An industrial control system includes a download verification subsystem to execute a verification test using the parameters stored in a configuration database before an execution subsystem downloads the recipe parameters to the input/output addresses of a piece of equipment to determine a first verification value. The download verification subsystem executes the verification test using the recipe parameters downloaded to the piece of equipment to determine a second verification value. The download verification subsystem compares the first and second verification values to determine whether the recipe parameters were downloaded to the input/output addresses of the piece of equipment successfully.

A method and apparatus for processing a real-time data stream. The method for processing a real-time data stream based on a rule includes selecting a rule corresponding to a current factory state with reference to a rule engine in which factory states are mapped to rules corresponding thereto, analyzing a real-time data stream to be used to control a factory resource, and, when a result of the analysis satisfies a condition defined in the rule, controlling a factory resource so as to take an action corresponding to the defined condition. According to the method, the real-time data streams collected in a factory or flowing from outside the factory are processed to be suitable for the environment in the factory, whereby the operating rate and productivity of the factory may be improved.

A quick dispatching rule screening method and apparatus are provided. The quick dispatching rule screening method includes following steps. A scheduling result and a corresponding scenario are obtained. A dispatching rule mining table is established according to the scheduling result, where the dispatching rule mining table includes a dispatching rule and an operation. A participation rate of each dispatching rule in the dispatching rule mining table is calculated. A contribution rate is calculated according to the participation rate to obtain a filter value. A selected dispatching rule is decided according to the filter value.

A method for allocating resources to machines of a production facility comprises: receiving forecast data indicating planned demands for the resources and a demand deviation for each resource; generating a new demand for each resource from the planned demand and the demand deviation of the resource in several iterations; assigning capacities to the new demands in each iteration by determining resource-machine combinations, wherein a priority is determined for each resource and each machine based on a set of priority rules, wherein the resource-machine combinations are determined by combining the resources and the machines according to the priorities; and generating a roll-out plan assigning resource-machine combinations to be rolled out to future time periods, wherein the roll-out plan is generated from the resource-machine combinations of different iterations and an estimated roll-out time for each resource-machine combination such that a total number of the resource-machine combinations to be rolled-out is minimized.

A KUBERNETES installation processes a script and invokes a scheduling agent in response to encountering an instruction to create a pod. The scheduling agent is an agent of an orchestrator and performs tasks such as identifying a selected node, creating multiple interface objects with multiple IP addresses, and creating storage volumes in coordination with the orchestrator. Upon creation, the pod may call a CNI that is an agent of the orchestrator in order to configure the pod to use the multiple interface objects. The pod may call a CSI that is an agent of the orchestrator in order to bind a storage volume to the pod. The scheduling agent may coordinate with the orchestrator to implement affinity and anti-affinity rules for placement of pods and storage volumes. The script may also be transformed by the orchestrator in order to insert instructions implementing affinity and anti-affinity rules.

A KUBERNETES installation processes a script and invokes a scheduling agent in response to encountering an instruction to create a pod. The scheduling agent is an agent of an orchestrator and performs tasks such as identifying a selected node, creating multiple interface objects with multiple IP addresses, and creating storage volumes in coordination with the orchestrator. Upon creation, the pod may call a CNI that is an agent of the orchestrator in order to configure the pod to use the multiple interface objects. The pod may call a CSI that is an agent of the orchestrator in order to bind a storage volume to the pod. The scheduling agent may coordinate with the orchestrator to implement affinity and anti-affinity rules for placement of pods and storage volumes. The script may also be transformed by the orchestrator in order to insert instructions implementing affinity and ant-affinity rules.

A recipe management system includes a versioning system that tracks the revision history of templates and their child instances. Modifications to templates and instances create new records with new primary key identifiers and version identifiers. However, each new version of a template or instance has the same root identifier as the prior versions. When a template is modified, a flag is set in its child instances, but they are not modified automatically. When an instance is modified, it has no effect on the parent template. At runtime, a recipe model is loaded to an equipment model to execute a recipe on a piece of equipment. Only approved versions of equipment models are used during execution, even if newer versions exist. During execution, new equipment models can be created. The recipe management system includes an execution engine that can be hosted as a standalone executable or in a system platform.

A quick dispatching rule screening method and apparatus are provided. The quick dispatching rule screening method includes following steps. A scheduling result and a corresponding scenario are obtained. A dispatching rule mining table is established according to the scheduling result, where the dispatching rule mining table includes a dispatching rule and an operation. A participation rate of each dispatching rule in the dispatching rule mining table is calculated. A contribution rate is calculated according to the participation rate to obtain a filter value. A selected dispatching rule is decided according to the filter value.

A recipe management system includes a versioning system that tracks the revision history of templates and their child instances. Modifications to templates and instances create new records with new primary key identifiers and version identifiers. However, each new version of a template or instance has the same root identifier as the prior versions. When a template is modified, a flag is set in its child instances, but they are not modified automatically. When an instance is modified, it has no effect on the parent template. At runtime, a recipe model is loaded to an equipment model to execute a recipe on a piece of equipment. Only approved versions of equipment models are used during execution, even if newer versions exist. During execution, new equipment models can be created. The recipe management system includes an execution engine that can be hosted as a standalone executable or in a system platform.

A work center in a manufacturing setup includes a machine learning model that uses a decision tree to facilitate the work of a supervisor on the production line to choose a machine to perform a particular operation on a particular part. The decision tree outputs a ranking of machines indicating the suitability of the ranked machines for performing the particular operation on the particular part.