CN105717093A - Cement characteristic analysis method based on large database recognition - Google Patents

Abstract

Provided is a cement characteristic analysis method based on large database recognition. According to the method, a laser-induced-breakdown spectroscopy technology is adopted, a calibrated sample is subjected to data collection under multiple experiment settings, and therefore a multi-dimensional calibrated sample spectral line intensity large database is established. When an unknown sample is detected, spectroscopic data is collected under the experiment settings identical to those of the calibrated sample, the sample to be detected is recognized from different dimensions, and the characteristic of the cement sample to be detected is directly obtained according to the recognition result or obtained by substituting the recognition result into a calibration model for calculation. According to the method, based on the cement spectral characteristic and the calculation principle of three rate values, a specific spectral line specific value is selected from a spectrum to be used for recognition; the result indicates that the recognition accuracy of the unknown sample can be remarkably improved through the method, and therefore the uncertainty of laser-induced-breakdown spectral measurement is reduced.

Description

A kind of cement characteristics based on large database concept identification analyzes method

Technical field

The present invention relates to one and utilize laser induced plasma spectral technique (LIBS), in conjunction with the LIBS quantitative analysis method of discriminant analysis.

Background technology

In recent years, laser induced plasma spectral technique (is called for short LIBS) owing to having high sensitivity, without sample pretreatment and realizes the advantages such as multielement measurement, becomes a kind of new laser analysis technology.The operation principle of this technology is: sample is carried out ablation and produces plasma by laser, then collection plasma sends optical signal input spectrum instrument are analyzed, and the height of the constituent content that the size of the intensity of spectral line that different wave length place is corresponding is corresponding with this spectral line is directly proportional.The many kinds of substance such as solid, liquids and gases can be analyzed by this technology, has the huge advantage realizing on-line checking, and therefore development speed is very fast.But due to the effect that the unstability of plasma itself, matrix effect and element disturb mutually so that the uncertainty of LIBS spectral measurement is relatively big, the precision of quantitative analysis and accuracy need to improve；

In order to improve the accuracy of LIBS quantitative analysis, Multielement statistical analysis method such as partial least square method is applied to LIBS spectrum analysis by people.Multielement statistical analysis method takes full advantage of the constituent content information comprised in spectrum, the accuracy of quantitative analysis more can be improved than traditional single argument calibrating method, for the shortcoming overcoming Multielement statistical analysis method to lack physical background, researcher proposes the Multielement statistical analysis method based on leading factor, the advantage that the method combines tradition univariate method and multivariate statistical method, both improve the precision of quantitative analysis, add again the robustness of calibration model.But due to the reason that the uncertainty of LIBS spectral measurement is bigger, still bigger for deviation between the group that the not homogeneous measurement of same sample obtains, particularly with the sample such as cement sample of relative complex, the deviation between group becomes apparent from, and has had a strong impact on the precision measured.Therefore how to increase the LIBS repeatability measured and become the problem that LIBS Technique Popularizing must solve.

According to bibliographical information, the method increasing the LIBS repeatability measured mainly has the following aspects: first, by improving the stability of performance improvement LIBS spectral signature the intensity of spectral line of hardware device, laser instrument as more stable in employing laser energy, improves the resolution etc. of spectrogrph；Second, the repeatability of measurement is increased by modulating plasma itself, for example with the method that space restriction or electric discharge strengthen, improve temperature and the electron density of plasma, reduce the fluctuation of plasma parameter itself, increase spectral intensity, thus reducing the relative standard deviation of characteristic spectral line intensity；3rd, being standardized processing by data processing method, plasma temperature, electron density and total population being folded to standard state, thus increasing the stability of LIBS spectrum；Generally speaking, these methods serve reasonable effect in lab analysis, but without carrying out systematized popularization and application.

Discriminant analysis method, as a kind of semiquantitative analysis means, is widely used in the sort research of sample at present, and the way that it combines with quantitative analysis is but without being furtherd investigate.

Summary of the invention

It is an object of the invention to provide a kind of cement characteristics based on large database concept identification and analyze method, improve the precision of LIBS quantitative analysis.

The technical scheme is that

Method analyzed by a kind of ature of coal cement based on large database concept identification, comprises the steps:

1) first by the known n kind cement slurry sample of various characteristics as calibration sample；Utilize LIBS system, adopt different experimental conditions to detect respectively every kind of calibration sample: arranging optical maser wavelength is λ, and laser energy is A, time delay is B, the spot diameter of laser focusing is C, and wherein, λ includes 1064nm, 532nm, 266nm, 193nm；70mJ≤A≤130mJ, 0.5 μ s≤B≤3 μ s；300 μm≤C≤1000 μm；Repeatedly change the value of at least one parameter in λ, A, B and C, there are p kind and arrange；

2) in p kind being arranged any one arrange lower every kind of calibration sample repeat impact t time (t >=50), obtain t × n characteristic spectrum of n kind calibration sample, from each characteristic spectrum, obtain the characteristic spectral line intensity matrix of various elements in calibration sample；

Jth kind calibration sample is obtained the intensity of spectral line matrix of characteristic spectrum:

$E^j=\left[\begin{array}{cccccc}{I}_1^j& I_2^j& ...& I_{i\×l}^j& ...& I_{k\×m}^j\end{array}\right]$

Wherein,Represent the intensity of spectral line that in jth kind calibration sample, the l article characteristic spectral line of i-th kind of element is corresponding,

I=1,2 ..., k；J=1,2 ..., n；L=1,2 ..., m

K is the kind number of element；N is the kind number of calibration sample；M is the number of certain element characteristic of correspondence spectral line；

From the intensity of spectral line matrix E^jIn select all characteristic spectral lines of elements Si, tetra-kinds of elements of Al, Fe, Ca, and obtain e spectral line strength ratio Ca/Si, Al/Si, Fe/Si and Al/Fe；Spectral line strength ratio matrix G^jIt is expressed as follows:

$G^j=\left[\begin{array}{cccccc}{R}_1^j& R_2^j& ...& R_g^j& ...& R_e^j\end{array}\right]$

Wherein,Represent the g spectral line strength ratio of jth kind sample；

Under any one is arranged in p kind is arranged, calculate average and the variance of each spectral line strength ratio, obtain spectral line strength ratio Mean MatrixWith spectral line strength ratio variance matrix F^j:

${\stackrel{\‾}{G}}^j=\left[\begin{array}{cccccc}{\stackrel{\‾}{R}}_1^j& {\stackrel{\‾}{R}}_2^j& ...& {\stackrel{\‾}{R}}_g^j& ...& {\stackrel{\‾}{R}}_e^j\end{array}\right]$

$F^j=\left[\begin{array}{cccccc}{D}_1^j& D_2^j& ...& D_g^j& ...& D_e^j\end{array}\right]$

Wherein,Represent the average of t g kind spectral line strength ratio,Represent the variance of t g kind spectral line strength ratio；G=1,2 ..., e；

3) step 2 is repeated), obtain comprising the characteristic spectrum large database concept of t × n × p characteristic spectrum that p kind arranges lower n kind calibration sample, calculate average and the variance of e spectral line strength ratio in characteristic spectrum large database concept；The n kind calibration sample comprised in characteristic spectrum large database concept is called calibration sample storehouse；

4) using characteristic a certain in the known n kind calibration sample of various characteristics as target property, under each is arranged in p kind is arranged, utilize multivariate to determine the calibration method target property to calibration sample and set up calibration model respectively；The expression formula of calibration model is as follows:

$C_{target}=\underset{g=1}{\overset{e}{\mathrm{\Σ}}}{d}_g{R}_g+b$

Wherein, R_gRepresent the g spectral line strength ratio, d_g, b be the constant determined by multivariate calibrating method matching；

5) with a kind of cement slurry sample of various characteristics the unknown for testing sample, first by LIBS system, lower detection testing sample is set in p kind, during p kind is arranged any one arrange under testing sample, repeated to impact s the characteristic spectrum (s >=50) obtained by s time, calculate average and the variance of each spectral line strength ratio in e spectral line strength ratio Ca/Si, Al/Si, Fe/Si and Al/Fe, obtain the Mean Matrix of testing sampleWith variance matrix F^x:

${\stackrel{\‾}{G}}^x=\left[\begin{array}{cccccc}{\stackrel{\‾}{R}}_1^x& {\stackrel{\‾}{R}}_2^x& ...& {\stackrel{\‾}{R}}_g^x& ...& {\stackrel{\‾}{R}}_e^x\end{array}\right]$

$F^x=\left[\begin{array}{cccccc}{D}_1^x& D_2^x& ...& D_g^x& ...& D_e^x\end{array}\right]$

Wherein,Represent sAverage；Represent sVariance；

Order:

$z=\frac{{\stackrel{\‾}{R}}_g^x-{\stackrel{\‾}{R}}_g^j}{\sqrt{\frac{D_g^x}{s}+\frac{D_g^j}{t}}}$

Can calculate for each spectral line strength ratio and obtain a z value；Different threshold value z is selected for different spectral line strength ratios₀, 2≤z₀≤ 4；If all of characteristic spectral line all meets z < z₀, then it is assumed that under current setting, testing sample is not significantly different from the characteristic spectrum of jth kind sample in calibration sample storehouse；

6) step 5 is repeated), under p kind is arranged, check the diversity between the characteristic spectrum of any one calibration sample in testing sample and calibration sample storehouse, if under p kind is arranged, testing sample all without significant difference, then finally determines that in testing sample and calibration sample storehouse, jth kind sample is same sample with the characteristic spectrum of jth kind sample in calibration sample storehouse；Directly obtain the value of the target property of testing sample, otherwise utilize step 4) in calibration model calculate target property value.

The various characteristics of above-mentioned cement slurry sample include the content of various element, lime saturation ratio, silicon rate and aluminum rate；Cement slurry sample includes powder sample and compressing sample；

The present invention has the following advantages and salience effect:

Discriminant analysis method is combined with quantitative analysis method unknown sample is predicted by the present invention so that the sample in data base is identified out, improves the repeatability of measurement result；Present invention employs different experiments to arrange same calibration sample is detected, each arranges the characteristic spectrum data obtaining a dimension；Kinds of experiments arranges the characteristic spectrum data base that can obtain different dimensions；Owing to plasma has different features under different experimental conditions, these features can be reflected by characteristic spectrum, from the diversity of different dimensional comparison testing samples and calibration sample when therefore carrying out discriminant analysis, it is possible to increase the accuracy of discriminant analysis；Particularly with the coal sample that composition is extremely complex, owing to matrix effect is notable, that measures is uncertain big, sample is easier to situation erroneous judgement occur when carrying out identification in data base, the present invention can greatly improve the accuracy of identification result, and then significantly reduces the uncertainty of complex sample detection.

Accompanying drawing explanation

Fig. 1 is the laser induced plasma spectroscopic system structured flowchart of the present invention.

Fig. 2 is that the present invention analyzes method flow schematic diagram.

In figure: 1 pulse laser；2 condenser lenses；3 samples；4 gather lens；5 optical fiber

6 spectrogrphs；7 computers.

Detailed description of the invention

Below in conjunction with accompanying drawing, the present invention is further illustrated.The present invention comprises the following steps:

1) first by the known n kind cement slurry sample of the various characteristics such as various constituent contents and lime saturation ratio, silicon rate and aluminum rate as calibration sample, cement sample can be powder sample, it is also possible to be compressing sample；Utilize LIBS system, adopt different experimental conditions to detect respectively every kind of calibration sample: arranging optical maser wavelength is λ, and laser energy is A, time delay is B, the spot diameter of laser focusing is C, and wherein, λ includes 1064nm, 532nm, 266nm, 193nm；70mJ≤A≤130mJ, 0.5 μ s≤B≤3 μ s；300 μm≤C≤1000 μm；Repeatedly change the value of at least one parameter in λ, A, B and C, there are p kind and arrange；With pulse laser 1 for excitation source; after condenser lens 2 focuses on, calibration sample 3 surface is acted on from the laser of laser emitting; plasma is produced at focus point; plasma cools down in the atmosphere of protective gas; the radiant light signal produced enters optical fiber 5 by gathering lens 4, and changes into the signal of telecommunication after spectrogrph 6 processes and gathered by computer 7；

2) in p kind being arranged any one arrange lower every kind of calibration sample repeat impact t time (t >=50), obtain t × n characteristic spectrum of n kind calibration sample, from each characteristic spectrum, obtain the characteristic spectral line intensity matrix of various elements in calibration sample；

Jth kind calibration sample is obtained the intensity of spectral line matrix of characteristic spectrum:

$E^j=\left[\begin{array}{cccccc}{I}_1^j& I_2^j& ...& I_{i\&times;l}^j& ...& I_{k\&times;m}^j\end{array}\right]$

Wherein,Represent the intensity of spectral line that in jth kind calibration sample, the l article characteristic spectral line of i-th kind of element is corresponding,

I=1,2 ..., k；J=1,2 ..., n；L=1,2 ..., m

K is the kind number of element；N is the kind number of calibration sample；M is the number of certain element characteristic of correspondence spectral line；

From the intensity of spectral line matrix E^jIn select all characteristic spectral lines of tetra-kinds of elements of Si, Al, Fe, Ca, and obtain e spectral line strength ratio Ca/Si, Al/Si, Fe/Si and Al/Fe；Spectral line strength ratio matrix G^jIt is expressed as follows:

$G^j=\left[\begin{array}{cccccc}{R}_1^j& R_2^j& ...& R_g^j& ...& R_e^j\end{array}\right]$

Wherein,Represent the g spectral line strength ratio of jth kind sample；

Under any one is arranged in p kind is arranged, calculate average and the variance of each spectral line strength ratio, obtain spectral line strength ratio Mean MatrixWith spectral line strength ratio variance matrix F^j:

${\stackrel{\&OverBar;}{G}}^j=\left[\begin{array}{cccccc}{\stackrel{\&OverBar;}{R}}_1^j& {\stackrel{\&OverBar;}{R}}_2^j& ...& {\stackrel{\&OverBar;}{R}}_g^j& ...& {\stackrel{\&OverBar;}{R}}_e^j\end{array}\right]$

$F^j=\left[\begin{array}{cccccc}{D}_1^j& D_2^j& ...& D_g^j& ...& D_e^j\end{array}\right]$

Wherein,Represent the average of t g kind spectral line strength ratio,Represent the variance of t g kind spectral line strength ratio；G=1,2 ..., e；

3) step 2 is repeated), obtain comprising the characteristic spectrum of t × n × p characteristic spectrum that p kind arranges lower n kind calibration sample

Large database concept；The n kind calibration sample comprised in characteristic spectrum large database concept is called calibration sample storehouse；

4) using characteristic a certain in the known n kind calibration sample of various characteristics as target property, under each is arranged in p kind is arranged, utilize multivariate to determine the calibration method target property to calibration sample and set up calibration model respectively；The expression formula of calibration model is as follows:

$C_{t\arg et}=\underset{g=1}{\overset{e}{\mathrm{\&Sigma;}}}{d}_g{R}_g+b$

Wherein, R_gRepresent the g spectral line strength ratio, d_g, b be the constant determined by multivariate calibrating method matching；

5) with a kind of cement slurry sample of various characteristics the unknown for testing sample, first by LIBS system, lower detection testing sample is set in p kind, during p kind is arranged any one arrange under testing sample, repeated to impact s the characteristic spectrum (s >=50) obtained by s time, calculate average and the variance of each spectral line strength ratio in e spectral line strength ratio Ca/Si, Al/Si, Fe/Si and Al/Fe, obtain the Mean Matrix of testing sampleWith variance matrix F^x:

${\stackrel{\&OverBar;}{G}}^x=\left[\begin{array}{cccccc}{\stackrel{\&OverBar;}{R}}_1^x& {\stackrel{\&OverBar;}{R}}_2^x& ...& {\stackrel{\&OverBar;}{R}}_g^x& ...& {\stackrel{\&OverBar;}{R}}_e^x\end{array}\right]$

$F^x=\left[\begin{array}{cccccc}{D}_1^x& D_2^x& ...& D_g^x& ...& D_e^x\end{array}\right]$

Wherein,Represent sAverage；Represent sVariance；

Order:

$z=\frac{{\stackrel{\&OverBar;}{R}}_g^x-{\stackrel{\&OverBar;}{R}}_g^j}{\sqrt{\frac{D_g^x}{s}+\frac{D_g^j}{t}}}$

Can calculate for each spectral line strength ratio and obtain a z value；Different threshold value z is selected for different spectral line strength ratios₀, 2≤z₀≤ 4；If all of characteristic spectral line all meets z < z₀, then it is assumed that under current setting, testing sample is not significantly different from the characteristic spectrum of jth kind sample in calibration sample storehouse；

6) step 5 is repeated), under p kind is arranged, check the diversity between the characteristic spectrum of any one calibration sample in testing sample and calibration sample storehouse, if under p kind is arranged, testing sample all without significant difference, then finally determines that in testing sample and calibration sample storehouse, jth kind sample is same sample with the characteristic spectrum of jth kind sample in calibration sample storehouse；Directly obtain the value of the target property of testing sample, otherwise utilize step 4) in calibration model calculate target property value.

For the sample outside calibration sample storehouse, after calculating obtains once result, this sample is added calibration sample storehouse, then once runs into this sample upper, it is possible to accurate recognition also improves the repeatability of its measurement.

Embodiment: in cement plant one group of cement slurry sample is carried out cement characteristics analysis.

1) this example uses 100 kinds of cement slurry samples as calibration sample, the result that the cement characteristics of calibration sample obtains through traditional off-line analysis is as shown in table 1: because sample size is more, the standard value of sample segment is omitted, respectively with lime saturation ratio, silicon rate and aluminum rate for target property.

Table 1 cement characteristics standard value

Utilize laser induced plasma spectroscopic system that 100 kinds of cement sample are detected, as shown in Figure 1: with pulse laser 1 for excitation source, after condenser lens 2 focuses on, cement sample 3 surface is acted on from the laser of laser emitting, plasma is produced at focus point, plasma cools down in the atmosphere of air, the radiant light signal produced is by adopting focus lens 4 by real-time collecting, change into the signal of telecommunication by optical fiber 5 and after spectrogrph 6 processes and gathered by computer 7, obtain the characteristic spectrum of one group of known cement sample of each elemental mass concentration, obtain the LIBS characteristic spectral line intensity matrix of various elements in cement sample further；Arranging optical maser wavelength is 532nm, and laser energy is 70mJ, 90mJ, 110mJ, and time delay is 0.5 μ s, 1 μ s, 1.5 μ s, and the spot diameter of laser focusing is 300 μm, 400 μm, 500 μm, there are 3 × 3 × 3=27 kind and arranges；

2) arrange at every kind and lower every kind of cement sample repeated impact 60 times, calculate the characteristic spectral line intensity of each characteristic spectrum, it is 250 that each spectrum selects the spectral line quantity corresponding to various element, therefrom select 8, Si line, 5, Al line, 10, Fe line, 4, Ca line, and obtain spectral line strength ratio Ca/Si32, Al/Si40, Fe/Si80 and Al/Fe50, add up to 202；

Under any one is arranged in p kind is arranged, calculate average and the variance of each spectral line strength ratio, obtain spectral line strength ratio Mean MatrixWith spectral line strength ratio variance matrix F^j: (j=1,2 ..., 100)；

3) a intensity of spectral line large database concept comprising 100 × 60 × 27 spectrum is set up；

4) under utilizing 100 kinds of calibration samples each being arranged in p kind is arranged, setting up the calibration model about lime saturation ratio, silicon rate and aluminum rate, the method setting up calibration model is that the deflected secondary air based on leading factor (refers to patent of invention: a kind of analysis of coal nature characteristics method based on leading factor in conjunction with partial least square method；The patent No.: 201310134235.5).

5) from 100 kinds of calibration samples, 10 kinds of samples (assuming that cement characteristics is unknown) are selected, from calibration sample storehouse, additionally choose 10 kinds of samples, add up to 20 kinds of samples as testing sample, the measurement effect of the checking present invention: for the first testing sample, adopt 27 kinds of settings, every kind arrange under repeat impact obtain 27 × 50 characteristic spectrums for 50 times；Every kind arranges down and obtains Mean MatrixWith variance matrix F^x: utilize formula (2) to calculate z value, threshold value z is set₀It is 3；Relatively various z and the z that lower every kind of calibration sample is set₀Value, it has been found that for the in calibration sample storehouse the 5th kind of sample, all of setting can be met z < z₀, then it is assumed that testing sample is not significantly different from the characteristic spectrum of the 5th kind of sample in calibration sample storehouse, and both are same samples；Successively to the 10 kinds of sample measurements selected in calibration sample storehouse and identification, final result shows, 10 kinds of samples by whole identifications out, can directly obtain the value of lime saturation ratio, silicon rate and aluminum rate.

In like manner, for choosing 5 kinds of samples outside calibration sample storehouse as testing sample, under arranging at all of 27 kinds, it is impossible to all meet z < z₀, then it is assumed that this sample is not belonging to calibration sample storehouse, substitutes into step 4) in calibration model in obtain the value of lime saturation ratio, silicon rate and aluminum rate.

It is as shown in the table that relative error result measured by cement slurry sample outside 5 kinds of calibration sample storehouses:

Relative error measured by 2.5 kinds of table cement slurry sample to be measured

The results show that this example obtains, as long as the sample in calibration sample storehouse, the present invention can exactly identification out, thus be effectively improved this sample measure repeatability and accuracy；For the sample outside calibration sample storehouse, after calculating obtains once result, this sample is added calibration sample storehouse, then once runs into this sample upper, it is possible to accurate recognition also improves the repeatability of its measurement.

Principles of the invention is:

Large database concept identification is a kind of method of discriminant analysis, when generally carrying out discriminant analysis, if every kind of calibration sample is only with the stack features spectrum input database foundation as identification, then owing to each characteristic spectral line of characteristic spectrum all has certain fluctuation range, cannot differentiate in fluctuation range so that the difference of the characteristic spectral line intensity of different types of sample is in；This is because at plasma in the process produced and develop, its temperature, electron density and total population constantly change and have certain uncertainty, so characteristic spectral line intensity also can fluctuate therewith.But, the intensity of the characteristic spectrum that cement sample shows under various experiment conditions and wave characteristic are regular governed；Such as when time delay is shorter, the atomic quantity in plasma is more, and atom line strength is bigger；And due to the effect of bremstrahlen, the fluctuation of the intensity of spectral line is also relatively larger；Along with the evolution of plasma, atom constantly ionizes, and now ion line intensity increases and the reduction of atom line strength；In plasma, the fully collision of other particles is made plasma more uniform by electronics, and the stability of the intensity of characteristic spectral line improves；Therefore along with the difference of experiment condition, the spectral catalogue of LIBS reveals different features；The present invention produces the understanding with evolutionary process based on plasma, propose when utilizing LIBS spectrum to carry out identification, the data arranging the multiple dimensions obtained from kinds of experiments go the diversity of sample survey, farthest increase the accuracy of identification, reduce the probability of erroneous judgement.

Cement is the sample that a kind of the Nomenclature Composition and Structure of Complexes is all extremely complex, and its Main elements content is relatively high and stable, and obtained LIBS characteristic spectral line fluctuation is less；And for the less and uneven element of content, then fluctuate often relatively larger, this adds the difficulty of identification to a certain extent.The three ratio of cement is the leading indicator of reflection cement performance, and three ratio is closely related with spectral line strength ratio Ca/Si, Al/Si, Fe/Si and Al/Fe of tetra-kinds of elements of Si, Al, Fe, Ca；The present invention is extracted the characteristic spectral line of corresponding element from LIBS spectral line and utilizes spectral line strength ratio to carry out identification, reduce plasma parameter based on the principle of internal standard method on the one hand and change the fluctuation brought, farthest it is extracted the spectral line information that can represent cement characteristics on the other hand, it is ensured that the success rate of sample identification in storehouse.

Discriminant analysis organically combines, with calibration model, the main thought that the certainty of measurement improving LIBS is the present invention.If a stack features spectrum can be carried out accurate identification, determine that it is a certain calibration sample in data base, then can directly give known target property value, it is calculated without further with calibration model, thus can reduce the uncertainty of measurement that plasma parameter fluctuation causes to a great extent；It has important advantages in that when the kind ratio of sample is relatively limited, and when the data base that sets up is relatively larger, data base inherently can include most similar sample, so utilizes discriminant analysis just can pick out the classification of major part testing sample.For the sample outside calibration sample storehouse, after calculating obtains once result, this sample is added calibration sample storehouse, then once runs into this sample upper, it is possible to accurate recognition also improves the repeatability of its measurement.The method combined of wanting large database concept identification and calibration model can improve the LIBS precision measured on the whole, is the effective ways of LIBS application.

Claims (4)

1. the cement characteristics based on large database concept identification analyzes method, it is characterised in that the method comprises the steps:

1) first by the known n kind cement slurry sample of various characteristics as calibration sample, utilize LIBS system, different experimental conditions is adopted to detect respectively every kind of calibration sample: arranging optical maser wavelength is λ, laser energy is A, time delay is B, the spot diameter of laser focusing is C, and wherein, λ includes 1064nm, 532nm, 266nm, 193nm；70mJ≤A≤130mJ, 0.5 μ s≤B≤3 μ s；300 μm≤C≤1000 μm；Repeatedly change the value of at least one parameter in λ, A, B and C, there are p kind and arrange；

2) in p kind being arranged any one arrange lower every kind of calibration sample repeat impact t time, obtain t × n characteristic spectrum of n kind calibration sample, from each characteristic spectrum, obtain the characteristic spectral line intensity matrix of various elements in calibration sample；

Jth kind calibration sample is obtained the intensity of spectral line matrix of characteristic spectrum:

$E^j=\left[\begin{array}{cccccc}{I}_1^j& I_2^j& ...& I_{i\&times;l}^j& ...& I_{k\&times;m}^j\end{array}\right]$

Wherein,Represent the intensity of spectral line that in jth kind calibration sample, the l article characteristic spectral line of i-th kind of element is corresponding,

I=1,2 ..., k；J=1,2 ..., n；L=1,2 ..., m

K is the kind number of element；N is the kind number of calibration sample；M is the number of certain element characteristic of correspondence spectral line；

From the intensity of spectral line matrix E^jIn select all characteristic spectral lines of elements Si, tetra-kinds of elements of Al, Fe, Ca, and obtain e spectral line strength ratio Ca/Si, Al/Si, Fe/Si and Al/Fe；Spectral line strength ratio matrix G^jIt is expressed as follows:

$G^j=\left[\begin{array}{cccccc}{R}_1^j& R_2^j& ...& R_g^j& ...& R_e^j\end{array}\right]$

Wherein,Represent the g spectral line strength ratio of jth kind sample；

Under any one is arranged in p kind is arranged, calculate average and the variance of each spectral line strength ratio, obtain spectral line strength ratio Mean MatrixWith spectral line strength ratio variance matrix F^j:

${\stackrel{\&OverBar;}{G}}^j=\&lsqb;{\begin{array}{cccccc}{\stackrel{\&OverBar;}{R}}_1^j& {\stackrel{\&OverBar;}{R}}_2^j& ...& {\stackrel{\&OverBar;}{R}}_g^j& ...& \stackrel{\&OverBar;}{R}\end{array}}_e^j\&rsqb;$

$F^j=\left[\begin{array}{cccccc}{D}_1^j& D_2^j& ...& D_g^j& ...& D_e^j\end{array}\right]$

Wherein,Represent the average of t g kind spectral line strength ratio,Represent the variance of t g kind spectral line strength ratio；G=1,2 ..., e；

3) step 2 is repeated), obtain comprising the characteristic spectrum large database concept of t × n × p characteristic spectrum that p kind arranges lower n kind calibration sample, calculate average and the variance of e spectral line strength ratio in characteristic spectrum large database concept；The n kind calibration sample comprised in characteristic spectrum large database concept is called calibration sample storehouse；

4) using characteristic a certain in the known n kind calibration sample of various characteristics as target property, under each is arranged in p kind is arranged, utilize multivariate to determine the calibration method target property to calibration sample and set up calibration model respectively；The expression formula of calibration model is as follows:

$C_{t\arg et}=\underset{g=1}{\overset{e}{\&Sigma;}}{d}_g{R}_g+b$

Wherein, R_gRepresent the g spectral line strength ratio, d_g, b be the constant determined by multivariate calibrating method matching；

5) with a kind of cement slurry sample of various characteristics the unknown for testing sample, first by LIBS system, lower detection testing sample is set in p kind, during p kind is arranged any one arrange under testing sample, repeated to impact s the characteristic spectrum obtained by s time, calculate average and the variance of each spectral line strength ratio in e spectral line strength ratio Ca/Si, Al/Si, Fe/Si and Al/Fe, obtain the Mean Matrix of testing sampleWith variance matrix F^x:

${\stackrel{\&OverBar;}{G}}^x=\left[\begin{array}{cccccc}{\stackrel{\&OverBar;}{R}}_1^x& {\stackrel{\&OverBar;}{R}}_2^x& ...& {\stackrel{\&OverBar;}{R}}_g^x& ...& {\stackrel{\&OverBar;}{R}}_e^x\end{array}\right]$

$F^x=\left[\begin{array}{cccccc}{D}_1^x& D_2^x& ...& D_g^x& ...& D_e^x\end{array}\right]$

Wherein,Represent sAverage；Represent sVariance；

Order:

$z=\frac{{\stackrel{\&OverBar;}{R}}_g^x-{\stackrel{\&OverBar;}{R}}_g^j}{\sqrt{\frac{D_g^x}{s}+\frac{D_g^j}{t}}}$

Can calculate for each spectral line strength ratio and obtain a z value；Different threshold value z is selected for different spectral line strength ratios₀, 2≤z₀≤ 4；If all of characteristic spectral line all meets z < z₀, then it is assumed that under current setting, testing sample is not significantly different from the characteristic spectrum of jth kind sample in calibration sample storehouse；

6) step 5 is repeated), under p kind is arranged, check the diversity between the characteristic spectrum of any one calibration sample in testing sample and calibration sample storehouse, if under p kind is arranged, testing sample all without significant difference, then finally determines that in testing sample and calibration sample storehouse, jth kind sample is same sample with the characteristic spectrum of jth kind sample in calibration sample storehouse；Directly obtain the value of the target property of testing sample, otherwise utilize step 4) in calibration model calculate target property value.

2. a kind of cement characteristics based on large database concept identification according to claim 1 analyzes method, it is characterised in that: t >=50；S >=50.

3. a kind of cement characteristics based on large database concept identification according to claim 1 analyzes method, it is characterised in that: the various characteristics of described cement slurry sample include the content of various elements in cement slurry, lime saturation ratio, silicon rate and aluminum rate.

4. a kind of cement characteristics based on large database concept identification according to claim 1 analyzes method, it is characterised in that: described cement sample includes powder sample and compressing sample.