多模态数据驱动的军事人员高原适应性评估模型研究进展OA

The plateau environment poses significant challenges to the physiology and operational capability of military personnel,which is also a key contributor to non-combat casualties.Traditional evaluation methods using single-modal indicators lack dynamic monitoring capabilities and fail to capture complex dynamic stress reactions.This paper reviews the research progress in multimodal data-driven evaluation models for plateau adaptability by analyzing the pathological mechanisms and influencing factors of plateau adaptability and outlining the limitations of current standards and single-modal machine learning.The construction of multimodal data-driven models is explored,focusing on both data architectures that combine physiological,behavioral and environmental modalities for military scenarios and cross-modal alignment and fusion techniques.Furthermore,military applications such as dynamic early warning of non-combat casualties,personalized acclimatization and intelligent military health support are described.Finally,future developments in multimodal data-driven evaluation models for plateau adaptability are predicted in hopes of contributing to the combat effectiveness of troops on the plateau.