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Please provide the digital‑twin interface for twin robot navigation and battery health for warehouse efficiency, showing the details for the robotic unit with serial number rbt‑20231108‑001—a PalBot‑X200 model manufactured by RoboLogix, running firmware version 3.2.5 that includes the latest motion‑control algorithms a...
autonomous_pallet_robot_fleet
{"group_id": "autonomous_pallet_robot_fleet"}
composed query does not need to verify expected output.
Given that the digital‑twin interface for twin solar irradiance is configured to maintain a targetTintLevel of 0.42 with an adjustmentSpeed of 0.6, operates in auto operationalMode for closed‑loop regulation, and its most recent diagnostic reported a lastErrorCode of E00 indicating normal operation, can this interface ...
smart_window_tint_control_system
{"group_id": "smart_window_tint_control_system"}
composed query does not need to verify expected output.
Please confirm the battery module reports a nominal capacity of 4.8 kWh, a state of charge of 76.3 % (about three‑quarters usable), a health metric of 92.5 % (reflecting remaining original performance), and a temperature of 23.7 °C, and explain how continuous tracking of capacity, state of charge, health, and temperatu...
marine_rover_energy_management
{"group_id": "marine_rover_energy_management"}
composed query does not need to verify expected output.
I’m using the unnamed digital‑twin interface as a tool for monitoring and controlling system parameters to twin the battery temperature to prevent overheating; the device is set to a target temperature of 27.3 °C with a maximum limit of 45.0 °C, running in auto control mode so it can automatically adjust heating and co...
ev_thermal_management
{"group_id": "ev_thermal_management"}
composed query does not need to verify expected output.
Could you provide the twin sound‑level data for maintaining occupant comfort using the acoustic sensor snd‑01a that’s installed in the floor2_zoneB area, which operates at a fixed 5 Hz sampling rate, measures acoustic pressure from 30 dB to 120 dB, and is positioned to monitor the local acoustic environment for reliabl...
building_acoustic_comfort_ai
{"group_id": "building_acoustic_comfort_ai"}
composed query does not need to verify expected output.
Please confirm that, using the twin‑nozzle dynamics digital‑twin interface to achieve uniform coating, the dispensing unit is configured with a 0.85 mm nozzle diameter, a 32.0° spray angle, and a pressure setpoint of 2.7 bar while the operating temperature is monitored at 42.5 °C within the optimal viscosity range and ...
robotic_paint_spray_optimization
{"group_id": "robotic_paint_spray_optimization"}
composed query does not need to verify expected output.
Please provide the full specifications and confirmation for the digital‑twin interface of the twin aerial imaging system used to apply precise fertilizer, including the version running firmware 3.2.1 with the latest feature set and security updates, configured with an immutable maximum altitude limit of 120.5 m, a reco...
drone_farm_nutrient_dosing
{"group_id": "drone_farm_nutrient_dosing"}
composed query does not need to verify expected output.
Please provide the digital‑twin interface for the precision alignment sensor (ID align‑sensor‑01) that is intended for twin‑pattern placement to ensure critical dimension accuracy, describing its purpose and capabilities, including that it continuously captures data at a high sampling rate of 5000.0 Hz for fine‑grained...
lithography_alignment_monitor
{"group_id": "lithography_alignment_monitor"}
composed query does not need to verify expected output.
Please provide a digital‑twin interface that forecasts space usage to optimize HVAC and lighting systems using model version 2024.11.0 (which defines its schema and behavior), generating predictions every 15.0 seconds and filtering each prediction with a confidence threshold of 0.8, with the underlying ML model last re...
smart_building_ai_occupancy_prediction
{"group_id": "smart_building_ai_occupancy_prediction"}
composed query does not need to verify expected output.
I’m interested in the digital‑twin interface that tracks degradation of lithium‑ion batteries in electric vehicles; please provide the current data for cell‑0012 (nominal capacity 3.2 Ah, current capacity 2.85 Ah, internal resistance 0.015 Ω, cumulative cycle count 342), then for pack‑01 (nominal capacity 85.0 kWh, mea...
battery_state_of_health
{"group_id": "battery_state_of_health"}
composed query does not need to verify expected output.
Please provide the digital‑twin interface details that simulate thrust efficiency and wear for remotely operated underwater vehicles, including the actuator engineered to deliver a maximum thrust of 135.7 N, draw a nominal current of 18.3 A to achieve that thrust, and accept a pulse‑width‑modulation command value of 12...
underwater_rov_thruster_performance
{"group_id": "underwater_rov_thruster_performance"}
composed query does not need to verify expected output.
Please provide the digital‑twin interface that forecasts joint wear in industrial robot arms with the following information: first, for the component identified by jointId joint‑2, a rotational actuator in the mechanical subsystem rated for a maximum torque of 125.7 N·m and currently positioned at an angle of 37.4 degr...
predictive_maintenance_robotic_arm
{"group_id": "predictive_maintenance_robotic_arm"}
composed query does not need to verify expected output.
I am working with the digital‑twin interface that tracks the real‑time temperature distribution inside the curing oven and need to (1) confirm that the most recent set‑point change to the target temperature of 185.3 units—limited by a ramp‑rate of 4.7 units per time interval and operating in Auto mode so the embedded l...
vulcanization_oven_temp_monitor
{"group_id": "vulcanization_oven_temp_monitor"}
composed query does not need to verify expected output.
Considering the digital twin interface that optimizes the collective pitch strategy to balance energy capture and loads and is currently configured to keep a target pitch angle of 13.7 degrees while limiting the pitch rate to 2.1 degrees per second in automatic mode, please: 1) provide turbine-07’s unique identifier, c...
wind_farm_blade_pitch_control_optimization
{"group_id": "wind_farm_blade_pitch_control_optimization"}
composed query does not need to verify expected output.
Please explain how the digital‑twin interface that generates optimal municipal waste‑truck routes minimizing distance and emissions uses a genetic_algorithm over a 24‑hour planning horizon, noting that the most recent execution was recorded at 2025‑11‑06T14:32:10Z, with each candidate route constrained to a maximum dis...
smart_city_waste_collection_routing_optimization
{"group_id": "smart_city_waste_collection_routing_optimization"}
composed query does not need to verify expected output.
I need a digital‑twin interface that can plan efficient hazardous‑waste pickup routes from industrial sites using a genetic‑algorithm routing engine that explores optimal paths across the network while enforcing a hard limit of 250.5 km for any generated route, pulls real‑time congestion data from the cityTrafficAPI so...
industrial_waste_collection_routing
{"group_id": "industrial_waste_collection_routing"}
composed query does not need to verify expected output.
Please provide the digital‑twin interface that models temperature layering in molten‑salt tanks to improve efficiency, adhering to model version 1.2.0 and simulating a storage tank with a maximum depth of 12.7 meters and a fluid density of 1910.5 kilograms per cubic meter while advancing the simulation in discrete 30.0...
solar_thermal_storage_stratification_monitoring
{"group_id": "solar_thermal_storage_stratification_monitoring"}
composed query does not need to verify expected output.
Using the digital‑twin interface that evaluates the cooling effect of street‑level trees on city temperatures, please confirm that the tree model instance representing a Quercus_rubra specimen has an 8.7‑meter canopy diameter, a leaf‑area index of 4.3, and a transpiration rate set to 0.58; then provide the road‑segment...
urban_heat_island_tree_mitigation
{"group_id": "urban_heat_island_tree_mitigation"}
composed query does not need to verify expected output.
Please provide the digital‑twin interface for the Twin CO₂ levels that manages garage ventilation, including its fixed 30‑second measurement interval (currently set to meet performance needs) synchronized with its last calibration on 2024‑09‑15 (with records retained for traceability), the fan operating at 1150.5 in au...
smart_parking_gas_ventilation_control
{"group_id": "smart_parking_gas_ventilation_control"}
composed query does not need to verify expected output.
Could you provide the digital‑twin interface that models canopy shading impact on the local microclimate, incorporating the reported vegetation coverage of 68.5 % (just over two‑thirds of the area), average canopy height of 22.3 m, and moderate canopy density classification so that I can calibrate wind‑load and water‑r...
forest_canopy_cooling_effect
{"group_id": "forest_canopy_cooling_effect"}
composed query does not need to verify expected output.
Please evaluate the leakage and environmental risks of offshore storage using the digital‑twin interface (detection threshold 0.08) whose calibration was performed on 2024‑03‑12 (logged for traceability and compliance) and whose routine upkeep was completed on 2025‑08‑20 with all functional checks confirming normal ope...
marine_storage_risk_assessment
{"group_id": "marine_storage_risk_assessment"}
composed query does not need to verify expected output.
Can you provide a comprehensive report on the twin power usage effectiveness for server farms that includes: first, details on the meter identified as pm-01a installed at the dc-main-entrance location, which gives direct access to the primary data‑center entry point, confirming that it is currently online, responsive, ...
data_center_power_efficiency_monitor
{"group_id": "data_center_power_efficiency_monitor"}
composed query does not need to verify expected output.
Please configure the system to monitor tension during 3D printing of fabrics using both the hardware sensor tension‑sensor‑01 (calibrated for 0–150 N, output in newtons, accuracy ±0.5 N, intended for high‑fidelity control loops, safety checks, static load verification, and dynamic stress testing to provide repeatable q...
textile_3d_print_tension_control
{"group_id": "textile_3d_print_tension_control"}
composed query does not need to verify expected output.
For the Twin rover power usage for mission planning digital‑twin, provide a concise summary that includes the power storage unit's current state of charge reported as 0.82 (about eighty‑two percent of nominal capacity), its health status set to Nominal confirming operation within expected performance thresholds, both a...
aerospace_rover_energy_consumption
{"group_id": "aerospace_rover_energy_consumption"}
composed query does not need to verify expected output.
Provide a full specification and status overview for the digital‑twin interface that detects abnormal vibrations in manufacturing equipment, including that it must acquire data at 2000 samples per second to capture high‑frequency dynamics, have a sensitivity of 0.005 to resolve very small signal variations, be calibrat...
industrial_vibration_monitoring_system
{"group_id": "industrial_vibration_monitoring_system"}
composed query does not need to verify expected output.
Could you provide the current snapshot of the twin peer-to-peer energy transaction blockchain—showing its ledger height of 128945, the most recent block hash a3f9c7e2b5d8f4a1c6e9b0d3f7a2c4e5b1d8f6a9c3e7b2d4f1a6c9e3b5d7f8a2, the ProofOfStake consensus mechanism, the total of 3,748,215 recorded transactions, and confirm ...
blockchain_energy_trading_platform
{"group_id": "blockchain_energy_trading_platform"}
composed query does not need to verify expected output.
I need to confirm that the twin airflow interface, which ensures safe underground conditions, is operating in automatic mode so its control logic can manage functions without human intervention, that the target airflow is precisely set to 350.5 and the actuator mechanisms are directed to maintain that ventilation rate,...
mining_ventilation_control_system
{"group_id": "mining_ventilation_control_system"}
composed query does not need to verify expected output.
I need a digital-twin interface for twin pathogen spread to guide hygiene protocols that uses version 1.0.0 of the epidemiological model as a stable baseline, encodes an infection rate of 0.13, sets the incubation period to 2.7 days, and is calibrated on 2025-10-20T07:45:00Z for reproducibility; it should operate under...
hospital_infection_control_monitor
{"group_id": "hospital_infection_control_monitor"}
composed query does not need to verify expected output.
Configure the digital‑twin interface to maintain voltage levels during load fluctuations with a target voltage of 13.8 volts, automatic regulation mode so the controller continuously adjusts output without manual intervention, a maximum adjustment rate limited to 0.5 volts per second to ensure smooth transitions and pr...
electric_grid_voltage_stability_control
{"group_id": "electric_grid_voltage_stability_control"}
composed query does not need to verify expected output.
Can you provide the details for the twin temperature layers in the solar thermal storage tanks, specifically the sensor deployed at layer index 3 within the multi‑layer monitoring architecture identified by sensor ID “temp‑layer‑3” that reports temperatures in Celsius and was calibrated on 2024‑08‑15T09:30:00Z with tha...
solar_thermal_storage_stratification_monitor
{"group_id": "solar_thermal_storage_stratification_monitor"}
composed query does not need to verify expected output.
Could you provide the details of the digital‑twin interface for the submarine propulsor thrust and efficiency, including its description and the key mechanical parameters (rotational speed 1472.3 rpm, blade pitch angle 13.7°, measured torque at the drivetrain 3421.8 Nm) that define the system’s mechanical state under l...
submarine_thruster_performance_monitor
{"group_id": "submarine_thruster_performance_monitor"}
composed query does not need to verify expected output.
Considering the digital‑twin interface that models temperature reduction from an urban tree canopy, which models a forested area for twin temperature reduction and includes a measured leaf area index of 3.7, vegetation covering 58.2 % of the total ground plane, an average tree height of 14.6 m, and a canopy density of ...
smart_forest_canopy_cooling_effect
{"group_id": "smart_forest_canopy_cooling_effect"}
composed query does not need to verify expected output.
Please confirm that the digital twin of the nutrient delivery system in our indoor farms shows the pump subsystem currently running with a flow rate setpoint of 15.2 and a pressure control loop maintaining a setpoint of 140.5 as enforced by the controller, and that the most recent maintenance timestamp is 2024-03-10T09...
smart_vertical_farm_nutrient_distribution
{"group_id": "smart_vertical_farm_nutrient_distribution"}
composed query does not need to verify expected output.
Please provide, via the digital‑twin interface, the detailed information for the warehouse robotic arm’s pick accuracy and force feedback: first, the sensor pick‑acc‑01 deployed in the picking subsystem, last calibrated on 2024‑09‑15, with an accuracyThreshold set to 0.25, that has passed ongoing health checks and is r...
warehouse_robotic_arm_quality
{"group_id": "warehouse_robotic_arm_quality"}
composed query does not need to verify expected output.
For the digital‑twin interface that monitors twin‑scale formation dynamics within wastewater‑treatment reactors, I need a comprehensive set of details: first, the rsensor‑01 unit is installed in the reactor‑3a zone where it continuously captures temperature and pressure directly at the point of interest to minimize con...
wastewater_scaling_monitor
{"group_id": "wastewater_scaling_monitor"}
composed query does not need to verify expected output.
Please provide a digital‑twin interface for the nutrient‑001 sensor installed in the river‑oxford‑section‑a monitoring zone that models nitrogen and phosphorus levels in river water, capturing data every 300 seconds with firmware version 3.2.1 (including the latest calibration algorithms and security patches), sampling...
water_quality_nutrient
{"group_id": "water_quality_nutrient"}
composed query does not need to verify expected output.
I’m looking for comprehensive documentation of the digital‑twin interface that performs twin signal processing to identify artifacts in wearable SpO₂ measurements, including how it operates at a 100 samples‑per‑second rate, applies a band‑pass filter in 256‑sample windows to balance latency and frequency resolution, ad...
spo2_artifact_detection
{"group_id": "spo2_artifact_detection"}
composed query does not need to verify expected output.
Can you return the complete digital‑twin definition and current runtime values for the hot‑water usage instance identified by sensorId temp‑sensor‑01, showing that it operates in auto heating mode with a target temperature of 55.5 °C, an isOn flag set to true, and a logged 342 heating cycles since the last reset, expla...
residential_water_heater
{"group_id": "residential_water_heater"}
composed query does not need to verify expected output.
I would like to use the digital‑twin interface for monitoring CO₂ levels and airflow in the underground parking to: first, confirm that the CO₂ sensor co2‑ugp‑01 installed in LevelB‑Section3 was calibrated on 2024‑03‑15T09:30:00Z, that this calibration timestamp is used for precise drift compensation, scheduled mainten...
parking_garage_ventilation
{"group_id": "parking_garage_ventilation"}
composed query does not need to verify expected output.
Provide the digital‑twin of pollutant dispersion across city neighborhoods that runs under model version v2.1.0 and generates forecasts extending 30 minutes beyond the current time for rapid decision loops, confirming it was last retrained on 2025-10-20 at 04:15 UTC to incorporate the newest operational data and report...
smart_city_air_quality
{"group_id": "smart_city_air_quality"}
composed query does not need to verify expected output.
I need the digital‑twin interface for the leak probability and contaminant migration in subsurface tanks covering several elements: first, for tank‑07a, a carbon‑steel storage unit with nominal liquid capacity 75,200.5 L, rated for internal pressure up to 3.2 bar, whose most recent inspection on 2024‑08‑20 confirmed it...
underground_storage_risk
{"group_id": "underground_storage_risk"}
composed query does not need to verify expected output.
Can the digital‑twin interface that simulates forces on mooring systems of floating converters (display name not specified) handle a configuration with four load‑bearing lines each exactly 525.7 units long made of high‑modulus polyethylene (HMPE) and rated for a maximum tension of 2 450 000.0 N per line, thereby meetin...
offshore_wave_mooring_dynamics
{"group_id": "offshore_wave_mooring_dynamics"}
composed query does not need to verify expected output.
Using the digital‑twin interface that adjusts panel orientation and cleaning schedule to maximize daily output, I see the device currently oriented at a current azimuth of 152.7° while aiming for a target azimuth of 158.3°, its tilt angle is set to a current 23.5° which aligns with the required operational geometry, an...
solar_farm_energy_yield_optimizer
{"group_id": "solar_farm_energy_yield_optimizer"}
composed query does not need to verify expected output.
Please provide a digital‑twin interface that simulates cyclic stresses on the mooring lines of floating wave converters, configured to run a deterministic simulation with a fixed integration interval and a step size of 0.05 s per calculation cycle, limited to a total simulation time of 3600.0 s, using a solver toleranc...
offshore_wave_mooring_fatigue_simulation
{"group_id": "offshore_wave_mooring_fatigue_simulation"}
composed query does not need to verify expected output.
Please provide a comprehensive overview of the digital‑twin interface that optimizes ventilation rates based on indoor CO₂ sensors and occupancy, including: the measurement interval of 60.0 seconds, the last calibration performed on 2024-03-10T09:15:00Z which serves as the reference point for accuracy checks, the repor...
smart_building_co2_ventilation
{"group_id": "smart_building_co2_ventilation"}
composed query does not need to verify expected output.
Provide the digital‑twin interface that predicts turbine output using wind speed, turbulence and turbine specifications, operating under model version 1.3.0, executing its control loop every 30 seconds, currently in a running status that confirms all required services are healthy, and using this version identifier to e...
wind_turbine_power_curve_estimator
{"group_id": "wind_turbine_power_curve_estimator"}
composed query does not need to verify expected output.
Could you confirm the current availability and provide the latest state for parking asset spot-42 (Level B – Section 3 – Spot 42) whose occupancy flag is true and whose most recent detection timestamp is 2025-11-07T14:23:45Z, and, in the same response, explain how the digital-twin interface allocates city parking spots...
smart_city_parking_management
{"group_id": "smart_city_parking_management"}
composed query does not need to verify expected output.
I need a consolidated view of the digital‑twin interface that issues alerts when water levels approach predefined flood thresholds, including: the sensor instance identified by sensorId rs‑07a located at geographic coordinates 45.1234, ‑122.5678, reporting all quantitative outputs in metric units and most recently cali...
river_flood_early_warning
{"group_id": "river_flood_early_warning"}
composed query does not need to verify expected output.
I need a full overview of the digital‑twin interface that manages battery charging to smooth solar output variability, with its optimization mode set to grid_balancing, a target smoothing factor of 0.78, the most recent execution recorded at 2025‑11‑07T12:45:30Z, isActive true, and also the operational envelope of its ...
solar_farm_energy_storage_optimizer
{"group_id": "solar_farm_energy_storage_optimizer"}
composed query does not need to verify expected output.
Can you provide a comprehensive description of the digital‑twin interface that allocates parking spaces in real‑time using demand forecasts and sensor data, including its configuration for a total of 250 spaces with 87 currently occupied, its use of the demandForecastWeighted algorithm that balances predicted demand wi...
smart_parking_management
{"group_id": "smart_parking_management"}
composed query does not need to verify expected output.
I’m looking for a digital‑twin interface that supports real‑time monitoring of electric‑vehicle battery degradation and capacity loss for the battery unit identified by serial number bp‑20231101‑07, manufactured by VoltEdge (a specialist in high‑power energy storage), which has a nominal capacity of 212.5 ampere‑hours ...
ev_battery_health_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:ev_battery_health_twin:BatteryPack;1", "@type": "Interface", "displayName": "Battery Pack", "description": "Represents the high‑voltage battery pack that supplies power to the vehicle. It aggregates the electrical characteristics of all cell modules and provides overall ...
{'interface': 'dtmi:ev_battery_health_twin:BatteryPack;1', 'dockerImage': 'registry.local/dtm/ev_battery_health_twin/BatteryPack:v1.0.0', 'serialNumber': 'bp-20231101-07', 'manufacturer': 'VoltEdge', 'nominalCapacityAh': 212.5, 'nominalVoltage': 400.0, 'stateOfCharge': 0, 'packVoltage': 0, 'packCurrent': 0}
Can you provide real‑time monitoring of the electric‑vehicle battery degradation and capacity loss for the high‑performance module mod‑0047, which is built from 96 nickel‑manganese‑cobalt (NMC) cells each rated to a maximum voltage of 4.2 V, with all parameters statically configured in the module’s property set to meet...
ev_battery_health_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:ev_battery_health_twin:CellModule;1", "@type": "Interface", "displayName": "Cell Module", "description": "Models an individual module composed of multiple battery cells within the pack. It records manufacturing identifiers, cell count, chemistry, and voltage limits. Tele...
{'interface': 'dtmi:ev_battery_health_twin:CellModule;1', 'dockerImage': 'registry.local/dtm/ev_battery_health_twin/CellModule:v1.0.0', 'moduleId': 'mod-0047', 'cellCount': 96, 'chemistry': 'NMC', 'maxCellVoltage': 4.2, 'averageCellVoltage': 0, 'maxCellTemperature': 0, 'degradationRate': 0}
I’m looking for a digital‑twin interface—display name not provided—described as delivering detailed telemetry for real‑time monitoring of electric‑vehicle battery degradation and capacity loss, and that includes the temperature sensor temp‑sensor‑01 installed in battery‑pack‑1‑module‑3 to provide localized thermal moni...
ev_battery_health_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:ev_battery_health_twin:TemperatureSensor;1", "@type": "Interface", "displayName": "Temperature Sensor", "description": "Captures ambient and thermal conditions surrounding the battery pack. Each sensor is identified, placed, and periodically calibrated to ensure accurate...
{'interface': 'dtmi:ev_battery_health_twin:TemperatureSensor;1', 'dockerImage': 'registry.local/dtm/ev_battery_health_twin/TemperatureSensor:v1.0.0', 'sensorId': 'temp-sensor-01', 'location': 'battery-pack-1-module-3', 'calibrationDate': '2024-09-15', 'temperature': 0, 'humidity': 0}
I’m looking to use the digital‑twin interface for real‑time monitoring of electric‑vehicle battery degradation and capacity loss, and I need to know that the instance runs model version 1.3.0 with the latest schema revisions, employs a randomForest algorithm as its core inference engine to balance bias and variance, an...
ev_battery_health_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:ev_battery_health_twin:HealthModel;1", "@type": "Interface", "displayName": "Health Model", "description": "Implements the analytics that estimate battery state of health based on telemetry trends. The model version, algorithm type, and last training date are tracked for...
{'interface': 'dtmi:ev_battery_health_twin:HealthModel;1', 'dockerImage': 'registry.local/dtm/ev_battery_health_twin/HealthModel:v1.0.0', 'modelVersion': '1.3.0', 'algorithm': 'randomForest', 'lastTrainingDate': '2024-09-30', 'stateOfHealth': 0, 'capacityLoss': 0, 'internalResistance': 0}
I’m looking for a digital‑twin interface that simulates the performance and scheduling of self‑driving public buses across routes, specifically the twin representing transit vehicle bus‑001, which is a NovaX‑200 model with a passenger capacity of 42 seats, currently marked as operational so it can be included in routin...
autonomous_bus_fleet_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:autonomous_bus_fleet_twin:bus;1", "@type": "Interface", "displayName": "Bus", "description": "Represents an individual autonomous bus within the fleet. It records vehicle identification, model details, and operational status for real‑time fleet management. The twin enabl...
{'interface': 'dtmi:autonomous_bus_fleet_twin:bus;1', 'dockerImage': 'registry.local/dtm/autonomous_bus_fleet_twin/bus:v1.0.0', 'busId': 'bus-001', 'model': 'NovaX-200', 'capacity': 42, 'isOperational': True, 'gpsLocation': 0, 'speed': 0, 'engineTemperature': 0}
I’d like to query the digital‑twin interface that simulates performance and scheduling of self‑driving public buses across routes; the current scheduling instance, identified by scheduleId sched‑20231106‑01 for the November 6 2023 batch, manages twelve activeRoutes, each a distinct path coordinated in real time, using ...
autonomous_bus_fleet_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:autonomous_bus_fleet_twin:routescheduler;1", "@type": "Interface", "displayName": "RouteScheduler", "description": "Manages assignment of buses to routes and schedules departures based on demand and traffic conditions. It evaluates route performance, adjusts headways, an...
{'interface': 'dtmi:autonomous_bus_fleet_twin:routescheduler;1', 'dockerImage': 'registry.local/dtm/autonomous_bus_fleet_twin/routescheduler:v1.0.0', 'scheduleId': 'sched-20231106-01', 'activeRoutes': 12, 'optimizationAlgorithm': 'genetic_algorithm', 'lastRunTimestamp': '2025-11-06T14:23:45Z', 'assignedBusCount': 0, 'a...
I'm looking at the digital‑twin that simulates performance and scheduling of self‑driving public buses across routes and need details on its battery storage unit: it has a rated maximum capacity of 300.0 kWh, a current state of charge of 185.7 kWh (about 62 % of nominal capacity), has undergone 2,473 charge cycles, and...
autonomous_bus_fleet_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:autonomous_bus_fleet_twin:batterymanagement;1", "@type": "Interface", "displayName": "BatteryManagement", "description": "Tracks the state of charge and health of each bus's battery pack. It provides predictions for remaining range and schedules charging sessions to mini...
{'interface': 'dtmi:autonomous_bus_fleet_twin:batterymanagement;1', 'dockerImage': 'registry.local/dtm/autonomous_bus_fleet_twin/batterymanagement:v1.0.0', 'maxCapacityKWh': 300.0, 'currentChargeKWh': 185.7, 'chargeCycleCount': 2473, 'temperatureC': 34.8, 'stateOfCharge': 0, 'estimatedRangeKm': 0, 'chargingPowerKw': 0}
I'm looking at the digital‑twin interface that simulates performance and scheduling of self‑driving public buses across routes, and it shows that bus‑07 has a design‑specified maximum capacity of 60 seats, is currently carrying 38 occupants (about 63 % utilization), and predictive analytics forecast a load of 45 passen...
autonomous_bus_fleet_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:autonomous_bus_fleet_twin:passengerload;1", "@type": "Interface", "displayName": "PassengerLoad", "description": "Measures the number of passengers on board each bus and predicts boarding/alighting at upcoming stops. It integrates with ticketing and occupancy sensors to ...
{'interface': 'dtmi:autonomous_bus_fleet_twin:passengerload;1', 'dockerImage': 'registry.local/dtm/autonomous_bus_fleet_twin/passengerload:v1.0.0', 'busId': 'bus-07', 'maxCapacity': 60, 'currentLoad': 38, 'loadForecast': 45, 'onBoardCount': 0, 'boardingRatePerMin': 0, 'alightingRatePerMin': 0}
Using the digital‑twin interface that simulates performance and scheduling of self‑driving public buses across routes, can you schedule a service for bus‑017, a transit bus operating on a city route, which was last serviced on 2024‑09‑20, has accumulated 15,800 miles since that service, and is now reporting fault code ...
autonomous_bus_fleet_twin
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:autonomous_bus_fleet_twin:maintenancetracker;1", "@type": "Interface", "displayName": "MaintenanceTracker", "description": "Records maintenance events, diagnostic codes, and service intervals for each bus. It predicts upcoming maintenance needs based on usage patterns an...
{'interface': 'dtmi:autonomous_bus_fleet_twin:maintenancetracker;1', 'dockerImage': 'registry.local/dtm/autonomous_bus_fleet_twin/maintenancetracker:v1.0.0', 'busId': 'bus-017', 'lastServiceDate': '2024-09-20', 'mileageSinceLastService': 15800, 'diagnosticCode': 'P0301', 'upcomingServiceDueInKm': 0, 'componentHealthSco...
The interface, which predicts tire tread loss and pressure changes under varying loads, reports a tread depth of 6.5 mm indicating moderate wear, an operating temperature of 68.3 °F within the expected range for normal use, an inflation rating of psi120 defining the maximum safe pressure, a sidewall condition that is g...
heavy_truck_tire_wear
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:heavy_truck_tire_wear:tire;1", "@type": "Interface", "displayName": "Tire", "description": "Represents a heavy‑truck tire with its physical state and wear characteristics. It captures static attributes such as tread depth and temperature as well as dynamic metrics used f...
{'interface': 'dtmi:heavy_truck_tire_wear:tire;1', 'dockerImage': 'registry.local/dtm/heavy_truck_tire_wear/tire:v1.0.0', 'treadDepth': 6.5, 'temperature': 68.3, 'inflationRating': 'psi120', 'sidewallCondition': 'good', 'manufactureDate': '2022-05-15T00:00:00Z', 'treadWearRate': 0, 'temperatureTrend': 0}
This interface, which predicts tire tread loss and pressure changes under varying loads, uses the pressure sensor identified as ps‑01a3 installed on the front_left_tire; it operates within a measurement range of 0‑120 psi with an accuracy of ±0.5 psi, was most recently calibrated on 2024‑08‑20 at 14:45 UTC with calibra...
heavy_truck_tire_wear
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:heavy_truck_tire_wear:pressure_sensor;1", "@type": "Interface", "displayName": "PressureSensor", "description": "Encapsulates the pressure sensor mounted on each tire, providing calibrated pressure readings and health diagnostics. It records sensor metadata, calibration ...
{'interface': 'dtmi:heavy_truck_tire_wear:pressure_sensor;1', 'dockerImage': 'registry.local/dtm/heavy_truck_tire_wear/pressure_sensor:v1.0.0', 'sensorId': 'ps-01a3', 'calibrationDate': '2024-08-20T14:45:00Z', 'accuracy': 0.5, 'measurementRange': '0-120 psi', 'location': 'front_left_tire', 'pressure': 0, 'pressureVaria...
The digital‑twin interface, which predicts tire tread loss and pressure changes under varying loads, reports an axle load of 5200.5 kg per wheel assembly, a total cargo weight of 21500.0 kg distributed across the vehicle’s axles, a balanced load distribution, a maximum load capacity of 8000.0 kg per axle, and applies a...
heavy_truck_tire_wear
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:heavy_truck_tire_wear:load_estimator;1", "@type": "Interface", "displayName": "LoadEstimator", "description": "Calculates the instantaneous load on each tire based on axle sensor data, cargo weight, and distribution patterns. It provides both static load limits and dynam...
{'interface': 'dtmi:heavy_truck_tire_wear:load_estimator;1', 'dockerImage': 'registry.local/dtm/heavy_truck_tire_wear/load_estimator:v1.0.0', 'axleLoad': 5200.5, 'cargoWeight': 21500.0, 'loadDistribution': 'balanced', 'maxLoadCapacity': 8000.0, 'safetyFactor': 1.15, 'loadFactor': 0, 'overloadAlert': 0}
I’m looking for the digital‑twin interface that predicts tire tread loss and pressure changes under varying loads; it runs a predictive model version 2024.09 that was most recently trained on 2024‑10‑15T08:30:00Z, generates forecasts over a 7.5‑day horizon, attaches a confidence score of 0.92 to each forecast, and cont...
heavy_truck_tire_wear
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:heavy_truck_tire_wear:wear_prediction;1", "@type": "Interface", "displayName": "WearPrediction", "description": "Runs the machine‑learning model that forecasts tire tread loss and pressure drop over a configurable horizon. It consumes inputs from tire state, pressure sen...
{'interface': 'dtmi:heavy_truck_tire_wear:wear_prediction;1', 'dockerImage': 'registry.local/dtm/heavy_truck_tire_wear/wear_prediction:v1.0.0', 'modelVersion': '2024.09', 'lastTrainingDate': '2024-10-15T08:30:00Z', 'predictionHorizon': 7.5, 'confidenceScore': 0.92, 'inputFeatures': 'treadDepth,pressure,load,temperature...
I’m looking for a digital‑twin interface that optimizes aerial delivery paths by accounting for wind, obstacles and battery limits, and that also supplies a precise geospatial reference for the asset it represents—specifically a latitude of 37.7749 degrees (northern hemisphere), a longitude of ‑122.4194 degrees (west o...
drone_delivery_route_opt
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:drone_delivery_route_opt:navigationEngine;1", "@type": "Interface", "displayName": "NavigationEngine", "description": "Manages real-time positional data and waypoint tracking for the delivery drone. It integrates GPS, inertial measurement, and altitude sensors to provide...
{'interface': 'dtmi:drone_delivery_route_opt:navigationEngine;1', 'dockerImage': 'registry.local/dtm/drone_delivery_route_opt/navigationEngine:v1.0.0', 'currentLatitude': 37.7749, 'currentLongitude': -122.4194, 'altitude': 120.5, 'poseUpdate': 0, 'gpsSignalQuality': 0}
Could you give me the current status of the aerial‑delivery‑optimizer interface, which optimizes aerial delivery paths considering wind, obstacles and battery limits, showing that the device reports a charge level of 82.3 percent, an operating voltage of 22.8 volts within the expected range, an internal temperature of ...
drone_delivery_route_opt
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:drone_delivery_route_opt:batteryManager;1", "@type": "Interface", "displayName": "BatteryManager", "description": "Monitors the drone's power subsystem, tracking charge state, voltage, and thermal conditions. It provides health metrics and triggers alerts when operating ...
{'interface': 'dtmi:drone_delivery_route_opt:batteryManager;1', 'dockerImage': 'registry.local/dtm/drone_delivery_route_opt/batteryManager:v1.0.0', 'chargeLevel': 82.3, 'voltage': 22.8, 'temperature': 34.7, 'healthStatus': 'Nominal', 'powerDraw': 0, 'temperatureAlert': 0}
I’m looking for the aerial delivery path optimization interface that considers wind, obstacles, and battery limits, and I need the current wind snapshot: a speed of 7.4 m/s, a prevailing direction of 212.5° toward the southwest quadrant, and a turbulence intensity of 0.12, which together indicate moderate airflow, low ...
drone_delivery_route_opt
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:drone_delivery_route_opt:windEstimator;1", "@type": "Interface", "displayName": "WindEstimator", "description": "Estimates ambient wind vectors using onboard sensors and atmospheric models. The data informs route adjustments and energy budgeting for safe aerial navigatio...
{'interface': 'dtmi:drone_delivery_route_opt:windEstimator;1', 'dockerImage': 'registry.local/dtm/drone_delivery_route_opt/windEstimator:v1.0.0', 'windSpeed': 7.4, 'windDirection': 212.5, 'turbulenceIntensity': 0.12, 'windVector': 0, 'gustDetected': 0}
I’m looking to confirm that the aerial‑delivery‑optimization interface, which plans routes based on wind, obstacles and battery limits, can maintain a 35.0‑meter detection range with all sensors reported as operational, uses a reactive avoidance mode that initiates immediate corrective actions when obstacles are detect...
drone_delivery_route_opt
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:drone_delivery_route_opt:obstacleAvoidance;1", "@type": "Interface", "displayName": "ObstacleAvoidance", "description": "Detects and classifies obstacles using lidar and vision sensors, then computes avoidance maneuvers. It continuously updates the flight controller to m...
{'interface': 'dtmi:drone_delivery_route_opt:obstacleAvoidance;1', 'dockerImage': 'registry.local/dtm/drone_delivery_route_opt/obstacleAvoidance:v1.0.0', 'detectionRange': 35.0, 'sensorStatus': 'operational', 'avoidanceMode': 'reactive', 'obstacleDetected': 0, 'avoidanceManeuver': 0}
The interface, which optimizes aerial delivery paths considering wind, obstacles, and battery limits, is configured with a maximum operational range of 85.7 (the furthest distance it can travel under standard conditions), maintains a battery reserve margin of 0.18 (an 18 % buffer above the minimum required energy to ac...
drone_delivery_route_opt
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:drone_delivery_route_opt:routePlanner;1", "@type": "Interface", "displayName": "RoutePlanner", "description": "Computes optimal delivery routes accounting for battery capacity, wind forecasts, and regulatory no‑fly zones. It can re‑plan dynamically when conditions change...
{'interface': 'dtmi:drone_delivery_route_opt:routePlanner;1', 'dockerImage': 'registry.local/dtm/drone_delivery_route_opt/routePlanner:v1.0.0', 'maxRange': 85.7, 'batteryReserveMargin': 0.18, 'preferredAltitude': 120.0, 'plannedRoute': 0, 'routeRecalculation': 0}
The interface, which tracks corrosion progression on ship hulls using sensor data and water chemistry, includes a component identified by sectionId sec-03a fabricated from high‑strength steel to provide the required load‑bearing capacity; it is protected by an epoxy zinc‑rich coating 18.2 mm thick, meeting the design s...
maritime_hull_corrosion
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:maritime_hull_corrosion:hull_section;1", "@type": "Interface", "displayName": "HullSection", "description": "Represents a distinct segment of a ship's hull whose structural integrity is monitored for corrosion. The twin captures material specifications, protective coatin...
{'interface': 'dtmi:maritime_hull_corrosion:hull_section;1', 'dockerImage': 'registry.local/dtm/maritime_hull_corrosion/hull_section:v1.0.0', 'sectionId': 'sec-03a', 'material': 'high-strength steel', 'coatingType': 'epoxy zinc-rich', 'thickness': 18.2, 'installationDate': '2014-09-15', 'corrosionRate': 0, 'temperature...
I want to track corrosion progression on ship hulls using sensor data and water chemistry, using the hull corrosion sensor identified as hull‑cor‑001 which is built to the CS‑EC‑200 model with a high‑precision measurement circuit, was last calibrated on 2024‑03‑15, has a measurement accuracy of 0.005, reports an operat...
maritime_hull_corrosion
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:maritime_hull_corrosion:corrosion_sensor;1", "@type": "Interface", "displayName": "CorrosionSensor", "description": "Models an electrochemical sensor mounted on the hull that measures corrosion potential and environmental conditions. The twin records calibration metadata...
{'interface': 'dtmi:maritime_hull_corrosion:corrosion_sensor;1', 'dockerImage': 'registry.local/dtm/maritime_hull_corrosion/corrosion_sensor:v1.0.0', 'sensorId': 'hull-cor-001', 'sensorModel': 'CS-EC-200', 'calibrationDate': '2024-03-15', 'accuracy': 0.005, 'powerStatus': 'operational', 'corrosionPotential': 0, 'temper...
The digital twin interface, which tracks corrosion progression on ship hulls using sensor data and water chemistry, monitors the unit deployed at hull‑port‑01 where it continuously records water characteristics; on the most recent collection performed on 2025‑10‑28 it extracted a sample from a depth of 2.3 meters below...
maritime_hull_corrosion
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:maritime_hull_corrosion:water_chemistry;1", "@type": "Interface", "displayName": "WaterChemistry", "description": "Captures periodic water quality samples taken near the hull to correlate chemical aggressiveness with observed corrosion. The twin stores sampling location,...
{'interface': 'dtmi:maritime_hull_corrosion:water_chemistry;1', 'dockerImage': 'registry.local/dtm/maritime_hull_corrosion/water_chemistry:v1.0.0', 'locationId': 'hull-port-01', 'samplingDepth': 2.3, 'lastSampleDate': '2025-10-28', 'salinity': 34.8, 'pH': 8.07, 'dissolvedOxygen': 0, 'sulfateConcentration': 0, 'temperat...
Using the digital‑twin interface that tracks corrosion progression on ship hulls using sensor data and water chemistry, I need to view the maintenance schedule identified by scheduleId hull‑sec‑2025‑03, which outlines the upcoming inspection timeline with the nextInspectionDate set for 2025‑04‑10, a high priorityLevel,...
maritime_hull_corrosion
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:maritime_hull_corrosion:maintenance_schedule;1", "@type": "Interface", "displayName": "MaintenanceSchedule", "description": "Represents the planned maintenance activities for hull sections based on corrosion diagnostics. The twin records schedule identifiers, upcoming in...
{'interface': 'dtmi:maritime_hull_corrosion:maintenance_schedule;1', 'dockerImage': 'registry.local/dtm/maritime_hull_corrosion/maintenance_schedule:v1.0.0', 'scheduleId': 'hull-sec-2025-03', 'nextInspectionDate': '2025-04-10', 'recommendedAction': 'apply epoxy coating to affected area', 'priorityLevel': 'high', 'respo...
I’d like to query the digital‑twin interface that models ventilation and contaminant levels inside passenger cabins, which continuously reports a fan speed of 1200.5 RPM, a damper position of 0.75 (meaning the damper is 75 % open), an airflow rate of 850.3 CFM, and a pressure differential across the filter of 12.7 Pa; ...
aircraft_cabin_air_quality
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:aircraft_cabin_air_quality:ventilation_system;1", "@type": "Interface", "displayName": "Ventilation System", "description": "Represents the cabin ventilation subsystem that regulates airflow rates, pressure differentials, and mixing of fresh and recirculated air. It moni...
{'interface': 'dtmi:aircraft_cabin_air_quality:ventilation_system;1', 'dockerImage': 'registry.local/dtm/aircraft_cabin_air_quality/ventilation_system:v1.0.0', 'fanSpeed': 1200.5, 'damperPosition': 0.75, 'airflowRate': 850.3, 'pressureDifferential': 12.7, 'airflow': 0, 'cabinPressure': 0}
I need information about the digital‑twin interface that models ventilation and contaminant levels inside passenger cabins, specifically regarding the air‑quality sensor cabin‑air‑01 installed in the front‑left cabin area to monitor passenger breathing zones; the sensor is identified by sensorId cabin‑air‑01, its calib...
aircraft_cabin_air_quality
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:aircraft_cabin_air_quality:cabin_air_sensor;1", "@type": "Interface", "displayName": "Cabin Air Sensor", "description": "Measures ambient air quality parameters inside the passenger cabin, including CO2 concentration, volatile organic compounds, and temperature. Provides...
{'interface': 'dtmi:aircraft_cabin_air_quality:cabin_air_sensor;1', 'dockerImage': 'registry.local/dtm/aircraft_cabin_air_quality/cabin_air_sensor:v1.0.0', 'sensorId': 'cabin-air-01', 'calibrationDate': '2024-08-15T10:30:00Z', 'location': 'front-left-cabin', 'co2Concentration': 0, 'tvoc': 0, 'temperature': 0, 'humidity...
I’m looking at the digital‑twin interface that models ventilation and contaminant levels inside passenger cabins, and I need details about the filter installed on 2022‑09‑01T12:00:00Z: it’s a HEPA type designed for high‑efficiency particle capture, shows a pressure drop of 85.7 Pa under nominal flow conditions, has a r...
aircraft_cabin_air_quality
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:aircraft_cabin_air_quality:filter_status;1", "@type": "Interface", "displayName": "Filter Status", "description": "Tracks the health and efficiency of HEPA and carbon filters used in the cabin air recirculation loop. Records pressure drop across filters and estimated rem...
{'interface': 'dtmi:aircraft_cabin_air_quality:filter_status;1', 'dockerImage': 'registry.local/dtm/aircraft_cabin_air_quality/filter_status:v1.0.0', 'filterType': 'HEPA', 'installedDate': '2022-09-01T12:00:00Z', 'pressureDrop': 85.7, 'serviceLifeHours': 6000, 'remainingLifePercent': 72.4, 'pressureDropTelemetry': 0, '...
The interface, whose display name and description aren’t provided, models ventilation and contaminant levels inside passenger cabins; it recorded event evt-20251106-03 with high severity at 2025‑11‑06T14:23:45Z, triggered the predefined response workflow, and I need it to activate HEPA filtration, increase fresh air in...
aircraft_cabin_air_quality
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:aircraft_cabin_air_quality:contamination_event;1", "@type": "Interface", "displayName": "Contamination Event", "description": "Captures detected contamination events such as smoke, aerosolized pathogens, or chemical releases. Logs event severity, timestamp, and recommend...
{'interface': 'dtmi:aircraft_cabin_air_quality:contamination_event;1', 'dockerImage': 'registry.local/dtm/aircraft_cabin_air_quality/contamination_event:v1.0.0', 'eventId': 'evt-20251106-03', 'severity': 'high', 'detectedAt': '2025-11-06T14:23:45Z', 'mitigationAction': 'activate HEPA filtration, increase fresh air inta...
I'm looking at the vibration monitoring interface that tracks vibration patterns to forecast track wear and maintenance needs, specifically the vib‑sensor‑01 which runs firmware version 2.3.1, samples data at 4000 samples per second, measures accelerations up to 196.2 m/s², reports all values in meters per second squar...
railway_track_vibration
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:railway_track_vibration:vibration_sensor;1", "@type": "Interface", "displayName": "Vibration Sensor", "description": "Captures high‑frequency vibration data from the rail track to detect anomalies and quantify dynamic loads. The sensor operates continuously, sampling at ...
{'interface': 'dtmi:railway_track_vibration:vibration_sensor;1', 'dockerImage': 'registry.local/dtm/railway_track_vibration/vibration_sensor:v1.0.0', 'sensorId': 'vib-sensor-01', 'samplingRate': 4000, 'measurementRange': 196.2, 'unit': 'm/s^2', 'firmwareVersion': '2.3.1', 'vibrationAmplitude': 0, 'vibrationFrequency': ...
This interface monitors vibration patterns to forecast track wear and maintenance needs; the digital twin instance runs model version 2024.09 with the latest schema updates, generates forecasts extending 30 days into the future, applies a confidence threshold of 0.87, and its underlying machine‑learning model was last ...
railway_track_vibration
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:railway_track_vibration:wear_forecast_model;1", "@type": "Interface", "displayName": "Wear Forecast Model", "description": "Runs machine‑learning inference on aggregated vibration data to estimate future track wear. The model outputs wear rates and confidence scores for ...
{'interface': 'dtmi:railway_track_vibration:wear_forecast_model;1', 'dockerImage': 'registry.local/dtm/railway_track_vibration/wear_forecast_model:v1.0.0', 'modelVersion': '2024.09', 'predictionHorizonDays': 30, 'confidenceThreshold': 0.87, 'lastTrainingDate': '2025-09-01T12:00:00Z', 'predictedWearRate': 0, 'wearProbab...
I’d like to use the vibration‑monitoring interface that forecasts track wear and maintenance needs, identified by scheduleId sched‑20251106‑01, to set up a maintenance window that starts at 2025‑11‑10T01:00:00Z and ends at 2025‑11‑10T05:00:00Z (a four‑hour interval), with a priorityLevel of 2 indicating a medium‑priori...
railway_track_vibration
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:railway_track_vibration:maintenance_scheduler;1", "@type": "Interface", "displayName": "Maintenance Scheduler", "description": "Coordinates maintenance activities based on wear forecasts and operational constraints. It defines maintenance windows, priority levels, and tr...
{'interface': 'dtmi:railway_track_vibration:maintenance_scheduler;1', 'dockerImage': 'registry.local/dtm/railway_track_vibration/maintenance_scheduler:v1.0.0', 'scheduleId': 'sched-20251106-01', 'maintenanceWindowStart': '2025-11-10T01:00:00Z', 'maintenanceWindowEnd': '2025-11-10T05:00:00Z', 'priorityLevel': 2, 'nextMa...
The interface, which monitors vibration patterns to forecast track wear and maintenance needs, reports a CPU utilization of 0.38, indicating a modest processing load under current conditions; memory usage is measured at 256.7 megabytes, reflecting the amount of RAM presently allocated to the application; disk space ava...
railway_track_vibration
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:railway_track_vibration:edge_processor;1", "@type": "Interface", "displayName": "Edge Processor", "description": "Runs near‑real‑time analytics at the edge, aggregating sensor telemetry and performing anomaly detection before forwarding to the cloud. Monitors its own res...
{'interface': 'dtmi:railway_track_vibration:edge_processor;1', 'dockerImage': 'registry.local/dtm/railway_track_vibration/edge_processor:v1.0.0', 'cpuUtilization': 0.38, 'memoryUsage': 256.7, 'diskSpaceAvailable': 15.2, 'networkLatencyMs': 8.4, 'processedTelemetryCount': 0, 'anomalyDetected': 0, 'processingLatencyMs': ...
I’m looking for a digital‑twin interface that monitors vibration patterns to forecast track wear and maintenance needs, specifically for the track segment identified as seg‑12b, which is positioned at latitude 42.3567° north, longitude ‑71.0923° west and sits at an elevation of 145.3 meters above sea level, so that its...
railway_track_vibration
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:railway_track_vibration:location_info;1", "@type": "Interface", "displayName": "Location Info", "description": "Provides static geospatial metadata for each track segment monitored by the twin. Includes precise coordinates, elevation, and geometric characteristics that i...
{'interface': 'dtmi:railway_track_vibration:location_info;1', 'dockerImage': 'registry.local/dtm/railway_track_vibration/location_info:v1.0.0', 'trackSegmentId': 'seg-12b', 'latitude': 42.3567, 'longitude': -71.0923, 'elevation': 145.3, 'segmentLengthMeters': 0, 'curvatureRadius': 0}
I'm looking at the digital‑twin interface that analyzes city scooter usage patterns for fleet redistribution and charging, which has recorded a total of 1,250,000 rides with an average ride duration of 14.3 minutes; the busiest period occurs during the peak hour of 17:00‑18:00, and the ride distribution across zones sh...
e_scooter_urban_usage
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:e_scooter_urban_usage:UsageAnalytics;1", "@type": "Interface", "displayName": "UsageAnalytics", "description": "Collects and processes ride data from all e‑scooters operating in the city. It aggregates total rides, average durations, and identifies peak usage hours to in...
{'interface': 'dtmi:e_scooter_urban_usage:UsageAnalytics;1', 'dockerImage': 'registry.local/dtm/e_scooter_urban_usage/UsageAnalytics:v1.0.0', 'totalRides': 1250000, 'averageRideDuration': 14.3, 'peakHour': '17:00-18:00', 'rideCountByZone': 'downtown:500000,uptown:400000,suburb:350000', 'ridesPerMinute': 0, 'avgSpeed': ...
I'm looking at the digital‑twin interface that analyzes city scooter usage patterns for fleet redistribution and charging; can you confirm that it’s configured to aim for a target redistribution ratio of 0.68, only permits redistribution actions between 07:00 and 11:00 each day, recorded the most recent redistribution ...
e_scooter_urban_usage
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:e_scooter_urban_usage:FleetRedistribution;1", "@type": "Interface", "displayName": "FleetRedistribution", "description": "Optimizes the spatial distribution of scooters across city zones based on demand forecasts. It stores target redistribution ratios, time windows, and...
{'interface': 'dtmi:e_scooter_urban_usage:FleetRedistribution;1', 'dockerImage': 'registry.local/dtm/e_scooter_urban_usage/FleetRedistribution:v1.0.0', 'targetRedistributionRatio': 0.68, 'redistributionWindowStart': '07:00', 'redistributionWindowEnd': '11:00', 'lastRedistributionTimestamp': '2025-10-28T09:45:12Z', 'man...
I'm looking at the digital‑twin interface that analyzes city scooter usage patterns for fleet redistribution and charging; it maintains a maximum charge level of 0.95 per unit of stored energy and enforces a minimum charge threshold of 0.25 to protect battery health, provisions a network of 14 charging stations, applie...
e_scooter_urban_usage
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:e_scooter_urban_usage:ChargingManagement;1", "@type": "Interface", "displayName": "ChargingManagement", "description": "Monitors charging infrastructure and schedules for the scooter fleet. It defines charge level thresholds, policy rules, and tracks the most recent char...
{'interface': 'dtmi:e_scooter_urban_usage:ChargingManagement;1', 'dockerImage': 'registry.local/dtm/e_scooter_urban_usage/ChargingManagement:v1.0.0', 'maxChargeLevel': 0.95, 'minChargeLevel': 0.25, 'chargingStationCount': 14, 'chargingPolicy': 'grid_balanced', 'lastChargingCycle': '2025-10-31T08:45:12Z', 'currentChargi...
I’m looking for a digital‑twin interface that analyzes city scooter usage patterns for fleet redistribution and charging, updating its position data every 5 seconds via a GPS locationProvider, storing the most recent coordinate 37.7749, ‑122.4194 in a lastKnownLocation property, accepting only readings whose measured e...
e_scooter_urban_usage
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:e_scooter_urban_usage:LocationTracking;1", "@type": "Interface", "displayName": "LocationTracking", "description": "Provides high‑frequency GPS updates for each scooter to enable routing and geofencing. It stores update frequency, accuracy thresholds, and the last known ...
{'interface': 'dtmi:e_scooter_urban_usage:LocationTracking;1', 'dockerImage': 'registry.local/dtm/e_scooter_urban_usage/LocationTracking:v1.0.0', 'gpsUpdateFrequency': 5, 'accuracyThreshold': 5.0, 'lastKnownLocation': '37.7749,-122.4194', 'geofenceRadius': 100.0, 'locationProvider': 'gps', 'latitude': 0, 'longitude': 0...
Can you tell me about the digital‑twin interface that analyzes city scooter usage patterns for fleet redistribution and charging, which reports that the battery has completed 412 full charge‑discharge cycles, currently has a health score of 81.3 % indicating moderate wear, a degradation rate of 0.09 per cycle, and that...
e_scooter_urban_usage
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:e_scooter_urban_usage:BatteryHealth;1", "@type": "Interface", "displayName": "BatteryHealth", "description": "Tracks the long‑term health of scooter batteries to predict maintenance needs. It records cycle counts, health scores, degradation rates, and warranty informatio...
{'interface': 'dtmi:e_scooter_urban_usage:BatteryHealth;1', 'dockerImage': 'registry.local/dtm/e_scooter_urban_usage/BatteryHealth:v1.0.0', 'batteryCycleCount': 412, 'healthScore': 81.3, 'degradationRate': 0.09, 'warrantyExpiration': '2027-11-30T23:59:59Z', 'replacementDue': False, 'voltage': 0, 'temperature': 0, 'curr...
I’m interested in the digital‑twin interface that predicts performance loss of photovoltaic panels over time and provides the full specification for panel‑0012—a SunPower solar panel rated at 350.0 W peak power under standard test conditions, installed on 2018‑06‑15, with its manufacturer and installation date supporti...
solar_farm_panel_degradation
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:solar_farm_panel_degradation:panel;1", "@type": "Interface", "displayName": "Panel", "description": "Represents an individual photovoltaic panel within the solar farm. It captures physical characteristics, installation metadata, and real‑time performance metrics that are...
{'interface': 'dtmi:solar_farm_panel_degradation:panel;1', 'dockerImage': 'registry.local/dtm/solar_farm_panel_degradation/panel:v1.0.0', 'panelId': 'panel-0012', 'manufacturer': 'SunPower', 'ratedPowerW': 350.0, 'installationDate': '2018-06-15', 'powerOutput': 0, 'temperature': 0, 'irradiance': 0}
Can you provide details on the inverter identified as inv‑01a, which is deployed in the power‑conversion subsystem and implements the INV‑5000X model with a maximum output capacity of 5000.0 W, currently running firmware version 3.2.1, and used to predict the performance loss of photovoltaic panels over time?
solar_farm_panel_degradation
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:solar_farm_panel_degradation:inverter;1", "@type": "Interface", "displayName": "Inverter", "description": "Models the power conversion unit that aggregates the output of multiple panels and feeds it into the grid. It records operational status, efficiency, and fault cond...
{'interface': 'dtmi:solar_farm_panel_degradation:inverter;1', 'dockerImage': 'registry.local/dtm/solar_farm_panel_degradation/inverter:v1.0.0', 'inverterId': 'inv-01a', 'modelNumber': 'INV-5000X', 'maxCapacityW': 5000.0, 'firmwareVersion': '3.2.1', 'outputPower': 0, 'efficiency': 0, 'temperature': 0}
I’m looking for a digital‑twin interface that predicts the performance loss of photovoltaic panels over time and includes a weather‑station model identified as ws‑01, positioned at latitude 34.0522 degrees north and longitude ‑117.1611 degrees west; the precise double‑precision coordinates serve as a unique key for dat...
solar_farm_panel_degradation
{"@context": "dtmi:dtdl:context;2", "@id": "dtmi:solar_farm_panel_degradation:weather_station;1", "@type": "Interface", "displayName": "WeatherStation", "description": "Provides ambient environmental data that directly affect photovoltaic performance, such as solar irradiance, temperature, and wind speed. By integratin...
{'interface': 'dtmi:solar_farm_panel_degradation:weather_station;1', 'dockerImage': 'registry.local/dtm/solar_farm_panel_degradation/weather_station:v1.0.0', 'stationId': 'ws-01', 'locationLatitude': 34.0522, 'locationLongitude': -117.1611, 'ambientTemperature': 0, 'globalIrradiance': 0, 'windSpeed': 0, 'precipitation'...
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Digital Twin Composition

Datasets for retrieving and filling DTDL digital-twin interfaces from natural-language requests. All parts live in this one repo as separate configs.

Two families

The configs come in two provenances that share the same schemas but must not be mixed:

  • synthetic (triplets, interfaces, fill_eval, topics, eval_small, eval_mid) — LLM-generated interfaces and everything derived from them. This is the training pool.
  • real (interfaces_real, triplets_real, fill_eval_real) — the same three schemas built from real vendor device interfaces. This is the evaluation pool.

The two catalogues share no interface ids. The retrieval model is trained on triplets and scored on triplets_real, so concatenating a *_real config with its synthetic namesake would leak the evaluation set into training.

Configs

config family split rows description
triplets synthetic train 27,771 Retrieval triplets: natural-language query, correct interface, different-topic negative.
interfaces synthetic train 27,770 The DTDL interface catalogue every other synthetic config refers to.
fill_eval synthetic train 27,770 Property-filling evaluation: a spec paragraph and the filled interface it implies.
topics synthetic train 6,097 Digital-twin topics; id is the middle segment of every synthetic dtmi interface id.
eval_small synthetic test 100 Composed end-to-end system queries, grouped by topic.
eval_mid synthetic test 100 Composed queries paired with a topic reference or a full interface.
interfaces_real real train 640 Usable DTDL models filtered from Azure/iot-plugandplay-models; topic is the vendor.
triplets_real real test 640 Retrieval triplets over the plug-and-play interfaces; the held-out retrieval eval set.
fill_eval_real real test 640 Property-filling evaluation over the plug-and-play interfaces.
from datasets import load_dataset

triplets = load_dataset("zirenx/digital-twin-composition", "triplets", split="train")           # synthetic, train
interfaces = load_dataset("zirenx/digital-twin-composition", "interfaces", split="train")
eval_triplets = load_dataset("zirenx/digital-twin-composition", "triplets_real", split="test")  # real, evaluation

Nested fields are JSON strings

positive, negative, contents, answer and interface hold JSON text, because DTDL payloads are not uniformly typed — contents mixes objects with bare strings, a schema may be a string or an object, and every fill_eval answer has its own key set. Storing them as strings keeps the data lossless; parse a column to get the original value:

import json
interface = json.loads(triplets[0]["positive"])
print(interface["@id"])  # dtmi:<topic>:<Name>;1

How the parts join

Every interface id has the form dtmi:<topic>:<Name>;1, and the topic columns are precomputed from the id for convenience. Within the synthetic family <topic> is normally a topics.id; within the real family it is the vendor name (Advantech, ASUS, …), which is deliberately absent from topics — that config describes the synthetic pool only.

fill_eval.interface_id and the id in a parsed triplets.positive both resolve to interfaces.id. The same holds inside the real family against interfaces_real, never across families.

Within each family the three configs are aligned one-to-one: same row count, and the same multiset of interface ids. Every positive and negative resolves to a row in that family's interface catalogue.

Provenance

The synthetic interfaces and topics are LLM-generated by the scripts in the digital-twin-composition project; triplet negatives are sampled from a different topic and verified by an LLM judge plus a lexical near-duplicate filter.

The real configs come from Azure/iot-plugandplay-models, Microsoft's public device-model index. That catalogue was filtered down to the DTDL models that are actually usable here — they parse, and they declare properties worth retrieving and filling — and the same triplet and fill-eval generators were then run over the survivors. Interface ids keep their upstream dtmi:<vendor>:<Model>;1 form, which is why the topic column of the real family holds a vendor name.

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