Figure 3 – Modular interoperable digital architecture for production drilling. 5. METHODS The validation of the proposed digital drilling architecture followed a structured methodological approach combining variable identification, system integration, data modeling, and comparative operational assessment. The objective was to evaluate the technical feasibility, resilience, and analytical capability of the framework under real large-scale mining conditions. In the first phase, systematic identification and prioritization of critical operational variables was conducted directly from drilling control systems. Variables were selected based on their mechanical relevance to drilling performance and their influence on rock–tool interaction. These included penetration rate, rotation torque, thrust force, air pressure, hole depth, drilling duration, and auxiliary operating states. Selection criteria focused on parameters that could support both productivity analysis and energy-based rock behavior interpretation. Subsequently, a data standardization model was defined to harmonize heterogeneous controller outputs. A unified naming convention, timestamp structure, and equipment identification schema were established to ensure cross-equipment comparability and long-term traceability. This normalization process enabled consolidation of multi-source data into a coherent operational dataset. Edge-level buffering mechanisms were configured to implement a store-and-forward strategy. Controlled communication interruption tests were performed to validate data continuity, synchronization integrity, and successful retransmission after network restoration. These tests confirmed the robustness of the architecture under intermittent connectivity scenarios typical of mobile equipment in open-pit mining environments. A comparative assessment before and after was conducted to evaluate the operational impact of the architecture. Key evaluation dimensions included data availability, decision latency, traceability granularity, and energy analytics capability. Baseline conditions were characterized by fragmented data sources and shift-based reporting, whereas post-implementation conditions enabled continuous acquisition and centralized visibility.
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