# Event-Driven Dynamics Model of Operating State Evolution for Cantilever Roadheader
## 1 Introduction  
With the rapid development of technologies such as the industrial Internet [1], 5G communication [2], and sensor network [3], the intelligent heading workface has significantly enhanced its perception capabilities for all elements, including the equipment operation status, personnel spatiotemporal information, geological information, and the roadway environment [4]. At the same time, the volume and dimensions of real-time state data have also increased substantially. In fact, due to the limitations of data computing capabilities, the increase in a large amount of high-dimensional data during the operation process of the intelligent cantilever roadheader cannot be directly applied to the equipment control decision, resulting in a significant waste of real-time state data [5]. Digital twin technology represents a groundbreaking paradigm that creates dynamic, bidirectional linkages between physical entities and their virtual models [6]. The key advantage of digital twin technology is its interactivity and collaborative capability, where the virtual model is able to monitor the physical entity in real time using sensor data, realize dynamic mapping, and validate the control strategy in the virtual environment, and then control the physical entity [7]. This process involves communication and cooperation between physical devices, information exchange between virtual models, and synchronization and dynamic adjustment between virtual and physical models [8]. In the domain of digital twins and virtual-physical interaction modeling, [PERSON] proposed a comprehensive theoretical framework for digital twin connectivity and interaction encompassing the following five core components: perception, communication, physical-virtual mapping, data-model coupling, and fusion. They established the \"accurate, real-time, consistent, safe, and reliable\" criteria for connectivity and interaction, which not only provides an in-depth analysis of each element along the temporal dimension but also constructs theoretical relationships between cross-dimensional elements. This work fundamentally strengthens the scientific rigor and practical applicability of digital twin connectivity and interaction [9; 10]. In intelligent mining systems, digital twin technology drives digital transformation and intelligent advancement across mine construction, extraction operations, monitoring systems, and maintenance processes. By establishing multidimensional equipment models that enable virtual-physical mapping and real-time interaction, this technology enhances mining safety, operational efficiency, and intelligent performance [11]. For remote intelligent excavation applications, digital twin solutions effectively address excavation challenges while boosting productivity and fostering intelligent development. Through state evolution dynamic modeling, the technology achieves real-time equipment monitoring and operational optimization, facilitating the critical transition from automated to fully intelligent excavation operations [12; 13]. The fundamental prerequisite for achieving effective virtual-physical interaction lies in maintaining temporal synchronization between the control decision states of physical entities and their digital counterparts. However, the inherent latency across system layers in tunneling systems, arising from sensor data acquisition, network transmission, and computational processing, creates fundamental synchronization challenges between virtual-physical spaces. This cumulative latency disrupts the alignment of decision-making states across virtual and physical systems, significantly diminishing the operational relevance of digital twin-derived decisions, particularly in the intelligent tunneling scenarios demanding rapid response coordination. In addition, the characteristic of physical control decision making is based on simple logical judgment and behavior execution through the collection of physical sensor data.