基于机器视觉的疫苗生产过程人工智能监测研究

Research on Artificial Intelligence Monitoring of Vaccine Production Process Based on Machine Vision

关键词:  机器视觉  疫苗生产  人工智能监测  深度学习

Key words:  Machine vision  Vaccine production  Artificial intelligence monitoring  Deep learni

摘要:

目的 随着疫苗生产数字化转型加快，疫苗生产过程关键阶段的高效监控和管理对于进一步提高疫苗质量保证水平至关重要。传统的人工监控方式存在差错或偏差分辨率低，纠错反应不及时，超大规模生产下监控效能偏低等问题。因此，机器视觉技术被应用于疫苗生产过程关键阶段的监控成为了疫苗生产领域数字化转型的新探索。方法 本研究以机器视觉技术为基础，针对疫苗生产过程的关键阶段进行人工智能视觉识别下的机器监控项目研究。我们选取了毒种领取和灭活剂添加两个场景，分别设计了相应的算法流程，并利用深度学习模型进行训练。通过大量数据训练和优化，算法的平均准确率达到了90%以上。结果 研究结果表明，机器视觉技术可以有效监测疫苗生产过程中的关键环节，减少人为差错或偏差，保证纠错反应能力、提高生产效率和产品质量保证水平。结论 本研究为机器视觉技术在疫苗超大规模生产过程中的应用提供了参考，也为疫苗产业未来智能化数字化发展提供了有前景的应用路径。

Abstract:

OBJECTIVE With the rapid development of the vaccine industry, the monitoring and management of the vaccine production process has become increasingly important. Traditional manual monitoring methods have issues such as low efficiency and susceptibility to errors. Therefore, machine vision technology has gradually been applied to monitor the vaccine production process. METHODS This research focuses on the application of machine vision technology for the key links in the vaccine production process. We selected two scenarios: the receipt of the virus strain and the addition of the inactivating agent, and designed corresponding algorithmic processes. Deep learning models were utilized for training. Through extensive data training and optimization, the average accuracy of the algorithms achieved over 90%. RESULTS The research results indicate that machine vision technology can effectively monitor key steps in the vaccine production process, reduce human errors, and improve production efficiency and product quality. CONCLUSION This research provides a reference for the application of machine vision technology in the vaccine production process and supports the intelligent development of the vaccine industry.