PMO管理模式下高校应用型科研项目进度风险评价研究  

Research on Progress Risk Evaluation of Applied Research Projects in Univer sities under PMO Management Mode

作　　者：桂郦娜[1] 林佳楠[1] 武斌 陆露 GUI Lina;LIN Jianan;WU Bin;LU Lu(Chengyi College,Jimei University,No rmal College,Xiamen,Fujian 361021,China;Jimei University,Normal College,Xiamen,Fujian 361021,China)

机构地区：[1]集美大学诚毅学院,福建厦门361021 [2]集美大学师范学院,福建厦门361021

出　　处：《德州学院学报》2023年第6期37-43,共7页Journal of Dezhou University

基　　金：福建省中青年教师教育科研项目“基于结构相似度与深度强化学习的新型边缘检测算法”(JAT220532)。

摘　　要：从高校科研管理部门的角度出发,基于AHP-BP神经网络优化模型,创建了一套高校应用型科研项目进度风险评价指标体系,适用于PMO管理模式。该研究明确了高校科研项目进度风险在该模式中的含义,13个二级指标从前期、实施、发表等各阶段进度风险的一级指标中提炼而得。同时运用AHP层次分析法对各层次指标进行赋权,并通过BP神经网络对这些评估指标的权重进行优化,从而确定最终的指标体系赋权。研究结果表明,成果水平、写作水平等对科研项目进度有相对较大影响,该研究使得评价结果更加科学合理、客观精准,能为科研项目管理人员提供项目进度风险管理的决策参考。From the perspective of research management departments in universities,based on the AHP-BP neural network optimization model,a set of progress risk evaluation index system for applied research projects in universities has been created,which is suitable for the PMO management mode.This study clarifies the meaning of progress risk in university research projects in this model,with 13 secondary indicators extracted from the pri-mary indicators of progress risk in various stages such as early stage,implementation,and publication.At the same time,the Analytic Hierarchy Process(AHP)is used to assign weights to each level of indicators,and the weights of these evaluation indicators are optimized through a BP neural network to determine the final indicator system weighting.The research results indicate that the level of achievement and writing have a relatively significant impact on the progress of scientific research projects.This study makes the evaluation results more scientific,reasonable,objective,and accurate,and can provide decision-making references for project schedule risk management for scientific research project managers.

关 键 词：应用型科研 进度风险 AHP BP神经网络 PMO模式 优化模型 

分 类 号：G644[文化科学—高等教育学]