Abstract
The advancement of Industry 4.0 and smart manufacturing has led to the generation of high-volume, multisource data that evolve in real-time within production scheduling systems. This poses significant challenges to traditional risk identification methods. Models that ignore topological relationships lack robustness when handling complex dependencies. Black-box deep learning models offer insufficient interpretability to support decision-making. Furthermore, complex graph neural networks entail substantial computational costs and cannot meet real-time requirements. To address these issues, this article proposes an attention-based lightweight graph neural network (AGNN) for big data analytics. The model incorporates a context-aware dynamic attention mechanism that adaptively adjusts node weights based on the global state, thereby enhancing robustness against uncertainties in the scheduling environment. The visuali
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ation of attention weights, combined with a business key performance indicator-aligned risk labeling strategy, enhances interpretability and provides a transparent basis for decisions. A lightweight two-layer, single-head architecture maintains high accuracy while compressing the parameter count to 9.8K and achieving an inference speed of 0.163 ms per sample, enabling real-time edge deployment. Importantly, AGNN establishes a data-to-decision link for big data-driven production decision-support systems. It not only identifies risks but also interprets their business impact and propagation pathways, enabling schedulers to prioritize interventions and optimi
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e resource allocation in a timely manner. This enhances the adaptability and intelligence of manufacturing systems in dynamic environments. Comparative experiments on a real-world marine shafting dataset demonstrate that AGNN significantly outperforms other baseline models. This study offers a robust, interpretable, and efficient risk identification solution for big data-driven smart manufacturing systems and promotes the application of computational intelligence in complex industrial scenarios within a big data context.