Abstract: To address the problems of low manual management efficiency and lack of data support for charging period division in university NMR spectrometers, the application of lightweight data analysis methods in charging management was explored. To overcome the limitation of the traditional “total machine time” indicator, a “daily weighted booking count” indicator was proposed, unifying equipment occupancy load and user service volume through weighting. Analysis of 19 684 actual records shows that the system achieves one-click automated billing; weighted values are significantly lower during 13:00-15:00 (5.4-5.7), and significantly higher during 1:00-2:00 and 22:00-23:00 (8.7 and 9.3); Wednesday and Thursday are the busiest, while Sunday is the quietest. The proposed method is zero-cost, easy to deploy, and applicable to both manually managed laboratories and as a data analysis plug-in for existing platforms, providing data-driven management reference for NMR laboratories.