AgentSkillsCN

Evaluate Mining Operation

评估矿业运营状况

SKILL.md

Skill: Evaluate Mining Operation Efficiency

Domain

mining

Description

Analyzes mining operation metrics to evaluate efficiency, safety compliance, and recommend optimization strategies based on operational benchmarks.

Tags

mining, operations, efficiency, safety-compliance, resource-extraction

Use Cases

  • •Operational efficiency assessment
  • •Equipment utilization analysis
  • •Safety compliance monitoring
  • •Production cost optimization

Proprietary Business Rules

Rule 1: Equipment Availability Targets

Minimum availability thresholds by equipment type with penalty calculations for downtime.

Rule 2: Ore Grade Reconciliation

Variance limits between predicted and actual ore grades trigger investigation.

Rule 3: Safety Incident Rate

Lost time injury frequency rate (LTIFR) thresholds by operation type.

Rule 4: Energy Intensity Benchmarks

Energy consumption per tonne benchmarks by mining method.

Input Parameters

  • •operation_id (string): Mining operation identifier
  • •mining_method (string): Open pit, underground, placer
  • •production_data (dict): Tonnes mined, ore grade, strip ratio
  • •equipment_metrics (list): Equipment availability and utilization
  • •safety_data (dict): Incidents, near misses, hours worked
  • •energy_consumption (dict): Fuel, electricity usage
  • •labor_data (dict): Workforce metrics

Output

  • •efficiency_score (float): Overall efficiency rating
  • •safety_rating (string): Safety performance rating
  • •bottlenecks (list): Identified operational constraints
  • •optimization_opportunities (list): Improvement recommendations
  • •benchmark_comparison (dict): Performance vs benchmarks

Implementation

The evaluation logic is implemented in operation_evaluator.py and references benchmarks from mining_benchmarks.csv.

Usage Example

python
from operation_evaluator import evaluate_operation

result = evaluate_operation(
    operation_id="MINE-001",
    mining_method="open_pit",
    production_data={"tonnes_mined": 500000, "ore_grade": 0.8, "strip_ratio": 3.5},
    equipment_metrics=[{"type": "haul_truck", "availability": 0.88, "utilization": 0.75}],
    safety_data={"lost_time_incidents": 1, "hours_worked": 250000},
    energy_consumption={"diesel_liters": 1500000, "electricity_kwh": 8000000},
    labor_data={"headcount": 350, "productivity_tonnes_per_person": 1428}
)

print(f"Efficiency Score: {result['efficiency_score']}")

Test Execution

python
from operation_evaluator import evaluate_operation

result = evaluate_operation(
    operation_id=input_data.get('operation_id'),
    mining_method=input_data.get('mining_method'),
    production_data=input_data.get('production_data', {}),
    equipment_metrics=input_data.get('equipment_metrics', []),
    safety_data=input_data.get('safety_data', {}),
    energy_consumption=input_data.get('energy_consumption', {}),
    labor_data=input_data.get('labor_data', {})
)