CN101666746B - Laser induced spectrum data processing method based on wavelet analysis - Google Patents
Laser induced spectrum data processing method based on wavelet analysis Download PDFInfo
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- CN101666746B CN101666746B CN2009100755782A CN200910075578A CN101666746B CN 101666746 B CN101666746 B CN 101666746B CN 2009100755782 A CN2009100755782 A CN 2009100755782A CN 200910075578 A CN200910075578 A CN 200910075578A CN 101666746 B CN101666746 B CN 101666746B
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Abstract
The invention relates to the technical field of spectral analysis, in particular to a laser induced spectrum data processing method based on wavelet analysis, which can improve the spectral analysis precision and efficiency and solve the problems that the current laser induced spectrum analysis results are influenced by ineffective measurement data, and the like. The laser induced spectrum data treatment method based on wavelet analysis comprises the following steps: (1) acquiring an effective measurement pattern template G<m>, extracting the effective measurement pattern template when a substance standard sample to be measured is calibrated, and carrying out calibration operation with the corresponding measurement data of the effective measurement pattern template to acquire calibration parameters; and (2) processing actual spectrum measurement data of the measured sample, comparing the actually measured spectrum data with the effective measurement pattern template in actual measurement, keeping the effective spectrum measurement data, and calculating the effective measurement spectrum data based on the calibration parameters to obtain the element content information of the measured sample. The invention can effectively improve the measurement precision, and acquire the effective measurement data of required number based on laser excitation measurement of limited number of time, thereby effectively prolonging the service life of a laser measurement system.
Description
Technical field
The present invention relates to field of spectral analysis technology, specifically is a kind of laser induced spectrum data processing method based on wavelet analysis that can improve spectral analysis precision and efficient.
Background technology
Laser-induced spectrum (Laser Induced Breakdown Spectroscopy, LIBS) be that a kind of laser excitation material that utilizes produces plasma, by analyzing the Atomic Emission Spectral Analysis technology that luminescence of plasma spectrum obtains material element component content information.But therefore advantages such as the LIBS technology has, and specimen preparation is simple, multielement Synchronization Analysis, the fast remote analysis of analysis speed are widely used in the qualitative or quantitative test of various materials, and great practical value is arranged.
But because the characteristic of laser plasma is subjected to the interference of matrix effect and some objective factors that are difficult to avoid easily, as factors such as laser intensity pulsation, sample surfaces characteristics, laser-induced spectrum exists problems such as randomness is big, poor repeatability, has influenced the degree of accuracy of quantitative test.
Be to eliminate the randomness and the undulatory property of laser induced spectrum measuring, improve the degree of accuracy of laser-induced spectrum quantitative test, employing at present repeatedly excites measuring method, and the direct averaged of measurement result is repeatedly obtained the laser-induced spectroscopic analysis result.But it is conspicuous repeatedly exciting and measuring the problem of bringing: the life-span that at first influences laser instrument, secondly influence the real-time of measuring, the most important thing is can't also include some invalid measured values to the direct averaged of measurement result repeatedly with avoiding, thus the precision that influence is measured and analyzed.
Summary of the invention
The present invention is subjected to problems such as invalid measurement data influences in order to solve present laser-induced spectroscopic analysis result, and a kind of laser induced spectrum data processing method based on wavelet analysis is provided.
The present invention adopts following technical scheme to realize: the laser induced spectrum data processing method based on wavelet analysis comprises the steps:
1) effective measurement pattern class template G
mObtaining step;
A, n standard model in the standard of physical sample sets to be measured carried out m time laser induced spectrum measuring respectively, and journal each time spectrum measuring data G corresponding with each standard model
I, j, constitute standard model spectrum measuring data set G={G
I, j, wherein, i=1,2 ..., n, j=1,2 ..., m, G
I, jExpression is carried out the spectrum measuring data that the j time laser induced spectrum measuring obtains to standard model i, and spectrum measuring data G
I, jRepresent in the sequence mode: G
I, j(k)=[X
1, X
2... X
k... X
N], N is a sequence length;
B, to each spectrum measuring data sequence G among the standard model spectrum measuring data set G
I, j(k) carry out L yardstick one-dimensional discrete stationary wavelet and decompose, 4≤L≤8 obtain respectively and each spectrum measuring data sequence G
I, j(k) Dui Ying high frequency coefficient of dissociation
With the low frequency coefficient of dissociation
C, with each spectrum measuring data sequence G
I, j(k) the corresponding low frequency wavelet coefficient of dissociation of difference
Carry out spectrum reconstruct, obtain and each spectrum measuring data sequence G
I, j(k) characteristic background spectroscopic data one to one
Constitutive characteristic background spectrum data acquisition
The characteristic background spectroscopic data
Represent in the sequence mode equally:
And sequence length and spectrum measuring data sequence G
I, j(k) sequence length is identical;
D, to characteristic background spectroscopic data set G
bIn the characteristic background spectroscopic data
Carry out cluster analysis, with characteristic background spectroscopic data set G
bBe divided into several mode class subclass
Promptly
Wherein, h=1,2 ..., H, H is for dividing the mode class subclass number that obtains with the set of characteristic background spectroscopic data according to cluster analysis; According to spectrum measuring data G
I, jWith the characteristic background spectroscopic data
One-to-one relationship and characteristic background spectroscopic data set G
bDivision, standard model spectrum measuring data set G is divided into and characteristic background spectroscopic data set G
bThe mode class subclass
Several mode class subclass G one to one
h, i.e. G={G
1, G
2..., G
h..., G
H;
E, to each mode class subclass G among the standard model spectrum measuring data set G
hThe spectrum measuring data G that comprises
I, jCarry out constituent content calibration computing, obtain and each mode class subclass G
hCalibrate parameter beta one to one
hWith the calibration operation result;
F, the standard value of selecting to calibrate operation result and test substance standard model differ minimum mode class subclass G
hThe pattern that is had is as effective measurement pattern, with this mode class subclass G
hCorresponding calibration parameter beta
hWhen being surveyed, calculates sample the calibration parameter that constituent content adopts;
G, extraction and this mode class subclass G
hCharacteristic of correspondence background spectrum data pattern class subclass
Select characteristic background spectroscopic data mode class subclass
In all characteristic background spectroscopic data sequences
The maximal value of middle position k is as the higher limit of effective measurement pattern class template sequence location k
The minimum value of position k is as the lower limit of effective measurement pattern class template sequence location k
Promptly
Form effective measurement pattern class template
Wherein, 1≤k≤N, E are characteristic background spectroscopic data mode class subclass
Middle characteristic background spectroscopic data sequence
Number, N is a characteristic background spectroscopic data sequence
Sequence length;
2), sample actual spectrum Measurement and Data Processing step;
H, sample is carried out the single laser spectral measurement of inducting, and write down this time spectrum measuring data G
j, j=1,2, And represent in the sequence mode: G
j(k)=[X
1, X
2..., X
k..., X
N], N is a sequence length;
I, to laser induced spectrum data sequence G
j(k) the one-dimensional discrete stationary wavelet that carries out the L yardstick decomposes, and obtains and laser induced spectrum data sequence G
j(k) Dui Ying high frequency coefficient of dissociation
With the low frequency coefficient of dissociation
Wherein, the L value is identical with L value among the step b;
J, the low frequency wavelet coefficient of dissociation that obtains with step I
Carry out spectrum reconstruct, obtain and laser induced spectrum data sequence G
j(k) characteristic of correspondence background spectrum data
The characteristic background spectroscopic data
Represent in the sequence mode equally:
And sequence length and spectrum measuring data sequence G
j(k) sequence length is identical;
K, judging characteristic background spectrum data sequence
Whether with effective measurement pattern class template
Coupling, promptly
Whether set up, as setting up this characteristic background spectrum
Belong to effective measurement pattern class, then with this characteristic background spectroscopic data
Measure spectrum data G
jEffectively;
1, to sample repeated execution of steps h to step k, up to the effective measure spectrum data G that obtains more than three times
j
M, effective measure spectrum data G to being obtained
jAccording to that obtain among the step f and effective measurement pattern class subclass G
hCorresponding calibration parameter beta
hCarry out constituent content and calculate, with the mean value of result of calculation constituent content analysis result as sample.
The spectrum reconstruct that the L yardstick one-dimensional discrete stationary wavelet used in the described method decomposes, carry out with the low frequency wavelet coefficient of dissociation, the cluster analysis that the characteristic background spectroscopic data is carried out and be known technology in the laser induced spectrum measuring technical field to the constituent content calibration computing that spectrum measuring data carries out, engineering technical personnel are known for this area.
Compared with prior art, the present invention at first when standard of physical sample to be measured is calibrated, extracts effective measurement pattern class template, and calibrates computing with the measurement data of effective measurement pattern class template correspondence, obtains the calibration parameter; Then, when sample is carried out actual measurement, the spectroscopic data that laser excitation obtains is compared with effective measurement pattern class template, the validity of checking spectroscopic data, reject invalid measure spectrum data, keep effective spectroscopic data, effective measure spectrum The data is calculated with the calibration parameter that effectively the measurement pattern class template is corresponding, obtain material composition constituent content information.Because the present invention is a foundation with effective measurement pattern class template in the actual measurement process, screen the validity (weighing promptly whether measurement result is effective each time) of measurement data, therefore, can in measuring, limited number of time laser excitation obtain effective measurement data of required number, need not to carry out laser-induced spectrum a large amount of times excites, can reduce laser-induced spectrum significantly and excite number of times, thereby effectively improve the life-span of laser measurement system; And the results of elemental analyses of sample calculates with effective measurement data, and therefore, measuring accuracy is high, and through check, the results of elemental analyses precision that obtains with the present invention has the raising more than 25%.
Description of drawings
Fig. 1 is the effective measurement pattern class template of the present invention G
mThe process flow diagram of obtaining step;
Fig. 2 is the process flow diagram of sample actual spectrum Measurement and Data Processing step of the present invention;
Fig. 3 is for to carry out exciting for 100 times resulting laser-induced spectrum figure to a certain sample;
Fig. 4 is for certain excites corresponding laser-induced spectrum figure among Fig. 3;
Fig. 5 be utilize the reconstruct of low frequency wavelet coefficient of dissociation with Fig. 4 in laser-induced spectrum characteristic of correspondence bias light spectrogram;
Fig. 6 be utilize the reconstruct of low frequency wavelet coefficient of dissociation with Fig. 3 in laser-induced spectrum characteristic of correspondence bias light spectrogram;
Fig. 7 is effective measurement pattern class template G
mSynoptic diagram.
Embodiment
Laser induced spectrum data processing method based on wavelet analysis comprises the steps:
1) effective measurement pattern class template G
mObtaining step, as shown in Figure 1;
A, n standard model in the standard of physical sample sets to be measured carried out m time laser induced spectrum measuring respectively, and journal each time spectrum measuring data G corresponding with each standard model
I, j(as shown in Figure 3), constitute standard model spectrum measuring data set G={G
I, j, wherein, i=1,2 ..., n, j=1,2 ..., m, G
I, jExpression is carried out the spectrum measuring data that the j time laser induced spectrum measuring obtains to standard model i, and spectrum measuring data G
I, jRepresent in the sequence mode: G
I, j(k)=[X
1, X
2... X
k... X
N], N is a sequence length; Wherein, the number of times m that standard of physical sample to be measured is carried out laser induced spectrum measuring generally is greater than and equals 50 times.
B, to each spectrum measuring data sequence G among the standard model spectrum measuring data set G
I, j(k) carry out L yardstick one-dimensional discrete stationary wavelet and decompose, 4≤L≤8 obtain respectively and each spectrum measuring data sequence G
I, j(k) Dui Ying high frequency coefficient of dissociation
With the low frequency coefficient of dissociation
C, with each spectrum measuring data sequence G
I, j(k) the corresponding low frequency wavelet coefficient of dissociation of difference
Carry out spectrum reconstruct, obtain and each spectrum measuring data sequence G
I, j(k) characteristic background spectroscopic data one to one
(as shown in Figure 6), constitutive characteristic background spectrum data acquisition
The characteristic background spectroscopic data
Represent in the sequence mode equally:
And sequence length and spectrum measuring data sequence G
I, j(k) sequence length is identical;
D, to characteristic background spectroscopic data set G
bIn the characteristic background spectroscopic data
Carry out cluster analysis, with characteristic background spectroscopic data set G
bBe divided into several mode class subclass
Promptly
Wherein, h=1,2 ..., H, H is for dividing the mode class subclass number that obtains with the set of characteristic background spectroscopic data according to cluster analysis; According to spectrum measuring data G
I, jWith the characteristic background spectroscopic data
One-to-one relationship and characteristic background spectroscopic data set G
bDivision, standard model spectrum measuring data set G is divided into and characteristic background spectroscopic data set G
bThe mode class subclass
Several mode class subclass G one to one
h, i.e. G={G
1, G
2..., G
h..., G
H;
E, to each mode class subclass G among the standard model spectrum measuring data set G
hThe spectrum measuring data G that comprises
I, jCarry out constituent content calibration computing, obtain and each mode class subclass G
hCalibrate parameter beta one to one
hWith the calibration operation result;
F, the standard value of selecting to calibrate operation result and test substance standard model differ minimum mode class subclass G
hThe pattern that is had is as effective measurement pattern, with this mode class subclass G
hCorresponding calibration parameter beta
hWhen being surveyed, calculates sample the calibration parameter that constituent content adopts;
G, extraction and this mode class subclass G
hCharacteristic of correspondence background spectrum data pattern class subclass
Select characteristic background spectroscopic data mode class subclass
In all characteristic background spectroscopic data sequences
The maximal value of middle position k is as the higher limit of effective measurement pattern class template sequence location k
The minimum value of position k is as the lower limit of effective measurement pattern class template sequence location k
Promptly
Form effective measurement pattern class template
As shown in Figure 7, wherein, 1≤k≤N, E are characteristic background spectroscopic data mode class subclass
Middle characteristic background spectroscopic data sequence
Number, N is a characteristic background spectroscopic data sequence
Sequence length;
2), sample actual spectrum Measurement and Data Processing step, as shown in Figure 2;
H, sample is carried out the single laser spectral measurement of inducting, and write down this time spectrum measuring data G
j, as shown in Figure 4, j=1,2, And represent in the sequence mode: G
j(k)=[X
1, X
2..., X
k..., X
N], N is a sequence length;
I, to laser induced spectrum data sequence G
j(k) the one-dimensional discrete stationary wavelet that carries out the L yardstick decomposes, and obtains and laser induced spectrum data sequence G
j(k) Dui Ying high frequency coefficient of dissociation
With the low frequency coefficient of dissociation
Wherein, the L value is identical with L value among the step b;
J, the low frequency wavelet coefficient of dissociation that obtains with step I
Carry out spectrum reconstruct, obtain and laser induced spectrum data sequence G
j(k) characteristic of correspondence background spectrum data
As shown in Figure 5; The characteristic background spectroscopic data
Represent in the sequence mode equally:
And sequence length and spectrum measuring data sequence G
j(k) sequence length is identical;
K, judging characteristic background spectrum data sequence
Whether with effective measurement pattern class template
Coupling, promptly
Whether set up, as setting up this characteristic background spectrum
Belong to effective measurement pattern class, then with this characteristic background spectroscopic data
Measure spectrum data G
jEffectively;
1, to sample repeated execution of steps h to step k, up to the effective measure spectrum data G that obtains more than three times
j
M, effective measure spectrum data G to being obtained
jAccording to that obtain among the step f and effective measurement pattern class subclass G
hCorresponding calibration parameter beta
hCarry out constituent content and calculate, with the mean value of result of calculation constituent content analysis result as sample.
Claims (1)
1. the laser induced spectrum data processing method based on wavelet analysis is characterized in that comprising the steps:
1) effective measurement pattern class template G
mObtaining step;
A, n standard model in the standard of physical sample sets to be measured carried out m time laser induced spectrum measuring respectively, and journal each time spectrum measuring data G corresponding with each standard model
I, j, constitute standard model spectrum measuring data set G={G
I, j, wherein, i=1,2 ..., n, j=1,2 ..., m, G
I, jExpression is carried out the spectrum measuring data that the j time laser induced spectrum measuring obtains to standard model i, and spectrum measuring data G
I, jRepresent in the sequence mode: G
I, j(k)=[X
1, X
2... X
k... X
N], N is a sequence length;
B, to each spectrum measuring data sequence G among the standard model spectrum measuring data set G
I, j(k) carry out L yardstick one-dimensional discrete stationary wavelet and decompose, 4≤L≤8 obtain respectively and each spectrum measuring data sequence G
I, j(k) Dui Ying high frequency coefficient of dissociation
With the low frequency coefficient of dissociation
C, with each spectrum measuring data sequence G
I, j(k) the corresponding low frequency wavelet coefficient of dissociation of difference
Carry out spectrum reconstruct, obtain and each spectrum measuring data sequence G
I, j(k) characteristic background spectroscopic data one to one
Constitutive characteristic background spectrum data acquisition
The characteristic background spectroscopic data
Represent in the sequence mode equally:
And sequence length and spectrum measuring data sequence G
I, j(k) sequence length is identical;
D, to characteristic background spectroscopic data set G
bIn the characteristic background spectroscopic data
Carry out cluster analysis, with characteristic background spectroscopic data set G
bBe divided into several mode class subclass
Promptly
Wherein, h=1,2 ..., H, H is for dividing the mode class subclass number that obtains with the set of characteristic background spectroscopic data according to cluster analysis; According to spectrum measuring data G
I, jWith the characteristic background spectroscopic data
One-to-one relationship and characteristic background spectroscopic data set G
bDivision, standard model spectrum measuring data set G is divided into and characteristic background spectroscopic data set G
bThe mode class subclass
Several mode class subclass G one to one
h, i.e. G={G
1, G
2..., G
h..., G
H;
E, to each mode class subclass G among the standard model spectrum measuring data set G
hThe spectrum measuring data G that comprises
I, jCarry out constituent content calibration computing, obtain and each mode class subclass G
hCalibrate parameter beta one to one
hWith the calibration operation result;
F, the standard value of selecting to calibrate operation result and test substance standard model differ minimum mode class subclass G
hThe pattern that is had is as effective measurement pattern, with this mode class subclass G
hCorresponding calibration parameter beta
hWhen being surveyed, calculates sample the calibration parameter that constituent content adopts;
G, extraction and this mode class subclass G
hCharacteristic of correspondence background spectrum data pattern class subclass
Select characteristic background spectroscopic data mode class subclass
In all characteristic background spectroscopic data sequences
The maximal value of middle position k is as the higher limit of effective measurement pattern class template sequence location k
The minimum value of position k is as the lower limit of effective measurement pattern class template sequence location k
Promptly
Form effective measurement pattern class template
Wherein, 1≤k≤N, E are characteristic background spectroscopic data mode class subclass
Middle characteristic background spectroscopic data sequence
Number, N is a characteristic background spectroscopic data sequence
Sequence length;
2), sample actual spectrum Measurement and Data Processing step;
H, sample is carried out the single laser spectral measurement of inducting, and write down this time spectrum measuring data G
j, j=1,2, And represent in the sequence mode: G
j(k)=[X
1, X
2..., X
k..., X
N], N is a sequence length;
I, to laser induced spectrum data sequence G
j(k) the one-dimensional discrete stationary wavelet that carries out the L yardstick decomposes, and obtains and laser induced spectrum data sequence G
j(k) Dui Ying high frequency coefficient of dissociation
With the low frequency coefficient of dissociation
Wherein, the L value is identical with L value among the step b;
J, the low frequency wavelet coefficient of dissociation that obtains with step I
Carry out spectrum reconstruct, obtain and laser induced spectrum data sequence G
j(k) characteristic of correspondence background spectrum data
The characteristic background spectroscopic data
Represent in the sequence mode equally:
And sequence length and spectrum measuring data sequence G
j(k) sequence length is identical;
K, judging characteristic background spectrum data sequence
Whether with effective measurement pattern class template
Coupling, promptly
Whether set up, as setting up this characteristic background spectrum
Belong to effective measurement pattern class, then with this characteristic background spectroscopic data
Measure spectrum data G
jEffectively;
1, to sample repeated execution of steps h to step k, up to the effective measure spectrum data G that obtains more than three times
j
M, effective measure spectrum data G to being obtained
jAccording to that obtain among the step f and effective measurement pattern class subclass G
hCorresponding calibration parameter beta
hCarry out constituent content and calculate, with the mean value of result of calculation constituent content analysis result as sample.
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