Mining is rapidly evolving into an AI driven cyber physical ecosystem where safety and operational reliability depend on robust perception, trustworthy distributed intelligence, and continuous monitoring of miners and equipment. However, real world mining environments impose severe constraints, including poor illumination, GPS denied conditions, irregular underground topologies and intermittent connectivity. These factors degrade perception accuracy, disrupt situational awareness and weaken distributed learning systems. At the same time, emerging cyber physical threats such as backdoor triggers, sensor spoofing, label flipping attacks, and poisoned model updates further jeopardize operational safety as mines adopt autonomous vehicles, humanoid assistance, and federated learning for collaborative intelligence. Energy constrained sensors also experience uneven battery depletion, creating blind spots in safety coverage and disrupting hazard detection pipelines. This paper presents a vision for a Unified Smart Safety and Security Architecture that integrates multimodal perception, secure federated learning, reinforcement learning, DTN enabled communication, and energy aware sensing into a cohesive safety framework. We introduce five core modules: Miner Finder, Multimodal Situational Awareness, Backdoor Attack Monitor, TrustFed LFD, and IoT driven Equipment Health Monitoring. These modules collectively address miner localization, hazard understanding, federated robustness, and predictive maintenance. Together, they form an end to end framework capable of guiding miners through obstructed pathways, identifying compromised models or sensors, and ensuring mission critical equipment reliability. This work outlines a comprehensive research vision for building a resilient and trustworthy intelligent mining system capable of maintaining operational continuity under adversarial conditions.

翻译：采矿正迅速演变为一个由人工智能驱动的信息物理生态系统，其安全性与运行可靠性依赖于鲁棒的感知、可信的分布式智能以及对矿工与设备的持续监控。然而，真实采矿环境存在诸多严苛限制，包括照明条件差、GPS拒止环境、不规则的地下拓扑结构以及间歇性网络连接。这些因素会降低感知精度、破坏态势感知能力并削弱分布式学习系统。与此同时，随着矿山采用自动驾驶车辆、仿人辅助设备以及用于协同智能的联邦学习，新兴的信息物理威胁（如后门触发器、传感器欺骗、标签翻转攻击和投毒模型更新）进一步危及运行安全。能量受限的传感器还会出现电池电量不均损耗，在安全覆盖范围内形成盲区，并破坏危险检测流程。本文提出了一种统一智能安全与保障架构的愿景，该架构将多模态感知、安全联邦学习、强化学习、容迟网络（DTN）使能的通信以及能量感知传感集成到一个统一的安全框架中。我们介绍了五个核心模块：矿工定位器、多模态态势感知、后门攻击监测器、TrustFed LFD以及物联网驱动的设备健康监测。这些模块共同应对矿工定位、危险理解、联邦学习鲁棒性和预测性维护等问题。它们共同构成了一个端到端的框架，能够引导矿工通过受阻路径、识别受损模型或传感器，并确保关键任务设备的可靠性。本工作概述了一个全面的研究愿景，旨在构建一个具有弹性且可信的智能采矿系统，能够在对抗性条件下维持运行的连续性。