CN115988724A - Plasma spectral data parameter optimization method, device and equipment - Google Patents
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Abstract
The invention discloses a plasma spectral data parameter optimization method, a device and equipment, belonging to the technical field of plasma diagnosis. And constructing a broadening effect function containing plasma parameters to be optimized and a wavelength sequence, and obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence. The method adopts an iterative algorithm, takes the plasma parameters to be optimized as an optimization target, executes the iterative algorithm according to the spectral line fitting contour and the spectral line intensity sequence, and finally obtains the optimized plasma parameters, wherein the optimized plasma parameters have higher precision and better diagnosis effect on the plasma.
Description
Technical Field
The application belongs to the technical field of plasma diagnosis, and particularly relates to a plasma spectral data parameter optimization method, device and equipment.
Background
Plasma spectra are mainly line spectra and continuum spectra. The line spectrum is generated when a neutral atom, ion, etc. in plasma is transited from its excited state with high energy level to lower energy level, and the intensity of the line emitted by a single particle is mainly determined by (1) the probability that the outer electron of the atom or ion is at the upper energy level, (2) the transition probability that such electron is transited from the upper energy level to the lower energy level, and (3) the probability that a photon is reabsorbed before escaping the plasma. However, the total intensity of the spectral lines is related to the density and temperature of electrons and ions, and each spectral line has its own intensity distribution rule, so that the information of the density, temperature and the like of electrons and ions can be obtained from the measurement of the intensity of the spectral lines by combining a theoretical model and atomic data in the spectrum. From the doppler effect, the macroscopic motion velocity of the plasma can be determined from the shift in the wavelength of the spectral line. The continuum is generated by electrons being accelerated or decelerated in the potential field of other particles. From the measurement of the intensity of the continuous spectrum, data such as electron density, temperature, etc. can also be obtained.
The spectral diagnostic method is one of the widely used methods for plasma diagnosis, and analyzes parameters such as ion temperature and electron density of plasma based on spectral data of the measured plasma, but the spectral diagnostic method in the prior art has low accuracy of plasma parameters measured, and is difficult to meet the diagnosis requirement.
Disclosure of Invention
The invention aims to provide a plasma spectral data parameter optimization method, a plasma spectral data parameter optimization device and plasma spectral data parameter optimization equipment, and aims to solve the problems that in the prior art, the plasma parameter measured by a spectral diagnostic method is low in precision and difficult to meet the diagnostic requirement.
A plasma spectral data parameter optimization method, comprising:
acquiring plasma spectral data, and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectral data;
constructing a broadening effect function containing plasma parameters to be optimized and the wavelength sequence;
obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence;
taking the plasma parameter to be optimized as an optimization target, and executing an iterative algorithm according to the spectral line fitting contour and the spectral line intensity sequence;
and outputting the optimized plasma parameters.
Preferably, in an implementable manner of the present application, after the acquiring the plasma spectrum data, the method further comprises:
and determining the optical thickness of the plasma, and correcting the spectral line intensity sequence according to the optical thickness of the plasma.
Preferably, in an implementable manner of the present application, the determining an optical thickness of the plasma and the correcting the sequence of line intensities according to the optical thickness of the plasma includes:
integrating the radius of the plasma based on Abelian transformation to obtain initial spectral line intensity;
obtaining spectral line intensity distribution according to the initial spectral line intensity based on Abelian inverse transformation;
determining an absorption coefficient from the spectral line intensity distribution;
determining the optical thickness of the plasma at different positions according to the absorption coefficient based on Abelian transformation;
correcting the spectral line intensities at different positions of the plasma according to the optical thicknesses at different positions of the plasma based on the beer-Lambert law to obtain corrected spectral line intensities at different positions of the plasma;
a sequence of line intensities is generated.
Preferably, in an implementable manner of the present application, after the acquiring the plasma spectrum data, the method further comprises: normalizing the plasma spectral data.
Preferably, in an implementable manner of the present application, the plasma parameters to be optimized include: ion temperature, electron density, and spectral line center wavelength;
the constructing of the broadening effect function comprising the plasma parameters to be optimized and the wavelength sequence comprises:
constructing an up-sampling wavelength sequence of the wavelength sequence, wherein the up-sampling wavelength sequence takes the maximum value and the minimum value of the wavelength sequence as boundaries;
constructing a Gaussian function according to the upsampling wavelength sequence, the ion temperature and the spectral line center wavelength; the Gaussian function is used for representing the Doppler broadened spectral line strength;
constructing a Lorentzian distribution function according to the up-sampling wavelength sequence, the electron density and the spectral line center wavelength; the Lorentzian distribution function is used to represent the line intensity of the Stokes broadening.
Preferably, in an implementable manner of the present application, said deriving a line fit profile from said broadening effect function and said sequence of wavelengths comprises:
performing discrete convolution on the Gaussian function, the Lorentzian distribution function and the up-sampling wavelength sequence;
and interpolating the discrete convolution result in the wavelength sequence to obtain the spectral line fitting profile.
Preferably, in an implementable manner of the present application, the performing an iterative algorithm according to the line fitting profile and the line intensity sequence with the plasma parameter to be optimized as an optimization target includes:
calculating a least squares error of the line-fit profile and the sequence of line intensities;
and taking the plasma parameters to be optimized as an optimization target, and reducing the least square error of the spectral line fitting profile and the spectral line intensity sequence based on an iterative algorithm until an iteration stop condition is met.
Preferably, in an implementable manner of the present application, the iteration stop condition is: the iteration stop times or the preset least square error value are satisfied.
A plasma spectral data parameter optimization apparatus, comprising:
the plasma spectrum data acquisition module is used for acquiring plasma spectrum data and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectrum data;
the construction module is used for constructing a broadening effect function containing plasma parameters to be optimized and the wavelength sequence;
the fitting module is used for obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence;
the iteration module is used for executing an iteration algorithm according to the spectral line fitting contour and the spectral line intensity sequence by taking the plasma parameters to be optimized as an optimization target;
and the output module is used for outputting the optimized plasma parameters.
A plasma spectral data parameter optimization device, comprising:
a processor and a memory;
the processor is connected with the memory through a communication bus;
the processor is used for calling and executing the program stored in the memory;
the memory for storing a program for at least performing a plasma spectroscopy data parameter optimization method as claimed in any preceding claim.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a plasma spectral data parameter optimization method which comprises the steps of obtaining plasma spectral data and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectral data. And constructing a broadening effect function containing plasma parameters to be optimized and a wavelength sequence, and obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence. The method adopts an iterative algorithm, takes the plasma parameters to be optimized as an optimization target, executes the iterative algorithm according to the spectral line fitting contour and the spectral line intensity sequence, and finally obtains the optimized plasma parameters, wherein the optimized plasma parameters have higher precision and better diagnosis effect on the plasma.
Drawings
Fig. 1 is a schematic flow chart of a plasma spectral data parameter optimization method according to an embodiment of the present invention.
Fig. 2 is a diagram illustrating an example of performing an inverse abelian transform in accordance with one embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a plasma spectral data parameter optimizing apparatus according to a second embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a plasma spectral data parameter optimizing apparatus according to a third embodiment of the present invention.
In the figure: an acquisition module-21; building a module-22; fitting module-23; iteration module-24; an output module-25; a processor-31; a memory-32.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a schematic flowchart of a plasma spectral data parameter optimization method according to an embodiment of the present application, and referring to fig. 1, a plasma spectral data parameter optimization method includes:
s11: acquiring plasma spectral data, and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectral data;
it will be appreciated that, to simplify the calculation, after the plasma spectral data has been acquired, the plasma spectral data is also normalized to give a maximum value of spectral intensity I max =1, and then the wavelength sequence { λ i And the line intensity series { I } i }。
It can be understood that, since the capillary discharge plasma is usually an optically thick plasma, the self-absorption phenomenon may occur due to the uneven distribution of plasma components and temperature, in this embodiment, the influence of the optical thickness of the plasma on the spectral line intensity is considered, after the plasma spectral data is obtained, the optical thickness of the plasma is also determined, and the spectral line intensity sequence is corrected according to the optical thickness of the plasma.
In this embodiment, the spectral line intensity sequence is mainly corrected based on the abelian inverse transformation, as shown in fig. 2, in a specific implementation, the radius of the plasma is integrated based on the abelian inverse transformation to obtain an initial spectral line intensity;
assuming that the plasma has rotational symmetry, i.e., satisfies the cylindrical assumption, such as the circularly symmetric region shown in FIG. 2, ε (r) is the emissivity at radius r, and ε (r) is integrated along path S from point A to point B to obtain the initial spectral line intensity I rad (y)：
For the convenience of calculation, the above formula is rewritten into a form of r as an integral factor, namely, abelian transformation, and is recorded as
Obtaining spectral line intensity distribution according to the initial spectral line intensity based on Abel inverse transformation;
if the initial line intensity I is known rad (y) solving the radiation intensity distribution, this process being called inverse Abelian transformation and noted
Determining an absorption coefficient according to the spectral line intensity distribution;
optical thickness and absorption coefficient kappa of a specific wavelength spectrum at different radial positions λ Correlation, and absorption coefficient κ λ The relation between the radiation coefficient and the radiation coefficient can be established by the Planck equation:
in the formula kappa λ 、ε λ And B λ The subscript λ of (a) represents a specific wavelength.
Determining the optical thickness of the plasma at different positions according to the absorption coefficient based on Abel transformation;
by the abelian transform, the optical thickness at spatial position y for a particular wavelength can be defined as:
correcting the spectral line intensities at different positions of the plasma according to the optical thicknesses at different positions of the plasma based on the beer-Lambert law to obtain corrected spectral line intensities at different positions of the plasma;
according to beer-Lambert's law, the corrected spectral line intensity has the following relationship with the initial spectral line intensity:
wherein,
is the corrected spectral line intensity; />
Is the initial spectral line intensity.
Based on this, after obtaining the corrected spectral line intensities at different positions of the plasma, a sequence of spectral line intensities can be further generated.
S12: constructing a broadening effect function containing plasma parameters to be optimized and a wavelength sequence;
it should be noted that the plasma parameters to be optimized include: ion temperature, electron density, and spectral line center wavelength;
constructing a broadening effect function containing parameters of the plasma to be optimized and the wavelength sequence, wherein the broadening effect function comprises the following steps:
constructing an up-sampling wavelength sequence of the wavelength sequence, wherein the up-sampling wavelength sequence takes the maximum value and the minimum value of the wavelength sequence as boundaries;
constructing a Gaussian function according to the up-sampling wavelength sequence, the ion temperature and the spectral line center wavelength; the Gaussian function is used for representing the Doppler broadened spectral line intensity;
constructing a Lorentz distribution function according to the up-sampling wavelength sequence, the electron density and the central wavelength of the spectral line; the Lorentzian distribution function is used to represent the line intensity of the Stokes broadening.
It is understood that the intensity of the stark-broadened spectral line follows a lorentz distribution:
wherein L is max Is the spectral line intensity amplitude; w is a L Stark spread (full width at half maximum)/nm, which is a lorentz line; lambda [ alpha ] 0 The center wavelength of the spectral line/nm.
The relationship between the stark spread and the electron density and electron temperature is as follows:
where ω is the electron impact parameter/s -1 ；n e Is electron density/cm -3 (ii) a Alpha is an electrostatic ion broadening parameter; t is e Is the electron temperature/K.
The first term to the right of the middle sign in the above equation represents the electron electric field contribution, and the second term is the ion correction factor, which represents the ion contribution. For non-like hydrogen atoms (the outermost layer has only one electron), the stark broadening is mainly affected by electrons, and thus the above equation can be simplified as:
w L ＝2×10 -18 ωn e
based on this, the lorentzian distribution function constructed according to the upsampling wavelength sequence, the electron density and the spectral line center wavelength is as follows:
it is understood that the doppler broadened spectral lines, whose intensity obeys a gaussian distribution:
wherein, G max Is the spectral line intensity amplitude; w is a D Doppler broadening (1/e of amplitude)/nm in a Gaussian line shape; lambda 0 The center wavelength of the spectral line/nm.
The relationship between the doppler spread and the particle temperature T (K) is:
wherein λ is nom Nominal center wavelength/nm; k is a radical of B Boltzmann constant, 1.39064852 (79). Times.10 -23 J·K -1 ；m ion Is the mass of the particles/kg; c is light velocity, 299,792, 458mS -1 。
Based on this, ion temperature (eV)
Comprises the following steps: />
Based on this, the gaussian function constructed from the up-sampling wavelength sequence, ion temperature and spectral line center wavelength is:
s13: obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence;
it should be noted that when multiple broadening effects are present in the radiating plasma, the resulting line profile is a convolution of each broadening profile. Therefore, a line fit profile is derived from the broadening effect function and the sequence of wavelengths, comprising: performing discrete convolution on the Gaussian function, the Lorentzian distribution function and the up-sampling wavelength sequence; and interpolating the discrete convolution result in the wavelength sequence to obtain a spectral line fitting profile.
In practice, for a typical capillary discharge plasma, the main broadening effects are Stark broadening and Doppler broadening, and the convolution produces a spectral profile that is Voigt (Kelvin) linear:
wherein A is v Effective intensity of Voigt line type, i.e. G max And L max The product of (a); by u 1 、u 2 、u 3 Respectively represents the ion temperature T and the electron density n e Sum line center wavelength λ 0 。
For is to
And &>
Performing discrete convolution with u 4 Representing the amplitude after convolution.
Wherein,
setting V (k) at { lambda i Interpolate to get V fit And will V fit The curve is used as Voigt linear line fitting profile.
S14: taking plasma parameters to be optimized as an optimization target, and executing an iterative algorithm according to the spectral line fitting contour and the spectral line intensity sequence;
performing an iterative algorithm based on the line fit profile and the sequence of line intensities, comprising: calculating the least square error of the spectral line fitting profile and the spectral line intensity sequence; and reducing the least square error of the spectral line fitting profile and the spectral line intensity sequence by taking the plasma parameters as an optimization target based on an iterative algorithm until an iteration stop condition is met.
In practice, the iteration stop conditions are: the iteration stop times or the preset least square error value are satisfied.
The formula for calculating the least squares error of the spectral line fit profile and the spectral line intensity sequence is:
preferably, the fruitIn the embodiment, the least square error is reduced by iterative calculation based on the variable simplex method of the unconstrained optimization method so as to optimize u 1 、u 2 、u 3 And u 4 The value of (c).
S15: and outputting the optimized plasma parameters.
In this embodiment, after the iteration is stopped, the finally optimized plasma parameter u is output 1 、u 2 I.e. ion temperature and electron density.
In this embodiment, a plasma spectral data parameter optimization method includes: and acquiring plasma spectral data, and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectral data. And constructing a broadening effect function containing the parameters of the plasma to be optimized and the wavelength sequence, and obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence. In the embodiment, an iterative algorithm is adopted, the plasma parameters to be optimized are taken as an optimization target, the iterative algorithm is executed according to the spectral line fitting profile and the spectral line intensity sequence, the optimized plasma parameters are finally obtained, the optimized plasma parameters are higher in precision, and the plasma diagnosis effect is better.
Example two
Fig. 3 is a schematic structural diagram of a plasma spectral data parameter optimizing device according to an embodiment of the present application, and referring to fig. 3, a plasma spectral data parameter optimizing device includes:
the acquisition module 21 is used for acquiring plasma spectral data and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectral data;
a construction module 22, configured to construct a broadening effect function including a plasma parameter to be optimized and the wavelength sequence;
the fitting module 23 is configured to obtain a spectral line fitting profile according to the broadening effect function and the wavelength sequence;
the iteration module 24 is used for executing an iteration algorithm according to the spectral line fitting contour and the spectral line intensity sequence by taking the plasma parameters to be optimized as an optimization target;
and the output module 25 is used for outputting the optimized plasma parameters.
In this embodiment, an apparatus for optimizing parameters of plasma spectral data includes: an acquisition module 21, a construction module 22, a fitting module 23, an iteration module 24 and an output module 25. In practice, the obtaining module 21 obtains the plasma spectrum data and determines the wavelength sequence and the spectral line intensity sequence of the plasma spectrum data. The construction module 22 constructs a broadening effect function containing the plasma parameters to be optimized and the wavelength sequence, and the fitting module 23 obtains a spectral line fitting profile according to the broadening effect function and the wavelength sequence. The iterative module 24 adopts an iterative algorithm, takes the plasma parameters to be optimized as an optimization target, executes the iterative algorithm according to the spectral line fitting profile and the spectral line intensity sequence, finally obtains optimized plasma parameters and outputs the optimized plasma parameters through the output module 25, and the optimized plasma parameters have higher precision and better diagnosis effect on the plasma.
A plasma spectral data parameter optimization device, further comprising:
and the correction module is used for determining the optical thickness of the plasma and correcting the spectral line intensity sequence according to the optical thickness of the plasma. The method is specifically used for integrating the radius of the plasma based on Abelian transformation to obtain the initial spectral line intensity; obtaining spectral line intensity distribution according to the initial spectral line intensity based on Abel inverse transformation; determining an absorption coefficient according to the spectral line intensity distribution; determining the optical thickness of the plasma at different positions according to the absorption coefficient based on Abelian transformation; correcting the spectral line intensities at different positions of the plasma according to the optical thicknesses at different positions of the plasma based on the beer-Lambert law to obtain corrected spectral line intensities at different positions of the plasma; a sequence of line intensities is generated.
And the normalization module is used for normalizing the plasma spectrum data.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a plasma spectral data parameter optimization apparatus according to an embodiment of the present application, and referring to fig. 4, the plasma spectral data parameter optimization apparatus includes:
a processor 21 and a memory 22;
the processor 21 and the memory 22 are connected by a communication bus;
the processor 21 is configured to call and execute a program stored in the memory 22;
a memory 22 for storing a program for performing at least one plasma spectral data parameter optimization method as in the above embodiment.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar contents in other embodiments may be referred to for the contents which are not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A plasma spectral data parameter optimization method is characterized by comprising the following steps:
acquiring plasma spectral data, and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectral data;
constructing a broadening effect function containing plasma parameters to be optimized and the wavelength sequence;
obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence;
taking the plasma parameter to be optimized as an optimization target, and executing an iterative algorithm according to the spectral line fitting contour and the spectral line intensity sequence; and outputting the optimized plasma parameters.
2. The method of claim 1, wherein the step of obtaining the plasma spectral data comprises determining an optical thickness of the plasma, and correcting the sequence of line intensities according to the optical thickness of the plasma.
3. A method as claimed in claim 2, wherein said determining an optical thickness of the plasma and correcting said sequence of line intensities according to said optical thickness of the plasma comprises the steps of:
integrating the radius of the plasma based on Abelian transformation to obtain initial spectral line intensity;
obtaining spectral line intensity distribution according to the initial spectral line intensity based on Abelian inverse transformation;
determining an absorption coefficient according to the spectral line intensity distribution;
determining the optical thickness of the plasma at different positions according to the absorption coefficient based on Abelian transformation;
correcting the spectral line intensities at different positions of the plasma according to the optical thicknesses at different positions of the plasma based on the beer-Lambert law to obtain corrected spectral line intensities at different positions of the plasma;
a sequence of line intensities is generated.
4. A method according to any one of claims 1 to 3, wherein the plasma spectral data is normalized after the plasma spectral data is acquired.
5. A plasma spectral data parameter optimization method according to any one of claims 1-3, wherein the plasma parameters to be optimized comprise: ion temperature, electron density, and spectral line center wavelength;
the constructing of the broadening effect function comprising the plasma parameters to be optimized and the wavelength sequence comprises:
constructing an up-sampling wavelength sequence of the wavelength sequence, wherein the up-sampling wavelength sequence takes the maximum value and the minimum value of the wavelength sequence as boundaries;
constructing a Gaussian function according to the up-sampling wavelength sequence, the ion temperature and the spectral line center wavelength; the Gaussian function is used for representing the Doppler broadened spectral line strength;
constructing a Lorentzian distribution function according to the up-sampling wavelength sequence, the electron density and the spectral line center wavelength; the Lorentzian distribution function is used to represent the line intensity of the Stokes broadening.
6. The method of claim 5, wherein said deriving a line fit profile from said broadening effect function and said sequence of wavelengths comprises:
performing discrete convolution on the Gaussian function, the Lorentzian distribution function and the up-sampling wavelength sequence;
and interpolating the discrete convolution result in the wavelength sequence to obtain the spectral line fitting profile.
7. The plasma spectral data parameter optimization method according to claim 5, wherein the performing an iterative algorithm according to the line fitting profile and the line intensity sequence with the plasma parameter to be optimized as an optimization target comprises:
calculating a least squares error of the line-fit profile and the sequence of line intensities;
and taking the plasma parameters to be optimized as an optimization target, and reducing the least square error of the spectral line fitting profile and the spectral line intensity sequence based on an iterative algorithm until an iteration stop condition is met.
8. The method of claim 7, wherein the iteration stop condition is: the iteration stop times or the preset least square error value are met.
9. A plasma spectral data parameter optimization apparatus, comprising:
the plasma spectrum data acquisition module is used for acquiring plasma spectrum data and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectrum data;
the construction module is used for constructing a broadening effect function containing the parameters of the plasma to be optimized and the wavelength sequence;
the fitting module is used for obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence;
the iteration module is used for executing an iteration algorithm according to the spectral line fitting contour and the spectral line intensity sequence by taking the plasma parameter to be optimized as an optimization target;
and the output module is used for outputting the optimized plasma parameters.
10. A plasma spectral data parameter optimization apparatus, comprising:
a processor and a memory;
the processor is connected with the memory through a communication bus;
the processor is used for calling and executing the program stored in the memory;
the memory for storing a program for performing at least one plasma spectral data parameter optimization method of any one of claims 1-8.
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