Influence of the combination of big data technology on the Spark platform with deep learning on elevator safety monitoring efficiency.

Abstract

To effectively minimize elevator safety accidents, big data technology is combined with deep learning technology based on the Spark platform. This study first introduces the relevant theories of elevator safety monitoring technology, namely big data technology and deep learning technology. Then, the fault types that occur in the running state of the elevator are identified, and a finite state machine model is established. An elevator fault monitoring method based on the Spark platform is proposed, namely finite state machine (FSM), and the results of elevator safety fault monitoring are evaluated. Based on deep learning, an elevator fault warning model is constructed and its early warning performance is evaluated. The results show that the study can realize real-time and effective monitoring in the operation state of the elevator, and can determine the fault type of the elevator by binding the abnormal operation state with the corresponding fault. The feasibility of the elevator safety monitoring efficiency is evaluated based on three indexes: mutual information, accuracy, and false positives. Compared with other algorithms, the proposed FSM algorithm has the largest mutual information (0.1337), the highest accuracy (0.9899), the lowest false positive rate (0.0624), and the lowest false negative rate (0.1126); compared with other models, the elevator fault warning model proposed in this study has the lowest root mean-square error (RMSE) value (0.0201), the highest accuracy (0.9834), the lowest Loss value (0.0012), and the shortest convergence time (88.2608s), indicating that the elevator safety monitoring system and elevator fault warning model are feasible. This study establishes a good direction for elevator safety monitoring efficiency in China.