Driver degradation prediction method and device, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides a driver degradation prediction method and device, electronic equipment and a readable storage medium, and belongs to the field of data processing. The methodcomprises: acquiring personal feature information and historical order receiving data of a driver to be tested; determining a first empirical value used for representing order receiving activity of the driver to be tested in a first target time period according to the historical order receiving data, determining a first grade corresponding to the driver to be tested according to the first empirical value, inputting the information of the driver to be tested into a pre-trained random forest model, and outputting a second empirical value; determining a second grade corresponding to the driver tobe tested according to the second empirical value; and comparing the first grade with the second grade, and predicting whether the driver to be tested is degraded or not according to a comparison result. In the scheme, the accuracy of predicting whether the driver to be tested is degraded or not in the future can be improved through the method, so that some measures can be taken for the driver needing to be degraded in the future to improve the order receiving enthusiasm of the driver, and the loss of the driver is avoided.

Description

technical field [0001] The present invention relates to the field of data processing, in particular to a method, device, electronic equipment and readable storage medium for driver demotion prediction. Background technique [0002] With the development of science and technology, the emergence of various taxi-hailing platforms has brought great convenience to users' travel. In order to attract more drivers to settle in and meet users' travel needs, taxi-hailing platforms The situation assigns different grades to each driver, and different reward mechanisms can be provided for drivers according to the grades. However, after some drivers are downgraded, because the rewards are not high, the motivation of the drivers to accept orders is not high, and even the number of drivers is reduced. Therefore, in order to avoid the loss of the number of drivers, it is necessary to monitor whether the drivers are downgraded in the future. [0003] The current method of predicting driver do...

AI Technical Summary

Problems solved by technology

[0002] With the development of science and technology, the emergence of various taxi-hailing platforms has brought great convenience to users' travel. In order to attract more drivers to settle in and meet users' travel needs, taxi-hailing platforms The situation assigns different grades to each driver, and different reward mechanisms can be provided for drivers according to the grades. However, after some drivers are downgraded, because the rewards are not high, the motivation of the drivers to accept orders is not high, and even the number of drivers is reduced. Therefore, in order to avoid the loss of the number of drivers, it is necessary to monitor whether the drivers are downgraded in the future

[0003] The current method of predicting driver downgrades is: pick out drivers whose recent income and orders are negative, and judge such drivers as drivers who may be downgraded. is less accurate

Images