引入新数据源的D-S融合检测方法

D-S Fusion Detection Method with New Data Sources

  • 摘要: 针对火灾检测时延过长的问题,该文引入新的火灾探测数据源,将模糊逻辑和D-S证据理论融合,提出一种信号火灾探测方法。该方法使用CO浓度、烟雾浓度、温度、O2浓度以及热释放速率等作为火灾探测数据源,建立火灾判别概率函数,计算各个数据源的无火、阴燃火和明火的判别概率,结合Jousselme距离为数据源分配权重,最终通过D-S证据理论对多源判别信息进行融合。仿真结果表明,该方法相比于未引入O2浓度和热释放速率的火灾探测方法,能提早3~5 s探测出火灾,提升了火灾探测及时性。

     

    Abstract: Aiming at the problem of excessive fire detection time delay, a new data source of fire detection is introduced. Fuzzy logic and D-S evidence theory are merged, and a signal fire detection method is proposed. This method uses CO concentration, smoke concentration, temperature, O2 concentration, and heat release rate as fire detection data sources. It establishes a fire discrimination probability function to calculate the discrimination probability of no fire, smoldering fire, and open flame for each data source. The data sources are assigned weights using the Jousselme distance, and the multi-source discriminated information is fused through D-S evidence theory. The simulation results show that this method can detect fire 3-5 seconds earlier compared with the fire detection method that does not introduce O2 concentration and heat release rate, thus improving the timeliness of fire detection.

     

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