CN116071169A - Dynamic adjustment method, device and equipment for car insurance quote and storage medium thereof - Google Patents
Dynamic adjustment method, device and equipment for car insurance quote and storage medium thereof Download PDFInfo
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Abstract
The embodiment of the application belongs to the technical field of artificial intelligence, is applied to the field of vehicle insurance pricing, and relates to a method, a device and equipment for dynamically adjusting vehicle insurance quotation and a storage medium thereof, wherein the method comprises the steps of collecting owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle; synchronously updating to a preset ODS library; acquiring analyzed vehicle related data, processing and analyzing the analyzed vehicle related data, and acquiring a characteristic value set corresponding to the vehicle related data; performing risk prediction to obtain a risk prediction value; and adjusting the quotation of the target vehicle according to the risk prediction value and a preset pricing adjustment formula. The insurance premium is determined according to the basic information of the vehicle owner, the basic information of the vehicle and the driving behavior information of the vehicle owner, the clients are easier to accept and accept, especially the clients with good driving behaviors can actually reduce the annual insurance premium, and the insurance premium is determined by combining the factors of the vehicle owner and the driving behaviors, so that the vehicle owner is facilitated to form good driving habits, and the occurrence of social traffic accidents is reduced.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, a device, and a storage medium for dynamically adjusting a car insurance quote.
Background
There are two basic principles for insurance product pricing: equal balance principle and big number principle. The principle of equal balance is to make the cash value of the net income of premium approximately equal to the cash value of the insurance claim. The large number rule means that when the number of insurance targets is sufficiently large, the difference between the expected loss and the objective loss is smaller. Accordingly, the insurer can predict risk relatively accurately and properly rate the insurance so that the income of the insurance charge in the insurance period is relatively balanced with the expenditure of the reimbursement and other fees. The pricing of the car insurance also follows the two principles, and the pricing formula of the car insurance before the car insurance fee is changed is as follows: premium = (vehicle price x rate + base premium) x adjustment coefficient; the fee-modified vehicle risk pricing formula is: premium = benchmark pure risk premium/(1-additional rate) ×rate adjustment coefficient, where: rate adjustment coefficient = no-claim preferential coefficient x autonomous underwriting coefficient x autonomous channel coefficient. The additional cost rate, the autonomous underwriting coefficient and the autonomous channel coefficient insurance company after the cost change have certain autonomous decision authority, and the core effect of the more reasonable and accurate pricing system in the car insurance competition is more remarkable.
At present, most insurance companies mainly determine coefficients according to the past year risk frequency and the number of violations of regulations of an owner, wherein a lot of errors exist, the risk probability of the owner cannot be accurately reflected, the obtained risk price also has deviation, most customers are difficult to satisfy, and the driving habit of a driver is difficult to be standardized in a way of adjusting the risk quotation.
Disclosure of Invention
The embodiment of the application aims to provide a dynamic car insurance quotation adjusting method, device and equipment and a storage medium thereof, so as to solve the problems that the car insurance quotation in the prior art is difficult to be satisfied by most clients and the driving habit of a driver is difficult to be standardized in a car insurance quotation adjusting mode.
In order to solve the above technical problems, the embodiments of the present application provide a dynamic adjustment method for a car insurance quote, which adopts the following technical scheme:
a dynamic adjustment method for a car insurance quote, comprising the following steps:
acquiring owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle through preset acquisition equipment;
synchronously updating the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information acquired each time to a preset ODS library based on a big data transmission technology;
Analyzing the owner basic information, the vehicle basic information and the owner driving behavior information which are acquired each time in the ODS library, acquiring analyzed vehicle related data, and storing the analyzed vehicle related data into a data warehouse DWD detail layer;
processing and analyzing vehicle-related data stored in a data warehouse DWD detail layer to obtain a characteristic value set corresponding to the vehicle-related data;
inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value;
and according to the risk prediction value and a preset pricing adjustment formula:
and carrying out quotation adjustment on the target vehicle, wherein the rate adjustment coefficient=no-claim preferential treatment coefficient×autonomous underwriting coefficient×autonomous channel coefficient, and the autonomous channel coefficient is a risk prediction value corresponding to the target vehicle.
Further, the step of collecting the main vehicle owner basic information, the vehicle basic information and the main vehicle owner driving behavior information corresponding to the target vehicle through the preset collecting device specifically includes:
collecting the main basic information and the vehicle basic information by adopting a mode of a monitoring component and a task trigger, wherein the main basic information comprises main age, main driving age, main name and main sudden acute medical history, and the vehicle basic information comprises the number of years of used vehicles, the driving mileage of the vehicles and the maintenance times of the vehicles;
And acquiring the driving behavior information of the vehicle owner by adopting a timing task mode, wherein the driving behavior information of the vehicle owner comprises driving mileage, emergency braking times, overspeed duration and lane-changing overtaking times of the target vehicle in a single timing period of the timing task.
Further, the step of collecting the main information and the vehicle basic information by adopting a monitoring assembly and a task trigger specifically includes:
the monitoring assembly is adopted to monitor an authoritative information platform which is pre-established with connection to acquire monitoring information, wherein the authoritative information platform is used for storing basic information of a vehicle owner and basic information of a vehicle;
identifying whether the main basic information of the vehicle and the basic information of the vehicle are changed according to the monitoring information;
if the vehicle owner basic information and the vehicle basic information are not changed, directly acquiring the vehicle owner basic information and the vehicle basic information which are acquired in advance;
if the vehicle owner basic information and the vehicle basic information are changed, the task trigger is started to acquire the vehicle owner basic information and the vehicle basic information after the change.
Further, the step of analyzing the owner basic information, the vehicle basic information and the owner driving behavior information collected in each time in the ODS library specifically includes:
Judging the change state of the currently acquired main owner basic information compared with the main owner basic information acquired last time by adopting a comparison and identification mode, and setting the change state as an analysis result corresponding to the main owner basic information;
counting the number of years of vehicle use, the mileage of the vehicle and the number of vehicle maintenance times in the vehicle basic information, and obtaining a statistical result as an analysis result corresponding to the vehicle basic information;
and calculating and acquiring driving behavior indexes corresponding to the vehicle owner according to the driving mileage, the sudden braking times, the overspeed time and the lane change overtaking times of the vehicle in the driving behavior information of the vehicle owner, and taking the driving behavior indexes as analysis results corresponding to the driving behavior information of the vehicle, wherein the driving behavior indexes comprise the sudden braking times in a unit kilometer, the lane change overtaking times in a unit kilometer, the overspeed running time in a unit time period, and the ratio of the average speed of overspeed running to the maximum speed limit.
Further, before executing the step of judging the change state of the currently collected main owner basic information compared with the main owner basic information collected last time by adopting the comparison and identification mode, the method further comprises:
The method comprises the steps of setting age grades, driving age grades and burst probability grades in advance according to the age of an owner, the driving age of the owner and the sudden acute medical history of the owner;
setting dynamic weights according to changes among age grades, driving age grades and burst probability grades respectively;
the step of judging the change state of the currently acquired main owner basic information compared with the main owner basic information acquired last time specifically comprises the following steps:
acquiring an age grade, a driving age grade and a burst probability grade corresponding to the last acquired main information of the vehicle owner;
acquiring an age grade, a driving age grade and a burst probability grade corresponding to the currently acquired main owner basic information;
according to the changes of the age level, the driving age level and the burst probability level and the corresponding dynamic weights, a comprehensive change value is obtained, and according to the positive and negative directions of the comprehensive change value, the change state of the currently acquired vehicle owner basic information compared with the vehicle owner basic information acquired last time is determined.
Further, before performing the step of processing and analyzing the vehicle-related data stored in the detail layer of the data repository DWD, the method further includes:
a numerical mapping table is preset for analysis results corresponding to the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information;
The steps for processing and analyzing the vehicle related data stored in the detail layer of the data warehouse DWD specifically comprise the following steps:
according to the numerical mapping table, mapping numerical values respectively corresponding to the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information are obtained;
the step of obtaining the characteristic value set corresponding to the vehicle-related data specifically includes:
and constructing a corresponding characteristic value set of the vehicle-related data according to the mapping value.
Further, before executing the step of inputting the feature value set into the trained risk scoring model to perform risk prediction and obtain a risk prediction value, the method further includes:
acquiring a plurality of characteristic value sets corresponding to the vehicles respectively in advance;
the feature value sets corresponding to the vehicles are used as training data to be input into an initialized risk scoring model for training;
according to training results, respectively setting corresponding weight values for each feature in the training data;
setting the weight value as a configuration parameter, configuring the risk scoring model, and obtaining a risk scoring model with the configured risk scoring model serving as the trained risk scoring model;
The step of inputting the characteristic value set into a trained risk scoring model to perform risk prediction and obtain a risk prediction value specifically comprises the following steps:
according to the characteristic values in the characteristic value set and the weight values corresponding to the characteristic values, carrying out weighted summation to obtain a weighted summation result;
and taking the weighted summation result as the risk prediction value.
In order to solve the above technical problems, the embodiments of the present application further provide a dynamic car insurance quote adjusting device, which adopts the following technical scheme:
a dynamic car insurance quote adjustment device, comprising:
the information acquisition module is used for acquiring owner basic information, vehicle basic information and owner driving behavior information corresponding to the target vehicle through preset acquisition equipment;
the synchronous updating module is used for synchronously updating the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information which are acquired each time to a preset ODS library based on a big data transmission technology;
the collected information analysis module is used for analyzing the vehicle owner basic information, the vehicle basic information and the vehicle owner driving behavior information which are collected in the ODS library each time, obtaining analyzed vehicle related data and storing the analyzed vehicle related data into a data warehouse DWD detail layer;
The characteristic value set acquisition module is used for acquiring a characteristic value set corresponding to vehicle-related data stored in the data warehouse DWD detail layer through processing and analyzing the vehicle-related data;
the model prediction module is used for inputting the characteristic value set into a trained risk scoring model, and performing risk prediction to obtain a risk prediction value;
the quotation calculation and adjustment module is used for adjusting a formula according to the risk prediction value and preset pricing:
and carrying out quotation adjustment on the target vehicle, wherein the rate adjustment coefficient=no-claim preferential treatment coefficient×autonomous underwriting coefficient×autonomous channel coefficient, and the autonomous channel coefficient is a risk prediction value corresponding to the target vehicle.
In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:
a computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which when executed by the processor implement the steps of the vehicle insurance quote dynamic adjustment method described above.
In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:
A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of a vehicle insurance quote dynamic adjustment method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
according to the vehicle insurance quotation dynamic adjustment method, the owner basic information, the vehicle basic information and the owner driving behavior information corresponding to the target vehicle are collected; synchronously updating to a preset ODS library; acquiring analyzed vehicle related data and storing the analyzed vehicle related data into a DWD detail layer of a data warehouse; processing and analyzing vehicle-related data stored in a data warehouse DWD detail layer to obtain a characteristic value set corresponding to the vehicle-related data; inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value; and according to the risk prediction value and a preset pricing adjustment formula, carrying out quotation adjustment on the target vehicle. The insurance premium is determined according to the basic information of the vehicle owner, the basic information of the vehicle and the driving behavior information of the vehicle owner, the clients are easier to accept and accept, especially the clients with good driving behaviors can actually reduce the annual insurance premium, and the insurance premium is determined by combining the factors of the vehicle owner and the driving behaviors, so that the vehicle owner is facilitated to form good driving habits, and the occurrence of social traffic accidents is reduced.
Drawings
For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a vehicle insurance quote dynamic adjustment method according to the present application;
FIG. 3 is a schematic diagram of one embodiment of a vehicle insurance quote dynamic adjustment device according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the method for dynamically adjusting the vehicle insurance quote provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the device for dynamically adjusting the vehicle insurance quote is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow chart of one embodiment of a dynamic car insurance quote adjustment method according to the present application is shown. The dynamic car insurance quotation adjusting method comprises the following steps:
In this embodiment, the preset collection device may be a vehicle ODB (international standard automobile communication interface) device, or may be a data collection device provided by a company for a specific user, for example: and the car networking equipment is provided for clients by safe car insurance.
In this embodiment, the step of collecting, by a preset collecting device, owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle specifically includes: collecting the main basic information and the vehicle basic information by adopting a mode of a monitoring component and a task trigger, wherein the main basic information comprises main age, main driving age, main name and main sudden acute medical history, and the vehicle basic information comprises the number of years of used vehicles, the driving mileage of the vehicles and the maintenance times of the vehicles; and acquiring the driving behavior information of the vehicle owner by adopting a timing task mode, wherein the driving behavior information of the vehicle owner comprises driving mileage, emergency braking times, overspeed duration and lane-changing overtaking times of the target vehicle in a single timing period of the timing task.
The vehicle owner basic information and the vehicle basic information are collected in the form of the monitoring and task trigger, so that corresponding change information is timely obtained when the vehicle owner basic information and the vehicle basic information change, the vehicle insurance quotation is quickly adjusted according to the vehicle owner basic information change and the vehicle basic information change, the adjustment of the vehicle insurance quotation is fully determined by combining the age, driving age and sudden acute medical history of the vehicle owner, the service years of the vehicle, the travelled mileage and the maintenance times, and the vehicle insurance quotation is more reasonable and scientific.
In this embodiment, the step of collecting the main information and the vehicle basic information by adopting a monitoring component and a task trigger specifically includes: the monitoring assembly is adopted to monitor an authoritative information platform which is pre-established with connection to acquire monitoring information, wherein the authoritative information platform is used for storing basic information of a vehicle owner and basic information of a vehicle; identifying whether the main basic information of the vehicle and the basic information of the vehicle are changed according to the monitoring information; if the vehicle owner basic information and the vehicle basic information are not changed, directly acquiring the vehicle owner basic information and the vehicle basic information which are acquired in advance; if the vehicle owner basic information and the vehicle basic information are changed, the task trigger is started to acquire the vehicle owner basic information and the vehicle basic information after the change.
The monitoring information is acquired from the authority information platform, wherein the monitoring information comprises the latest vehicle owner basic information and vehicle basic information, and the authority information platform can be a national motor vehicle registration management platform. The reliability of the acquired change information is ensured.
And 203, analyzing the owner basic information, the vehicle basic information and the owner driving behavior information which are acquired in the ODS library each time, acquiring analyzed vehicle related data, and storing the analyzed vehicle related data into a data warehouse DWD detail layer.
In this embodiment, the step of analyzing the owner basic information, the vehicle basic information and the owner driving behavior information collected in each time in the ODS library specifically includes: judging the change state of the currently acquired main owner basic information compared with the main owner basic information acquired last time by adopting a comparison and identification mode, and setting the change state as an analysis result corresponding to the main owner basic information; counting the number of years of vehicle use, the mileage of the vehicle and the number of vehicle maintenance times in the vehicle basic information, and obtaining a statistical result as an analysis result corresponding to the vehicle basic information; and calculating and acquiring driving behavior indexes corresponding to the vehicle owner according to the driving mileage, the sudden braking times, the overspeed time and the lane change overtaking times of the vehicle in the driving behavior information of the vehicle owner, and taking the driving behavior indexes as analysis results corresponding to the driving behavior information of the vehicle, wherein the driving behavior indexes comprise the sudden braking times in a unit kilometer, the lane change overtaking times in a unit kilometer, the overspeed running time in a unit time period, and the ratio of the average speed of overspeed running to the maximum speed limit.
The vehicle owner basic information, the vehicle basic information and the vehicle owner driving behavior information which are collected in the ODS library each time are analyzed, so that the vehicle insurance quotation adjustment can be realized by combining the vehicle owner basic information, the vehicle basic information and the vehicle owner driving behavior information together as the basis data of the vehicle insurance pricing in a later program. The insurance premium is determined according to the vehicle owner basic information, the vehicle basic information and the vehicle owner driving behavior information, so that the customer can accept approval more easily, especially the customer with good driving behavior can actually reduce the annual insurance premium.
In this embodiment, before executing the step of determining the change state of the currently collected main owner basic information compared with the main owner basic information collected last time by adopting the comparison and identification mode, the method further includes: the method comprises the steps of setting age grades, driving age grades and burst probability grades in advance according to the age of an owner, the driving age of the owner and the sudden acute medical history of the owner; dynamic weights are set according to changes between age levels, driving age levels, and burst probability levels, respectively.
By taking the age, the driving age and the sudden acute medical history of the vehicle owner as index conditions in one direction and setting dynamic weights according to different grades, the method is convenient for comprehensively evaluating the up-take risk quotation or the down-take risk quotation according to the change state of the vehicle owner after the vehicle owner changes, so that the quotation fully considers the self factors of the vehicle owner. And the premium is determined by combining the self factors of the vehicle owners and the driving behaviors, so that the vehicle owners are helped to form good driving habits, and the occurrence of traffic accidents in the whole society is reduced.
In this embodiment, the step of determining a change state of the currently collected main owner basic information compared with the main owner basic information collected last time specifically includes: acquiring an age grade, a driving age grade and a burst probability grade corresponding to the last acquired main information of the vehicle owner; acquiring an age grade, a driving age grade and a burst probability grade corresponding to the currently acquired main owner basic information; according to the changes of the age level, the driving age level and the burst probability level and the corresponding dynamic weights, a comprehensive change value is obtained, and according to the positive and negative directions of the comprehensive change value, the change state of the currently acquired vehicle owner basic information compared with the vehicle owner basic information acquired last time is determined.
The change state of the currently acquired vehicle owner basic information compared with the vehicle owner basic information acquired last time is judged, and the positive and negative pointing modes are adopted for representation, so that the risk indexes of the vehicle owners after the vehicle owner is convenient to identify after the vehicle owner is changed, compared with the vehicle owners before the vehicle owner is changed, the risk indexes of the vehicle owners after the vehicle owner is changed are stronger or weaker, different vehicle insurance quotes are provided due to the fact that the vehicle owners are different, the vehicle owner driving behavior is more scientific, the vehicle owner driving behavior is convenient to standardize, and the occurrence amount of all-society vehicle accidents is reduced.
And 204, processing and analyzing the vehicle-related data stored in the detail layer of the data warehouse DWD to acquire a characteristic value set corresponding to the vehicle-related data.
In this embodiment, before performing the steps of processing and analyzing the vehicle-related data stored in the detail layer of the data repository DWD, the method further includes: a numerical mapping table is preset for analysis results corresponding to the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information.
In this embodiment, the steps of processing and analyzing the vehicle-related data stored in the detail layer of the data warehouse DWD specifically include: and obtaining mapping values respectively corresponding to the main owner basic information, the vehicle basic information and the main owner driving behavior information according to the value mapping table.
In this embodiment, the step of obtaining the feature value set corresponding to the vehicle-related data specifically includes: and constructing a corresponding characteristic value set of the vehicle-related data according to the mapping value.
And the numerical mapping table is used for numerically representing the main owner basic information, the vehicle basic information and the main owner driving behavior information, so that analysis and processing are facilitated.
And 205, inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value.
In this embodiment, before executing the step of inputting the feature value set into the trained risk scoring model to perform risk prediction and obtain a risk prediction value, the method further includes: acquiring a plurality of characteristic value sets corresponding to the vehicles respectively in advance; the feature value sets corresponding to the vehicles are used as training data to be input into an initialized risk scoring model for training; according to training results, respectively setting corresponding weight values for each feature in the training data; and setting the weight value as a configuration parameter, configuring the risk scoring model, and obtaining a risk scoring model with the configured risk scoring model serving as the trained risk scoring model.
In this embodiment, the step of inputting the feature value set into the trained risk scoring model to perform risk prediction and obtain a risk prediction value specifically includes: according to the characteristic values in the characteristic value set and the weight values corresponding to the characteristic values, carrying out weighted summation to obtain a weighted summation result; and taking the weighted summation result as the risk prediction value.
The risk prediction value is obtained by setting a weight value for each feature and by a weighted summation mode, so that the risk prediction value is obtained by combining the main basic information, the vehicle basic information and the main driving behavior information, and the vehicle risk quotation adjustment is more scientific and the multi-factor characteristic is combined.
In this embodiment, the baseline net risk premium represents an expected premium that fully satisfies the claim payout needs within the guarantee period.
The method comprises the steps of collecting owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle; synchronously updating to a preset ODS library; acquiring analyzed vehicle related data and storing the analyzed vehicle related data into a DWD detail layer of a data warehouse; processing and analyzing vehicle-related data stored in a data warehouse DWD detail layer to obtain a characteristic value set corresponding to the vehicle-related data; inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value; and according to the risk prediction value and a preset pricing adjustment formula, carrying out quotation adjustment on the target vehicle. The insurance premium is determined according to the basic information of the vehicle owner, the basic information of the vehicle and the driving behavior information of the vehicle owner, the clients are easier to accept and accept, especially the clients with good driving behaviors can actually reduce the annual insurance premium, and the insurance premium is determined by combining the factors of the vehicle owner and the driving behaviors, so that the vehicle owner is facilitated to form good driving habits, and the occurrence of social traffic accidents is reduced.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
According to the embodiment of the application, the vehicle insurance premium is determined by combining the vehicle owner basic information, the vehicle basic information and the vehicle owner driving behavior information and adopting the model prediction to obtain the autonomous channel coefficient corresponding to the vehicle insurance, so that the client can accept approval more easily, particularly the client with good driving behavior can actually reduce the annual vehicle insurance premium, and the vehicle owner self factor and the driving behavior are combined to determine the premium, so that the vehicle owner can form good driving habit, and the occurrence of traffic accidents in the whole society is reduced.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a dynamic vehicle risk quote adjustment device, where an embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be specifically applied to various electronic devices.
As shown in fig. 3, the dynamic car insurance quote adjustment device 300 according to the present embodiment includes: an information acquisition module 301, a synchronization updating module 302, an acquisition information analysis module 303, a feature value set acquisition module 304, a model prediction module 305 and a quotation calculation adjustment module 306. Wherein:
the information acquisition module 301 is configured to acquire, through a preset acquisition device, owner basic information, vehicle basic information, and owner driving behavior information corresponding to a target vehicle;
the synchronous updating module 302 is configured to synchronously update the main vehicle owner basic information, the vehicle basic information and the main vehicle driving behavior information collected each time to a preset ODS library based on a big data transmission technology;
the collected information analysis module 303 is configured to analyze the owner basic information, the vehicle basic information and the owner driving behavior information collected in the ODS library each time, obtain analyzed vehicle related data, and store the analyzed vehicle related data in a data warehouse DWD detail layer;
The feature value set obtaining module 304 is configured to obtain a feature value set corresponding to vehicle-related data stored in a data warehouse DWD detail layer by processing and analyzing the vehicle-related data;
the model prediction module 305 is configured to input the feature value set into a trained risk score model, perform risk prediction, and obtain a risk prediction value;
a price calculation adjustment module 306, configured to adjust a formula according to the risk prediction value and a preset pricing:
and carrying out quotation adjustment on the target vehicle, wherein the rate adjustment coefficient=no-claim preferential treatment coefficient×autonomous underwriting coefficient×autonomous channel coefficient, and the autonomous channel coefficient is a risk prediction value corresponding to the target vehicle.
In some specific embodiments of the present application, the dynamic car insurance quote adjustment device 300 further includes a model training module, where the model training module is configured to obtain, in advance, a set of feature values corresponding to a plurality of vehicles respectively; the feature value sets corresponding to the vehicles are used as training data to be input into an initialized risk scoring model for training; according to training results, respectively setting corresponding weight values for each feature in the training data; and setting the weight value as a configuration parameter, configuring the risk scoring model, and obtaining a risk scoring model with the configured risk scoring model serving as the trained risk scoring model.
The method comprises the steps of collecting owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle; synchronously updating to a preset ODS library; acquiring analyzed vehicle related data and storing the analyzed vehicle related data into a DWD detail layer of a data warehouse; processing and analyzing vehicle-related data stored in a data warehouse DWD detail layer to obtain a characteristic value set corresponding to the vehicle-related data; inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value; and according to the risk prediction value and a preset pricing adjustment formula, carrying out quotation adjustment on the target vehicle. The insurance premium is determined according to the basic information of the vehicle owner, the basic information of the vehicle and the driving behavior information of the vehicle owner, the clients are easier to accept and accept, especially the clients with good driving behaviors can actually reduce the annual insurance premium, and the insurance premium is determined by combining the factors of the vehicle owner and the driving behaviors, so that the vehicle owner is facilitated to form good driving habits, and the occurrence of social traffic accidents is reduced.
Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by computer readable instructions, stored on a computer readable storage medium, that the program when executed may comprise the steps of embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 4a, a processor 4b, a network interface 4c communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 4a-4c is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may alternatively be implemented. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 4a includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 4a may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 4a may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 4a may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 4a is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a dynamic adjustment method for a car insurance quote. Further, the memory 4a may be used to temporarily store various types of data that have been output or are to be output.
The processor 4b may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 4b is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 4b is configured to execute computer readable instructions stored in the memory 4a or process data, such as computer readable instructions for executing the dynamic adjustment method for the car insurance quote.
The network interface 4c may comprise a wireless network interface or a wired network interface, which network interface 4c is typically used to establish a communication connection between the computer device 4 and other electronic devices.
The embodiment provides computer equipment, belongs to artificial intelligence technical field. The method comprises the steps of collecting owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle; synchronously updating to a preset ODS library; acquiring analyzed vehicle related data and storing the analyzed vehicle related data into a DWD detail layer of a data warehouse; processing and analyzing vehicle-related data stored in a data warehouse DWD detail layer to obtain a characteristic value set corresponding to the vehicle-related data; inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value; and according to the risk prediction value and a preset pricing adjustment formula, carrying out quotation adjustment on the target vehicle. The insurance premium is determined according to the basic information of the vehicle owner, the basic information of the vehicle and the driving behavior information of the vehicle owner, the clients are easier to accept and accept, especially the clients with good driving behaviors can actually reduce the annual insurance premium, and the insurance premium is determined by combining the factors of the vehicle owner and the driving behaviors, so that the vehicle owner is facilitated to form good driving habits, and the occurrence of social traffic accidents is reduced.
The present application also provides another embodiment, namely, a computer readable storage medium, where computer readable instructions are stored, where the computer readable instructions are executable by a processor, so that the processor performs the steps of the method for dynamically adjusting a car insurance quote as described above.
The embodiment provides a computer readable storage medium, which belongs to the technical field of artificial intelligence. The method comprises the steps of collecting owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle; synchronously updating to a preset ODS library; acquiring analyzed vehicle related data and storing the analyzed vehicle related data into a DWD detail layer of a data warehouse; processing and analyzing vehicle-related data stored in a data warehouse DWD detail layer to obtain a characteristic value set corresponding to the vehicle-related data; inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value; and according to the risk prediction value and a preset pricing adjustment formula, carrying out quotation adjustment on the target vehicle. The insurance premium is determined according to the basic information of the vehicle owner, the basic information of the vehicle and the driving behavior information of the vehicle owner, the clients are easier to accept and accept, especially the clients with good driving behaviors can actually reduce the annual insurance premium, and the insurance premium is determined by combining the factors of the vehicle owner and the driving behaviors, so that the vehicle owner is facilitated to form good driving habits, and the occurrence of social traffic accidents is reduced.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (10)
1. A dynamic adjustment method for a car insurance quote, comprising the steps of:
acquiring owner basic information, vehicle basic information and owner driving behavior information corresponding to a target vehicle through preset acquisition equipment;
synchronously updating the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information acquired each time to a preset ODS library based on a big data transmission technology;
analyzing the owner basic information, the vehicle basic information and the owner driving behavior information which are acquired each time in the ODS library, acquiring analyzed vehicle related data, and storing the analyzed vehicle related data into a data warehouse DWD detail layer;
processing and analyzing vehicle-related data stored in a data warehouse DWD detail layer to obtain a characteristic value set corresponding to the vehicle-related data;
inputting the characteristic value set into a trained risk scoring model, and carrying out risk prediction to obtain a risk prediction value;
and according to the risk prediction value and a preset pricing adjustment formula:
and carrying out quotation adjustment on the target vehicle, wherein the rate adjustment coefficient=no-claim preferential treatment coefficient×autonomous underwriting coefficient×autonomous channel coefficient, and the autonomous channel coefficient is a risk prediction value corresponding to the target vehicle.
2. The method for dynamically adjusting the vehicle insurance quote according to claim 1, wherein the step of collecting the owner basic information, the vehicle basic information and the owner driving behavior information corresponding to the target vehicle through a preset collecting device specifically includes:
collecting the main basic information and the vehicle basic information by adopting a mode of a monitoring component and a task trigger, wherein the main basic information comprises main age, main driving age, main name and main sudden acute medical history, and the vehicle basic information comprises the number of years of used vehicles, the driving mileage of the vehicles and the maintenance times of the vehicles;
and acquiring the driving behavior information of the vehicle owner by adopting a timing task mode, wherein the driving behavior information of the vehicle owner comprises driving mileage, emergency braking times, overspeed duration and lane-changing overtaking times of the target vehicle in a single timing period of the timing task.
3. The method for dynamically adjusting the quote price of the car insurance according to claim 2, wherein the step of collecting the basic information of the car owner and the basic information of the car by adopting a monitoring component and a task trigger specifically comprises the following steps:
the monitoring assembly is adopted to monitor an authoritative information platform which is pre-established with connection to acquire monitoring information, wherein the authoritative information platform is used for storing basic information of a vehicle owner and basic information of a vehicle;
Identifying whether the main basic information of the vehicle and the basic information of the vehicle are changed according to the monitoring information;
if the vehicle owner basic information and the vehicle basic information are not changed, directly acquiring the vehicle owner basic information and the vehicle basic information which are acquired in advance;
if the vehicle owner basic information and the vehicle basic information are changed, the task trigger is started to acquire the vehicle owner basic information and the vehicle basic information after the change.
4. The method for dynamically adjusting the quote price of the car insurance according to claim 1, wherein the step of analyzing the basic information of the car owner, the basic information of the car owner and the driving behavior information of the car owner collected each time in the ODS library specifically comprises the following steps:
judging the change state of the currently acquired main owner basic information compared with the main owner basic information acquired last time by adopting a comparison and identification mode, and setting the change state as an analysis result corresponding to the main owner basic information;
counting the number of years of vehicle use, the mileage of the vehicle and the number of vehicle maintenance times in the vehicle basic information, and obtaining a statistical result as an analysis result corresponding to the vehicle basic information;
and calculating and acquiring driving behavior indexes corresponding to the vehicle owner according to the driving mileage, the sudden braking times, the overspeed time and the lane change overtaking times of the vehicle in the driving behavior information of the vehicle owner, and taking the driving behavior indexes as analysis results corresponding to the driving behavior information of the vehicle, wherein the driving behavior indexes comprise the sudden braking times in a unit kilometer, the lane change overtaking times in a unit kilometer, the overspeed running time in a unit time period, and the ratio of the average speed of overspeed running to the maximum speed limit.
5. The method for dynamically adjusting a car insurance quote according to claim 4, wherein before said step of using a comparison and identification method to determine a change state of the currently collected main owner basic information compared with the main owner basic information collected last time is performed, said method further comprises:
the method comprises the steps of setting age grades, driving age grades and burst probability grades in advance according to the age of an owner, the driving age of the owner and the sudden acute medical history of the owner;
setting dynamic weights according to changes among age grades, driving age grades and burst probability grades respectively;
the step of judging the change state of the currently acquired main owner basic information compared with the main owner basic information acquired last time specifically comprises the following steps:
acquiring an age grade, a driving age grade and a burst probability grade corresponding to the last acquired main information of the vehicle owner;
acquiring an age grade, a driving age grade and a burst probability grade corresponding to the currently acquired main owner basic information;
according to the changes of the age level, the driving age level and the burst probability level and the corresponding dynamic weights, a comprehensive change value is obtained, and according to the positive and negative directions of the comprehensive change value, the change state of the currently acquired vehicle owner basic information compared with the vehicle owner basic information acquired last time is determined.
6. The method of claim 5, wherein prior to performing the step of processing and analyzing the vehicle-related data stored in the data warehouse DWD detail layer, the method further comprises:
a numerical mapping table is preset for analysis results corresponding to the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information;
the steps for processing and analyzing the vehicle related data stored in the detail layer of the data warehouse DWD specifically comprise the following steps:
according to the numerical mapping table, mapping numerical values respectively corresponding to the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information are obtained;
the step of obtaining the characteristic value set corresponding to the vehicle-related data specifically includes:
and constructing a corresponding characteristic value set of the vehicle-related data according to the mapping value.
7. The method of claim 1, wherein prior to performing the step of inputting the set of feature values into a trained risk scoring model for risk prediction, obtaining a risk prediction value, the method further comprises:
acquiring a plurality of characteristic value sets corresponding to the vehicles respectively in advance;
The feature value sets corresponding to the vehicles are used as training data to be input into an initialized risk scoring model for training;
according to training results, respectively setting corresponding weight values for each feature in the training data;
setting the weight value as a configuration parameter, configuring the risk scoring model, and obtaining a risk scoring model with the configured risk scoring model serving as the trained risk scoring model;
the step of inputting the characteristic value set into a trained risk scoring model to perform risk prediction and obtain a risk prediction value specifically comprises the following steps:
according to the characteristic values in the characteristic value set and the weight values corresponding to the characteristic values, carrying out weighted summation to obtain a weighted summation result;
and taking the weighted summation result as the risk prediction value.
8. A dynamic car insurance quote adjustment device, comprising:
the information acquisition module is used for acquiring owner basic information, vehicle basic information and owner driving behavior information corresponding to the target vehicle through preset acquisition equipment;
the synchronous updating module is used for synchronously updating the main vehicle basic information, the vehicle basic information and the main vehicle driving behavior information which are acquired each time to a preset ODS library based on a big data transmission technology;
The collected information analysis module is used for analyzing the vehicle owner basic information, the vehicle basic information and the vehicle owner driving behavior information which are collected in the ODS library each time, obtaining analyzed vehicle related data and storing the analyzed vehicle related data into a data warehouse DWD detail layer;
the characteristic value set acquisition module is used for acquiring a characteristic value set corresponding to vehicle-related data stored in the data warehouse DWD detail layer through processing and analyzing the vehicle-related data;
the model prediction module is used for inputting the characteristic value set into a trained risk scoring model, and performing risk prediction to obtain a risk prediction value;
the quotation calculation and adjustment module is used for adjusting a formula according to the risk prediction value and preset pricing:
and carrying out quotation adjustment on the target vehicle, wherein the rate adjustment coefficient=no-claim preferential treatment coefficient×autonomous underwriting coefficient×autonomous channel coefficient, and the autonomous channel coefficient is a risk prediction value corresponding to the target vehicle.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the vehicle insurance quote dynamic adjustment method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the vehicle insurance quote dynamic adjustment method according to any of claims 1 to 7.
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