基于动态知识流动特征的有向技术融合预测方法

Research on Directed Technology Fusion Prediction Method Based on Dynamic Knowledge Flow Characteristics

[研究目的]基于动态知识流动特征对有向的技术融合关系进行预测,有助于提高企业的研发效率及科技管理部门对科技资源的合理分配。[研究方法]首先,将IPC前4位代码作为技术单元,基于专利中主分类号与副分类号提取技术间的有向知识流动关系;然后,借鉴关联规则挖掘算法从重要性、强度和依赖度3个维度对知识流动关系进行筛选,将满足指标阈值的知识流动关系作为有向技术融合关系;最后,将有向技术融合关系预测转化为有监督的二分类任务,以机器学习算法为模型基础,结合前两个时期的技术间知识流动特征及其时序变化特征,预测下一个时期内的技术间是否存在技术融合关系。[研究结论]增材制造领域的实证分析结果显示,以代价敏感机器学习算法为预测算法,基于前两个时期的技术间知识流动特征及其时序变化特征可以有效预测下一阶段中的技术融合。随着技术的发展,技术间的单向融合愈发频繁,并逐渐演变为技术双向融合,甚至呈现多技术融合的现象。

[Research purpose]Predicting directed technology fusion relationships based on dynamic knowledge flow characteristics contributes to improving the R&D efficiency of enterprises and enables technology management departments to allocate technological resources more effectively.[Research method]Firstly,the first four digits of the International Patent Classification(IPC)codes were utilized as technology units.Directed knowledge flow relationships between technologies were extracted based on the primary class and supplementary class in patents.Subsequently,drawing inspiration from association rule mining algorithms,knowledge flow relationships were filtered based on three dimensions:importance,intensity,and dependence.Knowledge flow relationships meeting the specified threshold criteria were identified as directed technology fusion relationships.Finally,the prediction of directed technology fusion relationships was transformed into a supervised binary classification task.Machine learning algorithms served as the modeling foundation,incorporating the knowledge flow characteristics between technologies from the preceding two periods and their temporal variations to forecast the presence of directed technology fusion relationships in the subsequent period.[Research conclusion]The empirical analysis in the additive manufacturing domain reveals that,employing a cost-sensitive machine learning algorithm for prediction,based on the knowledge flow characteristics between technologies from the preceding two periods and their temporal variations,is effective in forecasting directed technology fusion in the subsequent period.As technology advances,the occurrence of one-way technology fusion becomes more frequent,evolving into two-way technology fusion,and even manifesting phenomena of multi-technology fusion.