创新创业理论研究与实践 ›› 2023, Vol. 6 ›› Issue (12): 74-76.

• 教育改革与发展 • 上一篇    下一篇

控制学科群智能化建设的探索

朱浩   

  1. 重庆邮电大学 自动化学院,重庆 400065
  • 出版日期:2023-06-25 发布日期:2023-09-18
  • 作者简介:朱浩(1982—),男,重庆人,博士,教授,研究方向:智能网联汽车协同感知,电子邮箱:zhuhao@cqupt.edu.cn。
  • 基金资助:
    2021年重庆市研究生教育教学改革研究项目“人工智能+控制学科群建设的探索与实践”(yjg212023); 2019年度重庆邮电大学国际化教育研究项目“依托中外科技合作的控制学科留学生英文课程建设——以自动控制原理为例”(GJJY19-1-02); 重庆邮电大学2020年“课程思政”建设项目“控制学科专业课融入课程思政的探索——以自动控制原理为例”(XKCSZ2042); 2022年重庆邮电大学教育教学改革研究项目“卓越工程师2.0培养模式探索与实践”(XJG22236); 2020年度重庆市研究生教育改革研究项目“‘新基建’背景下的智能网联汽车学科群建设的实践与探索”(YJG203077); 2020年度重庆邮电大学教育教学改革项目“‘新基建’背景下的机器人工程专业培养方案探索与研究”(XJG20201)

The Exploration on Intelligent Construction of Control Discipline Group

ZHU Hao   

  1. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
  • Online:2023-06-25 Published:2023-09-18

摘要: 在人工智能开发和新工科建设背景下,针对控制理论与控制工程、检测技术与自动化装置、系统工程、模式识别等控制学科,进行学科交叉融合、资源整合及优化。该文以科研实验为抓手,以平台产出的科研成果为支撑,培养高水平教师团队,构建以学生为中心的全过程实践能力培养模式,采用“特色工作室”的组织形式开展多维创新活动。目的为加快人工智能创新型人才的培养,形成以重庆邮电大学为中心的全员、全过程、全方位育人的新格局。解决了人工智能与控制学科群建设的四个问题:课程体系难以培养人工智能综合型人才、交叉互融团队缺乏人工智能领域运作经验、人才培养缺乏人工智能领域前沿知识探索、控制学科群课程联系不够紧密。

关键词: 人工智能, 学科交叉, 资源整合优化, 控制学科群建设, 人才培养, 科教融合

Abstract: In the context of the artificial intelligence (AI) development and new engineering construction, control theory and control engineering, detection technology and automation devices, systems engineering, pattern recognition and other control disciplines, for cross-discipline integration, resource integration and optimization. Based on the experimental research and supported by the achievements of scientific research, this paper aims to cultivate a high-level team of teachers and construct a student-centered training model for the whole process of practical ability, multi-dimensional innovation activities are carried out in the form of "Characteristic studio". Objective to accelerate the training of innovative talents in artificial intelligence (AI), and form a new pattern of all-round education with the Chongqing University of Posts and Telecommunications as the center. This paper has solved the following four difficulties in the construction of artificial intelligence and control discipline cluster: it is difficult for the curriculum system to train artificial intelligence integrated talents; the cross-integration team lacks operating experience in the field of artificial intelligence; the lack of advanced knowledge exploration in the field of artificial intelligence in personnel training and the lack of close connection between the courses of control subject group.

Key words: Artificial intelligence, Interdisciplinary, Resource integration and optimization, Construction of control subject group, Personnel training, Integration of science and education

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