The core quality data, such as interior ballistic performance, are seriously unbalanced in the plasticizing and molding process, which makes it difficult for traditional supervised learning methods to accurately predict this kind of index. A Tri-training weighted ensemble JITL-RVM model based on an integrated confidence evaluation strategy is proposed to solve the above problem. The method is based on Tri-training semi-supervised regression architecture and uses both labeled and unlabeled data for modeling. First of all, the traditional single similarity measure method is difficult to use to evaluate the real similarity between data samples reliably and stably. This method realizes diversity enhancement and data expansion of the data set for modelling through ensemble just-in-time modelling based on three homologous and heterogeneous mixed similarity measures. Secondly, a new integrated confidence evaluation strategy is used to select the unlabeled samples, and the pseudo-labeled data, which can improve the prediction performance of the model, can be selected. To improve the prediction effect of the model, the pseudo-label value of the data is revised continuously. The integrated confidence evaluation strategy can overcome many shortcomings of the traditional confidence evaluation method based on Co-training regression (Coreg). Finally, the final quality prediction value is obtained through weighted integration fusion, which reflects the difference between different models and further improves the prediction accuracy. The experimental results of interior ballistic performance prediction of single-base gun propellant show the effectiveness and superiority of the proposed method, and it can improve the RMSE, R², and PHR to 0.8074, 0.9644, and 93.3%, respectively.

The feeding and weighing of oxidizer agents is the key process of solid rocket motor propellant preparation, and its accuracy directly affects the burning performance of solid rocket motors. At present, the existing multi-batch feeding methods have the problem of low accuracy and high time consumption of the oxidizer agent. In this paper, an improved mass flow rate regulation method based on variable frequency control is proposed to improve accuracy and reduce time consumption. The nonlinear variation process of the mass flow rate during the opening and closing process of the air-operated pinch valve is analyzed. The periodic opening and closing frequency of the air-operated pinch valve is introduced to establish the mathematical model of the mass flow rate and frequency, and then, the model parameters are obtained through the discrete element method. The plan of the method of variable frequency regulation and the frequency parameters were determined using the multi-objective optimization method. The experiments are carried out, and the results show that compared to the existing multi-batch feeding method, optimized with the improved mass flow rate regulation methods based on the variable frequency control method, improved the feeding and weighing accuracy by 0.37% and reduced time consumption by 25.6%.