A method and system for measuring temperature and emissivity of high-temperature materials

Abstract

The present invention provides a method and system for measuring the temperature and emissivity of high-temperature materials, which relates to the field of non-contact measurement technology. The method comprises the following steps: collecting spectral data of high-temperature materials, performing radiation correction on the spectral data; selecting N within the spectral range of the spectral data; C The radiance value E of the spectral channel n and wavelength λ n Based on E n and λ n , construct the objective function; calculate the emissivity ε using the iterative calculation method for the objective function n Numerical solution of ε n The numerical solution is used to calculate the intermediate temperature value; the final temperature T of the high temperature material is calculated using the intermediate temperature value. OP ; and use T OP Calculating the emissivity ε of high-temperature materials obj (λ,T OP A measurement system is also provided, comprising an optical lens, a Fourier spectrometer, a laser, a servo turntable, and a data acquisition and processing unit. This method can process spectral data from a large number of spectral channels in real time, improving measurement efficiency and accuracy. The measurement system can also perform non-contact measurement of the temperature and emissivity of high-temperature materials.

Description

Technical Field

[0001] The present invention belongs to the field of non-contact measurement, and in particular relates to a method and a system for measuring the temperature and emissivity of a high-temperature material. Background Art

[0002] Temperature is a key physical quantity that indicates the degree of hotness or coldness of a target and plays a vital role in describing its radiation characteristics. Temperature measurement methods include contact and non-contact methods. Contact temperature measurement methods, such as thermocouples and thermometers, require direct contact with the target surface for temperature measurement, resulting in a narrow measurement range and instability of the target surface temperature field, limiting their use. Compared to non-contact temperature measurement methods like single-band and two-color temperature measurement, multispectral temperature measurement uses spectral data collected from the target to measure its temperature and simultaneously obtain its emissivity. This temperature measurement method can perform measurements without damaging the target surface, significantly extending its measurement range. It also offers simple operation, fast response, and a wide measurement range. It is suitable for measuring the temperature and emissivity of high-temperature materials and holds significant value in applications such as aircraft thermal monitoring and petrochemical refining monitoring.

[0003] According to Planck's law, the target's radiant energy is determined by its temperature and emissivity. Therefore, the core of multispectral thermometry lies in calculating the target's temperature and emissivity using known radiation wavelength and energy. Traditional multispectral thermometry typically relies on modeling how the target's emissivity varies with wavelength. The accuracy of the solution is highly dependent on the established emissivity-wavelength relationship model. However, in actual testing, establishing an accurate emissivity-wavelength relationship model is difficult due to the unclear target's radiation characteristics and the complexity of the emissivity distribution.

[0004] In recent years, researchers have proposed using iterative optimization methods such as gradient projection to apply to the objective function constructed from spectral data to inversely calculate the target temperature and emissivity. These methods do not rely on a model that models the relationship between emissivity and wavelength, but they require a Wien approximation of the objective function and a reference temperature model. The Wien approximation is often more effective for high-temperature spectral data in the shortwave band, but these methods cannot randomly process spectral data in arbitrary radiation bands. Furthermore, these methods are generally only applicable to data from a small number of spectral channels. When processing data containing a large number of spectral channels, the computational efficiency is low, making it difficult to meet the needs of real-time measurement. Summary of the Invention

[0005] To address the above issues, the present invention proposes a method for measuring the temperature and emissivity of high-temperature materials. The present invention adopts the multi-spectral temperature measurement method in non-contact temperature measurement, collects spectral data through a Fourier spectrometer, and performs iterative calculations based on the data to obtain the temperature and emissivity of the high-temperature material. The temperature measurement method provided by the present invention is highly efficient, the results are more accurate, and the emissivity of the high-temperature material can be obtained.

[0006] The technical solution adopted in the present invention is as follows:

[0007] A method for measuring the temperature and emissivity of a high-temperature material comprises the following steps:

[0008] Step 1: Collect spectral data of high-temperature materials;

[0009] Step 2: performing radiation correction on the spectral data;

[0010] Step 3: Select N within the spectral range of the spectral data. C The radiance E of each spectral channel n and wavelength λ n ;

[0011] Step 4: Based on the radiance E n and the wavelength value λ n , construct the objective function according to Planck's law

[0012]

[0013] Where c1 is Planck's first constant and c2 is Planck's second constant;

[0014] Step 5: For the objective function constructed in step 4, use the iterative calculation method to calculate the selected N C The emissivity ε of each spectral channel n Numerical solution of ;

[0015] Step 6: Using the emissivity ε n Numerical solution to calculate the intermediate temperature of high temperature materials;

[0016] Step 7: Calculate the final temperature T of the high temperature material using the intermediate temperature value OP ;

[0017] Step 8: Using the Final Temperature T of the High-Temperature Material OP Calculating the emissivity ε of high-temperature materials obj (λ,T OP ).

[0018] Furthermore, in step 3, select N C The radiance E of each spectral channel n and wavelength λn The way is:

[0019] In the spectral range of the spectral data, calculate the maximum spectral radiance E hMax and the minimum value E hMin , respectively select the corresponding radiance value

[0020]

[0021] Get N C The radiance value E of the spectral channel n and wavelength λ n .

[0022] Furthermore, in step 5, the specific calculation steps of the iterative algorithm used are:

[0023] N C The emissivity ε of each spectral channel n Form a vector Random initialization is performed according to the following expression:

[0024]

[0025] is a vector generated by random initialization, us is the upper limit of the search range, and its value is 1, ls is the lower limit of the search range, and its value is 0, and R1 is a vector composed of variable values ​​uniformly distributed between 0 and 1. According to the random initialization process, multiple vectors are generated to form a group as the initial input of the algorithm.

[0026] Substitute each vector in the generated group into the following formula in turn to iteratively calculate the intermediate solution:

[0027]

[0028] Among them, the parameters pn and rand are random numbers between 0 and 1. is the newly generated intermediate solution, is the candidate solution for the current iteration t, r1, r2, r3 and r4 are the indices of four vectors randomly drawn from the current population, and is a vector of random numbers between 0 and 1, the parameter δ ranges from 0.1 to 1.0, the scaling parameter χ ranges from 0.7 to 2.1, and β is the scaling factor, which is calculated as follows:

[0029]

[0030] vector The elements of are random numbers drawn from a normal distribution function with a mean of 0 and a standard deviation of 0, t is the current number of iterations, and T max It is the maximum number of times the function is calculated;

[0031] V I is an adaptive adjustment factor, and its calculation expression is

[0032] V I =β×r -1 P -1 (β,1),

[0033] r is a uniformly distributed random number between 0 and 1, -1 is the inverse of r, which is used as the search operator in the entire optimization process. -1 is the incomplete gamma inverse function corresponding to the variable β;

[0034] Will Substitute the objective function to calculate the function value, and update the vector solutions in the group and the current global optimal solution according to the following expression, namely:

[0035]

[0036] Among them, y t+1 represents the updated solution in the group, y g represents the global optimal solution;

[0037] In each iteration, the objective function is calculated according to the above rules and the parameter y is updated. g When the algorithm iterative calculation meets the termination condition, the iterative calculation ends and the global optimal solution y is output g , the y g That is the objective function The solution, that is, N C The emissivity ε of each spectral channel n Numeric value.

[0038] Furthermore, in step 5, the parameter and The mathematical expressions are:

[0039]

[0040] The parameter k is randomly generated between constants k1 and k2. The values ​​of k1 and k2 are 3.52 and 3.89 respectively. The mathematical expression of k is:

[0041] k=k1+R1(k2-k1),

[0042] R1 is a random number uniformly distributed between 0 and 1;

[0043] parameter and The calculation expression is:

[0044]

[0045] is a solution randomly selected from the output population after the tth iteration, is the optimal solution in the population after the tth iteration, is the current global optimal solution, and the calculation expression of norm(·) for the vector is:

[0046]

[0047] Among them, d represents the dimension of each variable in the group, The vector for normalizing the input, y j Then it is the data of the j-th dimension in the input vector;

[0048] parameter The calculation expression is:

[0049]

[0050] Represents the input vector, Y mean Represents the average value of each vector in the current group, and the expression is:

[0051]

[0052] N is the number of vectors in the group.

[0053] Furthermore, the condition for terminating the algorithm iteration calculation is to set the number of iterations T max , the current number of iterations t satisfies:

[0054] t>T max ,

[0055] Or the objective function value satisfies:

[0056]

[0057] Among them, δ is the threshold for iterative termination.

[0058] Furthermore, in step 6, the specific steps for calculating the intermediate temperature value are: N calculated in step 5 C The emissivity ε of each spectral channel n Substitute the value into the following formula to calculate the temperature value T on the selected spectral channel C :

[0059]

[0060] Then take the average temperature value T of each spectral channel O :

[0061]

[0062] T O This is the calculated intermediate temperature value.

[0063] Furthermore, in step 7, the final temperature T of the high temperature material is calculated using the intermediate temperature value. OP The specific steps are: follow the processing flow from step 3 to step 6, and repeat the spectral data processing N S times, calculate N S The middle temperature value T Oi (i=1,2,...,N s ), exclude one of the values ​​in turn, and calculate the remaining N s -1 standard deviation of the values ​​and get N s The standard deviation of the group values ​​is selected, and the group with the smallest standard deviation value is selected. According to the N s -1 the middle temperature value, calculate the average value, and use this value as the final temperature T of the high temperature material OP .

[0064] Furthermore, in step 8, the final temperature T of the high temperature material is calculated using step 7. OP According to Planck's law, the temperature of each spectral channel λ is calculated as T OP Blackbody radiance E b (λ,T OP ):

[0065]

[0066] E b (λ,T OP ) and high temperature material measurement radiance E obj (λ,T OP ) is divided by the emissivity ε of each spectral channel. obj (λ,T OP ):

[0067]

[0068] The present invention also provides a high-temperature material temperature and emissivity measurement system, comprising:

[0069] Optical lenses for collecting radiant energy from high-temperature materials; and

[0070] A Fourier spectrometer, comprising a data input terminal and a data output terminal, wherein the data input terminal is connected to the optical lens to receive the radiation energy collected by the optical lens; the Fourier spectrometer is used to generate spectral data from the radiation energy; and

[0071] A laser, provided on the Fourier spectrometer, for indicating the spectrum collection area, wherein the laser emission direction of the laser is parallel to the optical axis of the optical lens; and

[0072] a servo turntable, on which the Fourier spectrometer is disposed, and for adjusting the angle at which the Fourier spectrometer receives energy; and

[0073] A data acquisition processor is connected to the data output terminal. The data acquisition processor receives the spectral data transmitted by the Fourier spectrometer, and uses the above-mentioned measurement method for the temperature and emissivity of the high-temperature material to process the spectral data, inversely calculates the temperature and emissivity of the high-temperature material, and displays them.

[0074] Furthermore, the data acquisition processor is a computer.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] The present invention realizes long-distance non-contact measurement of temperature and emissivity of high-temperature materials. Without relying on a target emissivity and wavelength relationship model, it completes the inversion calculation of temperature and emissivity of randomly acquired high-temperature material spectral data. It does not use Wien approximation to construct the objective function. It can effectively act on spectral data with a large number of spectral channels and improves measurement accuracy, thus having a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0078] Figure 1 is a schematic structural diagram of a high-temperature material temperature and emissivity measurement system according to one embodiment of the present invention;

[0079] Figure 2 is a flow chart of a method for measuring temperature and emissivity of a high-temperature material in one embodiment of the present invention;

[0080] Figure 3is a spectral radiance distribution curve of a standard blackbody with a spectral range of 8.0 μm-12.0 μm and a temperature of 325° C. used for inversion calculation in one embodiment of the present invention;

[0081] Figure 4 The graph shows a theoretical emissivity curve and a calculated emissivity curve corresponding to the spectral radiation data of a standard black body with a spectral range of 8.0 μm-12.0 μm and a temperature of 325° C. calculated according to an embodiment of the present invention.

[0082] In the figure, 1. Optical lens; 2. Fourier spectrometer; 3. Laser; 4. Servo turntable; 5. Data acquisition processor. DETAILED DESCRIPTION

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0084] Example 1

[0085] The first embodiment of the present invention provides a method for measuring the temperature and emissivity of a high-temperature material, and its control flow chart is as follows: Figure 2 As shown, the specific steps include:

[0086] Step 1: Collect spectral data of high-temperature materials;

[0087] Step 2: performing radiation correction on the spectral data;

[0088] Step 3: Calculate the maximum spectral radiance E within the spectral range of the multispectral data hMax and the minimum value E hMin , respectively select the corresponding radiance value as:

[0089]

[0090] Then we can get N C The radiance value E of the spectral channel n and wavelength λ n .

[0091] Step 4: Based on N obtained in step 3 C The radiance value E of the spectral channel n and wavelength λ n , construct the objective function according to Planck's law:

[0092]

[0093] Among them, c1 is Planck's first constant, which is 3.7418×10 -16 W·m 2 , c2 is Planck's second constant, and its value is 1.4398×10 -2 m.K.

[0094] Step 5: The objective function constructed in step 4 is processed using an iterative calculation method. The specific calculation process is as follows:

[0095] N C The emissivity value ε of each spectral channel n Form a vector Random initialization is performed according to the following expression:

[0096]

[0097] is a vector generated by random initialization, us and ls are the upper and lower limits of the search range, and R1 is a vector consisting of variable values ​​uniformly distributed between 0 and 1. According to emissivity theory, the emissivity range is 0 to 1, so us and ls are 1 and 0, respectively. Following the above random initialization process, multiple vectors are generated and grouped together as the initial input to the algorithm.

[0098] Substitute each vector in the group generated by the above process into the following formula in turn to iteratively calculate the intermediate solution:

[0099]

[0100] Among them, the parameters pn and rand are random numbers between 0 and 1. is the newly generated intermediate solution, is the candidate solution for the current iteration t, r1, r2, r3, and r4 are the indices of four vectors randomly drawn from the current population. and is a vector of random numbers between 0 and 1. The parameter δ ranges from 0.1 to 1.0 and is set to 0.2 in this calculation. The scaling parameter χ ranges from 0.7 to 2.1 and is set to 1.0 in this calculation. β is the scaling factor, which is calculated as follows:

[0101]

[0102] vector The elements of are random numbers drawn from a normal distribution with a mean of 0 and a standard deviation of 0, t is the current number of iterations, and T max This is the maximum number of times the function is calculated.

[0103] V I is an adaptive adjustment factor, and its calculation expression is

[0104] V I =β×r -1 P -1 (β,1), (6)

[0105] r is a uniformly distributed random number between 0 and 1, -1 is the inverse of r, which is used as the search operator in the entire optimization process. -1 is the incomplete inverse gamma function corresponding to the variable β.

[0106] parameter and The mathematical expressions are:

[0107]

[0108] The parameter k is randomly generated between constants k1 and k2. The values ​​of k1 and k2 are 3.52 and 3.89 respectively. The mathematical expression of k is:

[0109] k=k1+R1(k2-k1), (10)

[0110] R1 is a random number uniformly distributed between 0 and 1.

[0111] parameter and The calculation expression is:

[0112]

[0113] is a solution randomly selected from the output population after the tth iteration, and is the optimal solution in the population after the tth iteration, is the current global optimal solution, and the calculation expression of norm(·) for the vector is:

[0114]

[0115] Among them, d represents the dimension of each variable in the group, The vector for normalizing the input, y j It is the data of the jth dimension in the input vector.

[0116] parameter The calculation expression is:

[0117]

[0118] Represents the input vector, Y mean Represents the average value of each vector in the current group, and the expression is:

[0119]

[0120] N is the number of vectors in the group.

[0121] Will Substitute the objective function to calculate the function value, and update the vector solutions in the group and the current global optimal solution according to the following expression, namely:

[0122]

[0123] Among them, y t+1 represents the updated solution in the group, y g represents the global optimal solution.

[0124] Set the number of iterations T max , in each iteration, calculate the objective function according to the above rules and update the parameter y g The condition for terminating the algorithm iteration calculation is that the current number of iterations t satisfies:

[0125] t>T max , (19)

[0126] Or the objective function value satisfies:

[0127]

[0128] Among them, δ is the threshold for iteration termination, and its value is a minimum.

[0129] The global optimal solution x output by iteration g That is the objective function The solution, that is, N C The emissivity ε of each spectral channel n Numeric value.

[0130] Step 6: Substitute the N calculated in step 5 C The emissivity ε of each spectral channel n Substitute the value into the following formula to calculate the temperature value T on the selected spectral channel C :

[0131]

[0132] Then take the average temperature value T of each spectral channel O :

[0133]

[0134] TO This is the calculated intermediate temperature value.

[0135] Step 7: Repeat the process of steps 3 to 6 to process the spectral data N times. S times, calculate N S The middle temperature value T Oi (i=1,2,...,N S ). After eliminating one of the values ​​in turn, calculate the remaining N s -1 standard deviation of the value. According to the above method, N S The standard deviation of the group values, select the group N with the smallest standard deviation value S -1 intermediate temperature value, calculate the average value, and use this value as the final temperature T of the high-temperature material OP .

[0136] Step 8: Use step 7 to calculate the final temperature T of the high temperature material OP According to Planck's law, the temperature of each spectral channel λ is calculated as T OP Blackbody radiance E b (λ,T OP ):

[0137]

[0138] E b (λ,T OP ) and high temperature material emittance E obj (λ,T OP ) is divided by the emissivity ε of each spectral channel. obj (λ,T OP ), which is the emissivity of high temperature materials ε obj (λ,T OP ):

[0139]

[0140] The present invention also verifies the measurement method of the temperature and emissivity of the above-mentioned high-temperature material based on the standard blackbody spectral data with a spectral range of 8.0μm-12.0μm and a temperature of 325°C. The spectral radiance distribution of the standard blackbody is shown in the figure, and the temperature and emissivity of the standard blackbody are calculated based on the spectral data.

[0141] The calculated emissivity curve of the standard blackbody spectral radiation data with a spectral range of 8.0μm-12.0μm and a temperature of 325℃ was obtained, and compared with the theoretical emissivity curve. Figure 4 .

[0142] Example 2

[0143] The second embodiment of the present invention provides a high temperature material temperature and emissivity measurement system, such as Figure 1 As shown, the above-mentioned method for measuring the temperature and emissivity of high-temperature materials can be performed by the measurement system, and the measurement system includes an optical lens 1 for collecting the radiation energy of the high-temperature material;

[0144] and a Fourier spectrometer 2, comprising a data input terminal and a data output terminal, wherein the data input terminal is connected to the optical lens 1 to receive the radiation energy collected by the optical lens 1; the Fourier spectrometer 2 is used to generate spectrum data from the radiation energy;

[0145] and a laser 3 for indicating a spectrum collection area, the laser 3 being disposed on the Fourier spectrometer 2, for example, by threaded connection, gluing, or the like; and calibrated so that the laser emission direction of the laser 3 is parallel to the optical axis of the optical lens 1;

[0146] and a servo turntable 4, on which the Fourier spectrometer 2 is disposed, and the angle at which the Fourier spectrometer 2 receives energy can be adjusted by adjusting the pitch and rotation angles of the servo turntable 4;

[0147] And a data acquisition processor 5 is connected to the data output end. The data acquisition processor 5 receives the spectral data transmitted by the Fourier spectrometer 2, and uses the measurement method of the temperature and emissivity of the high-temperature material described above to process the spectral data, inversely calculates the temperature and emissivity of the high-temperature material, and displays it. The data acquisition processor 5 can be a computer.

[0148] The measurement system can complete long-distance non-contact temperature and emissivity measurements of high-temperature materials. The specific measurement process is: adjusting the pitch and rotation angles of the servo turntable 4, turning on the laser 3 to indicate the orientation of the long-distance high-temperature material, and pointing the optical lens 1 at the high-temperature material. After turning off the laser 3, the Fourier spectrometer 2 receives the radiation energy of the high-temperature material collected by the optical lens 1 to complete spectrum collection, and transmits the spectrum data to the data acquisition processor 5. Using the measurement method of the high-temperature material temperature and emissivity described above, the spectrum data is processed by the data acquisition processor 5 to complete the temperature and emissivity measurement of the high-temperature material.

[0149] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0150] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for measuring the temperature and emissivity of high-temperature materials, characterized in that: The following steps are involved: Step 1: Collect spectral data of high-temperature materials; Step 2: performing radiation correction on the spectral data; Step 3: Select N within the spectral range of the spectral data. C The radiance value E of the spectral channel n and wavelength λ n ; Step 4: Based on the radiance value E n and the wavelength value λ n , construct the objective function according to Planck's law Where c1 is Planck's first constant and c2 is Planck's second constant; Step 5: For the objective function constructed in step 4, use the iterative calculation method to calculate the selected N C The emissivity ε of each spectral channel n Numerical solution of ; Step 6: Using the emissivity ε n Numerical solution to calculate the intermediate temperature of high temperature materials; Step 7: Calculate the final temperature T of the high temperature material using the intermediate temperature value OP ; Step 8: Using the Final Temperature T of the High-Temperature Material OP Calculating the emissivity ε of high-temperature materials obj (λ,T OP ).

2. The method for measuring the temperature and emissivity of a high-temperature material according to claim 1, characterized in that: In step 3, select N C The radiance value E of the spectral channel n and wavelength λ n The way is: In the spectral range of the spectral data, calculate the maximum spectral radiance E hMax and the minimum value E hMin , respectively select the corresponding radiance value Get N C The radiance value E of the spectral channel n and wavelength λ n .

3. The method for measuring the temperature and emissivity of a high-temperature material according to claim 1, characterized in that: In step 5, the specific calculation steps of the iterative algorithm used are: N C The emissivity ε of each spectral channel n Form a vector Random initialization is performed according to the following expression: is a vector generated by random initialization, us is the upper limit of the search range, and its value is 1, ls is the lower limit of the search range, and its value is 0, and R1 is a vector composed of variable values ​​uniformly distributed between 0 and 1. According to the random initialization process, multiple vectors are generated to form a group as the initial input of the algorithm. Substitute each vector in the generated group into the following formula in turn to iteratively calculate the intermediate solution: Among them, the parameters pn and rand are random numbers between 0 and 1. is the newly generated intermediate solution, is the candidate solution for the current iteration t, r1, r2, r3 and r4 are the indices of four vectors randomly drawn from the current population, and is a vector of random numbers between 0 and 1, the parameter δ ranges from 0.1 to 1.0, the scaling parameter χ ranges from 0.7 to 2.1, and β is the scaling factor, which is calculated as follows: vector The elements of are random numbers drawn from a normal distribution function with a mean of 0 and a standard deviation of 0, t is the current number of iterations, and T max It is the maximum number of times the function is calculated; V I is an adaptive adjustment factor, and its calculation expression is V I =β×r -1 P -1 (b,1), r is a uniformly distributed random number between 0 and 1, -1 is the inverse of r, which is used as the search operator in the entire optimization process. -1 is the incomplete gamma inverse function corresponding to the variable β; Will Substitute the objective function to calculate the function value, and update the vector solutions in the group and the current global optimal solution according to the following expression, namely: Among them, y t+1 represents the updated solution in the group, y g represents the global optimal solution; In each iteration, the objective function is calculated according to the above rules and the parameter y is updated. g When the algorithm iterative calculation meets the termination condition, the iterative calculation ends and the global optimal solution y is output g , the y g That is the objective function The solution, that is, N C The emissivity ε of each spectral channel n Numeric value.

4. The method for measuring the temperature and emissivity of a high-temperature material according to claim 3, characterized in that: In step 5, the parameters and The mathematical expressions are: The parameter k is randomly generated between constants k1 and k2. The values ​​of k1 and k2 are 3.52 and 3.89 respectively. The mathematical expression of k is: k=k1+R1(k2-k1), R1 is a random number uniformly distributed between 0 and 1; parameter and The calculation expression is: is a solution randomly selected from the output population after the tth iteration, is the optimal solution in the population after the tth iteration, is the current global optimal solution, and the calculation expression of norm(·) for the vector is: Among them, d represents the dimension of each variable in the group, The vector for normalizing the input, y j Then it is the data of the jth dimension in the input vector; parameter The calculation expression is: Represents the input vector, Y mean Represents the average value of each vector in the current group, and the expression is: N is the number of vectors in the group.

5. The method for measuring the temperature and emissivity of a high-temperature material according to claim 4, characterized in that: The condition for terminating the algorithm iteration calculation is to set the number of iterations T max , the current number of iterations t satisfies: t>T max , Or the objective function value satisfies: Among them, δ is the threshold for iterative termination.

6. The method for measuring the temperature and emissivity of a high-temperature material according to claim 5, characterized in that: In step 6, the specific steps for calculating the intermediate temperature value are: N calculated in step 5 C The emissivity ε of each spectral channel n Substitute the value into the following formula to calculate the temperature value T on the selected spectral channel C : Then take the average temperature value T of each spectral channel O : T O This is the calculated intermediate temperature value.

7. The method for measuring the temperature and emissivity of a high-temperature material according to claim 6, characterized in that: In step 7, the final temperature T of the high temperature material is calculated using the intermediate temperature value. OP The specific steps are: follow the processing flow from step 3 to step 6, and repeat the spectral data processing N S times, calculate N S The middle temperature value T Oi (i=1,2,...,N s ), exclude one of the values ​​in turn, and calculate the remaining N s -1 standard deviation of the values ​​and get N s The standard deviation of the group values ​​is selected, and the group with the smallest standard deviation value is selected. According to the N s -1 the middle temperature value, calculate the average value, and use this value as the final temperature T of the high temperature material OP .

8. The method for measuring the temperature and emissivity of a high-temperature material according to claim 7, characterized in that: In step 8, the final temperature T of the high temperature material is calculated using step 7. OP According to Planck's law, the temperature of each spectral channel λ is calculated as T OP Blackbody radiance E b (λ,T OP ): E b (λ,T OP ) and high temperature material measurement radiance E obj (λ,T OP ) is divided by the emissivity ε of each spectral channel. obj (λ,T OP ):

9. A high temperature material temperature and emissivity measurement system, characterized in that: include: Optical lens, used to collect radiation energy from high-temperature materials; and A Fourier spectrometer, comprising a data input terminal and a data output terminal, wherein the data input terminal is connected to the optical lens to receive the radiation energy collected by the optical lens; the Fourier spectrometer is used to generate spectral data from the radiation energy; and A laser is provided on the Fourier spectrometer and is used to indicate the spectrum collection area, and the laser emission direction of the laser is parallel to the optical axis of the optical lens; and A servo turntable, on which the Fourier spectrometer is arranged, and the servo turntable is used to adjust the angle at which the Fourier spectrometer receives energy; and A data acquisition processor is connected to the data output terminal, and receives spectral data transmitted by the Fourier spectrometer, and uses the method for measuring the temperature and emissivity of the high-temperature material as described in any one of claims 1 to 8 to process the spectral data, inversely calculates the temperature and emissivity of the high-temperature material, and displays them.

10. A high temperature material temperature and emissivity measurement system according to claim 9, characterized in that: The data acquisition processor is a computer.

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