基于犹豫模糊软集的专利质量评价模型

现有的专利质量评价方法以专家打分为主,需要专家对所评专利的全部指标给出一个评价值,并根据指标的相对重要程度分配权重值。但指标体系涉及不同领域的多个指标,专家对自己研究领域以外的指标难以做出准确评价,模糊性指标也使专家会在几个评价值之间犹豫。针对犹豫模糊软集可以更加准确的描述事物模糊本质,对可选对象没有限制,充分考虑专家决策的犹豫度得特性,本文提出一种基于犹豫模糊软集的专利质量评价模型,给出评价方法的具体步骤,并通过案例分析说明该方法的可行性与可靠性。

Patent Quality Evaluation Model Based on Hesitant Fuzzy Soft Set

The existing patent quality evaluation method is mainly based on expert scoring. Experts need to give an evaluation value to all the indicators of the patent evaluation, and assign the weight value according to the relative importance of the index. But index system involves multiple indicators in different fields. Experts cannot make accurate evaluation of indicators beyond their research fields, and fuzzy indicators also make experts hesitate between several evaluation values. For the hesitant fuzzy soft sets can more accurately describe the fuzzy nature of things, there is no restriction on the object, and fully consider the expert decision of hesitation characteristics. This paper proposes a patent quality evaluation based on hesitant fuzzy soft sets model, and gives specific evaluation procedures. The feasibility and reliability of the method are illustrated by case analysis.