Optimization of AI-driven communication systems for green hospitals in sustainable cities

Abstract

The green hospital is an important part for constructing Sustainable Cities. In this paper, system optimization algorithms based on artificial intelligence technologies are proposed by studying the communication system of the green hospital in the smart sustainable city, and the overall architecture design and detailed functional module design were carried out. The functions in the system can be divided into basic information management function, patient monitoring function, and remote self-test, etc. The medical equipment management system is characterized by the combination of Internet of Things technology, so that equipment managers can control the operating status of the equipment at any time, and can remotely upgrade and control the equipment through the system, and medical staff can view the measurement data of the equipment at the system interface and grasp the real-time information of treating patients to improve work efficiency. The accuracy of the improved particle swarm optimization (PSO) algorithm was compared with that of a single BP neural network algorithm. According to the requirement analysis of the health monitoring software and the overall design scheme of the system, each functional module of the health monitoring software is designed and the specific implementation of each functional module is carried out.