多模态认知架构与局部生成人工智能用于工业控制边缘设备上的混凝土工厂
Fernando Hidalgo-Castelo1, Antonio Guerrero-González1, Francisco García-Córdova1
1Department of Automation, Electrical Engineering, and Electronic Technology, Polytechnic University of Cartagena, 30203 Cartagena, Spain.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了用于混凝土工厂的对话式AI系统,使工业数据能够以自然语言访问. 边缘部署的AI显著加快了信息检索速度,民主化了操作洞察力.
科学领域:
- 工业自动化 工业自动化
- 人工智能的人工智能
- 边缘计算 边缘计算
背景情况:
- 在混凝土工厂访问工业数据耗时15-30分钟,需要专业知识.
- 目前的ERP,MES,SCADA和PLC等系统面临着可访问性挑战.
- 需要民主化,实时访问操作信息.
研究的目的:
- 开发一种对话式人工智能系统,用于自然语言访问工业信息.
- 解决混凝土工厂运营中的信息可访问性差距.
- 为了证明在工业应用的低成本边缘硬件上部署AI的可行性.
主要方法:
- 在Raspberry Pi 5上实现了五层认知架构.
- 在GGUF Q4_0格式中集成了一个量子化的Mistral-7B大型语言模型 (LLM).
- 使用了西班牙语语音识别/合成和工业协议 (OPC UA,MQTT,REST API).
主要成果:
- 实现了显著的响应时间改进:14.19s (简单),16.45s (中等),23.24s (复杂) 查询.
- 证明了系统可靠性,在30分钟的自主运行中成功率为100%.
- 保持了热稳定性,平均温度为69.3°C,低于压力值.
结论:
- 开发的人工智能系统通过自然语言实现了工业信息获取的民主化.
- 量子化LLM在工业环境的低成本边缘硬件上是可行的.
- 该系统确保数据隐私和云独立,提高运营效率.
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