基于循环神经网络的火灾风险预测及应用

Fire risk prediction based on recurrent neural networks and its application

介绍以循环神经网络为基础的火灾风险预测模型。该模型从历史火灾警情、单位及建筑基础信息、消防设施状况、检查与隐患记录等相关数据中提取多维度特征,进行深度学习与模型训练。目前,该模型已在四川省绵阳市试点应用,每季度对绵阳市共4.1万家单位未来90天的火灾风险概率进行预测,并依据预测概率优化“双随机、一公开”单位抽取规则,引导监督人员靶向抽查火灾风险较高的单位。实测结果表明,模型有效提升了日常消防监督检查的精准度。

The fire risk prediction model based on recurrent neural networks was introduced. The model extracts multidimensional features from historical fire alarm data, unit and building basic information, fire facilities situation, as well as inspection and hidden hazards records, and perform the deep learning and model training. The model has been applied in Mianyang, Sichuan, and predicts the fire risk probability of 41 thousand units in Mianyang in the future 90 days. According to the prediction probability, the rules for the selection of "double random, one public" units are optimized, and guide the supervisory staffs to the units with high fire risk. The results showed that, the model has effectively improved the accuracy of daily fire supervision.