基于多阶段评审的大规模创新类竞赛评比方案

Large-scale Innovation Competition Evaluation Scheme Based on Multi-stage Evaluation

当前,大规模创新类竞赛层出不穷,这类竞赛的评比因专家的主观差异等原因成了亟待解决的难题。关注大规模创新类竞赛评比方案的研究与设计,通过对已有竞赛的打分结果进行分析,综合对比多种不同评比方案的优缺点,探寻最优的评比方案,以尽可能使评审流程程序化、高效化,节约人力与时间资源。首先,构建专家分配模型确定评审专家“交叉分发”方案,运用改进模拟退火算法求解,验证了模型与算法的高精度和高效率;然后,构建加权模型对比4类标准分计算方法,设计基于专家权重的改进标准分计算方法;最后,考虑大极差对创新性的关联性,建立极差回归模型,进行基于极差的模型评估。所提模型与算法适用范围广,具有重要现实参考意义与高应用价值。

Currently,large-scale innovation competitions are constantly emerging.The evaluation of such competitions has become an urgent problem to be solved due to subjective differences among experts and other reasons.This paper focuses on the research and design of evaluation schemes for large-scale innovation competitions.Through the analysis of the scoring results of the exis-ting competitions,the advantages and disadvantages of various evaluation schemes are comprehensively compared to find the best evaluation,so as to make the review process as programmed and efficient as possible,saving manpower and time resources.Firstly,the text constructs an expert allocation model to determine the“cross distribution”plan for reviewing experts,and uses an improved simulated annealing algorithm to solve the problem.Secondly,the text constructs a weighted model to compare four types of standard score calculation methods,and designs an improved standard score calculation method based on expert weights.Lastly,considering the correlation between large range and innovation,a range regression model is established to evaluate the model based on range.The proposed model and algorithm are widely applicable,and have important practical reference significance,and high application value.