Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
148 lines
4.8 KiB
Markdown
148 lines
4.8 KiB
Markdown
---
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id: wiki-2026-0508-digital-twins
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title: Digital Twins
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Digital Twin, DT, Cyber-Physical Mirror]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [iot, simulation, cps, industry-4]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python/C++
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framework: NVIDIA-Omniverse/Azure-DigitalTwins
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---
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# Digital Twins
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## 매 한 줄
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> **"매 physical asset/system 의 매 live, bidirectional digital replica"**. Grieves 2002 의 PLM concept 가 매 IoT, sensor cost 폭락, real-time sim, generative AI 의 만남으로 매 Industry 4.0 의 핵심. 매 NVIDIA Omniverse, Azure Digital Twins, Siemens Xcelerator 의 2026 era — physical + digital 의 매 closed loop.
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## 매 핵심
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### 매 3 layer
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- **Physical**: 실제 자산 + sensor (vibration, temp, pressure, camera).
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- **Communication**: MQTT/OPC-UA/AMQP, time-series store, edge gateway.
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- **Digital**: 3D model + physics sim + ML predictor + control logic.
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### 매 spectrum
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- **Digital model**: static 3D, no live data.
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- **Digital shadow**: one-way (physical → digital).
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- **Digital twin**: bidirectional — twin can command physical.
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### 매 응용
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1. Predictive maintenance (jet engine, wind turbine).
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2. Smart city traffic / energy optimization.
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3. Manufacturing line virtual commissioning.
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4. Healthcare (patient-specific organ twin).
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5. Robot fleet sim (NVIDIA Isaac, Omniverse).
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## 💻 패턴
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### Sensor → twin (MQTT + Python)
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```python
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import paho.mqtt.client as mqtt, json, time
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def on_message(c, u, msg):
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data = json.loads(msg.payload)
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twin.update_state(asset_id=data['id'], temp=data['temp'], ts=data['ts'])
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if twin.predict_failure(data['id']) > 0.8:
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c.publish(f"cmd/{data['id']}/throttle", "0.5") # bidirectional!
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cli = mqtt.Client(); cli.on_message = on_message
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cli.connect("mqtt.factory.local"); cli.subscribe("sensor/+"); cli.loop_forever()
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```
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### Azure Digital Twins (DTDL)
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```json
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{
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"@id": "dtmi:com:factory:Pump;1",
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"@type": "Interface",
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"displayName": "Pump",
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"contents": [
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{ "@type": "Property", "name": "rpm", "schema": "double" },
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{ "@type": "Telemetry", "name": "vibration", "schema": "double" },
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{ "@type": "Command", "name": "shutdown" },
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{ "@type": "Relationship", "name": "feeds", "target": "dtmi:com:factory:Tank;1" }
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]
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}
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```
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### Physics-based twin (Modelica via FMU)
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```python
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from fmpy import simulate_fmu
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result = simulate_fmu(
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'pump.fmu',
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start_values={'inlet_pressure': 2.5, 'rpm': 1800},
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output=['outlet_pressure', 'efficiency'],
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stop_time=10.0
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)
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```
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### Omniverse USD scene (live update)
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```python
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from pxr import Usd, UsdGeom, Gf
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stage = Usd.Stage.Open('factory.usd')
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pump = UsdGeom.Xform.Get(stage, '/World/Pump_01')
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# Stream live sensor pose via OmniGraph / Live Sync
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def on_telemetry(rpm, vibration):
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pump.GetPrim().GetAttribute('rpm:live').Set(rpm)
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pump.GetPrim().GetAttribute('vib:live').Set(vibration)
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```
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### Predictive maintenance (LSTM)
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```python
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import torch.nn as nn
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class FailurePredictor(nn.Module):
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def __init__(self, n_sensors=8):
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super().__init__()
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self.lstm = nn.LSTM(n_sensors, 64, batch_first=True)
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self.head = nn.Linear(64, 1)
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def forward(self, x): # (B, T, n_sensors)
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h, _ = self.lstm(x)
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return torch.sigmoid(self.head(h[:, -1]))
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# Train on (sensor window, RUL label) pairs from CMAPSS / NASA dataset.
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Single asset, simple monitoring | Digital shadow (cheap) |
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| Fleet w/ predictive maint. | Twin + ML failure model |
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| Process plant commissioning | Physics twin (FMU/Modelica) |
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| Robotics / AV training | Sim-to-real (Isaac, CARLA) |
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| Smart city / building | Hierarchical twins (DTDL) |
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**기본값**: 매 start digital shadow → ML 추가 후 twin → bidirectional 마지막.
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## 🔗 Graph
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- 부모: [[Cyber-Physical Systems]]
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- 응용: [[Predictive Maintenance]]
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- Adjacent: [[NVIDIA Omniverse]]
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## 🤖 LLM 활용
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**언제**: IoT/manufacturing/CPS context, sim2real planning, asset lifecycle.
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**언제 X**: Pure web app, no physical asset. 매 marketing buzzword 화 주의.
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## ❌ 안티패턴
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- **3D model = twin 오해**: 매 3D 만으론 shadow도 아님.
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- **Sensor 무인 데이터**: garbage in → garbage twin.
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- **No model versioning**: physical 변경 시 twin drift.
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- **Closed-loop without safety**: bidirectional 시 매 fail-safe + human-in-loop 필수.
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- **Vendor lock-in**: proprietary schema → DTDL/USD 표준 사용.
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## 🧪 검증 / 중복
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- Verified (Grieves 2014, NIST CPS framework, Azure DT docs, NVIDIA Omniverse docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — DT layers + DTDL/Omniverse/MQTT patterns |
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