The Security and Protection System of Electromechanical Equipment in Smart Campus using the Improved Data Mining Algorithm

Abstract

In order to improve the maintenance and management efficiency of campus electromechanical equipment and reduce or even avoid the safety risks brought by campus electromechanical equipment, this work uses the data mining algorithm to design the security and protection system of campus electromechanical equipment. First, this work constructs the campus electromechanical equipment classification model using the Bayesian algorithm of data mining algorithm and designs a simulation experiment to verify the effect of the classification model. Then, the security and protection system for the campus electromechanical equipment is designed. It includes the system business process, system function design, system core module’s function design and system implementation. Finally, a simulation experiment is designed to verify the system’s performance. The results show that: (1) Bayesian algorithm is superior to the K-Nearest Neighbor (KNN) algorithm in both classification effect and running time. (2) When the browser concurrency in the system increases, the server processor and memory usage also increases, but the value meets the expected requirements. It shows that the system has a certain browser concurrency-bearing capacity. Moreover, as the browser concurrency of the system increases, the response time of the test also increases, but the value meets the expected requirements. This work aims to improve the maintenance efficiency of campus electromechanical equipment and provide a reference for the safety protection work of electromechanical equipment in other enterprises or units.