大学化学 >> 2026, Vol. 41 >> Issue (1): 276-288.doi: 10.12461/PKU.DXHX202504104

所属专题： 化学实验数字化设计竞赛获奖作品

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机器学习及大数据视域下分析化学实验数字化探索——以测量水体硬度为例

饶靖杰1, 蔡雯雯1, 赵佳辉1, 杨旭2, 颜紫嫣3, 张天锦1, 张航1   

  1. 1 江西师范大学化学与材料学院, 化学国家级实验教学示范中心(江西师范大学), 南昌 330022;
    2 江西师范大学计算机信息工程学院, 南昌 330022;
    3 江西师范大学软件学院, 南昌 330022
  • 收稿日期:2025-04-28 录用日期:2025-09-30 发布日期:2025-12-30
  • 通讯作者: 张航 E-mail:zhanghangjx@163.com Hang Zhang
  • 基金资助:
    江西省高等学校教学改革研究课题(JXJG-24-2-26)

Digital Exploration of Analytical Chemistry Experiments in the Context of Machine Learning and Big Data: A Case Study on Water Hardness Measurement

Jingjie Rao1, Wenwen Cai1, Jiahui Zhao1, Xu Yang2, Ziyan Yan3, Tianjin Zhang1, Hang Zhang1   

  1. 1 National Demonstration Center for Experimental Chemistry Education, College of Chemistry and Materials, Jiangxi Normal University, Nanchang 330022, China;
    2 School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China;
    3 School of Software, Jiangxi Normal University, Nanchang 330022, China
  • Received:2025-04-28 Accepted:2025-09-30 Published:2025-12-30
  • Contact: Hang Zhang E-mail:zhanghangjx@163.com

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摘要： 本文在数智教育背景下，以水体硬度测定为例，融合机器学习与大数据技术，构建分析化学实验数字化教学辅助系统。通过采集滴定图像，结合颜色直方图与SVM模型判定滴定进程，实现结果自动分析与评价及个性化学习反馈。该研究将传统实验与数字技术相结合，培养学生数据分析、跨学科思维与实践能力，为高校化学实验教学数字化提供了新方法。

关键词: 数字化教学, 高校化学实验, 机器学习, 数智化

Abstract: Within the framework of digital-intelligent education, this study develops a digital teaching assistance system for analytical chemistry experiments by integrating machine learning and big data technologies, using water hardness determination as a representative example. The system captures titration images and employs color histograms combined with a support vector machine (SVM) model to determine titration endpoints, enabling automated result analysis, evaluation, and personalized learning feedback. This research successfully bridges traditional experimental methods with digital technologies, fostering students’ competencies in data analysis, interdisciplinary thinking, and practical skills. The proposed approach offers an innovative methodology for promoting digital transformation in university-level analytical chemistry laboratory education.

Key words: Digital teaching, University-level chemistry experiments, Machine learning, Digital and intelligent