Application of Intelligent Computing and Machine Learning in Design and Optimization of Elevator PLC Control System

With the rise of tall buildings, elevators have become an indispensable part. With the acceleration of urbanization and the continuous growth of population, the number of high-rise buildings is increasing, and people's demand for elevators is also growing. The safety, reliability, comfort and efficiency of elevators directly affect people's daily life and work. Therefore, how to optimize elevator PLC (Programmable Logic Controller) control system through intelligent computing and machine learning technology to improve elevator performance and user experience is a hot issue in current research. In this paper, the PLC control system is analyzed, and the method of intelligent computing and machine learning is proposed to improve the system. This paper mainly uses the experimental method and the comparison method to compare the performance of the five signal control aspects of the control system and the performance of the algorithm. The experimental results show that the maximum throughput is the neural network, and the control system under this algorithm has the lowest response time (0.4s), the highest availability (98.95%), the lowest failure rate (0.005%) and the highest security (99.995%).