震后通信数据会发生异常变化,通过对通信数据异常进行分析,有助于提供有效的灾情数据以及更好地了解震后产生的影响,进而有效地为抗震救灾提供辅助支持。本文基于LSTM对震后的通信数据异常进行分析,研究内容主要包括通信数据流预处理、基于LSTM的异常检测模型以及数据分布变化检测模型。结果表明,本文模型能够对通信数据的异常变化进行识别,为后续的灾情分析提供数据。

After the earthquake,the communication data usually undergoes abnormal changes. Through the analysis of the communication data abnormality,effective disaster data can be provided,which is a basis for decision-making for earthquake relief. Analyzing abnormal communication data helps to better understand the impact of the earthquake and can provides auxiliary support for following-up command and disaster relief. This paper analyzes the communication data anomalies after the earthquake based on LSTM,which mainly includes:communication data stream preprocessing,anomaly detection model based on LSTM,and data distribution change detection model. The model in this paper is capable of identifying abnormal changes in communication data and provides disaster data for subsequent hazard analysis.

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国家重点研发计划(2018YFC1504500、2018YFC1504502)、国家自然科学基金青年基金项目(41807505)共同资助