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Driving risk evaluation method and device based on static state factor

A driving risk and factor technology, applied in the direction of instrumentation, finance, data processing applications, etc., can solve the problems that insurance companies cannot provide more accurate basis for formulating different premiums, and cannot accurately reflect the actual risk situation, etc.

Inactive Publication Date: 2017-07-25
南京人人保网络技术有限公司
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the rise of UBI Internet of Vehicles insurance and big data, there is an urgent need for a scientific method to evaluate the driver's driving risk. The evaluation results can be used as the support of the driving risk data of the test vehicle owner to remind and urge the owner to improve driving habits. , so as to improve the safety awareness of car owners, and can also provide a basis for insurance companies to formulate different levels of premiums. At present, the insurance evaluation of UBI at home and abroad is only after assigning appropriate weights to risk factors and doing simple fitting and calculation. But The evaluation results obtained by this method cannot accurately reflect the actual risk situation, nor can it provide a more accurate basis for insurance companies to formulate different premiums for different customers.

Method used

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  • Driving risk evaluation method and device based on static state factor
  • Driving risk evaluation method and device based on static state factor
  • Driving risk evaluation method and device based on static state factor

Examples

Experimental program
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no. 1 example

[0029] Please refer to figure 2 , figure 2 It is a flow chart of a static factor-based driving risk assessment method provided in the first embodiment of the present invention, and the method specifically includes the following steps:

[0030] Step S110: Obtain the static data of the first passenger and vehicle, wherein the static data of the first passenger and vehicle includes the personal characteristic data of the first vehicle owner, the first physical parameter data of the vehicle and the first accident data.

[0031]Firstly, the static data of the first passenger and the vehicle are obtained. The static data of the first passenger and the vehicle include the personal characteristic data of the first vehicle owner, the physical parameter data of the first vehicle and the first emergency data. These data all represent the static factors related to the vehicle owner or the vehicle. Among them, the personal characteristic data of the first car owner may include data such...

no. 2 example

[0042] Please refer to image 3 , image 3 It is a flowchart of a driving risk assessment method based on static factors provided by the second embodiment of the present invention, the method specifically includes the following steps:

[0043] Step S210: Obtain a plurality of second passenger-vehicle static data, wherein each second passenger-vehicle static data includes second vehicle owner personal characteristic data, second vehicle physical parameter data, and second accident data.

[0044] In order to build a risk prediction model and improve the accuracy of the evaluation results, it is necessary to obtain a plurality of second passenger-vehicle static data, each of which includes the second vehicle owner's personal characteristic data, the second vehicle physical parameter data and The second accident data, wherein the personal characteristic data of the second car owner can include data such as the traffic violation situation of the car owner within a preset time, the...

no. 3 example

[0070] Please refer to Figure 4 , Figure 4 A structural block diagram of a static factor-based driving risk assessment device 200 is provided for the third embodiment of the present invention, and the device specifically includes:

[0071] The first acquisition module 210 is configured to acquire the first passenger-vehicle static data, wherein the first passenger-vehicle static data includes the first owner's personal characteristic data, the first vehicle physical parameter data, and the first accident data.

[0072] The risk prediction module 220 is configured to input the static data of the first passenger and vehicle into a pre-acquired risk prediction model for risk prediction.

[0073] An evaluation result obtaining module 230, configured to obtain an evaluation result according to the first passenger-vehicle static data and the risk prediction model.

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Abstract

The invention provides a driving risk evaluation method and device based on a static state factor, and belongs to the field of risk prediction. The method comprises that first human-vehicle static state data including first vehicle-owner personal characteristic data, first vehicle physical parameter data and first insurance accident occurrence data is obtained; the first human-vehicle static state data is input to a pre-obtained risk prediction model to implement risk prediction; and an evaluation result is obtained according to the first human-vehicle static state data and the risk prediction model. An evaluation result is high in accuracy, insurance companies can identity and screen driving risks via data output, the integrated loss ratio is reduced, and a win-win effect is achieved; technical and data support is provided for innovative and different pricing of the auto insurance industry, and fairer and more reasonable commercial insurance price is provided for vehicle owners who drives safely.

Description

technical field [0001] The present invention relates to the field of risk prediction, in particular to a static factor-based driving risk assessment method and device. Background technique [0002] With the rise of UBI Internet of Vehicles insurance and big data, there is an urgent need for a scientific method to evaluate the driver's driving risk. The evaluation results can be used as the support of the driving risk data of the test vehicle owner to remind and urge the owner to improve driving habits. , so as to improve the safety awareness of car owners, and can also provide a basis for insurance companies to formulate different levels of premiums. At present, the insurance evaluation of UBI at home and abroad is only after assigning appropriate weights to risk factors and doing simple fitting and calculation. But The evaluation results obtained by this method cannot accurately reflect the actual risk situation, nor can it provide a more accurate basis for insurance compan...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q40/08
CPCG06Q10/04G06Q40/08
Inventor 帅勇
Owner 南京人人保网络技术有限公司
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