基于边缘计算的建筑消防设施物联网状态实时诊断研究

张小雨

doi:10.12184/wspkjllysjWSP2634-792X37.20260705

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Volume 7, Issue 5

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基于边缘计算的建筑消防设施物联网状态实时诊断研究
基于边缘计算的建筑消防设施物联网状态实时诊断研究
合肥市包河区消防救援局，安徽合肥 230000
Vol. 7, Issue 5, Pages: 168-172(2026)
Published：30 May 2026
DOI：10.12184/wspkjllysjWSP2634-792X37.20260705
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张小雨.基于边缘计算的建筑消防设施物联网状态实时诊断研究[J].科技理论与实践,2026,07(05):168-172.

张小雨. (2026). 基于边缘计算的建筑消防设施物联网状态实时诊断研究. Theory and Practice of Science and Technology, 7(5), 168-172..
张小雨.基于边缘计算的建筑消防设施物联网状态实时诊断研究[J].科技理论与实践,2026,07(05):168-172. DOI： 10.12184/wspkjllysjWSP2634-792X37.20260705.

张小雨. (2026). 基于边缘计算的建筑消防设施物联网状态实时诊断研究. Theory and Practice of Science and Technology, 7(5), 168-172.. DOI： 10.12184/wspkjllysjWSP2634-792X37.20260705.

摘要

随着城市化进程的加快和高层建筑的日益增多，建筑消防安全面临着前所未有的挑战。传统的消防设施监测系统通常依赖于云计算架构，存在数据传输延迟高、带宽消耗大以及在网络不稳定时容易失效等问题，难以满足火灾等突发事件对实时性和可靠性的严苛要求。本文提出了一种基于边缘计算的建筑消防设施物联网状态实时诊断框架。该框架将计算资源和数据分析能力下沉至靠近数据源的边缘节点，结合轻量级深度学习算法和物联网（IoT）传感器网络，实现了对消防水泵、烟感探测器、电气火灾监控等关键消防设施运行状态的毫秒级实时监测与故障诊断。研究探讨了边缘计算与云端协同的系统架构，分析了基于轻量化神经网络（如1D-CNN、Bi-LSTM等）的异常检测与预测性维护模型，并结合数字孪生技术和MQTT等轻量级通信协议，提升了系统的数据融合与可视化能力。分析表明，基于边缘计算的消防物联网系统在降低响应延迟、减轻云端负荷、提升故障识别准确率方面具有显著优势，为现代智慧建筑的消防安全管理提供了高效、可靠的解决方案。

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references

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