Method and device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology

Abstract

The present invention provides a method and device for measuring flame temperature by combining autoradiation spectroscopy and imaging technology. The method comprises: measuring the flame emissivity corresponding to each wavelength; measuring the flame radiation intensity distribution; establishing an iterative radiation intensity model based on the flame emissivity corresponding to each wavelength; establishing a residual square iterative model based on the iterative radiation intensity model according to the actual radiation intensity of any two of the R, G, and B channels obtained from the flame radiation intensity distribution; setting an initial iteration value for the flame temperature; and iterating using a Newton iteration method based on the residual square iterative model according to the initial iteration value for the flame temperature until the result of the residual square iterative model meets a convergence condition, outputting the flame temperature input to the residual square iterative model in this iteration as the final flame temperature. The calculation results of the present invention are more accurate and more consistent with the laws of flame radiation.

Description

Technical Field

[0001] The present invention relates to the field of spectral technology, and in particular to a method and device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology. Background Art

[0002] Monitoring the flame combustion temperature field in the furnace is an important means for operators to observe combustion conditions, analyze combustion performance, and regulate combustion load. However, the common flame temperature imaging technology uses a two-color method based on the gray nature assumption to calculate the temperature, ignoring the variation of flame emissivity with wavelength. As a result, the measurement results do not conform to the actual flame radiation law and the measurement accuracy is too low. Summary of the Invention

[0003] The purpose of the embodiments of the present invention is to provide a method and device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology. The calculation results of the method and device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology are more accurate and more in line with the flame radiation law.

[0004] To achieve the above-mentioned objectives, an embodiment of the present invention provides a method for measuring flame temperature by combining self-radiation spectroscopy and imaging technology, the method comprising: measuring the flame emissivity corresponding to each wavelength; measuring the flame radiation intensity distribution; establishing an iterative radiation intensity model based on the flame emissivity corresponding to each wavelength based on Planck's law, the iterative radiation intensity model including parameters of the flame temperature; establishing a residual square iterative model based on the iterative radiation intensity model according to the actual radiation intensity of any two channels among the R, G, and B channels obtained from the flame radiation intensity distribution, the residual square iterative model including parameters of the flame temperature; setting an initial iteration value of the flame temperature; iterating using the Newton iteration method based on the residual square iterative model according to the initial iteration value of the flame temperature, until the result of the residual square iterative model meets the convergence condition, and outputting the flame temperature input to the residual square iterative model in this iteration as the final flame temperature.

[0005] Preferably, the iterative radiation intensity model is:

[0006]

[0007] Among them, ε i is the flame emissivity corresponding to each wavelength, η i is the spectral response coefficient of any channel among the three channels R, G, and B, λ i is the wavelength, T is the flame temperature, and C1 and C2 are Planck constants.

[0008] Preferably, for R and G channels, the residual square iterative model is:

[0009]

[0010] Among them, I R is the actual radiation intensity of the R channel, I G is the actual radiation intensity of the G channel, ε i is the flame emissivity corresponding to each wavelength, is the spectral response coefficient of the R channel, is the spectral response coefficient of the G channel, λ i is the wavelength, T is the flame temperature, and C1 and C2 are Planck constants.

[0011] Preferably, the iterative correction value is calculated by the following formula:

[0012]

[0013] Where m is the number of iterations, ΔT m+1 is the iterative correction value, f m (T) is the result of the residual square iterative model at the mth iteration, For small deviations in temperature.

[0014] Preferably, the convergence condition is:

[0015]

[0016] Among them, the f m+1 is the result of the iterative residual square iterative model, f m The result of the iterative model is the residual square before the iteration.

[0017] An embodiment of the present invention also provides a device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology, the device including: a measuring unit, a model building unit and an iteration unit, wherein the measuring unit is used to: measure the flame emissivity corresponding to each wavelength; measure the flame radiation intensity distribution; the model building unit is used to: establish an iterative radiation intensity model based on Planck's law and the flame emissivity corresponding to each wavelength, the iterative radiation intensity model including the parameters of the flame temperature; establish a residual square iteration model based on the iterative radiation intensity model according to the actual radiation intensity of any two channels among the R, G, and B channels obtained from the flame radiation intensity distribution, the residual square iteration model including the parameters of the flame temperature; the iteration unit is used to: set an initial iteration value of the flame temperature; according to the initial iteration value of the flame temperature, iterate using the Newton iteration method based on the residual square iteration model until the result of the residual square iteration model meets the convergence condition, and output the flame temperature of the residual square iteration model input in this iteration as the final flame temperature.

[0018] Preferably, the iterative radiation intensity model is:

[0019]

[0020] Among them, ε i is the flame emissivity corresponding to each wavelength, η i is the spectral response coefficient of any channel among the three channels R, G, and B, λ i is the wavelength, T is the flame temperature, and C1 and C2 are Planck constants.

[0021] Preferably, for R and G channels, the residual square iterative model is:

[0022]

[0023] Among them, I R is the actual radiation intensity of the R channel, I G is the actual radiation intensity of the G channel, ε i is the flame emissivity corresponding to each wavelength, is the spectral response coefficient of the R channel, is the spectral response coefficient of the G channel, λ i is the wavelength, T is the flame temperature, and C1 and C2 are Planck constants.

[0024] Preferably, the iterative correction value is calculated by the following formula:

[0025]

[0026] Where m is the number of iterations, ΔT m+1 is the iterative correction value, f m (T) is the result of the residual square iterative model at the mth iteration, For small deviations in temperature.

[0027] Preferably, the convergence condition is:

[0028]

[0029] Among them, the f m+1 is the result of the iterative residual square iterative model, f m The result of the iterative model is the residual square before the iteration.

[0030] Through the above technical solution, an embodiment of the present invention provides a method and device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology. This method and device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology couples the emissivity that varies with wavelength into the calculation process, thereby improving the theoretical derivation of the temperature field based on the flame radiation characteristics. It is a temperature field calculation method that is more consistent with the laws of flame radiation and has more accurate calculation results.

[0031] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0033] Figure 1 This is a flow chart of a method for measuring flame temperature by combining self-radiation spectroscopy and imaging technology provided by one embodiment of the present invention;

[0034] Figure 2 This is a radiation intensity diagram of the R channel of a laboratory ethylene laminar flame under stable combustion conditions, obtained by using a CMOS camera, provided by one embodiment of the present invention;

[0035] Figure 3 This is a flame temperature distribution diagram obtained by using only the shooting data of a CMOS camera and based on a two-color method, provided by an embodiment of the present invention;

[0036] Figure 4 The method of the present invention provided by one embodiment of the present invention introduces the radiation spectrum of the flame and obtains the flame temperature distribution diagram based on the Newton iteration method;

[0037] Figure 5 This is a normalized temperature curve obtained by the two-color method and the method of the present invention provided in one embodiment of the present invention;

[0038] Figure 6 The temperature variation curve along the height at the left edge of the flame measured by the two-color method and the method of the present invention provided in one embodiment of the present invention;

[0039] Figure 7 It is a flame temperature difference diagram of flame distribution measured by the two-color method and the method of the present invention provided in one embodiment of the present invention;

[0040] Figure 8 This is a structural block diagram of a device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology, provided in one embodiment of the present invention.

[0041] Description of Reference Numerals

[0042] 1. Measurement unit 2. Model building unit

[0043] 3 Iteration Units DETAILED DESCRIPTION

[0044] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0045] In one embodiment of the present invention, a spectrometer is used to measure the flame emissivity corresponding to each wavelength, and a CMOS camera is used to measure the flame radiation intensity distribution. An initial temperature value is randomly selected, and the iterative radiation intensity is calculated based on Planck's law and the known flame emissivity. The data with the closest detection time of the spectrometer and CMOS camera are retrieved and grouped together for iterative calculation. The residual square f function equation is established until the residual square error converges, and the converged value is recorded as the final output temperature result.

[0046] Figure 1 FIG. 1 is a flow chart of a method for measuring flame temperature by combining self-radiation spectroscopy and imaging technology according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0047] Step S101, measuring the flame emissivity corresponding to each wavelength;

[0048] Among them, a spectrometer can be used to measure the flame emissivity corresponding to each wavelength.

[0049] Step S102, measuring the flame radiation intensity distribution;

[0050] Among them, a CMOS camera can be used to measure the flame radiation intensity distribution.

[0051] Step S103, based on Planck's law, an iterative radiation intensity model is established according to the flame emissivity corresponding to each wavelength, wherein the iterative radiation intensity model includes a parameter of the flame temperature;

[0052] Wherein, the iterative radiation intensity model is:

[0053]

[0054] Among them, ε i is the flame emissivity corresponding to each wavelength, η i is the spectral response coefficient of any channel among the three channels R, G, and B, λ i is the wavelength, T is the flame temperature, C1 and C2 are Planck constants, C1=1.4388×10 -2 W·m 2 C2=3.742×10 - 16 m·K

[0055] The detection time of the spectrometer data corresponding to the iterative radiation intensity model and the CMOS data corresponding to the actual radiation intensity is approximately the same, specifically, the time difference is less than 0.2s. The radiation characteristics of the flame radiation intensity calculation process are approximately uniform within the flame field at the same time. For the R and G channels, it is specifically expressed as:

[0056]

[0057] Where ξ is the flame radiation characteristic detected, ε i is the flame emissivity corresponding to each wavelength, is the spectral response coefficient of the R channel, is the spectral response coefficient of the G channel;

[0058] Step S104, establishing a residual square iterative model based on the iterative radiation intensity model according to the actual radiation intensity of any two channels among the R, G, and B channels obtained from the flame radiation intensity distribution, wherein the residual square iterative model includes a flame temperature parameter;

[0059] Wherein, preferably, for R and G channels, the residual square iterative model is:

[0060]

[0061] Among them, I R is the actual radiation intensity of the R channel, I G is the actual radiation intensity of the G channel, ε i is the flame emissivity corresponding to each wavelength, is the spectral response coefficient of the R channel, is the spectral response coefficient of the G channel, λ i is the wavelength, T is the flame temperature, and C1 and C2 are Planck constants.

[0062] It is understandable that the present invention can also establish a residual square iterative model based on two channels in other combinations, which will not be described in detail here.

[0063] Step S105, setting the iterative initial value of the flame temperature;

[0064] The iterative initial value T0 of the flame temperature can be set. The iterative initial value T0 is used as the initial data to be substituted into formula (3) to obtain f0(T), which can be regarded as the 0th iteration.

[0065] Step S106 , according to the initial iteration value of the flame temperature, based on the residual square iteration model, adopt Newton iteration method to iterate until the result of the residual square iteration model meets the convergence condition, and output the flame temperature of the residual square iteration model input in this iteration as the final flame temperature.

[0066] The correction value of the Newton iteration method is calculated by the following formula:

[0067]

[0068] Where m is the number of iterations, ΔT m+1 is the iterative correction value, f m (T) is the result of the residual square iterative model at the mth iteration, is a small deviation of temperature. In the iterative calculation process, factors such as iteration accuracy and convergence speed are comprehensively considered. The small temperature deviation is taken as 0.1K.

[0069] After obtaining the correction value, the temperature can be corrected as follows:

[0070] Correct the temperature as shown below:

[0071] T m+1 =T m +ΔT m+1 , (5)

[0072] Among them, T m+1 is the temperature value to be substituted into formula (3) next time.

[0073] Each time the temperature value is substituted into formula (3), the result of the residual square iterative model will be obtained, and the following formula will be used to determine whether the convergence condition is met:

[0074]

[0075] Among them, the f m+1 is the result of the iterative residual square iterative model, f m The result of the iterative model is the residual square before the iteration.

[0076] If the above formula (6) is satisfied, it is considered that the convergence condition is met, and the flame temperature of the input formula (3) of this iteration is output as the final flame temperature. If the condition is not met, the temperature is iterated by repeating formula (4) until the convergence condition is met.

[0077] The present invention also provides an embodiment of the method of the present invention as follows:

[0078] A CMOS camera was used to measure the radiant intensity distribution of a laboratory ethylene laminar flame. A spectrometer was positioned directly above the camera at the same angle as the camera, measuring the spectral radiation at a vertical height of 3 cm from the center of the flame (with the burner nozzle as 0 cm). The radiation data, obtained by the spectrometer and CMOS camera almost simultaneously during the stable combustion of a laboratory ethylene laminar flame, were used as a calculation example.

[0079] Figure 2 The R-channel radiation intensity diagram of a laboratory ethylene laminar flame under stable combustion conditions, obtained by using a CMOS camera and provided in one embodiment of the present invention, shows that the flame radiation intensity distribution presents a clear layered axisymmetric structure, and the radiation intensity on both sides of the middle of the flame near the flame edge is significantly higher than that in other parts. Figure 3 The flame temperature distribution diagram provided by an embodiment of the present invention is obtained by using only the shooting data of the CMOS camera and based on the two-color method. Figure 4 The present invention provides an embodiment of the present invention. The invention introduces the flame radiation spectrum and the flame temperature distribution diagram obtained based on the Newton iteration method. By comparison, it can be seen that the high temperature areas of the two are concentrated in the middle of the flame near the flame edge on both sides. Figure 2 consistent, but Figure 4 The high temperature area is narrower.

[0080] In order to further study the difference in temperature field results obtained by the two methods, the temperature variation data of the flame at a vertical height of 3 cm (through the high temperature area of the flame) with the flame width were selected and normalized, as shown in the following example: Figure 5 As shown. The temperature measured by the two-color method is higher than that by the combined self-radiation spectroscopy and imaging technology, and the temperature change trend of the two methods is basically the same through the normalized temperature distribution trend. Subsequently, the temperature change curve along the height at the left edge of the flame measured by the two methods was selected, as shown in Figure 6 As shown. By comparison, it can be seen that the highest temperature of method 1 is located at a pixel height of 101, which is 1971.55K, while the highest temperature of method 2 (the present invention) is located at a pixel height of 201, which is 2053.50K. Therefore, combining the distribution of the flame along the width and height measured by the two methods, it can be seen that method 2 corrects the temperature value by introducing emissivity. In order to further observe the global difference in the temperature measurement results of the two methods, the temperature distribution measured by method 1 is subtracted from the temperature distribution measured by method 2 to obtain the flame temperature difference diagram, as shown Figure 7 The results show that the temperature difference distribution has an obvious layered axisymmetric structure.

[0081] Figure 8 FIG. 1 is a block diagram of a device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology according to an embodiment of the present invention. Figure 8As shown, the device includes: a measuring unit 1, a model building unit 2 and an iterative unit 3, wherein the measuring unit 1 is used to: measure the flame emissivity corresponding to each wavelength; measure the flame radiation intensity distribution; the model building unit 2 is used to: establish an iterative radiation intensity model based on the flame emissivity corresponding to each wavelength based on Planck's law, and the iterative radiation intensity model includes the parameters of the flame temperature; establish a residual square iterative model based on the iterative radiation intensity model according to the actual radiation intensity of any two channels among the R, G, and B channels obtained from the flame radiation intensity distribution, and the residual square iterative model includes the parameters of the flame temperature; the iterative unit 3 is used to: set an initial iteration value of the flame temperature; according to the initial iteration value of the flame temperature, iterate using the Newton iteration method based on the residual square iterative model until the result of the residual square iterative model meets the convergence condition, and output the flame temperature of the residual square iterative model input in this iteration as the final flame temperature.

[0082] Preferably, the iterative radiation intensity model is:

[0083]

[0084] Among them, ε i is the flame emissivity corresponding to each wavelength, η i is the spectral response coefficient of any channel among the three channels R, G, and B, λ i is the wavelength, T is the flame temperature, and C1 and C2 are Planck constants.

[0085] Preferably, for R and G channels, the residual square iterative model is:

[0086]

[0087] Among them, I R is the actual radiation intensity of the R channel, I G is the actual radiation intensity of the G channel, ε i is the flame emissivity corresponding to each wavelength, is the spectral response coefficient of the R channel, is the spectral response coefficient of the G channel, λ i is the wavelength, T is the flame temperature, and C1 and C2 are Planck constants.

[0088] Preferably, the iterative correction value is calculated by the following formula:

[0089]

[0090] Where m is the number of iterations, ΔT m+1 is the iterative correction value, f m (T) is the result of the residual square iterative model at the mth iteration, For small deviations in temperature.

[0091] Preferably, the convergence condition is:

[0092]

[0093] Among them, the f m+1 is the result of the iterative residual square iterative model, f m The result of the iterative model is the residual square before the iteration.

[0094] The embodiment of the apparatus for measuring flame temperature by combining self-radiation spectroscopy and imaging technology described above is similar to the embodiment of the method for measuring flame temperature by combining self-radiation spectroscopy and imaging technology described above, and will not be described in detail here.

[0095] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0100] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0101] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, commodity, or apparatus that includes the element.

[0103] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for measuring flame temperature by combining self-radiation spectroscopy and imaging technology, characterized in that: The method comprises: Measure the flame emissivity corresponding to each wavelength; Measure the flame radiation intensity distribution; Based on Planck's law, an iterative radiation intensity model is established according to the flame emissivity corresponding to each wavelength. The iterative radiation intensity model includes the parameter of flame temperature. The iterative radiation intensity model is: Among them, ε i is the flame emissivity corresponding to each wavelength, η i is the spectral response coefficient of any channel among the three channels R, G, and B, λ i is the wavelength, T is the flame temperature, C1 and C2 are Planck constants; According to the actual radiation intensity of any two channels among the R, G, and B channels obtained from the flame radiation intensity distribution, a residual square iterative model is established based on the iterative radiation intensity model. The residual square iterative model includes the parameters of the flame temperature. For the R and G channels, the residual square iterative model is: Among them, I R is the actual radiation intensity of the R channel, I G is the actual radiation intensity of the G channel, ε i is the flame emissivity corresponding to each wavelength, is the spectral response coefficient of the R channel, is the spectral response coefficient of the G channel, λ i is the wavelength, T is the flame temperature, C1 and C2 are Planck constants; Set the initial value of the flame temperature iteration; According to the iterative initial value of the flame temperature, based on the residual square iterative model, Newton iteration method is used to iterate until the result of the residual square iterative model meets the convergence condition, and the flame temperature of the residual square iterative model input in this iteration is output as the final flame temperature.

2. The method for measuring flame temperature by combining self-radiation spectroscopy and imaging technology according to claim 1, characterized in that: The iterative correction value is calculated by the following formula: Where m is the number of iterations, ΔT m+1 is the iterative correction value, f m (T) is the result of the residual square iterative model at the mth iteration, For small deviations in temperature.

3. The method for measuring flame temperature by combining self-radiation spectroscopy and imaging technology according to claim 1, characterized in that: The convergence condition is: Among them, the f m+1 is the result of the iterative residual square iterative model, f m The result of the iterative model is the residual square before the iteration.

4. A device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology, characterized in that: The device includes: measurement unit, model building unit, and iteration unit, where The measuring unit is used for: Measure the flame emissivity corresponding to each wavelength; Measure flame radiation intensity distribution; The model building unit is used to: Based on Planck's law, an iterative radiation intensity model is established according to the flame emissivity corresponding to each wavelength. The iterative radiation intensity model includes the parameter of flame temperature. The iterative radiation intensity model is: Among them, ε i is the flame emissivity corresponding to each wavelength, η i is the spectral response coefficient of any channel among the three channels R, G, and B, λ i is the wavelength, T is the flame temperature, C1 and C2 are Planck constants; According to the actual radiation intensity of any two channels among the R, G, and B channels obtained from the flame radiation intensity distribution, a residual square iterative model is established based on the iterative radiation intensity model. The residual square iterative model includes the parameters of the flame temperature. For the R and G channels, the residual square iterative model is: Among them, I R is the actual radiation intensity of the R channel, I G is the actual radiation intensity of the G channel, ε i is the flame emissivity corresponding to each wavelength, is the spectral response coefficient of the R channel, is the spectral response coefficient of the G channel, λ i is the wavelength, T is the flame temperature, C1 and C2 are Planck constants; The iteration unit is used to: Set the initial value of the flame temperature iteration; According to the iterative initial value of the flame temperature, based on the residual square iterative model, Newton iteration method is used to iterate until the result of the residual square iterative model meets the convergence condition, and the flame temperature of the residual square iterative model input in this iteration is output as the final flame temperature.

5. The device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology according to claim 4, characterized in that: The iterative correction value is calculated by the following formula: Where m is the number of iterations, ΔT m+1 is the iterative correction value, f m (T) is the result of the residual square iterative model at the mth iteration, For small deviations in temperature.

6. The device for measuring flame temperature by combining self-radiation spectroscopy and imaging technology according to claim 4, characterized in that: The convergence condition is: Among them, the f m+1 is the result of the iterative residual square iterative model, f m The result of the iterative model is the residual square before the iteration.

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