###### Abstract  
Accurate temperature prediction is a prerequisite for precise thermal control of an aerial camera, which is crucial for capturing high-resolution images. This paper introduces a methodology for improving the precision of temperature predictions generated by thermal network models for aerial cameras, which involves correcting the structure and parameters of the thermal network. The passive thermal control optimization design of the frame insulation structure and convective heat transfer coefficients ensures that key thermal parameter values are maintained within optimum ranges. Following an analysis of the change trend consistency between the predicted temperatures and the transient thermal experimental data, the thermal network structure is modified to reduce the discrepancy between the thermal network and physical models. Using the Monte Carlo algorithm, parameter spaces for key thermal parameters are explored using different sampling numbers. The optimal parameter values are then derived by satisfying the tolerance limit condition and minimizing the objective function. As the sampling number increases, the RMSE between the predicted and experimental results decreases from 1.82 \({}^{\circ}\)C to 1.44 \({}^{\circ}\)C, and the R-squared value increases from 0.78 to 0.91. The proposed correction method demonstrates its efficiency and validity in refining the accuracy of thermal network models for aerial cameras. a 2025 28 March 2025 16 April 2025 17  
the context of developing a digital twin system for an aerial camera, where prompt and precise thermal predictions are of paramount importance, the LPTN model's computational efficiency provides a robust foundation for achieving this objective [9; 10]. When establishing an LPTN model, certain simplifications and assumptions are inevitably made, potentially leading to modeling errors. Factors such as variations in processing techniques, surface properties, and the surrounding environment can lead to deviations between actual physical properties and calculated values [11; 12]. It is therefore essential to update the LPTN model according to test data to guarantee more precise calculations of temperature distributions. Furthermore, the continuous refinement of the LPTN model can ensure that the digital twin system remains accurate and reliable, mirroring the real-world performance of the aerial camera. A substantial body of research has been conducted on the subject of refining thermal network models associated with aerospace instruments. In their seminal work, [PERSON] et al. [13] proposed a systematic approach to calibrating satellite thermal network models. This methodology is firmly grounded in empirical data derived from thermal balance tests. [PERSON] et al. [14] enhanced pivotal parameters within the thermal network model through the application of statistical regression analysis techniques. In a successful implementation of the particle swarm optimization approach, [PERSON] et al. [15] modified thermal model parameters with a primary emphasis on optimizing linear conductivity. [PERSON] et al. [16] employed the Jacobian matrix formulation in conjunction with the Moore-Penrose pseudo-inverse method to effectively reduce the uncertainty associated with the parameters within the thermal mathematical model. [PERSON] et al. [17] presented a thermal model updating approach that employs the Kriging model as a surrogate tool to refine the thermal design parameters of a solar spectrometer, thus obviating the necessity for repetitive finite element analysis iterations. [PERSON] et al. [18] employed a combination of Latin hypercube sampling and the coordinate rotation method to enhance the accuracy of the spacecraft's thermal analysis model. The Monte Carlo method, as a computational method based on probabilistic statistical theory, has also been employed to estimate the optimal values of thermal network parameters. [PERSON] and [PERSON] [19] were the first to apply the Monte Carlo stochastic approximation method in satellite thermal analysis.