多光谱测温依据黑体辐射定律, 通过辐射光强、 多组波长即能推测出温度值, 克服了比色测温要求光谱单一和比色光谱相近的约束, 在工程实际中得到了广泛的应用。 在多光谱温度反演的过程中, 光谱发射率的求解及多光谱数据处理是精确测温的关键。 目前, 光谱发射率的求解大多以光谱发射率假设模型为主要的方法, 当假设模型与实际情况接近时, 反演的温度与光谱发射率精度很高, 当二者不相符时, 反演的结果与实际情况相差甚大, 对于复杂材料和燃烧过程中材料性能动态变化情况下的测温, 以光谱发射率假设模型的方法存在盲目性; 近年来, 基于神经网络的深度学习的方法应用于多光谱测温, 避免了光谱发射率假设模型, 可建立温度与多光谱的非线性统计规律关系, 但需要海量数据与超强算力支撑, 且建模过程复杂。 针对上述问题, 提出了一种基于多元极值优化的多光谱温度测量方法(MEVO), 该方法利用不同温度下多光谱信号之间的关联性, 通过分析在多光谱温度反演过程中各通道测量温度之间的联系, 基于多光谱辐射测温原理以及温度反演过程中各通道数据之间的信息关联, 建立多元温差关联函数, 通过关联函数的寻优, 建立高精度测温模型。 该方法将建模过程简化为多元温差函数的寻优问题, 避免了光谱发射率与其他物理量的关系假设, 降低了深度学习方法对数据样本量的要求, 简化了多光谱温度测量的过程。 为了验证该方法的可行性与可靠性, 利用一套简单的8通道多谱测温装置进行实验验证, 实验中认定黑体炉发射的温度是标准值, 在1 923.15～2 273.15 K温区内对468～603 nm波段的光谱数据进行标定, 实现了基于多元极值优化的多光谱温度测量, 其测温精度在0.5%左右, 温度反演时间在2.5 s以内。 与二次测量法(SMM)、 神经网络方法相比反演精度有所提高; 反演速度与SMM法相比有大幅度提升。

Multispectral thermometry is based on Blackbody radiationlaw, and the temperature value can be calculated based on the radiation intensity and multiple sets of wavelengths. This method has become widely used in engineering practice, as it overcomes the constraints of the single spectrum and similar colorimetric spectrum requirements for colorimetric temperature measurement. In multispectral temperature inversion, the solution of spectral emissivity and multispectral data processing are the keys to accurate temperature measurement. At present, the solution of spectral emissivity is mostly based on the assumption model of spectral emissivity. When the hypothetical model is close to reality, the accuracy of the inverted temperature and spectral emissivity is very high; otherwise, the inversion result deviates significantly. For the temperature measurement of complex materials and the dynamic changes of material properties during the combustion process, the method of assuming the model of spectral emissivity is groundless; In recent years, the deep learning method based on the neural network has been applied to multispectral temperature measurement, which avoids the assumption model of spectral emissivity, and can establish the nonlinear statistical relationship between temperature and multi spectrum, but it requires massive data and supercomputing power support, and the modeling process is complicated.In order to solve the above problems, this paper proposes a multispectral temperature measurement method named multi-element extreme value optimization (MEVO) measurement method. This method utilizes the correlation between multispectral signals at different temperatures, and by analyses the relationship between the measured temperatures of each channel in the process of multispectral temperature inversion, based on the principle of multispectral radiation temperature measurement and the information correlation between the data of each channel in the process of temperature inversion, establish a multivariate temperature difference correlation function, and establish a high-precision temperature measurement model through the optimization of the correlation function. This method simplifies the modeling process to the optimization problem of multivariate temperature difference function, avoids the assumption of the relationship between spectral emissivity and other physical quantities, reduces the requirement of data sample size for deep learning methods, and simplifies the process of multispectral temperature measurement. A simple 8-channel temperature measuring device was used for experimental verification. In the experiment, we determined that the temperature emitted by the Blackbody furnace was the standard value. The spectral data of the 468~603 nm band in the 1 923.15~2 273.15 K temperature zone was calibrated, and the multispectral thermometry based on the optimization of multiple extreme values was realized. The temperature measurement accuracy is about 0.5%, and the temperature inversion time is within 2.5 s. Compared with the second measurement method (SMM) and the neural network method, the inversion accuracy is substantially improved. Moreover, the inversion speed is significantly faster than the SMM method.