METHOD FOR CLASSIFYING FAILURE CONSUMER DEVICES ON-LINE, ELECTRONIC DEVICE EMPLOYING METHOD, AND COMPUTER READABLE STORAGE MEDIUM

1 . A method for classifying failure, applicable in an electronic device; the electronic device comprises a non-transitory storage medium with program codes and a processor; the processor executes the program codes in the non-transitory storage medium to implement the method; the method comprises:

obtaining training set; the training set comprises repairable failure information and total failure information;

selecting each first key information in the repairable failure information and each second key information in the total failure information;

computing a first term frequency-inverse document frequency (TF-IDF) value of each first key information and a second TF-IDF value of each second key information;

creating a bank of first feature information obtained based on the first TF-IDF value and a first threshold value;

creating a bank of second feature information obtained based on the second TF-IDF value and a second threshold value; and

training a classifier of failures based on the first feature information bank and the second feature information bank and classifying target failure information by the trained classifier of failures.

2 . The method of claim 1 , wherein the step of selecting each first key information in the repairable failure information and each second key information in the total failure information comprises:

selecting first critical level failure information from the repairable failure information as the first key information based on a severity level of the repairable failure information; and

selecting second critical level failure information from the total failure information as the second key information based on a severity level of the total failure information.

3 . The method of claim 1 , wherein the step of creating a bank of first feature information obtained based on the first TF-IDF value and a first threshold value comprises:

determining whether the first TF-IDF value is larger than the first threshold value;

confirming the first key information corresponding to the first TF-IDF value as the first feature information of the repairable failure information; and

creating the first feature information bank based on the first feature information.

4 . The method of claim 3 , wherein the step of creating a bank of second feature information obtained based on the second TF-IDF value and a second threshold value comprises:

determining whether the second TF-IDF value is larger than the second threshold value;

confirming the second key information corresponding to the second TF-IDF value as the second feature information of the total failure information; and

creating the second feature information bank based on the second feature information.

5 . The method of claim 4 , wherein the step of training a classifier of failures based on the first feature information bank and the second feature information bank comprises:

obtaining a first number of feature information from the first feature information bank and the second feature information bank in a first specified ratio as target training data;

inputting target training data into a specified neural network frame for training to obtain the classifier of failures; the neural network frame comprises a KERAS frame and a TENSORFLOW frame;

obtaining a second number of feature information from the first feature information bank and the second feature information bank in a second specified ratio as target testing data; and

testing the classifier of failures based on the target testing data to obtain a testing ratio; and

confirming the classifier of failures as a target classifier of failures when the testing ratio is larger than the specified threshold value.

6 . The method of claim 5 , wherein the method further comprises:

extracting third key information in target failure information;

computing a third TF-IDF value of each third key information;

determining whether the third TF-IDF value is larger than a third threshold value;

confirming the third key information corresponding to the third TF-IDF value as the third feature information of the target failure information; and

classifying the target failure information by the trained classifier of failures based on the third key information.

7 . The method of claim 1 , wherein the method further comprises:

determining whether failure information is loaded on a cloud platform;

transferring failure information to a local database through a Restful application interface when the failure information is loaded on the cloud platform;

writing the failure information into the local database and classifying the failure information to obtain a prediction value;

writing the prediction value into the local database; and

transferring prediction value to the cloud platform through the Restful application interface to be displayed on the cloud platform.

8 . The method of claim 1 , wherein the method further comprises:

setting an updating period; and

re-training the classifier of failures after each updating period.

9 . An electronic device comprises a non-transitory storage medium with program codes, which when being executed by a processor, cause the processor to:

obtain training set; the training set comprises repairable failure information and total failure information;

select each first key information in the repairable failure information and each second key information in the total failure information;

compute a first term frequency-inverse document frequency (TF-IDF) value of each first key information and a second TF-IDF value of each second key information;

create a bank of first feature information obtained based on the first TF-IDF value and a first threshold value;

create a bank of second feature information obtained based on the second TF-IDF value and a second threshold value; and

train a classifier of failures based on the first feature information bank and the second feature information bank and classify target failure information by the trained classifier of failures.

10 . The electronic device of claim 9 , wherein the processor to select each first key information in the repairable failure information and each second key information in the total failure information comprises:

select first critical level failure information from the repairable failure information as the first key information based on a severity level of the repairable failure information; and

select second critical level failure information from the total failure information as the second key information based on a severity level of the total failure information.

11 . The electronic device of claim 9 , wherein the processor to create a bank of first feature information obtained based on the first TF-IDF value and a first threshold value comprises:

determine whether the first TF-IDF value is larger than the first threshold value;

confirm the first key information corresponding to the first TF-IDF value as the first feature information of the repairable failure information; and

create the first feature information bank based on the first feature information.

12 . The electronic device of claim 11 , wherein the processor to create a bank of second feature information obtained based on the second TF-IDF value and a second threshold value comprises:

determine whether the second TF-IDF value is larger than the second threshold value;

confirm the second key information corresponding to the second TF-IDF value as the second feature information of the total failure information; and

create the second feature information bank based on the second feature information.

13 . The electronic device of claim 12 , wherein the process to train a classifier of failures based on the first feature information bank and the second feature information bank comprises:

obtain a first number of feature information from the first feature information bank and the second feature information bank in a first specified ratio as target training data;

input target training data into a specified neural network frame for training to obtain the classifier of failures; the neural network frame comprises a KERAS frame and a TENSORFLOW frame;

obtain a second number of feature information from the first feature information bank and the second feature information bank in a second specified ratio as target testing data; and

test the classifier of failures based on the target testing data to obtain a testing ratio; and

confirm the classifier of failures as a target classifier of failures when the testing ratio is larger than the specified threshold value.

14 . The electronic device of claim 13 , wherein the processor further:

extract third key information in target failure information;

compute a third TF-IDF value of each third key information;

determine whether the third TF-IDF value is larger than a third threshold value;

confirm the third key information corresponding to the third TF-IDF value as the third feature information of the target failure information; and

classify the target failure information by the trained classifier of failures based on the third key information.

15 . The electronic device of claim 1 , wherein the processor further:

determine whether failure information is loaded on a cloud platform;

transfer failure information to a local database through a Restful application interface when the failure information is loaded on the cloud platform;

write the failure information into the local database and classify the failure information to obtain a prediction value;

write the prediction value into the local database; and

transfer prediction value to the cloud platform through the Restful application interface to be displayed on the cloud platform.

16 . The electronic device of claim 9 , wherein the processor further:

set an updating period; and

re-train the classifier of failures after each updating period.

17 . A computer readable storage medium stores program codes; the program codes are executed by at least one processor to implement the following steps:

obtaining training set; the training set comprises repairable failure information and total failure information;

selecting each first key information in the repairable failure information and each second key information in the total failure information;

computing a first term frequency-inverse document frequency (TF-IDF) value of each first key information and a second TF-IDF value of each second key information;

creating a bank of first feature information obtained based on the first TF-IDF value and a first threshold value;

creating a bank of second feature information obtained based on the second TF-IDF value and a second threshold value; and

training a classifier of failures based on the first feature information bank and the second feature information bank, and classifying target failure information by the trained classifier of failures.

18 . The computer readable storage medium of claim 17 , wherein the step of selecting each first key information in the repairable failure information and each second key information in the total failure information comprises:

selecting first critical level failure information from the repairable failure information as the first key information based on a severity level of the repairable failure information; and

selecting second critical level failure information from the total failure information as the second key information based on a severity level of the total failure information.

19 . The computer readable storage medium of claim 18 , wherein the step of creating a bank of second feature information obtained based on the second TF-IDF value and a second threshold value comprises:

determining whether the first TF-IDF value is larger than the first threshold value;

confirming the first key information corresponding to the first TF-IDF value as the first feature information of the repairable failure information; and

creating the first feature information bank based on the first feature information.

20 . The computer readable storage medium of claim 19 , wherein the step of training a classifier of failures based on the first feature information bank and the second feature information bank, and classifying target failure information by the trained classifier of failures comprises:

obtaining a first number of feature information from the first feature information bank and the second feature information bank in a first specified ratio as target training data;

inputting target training data into a specified neural network frame for training to obtain the classifier of failures; the neural network frame comprises a KERAS frame and a TENSORFLOW frame;

obtaining a second number of feature information from the first feature information bank and the second feature information bank in a second specified ratio as target testing data; and

testing the classifier of failures based on the target testing data to obtain a testing ratio; and

confirming the classifier of failures as a target classifier of failures when the testing ratio is larger than the specified threshold value.