Abstract
In high-risk industrial settings, the proliferation of sensor data provides crucial support for fundamental research on safety events (SEs) and for precise risk analysis. However, existing data-driven methods struggle to reveal the nonlinear couplings among multiple factors and lack a systematic framework to explain how these factors jointly contribute to different types of SEs. To address these limitations, this study proposes a theory–data integrated model for multitype SE risk identification and assessment. From a cross-scale emergence perspective, the study elucidates that SEs arise from a chain-evolution process driven by the nonlinear coupling of diverse environmental factors and constructs a theoretical framework describing hierarchical factor associations and cross-scale interactions. Building upon this theoretical foundation, a data-driven risk identification and assessment model (RIAM) is established. This model quantifies the contributions of measurable environmental factors (MEFs) that significantly influence SEs through online learning. Experimental results demonstrate that the proposed model effectively captures the cumulative risk effects arising from multi-factor coupling, thereby enhancing both event identification accuracy and model interpretability. This study provides a novel perspective and methodological foundation for SE prediction and integrated prevention in complex industrial environments.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding
This work was supported by the National Natural Science Foundation of China (NO.52404233).
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Conceptualization, Zhixin Jin and Qian Liu; methodology, Junqiao Li and Qian Liu; software, Junqiao Li and Qian Liu; validation, Qian Liu; formal analysis, Qian Liu; investigation, Qian Liu; resources, Junqiao Li and Qian Liu; data curation, Junqiao Li and Qian Liu; writing—original draft preparation, Qian Liu; writing—review and editing, Qian Liu; visualization, Qian Liu; supervision, Junqiao Li and Qian Liu; Project administration, Junqiao Li; Funding acquisition, Junqiao Li.
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Liu, Q., Li, J. & Jin, Z. Risk identification and assessment for multitype safety events under the coupling of environmental factors. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39940-3
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DOI: https://doi.org/10.1038/s41598-026-39940-3
