Evidence theory is widely used in decision-making and reasoning systems. In
previous research, Transferable Belief Model (TBM) is a commonly used
evidential decision making model, but TBM is a non-preference model. In order
to better fit the decision making goals, the Evidence Pattern Reasoning Model
(EPRM) is proposed. By defining pattern operators and decision making
operators, corresponding preferences can be set for different tasks. Random
Permutation Set (RPS) expands order information for evidence theory. It is hard
for RPS to characterize the complex relationship between samples such as
cycling, paralleling relationships. Therefore, Random Graph Set (RGS) were
proposed to model complex relationships and represent more event types. In
order to illustrate the significance of RGS and EPRM, an experiment of aircraft
velocity ranking was designed and 10,000 cases were simulated. The
implementation of EPRM called Conflict Resolution Decision optimized 18.17\% of
the cases compared to Mean Velocity Decision, effectively improving the
aircraft velocity ranking. EPRM provides a unified solution for evidence-based
decision making.

提出了一种证据模式推理模型（EPRM），并引入了随机图集（RGS）和随机排列集（RPS）以建模复杂关系和表示更多事件类型，通过冲突解决决策实施的 EPRM 相对于平均速度决策优化了 18.17％的案例，有效改进了飞机速度排序。