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  Blast Performance Twin Data Integration Schema (JSON Schema)
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DEFINITION
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The Blast Performance Twin Data Integration Schema is a standardized JSON Schema
defining the structure, semantics, and validation rules for ingesting, harmonizing,
and exchanging blast-related operational, geotechnical, and performance data into a
mine digital twin. It ensures interoperability across drilling, blasting,
fragmentation analysis, and post-blast monitoring systems by enforcing consistent
data typing, units, temporal alignment, and spatial referencing. The schema serves as
the canonical contract between field sensors, engineering software, and twin
analytics engines.

OVERVIEW
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The Blast Performance Twin Data Integration Schema bridges domain-specific blast
engineering practices with modern digital twin architecture. It formalizes how
heterogeneous data—such as drill hole coordinates and deviation logs, explosive
loading parameters (type, density, stemming), initiation timing sequences, high-speed
camera-derived fragment size distributions (via Rosin-Rammler analysis), and
post-blast muck pile LiDAR scans—are modeled, normalized, and semantically linked
within a unified JSON-based payload. Central to the schema is the concept of 'blast
event provenance', which captures traceability from design (e.g., blast design
software exports) through execution (e.g., electronic detonator firing logs) to
verification (e.g., fragmentation image analysis metadata). Validation rules enforce
unit consistency (e.g., all lengths in meters, pressures in kPa), temporal precision
(ISO 8601 UTC timestamps with microsecond resolution), and spatial referencing (WGS84
or local mine grid with CRS identifier). Furthermore, the schema supports versioned
extensions for emerging modalities—such as acoustic emission signatures or
drone-based thermal imaging of post-blast rock stress patterns—ensuring forward
compatibility while maintaining backward validation integrity. Its implementation
enables real-time ingestion pipelines, automated KPI derivation (e.g., weighted
average fragment size, powder factor efficiency), and closed-loop feedback to blast
design optimization algorithms.

KEY COMPONENTS
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1. BlastEventMetadata
2. DrillHoleGeometry
3. ExplosiveLoadingProfile
4. FragmentationAssessment
5. SensorObservationBundle

APPLICATIONS
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  - Real-time blast performance monitoring dashboards
  - AI-driven blast design optimization using historical twin data
  - Regulatory compliance reporting with auditable data lineage

KEY FORMULAS
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Rosin-Rammler Distribution Parameter (x₅₀): x₅₀ = (ln(2) / k)^(1/n)
  -> Calculates the median fragment size (x₅₀) from Rosin-Rammler distribution parameters k (scale) and n (shape), derived from image-based fragmentation analysis.
Powder Factor: PF = TotalExplosiveMass / RockVolume
  -> Measures explosive efficiency in kg/m³; used to benchmark blast economy and correlate with fragmentation outcomes.
Blast Efficiency Index (BEI): BEI = (x₅₀_designed / x₅₀_actual) × (PF_actual / PF_designed)
  -> Quantifies overall blast performance deviation from design intent; values >1 indicate better-than-expected fragmentation per unit explosive.

RELATED CONCEPTS
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  - Mine Digital Twin
  - Interoperable Data Infrastructure (IDI)
  - ISO 19115 Geospatial Metadata
  - OPC UA for Mining
  - Digital Thread

REFERENCES
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ICMM Digital Twin Framework for Mining Operations (https://www.icmm.com/en-gb/publications/digital-twin-framework-mining-operations)
ISO/IEC 33001:2023 — Information technology — Systems and software engineering — Capability assessment models (https://www.iso.org/standard/79611.html)
Blast Performance Twin: A Reference Architecture (SME 2022 Technical Paper) (https://www.smenet.org/resources/blast-performance-twin-reference-architecture)

TAGS
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blasting, digital-twin, json-schema, mining-automation, interoperability