构建可实时交互的制造智能平台，提升运维效率与决策能力
Yantra AI -- An intelligence platform which interacts with manufacturing operations
- 融合机器学习与GPT-4虚拟助手，实现预测性维护与实时决策支持
- 使用随机森林与孤立森林模型，识别异常并降低设备停机时间
- 通过Streamlit实现可视化交互，适合智能制造与工业优化场景
工业4.0快速发展，推动智能制造中实时追踪、机器学习与人工智能系统的广泛应用。本论文聚焦于为XRIT设计并验证一个智能生产系统，解决能源管理、预测性维护与AI驱动决策支持等关键问题。系统集成随机森林分类器用于主动维护，孤立森林用于异常检测，提升决策效率与减少停机时间。通过Streamlit实现实时数据可视化，提供可交互的仪表盘，使工人能即时获取操作洞察。系统基于模拟数据测试，具备可扩展性，支持未来在真实生产环境中的实时部署。引入基于GPT-4的虚拟助理，提供实时信息响应，简化复杂问题处理，优化运营决策。测试表明，系统显著提升工作效率、能源管理能力与维修计划水平。未来工作将聚焦于实时数据融合与系统持续优化。
原文摘要 · Abstract (English)
Industry 4.0 is growing quickly, which has changed smart production by encouraging the use of real-time tracking, machine learning, and AI-driven systems to make operations run more smoothly. The main focus of this dissertation is on creating and testing an intelligent production system for XRIT that solves important problems like energy management, predictive maintenance, and AI-powered decision support. Machine learning models are built into the system, such as the Random Forest Classifier for proactive maintenance and the Isolation Forest for finding outliers. These models help with decision-making and reducing downtime. Streamlit makes real-time data visualisation possible, giving workers access to dashboards that they can interact with and see real-time observations.The system was tested with fake data and is made to be scalable, so it can be used in real time in XRIT's production setting. Adding an AI-powered virtual assistant made with GPT-4 lets workers get real-time, useful information that makes complicated questions easier to answer and improves operational decisions. The testing shows that the system makes working efficiency, energy management, and the ability to plan repairs much better. Moving the system to real-time data merging and looking for other ways to make it better will be the main focus of future work.
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