Industrial Internet of Things (IoT) for determining reparability of defective product, control method, and storage medium thereof

An industrial Internet of Things (IoT) for determining repairability of a defective product, including:

• an obtaining module, a total correction cost determination module, and a defective product processing module, wherein
• the obtaining module is configured to obtain defective product data;
• the total correction cost determination module is configured to:
• in response to a determination that a number of the defective product is larger than 1, perform a filtering operation on a defective product vector of the defective product to obtain a first defective product vector and a second defective product vector, the defective product vector being constructed based on the defective product data, wherein the first defective product vector is a filtered-out defective product vector, and the second defective product vector is a defective product vector left after filtering;
• determine, based on the first defective product vector, a first total correction cost;
• cluster the second defective product vector to determine at least one cluster center set;
• for each of the at least one cluster center set, determine, based on one or more center set features, a number of raw material and manpower required by the cluster center set through a prediction model, the prediction model being a machine learning model;
• wherein the prediction model is obtained through training based on a plurality of labeled training samples, and a training process of the prediction model includes:
• inputting the plurality of labeled training samples to an initial prediction model, constructing a loss function by the labels and an output result of the initial prediction model, and updating a parameter of the initial prediction model iteratively based on the loss function;
• wherein when the loss function of the initial prediction model satisfies a preset condition, the training process is completed, and a trained prediction model is obtained;
• and the training samples including sample center set features of a plurality of sample cluster center sets, and the labels indicating a number of raw material and manpower required for the sample cluster center sets;
• determine, based on the number of raw material and the manpower required by the each of the at least one cluster center set, at least one second total correction cost;
• and
• determine, based on the first total correction cost and the at least one second total correction cost, a total correction cost;
• and
• the defective product processing module is configured to determine, based on the total correction cost and a preset cost, whether the defective product is repairable, and perform repair of the defective product based on the determination that the defective product is repairable.