Temperature and emissivity joint inversion method for multispectral temperature measurement

Abstract

The invention discloses a temperature and emissivity joint inversion method for multispectral temperature measurement, and relates to the field of non-contact temperature measurement of high-temperature targets. In order to solve the defect that in the prior art, combined inversion of the emissivity cannot be realized under the condition that the emissivity does not need to be known in advance, the technical scheme provided by the invention comprises the following steps: establishing a multispectral temperature measurement model, constructing a temperature calculation model under each channel, and outputting a plurality of sub-objective functions for optimization; constructing a weighted objective function used for optimization solution; outputting a global optimal solution as an initial point; constructing an optimization model with a penalty function, and outputting the target temperature and the spectral emissivity; and performing simulation verification based on the inversion result, performing error evaluation by setting a typical emissivity curve and a real temperature, and outputting an algorithm precision index. The method is suitable for non-contact high-precision temperature and emissivity combined measurement of a high-temperature target in a complex spectrum environment.

Description

Technical Field

[0001] This paper relates to the field of non-contact temperature measurement of high-temperature targets, and specifically to the joint inversion of temperature and emissivity using multi-spectral temperature measurement. Background Art

[0002] In the field of non-contact temperature measurement of high-temperature targets, multispectral radiation temperature measurement technology has become a hot topic in current research and engineering applications due to its strong anti-interference ability and high-temperature adaptability. This method generally relies on multiple infrared measurement channels in different bands. By receiving the radiation intensity of the target object at different wavelengths and combining it with radiation theory models, the true temperature of the target is indirectly deduced. However, in practical applications, due to the unknown or variable emissivity of the target, traditional temperature inversion methods based on single-channel or two-channel ratio methods are susceptible to factors such as systematic errors and channel response differences, resulting in low accuracy and even instability in the inversion results.

[0003] In recent years, researchers have proposed strategies such as multi-objective optimization, nonlinear fitting, and intelligent optimization algorithms to improve inversion accuracy. For example, some literature utilizes intelligent algorithms such as genetic algorithms and particle swarm optimization to jointly invert temperature and emissivity parameters, improving the global convergence of the results. Other studies have also introduced statistical methods such as Bayesian estimation into temperature inversion to suppress the influence of random measurement noise. However, these methods often suffer from the following problems: On the one hand, the construction of the model objective function does not fully consider the measurement consistency between different channels and the need to suppress anomalous extreme values; on the other hand, the optimization algorithms either lack global search capabilities and are prone to falling into local optimality (such as SQP-type methods), or have slow convergence and low local accuracy (such as heuristic methods such as standard PSO), making it difficult to strike a balance between accuracy and efficiency.

[0004] In addition, in some existing studies, the stability and generalizability of the inversion algorithm have not been fully verified on typical materials or real measurement data, resulting in the scheme performing well in a simulation environment, but the accuracy drops significantly under complex working conditions, limiting its practical application process.

[0005] In summary, the existing technology has the defect of being unable to achieve high-precision and high-stability joint inversion of temperature and emissivity without the need to know the emissivity in advance. Summary of the Invention

[0006] In order to solve the defect in the prior art that the prior art cannot achieve high-precision and high-stability joint inversion of temperature and emissivity without prior knowledge of the emissivity, the technical solution provided by the present invention is as follows: A method for joint inversion of temperature and emissivity using multispectral thermometry, comprising: Establish a multi-spectral temperature measurement model, collect the output voltage, center wavelength and blackbody calibration parameters of each spectral channel, build a temperature calculation model for each channel, and output the steps of multiple sub-objective functions for optimization; Construct three sub-objective functions: temperature consistency, extreme value suppression, and channel difference constraint, and integrate them into a single objective function through weighting, and output the steps for optimizing the weighted objective function; Based on the weighted objective function, a particle swarm optimization algorithm is used to perform a global search and output the global optimal solution as the initial point; Taking the global optimal solution as the starting point, an improved sequential quadratic programming algorithm is introduced to construct an optimization model with a penalty function. The quasi-Newton method is combined with the Armojo line search strategy to update the Hessian matrix and output the target temperature and spectral emissivity. Based on the above inversion results, simulation verification is performed, error evaluation is performed by setting a typical emissivity curve and actual temperature, and the algorithm accuracy index is output.

[0007] Furthermore, a preferred embodiment is provided in which the temperature consistency objective function is constructed based on the square of the difference between the temperature measured in each channel and the average temperature.

[0008] Furthermore, a preferred embodiment is provided in which an extreme value suppression objective function is used to penalize abnormal fluctuations in individual channel measurement values.

[0009] Furthermore, a preferred embodiment is provided in which the channel difference constraint function is used to constrain the temperature difference between any two channels to not exceed a set threshold.

[0010] Furthermore, a preferred embodiment is provided in which the particle swarm optimization algorithm sets parameters of population size, inertia weight and acceleration factor, and uses a maximum number of iterations to limit the search range.

[0011] Furthermore, a preferred embodiment is provided in which the sequential quadratic programming algorithm uses the BFGS method to update the Hessian matrix and embeds inequality and equality constraints into the objective function through a penalty function method.

[0012] A temperature and emissivity joint inversion device for multi-spectral temperature measurement is also provided, comprising: Establish a multi-spectral temperature measurement model, collect the output voltage, center wavelength and blackbody calibration parameters of each spectral channel, build a temperature calculation model for each channel, and output a module for multiple sub-objective functions for optimization; Construct three sub-objective functions: temperature consistency, extreme value suppression, and channel difference constraint. These sub-objective functions are then weighted and integrated into a single objective function. The module then outputs the weighted objective function for optimization. Based on the weighted objective function, a particle swarm optimization algorithm is used to perform a global search and output the global optimal solution as the module of the initial point; Taking the global optimal solution as the starting point, an improved sequential quadratic programming algorithm is introduced to construct an optimization model with a penalty function. The quasi-Newton method is combined with the Armojo line search strategy to update the Hessian matrix and output the module of target temperature and spectral emissivity. Based on the above inversion results, simulation verification is carried out, error evaluation is performed by setting typical emissivity curves and actual temperatures, and a module is created to output algorithm accuracy indicators.

[0013] A computer storage medium is also provided for storing a computer program, and when the computer program is read by a computer, the computer executes the method.

[0014] A computer is also provided, comprising a processor and a storage medium, wherein when the processor reads a computer program stored in the storage medium, the computer executes the method.

[0015] A computer program product is also provided, which is a computer program that implements the method when the computer program is executed.

[0016] Compared with the prior art, the technical solution provided by the present invention is beneficial in that: By introducing three objective functions—temperature consistency, extreme value suppression, and channel difference constraint—and integrating them into a unified, weighted single objective function, the temperature inversion algorithm not only pursues the minimum deviation from the average temperature but also possesses the ability to suppress outliers and constrain the consistency of errors within the channels. Compared to traditional research that constructs a single objective function based solely on average temperature error, this method provides a more comprehensive measure of algorithm optimization effectiveness and significantly reduces systematic errors caused by fluctuations in individual channels in multi-channel measurements.

[0017] The particle swarm optimization (PSO) algorithm is used to perform a global search of the initial parameter space, obtaining a relatively optimal solution as the starting point. The sequential quadratic programming (SQP) algorithm is then introduced for local refinement, improving the convergence and accuracy of the optimization results. This hybrid global and local strategy effectively avoids the drawbacks of the traditional SQP algorithm, which often falls into local optimal solutions, while also addressing the shortcomings of the PSO algorithm in terms of convergence accuracy and speed. This improves the algorithm's stability and global optimization capabilities without increasing the computational burden.

[0018] The penalty function approach, combined with BFGS to update the Hessian matrix and the Armojo line search mechanism, ensures algorithm convergence and iterative stability under the constraints. Unlike some literature that directly uses standard optimization solvers for black-box processing, this approach makes targeted improvements to each step of the SQP process, thereby improving numerical stability under complex objective functions and providing greater adaptability to high-dimensional and nonlinear constrained problems.

[0019] Finally, four representative emissivity curves (monotonically increasing / decreasing, convex, and concave) were selected for simulation verification, achieving a maximum error within 10K and an error rate as low as 0.6%. Unlike traditional methods that often rely on idealized models for verification, this approach is more realistic and can more effectively test the algorithm's adaptability to complex emissivity variations. This demonstrates the algorithm's feasibility and practical value in complex multispectral high-temperature scenarios. It is suitable for non-contact, high-precision combined temperature and emissivity measurements of high-temperature targets in complex spectral environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Flow chart of the method.

[0021] Figure 2 These are simulation verification diagrams for four complex emissivity changes.

[0022] Figure 3 Schematic diagram of four typical emissivity curves. DETAILED DESCRIPTION

[0023] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically: Embodiment 1: This embodiment provides a method for joint inversion of temperature and emissivity using multispectral temperature measurement, including: Establish a multi-spectral temperature measurement model, collect the output voltage, center wavelength and blackbody calibration parameters of each spectral channel, build a temperature calculation model for each channel, and output the steps of multiple sub-objective functions for optimization; Construct three sub-objective functions: temperature consistency, extreme value suppression, and channel difference constraint, and integrate them into a single objective function through weighting, and output the steps for optimizing the weighted objective function; Based on the weighted objective function, a particle swarm optimization algorithm is used to perform a global search and output the global optimal solution as the initial point; Taking the global optimal solution as the starting point, an improved sequential quadratic programming algorithm is introduced to construct an optimization model with a penalty function. The quasi-Newton method is combined with the Armojo line search strategy to update the Hessian matrix and output the target temperature and spectral emissivity. Based on the above inversion results, simulation verification is performed, error evaluation is performed by setting a typical emissivity curve and actual temperature, and the algorithm accuracy index is output.

[0024] The temperature consistency objective function is constructed based on the square of the difference between the measured temperature of each channel and the average temperature.

[0025] The extreme value suppression objective function is used to penalize abnormal fluctuations in the measurement values ​​of individual channels.

[0026] The channel difference constraint function is used to constrain the temperature difference between any two channels to not exceed a set threshold.

[0027] The particle swarm optimization algorithm sets the population size, inertia weight and acceleration factor parameters, and uses the maximum number of iterations to limit the search range.

[0028] The sequential quadratic programming algorithm uses the BFGS method to update the Hessian matrix and embeds the inequality and equality constraints into the objective function through the penalty function method.

[0029] Implementation Method 2: This implementation method further limits the technical solution provided in Implementation Method 1. Specifically: A high-precision joint temperature and emissivity inversion method, without requiring prior knowledge of emissivity, is suitable for target temperature identification and emissivity fitting in multispectral non-contact temperature measurement systems. The method consists of four main stages: establishing a multispectral temperature measurement model, constructing and integrating multiple objective functions into a single objective function, finding the optimal solution using a PSO-SQP hybrid optimization algorithm, and conducting simulations and real-world data verification.

[0030] The first step is to establish a multi-spectral temperature measurement model.

[0031] This step is the foundation of the entire inversion method. Its core is to construct a radiative transfer model under multiple spectral channels using radiation laws to accurately express the physical relationship between temperature, emissivity, and channel measurement signals.

[0032] First, a multispectral radiation thermometer is used to obtain the target's radiation voltage output signals in multiple wavelength bands. Each channel corresponds to a central wavelength, and the measurement results are affected by the target surface temperature, the material's spectral emissivity, and environmental influences. To eliminate the effects of environmental interference and system response, blackbody calibration data under known laboratory conditions is incorporated into the temperature measurement model. This includes the standard radiation intensity or voltage response value for each channel at a reference temperature.

[0033] Next, based on Planck's radiation law and proportional calibration, an inversion expression for the target temperature in each channel is derived. Since the emissivity is unknown, the model can only provide an indirect temperature expression with the emissivity as a variable. Different channels correspond to a common true temperature at the same measurement moment, but due to instrument errors, random noise, and differences in channel sensitivity, the temperature values ​​calculated independently by each channel may deviate. Therefore, in subsequent optimization, a unified multi-objective control mechanism is needed to ensure the consistency of the temperature output by each channel and improve the final inversion accuracy.

[0034] Finally, the indirect temperature expression and mutual error characteristics of each channel are output to provide input basis for the design of the objective function.

[0035] The second step is to construct multi-objective functions and integrate them into a single-objective optimization model.

[0036] In order to solve the problems of large deviation of multi-channel measurement results and poor inversion stability, this step designs multiple physically meaningful optimization objective functions based on the temperature model established in the first step and integrates them into a unified optimization index.

[0037] Specifically, the objective functions constructed include: (1) Temperature consistency objective function, which is used to measure the difference between the independent inverted temperature of each channel and its mean, reflecting the overall measurement consistency; (2) Extreme value suppression objective function, which is used to identify and suppress abnormal deviations of individual channels caused by noise or system imbalance, thereby improving robustness to occasional errors; (3) Channel difference constraint function, which is used to limit the temperature difference between any channels to a reasonable physical range and enhance the overall constraint of the inversion model.

[0038] The three objective functions described above are integrated using a linear weighting method to construct a weighted single objective function. To maintain the algorithm's universality and parameter-free nature, the weights are initially set to be equal, meaning that each sub-objective has an equal impact on the final optimization value. In practice, weights can be adjusted based on different measurement scenarios to accommodate specific constraints or preferences.

[0039] Finally, the integrated objective function is constructed as a standard constrained optimization problem, whose optimization variables include the emissivity parameter group and the common target temperature, which serves as the unified input for subsequent global and local optimization algorithms.

[0040] The third step is to use the PSO algorithm to obtain the initial value.

[0041] Given the nonlinear, multimodal structure of the integrated objective function, traditional gradient-based optimization algorithms lack global search capabilities and are prone to falling into local minima. This step introduces the particle swarm optimization (PSO) algorithm as a global search tool to find optimal initial parameter values ​​within the feasible region.

[0042] The PSO algorithm simulates the foraging behavior of bird flocks and approaches the optimal solution through the collaborative search of multiple particles in the solution space. First, the initial position and velocity of the particle swarm are randomly initialized, and key algorithm parameters are set, including the number of particles, maximum number of iterations, individual learning factor, group learning factor, and inertia factor.

[0043] Subsequently, the particle swarm continuously updates its individual position and velocity under the guidance of the objective function, records the individual historical optimal value and the global optimal value of the swarm, and iterates until it reaches a stable stage. In each round of iteration, the particle chooses the next movement direction based on the objective function value of the current position and historical experience information.

[0044] When the preset accuracy condition or the maximum number of iterations is reached, the algorithm outputs the initial values ​​of the emissivity and temperature corresponding to the global optimal particle. This initial solution is usually located in the high-quality solution space, providing a better starting point for subsequent gradient methods and effectively avoiding falling into non-optimal stable points.

[0045] The fourth step is to use the improved SQP algorithm for local fine optimization.

[0046] After obtaining a high-quality initial solution from the PSO output, this step further introduces the Sequential Quadratic Programming (SQP) algorithm to perform a local, refined solution to the problem under constraints. SQP is an efficient nonlinear programming algorithm based on the Lagrange multiplier method and the quasi-Newton method, and is suitable for solving constrained continuous variable optimization problems.

[0047] First, the Lagrange function of the original optimization problem is constructed, combining the objective function with all constraints. In each iteration, the original problem is linearly approximated as a quadratic programming subproblem, using the gradient information at the current point as the linear term. A quasi-Newton method (such as the BFGS algorithm) estimates the Hessian matrix as the quadratic term, thus avoiding the direct solution of the second-order derivative of the objective function.

[0048] Considering that both inequality constraints and equality constraints must be satisfied during the solution process, this step uses the penalty function method to process all constraints, embeds the cost of violating the constraints into the objective function, and controls the cost intensity of the constraint violation degree by setting the penalty factor.

[0049] To enhance the algorithm's numerical stability and convergence efficiency, this step introduces the Armojo line search strategy. By setting a descent factor and a reduction coefficient to control the step size in each update round, the objective function decreases monotonically after each update, avoiding oscillations or invalid moves. Furthermore, a dynamic step-size adjustment mechanism adaptively adjusts the current iteration amplitude based on historical step-size trends, further improving convergence speed for complex models.

[0050] Ultimately, when the optimization process meets the specified termination criteria (e.g., the objective function change is less than a threshold, the maximum number of iterations has been reached, or the gradient norm approaches zero), the current emissivity solution and target temperature solution are output. This result is the final solution to the inversion problem, with high accuracy and stability, and can be directly used for subsequent simulation verification or engineering measurement tasks.

[0051] Implementation Method 3: Combination Figure 1 This embodiment further describes the above technical solution in detail through specific examples, specifically: This method aims to accurately invert the target's true temperature and spectral emissivity without knowing the emissivity in advance. Its core process is: Establish a multi-objective temperature inversion model → Construct the objective function and unify it into a single-objective optimization → Introduce PSO to obtain initial values ​​→ Use improved SQP to solve the optimal solution → Apply to simulation and experimental verification.

[0052] Specific steps to sort out: 1. Establishment of multispectral temperature measurement model Output: Objective function .

[0053] Establish a channel measurement model based on the radiation equation; Introduce temperature consistency target F1, extreme value suppression target F2, and channel difference constraint F3; For a multi-spectral radiation thermometer with N spectral channels, the output signal of the Nth channel is collected. At the reference temperature T ’ Under , the blackbody radiation received by the i-th channel is collected; According to the ratio, a temperature measurement model is established: Assume that the temperature measured in the nth channel is T n ,but Blackbody temperature T n ’ (known), is the unknown quantity in the formula, Indicates the target temperature obtained by inversion calculation under the nth channel, is the fitting constant of the nth channel at the reference temperature, which is related to the channel wavelength and calibration data. is the emissivity influence coefficient corresponding to the nth channel, represents the target firing rate parameter of the nth channel.

[0054] Since the target temperature at a specific time is unique, the temperature measured by each channel should theoretically be equal. However, random errors in the measurement cause the temperature of each channel to be different and not completely equal. According to error theory, the smaller the standard deviation of the target temperature measured by different channels, the higher the measurement reliability and the higher the measurement accuracy. Assume that the target temperature measured by each channel is T n , establish an objective function in, Represents the temperature consistency objective function N represents the total number of spectral channels, x is the parameter vector to be optimized, including variables such as emissivity, is the average value of the inverted temperature of all channels: For F1, if the temperature of one channel at the iteration point deviates significantly from the true value, the average value of the channel temperature will also deviate significantly from the true value. This will have an adverse effect on subsequent calculations. To avoid this situation, a second objective function F2 is proposed: represents the extreme value suppression objective function, is the temperature inverted by the channels except the nth channel.

[0055] Ideally, the temperatures of any two channels are equal. This means the difference between them is 0: Therefore, the following objective function F3 can be constructed: represents the channel difference constrained objective function.

[0056] Using the weighted method Integrate into a comprehensive objective function F' and construct a constrained optimization problem.

[0057] From the above analysis, for the real temperature inversion problem, it is difficult to evaluate the accuracy of the algorithm results based on the value of a single objective function. Therefore, the linear weighted method is used to combine the advantages and disadvantages of each objective function and calculate the accuracy of the algorithm results according to F. i (x) The importance of , the linear weighted method transforms the target optimization problem into: represents the weighted comprehensive objective function, is the i-th sub-objective function, corresponding to temperature consistency, extreme value suppression and channel difference constraints, is the weight coefficient corresponding to the i-th objective function, Represents a typical single-objective optimization problem. When the condition When holds, the optimal solution is a valid solution to the corresponding multi-objective optimization problem.

[0058] When the importance of each sub-objective function is unknown, equal weight treatment of F'(x) is a relatively compromise method, that is, all ωi are taken as 1 / 3.

[0059] To sum up, the single objective function is finally generated, and the following constrained optimization problem is constructed, and the final form is as follows: represents the integrated single-objective optimization function, is the emission rate variable to be optimized for the nth channel, are the calibration coefficient and emissivity weight coefficient of the nth channel respectively, The first term corresponds to the temperature consistency objective function, which calculates the sum of squares of the deviations between the temperature of each channel and the average temperature. The second term corresponds to the extreme value suppression objective function, which suppresses the deviation between the temperature of a certain channel and the mean temperature of the other channels. The third term corresponds to the channel difference constraint function, which constrains the deviation between any two channel temperatures. The three objective functions are equal weighted Weighted combination.

[0060] As input for the next step: Form a mathematical expression of the constrained optimization problem to prepare for algorithm optimization.

[0061] 2. PSO-SQP hybrid optimization algorithm design PSO has a strong global search capability and is combined with the SQP algorithm to achieve algorithm optimization.

[0062] 2.1 PSO algorithm: providing initial values Initialize the particle swarm position and velocity; Set parameters (such as c1, c2, w, popsize, speed boundary, etc.); Search for the global optimal particle as the initial value of SQP.

[0063] 2.2 Improved SQP algorithm: refined local search Construct a quadratic programming subproblem; Update the Hessian matrix using BFGS; Introducing Armojo line search and dynamic step size to ensure stability and convergence; The constraints are converted into penalty terms and embedded into the objective function.

[0064] As input for the next step: forming a stable and efficient optimization framework to invert the true temperature and emissivity.

[0065] The SQP algorithm is an effective method for solving constrained optimization problems. The SQP algorithm simplifies the original problem into a quadratic programming problem under a certain approximate solution and attempts to find the optimal solution. If the solution is not optimal, the SQP algorithm will use the solution as a new approximate solution to construct a new quadratic programming problem and continue iterating. The corresponding Lagrangian function is: represents the Lagrangian function, which is used to construct optimization problems with constraints. is the original objective function, is the Lagrange multiplier vector, corresponding to the constraints in the optimization problem, x is the variable vector to be optimized, It represents the inner product term of the Lagrange multiplier and the variable, which is used to integrate the constraints into the objective function.

[0066] This function is used in the SQP algorithm to combine the main function with constraint information to calculate the gradient and Hessian.

[0067] The basic iterative format of the SQP algorithm is: is an approximate solution, is the step size obtained by linear search, is the solution to the following quadratic programming problem.

[0068] is the Hessian matrix of the Lagrangian. may not be positive definite, so the direction is not necessarily decreasing. Therefore, the quasi-Newton method for unconstrained optimization problems is introduced. The approximate matrix replace The algorithm adjusts the approximation of the Hessian matrix based on the current step size and update direction. It uses the Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula for correction. The problems corrected are: In addition, the quadratic programming sub-problem and Armojo line search and dynamic step size adjustment strategies are introduced. By customizing the optimization problem, the constraint influence is incorporated into the objective function to form a comprehensive value function. : in is a penalty coefficient that controls the intensity of the penalty for constraint violations. and In order to ensure the effectiveness of the step size, the Armojo line search strategy is introduced and optimized in combination with the dynamic step size adjustment strategy. Specifically, in order to find a suitable step size , which effectively reduces the updated objective function and enhances the stability and convergence speed of the algorithm. This is especially true in high-dimensional and highly constrained optimization problems, where it effectively avoids common numerical instability problems and ensures the quality of the solution. The Armojo line search strategy is as follows: in is the current step size, is a constant that controls the step size update.

[0069] Considering the complementary strengths and weaknesses of the PSO and SQP algorithms in global and local optimization, a hybrid optimization algorithm was proposed, integrating the PSO algorithm with the SQP algorithm. The traditional SQP trajectory optimization algorithm requires a good initial trajectory to achieve good optimization results. While the algorithm's global search capabilities are weak, its local search capabilities are strong. It can converge to a local minimum point in a relatively short time. However, for objective functions with multiple local minimum points, the SQP algorithm is very sensitive to the initial point and can easily become trapped in a local minimum point, preventing it from achieving the global optimal solution. Therefore, a hybrid optimization algorithm was proposed, combining the PSO algorithm with the SQP algorithm. The initial point was obtained through iterative PSO, and the global optimal solution was then obtained using the SQP algorithm.

[0070] Implementation method 4 elaborates on the verification process and significant effects of the multi-spectral radiation temperature inversion algorithm, focusing on its superior performance in scenarios close to actual engineering. Figure 3 The following are explained: To fully test the algorithm's adaptability to complex and unknown emissivity variations, this implementation selected four real or highly realistic emissivity curves with typical morphological characteristics as validation samples: Model A: monotonically decreasing emissivity; Model B: monotonically increasing emissivity; Model C: convex emissivity (increasing first, then decreasing); and Model D: concave emissivity (decreasing first, then increasing). These four curves essentially cover the primary emissivity variation patterns that high-temperature materials may encounter in actual operating conditions, providing a rigorous benchmark for testing the algorithm's robustness.

[0071] For the four typical emissivity curves described above, the emissivity results obtained by the algorithm were compared with the target emissivity. The inversion results were highly consistent with the true values. The maximum absolute temperature error was strictly controlled within 10K, with the error rate as low as 0.6% under some operating conditions. This level of accuracy significantly outperforms many traditional methods that rely on idealized models for verification, fully demonstrating the algorithm's effectiveness and high accuracy in handling complex emissivity variations.

[0072] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable modification and improvement of the present invention, combination of embodiments and equivalent replacement based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for joint inversion of temperature and emissivity using multispectral thermometry, characterized in that: include: Establish a multi-spectral temperature measurement model, collect the output voltage, center wavelength and blackbody calibration parameters of each spectral channel, build a temperature calculation model for each channel, and output the steps of multiple sub-objective functions for optimization; Construct three sub-objective functions: temperature consistency, extreme value suppression, and channel difference constraint, and integrate them into a single objective function through weighting, and output the steps for optimizing the weighted objective function; Based on the weighted objective function, a particle swarm optimization algorithm is used to perform a global search and output the global optimal solution as the initial point; Taking the global optimal solution as the starting point, an improved sequential quadratic programming algorithm is introduced to construct an optimization model with a penalty function. The quasi-Newton method is combined with the Armojo line search strategy to update the Hessian matrix and output the target temperature and spectral emissivity. Based on the above inversion results, simulation verification is performed, error evaluation is performed by setting a typical emissivity curve and actual temperature, and the algorithm accuracy index is output.

2. The method for joint inversion of temperature and emissivity using multispectral temperature measurement according to claim 1, characterized in that: The temperature consistency objective function is constructed based on the square of the difference between the measured temperature of each channel and the average temperature.

3. The method for joint inversion of temperature and emissivity using multispectral temperature measurement according to claim 1, characterized in that: The extreme value suppression objective function is used to penalize abnormal fluctuations in the measurement values ​​of individual channels.

4. The method for joint inversion of temperature and emissivity using multispectral temperature measurement according to claim 1, characterized in that: The channel difference constraint function is used to constrain the temperature difference between any two channels to not exceed a set threshold.

5. The method for joint inversion of temperature and emissivity using multispectral temperature measurement according to claim 1, characterized in that: The particle swarm optimization algorithm sets the population size, inertia weight and acceleration factor parameters, and uses the maximum number of iterations to limit the search range.

6. The method for joint inversion of temperature and emissivity using multispectral temperature measurement according to claim 1, characterized in that: The sequential quadratic programming algorithm uses the BFGS method to update the Hessian matrix and embeds the inequality and equality constraints into the objective function through the penalty function method.

7. A temperature and emissivity joint inversion device for multi-spectral temperature measurement, characterized in that: include: Establish a multi-spectral temperature measurement model, collect the output voltage, center wavelength and blackbody calibration parameters of each spectral channel, build a temperature calculation model for each channel, and output a module for multiple sub-objective functions for optimization; Construct three sub-objective functions: temperature consistency, extreme value suppression, and channel difference constraint. These sub-objective functions are then weighted and integrated into a single objective function. The module then outputs the weighted objective function for optimization. Based on the weighted objective function, a particle swarm optimization algorithm is used to perform a global search and output the global optimal solution as the module of the initial point; Taking the global optimal solution as the starting point, an improved sequential quadratic programming algorithm is introduced to construct an optimization model with a penalty function. The quasi-Newton method is combined with the Armojo line search strategy to update the Hessian matrix and output the module of target temperature and spectral emissivity. Based on the above inversion results, simulation verification is carried out, error evaluation is performed by setting typical emissivity curves and actual temperatures, and a module is created to output algorithm accuracy indicators.

8. A computer storage medium for storing a computer program, characterized in that When the computer program is read by a computer, the computer executes the method according to claim 1 .

9. A computer comprising a processor and a storage medium, characterized in that When the processor reads the computer program stored in the storage medium, the computer executes the method according to claim 1 .

10. A computer program product, being a computer program, characterized in that When the computer program is executed, the method according to claim 1 is implemented.

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