Design of Health Healing Lighting in a Medical Center Based on Intelligent Lighting Control System

Design of Health Healing Lighting in a Medical Center Based on Intelligent Lighting Control System

Yan Huang, Minmin Li
DOI: 10.4018/IJITSA.331399
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Abstract

The intelligent lighting control system will unify the management of lighting equipment in outpatient buildings, inpatient buildings, and other buildings of the hospital, thereby improving the service quality of the hospital, providing a comfortable and relaxed working environment for medical staff, and providing a warm and comfortable treatment environment for patients. This project aims to develop a health rehabilitation environment lighting automation control system based on combining Zigbee Wide Ad Hoc Network and RFID (Radio Frequency Identification) technology. It can be customized according to the comfort needs of patients and can achieve lighting adjustments before the patient arrives. A PSO-based intelligent lighting control method has been proposed. To overcome the shortcomings of PSO such as being prone to local minima and premature convergence, a new PSO optimization method is proposed based on the inertia weight PSO and combined with genetic optimization theory. This method can not only learn experience from individuals in the group but also avoid the possibility of parent particles falling into local extremum. Compared with the original particle swarm optimization algorithm, the new particle swarm optimization algorithm can quickly find the optimal combination of lighting devices, improve computational efficiency by 6.208%, and also reduce the energy consumption of the calculated lighting devices, indicating the advantages of the new particle swarm optimization algorithm.
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1. Introduction

To make the indoor environment more comfortable and convenient, traditional lighting control methods are no longer suitable for indoor environments, and more intelligent lighting control methods must be adopted. Modern hospitals have excellent lighting systems that can release patients' negative emotions in a bright and comfortable environment, allowing patients to wait for treatment with peace of mind, bringing positive effects to treatment, ensuring that medical workers can efficiently and quickly complete all work, reducing physical and mental fatigue of medical staff, and thereby improving work efficiency (Yilmaz, F. S, 2018). The intelligent lighting control system will unify the management of lighting equipment in outpatient buildings, inpatient buildings, and other buildings of the hospital, thereby improving the service quality of the hospital, providing a comfortable and relaxed working environment for medical staff, and providing a warm and comfortable treatment environment for patients. Appropriate lighting design can create a healthy, clean, and comfortable surgical setting, positively affects onpositively reducing patients' tension and improves their physical and mental health. A good lighting environment for medical staff can slow down the tense working atmosphere and improve work efficiency (Guo Yujian, et al., 2019; Tang & Zhang, 2022).

The two commonly used methods are: firstly, to modify the lighting fixtures themselves to reduce energy consumption; The second is to transform the monitoring and management mode of lighting equipment, develop intelligent lighting equipment, and make lighting equipment more convenient and energy-saving. Smart lighting control system has been put forward by experts and scholars at home and abroad, and related research and application have been carried out (Wang, S., Su, D., & Wu, Y, 2022). The management and control of lighting equipment can be divided into three stages: manual control, timer photosensitive control and computer control. Jenny et al. combined the Internet of Things technology with the lighting system, and ZigBee and WiFiZigBee and WiFi controlled the lamps controlled the lamps. The lighting lamps' start, stop and brightness were mainly studied (Jenny, & Donelan, 2016). Rubenstone uses ANN(Artificial neural network) to study the design method of intelligent lighting (Rubenstone, J, 2018). Because the artificial neural network can self-learn, it can continuously accumulate experience through training, helping designers design intelligent lighting systems, thus saving a lot of work for designers. Archer et al. improved PSO(Particle swarm optimization) and applied it to the indoor smart lighting system (Archer, G. S, 2017). When adjusting the brightness of indoor artificial light sources, PSO was used to optimize the control strategy of the intelligent lighting system, which not only ensured the lighting needs of people but also made the outdoor natural light reasonably applied and reduced the energy consumption of the lighting system. Gentile et al. used the prior information of light sensor calibration to control sunlight adaptive lighting (Gentile, N, et al., 2018). The purpose is to introduce natural light reasonably and realize energy saving. At home, some corresponding scholars study natural lighting (Dikel, E. E, et al., 2018; Iyamu, & Mlambo, 2022).

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