基于数据的产学研协同创新关键动力优化

Optimization of Key Dynamic of Industry-University-Research Collaborative Innovation Based on the Data

为确定产学研协同创新的关键动力,首先,对影响产学研协同创新的各种因素进行了分析,对产学研主体间的协同关系进行了研究。然后,借助Agent理论方法,构建了一个主体间协同创新的动力结构模型,对各主体Agent的功能,相互间的协同工作过程和动力机制进行了设计,进而对产学研各主体间的协同创新动力进行了提纯。其次,利用粒子群算法(PSO),对产学研各主体间的协同创新动力机制进行了优化,进而运用2010年《中国科技统计年鉴》中31个省市的统计数据为样本对关键动力优化算法进行了实证检验。结果表明,在产学研大数据环境下,多Agent的产学研协同创新关键动力模型能精确表达了产学研三个主体之间的协同关系,优化后的PSO算法能以较少的迭代次数得出了影响关键动力的临界点,确定了影响产学研协同创新的关键动力因素,并提出了具体对策建议。

To determine the key drivers of Industry-University-Research collaborative innovation,firstly,various factors which affect collaborative innovation are analyzed,and the synergy relationships of subjects among industry,university and research are studied.Then,through agent theoretical method,a dynamic structural model of collaborative innovation for inter-subject is built,the functions of each body agent,mutual collaborative work process and dynamic mechanism has been designed,and then the main driving force for synergy innovation between bodies is purified.Secondly,the synergy innovation dynamic mechanism between Industry-University-Research bodies is optimized via particle swarm optimization(PSO).Finally,to use the statistical data from 31 provinces and municipalities of 2010 the China Science and Technology Statistics Yearbook as the sample,the critical dynamic optimization algorithm is empirically tested.Results indicate that,in the big data environment of Industry-University-Research,the key dynamic model of Industry-University-Research based on multi-agent could accurately express the synergy relationship between three main bodies of Industry-University-Research.The optimized PSO algorithm has a relatively low number of iterations to obtain the critical point that affect the key dynamic.Key dynamic factors affecting Industry-University-Research collaborative innovation are determind,and specific recommendations for countermeasures are proposed.