Driving intention prediction and processing method and device, equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a driving intention prediction and processing method and device, equipment and a storage medium, which are used for improving the accuracy of predicting the driving intention of a user. The driving intention prediction and processing method comprises the following steps: acquiring position service track information, action hotspot track information and credit information according to a driving intention prediction request; determining a target feature factor data set based on the position service track information, the action hotspot track information and the credit information; obtaining a driving intention prediction result from the target feature factor data set through a preset risk user prediction model, and judging whether the driving intention prediction result is smaller than a preset risk threshold value or not; if yes, obtaining and pushing vehicle compensation business order information; and if not, generating and pushing a driving intention risk assessment report and early warning information of a target object. In addition, the invention also relates to a block chain technology, and the driving intention risk assessment report can be stored in a block chain node.

Description

technical field [0001] The present invention relates to the technical field of neural network of artificial intelligence, in particular to a driving intention prediction and processing method, device, equipment and storage medium. Background technique [0002] At present, there is a compensation standard for vehicle damage caused by traffic accidents. According to the compensation standard, economic compensation for damaged vehicles can reduce the economic pressure of car owners. However, in the case of vehicle damage caused by traffic accidents, drivers deliberately rear-end or wipe the car to obtain vehicle compensation, which hinders the healthy development of vehicle compensation companies. [0003] Traditional risk identification schemes use models based on historical fraud data and specific rules for judging fraud that insurance companies have developed based on experience. The data that can be used to detect fraud is often limited to claims data collected by auto com...

AI Technical Summary

Problems solved by technology

In the vehicle damage compensation business, drivers can cooperate with each other to obtain vehicle compensation by forging call records or transaction records. However, through traditional risk identification schemes, the accuracy of predicting the user's driving intention is low, and the vehicle loss compensation is reliable. low sex problem

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