摘要
利用信息化的手段辅助园林绿化管理的日常工作能够大幅提高管理效率和管理的精准化水平。准确、充分和前沿的用户需求分析,是建立城市园林绿化信息化管理系统和平台顶层架构、功能组成、模块设计等的基础。但由于信息化建设和园林绿化行业管理在专业上存在偏差,使得信息化的需求分析和行业的预测工作沟通壁垒较高。结合大数据的特点,提出一套充分结合用户使用评价、网络新闻、会议记录等文本型大数据,利用文本挖掘方法进行城市园林绿化信息化管理的需求分析和预测的科学方法。分析了三类文本挖掘得出的高频词特点,结合前期对城市园林绿化信息化行业的实际调研,得出了当前中国园林绿化的管理目标在层级转变,办公自动化、物联网感知等前端管理逐步突显的需求特点,为推动园林绿化行业信息化工作提出了建设方向。
Assisting daily landscaping management with the means of informationization can greatly improve the efficiency and precision of management.Accurate,full and frontier user demand analysis is the basis for the establishment of an urban landscaping information management system,and platform top-level architecture, functional composition,and module design.However,due to the specialty deviation in information construction and landscaping industry management,the communication barriers between the informatization demand analysis and industry forecasting are high.According to the characteristics of big data,this paper puts forward a set of scientific methods for demand analysis and forecast of urban landscaping information management with the text mining method,which fully combines the user application evaluation,network news and meeting minutes and other text big data.It analyzes the characteristics of high frequency words obtained from the three types of text mining and, along with the actual investigation of the urban landscaping information industry in the early stage,to reach the characteristics of demand prominent in the front-end management of hierarchical transformation,office automation, Internet of Things awareness in the current management goal of Chinese landscaping,putting forward the direction of construction to promote the information work of landscaping industry.
作者
季珏
安超
李波茵
伍雍涵
JI Jue;AN Chao;LI Boyin;WU Yonghan(the Urban-Rural Planning Administration Center,Ministry of Housing and Urban-Rural Development)
出处
《风景园林》
2019年第8期44-47,共4页
Landscape Architecture
基金
国家重点研发计划(编号2017YFB0503903)~~
关键词
风景园林
文本挖掘
信息化管理
需求分析
园林绿化
landscape architecture
text mining
information management
demand forecasting
landscaping