桐乡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：桐乡市链主企业
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 Tongxiang City

1. Proactive identification of corporate technological needs: Proactively uncover the upcoming technological demands of enterprises based on their in-house R&D activities.
2. Matching technical experts for enterprises: Resolve the matching gap between leading chain enterprises and university talents, and assist enterprises in finding targeted technical experts.
3. Technical direction confirmation: Cooperate with matched university experts to conduct in-depth on-site investigations at enterprises, and help enterprises determine their technical development directions.
4. Facilitating industry-university-research cooperation: Provide access channels for enterprises requiring external R&D support, and promote industry-university-research cooperation among leading chain enterprises.
5. Promoting the transformation of university scientific and technological achievements: Connect university faculty with enterprises in need, and accelerate the commercialization 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 framework of the internal system, match patent descriptions with technical classification keywords, and automatically add each matched keyword as an enterprise technical tag.
3. Calculation of patent tag concentration: For each hit of a keyword, increment the hit count by 1; select the top 5 tags with the highest total hit counts (max Σi) as the official technical tags for the enterprise.
4. University expert talent information collection and talent labeling: Collect talent information of university experts, collect corresponding patent data for each collected talent, analyze the patent data to add labels to the talents, including the number of keyword hits.
5. Tag-based matching: Match university talents based on the technical tags of enterprises, and recommend the top 10 university experts with the highest keyword hit counts to the enterprises.

搜集汇总

数据集介绍

特点

该数据集提供了桐乡市链主企业的研发需求与高校专家人才的智能匹配信息，包含企业专利数据、技术需求、匹配的高校专家等，旨在通过数据分析和算法匹配，促进企业与高校之间的技术合作和科技成果转化。