Research on Precise Training Mode of Intelligent Logistics Talents in Higher Vocational Colleges Based on Big Data Analysis
- DOI
- 10.2991/978-2-38476-346-7_9How to use a DOI?
- Keywords
- big data analysis; higher vocational college; intelligent logistics talents; precise training
- Abstract
With the rapid development of logistics industry, the demand for logistics talents is also increasing. Logistics education in higher vocational colleges plays an important role in training logistics talents. However, there are still some deficiencies in the training mode of logistics talents in higher vocational colleges. Therefore, This paper introduces a refined training framework for developing skilled logistics professionals in higher vocational education, utilizing big data analytics. Through big data analysis of the logistics industry, we can determine the demand type and quantity of logistics talents, and design an accurate training program according to the demand type and quantity. Through the analysis of big data and the research of precise training mode, this paper provides a new idea for the training of logistics talents in higher vocational colleges.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ke Wang PY - 2024 DA - 2024/12/27 TI - Research on Precise Training Mode of Intelligent Logistics Talents in Higher Vocational Colleges Based on Big Data Analysis BT - Proceeding of the 2024 International Conference on Diversified Education and Social Development (DESD 2024) PB - Atlantis Press SP - 63 EP - 69 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-346-7_9 DO - 10.2991/978-2-38476-346-7_9 ID - Wang2024 ER -