基于LSTM和注意力机制的安全事故等级分类

Classification Of Safety Accident Level Based On Lstm And Attention Mechanism

针对传统矿山安全事故数据分类的耗时、低效等问题,提出一种基于长短期记忆网络(LSTM)和注意力机制结合的矿山安全事故分类方法。首先对安全管理局的所有相关矿山事故数据进行分词和去停用词等预处理操作;然后将数据输入到LSTM网络进行预训练并结合注意力机制根据预训练结果进行模型优化;最后利用分类模型对矿山事故数据进行安全等级分类。根据实验结果表明,与只使用LSTM的分类方法相比提出的分类方法准确率提高了39%,有效解决了传统矿山安全事故分类方法中存在的问题。

Aiming at the time-consuming and inefficient problems of traditional mine safety accident data classification,a mine safety accident classification method based on LSTM and attention mechanism is proposed.Firstly,all relevant mine accident data of the Safety Management Bureau are pre-processed by word segmentation and word removal,then the data are input into LSTM network for pre-training and attention mechanism is used to optimize the model according to the pre-training results.Finally,the classification model is used to classify the mine accident data.The experimental results show that the accuracy of the proposed classification method is 39%higher than that of the LSTM-only classification method,which effectively solves the problems existing in the traditional mine safety accident classification method.