Temperature and humidity calibration chambers, which provide controlled environments for instrument testing and validation, are widely applied in the aerospace and biomedicine fields. However, traditional fixed calibration points fail to adapt to complex operational requirements and exhibit problems including a limited coverage range and low efficiency. To address these challenges, this study develops a Gaussian Process-based Multi-Fidelity Bayesian Optimization (GP-MFBO) framework for optimal selection of temperature and humidity calibration points. The framework integrates the following three key components: (1) a three-layer progressive multi-fidelity modeling system comprising physical analytical models, computational fluid dynamics (CFD) numerical simulations, and experimental verification; (2) a systematic uncertainty quantification system covering model uncertainty, parameter uncertainty, and observation uncertainty; and (3) an adaptive acquisition function that balances uncertainty penalty mechanisms and multi-fidelity information gain evaluation. The experimental results demonstrate that the proposed GP-MFBO method achieves optimal calibration point combinations with a temperature uniformity score of 0.149 and humidity uniformity score of 2.38, approaching theoretical optimal solutions within 4.5% and 3.6%, respectively. Compared to standard Gaussian process, Co-Kriging, two-stage optimization, polynomial regression, and traditional single-fidelity methods, GP-MFBO achieves uniformity score improvements of up to 81.7% and 76.3% for temperature and humidity, respectively. The prediction confidence interval coverage reaches 94.2%, outperforming all comparative methods. This research provides a rigorous theoretical foundation and technical solution for the scientific design and reliable operation of large-space temperature and humidity calibration systems.

The CMOS temperature sensor, with its advantages of low power consumption, high integration, and digital output, has been widely adopted in smart healthcare, IoT, and wearable devices. To address performance variations caused by process deviations during mass production, this study proposed an efficient multi-temperature-point calibration method. The calibration system employed standard platinum resistance thermometers, thermal chambers, and precision electrical measurement equipment. The stability was calculated using Bessel’s formula, confirming the repeatability of the calibration methodology. Experimental results demonstrated that calibrated sensors achieved significantly reduced measurement errors within the -55 °C to 215 °C range, with stability performance better than 0.41 °C. The research proved that both standard voltage and current inputs could enable rapid calibration, providing an effective solution for reducing production costs and enhancing product consistency.

Liquid-nitrogen biobanks require stable and spatially uniform vapor-phase temperatures for long-term biological-sample storage. Transient computational fluid dynamics models were developed for automated and non-automated configurations under empty-, half-, and full-load conditions. Loading was represented using different porosity settings for homogenized storage regions, and transient temperature evolution and monitoring-point temperature uniformity were evaluated. In the automated biobank, the final monitoring-point average temperature increased from −187.90 °C under an empty load to −183.21 °C under a full load, while the final monitoring-point temperature difference increased from 10.67 °C to 13.70 °C. In the non-automated biobank, the corresponding average temperature increased from −169.65 °C to −165.38 °C, and the temperature difference increased from 7.81 °C to 10.25 °C. In both configurations, the warmest monitoring locations were more responsive to loading than the coldest locations, indicating increased spatial non-uniformity. Under their respective modeled conditions, the automated configuration reached lower monitoring-point average temperatures but exhibited larger temperature differences, whereas the non-automated configuration showed higher average temperatures but smaller differences. Because the models differed in scale, internal arrangement, thermal boundaries, and monitoring layout, the cross-configuration comparison is descriptive rather than normalized. The results provide a model-based basis for structural screening, loading management, and functional temperature-monitoring strategies.