Structure-aware fusion learning and intelligent decision support based on dynamic flight parameter hypergraphs in...

To address the bottlenecks in fusion analysis and decision support caused by strongly coupled dynamics, significant non-stationary disturbances, and cross-condition distribution shifts under high-frequency sampling conditions of multi-source flight parameters in flight test experiments, this paper proposes a structural perception fusion representation learning framework based on a dynamic flight parameter hypergraph. The method employs dual-stream gated multi-scale encoding in the temporal dimension to achieve scale decoupling and adaptive fusion, which respectively characterize transient mutations and low-frequency trends while suppressing noise-induced spurious correlations, thereby generating robust channel-level representations. In the structural dimension, flight parameter channels are modeled as nodes, enabling end-to-end learning of hypergraph associations that vary with window contents, and achieving cross-channel collaborative fusion through high-order message passing and normalized propagation across nodes, hyperedges, and nodes. A structural fingerprint is also constructed to support traceable analysis of key channel groups and collaborative mechanisms. At the geometric representation level, supervised contrastive learning constraints are introduced to compress intra-class variance and enlarge inter-class separation, enhancing metric consistency and cross-domain robustness of the embedding space, allowing the same representation to simultaneously adapt to state recognition, similar segment retrieval, and retrieval-enhanced prediction. Evaluated on complex engineering benchmarks and multiple random seeds, the proposed method outperforms sequence baselines such as LSTM and TCN in metrics such as F1 and PR-AUC. Ablation experiments further validate the critical contributions of dynamic hypergraph structural modeling and multi-scale encoding; changes in gated statistics and hyperedge allocation entropy reveal structural reconstruction patterns triggered by collaborative events. Results indicate that this framework provides a stable, reusable, and interpretable foundation for fusion representations in flight state assessment.