突发事件驱动的应急情报分析框架构建

Method Driven by Unexpected Events for Constructing an Emergency Information Analysis Framework

目前我国正处在经济转型升级关键时期,各类突发事件频发,提高突发事件快速应急响应的效率成为亟待解决的问题,针对多源异构的各类突发事件信息泛滥、组织缺乏针对性、信息流动不畅、利用程度低下等现状,从情报视角探讨突发事件快速响应中应急情报系统的分析和应用过程,明确应急情报分析特征,以大数据为基、应急决策为标、以资源和问题为双驱动、以情报分析方法为力,将应急情报分析过程分解为事前预警、事中响应和事后总结三个阶段,结合应急事件处理的经验知识,构建形式-特征-决策三级应急情报融合框架,对突发事件多源信息进行多层次深度分析融合,针对不同阶段不同用户的要求,进行相应的突发事件应急信息关联、分析、融合,促进支撑突发事件快速响应的应急情报产生,为突发事件快速高效响应提供决策支撑。

At present, as our country is in the critical period of economic transformation and upgrading, all types of emergencies occur frequently. Improving the efficiency of rapid response to emergencies has become an important problem that needs to be solved urgently. Problems related to multi-source heterogeneous emergencies include information overload, lack of specificity in organization, and uncomfortable flow of information and underutilization. With the aim of solving these problems, this paper discusses the analysis and application of an emergency intelligence system for rapid emergency response from the perspective of intelligence, and clarifies the characteristics of emergency intelligence analysis. Emergency intelligence analysis is divided into three stages of advance warning, response to the incident, which meet the requirements of real-time, high accuracy, and quick reactivity for the emergency environment based on big data, emergency decision, resources and problems, and the intelligence analysis method. With the help of experiential knowledge of processed emergencies, the three-level emergency intelligence fusion framework of forms-features-decision is built. It analyses and combines multi-source information of emergencies hierarchically and deeply. It promotes the emergence of emergency information, supporting a rapid response to unexpected incidents by associating, analyzing, and combining multi-source information according to the requirements of different stages and users. This paper provides information support for emergency decision-making at different stages.