Automation is the guiding principle of this new era, and despite the problems that humanity faces as a result of automation, technology has greatly benefitted people by streamlining challenging jobs across many industries. The mining business, where there are frequently unforeseen mishaps, is one such industry that requires complete automation. In this work, a new simulative processing environment termed DTFL‐DF—Digital twin federated learning decision forest a digital twin environment that is tailored to handle unforeseen fire incidents—is offered as a means of avoiding these unplanned catastrophes in the mining industry. Although the design presented here is intended for usage in the mining sector, it can also be applied to other sectors. The overall technological contribution of this study is to guarantee the processing of real‐time data in order to successfully handle mission‐critical operations without relying on past data. This is accomplished by adapting the digital twin's original design and distributing the processing environment within the edge‐fog layer. Results analysis in the form of robustness analysis, performance evaluation of the classification model, etc. provides strong support for the suggested methodology. For handling the decentralized training procedure, a brand‐new algorithm termed FL‐DF is put forth in order to speed up classification and prevent any sort of catastrophe.

中文翻译：

---

DTFL-DF：由联邦学习决策林支持的数字孪生架构，可减少采矿业火灾事故

自动化是这个新时代的指导原则，尽管人类因自动化而面临问题，但技术通过简化许多行业的挑战性工作，极大地造福了人们。采矿业经常发生不可预见的事故，正是需要完全自动化的行业之一。在这项工作中，提供了一种称为 DTFL-DF（数字孪生联合学习决策林）的新模拟处理环境，这是一种专为处理不可预见的火灾事件而定制的数字孪生环境，可作为避免采矿业中这些意外灾难的一种手段。尽管此处介绍的设计旨在用于采矿业，但它也可以应用于其他领域。这项研究的总体技术贡献是保证实时数据的处理，以便在不依赖过去数据的情况下成功处理关键任务操作。这是通过调整数字孪生的原始设计并将处理环境分布在边缘雾层内来实现的。稳健性分析、分类模型性能评估等形式的结果分析为建议的方法提供了强有力的支持。为了处理分散的训练过程，提出了一种称为 FL-DF 的全新算法，以加快分类速度并防止任何类型的灾难。