宁波市链主企业研发需求与高校专家人才智能匹配数据

适用范围：宁波市链主企业
1.主动挖掘企业技术需求：可以根据自身技术研发，主动挖掘企业下一步的技术需求。
2.为企业匹配技术专家：解决链主企业和高校人才之间的匹配问题，帮助企业找对应的技术专家。
3.选择技术方向：匹配高校专家，深入企业调研，协助企业确定技术方向。
4.促成企业产学研合作：为有需要借助外力研发的企业提供渠道，促进链主企业的产学研合作。
5.创建高校科技成果转化。为高校老师链接有需求的企业，促进科技成果转化。1.采集企业专利数据（发明专利、发明授权、实用新型专利、外观设计专利）
2.进行专利数据清洗，并根据内部系统的技术分类为专利加标签，采用技术分类关键词匹配专利描述，将每个匹配到的关键词自动添加为企业技术标签
3.计算专利标签集中度，关键词每命中一次i+1，采用命中次数maxΣi 最高的前5个标签为企业标签
4.采集高校专家人才信息，并根据人才信息采集专利数据，分析专利数据为人才贴标签（含关键词命中次数）
5.标签匹配：根据企业标签匹配高校人才，为企业推进命中关键词最高的前10名高校专家

Applicable Scope: Leading Industrial Chain Enterprises in Ningbo

1. Proactive identification of enterprise technological needs: Proactively identify the next-stage technological requirements of enterprises based on their own R&D activities.
2. Matching technical experts for enterprises: Address the matching gap between leading industrial chain enterprises and university talents, and help enterprises find suitable technical experts.
3. Selecting technological directions: Collaborate with matched university experts to conduct in-depth on-site investigations at enterprises and assist enterprises in confirming their technological directions.
4. Facilitating industry-university-research cooperation: Provide channels for enterprises requiring external R&D support, and promote industry-university-research cooperation among leading industrial chain enterprises in Ningbo.
5. Promoting the commercialization of university scientific and technological achievements: Connect university professors with enterprises with demand, so as to facilitate the transformation of scientific and technological achievements.

1. Collect enterprise patent data, including invention patents, granted invention patents, utility model patents, and design patents.
2. Clean the collected patent data, add labels to patents based on the technical classification of the internal system, and automatically add each matched keyword as an enterprise technical label by matching the patent description with technical classification keywords.
3. Calculate the concentration of patent labels: For each keyword, increment its hit count by 1 each time it is matched, and select the top 5 labels with the highest total hit count (Σi) as the enterprise's official technical labels.
4. Collect information of university experts and their associated patent data, analyze the patent data to assign labels to the talents, including the keyword hit counts.
5. Label matching: Match university talents based on the enterprise's technical labels, and recommend the top 10 university experts with the highest keyword hit counts to promote cooperation for the enterprise.

搜集汇总

数据集介绍

特点

该数据集包含523条记录，每季度更新，主要用于宁波市链主企业的技术需求挖掘、高校专家人才匹配、技术方向选择和产学研合作促进。数据集通过专利数据清洗和标签匹配算法，帮助企业找到合适的技术专家，促进科技成果转化。