A computer-implemented method of scheduling jobs of a manufacturing or logistics process using a priority function. The priority function is evaluated on multiple to be scheduled jobs to obtain multiple respective priority values. The priority function is defined to invoke a kernel function. The kernel function is defined to compare representations of two respective jobs. Evaluating the priority function on a selected job comprises evaluating the kernel function on representations of the selected job and one or more reference jobs. The schedule for the multiple jobs is determined based on the priority values and is then output to enable the multiple jobs to be carried out according to the schedule.

A software defined distributed control system (SDCS) in a process plant includes an application layer that includes a plurality of containers instantiated in a data cluster. Each of the containers is an isolated execution environment executing within the local operating system of a respective computing node. The containers cooperate to facilitate execution of a control strategy in the SDCS, and includes a hyper converged infrastructure (HCI) operating across the data cluster, which HCI is configured to communicate with the application layer via an adapter service. The HCI includes software-defined (SD) compute resources, SD storage resources, SD networking resources, and an orchestrator service. The orchestrator service is programmed to configure a first container to include a service executing within the first container. It also assigns the first container to execute on an available hardware resource to control a plurality of field devices operating in the process plant.

A software defined distributed control system (SDCS) in a process plant includes an application layer that includes a plurality of containers instantiated in a data cluster. Each of the containers is an isolated execution environment executing within the local operating system of a respective computing node. The containers cooperate to facilitate execution of a control strategy in the SDCS, and includes a hyper converged infrastructure (HCI) operating across the data cluster, which HCI is configured to communicate with the application layer via an adapter service. The HCI includes software-defined (SD) compute resources, SD storage resources, SD networking resources, and an orchestrator service. The orchestrator service is programmed to configure a first container to include a service executing within the first container. It also assigns the first container to execute on an available hardware resource to control a plurality of field devices operating in the process plant.

A process control system includes a plurality of field devices operating to control a process in a process plant. A communication infrastructure couples the plurality of field devices to a software-defined control system (SDCS) that receives data from the field devices and transmits instructions to the field devices. A data cluster, executing the SDCS, includes a plurality of compute nodes, each of which includes a processor executing an operating system, a memory, and a communication resource coupled to one or more other compute nodes in the data cluster. A plurality of instantiated containers, each of which is an isolated execution environment within the operating system of the compute node on which the container is instantiated, cooperate to facilitate execution of a control strategy in the SDCS. At least one of the containers in the SDCS is pinned to a component in the SDCS.

A process control system includes a plurality of field devices operating to control a process in a process plant. A communication infrastructure couples the plurality of field devices to a software-defined control system (SDCS) that receives data from the field devices and transmits instructions to the field devices. A data cluster, executing the SDCS, includes a plurality of compute nodes, each of which includes a processor executing an operating system, a memory, and a communication resource coupled to one or more other compute nodes in the data cluster. A plurality of instantiated containers, each of which is an isolated execution environment within the operating system of the compute node on which the container is instantiated, cooperate to facilitate execution of a control strategy in the SDCS. At least one of the containers in the SDCS is pinned to a component in the SDCS.

A software defined (SD) process control system (SDCS) implements controller and other process control-related business logic as logical abstractions (e.g., application layer services executing in containers, VMs, etc.) decoupled from hardware and software computing platform resources. An SD networking layer of the SDCS utilizes process control-specific operating system support services to manage the usage of the computing platform resources and the creation, deletion, modifications, and networking of application layer services with devices disposed in the field environment and with other services, responsive to the requirements and needs of the business logic and dynamically changing conditions of SDCS hardware and/or software assets during run-time of the process plant (such as performance, faults, addition/deletion of hardware and/or software assets, etc.). A visualization system of the SDCS provides a user with a view as to the state of the SDCS as currently configured/running on the computing platform to enable a user to view currently configured interrelationships between logical elements of the control system and other logical and/or physical elements of the control system. The visualization system also provides performance metrics of the system as currently configured to enable a user to understand the operational health of the control system as currently configured.

An event waveform extracting unit (3) extracts an event waveform from time-series data. A co-occurrence rate calculating unit (4) calculates co-occurrence rates of event waveforms among the time-series data. A grouping unit (5) classifies the time-series data into groups depending the co-occurrence rates of the event waveforms. An event information generating unit (6) determines the time at which the periods during which event waveforms occur overlap with each other among the time-series data included in each group, and generates event information identifying an event related to the event waveforms on the basis of the determined time.

Embodiments of the present disclosure provide systems and methods for controlling a manufacturing process in a manner that protects sensitive information from misuse by different entities involved in the manufacturing process. According to the present disclosure, a blueprint providing information regarding subcomponents of a product to be manufactured may be provided to a synthesizer device. The synthesizer device may engage in two-party computation with IP providers to generate a set of machine commands, which may be encrypted, and then provide a message including the set of machine commands to a manufacturer device. The manufacturer device may obtain authorization from the IP provider(s) based on the message, where the authorization may enable the manufacturer device to configure a manufacturing process in accordance with the set of machine commands to manufacture the subcomponents of the product.

A distributed light-guided processing method includes obtaining an order from a requester, for at least one completed product. Raw components are provided to at least one selected remote processing location. The selected remote processing location includes a light guided system. Work instructions are provided to the selected remote processing location, wherein the work instructions enable the light guided system to guide construction of the completed product. The completed product is processed, using at least the raw components, the work instructions, and the light guided system. The completed product is shipped from the selected remote processing location upon completion of the processing.

The systems and methods described provide improved process control operating range and capabilities and integrate process control monitoring and management with broader process automation (PA) systems process management, extending the real-time operation and control of a process control system to process handling of a PA system, and extending PA-style process management by adding real-time process controls and monitoring, and adding new functionality by permitting management of these processes to externally defined completion goals. This combination provides new functionality in dynamically determined process flexibility, extended operating range and extended process recipe definition capabilities for process control systems using this technology, and provides improved error recovery and exception handling of traditional PA systems.