基于GIS的环保智能可视化管理系统的研发及应用
Research and Application of Environmental Protection Intelligent Visual Management System Based on GIS
杨文玉1
作者信息
- 1. 化学品安全全国重点实验室,山东青岛 266104;中石化安全工程研究院有限公司,山东青岛 266104
- 折叠
摘要
基于石化企业环保管理业务特点及需求,从环保智能可视化管理角度提出了基于GIS的环保智能地图管理系统的技术架构,主要解决现有技术难以实现环保管理一体化、系统化、智能化的问题.通过建立基于GIS的环保智能可视化管理系统,将物联网、自动控制、网络通信、GIS以及大数据等信息技术引进环保管理领域,搭建了企业环保数据的价值挖掘和深化应用模式,解决了环保管理业务平台独立、数据多源和分散决策的问题,实现了企业废水、废气、固废、土壤及地下水数据的融合应用,全面支撑企业污染源和环保治理设施的环保智能监控管理,为企业环保过程管控和科学决策提供技术支撑.
Abstract
Based on the characteristics and require-ments of environmental protection management in petrochemical enterprises,the technical framework of environmental protection intelligent map manage-ment system based on GIS was proposed from the perspective of environmental protection intelligent visual management,which mainly solved the prob-lem that the existing technology was difficult to real-ize the integration,systematization and intelligence of environmental protection management.Through the establishment of a GIS-based environmental pro-tection intelligent visual management system,infor-mation technologies such as Internet of Things,au-tomatic control,network communication,GIS and big data were introduced into the field of environ-mental protection management,establishing a value mining and deepening application model for enter-prise environmental protection data,and solving the problems of independent environmental management business platform,multi-source data and decentral-ized decision-making.The integrated application of enterprise wastewater,waste gas,solid waste,soil and groundwater data were realized.The environ-mental intelligent monitoring and management which fully supported enterprise pollution sources and en-vironmental protection management facilities,pro-vided technical support for enterprise environmental protection process control and scientific decision-making.
关键词
地理信息技术/污染源/数字化/环保管理/智能化Key words
geographic information technology/pol-lution sources/digitization/environmental manage-ment/intelligent引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据