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Challenge

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.

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Challenge brief

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.

Rules

Participation guide

Read-only advisory tool; produces draft maintenance calendars without commanding field instrumentation. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Correctly flags sensors exceeding tolerance thresholds. Calculates replacement due dates based on historical calibration intervals. Emits valid JSON schedule artifacts matching the schema.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Terminal dashboard showing sensor drift variance and calendar calibration rosters.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Read-only advisory tool; produces draft maintenance calendars without commanding field instrumentation.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

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.

Resources and assets

Reference links and supporting material

Dataset notes

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.

Evaluation checks
  • 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.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 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

Action Space
  • Pydantic AI · Optional
Orchestration
  • 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.

Frequently Asked Questions about Build a Sparkplug B Sensor Drift Calibration Rebalancer