In this paper, we present our submission to the bilingual machine translation task of CCMT 2024 for the Chinese \(\leftrightarrow \) English constrained scenarios. Our systems are based on the Transformer architecture, the submitted system is an ensemble of multiple models of Transformer-variant for all directions. Only the released constrained data were used as our training data, with fine-grained data filtering and augmentation strategies. Additionally, we used various training techniques such as HyPe tuning, back-translation, forward translation, R-Drop, and alternated translation. The experimental results show that R-Drop, increasing data diversity, and model ensemble are the most effective methods in enhancing model performance.