结合信息熵与方案偏好的城市高层火灾救援方案推荐

Recommendation of urban high⁃rise fire rescue scheme based on information entropy and scheme preference

由于城市高层建筑火灾救援难度较大,往往造成严重的人员伤亡和经济损失。计算机科学对高层火灾救援决策的研究较少,而人工智能技术可以应用于该方面的研究。文中依据城市高层建筑的过火面积,结合信息熵与方案主观偏好值对城市高层建筑火灾救援方案的指标进行分析并做出救援决策。真实算例与现实情况分析表明,当城市高层建筑火灾过火面积分别达到5 000~10 000 m^(2)、10 000~20 000 m^(2)、20 000 m^(2)以上时,可以分别派出46~77辆消防车、392~410名消防员,80~127辆消防车、410~654名消防员,127~148辆消防车、550~654名消防员。该模型提出的对方案有主观偏好的熵权决策方法能够较为有效地解决城市高层火灾救援问题,并在基于偏好信息的多属性决策问题中提出了一个比较好的方法。

Due to the difficulty of fire rescue in urban high⁃rise buildings,it often causes serious casualties and economic losses.As computer science has less research on high⁃rise fire rescue decision⁃making,artificial intelligence technology can be applied to this research.Based on the fire area of urban high⁃rise buildings,the indexes of urban high⁃rise building fire rescue plan are analyzed by combing with information entropy and subjective preference value of the scheme,and the rescue decision is made.The real example and the analysis of the real situation show that when the fire areas of urban high⁃rise buildings reach 5000~10000 m^(2),10000~20000 m^(2) and more than 20000 m^(2) respectively,46~77 fire trucks and 392~410 firefighters,80~127 fire trucks and 410~654 firefighters,and 127~148 fire trucks and 550~654 firefighters can be dispatched respectively.The entropy weight decision⁃making method with subjective preference for the scheme proposed by the model can effectively solve the urban high⁃rise fire rescue problem,and a better method is proposed in the multi⁃attribute decision⁃making problem based on preference information.