539 Drilling directly influences downstream fragmentation, which in turn affects comminution energy demand—one of the most energy-intensive stages in mining operations. By enabling real-time calculation and visualization of energy-related indicators such as specific energy per drilled meter, the proposed architecture supports more energy-aware operational decisions. Although the architecture does not directly reduce energy consumption, it provides the analytical foundation required to identify deviations, understand rock–tool interaction dynamics, and adjust operational parameters proactively. Such capability contributes to reducing cumulative inefficiencies that propagate along the production chain. From a data governance perspective, the consolidation of heterogeneous control signals into a standardized, traceable dataset strengthens transparency and accountability in operational management. Structured traceability by hole, equipment, and geological domain improves the reliability of performance assessments and supports evidence-based continuous improvement processes. In an environment where societal expectations increasingly demand responsible resource extraction, robust data governance becomes an essential pillar of operational credibility. The resilience mechanisms embedded in the architecture, particularly edge-level buffering and secure wireless transport, enhance data integrity under real mining conditions characterized by mobility and variable connectivity. Reliable data continuity reduces operational blind spots and strengthens the foundation for advanced analytics and long-term optimization strategies. Furthermore, democratizing access to operational intelligence through AI-assisted natural language interaction reduces technical barriers and accelerates decision-making across supervisory levels. Enabling broader organizational access to structured information fosters alignment between drilling, planning, and downstream operations, contributing to a more integrated production ecosystem. The framework presented in this study is inherently replicable in other large-scale mining environments facing similar interoperability and connectivity challenges. By prioritizing open standards, modular scalability, and energy-oriented analytics, the architecture supports the transition from isolated automation to integrated digital ecosystems. In summary, advancing interoperable digital infrastructures in drilling operations is not solely a technological improvement but a strategic step toward building more efficient, transparent, and accountable mining systems. Such evolution is essential for sustaining responsible mineral supply under the increasing pressures of the global energy transition. 8. CONCLUSIONS This study demonstrates the technical feasibility and operational value of implementing a vendorneutral, interoperable digital architecture for production drilling in large-scale mining environments. By integrating heterogeneous control systems into a unified data governance framework supported by resilient edge computing and secure wireless transport, the proposed model enables continuous acquisition, structured consolidation, and real-time accessibility of drilling data. The implementation of standardized data structures across multi-vendor fleets significantly improved traceability, reduced reporting inconsistencies, and enhanced comparability between equipment units. The integration of specific energy calculation at the individual hole level provided practical analytical insight into rock–tool interaction dynamics and geological variability, supporting more informed and energyaware operational decisions. Beyond localized process optimization, architecture establishes a scalable foundation for broader digital integration across the mining value chain. The combination of interoperability, resilience under variable connectivity, and AI-assisted data interaction transforms raw control signals into structured operational intelligence accessible across supervisory levels.
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