Build a Sparkplug B Sensor Drift Calibration Rebalancer
Evaluate normalized Sparkplug B dataset metrics against sensor calibration thresholds to detect measurement drift. Synthesize a recalibration and replacement schedule for instrumentation technicians.
What you are building
The core problem, expected build, and operating context for this challenge.
Evaluate normalized Sparkplug B dataset metrics against sensor calibration thresholds to detect measurement drift. Synthesize a recalibration and replacement schedule for instrumentation technicians.
Instrumentation Lead
Ingest normalized Sparkplug B device metric records containing calibration dates and drift standard deviations.
Compare sensor variance against published allowable tolerance thresholds.
Generate a prioritized recalibration schedule grouped by line and technician specialization.
Export a simulated maintenance dispatch record for supervisor sign-off.
Terminal dashboard showing sensor drift variance and calendar calibration rosters.
Generate local synthetic JSON mock streams containing normalized Sparkplug B metric records with sensor serials and baseline offsets. Starter examples below are synthetic. They are not downloaded records.
Eclipse Sparkplug Utilizes Sparkplug B metric property metadata, dataset types, and device lifecycle states outlined in Source 2.
pydantic-ai, fastapi. Pydantic AI provides strict schema enforcement on nested metric records and validation of calibration schedules. Local small LLM (e.g., Llama 3 8B) or hosted Claude via API for schedule synthesis.
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
Correctly flags sensors exceeding tolerance thresholds.
Calculates replacement due dates based on historical calibration intervals.
Emits valid JSON schedule artifacts matching the schema.
```json [ { "input": { "device_id": "EDGE-NODE-01/DEV-TEMP-99", "metric_name": "FurnaceTemp", "last_calibrated_days": 190, "drift_deviation_pct": 4.2, "max_allowed_drift_pct": 2.5 }, "expected_output": { "device_id": "EDGE-NODE-01/DEV-TEMP-99", "action_required": "RECALIBRATE", "priority": "HIGH", "estimated_labor_hours": 1.5, "recommended_window": "NEXT_DOWNTIME_SHIFT" } }, { "input": { "device_id": "EDGE-NODE-02/DEV-PRESS-04", "metric_name": "HydraulicPressure", "last_calibrated_days": 45, "drift_deviation_pct": 0.8, "max_allowed_drift_pct": 3 }, "expected_output": { "device_id": "EDGE-NODE-02/DEV-PRESS-04", "action_required": "MONITOR", "priority": "LOW", "estimated_labor_hours": 0, "recommended_window": "ROUTINE_CYCLE" } } ] ```
Read-only advisory tool; produces draft maintenance calendars without commanding field instrumentation. Use mock external writes. Require human review before any real deployment.
Participation guide
Read-only advisory tool; produces draft maintenance calendars without commanding field instrumentation. Use mock external writes and human review.
How work is evaluated
Correctly flags sensors exceeding tolerance thresholds. Calculates replacement due dates based on historical calibration intervals. Emits valid JSON schedule artifacts matching the schema.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Ingest normalized Sparkplug B device metric records containing calibration dates and drift standard deviations. Compare sensor variance against published allowable tolerance thresholds. Generate a prioritized recalibration schedule grouped by line and technician specialization. Export a simulated maintenance dispatch record for supervisor sign-off.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Correctly flags sensors exceeding tolerance thresholds. Calculates replacement due dates based on historical calibration intervals. Emits valid JSON schedule artifacts matching the schema.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Terminal dashboard showing sensor drift variance and calendar calibration rosters.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Read-only advisory tool; produces draft maintenance calendars without commanding field instrumentation.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Ingest normalized Sparkplug B device metric records containing calibration dates and drift standard deviations.
Compare sensor variance against published allowable tolerance thresholds.
Generate a prioritized recalibration schedule grouped by line and technician specialization.
Export a simulated maintenance dispatch record for supervisor sign-off.
Reference links and supporting material
Generate local synthetic JSON mock streams containing normalized Sparkplug B metric records with sensor serials and baseline offsets. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Pydantic AI
Typed Python agent framework.
Evaluated output
No output checks are configured.
- Ingest normalized Sparkplug B device metric records containing calibration dates and drift standard deviati...
- Correctly flags sensors exceeding tolerance thresholds. Calculates replacement due dates based on historica...
- Terminal dashboard showing sensor drift variance and calendar calibration rosters.
- 2 public reference cases
- 2 configured tools
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Pydantic AI · Optional
- Pydantic AI · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.