chore(brain): ASTRA 성장 자산 동기화 — 기능 인벤토리·growth(약점프로필/학습큐)·일화기억·장기기억·회의록 원문

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+---
+id: wiki-2026-0508-data-twins
+title: Data Twins (Digital Twins)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Digital Twin, Cyber-Physical Twin, Virtual Replica]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.88
+verification_status: applied
+tags: [digital-twin, iot, simulation, industry-4-0, modeling]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python/C++
+  framework: Azure Digital Twins / Omniverse / Modelica
+---
+
+# Data Twins (Digital Twins)
+
+## 매 한 줄
+> **"매 digital twin 의 핵심: live data binding + physics-aware simulation + bidirectional sync"**. 매 2002 Michael Grieves 의 PLM 컨셉 으로 시작, 매 NASA Apollo 13 의 ground simulation 이 ancestor. 매 2026 현재 NVIDIA Omniverse, Azure Digital Twins, AWS IoT TwinMaker, 매 LLM-grounded 산업 simulation 으로 manufacturing / smart-city / healthcare 의 mainstream.
+
+## 매 핵심
+
+### 매 3 fidelity levels
+- **Descriptive twin**: 매 static data + dashboard.
+- **Predictive twin**: 매 ML / physics simulation — 매 forecast.
+- **Prescriptive twin**: 매 optimize + actuate back to physical asset.
+
+### 매 components
+- **Sensor layer**: 매 IoT (MQTT, OPC UA, CAN bus).
+- **Time-series store**: 매 InfluxDB, Timescale, AWS Timestream.
+- **Twin graph**: 매 ontology (DTDL, asset hierarchy).
+- **Simulation kernel**: 매 Modelica, Omniverse PhysX, OpenFOAM.
+- **Closed-loop controller**: 매 actuator command back.
+
+### 매 응용
+1. Manufacturing (Siemens, GE Predix — turbine twin).
+2. Smart city (Singapore Virtual Singapore, Shanghai twin).
+3. Healthcare (heart twin for surgery planning).
+4. Supply chain (warehouse / fleet simulation).
+5. Building / HVAC optimization (BIM + live sensor).
+
+## 💻 패턴
+
+### DTDL (Digital Twins Definition Language)
+```json
+{
+  "@context": "dtmi:dtdl:context;3",
+  "@id": "dtmi:com:example:Turbine;1",
+  "@type": "Interface",
+  "displayName": "Turbine",
+  "contents": [
+    { "@type": "Telemetry", "name": "rpm", "schema": "double" },
+    { "@type": "Telemetry", "name": "tempC", "schema": "double" },
+    { "@type": "Property", "name": "model", "schema": "string" },
+    { "@type": "Command", "name": "shutdown" }
+  ]
+}
+```
+
+### Azure Digital Twins (Python SDK)
+```python
+from azure.digitaltwins.core import DigitalTwinsClient
+from azure.identity import DefaultAzureCredential
+
+client = DigitalTwinsClient(url, DefaultAzureCredential())
+twin = {
+  "$metadata": {"$model": "dtmi:com:example:Turbine;1"},
+  "rpm": 3500.0, "tempC": 78.5, "model": "T-900"
+}
+client.upsert_digital_twin("turbine-42", twin)
+# Query
+for t in client.query_twins("SELECT * FROM digitaltwins WHERE tempC > 80"):
+    print(t)
+```
+
+### MQTT ingestion → twin update
+```python
+import paho.mqtt.client as mqtt, json
+
+def on_message(client, userdata, msg):
+    data = json.loads(msg.payload)
+    update_twin(data["device_id"], {"rpm": data["rpm"]})
+
+c = mqtt.Client()
+c.on_message = on_message
+c.connect("broker.local", 1883)
+c.subscribe("plant/+/telemetry")
+c.loop_forever()
+```
+
+### Physics simulation (FMU via Modelica)
+```python
+from fmpy import simulate_fmu
+result = simulate_fmu("turbine.fmu",
+                      start_time=0, stop_time=60, step_size=0.01,
+                      input=[("inlet_pressure", input_signal)])
+```
+
+### NVIDIA Omniverse (USD asset twin)
+```python
+import omni.usd
+from pxr import UsdGeom, Gf
+stage = omni.usd.get_context().get_stage()
+turbine = UsdGeom.Xform.Define(stage, "/World/Turbine")
+turbine.AddRotateYOp().Set(Gf.Vec3f(0, rpm * dt * 6, 0))  # live RPM
+```
+
+### Anomaly-driven actuation (closed loop)
+```python
+def control_loop(twin):
+    if twin.tempC > 95:
+        send_command(twin.id, "reduce_load", value=20)
+        log(f"Twin {twin.id} thermal protection triggered")
+```
+
+### LLM-augmented twin Q&A
+```python
+import anthropic
+client = anthropic.Anthropic()
+def ask_twin(twin_state, question):
+    return client.messages.create(
+        model="claude-opus-4-7-20260101",
+        max_tokens=512,
+        system="You are an expert in industrial twin diagnostics.",
+        messages=[{"role": "user",
+                   "content": f"State: {twin_state}\nQ: {question}"}]
+    ).content[0].text
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Asset monitoring only | Descriptive (dashboard) |
+| Predictive maintenance | Predictive (ML on telemetry) |
+| Autonomous operation | Prescriptive (closed-loop) |
+| 3D / VR walkthrough | Omniverse / USD |
+| Cloud-managed | Azure Digital Twins / AWS TwinMaker |
+| Edge constraints | Local twin + sync (KubeEdge) |
+
+**기본값**: 매 industrial use 의 Azure Digital Twins + DTDL ontology, 매 3D viz 의 Omniverse.
+
+## 🔗 Graph
+- 부모: [[Cyber-Physical Systems]]
+- 변형: [[Predictive Maintenance]]
+- Adjacent: [[클라우드_인프라_및_IaC_운영_표준|IoT]] · [[Edge Computing]] · [[Time Series]]
+
+## 🤖 LLM 활용
+**언제**: 매 twin schema (DTDL) drafting, 매 anomaly explanation, 매 operator natural-language query, 매 simulation scenario generation.
+**언제 X**: 매 hard-real-time control loop — 매 LLM latency 의 unfit. 매 deterministic control 은 PID / MPC.
+
+## ❌ 안티패턴
+- **Twin = dashboard 의 단순 rebrand**: 매 simulation / closed-loop 없으면 그냥 monitoring.
+- **No data quality validation**: 매 garbage sensor → garbage twin.
+- **Twin without versioning**: 매 schema drift / model evolution 의 disaster.
+- **Tight coupling to vendor**: 매 vendor lock — 매 DTDL / OPC UA 같은 standards 사용.
+- **Ignoring security**: 매 closed-loop = attacker 의 actuation = physical damage.
+- **One twin for everything**: 매 hierarchical decomposition (asset → system → plant) 의 사용.
+
+## 🧪 검증 / 중복
+- Verified (Grieves 2002, NASA twin paradigm, Microsoft DTDL spec, ISO 23247, NVIDIA Omniverse docs 2025).
+- 신뢰도 B+ (terminology / scope 의 industry variation).
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Digital twin patterns + Omniverse / DTDL |