Enhancing University Logistics Management through IoT Technology in the Context of Bioinformatics Engineering

Abstract

This paper first discusses the principles and processes of bioinformatics technology and bioinformatics engineering, then analyzes the level of bioinformatics technology use in university logistics management by taking the example of fingerprint access control in universities. The fingerprint image is collected, the quality of the image is evaluated, detailed features are extracted from the grayscale image and the pre-processed skeleton of the lines, and the current set of fingerprint features is matched with the pre-stored fingerprint feature templates to determine if they match, and the feature templates are created and stored in the fingerprint library. Finally, fingerprint classification and recognition results and fingerprint matching time are analyzed to evaluate the performance of the fingerprint access control system. The correct average rate of fingerprint classification and recognition was 94%. The average time for each fingerprint acquisition is 0.3231s, the recognition time based on singularity base point is 0.9123s, while the recognition time based on bifurcation base point is 0.9871s, and the single-store matching time between fingerprint templates is about 0.005s, which indicates that the system has good recognition performance. The research in this paper provides reference and reference for upgrading and optimization of university logistics management and lays the foundation for the integration of university logistics management with modern technology.