Abstract When the object surface temperature in high temperature environment is measured by multi-spectral temperature measurement method, the radiation generated by the reflection of the high temperature background on the surface of the object and the radiation emitted by the object itself are both received by the pyrometer. For one thing, the amount of radiation received by the pyrometer increases. On the other hand, the surface emissivity model may change. So a large temperature measurement error may occur. In order to accurately measure the true surface temperature of an object in a high temperature environment, a multi-spectral temperature measurement method based on adaptive emissivity model is proposed in this paper. First, BP neural network is used to identify the shape of spectral data and obtain the correspondence between spectral data and emissivity model, then output the emissivity model that conforms to the measured target and specific environment. Moreover, the traditional NSGA-II algorithm (Non-dominant sorting genetic algorithm-II) is prone to falling into local optimal solutions and the numerical values obtained are not accurate enough. In this paper, the concepts of clustering algorithm, difference operator and symmetrical solution are introduced in the process of crossover and mutation, and then the new algorithm of INSGA-II (Improved Non-dominant sorting genetic algorithm-II)) is proposed. On the basis of this algorithm, the emissivity model of BP network is combined to solve the target true temperature. The experimental results show that the maximum temperature measurement error can reach 60K if the influence of high temperature background is ignored. After removing the influence of high temperature background, the non-adaptive emissivity model was used to obtain the maximum temperature error of 30K, and the fluctuation between adjacent temperature points was large. The multi-spectral temperature measurement method based on adaptive emissivity model proposed in this paper can obtain more accurate temperature measurement results with the maximum error of 8K.

中文翻译：

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高温背景下基于自适应发射率模型的多光谱测温

摘要 用多光谱测温法测量高温环境下物体表面温度时，物体表面高温背景反射产生的辐射和物体自身发出的辐射都被物体接收到。高温计。一方面，高温计接收到的辐射量增加。另一方面，表面发射率模型可能会改变。因此可能会出现较大的温度测量误差。为了准确测量高温环境下物体的真实表面温度，提出了一种基于自适应发射率模型的多光谱测温方法。第一的，BP神经网络用于识别光谱数据的形状，获得光谱数据与发射率模型的对应关系，然后输出符合被测目标和特定环境的发射率模型。而且，传统的NSGA-II算法（非显性排序遗传算法-II）容易陷入局部最优解，得到的数值不够准确。本文在交叉和变异过程中引入了聚类算法、差分算子和对称解的概念，进而提出了新的算法INSGA-II(Improved Non-dominant排序遗传算法-II)。在该算法的基础上，结合BP网络的发射率模型求解目标真实温度。实验结果表明，忽略高温背景的影响，测温最大误差可达60K。去除高温背景影响后，采用非自适应发射率模型得到最大温度误差为30K，相邻温度点间波动较大。本文提出的基于自适应发射率模型的多光谱测温方法可以获得更准确的测温结果，最大误差为8K。且相邻温度点间波动较大。本文提出的基于自适应发射率模型的多光谱测温方法可以获得更准确的测温结果，最大误差为8K。且相邻温度点间波动较大。本文提出的基于自适应发射率模型的多光谱测温方法可以获得更准确的测温结果，最大误差为8K。