Contenu connexe Similaire à AWS Intelligent at Edge for IoT (20) Plus de Amazon Web Services (20) AWS Intelligent at Edge for IoT1. AWS Intelligent at Edge for IoT
Young Yang
AWS Solutions Architect
beyoung@amazon.com
Drive Warehouse Efficiencies with the Same AWS IoT
technology
That Powers Amazon Fulfillment
2. 亞馬遜人工智慧應用服務- 以亞馬遜物流為例
Amazon Kindle
Reader
Revolutionizing the reading
experience
Amazon Fresh
Grocery Delivery
Amazon Go
Advanced Shopping
Amazon Studios
Revolutionizing Music and
Film Production
尊榮會員服務 雲端服務 (44%全球市佔) 智慧電視智慧語音助理 客戶消費體驗回顧市集
一小時貨物送達服務 新創產品銷售
電子書
一鍵點擊訂購服務
亞馬遜物流
無人機運送
一日生鮮送達服務 無人商店
影音串流服務
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What you expect in this session
The Transformation Path
from Monolithic to Microservices
in
Amazon.com Fulfillment Center
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Transition period
System integrators point of view:
From User
To
Development Collaboration
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Big bang or incremental change?
Evolution
Not revolution
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Machine as a service ≠ IIoT
Equipment life cycle
Cloud connectivity issues
Security
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Challenges
Monetization
mechanism
Controller
Permission
Sensors Actuators
Must connect to millions of end points
spread out over millions of acres
CPU load is highly variable and must be
completed in a fixed time window
End user engagement needs
to assume a very mobile and
non-technical customer
Multi-system orchestration approach
needed to combine all required data in
near real-time
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Challenges
Monetization
mechanism
Controller
Permission
Sensors Actuators
Must connect to millions of end points
spread out over millions of acres
CPU load is highly variable and must be
completed in a fixed time window
End user engagement needs
to assume a very mobile and
non-technical customer
Multi-system orchestration approach
needed to combine all required data in
near real-time
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Challenges
Monetization
mechanism
Controller
Permission
Sensors Actuators
Must connect to millions of end points
spread out over millions of acres
CPU load is highly variable and must be
completed in a fixed time window
End user engagement needs
to assume a very mobile and
non-technical customer
Multi-system orchestration approach
needed to combine all required data in
near real-time
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Challenges
Monetization
mechanism
Controller
Permission
Sensors Actuators
Must connect to millions of end points
spread out over millions of acres
CPU load is highly variable and must be
completed in a fixed time window
End user engagement needs
to assume a very mobile and
non-technical customer
Multi-system orchestration approach
needed to combine all required data in
near real-time
14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Challenges
Monetization
mechanism
Controller
Permission
Sensors Actuators
Must connect to millions of end points
spread out over millions of acres
CPU load is highly variable and must be
completed in a fixed time window
End user engagement needs
to assume a very mobile and
non-technical customer
Multi-system orchestration approach
needed to combine all required data in
near real-time
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Data from Assets – The Foundation of Digital Twins
Unable to link
data together
96%
of industrial
state data is not used
Data collected
too
infrequently
39%
of Manufacturers do not
regularly collect data
Data difficult
to access
66%
of industrial companies
find data is difficult to
access
Why?
SCM World/Cisco “Smart Manufacturing & the Internet of Things 2015”
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Decades of this...
Source: https://xkcd.com/927/
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... resulted in this!
Operations (OT) Enterprise (IT)
IT Systems
CRM
Asset Management
ERP
Supply Chain
Finance
Maintenance
Compliance
Shopfloor
Single machine with
multiple components
following different
standards
Complete production
line likely to have
many machines with
different protocols
Challenge: Get data
from OT to IT and
make it usable!
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ISA-S99, Industrial Automation and Control Systems Security
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Manufacturing networking 101
Internet
Enterprise
network
Manufacturing
network
VLAN
VLAN
VLAN
VLAN
MES/Historian Enterprise apps
Shop floor
ISA-S99, Industrial Automation and Control Systems Security
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Shop Floor to AWS Cloud Connectivity
Internet
Enterprise
DMZ
Industrial
Machine Tool / PLC
ISA-S99, Industrial Automation and Control Systems Security
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Shop Floor to AWS Cloud Connectivity
Internet
Enterprise
DMZ
Industrial
Greengrass
VPC
VPN Connection Direct Connect
Machine Tool / PLC
ISA-S99, Industrial Automation and Control Systems Security
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Greengrass/VPC
Internet
Corporate
DMZ
Industrial
Greengrass
VPC
ISA-S99, Industrial Automation and Control Systems Security
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Manufacturing networking - IoT connectivity
Internet
Enterprise
network
Manufacturing
network
VLAN
VLAN
VLAN
VLAN
MES/Historian Enterprise apps
Shop floor
AWS IoT
AWS Greengrass
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Operations (OT)
Solving Industrial Data Extraction
Factory Machines
Enterprise (IT)
IT Systems
CRM
Asset Management
ERP
Supply Chain
Finance
Maintenance
Compliance
Protocol
conversion
Modbus
conversion
OPC UA
conversion
Gateway
Custom / Proprietary
Protocol
MQTT
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IIoT Design Pattern – ETL @ Edge
Load
• Buffering
• Compression
• Gateway Cloud
Transform
• Formats (CVS JSON)
• Filtering
• Data translation
Extract
• Protocol conversion
• Data rate
• Polling vs Events
Operations (OT)
Gateway
Extract Transform Load
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Industrial communication protocols
Sends data to
PLCs or RTUs
Feeds data to
SCADA system
Supervise and
control from an
operational terminal
programmable logic controllers (PLCs) or
remote terminal units (RTUs).
IEC 61158 SCADA Protocols
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Communication requirements
- Throughput
- Scheduled
- Low downtime
- Hostile environments
- Scalable
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Fieldbus examples
Non-ethernet Ethernet
• Modbus RTU/ASCII
• Profibus
• DeviceNet (CIP)
• ControlNet (CIP)
• IO-link
• AS-i
• Modbus/TCP
• Profinet
• Ethernet/IP (CIP)
• CC-Link
• PowerLink
• EtherCAT
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Supervisory examples
Ethernet
• Modbus/TCP
• S7-Comm
• Ethernet/IP
• PCCC
• SLMP
• OPC-UA/OPC-DA
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SCADA Protocols:
The Good, the Bad, and the Ugly
Allen-Bradley DF1
Allen-Bradley DH+
Allen-Bradley EN/IP
Amocam
ARCNet
ATS
BITbus
CANbus
CA
CCM2
CDCI
CDCII
Conitel
DeviceNet
Daniel
DL130
DNP 3.0
Elliott
Enron Modbus
F&M
Ferranti MK2A
Galveston-Houston
GPE
GSI
Harris
5000/5500/6000
Hansa S002
HART
Hayes
Honeywell DE
Kodata
L&J
LANDAC
Landis & Gyr
Micromotion
Flowscale
MODBUS ASCII
MODBUS RTU
MODBUS Plus
MPS9000
MTS
Omron Host Link
Optomux
PERT 2631
Plessey TC6
RDACSII
REDAC 70H
RNIM
Siemens 3964R
Siemens RK512
SLIP
SNET -I
SNET –II
GE SRTP
TANO Model 10
TANO Model 100
Tejas
Total-Flow
Transit Bus
TRW 9550
Valmet Series 5
Transmitton MT700
TRW S-70
TRW S-703
Varec
Wesdac 4F
WISP
Wireless HART
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SCADA Systems have been around for over 40 years now. From an
operations point of view, they have been doing their jobs quite
well.
But we need to re-think SCADA architectures and infrastructures
to not only meet the strict requirements of a mission-critical real-
time control system, but also provide this business-critical
information to other data consumers within the enterprise.
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Operations (OT)Enterprise (IT)
Manufacturing environment
IT Systems
CRM
Asset Management
ERP
Supply Chain
Finance
Maintenance
Compliance
SCADA,
DCS, etc.
Various Protocols
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International standards
IEC 61131-3
Programmable Logic Controllers (PLC)
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Hardware
Siemens
Allen Bradley
Mitsubishi
CoDeSys
SoftPLCsEstablished PLCs
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Chosen design
Industrial PC—Linux based OS
SoftPLC
On premises SCADA system,
running on Linux hosts.
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Ignition: The Ultimate in Data Acquisition
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AWS Injector
This new Injector module easily connects any tag data from the
Ignition platform into the Amazon Web Services (AWS) cloud
services infrastructure. With a simple configuration, tag data will
flow into Amazon Kinesis.
• Connects to any TAG Data (including all properties)
• Easy to configure
• For use on Ignition Gateway or Ignition Edge
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From Local Server to AWS Cloud
EC2 RDS
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Industrial Equipment
Manufacturing Execution Systems (MES)
Ignition Edge & AWS Greengrass
L0
L1
L2
L3
L4
L5
ERP/SAP (financials, etc.)
Cloud
MQTT-SP-B
AWS IoT
Telemetry channel
(MQTT)
File channel
(HTTPS)
AB CIP Protocol
Ignition Gateway
PLC
AWS Greengrass
on Raspberry Pi
AB PLC –
Micrologix 1100
Machines
AB CIP/Modbus/OPC/
other industrial protocols
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ISA-95 in the context of the AWS Cloud
Level 1
Level 2
Line/machine
control
Animation
direct control
Level 3
Level 4
Description
Line/machine
supervision
Manufacturing
Operations
Management
Business
planning &
logistics
MES/
Historian
ERP/PLP/SCM
App/SystemFunction
Line/cell
execution
Business
operations
SCADA/HMI
Supervisory
control
DCS/PLC/RTU
Level 0
Physical
values
Raw data
event signals
I/O Sensor
AWS
Services
Enterprise
apps in the
cloud
Data
ingestion &
analytics
AWS
Greengrass
IoT Device
FreeRTOS
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Benefits of the transformation
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Cost Saving
Business viability
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When the impact of change is small,
release velocity can increase
Monolith
Does everything
Microservices
Does one thing
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AWS CodeDeploy
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Existing designs
W M S
W C S
M a c h i n e
s e n s o r s / a c t u a t o r s
Rack mount ed in data center
Local servers
Rigid monolit hi c design
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New design
W M S
W C S
M a c h i n e
s e n s o r s / a c t u a t o r s
AWS
Local servers and Greengr as s
Modul ar design
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International standards
ANSI ISA-S88
Batch control
Enterprise Site Area
Process
Cell
Amazon
Customer
Fulfillment
Site A
Site B
. . .
Pack
Receive
. . .
Pack Machine
Labeler
. . .
1:n 1:n 1:n
Unit
1:n
Glue System
. . .
Equipment
Module
1:n
Conveyor
Divert
. . .
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Modular design
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Challenges
Monetization
mechanism
Controller
Permission
Sensors Actuators
Must connect to millions of end points
spread out over millions of acres
CPU load is highly variable and must be
completed in a fixed time window
End user engagement needs
to assume a very mobile and
non-technical customer
Multi-system orchestration approach
needed to combine all required data in
near real-time
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AWS IoT Buttons/Lambda/messaging
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AWS IoT Buttons/Lambda/messaging
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Operator can call assistance from:
- Water spider (one click)
- Engineer (long click)
Results:
- Text message to the right person
- Message on SCADA
AWS IoT Buttons/Lambda/messaging
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Low Latency
Low Volume
High Latency
High Volume
Technique Need Response Applications
Cloud
Massive datasets for long
term analysis
Days to Minutes • Deep Learning
Fog
M2M communication and
analysis of batch data
from multiple devices
Minutes to Milliseconds • Industrial
• Wearables – Data
Tracking
Edge
Real-time decision making
off of one devices data
Milliseconds to
Nanoseconds
• Real-time traffic
Monitoring
• Robotics
• Streaming Video
• VR/AR
Edge Fog and Cloud Computing
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Examples of learning
Classification
Prediction & forecasting
Route optimization
Anomaly detection
Object identification
Language processing
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Key applications Vision, Robotics & Pattern Recognition
Source McKinsey
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SP BarCode
- Shipping information
- Package Contents
Carton BarCode
- Carton weight
- Carton dimensions
Shipping Label
- Shipping information
- Delivery Vendor (UPS 2nd Day Air)
- Vendor tracking code
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AWS Greengrass
Lambda Function Machine Learning
Cameras
Scale Sensors
Actual weight + tolerance < Expected weight: (weight of package contents + carton weight)
Actual Shipping Label <> Expected Shipping Label
ML Models:
- CV: Segmentation
- CV: Text Extraction
WMS
The intelligent at Edge for IoT
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Shipping label application
LPA2
BCR
Kinesis
Streams
3rd party
software
SNS Topic
S3Storage
Lambda
LPA 1
S3Storage
• Location ID
• Item ID
• UoM[n]
Elastic Beanstalk
EC2
Amazon
QuickSight
Amazon
Redshift
AWS IoT
UoM: Unit of Measures, weight, height, width, length, and etc.
BCR: Bar Code Reader
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Sortation
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Sortation
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Sortation
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Sortation
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Sortation
BCR
AWS IoT
• Location ID
• Induct ID
• Item ID
• UoM[n]
A
B
C
D
E
M
XT
• Vibration
sensors
• Motor current
• Speed
JT
Energy consumption
• Wh (consumed/generated)
• Phase voltages
• Phase currents
• Active power/phase
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Greengrass on existing machines
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Greengrass on new machines
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Data from Assets – The Foundation of Digital Twins
Unable to link
data together
96%
of industrial
state data is not used
Data collected
too
infrequently
39%
of Manufacturers do not
regularly collect data
Data difficult
to access
66%
of industrial companies
find data is difficult to
access
Why?
SCM World/Cisco “Smart Manufacturing & the Internet of Things 2015”
Greengrass/VPC
Internet
Corporate
DMZ
Industrial
Greengrass
VPC
ISA-S99, Industrial Automation and Control Systems Security
Operations (OT)
Solving Industrial Data Extraction
Factory Machines
Enterprise (IT)
IT Systems
CRM
Asset Management
ERP
Supply Chain
Finance
Maintenance
Compliance
Protocol
conversion
Modbus
conversion
OPC UA
conversion
Gateway
Custom / Proprietary
Protocol
MQTT
75. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Industrial Equipment
Manufacturing Execution Systems (MES)
Ignition Edge & AWS Greengrass
L0
L1
L2
L3
L4
L5
ERP/SAP (financials, etc.)
Cloud
MQTT-SP-B
AWS IoT
Telemetry channel
(MQTT)
File channel
(HTTPS)
AB CIP Protocol
Ignition Gateway
PLC
AWS Greengrass
on Raspberry Pi
AB PLC –
Micrologix 1100
Machines
AB CIP/Modbus/OPC/
other industrial protocols
New design
W M S
W C S
M a c h i n e
s e n s o r s / a c t u a t o r s
AWS
Local servers and Greengrass
Modular design
AWS Greengrass
Lambda Function Machine Learning
Cameras
Scale Sensors
ML Models:
- CV: Segmentation
- CV: Text Extraction
WMS
The intelligent at Edge for IoT
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Smart operations
Smart products and
services
Connectivity and internet technology
Smart sensors and actuators
Cloud processing and storage
Big data analytics and artificial intelligence
Two predominant outcomes
Smart products and services are brought to market
with smart operations
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Smart Products and Services Capabilities
Machine
Connectivity
Machine Data
Integration Platform
Machine as a Service
Advanced
Machine Optimization
• Rapid commissioning
• Machine security
• Enable template-
based machine
provisioning
• Factory / On-prem
• Enable machine OEE
monitoring
• MTC & OPC offload
• Scaled factory data
acquisition
• Advanced security
• Process health
• Identity security
framework
• Machine to cloud
framework
• Machine tuning
• Predictive
maintenance
• Secure bi-directional
data
• Secure remote access
• Time sensitive
networking
• High speed,
standards based
machine I/O and
control networking
• Advanced control
integration with HMI
visibility of network
• Advanced analytics
• Machine Learning
Value
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Thank You
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AWS industrial IoT reference architecture
Amazon
SNS
AWS Greengrass
IoT rule (all data)
Amazon S3
Data Lake
Kinesis Data
Firehose
Protocol
conversion
Email
AWS
SMS
Industrial equipment
Industrial equipment
Amazon
Glacier
Kinesis Data
AnalyticsProtocol
conversion
ML
inference
AWS IoT/AWS Greengrass/
AWS IoT Device Management/
AWS Device Defender
Amazon
SageMaker
ML models
Amazon QuickSight
Amazon Kinesis
Streams
Kinesis Data
Firehose
IoT anomaly
data repository
Amazon
Athena
Amazon
Athena
IoTrule(alerts)
Realtimeand
historicalvisualization
CloudWatch
Amazon Cognito
CloudTrail
AWS Config
IoT Cert
IAM
AWS IoT Analytics
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AWS manufacturing reference architecture (Brownfield)
AWS
Greengrass
Amazon S3
Data Lake
Kinesis Data
Firehose
MES
Factory machines
Protocol
conversion
ML
inference
AWS IoT
Amazon SageMaker
Machine Learning
Amazon QuickSight
Data visualization/reporting
Amazon
Athena
Historian
Storage
gateway
Amazon EMR
Amazon
EBS
Amazon
EC2
AWS
Batch
Amazon
AppStream
Amazon
EBS
Amazon
EC2
HPC workloads
Enterprise workloads (SAP)
AWS
DMS
Amazon
RDS
Local servers
Amazon Redshift
Data warehouse
Dataingestion
API Gateway
interfaces
N:1
Plant
manager
Quality
manager
H&S officer
Production
planner
Plant
maintenance
Production
manager
Engineer
OSI PI connector
AWS Snowball Edge
Data migration
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AWS Manufacturing Reference Architecture (Brownfield)
Greengrass
Edge/GW
S3
Data Lake
Kinesis
MES
Factory Machines
ML
Inference
IoT Core
Sage Maker
ML
QuickSight
Business
Intelligence
Athena
Historian
Storage GW
EMR
EBS EC2 Batch AppStreamEBS EC2
E&D Workloads
(PLM/HPC/CAE)
Enterprise Workloads
(SAP ERP/CRM)DMS RDS
Local Servers
RedShift
Data Warehouse
DataIngestion
API
N:1
SiteWise
Snowball Edge
Smart Products
DynamoDB Lambda
IoT Core
Amazon Forecast
Plant Maint. Planning
Business Functions
Greengrass
Connectors
IoT Analytics
Timestream
Outpost
IoT Events
EC2
Lambda
Business Logic
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Workshop Architecture
Node Red
Lambda
function
VPC
Subnet
Windows
EC2
PLC
EC2 Instance
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Ignition
AWS Greengrass
Lambda Function