University laboratories are essential for experimental teaching, scientific research, and technological innovation, but they also pose multiple safety risks, including hazardous chemicals, specialized equipment, biological materials, radiation sources, electrical facilities, and laboratory waste. In many universities, laboratory safety inspections are an important management tool; however, several practical problems persist, including insufficient integration of inspection forces, weak linkage between inspection and rectification, limited professional support for high-risk hazards, and inadequate digital traceability. These challenges make it difficult to convert inspection results into continuous risk control. Considering the requirements of laboratory safety governance and the dual-prevention mechanism of risk grading control and hidden-hazard investigation, this study aims to construct a systematic, collaborative, and closed-loop laboratory safety inspection system, “four inspections and one supervision,” and to explore its operational logic and practical value in university laboratory safety management.

A problem-oriented design approach was adopted. Based on national policies, laboratory safety standards, and the actual needs of university laboratories, the study first analyzed the limitations of conventional inspections, particularly the separation of hazard identification, rectification assignment, process tracking, and final verification. A microgrid-based responsibility structure was then designed, linking laboratory rooms, personnel, hazardous sources, and management roles to specific responsibility units. Supported by a PC- and mobile-based laboratory safety inspection system, the process was digitized from task creation and automatic notification to on-site recording, rectification dispatch, progress tracking, review, and closure. The “four inspections” comprise self-inspection, mutual inspection, specialized inspection, and comprehensive inspection. “One supervision” refers to safety oversight, emphasizing external professional diagnosis and rectification implementation. Self-inspection ensures routine responsibilities at the college and laboratory levels; mutual inspection promotes cross-unit perspectives and experience sharing; specialized inspection targets high-risk professional fields; comprehensive inspection focuses on hierarchical, risk-based oversight; and supervision ensures hidden hazards are corrected through a closed-loop mechanism. Institutional, organizational, technological, and safety-culture measures were also established to support stable system operation.

The system transformed laboratory safety inspection from a fragmented activity into an integrated governance process. The microgrid structure clarified relationships between inspection tasks and responsible personnel, enabling hidden hazards to be assigned and tracked until completion. The combination of self, mutual, specialized, and comprehensive inspections improved the coverage and depth of hazard identification, detecting common risks through routine management and professional or concealed risks through targeted expert-supported inspections. The information platform enhanced efficiency, transparency, and traceability, managing inspection records, rectification requirements, responsible personnel, deadlines, review results, and closure status online, thereby reducing unresolved or repeatedly neglected hazards. The supervision mechanism strengthened accountability by linking inspection findings with rectification verification, notification, interviews, and assessment measures. In practice, the system fostered a coordinated pattern in which university-level management, colleges, laboratories, experts, and frontline users actively participate in safety governance according to their roles.

The “four inspections and one supervision” system provides a practical framework for improving university laboratory safety inspections. Its core value lies in integrating multiple inspection methods, grid-based responsibility assignment, professional support, and information-based closed-loop management into a single governance mechanism. The system addresses the common issue of emphasizing inspection while neglecting rectification, strengthens the connection between risk identification and rectification, and supports the transition from passive, fragmented control to proactive, systematic safety management. This framework may serve as a reference for universities seeking to establish a normalized, traceable, and sustainable mechanism for laboratory safety inspection and hidden-hazard rectification.

This study aims to address the critical issues faced by university laboratories, which are central to academic research and educational activities. The primary challenges that university laboratories face are the low level of informatization in performance evaluation, frequent safety incidents, and uneven distribution of resources. These issues not only hinder the operational efficiency of the laboratories but also pose significant risks to the safety of faculty and students, as well as the integrity of research outcomes. The importance of this study lies in its potential to enhance the management and operational efficiency of laboratories, thereby fostering a safer and more productive research environment that can meet the demands of modern academic research.

The development of the performance evaluation index system is based on an integrated laboratory data platform. This system employs the Fuzzy Analytic Hierarchy Process-Entropy Weight Method (FAHP-EWM) to determine the weights of various performance indicators. This method is chosen for its ability to integrate expert opinions with objective entropy measures, ensuring a balanced assessment. The importance of different performance indicators is evaluated, and entropy is used to measure the amount of information that each indicator provides, helping to identify which indicators are the most informative. This dual approach allows for a more nuanced understanding of the contribution of each indicator to overall performance. The Fuzzy Comprehensive Evaluation approach is then applied to assess the overall performance of the laboratories. This approach involves defining evaluation criteria and membership functions for each criterion, and then converting performance data into fuzzy values. These values are aggregated to provide an overall performance score. This method is particularly useful for handling uncertainty and imprecision in real-world performance data. It allows for a more flexible and comprehensive evaluation that can adapt to varying conditions and data quality. Additionally, input-output analysis is integrated to assess the operational efficiency of laboratories. This involves analyzing the inputs (resources, time, etc.) and outputs (research outcomes, publications, etc.) of the laboratories to determine their efficiency. This analysis reveals underutilized resources and inefficiencies, providing clear indications for process optimization. This method provides a quantitative measure of how effectively the laboratories convert inputs into valuable outputs, which is crucial for optimizing resource use and enhancing overall performance. The system is scalable and adaptable, making it suitable for laboratories of various sizes and research focuses. This adaptability ensures that the system can be tailored to the specific needs of each laboratory, enhancing its applicability and effectiveness. The integration of these methods into a single system enables a comprehensive evaluation that considers multiple aspects of laboratory performance, ranging from safety and resource management to research output.

The empirical results of implementing the proposed system demonstrate significant improvements in evaluation informatization. The probability of accidents is notably reduced, and the protection of national assets and the safety of faculty and students are enhanced. The system also contributes to the optimization of resource allocation, ensuring that resources are used efficiently and effectively, which is crucial for the long-term sustainability of laboratories. These results highlight the system's ability to provide innovative insights into the management of university laboratories, offering a scientific, standardized, and efficient approach to laboratory management.

This study presents a robust and comprehensive framework for evaluating and enhancing the performance of university laboratories. The integration of advanced analytical techniques with practical management strategies has yielded a system that is both scientifically sound and operationally feasible. The findings and methodologies presented in this study are expected to serve as a valuable reference for academic institutions and researchers worldwide, promoting the continuous improvement of laboratory management practices. By providing a structured and systematic approach to laboratory performance evaluation, this study aims to facilitate the transition toward more efficient, sustainable, and safe laboratory practices, ultimately contributing to the advancement of knowledge and innovation in academia.