docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동
최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치. 콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서 전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한 업데이트0615/무제 3.canvas 뿐).
This commit is contained in:
Antigravity Agent
@@ -0,0 +1,168 @@
|
||||
---
|
||||
id: wiki-2026-0508-digital-twin
|
||||
title: Digital Twin
|
||||
category: 10_Wiki/Topics
|
||||
status: verified
|
||||
canonical_id: self
|
||||
aliases: [digital-twin, virtual-replica, cyber-physical-system]
|
||||
duplicate_of: none
|
||||
source_trust_level: A
|
||||
confidence_score: 0.9
|
||||
verification_status: applied
|
||||
tags: [digital-twin, iot, simulation, cps]
|
||||
raw_sources: []
|
||||
last_reinforced: 2026-05-10
|
||||
github_commit: pending
|
||||
tech_stack:
|
||||
language: python
|
||||
framework: NVIDIA-Omniverse/Azure-Digital-Twins
|
||||
---
|
||||
|
||||
# Digital Twin
|
||||
|
||||
## 매 한 줄
|
||||
> **"매 digital twin 의 매 physical asset 의 living mirror"**. 매 sensor stream 가 매 simulation model 에 feed → 매 prediction / what-if / control. 2026 의 매 NVIDIA Omniverse + OpenUSD, Azure Digital Twins, AWS IoT TwinMaker 가 매 enterprise standard. 매 LLM-augmented reasoning over twin (Claude Opus 4.7 + DTDL graph query) 의 매 emerging.
|
||||
|
||||
## 매 핵심
|
||||
|
||||
### 매 3-tier
|
||||
- **Digital Model** — 매 static representation, 매 sync X.
|
||||
- **Digital Shadow** — 매 one-way sync (physical → digital).
|
||||
- **Digital Twin** — 매 bidirectional sync (digital → physical control 의 가능).
|
||||
|
||||
### 매 ingredient
|
||||
- **3D geometry** (OpenUSD, glTF).
|
||||
- **Telemetry** (MQTT, OPC UA, AVRO over Kafka).
|
||||
- **Physics / behavior** (FMU, Modelica, Isaac Sim, Omniverse PhysX).
|
||||
- **Ontology / DTDL** (Digital Twins Definition Language).
|
||||
- **AI layer** (anomaly detection, forecasting, RL policy).
|
||||
|
||||
### 매 응용
|
||||
1. **Manufacturing**: BMW iFactory — 매 line 의 reconfigure 의 digital first.
|
||||
2. **City** — Singapore Virtual Singapore, Helsinki 3D+.
|
||||
3. **Energy grid** — 매 outage prediction, demand response.
|
||||
4. **Healthcare** — patient-specific cardiac twin (Dassault Living Heart).
|
||||
5. **Robotics fleet** — 매 Isaac Sim 의 sim-to-real RL training.
|
||||
|
||||
## 💻 패턴
|
||||
|
||||
### Azure Digital Twins (DTDL v3)
|
||||
```json
|
||||
{
|
||||
"@context": "dtmi:dtdl:context;3",
|
||||
"@id": "dtmi:com:factory:Pump;1",
|
||||
"@type": "Interface",
|
||||
"contents": [
|
||||
{ "@type": "Property", "name": "serialNumber", "schema": "string" },
|
||||
{ "@type": "Telemetry", "name": "rpm", "schema": "double" },
|
||||
{ "@type": "Telemetry", "name": "temperature", "schema": "double" },
|
||||
{ "@type": "Command", "name": "shutdown" },
|
||||
{ "@type": "Relationship", "name": "feedsInto", "target": "dtmi:com:factory:Tank;1" }
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### MQTT → twin update (Python)
|
||||
```python
|
||||
import paho.mqtt.client as mqtt
|
||||
from azure.digitaltwins.core import DigitalTwinsClient
|
||||
|
||||
dt = DigitalTwinsClient("https://factory.api.weu.digitaltwins.azure.net", credential)
|
||||
|
||||
def on_msg(client, _, msg):
|
||||
payload = json.loads(msg.payload)
|
||||
patch = [{"op": "replace", "path": "/rpm", "value": payload["rpm"]},
|
||||
{"op": "replace", "path": "/temperature", "value": payload["temp"]}]
|
||||
dt.update_digital_twin(payload["twin_id"], patch)
|
||||
|
||||
c = mqtt.Client()
|
||||
c.on_message = on_msg
|
||||
c.connect("mqtt.factory.local", 1883)
|
||||
c.subscribe("factory/+/telemetry")
|
||||
c.loop_forever()
|
||||
```
|
||||
|
||||
### Twin graph query (Cypher-like)
|
||||
```text
|
||||
SELECT pump, tank
|
||||
FROM DIGITALTWINS pump
|
||||
JOIN tank RELATED pump.feedsInto
|
||||
WHERE pump.temperature > 85
|
||||
AND IS_OF_MODEL(pump, 'dtmi:com:factory:Pump;1')
|
||||
```
|
||||
|
||||
### Omniverse + OpenUSD scene composition
|
||||
```python
|
||||
from pxr import Usd, UsdGeom, Sdf
|
||||
stage = Usd.Stage.CreateNew("factory.usda")
|
||||
factory = UsdGeom.Xform.Define(stage, "/Factory")
|
||||
pump = stage.OverridePrim("/Factory/Pump_42")
|
||||
pump.CreateAttribute("custom:rpm", Sdf.ValueTypeNames.Float).Set(1480.0)
|
||||
pump.CreateAttribute("custom:temperature", Sdf.ValueTypeNames.Float).Set(72.3)
|
||||
stage.Save()
|
||||
```
|
||||
|
||||
### Anomaly detection on twin stream
|
||||
```python
|
||||
from river import anomaly # online learning
|
||||
detector = anomaly.HalfSpaceTrees(seed=42)
|
||||
async for event in kafka_consumer("factory.telemetry"):
|
||||
score = detector.score_one({"rpm": event.rpm, "temp": event.temp})
|
||||
detector.learn_one({"rpm": event.rpm, "temp": event.temp})
|
||||
if score > 0.95:
|
||||
await dt.update_relationships(event.twin_id, "alert_state", "anomaly")
|
||||
```
|
||||
|
||||
### LLM reasoning over twin graph
|
||||
```python
|
||||
graph_context = dt.query_twins("SELECT * FROM digitaltwins WHERE temperature > 80")
|
||||
response = anthropic.messages.create(
|
||||
model="claude-opus-4-7",
|
||||
system="You analyze factory digital twin state for root-cause hypotheses.",
|
||||
messages=[{"role": "user", "content": f"Twins: {graph_context}\nWhy is line 3 throughput dropping?"}],
|
||||
)
|
||||
```
|
||||
|
||||
### Sim-to-real RL (Isaac Sim)
|
||||
```python
|
||||
from omni.isaac.gym.vec_env import VecEnvBase
|
||||
env = VecEnvBase(headless=True)
|
||||
# 매 4096 parallel pump sims 의 train, 매 policy 가 real pump 에 deploy.
|
||||
```
|
||||
|
||||
## 매 결정 기준
|
||||
| 상황 | Approach |
|
||||
|---|---|
|
||||
| 매 high-fidelity physics | NVIDIA Omniverse + Isaac Sim |
|
||||
| 매 enterprise IoT graph | Azure Digital Twins (DTDL) |
|
||||
| 매 AWS-native | AWS IoT TwinMaker |
|
||||
| 매 city / GIS | CesiumJS + 3D Tiles |
|
||||
| 매 scientific sim | Modelica + FMU |
|
||||
|
||||
**기본값**: Azure Digital Twins or AWS TwinMaker for graph + telemetry; Omniverse for 3D/physics; OpenUSD for interchange.
|
||||
|
||||
## 🔗 Graph
|
||||
- 부모: [[Cyber-Physical-Systems]]
|
||||
- 응용: [[Predictive Maintenance]]
|
||||
- Adjacent: [[Control_Systems_Engineering|Control-Systems-Engineering]] · [[MQTT]]
|
||||
|
||||
## 🤖 LLM 활용
|
||||
**언제**: 매 twin graph 의 natural-language query → DTDL/SQL translation, 매 anomaly explanation, 매 maintenance work order generation.
|
||||
**언제 X**: 매 hard-realtime control loop (sub-ms). 매 safety-critical actuation (deterministic controller 의 사용).
|
||||
|
||||
## ❌ 안티패턴
|
||||
- **3D model only**: 매 telemetry 가 X — 매 just CAD viewer.
|
||||
- **No bidirectional channel**: 매 just shadow, 매 not twin.
|
||||
- **Monolithic schema**: 매 DTDL inheritance / interfaces 의 사용.
|
||||
- **Synchronous queries on hot path**: 매 read replica / cache.
|
||||
- **No data retention policy**: 매 telemetry storage cost 가 explodes — tiered storage (hot Kafka → warm Parquet → cold S3).
|
||||
|
||||
## 🧪 검증 / 중복
|
||||
- Verified (Microsoft DTDL v3 spec, NVIDIA Omniverse docs, AWS IoT TwinMaker, Gartner 2025 digital twin report).
|
||||
- 신뢰도 A.
|
||||
|
||||
## 🕓 Changelog
|
||||
| 날짜 | 변경 |
|
||||
|---|---|
|
||||
| 2026-05-08 | Phase 1 |
|
||||
| 2026-05-10 | Manual cleanup — digital twin tiers, DTDL, Omniverse, sim-to-real |
|
||||
Reference in New Issue
Block a user