基于大数据的智能制造岗位与技能需求研究
Research on intelligent manufacturing positions and skill requirements based on big data
刘祺彬 1高祥兰 2何凤琴 1李新元1
作者信息
- 1. 上海师范大学 信息与机电工程学院,上海 201418
- 2. 上海立达学院 数字科学学院,上海 201609
- 折叠
摘要
在不违反相关协议准则的情况下,通过爬虫技术获取智能制造岗位数据,并对其进行清洗与脱敏处理.应用Jieba中文分词工具、K-means聚类算法与隐含狄利克雷分布(LDA)模型,将岗位名称分为6类,将技能集分为8类.最后,构建需求矩阵并归一化处理,得到各技能集对岗位簇的重要程度,为专业选择、课程建设与从业人员发展提供参考.
Abstract
Without violating relevant protocol guidelines,the intelligent manufacturing job data was obtained by crawler technology,which was cleaned and desensitized in this paper.By Jieba Chinese text segmentation,as well as clustering algorithms such as K-means clustering algorithm and latent Dirichlet allocation(LDA)model,job titles were categorized into six clusters.Besides,skills were classified into eight clusters.Finally,a demand matrix was constructed and normalized,revealing the importance of each skill set to job clusters.The research was able to provide reference of choosing majors,curriculum development and the professional development of practitioners.
关键词
智能制造/大数据分析/K-means/隐含狄利克雷分布(LDA)模型/需求评估Key words
intelligent manufacturing/big data analysis/K-means/latent Dirichlet allocation(LDA)model/demand assessment引用本文复制引用
出版年
2024
NETL
NSTL
万方数据