The aim of this study is to solve the problem of it being difficult to obtain quantitative step signals when testing the time constant of thermocouples using the laser excitation method, thereby restricting the accuracy and repeatability of the test of the time constant of thermocouples. This paper designs a thermocouple time constant testing system in which laser power can be adjusted in real time. The thermocouple to be tested and a colorimetric thermometer with a faster response speed are placed on a pair of conjugate focal points of an elliptic mirror. By taking advantage of the aberration-free imaging characteristic of the conjugate focus, the temperature measured by the colorimetric thermometer is taken as the true value on the surface of the thermocouple so as to adjust the output power of the laser in real time, make the output curve of the thermocouple reach a steady state, and calculate the time constant of the thermocouple. This paper simulates and analyzes the effects of adjusting PID parameters using quantum neural networks. By comparing this with the method of optimizing PID parameters with BP neural networks, the superiority of the designed QNN-PID controller is proven. The designed controller was applied to the test system, and the dynamic response curves of the thermocouple reaching equilibrium at the expected temperatures of 800 °C, 900 °C, 1000 °C, 1050 °C, and 1100 °C were obtained. Through calculation, it was obtained that the time constants of the tested thermocouples were all within 150 ms, proving that this system can be used for the time constant test of rapid thermocouples. This also provides a basis for the selection of thermocouples in other subsequent temperature tests. Meanwhile, repeated experiments were conducted on the thermocouple test system at 1000 °C, once again verifying the feasibility of the test system and the repeatability of the experiment.

This work presents a method for measuring flame temperatures through an imaging technique that combines spectral analysis with two-color pyrometry. Initially, we employed Laser-Induced Breakdown Spectroscopy (LIBS) to analyze the radiation spectrum of nitrocellulose, selecting 694 nm and 768 nm as the two spectral lines for temperature measurement. Subsequently, we constructed a temperature measurement system utilizing two sCMOS cameras and conducted calibration within the range of 600 to 1000 °C, achieving a maximum temperature measurement uncertainty of 3.43%. Finally, we successfully performed two-dimensional temperature field detection and imaging of nitrocellulose flames of varying qualities, achieving a flame image resolution of 2048 (H) × 2048 (V). In comparison to traditional two-color infrared thermometers and Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology, the maximum relative temperature measurement error was 2.1%. This work provides technical insights into the development of high-resolution, low-cost flame temperature imaging technology applicable across a wide range of fields.

In recent years, the frequency and severity of large-scale forest fires have increased globally, threatening forest ecosystems, human lives, and property while potentially triggering cascading ecological and social crises. Despite significant advancements in remote sensing-based forest fire monitoring, early warning systems, and fire risk zoning, post-fire thermal damage assessment remains insufficiently addressed. This study introduces an innovative approach combining multispectral imaging with a dual annealing constrained optimization algorithm to enable dynamic monitoring of fire temperature distribution. Based on this method, we develop a dynamic thermal damage assessment model to quantify thermal impacts during forest fires. The proposed model provides valuable insights for defining thermal damage zones, optimizing evacuation strategies, and supporting firefighting operations, ultimately enhancing emergency response and forest fire management efficiency.