SlideShare une entreprise Scribd logo
1  sur  168
Télécharger pour lire hors ligne
김 대 영
2019년 10월 17일
교수, 전산학부, KAIST
Director, Auto-ID Labs, KAIST
kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org
Internet of Trains: GS1 국제표준기반의 철도산업
디지털 트랜스포메이션 전략과 구축 기술
김 대 영
2020년 6월 30일
14:00 ~15:40 (Webinar)
교수, 전산학부, KAIST
Director, Auto-ID Labs, KAIST
kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org
© Auto-ID Lab Korea / KAIST
Slide 3
SuperX를 위한 Internet of Trains
(Open global language, Digital twin)
서울역(Location ID) 에서 19:30(Time) 출발(Vocabulary)하여,
부산역에 22:08 도착하는, 총 20 차량 (2 기관차, 18 객차)(Assets IDs)
으로 구성된 KTX169(Passage ID) 편이, 방금 19:31 분에 서울역을
출발(Vocabulary) 하였다.
KTX169(Passage ID) 편이 방금 20:28(Time) 대전역 (Location ID)
도착 (Vocabulary) 하였는데, 앞 기관차(Asset ID) 의 앞바퀴
온도(Vocabulary)가 250°C (Vocabulary Element) 이다.
© Auto-ID Lab Korea / KAIST
Slide 4
목차
Part I – 철도산업에서의 디지털 트랜스포메이션
Part II – 유럽, 호주, 인도등 세계 현황
Part III – GS1 Standards (GS1 국제표준, 산업표준)
Part IV – GS1 Rail Standards
Part V – 디지털인프라, 디지털 SoC 인프라
Part I – 철도산업에서의 디지털 트랜스포메이션
(GS1’s Perspective)
© Auto-ID Lab Korea / KAIST
Slide 6
유럽은 왜 GS1 국제표준을 활용하며
철도산업 디지털 트랜스포메이션에 뛰어 들었나?
안전 관리 시스템
(SMS: Safety Management System)
독립된 유지보수 담당
(ECM: Entity in Charge of Maintenance)
유럽연합규정에 따른 철도운영호환성
평가 및 인증
(TSI: Technical Specifications for
Interoperability)
CE 마크
(Conformite Europeenne Mark)
© Auto-ID Lab Korea / KAIST
Slide 7
Top 10 resources for rail industry standards and
regulatory guidance (탑10 철도산업 표준, 가이드라인)
• The rail industry is subject to a variety of standards (표준) and regulations (규정), these
standards change over time as more effective safety measures are identified and new
safety and operational risks are recognized.
• We’ve rounded up the top 10 resources for rail industry standards and regulatory
guidance, including:
• The International Organization for Standardization (ISO)
• The European Rail Industry – UNIFE
• GS1
• International Rail Industry Standard (IRIS)
• American National Standards Institute (ANSI)
• The Rail Committee on Information Standards (RailCIS)
• Association of American Railroads (AAR)
• Federal Railroad Administration (FRA)
• Railway Age
• International Railway Journal
https://www.mpofcinci.com/blog/rail-industry-standards-resources/
© Auto-ID Lab Korea / KAIST
Slide 8
“공급망을 자동화하고 간소화하기 위해 공통 표준과 언어
가 종종 필요합니다. GS1 표준의 도움으로 유럽 철도 산업
은 철도 안전, 효율성 및 경쟁력 향상을 목표로 이 경로를
시작하고 있습니다” - 구글번역
Les premiers pas vers le "rail cloud"
https://www.lantenne.com/Les-premiers-pas-vers-le-rail-cloud_a41020.html
"레일 클라우드"를 향한 첫 걸음
© Auto-ID Lab Korea / KAIST
Slide 9
Le ferroviaire en Europe sur les rails des standards GS1
- 유럽의 철도는 GS1 표준의 난간에 기대어 있다
https://www.actu-transport-logistique.fr/bibliotheque-numerique/supply-chain-magazine/5/parole-dexpert/le-ferroviaire-en-europe-sur-les-rails-des-
standards-gs1-456461.php
"디지털 변환 시대에 철도는 예비 부
품의 추적 성 보장, 생산 및 유지 보
수 비용 합리화, 운영 성과 향상과
같은 여러 가지 문제에 적응하고 대
응해야합니다. 이를 달성하기 위해
이 분야의 주요 업체들은 프로세스
에 GS1 표준을 채택하기로 결정했
습니다.
© Auto-ID Lab Korea / KAIST
Slide 10
GS1 국제표준 적용 국가
Part II – 유럽, 호주 등 세계 현황
스웨덴
독일 폴란드
오스트리아
스위스
독일
스페인
영국
네덜란드
벨기에
이탈리아
인도
호주
© Auto-ID Lab Korea / KAIST
Slide 14
스위스 (SBB CFF FFS)
© Auto-ID Lab Korea / KAIST
Slide 15
프랑스 TGV2020과 GS1국제표준
"2 년 동안 테스트 한 결과,
GS1 식별자는 TGV 2020
에 확실히 채택되었습니다.
그것은 미래의 고속 열차의
제조를 위한 사양에 포함되
었습니다. 또한 2020 년 1
월부터 RER NG 테스트 열
차에서 테스트 될 예정입니
다." [구글번역]
https://www.constructioncayola.com/rail/article/2019/06/27/125027/tracabilite-des-pieces-filiere-ferroviaire
© Auto-ID Lab Korea / KAIST
Slide 16
호주 i-Trace 프로젝트
© Auto-ID Lab Korea / KAIST
Slide 17
© Auto-ID Lab Korea / KAIST
Slide 19
Part IV – GS1 Standards
(GS1 국제표준기구)
© Auto-ID Lab Korea / KAIST
Slide 23
GS1 (Global Standards 1) 국제표준기구
(데이터, 비즈니스, 산업, 사물인터넷)
산업 분야에서
데이터를 표준화하고
공유하는 글로벌
플랫폼 제공
114개국 국가-MO (Member Organizations), 170개 국가 공식 활용
2백만 이상의 기업 멤버로 구성된 비영리 국제표준기구
한국은 대한상공회의소 산하의 GS1 Korea (유통물류진흥원) 와
Auto-ID Labs, KAIST GS1 사물인터넷 국제표준공동연구소 운영
(KAIST, MIT, Cambridge, ETH Zurich, Keio, Fudan University)
© Auto-ID Lab Korea / KAIST
Slide 24
여권으로 보는 국제표준 데이터 구성
데이터 DATA
식별자 (Identifier)
예) 여권 번호, 여권 발급 장소, 공항 출입국 심사대, 출입국 기기 식별자 등
어휘 (Vocabulary)
예) 사람 이름, 성별, 주민등록번호, 국적, 여권발급, 비자발급, 출입국 기록등
마스터 데이터 (Master Data)
변하지 않는 데이터
예) 이름, 성별, 국적, 사진
트랜잭션 데이터 (Transaction Data)
비즈니스 관련 데이터
예) 여권 발급, 수수료 납부, 비자 발급
이벤트 데이터 (Event Data)
발생 이벤트 관련 데이터
예) 출국 및 입국 이벤트, 출입국 특이 사항 이벤트
이미지 출처: 외교부 www.mofa.go.kr
© Auto-ID Lab Korea / KAIST
Slide 25
GS1 Company Prefix (식별자의 시작)
• GS1 국제 생태계에 진입하기 위해, 각 기업이나 기관은 국가코드와 기업/기관 코드로 이루어진 GS1
Company Prefix (GCP) 를 확보해야 함
• GS1 Company Prefix 를 기반으로 13종류이상의 다양한 글로벌 GS1 식별자를 사용할 수 있음 (GTIN, GRAI,
GIAI, GLN, GSRN, ….)
© Auto-ID Lab Korea / KAIST
Slide 26
주요 GS1 글로벌 식별자 (1/2)
상품 코드 (GTIN, Global Trade Item Number) 팔렛트 (GRAI, Global Returnable Asset Identifier) 기차, 선로, 장비 (GIAI, Global Individual Asset Identifier)
기차역, 조차장, 선로, 웍샵 (GLN, Global Location Number) 인증서, 계약서류등
(GDTI, Global Document Type Identifier)기관사, 승무원, 승객등 서비스 제공자와 제공받는자
(GSRN, Global Service Relation Number)
© Auto-ID Lab Korea / KAIST
Slide 27
주요 GS1 글로벌 식별자 (2/2)
기차 부품 코드 (CPID, Components and
Parts IDentifier)
책 및 잡지 (ISBN, ISSN)
할인 쿠폰 (GCN, Global Coupon Number)
운송 코드 (GSIN, Global Shipment Identification Number) 탁송코드 (GINC, Global Identification
Number for Consignment)
배송 박스 코드 (SSCC, Serialized Shipping
Container Code)
* 모델번호 (GMN, Global Model Number)
9791185343426
* GMN (Global Model Number)
© Auto-ID Lab Korea / KAIST
Slide 28
GS1 Application Identifiers
(응용 식별자)
~~~~
© Auto-ID Lab Korea / KAIST
Slide 29
GS1 GDSN, Global Registry Attributes [마스터 데이터]
B2B 상품 데이터 (+GS1 Source, GS1 Registry Platform)
- 표준화된 상품(사물) 데이터를 글로벌 하게 공유하기 위한 표준과 분산
데이터풀 (데이터베이스) 시스템
- 2020년 5월 1일 현재 전세계 43개 Data Pool 운영
• B2C의 상세 상품 데이터를 제공하기 위한 노력이 GDSN,
GS1 Source, GS1 Registry Platform 를 통해 상호 보완으로
진행되고 있음. (예. 식품의 성분, 인증, 앨러지 정보등)
© Auto-ID Lab Korea / KAIST
Slide 30
EPC Information Service (EPCIS)
GS1의 데이터공유 6하원칙
GS1의 6하원칙
WHO 스위스 철도회사
GS1 GLN
WHAT 프랑스 TGV 기차
GS1 GIAI
WHERE 베른역
GS1 GLN/SGLN + geo
WHEN 방금 / 어제
UTC
HOW GS1에는 없습니다. 어떻게 그
럴수가 있니?
WHY 도착했다
GS1 CBV Vocabulary/Data
Dictionary
© Auto-ID Lab Korea / KAIST
Slide 31
➢ 사물인터넷 이벤트 데이터의 상황을 표기 하기 위한 표준화된 어휘 (Common, Standard/User Vocabulary)
➢ Identifier 의 Syntax 와 Vocabulary Element 표준화
➢ 전산업에 공용인 CBV와 산업별, 국가별, 기업별로 새로운 Standard/User Vocabulary 표준화 가능
(1) urn:epcglobal:cbv:bizstep:assembling
(4) bizstep:arriving
(3) bizstep:departing
(11)bizstep: destroying
(7) bizstep:inspecting
(5) bizstep:unloading
(하차: 컨테이너 등)
(9) bizstep:repairing
<disposition>https://gs1.org/cbv/rail/di
sp/available<disposition>
(10) bizstep:replacing
(6) bizstep:retail_selling
(2) bizstep:loading
(상차: 컨테이너 등)
(8)
<bizStep>https://gs1.org/cbv/rail/disp/pl
anning_replacement</bizStep> (산업
표준 예제)
산업별 확장 지원
(철도산업, 수산업, 헬스케어,
스마트시티,…)
Also adopted by ISO/IEC
ISO/IEC 19987:2015 standard
비즈니스 프로세스 (마스터, 트랜잭션, 이벤트 데이터)를 기술하는 표준 어휘
Common Business Vocabulary (CBV)
(11)urn:epcglobal:cbv:disp:
Damaged
© Auto-ID Lab Korea / KAIST
Slide 32
GS1 국제 표준 – Lifetime 프로세스
모델링 기술
• ObjectEvent – 상품의 생성/관찰/제거
• AggregationEvent – 상품의 포장/적재
• TransactionEvent – 상품의 판매 상황
• TransformationEvent – 상품의 가공 상황
• Association Event – Coming soon
생성부터 판매까지 상품(사물)의 lifetime을
기록할 수 있는 데이터 모델을 통해서 상품
을 효율적으로 관리
What, Where, When, Why 를 통한 상세한 이벤트 정보 입력
© Auto-ID Lab Korea / KAIST
Slide 33
• EPC Information Service (EPCIS)
• Current Version 1.2, Oct. 2016
• Also adopted by ISO/IEC
ISO/IEC 19987:2015 standard
사물인터넷 이벤트를 저장하는 분산 연합 데이터 베이스
/ 왼쪽 그림 처럼 EPCIS 가 중앙에 하나 있는 게 아니라,
철도부품회사, 기차제작회사, 철도운영회사등이 제각기 운영하고,
가상으로는 단일의 데이터 저장소로 보임
- 기존 시스템 대체가 아니라, 별개 시스템 설치 후 쉽게 연결함
➢ RFID를 위한 EPCglobal 표준은 지난 5년간
확장되어, 사물인터넷의 인프라 프레임웍으로 발전중임
EPC Information Service (EPCIS)
트랜잭션 데이터와 이벤트 데이터 공유 (+EDI)
© Auto-ID Lab Korea / KAIST
Slide 34
GS1-Traceability (이력추적표준) Factory
▪ GS1 Global Traceability Standard에서는
▪ 상품(사물)의 생산-유통-소비 라이프사이클에 따른 이력 추적 관리 (TRACK)
▪ 최종 상품의 원산지 확인을 위한 역 이력 관리 (TRACE)
▪ 시스템 구축 가이드라인을 제시
© Auto-ID Lab Korea / KAIST
Slide 35
GS1 Digital Link + ONS
(QR 표준: 서비스 표준접근)
국제표준QR
(국가, 기업, 상품, 로트, 일련번호, 생
산연월일, 유통기한등)
© Auto-ID Lab Korea / KAIST
Slide 36
블록체인 (식품, 의약품, Identifier 등, 철도산업?)
GS1 표준 데이터로 상호 운용성 확보
© Auto-ID Lab Korea / KAIST
Slide 37
• Oliot Open Source Project
• http://oliot.org
GS1 EPCglobal 데이터 및 서비스 공유 국제표준 구현
• GS1 Source
• Pedigree
• Traceability & Recall
• ONS
• DS
• EPCIS
• F&C
• IoT connectivity Layer, etc.
Oliot Open Source Project (KAIST)
Apache License
© Auto-ID Lab Korea / KAIST
Slide 38
6 Continents, 103 countries, 1147 Cities
13618 Organizations/Companies/Individuals
2020.05.26.
2014년 6월25일 (Oliot 1.0)
2016년 (Oliot 1.2)
2020년 하반기 예정 (Oliot 2.0))
Part V – GS1 Rail Standards
© Auto-ID Lab Korea / KAIST
Slide 40
GS1 Rail Standards
• Identification of components and parts in the rail industry (식별체계)
• Application standard: "Identification of components and parts in the rail industry"
• Identification in Rail – Do’s and Don’t’s
• Brochure: "Improving safety and efficiency in the rail industry“
• Vehicle Visibility Standard (철도차량 일생 데이터 공유)
• Application standard: "GS1 EPCIS for Rail Vehicle Visibility"
• Brochure: "GS1 application standard for visibility in rail“
• Exchange of component/part lifecycle data in the rail industry (구성품/파
트 일생 데이터 공유)
• Application standard: "Exchange of component/part lifecycle data in the rail
industry“
© Auto-ID Lab Korea / KAIST
Slide 42
The Scope of the Document (표준 내용)
• GS1 identification keys (식별자) and attributes (속성) for the identification of
parts and components
• Interoperability (상호운용성) means the ability of a rail system to allow the safe and
uninterrupted movement of trains
• rolling stock of operator A (철도회사) can operate on infrastructure of infrastructure
managers B, C, D, etc., (철도인프라 회사) because the parts where the systems meet
(wheelsets, rails, ETCS-components, pantographs, switches, toilet drains, etc.) are guaranteed
to be compatible
• A key enabler will be the ability to unambiguously identify MRO-objects across the
systems and processes of all stakeholders.
• MRO-objects will need to be identified on class-level (클래스), lot-level(로트) and more and
more frequently up to serial-level (시리얼)
© Auto-ID Lab Korea / KAIST
Slide 44
Lifecycle identification of MRO-objects
- Value Chain (글로벌 밸류 체인)
• Today’s rail manufacturing
and MRO industry has
become global, with a
relatively small number of
system suppliers relying on
an ever more fragmented
international supply chain
with a network of specialised
suppliers for key
components and assemblies.
• 한번 주어진 이름은 변함없이
~~~
© Auto-ID Lab Korea / KAIST
Slide 45
Lifecycle identification of MRO-objects
Business processes – Process Roles (구성원)
© Auto-ID Lab Korea / KAIST
Slide 46
Lifecycle identification of MRO-objects
Need for traceability (일생 이력추적 요구)
Regulatory requirements (규정 요구사항)
“According to recent European legislation (see section 2) rail and rail network
operators must develop and maintain management systems which guarantee a safe and
stable operation as well as the interoperability of the assets used.”
This entails that all MRO-objects will undergo a risk analysis (리스크 분석) reflecting
their potential impact on safety. Moreover, a configuration management (구성/형상 관리)
is compulsory, as required by regulations 445/2011. 1169/2010 and 1158/2010.
Maintenance strategies (유지보수운영 요구사항)
One of the main defining elements of the rail industry is the fact that a substantial
number of MRO-objects (in rolling stock as well as in rail infrastructure) is procured for
a long-use life cycle of up to 60 years.(60년이상 운용) Such MRO-objects need to be
maintained, refurbished or replaced on a regular or on an ad-hoc basis.
© Auto-ID Lab Korea / KAIST
Slide 47
Lifecycle identification of MRO-objects
Configuration management (구성/형상 관리)
The system integrator (예. Alstom) will have a design BOM of the locomotive, and will create a
manufacturing BOM for each manufactured locomotive.
The sub system manufacturer of the brake system (예. ABB) will have a design BOM and a manufacturing
BOM for the sub system, consisting of several components that need to be integrated by the system
integrator.
Based on the data from the suppliers the system integrator will create an installation BOM. In that BOM the
brake system as a ‘whole’ will not be present, but primarily the serialised physical components that make up
the system.
© Auto-ID Lab Korea / KAIST
Slide 48
Identification and marking principles
(식별과 마킹)
Marking events during the MRO-object lifecycle Identification levels and GS1 identification keys
© Auto-ID Lab Korea / KAIST
Slide 49
Identification and marking principles
- Identification and marking scenarios
© Auto-ID Lab Korea / KAIST
Slide 51
The application standard has been created by a
team of rail stakeholders, solution providers and
GS1 Member Organisations. This represents a
significant achievement in collaboration and
consensus on the use of GS1 standards in the rail
sector.
Rail Vehicle Visibility
© Auto-ID Lab Korea / KAIST
Slide 53
Rail Vehicle Visibility
• Business needs
• The needs for information sharing (데이터 공유):
• Tracking of vehicles (철도차량 추적) – 한 국가 내 뿐만 아니라, 국경을 넘어서 국가간 추적/모니터링
• Associate the vehicle data with the Wayside Train Monitoring System (WTMS) data (차량 데이터
와 선로 차량 모니터링 시스템 데이터 융합) about vehicles (철도 차량) and vehicle components (차량
부품) to enhance preventive maintenance
• The use of RFID for railway vehicles becomes more and more popular..
Hot Axle-Box Detectors
(HABD) or hot-box detectors
Wheel impact load detectors
(WILD)
Acoustic Axle Bearing
Monitoring (AABM)
Automatic pantograph
monitoring systems (APMS)
© Auto-ID Lab Korea / KAIST
Slide 54
Rail Vehicle Visibility
• RFID enabled Automatic Vehicle Identification
(AVI) systems
• Identify all tagged vehicles and their order in
the train / Detect the presence of vehicles
with missing or broken tags and their relative
location in the train
• Important for the WTMS use case, since it
enables the measurement results to be
linked to the correct vehicles in a train set
• travel direction, the orientation, axle count,
speed, and length of each vehicle
• Enable train level information exchanges
• Ex) A train entering or leaving a yard and
the composition/formation of the train
• Train Management System (TMS)
• The information from the TMS can be used to
generate additional event data.
• Ex) The train entering or leaving an area
can be deduced by combining data from
a TMS and data from previously read
points provided by the AVI system.
Cross River Rail: How the European Train
Control System works
Vehicle Identification
© Auto-ID Lab Korea / KAIST
Slide 56
The GS1 system increases security when
capturing data
© Auto-ID Lab Korea / KAIST
Slide 57
Vehicle Identification
• Vehicle identification with “master” GIAI
• Identify each rail vehicle as an asset
• EPC URI is used to represent rail vehicles which are included in
EPCIS events.
• Ex) urn:epc:id:giai:4012345.98765432198765432
• Unambiguously determine static information about the rail vehicle
• Rail vehicle type, axle count, vehicle owner, etc. (master data)
• Not physical, deduced by proxy GIAI
© Auto-ID Lab Korea / KAIST
Slide 58
Vehicle Identification
• AIDC device identification with “proxy” GIAI
• Each of these tags is identified by a unique GIAI
• Ex) tag 1 of 2: urn:epc:tag:giai-96:1.4012345.18765432198765432
tag 2 of 2: urn:epc:tag:giai-96:1.4012345.28765432198765432
• These “device” GIAIs serve as “proxy” representation
Rail vehicle with multiple tags - top views Rail vehicle with multiple tags - side
views
Read Point and Business
Location Identification
How locations can be identified in a rail context
© Auto-ID Lab Korea / KAIST
Slide 60
Read Point and Business Location
Identification
• Read Points (센서나 장치가 읽은 물리적 위치 식별자)
• A location that is meant to identify the most specific place at which
an EPCIS event took place (Identified using the SGLN)
• Unique Read Point – Unambiguously determine the read point’s
physical location, line name/ID, and track name/ID
AVI system monitoring a single track AVI system monitoring multiple tracks
© Auto-ID Lab Korea / KAIST
Slide 61
Read Point and Business Location
Identification
• Business location (비즈니스 위치 식별자)
• The location where the rail vehicle is assumed to be following the
event (Assumed to be valid until superseded by the business
location of a subsequent event pertaining to the rail vehicle)
• Used to tell the location where the vehicles or trains are found after
the event took place
• Ex) track section, station, shunting yard, or specific shunting yard location
• Used to serve asset tracking needs
Determining vehicle and train
visibility data
© Auto-ID Lab Korea / KAIST
Slide 63
Determining vehicle and train visibility data
Determining Train Direction
• Determining the orientation of the rail vehicle
• The correct assignment of measurement values
requires the direction of travel of the vehicle.
• The orientation of a rail vehicle is determined by:
• The observed tag
• The train direction
Train direction indicator = 2 (compass direction = NE)
t1: Vehicle 2 – tag 2, vehicle end 2 passed first
t2: Vehicle 1 – tag 1, vehicle end 1 passed first
© Auto-ID Lab Korea / KAIST
Slide 64
Determining vehicle and train visibility data
• Determining Source and Destination (출발역, 종착역)
• Parties with access to the railroad plan – can derive this based on
the provided read points and direction information (철도편 정보 있
을시)
• Parties that do not have access – utilize the Source and Destination
elements in EPCIS (이벤트 데이터에 포함)
• Identified with SGLN
© Auto-ID Lab Korea / KAIST
Slide 65
Determining vehicle and train visibility data
• Determining a train passage (기차편)
• The AVI can detect whether observed vehicles are connected in the
same train-set.
• A separate EPCIS event for each vehicle observation (차량 하나 마다
EPCIS 이벤트 생성)
• Passage identifier (기차편명) should be included as an EPCIS
Business Transaction.
• A Transaction Event will be used to list all observed rail vehicles.
• The train number can be used to link to information in other train
management systems.
Sharing vehicle and train
visibility data with EPCIS
Special issues when applying Epcis functions to railway vehicle visibility
© Auto-ID Lab Korea / KAIST
Slide 67
Sharing vehicle and train visibility data with
EPCIS (critical tracking events)
Standard Rail Journey Diagram
© Auto-ID Lab Korea / KAIST
Slide 68
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• ObjectEvent (action OBSERVE) – serves as an observation of a
uniquely identified rail vehicle in passage along its journey, or upon
its arrival at or departure from a terminus
(철도 차량 한대씩 마다 이벤트 보고)
• TransactionEvent (action ADD) – serves as a “summary” event
following the observation of a passing train’s trailing vehicle,
reiterating the proxy GIAIs of positively identified vehicles, as well as
relevant totals for all vehicles
(마지막 차량 통과후 기차편에 대한 모든 정보
이벤트 보고)
© Auto-ID Lab Korea / KAIST
Slide 69
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• What
• Indicates the objects to which the EPCIS event pertains
• Each observed rail vehicle should be captured in a separate ObjectEvent.
• The epcList element should contain only the master GIAI of the observed
vehicle.
• Ex)
• A passage should be defined using a TransactionEvent.
• The epcList element includes the master GIAIs of all observed, positively
identified rail vehicles.
• Ex)
상세내용은 부록 참고
Examples of Rail Visibility
Events
© Auto-ID Lab Korea / KAIST
Slide 71
Examples of Rail Visibility Events
• Rail vehicle observations
• Two tags were observed
• First tag 2 of vehicle 676, after that tag 1 of vehicle 070
• The passage ID for both observed vehicles is the same
• Part of the same train set
070 676
Read point
1 2
12
© Auto-ID Lab Korea / KAIST
Slide 72
Examples of Rail Visibility Events
• Rail vehicle observations
Rail vehicle observation – Object event 1
© Auto-ID Lab Korea / KAIST
Slide 73
Examples of Rail Visibility Events
• Rail vehicle observations
Rail vehicle observation – Object event 2
© Auto-ID Lab Korea / KAIST
Slide 74
Examples of Rail Visibility Events
• Rail vehicle changing direction
Illustration of direction change – Object event 1
© Auto-ID Lab Korea / KAIST
Slide 75
Examples of Rail Visibility Events
• Rail vehicle changing direction
Illustration of direction change – Object event 2
© Auto-ID Lab Korea / KAIST
Slide 76
Examples of Rail Visibility Events
• Rail vehicle changing direction
Illustration of direction change – Object event 3
© Auto-ID Lab Korea / KAIST
Slide 77
Examples of Rail Visibility Events
• Train passage transaction event (including untagged vehicle)
• How a train passage can be expressed using a transaction event
• A train passage which consists of three rail vehicles
Train passage
© Auto-ID Lab Korea / KAIST
Slide 78
Examples of Rail Visibility Events
• Train passage transaction event (including untagged vehicle)
Train passage transaction event
© Auto-ID Lab Korea / KAIST
Slide 79
Examples of Rail Visibility Events
• Train passage transaction event (including untagged vehicle)
Train passage transaction event
EPCIS Query examples for
rail vehicle visibility
© Auto-ID Lab Korea / KAIST
Slide 81
EPCIS Query examples for rail vehicle
visibility
• EPCIS Query Control Interface
• On-demand (synchronous) – a client
makes a request through the EPCIS
Query Control Interface and receives a
response immediately
(바로 찾고자 하는 데이터를 보내주세요)
• Standing request (asynchronous) – a
client establishes a subscription for a
periodic query. Each time the periodic
query is executed, the results are
delivered asynchronously to a recipient
via the EPCIS Query Callback Interface
(원하는 데이터가 발생하면, 이리로 보내주
세요)
EPCIS Capturing Application
EPCIS Capture Interface
EPCIS
Repository
EPCIS Query Interface
(Control and Callback)
Business App.
Fig. EPCIS and its scope
© Auto-ID Lab Korea / KAIST
Slide 82
EPCIS Query examples for rail vehicle
visibility
• On-demand queries via EPCIS Query Control Interface
• Observations of a specified vehicle since a specified date/time
• Example of on-demand query by vehicle
• Observations of all vehicles at a specified read point in a specified
window of time
• Example of on-demand query by read point
© Auto-ID Lab Korea / KAIST
Slide 83
EPCIS Query examples for rail vehicle
visibility
• On-demand queries via EPCIS Query Control Interface
• Events for a given passage
• Example of on-demand query by passage ID
• Passage-level queries
• Example of on-demand query for passage events
© Auto-ID Lab Korea / KAIST
Slide 84
EPCIS Query examples for rail vehicle
visibility
• Standing queries (Subscriptions) via EPCIS Query Call-back Interface
• Notification whenever a specified vehicle is observed at any read point
• Example of standing query by vehicle
• Notification whenever any uniquely identified vehicle is observed a ta
specified read point
• Example of standing query by read point
© Auto-ID Lab Korea / KAIST
Slide 86
Business Intention
• Rail stakeholders can develop and share manufacturing & maintenance
and usage information, enabling rail equipment operators to
consistently fulfil tracking and tracing needs while reducing overall costs.
• strongly facilitate tracking and tracing throughout the complete lifecycle
of, for instance, an individual object, across companies and borders;
• unify the data exchange process requirements by rail and rail network
operators in regards to unit suppliers and manufacturers; and
• thereby allow for new supply chain design possibilities (e.g., stock and
supply sharing, pay per use, user specific R&D programmes, etc.)
© Auto-ID Lab Korea / KAIST
Slide 87
Visibility events for rail manufacturing and MRO
- Mapping of rail business processes to visibility events
© Auto-ID Lab Korea / KAIST
Slide 88
Visibility events for rail manufacturing and MRO
- Rolling stock visibility events
Data exchanges during lifecycle of rolling stock
Rolling stock visibility events
© Auto-ID Lab Korea / KAIST
Slide 89
Visibility events for rail manufacturing and MRO
- Rolling stock visibility events
© Auto-ID Lab Korea / KAIST
Slide 90
Visibility events for rail manufacturing and MRO
- Rolling stock visibility events
© Auto-ID Lab Korea / KAIST
Slide 91
Visibility events for rail manufacturing and MRO
- Rolling stock visibility events
© Auto-ID Lab Korea / KAIST
Slide 92
Visibility events for rail manufacturing and MRO
- Infrastructure visibility events
Data exchanges during infrastructure projects
Infrastructure visibility events
© Auto-ID Lab Korea / KAIST
Slide 93
Visibility events for rail manufacturing and MRO
- Infrastructure visibility events
© Auto-ID Lab Korea / KAIST
Slide 94
Visibility events for rail manufacturing and MRO
- Infrastructure visibility events
© Auto-ID Lab Korea / KAIST
Slide 95
Visibility events for rail manufacturing and MRO
© Auto-ID Lab Korea / KAIST
Slide 96
Master Data
- Trade item (class-level) master data
© Auto-ID Lab Korea / KAIST
Slide 97
Master Data
- Trade item (instance/lot level) master data
© Auto-ID Lab Korea / KAIST
Slide 101
Location and party master data
- Location and party master data attributes
Location master data attributes
may be used to describe a location
identifier;
this identifier SHOULD be a Global
Location Number (GLN), expressed
in EPC URI format as an SGLN,
whether the location identifier is
used as a EPCIS Read Point,
Business Location, Source, or
Destination.
© Auto-ID Lab Korea / KAIST
Slide 102
Location and party master data
- Geofence polygons (GFP extension)
© Auto-ID Lab Korea / KAIST
Slide 103
Rail-specific EPCIS event extensions
(철도 산업에 특화된 이벤트 확장 데이터)
• Each Rail-specific extension is assigned the following
namespace identifier:
https://gs1.org/cbv/rail
The namespace should be declared, along with the EPCIS
standard namespace(s), in the beginning of the EPCIS header,
as follows:
© Auto-ID Lab Korea / KAIST
Slide 104
Rail-specific EPCIS event extensions
Updated Configuration Data (UCD)
• updates to configuration that are generated for instance/lot (LGTIN, SGTIN or GIAI) following
repair/refurbishment of an assembly/vehicle/infrastructure – potentially superseding original
CMD & ILMD values – are not considered master data
© Auto-ID Lab Korea / KAIST
Slide 105
Rail-specific EPCIS event extensions
Runtime Condition Data (RCD)
Sensor/runtime data may be obtained in two major
ways:
By sensors directly affixed to the object in question, which
mainly measure relevant properties for the object itself
(e.g., internal activation cycles of an instance),
By inheritance from parent objects in an aggregated
assembly. An example of inherited (or “global”)
sensor/runtime data is the mileage recorded on rail vehicle
level, which could be used to update the mileage of
relevant components and parts, taking the time of
installation into account.
© Auto-ID Lab Korea / KAIST
Slide 106
Rail-specific EPCIS event extensions
Relative Position of Child (CRP)
Where it is necessary to express relative position of assembled and/or installed components within their
parent assembly, as per externally maintained standards, including but not limited to:
• EN 15380-2: logical description in train (similar to e-class)
• EN 15380-3: position installation description (e.g., left/right)
EN 15380 -
Railway
applications.
Classification
system for
railway
vehicles
© Auto-ID Lab Korea / KAIST
Slide 107
Rail-specific EPCIS event extensions
Leading Part (LP)
When it comes to the identification of assemblies, two main scenarios can occur in practice:
1. The ID of the leading part is different from the ID of the assembly.
2. The ID of the leading part is used as the ID of the assembly (when the part is in assembled
state).
Since each party is free to apply either one of the scenarios, and a given party’s approach may
not be known in advance, it is important to include sufficient information to eliminate ambiguity.
The party SHALL always transmit the leading part ID.
© Auto-ID Lab Korea / KAIST
Slide 108
Rail-specific EPCIS event extensions
Leading Part (LP)
© Auto-ID Lab Korea / KAIST
Slide 109
Rail-specific EPCIS event extensions
Inspection Report (IR)
1. OBSERVE event, business step inspecting
2. OBSERVE event, business step repairing
© Auto-ID Lab Korea / KAIST
Slide 110
Rail-specific EPCIS event extensions
Inspection Report (IR)
1. OBSERVE event, business step inspecting
2. OBSERVE event, business step repairing
© Auto-ID Lab Korea / KAIST
Slide 111
Rail-specific EPCIS event extensions
Planned Replacement (PR)
To satisfy Infrastructure
planning requirements,
planned replacement
parts and planned
replacement dates MAY be
specified at the time of
commissioning/original
installation of the planned
replacement’s
predecessor (i.e., of the
currently installed part)
by means of a Rail-
specific extension to an
Object Event or an
Aggregation Event
© Auto-ID Lab Korea / KAIST
Slide 112
EPCIS Query examples
Part V – 디지털 인프라, 디지털 SOC 인프라
디지털 인프라
© Auto-ID Lab Korea / KAIST
Slide 115
디지털 인프라, SoC의 디지털화? 국제 표준이 기본입니다.
[schema.org + GS1 + BIM/3D GIS]
• TrainStation - schema.org - https://schema.org/TrainStation
(웹 데이터 국제 고속도로)
• Rail Standards | GS1 - https://www.gs1.org/…/technical-industr…/rail/rail-standards
(산업 데이터 국제 고속도로)
• IFC Rail - buildingSMART International - https://www.buildingsmart.org/ifc-rail-
candidate-standard-…/
(공간 데이터 국제고속도로)
Schema.org, GS1, BIM 모두 기차역은 GS1의 GLN(Global Location Number) 식별자를 가집니다
© Auto-ID Lab Korea / KAIST
Slide 116
관련 슬라이드 및 동영상 자료
[0] GS1 튜토리얼 세미나 자료
https://www.slideshare.net/gatordkim/gs1-tutorial-korean-by-daeyoung-kim-autoid-labs-kaist
[1] GS1 튜토리얼 세미나 발표동영상 (1/3)
https://youtu.be/rNaUpbO0fqY
[2] GS1 튜토리얼 세미나 발표동영상 (2/3)
https://youtu.be/4I0HNSM_Veg
[3] GS1 튜토리얼 세미나 발표동영상 (3/3)
https://youtu.be/cvC3B6vFqPg
[4] Smart City Position Paper - GS1 Standards Perspective
https://www.slideshare.net/gatordkim/smart-city-position-paper-gs1-standards-perspective
[5] GS1 EPCIS, CBV 세미자자료
https://www.slideshare.net/gatordkim/gs1-epcis-and-cbv-tutorial-autoid-labs-kaist-apr-28-2020
[6] GS1 EPCIS, CBV 세미나 발표동영상
https://www.youtube.com/watch?v=d-ubhyXyT3A
[7] GS1 ONS, Digital Link 세미나자료
https://www.slideshare.net/gatordkim/gs1-ons-and-digital-link-tutorial-autoid-labs-kaist-apr-28-2020
[8] GS1 ONS, Digital Link 세미나 발표동영상
https://www.youtube.com/watch?v=bs3OjSpyH60
[9] GS1발 데이터혁명시리즈 1 - 안전한 수산물 확보와 해양생물 보존을 위한 글로벌 수산물 이력 추적 시스템 구축 동향과 기술 – 세미나 자료
https://www.slideshare.net/gatordkim/digital-revolution-series-1seafood-industry
[10] GS1발 데이터혁명시리즈 1 - 안전한 수산물 확보와 해양생물 보존을 위한 글로벌 수산물 이력 추적 시스템 구축 동향과 기술 – 발표 동영상 자료
https://www.youtube.com/watch?v=ohuQyBsE8fQ&feature=youtu.be
Identify,
Capture,
Share,
Use,
and Imagine
Thank you
© Auto-ID Lab Korea / KAIST
Slide 119
FDA’s New Era of Smarter Food Safety
November 26, 2019
Railroads Need to Step Up Their Game, New Report Says
It reveals major concerns of shippers about rail’s service
quality, reliability, degree of communication, flexibility and
cost.
why railroads should be focusing on digital transformation
and the end-to-end supply chain.
https://www.supplychainbrain.com/articles/30529-a-new-report-says-railroads-need-
to-step-up-their-game
First of all, it's more than just that railways need to change. There
needs to be a coming together of carriers, shippers and investors.
We interviewed those groups individually. Now we need to create a
forum where they're sitting together, much like GS1 does with its global
council structure, where everybody comes to the table, puts their
differences aside, signs NDAs [non-disclosure agreements], and
comes up with solutions that will benefit the entire ecosystem.
SCB: What steps should railroads be taking to meet shippers’
concerns and improve overall performance?
© Auto-ID Lab Korea / KAIST
Slide 120
© Auto-ID Lab Korea / KAIST
Slide 121
https://www.itln.in/indian-railways-to-track-350000-wagons-and-coaches-using-rfid-by-2021
© Auto-ID Lab Korea / KAIST
Slide 124
© Auto-ID Lab Korea / KAIST
Slide 125
© Auto-ID Lab Korea / KAIST
Slide 127
The Scope of the Document
• This document explains how to use the GS1 identification keys and attributes for the
identification of parts and components in the rail industry.
• In the rail sector interoperability means the ability of a rail system to allow the safe and
uninterrupted movement of trains while accomplishing the required performance level.
• This helps to ensure that rolling stock of operator A can operate on infrastructure of
infrastructure managers B, C, D, etc., because the parts where the systems meet (wheelsets, rails,
ETCS-components, pantographs, switches, toilet drains, etc.) are guaranteed to be compatible
due to international norms.
This standard is intended to be used by all parties involved in rail manufacturing, maintenance,
repair, and overhaul processes. These include:
• Manufacturers (system integrators, system manufacturers, component supplier),
• Operators (rail network operators, rail operators),
• Service providers (MRO workshops, project contractors, logistics service providers, and
• Regulators.
© Auto-ID Lab Korea / KAIST
Slide 128
The Scope of the Document
• At the same time the rail industry is being challenged by its customers to improve reliability
and quality, and by regulatory bodies to implement measures aimed at further improving safety.
• As a result manufacturing, maintenance, repair and overhaul (in short Manufacturing & MRO)
processes have become far more international and complex than before. This drives the need
for greater interoperability among rail manufacturing & MRO process stakeholders and among
their systems and supply chains.
• In order to meet these challenges, the entire rail industry must improve its manufacturing &
MRO processes and in particular develop capabilities for reliable life cycle tracking of
components and parts (referred to as MRO-objects in this standard) across companies, supply
chains and over life cycles of up to 60 years.
• A key enabler will be the ability to unambiguously identify MRO-objects across the systems and
processes of all stakeholders. Depending on the operational and safety characteristics as well as
legal requirements MRO-objects will need to be identified on class-level, lot-level and more and
more frequently up to serial-level.
© Auto-ID Lab Korea / KAIST
Slide 129
Lifecycle identification of MRO-objects
Business processes – Roles & Responsibilities
© Auto-ID Lab Korea / KAIST
Slide 130
Lifecycle identification of MRO-objects
Need for traceability (일생 이력추적 요구)
The maintenance organisations responsible for the objects needing maintenance will act
based upon a wide variety of triggers that will signal that objects require planned or
emergency or ad-hoc maintenance. (Maintenance 를 위한 표준 데이터 공유)
<Types of maintenance strategies>
© Auto-ID Lab Korea / KAIST
Slide 131
Lifecycle identification of MRO-objects
Configuration management
Composite MRO-objects will be manufactured and maintained using a bill-of-material (BOM).
Composite MRO-objects may contain other composite MRO-objects (produced by other
manufacturers), which means that it must be possible to link BOMs.
Three types of BOMs that may be applied, each with specific characteristics, are:
1. Design BOM: A standard BOM used in conjunction with the technical design, used as a basis for
the manufacturing process. It will define the MRO-objects in terms of their type and position, but will
not contain any serialised IDs or lot level IDs.
2. Manufacturing BOM: An instance BOM that is created during the manufacturing process and
defines the MRO-object ‘as built’. It will contain a mixture of serialised and non-serialised IDs of the
contained instances. Composite MRO-objects sourced from another party should have a serialised ID
allowing to link to the manufacturing BOM of the supplier. This linking of instance BOMs is an
essential aspect.
3. Installation BOM: An instance BOM that is used by the operator and the manufacturer’s after sales
service organisation and used for the maintenance process. Like the manufacturing BOM this is an
instance BOM, but unlike the manufacturing BOM the installation BOM will only contain instances
that can be physically identified (serialised MRO-objects).
© Auto-ID Lab Korea / KAIST
Slide 132
Lifecycle identification of MRO-objects
Configuration management
The system integrator will have a design BOM of the locomotive, and will create a manufacturing BOM for
each manufactured locomotive.
The sub system manufacturer of the brake system will have a design BOM and a manufacturing BOM for
the sub system, consisting of several components that need to be integrated by the system integrator.
Based on the data from the suppliers the system integrator will create an installation BOM. In that BOM the
brake system as a ‘whole’ will not be present, but primarily the serialised physical components that make
up the system.
© Auto-ID Lab Korea / KAIST
Slide 133
Identification and marking principles
- Identification and marking scenarios
© Auto-ID Lab Korea / KAIST
Slide 134
Significant business benefits for all players
(표준 데이터 수집의 목적)
Due to limited visibility and
information about rail vehicles, it’s
difficult for rail operators to plan and
meet customer demands for timely
deliveries and updates.
Provides roadmap for rail
stakeholders to gain visibility of
rolling stock and access to real-time
information
(적절한 시간에 유지보수)
(분석과 사고 조사) (안전관련 정보 공유)
(고장 탐지) (리콜 관리)
© Auto-ID Lab Korea / KAIST
Slide 135
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• When
• eventTime – the time at which the vehicle was observed (Object events), the
time at which the first (leading) vehicle of a passing trainset was observed
(Transaction events)
• recordTime – the date and time at which this event was recorded by an EPCIS
Repository
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 136
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Where
• Indicates the location at which the EPCIS event was observed, as well as the
whereabouts of the object subsequent to the event
• readPoint – the SGLN corresponding to the event’s location
• bizLocation – the SGLN corresponding to the object’s whereabouts
subsequent to the event
• For all object events and transaction events, either the readPoint or the
bizLocation or both should be populated.
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 137
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Why
• Reflects the business context (“Business Step”) of the EPCIS event, as well as
the status (“Disposition”) of the object subsequent to the event
• Business Step
• Specifies the business process linked to the EPCIS event
• Ex)
• Disposition
• Denotes the status of an object subsequent to the EPCIS event
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 138
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Why
• Source/Destination
• Use the urn:epcglobal:cbv:sdt:location source/destination type identifier
with SGLN
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 139
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Why
• Business Transactions
• EPCIS Business Transactions are defined using a combination of Business
Transaction Type and Business Transaction ID
• Rail sector-specific vehicle visibility applications should use HTTP URLs for business
transaction identifiers
• To share information about a passage, the bizTransaction element can be used
in two ways:
• as a transaction reference in a rail visibility ObjectEvent, to indicate which
events belong to the same ‘passage’
• as a transaction type in a rail visibility TransactionEvent, to specify totals
and tag IDs for a particular ‘passage’
• Ex) transaction reference
© Auto-ID Lab Korea / KAIST
Slide 140
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Extension elements
• All of these elements should be specified using the namespace
urn:gs1:epcisapp:rail
• Direction elements (for object and transaction events)
• directionIndicator
• 0 : the direction was not detected
• 1 : the first direction in the rail network
• 2 : the second direction in the rail network
• compassDirection
• Using a cardinal (N, S, W, E) or inter-cardinal (NW, NE, SE, SW)
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 141
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Extension elements
• Object event elements
• vehicleOrientation
• 1 : vehicle end one is leading, relative to the direction of travel
• 2 : vehicle end two is leading, relative to the direction of travel
• 0 : leading vehicle end not determined
• vehiclePosition – A number identifying the relative position of the rail vehicle
within the passage
• vehicleAxleCount – The number of axles of the vehicle
• proxyGIAI – the GIAI(s) of the observed tag(s)
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 142
Sharing vehicle and train visibility data with
EPCIS
• EPCIS event data
• Transaction event elements (optional)
• trainAxleCount – the total number of axles observed for the passage of the entire trainset
• trainVehicleCount – the total number of vehicles observed for the passage of the entire trainset
• Vehicle – information on each of the observed vehicles
• vehiclePosition – the relative position of the vehicle in the passage
• vehicleAxleCount – the number of axles of the observed vehicle
• vehicleUniqueIdentified – indicates whether the ID of the observed rail vehicle was captured
• vehicleMasterGIAI – optional element specifying the ID of the rail vehicle
• Ex)
© Auto-ID Lab Korea / KAIST
Slide 143
Master Data
- Name Space
© Auto-ID Lab Korea / KAIST
Slide 144
Rail-specific EPCIS event extensions
Updated Configuration Data (UCD)
• By contrast, updates to configuration that are generated for instance/lot (LGTIN, SGTIN or GIAI)
following repair/refurbishment of an assembly/vehicle/infrastructure – potentially superseding
original CMD & ILMD values – are not considered master data, but instead SHALL be reflected
in a Rail-specific extension for Updated Configuration Data (UCD).
• The attributes SHALL be placed within an XML element rail:UpdatedConfigurationData.
© Auto-ID Lab Korea / KAIST
Slide 145
Rail-specific EPCIS event extensions
Runtime Condition Data (RCD)
Sensor/runtime data may be obtained in two major ways:
■ By sensors directly affixed to the object in question, which mainly measure relevant properties
for the object itself (e.g., internal activation cycles of an instance),
■ By inheritance from parent objects in an aggregated assembly. An example of inherited (or
“global”) sensor/runtime data is the mileage recorded on rail vehicle level, which could be used to
update the mileage of relevant components and parts, taking the time of installation into account.
Rules for “direct sensor data”
Each attached sensor SHOULD be identified uniquely with a GIAI or SGTIN, and the sensor data
SHOULD be specified in the RCD extension of the ObjectEvent related to the sensor in question.
Sensor installation should be captured by means of Aggregation events (action: ADD/business
step: installing/disposition: active) including the sensor as child and the object to which the sensor
is affixed as parent.
Rules for “inherited sensor data”
Handling of sensor/runtime data inheritance is at the discretion of the operators that integrate
these parts and subcomponents. The values will be specified in the RCD extension of an
ObjectEvent related to the object in question.
When disaggregating, repairing or refurbishing, inherited sensor values SHOULD be updated to
reflect current values of the parent and impacted children.
© Auto-ID Lab Korea / KAIST
Slide 146
Rail-specific EPCIS event extensions
Runtime Condition Data (RCD)
© Auto-ID Lab Korea / KAIST
Slide 147
Rail-specific EPCIS event extensions
Relative Position of Child (CRP)
Where it is necessary to express relative position of assembled and/or installed components within
their parent assembly, as per externally maintained standards, including but not limited to:
■ EN 15380-2: logical description in train (similar to e-class)
■ EN 15380-3: position installation description (e.g., left/right)
…each such component must be expressed as an “only child” without siblings when aggregated to
its parent in an EPCIS Aggregation Event.
© Auto-ID Lab Korea / KAIST
Slide 148
Rail-specific EPCIS event extensions
Leading Part (LP)
When it comes to the identification of assemblies, two main scenarios can occur in practice:
1. The ID of the leading part is different from the ID of the assembly.
2. The ID of the leading part is used as the ID of the assembly (when the part is in assembled
state).
Since each party is free to apply either one of the scenarios, and a given party’s approach may
not be known in advance, it is important to include sufficient information to eliminate ambiguity.
The party SHALL always transmit the leading part ID.
© Auto-ID Lab Korea / KAIST
Slide 149
Rail-specific EPCIS event extensions
Leading Part (LP)
© Auto-ID Lab Korea / KAIST
Slide 150
Rail-specific EPCIS event extensions
Leading Part (LP)
© Auto-ID Lab Korea / KAIST
Slide 151
Rail-specific EPCIS event extensions
Leading Part (LP)
© Auto-ID Lab Korea / KAIST
Slide 152
Rail-specific EPCIS event extensions
Leading Part (LP)
© Auto-ID Lab Korea / KAIST
Slide 153
Rail-specific EPCIS event extensions
Inspection Report (IR)
1. OBSERVE event, business step inspecting
2. OBSERVE event, business step repairing
© Auto-ID Lab Korea / KAIST
Slide 154
Rail-specific EPCIS event extensions
Inspection Report (IR)
© Auto-ID Lab Korea / KAIST
Slide 155
Rail-specific EPCIS event extensions
Planned Replacement (PR)
To satisfy Infrastructure planning requirements, planned replacement parts and planned
replacement dates MAY be specified at the time of commissioning/original installation of the
planned replacement’s predecessor (i.e., of the currently installed part) by means of a Rail-
specific extension to an Object Event or an Aggregation Event, as indicated below.
A lone part MAY be designated for replacement by multiple parts; in this case there SHALL be a
list of GTINs designated as replacement parts.
© Auto-ID Lab Korea / KAIST
Slide 156
Rail-specific EPCIS event extensions
Planned Replacement (PR)
© Auto-ID Lab Korea / KAIST
Slide 157
Rail-specific EPCIS event extensions
Planned Replacement (PR)
© Auto-ID Lab Korea / KAIST
Slide 158
Rail Vehicle Visibility
• Asset tracking applications
• Normal operative functions where the location of specific vehicles
needs to be known
• Vehicle level management
• Tracking the location and status of each vehicle as they travel
• Independent of the train
• RFID allows for automatic collection of all measurement results
and creating statistical data of measurement results per vehicle.
• Potential future applications:
• Real-time cargo tracking
• Planning vehicle availability
• Estimating the vehicle distance travelled for planning preventive maintenance
© Auto-ID Lab Korea / KAIST
Slide 159
Rail Vehicle Visibility
• RFID enabled Automatic Vehicle
Identification (AVI) systems
• Fixed readers and wheel sensors (at trackside)
• The fixed trackside readers identify the vehicles of
the passing train.
• Identify all tagged vehicles and their order in the
train
• Detect the presence of vehicles with missing or
broken tags and their relative location in the
train
• Important for the WTMS use case, since it enables
the measurement results to be linked to the correct
vehicles in a train set
• Determine the travel direction, the orientation,
axle count, speed, and length of each vehicle
• Enable train level information exchanges
• Ex) A train entering or leaving a yard and the
composition/formation of the train
Fixed trackside RFID configuration
© Auto-ID Lab Korea / KAIST
Slide 160
Rail Vehicle Visibility
• Train Management System (TMS)
• A system used to control railway operations
• Detect and control movement of trains on a track
• The information from the TMS can be used to generate additional
event data.
• Ex) The train entering or leaving an area can be deduced by combining data
from a TMS and data from previously read points provided by the AVI system.
Cross River Rail: How the
European Train Control System
works
© Auto-ID Lab Korea / KAIST
Slide 161
Scope of the document
• It explains how to implement the GS1 EPCIS standard to
exchange of component/part lifecycle data in the rail industry.
The scope of this document includes:
• Lifecycle/business events that will require a set of key data elements to
be recorded (and shared)
• Roles and responsibilities related to recording and data sharing
• Data structures and definitions
• XML-syntax representations for each message
• Message exchange scenarios
© Auto-ID Lab Korea / KAIST
Slide 162
Business Intention
• Rail stakeholders can develop and share manufacturing & maintenance
and usage information, enabling rail equipment operators to
consistently fulfil tracking and tracing needs while reducing overall costs.
• strongly facilitate tracking and tracing throughout the complete lifecycle
of, for instance, an individual object, across companies and borders;
• unify the data exchange process requirements by rail and rail network
operators in regards to unit suppliers and manufacturers; and
• thereby allow for new supply chain design possibilities (e.g., stock and
supply sharing, pay per use, user specific R&D programmes, etc.)
© Auto-ID Lab Korea / KAIST
Slide 163
Master Data
- Trade item master data
This section specifies master data attributes that may be used to describe a trade item identifier that appears in the “what”
dimension of an EPCIS event. “trade item” refers to all MRO objects, including items from manufacturers.
© Auto-ID Lab Korea / KAIST
Slide 164
Master Data
- Trade item (class-level) master data
Class-level master data related to a GTIN may undergo changes over time. This can lead to multiple sets of class-level master data for the
same GTIN. In order to distinguish these sets, the functional status and revision status SHALL be expressed as attributes of the
VocabularyElement.
Rail-specific enhancement for class-level master data
© Auto-ID Lab Korea / KAIST
Slide 165
Location and party master data
This section specifies master data attributes that may be used to describe a
location identifier. The following general rules apply:
■ Location master data attributes may be used to describe a location identifier; this
identifier SHOULD be a Global Location Number (GLN), expressed in EPC URI format as
an SGLN, whether the location identifier is used as a EPCIS Read Point, Business
Location, Source, or Destination.
■ A Rail Component/Part EPCIS document MAY include any of the master data attributes
specified in this section within the master data section of the EPCIS Document
header, subject to the constraints specified elsewhere in this section.
■ The master attributes specified in this section may also be used in an EPCIS Master
Data Document or in the response to an EPCIS Master Data Query.
■ A Rail Component/Part EPCIS document SHALL NOT include any of the master data
attributes specified in this section in the ILMD section of an EPCIS event.
© Auto-ID Lab Korea / KAIST
Slide 166
Examples of Rail Visibility Events
SBB (Swiss Federal Railways) and other international rail
operators have decided to implement standardised labelling.
This guarantees a transparent flow of materials and
information throughout their entire life-cycles, including
production, storage, installation, operation and repairs.
© Auto-ID Lab Korea / KAIST
Slide 167
Examples of Products
© Auto-ID Lab Korea / KAIST
Slide 168
Case study

Contenu connexe

Tendances

Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...
Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...
Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...Amazon Web Services
 
클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사
클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사
클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사Amazon Web Services Korea
 
Solution deck capgemini cloud assessment
Solution deck capgemini cloud assessmentSolution deck capgemini cloud assessment
Solution deck capgemini cloud assessmentAdobe
 
DMPs are Dead. Welcome to the CDP Era.
DMPs are Dead. Welcome to the CDP Era.DMPs are Dead. Welcome to the CDP Era.
DMPs are Dead. Welcome to the CDP Era.mParticle
 
AI Data Acquisition and Governance: Considerations for Success
AI Data Acquisition and Governance: Considerations for SuccessAI Data Acquisition and Governance: Considerations for Success
AI Data Acquisition and Governance: Considerations for SuccessDatabricks
 
Data Governance
Data GovernanceData Governance
Data GovernanceRob Lux
 
Sisense Introduction PPT
Sisense Introduction PPTSisense Introduction PPT
Sisense Introduction PPTKhirod Sahu
 
Introduce kongtech co., ltd. (콩테크 회사소개서)
Introduce kongtech co., ltd. (콩테크 회사소개서)Introduce kongtech co., ltd. (콩테크 회사소개서)
Introduce kongtech co., ltd. (콩테크 회사소개서)콩테크(kongtech)
 
Ross Chayka. Gartner Hype Cycle
Ross Chayka. Gartner Hype CycleRoss Chayka. Gartner Hype Cycle
Ross Chayka. Gartner Hype CycleLviv Startup Club
 
디지털트윈, 스마트시티, 메타버스
디지털트윈, 스마트시티, 메타버스디지털트윈, 스마트시티, 메타버스
디지털트윈, 스마트시티, 메타버스SANGHEE SHIN
 
Black Box Global Corporate Presentation - Jul'23
Black Box Global Corporate Presentation - Jul'23Black Box Global Corporate Presentation - Jul'23
Black Box Global Corporate Presentation - Jul'23Black Box
 
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
 
The People Model & Cloud Transformation - Transformation Day Public Sector Lo...
The People Model & Cloud Transformation - Transformation Day Public Sector Lo...The People Model & Cloud Transformation - Transformation Day Public Sector Lo...
The People Model & Cloud Transformation - Transformation Day Public Sector Lo...Amazon Web Services
 
Customer Data Platform 101
Customer Data Platform 101Customer Data Platform 101
Customer Data Platform 101Kiyoto Tamura
 
Creating an Enterprise AI Strategy
Creating an Enterprise AI StrategyCreating an Enterprise AI Strategy
Creating an Enterprise AI StrategyAtScale
 
Developing Data Products
Developing Data ProductsDeveloping Data Products
Developing Data ProductsPeter Skomoroch
 
[Datanest] AI startup in Indonesia - March 2018
[Datanest] AI startup in Indonesia - March 2018[Datanest] AI startup in Indonesia - March 2018
[Datanest] AI startup in Indonesia - March 2018Thibaud Plaquet
 

Tendances (20)

Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...
Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...
Transforming Consumer Banking with a 100% Cloud-Based Bank (FSV204) - AWS re:...
 
클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사
클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사
클라우드 뉴노멀 시대의 글로벌 혁신 기업들의 디지털 트랜스포메이션 :: 정우진 이사
 
Solution deck capgemini cloud assessment
Solution deck capgemini cloud assessmentSolution deck capgemini cloud assessment
Solution deck capgemini cloud assessment
 
DMPs are Dead. Welcome to the CDP Era.
DMPs are Dead. Welcome to the CDP Era.DMPs are Dead. Welcome to the CDP Era.
DMPs are Dead. Welcome to the CDP Era.
 
AI Data Acquisition and Governance: Considerations for Success
AI Data Acquisition and Governance: Considerations for SuccessAI Data Acquisition and Governance: Considerations for Success
AI Data Acquisition and Governance: Considerations for Success
 
AWS Manufacturing.pdf
AWS Manufacturing.pdfAWS Manufacturing.pdf
AWS Manufacturing.pdf
 
Data Governance
Data GovernanceData Governance
Data Governance
 
Sisense Introduction PPT
Sisense Introduction PPTSisense Introduction PPT
Sisense Introduction PPT
 
Introduce kongtech co., ltd. (콩테크 회사소개서)
Introduce kongtech co., ltd. (콩테크 회사소개서)Introduce kongtech co., ltd. (콩테크 회사소개서)
Introduce kongtech co., ltd. (콩테크 회사소개서)
 
Ross Chayka. Gartner Hype Cycle
Ross Chayka. Gartner Hype CycleRoss Chayka. Gartner Hype Cycle
Ross Chayka. Gartner Hype Cycle
 
Big Data Security and Governance
Big Data Security and GovernanceBig Data Security and Governance
Big Data Security and Governance
 
디지털트윈, 스마트시티, 메타버스
디지털트윈, 스마트시티, 메타버스디지털트윈, 스마트시티, 메타버스
디지털트윈, 스마트시티, 메타버스
 
Black Box Global Corporate Presentation - Jul'23
Black Box Global Corporate Presentation - Jul'23Black Box Global Corporate Presentation - Jul'23
Black Box Global Corporate Presentation - Jul'23
 
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
 
Workshop: Make the Most of Customer Data Platforms - David Raab
Workshop: Make the Most of Customer Data Platforms - David RaabWorkshop: Make the Most of Customer Data Platforms - David Raab
Workshop: Make the Most of Customer Data Platforms - David Raab
 
The People Model & Cloud Transformation - Transformation Day Public Sector Lo...
The People Model & Cloud Transformation - Transformation Day Public Sector Lo...The People Model & Cloud Transformation - Transformation Day Public Sector Lo...
The People Model & Cloud Transformation - Transformation Day Public Sector Lo...
 
Customer Data Platform 101
Customer Data Platform 101Customer Data Platform 101
Customer Data Platform 101
 
Creating an Enterprise AI Strategy
Creating an Enterprise AI StrategyCreating an Enterprise AI Strategy
Creating an Enterprise AI Strategy
 
Developing Data Products
Developing Data ProductsDeveloping Data Products
Developing Data Products
 
[Datanest] AI startup in Indonesia - March 2018
[Datanest] AI startup in Indonesia - March 2018[Datanest] AI startup in Indonesia - March 2018
[Datanest] AI startup in Indonesia - March 2018
 

Similaire à GS1 Data Revolution Series 2 - Internet of Trains

(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...Daeyoung Kim
 
Soscon2016 daeyoungkim-kaist - final
Soscon2016 daeyoungkim-kaist - finalSoscon2016 daeyoungkim-kaist - final
Soscon2016 daeyoungkim-kaist - finalDaeyoung Kim
 
Oliot samsung-daeyoungkim-kaist wide-version-final
Oliot samsung-daeyoungkim-kaist wide-version-finalOliot samsung-daeyoungkim-kaist wide-version-final
Oliot samsung-daeyoungkim-kaist wide-version-finalDaeyoung Kim
 
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...Daeyoung Kim
 
Internet of Things Platform for Open Process, Open Data, and Open Service
Internet of Things Platform for Open Process, Open Data, and Open ServiceInternet of Things Platform for Open Process, Open Data, and Open Service
Internet of Things Platform for Open Process, Open Data, and Open ServiceDaeyoung Kim
 
Eu fp7-h2020-experiences-daeyoung kim-kaist
Eu fp7-h2020-experiences-daeyoung kim-kaistEu fp7-h2020-experiences-daeyoung kim-kaist
Eu fp7-h2020-experiences-daeyoung kim-kaistDaeyoung Kim
 
GS1 EPCglobal framework and Oliot Project Overview
GS1 EPCglobal framework and Oliot Project OverviewGS1 EPCglobal framework and Oliot Project Overview
GS1 EPCglobal framework and Oliot Project OverviewDaeyoung Kim
 
Oliot daeyoungkim-kaist-2015 - final - short
Oliot daeyoungkim-kaist-2015 - final - shortOliot daeyoungkim-kaist-2015 - final - short
Oliot daeyoungkim-kaist-2015 - final - shortDaeyoung Kim
 
Iot ecosystem-challenges-daeyoungkim-auto-id-labs-kaist
Iot ecosystem-challenges-daeyoungkim-auto-id-labs-kaistIot ecosystem-challenges-daeyoungkim-auto-id-labs-kaist
Iot ecosystem-challenges-daeyoungkim-auto-id-labs-kaistDaeyoung Kim
 
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...Daeyoung Kim
 
GS1 smart city platforms and case studies
GS1 smart city platforms and case studiesGS1 smart city platforms and case studies
GS1 smart city platforms and case studiesDaeyoung Kim
 
The Road to Internet of Things
The Road to Internet of ThingsThe Road to Internet of Things
The Road to Internet of ThingsDaeyoung Kim
 
Kaist snail-20150122
Kaist snail-20150122Kaist snail-20150122
Kaist snail-20150122Daeyoung Kim
 
ISC Cloud13 Sill - Crossing organizational boundaries in cloud computing
ISC Cloud13 Sill - Crossing organizational boundaries in cloud computingISC Cloud13 Sill - Crossing organizational boundaries in cloud computing
ISC Cloud13 Sill - Crossing organizational boundaries in cloud computingAlan Sill
 
Itu telecom-world-2017-autoidlabs-kaist-consortium
Itu telecom-world-2017-autoidlabs-kaist-consortiumItu telecom-world-2017-autoidlabs-kaist-consortium
Itu telecom-world-2017-autoidlabs-kaist-consortiumDaeyoung Kim
 
GS1/Oliot EPCIS and Next
GS1/Oliot EPCIS and NextGS1/Oliot EPCIS and Next
GS1/Oliot EPCIS and NextDaeyoung Kim
 
Global Seafood Traceability System
Global Seafood Traceability SystemGlobal Seafood Traceability System
Global Seafood Traceability SystemDaeyoung Kim
 
AWSの提供するioTソリューションと実例
AWSの提供するioTソリューションと実例AWSの提供するioTソリューションと実例
AWSの提供するioTソリューションと実例Takashi Koyanagawa
 
Digital revolution series 1-seafood industry
Digital revolution series 1-seafood industryDigital revolution series 1-seafood industry
Digital revolution series 1-seafood industryDaeyoung Kim
 

Similaire à GS1 Data Revolution Series 2 - Internet of Trains (20)

(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
(Final) Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Stan...
 
Soscon2016 daeyoungkim-kaist - final
Soscon2016 daeyoungkim-kaist - finalSoscon2016 daeyoungkim-kaist - final
Soscon2016 daeyoungkim-kaist - final
 
Oliot samsung-daeyoungkim-kaist wide-version-final
Oliot samsung-daeyoungkim-kaist wide-version-finalOliot samsung-daeyoungkim-kaist wide-version-final
Oliot samsung-daeyoungkim-kaist wide-version-final
 
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
GS1 standards and Blockchain Technology for Traceability in Agriculture and S...
 
Internet of Things Platform for Open Process, Open Data, and Open Service
Internet of Things Platform for Open Process, Open Data, and Open ServiceInternet of Things Platform for Open Process, Open Data, and Open Service
Internet of Things Platform for Open Process, Open Data, and Open Service
 
test
testtest
test
 
Eu fp7-h2020-experiences-daeyoung kim-kaist
Eu fp7-h2020-experiences-daeyoung kim-kaistEu fp7-h2020-experiences-daeyoung kim-kaist
Eu fp7-h2020-experiences-daeyoung kim-kaist
 
GS1 EPCglobal framework and Oliot Project Overview
GS1 EPCglobal framework and Oliot Project OverviewGS1 EPCglobal framework and Oliot Project Overview
GS1 EPCglobal framework and Oliot Project Overview
 
Oliot daeyoungkim-kaist-2015 - final - short
Oliot daeyoungkim-kaist-2015 - final - shortOliot daeyoungkim-kaist-2015 - final - short
Oliot daeyoungkim-kaist-2015 - final - short
 
Iot ecosystem-challenges-daeyoungkim-auto-id-labs-kaist
Iot ecosystem-challenges-daeyoungkim-auto-id-labs-kaistIot ecosystem-challenges-daeyoungkim-auto-id-labs-kaist
Iot ecosystem-challenges-daeyoungkim-auto-id-labs-kaist
 
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
Tutorial: Standardization Efforts for Smart Cities - GS1/ISO/IEC Standards At...
 
GS1 smart city platforms and case studies
GS1 smart city platforms and case studiesGS1 smart city platforms and case studies
GS1 smart city platforms and case studies
 
The Road to Internet of Things
The Road to Internet of ThingsThe Road to Internet of Things
The Road to Internet of Things
 
Kaist snail-20150122
Kaist snail-20150122Kaist snail-20150122
Kaist snail-20150122
 
ISC Cloud13 Sill - Crossing organizational boundaries in cloud computing
ISC Cloud13 Sill - Crossing organizational boundaries in cloud computingISC Cloud13 Sill - Crossing organizational boundaries in cloud computing
ISC Cloud13 Sill - Crossing organizational boundaries in cloud computing
 
Itu telecom-world-2017-autoidlabs-kaist-consortium
Itu telecom-world-2017-autoidlabs-kaist-consortiumItu telecom-world-2017-autoidlabs-kaist-consortium
Itu telecom-world-2017-autoidlabs-kaist-consortium
 
GS1/Oliot EPCIS and Next
GS1/Oliot EPCIS and NextGS1/Oliot EPCIS and Next
GS1/Oliot EPCIS and Next
 
Global Seafood Traceability System
Global Seafood Traceability SystemGlobal Seafood Traceability System
Global Seafood Traceability System
 
AWSの提供するioTソリューションと実例
AWSの提供するioTソリューションと実例AWSの提供するioTソリューションと実例
AWSの提供するioTソリューションと実例
 
Digital revolution series 1-seafood industry
Digital revolution series 1-seafood industryDigital revolution series 1-seafood industry
Digital revolution series 1-seafood industry
 

Plus de Daeyoung Kim

주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼Daeyoung Kim
 
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환Daeyoung Kim
 
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개Daeyoung Kim
 
데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)Daeyoung Kim
 
gs1 standards in building smart cities
gs1 standards in building smart citiesgs1 standards in building smart cities
gs1 standards in building smart citiesDaeyoung Kim
 
Smartship in GS1's perspective
Smartship in GS1's perspectiveSmartship in GS1's perspective
Smartship in GS1's perspectiveDaeyoung Kim
 
GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017Daeyoung Kim
 
GS1 standards - Jan. 2017
GS1 standards - Jan. 2017GS1 standards - Jan. 2017
GS1 standards - Jan. 2017Daeyoung Kim
 
Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021Daeyoung Kim
 
GS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 HealthcareGS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 HealthcareDaeyoung Kim
 
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)Daeyoung Kim
 
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)Daeyoung Kim
 
Smart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspectiveSmart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspectiveDaeyoung Kim
 
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAISTGS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAISTDaeyoung Kim
 
GS1 Standards in Building Smart Cities
GS1 Standards in Building Smart CitiesGS1 Standards in Building Smart Cities
GS1 Standards in Building Smart CitiesDaeyoung Kim
 
Soscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaistSoscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaistDaeyoung Kim
 
Lh iot-bigdata-20181023
Lh iot-bigdata-20181023Lh iot-bigdata-20181023
Lh iot-bigdata-20181023Daeyoung Kim
 
GS1 Railway - Internet of Trains
GS1 Railway - Internet of TrainsGS1 Railway - Internet of Trains
GS1 Railway - Internet of TrainsDaeyoung Kim
 
Data and Service Driven Smart City Platform and Urban Technology Alliance
Data and Service Driven Smart City Platform and Urban Technology AllianceData and Service Driven Smart City Platform and Urban Technology Alliance
Data and Service Driven Smart City Platform and Urban Technology AllianceDaeyoung Kim
 
GS1 food ecosystem trial in Korea
GS1 food ecosystem trial in Korea GS1 food ecosystem trial in Korea
GS1 food ecosystem trial in Korea Daeyoung Kim
 

Plus de Daeyoung Kim (20)

주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
주소기반혁신성장 산업 - 주소가 바꿀 미래 사회와 산업 - 행정안전부와 주소포럼
 
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
Standards and AI Transformation (SAX) 국제표준과 인공지능 기반의 철도산업 디지털 전환
 
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
기후대응을 위한 EU 디지털제품여권법 동향과 GS1 국제표준 적용 방안 소개
 
데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)데이터공유 농축산식품-GS1적용(김대영)
데이터공유 농축산식품-GS1적용(김대영)
 
gs1 standards in building smart cities
gs1 standards in building smart citiesgs1 standards in building smart cities
gs1 standards in building smart cities
 
Smartship in GS1's perspective
Smartship in GS1's perspectiveSmartship in GS1's perspective
Smartship in GS1's perspective
 
GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017GS1 standards in agriculture - Jan. 2017
GS1 standards in agriculture - Jan. 2017
 
GS1 standards - Jan. 2017
GS1 standards - Jan. 2017GS1 standards - Jan. 2017
GS1 standards - Jan. 2017
 
Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021Gs1au newsletter-building-march-2021
Gs1au newsletter-building-march-2021
 
GS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 HealthcareGS1 Data Revolution Series #3 Healthcare
GS1 Data Revolution Series #3 Healthcare
 
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 ONS and Digital Link Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
 
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
GS1 EPCIS and CBV Tutorial, Auto-ID Labs, KAIST (Apr 28, 2020)
 
Smart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspectiveSmart city position paper - GS1 standards perspective
Smart city position paper - GS1 standards perspective
 
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAISTGS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
GS1 Tutorial (Korean) by Daeyoung Kim, Auto-ID Labs, KAIST
 
GS1 Standards in Building Smart Cities
GS1 Standards in Building Smart CitiesGS1 Standards in Building Smart Cities
GS1 Standards in Building Smart Cities
 
Soscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaistSoscon2019 oliot-auto-id-labs-kaist
Soscon2019 oliot-auto-id-labs-kaist
 
Lh iot-bigdata-20181023
Lh iot-bigdata-20181023Lh iot-bigdata-20181023
Lh iot-bigdata-20181023
 
GS1 Railway - Internet of Trains
GS1 Railway - Internet of TrainsGS1 Railway - Internet of Trains
GS1 Railway - Internet of Trains
 
Data and Service Driven Smart City Platform and Urban Technology Alliance
Data and Service Driven Smart City Platform and Urban Technology AllianceData and Service Driven Smart City Platform and Urban Technology Alliance
Data and Service Driven Smart City Platform and Urban Technology Alliance
 
GS1 food ecosystem trial in Korea
GS1 food ecosystem trial in Korea GS1 food ecosystem trial in Korea
GS1 food ecosystem trial in Korea
 

Dernier

The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...
The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...
The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...kalichargn70th171
 
AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
AI Mastery 201: Elevating Your Workflow with Advanced LLM TechniquesAI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
AI Mastery 201: Elevating Your Workflow with Advanced LLM TechniquesVictorSzoltysek
 
8257 interfacing 2 in microprocessor for btech students
8257 interfacing 2 in microprocessor for btech students8257 interfacing 2 in microprocessor for btech students
8257 interfacing 2 in microprocessor for btech studentsHimanshiGarg82
 
The title is not connected to what is inside
The title is not connected to what is insideThe title is not connected to what is inside
The title is not connected to what is insideshinachiaurasa2
 
Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVshikhaohhpro
 
%in kempton park+277-882-255-28 abortion pills for sale in kempton park
%in kempton park+277-882-255-28 abortion pills for sale in kempton park %in kempton park+277-882-255-28 abortion pills for sale in kempton park
%in kempton park+277-882-255-28 abortion pills for sale in kempton park masabamasaba
 
Chinsurah Escorts ☎️8617697112 Starting From 5K to 15K High Profile Escorts ...
Chinsurah Escorts ☎️8617697112  Starting From 5K to 15K High Profile Escorts ...Chinsurah Escorts ☎️8617697112  Starting From 5K to 15K High Profile Escorts ...
Chinsurah Escorts ☎️8617697112 Starting From 5K to 15K High Profile Escorts ...Nitya salvi
 
%in tembisa+277-882-255-28 abortion pills for sale in tembisa
%in tembisa+277-882-255-28 abortion pills for sale in tembisa%in tembisa+277-882-255-28 abortion pills for sale in tembisa
%in tembisa+277-882-255-28 abortion pills for sale in tembisamasabamasaba
 
%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein
%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein
%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfonteinmasabamasaba
 
introduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdf
introduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdfintroduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdf
introduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdfVishalKumarJha10
 
10 Trends Likely to Shape Enterprise Technology in 2024
10 Trends Likely to Shape Enterprise Technology in 202410 Trends Likely to Shape Enterprise Technology in 2024
10 Trends Likely to Shape Enterprise Technology in 2024Mind IT Systems
 
Unlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language ModelsUnlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language Modelsaagamshah0812
 
%in Bahrain+277-882-255-28 abortion pills for sale in Bahrain
%in Bahrain+277-882-255-28 abortion pills for sale in Bahrain%in Bahrain+277-882-255-28 abortion pills for sale in Bahrain
%in Bahrain+277-882-255-28 abortion pills for sale in Bahrainmasabamasaba
 
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...panagenda
 
The Top App Development Trends Shaping the Industry in 2024-25 .pdf
The Top App Development Trends Shaping the Industry in 2024-25 .pdfThe Top App Development Trends Shaping the Industry in 2024-25 .pdf
The Top App Development Trends Shaping the Industry in 2024-25 .pdfayushiqss
 
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...Shane Coughlan
 
Direct Style Effect Systems - The Print[A] Example - A Comprehension Aid
Direct Style Effect Systems -The Print[A] Example- A Comprehension AidDirect Style Effect Systems -The Print[A] Example- A Comprehension Aid
Direct Style Effect Systems - The Print[A] Example - A Comprehension AidPhilip Schwarz
 
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdfLearn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdfkalichargn70th171
 

Dernier (20)

The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...
The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...
The Guide to Integrating Generative AI into Unified Continuous Testing Platfo...
 
AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
AI Mastery 201: Elevating Your Workflow with Advanced LLM TechniquesAI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques
 
8257 interfacing 2 in microprocessor for btech students
8257 interfacing 2 in microprocessor for btech students8257 interfacing 2 in microprocessor for btech students
8257 interfacing 2 in microprocessor for btech students
 
The title is not connected to what is inside
The title is not connected to what is insideThe title is not connected to what is inside
The title is not connected to what is inside
 
Microsoft AI Transformation Partner Playbook.pdf
Microsoft AI Transformation Partner Playbook.pdfMicrosoft AI Transformation Partner Playbook.pdf
Microsoft AI Transformation Partner Playbook.pdf
 
Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTV
 
%in kempton park+277-882-255-28 abortion pills for sale in kempton park
%in kempton park+277-882-255-28 abortion pills for sale in kempton park %in kempton park+277-882-255-28 abortion pills for sale in kempton park
%in kempton park+277-882-255-28 abortion pills for sale in kempton park
 
CHEAP Call Girls in Pushp Vihar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Pushp Vihar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICECHEAP Call Girls in Pushp Vihar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Pushp Vihar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
 
Chinsurah Escorts ☎️8617697112 Starting From 5K to 15K High Profile Escorts ...
Chinsurah Escorts ☎️8617697112  Starting From 5K to 15K High Profile Escorts ...Chinsurah Escorts ☎️8617697112  Starting From 5K to 15K High Profile Escorts ...
Chinsurah Escorts ☎️8617697112 Starting From 5K to 15K High Profile Escorts ...
 
%in tembisa+277-882-255-28 abortion pills for sale in tembisa
%in tembisa+277-882-255-28 abortion pills for sale in tembisa%in tembisa+277-882-255-28 abortion pills for sale in tembisa
%in tembisa+277-882-255-28 abortion pills for sale in tembisa
 
%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein
%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein
%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein
 
introduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdf
introduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdfintroduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdf
introduction-to-automotive Andoid os-csimmonds-ndctechtown-2021.pdf
 
10 Trends Likely to Shape Enterprise Technology in 2024
10 Trends Likely to Shape Enterprise Technology in 202410 Trends Likely to Shape Enterprise Technology in 2024
10 Trends Likely to Shape Enterprise Technology in 2024
 
Unlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language ModelsUnlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language Models
 
%in Bahrain+277-882-255-28 abortion pills for sale in Bahrain
%in Bahrain+277-882-255-28 abortion pills for sale in Bahrain%in Bahrain+277-882-255-28 abortion pills for sale in Bahrain
%in Bahrain+277-882-255-28 abortion pills for sale in Bahrain
 
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
 
The Top App Development Trends Shaping the Industry in 2024-25 .pdf
The Top App Development Trends Shaping the Industry in 2024-25 .pdfThe Top App Development Trends Shaping the Industry in 2024-25 .pdf
The Top App Development Trends Shaping the Industry in 2024-25 .pdf
 
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...
 
Direct Style Effect Systems - The Print[A] Example - A Comprehension Aid
Direct Style Effect Systems -The Print[A] Example- A Comprehension AidDirect Style Effect Systems -The Print[A] Example- A Comprehension Aid
Direct Style Effect Systems - The Print[A] Example - A Comprehension Aid
 
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdfLearn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
 

GS1 Data Revolution Series 2 - Internet of Trains

  • 1. 김 대 영 2019년 10월 17일 교수, 전산학부, KAIST Director, Auto-ID Labs, KAIST kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org Internet of Trains: GS1 국제표준기반의 철도산업 디지털 트랜스포메이션 전략과 구축 기술 김 대 영 2020년 6월 30일 14:00 ~15:40 (Webinar) 교수, 전산학부, KAIST Director, Auto-ID Labs, KAIST kimd@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org
  • 2.
  • 3. © Auto-ID Lab Korea / KAIST Slide 3 SuperX를 위한 Internet of Trains (Open global language, Digital twin) 서울역(Location ID) 에서 19:30(Time) 출발(Vocabulary)하여, 부산역에 22:08 도착하는, 총 20 차량 (2 기관차, 18 객차)(Assets IDs) 으로 구성된 KTX169(Passage ID) 편이, 방금 19:31 분에 서울역을 출발(Vocabulary) 하였다. KTX169(Passage ID) 편이 방금 20:28(Time) 대전역 (Location ID) 도착 (Vocabulary) 하였는데, 앞 기관차(Asset ID) 의 앞바퀴 온도(Vocabulary)가 250°C (Vocabulary Element) 이다.
  • 4. © Auto-ID Lab Korea / KAIST Slide 4 목차 Part I – 철도산업에서의 디지털 트랜스포메이션 Part II – 유럽, 호주, 인도등 세계 현황 Part III – GS1 Standards (GS1 국제표준, 산업표준) Part IV – GS1 Rail Standards Part V – 디지털인프라, 디지털 SoC 인프라
  • 5. Part I – 철도산업에서의 디지털 트랜스포메이션 (GS1’s Perspective)
  • 6. © Auto-ID Lab Korea / KAIST Slide 6 유럽은 왜 GS1 국제표준을 활용하며 철도산업 디지털 트랜스포메이션에 뛰어 들었나? 안전 관리 시스템 (SMS: Safety Management System) 독립된 유지보수 담당 (ECM: Entity in Charge of Maintenance) 유럽연합규정에 따른 철도운영호환성 평가 및 인증 (TSI: Technical Specifications for Interoperability) CE 마크 (Conformite Europeenne Mark)
  • 7. © Auto-ID Lab Korea / KAIST Slide 7 Top 10 resources for rail industry standards and regulatory guidance (탑10 철도산업 표준, 가이드라인) • The rail industry is subject to a variety of standards (표준) and regulations (규정), these standards change over time as more effective safety measures are identified and new safety and operational risks are recognized. • We’ve rounded up the top 10 resources for rail industry standards and regulatory guidance, including: • The International Organization for Standardization (ISO) • The European Rail Industry – UNIFE • GS1 • International Rail Industry Standard (IRIS) • American National Standards Institute (ANSI) • The Rail Committee on Information Standards (RailCIS) • Association of American Railroads (AAR) • Federal Railroad Administration (FRA) • Railway Age • International Railway Journal https://www.mpofcinci.com/blog/rail-industry-standards-resources/
  • 8. © Auto-ID Lab Korea / KAIST Slide 8 “공급망을 자동화하고 간소화하기 위해 공통 표준과 언어 가 종종 필요합니다. GS1 표준의 도움으로 유럽 철도 산업 은 철도 안전, 효율성 및 경쟁력 향상을 목표로 이 경로를 시작하고 있습니다” - 구글번역 Les premiers pas vers le "rail cloud" https://www.lantenne.com/Les-premiers-pas-vers-le-rail-cloud_a41020.html "레일 클라우드"를 향한 첫 걸음
  • 9. © Auto-ID Lab Korea / KAIST Slide 9 Le ferroviaire en Europe sur les rails des standards GS1 - 유럽의 철도는 GS1 표준의 난간에 기대어 있다 https://www.actu-transport-logistique.fr/bibliotheque-numerique/supply-chain-magazine/5/parole-dexpert/le-ferroviaire-en-europe-sur-les-rails-des- standards-gs1-456461.php "디지털 변환 시대에 철도는 예비 부 품의 추적 성 보장, 생산 및 유지 보 수 비용 합리화, 운영 성과 향상과 같은 여러 가지 문제에 적응하고 대 응해야합니다. 이를 달성하기 위해 이 분야의 주요 업체들은 프로세스 에 GS1 표준을 채택하기로 결정했 습니다.
  • 10. © Auto-ID Lab Korea / KAIST Slide 10 GS1 국제표준 적용 국가
  • 11.
  • 12. Part II – 유럽, 호주 등 세계 현황
  • 14. © Auto-ID Lab Korea / KAIST Slide 14 스위스 (SBB CFF FFS)
  • 15. © Auto-ID Lab Korea / KAIST Slide 15 프랑스 TGV2020과 GS1국제표준 "2 년 동안 테스트 한 결과, GS1 식별자는 TGV 2020 에 확실히 채택되었습니다. 그것은 미래의 고속 열차의 제조를 위한 사양에 포함되 었습니다. 또한 2020 년 1 월부터 RER NG 테스트 열 차에서 테스트 될 예정입니 다." [구글번역] https://www.constructioncayola.com/rail/article/2019/06/27/125027/tracabilite-des-pieces-filiere-ferroviaire
  • 16. © Auto-ID Lab Korea / KAIST Slide 16 호주 i-Trace 프로젝트
  • 17. © Auto-ID Lab Korea / KAIST Slide 17
  • 18.
  • 19. © Auto-ID Lab Korea / KAIST Slide 19
  • 20.
  • 21.
  • 22. Part IV – GS1 Standards (GS1 국제표준기구)
  • 23. © Auto-ID Lab Korea / KAIST Slide 23 GS1 (Global Standards 1) 국제표준기구 (데이터, 비즈니스, 산업, 사물인터넷) 산업 분야에서 데이터를 표준화하고 공유하는 글로벌 플랫폼 제공 114개국 국가-MO (Member Organizations), 170개 국가 공식 활용 2백만 이상의 기업 멤버로 구성된 비영리 국제표준기구 한국은 대한상공회의소 산하의 GS1 Korea (유통물류진흥원) 와 Auto-ID Labs, KAIST GS1 사물인터넷 국제표준공동연구소 운영 (KAIST, MIT, Cambridge, ETH Zurich, Keio, Fudan University)
  • 24. © Auto-ID Lab Korea / KAIST Slide 24 여권으로 보는 국제표준 데이터 구성 데이터 DATA 식별자 (Identifier) 예) 여권 번호, 여권 발급 장소, 공항 출입국 심사대, 출입국 기기 식별자 등 어휘 (Vocabulary) 예) 사람 이름, 성별, 주민등록번호, 국적, 여권발급, 비자발급, 출입국 기록등 마스터 데이터 (Master Data) 변하지 않는 데이터 예) 이름, 성별, 국적, 사진 트랜잭션 데이터 (Transaction Data) 비즈니스 관련 데이터 예) 여권 발급, 수수료 납부, 비자 발급 이벤트 데이터 (Event Data) 발생 이벤트 관련 데이터 예) 출국 및 입국 이벤트, 출입국 특이 사항 이벤트 이미지 출처: 외교부 www.mofa.go.kr
  • 25. © Auto-ID Lab Korea / KAIST Slide 25 GS1 Company Prefix (식별자의 시작) • GS1 국제 생태계에 진입하기 위해, 각 기업이나 기관은 국가코드와 기업/기관 코드로 이루어진 GS1 Company Prefix (GCP) 를 확보해야 함 • GS1 Company Prefix 를 기반으로 13종류이상의 다양한 글로벌 GS1 식별자를 사용할 수 있음 (GTIN, GRAI, GIAI, GLN, GSRN, ….)
  • 26. © Auto-ID Lab Korea / KAIST Slide 26 주요 GS1 글로벌 식별자 (1/2) 상품 코드 (GTIN, Global Trade Item Number) 팔렛트 (GRAI, Global Returnable Asset Identifier) 기차, 선로, 장비 (GIAI, Global Individual Asset Identifier) 기차역, 조차장, 선로, 웍샵 (GLN, Global Location Number) 인증서, 계약서류등 (GDTI, Global Document Type Identifier)기관사, 승무원, 승객등 서비스 제공자와 제공받는자 (GSRN, Global Service Relation Number)
  • 27. © Auto-ID Lab Korea / KAIST Slide 27 주요 GS1 글로벌 식별자 (2/2) 기차 부품 코드 (CPID, Components and Parts IDentifier) 책 및 잡지 (ISBN, ISSN) 할인 쿠폰 (GCN, Global Coupon Number) 운송 코드 (GSIN, Global Shipment Identification Number) 탁송코드 (GINC, Global Identification Number for Consignment) 배송 박스 코드 (SSCC, Serialized Shipping Container Code) * 모델번호 (GMN, Global Model Number) 9791185343426 * GMN (Global Model Number)
  • 28. © Auto-ID Lab Korea / KAIST Slide 28 GS1 Application Identifiers (응용 식별자) ~~~~
  • 29. © Auto-ID Lab Korea / KAIST Slide 29 GS1 GDSN, Global Registry Attributes [마스터 데이터] B2B 상품 데이터 (+GS1 Source, GS1 Registry Platform) - 표준화된 상품(사물) 데이터를 글로벌 하게 공유하기 위한 표준과 분산 데이터풀 (데이터베이스) 시스템 - 2020년 5월 1일 현재 전세계 43개 Data Pool 운영 • B2C의 상세 상품 데이터를 제공하기 위한 노력이 GDSN, GS1 Source, GS1 Registry Platform 를 통해 상호 보완으로 진행되고 있음. (예. 식품의 성분, 인증, 앨러지 정보등)
  • 30. © Auto-ID Lab Korea / KAIST Slide 30 EPC Information Service (EPCIS) GS1의 데이터공유 6하원칙 GS1의 6하원칙 WHO 스위스 철도회사 GS1 GLN WHAT 프랑스 TGV 기차 GS1 GIAI WHERE 베른역 GS1 GLN/SGLN + geo WHEN 방금 / 어제 UTC HOW GS1에는 없습니다. 어떻게 그 럴수가 있니? WHY 도착했다 GS1 CBV Vocabulary/Data Dictionary
  • 31. © Auto-ID Lab Korea / KAIST Slide 31 ➢ 사물인터넷 이벤트 데이터의 상황을 표기 하기 위한 표준화된 어휘 (Common, Standard/User Vocabulary) ➢ Identifier 의 Syntax 와 Vocabulary Element 표준화 ➢ 전산업에 공용인 CBV와 산업별, 국가별, 기업별로 새로운 Standard/User Vocabulary 표준화 가능 (1) urn:epcglobal:cbv:bizstep:assembling (4) bizstep:arriving (3) bizstep:departing (11)bizstep: destroying (7) bizstep:inspecting (5) bizstep:unloading (하차: 컨테이너 등) (9) bizstep:repairing <disposition>https://gs1.org/cbv/rail/di sp/available<disposition> (10) bizstep:replacing (6) bizstep:retail_selling (2) bizstep:loading (상차: 컨테이너 등) (8) <bizStep>https://gs1.org/cbv/rail/disp/pl anning_replacement</bizStep> (산업 표준 예제) 산업별 확장 지원 (철도산업, 수산업, 헬스케어, 스마트시티,…) Also adopted by ISO/IEC ISO/IEC 19987:2015 standard 비즈니스 프로세스 (마스터, 트랜잭션, 이벤트 데이터)를 기술하는 표준 어휘 Common Business Vocabulary (CBV) (11)urn:epcglobal:cbv:disp: Damaged
  • 32. © Auto-ID Lab Korea / KAIST Slide 32 GS1 국제 표준 – Lifetime 프로세스 모델링 기술 • ObjectEvent – 상품의 생성/관찰/제거 • AggregationEvent – 상품의 포장/적재 • TransactionEvent – 상품의 판매 상황 • TransformationEvent – 상품의 가공 상황 • Association Event – Coming soon 생성부터 판매까지 상품(사물)의 lifetime을 기록할 수 있는 데이터 모델을 통해서 상품 을 효율적으로 관리 What, Where, When, Why 를 통한 상세한 이벤트 정보 입력
  • 33. © Auto-ID Lab Korea / KAIST Slide 33 • EPC Information Service (EPCIS) • Current Version 1.2, Oct. 2016 • Also adopted by ISO/IEC ISO/IEC 19987:2015 standard 사물인터넷 이벤트를 저장하는 분산 연합 데이터 베이스 / 왼쪽 그림 처럼 EPCIS 가 중앙에 하나 있는 게 아니라, 철도부품회사, 기차제작회사, 철도운영회사등이 제각기 운영하고, 가상으로는 단일의 데이터 저장소로 보임 - 기존 시스템 대체가 아니라, 별개 시스템 설치 후 쉽게 연결함 ➢ RFID를 위한 EPCglobal 표준은 지난 5년간 확장되어, 사물인터넷의 인프라 프레임웍으로 발전중임 EPC Information Service (EPCIS) 트랜잭션 데이터와 이벤트 데이터 공유 (+EDI)
  • 34. © Auto-ID Lab Korea / KAIST Slide 34 GS1-Traceability (이력추적표준) Factory ▪ GS1 Global Traceability Standard에서는 ▪ 상품(사물)의 생산-유통-소비 라이프사이클에 따른 이력 추적 관리 (TRACK) ▪ 최종 상품의 원산지 확인을 위한 역 이력 관리 (TRACE) ▪ 시스템 구축 가이드라인을 제시
  • 35. © Auto-ID Lab Korea / KAIST Slide 35 GS1 Digital Link + ONS (QR 표준: 서비스 표준접근) 국제표준QR (국가, 기업, 상품, 로트, 일련번호, 생 산연월일, 유통기한등)
  • 36. © Auto-ID Lab Korea / KAIST Slide 36 블록체인 (식품, 의약품, Identifier 등, 철도산업?) GS1 표준 데이터로 상호 운용성 확보
  • 37. © Auto-ID Lab Korea / KAIST Slide 37 • Oliot Open Source Project • http://oliot.org GS1 EPCglobal 데이터 및 서비스 공유 국제표준 구현 • GS1 Source • Pedigree • Traceability & Recall • ONS • DS • EPCIS • F&C • IoT connectivity Layer, etc. Oliot Open Source Project (KAIST) Apache License
  • 38. © Auto-ID Lab Korea / KAIST Slide 38 6 Continents, 103 countries, 1147 Cities 13618 Organizations/Companies/Individuals 2020.05.26. 2014년 6월25일 (Oliot 1.0) 2016년 (Oliot 1.2) 2020년 하반기 예정 (Oliot 2.0))
  • 39. Part V – GS1 Rail Standards
  • 40. © Auto-ID Lab Korea / KAIST Slide 40 GS1 Rail Standards • Identification of components and parts in the rail industry (식별체계) • Application standard: "Identification of components and parts in the rail industry" • Identification in Rail – Do’s and Don’t’s • Brochure: "Improving safety and efficiency in the rail industry“ • Vehicle Visibility Standard (철도차량 일생 데이터 공유) • Application standard: "GS1 EPCIS for Rail Vehicle Visibility" • Brochure: "GS1 application standard for visibility in rail“ • Exchange of component/part lifecycle data in the rail industry (구성품/파 트 일생 데이터 공유) • Application standard: "Exchange of component/part lifecycle data in the rail industry“
  • 41.
  • 42. © Auto-ID Lab Korea / KAIST Slide 42 The Scope of the Document (표준 내용) • GS1 identification keys (식별자) and attributes (속성) for the identification of parts and components • Interoperability (상호운용성) means the ability of a rail system to allow the safe and uninterrupted movement of trains • rolling stock of operator A (철도회사) can operate on infrastructure of infrastructure managers B, C, D, etc., (철도인프라 회사) because the parts where the systems meet (wheelsets, rails, ETCS-components, pantographs, switches, toilet drains, etc.) are guaranteed to be compatible • A key enabler will be the ability to unambiguously identify MRO-objects across the systems and processes of all stakeholders. • MRO-objects will need to be identified on class-level (클래스), lot-level(로트) and more and more frequently up to serial-level (시리얼)
  • 43.
  • 44. © Auto-ID Lab Korea / KAIST Slide 44 Lifecycle identification of MRO-objects - Value Chain (글로벌 밸류 체인) • Today’s rail manufacturing and MRO industry has become global, with a relatively small number of system suppliers relying on an ever more fragmented international supply chain with a network of specialised suppliers for key components and assemblies. • 한번 주어진 이름은 변함없이 ~~~
  • 45. © Auto-ID Lab Korea / KAIST Slide 45 Lifecycle identification of MRO-objects Business processes – Process Roles (구성원)
  • 46. © Auto-ID Lab Korea / KAIST Slide 46 Lifecycle identification of MRO-objects Need for traceability (일생 이력추적 요구) Regulatory requirements (규정 요구사항) “According to recent European legislation (see section 2) rail and rail network operators must develop and maintain management systems which guarantee a safe and stable operation as well as the interoperability of the assets used.” This entails that all MRO-objects will undergo a risk analysis (리스크 분석) reflecting their potential impact on safety. Moreover, a configuration management (구성/형상 관리) is compulsory, as required by regulations 445/2011. 1169/2010 and 1158/2010. Maintenance strategies (유지보수운영 요구사항) One of the main defining elements of the rail industry is the fact that a substantial number of MRO-objects (in rolling stock as well as in rail infrastructure) is procured for a long-use life cycle of up to 60 years.(60년이상 운용) Such MRO-objects need to be maintained, refurbished or replaced on a regular or on an ad-hoc basis.
  • 47. © Auto-ID Lab Korea / KAIST Slide 47 Lifecycle identification of MRO-objects Configuration management (구성/형상 관리) The system integrator (예. Alstom) will have a design BOM of the locomotive, and will create a manufacturing BOM for each manufactured locomotive. The sub system manufacturer of the brake system (예. ABB) will have a design BOM and a manufacturing BOM for the sub system, consisting of several components that need to be integrated by the system integrator. Based on the data from the suppliers the system integrator will create an installation BOM. In that BOM the brake system as a ‘whole’ will not be present, but primarily the serialised physical components that make up the system.
  • 48. © Auto-ID Lab Korea / KAIST Slide 48 Identification and marking principles (식별과 마킹) Marking events during the MRO-object lifecycle Identification levels and GS1 identification keys
  • 49. © Auto-ID Lab Korea / KAIST Slide 49 Identification and marking principles - Identification and marking scenarios
  • 50.
  • 51. © Auto-ID Lab Korea / KAIST Slide 51 The application standard has been created by a team of rail stakeholders, solution providers and GS1 Member Organisations. This represents a significant achievement in collaboration and consensus on the use of GS1 standards in the rail sector.
  • 53. © Auto-ID Lab Korea / KAIST Slide 53 Rail Vehicle Visibility • Business needs • The needs for information sharing (데이터 공유): • Tracking of vehicles (철도차량 추적) – 한 국가 내 뿐만 아니라, 국경을 넘어서 국가간 추적/모니터링 • Associate the vehicle data with the Wayside Train Monitoring System (WTMS) data (차량 데이터 와 선로 차량 모니터링 시스템 데이터 융합) about vehicles (철도 차량) and vehicle components (차량 부품) to enhance preventive maintenance • The use of RFID for railway vehicles becomes more and more popular.. Hot Axle-Box Detectors (HABD) or hot-box detectors Wheel impact load detectors (WILD) Acoustic Axle Bearing Monitoring (AABM) Automatic pantograph monitoring systems (APMS)
  • 54. © Auto-ID Lab Korea / KAIST Slide 54 Rail Vehicle Visibility • RFID enabled Automatic Vehicle Identification (AVI) systems • Identify all tagged vehicles and their order in the train / Detect the presence of vehicles with missing or broken tags and their relative location in the train • Important for the WTMS use case, since it enables the measurement results to be linked to the correct vehicles in a train set • travel direction, the orientation, axle count, speed, and length of each vehicle • Enable train level information exchanges • Ex) A train entering or leaving a yard and the composition/formation of the train • Train Management System (TMS) • The information from the TMS can be used to generate additional event data. • Ex) The train entering or leaving an area can be deduced by combining data from a TMS and data from previously read points provided by the AVI system. Cross River Rail: How the European Train Control System works
  • 56. © Auto-ID Lab Korea / KAIST Slide 56 The GS1 system increases security when capturing data
  • 57. © Auto-ID Lab Korea / KAIST Slide 57 Vehicle Identification • Vehicle identification with “master” GIAI • Identify each rail vehicle as an asset • EPC URI is used to represent rail vehicles which are included in EPCIS events. • Ex) urn:epc:id:giai:4012345.98765432198765432 • Unambiguously determine static information about the rail vehicle • Rail vehicle type, axle count, vehicle owner, etc. (master data) • Not physical, deduced by proxy GIAI
  • 58. © Auto-ID Lab Korea / KAIST Slide 58 Vehicle Identification • AIDC device identification with “proxy” GIAI • Each of these tags is identified by a unique GIAI • Ex) tag 1 of 2: urn:epc:tag:giai-96:1.4012345.18765432198765432 tag 2 of 2: urn:epc:tag:giai-96:1.4012345.28765432198765432 • These “device” GIAIs serve as “proxy” representation Rail vehicle with multiple tags - top views Rail vehicle with multiple tags - side views
  • 59. Read Point and Business Location Identification How locations can be identified in a rail context
  • 60. © Auto-ID Lab Korea / KAIST Slide 60 Read Point and Business Location Identification • Read Points (센서나 장치가 읽은 물리적 위치 식별자) • A location that is meant to identify the most specific place at which an EPCIS event took place (Identified using the SGLN) • Unique Read Point – Unambiguously determine the read point’s physical location, line name/ID, and track name/ID AVI system monitoring a single track AVI system monitoring multiple tracks
  • 61. © Auto-ID Lab Korea / KAIST Slide 61 Read Point and Business Location Identification • Business location (비즈니스 위치 식별자) • The location where the rail vehicle is assumed to be following the event (Assumed to be valid until superseded by the business location of a subsequent event pertaining to the rail vehicle) • Used to tell the location where the vehicles or trains are found after the event took place • Ex) track section, station, shunting yard, or specific shunting yard location • Used to serve asset tracking needs
  • 62. Determining vehicle and train visibility data
  • 63. © Auto-ID Lab Korea / KAIST Slide 63 Determining vehicle and train visibility data Determining Train Direction • Determining the orientation of the rail vehicle • The correct assignment of measurement values requires the direction of travel of the vehicle. • The orientation of a rail vehicle is determined by: • The observed tag • The train direction Train direction indicator = 2 (compass direction = NE) t1: Vehicle 2 – tag 2, vehicle end 2 passed first t2: Vehicle 1 – tag 1, vehicle end 1 passed first
  • 64. © Auto-ID Lab Korea / KAIST Slide 64 Determining vehicle and train visibility data • Determining Source and Destination (출발역, 종착역) • Parties with access to the railroad plan – can derive this based on the provided read points and direction information (철도편 정보 있 을시) • Parties that do not have access – utilize the Source and Destination elements in EPCIS (이벤트 데이터에 포함) • Identified with SGLN
  • 65. © Auto-ID Lab Korea / KAIST Slide 65 Determining vehicle and train visibility data • Determining a train passage (기차편) • The AVI can detect whether observed vehicles are connected in the same train-set. • A separate EPCIS event for each vehicle observation (차량 하나 마다 EPCIS 이벤트 생성) • Passage identifier (기차편명) should be included as an EPCIS Business Transaction. • A Transaction Event will be used to list all observed rail vehicles. • The train number can be used to link to information in other train management systems.
  • 66. Sharing vehicle and train visibility data with EPCIS Special issues when applying Epcis functions to railway vehicle visibility
  • 67. © Auto-ID Lab Korea / KAIST Slide 67 Sharing vehicle and train visibility data with EPCIS (critical tracking events) Standard Rail Journey Diagram
  • 68. © Auto-ID Lab Korea / KAIST Slide 68 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • ObjectEvent (action OBSERVE) – serves as an observation of a uniquely identified rail vehicle in passage along its journey, or upon its arrival at or departure from a terminus (철도 차량 한대씩 마다 이벤트 보고) • TransactionEvent (action ADD) – serves as a “summary” event following the observation of a passing train’s trailing vehicle, reiterating the proxy GIAIs of positively identified vehicles, as well as relevant totals for all vehicles (마지막 차량 통과후 기차편에 대한 모든 정보 이벤트 보고)
  • 69. © Auto-ID Lab Korea / KAIST Slide 69 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • What • Indicates the objects to which the EPCIS event pertains • Each observed rail vehicle should be captured in a separate ObjectEvent. • The epcList element should contain only the master GIAI of the observed vehicle. • Ex) • A passage should be defined using a TransactionEvent. • The epcList element includes the master GIAIs of all observed, positively identified rail vehicles. • Ex) 상세내용은 부록 참고
  • 70. Examples of Rail Visibility Events
  • 71. © Auto-ID Lab Korea / KAIST Slide 71 Examples of Rail Visibility Events • Rail vehicle observations • Two tags were observed • First tag 2 of vehicle 676, after that tag 1 of vehicle 070 • The passage ID for both observed vehicles is the same • Part of the same train set 070 676 Read point 1 2 12
  • 72. © Auto-ID Lab Korea / KAIST Slide 72 Examples of Rail Visibility Events • Rail vehicle observations Rail vehicle observation – Object event 1
  • 73. © Auto-ID Lab Korea / KAIST Slide 73 Examples of Rail Visibility Events • Rail vehicle observations Rail vehicle observation – Object event 2
  • 74. © Auto-ID Lab Korea / KAIST Slide 74 Examples of Rail Visibility Events • Rail vehicle changing direction Illustration of direction change – Object event 1
  • 75. © Auto-ID Lab Korea / KAIST Slide 75 Examples of Rail Visibility Events • Rail vehicle changing direction Illustration of direction change – Object event 2
  • 76. © Auto-ID Lab Korea / KAIST Slide 76 Examples of Rail Visibility Events • Rail vehicle changing direction Illustration of direction change – Object event 3
  • 77. © Auto-ID Lab Korea / KAIST Slide 77 Examples of Rail Visibility Events • Train passage transaction event (including untagged vehicle) • How a train passage can be expressed using a transaction event • A train passage which consists of three rail vehicles Train passage
  • 78. © Auto-ID Lab Korea / KAIST Slide 78 Examples of Rail Visibility Events • Train passage transaction event (including untagged vehicle) Train passage transaction event
  • 79. © Auto-ID Lab Korea / KAIST Slide 79 Examples of Rail Visibility Events • Train passage transaction event (including untagged vehicle) Train passage transaction event
  • 80. EPCIS Query examples for rail vehicle visibility
  • 81. © Auto-ID Lab Korea / KAIST Slide 81 EPCIS Query examples for rail vehicle visibility • EPCIS Query Control Interface • On-demand (synchronous) – a client makes a request through the EPCIS Query Control Interface and receives a response immediately (바로 찾고자 하는 데이터를 보내주세요) • Standing request (asynchronous) – a client establishes a subscription for a periodic query. Each time the periodic query is executed, the results are delivered asynchronously to a recipient via the EPCIS Query Callback Interface (원하는 데이터가 발생하면, 이리로 보내주 세요) EPCIS Capturing Application EPCIS Capture Interface EPCIS Repository EPCIS Query Interface (Control and Callback) Business App. Fig. EPCIS and its scope
  • 82. © Auto-ID Lab Korea / KAIST Slide 82 EPCIS Query examples for rail vehicle visibility • On-demand queries via EPCIS Query Control Interface • Observations of a specified vehicle since a specified date/time • Example of on-demand query by vehicle • Observations of all vehicles at a specified read point in a specified window of time • Example of on-demand query by read point
  • 83. © Auto-ID Lab Korea / KAIST Slide 83 EPCIS Query examples for rail vehicle visibility • On-demand queries via EPCIS Query Control Interface • Events for a given passage • Example of on-demand query by passage ID • Passage-level queries • Example of on-demand query for passage events
  • 84. © Auto-ID Lab Korea / KAIST Slide 84 EPCIS Query examples for rail vehicle visibility • Standing queries (Subscriptions) via EPCIS Query Call-back Interface • Notification whenever a specified vehicle is observed at any read point • Example of standing query by vehicle • Notification whenever any uniquely identified vehicle is observed a ta specified read point • Example of standing query by read point
  • 85.
  • 86. © Auto-ID Lab Korea / KAIST Slide 86 Business Intention • Rail stakeholders can develop and share manufacturing & maintenance and usage information, enabling rail equipment operators to consistently fulfil tracking and tracing needs while reducing overall costs. • strongly facilitate tracking and tracing throughout the complete lifecycle of, for instance, an individual object, across companies and borders; • unify the data exchange process requirements by rail and rail network operators in regards to unit suppliers and manufacturers; and • thereby allow for new supply chain design possibilities (e.g., stock and supply sharing, pay per use, user specific R&D programmes, etc.)
  • 87. © Auto-ID Lab Korea / KAIST Slide 87 Visibility events for rail manufacturing and MRO - Mapping of rail business processes to visibility events
  • 88. © Auto-ID Lab Korea / KAIST Slide 88 Visibility events for rail manufacturing and MRO - Rolling stock visibility events Data exchanges during lifecycle of rolling stock Rolling stock visibility events
  • 89. © Auto-ID Lab Korea / KAIST Slide 89 Visibility events for rail manufacturing and MRO - Rolling stock visibility events
  • 90. © Auto-ID Lab Korea / KAIST Slide 90 Visibility events for rail manufacturing and MRO - Rolling stock visibility events
  • 91. © Auto-ID Lab Korea / KAIST Slide 91 Visibility events for rail manufacturing and MRO - Rolling stock visibility events
  • 92. © Auto-ID Lab Korea / KAIST Slide 92 Visibility events for rail manufacturing and MRO - Infrastructure visibility events Data exchanges during infrastructure projects Infrastructure visibility events
  • 93. © Auto-ID Lab Korea / KAIST Slide 93 Visibility events for rail manufacturing and MRO - Infrastructure visibility events
  • 94. © Auto-ID Lab Korea / KAIST Slide 94 Visibility events for rail manufacturing and MRO - Infrastructure visibility events
  • 95. © Auto-ID Lab Korea / KAIST Slide 95 Visibility events for rail manufacturing and MRO
  • 96. © Auto-ID Lab Korea / KAIST Slide 96 Master Data - Trade item (class-level) master data
  • 97. © Auto-ID Lab Korea / KAIST Slide 97 Master Data - Trade item (instance/lot level) master data
  • 98.
  • 99.
  • 100.
  • 101. © Auto-ID Lab Korea / KAIST Slide 101 Location and party master data - Location and party master data attributes Location master data attributes may be used to describe a location identifier; this identifier SHOULD be a Global Location Number (GLN), expressed in EPC URI format as an SGLN, whether the location identifier is used as a EPCIS Read Point, Business Location, Source, or Destination.
  • 102. © Auto-ID Lab Korea / KAIST Slide 102 Location and party master data - Geofence polygons (GFP extension)
  • 103. © Auto-ID Lab Korea / KAIST Slide 103 Rail-specific EPCIS event extensions (철도 산업에 특화된 이벤트 확장 데이터) • Each Rail-specific extension is assigned the following namespace identifier: https://gs1.org/cbv/rail The namespace should be declared, along with the EPCIS standard namespace(s), in the beginning of the EPCIS header, as follows:
  • 104. © Auto-ID Lab Korea / KAIST Slide 104 Rail-specific EPCIS event extensions Updated Configuration Data (UCD) • updates to configuration that are generated for instance/lot (LGTIN, SGTIN or GIAI) following repair/refurbishment of an assembly/vehicle/infrastructure – potentially superseding original CMD & ILMD values – are not considered master data
  • 105. © Auto-ID Lab Korea / KAIST Slide 105 Rail-specific EPCIS event extensions Runtime Condition Data (RCD) Sensor/runtime data may be obtained in two major ways: By sensors directly affixed to the object in question, which mainly measure relevant properties for the object itself (e.g., internal activation cycles of an instance), By inheritance from parent objects in an aggregated assembly. An example of inherited (or “global”) sensor/runtime data is the mileage recorded on rail vehicle level, which could be used to update the mileage of relevant components and parts, taking the time of installation into account.
  • 106. © Auto-ID Lab Korea / KAIST Slide 106 Rail-specific EPCIS event extensions Relative Position of Child (CRP) Where it is necessary to express relative position of assembled and/or installed components within their parent assembly, as per externally maintained standards, including but not limited to: • EN 15380-2: logical description in train (similar to e-class) • EN 15380-3: position installation description (e.g., left/right) EN 15380 - Railway applications. Classification system for railway vehicles
  • 107. © Auto-ID Lab Korea / KAIST Slide 107 Rail-specific EPCIS event extensions Leading Part (LP) When it comes to the identification of assemblies, two main scenarios can occur in practice: 1. The ID of the leading part is different from the ID of the assembly. 2. The ID of the leading part is used as the ID of the assembly (when the part is in assembled state). Since each party is free to apply either one of the scenarios, and a given party’s approach may not be known in advance, it is important to include sufficient information to eliminate ambiguity. The party SHALL always transmit the leading part ID.
  • 108. © Auto-ID Lab Korea / KAIST Slide 108 Rail-specific EPCIS event extensions Leading Part (LP)
  • 109. © Auto-ID Lab Korea / KAIST Slide 109 Rail-specific EPCIS event extensions Inspection Report (IR) 1. OBSERVE event, business step inspecting 2. OBSERVE event, business step repairing
  • 110. © Auto-ID Lab Korea / KAIST Slide 110 Rail-specific EPCIS event extensions Inspection Report (IR) 1. OBSERVE event, business step inspecting 2. OBSERVE event, business step repairing
  • 111. © Auto-ID Lab Korea / KAIST Slide 111 Rail-specific EPCIS event extensions Planned Replacement (PR) To satisfy Infrastructure planning requirements, planned replacement parts and planned replacement dates MAY be specified at the time of commissioning/original installation of the planned replacement’s predecessor (i.e., of the currently installed part) by means of a Rail- specific extension to an Object Event or an Aggregation Event
  • 112. © Auto-ID Lab Korea / KAIST Slide 112 EPCIS Query examples
  • 113. Part V – 디지털 인프라, 디지털 SOC 인프라
  • 115. © Auto-ID Lab Korea / KAIST Slide 115 디지털 인프라, SoC의 디지털화? 국제 표준이 기본입니다. [schema.org + GS1 + BIM/3D GIS] • TrainStation - schema.org - https://schema.org/TrainStation (웹 데이터 국제 고속도로) • Rail Standards | GS1 - https://www.gs1.org/…/technical-industr…/rail/rail-standards (산업 데이터 국제 고속도로) • IFC Rail - buildingSMART International - https://www.buildingsmart.org/ifc-rail- candidate-standard-…/ (공간 데이터 국제고속도로) Schema.org, GS1, BIM 모두 기차역은 GS1의 GLN(Global Location Number) 식별자를 가집니다
  • 116. © Auto-ID Lab Korea / KAIST Slide 116 관련 슬라이드 및 동영상 자료 [0] GS1 튜토리얼 세미나 자료 https://www.slideshare.net/gatordkim/gs1-tutorial-korean-by-daeyoung-kim-autoid-labs-kaist [1] GS1 튜토리얼 세미나 발표동영상 (1/3) https://youtu.be/rNaUpbO0fqY [2] GS1 튜토리얼 세미나 발표동영상 (2/3) https://youtu.be/4I0HNSM_Veg [3] GS1 튜토리얼 세미나 발표동영상 (3/3) https://youtu.be/cvC3B6vFqPg [4] Smart City Position Paper - GS1 Standards Perspective https://www.slideshare.net/gatordkim/smart-city-position-paper-gs1-standards-perspective [5] GS1 EPCIS, CBV 세미자자료 https://www.slideshare.net/gatordkim/gs1-epcis-and-cbv-tutorial-autoid-labs-kaist-apr-28-2020 [6] GS1 EPCIS, CBV 세미나 발표동영상 https://www.youtube.com/watch?v=d-ubhyXyT3A [7] GS1 ONS, Digital Link 세미나자료 https://www.slideshare.net/gatordkim/gs1-ons-and-digital-link-tutorial-autoid-labs-kaist-apr-28-2020 [8] GS1 ONS, Digital Link 세미나 발표동영상 https://www.youtube.com/watch?v=bs3OjSpyH60 [9] GS1발 데이터혁명시리즈 1 - 안전한 수산물 확보와 해양생물 보존을 위한 글로벌 수산물 이력 추적 시스템 구축 동향과 기술 – 세미나 자료 https://www.slideshare.net/gatordkim/digital-revolution-series-1seafood-industry [10] GS1발 데이터혁명시리즈 1 - 안전한 수산물 확보와 해양생물 보존을 위한 글로벌 수산물 이력 추적 시스템 구축 동향과 기술 – 발표 동영상 자료 https://www.youtube.com/watch?v=ohuQyBsE8fQ&feature=youtu.be
  • 119. © Auto-ID Lab Korea / KAIST Slide 119 FDA’s New Era of Smarter Food Safety November 26, 2019 Railroads Need to Step Up Their Game, New Report Says It reveals major concerns of shippers about rail’s service quality, reliability, degree of communication, flexibility and cost. why railroads should be focusing on digital transformation and the end-to-end supply chain. https://www.supplychainbrain.com/articles/30529-a-new-report-says-railroads-need- to-step-up-their-game First of all, it's more than just that railways need to change. There needs to be a coming together of carriers, shippers and investors. We interviewed those groups individually. Now we need to create a forum where they're sitting together, much like GS1 does with its global council structure, where everybody comes to the table, puts their differences aside, signs NDAs [non-disclosure agreements], and comes up with solutions that will benefit the entire ecosystem. SCB: What steps should railroads be taking to meet shippers’ concerns and improve overall performance?
  • 120. © Auto-ID Lab Korea / KAIST Slide 120
  • 121. © Auto-ID Lab Korea / KAIST Slide 121 https://www.itln.in/indian-railways-to-track-350000-wagons-and-coaches-using-rfid-by-2021
  • 122.
  • 123.
  • 124. © Auto-ID Lab Korea / KAIST Slide 124
  • 125. © Auto-ID Lab Korea / KAIST Slide 125
  • 126.
  • 127. © Auto-ID Lab Korea / KAIST Slide 127 The Scope of the Document • This document explains how to use the GS1 identification keys and attributes for the identification of parts and components in the rail industry. • In the rail sector interoperability means the ability of a rail system to allow the safe and uninterrupted movement of trains while accomplishing the required performance level. • This helps to ensure that rolling stock of operator A can operate on infrastructure of infrastructure managers B, C, D, etc., because the parts where the systems meet (wheelsets, rails, ETCS-components, pantographs, switches, toilet drains, etc.) are guaranteed to be compatible due to international norms. This standard is intended to be used by all parties involved in rail manufacturing, maintenance, repair, and overhaul processes. These include: • Manufacturers (system integrators, system manufacturers, component supplier), • Operators (rail network operators, rail operators), • Service providers (MRO workshops, project contractors, logistics service providers, and • Regulators.
  • 128. © Auto-ID Lab Korea / KAIST Slide 128 The Scope of the Document • At the same time the rail industry is being challenged by its customers to improve reliability and quality, and by regulatory bodies to implement measures aimed at further improving safety. • As a result manufacturing, maintenance, repair and overhaul (in short Manufacturing & MRO) processes have become far more international and complex than before. This drives the need for greater interoperability among rail manufacturing & MRO process stakeholders and among their systems and supply chains. • In order to meet these challenges, the entire rail industry must improve its manufacturing & MRO processes and in particular develop capabilities for reliable life cycle tracking of components and parts (referred to as MRO-objects in this standard) across companies, supply chains and over life cycles of up to 60 years. • A key enabler will be the ability to unambiguously identify MRO-objects across the systems and processes of all stakeholders. Depending on the operational and safety characteristics as well as legal requirements MRO-objects will need to be identified on class-level, lot-level and more and more frequently up to serial-level.
  • 129. © Auto-ID Lab Korea / KAIST Slide 129 Lifecycle identification of MRO-objects Business processes – Roles & Responsibilities
  • 130. © Auto-ID Lab Korea / KAIST Slide 130 Lifecycle identification of MRO-objects Need for traceability (일생 이력추적 요구) The maintenance organisations responsible for the objects needing maintenance will act based upon a wide variety of triggers that will signal that objects require planned or emergency or ad-hoc maintenance. (Maintenance 를 위한 표준 데이터 공유) <Types of maintenance strategies>
  • 131. © Auto-ID Lab Korea / KAIST Slide 131 Lifecycle identification of MRO-objects Configuration management Composite MRO-objects will be manufactured and maintained using a bill-of-material (BOM). Composite MRO-objects may contain other composite MRO-objects (produced by other manufacturers), which means that it must be possible to link BOMs. Three types of BOMs that may be applied, each with specific characteristics, are: 1. Design BOM: A standard BOM used in conjunction with the technical design, used as a basis for the manufacturing process. It will define the MRO-objects in terms of their type and position, but will not contain any serialised IDs or lot level IDs. 2. Manufacturing BOM: An instance BOM that is created during the manufacturing process and defines the MRO-object ‘as built’. It will contain a mixture of serialised and non-serialised IDs of the contained instances. Composite MRO-objects sourced from another party should have a serialised ID allowing to link to the manufacturing BOM of the supplier. This linking of instance BOMs is an essential aspect. 3. Installation BOM: An instance BOM that is used by the operator and the manufacturer’s after sales service organisation and used for the maintenance process. Like the manufacturing BOM this is an instance BOM, but unlike the manufacturing BOM the installation BOM will only contain instances that can be physically identified (serialised MRO-objects).
  • 132. © Auto-ID Lab Korea / KAIST Slide 132 Lifecycle identification of MRO-objects Configuration management The system integrator will have a design BOM of the locomotive, and will create a manufacturing BOM for each manufactured locomotive. The sub system manufacturer of the brake system will have a design BOM and a manufacturing BOM for the sub system, consisting of several components that need to be integrated by the system integrator. Based on the data from the suppliers the system integrator will create an installation BOM. In that BOM the brake system as a ‘whole’ will not be present, but primarily the serialised physical components that make up the system.
  • 133. © Auto-ID Lab Korea / KAIST Slide 133 Identification and marking principles - Identification and marking scenarios
  • 134. © Auto-ID Lab Korea / KAIST Slide 134 Significant business benefits for all players (표준 데이터 수집의 목적) Due to limited visibility and information about rail vehicles, it’s difficult for rail operators to plan and meet customer demands for timely deliveries and updates. Provides roadmap for rail stakeholders to gain visibility of rolling stock and access to real-time information (적절한 시간에 유지보수) (분석과 사고 조사) (안전관련 정보 공유) (고장 탐지) (리콜 관리)
  • 135. © Auto-ID Lab Korea / KAIST Slide 135 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • When • eventTime – the time at which the vehicle was observed (Object events), the time at which the first (leading) vehicle of a passing trainset was observed (Transaction events) • recordTime – the date and time at which this event was recorded by an EPCIS Repository • Ex)
  • 136. © Auto-ID Lab Korea / KAIST Slide 136 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Where • Indicates the location at which the EPCIS event was observed, as well as the whereabouts of the object subsequent to the event • readPoint – the SGLN corresponding to the event’s location • bizLocation – the SGLN corresponding to the object’s whereabouts subsequent to the event • For all object events and transaction events, either the readPoint or the bizLocation or both should be populated. • Ex)
  • 137. © Auto-ID Lab Korea / KAIST Slide 137 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Why • Reflects the business context (“Business Step”) of the EPCIS event, as well as the status (“Disposition”) of the object subsequent to the event • Business Step • Specifies the business process linked to the EPCIS event • Ex) • Disposition • Denotes the status of an object subsequent to the EPCIS event • Ex)
  • 138. © Auto-ID Lab Korea / KAIST Slide 138 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Why • Source/Destination • Use the urn:epcglobal:cbv:sdt:location source/destination type identifier with SGLN • Ex)
  • 139. © Auto-ID Lab Korea / KAIST Slide 139 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Why • Business Transactions • EPCIS Business Transactions are defined using a combination of Business Transaction Type and Business Transaction ID • Rail sector-specific vehicle visibility applications should use HTTP URLs for business transaction identifiers • To share information about a passage, the bizTransaction element can be used in two ways: • as a transaction reference in a rail visibility ObjectEvent, to indicate which events belong to the same ‘passage’ • as a transaction type in a rail visibility TransactionEvent, to specify totals and tag IDs for a particular ‘passage’ • Ex) transaction reference
  • 140. © Auto-ID Lab Korea / KAIST Slide 140 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Extension elements • All of these elements should be specified using the namespace urn:gs1:epcisapp:rail • Direction elements (for object and transaction events) • directionIndicator • 0 : the direction was not detected • 1 : the first direction in the rail network • 2 : the second direction in the rail network • compassDirection • Using a cardinal (N, S, W, E) or inter-cardinal (NW, NE, SE, SW) • Ex)
  • 141. © Auto-ID Lab Korea / KAIST Slide 141 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Extension elements • Object event elements • vehicleOrientation • 1 : vehicle end one is leading, relative to the direction of travel • 2 : vehicle end two is leading, relative to the direction of travel • 0 : leading vehicle end not determined • vehiclePosition – A number identifying the relative position of the rail vehicle within the passage • vehicleAxleCount – The number of axles of the vehicle • proxyGIAI – the GIAI(s) of the observed tag(s) • Ex)
  • 142. © Auto-ID Lab Korea / KAIST Slide 142 Sharing vehicle and train visibility data with EPCIS • EPCIS event data • Transaction event elements (optional) • trainAxleCount – the total number of axles observed for the passage of the entire trainset • trainVehicleCount – the total number of vehicles observed for the passage of the entire trainset • Vehicle – information on each of the observed vehicles • vehiclePosition – the relative position of the vehicle in the passage • vehicleAxleCount – the number of axles of the observed vehicle • vehicleUniqueIdentified – indicates whether the ID of the observed rail vehicle was captured • vehicleMasterGIAI – optional element specifying the ID of the rail vehicle • Ex)
  • 143. © Auto-ID Lab Korea / KAIST Slide 143 Master Data - Name Space
  • 144. © Auto-ID Lab Korea / KAIST Slide 144 Rail-specific EPCIS event extensions Updated Configuration Data (UCD) • By contrast, updates to configuration that are generated for instance/lot (LGTIN, SGTIN or GIAI) following repair/refurbishment of an assembly/vehicle/infrastructure – potentially superseding original CMD & ILMD values – are not considered master data, but instead SHALL be reflected in a Rail-specific extension for Updated Configuration Data (UCD). • The attributes SHALL be placed within an XML element rail:UpdatedConfigurationData.
  • 145. © Auto-ID Lab Korea / KAIST Slide 145 Rail-specific EPCIS event extensions Runtime Condition Data (RCD) Sensor/runtime data may be obtained in two major ways: ■ By sensors directly affixed to the object in question, which mainly measure relevant properties for the object itself (e.g., internal activation cycles of an instance), ■ By inheritance from parent objects in an aggregated assembly. An example of inherited (or “global”) sensor/runtime data is the mileage recorded on rail vehicle level, which could be used to update the mileage of relevant components and parts, taking the time of installation into account. Rules for “direct sensor data” Each attached sensor SHOULD be identified uniquely with a GIAI or SGTIN, and the sensor data SHOULD be specified in the RCD extension of the ObjectEvent related to the sensor in question. Sensor installation should be captured by means of Aggregation events (action: ADD/business step: installing/disposition: active) including the sensor as child and the object to which the sensor is affixed as parent. Rules for “inherited sensor data” Handling of sensor/runtime data inheritance is at the discretion of the operators that integrate these parts and subcomponents. The values will be specified in the RCD extension of an ObjectEvent related to the object in question. When disaggregating, repairing or refurbishing, inherited sensor values SHOULD be updated to reflect current values of the parent and impacted children.
  • 146. © Auto-ID Lab Korea / KAIST Slide 146 Rail-specific EPCIS event extensions Runtime Condition Data (RCD)
  • 147. © Auto-ID Lab Korea / KAIST Slide 147 Rail-specific EPCIS event extensions Relative Position of Child (CRP) Where it is necessary to express relative position of assembled and/or installed components within their parent assembly, as per externally maintained standards, including but not limited to: ■ EN 15380-2: logical description in train (similar to e-class) ■ EN 15380-3: position installation description (e.g., left/right) …each such component must be expressed as an “only child” without siblings when aggregated to its parent in an EPCIS Aggregation Event.
  • 148. © Auto-ID Lab Korea / KAIST Slide 148 Rail-specific EPCIS event extensions Leading Part (LP) When it comes to the identification of assemblies, two main scenarios can occur in practice: 1. The ID of the leading part is different from the ID of the assembly. 2. The ID of the leading part is used as the ID of the assembly (when the part is in assembled state). Since each party is free to apply either one of the scenarios, and a given party’s approach may not be known in advance, it is important to include sufficient information to eliminate ambiguity. The party SHALL always transmit the leading part ID.
  • 149. © Auto-ID Lab Korea / KAIST Slide 149 Rail-specific EPCIS event extensions Leading Part (LP)
  • 150. © Auto-ID Lab Korea / KAIST Slide 150 Rail-specific EPCIS event extensions Leading Part (LP)
  • 151. © Auto-ID Lab Korea / KAIST Slide 151 Rail-specific EPCIS event extensions Leading Part (LP)
  • 152. © Auto-ID Lab Korea / KAIST Slide 152 Rail-specific EPCIS event extensions Leading Part (LP)
  • 153. © Auto-ID Lab Korea / KAIST Slide 153 Rail-specific EPCIS event extensions Inspection Report (IR) 1. OBSERVE event, business step inspecting 2. OBSERVE event, business step repairing
  • 154. © Auto-ID Lab Korea / KAIST Slide 154 Rail-specific EPCIS event extensions Inspection Report (IR)
  • 155. © Auto-ID Lab Korea / KAIST Slide 155 Rail-specific EPCIS event extensions Planned Replacement (PR) To satisfy Infrastructure planning requirements, planned replacement parts and planned replacement dates MAY be specified at the time of commissioning/original installation of the planned replacement’s predecessor (i.e., of the currently installed part) by means of a Rail- specific extension to an Object Event or an Aggregation Event, as indicated below. A lone part MAY be designated for replacement by multiple parts; in this case there SHALL be a list of GTINs designated as replacement parts.
  • 156. © Auto-ID Lab Korea / KAIST Slide 156 Rail-specific EPCIS event extensions Planned Replacement (PR)
  • 157. © Auto-ID Lab Korea / KAIST Slide 157 Rail-specific EPCIS event extensions Planned Replacement (PR)
  • 158. © Auto-ID Lab Korea / KAIST Slide 158 Rail Vehicle Visibility • Asset tracking applications • Normal operative functions where the location of specific vehicles needs to be known • Vehicle level management • Tracking the location and status of each vehicle as they travel • Independent of the train • RFID allows for automatic collection of all measurement results and creating statistical data of measurement results per vehicle. • Potential future applications: • Real-time cargo tracking • Planning vehicle availability • Estimating the vehicle distance travelled for planning preventive maintenance
  • 159. © Auto-ID Lab Korea / KAIST Slide 159 Rail Vehicle Visibility • RFID enabled Automatic Vehicle Identification (AVI) systems • Fixed readers and wheel sensors (at trackside) • The fixed trackside readers identify the vehicles of the passing train. • Identify all tagged vehicles and their order in the train • Detect the presence of vehicles with missing or broken tags and their relative location in the train • Important for the WTMS use case, since it enables the measurement results to be linked to the correct vehicles in a train set • Determine the travel direction, the orientation, axle count, speed, and length of each vehicle • Enable train level information exchanges • Ex) A train entering or leaving a yard and the composition/formation of the train Fixed trackside RFID configuration
  • 160. © Auto-ID Lab Korea / KAIST Slide 160 Rail Vehicle Visibility • Train Management System (TMS) • A system used to control railway operations • Detect and control movement of trains on a track • The information from the TMS can be used to generate additional event data. • Ex) The train entering or leaving an area can be deduced by combining data from a TMS and data from previously read points provided by the AVI system. Cross River Rail: How the European Train Control System works
  • 161. © Auto-ID Lab Korea / KAIST Slide 161 Scope of the document • It explains how to implement the GS1 EPCIS standard to exchange of component/part lifecycle data in the rail industry. The scope of this document includes: • Lifecycle/business events that will require a set of key data elements to be recorded (and shared) • Roles and responsibilities related to recording and data sharing • Data structures and definitions • XML-syntax representations for each message • Message exchange scenarios
  • 162. © Auto-ID Lab Korea / KAIST Slide 162 Business Intention • Rail stakeholders can develop and share manufacturing & maintenance and usage information, enabling rail equipment operators to consistently fulfil tracking and tracing needs while reducing overall costs. • strongly facilitate tracking and tracing throughout the complete lifecycle of, for instance, an individual object, across companies and borders; • unify the data exchange process requirements by rail and rail network operators in regards to unit suppliers and manufacturers; and • thereby allow for new supply chain design possibilities (e.g., stock and supply sharing, pay per use, user specific R&D programmes, etc.)
  • 163. © Auto-ID Lab Korea / KAIST Slide 163 Master Data - Trade item master data This section specifies master data attributes that may be used to describe a trade item identifier that appears in the “what” dimension of an EPCIS event. “trade item” refers to all MRO objects, including items from manufacturers.
  • 164. © Auto-ID Lab Korea / KAIST Slide 164 Master Data - Trade item (class-level) master data Class-level master data related to a GTIN may undergo changes over time. This can lead to multiple sets of class-level master data for the same GTIN. In order to distinguish these sets, the functional status and revision status SHALL be expressed as attributes of the VocabularyElement. Rail-specific enhancement for class-level master data
  • 165. © Auto-ID Lab Korea / KAIST Slide 165 Location and party master data This section specifies master data attributes that may be used to describe a location identifier. The following general rules apply: ■ Location master data attributes may be used to describe a location identifier; this identifier SHOULD be a Global Location Number (GLN), expressed in EPC URI format as an SGLN, whether the location identifier is used as a EPCIS Read Point, Business Location, Source, or Destination. ■ A Rail Component/Part EPCIS document MAY include any of the master data attributes specified in this section within the master data section of the EPCIS Document header, subject to the constraints specified elsewhere in this section. ■ The master attributes specified in this section may also be used in an EPCIS Master Data Document or in the response to an EPCIS Master Data Query. ■ A Rail Component/Part EPCIS document SHALL NOT include any of the master data attributes specified in this section in the ILMD section of an EPCIS event.
  • 166. © Auto-ID Lab Korea / KAIST Slide 166 Examples of Rail Visibility Events SBB (Swiss Federal Railways) and other international rail operators have decided to implement standardised labelling. This guarantees a transparent flow of materials and information throughout their entire life-cycles, including production, storage, installation, operation and repairs.
  • 167. © Auto-ID Lab Korea / KAIST Slide 167 Examples of Products
  • 168. © Auto-ID Lab Korea / KAIST Slide 168 Case study