杭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：杭州市链主企业
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）

Scope of Application: Leading Chain Enterprises in Hangzhou
1. Proactive Technological Demand Excavation: Enterprises can proactively identify their upcoming technological demands based on their own research and development activities.
2. Technological Expert Matching for Enterprises: Resolve the matching problem between leading chain enterprises and university talents, and help enterprises find appropriate technological experts.
3. Technological Direction Selection: Collaborate with university experts to conduct in-depth on-site investigations at enterprises, and assist enterprises in confirming their technological development directions.
4. Promotion of Enterprise Industry-University-Research Cooperation: Provide channels for enterprises requiring external R&D support, and promote industry-university-research cooperation among leading chain enterprises.
5. Promotion of University Scientific and Technological Achievement Transformation: Connect university professors with enterprises in need, so as to accelerate the transformation of scientific and technological achievements.

1. Regular Collection and Update of Enterprise Patent Data: Including invention publication patents, invention authorization patents, utility model patents, and design patents (invention publication and invention authorization represent different stages of enterprise patent status and will not overlap).
2. Patent Data Cleaning and Tagging: Clean patent data, add labels to patents based on the internal system's technical classification system, match patent descriptions with technical classification keywords, and automatically add each matched keyword as an enterprise's technical tag.
3. Patent Tag Concentration Calculation: For each hit of a keyword, increment the count by 1; select the top 5 tags with the highest total hit count (max Σi) as the enterprise's official technical tags.
4. University Expert Talent Information Collection and Tagging: Collect information on university expert talents, and gather patent data of the experts based on their personal information (name + affiliated institution). Analyze the patent data using the methods described in Steps 2 and 3 to add tags (including keyword hit counts) to the talents.
5. Tag-based Matching and Expert Recommendation: Match the tags of enterprises with those of university talents, and recommend to enterprises the top 10 university experts with the highest total matching keyword hit counts (calculated as the sum of hit counts of a single tag from the enterprise and that from the talent). If fewer than 10 experts meet the criteria, display all available ones.

搜集汇总

数据集介绍

特点

该数据集旨在通过智能匹配杭州市链主企业的研发需求与高校专家人才，促进产学研合作。数据集包含企业专利信息、技术需求和匹配的高校专家数据，通过算法规则实现精准匹配，适用于企业技术需求挖掘和专家人才推荐。