## 1 Introduction
In this circumstance, the Monte Carlo method can be used to calculate the radiative heat transfer between the infrared heating cage and the spacecraft concerning the random sampling method and the statistical principle so that while the angular coefficients are obtained between the infrared heating cage and the different partitions of the spacecraft, the distribution of the heat flow reaching the surface of the spacecraft can be further obtained under different heating powers [15; 16]. Monte Carlo methods are an efficient way to find corner coefficients. For simple surface geometric arrangements, [PERSON] et al. [17] proposed more than 250 angular coefficient factors for specific geometries and current surface arrangements. [PERSON] et al. [18] proposed some exchange factors for simple two-dimensional geometries and compared them with analytic solutions. [PERSON] et al. [19] dealt with the computation of exchange factors between non-gray, non-diffuse surfaces in closed and open geometries. He also pointed out that for open geometry models, the energy partitioning method of [PERSON] et al. [20; 21] is very effective in reducing the computation time. [PERSON] [22; 23] used the Monte Carlo method to simulate the heat flow of cylindrical and circular truncated cone-shaped infrared heating cages and analyzed the influence of parallel distance between the infrared heating cage and the heated surface on the edge effect and the influence of the taper angle of the infrared cage on the edge effect, respectively, to obtain the optimal design method of the infrared heating cage, to improve the homogeneity of the simulation of the density of heat flow on the surface of the spacecraft. Nevertheless, there is still room for improvement in the thermal design of infrared heating cages based on the Monte Carlo method, such as the complicated process of calculating the radiation angle coefficients and the low computational efficiency [24]. To solve the problems of complex modeling and the slow calculation of Monte Carlo-based heat flow simulation of an infrared heating cage, this paper carries out research on the rapid prognostic technology of infrared heating cages based on particle swarm optimization-support vector regression (PSO-SVR), and the spacecraft structure is decomposed into planar, cylindrical, and circular truncated conic. The spacecraft structure is decomposed into three types: planar type, cylindrical type, and circular truncated conic type, and the PSO-SVR method is used to establish an agent model to obtain the correspondence between the partition parameters such as partition heights, widths, and distances and the angular coefficients. In the design of an infrared thermal cage, the corner coefficients can be obtained directly through the agent model to realize rapid calculation. To achieve the above purpose, this paper is structured as follows: the spacecraft structure and infrared heating cage partitions used for the rapid prognostic model are modeled in Section 2. In Section 3, the PSO-SVR proxy model is theoretically derived, and a general model for rapid prediction of heat flow for different infrared heating cage configurations is established. In Section 4, the developed rapid prognostic model is validated, analyzed, and compared with the Monte Carlo method. In Section 5, the validity of the model is verified using a small thermal control star as a case study. Conclusions are subsequently drawn based on the synthesis of these simulations and experimental insights.