An operation determination apparatus includes an image obtainer that obtains a captured image of a worker gripping a component and performing an operation of attaching the component to a workpiece, a component detector that detects a component in the captured image obtained by the image obtainer, a grip determiner that determines, based on a detection result from the component detector, whether the worker is gripping a component and whether the component gripped by the worker is a component to be attached to the workpiece in the operation, and a notification controller that controls a predetermined notification to be provided in response to the grip determiner determining that the component gripped by the worker is not the component to be attached to the workpiece in the operation.

Provided is a collaborative-robot risk assessment guidance device (1) including: an input device (2) that receives inputs from a user; a storage device (3) that stores information; a display device (4) that displays the information; and at least one processing device (5), wherein the processing device (5) prompts the user to input, by means of the input device (2), basic information including conditions for collaborating with people, prompts the user to select, by means of the input device (2), at least one candidate information item for hazard sources stored in the storage device (3) in association with the input basic information, performs a risk assessment on a basis of risk specifics information stored in the storage device (3) in association with the selected candidate information item for the hazard sources, determines whether a result of the risk assessment is at or below an acceptable risk, and displays, on the display device (4), countermeasure proposals for separating people and a collaborative robot without partitioning a space therebetween in a case in which the result of the risk assessment is not at or below the acceptable risk.

Provided is an LSTM-based hot-rolling roll-bending force predicting method including the steps of acquiring final rolling data of a stand of a stainless steel rolling mill when performing a hot rolling process, and dividing the data into a training set traindata and a test set testdata; normalizing the traindata; building a matrix P; using a last row of the matrix P as a label of the training set, namely a true value; calculating and updating an output value and the true value of a network; after network training is completed, taking the last m output data of the LSTM network as an input at a next moment, and then obtaining an output of the network at the next moment, wherein the output is a predicted value of the roll-bending force at the next moment; repeating the steps until a sufficient number of prediction data is obtained; and comparing the processed data with the true value in the testdata to check the validity of the network.

A method for optimizing production of sheet-metal parts, the production comprising cutting out and singularizing the sheet-metal parts and bending the sheet-metal parts, wherein the method includes: (A) training a neural network, which is executed on a Monte Carlo tree search framework, by means of supervised learning and self-play with reinforcement learning; (B) recording constraints for the sheet-metal parts, the constraints comprising geometric data of the sheet-metal parts; (C) creating an optimized production schedule by way of the neural network; and (D) outputting the production schedule.

A system for assisting the design of manufactured components requiring post-processing and comprising an input module receiving original design data relative to engineering requirements of a component and manufacturing and post-processing data relative to the component. The system also comprising a compensation determination module receiving the original design data and the manufacturing and post-processing data from the input module, predicting the geometrical deviations created by the at least one post-processing procedure and generating dimension compensation data defining compensations for each one of the features of the component using at least one machine learning model generated by a machine learning algorithm trained using a training dataset. The system further comprises a compensated model generation module configured to receive the compensation data and to generate a compensated Computer Assisted Design (CAD) model therefrom. A computer implemented method for assisting the design of manufactured components requiring post-processing is also provided.

The present invention provides a data fusion and reconstruction method for fine chemical industry safety production based on a virtual knowledge graph. In view of the characteristics of fine chemical industry safety production data, such as a large amount of structured data, a multi-source heterogeneous database and a strong sequential logic, the present invention innovatively proposes a method of using a virtual knowledge graph to complete the fusion and reconstruction of a traditional database for fine chemical industry. The present invention fuses static structured knowledge in the field of fine chemical industry with a real-time dynamic database for chemical industry safety production in the concept of ontologies for the first time to organize time series data in the form of entities. In addition, the mapping rules of the existing OBDA system are improved based on a data set of the present invention.

The confidentiality of data is maintained in a case where analysis of an operation state of a facility is entrusted to the outside. An embodiment of the present invention is configured to chronologically store log data in a first storage medium, and store attribute information indicating a relevance between a type of a failure expected to occur in a facility and each of a plurality of data users; The embodiment is further configured to select, at the occurrence of a failure in the facility, a data user who has a relevance to a type of the failure from among the plurality of data users based on the attribute information, selectively read log data relating to an operation state of the facility in which the failure has occurred, and transmits the read log data to the selected data user.

A data processing method includes: obtaining a defect type of a sample set in response to a first input of a user on a first interface, the sample set including samples, each sample having a first parameter used to represent a defect degree of the sample with regard to the defect type and a second parameter used to represent device informations of sample production devices through which the sample passes; calculating yield purity indexes of sample production devices on the samples based on first parameters and second parameters of the samples, so as to obtain influencing parameters of the sample production devices, an influencing parameter of each sample production device being used to represent an influence degree to which the sample production device affects an occurrence of the defect type on the samples; and displaying the influencing parameters of the sample production devices on a second interface.

Provided is a contribution degree calculation system and computer program product for calculating the degree of contribution of “displays” serving as means for mediating supply and purchase of a product and/or service between a user who considers purchase of the product and/or service and a supplier that supplies the product and/or service.

The contribution degree calculation system includes a user action history acquisition unit that acquires the user action history, which is the history of actions taken by the user through the displays, a user purchase history acquisition unit that acquires the user purchase history, which is the history of purchase of the product and/or service by the user, a supplier progress history acquisition unit that acquires the supplier progress history, which is the progress history of supply of the product and/or service by the supplier, and a contribution degree calculator that calculates the degree of contribution of each of the displays associated with the actions of the user on the basis of the user action history, user purchase history, and supplier progress history.

A design support system for permitting a design that easily meets desired conditions regarding an entire item group is provided. An automatic estimation system acting as the design support system comprises: an item recognition section that recognizes each of the items included in the item group by individually recognizing elements making up the item; a designated condition recognition section that recognizes a designated condition from the manufacturing conditions; and a recommended-to-be-examined element recognition section that recognizes, with respect to the designated condition recognized, the element corresponding to any of the manufacturing conditions that is recommended to be examined for changes. The recommended-to-be-examined element recognition section displays a model of the item group by highlighting the element for which the manufacturing condition is recommended to be examined for changes.