The present invention relates to the technical field of industrial automation, and in particular to a method and system for quick customized-design of an intelligent workshop. The method comprises the following steps: step A: acquiring design requirement information of a production line, and performing modeling in a simulation system according to the design requirement information; step B: performing action planning of a physical stand-alone device, performing logistics and motion planning of articles being processed, and compiling motion and action control scripts; step C: establishing, by the digital twin technology, a communication channel among a PLC system of the workshop digitization model, a PLC system of a physical workshop device and a host computer; and, step D: outputting a three-dimensional digital twin model as a blueprint for follow-up design and development of the stand-alone device, a control system and an execution system.

A method, a system, and a computer readable medium for managing recharging of a shared electric vehicle are provided. The method includes determining whether an energy storage device of the electric vehicle requires charging, identifying a charging method based on a plurality of parameters including an ambient temperature, a current state of charge, a current load, and a power estimate for a planned route, altering the planned route of the electric vehicle to enable charging based on the identified method, and charging a second energy storage device associated with a second electric vehicle during transit via the electric vehicle. The charging method includes swapping electric vehicles, exchanging the energy storage device, and charging via a charging bot.

Provided is a method for resource planning in a factory based on simulations. The method for resource planning may comprise: modeling factory resources as capacity buckets; allocating a plurality of demands to the modeled capacity buckets; and, constructing factory resource planning by performing capacity bucket simulations (CBSs) based on the factory resources to which the plurality of demands are allocated.

A system may include an insighter engine configured to access conceptual plans for previously manufactured products, and a given conceptual plan may include a bill of materials (BoM), a bill of processes (BoP), and a bill of resources (BoR). The insighter engine may be configured to represent the conceptual plans according to an insighter ontology and apply machine learning, using the conceptual plans represented according to the insighter ontology as training data, to learn a manufacturing constraint not already represented in the conceptual plans. The system may also include a predictor engine configured to access a BoM for a variant product that differs from the previously manufactured products and apply the learned manufacturing constraint to generate a predicted BoP and a predicted BoR for the BoM of the variant product.

An integrated safety-evaluation with labor-time-standard system is provided that includes a work-task manager, integrated module and ergonomic safety evaluator. The work-task manager may be configured to receive a work instruction and determine work elements applicable to the work instruction, with the work elements may have respective associated elemental unit times, elemental risk ratings and frequency values. The integrated module may be configured to receive the elemental unit times, elemental risk ratings and frequency values for the work elements, and calculate a labor time standard and ergonomic safety rating therefrom. And the ergonomic safety evaluator may be configured to receive the labor time standard and ergonomic safety rating and perform an ergonomic safety evaluation therefrom. In this regard, the ergonomic safety evaluator may be configured to perform the ergonomic safety evaluation to determine whether to release or reject the work instruction.

The present invention relates to the technical field of industrial automation, and in particular to a method and system for quick customized-design of an intelligent workshop. The method comprises the following steps: step A: acquiring design requirement information of a production line, and performing modeling in a simulation system according to the design requirement information; step B: performing action planning of a physical stand-alone device, performing logistics and motion planning of articles being processed, and compiling motion and action control scripts; step C: establishing, by the digital twin technology, a communication channel among a PLC system of the workshop digitization model, a PLC system of a physical workshop device and a host computer; and, step D: outputting a three-dimensional digital twin model as a blueprint for follow-up design and development of the stand-alone device, a control system and an execution system.

A cell controller in each manufacturing cell includes a manufacturing instruction determination part that determines the types of manufacturable parts to be preferentially manufactured and the number of parts to be manufactured, based on order information and inventory information that are stored in the storage part of an administrative server, the manufacturing instruction determination part indicating the determination to a drive controller. In a cell production system configured thus, each manufacturing cell can timely manufacture multiple types of parts according to a status change of, for example, a part order or the inventory of materials.

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.

A system and method for optimizing resource usage of a robot, the method including: receiving a first request to execute a first task, where the first task requires a first set of resources of the robot; causing the execution of the first task; receiving a second request to execute at least a second task, wherein the second task requires a second set of resources of the robot; determining whether any resources of the first set of resources and the second set of resources includes at least one overlapping resource; modifying at least one of the first task and the at least a second task when at least one overlapping resource is determined by omitting at the least one overlapping resource; and executing of the first task and the at least a second task as modified.

The present disclosure provides a system and method for inverse inference in a chain of manufacturing processes using Bayesian networks is provided. The method generates a composite Bayesian network model for a chain of manufacturing processes from Bayesian network models of the unit processes in the chain. The models of unit processes might have been learned independently in other contexts and stored in a knowledge repository. Models relevant for the current problem context are obtained from the knowledge repository and checked for compatibility using ontological information about their inputs and outputs. The obtained compatible Bayesian network models of unit processes are composed to generate a composite Bayesian network model for the chain. The generated composite Bayesian network model is sampled to perform inverse inference.