# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Selection of Data Collection Equipment  
This technology system is based on an energy self-sufficient, autonomous mobile station designed to operate in real time in highly complex mining environments. The unit is mounted on a trailer-type chassis with towable traction, which incorporates a photovoltaic generation system consisting of a 300 W monocrystalline solar panel and a deep-cycle battery bank (12 V, 100 Ah) connected to an MPPT regulator. This configuration guarantees continuous operating autonomy for at least 48 h in low solar radiation conditions.  
The sensor system is composed of IP optical cameras with day and night vision, integrated into a vertical telescopic mast that allows surveillance at different heights, adapting to the topography of the mine pit. The cameras used have infrared vision capacity (IR) of up to 60 m, H.265+ compression, and 4 MP resolution, with a fixed 6 mm lens, making them suitable for environments with dust, fog, or variable lighting. The sensors capture images at 25-30 fps and feed directly to the edge analysis system, without requiring permanent connectivity to external networks.  
Processing of the captured data is performed locally by a mini-PC type industrial computing unit equipped with a quad-core Intel Celeron processor, 8 GB of RAM, and a 256 GB solid disk, all housed in a weatherproof IP65 enclosure. Running on this unit is an optimized YOLO11 model, pre-trained to recognize heavy machinery, people, and wildlife. The model is capable of operating with latencies below 50 ms, thanks to a convolution-based architecture with Leaky ReLU activation and regularization techniques such as dropout (0.1) and label smoothing. To ensure efficient operation in the field, the station integrates a hybrid wireless communications system.The first subsystem, based on UHF radio frequency at 915 MHz, allows the transmission of discrete data (alerts, crossing events) with an effective range of up to 8 km line-of-sight and response times in the order of milliseconds. This channel is essential for activating control mechanisms and visual or audible alerts in critical situations.  
Figure 3: Methodological proposal for this research.  
The second subsystem corresponds to high-capacity transmission via 5 GHz band Wi-Fi (IEEE 802.11 ad) [73], which facilitates real-time video transfer from the station to a remote computer or server. The connection is made using high-gain DUAL OMNI antennas (5 dBi), configured in 2 \(\times\) 2 MIMO mode, allowing stability against electromagnetic interference typical of the mining environment. The modularity of the station allows it to be repositioned at multiple strategic points in the pit, covering sectors where visual monitoring of the movement of trucks, drills, or operators is required. This repositioning capability allows surveillance to be adapted according to operational changes in the mining process, ensuring continuous monitoring of critical areas. All captured information can be stored locally, transmitted to the Central Operational Information Centre (CIO), or sent to the cloud, depending on the type of connection available.