基于Apriori算法综合管廊火灾识别预警系统

为了有效识别和预防由不安全因素引起的综合管廊火灾事故，利用多种传感器数据融合方法对综合管廊中可能导致火灾的各种危险因子进行监测，并提出了以PLC为下位机、NI公司开发的LabVIEW作为上位机的火灾危险识别与预警系统。该系统采用改进的关联规则Apriori算法对管廊中诸多引发火灾的危险因素进行了分析，并对分析结果进行了量化。结果表明，该系统能够迅速识别综合管廊中火灾事故的环境因素，并提前作出预警，为城市地下综合管廊火灾预警工程应用提供参考和借鉴。

Fire danger identification and early warningsystem for urban underground comprehensivepipe gallery based on Apriori algorithm

 In order to effectively identify and prevent fire accidents in the comprehensive gallery of pipes caused by uncertain factors, this paper uses a variety of sensor data fusion methods to monitor various risk factors that can cause fire in the integrated pipe gallery, and proposes a comprehensive gallery fire hazard identification and early warning system with PLC as lower computer and LabVIEW developed by NI Corporation as the upper computer. The system uses the improved Apriori algorithm regulation to analyze many gallery fire risk factors, and quantifies the results of the analysis. The results show that the system can quickly identify the environmental factors that cause fire accidents in the comprehensive gallery of pipes, and provide early warning, providing a reference for the application of urban underground comprehensive gallery fire warning engineering.