IoT Execution Engine - speed in digitization
Away from rigid, hard to change IT and hardware systems to more influenceability and intelligence in the monitoring and control of systems
With the IoT Execution Engine, plant and sensor data can be collected in real time, modeled in the context of the value-added process and all information and status changes recorded in the Digital Twin can be analyzed and persisted during operation.
- Processing of IoT signals in real time
- Creation of context and behavior models
- Creation of Digital Twin models for plants, products and processes
- Derivation of improvement measures
IoT Execution Engine - speed in digitization
Away from rigid, hard to change IT and hardware systems to more influenceability and intelligence in the monitoring and control of systems
With the IoT Execution Engine, plant and sensor data can be collected in real time, modeled in the context of the value-added process and all information and status changes recorded in the Digital Twin can be analyzed and persisted during operation.
- Processing of IoT signals in real time
- Creation of context and behavior models
- Creation of Digital Twin models for plants, products and processes
- Derivation of improvement measures
The IoT Execution Engine enables intelligently networked and autonomous production
The Micro-Service Platform enables the:
- Modelling of subject-specific digital twins for systems, products and processes by the user
- Configurative connection of hardware and external signals without coding
- Persistent storage of all IoT signals in the digital file
- Visualization of signals and context based on a widget library
- Analysis, optimization and control of production through AI and machine learning
Context-based
Consistent behaviour model from the process model to the signal level
Integrative
Simple and flexible process, system (e.g. SAP) and device integration (e.g. OPC UA)
Real-time capable
Semantic engine with massively parallel real-time information processing
No Coding
Flexible modeling and configuration via graphical user interfaces
The IoT Execution Engine enables intelligently networked and autonomous production
The Micro-Service Platform enables the:
- Modelling of subject-specific digital twins for systems, products and processes by the user
- Configurative connection of hardware and external signals without coding
- Persistent storage of all IoT signals in the digital file
- Visualization of signals and context based on a widget library
- Analysis, optimization and control of production through AI and machine learning
Context-based
Consistent behaviour model from the process model to the signal level
Integrative
Simple and flexible process, system (e.g. SAP) and device integration (e.g. OPC UA)
Real-time capable
Semantic engine with massively parallel real-time information processing
No Coding
Flexible modeling and configuration via graphical user interfaces
Components of the IoT Execution Engine
Twin Modeler
Modeling and
configuration of the
Digital Twin models
Twin Execution
Runtime environment
and real-time control
of events
Twin Digital File
Real-time persistence of all parameters in context and controlled access to them
Twin Live Dashboard
Visualization for the end user and intervention in the execution level
Twin Validation
Validation of process logic,
process descriptions, consistency
check
Twin Device Connector
Flexible connection and intelligent data retrieval of plant protocols (OPC UA, S7)
Twin Device Builder
Configuration of the device connection and automatic recognition of new parameters
Twin Control Center
Sanity Twin Infrastructure, Service Discovery, Availability, runtime-environment
Twin AI
Advances Analytics in the context of the Digital Twin (Predictive-, Prescriptive-Analytics)
e.g. Machine Learning Analysis of machine parameters in relation to use case
Standard features of the IoT Execution Engine
Flexible Machine Connection and Data Retrieval
Learn more
Reading of plant parameters from a PLC without coding via standard protocols, such as OPC-UA, MQTT, S7, TCP Client/Server, REST
Messages, Alarms and Anomaly Detection
Learn more
Messages and recommendations for action are generated automatically and are available to the user filterable according to various criteria
Flexible Operation,
Reliability
Learn more
Availability of all operational data to protect your sensitive data for cloud-native, hybrid or on-premise operation
Integration Layer for the Shopfloor
Learn more
High-performance integration layer for connecting all production systems, devices, actuators, sensors & networking the IT landscape (e.g. ERP, MES)
Persistence of Data in the Context of Value Creation
Learn more
Persistent and historized storage of all signals and information about state changes including display for selected time in the digital file
User-specific
Visualization
Learn more
Flexible configuration of user-specific dashboards via standardized widget library on different (mobile) devices
Modelling of
Digital Twins
Learn more
Rapid implementation of subject-specific behavioral models through the use of predefined modules and ready-made ontological models
Intelligent, AI-based Analyses
Learn more
Integration of Data Analytics Services for pattern, threshold, correlation detection & for the application of Supervised & Unsupervised Machine Learning
Condition Monitoring &
Quality Monitoring
Learn more
User get a view of production, process & product configured for their specific role with necessary calculations & information
Standard features of the IoT Execution Engine
Flexible Machine Connection and Data Retrieval
Learn more
Reading of plant parameters from a PLC without coding via standard protocols, such as OPC-UA, MQTT, S7, TCP Client/Server, REST
Messages, Alarms and Anomaly Detection
Learn more
Messages and recommendations for action are generated automatically and are available to the user filterable according to various criteria
Flexible Operation,
Reliability
Learn more
Availability of all operational data to protect your sensitive data for cloud-native, hybrid or on-premise operation
Integration Layer for the Shopfloor
Learn more
High-performance integration layer for connecting all production systems, devices, actuators, sensors & networking the IT landscape (e.g. ERP, MES)
Persistence of Data in the Context of Value Creation
Learn more
Persistent and historized storage of all signals and information about state changes including display for selected time in the digital file
User-specific
Visualization
Learn more
Flexible configuration of user-specific dashboards via standardized widget library on different (mobile) devices
Modelling of
Digital Twins
Learn more
Rapid implementation of subject-specific behavioral models through the use of predefined modules and ready-made ontological models
Intelligent, AI-based Analyses
Learn more
Integration of Data Analytics Services for pattern, threshold, correlation detection & for the application of Supervised & Unsupervised Machine Learning
Condition Monitoring &
Quality Monitoring
Learn more
User get a view of production, process & product configured for their specific role with necessary calculations & information
The key to implementing Industry 4.0 and IIoT
1
- Description of the process and behavioral logic of the system by modeling the functional relationships including all dependencies, states and properties
2
- Configurative connection of hardware and networking with existing IT infrastructure (e.g. ERP, MES)
- Capture, storage and processing of data streams in real time
3
- Providing the context for machine learning
- Interactive dashboards for visualization and advanced analytics
4
- Visualization of fault messages, anomalies and trends through Advanced Analytics
- Worker guidance through recommendations for action in case of deviations/anomalies
5
- Visualization of fault messages, anomalies and trends through Advanced Analytics
- Worker guidance through recommendations for action in case of deviations/anomalies
The key to implementing Industry 4.0 and IIoT
1
- Description of the process and behavioral logic of the system by modeling the functional relationships including all dependencies, states and properties
2
- Configurative connection of hardware and networking with existing IT infrastructure (e.g. ERP, MES)
- Capture, storage and processing of data streams in real time
3
- Providing the context for machine learning
- Interactive dashboards for visualization and advanced analytics
4
- Visualization of fault messages, anomalies and trends through Advanced Analytics
- Worker guidance through recommendations for action in case of deviations/anomalies
5
- Visualization of fault messages, anomalies and trends through Advanced Analytics
- Worker guidance through recommendations for action in case of deviations/anomalies
