function of calculating is more accurate.When sampling number n is more than the number of parameters m of transmission function, the method finding out the parameter of optimal transfer function, it is called matching.Find out the parameter of the best, it is necessary to first one error evaluation function of definition assesses the quality of two parameters.Tradition method of least square is estimated.

Such as, by below equation group, calculate parameter a, b, the c of best above-mentioned quadratic polynomial transmission function:

$\left\{\begin{array}{c}{\mathrm{Rxp}}_0=a\×{\mathrm{AD}}_0\×{\mathrm{AD}}_0+b\×{\mathrm{AD}}_0+c\\ {\mathrm{Rxp}}_1=a\×{\mathrm{AD}}_1\×{\mathrm{AD}}_1+b\×{\mathrm{AD}}_1+c\\ {\mathrm{Rxp}}_2=a\×{\mathrm{AD}}_2\×{\mathrm{AD}}_2+b\×{\mathrm{AD}}_2+c\\ {\mathrm{Rxp}}_3=a\×{\mathrm{AD}}_3\×{\mathrm{AD}}_3+b\×{\mathrm{AD}}_3+c\end{array}\right.$

AD in above-mentioned equation group₀、AD₁、AD₂、AD₃, and Rxp₀、Rxp₁、Rxp₂、Rxp₃For known input value；Owing to unknown number number is less than the number of equation, a, b, c generally without solution, but can find the value of the best to make the left side and the right deviation minimum.The error evaluation result of the traditional method of least square weighing deviation size is:

$s(a,b,c)=\underset{i=0}{\overset{3}{\mathrm{\Σ}}}{({\mathrm{Rxp}}_i-\mathrm{Rxp}{\mathrm{Mon}}_i)}^2$

Wherein, RxpMon_i=a×AD_i×AD_i+b×AD_i+c；Take different a, b, c so that (a, b, this group parameter of the more little explanation of value c) is more good, and (a, b, c) for minima namely to make s for s.

S202: m the parameter transmitting function is asked by error evaluation result respectively partial derivative, and makes its partial derivative equal to 0, obtain m equation, form an equation group.

Equation group in previous example is as follows:

$\left\{\begin{array}{c}\frac{\∂s(a,b,c)}{\∂a}=0\\ \frac{\∂s(a,b,c)}{\∂b}=0\\ \frac{\∂s(a,b,c)}{\∂c}=0\end{array}\right.$

S203: solve this equation group, obtains one group of parameter, as calibration result.

Herein above in example, solving this equation group, obtain the value of a group of a, b, c, now (a, b, c) for minima for s.This class value is according to the optimal parameter that method of least square is error evaluation function.Method of least square can so that all sampled points according to transmission function result of calculation and actual value between absolute error quadratic sum minimum.

But, it was found by the inventors of the present invention that prior art calibration steps application scenario is very limited, such as, in using the occasion of nonlinear transmission function or nonlinear error evaluation function, error evaluation result is extremely complex function, and the equation group of its partial derivative composition is difficult to resolve very much；Thus the calibration steps of prior art can not be applied substantially in these occasions.Such as, the application scenarios such as the optical power monitoring result in optical communication field require that relative error is minimum, then the valuation functions of its preferably parameter is the relative error valuation functions of the composition such as logarithmic function and addition function, if the calibration steps of application prior art, then the equation group of its partial derivative composition is difficult to resolve very much；It is to say, the calibration steps of prior art is not suitable for the optical power monitoring occasion in the only small optical communication field of requirement relative error.

Additionally, in the process of solving an equation, the sampled value used as given value and the number of expected value are corresponding with the number of the parameter of transmission function, it is common that equal or more than the number of parameter several；It is known that the given value used is limited, then the accuracy of the result of calculation generally yielded is also just limited.

Therefore, the calibration steps application scenario of prior art is very limited in sum, and the accuracy of the parameter of the transmission function calibrated out is not high.

Summary of the invention

The embodiment provides a kind of calibrating installation and to the method transferring function by calibration in monitoring device, in order to provide application scenario more calibration steps, and improve the accuracy of the parameter of the transmission function calibrated out.

According to an aspect of the invention, it is provided a kind of to the method transferring function by calibration in monitoring device, including:

M parameter of transmission function to be calibrated in described monitoring device is set to the parameter value of the 1st iteration, respectively

Carry out iterative process at least one times；Wherein in iteration j process, if according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueThe error evaluation value s calculated is less than the assessment desired value set, it is determined that the parameter value of iteration j is calibration result；

Otherwise, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, continue+1 iterative process of jth；

Wherein, j is natural number, and n is more than m；Rxp₁~Rxp_nFor n predetermined value of the monitored target of output in calibration process；AD₁~AD_nN the sampled value respectively obtained after the monitored target of described n predetermined value being sensed, samples for described monitoring device.

It is preferred that described contraction algorithm is specially Lay covers Burger-Ma Kuite levenberg-marquardt algorithm or Gauss-Newton Methods.

Wherein, described according to AD₁~AD_n、Rxp₁～Rxp_n, iteration j parameter valueThe error evaluation value s calculated is specially relative error assessed value RS, determines according to equation below 1:

$\mathrm{RS}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|{\log }_{10}\left(g({\mathrm{AD}}_i,x_1^j...x_m^j)\right)-{\log }_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 1)

Wherein, g () represents described transmission function.

According to contraction algorithm, the parameter value of iteration j is adjusted it is preferred that described, obtains the parameter value of+1 iteration of jth, specifically include:

According to described levenberg-marquardt algorithm, calculate Jacobian matrix W；Element in described Jacobian matrix W is determined according to equation below 3:

$w_{h,l}=\frac{{\∂\mathrm{err}}_h}{{\∂x}_l^j}$ (formula 3)

Wherein, w_h,lFor the element arranged of the h row l in W；H is the natural number less than or equal to n, and l is the natural number less than or equal to m；err_hDetermine according to equation below 4:

${\mathrm{err}}_h={\log }_{10}\left(g({\mathrm{AD}}_i,x_1^j...x_m^j)\right)-{\log }_{10}\left({\mathrm{Rxp}}_h\right)$ (formula 4)

Carry out Lay at least one times and cover the adjustment of Burger-Ma Kuite parameter matrix D；Wherein, in the q time adjustment process:

Calculate, according to equation below 5, the Lay adjusted the q time and cover Burger-Ma Kuite parameter matrix D_q:

D_q=[W^T×W+u×I]^-1×W^T× ERR(formula 5)

Wherein, u is decay factor, is set to initial value u before the 1st iterative process₀；I is unit matrix；ERR is by err₁~err_nThe vector constituted；

According to equation below 6, use D_qAdjustAfter, obtain

X_q=X-D_q(formula 6)

Wherein, X serves as reasonsThe vector constituted, X_qServe as reasonsThe vector constituted；

According to equation below 7, useCalculate s_q:

$s_q=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|{\log }_{10}\left(g({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})\right)-{\log }_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 7)

Judge s_qCompared to whether s improves；If judging not improve, then, after increasing decay factor u, continue the adjustment that Lay covers Burger-Ma Kuite parameter matrix the q+1 time；Otherwise, by D_qBurger-Ma Kuite parameter matrix D is covered as the Lay finally determined in iteration j process；Wherein, q is natural number；

Cover Burger-Ma Kuite parameter matrix D according to the Lay finally determined in iteration j process, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth.

Wherein, described according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueThe error evaluation value s calculated is specially relative error assessed value RS, determines according to equation below 11:

$\mathrm{RS}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|\mathrm{RxpMon}\_{\mathrm{dbm}}_i-10\×{\mathrm{Log}}_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 11)

Wherein, RxpMon_dbm_iDetermine according to equation below group 12:

(formula group 12)

Wherein, a, b are setup parameter；RxpMon_iDetermine according to equation below 13:

$\mathrm{Rxp}{\mathrm{Mon}}_i=g({\mathrm{AD}}_i,x_1^j...x_m^j)$ (formula 13)

Wherein, g () represents described transmission function.

Or, described according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, specifically include:

According to described levenberg-marquardt algorithm, calculate Jacobian matrix W；Element in described Jacobian matrix W is determined according to equation below 3:

$w_{h,l}=\frac{{\∂\mathrm{err}}_h}{{\∂x}_l^j}$ (formula 3)

Wherein, w_h,lFor the element arranged of the h row l in W；H is the natural number less than or equal to n, and l is the natural number less than or equal to m；err_hDetermine according to equation below 14:

err_h=RxpMon_dbm_h-10×Log₁₀(Rxp_h) (formula 14)

Wherein, RxpMondbm_hDetermine according to equation below group 15:

(formula group 15)

Wherein, a, b are setup parameter；RxpMon_hDetermine according to equation below 16:

$\mathrm{Rxp}{\mathrm{Mon}}_h=g({\mathrm{AD}}_h,x_1^j...x_m^j)$ (formula 16)

Carry out Lay at least one times and cover the adjustment of Burger-Ma Kuite parameter matrix D；Wherein, in the q time adjustment process:

Calculate, according to equation below 5, the Lay adjusted the q time and cover Burger-Ma Kuite parameter matrix D_q:

D_q=[W^T×W+u×I]^-1×W^T× ERR(formula 5)

Wherein, u is decay factor, is set to initial value u before the 1st iterative process₀；I is unit matrix；ERR is by err₁~err_nThe vector constituted；

According to equation below 6, use D_qAdjustAfter, obtain

X_q=X-D_q(formula 6)

Wherein, X serves as reasonsThe vector constituted, X_qServe as reasonsThe vector constituted；

According to equation below 17, useCalculate s_q:

$s_q=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|\mathrm{RxpMon}\_{\mathrm{dbm}}_i^q-10\×{\log }_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 17)

Wherein, g () represents described transmission function；Determine according to equation below group 18:

(formula group 18)

Wherein, a, b are setup parameter；Determine according to equation below 19:

$\mathrm{Rxp}{\mathrm{Mon}}_i^q=g({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})$ (formula 19)

Judge s_qCompared to whether s improves；If judging not improve, then, after increasing decay factor u, continue the adjustment that Lay covers Burger-Ma Kuite parameter matrix the q+1 time；Otherwise, by D_qBurger-Ma Kuite parameter matrix D is covered as the Lay finally determined in iteration j process；Wherein, q is natural number；

Cover Burger-Ma Kuite parameter matrix D according to the Lay finally determined in iteration j process, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth.

Or, described according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueThe error evaluation value s calculated is specially minimum mean-square error assessed value MS, determines according to equation below 2:

$\mathrm{WS}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(g({\mathrm{AD}}_i,x_1^j...x_m^j)-{\mathrm{Rxp}}_i)}^2$ (formula 2)

Wherein, g () represents described transmission function.

Wherein, described according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, specifically include:

According to described levenberg-marquardt algorithm, calculate Jacobian matrix W；Element in described Jacobian matrix W is determined according to equation below 3:

$w_{h,l}=\frac{{\∂\mathrm{err}}_h}{{\∂x}_l^j}$ (formula 3)

Wherein, w_h,lFor the element arranged of the h row l in W；H is the natural number less than or equal to n, and l is the natural number less than or equal to m；err_hDetermine according to equation below 8:

${\mathrm{err}}_h={(g({\mathrm{AD}}_h,x_1^j...x_m^j)-{\mathrm{Rxp}}_h)}^2$ (formula 8)

Carry out Lay at least one times and cover the adjustment of Burger-Ma Kuite parameter matrix D；Wherein, in the q time adjustment process:

Calculate, according to equation below 5, the Lay adjusted the q time and cover Burger-Ma Kuite parameter matrix D_q:

D_q=[W^T×W+u×I]^-1×W^T× ERR(formula 5)

Wherein, u is decay factor, is set to initial value u before the 1st iterative process₀；I is unit matrix；ERR is by err₁~err_nThe vector constituted；

According to equation below 6, use D_qAdjustAfter, obtain

X_q=X-D_q(formula 6)

Wherein, X serves as reasonsThe vector constituted, X_qServe as reasonsThe vector constituted；

According to equation below 9, useCalculate s_q:

$s_q=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(g({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})-{\mathrm{Rxp}}_i)}^2$ (formula 9)

Judge s_qCompared to whether s improves；If judging not improve, then, after increasing decay factor u, continue the adjustment that Lay covers Burger-Ma Kuite parameter matrix the q+1 time；Otherwise, by D_qBurger-Ma Kuite parameter matrix D is covered as the Lay finally determined in iteration j process；Wherein, q is natural number；

Cover Burger-Ma Kuite parameter matrix D according to the Lay finally determined in iteration j process, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth.

According to another aspect of the present invention, additionally provide a kind of calibrating installation, including:

Data acquisition module, is used for obtaining AD₁~AD_n、Rxp₁~Rxp_n；Wherein, Rxp₁~Rxp_nFor n predetermined value of the monitored target of output in calibration process；AD₁~AD_nN the sampled value respectively obtained after the monitored target of described n predetermined value being sensed, samples for monitoring device；

Iteration module, for being set to the parameter value of the 1st iteration, respectively by m parameter of transmission function to be calibrated in monitoring deviceAfter, carry out iterative process at least one times；Wherein in iteration j process, if according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueThe error evaluation value s calculated is less than setting assessed value, it is determined that the parameter value of iteration j is calibration result and exports；Otherwise, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, continue+1 iterative process of jth；Wherein, j is natural number, and m is less than n；

Parameter setting module, for receiving the calibration result of described iteration module output, and updates the parameter transmitting function in described monitoring device according to the calibration result received.

Wherein, described iteration module includes:

Iteration control unit, for being set to the parameter value of the 1st iteration, respectively by m parameter of transmission function to be calibrated in monitoring deviceAfter, control iterative process at least one times；When iteration j process starts, send the parameter value of error evaluation notice and iteration j

Error evaluation unit, after receiving the error evaluation notice that described iteration control unit sends, according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueDescribed iteration control unit it is sent to after calculating error evaluation value s；

Described iteration control unit is additionally operable to if it is determined that described error evaluation value s is less than setting assessed value, it is determined that the parameter value of iteration j is calibration result and exports；Otherwise, adjustment notice is sent；

Parameter adjustment unit, after receiving the adjustment notice that described iteration control unit sends, is adjusted the parameter value of iteration j according to contraction algorithm, obtains the parameter value of+1 iteration of jth and be sent to described iteration control unit；

After described iteration control unit receives the parameter value of+1 iteration of jth that described parameter adjustment unit sends, start+1 iterative process of jth.

It is preferred that described monitoring device is arranged in optical module, described monitored target is the luminous power of laser.

In the technical scheme of the embodiment of the present invention, owing to adopting contraction algorithm, the parameter of iterative computation transmission function, avoid the process solved an equation, in an iterative process for the calculating process of the parameter of nonlinear transmission function uncomplicated, for non-minimum square law error evaluation function, for instance the calculating process of relative error valuation functions is also uncomplicated, Arbitrary Transfer Function and error evaluation function can be used, thus the calibration steps of the present invention has more is widely applied occasion；

And, comprise the error evaluation function of logarithm operation, it is possible to make absolute error minimum, minimum in the application of target with relative error at the optical power monitoring etc. of optical communication, it is possible to significantly improve the precision of calculating.The minimum fitting algorithm corresponding for the error evaluation function of target of this absolute error being traditional cannot be accomplished.

Further, the parameter of transmission function is estimated by the technical scheme of the embodiment of the present invention according to relative error assessed value, the parameter making the transmission function calibrated out has minimum relative error assessed value, it is adaptable to monitored target has the calibration of the monitoring device of wider dynamic range.

Further, it is contemplated that the Log function used in the valuation functions of relative error assessed value can not meet the contraction algorithm requirement to the continuity of a function；Therefore, in the embodiment of the present invention, adopt piecewise function solve this problem so that valuation functions have employed Log function still can apply contraction algorithm, it is adaptable to Log operand be likely to occur input negative value and imponderable situation.

Accompanying drawing explanation

Fig. 1 is the monitoring device internal structure schematic diagram of prior art；

Fig. 2 is the method flow diagram that the parameter in the transmission function in monitoring device is calibrated of prior art；

Fig. 3 is the monitoring device internal structure block diagram of the embodiment of the present invention；

Fig. 4 be the embodiment of the present invention to the method flow diagram that is calibrated of parameter in the transmission function in monitoring device；

The application Lay that Fig. 5 is the embodiment of the present invention covers the method flow diagram that Burger-Ma Kuite algorithm is iterated；

Fig. 6 is the internal structure block diagram of the calibrating installation of the embodiment of the present invention.

Detailed description of the invention

For making the purpose of the present invention, technical scheme and advantage clearly understand, referring to accompanying drawing and enumerate preferred embodiment, the present invention is described in more detail.However, it is necessary to illustrate, the many details listed in description are only used to make reader that one or more aspects of the present invention are had a thorough explanation, can also realize the aspects of the invention even without these specific details.

The term such as " module " used in this application, " system " is intended to include the entity relevant to computer, for instance but it is not limited to hardware, firmware, combination thereof, software or executory software.Such as, module it may be that it is not limited to: the process run on processor, processor, object, executable program, the thread of execution, program and/or computer.For example, application program and this computing equipment of computing equipment running can be modules.One or more modules may be located in an executory process and/or thread, and module can also on a computer and/or be distributed in two or more between multiple stage computer.

The calibration steps of prior art is analyzed by the present inventor, find that the method solved an equation is minimum in some practical applications of target with relative error, due to nonlinear transmission function, goal-based assessment function with non-minimum square law, such as with logarithmic function, the transmission function of piecewise function etc., and the equation group that its partial derivative forms is extremely complex, solve an equation very difficult realization, be not very suitable for practical application.Therefore, the present inventor considers to adopt the parameter of a kind of iterative calculation method calibration transmission function utilizing contraction algorithm, avoid the process solved an equation, thus adopting the method that contraction algorithm is iteratively calibrated to can apply to the occasion of nonlinear transmission function, it is possible to be applied to the occasion of non-minimum square law error evaluation function；Further, it is modified the targets such as absolute error is minimum of can realizing of goal-based assessment function, has broader practice scene.

The technical scheme of the embodiment of the present invention is described in detail below in conjunction with accompanying drawing.The calibration system that the embodiment of the present invention provides includes: monitoring device and calibrating installation；

Wherein, monitoring device is as it is shown on figure 3, include: conversion equipment 300, sampling apparatus 301, estimation block 302；

Conversion equipment 300 is used for sensing monitored target, and exports corresponding induced signal；In actual applications, conversion equipment 300 is different according to the difference of monitored target；Such as, if monitored target is optical signal, then conversion equipment 300 can pass through photodiode and associated peripheral circuits composition；If monitored target is temperature, then conversion equipment 300 can be realized by temperature sensor；If monitored target is high-tension electricity, then conversion equipment 300 can be realized by transformer.

Sampling apparatus 301, for the induced signal of conversion equipment 300 output is sampled, exports sampled value；

Estimation block 302 calculates corresponding estimated value for each sampled value by transferometer after receiving the sampled value of sampling apparatus 301 output, and the monitor value as monitored target exports.The parameter of the transmission function that estimation block 302 uses can deposit in Flash(nonvolatile semiconductor memory member) in.

Before above-mentioned monitoring device comes into operation, calibrating installation the parameter of the transmission function in estimation block 302 is calibrated, namely determines the parameter transmitting function in monitoring device.

When the parameter transmitting function in estimation block 302 is calibrated, the value controlling monitored target is some predetermined value Rxp₁~Rxp_n.；Wherein, n is natural number；This control process both can be through automatic or manual adjustment and can export the instrument of monitored target and complete, and namely controlled described instrument with some setting value Rxp₁~Rxp_nOutput monitored target.Such as, in optical module in order to monitor the monitoring device of laser power, monitored target is the luminous power of laser, and calibration personnel can export, by light power meter, the laser that some luminous powers are predetermined value.

Conversion equipment 300 senses monitored target, and after exporting corresponding induced signal, sampling apparatus 301, for the induced signal of conversion equipment 300 output is sampled, obtains the predetermined value Rxp with n monitored target₁~Rxp_nCorresponding n sampled value AD₁~AD_n.Sampling apparatus 301 exports n sampled value AD₁~AD_nTo estimation block 302.

For calibrating installation, it is necessary to according to AD₁~AD_n, and Rxp₁~Rxp_nDetermine the parameter of the transmission function minimum so that error evaluation value；

Wherein, error evaluation value can be relative error assessed value, it is also possible to be method of least square error evaluation value；

The calibration steps of the present invention is to adopt contraction algorithm, calculates the parameter of transmission function in an iterative manner, so as to the error evaluation value calculated is close to a smaller value gradually.

In fact, if calculated the parameter of the minimum transmission function of relative error assessed value by the method solved an equation of prior art, owing to relating to logarithm operation, so object function also has logarithm operation, its partial derivative is also complicated nonlinear function, need the extremely complex equation group surmounted function, logic is extremely complex, and operand is also very huge, therefore, it is difficult to apply in practice, prior art is little to the accuracy adopting relative error assessed value to assess the parameter transmitting function.And this is for needing the occasion of comparatively accurate relative error, the calibration steps of prior art can not meet the requirement of the relative error of measurement.Such as, optical module is when carrying out the optical power monitoring of laser, and the dynamic range of the luminous power of the laser of its reception is the dynamic range of the reception of typical optical module is 20 ~ 25dB, the namely scope of about about 100 ~ 316 times.Namely the maximum signal that can receive is than minimum big more than 100 times of the signal that can receive.Because this receives the non-constant width of dynamic range of signal, while bringing difficulty to monitoring, also bring the problem that another one monitored results is weighed.If the parameter of the transmission function of optical module is based on what method of least square error evaluation value was determined, then its monitoring result caused is likely: 0.1mW and when 0.001mW monitor deviation be similarly 0.001mW, but for the luminous power of 0.001mW, this error has reached 100%；Therefore, the parameter of the transmission function determined according to method of least square error evaluation value is not particularly suited for monitored target and has the situation of wider dynamic range.

And the calibration steps of the present invention is owing to being adopt contraction algorithm, calculate the parameter of transmission function in an iterative manner, so as to the error evaluation value calculated is close to a smaller value gradually；And owing to the calculating formula in iterative process is relatively simple, so decreasing operand, save operation time, take less calculation resources；And, adopt contraction algorithm, calculate in the process of parameter of transmission function in an iterative manner, if use relative error assessed value as error evaluation value without increasing how many amounts of calculation, be more suitable for the determination of parameter of transmission function that monitored target has the monitoring device of wider dynamic range.

The calibrating installation that the embodiment of the present invention provides is to the method transferring function by calibration in monitoring device, and flow chart as shown in Figure 4, comprises the steps:

S401: calibrating installation obtains the predetermined value Rxp of n above-mentioned monitored target₁~Rxp_n, and corresponding n sampled value AD₁~AD_n。

Specifically, calibrating installation can obtain above-mentioned sampled value AD from the estimation block 302 of monitoring device₁~AD_n, the predetermined value Rxp of monitored target is obtained from the instrument of above-mentioned output monitored target₁~Rxp_n。

S402: m parameter of calibrating installation transmission function g () to using in estimation block 302 first initializes before being calibrated.

The initialization that calibrating installation carries out includes:

The initial value arranging iterations j is 1；J is natural number；

M the parameter for transmitting function arranges initial value: m parameter is set to the parameter value of the 1st iteration, respectivelyIf quickening contraction speed, it is possible to rule of thumb arrange

Assessment desired value eTarget is set；Such as can arrange eTarget is 0.0001.

S403: in iteration j process, according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueThe error evaluation value s calculated.

Specifically, the error evaluation value s calculated can be that relative error assessed value RS, RS can determine according to equation below 1:

$\mathrm{RS}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|{\log }_{10}\left(g({\mathrm{AD}}_i,x_1^j...x_m^j)\right)-{\log }_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 1)

Wherein, g () represents described transmission function.

Or, the error evaluation value s calculated can be that minimum mean-square error assessed value MS, MS determine according to equation below 2:

$\mathrm{WS}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(g({\mathrm{AD}}_i,x_1^j...x_m^j)-{\mathrm{Rxp}}_i)}^2$ (formula 2)

Further, it is contemplated that computationally state in the process of formula 1, due to the object of Log computing be necessary on the occasion of, ifNegative value occurs, calculating can be made to make mistakes；It is true that the contraction algorithm used in subsequent step usually requires that valuation functions is continuous print；And Log function is discontinuous, because Log (0) is minus infinity；So, the valuation functions used in contraction algorithm has been improved by the present invention, so as to continuously；

Therefore, the another kind of valuation functions calculating relative error assessed value RS can be as follows, namely calculates RS according to equation below 11:

$\mathrm{RS}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|\mathrm{RxpMon}\_{\mathrm{dbm}}_i-10\×{\mathrm{Log}}_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 11)

RxpMon_dbm in formula 11_iDetermine according to equation below group 12:

(formula group 12)

Wherein, a, b are the parameter set, for instance can set that a be 40.001, b is 0.0001；RxpMon_iDetermine according to equation below 13:

$\mathrm{Rxp}{\mathrm{Mon}}_i=g({\mathrm{AD}}_i,x_1^j...x_m^j)$ (formula 13)

S404: in iteration j process, it is judged that whether the error evaluation value s calculated is less than the assessment desired value eTarget set；If less than, perform step S410；Otherwise, step S405 is performed.

In this step, the error evaluation value s calculated in iteration j process is compared by calibrating installation with assessment desired value eTarget；If error evaluation value s is less than assessment desired value eTarget, then the parameter in iteration j process is describedRequirement has been reached, it is possible to export as calibration result, terminate this calibration process, jump to step S410 when carrying out error evaluation；Otherwise, calculate the parameter in iterative process next time, perform step S405, proceed iteration.

S405: the parameter value of iteration j is adjusted according to contraction algorithm, obtains the parameter value of+1 iteration of jth, jumps to step S403 after making j=j+1.

Specifically, the parameter value of iteration j is carried out shrinking adjustment by calibrating installation according to contraction algorithm, obtains the parameter value of+1 iteration of jth, jumps to step S403 after making j=j+1；The contraction algorithm adopted can be that Lay covers Burger-Ma Kuite levenberg-marquardt algorithm or Gauss-Newton Methods.

S406: the parameter value of iteration j is exported as calibration result in the estimation block 302 of monitoring device, terminate this calibration process.

Specifically, calibrating installation is after error evaluation value reaches requirement, the parameter value of iteration j is exported in the estimation block 302 of monitoring device as calibration result, then monitoring device is after coming into operation, sampled value can be estimated by estimation block 302 according to the parameter of the transmission function of calibrating installation write, obtains the satisfactory estimated value of error.

Cover Burger-Ma Kuite levenberg-marquardt algorithm for Lay below to tell about in detail, according to contraction algorithm, the parameter value of iteration j is adjusted, the method obtaining the parameter value of+1 iteration of jth；Those skilled in the art can according to technology contents disclosed by the invention, it is achieved the iteration of other contraction algorithm；Under the premise without departing from the principles of the invention, the alternative manner of other contraction algorithm is also regarded as protection scope of the present invention.In above-mentioned steps S405, cover Burger-Ma Kuite levenberg-marquardt algorithm according to Lay and carry out the method flow of parameter iteration, as it is shown in figure 5, comprise the steps:

S500: arranging the initial value adjusting number of times q is 1；Q is natural number.

S501: the parameter value according to iteration j, calculates Jacobian matrix W.

Specifically, cover Burger-Ma Kuite levenberg-marquardt algorithm according to Lay, calculate Jacobian matrix W；Element in described Jacobian matrix W is determined according to equation below 3:

$w_{h,l}=\frac{{\∂\mathrm{err}}_h}{{\∂x}_l^j}$ (formula 3)

In formula 3, w_h,lFor the element arranged of the h row l in W；H is the natural number less than or equal to n, and l is the natural number less than or equal to m；Calculating the difference stepping used in above-mentioned formula 3 isWherein, eps is setting value, those skilled in the art can experience set.

If the error evaluation value s calculated in above-mentioned steps S403 is relative error assessed value RS, then correspondingly, err_hDetermine according to equation below 4:

${\mathrm{err}}_h={\log }_{10}\left(g({\mathrm{AD}}_h,x_1^j...x_m^j)\right)-{\log }_{10}\left({\mathrm{Rxp}}_h\right)$ (formula 4)

Similar with what be previously mentioned in above-mentioned steps S403, if considering, the object of Log computing is necessary on the occasion of, then err_hCan determine according to equation below 14:

err_h=RxpMon_dbm_h-10×Log₁₀(Rxp_h) (formula 14)

RxpMon_dbm_hDetermine according to equation below group 15:

(formula group 15)

Wherein, a, b are the parameter set, for instance can set that a be 40.001, b is 0.0001；RxpMon_hDetermine according to equation below 16:

$\mathrm{Rxp}{\mathrm{Mon}}_h=g({\mathrm{AD}}_h,x_1^j...x_m^j)$ (formula 16)

If the error evaluation value s calculated in above-mentioned steps S403 is minimum mean-square error assessed value MS, then correspondingly, err_hDetermine according to equation below 8:

${\mathrm{err}}_h={(g({\mathrm{AD}}_h,x_1^j...x_1^j)-{\mathrm{Rxp}}_h)}^2$ (formula 8)

Further, after calculating Jacobian matrix W, it is possible to W is carried out QR decomposition, Householder transformation is utilized to find an orthogonal matrix Q, a upper trapezoid matrix R, and a permutation matrix P so that W × P=Q × R；Then with W × (Q^T× ERR) judge whether orthogonal；Wherein, ERR is by err₁~err_nThe vector constituted.

If orthogonal, parameter and the err of transmission function are described_hUncorrelated, then follow-up parameter adjustment and iteration are without in all senses, it is necessary to reacquire Rxp₁~Rxp_n, and AD₁~AD_n, jump to above-mentioned steps S401.

S502: in the adjustment process of the q time Lay illiteracy Burger-Ma Kuite parameter matrix D, calculates the Lay adjusted the q time and covers Burger-Ma Kuite parameter matrix D_q。

Specifically, the Lay that the q time adjusts covers Burger-Ma Kuite parameter matrix D_qCalculate according to equation below 5:

D_q=[W^T×W+u×I]^-1×W^T× ERR(formula 5)

In formula 5, u is decay factor, in the initialization procedure of the above-mentioned steps S402 before the 1st iterative process, it is possible to be set to initial value u₀；I is unit matrix；ERR is by err₁~err_nThe vector constituted.

S503: in the q time adjustment process, uses D_qThe parameter value of iteration j is carried out the q time adjustment.

Specifically, according to equation below 6, use D_qRightAfter carrying out the q time adjustment, obtain

X_q=X-D_q(formula 6)

In formula 6, X serves as reasonsThe vector constituted, X_qServe as reasonsThe vector constituted.

S504: in the q time adjustment process, calculates the error evaluation value RS in the q time adjustment process_q。

Specifically, if the error evaluation value s calculated in above-mentioned steps S403 is relative error assessed value RS, then correspondingly, according to equation below 7, useCalculate s_q:

$s_q=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|{\log }_{10}\left(g({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})\right)-{\log }_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 7)

Similar with what be previously mentioned in above-mentioned steps S403, if considering, the object of Log computing is necessary on the occasion of, then s_qCan determine according to equation below 17:

$s_q=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}|\mathrm{RxpMon}\_{\mathrm{dbm}}_i^q-10\×{\log }_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 17)

Wherein, g () represents described transmission function；Determine according to equation below group 18:

(formula group 18)

Wherein, a, b are the parameter set, for instance can set that a be 40.001, b is 0.0001；Determine according to equation below 19:

$\mathrm{Rxp}{\mathrm{Mon}}_i^q=g({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})$ (formula 19)

If the error evaluation value s calculated in above-mentioned steps S403 is minimum mean-square error assessed value MS, then correspondingly, according to equation below 9, useCalculate s_q:

$s_q=\underset{i=1}{\stackrel{n}{\mathrm{\Σ}}}{(g({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})-{\mathrm{Rxp}}_i)}^2$ (formula 9)

S505: in the q time adjustment process, it is judged that the error evaluation value s in the q time adjustment process_qWhether improve；If judging not improve, then perform step S507；Otherwise, step S506 is performed.

In this step, it is judged that s_qIf specifically may is that s compared to the s method whether improved_qLess than s, specification error assessed value reduces, then judge s_qImprove compared to s；If s_qMore than or equal to s, then show not improve；

Or, ifLess than setting ratio, then judge there is improvement；Otherwise judge not improve；Such asLess than setting ratio 0.9999, then judge there is improvement；Otherwise judge not improve.

If judging not improve, then the space that the adjustment of parameter has not improved is described, jumps to step S507, terminate this and take turns adjustment, enter iterative process next time；If judging there is improvement, then illustrate that adjusting of parameter also has the space improved, it is possible to continue to adjust next time, jump to step S506.

S506: increase decay factor u, after making q=q+1, the adjustment continuing the q+1 time Lay illiteracy Burger-Ma Kuite parameter matrix jumps to step S502.

Specifically, it is possible to after decay factor u is increased 10 times, make q=q+1, jump to step S502.

S507: determine that the Lay adjusted covers Burger-Ma Kuite parameter matrix D the q time_qCover Burger-Ma Kuite parameter matrix D for Lay final in iteration j process, according to the D finally determined, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth.

In this step, according to the D finally determined, the parameter value of iteration j being adjusted, the parameter value obtaining+1 iteration of jth is similar with above-mentioned formula 6, obtains the parameter value of+1 iteration of jth with specific reference to equation below 10:

X^j+1=X^j-D(formula 10)

In formula 10, X^jServe as reasonsThe vector constituted, X^j+1For the vector being made up of the parameter value of+1 iteration of jth.

Based on above-mentioned calibration steps, the internal structure block diagram of the calibrating installation that the embodiment of the present invention provides, as shown in Figure 6, and including: data acquisition module 601, iteration module 602, parameter setting module 603.

Data acquisition module 601 is used for obtaining AD₁~AD_n、Rxp₁~Rxp_n；Wherein, Rxp₁~Rxp_nFor n predetermined value of the monitored target of output in calibration process；AD₁~AD_nN the sampled value respectively obtained after the monitored target of described n predetermined value being sensed, samples for monitoring device；Sampled value that data acquisition module 601 obtains and the predetermined value of monitored target both can be through interface by being manually entered, it is also possible to be, after data acquisition module 601 communicates with the estimation block 302 monitored in device, obtain sampled value AD from estimation block 302₁~AD_n, and the predetermined value Rxp of the instrument communication acquisition monitored target with output monitored target₁~Rxp_n

Iteration module 602 for being set to the parameter value of the 1st iteration by m parameter of transmission function to be calibrated in monitoring device, respectivelyAfter, carry out iterative process at least one times according to the data that data acquisition module 601 obtains；Wherein in iteration j process, if according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueThe error evaluation value s calculated is less than setting assessed value, it is determined that the parameter value of iteration j is calibration result and exports；Otherwise, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, continue+1 iterative process of jth；Wherein, j is natural number, and m is less than n；Iteration module 602 is iterated and is calibrated the method for result and describes in detail in above-mentioned Fig. 4, the method step shown in 5, repeats no more herein.

Parameter setting module 603 is for receiving the calibration result of iteration module 602 output, and updates the parameter transmitting function in described monitoring device according to the calibration result received.

Further, iteration module 602 may include that iteration control unit 611, error evaluation unit 612, parameter adjustment unit 613.

Iteration control unit 611 for being set to the parameter value of the 1st iteration by m parameter of transmission function to be calibrated in monitoring device, respectivelyAfter, control iterative process at least one times；When iteration j process starts, send the parameter value of error evaluation notice and iteration j

After the error evaluation notice that error evaluation unit 612 sends for receiving iteration control unit 611, according to AD₁~AD_n、Rxp₁~Rxp_n, iteration j parameter valueDescribed iteration control unit it is sent to after calculating error evaluation value s；

Iteration control unit 611 is additionally operable to if it is determined that described error evaluation value s is less than setting assessed value, it is determined that the parameter value of iteration j is calibration result and exports；Otherwise, adjustment notice is sent；

After the adjustment notice that parameter adjustment unit 613 sends for receiving iteration control unit 611, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth and be sent to for control unit 611；

After iteration control unit 611 receives the parameter value of+1 iteration of jth that parameter adjustment unit 613 sends, start+1 iterative process of jth.

In the technical scheme of the embodiment of the present invention, owing to adopting contraction algorithm, the parameter of iterative computation transmission function, avoid the process solved an equation, in an iterative process for the calculating process of the result of nonlinear transmission function uncomplicated, for non-minimum square law error evaluation function, for instance the calculating process of the result of relative error valuation functions is also uncomplicated, thus the calibration steps of the present invention has more is widely applied occasion；

Further, the parameter of transmission function is estimated by the technical scheme of the embodiment of the present invention according to relative error assessed value, the parameter making the transmission function calibrated out has minimum relative error assessed value, it is adaptable to monitored target has the calibration of the monitoring device of wider dynamic range.

Further, it is contemplated that the Log function used in the valuation functions of relative error assessed value can not meet the contraction algorithm requirement to the continuity of a function；Therefore, in the embodiment of the present invention, adopt piecewise function to solve this problem, so that valuation functions have employed Log function still can apply contraction algorithm, it is adaptable to Log operand is likely to occur the situation of negative value.

One of ordinary skill in the art will appreciate that all or part of step realizing in above-described embodiment method can be by the hardware that program carrys out instruction relevant and completes, this program can be stored in a computer read/write memory medium, as: ROM/RAM, magnetic disc, CD etc..

The above is only the preferred embodiment of the present invention; it should be pointed out that, for those skilled in the art, under the premise without departing from the principles of the invention; can also making some improvements and modifications, these improvements and modifications also should be regarded as protection scope of the present invention.

Claims (6)

1. the method transferring function by calibration to monitoring in device, it is characterised in that including:

M parameter of transmission function to be calibrated in described monitoring device is set to the parameter value of the 1st iteration, respectively

Carry out iterative process at least one times；Wherein in iteration j process, if according to AD₁～AD_n、Rxp₁～Rxp_n, iteration j parameter valueThe error evaluation value s calculated is less than the assessment desired value set, it is determined that the parameter value of iteration j is calibration result；

Otherwise, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, continue+1 iterative process of jth；

Wherein, j is natural number, and n is more than m；Rxp₁～Rxp_nFor n predetermined value of the monitored target of output in calibration process；AD₁～AD_nN the sampled value respectively obtained after the monitored target of described n predetermined value being sensed, samples for described monitoring device；

Described contraction algorithm is specially Lay and covers Burger-Ma Kuite algorithm or Gauss-Newton Methods；

Described according to AD₁～AD_n、Rxp₁～Rxp_n, iteration j parameter valueThe error evaluation value s calculated is specially relative error assessed value RS, determines according to equation below 1:

$RS=\underset{i=1}{\overset{n}{\Σ}}|{\mathrm{Log}}_{10}\left(g\right({\mathrm{AD}}_i,x_1^j...x_m^j\left)\right)-{\mathrm{Log}}_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 1)

Wherein, g () represents described transmission function.

2. the method for claim 1, it is characterised in that described according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, specifically includes:

Cover Burger-Ma Kuite algorithm according to described Lay, calculate Jacobian matrix W；Element in described Jacobian matrix W is determined according to equation below 3:

$w_{h,l}=\frac{\∂{\mathrm{err}}_h}{\∂x_l^j}$ (formula 3)

Wherein, w_h,lFor the element arranged of the h row l in W；H is the natural number less than or equal to n, and l is the natural number less than or equal to m；err_hDetermine according to equation below 4:

${\mathrm{err}}_h={\mathrm{Log}}_{10}\left(g\right({\mathrm{AD}}_i,x_1^j...x_m^j\left)\right)-{\mathrm{Log}}_{10}\left({\mathrm{Rxp}}_h\right)$ (formula 4)

Carry out Lay at least one times and cover the adjustment of Burger-Ma Kuite parameter matrix D；Wherein, in the q time adjustment process:

Calculate, according to equation below 5, the Lay adjusted the q time and cover Burger-Ma Kuite parameter matrix D_q:

D_q=[W^T×W+u×I]^-1×W^T× ERR (formula 5)

Wherein, u is decay factor, is set to initial value u before the 1st iterative process₀；I is unit matrix；ERR is by err₁～err_nThe vector constituted；

According to equation below 6, use D_qAdjustAfter, obtain

X_q=X-D_q(formula 6)

Wherein, X serves as reasonsThe vector constituted, X_qServe as reasonsThe vector constituted；

According to equation below 7, useCalculate s_q:

$s_q=\underset{i=1}{\overset{n}{\Σ}}|{\mathrm{Log}}_{10}\left(g\right({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q}\left)\right)-{\mathrm{Log}}_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 7)

Judge s_qCompared to whether s improves；If judging not improve, then, after increasing decay factor u, continue the adjustment that Lay covers Burger-Ma Kuite parameter matrix the q+1 time；Otherwise, by D_qBurger-Ma Kuite parameter matrix D is covered as the Lay finally determined in iteration j process；Wherein, q is natural number；

Cover Burger-Ma Kuite parameter matrix D according to the Lay finally determined in iteration j process, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth.

3. the method transferring function by calibration to monitoring in device, it is characterised in that including:

M parameter of transmission function to be calibrated in described monitoring device is set to the parameter value of the 1st iteration, respectively

Carry out iterative process at least one times；Wherein in iteration j process, if according to AD₁～AD_n、Rxp₁～Rxp_n, iteration j parameter valueThe error evaluation value s calculated is less than the assessment desired value set, it is determined that the parameter value of iteration j is calibration result；

Otherwise, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, continue+1 iterative process of jth；

Wherein, j is natural number, and n is more than m；Rxp₁～Rxp_nFor n predetermined value of the monitored target of output in calibration process；AD₁～AD_nN the sampled value respectively obtained after the monitored target of described n predetermined value being sensed, samples for described monitoring device；

Described contraction algorithm is specially Lay and covers Burger-Ma Kuite algorithm or Gauss-Newton Methods；

Described according to AD₁～AD_n、Rxp₁～Rxp_n, iteration j parameter valueThe error evaluation value s calculated is specially relative error assessed value RS, determines according to equation below 11:

$RS=\underset{i=1}{\overset{n}{\Σ}}|RxpMon\_{\mathrm{dbm}}_i-10\×{\mathrm{Log}}_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 11)

Wherein, RxpMon_dbm_iDetermine according to equation below group 12:

Wherein, a, b are setup parameter；RxpMon_iDetermine according to equation below 13:

${\mathrm{RxpMon}}_i=g({\mathrm{AD}}_i,x_1^j...x_m^j)$ (formula 13)

Wherein, g () represents described transmission function.

4. method as claimed in claim 3, it is characterised in that described according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, specifically includes:

Cover Burger-Ma Kuite algorithm according to described Lay, calculate Jacobian matrix W；Element in described Jacobian matrix W is determined according to equation below 3:

$w_{h,l}=\frac{\∂{\mathrm{err}}_h}{\∂x_l^j}$ (formula 3)

Wherein, w_h,lFor the element arranged of the h row l in W；H is the natural number less than or equal to n, and l is the natural number less than or equal to m；err_hDetermine according to equation below 14:

err_h=RxpMon_dbm_h-10×Log₁₀(Rxp_h) (formula 14)

Wherein, RxpMon_dbm_hDetermine according to equation below group 15:

Wherein, a, b are setup parameter；RxpMon_hDetermine according to equation below 16:

${\mathrm{RxpMon}}_h=g({\mathrm{AD}}_h,x_1^j...x_m^j)$ (formula 16)

Carry out Lay at least one times and cover the adjustment of Burger-Ma Kuite parameter matrix D；Wherein, in the q time adjustment process:

Calculate, according to equation below 5, the Lay adjusted the q time and cover Burger-Ma Kuite parameter matrix D_q:

D_q=[W^T×W+u×I]^-1×W^T× ERR (formula 5)

Wherein, u is decay factor, is set to initial value u before the 1st iterative process₀；I is unit matrix；ERR is by err₁～err_nThe vector constituted；

According to equation below 6, use D_qAdjustAfter, obtain

X_q=X-D_q(formula 6)

Wherein, X serves as reasonsThe vector constituted, X_qServe as reasonsThe vector constituted；

According to equation below 17, useCalculate s_q:

$s_q=\underset{i=1}{\overset{n}{\Σ}}|RxpMon\_{\mathrm{dbm}}_i^q-10\×{\mathrm{Log}}_{10}\left({\mathrm{Rxp}}_i\right)|$ (formula 17)

Wherein, g () represents described transmission function；Determine according to equation below group 18:

Wherein, a, b are setup parameter；Determine according to equation below 19:

${\mathrm{RxpMon}}_i^q=g({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})$ (formula 19)

Judge s_qCompared to whether s improves；If judging not improve, then, after increasing decay factor u, continue the adjustment that Lay covers Burger-Ma Kuite parameter matrix the q+1 time；Otherwise, by D_qBurger-Ma Kuite parameter matrix D is covered as the Lay finally determined in iteration j process；Wherein, q is natural number；

Cover Burger-Ma Kuite parameter matrix D according to the Lay finally determined in iteration j process, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth.

5. the method transferring function by calibration to monitoring in device, it is characterised in that including:

M parameter of transmission function to be calibrated in described monitoring device is set to the parameter value of the 1st iteration, respectively

Carry out iterative process at least one times；Wherein in iteration j process, if according to AD₁～AD_n、Rxp₁～Rxp_n, iteration j parameter valueThe error evaluation value s calculated is less than the assessment desired value set, it is determined that the parameter value of iteration j is calibration result；

Otherwise, according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, continue+1 iterative process of jth；

Wherein, j is natural number, and n is more than m；Rxp₁～Rxp_nFor n predetermined value of the monitored target of output in calibration process；AD₁～AD_nN the sampled value respectively obtained after the monitored target of described n predetermined value being sensed, samples for described monitoring device；

Described contraction algorithm is specially Lay and covers Burger-Ma Kuite algorithm or Gauss-Newton Methods；

Described according to AD₁～AD_n、Rxp₁～Rxp_n, iteration j parameter valueThe error evaluation value s calculated is specially minimum mean-square error assessed value MS, determines according to equation below 2:

$WS=\underset{i=1}{\overset{n}{\Σ}}{\left(g\right({\mathrm{AD}}_i,x_1^j...x_m^j)-{\mathrm{Rxp}}_i)}^2$ (formula 2)

Wherein, g () represents described transmission function.

6. method as claimed in claim 5, it is characterised in that described according to contraction algorithm, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth, specifically includes:

Cover Burger-Ma Kuite algorithm according to described Lay, calculate Jacobian matrix W；Element in described Jacobian matrix W is determined according to equation below 3:

$w_{h,l}=\frac{\∂{\mathrm{err}}_h}{\∂x_l^j}$ (formula 3)

Wherein, w_h,lFor the element arranged of the h row l in W；H is the natural number less than or equal to n, and l is the natural number less than or equal to m；err_hDetermine according to equation below 8:

${\mathrm{err}}_h={\left(g\right({\mathrm{AD}}_h,x_1^j...x_m^j)-{\mathrm{Rxp}}_h)}^2$ (formula 8)

Carry out Lay at least one times and cover the adjustment of Burger-Ma Kuite parameter matrix D；Wherein, in the q time adjustment process:

Calculate, according to equation below 5, the Lay adjusted the q time and cover Burger-Ma Kuite parameter matrix D_q:

D_q=[W^T×W+u×I]^-1×W^T× ERR (formula 5)

Wherein, u is decay factor, is set to initial value u before the 1st iterative process₀；I is unit matrix；ERR is by err₁～err_nThe vector constituted；

According to equation below 6, use D_qAdjustAfter, obtain

X_q=X-D_q(formula 6)

Wherein, X serves as reasonsThe vector constituted, X_qServe as reasonsThe vector constituted；

According to equation below 9, useCalculate s_q:

$s_q=\underset{i=1}{\overset{n}{\Σ}}{\left(g\right({\mathrm{AD}}_i,x_1^{j,q}...x_m^{j,q})-{\mathrm{Rxp}}_i)}^2$ (formula 9)

Judge s_qCompared to whether s improves；If judging not improve, then, after increasing decay factor u, continue the adjustment that Lay covers Burger-Ma Kuite parameter matrix the q+1 time；Otherwise, by D_qBurger-Ma Kuite parameter matrix D is covered as the Lay finally determined in iteration j process；Wherein, q is natural number；

Cover Burger-Ma Kuite parameter matrix D according to the Lay finally determined in iteration j process, the parameter value of iteration j is adjusted, obtains the parameter value of+1 iteration of jth.