基于智能照明控制系统的医疗中心健康康复照明设计

IF 0.8 Q4 Computer Science
Yan Huang, Minmin Li
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引用次数: 0

摘要

智能照明控制系统将统一医院门诊楼、住院楼及其他建筑的照明设备管理,从而提高医院的服务质量,为医护人员提供舒适轻松的工作环境,为患者提供温馨舒适的治疗环境。本项目旨在开发基于Zigbee Wide Ad Hoc Network和RFID(无线射频识别)技术相结合的健康康复环境照明自动化控制系统。它可以根据患者的舒适度需求进行定制,并可以在患者到达之前实现灯光调节。提出了一种基于粒子群算法的智能照明控制方法。针对粒子群算法容易出现局部极小和过早收敛的缺点,提出了一种基于惯性权值粒子群算法并结合遗传优化理论的粒子群优化方法。这种方法既可以从群体中个体学习经验,又可以避免母粒子陷入局部极值的可能性。与原有的粒子群优化算法相比,新的粒子群优化算法可以快速找到照明器件的最优组合,计算效率提高了6.208%,同时也降低了计算照明器件的能耗,表明了新粒子群优化算法的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Health Healing Lighting in a Medical Center Based on Intelligent Lighting Control System
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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来源期刊
自引率
12.50%
发文量
29
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