面向企业模糊型技术需求的信息融合方法研究

Research on Information Fusion Method for Enterprise Fuzzy Technical Requirements

[目的/意义]:实现需求与供给的精准匹配是产教融合的关键所在,对科技成果转化、技术创新等具有重要意义。然而,企业在发布技术需求时,因商业机密保护等原因,导致技术需求文本描述常具有模糊性,难以与技术供给进行精准匹配。[方法/过程]:设计了面向企业模糊型技术需求的两阶段模型,首先使用BERT+BiLSTM+CRF算法提取企业发布项目中的需求实体,然后通过挖掘企业基本信息,结合需求实体作为检索条件爬取相关专利成果,通过TF-IDF算法获取专利中关键词作为补充信息,精准化技术需求描述,并识别出技术的聚焦方向与发展前沿。[结果/结论]:以机械制造领域内某企业真实需求作为案例,展示了该方法的完整处理过程,挖掘与TBM相关的施工管理、工件改进、系统研发等关键技术点;通过跨时间维度识别技术需求聚焦方向,TBM与人工智能、大数据、机器学习、探测等技术相结合将是未来发展的趋势。

[Purpose/Significance]:Achieving the accurate matching of demand and supply is the key to the integration of industry and education,which is of great significance to the transformation of scientific and technological achievements and technological innovation.However,when enter-prises release technical requirements,due to trade secret protection and other reasons,the text description of technical requirements is often ambiguous,and it is difficult to accurately match the technical supply.[Method/Process]:A two-stage model for the fuzzy technical needs of enterprises was designed.Firstly,the BERT+BiLSTM+CRF algorithm is used to extract the demand entities in the enterprise release projects,and then the relevant patent achievements are crawled by mining the basic information of the enterprise,combined with the demand entities as the search condi-tions,and the keywords in the patents are obtained as supplementary information through the TF-IDF algorithm,so as to accurately describe the technical requirements and identify the focus direction and development frontier of the technology.[Result/Conclusion]:Taking the real needs of an enterprise in the field of machinery manufacturing as a case,the complete processing process of the method is demonstrated,and the key technical points related to TBM such as construction management,workpiece improvement,and system research and development are excavated.By identifying the focus direction of technology demand across time dimensions,the combination of TBM with artificial intelligence,big data,machine learning,detection and other technologies would be the trend of future development.