基于5G与边缘智能的矿山多模态感知协同系统设计与应用

Design and Application of a Multimodal Perception Collaboration System for Mines Based on 5G and Edge Intelligence

摘要: 针对山西省矿山智能化转型中存在的“数据孤岛”、系统协同弱、安全响应慢等瓶颈，本文设计并实现了一种基于5G与边缘智能的多模态感知协同平台。该平台采用分层微服务架构，核心创新在于构建了一个支持可视化编排的动态规则引擎，并提出了一个结合卡尔曼滤波与LSTM网络的混合数据融合算法，以实现多源异构数据的时空对齐与质量优化。通过“矿山大脑”客户端与联动控制模块，实现了对人员、设备、环境全要素的闭环管控。在山西省某大型铜矿的工业性试验表明，该平台使设备故障发现效率提升超40%，安全事件平均处置时间缩短至15分钟以内。本工作不仅验证了平台的技术有效性，还通过成本效益与5G必要性分析，论证了其向中小型矿山推广的可行性。

Abstract: Aiming at the bottlenecks such as “data silos”, weak system collaboration and slow safety response in the intelligent transformation of mines in Shanxi Province, this paper designs and implements a multi-modal perception collaboration platform based on 5G and edge intelligence. The platform adopts a hierarchical microservice architecture, with its core innovation lying in the construction of a dynamic rule engine that supports visual orchestration, as well as the proposal of a hybrid data fusion algorithm combining Kalman filtering and LSTM network, so as to realize spatio-temporal alignment and quality optimization of multi-source heterogeneous data. Through the “Mine Brain” client and the linkage control module, closed-loop management and control of all elements including personnel, equipment and environment are achieved. Industrial tests conducted in a large copper mine in Shanxi Province show that the platform improves equipment fault detection efficiency by more than 40% and reduces the average handling time of safety incidents to less than 15 minutes. This work not only verifies the technical effectiveness of the platform, but also demonstrates the feasibility of its popularization in small and medium-sized mines through cost-benefit analysis and necessity analysis of 5G application.