Research prospects for smart laboratory safety based on cross-technological integration

With the rapid development of higher education in China, experiments are becoming increasingly diverse and new methods are constantly emerging. Experimental research is showing a trend toward interdisciplinary collaboration, and traditional safety management methods are proving inadequate for effective prevention of accidents, such as fires, explosions, and poisoning. In recent years, a series of laboratory accidents in universities have exposed shortcomings in the traditional management systems to identify and warn against dynamic risks. Consequently, integrating cutting-edge technologies, such as artificial intelligence, to establish an intelligent, proactive laboratory safety risk prevention and control system has become an urgent necessity for enhancing the effectiveness of laboratory safety management in higher education institutions.

To enhance the effectiveness of laboratory safety management and prevent various types of accidents, this study systematically reviews the current state of the application of the five-in-one technology—artificial intelligence, neural networks, image recognition, geographic information systems (GIS), and visualization—in laboratory safety, and proposes the construction of an integrated technological framework comprising “AI-neural network-image recognition-GIS-visualization” integrated technological framework. Building upon this, a multitiered intelligent laboratory safety management framework comprising the following specific steps is designed: (1) a laboratory safety knowledge graph is constructed based on multisource data. The graph deploys the ALBERT-BiLSTM-CRF model for entity recognition and uses the Neo4j graph database for knowledge storage and associative reasoning;(2) an intelligent agent is developed for risk perception and digital-intelligent control. A closed-loop “perception-identification-decision-making” system is constructed, and the Dempster–Shafer evidence theory is integrated with dynamic risk assessment models and a cloud computing platform for real-time monitoring and intelligent discrimination of multidimensional information on personnel, equipment, environment, and procedures;(3) A university laboratory safety risk assessment and intelligent monitoring/early warning system is established, integrating multimodal sensing networks, dynamic knowledge graphs, and agent-driven decision-making to form a complete technical chain spanning data collection, risk modeling, and early-warning response.

The five-in-one technology proposed in this paper offers an innovative approach to laboratory safety management in higher education institutions, reflecting the trend toward the deep integration of modern information technology and safety management. Although the proposed technology still faces challenges such as high implementation costs, data security risks, and a shortage of specialized personnel during roll-out, with continuous advancements in technologies, such as artificial intelligence, digital twins, and edge computing, safety inspection robots with enhanced intelligence, visualization, and automation capabilities are expected to be developed in the future. By integrating multimodal sensing, intelligent algorithms, and visual interaction, these robots will be capable of real-time monitoring of environmental parameters, equipment status, and personnel behavior, automatically performing risk assessments and issuing tiered alerts. This will ensure round-the-clock intelligent inspection support for laboratories.

This research not only drives the continuous development of laboratory safety management toward greater precision, proactivity, and universal accessibility but also provides a solid guarantee for the high-quality advancement of higher education and the safety of faculty and students during experimental research.