引用本文:
石青玉, 王艺清, 苏自乾, 傅旦赞, 秦家骏, 刘悦彤, 张永策, 宿艳. 基于计算机视觉的AI滴定分析实验[J]. 大学化学,
2026, 41(1): 57-63.
doi:
10.12461/PKU.DXHX202505001
Citation: Qingyu Shi, Yiqing Wang, Ziqian Su, Danzan Fu, Jiajun Qin, Yuetong Liu, Yongce Zhang, Yan Su. AI Titration Analysis Experiment Based on Computer Vision[J]. University Chemistry, 2026, 41(1): 57-63. doi: 10.12461/PKU.DXHX202505001
Citation: Qingyu Shi, Yiqing Wang, Ziqian Su, Danzan Fu, Jiajun Qin, Yuetong Liu, Yongce Zhang, Yan Su. AI Titration Analysis Experiment Based on Computer Vision[J]. University Chemistry, 2026, 41(1): 57-63. doi: 10.12461/PKU.DXHX202505001
基于计算机视觉的AI滴定分析实验
摘要:
本项目通过结合单片机控制技术和步进电机，实现了对注射器的精确操控，进而构建了一个基于计算机视觉的人工智能滴定分析系统。该系统采纳了一种创新的计算机视觉方案，通过ResNet神经网络分类算法，依据指示剂颜色变化智能判定滴定终点。在23级分析化学实验课中，40名学生在4个学时内成功完成了实验，显示出该系统易于操作和学习。在首届智能实验挑战校赛中，57名学生组成的19支队伍不仅完成了比赛，更有4支队伍自主设计了新的硬件系统和软件算法，展现创新潜力。为了降低成本并便于推广，项目采用了商品化组件和3D打印技术。这些技术的融合不仅为分析化学基础实验的教学创新提供了新的方向，也为未来在智能实验领域的探索研究打下了坚实的基础。
English
AI Titration Analysis Experiment Based on Computer Vision
Abstract:
This project integrates microcontroller technology with stepper motors to achieve precise syringe control, establishing an artificial intelligence-based titration analysis system utilizing computer vision. The system implements an innovative visual approach employing the ResNet neural network classification algorithm to intelligently identify titration endpoints through indicator color changes. During the 23rd-level analytical chemistry laboratory course, 40 students successfully completed the experiment within a 4-hour session, demonstrating the system’s user-friendly operation and ease of learning. In the inaugural Intelligent Experiment Challenge competition, 19 teams comprising 57 participants not only completed the assigned tasks but also showcased innovative potential, with 4 teams independently developing novel hardware systems and software algorithms. To enhance cost-effectiveness and scalability, the project utilizes commercial components and 3D printing technology. This technological integration not only pioneers new directions for teaching innovation in fundamental analytical chemistry experiments but also establishes a robust foundation for future research in intelligent experimental systems.
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