基于模糊相容关系的公共卫生应急决策模型设计

Design of Public Health Emergency Decision Model Based on Fuzzy Compatibility

目的传统的应急决策模型中,使用等价类方法设置模型论域,导致模型在事件决策运算中模糊近似空间划分准确性不佳,为了提高决策结论精度,提出基于模糊相容关系的公共卫生应急决策模型设计。方法使用模糊相容关系设置模型论域,建立模型粗糙集,应用粗糙集中的论域使用马尔科夫预测方法对态势进行预测。根据预测结果,设置物资需求公式和多部门协同决策矩阵得到事件决策数据,使用决策数据,通过三角模法划分模糊近似空间,计算模糊近似空间中的相容类,得出决策结论值,完成决策模型设计。使用曾发生的SARS疫情事件对公共卫生应急决策模型进行实验,实验中采取基于双论域量化模糊粗糙集的公共卫生应急决策模型、基于态势预测的非常规突发事件应急决策模型以及传统的Pawlak经典粗糙集决策模型与研究设计的模型进行对比。结果研究设计的模型决策结论数值与实际的事件决策结论值更接近。结论模型决策精度更高,可以为该领域的相关研究提供应急决策方面的参考。

Objective In the traditional emergency decision-making model,the equivalent class method is used to set the domain of the model,which leads to the poor accuracy of the fuzzy approximate space division in the event decision-making operation of the model.To improve the accuracy of the decision-making conclusion,a public health emergency decision-making model design based on fuzzy compatibility relation is proposed.Methods Fuzzy compatible relation was used to set the universe of model,and the rough set of model was established.The universe of rough set was applied to predict the situation by using Markov prediction method.According to the prediction results,the material demand formula and multi-department collaborative decision matrix were set to get the event decision data.The fuzzy approximate space was divided by using the decision data,and the consistent classes in the fuzzy approximate space were calculated.The decision conclusion value was obtained and the decision model design completed.In the experiment,the decision-making model of public health emergency was tested by using the previous SARS epidemic events.In the experiment,the public health emergency decision-making model based on double universe quantitative fuzzy rough set,unconventional emergency decision-making model based on situation prediction and traditional Pawlak classical rough set decision-making model are used to compare with the designed model.Results The value of the decision-making conclusion of the model designed in the study was closer to the actual decision-making value.Conclusion It is proved that the accuracy of the model is higher,which can provide reference for related research in this field.