Abstract: Performance-based smart contracts in industrial automation (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores executable components; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a blockchain component that stores data representative of an output of an industrial automation device to an industrial blockchain, and an execution component that, in response to the output satisfying a smart contract stored on the industrial blockchain, facilitates execution of an element of the smart contract.

Abstract: Industrial automation blockchain data management (e.g., using a computerized tool) is enabled. For example, a system can comprise: a transfer component that, using a defined conversion algorithm, converts first data from a first industrial blockchain to second data applicable to a second industrial blockchain, other than the first industrial blockchain, a blockchain component that writes the second data to the second industrial blockchain, and a user interface component that renders an output indicative of the writing of the second data to the second industrial blockchain.

Abstract: Industrial security using blockchain or NFTs (e.g., using a computerized tool) is enabled. For example, an industrial system can comprise: a memory that stores executable components, and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising a minting component that mints a non-fungible token associated with a product generated using industrial automation equipment, wherein the non-fungible token comprises a first verification element, and a verification component that: verifies the product based on a comparison of a first aspect of the product to the first verification element, and generates an output representative of the comparison.

Abstract: Industrial blockchain digital twin change management (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a blockchain component that stores a digital twin fingerprint for a digital twin of an industrial automation system to an immutable blockchain ledger and stores a configuration fingerprint representative of a configuration of the industrial automation system to the immutable blockchain ledger, wherein the digital twin fingerprint is associated with the configuration fingerprint, a change component that determines a change to the industrial automation system, and an update component that facilitates creation of an updated digital twin, representative of the change, and updates the digital twin fingerprint and the configuration fingerprint based on the change.

Abstract: Blockchain-enabled digital twins for industrial automation systems (e.g., using a computerized tool) are enabled.

Abstract: Industrial blockchain enabled automation control (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: an authorization component that determines, according to a defined authorization criterion, authorization of a request for a license for industrial automation software, and a license component that, in response to the determination by the authorization component that the request is authorized, mints a non-fungible token comprising the license for the industrial automation software.

Abstract: An industrial data broker system receives contextualized industrial data from one or more industrial devices that support data modeling at the device level. The received industrial data is augmented with contextualization metadata that defines correlations between the data relevant to an analytical objective, and labels specifying analytic topics to which each data item is relevant. The broker system allows external systems, such as analytic systems, to subscribe to topics of interest, and streams a subset of contextualized device data relevant to the topic of interest to the external system for analysis.

Abstract: Blockchain-enabled industrial devices and associated systems are configured to support the use of industrial blockchains in connection with product and machine tracking, subscription-based industrial services, device lifecycle management, and other functions. Collections of industrial devices can collectively serve as an industrial blockchain system, with multiple such systems within a supply chain yielding an industrial blockchain ecosystem. This architecture can create distributed, decentralized, tamper-proof records of manufacturing statistics for a product, a product's history within the larger supply chain, industrial asset usage histories that can be leveraged in connection with lifecycle management, machine usage history for use in connection with subscription-based machine operation, and other such information.

Abstract: A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.

Abstract: A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.

Abstract: A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.

Abstract: A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.

Abstract: Blockchain-enabled industrial devices and associated systems are configured to support the use of industrial blockchains in connection with product and machine tracking, subscription-based industrial services, device lifecycle management, and other functions. Collections of industrial devices can collectively serve as an industrial blockchain system, with multiple such systems within a supply chain yielding an industrial blockchain ecosystem. This architecture can create distributed, decentralized, tamper-proof records of manufacturing statistics for a product, a product's history within the larger supply chain, industrial asset usage histories that can be leveraged in connection with lifecycle management, machine usage history for use in connection with subscription-based machine operation, and other such information.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: A smart gateway platform leverages pre-defined industrial expertise to identify limited subsets of available industrial data deemed relevant to a desired business objective, and to collect and model this relevant data to apply useful constraints on subsequent artificial intelligence or machine learning analytics applied to the data. This approach can reduce the data space to which AI analytics are applied and assist data analytic systems to more quickly derive valuable insights and business outcomes. In some embodiments, the smart gateway platform can operate within the context of a multi-level industrial analytic system, feeding pre-modeled data to one or more AI or machine learning systems executing on one or more different levels of an industrial enterprise. The multi-level industrial analytic system can also further refine modeled industrial data as the data moves upward through the system (e.g., from the device level to higher levels).

Abstract: An industrial data broker system receives contextualized industrial data from one or more industrial devices that support data modeling at the device level. The received industrial data is augmented with contextualization metadata that defines correlations between the data relevant to an analytical objective, and labels specifying analytic topics to which each data item is relevant. The broker system allows external systems, such as analytic systems, to subscribe to topics of interest, and streams a subset of contextualized device data relevant to the topic of interest to the external system for analysis.