基于云计算的地震网络数据安全态势感知仿真

Simulation of Seismic Network Data Security Situation Awareness Based on Cloud Computing

地震发生前具有特殊波动响应特征,通过地震台网灾害信息可确定震灾范围,但是网络个人用户无法通过终端服务器及时查询地震数据,导致对地震的安全感知出现延迟问题。针对地震数据处理的任务密集、流程复杂的计算需求,提出基于云计算的地震网络数据安全态势感知方法。在地震节点布局中融入贝叶斯结构,构成混合结构化安全态势采集模型。应用数据分类方法估计出地震态势数据,提取地震发生的隶属度特征,构建地震网络数据安全态势感知模型。按照区域影响因子算出地震信息量,利用相似原则区分灾害程度,检测地震发生范围的安全态势。实验结果证明:研究方法感知获取的地震层间位移与震中国方位角和实际情况一致,偏差较小,能够明确当前地震区域的安全态势。

Before an earthquake occurs,there are special wave response characteristics,and the scope of the earthquake can be determined through the disaster information of the seismic network.However,individual network users are unable to query earthquake data in a timely manner through terminal servers,resulting in delayed perception of earthquake safety.In this paper,a cloud computing based seismic network data security situational awareness method is proposed to address the computationally intensive and complex processing requirements of seismic data processing.Bayesian structure was incorporated into the seismic node layout to form a hybrid model for security situation acquisition.The data classification method was adopted to estimate seismic situation data and extract the membership characteristics of earthquake,thus constructing a model of seismic network data security situation awareness.Then,the amount of seismic information was calculated according to regional influence factors.Finally,the similarity principle was used to distinguish the disaster degree and thus to detect the safety situation of seismic region.The experimental results show that the displacement between seismic layers sensed by the proposed method is consistent with the epicenter azimuth and actual situation,and the deviation is small,so the method can clarify the current safety situation of seismic region.