Long-term operation mechanism for the dual prevention system in university laboratories

University laboratories are high-risk sites with concentrated hazardous sources. In recent years, frequent laboratory safety accidents have revealed major weaknesses in safety management, including incomplete risk identification, ineffective implementation of control measures, and failure to rectify identified hazards. Under the mandatory requirements of national education authorities, most universities have established an institutional framework for a dual prevention system covering risk-based hierarchical control and safety hazard investigation and treatment. Nevertheless, a widespread bottleneck emerges in practical operation: the system suffers from “formal construction without effective application,” manifested as disjointed risk identification and on-site conditions, decoupled safety hazard inspection and rectification, and derailed accountability implementation and assessment. Existing domestic studies mostly focus on the structural construction of the dual prevention system, while systematic research on long-term stable operations and self-adaptive optimization is lacking. To fill this research gap, this paper aims to answer three core questions: where the driving force of the system originates, how to sustain its stable operation, and how to continuously improve its management efficiency.

This paper innovatively couples incentive theory, systems theory, and plan-do-check-act (PDCA) cycle theory to construct a three-level progressive framework consisting of motivation, guarantee, and optimization layers. Additionally, it proposes a five-in-one collaborative mechanism including motivation stimulation, technical support, process control, collaborative supervision, and continuous optimization. In the motivation layer, a grid-based accountability system, multidimensional incentive policies, and a near-miss incident no-blame reporting mechanism are designed to encourage internal participation from teachers and students. The guarantee layer integrates digital intelligent management platforms, artificial intelligence (AI) visual recognition, and Internet of Things real-time monitoring as technical support; builds a nested PDCA dual-cycle model combining annual macro and daily micro cycles for process control; and establishes a three-dimensional supervision network composed of school cross-inspection, third-party independent audit, and regular safety conferences. The optimization layer forms an endogenous experience explicit transformation channel through quantitative effectiveness evaluation, feedback from typical accident cases, and institutionalization of grassroots management experience. A two-year pilot test was carried out in chemical engineering laboratories of Nantong Institute of Technology to verify the practical effect of the proposed mechanism, with multiple quantitative indicators set for comparative analysis before and after implementation.

The pilot operation data demonstrate remarkable optimization effects brought by the long-term operation mechanism. The average safety hazard rectification cycle is shortened from 12 to 7 days, a reduction of nearly 40%; the recurrence rate of repeated safety hazards drops sharply from 27% to 6%; and the number of voluntarily reported near-miss incidents rises from 2 to 15, representing considerable improvement in active safety awareness. In terms of personnel safety literacy, the standardized safety behavior score of teachers and students increases from 72 to 91, the awareness rate of safety management regulations rises from 67% to 89%, and the recognition proportion of individual main safety accountability grows from 69% to 87%. The mechanism effectively resolves the dilemma of superficial system operation caused by accountability vacuum, formalized inspection, mismatched resource allocation, insufficient safety culture, and isolated safety data. Meanwhile, the pilot reveals restrictive factors, including insufficient sustained funding for intelligent platforms and uneven execution efficiency across individual laboratories.

The three-layer framework and five-in-one collaborative mechanism constructed in this study can provide systematic theoretical support and replicable practical paths for universities to realize intrinsic laboratory safety. They fundamentally resolve the long-standing pain point of “built but unused” in the dual prevention system by endowing the system with endogenous driving force, standardized operation guarantee, and self-evolution capacity. Subsequent research will develop quantitative evaluation tools to measure the long-term operational effectiveness of the dual prevention system, conduct cross-university empirical research, and build an AI-driven dynamic risk early warning model to further improve the whole-chain laboratory safety governance system of higher education institutions.