Abstract: An AI-based platform for enabling intelligent orchestration and management of at least one operating process is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a prediction of an energy pattern associated with the at least one operating process. The AI-based platform is also configured to manage the at least one operating process based on the prediction of the energy pattern.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of: a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may provide a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.

Abstract: A VCN process may configure a set of sub-level computing devices for communication with a primary computing device, wherein the primary computing device manages the set of sub-level computing devices to orchestrate performance of a set of value chain network entities. A VCN process may receive, by the set of sub-level computing devices, a primary command from the primary computing device, wherein the primary command is one of a task or a request associated with the value chain network. A VCN process may assign at least a portion of one or more operating devices capable of fulfilling the primary command as a set of one or more computing devices to be managed by the set of sub-level computing devices, wherein the sub-level computing device is a computing device that manages or executes performance of a particular entity or relationship of the value chain network.

Abstract: A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A VCN process may determine a task to be completed for the value chain network based upon, at least in part, on an output of the set of AI-based learning models. A VCN process may execute the task to facilitate an improvement in the value chain network.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a potential risk in the value chain based upon, at least in part, an output of the AI-based learning classification. A VCN process may execute an action to mitigate the potential risk in the value chain network.

Abstract: A VCN process may receive information associated with a set of value chain network entities. A VCN process may provide the information to a first set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the first set of AI-based learning models is trained to generate a prediction of future demand for an item. A VCN process may provide the information to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity. A VCN process may determine a potential risk in the value chain network associated with the at least one value chain network entity based upon, at least in part, an output of the AI-based learning models.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of simulation systems that are configured to perform at least one simulation of at least one energy-related operation performed by a set of entities. The set of simulation systems is also configured to determine, based on the at least one simulation, energy associated with the set of entities.

Abstract: A VCN process may configure a set of secondary computing devices of a set of value chain network entities for communication with a primary computing device of an enterprise operator, wherein the primary computing device manages the set of secondary computing devices. A VCN process may receive, by at least one member of the set of secondary computing devices, a set of primary commands from the primary computing device, wherein each of the set of primary commands is at least one of a task or a request. A VCN process may assign at least a portion of one or more computing devices capable of fulfilling the set of primary commands as a set of one or more computing devices to be managed by the set of secondary computing devices.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a digital twin system including a digital twin of an energy operating asset, the digital twin of the energy operating asset including at least one of an energy generation digital twin; an energy storage digital twin, an energy delivery digital twin, and an energy consumption digital twin, an energy simulation system configured to generate a simulation of energy-related behavior of the energy operating asset; and a machine-learning system configured to generate a predicted state of the energy operating asset. The simulation of energy-related behavior includes a simulation of carbon emissions of the energy operating asset based on historical patterns, current states, and the predicted state of the energy operating asset.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a digital twin system including a plurality of digital twins of energy operating assets, the plurality of digital twins of energy operating assets including at least one energy generation digital twin, energy storage digital twin, energy delivery digital twin, and/or energy consumption digital twin, and a set of energy simulation systems configured to generate a simulation of energy-related behavior of at least one of the plurality of digital twins of energy operating assets, and a machine-learning system configured to generate a predicted state of at least one of the energy operating assets. The simulation of energy-related behavior is based on historical patterns, current states, and the predicted state of at least one of the energy operating assets.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, energy storage, energy delivery, and/or energy consumption, wherein the data is collected from a set of edge devices that are in operational control of a set of distributed energy resources; and a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

Abstract: An AI-based platform for managing energy is provided herein. The AI-based platform is configured to produce at least one operating parameter for energy-related operations of a set of infrastructure assets. The at least one operating parameter is based on a data set of energy-related data associated with the set of infrastructure assets. The data set of energy-related data is at least partially generated by a set of sensors associated with a set of edge devices.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems configured to collect data relating to energy generation, energy storage, energy delivery, and/or energy consumption, wherein the data is collected from a set of edge devices that are in operational control of a set of distributed energy resources and a set of legacy grid-based energy resources, an adaptive energy data pipeline configured to receive collected data from the autonomous data handling systems and communicate the received data across a set of nodes in a network; and a machine-learning system configured to generate predictions related to operation of a plurality of energy resources of a power grid. The predictions related to operation of the energy resources of a power grid include at least one of predictive maintenance, outage detection, or load forecasting.

Abstract: An AI-based platform for enabling intelligent orchestration and management of at least one operating process is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a prediction of an energy pattern associated with the at least one operating process. The AI-based platform is also configured to manage the at least one operating process based on the prediction of the energy pattern.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems that are configured to collect data relating to energy generation, energy storage, energy delivery, and/or energy consumption, wherein the data is collected from a set of artificial-intelligence enabled edge devices that are in operational control of a set of distributed energy resources and a set of legacy grid-based energy resources; and an adaptive energy data pipeline. The set of adaptive, autonomous data handling systems is configured to determine, based on the collected data, a mix of at least one of energy generation, energy storage, energy delivery, and/or energy consumption characteristics for a set of energy resources that are in local communication with the set of artificial-intelligence enabled edge devices and output a data set that represents a constituent proportion of the mix.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a recommendation of a set of operating parameters for satisfaction of an energy demand of a set of entities within a defined domain. The recommendation is based on a data set of energy generation, energy storage, energy delivery, and/or energy consumption information. The artificial intelligence system has been trained on a set of outcomes associated with energy generation, energy storage, energy delivery, and/or energy consumption.

Abstract: Systems and methods for integrating a gaming engine and a smart contract system in a platform are provided. The gaming engine is programmed with a software development environment and an architecture that provides a set of gaming engine services with predefined tools for digital content developers to create a set of game engine generated environments. The smart contract system programmed with smart contract services associated with transactions that are based on electronically verifiable conditions. The integration platform is programmed with an execution framework that is common to the gaming engine and to the smart contract system to integrate the smart contract services with at least one of the gaming engine and the set of game engine generated environments.