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

Claim 10:
11. A processing method, causing a processing circuitry to
refer to a plurality of data sets, each of the data sets including previous plan data and performance data, the previous plan data being of a time series of a relationship between time and a target production volume of a previous plan, the performance data being of performance with respect to the previous plan;
calculate a plurality of first evaluation values for a plurality of sets of the performance data by using distances between new plan data and each of a plurality of sets of the previous plan data, the new plan data being of a time series of a relationship between time and a target production volume of a new plan, the plurality of first evaluation values being of an evaluation as a prediction of performance with respect to the new plan data;
input a new plan image to a first model and calculating a plurality of second evaluation values for the plurality of sets of performance data by using a classification result of the new plan image output from the first model, the new plan image being of the relationship between time and the target production volume of the new plan, the plurality of second evaluation values being of an evaluation as a prediction of the performance with respect to the new plan data; and
extract at least one of the plurality of sets of performance data by using the plurality of first evaluation values and the plurality of second evaluation values,
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.