METHOD FOR CLASSIFICATION PARTS, SYSTEM OF PROCESSING, AND ELECTRONIC DEVICE
A method for classification parts and components of a manufacturable device and a system of processing data relevant to such parts obtains data of the components, the components comprising many different types of parts to be assembled together. A clustering model can cluster the data to each cluster and the labeling module would label for the data in each cluster. A classification model classifies and assembles data into data classes that are labeled, also indicating assembly of the different types of components. The disclosure also provides an electronic device and a non-transitory storage medium.
1 . A classification processing method comprising:
obtaining data of components to be assembled; wherein the components to be assembled comprises different types of the components that are assembled with each other;
using a clustering model to cluster the data to obtain each cluster;
setting labels on the data in each cluster to obtain labeled data;
using a classification model to classify the labeled data to obtain classification results, and indicating mutual assembly of different types of the components.
2 . The classification processing method according to claim 1 , further comprising:
obtaining training data of the different types of the mutually assembled components;
training the clustering model to cluster the training data to obtain each training cluster;
adjusting the clustering model according to the preset balance parameters and the training clusters to obtain a clustering model, and the preset balance parameters are used to balance the amount of data in the training clusters.
3 . The classification processing method according to claim 2 , further comprising:
setting labels on the training data of each training cluster to obtain labeled training data;
training the classification model to classify the labeled training data to obtain the training classification result;
adjusting the classification model according to preset classification parameters and the training classification result to obtain a classification model; wherein the preset classification parameter makes the classification result of the different types of mutually assembled components consistent.
4 . The classification processing method according to claim 1 , further comprising:
loading production data of a machine that produces the components to be assembled, to obtain dimensional data of the components to be assembled according to the production data;
obtaining size data of the components to be assembled by measuring the size of the components to be assembled.
5 . The performance tuning method according to claim 4 , further comprising:
obtaining standard components size corresponding to each cluster component;
setting labels according to the size data of the components in each cluster and the corresponding standard component size.
6 . An electronic device comprising:
a storage device; and
a processor;
wherein the storage device stores one or more programs, which when executed by the processor, cause the processor to:
obtain data of components to be assembled; wherein the components to be assembled comprises different types of the components that are assembled with each other;
use a clustering model to cluster the data to obtain each cluster;
set labels on the data in each cluster to obtain labeled data
use a classification model to classify the labeled data to obtain classification results, and indicating mutual assembly of different types of the components.
7 . The electronic device according to claim 6 , wherein the processor is further caused to:
obtain training data of the different types of the mutually assembled components;
train the clustering model to cluster the training data to obtain each training cluster;
adjust the clustering model according to the preset balance parameters and the training clusters to obtain a clustering model, and the preset balance parameters are used to balance the amount of data in the training clusters.
8 . The electronic device according to claim 7 , wherein the processor is further caused to:
set labels on the training data of each training cluster to obtain labeled training data;
train a classification model to classify the labeled training data to obtain the training classification result;
adjust the classification model according to preset classification parameters and the training classification result to obtain a classification model; wherein the preset classification parameter makes the classification result of the different types of mutually assembled components consistent.
9 . The electronic device according to claim 8 , wherein the processor is further caused to:
load production data of a machine that produces the components to be assembled, to obtain dimensional data of the components to be assembled according to the production data;
obtain size data of the components to be assembled by measuring the size of the components to be assembled.
10 . The electronic device according to claim 9 , further causing the at least one processor to:
obtain standard components size corresponding to each cluster component;
set labels according to the size data of the components in each cluster and the corresponding standard component size.
11 . A non-transitory storage medium having stored thereon instructions that, when executed by a processor of an electronic device, causes the processor to perform a performance tuning method, the method comprising:
obtaining data of components to be assembled; wherein the components to be assembled comprises different types of the components that are assembled with each other;
using a clustering model to cluster the data to obtain each cluster;
setting labels on the data in each cluster to obtain labeled data;
using a classification model to classify the labeled data to obtain classification results, and indicating mutual assembly of different types of the components.
12 . The non-transitory storage medium according to claim 11 , further comprising:
obtaining training data of the different types of the mutually assembled components;
training the clustering model to cluster the training data to obtain each training cluster;
adjusting the clustering model according to the preset balance parameters and the training clusters to obtain a clustering model, and the preset balance parameters are used to balance the amount of data in the training clusters.
13 . The non-transitory storage medium according to claim 12 ,
setting labels on the training data of each training cluster to obtain labeled training data;
training the classification model to classify the labeled training data to obtain the training classification result;
adjusting the classification model according to preset classification parameters and the training classification result to obtain a classification model; wherein the preset classification parameter makes the classification result of the different types of mutually assembled components consistent
14 . The non-transitory storage medium according to claim 13 , further comprising:
loading production data of a machine that produces the components to be assembled, to obtain dimensional data of the components to be assembled according to the production data;
obtaining size data of the components to be assembled by measuring the size of the components to be assembled.
15 . The non-transitory storage medium according to claim 14 , further comprising:
obtaining standard components size corresponding to each cluster component;
setting labels according to the size data of the components in each cluster and the corresponding standard component size.