Patent ID: 11900298
Assignee: KABUSHIKI KAISHA TOSHIBA
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A processing device, comprising a processing circuitry,
the processing circuitry accepting new plan data of a time series of a relationship between time and a target production volume of a new plan,
the processing circuitry displaying a first ranking based on distances between the new plan data and each of a plurality of sets of previous plan data, each of the plurality of sets of previous plan data being of a time series of a relationship between time and a target production volume of a previous plan, the first ranking being displayed by arranging not less than one of a plurality of sets of performance data, the plurality of sets of performance data being respectively of performance with respect to the plurality of sets of previous plan data,
the device inputting a new plan image to a first model and displaying a second ranking based on an output result from the first model, the new plan image being of the relationship between time and the target production volume of the new plan, the second ranking being displayed by arranging not less than one of the plurality of sets of performance data,
wherein
the first model includes a convolutional neural network,
the first model is trained using a value of a class as a label for each of the plurality of sets of performance data by using a previous plan image as input data, and
the previous plan image is of the relationship between time and the target production volume of the previous plan,
the processing circuitry inputs production data of the new plan to a second model, which includes a recurrent neural network (RNN), and acquires second prediction data output from the second model,
the production data is of a relationship between time and at least one production parameter selected from the group consisting of an operation rate of equipment, an occurrence rate of discrepancies, a maintenance rate, and an occurrence rate of defective components, and
the second prediction data is of a time series of a relationship between time and a production volume.