The present disclosure discloses an Industrial Internet of Things (IIoT) system for automatic control of production line manufacturing parameters, which comprises a user platform, a service platform, a management platform, a sensor network platform and an object platform that interact in turn. The service platform adopts centralized layout, and the management platform and the sensor network platform adopt independent layout. The present disclosure also discloses a control method of the IIoT for automatic control of production line manufacturing parameters. The present disclosure builds the IIoT based on the five platform structure, in which the sensor network platform and the management platform are arranged independently, and each corresponding platform includes a plurality of independent sub-platforms, so that the independent sensor network platform and the management platform can be used for each production line device to form an independent data processing channel and transmission channel, and reduce the data processing capacity and transmission capacity of each platform.

According to an embodiment, an information processing device includes one or more processors. The processors calculate a first degree of influence of a plurality of variables on output data, and a frequency at which the plurality of variables are selected as a variable influencing the output data, based on K first models. The K first models are models estimated using a plurality of pieces of input data including the plurality of variables. The plurality of input data are obtained in K periods. K is an integer of 2 or more. The first model receives input of the input data including the plurality of variables and outputs the output data. The processors output the first degree of influence and the frequency in association with each other.

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

Provided is a method of optimizing parameter intervals of manufacturing processes based on prediction intervals. The method includes: collecting process data by applying an experiment design method to a target process; training a second-order polynomial regression model based on the collected process data; estimating importance values of each input variable with respect to each output variable using the second-order polynomial regression model; defining an objective function for process optimization based on the second-order polynomial regression model; optimizing each parameter value by applying an optimization algorithm to the defined objective function; and optimizing each parameter interval including the optimized parameter value in an input space using the prediction interval of the second-order polynomial regression model.

A method of hierarchical machine learning includes receiving a topology model having information on hierarchical relations between components of the industrial plant, determining a representation hierarchy comprising a plurality of levels, wherein each representation on a higher level represents a group of representations on a lower level, wherein the representations comprise a machine learning model, and training an output machine learning model using the determined hierarchical representations.

The present invention includes: (a) a method for improving data to be processed for bottleneck detection, by cleaning corrupt or outlier data; (b) a method for improved analysis of bottleneck data using a plurality of rules for categorization; and (c) a method for improved display and/or allowing improved user feedback for bottleneck data using multivariate analysis and display. These methods can be used alone, or preferably be combined in whole or in part together to improve performance of an industrial process. A system is also provided.

A non-transitory computer readable storage medium stores an operation improvement assistance program that is a program for causing a computer to function as an operation improvement assistance device that assists improvement of an operation status of a device or improvement of an outcome by operation of the device. The operation improvement assistance program causes a computer to execute process procedures including: predicting output data indicating the operation status or the outcome from input data including each value of a plurality of feature amounts related to operation of the device; extracting a target feature amount whose value is changeable in prediction of the output data, from among the plurality of feature amounts; a step of simulating the predicted output data by changing a value of the target feature amount; and presenting a simulation result of the output data.

A replacement part for replacing an original mechanical machine part having has an original mechanical configuration with original part descriptive data is made by first receiving performance data obtained by monitoring the machine during operation with the original machine part with one or more sensors and then sending the performance data to a modeling server. The modeling server then calculates multiple optimized mechanical configurations of the replacement part with one or more modeling algorithms based on different optimization criteria using at least the original part descriptive data and the received performance data. Then a selection of several performance options representing the multiple mechanical configurations of the replacement part are provided, one of which is selected by a user. Finally a replacement part is made with the final optimized configuration corresponding to the selected performance option or sending optimized part descriptive or construction data with the final optimized configuration.

A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.