让工厂旧系统轻松用上机器学习，不改硬件、不停产。
Proposing a Framework for Machine Learning Adoption on Legacy Systems
- 通过API将模型与生产环境解耦，避免系统升级
- 浏览器端交互控制模型参数，零停机维护
- 适合中小制造企业快速部署ML提升质量
机器学习对工业竞争力至关重要，但老旧系统升级成本高、中断大，制约了其广泛应用，尤其对中小企业。本文提出一种基于API的实用框架，将机器学习模型生命周期与生产环境解耦。通过轻量级浏览器界面，使领域专家无需本地硬件升级即可使用模型分析能力，实现模型维护零停机。该人机协同方法赋予专家对模型参数的实时交互控制权，增强信任并无缝嵌入现有流程。该方案有效降低财务与运营风险，为制造业提供可扩展、易获取的路径，提升生产质量与安全，强化竞争优势。
原文摘要 · Abstract (English)
The integration of machine learning (ML) is critical for industrial competitiveness, yet its adoption is frequently stalled by the prohibitive costs and operational disruptions of upgrading legacy systems. The financial and logistical overhead required to support the full ML lifecycle presents a formidable barrier to widespread implementation, particularly for small and medium-sized enterprises. This paper introduces a pragmatic, API-based framework designed to overcome these challenges by strategically decoupling the ML model lifecycle from the production environment. Our solution delivers the analytical power of ML to domain experts through a lightweight, browser-based interface, eliminating the need for local hardware upgrades and ensuring model maintenance can occur with zero production downtime. This human-in-the-loop approach empowers experts with interactive control over model parameters, fostering trust and facilitating seamless integration into existing workflows. By mitigating the primary financial and operational risks, this framework offers a scalable and accessible pathway to enhance production quality and safety, thereby strengthening the competitive advantage of the manufacturing sector.
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