Intelligent threshold spectrum anomaly detection method and system based on machine learning

The invention discloses an intelligent threshold spectrum anomaly detection method and system based on machine learning, and the system comprises a data obtaining module which obtains weather data and electromagnetic energy data of a time sequence; the data processing module is used for generating an intelligent threshold by using the trained model to judge the electromagnetic energy data at the T moment to detect an abnormal frequency spectrum; wherein a predicted value is determined through a trained model, the intelligent threshold is generated according to the predicted value, the model is obtained through training by using multiple groups of training data, and each group of data of the multiple groups of training data comprises historical weather data and historical energy data. The method has the advantage of better screening out electromagnetic energy abnormity caused by an unknown target or an unauthorized target. 本发明公开了一种基于机器学习的智能门限频谱异常检测方法及系统，包括数据获取模块，获取时序的天气数据和电磁能量数据；数据处理模块，用于利用训练好的模型生成智能门限对T时刻的电磁能