Fig. 1: Research framework.

a Illustrates the proposed SSL-MISS-Net architecture. Self-supervised and Self-attention Learning (SSL) consists of two main components: the pretrained Self-Supervised Learning (b) and the Transformer-based Bimodal Feature Coupling Module (c). The Self-Supervised Learning employing dynamic full-masking strategy effectively explores inter-modality correlations to establish robust cross-modal mapping networks. The Transformer-based Bimodal Feature Coupling Module (BFCM) dynamically regulates modality feature weights via attention mechanisms, reinforcing salient bimodal features. SSL-MISS-Net establishes a novel Missing-label Synergistic-optimized Strategy (MISS) by the Labeling prior knowledge-based GCN Learning Module (d) with multi-task loss function based on missing-label encoding (e). The multi-task loss function based on missing-label encoding (\({{\mathcal{L}}}_{{ML}}\)) optimizes missing label samples through adaptive recalibration of their loss weights; the Labeling prior knowledge-based GCN Learning Module (LGLM) explores the dependencies relationships between labels, enabling the network to accurately infer missing annotations. f Illustrates the number and distribution ratio of the nine data centers. g Shows the division of the dataset in this study.