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2nd/10_Wiki/Topics/Domain_Programming/DevOps_and_Security/Digital Thread Integration.md
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Antigravity Agent c24165b8bc refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-11 11:05:56 +09:00

4.5 KiB

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack
id title category status canonical_id aliases duplicate_of source_trust_level confidence_score verification_status tags raw_sources last_reinforced github_commit tech_stack
wiki-2026-0508-digital-thread-integration Digital Thread Integration 10_Wiki/Topics verified self
Digital Thread
Industrial Data Thread
none A 0.9 applied
iiot
digital-thread
manufacturing
plm
2026-05-10 applied
language framework
Python/SQL OPC UA/MQTT/Kafka

Digital Thread Integration

매 한 줄

"매 product lifecycle 의 모든 data 가 single linked thread 로 흐르는 것.". Digital Thread 는 design (CAD) → engineering (PLM) → manufacturing (MES) → operations (IoT) → service (CRM) 까지 매 traceable, queryable 하게 연결하는 매 manufacturing/aerospace 의 backbone. 2026 의 standard: ISA-95 + OPC UA + Asset Administration Shell (AAS) + RAMI 4.0 + IDS data spaces.

매 핵심

매 Digital Thread vs Digital Twin

  • Thread: 매 data lineage — design intent ↔ as-built ↔ as-maintained.
  • Twin: 매 simulation model of a specific asset.
  • 관계: Twin 은 Thread 의 cross-section snapshot.

매 Layer Stack

  1. Edge: PLC, sensor, OPC UA server.
  2. Connectivity: MQTT, OPC UA, MTConnect.
  3. Stream: Kafka / Pulsar — high-throughput.
  4. Storage: time-series (InfluxDB, TimescaleDB), data lake (Iceberg).
  5. Semantic: Asset Administration Shell, ontology (W3C SOSA/SSN).
  6. Application: PLM (Teamcenter, Windchill), MES, ERP.

매 응용

  1. Aerospace — 매 part traceability, certification.
  2. Automotive — 매 EV battery passport (EU 2027 mandate).
  3. Industrial maintenance — 매 predictive + service history.
  4. Pharma — 매 batch genealogy.
  5. EU Digital Product Passport — 매 sustainability.

💻 패턴

OPC UA client (Python)

from asyncua import Client
async def main():
    async with Client(url="opc.tcp://plant.local:4840") as c:
        node = c.get_node("ns=2;s=Line1.Press.Temp")
        async for v in node.subscribe_data_change(callback):
            pass

MQTT Sparkplug B (manufacturing-standard payload)

import paho.mqtt.client as mqtt
import sparkplug_b as sp
payload = sp.getDdataPayload()
sp.addMetric(payload, "Temp", None, sp.MetricDataType.Float, 72.5)
client.publish("spBv1.0/PlantA/DDATA/Edge1/Press1", payload.SerializeToString())

Asset Administration Shell submodel

{
  "idShort": "Nameplate",
  "submodelElements": [
    {"idShort":"ManufacturerName","value":"Acme"},
    {"idShort":"SerialNumber","value":"SN-A1B2"},
    {"idShort":"YearOfConstruction","value":"2026"}
  ]
}

Kafka pipeline edge → lake

from confluent_kafka import Producer
import pyarrow.parquet as pq
p = Producer({'bootstrap.servers':'kafka:9092','compression.type':'zstd'})
p.produce('plant.line1.temp', key=part_id, value=msg.SerializeToString())
# downstream Flink/Spark → Iceberg table

Digital Product Passport (EU 2027)

{
  "productId": "urn:gtin:01234567890128",
  "carbonFootprintKg": 12.4,
  "materials": [{"name":"Li","massGrams":1200}],
  "recycledContentPercent": 18,
  "linkedTwin": "urn:twin:battery:SN-A1B2"
}

매 결정 기준

상황 Approach
Field bus modernization OPC UA
IoT-style telemetry MQTT Sparkplug B
Cross-vendor semantics AAS / IDS
Stream backbone Kafka / Pulsar
Time-series store TimescaleDB / Influx
Lake Iceberg + Trino

기본값: 매 OPC UA + MQTT Sparkplug B → Kafka → Iceberg.

🔗 Graph

🤖 LLM 활용

언제: ontology mapping, anomaly summary, maintenance work-order draft. 언제 X: 매 safety-critical PLC logic — formal verification 만.

안티패턴

  • Polling 오버: 매 OPC UA subscribe 사용 — pub/sub.
  • Untimestamped data: 매 Thread 의 핵심은 시간 lineage.
  • Vendor lock: 매 proprietary protocol — open standards 사용.
  • No identity: 매 GS1, urn 등 stable id 필수.

🧪 검증 / 중복

  • Verified: ISA-95 spec; OPC UA Part 1; Plattform Industrie 4.0 AAS spec; EU DPP regulation 2024.
  • 신뢰도 A.

🕓 Changelog

날짜 변경
2026-05-08 Phase 1
2026-05-10 Manual cleanup — Thread vs Twin + OPC UA/MQTT/AAS