金华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：金华市链主企业
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

Scope of Application: Leading Chain Enterprises in Jinhua City
1. Proactive identification of enterprise technological needs: Proactively uncover upcoming technological demands of enterprises based on their in-house technology R&D efforts.
2. Technical expert matching for enterprises: Resolve the matching gap between leading chain enterprises and university technical talents, and help enterprises find targeted technical experts.
3. Technical direction confirmation: Cooperate with matched university experts to conduct in-depth on-site research at enterprises and assist them in confirming their technical development directions.
4. Promotion of industry-university-research cooperation: Provide channels for enterprises requiring external R&D support, and facilitate industry-university-research cooperation among leading chain enterprises.
5. University scientific and technological achievement transformation facilitation: Connect university faculty with enterprises in need, so as to promote the transformation of scientific and technological achievements.1. Enterprise patent data collection: Collect various types of patents including invention patents, granted invention patents, utility model patents, and design patents.
2. Patent data cleaning and labeling: Clean the collected patent data, add labels to patents based on the technical classification of the internal system, match patent descriptions with technical classification keywords, and automatically add each matched keyword as an enterprise technical tag.
3. Patent tag concentration calculation: Increment the hit count of each keyword by 1 for each match. Select the top 5 tags with the highest total hit count (maxΣi) as the final technical tags for the enterprise.
4. University expert talent information collection and labeling: Collect information about university expert talents, gather their associated patent data, analyze the patent data to assign labels to the talents, including the number of keyword hits.
5. Tag matching: Match university talents with enterprise technical tags, and recommend the top 10 university experts with the highest keyword hit counts to the corresponding enterprises.

搜集汇总

数据集介绍

特点

该数据集包含628条金华市链主企业的研发需求与高校专家人才的匹配数据，每季度更新一次。主要应用于挖掘企业技术需求、匹配技术专家、促成产学研合作等场景，通过专利数据清洗和标签匹配算法实现智能匹配。