An LSTM-GNN-Based Early Warning System for Laboratory Biosafety Using BIM and IoT-Driven Multi-Source Data Fusion - Cao, Jun (Informatica, 2025)
To address the challenges of traditional laboratory biosafety monitoring (e.g., data lag, single-dimensionality, rigid early warning), this paper designs an integrated system combining "BIM + IoT + LSTM-GNN Intelligent Algorithm" with a four-layer "Perception-Transmission-Platform-Application" architecture. Hardware includes the SHT35 temperature and humidity sensor (±0.1°C/±2% RH), BV6000 pathogen sensor (0.01 CFU/mL lower limit), and UWB positioning tags (0.3–1m accuracy). Software adopts a MySQL+Redis database, and a dynamic early warning algorithm integrating an improved attention mechanism, LSTM (for temporal feature extraction), and GNN (for spatial correlation mining) is proposed. Experiments were conducted on 15,000 datasets (covering 10 scenarios) with three control systems: traditional threshold system, single LSTM system, and IoT static monitoring system. Key results include: (1) Temperature-humidity error rates: 0.5% (normal scenarios), 0.7% (mild abnormal), 0.8% (severe abnormal); (2) Response time: 0.3s for 10,000 datasets; (3) Early warning accuracy: 98.5% (false positive rate 1.2%, false negative rate 0.3%); (4) 72-hour data transmission success rate: 99.9%. SPSS significance analysis (p<0.01) confirms the system outperforms control solutions, fully meeting laboratories’ dynamic safety management needs.