Method and apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy

Abstract

This invention provides a method and apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy. The method includes: measuring the radiation intensity curve of a target flame using a spectrometer; establishing a first calculation model for the spectral radiation intensity considering the spectral broadening effect, the first calculation model including parameters of water concentration and flame temperature in the flame; establishing a second calculation model based on the radiation intensity curve and the first calculation model to calculate the deviation between the measured radiation intensity and the calculated radiation intensity; using the second calculation model, taking the water concentration and flame temperature in the flame as the particle positions and the change in particle velocity in a single iteration as the velocity, iterating using a random particle swarm optimization algorithm to obtain the current global optimal position of the particles; stopping the iteration when the current global optimal position meets the set accuracy requirements or the number of iterations reaches the set maximum number of iterations, and outputting the results to obtain the water concentration and flame temperature in the flame. This method is simple to operate and the equipment cost is low.

Description

Technical Field

[0001] This invention relates to the field of spectroscopy, and more specifically to a method and apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy. Background Technology

[0002] In high-temperature combustion chambers, accurate and real-time measurement of a class of substances in the environment can infer the operating status of the combustion system and improve combustion efficiency. With the increasing demand for efficient and clean combustion, the development of accurate and convenient methods for measuring substance temperature and concentration has become an inevitable trend. Among various measurement methods, optical measurement methods are gaining increasing attention due to their advantages such as immediacy and non-contact operation. Laser absorption-based methods, with their high signal-to-noise ratio, have been widely used in combustion detection. However, for large-scale combustion systems, this method requires high-power lasers, leading to system complexity, high equipment costs, and limited application in industrial fields. Conversely, methods based on flame self-emission spectroscopy for measuring substance concentration and temperature are simple to operate and have low equipment costs. In clean combustion environments, high-temperature gases such as water, carbon monoxide, and carbon dioxide are the main combustion products in the flame. Since water has a wide emission spectrum distribution and strong radiative power, with two strong absorption bands near 1.4 and 1.9 μm, measuring water concentration and temperature in the flame based on flame self-emission spectroscopy is a preferred method. Summary of the Invention

[0003] The purpose of this invention is to provide a method and apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy. This method and apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy is simple to operate and has low equipment cost.

[0004] To achieve the above objectives, embodiments of the present invention provide a method for measuring water concentration and flame temperature in a flame based on emission spectroscopy. The method includes: measuring the radiation intensity curve of a target flame using a spectrometer; establishing a first calculation model for the spectral radiation intensity considering the spectral broadening effect, the first calculation model including parameters of water concentration and flame temperature in the flame; establishing a second calculation model for the deviation between the calculated radiation intensity and the measured radiation intensity based on the radiation intensity curve and the first calculation model; using the water concentration and flame temperature in the flame as the position of particles and the change in particle velocity in a single iteration as the velocity, iterating using a random particle swarm optimization algorithm to obtain the current global optimal position of the particles; stopping the iteration when the current global optimal position meets a set accuracy requirement or the number of iterations reaches a set maximum number of iterations, and outputting the results to obtain the water concentration and flame temperature in the flame.

[0005] Preferably, the first calculation model is:

[0006]

[0007] in, To account for the spectral radiation intensity after considering the spectral line broadening effect, j(λ) is the spectrometer instrument function, L is the flame thickness, k is the spectral absorption coefficient calculated by water concentration, c1 and c2 are Planck's first and second constants respectively, λ is the wavelength, and T is the flame temperature.

[0008] Preferably, the second calculation model is:

[0009]

[0010] in, To calculate the deviation between the radiation intensity and the measured radiation intensity, I 1，meas To measure radiation intensity, T is the flame temperature. This refers to the water concentration.

[0011] Preferably, the random particle swarm optimization algorithm is used iteratively to obtain the current global optimal position of the particles, including: Step 1), randomly selecting the initial position and initial velocity of all particles; Step 2), calculating the initial fitness function value, using the initial position as the current individual optimal position of each particle, and selecting the position with the smallest function value among all initial positions of particles as the current global optimal position; Step 3), updating the velocity and position of the particles; Step 4), using the updated particles, calculating the updated fitness function value, if the updated fitness function value of one particle is lower than the current individual optimal position function value of that particle, then the current individual optimal position of that particle is replaced with the updated position of the particle; Step 5), comparing the current individual optimal position function values ​​of all particles with the current global optimal position function value, if the current individual optimal position function value of one particle is lower than the current global optimal position function value, then the current global optimal position is replaced with the current individual optimal position function value.

[0012] Preferably, after obtaining the current global optimal position of the particle by iterating using the random particle swarm algorithm, the method further includes: when the current global optimal position does not meet the set accuracy requirement and the number of iterations has not reached the set maximum number of iterations, returning to step 3) to continue iterating.

[0013] This invention also provides an apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy. The apparatus includes a radiation measurement unit, a model building unit, and a particle swarm optimization (PSO) unit. The radiation measurement unit is used to measure the radiation intensity curve of a target flame using a spectrometer. The model building unit is used to: establish a first calculation model for the spectral radiation intensity considering spectral broadening effects, the first calculation model including parameters for water concentration and flame temperature; and establish a second calculation model based on the radiation intensity curve and the first calculation model to calculate the deviation between the measured radiation intensity and the measured radiation intensity. The PSO unit is used to: based on the second calculation model, using the water concentration and flame temperature as particle positions and the change in particle velocity in a single iteration as the velocity, iterate using a random particle swarm optimization (PSO) algorithm to obtain the current global optimal position of the particles; stop iterating when the current global optimal position meets a set accuracy requirement or the number of iterations reaches a set maximum number of iterations, and output the results to obtain the water concentration and flame temperature.

[0014] Preferably, the first calculation model is:

[0015]

[0016] in, To account for the spectral radiation intensity after considering the spectral line broadening effect, j(λ) is the spectrometer instrument function, L is the flame thickness, k is the spectral absorption coefficient calculated by water concentration, c1 and c2 are Planck's first and second constants respectively, λ is the wavelength, and T is the flame temperature.

[0017] Preferably, the second calculation model is:

[0018]

[0019] in, To calculate the deviation between the radiation intensity and the measured radiation intensity, I 1，meas To measure radiation intensity, T is the flame temperature. This refers to the water concentration.

[0020] Preferably, the particle swarm optimization (PSO) unit is used for: step 1), randomly selecting the initial position and initial velocity of all particles; step 2), calculating the initial fitness function value, taking the initial position as the current individual optimal position of each particle, and selecting the position with the smallest function value among all initial positions of particles as the current global optimal position; step 3), updating the velocity and position of the particles; step 4), using the updated particles, calculating the updated fitness function value, and if the updated fitness function value of one particle is lower than the current individual optimal position function value of that particle, then the current individual optimal position of that particle is replaced with the updated position of the particle; step 5), comparing the current individual optimal position function values ​​of all particles with the current global optimal position function value, and if one of the current individual optimal position function values ​​is lower than the current global optimal position function value, then the current global optimal position is replaced with the current individual optimal position function value.

[0021] Preferably, the particle swarm algorithm unit is further configured to: return to step 3) to continue iterating when the current global optimal position does not meet the set accuracy requirement and the number of iterations has not reached the set maximum number of iterations.

[0022] Through the above technical solution, the embodiments of the present invention provide a method and apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy. The method and apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy establishes a second calculation model for calculating the deviation between the calculated radiation intensity and the measured radiation intensity, and uses a particle swarm optimization algorithm to calculate the water concentration and flame temperature in the flame. It is simple to operate and the equipment cost is low.

[0023] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0025] Figure 1 This is a flowchart of a method for measuring water concentration and flame temperature in a flame based on emission spectroscopy, provided in an embodiment of the present invention;

[0026] Figure 2 This is a flowchart of a calculation using a random particle swarm optimization algorithm provided in an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of a radiation intensity curve provided in an embodiment of the present invention;

[0028] Figure 4aThis is a schematic diagram comparing the reconstructed radiation intensity and the measured radiation intensity curves provided in an embodiment of the present invention;

[0029] Figure 4b This is an embodiment of the present invention that provides a relative error curve for reconstructing radiation intensity based on a measured radiation intensity curve;

[0030] Figure 5 This is a schematic diagram of a device for measuring water concentration and flame temperature in a flame based on emission spectroscopy, provided in an embodiment of the present invention.

[0031] Explanation of reference numerals in the attached figures

[0032] 1. Radiation Measurement Unit 2. Model Building Unit

[0033] 3 Particle Swarm Algorithm Unit Detailed Implementation

[0034] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0035] Figure 1 This is a flowchart of a method for measuring water concentration and flame temperature in a flame based on emission spectroscopy, provided by an embodiment of the present invention. Figure 1 As shown, the method includes:

[0036] Step S101: Use a spectrometer to measure the radiation intensity curve of the target flame;

[0037] In this invention, the radiation signal of the flame under test is measured by a spectrometer, and then the radiation signal is processed using the spectral response value obtained by calibration to obtain the spectral radiation intensity distribution curve.

[0038] Step S102: Establish a first calculation model for the spectral radiance considering the spectral line broadening effect. The first calculation model includes parameters for water concentration and flame temperature in the flame.

[0039] According to Planck's radiation law, the formula for calculating spectral radiation intensity can be expressed as:

[0040]

[0041] In the formula, ε(λ) is the emissivity, λ is the wavelength, c1 and c2 are Planck's first and second constants, respectively, and T is the flame temperature.

[0042] For a homogeneous flame with a certain flame thickness L, the emissivity ε(λ) can be expressed as:

[0043] ε(λ)=1-e -Lk(2)

[0044] Wherein, neglecting particle radiation, the spectral absorption coefficient k is the gas absorption coefficient k f ,Right now:

[0045] k = k f (3)

[0046] k can be calculated using a water gas database. f The calculation formula is:

[0047]

[0048] Where N represents the density of gaseous water molecules, S i，λ (T) represents the spectral intensity of the i-th spectral line, F i (λ) is the Lorentz line type function. Spectral line intensity S i，λ (T) can be derived from the linear intensity S at the reference temperature. i，λ (T0) Extrapolation:

[0049]

[0050] Where T0, Q v Q r ,h,k B E″, c, and λ0 represent the reference temperature, vibrational partition function, rotational partition function, Planck constant, Boltzmann constant, low-level energy of the absorption line, speed of light, and center wavelength, respectively.

[0051] The Lorentz linear function is as follows:

[0052]

[0053]

[0054] Where, γ i Indicates the broadening of the i-th spectral line, T0, n, γ air γ self P and P s These are the reference temperature, temperature correlation coefficient, air broadening, gas self-broadening, total atmospheric pressure, and gas partial pressure, respectively.

[0055] Considering the spectral broadening effect caused by experimental factors such as the spectrometer, the above radiation intensities need to be convolved. The convolution formula is as follows:

[0056]

[0057] Among them, I λ Radiation intensity calculated according to Planck's law. To account for the spectral broadening effect in the experimental spectrum, j(λ) is the spectrometer instrument function. When high precision is not required, a Gaussian line shape function can usually be used to describe it. The Gaussian line shape function is as follows:

[0058]

[0059] Where, D λ D λ is the full width at half maximum (FWHM) of the Gaussian line, the height at half the maximum value of the spectral line, λ is the wavelength, and λ0 is the center wavelength of the line.

[0060] Therefore, combining formulas (1)-(9), the first calculation model is:

[0061]

[0062] in, To account for the spectral radiation intensity after considering the spectral line broadening effect, j(λ) is the spectrometer instrument function, L is the flame thickness, k is the spectral absorption coefficient, c1 and c2 are Planck's first and second constants, respectively, λ is the wavelength, and T is the flame temperature. Furthermore, j(λ) can be calculated using formula (9), and k can be calculated using formulas (3)-(7).

[0063] Step S103: Based on the radiation intensity curve and the first calculation model, establish a second calculation model to calculate the deviation between the radiation intensity and the measured radiation intensity;

[0064] The second calculation model is as follows:

[0065]

[0066] in, To calculate the deviation between the radiation intensity and the measured radiation intensity, I 1，meas To measure radiation intensity, T is the flame temperature. This refers to the water concentration.

[0067] Step S104: According to the second calculation model, the position of the particle is determined by the water concentration and flame temperature in the flame, and the velocity is determined by the change in the particle in a single iteration. The random particle swarm algorithm is used to iterate and obtain the current global optimal position of the particle.

[0068] Among them, such as Figure 2 As shown, the current global optimal position of the particle is obtained by iteratively applying the random particle swarm optimization algorithm, including:

[0069] Step 1), randomly select the initial position and initial velocity of all particles;

[0070] The process involves first defining the iteration range for flame temperature and gas concentration, specifying the maximum number of iterations and the maximum permissible error, inputting measured radiation intensity information, treating water concentration and flame temperature as particle positions, treating the change in particle velocity in a single iteration as velocity, and randomly selecting all particle positions and velocities as initial positions and initial velocities.

[0071] Step 2), calculate the initial fitness function value, and select the position with the smallest function value among all the initial positions of the particles as the current global optimal position.

[0072] The fitness function is as follows:

[0073]

[0074] In the formula, i represents the particle order and M represents the number of detection wavelengths.

[0075] Step 3) Update the particle's velocity and position;

[0076] The particle's velocity and position are updated using the following formula:

[0077] υ ij (n+1)=c1r1[p ij (n)-x ij (n)]+c2r2[p g (n)-x ij (n)] (13)

[0078] x ij (n+1)=x ij (n)+υ ij (n+1) (14)

[0079] In the formula, i represents the particle order, j represents the particle dimension (concentration and temperature), n represents the iteration number, c1 and c2 are acceleration constants, r1 and r2 are random numbers between 0 and 1, and υ ij x ij p ij and p g This represents the particle velocity, position, individual optimal position, and global optimal position of particle i in the j-th dimension.

[0080] Step 4) Using the updated particles, calculate the updated fitness function value. If the updated fitness function value of one of the particles is lower than the current individual best position function value of the particle, then replace the current individual best position of the particle with the position of the updated particle.

[0081] Step 5) Compare the current individual optimal position function value of all particles with the current global optimal position function value. If one of the current individual optimal position function values ​​is lower than the current global optimal position function value, then replace the current global optimal position with the current individual optimal position function value.

[0082] Step S105: Stop iterating when the current global optimal position meets the set accuracy requirement or the number of iterations reaches the set maximum number of iterations, and output the results to obtain the water concentration and flame temperature in the flame.

[0083] If the current global optimal position does not meet the set accuracy requirement and the number of iterations has not reached the set maximum number of iterations, return to step 3) to continue iterating. It can be understood that when iteration stops, the current global optimal position is the desired water concentration and flame temperature in the flame.

[0084] The following is a practical example of using the method described above for calculation:

[0085] The radiant intensity of a planar flame was measured using a spectrometer. A Hencken burner was used. The combustion gas nozzle diameter was 60 mm, and the combustion gas consisted of a premixed gas of 0.4 L / min ethylene and 1.4 L / min oxygen, with a protective gas flow of 15 L / min nitrogen. The radiant intensity curve was measured radially at a flame height 2 mm above the nozzle center. Water concentration and flame temperature were calculated using the radiant intensity curve measured at this location. The HITRAN 2016 database was used, and the calculated spectral range was 1300–1900 nm.

[0086] Figure 3 The response values ​​of the spectrometer and the radiation intensity curve obtained after calibration are presented. Radiation intensity information is input, and then iterative calculations are performed using a random particle swarm optimization algorithm. Iteration results: water concentration and flame temperature are 0.4176 K and 1431 K, respectively. Figure 4a A comparison is given between the reconstructed radiation intensity calculated using the iterative results and the measured radiation intensity curves. Figure 4b The relative error curve for reconstructing radiation intensity is given, based on the measured radiation intensity curve.

[0087] Depend on Figure 4b It can be seen that the maximum relative error is less than 5%, and it occurs in the spectral range where the radiation intensity values ​​are relatively small. In the 1350-1900nm spectral range, the maximum relative error is less than 1%, indicating that the reconstructed radiation intensity and the measured radiation intensity are in good agreement.

[0088] Figure 5 This is a structural block diagram of a device for measuring water concentration and flame temperature in a flame based on emission spectroscopy, according to an embodiment of the present invention. Figure 3 As shown, the device includes: a radiation measurement unit 1, a model building unit 2, and a particle swarm optimization (PSO) algorithm unit 3. The radiation measurement unit 1 is used to measure the radiation intensity curve of the target flame using a spectrometer. The model building unit 2 is used to: establish a first calculation model for the spectral radiation intensity considering the spectral broadening effect, the first calculation model including parameters of water concentration and flame temperature in the flame; and establish a second calculation model based on the radiation intensity curve and the first calculation model to calculate the deviation between the measured radiation intensity and the calculated radiation intensity. The particle swarm optimization (PSO) algorithm unit 3 is used to: based on the second calculation model, using the water concentration and flame temperature in the flame as the particle positions, and the change in particle velocity in a single iteration as the velocity, iterate using a random particle swarm optimization algorithm to obtain the current global optimal position of the particles; stop iterating when the current global optimal position meets the set accuracy requirements or the number of iterations reaches the set maximum number of iterations, and output the results to obtain the water concentration and flame temperature in the flame.

[0089] Preferably, the first calculation model is:

[0090]

[0091] in, To account for the spectral radiation intensity after considering the spectral line broadening effect, j(λ) is the spectrometer instrument function, L is the flame thickness, k is the spectral absorption coefficient, c1 and c2 are Planck's first and second constants, respectively, λ is the wavelength, and T is the flame temperature.

[0092] Preferably, the second calculation model is:

[0093]

[0094] in, To calculate the deviation between the radiation intensity and the measured radiation intensity, I 1，meas To measure radiation intensity, T is the flame temperature. This refers to the water concentration.

[0095] Preferably, the particle swarm optimization unit 3 is used for: step 1), randomly selecting the initial position and initial velocity of all particles; step 2), calculating the initial fitness function value, taking the initial position as the current individual optimal position of each particle, and selecting the position with the smallest function value among all initial positions of particles as the current global optimal position; step 3), updating the velocity and position of the particles; step 4), using the updated particles, calculating the updated fitness function value, and if the updated fitness function value of one particle is lower than the current individual optimal position function value of that particle, then the current individual optimal position of that particle is replaced with the updated position of the particle; step 5), comparing the current individual optimal position function value of all particles with the current global optimal position function value, and if one of the current individual optimal position function values ​​is lower than the current global optimal position function value, then the current global optimal position is replaced with the current individual optimal position function value.

[0096] Preferably, the particle swarm algorithm unit 3 is further configured to: return to step 3) to continue iterating when the current global optimal position does not meet the set accuracy requirements and the number of iterations has not reached the set maximum number of iterations.

[0097] The embodiments of the apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy described above are similar to the embodiments of the method for measuring water concentration and flame temperature in a flame based on emission spectroscopy described above, and will not be repeated here.

[0098] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0104] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0106] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for measuring water concentration and flame temperature in a flame based on emission spectroscopy, characterized in that, The method includes: The radiation intensity curve of the target flame was obtained by measuring it with a spectrometer; A first calculation model for spectral radiance considering the spectral line broadening effect is established. This first calculation model includes parameters for water concentration and flame temperature within the flame. The first calculation model is as follows: ,in, To account for the spectral radiance after considering the spectral line broadening effect, Here, L is the instrument function of the spectrometer, k is the flame thickness, and k is the spectral absorption coefficient, calculated using water concentration. and These are Planck's first and second constants, respectively. For wavelength, Flame temperature; Based on the radiation intensity curve and the first calculation model, a second calculation model is established to consider the deviation between the spectral radiation intensity and the measured radiation intensity after considering the spectral line broadening effect. According to the second calculation model, the position of the particle is determined by the water concentration and flame temperature in the flame, and the velocity is determined by the change in the particle in a single iteration. The random particle swarm algorithm is used to iterate and obtain the current global optimal position of the particle. The iteration stops when the current global optimal position meets the set accuracy requirement or the number of iterations reaches the set maximum number of iterations, and the results are output to obtain the water concentration and flame temperature in the flame.

2. The method for measuring water concentration and flame temperature in a flame based on emission spectroscopy according to claim 1, characterized in that, The second calculation model is: ， in, To calculate the deviation between the radiation intensity and the measured radiation intensity, To measure radiation intensity, The flame temperature This refers to the water concentration.

3. The method for measuring water concentration and flame temperature in a flame based on emission spectroscopy according to claim 1, characterized in that, Using a random particle swarm optimization algorithm for iterative processing, the current global optimal position of the particle is obtained as follows: Step 1), randomly select the initial position and initial velocity of all particles; Step 2), calculate the initial fitness function value, and select the position with the smallest function value among all the initial positions of the particles as the current global optimal position. Step 3) Update the particle's velocity and position; Step 4) Using the updated particles, calculate the updated fitness function value. If the updated fitness function value of one of the particles is lower than the current individual best position function value of that particle, then replace the current individual best position of that particle with the position of the updated particle. Step 5): Compare the current individual optimal position function value of all particles with the current global optimal position function value. If one of the current individual optimal position function values ​​is lower than the current global optimal position function value, then replace the current global optimal position with the current individual optimal position function value.

4. The method for measuring water concentration and flame temperature in a flame based on emission spectroscopy according to claim 3, characterized in that, After iterating using a random particle swarm optimization algorithm to obtain the current global optimal position of the particles, the method further includes: If the current global optimal position does not meet the set accuracy requirement and the number of iterations has not reached the set maximum number of iterations, return to step 3) to continue iterating.

5. A device for measuring water concentration and flame temperature in a flame based on emission spectroscopy, characterized in that, The device includes: The unit comprises a radiation measurement unit, a model building unit, and a particle swarm optimization algorithm unit. The radiation measurement unit is used to measure the radiation intensity curve of the target flame using a spectrometer; The model building unit is used for: A first calculation model for spectral radiance considering the spectral line broadening effect is established. This first calculation model includes parameters for water concentration and flame temperature within the flame. The first calculation model is as follows: ,in, To account for the spectral radiance after considering the spectral line broadening effect, Here, L is the instrument function of the spectrometer, k is the flame thickness, and k is the spectral absorption coefficient, calculated using water concentration. and These are Planck's first and second constants, respectively. For wavelength, Flame temperature; Based on the radiation intensity curve and the first calculation model, a second calculation model is established to consider the deviation between the spectral radiation intensity and the measured radiation intensity after considering the spectral line broadening effect. The particle swarm optimization algorithm unit is used for: According to the second calculation model, the position of the particle is determined by the water concentration and flame temperature in the flame, and the velocity is determined by the change in the particle in a single iteration. The random particle swarm algorithm is used to iterate and obtain the current global optimal position of the particle. The iteration stops when the current global optimal position meets the set accuracy requirement or the number of iterations reaches the set maximum number of iterations, and the results are output to obtain the water concentration and flame temperature in the flame.

6. The apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy according to claim 5, characterized in that, The second calculation model is: ， in, To calculate the deviation between the radiation intensity and the measured radiation intensity, To measure radiation intensity, The flame temperature This refers to the water concentration.

7. The apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy according to claim 5, characterized in that, The particle swarm optimization algorithm unit is used for: Step 1), randomly select the initial position and initial velocity of all particles; Step 2), calculate the initial fitness function value, and select the position with the smallest function value among all the initial positions of the particles as the current global optimal position. Step 3) Update the particle's velocity and position; Step 4) Using the updated particles, calculate the updated fitness function value. If the updated fitness function value of one of the particles is lower than the current individual best position function value of that particle, then replace the current individual best position of that particle with the position of the updated particle. Step 5): Compare the current individual optimal position function value of all particles with the current global optimal position function value. If one of the current individual optimal position function values ​​is lower than the current global optimal position function value, then replace the current global optimal position with the current individual optimal position function value.

8. The apparatus for measuring water concentration and flame temperature in a flame based on emission spectroscopy according to claim 7, characterized in that, The particle swarm optimization algorithm unit is also used for: If the current global optimal position does not meet the set accuracy requirement and the number of iterations has not reached the set maximum number of iterations, return to step 3) to continue iterating.

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