CN113781244A - Method and system for generating vehicle insurance premium, electronic device and storage medium - Google Patents
Method and system for generating vehicle insurance premium, electronic device and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a system for generating vehicle insurance premium, an electronic device and a storage medium, wherein the method for generating the vehicle insurance premium comprises the following steps: acquiring driving behavior data corresponding to a vehicle; inputting the driving behavior data into a driving behavior scoring model to obtain a driving behavior score corresponding to the vehicle; and determining the insurance premium according to the driving behavior score. According to the method and the system for generating the vehicle insurance premium, the electronic device and the storage medium, provided by the invention, through the analysis of the driving behavior data corresponding to the vehicle, the risk level generated in the use process of the vehicle can be more accurately distinguished, the generated vehicle insurance premium is more reasonable, and the perception of the vehicle owner on the driving behavior of the vehicle owner can be improved through the vehicle insurance premium, so that the unreasonable driving habit is improved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a method and a system for generating vehicle insurance premium, an electronic device and a storage medium.
Background
Traditional car insurance pricing is typically based on the demographic attributes of the car owner, such as gender, age; and vehicle attributes such as vehicle type, vehicle price, vehicle age; the risk of accident claims settlement of the vehicle is distinguished by information such as historical insurance and violation records, and corresponding premium price is made. However, the above factors are not factors directly causing an accident, and there is a lack of accuracy in predicting the risk of an accident of a vehicle by the above factors.
Disclosure of Invention
The invention aims to overcome the defects that the basis for generating the insurance premium of the vehicle insurance is not reasonable enough and the determination and consideration of the insurance premium of the vehicle insurance are not comprehensive in the prior art, and provides a method and a system for generating the insurance premium of the vehicle insurance, electronic equipment and a storage medium.
The invention solves the technical problems through the following technical scheme:
the invention provides a method for generating vehicle insurance premium, which comprises the following steps:
acquiring driving behavior data corresponding to a vehicle;
inputting the driving behavior data into a driving behavior scoring model to obtain a driving behavior score corresponding to the vehicle;
and determining the insurance premium according to the driving behavior score.
Preferably, the step of determining the insurance premium according to the driving behavior score comprises:
acquiring mileage unit price according to the driving behavior score and the target mileage; the mileage unit price and the driving behavior score are positively correlated;
and determining the vehicle insurance premium corresponding to the target mileage according to the target mileage and the mileage unit price.
Preferably, the driving behavior data includes at least one of the number of rapid accelerations per unit distance, the number of emergency avoidance, the number of emergency braking, and the duration of fatigue driving.
Preferably, the step of obtaining the driving behavior data corresponding to the vehicle includes:
and acquiring driving behavior data corresponding to the vehicle through a block chain.
The invention also provides a system for generating the insurance premium of the vehicle insurance, which is characterized by comprising the following steps:
the acquisition module is used for acquiring driving behavior data corresponding to the vehicle;
the scoring module is used for inputting the driving behavior data into a driving behavior scoring model so as to obtain a driving behavior score corresponding to the vehicle;
and the premium generation module is used for determining the insurance premium according to the driving behavior score.
Preferably, the unit price acquiring unit is used for acquiring mileage unit price according to the driving behavior score and the target mileage; the mileage unit price and the driving behavior score are positively correlated;
and the premium determining unit is used for determining the vehicle insurance premium corresponding to the target mileage according to the target mileage and the mileage unit price.
Preferably, the driving behavior data includes at least one of the number of rapid accelerations per unit distance, the number of emergency avoidance, the number of emergency braking, and the duration of fatigue driving.
Preferably, the obtaining module is specifically configured to obtain driving behavior data corresponding to the vehicle through a blockchain.
The method and the system for generating the insurance premium, the electronic device and the storage medium provided by the invention have the positive effects that the risk generated in the using process of the vehicle can be more accurately distinguished through analyzing the driving behavior data corresponding to the vehicle, the generated insurance premium is more reasonable, and the perception of the vehicle owner on the driving behavior of the vehicle owner can be improved through the insurance premium, so that the unreasonable driving habit is improved.
Drawings
Fig. 1 is a schematic flow chart of a method for generating a vehicle insurance premium according to embodiment 1 of the present invention.
Fig. 2 is a flowchart illustrating a process of calculating a vehicle insurance premium according to embodiment 1 of the present invention.
Fig. 3 is a block diagram of a vehicle insurance premium generation system according to embodiment 2 of the present invention.
Fig. 4 is a block diagram of an electronic device according to embodiment 3 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
Referring to fig. 1, the embodiment specifically provides a method for generating a vehicle insurance premium, which includes the steps of:
s1, obtaining driving behavior data corresponding to the vehicle.
S2, inputting the driving behavior data into a driving behavior scoring model to obtain a driving behavior score corresponding to the vehicle;
and S3, determining the insurance premium according to the driving behavior score.
The embodiment generates the insurance premium based on the driving behavior corresponding to the vehicle, namely the higher the driving behavior evaluation of the vehicle owner is, the smaller the driving risk is considered; accordingly, a lower insurance premium for the vehicle is determined.
In one example, the insurance premium of the present embodiment may be generated directly from the driving behavior of the previous period (e.g., the previous year, season, or a certain amount).
In yet another example, the current insurance premium may be divided into a first-term premium and a renewal premium by mileage. For example, when the car owner buys the car insurance each year, the car owner firstly pays a first-stage premium, and the first-stage premium contains a certain mileage amount; after the first amount is used up, the mileage amount is purchased according to mileage unit price. Of course, the first-stage insurance fee can also be determined according to the mileage amount and the validity period, for example, the mileage amount of the first-stage insurance fee is used up or the validity period is expired, which are both considered as the termination of the corresponding insurance responsibility.
For the initial premium, the basic value premium of the initial premium can be determined according to the traditional car insurance pricing result, and the initial payment proportion p is setfirstThen the first premium of the owner is premium pfirst. For the mileage amount corresponding to the first-stage premium, the mileage can be divided into quantiles according to the annual mileage of the whole vehiclexTo make the determination. Example (b)As specified by x<50 hours corresponds to the annual mileage. Wherein the annual mileage of all car owners, i.e. the kilometers displayed by the current stopwatch sensor/actual age (year) of car owner, e.g. mile10,mile20,…,mile50Respectively representing quantiles of 10% -50% of the annual mileage of the whole vehicle owners, wherein the annual mileage is 50% of mile of the whole vehicle owners50Is 10000 km.
After the initial premium is determined, purchasing mileage amount according to mileage unit price on the basis of the initial premium; the product of the mileage quota and the mileage unit price is the renewal premium, so that the final insurance premium is determined on the basis of the first-stage premium determination. Referring to fig. 2, a flow diagram of a process for calculating the insurance premium described above is shown, wherein a driving behavior scoring model may be used to score driving behavior data. Those skilled in the art will appreciate that the method of generating the insurance premium of the present embodiment may include, but is not limited to, determining a renewal premium. Furthermore, the above mentioned determination of the first-term premium is only one implementation, and does not impose any limitation on the inventive vehicle insurance premium generation method.
Specifically, step S1 obtains driving behavior data of the vehicle, and step S2 processes the driving behavior data through a driving behavior score model to obtain a driving behavior score corresponding to the vehicle.
The driving behavior data of the vehicle obtained in step S1 may be processed for a manually driven vehicle or a current hybrid automatic driving vehicle, that is, the driving behavior of the vehicle includes both a manual driving behavior in which an owner of the vehicle participates and a driving behavior in which the vehicle is automatically controlled. It will be appreciated that both should be processed separately when scoring the above-described vehicle driving behavior.
In a preferred embodiment, the driving behavior data includes, but is not limited to, the number of rapid accelerations per unit distance, the number of emergency avoidance, the number of emergency braking, and the duration of fatigue driving. The fatigue driving time length can be obtained by judging the time length of continuous driving or judging the time length of a manual driving mode in the intelligent vehicle by combining the set fatigue driving time length threshold, for example, if the time length exceeds the fatigue driving time length threshold by 3 hours, the fatigue driving is judged to be the fatigue driving.
First, in the case of the manual driving behavior, the control operation of the vehicle body includes drive control, brake control, and steering control.
In addition, with the maturity of intelligent vehicle technology, the vehicle can continuously obtain data when the vehicle drives through various configured sensors and analyze the driving habits of the vehicle owner based on the data, and then the driving behavior risk of the vehicle owner is prejudged. The difference between the smart-drive vehicle and the conventional vehicle is the redundancy, for example by adding clutches, engine control units, redundant steering motors and force feedback. The direction and angle of steering wheel rotation is controlled by a decision-making system based on a deep learning model of road detection or driving patterns through information collected by a perception system.
The intelligent driving can send out corresponding prompts when manual driving and automatic driving are carried out alternately, for example, the intelligent driving indicates that the current driving is the automatic driving control steering wheel through a green light; and a blue light indicates that the steering wheel needs to be manually taken over. Even so, in most cases the driver is required to place his or her hands on the steering wheel.
The steering control corresponds to a vehicle body control for turning the vehicle, i.e., a steering wheel control action that is generally understood. For a vehicle with an automatic steering function, when the vehicle is controlled manually, the steering wheel is rotated through manual operation, steering data collected when the steering wheel is rotated can be used for judging parameters such as steering amplitude and the like, so that a lane changing scene implemented by manual behaviors is restored, and accordingly, the emergency avoidance times of a unit distance can be obtained on the basis of the lane changing scene.
The drive control corresponds to a vehicle body control action for driving the vehicle, for achieving acceleration of the vehicle, i.e., an accelerator action. The electronic accelerator converts the driving intention of a driver into an electronic signal through an electronic structure in a pedal, transmits the electronic signal to an engine controller through a bus, and adjusts a whole vehicle power system to achieve the driving purpose after the electronic accelerator is operated according to the working condition of the whole vehicle. The opening of a throttle valve of an electronic accelerator pedal is controlled by a motor, the intention of acceleration is transmitted to an engine controller through a position sensor of the accelerator pedal, and the engine controller realizes the adjustment of a throttle valve body through the motor. Meanwhile, the electronic throttle opening sensor feeds back the detected opening signal to the engine controller to realize the closed-loop control of the electronic throttle opening. When the electronic accelerator is manually operated to step on the pedal, related parameters can be obtained, vehicle body control actions implemented by manual driving behaviors can be obtained based on main parameters of the electronic accelerator, such as the acceleration intention signal, the position sensor parameter, the throttle opening degree signal and the like, a brake force value corresponding to the operation is obtained, parameters of acceleration and the like in the acceleration process are further judged, and driving behavior data, such as the number of rapid acceleration times in unit distance and the like, are obtained.
The braking control corresponds to braking action on a vehicle body, for example, in manual driving operation by adopting an electronic brake, when an electronic brake pedal is stepped on, parameters such as electronic brake liquid pressure and the like can be obtained, so that a corresponding braking force value is obtained, further, parameters such as acceleration and the like in a deceleration process are judged, and driving behavior data such as emergency braking times of a unit distance and the like are obtained.
While the vehicle is in the automatic driving mode, the control of the vehicle is usually based on an automatic driving instruction of a vehicle computing unit and is realized by a line control system. Finally, the vehicle body control such as acceleration, deceleration, steering and the like is also realized. The vehicle computing unit is usually integrated in a TBOX (vehicle box), and is mainly based on a central processing unit, a graphic processor and a programmable chip architecture, and generates an automatic driving instruction based on fusion data obtained after processing by a related algorithm, so as to realize autonomous driving control of a vehicle. The method comprises a longitudinal control instruction and a transverse control instruction. The longitudinal control command, i.e. the speed control, includes control commands related to acceleration and deceleration, such as when to accelerate, when to decelerate, how much to accelerate, etc. Lateral control commands are behavior control, how to change lanes, when to overtake, etc. In addition, the automatic driving command sent by the vehicle computing unit can control a gearbox and even an engine to cooperate with the vehicle to finish the speed change and the steering action.
As a preferred embodiment, step S1 may obtain the driving behavior data corresponding to the vehicle through the block chain. Blockchain networks are public networks that can provide low-cost development, deployment, operation, maintenance, interworking, and car networking applications. The block chain application participants do not need to set a server or cloud service to build the own block chain operation environment, and the car networking information can be realized only by setting an interface for accessing the block chain network at the terminal or directly using the unified service provided by the service network.
In this embodiment, the block chain network may be logged in by acquiring a unique identifier of the vehicle, such as a frame number, an owner identification number, a vehicle-corresponding policy number, and the like, as an account. Parameter state information generated in the vehicle driving process is sent to a block chain technology cochain in real time, and meanwhile, encryption of vehicle information can be guaranteed through unique identification login. On the basis, the driving process of the vehicle can be traced back through a time axis according to the established authority, and the vehicle driving process information encrypted through the block chain not only has a timestamp with high confidence level, but also can be prevented from being tampered, and the privacy of the vehicle owner is guaranteed. This embodiment acquires vehicle driving behavior data through the block chain, can guarantee transmission real-time, confidentiality, the traceability of vehicle driving behavior data, not only has high confidence level, and the subsequent processing of being convenient for.
Step S2 inputs the driving behavior data into the driving behavior score model to obtain the driving behavior score corresponding to the vehicle. Of course, the driving behavior scoring model may be implemented by obtaining driving behavior data from the blockchain network at the cloud, or may be implemented in a local or local area network server.
Wherein the driving score calculation may include, but is not limited to, the following:
1) driving behavior scoring model: inputting driving behavior data x1,x2,…,xnFor example, the number of rapid acceleration times per hundred kilometers and the fatigue driving time per hundred kilometers, etc., to obtain the predicted value of the claim rate score of the vehicle owner's historical policy, i.e., to establish the model f (x)1,x2,…,xn)＝score。
2) Standardized model scoring: suppose that each owner i gets a score of score through the above modeli(ii) a As shown in FIG. 2, the calendar year insurance policy of all vehicle owners can be obtained from the claims dataAverage odds of claim (typically 60% to 80%), according to score aboveiFinding one or more vehicle owners with the same or close to the claim rate, and obtaining the driving behavior score corresponding to the claim rate close based on the obtained driving behavior score (if the claim rate of the plurality of vehicle owners is the same or close to the claim rate, the average number, the median and the like can be obtained according to the driving behavior scores of the plurality of vehicle owners)claim. The final score for each owner i is calculated as scorei＝scorei/scoreclaimNamely, the driving behavior score of each vehicle owner is expressed by the multiple of the score corresponding to the average payout rate.
In an alternative embodiment, step S3 includes:
s31, acquiring mileage unit price according to the driving behavior score and the target mileage; the mileage unit price and the driving behavior score are positively correlated;
and S32, determining the vehicle insurance premium corresponding to the target mileage according to the target mileage and the mileage unit price.
Calculating the mileage unit price at step S31 may include, but is not limited to, the following:
suppose that the current driving score of a vehicle owner is scoreiThen, the mileage unit price of premium renewal:
unitprice＝premium*max(0,g(scorei)-pfirst))/(mile50-milex)。
wherein g () is a score adjustment function to scale the distribution of scores and make g (score)i) Has a minimum value as large as pfirstI.e., the first payment rate of the first premium. g () may take the function ln (), sqrt (), () < lambda > n, etc.
Thus mileage unit price and driving behavior scoreiIs in positive correlation. And the mileage unit price is updated each time using the current latest driving score of the owner.
Step S32 calculates the insurance premium, for example, determining a renewal premium based on the mileage unit price and the target mileage: t is unit price target mileage. And determining the vehicle insurance premium corresponding to the target mileage by combining the initial premium.
The method for generating the insurance premium of the present embodiment is specifically described below by a insurance premium calculation process, but those skilled in the art will appreciate that the calculation process does not constitute a limitation to the method.
Firstly, determining initial premium and mileage limit: the base value of the vehicle insurance premium is 5000 yuan. First payment ratio pfirstCalculated as 30%.
Assume quantile mile of annual mileage of all car owners30＝3000km；mile50＝10000km。
The first-stage premium of a certain vehicle is 5000 x 30 percent, namely 1500 Yuan, and the vehicle contains a mileage quota of 3000 km;
each time, the mileage quota is regarded as a period, the user can be reminded to renew the fee in time before the quota is used up, the renewal fee premium of the next period is calculated at the moment, and three-gear mileage quotas such as 1000km, 3000km and 5000km are provided.
Assuming that the average mileage of a certain vehicle in each month is 1000km, the mileage amount of 3000km is used up in 4 months, assuming that the driving behavior score is 0.9 at that time, if the vehicle continuously buys 1000km, the renewal premium is paid:
unitprice＝premium*max(0,g(scorei)-pfirst))/(mile50-milex)
5000 x (0.9-0.3)/(10000-;
assuming that the 1000km limit is exhausted by the month of 5, the driving behavior score is assumed to drop to 0.8, and if 3000km is continuously purchased, a renewal fee is required to be paid:
3, 5000, (0.8-0.3)/(10000-.
According to the method for generating the vehicle insurance premium, through analysis of the driving behavior data corresponding to the vehicle, the risk level generated in the using process of the vehicle can be distinguished more accurately, the generated vehicle insurance premium is more reasonable, and the perception of the vehicle owner on the driving behavior of the vehicle owner can be improved through the vehicle insurance premium, so that unreasonable driving habits are improved. Specifically, the mileage unit price is determined by setting a first-period premium and a renewal premium, and scoring the driving behavior data in the calculation of the renewal premium, so that the premium differentiation of different risk car owners is realized, and the perception of the car owners on the premium is strengthened by the renewal based on mileage. In addition, by reasonably setting the calculation parameters of the mileage unit price, the risk that the mileage unit price is difficult to calculate and the premium possibly cannot be collected in the past is avoided. And finally, the driving behavior score of the vehicle owner is updated in real time to influence the renewal price, so that the vehicle owner can improve the driving behavior of the vehicle owner, and the accident rate is reduced.
Example 2
Referring to fig. 3, the embodiment specifically provides a system for generating a vehicle insurance premium, including:
the system comprises an acquisition module 1, a display module and a control module, wherein the acquisition module is used for acquiring driving behavior data corresponding to a vehicle;
the scoring module 2 is used for inputting the driving behavior data into the driving behavior scoring model so as to obtain a driving behavior score corresponding to the vehicle;
and the premium generation module 3 is used for determining the insurance premium according to the driving behavior score.
The embodiment generates the insurance premium based on the driving behavior corresponding to the vehicle, namely the higher the driving behavior evaluation of the vehicle owner is, the smaller the driving risk is considered; accordingly, a lower insurance premium for the vehicle is determined.
In one example, the insurance premium of the present embodiment may be generated directly from the driving behavior of the previous period (e.g., the previous year, season, or a certain amount).
In yet another example, the current insurance premium may be divided into a first-term premium and a renewal premium by mileage. For example, when the car owner buys the car insurance each year, the car owner firstly pays a first-stage premium, and the first-stage premium contains a certain mileage amount; after the first amount is used up, the mileage amount is purchased according to mileage unit price. Of course, the first-stage insurance fee can also be determined according to the mileage amount and the validity period, for example, the mileage amount of the first-stage insurance fee is used up or the validity period is expired, which are both considered as the termination of the corresponding insurance responsibility.
For the first-term premium, the basis of the first-term premium can be determined according to the traditional car insurance pricing resultValue premium, and set the top-payment ratio pfirstThen the first premium of the owner is premium pfirst. For the mileage amount corresponding to the first-stage premium, the mileage can be divided into quantiles according to the annual mileage of the whole vehiclexTo make the determination. For example, specify x<50 hours corresponds to the annual mileage. Wherein the annual mileage of all car owners, i.e. the kilometers displayed by the current stopwatch sensor/actual age (year) of car owner, e.g. mile10,mile20,…,mile50Respectively representing quantiles of 10% -50% of the annual mileage of the whole vehicle owners, wherein the annual mileage is 50% of mile of the whole vehicle owners50Is 10000 km.
After the initial premium is determined, purchasing mileage amount according to mileage unit price on the basis of the initial premium; the product of the mileage quota and the mileage unit price is the renewal premium, so that the final insurance premium is determined on the basis of the first-stage premium determination. Referring to fig. 2, a flow diagram of a process for calculating the insurance premium described above is shown, wherein a driving behavior scoring model may be used to score driving behavior data. Those skilled in the art will appreciate that the method of generating the insurance premium of the present embodiment may include, but is not limited to, determining a renewal premium. Furthermore, the above mentioned determination of the first-term premium is only one implementation, and does not impose any limitation on the inventive vehicle insurance premium generation method.
Specifically, the obtaining module 1 obtains driving behavior data of the vehicle, and the scoring module 2 processes the driving behavior data through a driving behavior scoring model to obtain a driving behavior score corresponding to the vehicle.
The obtaining module 1 obtains the driving behavior data of the vehicle, and the data can be processed for a manually driven vehicle or a current hybrid automatic driving vehicle, that is, the driving behavior of the vehicle has the manual driving behavior participated by the vehicle owner and the driving behavior of the vehicle automatic control. It will be appreciated that both should be processed separately when scoring the above-described vehicle driving behavior.
In a preferred embodiment, the driving behavior data includes, but is not limited to, the number of rapid accelerations per unit distance, the number of emergency avoidance, the number of emergency braking, and the duration of fatigue driving. The fatigue driving time length can be obtained by judging the time length of continuous driving or judging the time length of a manual driving mode in the intelligent vehicle by combining the set fatigue driving time length threshold, for example, if the time length exceeds the fatigue driving time length threshold by 3 hours, the fatigue driving is judged to be the fatigue driving.
First, in the case of the manual driving behavior, the control operation of the vehicle body includes drive control, brake control, and steering control.
In addition, with the maturity of intelligent vehicle technology, the vehicle can continuously obtain data when the vehicle drives through various configured sensors and analyze the driving habits of the vehicle owner based on the data, and then the driving behavior risk of the vehicle owner is prejudged. The difference between the smart-drive vehicle and the conventional vehicle is the redundancy, for example by adding clutches, engine control units, redundant steering motors and force feedback. The direction and angle of steering wheel rotation is controlled by a decision-making system based on a deep learning model of road detection or driving patterns through information collected by a perception system.
The intelligent driving can send out corresponding prompts when manual driving and automatic driving are carried out alternately, for example, the intelligent driving indicates that the current driving is the automatic driving control steering wheel through a green light; and a blue light indicates that the steering wheel needs to be manually taken over. Even so, in most cases the driver is required to place his or her hands on the steering wheel.
The steering control corresponds to a vehicle body control for turning the vehicle, i.e., a steering wheel control action that is generally understood. For a vehicle with an automatic steering function, when the vehicle is controlled manually, the steering wheel is rotated through manual operation, steering data collected when the steering wheel is rotated can be used for judging parameters such as steering amplitude and the like, so that a lane changing scene implemented by manual behaviors is restored, and accordingly, the emergency avoidance times of a unit distance can be obtained on the basis of the lane changing scene.
The drive control corresponds to a vehicle body control action for driving the vehicle, for achieving acceleration of the vehicle, i.e., an accelerator action. The electronic accelerator converts the driving intention of a driver into an electronic signal through an electronic structure in a pedal, transmits the electronic signal to an engine controller through a bus, and adjusts a whole vehicle power system to achieve the driving purpose after the electronic accelerator is operated according to the working condition of the whole vehicle. The opening of a throttle valve of an electronic accelerator pedal is controlled by a motor, the intention of acceleration is transmitted to an engine controller through a position sensor of the accelerator pedal, and the engine controller realizes the adjustment of a throttle valve body through the motor. Meanwhile, the electronic throttle opening sensor feeds back the detected opening signal to the engine controller to realize the closed-loop control of the electronic throttle opening. When the electronic accelerator is manually operated to step on the pedal, related parameters can be obtained, vehicle body control actions implemented by manual driving behaviors can be obtained based on main parameters of the electronic accelerator, such as the acceleration intention signal, the position sensor parameter, the throttle opening degree signal and the like, a brake force value corresponding to the operation is obtained, parameters of acceleration and the like in the acceleration process are further judged, and driving behavior data, such as the number of rapid acceleration times in unit distance and the like, are obtained.
The braking control corresponds to braking action on a vehicle body, for example, in manual driving operation by adopting an electronic brake, when an electronic brake pedal is stepped on, parameters such as electronic brake liquid pressure and the like can be obtained, so that a corresponding braking force value is obtained, further, parameters such as acceleration and the like in a deceleration process are judged, and driving behavior data such as emergency braking times of a unit distance and the like are obtained.
While the vehicle is in the automatic driving mode, the control of the vehicle is usually based on an automatic driving instruction of a vehicle computing unit and is realized by a line control system. Finally, the vehicle body control such as acceleration, deceleration, steering and the like is also realized. The vehicle computing unit is generally integrated in the TBOX, and is mainly based on a central processing unit, a graphic processor and a programmable chip architecture, and generates an automatic driving instruction based on fusion data obtained after processing by a related algorithm, so as to realize autonomous driving control of a vehicle. The method comprises a longitudinal control instruction and a transverse control instruction. The longitudinal control command, i.e. the speed control, includes control commands related to acceleration and deceleration, such as when to accelerate, when to decelerate, how much to accelerate, etc. Lateral control commands are behavior control, how to change lanes, when to overtake, etc. In addition, the automatic driving command sent by the vehicle computing unit can control a gearbox and even an engine to cooperate with the vehicle to finish the speed change and the steering action.
As a preferred embodiment, the obtaining module 1 may obtain the driving behavior data corresponding to the vehicle through the block chain. Blockchain networks are public networks that can provide low-cost development, deployment, operation, maintenance, interworking, and car networking applications. The block chain application participants do not need to set a server or cloud service to build the own block chain operation environment, and the car networking information can be realized only by setting an interface for accessing the block chain network at the terminal or directly using the unified service provided by the service network.
In this embodiment, the block chain network may be logged in by acquiring a unique identifier of the vehicle, such as a frame number, an owner identification number, a vehicle-corresponding policy number, and the like, as an account. Parameter state information generated in the vehicle driving process is sent to a block chain technology cochain in real time, and meanwhile, encryption of vehicle information can be guaranteed through unique identification login. On the basis, the driving process of the vehicle can be traced back through a time axis according to the established authority, and the vehicle driving process information encrypted through the block chain not only has a timestamp with high confidence level, but also can be prevented from being tampered, and the privacy of the vehicle owner is guaranteed. This embodiment acquires vehicle driving behavior data through the block chain, can guarantee transmission real-time, confidentiality, the traceability of vehicle driving behavior data, not only has high confidence level, and the subsequent processing of being convenient for.
The scoring module 2 inputs the driving behavior data into the driving behavior scoring model to obtain the driving behavior score corresponding to the vehicle. Of course, the driving behavior scoring model may be implemented by obtaining driving behavior data from the blockchain network at the cloud, or may be implemented in a local or local area network server.
Wherein the driving score calculation may include, but is not limited to, the following:
1) driving behavior scoring model: inputting driving behavior data x1,x2,…,xnFor example, the number of rapid acceleration times per hundred kilometers and the fatigue driving time per hundred kilometers, etc., to obtain the predicted value of the claim rate score of the vehicle owner's historical policy, i.e., to establish the model f (x)1,x2,…,xn)＝score。
2) Standardized model scoring: suppose that each owner i gets a score of score through the above modeli(ii) a As shown in FIG. 2, the average payout rate of all owners' calendar year insurance policies is obtained from the claims settlement data as claim (usually 60% to 80%), according to the scoreiFinding one or more vehicle owners with the same or close to the claim rate, and obtaining the driving behavior score corresponding to the claim rate close based on the obtained driving behavior score (if the claim rate of the plurality of vehicle owners is the same or close to the claim rate, the average number, the median and the like can be obtained according to the driving behavior scores of the plurality of vehicle owners)claim. The final score for each owner i is calculated as scorei＝scorei/scoreclaimNamely, the driving behavior score of each vehicle owner is expressed by the multiple of the score corresponding to the average payout rate.
In an alternative embodiment, the premium generation module 3 comprises:
a unit price obtaining unit 31, configured to obtain a mileage unit price according to the driving behavior score and the target mileage; the mileage unit price and the driving behavior score are positively correlated;
and the premium determining unit 32 is used for determining the vehicle insurance premium corresponding to the target mileage according to the target mileage and the mileage unit price.
The unit price acquisition unit 31 may calculate the mileage unit price by, but not limited to, the following means:
suppose that the current driving score of a vehicle owner is scoreiThen, the mileage unit price of premium renewal:
unitprice＝premium*max(0,g(scorei)-pfirst))/(mile50-milex)。
wherein g () is a score adjustment function to scale the distribution of scores and make g (score)i) Has a minimum value as large as pfirstI.e., the first payment rate of the first premium. g () may take the function ln (), sqrt (), () < lambda > n, etc.
Thus mileage unit price and driving behavior scoreiIn the positive phaseAnd off. And the mileage unit price is updated each time using the current latest driving score of the owner.
The premium determination unit 32 calculates a vehicle insurance premium, for example, determining a vehicle insurance premium based on the mileage unit price and the target mileage: t is unit price target mileage. And determining the vehicle insurance premium corresponding to the target mileage by combining the initial premium.
The method for generating the insurance premium of the present embodiment is specifically described below by a insurance premium calculation process, but those skilled in the art will appreciate that the calculation process does not constitute a limitation to the method.
Firstly, determining initial premium and mileage limit: the base value of the vehicle insurance premium is 5000 yuan. First payment ratio pfirstCalculated as 30%.
Assume quantile mile of annual mileage of all car owners30＝3000km；mile50＝10000km。
The first-stage premium of a certain vehicle is 5000 x 30 percent, namely 1500 Yuan, and the vehicle contains a mileage quota of 3000 km;
each time, the mileage quota is regarded as a period, the user can be reminded to renew the fee in time before the quota is used up, the renewal fee premium of the next period is calculated at the moment, and three-gear mileage quotas such as 1000km, 3000km and 5000km are provided.
Assuming that the average mileage of a certain vehicle in each month is 1000km, the mileage amount of 3000km is used up in 4 months, assuming that the driving behavior score is 0.9 at that time, if the vehicle continuously buys 1000km, the renewal premium is paid:
unitprice＝premium*max(0,g(scorei)-pfirst))/(mile50-milex)
5000 x (0.9-0.3)/(10000-;
assuming that the 1000km limit is exhausted by the month of 5, the driving behavior score is assumed to drop to 0.8, and if 3000km is continuously purchased, a renewal fee is required to be paid:
3, 5000, (0.8-0.3)/(10000-.
The generating system of the vehicle insurance premium of the embodiment can more accurately distinguish the risk level generated in the using process of the vehicle through the analysis of the driving behavior data corresponding to the vehicle, the generated vehicle insurance premium is more reasonable, and the perception degree of the vehicle owner to the driving behavior can be improved through the vehicle insurance premium, so that the unreasonable driving habit is improved. Specifically, the mileage unit price is determined by setting a first-period premium and a renewal premium, and scoring the driving behavior data in the calculation of the renewal premium, so that the premium differentiation of different risk car owners is realized, and the perception of the car owners on the premium is strengthened by the renewal based on mileage. In addition, by reasonably setting the calculation parameters of the mileage unit price, the risk that the mileage unit price is difficult to calculate and the premium possibly cannot be collected in the past is avoided. And finally, the driving behavior score of the vehicle owner is updated in real time to influence the renewal price, so that the vehicle owner can improve the driving behavior of the vehicle owner, and the accident rate is reduced.
Example 3
Fig. 4 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention. The electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and the processor executes the program to realize the vehicle insurance premium generation method in the embodiment 1. The electronic device 30 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 4, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM)321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as a method of generating a vehicle insurance premium in embodiment 1 of the present invention, by running the computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown in FIG. 4, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium on which a computer program is stored, the program implementing the steps in the vehicle insurance premium generation method in embodiment 1 when executed by a processor.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program codes for causing a terminal device to execute steps in the method for generating a vehicle insurance premium in implementation example 1 when the program product runs on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A method for generating a vehicle insurance premium, the method comprising the steps of:
acquiring driving behavior data corresponding to a vehicle;
inputting the driving behavior data into a driving behavior scoring model to obtain a driving behavior score corresponding to the vehicle;
and determining the insurance premium according to the driving behavior score.
2. The method of generating a vehicle insurance premium according to claim 1, wherein the step of determining the vehicle insurance premium according to the driving behavior score includes:
acquiring mileage unit price according to the driving behavior score and the target mileage; the mileage unit price and the driving behavior score are positively correlated;
and determining the vehicle insurance premium corresponding to the target mileage according to the target mileage and the mileage unit price.
3. The method of claim 1, wherein the driving behavior data includes at least one of a number of rapid accelerations per unit distance, a number of emergency exits per unit distance, a number of emergency brakes per unit distance, and a fatigue driving duration.
4. The method for generating a vehicle insurance premium according to any one of claims 1 to 3, wherein the step of acquiring the driving behavior data corresponding to the vehicle includes:
and acquiring driving behavior data corresponding to the vehicle through a block chain.
5. A system for generating a vehicle insurance premium, the system comprising:
the acquisition module is used for acquiring driving behavior data corresponding to the vehicle;
the scoring module is used for inputting the driving behavior data into a driving behavior scoring model so as to obtain a driving behavior score corresponding to the vehicle;
and the premium generation module is used for determining the insurance premium according to the driving behavior score.
6. The method of generating a car insurance premium of claim 5, wherein the premium generation module comprises:
the unit price obtaining unit is used for obtaining mileage unit price according to the driving behavior score and the target mileage; the mileage unit price and the driving behavior score are positively correlated;
and the premium determining unit is used for determining the vehicle insurance premium corresponding to the target mileage according to the target mileage and the mileage unit price.
7. The method of claim 5, wherein the driving behavior data includes at least one of a number of rapid accelerations per unit distance, a number of emergency exits per unit distance, a number of emergency brakes per unit distance, and a fatigue driving duration.
8. The method for generating vehicle insurance premium according to claim 5-7, wherein the acquiring module is specifically configured to acquire the driving behavior data corresponding to the vehicle through a blockchain.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the method for generating a vehicle insurance premium when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of generating a vehicle insurance premium of any one of claims 1 to 4.
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