Build a Sparkplug B Operator Knowledge Capture and Anomaly Explainer
Capture operator voice-to-text notes and associate them with Sparkplug B edge telemetry anomalies. Generate contextual operating guides and shift resolution playbooks for recurring production disturbances.
What you are building
The core problem, expected build, and operating context for this challenge.
Capture operator voice-to-text notes and associate them with Sparkplug B edge telemetry anomalies. Generate contextual operating guides and shift resolution playbooks for recurring production disturbances.
Production Shift Team Leader
Listen for unacknowledged alarm sequences and process excursions published via Sparkplug B edge payloads.
Prompt the active machine operator for contextual resolution observations via mobile terminal voice-to-text.
Link operator natural language explanations with underlying metric timestamps, tags, and alarm aliases.
Assemble indexed shift knowledge base entries to accelerate troubleshooting of matching future events.
Voice-enabled tablet operator interface featuring incident summaries and one-click knowledge approval.
Construct paired datasets of Sparkplug B edge metric bursts and simulated operator incident resolution transcripts. Starter examples below are synthetic. They are not downloaded records.
Eclipse Sparkplug Relies on Sparkplug B metric aliases and historical data structures defined in the Eclipse specification.
semantic-kernel, pydantic, pytest. Semantic Kernel integrates multi-modal voice plugins with enterprise operational memory indexing. Requires hosted or local reasoning model supporting structured JSON function calling.
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.
Associates operator narrative explanations to matching telemetry incident timestamps.
Extracts verified root-cause tags from unstructured operator notes.
Stores structured operational playbooks retrievable by alarm alias code.
Rejects entries where operator resolution description lacks actionable steps.
```json [ { "input": { "incident_id": "INC-883", "alarm_metric": "HydraulicPressure_Low", "alias": 44, "metric_value": 42.1, "threshold": 60, "operator_note": "Found bypass valve loose after product changeover. Hand tightened valve handle and pressure returned to 65 PSI." }, "expected_output": { "incident_id": "INC-883", "metric_alias": 44, "cause_category": "VALVE_MECHANICAL_MISALIGNMENT", "action_taken": "Hand tightened bypass valve handle", "playbook_entry_created": true, "playbook_title": "Hydraulic Pressure Low - Check Bypass Valve Position" } }, { "input": { "incident_id": "INC-884", "alarm_metric": "MotorTemp_High", "alias": 52, "metric_value": 98.4, "threshold": 85, "operator_note": "It was hot." }, "expected_output": { "incident_id": "INC-884", "metric_alias": 52, "cause_category": "UNSPECIFIED", "action_taken": "NONE_RECORDED", "playbook_entry_created": false, "playbook_title": null } } ] ```
Maintains informational operator logbooks; never executes direct parameter changes on active machinery. Use mock external writes. Require human review before any real deployment.
Participation guide
Maintains informational operator logbooks; never executes direct parameter changes on active machinery. Use mock external writes and human review.
How work is evaluated
Associates operator narrative explanations to matching telemetry incident timestamps. Extracts verified root-cause tags from unstructured operator notes. Stores structured operational playbooks retrievable by alarm alias code. Rejects entries where operator resolution description lacks actionable steps.
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
Listen for unacknowledged alarm sequences and process excursions published via Sparkplug B edge payloads. Prompt the active machine operator for contextual resolution observations via mobile terminal voice-to-text. Link operator natural language explanations with underlying metric timestamps, tags, and alarm aliases. Assemble indexed shift knowledge base entries to accelerate troubleshooting of matching future events.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Associates operator narrative explanations to matching telemetry incident timestamps. Extracts verified root-cause tags from unstructured operator notes. Stores structured operational playbooks retrievable by alarm alias code. Rejects entries where operator resolution description lacks actionable steps.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Voice-enabled tablet operator interface featuring incident summaries and one-click knowledge approval.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Maintains informational operator logbooks; never executes direct parameter changes on active machinery.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Listen for unacknowledged alarm sequences and process excursions published via Sparkplug B edge payloads.
Prompt the active machine operator for contextual resolution observations via mobile terminal voice-to-text.
Link operator natural language explanations with underlying metric timestamps, tags, and alarm aliases.
Assemble indexed shift knowledge base entries to accelerate troubleshooting of matching future events.
Reference links and supporting material
Construct paired datasets of Sparkplug B edge metric bursts and simulated operator incident resolution transcripts. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Semantic Kernel
Agent Systems · Agent Frameworks
Evaluated output
No output checks are configured.
- Listen for unacknowledged alarm sequences and process excursions published via Sparkplug B edge payloads. P...
- Associates operator narrative explanations to matching telemetry incident timestamps. Extracts verified roo...
- Voice-enabled tablet operator interface featuring incident summaries and one-click knowledge approval.
- 2 public reference cases
- 1 configured tool
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
- Semantic Kernel · 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.