CN117893333A - New energy vehicle insurance method, medium and equipment based on historical driving data - Google Patents
New energy vehicle insurance method, medium and equipment based on historical driving data Download PDFInfo
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Abstract
The invention provides a new energy vehicle insurance method, medium and equipment based on historical driving data, wherein the method comprises the following steps: acquiring historical driving behavior data of a target driver in a plurality of continuous set time periods before the target time period; according to the historical driving behavior data, historical driving habit data and historical driving technical grade of a target driver in each set time period are obtained; under the condition that each historical driving technology does not meet the preset technical stability condition, predicting expected driving habit data and expected driving skill grade of a target driver in a target time period according to each historical driving skill grade and each historical driving habit data; and acquiring vehicle attribute information of the target vehicle, and acquiring a insurance pricing strategy of the target vehicle in a target time period by adopting a UBI insurance model according to the vehicle attribute information, the expected driving technical grade and the expected driving habit data. The technical scheme of the invention can improve the accuracy of insurance pricing.
Description
Technical Field
The invention relates to the technical field of vehicle insurance, in particular to a new energy vehicle insurance method, medium and equipment based on historical driving data.
Background
The UBI (Usage-Based Insurance) model is an Insurance model for determining premium Based On Usage, and the driving habit, driving technique, vehicle information, surrounding environment and other data of a driver can be integrated through networking equipment such as internet of vehicles, smart phones, OBD (On-Board Diagnostics), and the like, so as to establish a multi-dimensional model of people, vehicles and environment for pricing. The core concept of the UBI insurance model is to give premium benefits to drivers with safe driving behaviors, and the popularization of the insurance model can not only enable insurance companies to strengthen the vehicle insurance pricing capability, but also generate good personal and social effects to guide the drivers to form good driving habits.
The existing UBI insurance model is used for making insurance pricing strategies according to the previous driving technology information and driving habit information of a driver, but the driving habits and driving technologies of the driver are correspondingly changed along with the increase of driving ages and the accumulation of driving experiences, so that the deviation between the insurance pricing strategies obtained by the UBI insurance model and the actual conditions of the driver is caused, and the problem of poor user experience is caused.
Disclosure of Invention
The invention provides a new energy vehicle insurance method, medium and equipment based on historical driving data, which are used for solving the problem that an insurance pricing strategy obtained by adopting a UBI insurance model in the prior art has deviation from the actual situation of a driver, and achieving the purpose of improving user experience.
To solve at least the above technical problems, in a first aspect, the present invention provides a new energy vehicle insurance method based on historical driving data, including:
acquiring historical driving behavior data of a target driver in a plurality of continuous set time periods before the target time period;
according to the historical driving behavior data, historical driving habit data and historical driving technical grade of the target driver in the set time period are obtained;
under the condition that each historical driving technology does not meet the preset technical stability condition, according to each historical driving technology grade and each historical driving habit data, predicting expected driving habit data and expected driving technology grade of the target driver in the target time period;
acquiring vehicle attribute information of a target vehicle, wherein the vehicle attribute information at least comprises a power battery parameter, a driving motor parameter and a vehicle body size of the target vehicle;
and obtaining the insurance pricing strategy of the target vehicle in the target time period according to the vehicle attribute information, the expected driving technical grade and the expected driving habit data by adopting a UBI insurance model.
Further, the obtaining, according to each of the historical driving behavior data, historical driving habit data and driving skill level of the target driver in each of the set time periods includes:
acquiring first historical driving operation data of the target driver under a plurality of first preset working conditions according to each historical driving behavior data;
and carrying out statistical analysis on each first historical driving operation data to obtain the historical driving habit data.
Further, the performing statistical analysis on each of the first historical driving operation data to obtain the historical driving habit data includes:
and acquiring a preset statistical analysis model, and carrying out statistical analysis on the first driving operation data by adopting the preset statistical analysis model so as to obtain the historical driving habit data.
Further, the obtaining, according to each of the historical driving behavior data, historical driving habit data and driving skill level of the target driver in each of the set time periods includes:
acquiring second driving operation data of the target driver under a plurality of second preset working conditions according to each historical driving behavior data;
and evaluating the driving technology of the target driver according to the second driving operation data to obtain each historical driving technology grade.
Further, the evaluating the driving technology of the target driver according to the second driving operation data to obtain the driving technology grade includes:
and acquiring a preset technical grade evaluation model, and evaluating each second driving operation data by adopting the preset technical grade evaluation model to obtain each driving technical grade.
Further, after the historical driving habit data and the historical driving skill level of the target driver in each set period are obtained, the method further comprises:
acquiring the actual driving technology level of the target driver according to each historical driving technology level under the condition that each historical driving technology level meets the preset technology stability condition;
judging whether the actual driving technical grade is smaller than or equal to a preset technical grade;
if yes, predicting expected driving habit data of the target driver in the target time period according to each historical driving habit data;
and obtaining the insurance pricing strategy of the target vehicle in the target time period according to the vehicle attribute information, the actual driving technical grade and the expected driving habit data by adopting a UBI insurance model.
Further, after the determining that the actual driving skill level is less than or equal to the preset skill level, the method further includes:
judging whether the driving habit of the target driver is stable or not according to each historical driving habit data under the condition that the actual driving skill level is larger than the preset skill level;
if so, carrying out statistical analysis on each historical driving habit data to obtain expected driving habit data of the target driver in the target time period
Further, after the determining whether the driving habit of the target driver is stable according to each of the historical driving habit data, the method further includes:
and if the driving habit data is unstable, predicting expected driving habit data of the target driver in the target time period according to each historical driving habit data.
In a second aspect, the present invention also provides a machine-readable storage medium having stored thereon a machine-executable program which, when executed by a processor, implements any of the above-described methods of securing a new energy vehicle based on historical driving data.
In a third aspect, the present invention also provides a computer device, including a memory, a processor, and a machine executable program stored on the memory and running on the processor, where the processor implements any one of the above new energy vehicle insurance methods based on historical driving data when executing the machine executable program.
According to the technical scheme provided by the invention, under the condition that the historical driving technology of the target driver in each set time period does not meet the preset technical stability condition, the expected driving habit data and the expected driving habit data of the target driver in the target time period are predicted according to the historical driving skill grade and the historical driving habit data of the target driver, and the UBI insurance model is adopted, so that the insurance pricing strategy of the target vehicle in the target time period is obtained according to the vehicle attribute information, the expected driving skill grade and the expected driving habit data. According to the technical scheme, the expected driving habit data and the expected driving habit level of the target driver in the target time period can be accurately predicted according to the historical driving habit data and the historical driving habit data of the target driver, so that the insurance pricing strategy of the target vehicle in the target time period, calculated according to the expected driving habit data and the expected driving habit level by adopting the UBI insurance model, is higher in accuracy compared with the prior art, and can achieve the purpose of improving user experience.
The above, as well as additional objectives, advantages, and features of the present invention will become apparent to those skilled in the art from the following detailed description of a specific embodiment of the present invention when read in conjunction with the accompanying drawings.
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Some specific embodiments of the invention will be described in detail hereinafter by way of example and not by way of limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions. It will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the accompanying drawings:
FIG. 1 is a schematic flow chart of a new energy vehicle insurance method based on historical driving data in accordance with one embodiment of the invention;
FIG. 2 is a schematic flow chart of acquiring historical driving habit data of a target driver for each set period of time according to one embodiment of the invention;
FIG. 3 is a schematic flow chart of acquiring historical driving skill levels for a target driver over various set time periods in accordance with one embodiment of the invention;
FIG. 4 is a schematic flow chart of a new energy vehicle insurance method based on historical driving data in accordance with another embodiment of the invention;
FIG. 5 is a schematic flow chart of a new energy vehicle insurance method based on historical driving data in accordance with another embodiment of the invention;
FIG. 6 is a schematic flow chart diagram of a new energy vehicle insurance method based on historical driving data in accordance with another embodiment of the invention;
FIG. 7 is a schematic diagram of a machine-readable storage medium according to one embodiment of the invention;
FIG. 8 is a schematic diagram of a computer device according to one embodiment of the invention.
Detailed Description
A new energy vehicle insurance method, medium and apparatus based on historical driving data according to an embodiment of the present invention will be described below with reference to fig. 1 to 8. In the description of the present embodiment, it should be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature, i.e. one or more such features. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. When a feature "comprises or includes" a feature or some of its coverage, this indicates that other features are not excluded and may further include other features, unless expressly stated otherwise.
In the description of the present embodiment, a description referring to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In some embodiments of the present invention, a flow of the new energy vehicle insurance method based on historical driving data of the present invention is shown in fig. 1, and the method includes the following steps:
step S101: acquiring historical driving behavior data of a target driver in a plurality of continuous set time periods before the target time period;
step S102: according to the historical driving behavior data of the target driver in each set time period, obtaining the historical driving habit data and the historical driving technical grade of the target driver in each set time period;
step S103: judging whether each historical driving technology grade meets a preset technology stability condition;
if not, executing step S104;
step S104: according to the historical driving technology grades and the historical driving habit data, predicting expected driving habit data and expected driving technology grades of a target driver in a target time period;
step S105: acquiring vehicle attribute information of a target vehicle;
step S106: and obtaining the insurance pricing strategy of the target driver in the target time period according to the vehicle attribute information of the target vehicle, the expected driving technical grade and the expected driving habit data of the target driver in the target time period by adopting the UBI insurance model.
In the above step S101, an OBD (On-Board-Diagnostics) device may be installed On the target vehicle, and driving behavior data of the target driver, that is, historical driving behavior data of the target driver, may be collected by the OBD device in a continuous plurality of set periods before the target period.
In the present embodiment, the historical driving behavior data of the target driver includes, but is not limited to, average driving speed, driving frequency, emergency braking frequency, driving mileage, charging frequency, emergency acceleration frequency, emergency deceleration frequency, audio device use frequency, navigation device use frequency, overtaking driving frequency.
In the step S102, historical driving habit data of the target driver in each set period may be obtained by performing statistical analysis on the historical driving behavior data of the target driver in each set period; and evaluating the historical driving behavior data of the target driver in each set time period to obtain the historical driving technical grade of the target driver in each set time period.
In the step S103, a variance value of each historical driving technology level may be calculated first, and if the variance value is less than or equal to a set threshold value, it is determined that each historical driving technology meets a preset technology stability condition; otherwise, if the variance value is larger than the set threshold value, judging that each historical driving technology grade does not meet the preset technology stability condition.
In this embodiment, the detected driving skill level and driving habit data of the user may be stored in a database, and in the process of executing step S104, the user having the greatest similarity with the target driver may be selected from the database according to each historical driving habit data and each historical driving skill level of the target driver, and the user may be used as a reference user of the target driver; and then, referring to the driving habit data and the driving skill grade of the user in the target time period in the database to obtain the expected driving habit data and the expected driving skill grade of the target driver in the target time period.
In the process of screening reference users of a target driver from a database, the similarity between each user in the database and the target driver needs to be calculated, and the calculation method comprises the following steps:
firstly, a driving habit data table is obtained, and driving habit indexes corresponding to various driving habits are stored in a driving habit database; and then, inquiring the driving habit data table according to the historical driving habit data of the target driver in each set time period to obtain the driving habit index of the target driver in each set time period. Setting the number of the set time periods as N, wherein the historical driving habit index of the ith set time period is P i Historical driving skill grade of Q i One of the users in the database has a driving habit index of P 'in the ith set time period' i The driving technical grade is Q' i The similarity gamma between the target driver and the user is
Wherein a is 1 To weight driving habit, a 2 Is the weight of driving technology.
In the above step S105, the vehicle attribute information of the target vehicle includes at least the power battery parameter, the drive motor parameter, and the vehicle body size of the target vehicle.
In the above step S106, the UBI insurance model is:
Y＝α 1 X 1 +α 2 X 2 +α 3 X 3 +μ
wherein Y represents the insurance pricing of the target vehicle over the target period of time, X 1 An expected driving habit index, alpha, representing the target driver in the target time period 1 Is a driving habit coefficient; x is X 2 Representing the expected driving skill level, alpha, of the target driver in the target time period 2 Is a driving technical coefficient; x is X 3 Vehicle index, alpha, representing target vehicle 3 Representing the vehicle coefficient; u represents a constant term.
In this embodiment, a vehicle information table may be acquired first, in which vehicle indexes corresponding to various vehicle attribute information are stored; after obtaining the vehicle attribute information of the target vehicle, the vehicle information table may be queried according to the vehicle attribute information of the target vehicle to obtain the vehicle index of the target vehicle. After the expected driving habit data of the target driver in the target time period is obtained, the driving habit data table is queried according to the expected driving habit data, so that the expected driving habit index of the target driver in the target time period is obtained. And carrying expected driving habit indexes, expected driving technical grade and vehicle indexes of the target vehicle of the target driver in the target time period into the UBI insurance pricing model to obtain insurance pricing of the target vehicle in the target time period.
According to the new energy vehicle insurance method based on the historical driving data, the expected driving habit data and the expected driving skill level of the target driver in the target time period can be accurately predicted according to the historical driving skill level and the historical driving habit data of the target driver, so that the insurance pricing strategy of the target vehicle in the target time period calculated according to the expected driving habit data and the expected driving skill level by adopting the UBI insurance model is higher in accuracy compared with the prior art, and the purpose of improving user experience can be achieved.
In some embodiments of the present invention, the method for obtaining the historical driving habit data of the target driver in each set period according to the historical driving behavior data of the target driver in each set period in the step S102 is shown in fig. 2, and includes:
step S201: acquiring first historical driving operation data of a target driver under a plurality of first preset working conditions according to the historical driving behavior data of the target driver in each set time period;
step S202: and carrying out statistical analysis on the first historical driving operation data to obtain historical driving habit data of the target driver in each set time period.
In the above step S201, the first preset condition may include night, left turn, right turn, red light, green light, intersection, one-way road. The first historical driving operation data includes operation information of the target driver on the target vehicle under each first preset working condition, such as operation information of car lights, steering lights, brakes, accelerator, multimedia and electronic equipment.
In the step S202, the first historical driving operation data are statistically analyzed according to the first preset conditions, so as to obtain the driving habit of the target driver under the first preset conditions, i.e. the historical driving habit data of the target driver in the set time periods.
In this embodiment, the statistical analysis is performed on each first driving operation data, which means that the first driving operation data under each first preset working condition is counted to obtain the duty ratio of the number of each first driving operation data under each first preset working condition, and the first driving operation data with the duty ratio greater than the preset duty ratio is used as the historical driving habit data under the corresponding first preset working condition.
By means of the setting mode of the embodiment, corresponding first historical driving operation data can be counted according to the first preset working condition, so that historical driving habit data of a target driver in each set time period can be obtained, and accuracy and reliability of the obtained historical driving habit data are improved.
In some embodiments of the present invention, the performing the statistical analysis on each of the first historical driving operation data in the step S202 includes:
acquiring a preset statistical analysis model, wherein the statistical analysis model can be a cluster analysis model, and taking similar driving operations under unified first preset working conditions as the same driving operation, wherein whether the driving operations are similar or not can be defined by an insurance company; and then carrying out statistical analysis on each first historical driving operation data by adopting a preset statistical analysis model so as to obtain historical driving habit data of the target driver in each set time period.
In this embodiment, the historical driving habit data of the target driver in each set period is obtained through the preset statistical analysis model, so that the work efficiency of obtaining each historical driving habit data can be improved.
In some embodiments of the present invention, the method for obtaining the historical driving skill level of the target driver in each set period according to the historical driving behavior data of the target driver in each set period in the step S102 is shown in fig. 3, and includes the following steps:
step S301: acquiring second historical driving operation data of the target driver under a plurality of second preset working conditions according to the historical driving behavior data of the target driver in the set time period;
step S302: and evaluating the driving technology of the target driver according to the second historical driving operation data to obtain the historical driving technology grade of the target driver in each set time period.
In the step S301, the second preset operating conditions may include a highway operating condition, a traffic jam operating condition, a snowy and rainy weather operating condition, a night driving operating condition, and a mountain road driving operating condition, and the second historical driving operation data includes a driving speed, an acceleration frequency, a deceleration frequency, a street lamp on state, and a distance from surrounding obstacles.
In the step S302, a score under each second preset working condition may be obtained according to each second historical driving operation data; and then calculating the driving technical grade of the target driver in each set time period according to the weight of each second preset working condition, wherein the driving technical grade is the historical driving technical grade of the target driver in the corresponding set time period.
In this embodiment, the second preset working condition is an extreme environment working condition, and under this working condition, the driving operation data of the target driver can accurately reflect the driving technique of the target driver. Therefore, the setting manner of the present embodiment can accurately acquire the historical driving skill level of the target driver in each set period.
In some embodiments of the present invention, the step S302 includes:
and acquiring a preset technical grade evaluation model, and evaluating each second historical driving operation data by adopting the preset technical grade evaluation model to obtain the historical driving technical grade of the target driver in each set time period.
In this embodiment, a preset technical grade evaluation model may be established according to the driving operation scoring rule of the target driver under each second preset condition and the weight of each second preset condition, for example, assuming that the number of second preset conditions in one set period is M, where the weight of the jth second preset condition is β j A driving operation score of f j The historical driving skill level H for the set period of time is
And then, inputting the second historical driving operation data into the preset technical grade evaluation model to evaluate the driving level of the target driver in each set time period, and accurately and rapidly acquiring the historical driving technical grade of the target driver in each set time period.
In some embodiments of the present invention, as shown in fig. 4, after determining whether each historical driving technique satisfies the preset technique stability condition in the step S103, the method further includes:
if each of the history driving techniques satisfies the preset technique stability condition, step S107 is performed
Step S107: acquiring the actual driving technical grade of the target driver according to each historical driving technical grade;
step S108: judging whether the actual driving technical grade of the target driver is smaller than or equal to a preset technical grade;
if yes, go to step S109;
step S109: according to the historical driving habit data of the target driver in each set time period, predicting the expected driving habit data of the target driver in the target time period;
step S110: and obtaining the insurance pricing strategy of the target driver in the target time period according to the vehicle attribute information of the target vehicle, the actual driving technical grade and the expected driving habit data of the target driver in the target time period by adopting the UBI insurance model.
In the above step S107, the average value of the historic driving skill levels may be used as the actual driving skill level of the target driver.
Since a large change does not occur in the driving skill level of the target driver in the target period in the case where each of the history driving techniques satisfies the preset skill stabilizing condition, if the actual driving skill level of the target driver is less than or equal to the preset skill level, the driving habits of the target driver may change. Therefore, the actual driving technology grade of the target driver is obtained according to each historical driving technology grade, the expected driving habit data of the target driver in the target time period is predicted under the condition that the actual driving technology grade of the target driver is smaller than or equal to the preset technology grade, and then the UBI insurance model is adopted, and the insurance pricing of the target driver in the target time period is calculated according to the actual driving technology grade and the expected driving habit data, so that the rationality of insurance pricing is improved.
In some embodiments of the present invention, as shown in fig. 5, after determining in the step S108 that the actual driving skill level of the target driver is less than or equal to the preset skill level, the method further includes:
if the actual driving skill level of the target driver is greater than the preset skill level, step S111 is executed:
step S111: judging whether the driving habit of the target driver is stable or not according to the historical driving habit data of each set time period;
if so, executing step S112;
step S112: statistical analysis is performed on the historical driving habit data of each set period of time to obtain expected driving habit data of the target driver in the target period of time, and then step S110 is performed.
In the step S111, the driving habit type of each set period may be obtained according to the historical driving habit data of each set period, and then the variance between the driving habit types may be calculated to determine whether the driving habit of the target driver is stable.
In the above step S112, the driving habit types of each set period may be averaged, and then the average value is used as the expected driving habit type of the set period in the target period, and the driving habit data corresponding to the expected driving habit type is used as the preset driving habit data of the target driver in the target period.
Under the condition that the actual driving technical grade of the target driver is larger than the preset technical grade, if the driving habit of the target driver is stable, the historical driving habit data of each set time period is only required to be used as the expected driving habit data of the target time period, so that the speed of acquiring the expected driving habit data of the target driver is improved.
In some embodiments of the present invention, as shown in fig. 6, after determining whether the driving habit of the target driver is stable according to the historical driving habit data of each set period in the step S111, the method further includes:
if the driving habit of the target driver is unstable, executing step S113;
step S113: the expected driving habit data of the target driver in the target period is predicted according to each of the historical driving habit data, and then step S110 is performed.
In this embodiment, curve fitting may be performed according to the driving habit types of the target driver in each set period, so as to obtain a driving habit variation curve of the target driver, and according to the variation curve, expected driving habit data of the target driver in the target period is predicted.
The technical scheme provided by the embodiment can improve the reliability of acquiring the expected driving habit of the target driver in the target time period.
An embodiment of the invention also provides a machine-readable storage medium and a computer device. FIG. 7 is a schematic diagram of a machine-readable storage medium 830 in accordance with an embodiment of the invention; fig. 8 is a schematic diagram of a computer device 900 according to one embodiment of the invention. The machine-readable storage medium 830 has stored thereon a machine-executable program 840, which when executed by a processor, implements the new energy vehicle insurance method based on historical driving data of any of the above embodiments.
The computer device 900 may include a memory 920, a processor 910, and a machine executable program 840 stored on the memory 920 and running on the processor 910, and the processor 910 implements the new energy vehicle insurance method based on historical driving data of any of the above embodiments when executing the machine executable program 840.
It should be noted that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any machine-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
For the purposes of this description of the embodiment, a machine-readable storage medium 830 can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the machine-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
The computer device 900 may be, for example, a server, a desktop computer, a notebook computer, a tablet computer, or a smartphone. In some examples, computer device 900 may be a cloud computing node. Computer device 900 may be described in the general context of computer-system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer device 900 may be implemented in a distributed cloud computing environment where remote processing devices coupled via a communications network perform tasks. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Computer device 900 may include a processor 910 adapted to execute stored instructions, a memory 920 providing temporary storage for the operation of the instructions during operation. Processor 910 may be a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. Memory 920 may include Random Access Memory (RAM), read only memory, flash memory, or any other suitable storage system.
Processor 910 may be connected by a system interconnect (e.g., PCI-Express, etc.) to an I/O interface (input/output interface) adapted to connect computer device 900 to one or more I/O devices (input/output devices). The I/O devices may include, for example, a keyboard and a pointing device, which may include a touch pad or touch screen, among others. The I/O device may be a built-in component of the computer device 900 or may be a device externally connected to the computing device.
The processor 910 may also be linked by a system interconnect to a display interface suitable for connecting the computer device 900 to a display device. The display device may include a display screen as a built-in component of the computer device 900. The display device may also include a computer monitor, television, projector, or the like, that is externally connected to the computer device 900. Further, a network interface controller (network interface controller, NIC) may be adapted to connect the computer device 900 to a network through a system interconnect. In some embodiments, the NIC may use any suitable interface or protocol (such as an internet small computer system interface, etc.) to transfer data. The network may be a cellular network, a radio network, a Wide Area Network (WAN), a Local Area Network (LAN), or the internet, among others. The remote device may be connected to the computing device through a network.
By now it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been shown and described herein in detail, many other variations or modifications of the invention consistent with the principles of the invention may be directly ascertained or inferred from the present disclosure without departing from the spirit and scope of the invention. Accordingly, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.
Claims (10)
1. A new energy vehicle insurance method based on historical driving data, comprising:
acquiring historical driving behavior data of a target driver in a plurality of continuous set time periods before the target time period;
according to the historical driving behavior data, historical driving habit data and historical driving technical grade of the target driver in the set time period are obtained;
under the condition that each historical driving technology does not meet the preset technical stability condition, according to each historical driving technology grade and each historical driving habit data, predicting expected driving habit data and expected driving technology grade of the target driver in the target time period;
acquiring vehicle attribute information of a target vehicle, wherein the vehicle attribute information at least comprises a power battery parameter, a driving motor parameter and a vehicle body size of the target vehicle;
and obtaining the insurance pricing strategy of the target vehicle in the target time period according to the vehicle attribute information, the expected driving technical grade and the expected driving habit data by adopting a UBI insurance model.
2. The method for protecting a new energy vehicle based on historical driving data according to claim 1, wherein said obtaining historical driving habit data and driving skill level of said target driver in each of said set time periods based on each of said historical driving behavior data comprises:
acquiring first historical driving operation data of the target driver under a plurality of first preset working conditions according to each historical driving behavior data;
and carrying out statistical analysis on each first historical driving operation data to obtain the historical driving habit data.
3. The method of claim 2, wherein said statistically analyzing each of said first historical driving operation data to obtain said historical driving habit data comprises:
and acquiring a preset statistical analysis model, and carrying out statistical analysis on the first driving operation data by adopting the preset statistical analysis model so as to obtain the historical driving habit data.
4. The method for protecting a new energy vehicle based on historical driving data according to claim 1, wherein said obtaining historical driving habit data and driving skill level of said target driver in each of said set time periods based on each of said historical driving behavior data comprises:
acquiring second driving operation data of the target driver under a plurality of second preset working conditions according to each historical driving behavior data;
and evaluating the driving technology of the target driver according to the second driving operation data to obtain each historical driving technology grade.
5. The method for protecting a new energy vehicle based on historical driving data as recited in claim 4, wherein said evaluating the driving technique of said target driver based on said second driving operation data to obtain said driving technique class comprises:
and acquiring a preset technical grade evaluation model, and evaluating each second driving operation data by adopting the preset technical grade evaluation model to obtain each driving technical grade.
6. The method for protecting a new energy vehicle based on historical driving data according to claim 1, further comprising, after said obtaining historical driving habit data and historical driving skill level of said target driver for each of said set time periods:
acquiring the actual driving technology level of the target driver according to each historical driving technology level under the condition that each historical driving technology level meets the preset technology stability condition;
judging whether the actual driving technical grade is smaller than or equal to a preset technical grade;
if yes, predicting expected driving habit data of the target driver in the target time period according to each historical driving habit data;
and obtaining the insurance pricing strategy of the target vehicle in the target time period according to the vehicle attribute information, the actual driving technical grade and the expected driving habit data by adopting a UBI insurance model.
7. The method for protecting a new energy vehicle based on historical driving data as recited in claim 6, wherein,
after the actual driving technical grade is less than or equal to the preset technical grade, the method further comprises:
judging whether the driving habit of the target driver is stable or not according to each historical driving habit data under the condition that the actual driving skill level is larger than the preset skill level;
if so, carrying out statistical analysis on each historical driving habit data to obtain expected driving habit data of the target driver in the target time period
8. The method for protecting a new energy vehicle based on historical driving data as recited in claim 7, wherein,
after the step of judging whether the driving habit of the target driver is stable according to each historical driving habit data, the method further comprises the following steps:
and if the driving habit data is unstable, predicting expected driving habit data of the target driver in the target time period according to each historical driving habit data.
9. A machine-readable storage medium, having stored thereon a machine-executable program which, when executed by a processor, implements the new energy vehicle insurance method based on historical driving data according to any one of claims 1 to 8.
10. A computer device comprising a memory, a processor and a machine executable program stored on the memory and running on the processor, wherein the processor, when executing the machine executable program, implements the method of insurance of a new energy vehicle based on historical driving data according to any one of claims 1-8.
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