Fusion of discrete belief structures gets much attention owing to the widespread existence of discrete information in decision analysis, expert system and other fields. Recently, one method that combines discrete evidence was proposed by establishing an optimization model. However, the existing optimization model of discrete belief structure has a problem of high computational complexity since it needs to calculate all the cases of deterministic evidence combination to obtain maximum and minimum values of each focal element in combination result. In this paper, a novel method of discrete evidence fusion is proposed to reduce the computational complexity by considering the consistency of belief structures of evidence groups and finding the most consistent and most inconsistent cases. Example and application are given to illustrate the effectiveness and rationality of the proposed method.

由于决策分析，专家系统等领域中离散信息的广泛存在，离散信念结构的融合备受关注。最近，通过建立优化模型提出了一种结合离散证据的方法。然而，现有的离散信念结构优化模型存在计算复杂度高的问题，因为它需要计算所有确定性证据组合的情况，以获得组合结果中每个焦点元素的最大值和最小值。本文提出了一种新的离散证据融合方法，通过考虑证据组信念结构的一致性并找到最一致和最不一致的案例来降低计算复杂度。