Data processing method and device, storage medium and electronic device

Abstract

The embodiment of the invention provides a data processing method and device, a storage medium and an electronic device, and the method comprises the steps of receiving a to-be-predicted communicationidentifier, and obtaining the feature data of multiple dimensions of the to-be-predicted communication identifier; inputting the feature data of the multiple dimensions into a deep learning model toobtain the calculation data of the to-be-predicted communication identifier; and predicting the type of the to-be-predicted communication identifier according to the calculation data. The communication identifier type prediction is realized through the deep learning model, and the efficiency and the accuracy of the communication identifier prediction are improved, so that the waste of time and cost during communication with the first type of communication identifier is avoided.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular, to a data processing method, device, storage medium and electronic equipment. Background technique [0002] With the popularization of mobile communication, there are a large number of downtime, arrears, invalid, empty phone numbers, and only when these phone numbers are difficult to identify. For example, when we dial a phone number that is in arrears, we will find that the phone number has been stopped for arrears. If we send a text message to the phone number that is in arrears, there will be no prompt at all. [0003] A method for inferring an empty number is proposed in the related art. The method is mainly based on the data of the operator, and it is judged that a phone number with a recent calling behavior is a non-empty number. However, this method has the following problems: [0004] 1. Single source of data. For example, if there is a call behavior, it is jud...

AI Technical Summary

Problems solved by technology

[0004] 1. Single source of data

For example, if there has been a call behavior, it will be judged as a non-empty number without considering other factors, which may easily lead to misjudgment

[0005] 2. Poor real-time performance

For example, if a mobile phone with a call behavior in the past three months is a non-empty mobile phone number, it is impossible to determine the mobile phone number that has been shut down within the past three months; if a mobile phone with a call behavior in the past month is a non-empty mobile phone number, Mobile phone numbers that have been shut down within the past month

[0006] 3. Low matching degree

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