查询结果:   刘博,陈冠益,马云龙.基于云服务的智慧医院能源效率管理系统的研究[J].计算机应用与软件,2017,34(5):104 - 109.
中文标题
基于云服务的智慧医院能源效率管理系统的研究
发表栏目
应用技术与研究
摘要点击数
821
英文标题
RESEARCH ON SMART HOSPITAL ENERGY EFFICIENCY MANAGEMENT SYSTEM BASED ON CLOUD SERVICE
作 者
刘博 陈冠益 马云龙 Liu Bo Chen Guanyi Ma Yunlong
作者单位
天津大学环境科学与工程学院 天津 300072 同济大学电子与信息工程学院 上海 201804    
英文单位
College of Environmental Science and Engineering,Tianjin University,Tianjin 300072,China College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China    
关键词
云服务 物联网 智慧医院 能耗预测 管理系统
Keywords
Cloud services Internet of Things Smart hospital Energy efficiency Management system
基金项目
作者资料
刘博,高工,主研领域:医疗信息化,环境工程技术及能源管理。陈冠益,教授。马云龙,副研究员。 。
文章摘要
随着医疗机构医疗技术的进步以及医疗机构功能的不断完善,就医环境与工作环境的不断优化,我国医疗机构电气煤单位面积能源消耗每年递增, 医疗机构能源和环境质量管理仍存在粗放式现象,如设备资料缺少、台账不清、制度执行力较弱、员工队伍失衡等问题。而目前绝大多数医疗机构都没有开展能源诊断以及系统管理,医疗机构节能和环境质量管理发展潜力很大。基于云服务以及物联网技术,主要研究面向智慧医院能效管理的云服务架构,以及管理评价方面的关键问题,利用物联网监测获取数据,通过信息平台集成和分析数据,结合回归分析与BP神经网络的方法对能耗数据进行预测,确立能源效率与环境质量综合表征的智慧医院指标体系,并开展相关评估与管控研究,从而打造节能高效、环境舒适的智慧节能医院,有效推进医疗机构绿色节能建设的发展。
Abstract
With the development of medical technology and the improvement of the function of medical institution, the medical environment and working environment are constantly optimized. In China, the energy consumption per unit area of electric and coal is increasing every year. The management of energy and environmental quality of medical institutions still has extensive phenomena, such as lack of equipment information, unclear accounting, weak institutional execution and imbalance of staff team. At present, the vast majority of medical institutions do not carry out energy diagnosis and management system, and medical institutions, energy saving and environmental quality management development potential is great. Based on cloud service and Internet of Things, this paper mainly focuses on the cloud service architecture for smart hospital energy efficiency management and the key issues in management evaluation. The data are collected by means of Internet of Things monitoring, and are integrated and analyzed by information platform, and the energy consumption data are predicted by regression analysis and BP neural network. To establish a comprehensive index of energy efficiency and environmental quality indicators of the smart hospital system, and to carry out the relevant assessment and control research to create energy efficient and comfortable environment of the smart energy-saving hospitals, and effectively promote the development of green energy-saving medical institutions.
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