湖州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：湖州市链主企业
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.定期采集更新企业专利数据：发明公布专利、发明授权专利、实用新型专利和外观设计专利（发明公布和发明授权是不同阶段的企业专利状态，不会重复）
2.进行专利数据清洗，并根据内部系统的技术分类体系为专利加标签，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（姓名+单位）采集专家人才专利数据，采用步骤2和3的方法分析专利数据并为人才添加标签（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标签，为企业推荐命中关键词匹配次数最高（按照企业单个标签次数与人才单个标签之和取数）的前10名高校专家（少于10名则全部展示）

Applicable Scope: Leading Enterprises in Huzhou Industrial Chain
1. Proactively identify enterprise technical demands: Proactively uncover the upcoming technical requirements of enterprises based on their own technological R&D efforts.
2. Match technical experts for enterprises: Address the matching gap between leading industrial chain enterprises and university talents, and help enterprises find corresponding technical experts.
3. Technical Direction Determination: Collaborate with university experts to conduct in-depth on-site investigations in enterprises, and assist enterprises in confirming their technical development directions.
4. Promote industry-university-research cooperation: Provide channels for enterprises that need external R&D support, and facilitate industry-university-research cooperation among leading industrial chain enterprises.
5. Facilitate university scientific and technological achievement transformation: Connect university teachers with enterprises in need, so as to promote the transformation of scientific and technological achievements.

1. Regularly collect and update enterprise patent data: Including invention publication patents, invention grant patents, utility model patents, and design patents. Note: Invention publication and invention grant patents correspond to different stages of enterprise patent status and will not overlap.
2. Conduct patent data cleaning, and add labels to patents according to the technical classification system of the internal system. Match patent descriptions with technical classification keywords, and automatically add each matched keyword as an enterprise technical tag.
3. Calculate the concentration of patent tags: For each keyword, increment the hit count by 1 for each match, and select the top 5 tags with the highest total hit count (max Σi) as the enterprise's official technical tags.
4. Collect information on university expert talents, and collect patent data of experts based on their personal information (name + affiliated institution). Analyze the patent data using the methods described in Steps 2 and 3, and add tags (including keyword hit counts) to the experts.
5. Tag matching: Match the enterprise tags with the university talent tags, and recommend the top 10 university experts with the highest total keyword matching scores (calculated by summing the hit counts of each matching individual tag between the enterprise and the expert) for the enterprise. Display all matched experts if fewer than 10 are available.

搜集汇总

数据集介绍

特点

该数据集包含547条湖州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。数据集通过专利数据清洗和标签匹配算法，帮助企业挖掘技术需求、匹配技术专家，并促进产学研合作和科技成果转化。