A 95598 telephone traffic work order prediction and transaction early warning method based on LSTM deep learning

Abstract

The invention discloses a 95598 telephone traffic work order prediction and transaction early warning method based on LSTM deep learning, and relates to a power telephone traffic work order analysis method. At present, an abnormal threshold value is artificially determined through a comparison value, a loop ratio value and an amplification value, and the threshold value cannot be accurately and scientifically set in real time, so that the monitoring and early warning, problem positioning and trend prediction capabilities are insufficient. Based on the LSTM neural network deep learning technology, a scientific index transaction prediction model is established, the mathematical relationship of all indexes is studied, and short-term telephone traffic work order confidence transaction prediction and intelligent early warning application are achieved. According to the technical scheme, index analysis early warning is obtained from a large number of indexes more efficiently, more lean and more intelligently, and the working efficiency of customer service index analysis and quality control is improved. The defect that traditional curve fitting modeling needs periodic model correction is overcome, online real-time dynamic learning prediction and early warning analysis are supported, and the monitoring early warning, problem positioning and trend prediction capabilities of daily indexesare improved.

Description

technical field [0001] The present invention relates to a method for analyzing electric power telephone service orders, in particular to a method for predicting and pre-alarming 95598 telephone service orders based on LSTM deep learning. Background technique [0002] How to predict the early warning of traffic ticket changes in the short term has become one of the key points and difficulties in the daily analysis work of 95598. Traditional methods such as relying on manual data review and manual cleaning of data for index prediction have seriously failed to keep up with development needs. The analysis mode is single, inefficient, and poor in timeliness. And a waste of human resources. At present, the transaction threshold is determined artificially through year-on-year, month-on-month, and increase values, and the threshold cannot be set in a real-time, accurate, and scientific manner, resulting in insufficient monitoring and early warning, problem location, and trend predic...

AI Technical Summary

Problems solved by technology

[0002] How to predict the early warning of traffic ticket changes in the short term has become one of the key points and difficulties in the daily analysis work of 95598. Traditional methods such as relying on manual data review and manual cleaning of data for index prediction have seriously failed to keep up with development needs. The analysis mode is single, inefficient, and poor in timeliness. and waste human resources

At present, the transaction threshold is determined artificially through year-on-year, month-on-year, and increase values, and the threshold cannot be set in a real-time, accurate, and scientific manner, resulting in insufficient monitoring and early warning, problem location, and trend prediction capabilities.

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