论文部分内容阅读
应急信息监测是实现突发事件应急管理的前提条件,也是应急情报体系建设的重要一环。为及时预警、应对和处理突发事件,本文讨论了面向突发事件的应急信息监测系统的设计原则和功能模块,并探讨了应急信息监测存储系统的结构模型。其中,数据的有效存储是支撑情报人员进行进一步数据分析,实现突发事件预警和应对工作的物理基础。然而大数据环境下,突发事件信息监测网络收集的海量数据对数据的存储及有效利用提出了挑战。因此,本文基于遗传算法提出一种存储优化策略能够寻找到最优的数据指标聚类集合,进而有效的减少数据的存储空间,最后经过实验仿真验证了其有效性。
Emergency information monitoring is a prerequisite for emergencies emergency management, but also an important part of the construction of emergency intelligence system. In order to timely alert, deal with and deal with unexpected events, this paper discusses the design principles and functional modules of emergency information monitoring system for emergencies and explores the structural model of emergency information monitoring storage system. Among them, the effective data storage is to support the intelligence personnel for further data analysis, to achieve the physical basis of early warning and response to emergencies. However, in the big data environment, massive data collected by the emergency information monitoring network poses a challenge to the storage and efficient use of data. Therefore, this paper proposes a storage optimization strategy based on genetic algorithm to find the optimal clustering set of data indicators, and then effectively reduce the storage space of data. At last, the validity of the algorithm is verified through experimental simulation.