基于粗糙灰色预测的装备维修资源需求预测模型

面向装备维修资源保障任务，首先采用粗糙集理论，通过属性约简算法，简化装备维修备件资源消耗影响因素，在此基础上利用灰色预测模型，对基于虚拟仓储的装备维修资源需求进行预测，通过与单一的灰色预测方法结果相比较，将粗糙集与灰色预测模型相融合的方法应用于装备维修备件资源预测的结果可靠、信息准确，并且预测值与实用值的相对误差和绝对误差很小，达到了准确预测的效果；从而验证了此模型与算法的有效性，为信息化战争中提高装备维修备件资源保障功能提供理论与方法支持。

Consumption Forecasting of Equipment Maintenance Support Resources Based on Rough Set-Gray Theory

Faced to the assignment of equipment maintenance support resources,firstly,we adopted the rough set theory and simplified equipment maintenance spare parts resource consumption influence factors through attribute reduction algorithms,and on the basis of using grey prediction model,the demand for equipment maintenance based on virtual storage resources was forecasted,and compared with the results of single grey prediction method,the method of combining the rough sets and grey forecasting model of applied in equipment maintenance spare parts resources prediction result is reliable,and is accurate information,and the predicted value and the practical value of relative error and absolute error is very small,which achieved the effect of accurate prediction.As a result,this model and algorithum were proved to be effective to provide theoretical and practical support for equipment maintenance spare resources in information warfare.