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Challenge

Digital Twin OPC UA Stream Anomaly Detector with Vercel AI SDK

Simulating physical assets in real-time digital twins requires constant calibration against live OPC UA sensor telemetry streams. Build a streaming digital twin synchronizer using Vercel AI SDK that continuously compares twin predictions against physical sensor streams and adjusts state models dynamically. Maintain synchronization accuracy above 94% across high-vibration operating conditions.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an OPC UA digital twin calibration agent using Vercel AI SDK streaming workflows and custom state estimation tools.

Delivery guide

How work is evaluated

Evaluation

Evaluates digital twin state model synchronization precision on streaming sensor streams.

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
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

sync_status

Ensure sync status evaluates to true

Binary check

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

Dimension 2

synchronization_accuracy

Accuracy score of calibrated digital twin state vs physical ground truth • target: 0.94 • range: 0-1

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

  • Implement AI SDK streaming pipelines for continuous digital twin sensor processing

  • Build custom tool calls for executing state vector physics adjustments

  • Achieve 94%+ synchronization precision between model dynamic prediction and physical readings

Resources and assets

Reference links and supporting material

Dataset notes

OPC UA sensor telemetry streams generated from physical pump test stand models.

Challenge setup

Evaluates digital twin state model synchronization precision on streaming sensor streams.

Challenge input

JSON sensor stream and current twin dynamic parameters

Vercel AI SDK

Provides low-latency stream handling for real-time sensor loops.

Evaluated output

JSON calibrated parameter vector and drift score

Evaluation checks
  • Ensure sync status evaluates to true
  • Accuracy score of calibrated digital twin state vs physical ground truth • target: 0.94 • range: 0-1
Configured evaluation evidence
  • Benchmark: Digital Twin Calibration Benchmark (DT-CAL-2025)
  • Synchronization Accuracy target: 0.94
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

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
  • Vercel AI SDK · Required
Orchestration
  • Vercel AI SDK · Required

Evaluation contract

  • sync_status · Weight 1
  • synchronization_accuracy · Weight 1

Metrics

  • synchronization_accuracy · Target 0.94

Benchmarks

  • Digital Twin Calibration Benchmark (DT-CAL-2025)
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON sensor stream and current twin dynamic parameters

    Output: JSON calibrated parameter vector and drift score

    Execution harness: JavaScript

Evaluation tools

  • Vercel AI SDK · Streaming Engine

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Digital Twin OPC UA Stream Anomaly Detector with Vercel AI SDK