LOADING
Cycle and waiting
01OPEN-PIT MINING · AOI
Intelligence for every cycle. Context for every decision.
Connect the pit, equipment and ore flow. AOI combines visual evidence, process data and operating context to support safety, productivity, continuity, recovery and material control in open-pit mining.
Prioritize a mine use case
AOI / MINE INTELLIGENCE OPEN PIT
RGB · THERMAL · LiDAR · OT
01 / AOI MINE PULSEA SHARED OPERATIONAL VIEW
The pulse of the mine. One connected operation.
A shared view of safety, cycles, condition and material for supervisors, operations and maintenance.
CONTINUITY
Condition and events
SAFETY
Exposure and zones
MATERIAL
Quality and dilution
AICE observes
RGB, thermal, LiDAR and operating data.
AOI understands
States, sequences and process relationships.
Predictive anticipates
Trends, anomalies and likely deviations.
AI Process Operator recommends
Actions with operational evidence and context.
AICE OBSERVES›AOI UNDERSTANDS›PREDICTIVE ANTICIPATES›AI PROCESS OPERATOR RECOMMENDS
02 / MINING USE CASESMINE-SITE CAPABILITIES
From the pit to the process.
Start with an operational problem and connect the signals, assets and indicators needed to evaluate it.
AOI / LOADING AND HAULAGE01LOADING AND HAULAGE
Shovel-truck cycle optimization
Measure the actual sequence to reduce variation across truck positioning, loading and departure.
WHAT AOI OBSERVES
- Truck presence and queues, shovel availability and waiting times.
- Loading start and end, observable passes and stage duration.
- Equipment interactions and visible causes of stoppage.
WHAT IT CAN RECOMMEND
- Review recurring waits and adjust coordination with dispatch.
- Compare faces and shifts using a shared cycle definition.
- Prioritize interventions at the stage with the greatest loss.
AOI / BUCKET · TRAMP-MATERIAL PREVENTION02BUCKET · TRAMP-MATERIAL PREVENTION
Shovel and loader bucket teeth
Identify missing teeth or visible changes, then relate the event to material that may have entered the circuit.
AT THE SHOVEL OR LOADER
- Compare visible tooth count, position and condition.
- Record a missing tooth or change with image, equipment and event time.
- Relate the alert to potentially affected cycles and haul trucks.
ON THE CONVEYOR
- Look for visible foreign objects in the ore flow.
- Relate the source alert to the material route.
- Support inspection, removal or a belt stop under the approved procedure.
Detection depends on visibility and coverage. An automatic stop requires integration and interlocks validated by the mine.
AOI / CRUSHING AND CONVEYING03CRUSHING AND CONVEYING
Crushers, conveyors, idlers and chutes
Visibility into buildup, visible tramp material and conditions that may affect continuity.
PRIMARY CRUSHER AND DUMP POCKET
- Occupancy and buildup in the dumping area.
- Oversize, spillage and possible entry of bucket teeth or other tramp material.
- Truck arrival, dumping and release sequence.
CONVEYORS, IDLERS AND CHUTES
- Observe particle size, oversize and visible tramp material in the flow.
- Detect thermal anomalies in idlers, motors and gearboxes.
- Identify spillage, misalignment and chute blockage.
Thermal measurement requires configuration by asset, emissivity and viewing conditions.
AOI / CONVEYOR PARTICLE-SIZE ANALYSIS04CONVEYOR PARTICLE-SIZE ANALYSIS
Measure particle size. Understand the feed.
Estimate apparent ore size on the conveyor and track its size distribution during operation.
WHAT AOI MEASURES
- Apparent fragment size and distribution across configurable ranges in millimeters.
- Trends in fines, medium and coarse material, and visible oversize.
- Estimated P80, subject to calibration and correlation with sampling.
DECISIONS IT CAN SUPPORT
- Detect fragmentation changes and feed deviations.
- Relate size to ore source, shift and crushing performance.
- Support feed adjustments and prioritize oversize review.
Surface-based visual measurement requires scale, lighting and sieve validation. Hidden material and fines that are not visible limit the estimate.
AOI / CRITICAL-ASSET CONDITION05CRITICAL-ASSET CONDITION
See the deviation. Anticipate deterioration.
Relate video and Thermal Analytics signals to asset history and evolution to prioritize inspection.
WHAT IT OBSERVES AND CORRELATES
- Video: signs of leakage, displaced parts or visible vibration.
- Thermal: hot spots, asymmetry and persistent temperature changes.
- Available context: load, environment, asset history and operating signals.
WHAT PREDICTIVE ADDS
- Recognize deviations and recurrence against typical behavior.
- Prioritize haul trucks, shovels, motors, gearboxes, bearings and idlers.
- Suggest inspection timing based on criticality and trend.
A video scene does not measure temperature or confirm a failure. Models and thermal measurements are validated for each asset.
AOI / SAFETY AND MAINTENANCE06SAFETY AND MAINTENANCE
People in the context of operations.
Presence, distance, identity and activity help focus attention on interactions and critical zones.
IN THE PIT AND ON ROUTES
- People near equipment and at defined risk points.
- Intrusion into or dwell time in exclusion zones.
- Events associated with equipment, location and time.
IN WORKSHOPS
- Visible PPE use and occupancy of work areas.
- Access and dwell time through Identity where appropriate.
- Activity traceability and thermal condition during inspections.
AOI / VIRTUAL GEOTECHNICAL MARKING07VIRTUAL GEOTECHNICAL MARKING
Risk zones visible from the cab.
Augmented reality and georeferencing display zones approved by Geotechnical teams and may reduce exposure while marking them in the field.
GEOTECHNICAL TEAMS DEFINE AND MANAGE
- Polygons, lines and virtual marks from approved GIS/CAD information.
- Validity and version control for operational risk zones.
- A supervision panel and marking history for traceability.
THE OPERATOR SEES
- Georeferenced zones overlaid on terrain through an in-cab AR device.
- GNSS RTK positioning and Edge processing to register the view.
- Virtual references to carry out the task from a front-end loader.
Proposed integration use case. Geotechnical teams approve zones; accuracy and usability are validated on site. It does not replace slope-stability monitoring systems.
AOI / STOCKPILES AND QUALITY08STOCKPILES AND QUALITY
Material needs context too.
Link observable ore condition to its location, movement and destination.
LOCATE AND INSPECT
- Track movements, dumping areas and segregation by material type.
- Inspect visible oversize, foreign material and approximate visual particle size.
ESTIMATE AND RECONCILE
- Estimate geometry and volume using 3D measurement.
- Compare estimates with operational movement and inventory.
Visual appearance does not replace an ore-grade assay. Classification and inventory require calibration and validation against mine data.
AOI / LOADING DILUTION CONTROL09LOADING DILUTION CONTROL
See the contact. Guide extraction.
LiDAR, AI vision and the mine plan relate bucket position to target material.
ANALYSIS SEQUENCE
- Reconstruct face geometry and its changes after each sequence.
- Locate the bucket path and estimated extraction point.
- Compare cycles against polygons, bench, phase and block model.
CONTROL CONTEXT
- Relate geometry, target material and loading cycles.
- Provide evidence for Geology and Operations to review contacts.
Ore/waste classification must be validated for the mine geology and visual conditions.
AOI / DILUTION SCORE · RECOMMENDATION10DILUTION SCORE · RECOMMENDATION
From observed deviation to a recommendation.
A conceptual indicator to prioritize cycles and contacts with greater dilution risk.
HOW IT IS BUILT
- Combines geometric deviation, estimated share outside target, proximity to contact and classification confidence.
- Weights and thresholds are calibrated with Geology and Operations.
- Brochure example: North Face / Shovel 03, last 20 cycles; illustrative score 70/100 and 3 cycles flagged for review.
WHAT AI PROCESS OPERATOR / MINCORE MAY RECOMMEND
- Review the east-edge sequence, validate the contact with Geology and adjust the next pass toward the approved polygon interior.
- Learn from lower-deviation passes: attack angle, depth, extraction order and shovel position.
The illustrative score is not a dilution percentage or grade measurement. Every recommendation requires operational validation.
03 / AOI ECOSYSTEMAOI ECOSYSTEM
Eight modules. One operational view.
The combination depends on the mine area, available data and the decision operations need to improve.
AI Safety
PPE, presence and safety events.
Process Intelligence
Sequences, timing and process states.
Thermal Analytics
Temperature, trends and thermal condition.
Predictive Intelligence
Anomalies and anticipation of deviations.
Proximity Awareness
Person-equipment interaction and defined zones.
Identity
Identity, access and event traceability.
Quality & Inventory
Inspection, material and observable inventory.
AI Process Operator / MINCORE
Recommendations and operational learning.
04REFERENCE ARCHITECTURE
SOURCESRGB / thermal / LiDAR / OT
AICE + AOIObservation / context
DECISIONAlerts / dashboard / guidance
Integration with dispatch, historians and plant systems depends on the interfaces available.
05SCOPE AND VALIDATION
Start with evidence that can be validated.
Camera coverage, lighting, scale, asset calibration, process data and available interfaces define each use case. Alerts and recommendations are validated with responsible teams before actions are automated.
06START WITH A HIGH-VALUE DECISION
One priority area. Measurable impact.
Define the use case, evidence and indicators to bring AOI into your mine operation.
01Prioritize
Safety, continuity or dilution.
02Validate
Sources, coverage and acceptance criteria.
03Scale
Compare results and extend across assets.