Construction and Application of Intelligent Evaluation System for Preschool Education Quality Integrating Multi-Source Big Data
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Abstract
With the rapid advancement of intelligent sensing networks, edge computing, and Electromagnetic Waves, Antennas and Propagation technologies, efficient multimodal information fusion has become a fundamental requirement for data-driven monitoring and decision support in complex cyber–physical systems. To address the challenges of heterogeneous data inconsistency, spatiotemporal misalignment, and limited semantic interaction, this study proposes an intelligent evaluation framework integrating multi-source big data through Digital Twin–Knowledge Graph (DT-KG) fusion and a Multimodal Spatiotemporal Alignment Transformer Network (MSAT-Net). The proposed architecture combines video, audio, and textual information using heterogeneous feature encoding, cross-modal relative position encoding, adaptive gating fusion, and multi-head self-attention to construct unified semantic representations and interpretable quantitative evaluation models. Edge computing and differential privacy mechanisms are incorporated to enable real-time data acquisition while ensuring secure information processing and privacy preservation. Experimental results demonstrate that the proposed system achieves an evaluation accuracy of 92.3%, a Pearson correlation coefficient of 0.887 with ECERS-3 expert assessment, and significant improvements in robustness, inference efficiency, and long-term quality monitoring capability. Beyond preschool education, the proposed multimodal fusion framework provides an effective methodology for distributed sensing, semantic information propagation, adaptive data fusion, and communication-oriented intelligent monitoring, offering valuable engineering references for applications in Electromagnetic Waves, Antennas and Propagation.
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