Methods and systems consistent with the present invention provide a data processing system having a business object model reflecting the data used during a business transaction. Consistent interfaces are generated from the business object model. These interfaces are suitable for use across industries, across businesses, and across different departments within a business during a business transaction.

Consistent set of interfaces derived from a business object model

This paper presents an architecture for a future electric power distribution system that is suitable for plug-and-play of distributed renewable energy and distributed energy storage devices. Motivated by the success of the (information) Internet, the architecture described in this paper was proposed by the NSF FREEDM Systems Center, Raleigh, NC, as a roadmap for a future automated and flexible electric power distribution system. In the envisioned “Energy Internet,” a system that enables flexible energy sharing is proposed for consumers in a residential distribution system. The key technologies required to achieve such a vision are presented in this paper as a result of the research partnership of the FREEDM Systems Center.

The Future Renewable Electric Energy Delivery and Management (FREEDM) System: The Energy Internet

This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.

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Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges

https://bibliotecadigital.ipb.pt/bitstream/10198/1536/4/Agent-based%20distributed%20manufacturing%20control.pdf

Agent-based distributed manufacturing control: A state-of-the-art survey

Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include a time-series data component to receive time-series data from time-series data sources. The system may include a relational data component to receive relational data from relational data sources. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to extract, transform, and load aggregate data into a multi-dimensional data store; implement a type layer over a plurality of data stores comprising the key-value store, the relational database, and the multi-dimensional data store. The data services component may include definitions for a plurality of types based on the plurality of data stores.

Systems, methods, and devices for an enterprise internet-of-things application development platform

A cloud-based infrastructure facilitates gathering, transmitting, and remote storage of control and automation data using an agent-based communication channel. The infrastructure collects the industrial data from an industrial enterprise and intelligently sorts and organizes the acquired data based on selected criteria. Message queues can be configured on the cloud platform to segregate the industrial data according to priority, data type, or other criteria. Behavior assemblies stored in customer-specific manifests on the cloud platform define customer-specific preferences for processing data stored in the respective message queues. An agent-based analytics framework in the cloud platform performs desired analytics on the data using distributed parallel processing.

Remote industrial monitoring and analytics using a cloud infrastructure

This paper provides a comprehensive overview of long-term R&D activities of the Rockwell Automation (RA) company in the field of holonic and agent-based manufacturing control systems. It presents a coherent framework of methodologies for designing agent-based control systems, tools that support implementation and validation of them, and agent applications that were developed for various industrial systems. The common attribute is the integration of novel techniques, such as multiagent systems or semantics with the current “old-fashioned” automation architectures represented mainly by programmable logic controllers. Retaining the ability of the control system to meet the strict real-time constraints and concurrently raise its intelligence has always been of highest importance. The paper does not focus merely on RA's work, but discusses all its aspects in a context of other existing works in this area.

Rockwell Automation's Holonic and Multiagent Control Systems Compendium

A cloud-based infrastructure facilitates gathering, transmitting, and remote storage of control and automation data using an agent-based communication channel. The infrastructure collects the industrial data from an industrial enterprise and intelligently sorts and organizes the acquired data based on selected criteria. Message queues can be configured on the cloud platform to segregate the industrial data according to priority, data type, or other criteria. Behavior assemblies stored in customer-specific manifests on the cloud platform define customer-specific preferences for processing data stored in the respective message queues. Web-based tools can deliver automated notifications to an end user device based on analysis of the stored data, and allow the user to remotely view the stored data.

Remote industrial monitoring using a cloud infrastructure

A simulation environment for running a process simulation used to validate an industrial control program. The simulation environment exposes the I/O module configurations defined in the control program and retrieves module configuration information therefrom. This I/O module configuration information is combined with generic, module-specific I/O module profiles to create a pool of available controller I/O points, which can be selectively associated with I/O points in the simulation to create an I/O point mapping. During control program validation, simulated I/O data is exchanged between the process simulation and the I/O module instances in the controller in accordance with the I/O point mapping.

Industrial simulation using redirected I/O module configurations

A cloud-based multi-tier cyber analytics system is provided for integration of cloud-side and on-premise analytics for industrial systems. The analytics system includes an emulation runtime engine that executes a virtualized controller on a cloud platform. The runtime engine serves as a core analytics component by providing a control-level analytics engine with application programming interfaces (APIs) that enable seamless interaction of distributed simulations, cloud level services, and hardware industrial controllers. A cloud-based framework integrates soft control, hard control, and simulation with cloud-level services, and includes components that facilitate near real-time data streaming from the plant floor to the cloud platform to yield an industrial Internet of Things (IoT).

Francisco P. Maturana

Papers

Remote industrial monitoring and analytics using a cloud infrastructure

Rockwell Automation's Holonic and Multiagent Control Systems Compendium

Remote industrial monitoring using a cloud infrastructure

Industrial simulation using redirected I/O module configurations

Cloud-based emulation and modeling for automation systems