As soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems, its precise determination provides crucial technical support for assessing soil quality, calculating the potential of terrestrial carbon sinks, and promoting low-carbon agriculture. In recent years, elemental analysis has become the mainstream method for detecting SOC content; however, the pretreatment step for removing inorganic carbon directly affects measurement accuracy. The silver cup and centrifugation methods are two widely used and relatively stable pretreatment approaches. However, existing studies mostly focus on standard soil compounds, neglecting the influence of key factors such as land use types, soil-forming environments, and soil matrix compositions. This narrowness makes it difficult to provide method selection references for the efficient and precise detection of large quantities of various soil types. Therefore, this study selected soils from four typical land use types—agricultural land, grassland, urban green space, and sandy wasteland—to systematically explore their impact on measurement results obtained using the two pretreatment methods, thereby providing a scientific basis and technical support for the efficient and precise detection of organic carbon in different soil types.

The study used a VELP CN802 carbon-nitrogen analyzer to determine the SOC content based on the Dumas dry combustion method. After strictly verifying the instrument’s precision and accuracy, the accuracy and reproducibility of the silver cup and centrifugation methods were evaluated using standard soil compounds. Subsequently, 39 soil samples from multiple provinces (regions), including Heilongjiang, Jilin, Inner Mongolia, and Shaanxi, were selected. For each sample, three replicates were prepared using the same pretreatment methods, and each sample was measured three times consecutively. The average SOC content of each sample was calculated based on the mean of nine measurements, and the relative difference between the values from the two pretreatment methods was compared to assess inter-method consistency and variance.

The results showed that both pretreatment methods had good stability. The silver cup method had higher accuracy, while the centrifugation method was low-cost and convenient for batch operations. Land use types significantly affected assessment consistency by altering the SOC content, component characteristics, and the inorganic carbon state. The relative difference generally followed the order agricultural land < urban green space < grassland < sandy wasteland, and it was significantly positively correlated with soil pH. The conversion equation (y = 1.0191x + 0.0374, R² = 0.9995) based on the data obtained could effectively and correctly measure the centrifugation method data, reducing the relative error to 0.04%–5.15%, meeting industry standards and achieving a balance between detection cost and accuracy.

This study provides referential methods for the large-scale, efficient, and precise determination of SOC in different land use types. However, due to the limited number and coverage of samples, the influence of microscopic factors such as soil texture and SOC components could not be deeply analyzed. Thus, the generalizability of the conclusions and correction models needs verification. In the future, sample type and spatial coverage can be expanded, and deviation mechanisms can be analyzed in combination with microscopic techniques to build a multi-method optimization system and a universal correction database, providing a more reliable technical basis for the precise detection of SOC content.

Intensive management is a crucial measure for optimizing the allocation and sharing of scientific research equipment in universities. Exploring the practical difficulties in its implementation and overcoming management bottlenecks based on advanced concepts are crucial for enhancing the scientific competitiveness of university research. Small high-temperature equipment, as an essential infrastructure for experimental teaching and scientific research, is widely applied in chemistry, geography, and life sciences. However, during its operation, it encounters multiple safety hazards such as high-temperature burns, fire explosions, and electrical short circuits. Under the intensive management model, as the scope of equipment sharing expands and the usage frequency increases, the contradiction between the increase in the safety risk index and the demand for sharing, the contradiction between frequent use and maintenance guarantee, and the contradiction between differentiated experimental needs and instrument function adaptation have become increasingly prominent, severely affecting the safe and stable operation of the laboratory and the improvement of research quality. Therefore, a systematic solution needs to be urgently developed.

This study used the State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction with a fishbone diagram as an example to overcome the existing drawbacks of the intensive management of small high-temperature equipment and deeply analyze the causes of these drawbacks. The Six Major White Hats method from enterprise lean production management was introduced, and its feasibility and adaptability for intensive management of small high-temperature equipment were demonstrated from the perspectives of concept-scenario compatibility and multi-concept synergy. Based on the feasibility analysis, the following targeted implementation paths were further proposed: “planning white hat”: to coordinate resource layout and optimize the allocation system, “standard white hat”: to standardize the entire process operation and strengthen safety control, “training white hat”: to enhance personnel safety literacy and lay a solid foundation for standardized operation, “inspection white hat”: to improve the risk prevention network and reduce potential equipment hazards, “emergency white hat”: to establish a response plan system and enhance the ability to respond to emergencies, and “improvement white hat”: to dynamically iterate the management mechanism and promote continuous upgrading of the model, ultimately building a lifecycle management system centered on “preventing risks, standardized operation, resource optimization allocation, and continuous improvement.”

The implementation proved that this innovative management system, with its refined, standardized, and collaborative management logic, not only specifically resolved the inherent mechanism defects of the intensive management model but also effectively balanced the adverse impacts of the external environment, thereby effectively promoting the transformation and upgradation of small high-temperature equipment management from passive response to active optimization and demonstrating outstanding application effects for strengthening safety control, promoting the release of equipment usage efficiency, and supporting scientific research breakthroughs.

This study proposes a scientific and efficient operational model for the intensive management of small-scale equipment in universities and provides new insights into balancing safety and efficiency in shared research equipment systems. Moreover, the cross-disciplinary application of this management tool offers a practical reference for advancing refined management in university research laboratories.