冲击物理温度是**弹药性能测试、 极端环境材料状态的重要参量, 温度的精准获取在**建设和工业制造领域有重要意义。 冲击过程由于持续时间短、 较难接触式测量以及温度范围广等特性, 常规测温方法较难满足要求。 利用多光谱辐射测温法, 获取材料辐射强度值, 以普朗克辐射定律为基础建立温度反演模型, 从而获取目标的冲击物理温度值。 实际中, 由于不同目标发射率存在一定随机性, 在模型反演温度时误差较大。 冲击压缩下材料的结构可能发生相变, 发射率随之变化, 因此直接将发射率模型假定用于冲击物理温度求解, 很难准确的获取温度值。 基于约束优化理论, 将多光谱测温实验中温度求解问题转为约束优化问题。 针对每个通道获取到的温度值应该相同, 将物体发射率限制在特定范围, 利用约束优化算法计算获取目标温度和发射率, 克服了未知发射率对于冲击物理温度求解的障碍。 同时, 将平衡优化器算法（EO）与序列二次规划法（SQP）相结合应用于温度模型的求解中, 避免了单一算法求解稳定性差和速度慢的缺点, 提高了温度反演的效率和准确性。 对四种常见的发射率模型在3 000 K时的发射率数据进行了仿真验证, 结果表明温度反演误差均小于1%, 反演时间小于3 s。 最后利用本算法对冲击压缩下金属铜的温度进行了反演, 并与最小二乘法和内点罚函数法进行了对比, 结果表明所提出的方法得到金属铜的冲击物理温度值更接近理论计算结果。 因此, 该方法为其他未知发射率模型目标的冲击物理温度值获取, 提供了一种有效的反演方法。

The physical temperature of shock is an important parameter for weapon ammunition performance testing and the characterization of material states in extreme environments. Accurate acquisition of temperature has vital significance in the fields of national defence and industrial manufacturing. The shock process has characteristics of short duration, difficulty in contact-based measurements, and a wide temperature range, which can cause the failure of conventional temperature measurement methods. This paper proposes a temperature measurement method based on multi-spectral radiometry, which obtain the values of material radiation intensity. The inversion model based on Plancks radiation law can obtain the shock physical temperature value. In practice, the randomness of different target emissivity can cause large errors using the temperature inversion model. The emissivity model of the material during the shock process is more difficult to obtain. Meanwhile, the materials structure under shock may change in phase, which leads to a change in the emissivity model. Therefore, it is difficult to accurately obtain the shock physical temperature value by directly assuming the emissivity model. In this paper, the temperature calculation in multi-spectral temperature measurement experiments turned into a constrained optimization problem based on the constrained optimization theory. The temperature value obtained for each channel should be the same, limiting the object emissivity to a specific range. The constraint optimization algorithm calculates the target temperature and emissivity, which can overcome unknown emissivity for a shock physical temperature solution. At the same time, the combination of Equilibrium Optimizer and Sequential Quadratic Programming is applied to the solution of the temperature model, which avoids the shortcomings of poor stability and slow speed of a single algorithm. It improves the efficiency and accuracy of temperature inversion. The emissivity data of four common emissivity models at 3 000 K are simulated and verified. The results show that the temperature inversion error is less than 1%, and an inversion time is within 3 seconds. Finally, the temperature inversion of copper under shock compression is carried out using this algorithm. Compared with the Least Squares Method and Interior Point Penalty Function Method, the results indicate that the method proposed in this paper obtains the impact physical temperature value of copper more closely to the theoretical calculation. Therefore, this method provides an effective inversion approach for obtaining the physical temperature of other targets with unknown emissivity models.