Construction and Quantitative Evaluation of Basketball Smart Teaching Scenarios Integrating Ideological and Political Elements

Accurate perception and quantitative assessment of human activities in complex interactive environments require efficient sensing, reliable data fusion, and real-time contextual understanding. This study proposes an intelligent scenario construction and evaluation framework based on multimodal sensing and digital twin technologies. A heterogeneous sensing architecture integrating ultra-wideband (UWB) localization, inertial measurement units (IMUs), computer vision systems, and physiological sensor networks is developed to acquire synchronized spatiotemporal data from dynamic human activities. To achieve semantic understanding of complex behavioral interactions, a domain knowledge graph is introduced to establish contextual relationships among activity events, interaction patterns, and assessment objectives, enabling real-time generation of a digital twin environment. Furthermore, a data-driven evaluation model incorporating multi-head attention mechanisms is designed to perform feature fusion, representation learning, and quantitative assessment from multimodal sensor streams. Experimental results demonstrate that the proposed framework effectively improves activity recognition, interaction analysis, and real-time assessment performance. By integrating wireless sensing, multimodal information fusion, digital twin modeling, and intelligent data analytics, the proposed framework provides an engineering-oriented methodology for human-centric monitoring, distributed sensing environments, and intelligent perception systems.