No abstract

In order to solve the problem of long product lead time, accurate demand forecasting for space science payload components is of great significance to the development of China’s space science industry. In view of the unsteady, nonlinear, and small sample characteristics of space science payload component demand, this paper proposes the EEMD-CC&CV-MPSO-SVR model to predict the future demand of space science payload components. First, this paper effectively adopts EEMD to decompose the normalized demand sequence and analyze the stationarity of each subsequence. The sequence complexity is distinguished by sample entropy, and the optimum kernel function CC-MPSO-SVR and CV-MPSO-SVR prediction models are established for high-complexity and low-complexity sequences, respectively. Finally, the prediction results of each subsequence are ensemble to form a total prediction. Experimental results shows that the model proposed in this paper performs better than single benchmark models and other hybrid models in terms of prediction performance and robustness. It can effectively predict the quantity and trend of the demand for China’s space science payload components, which provide decision-making basis for the government to formulate policies, demand-side procurement, and supply-side inventory control.

Commercial off-the-shelf (COTS) components have been widely used in the aerospace at home and abroad, but there is no systematic theory of quantitative evaluation of the risk. Therefore, based on Shannon’s information entropy, the risk information entropy is proposed to describe the risk uncertainty of COTS components in space application. And a theoretical framework of COTS components space application is established, which includes calculation and decrease of the entropy. According to whether the failure mode and failure frequency ratio are known, different risk information entropy models are established for components. Based on this, a system risk information entropy model is established, and a continuous optimization method for the risk information entropy model is proposed. In addition, it introduces how to achieve system entropy reduction from several aspects, such as selection guidance, application certification, reliability reinforcement, and status detection.