In multispectral thermometry, spectral emissivity calculation is crucial for temperature measurement. Constraint optimization algorithms require a shrunken emissivity search range and an appropriate initial solution to ensure high accuracy of temperature calculation. We propose a novel method without these requirements. In our method, the trend of the emissivity−wavelength curve is utilized to establish constraints, and a genetic algorithm is utilized as the optimization tool. Experiments reveal that the proposed method significantly improves the accuracy of temperature calculation for multispectral thermometry within a relative error of 1%. Our method can be applied to some high-precision temperature measurements (that require relative error less than 1%), such as the measurement of subtle variations in temperature distribution for aerospace engine plumes, and the measurement of slight changes in temperature during laser processing.

在多光谱测温中，光谱发射率计算对于温度测量至关重要。约束优化算法需要缩小的发射率搜索范围和适当的初始解，以确保温度计算的高精度。我们提出了一种没有这些要求的新颖方法。在我们的方法中，利用发射率-波长曲线的趋势建立约束，并使用遗传算法作为优化工具。实验表明，该方法显着提高了多光谱测温的温度计算精度，相对误差为1％。我们的方法可以应用于一些高精度温度测量（要求相对误差小于1％），