CN118115290A - Insurance method, medium and equipment based on new energy vehicle configuration parameters - Google Patents
Insurance method, medium and equipment based on new energy vehicle configuration parameters Download PDFInfo
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Abstract
The invention provides an insurance method, medium and equipment based on new energy vehicle configuration parameters, 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, and predicting expected driving behavior data of the target driver in the target time period according to the historical driving behavior data; acquiring configuration parameters of a target vehicle and the existing damage degree of each key device on the target vehicle; calculating the expected damage degree of each key device in the target time period according to the expected driving behavior data and the current damage degree; and calculating the insurance pricing strategy of the target vehicle in the target time period according to the configuration parameters, the expected driving behavior data and the expected damage degree by adopting a UBI insurance optimization model. The technical scheme of the invention can solve the problem that the insurance pricing strategy obtained in the prior art has deviation from the actual condition of the vehicle, and achieves the purpose of improving user experience.
Description
Technical Field
The invention relates to the technical field of vehicle insurance, in particular to an insurance method, medium and equipment based on new energy vehicle configuration parameters.
Background
The UBI (Usage-Based Insurance) model is an Insurance model for determining a premium Based On Usage, and can integrate driving habit, driving technology, vehicle information, surrounding environment and other data of a driver through networking equipment such as a car networking, a smart phone, an On-Board Diagnostic (OBD) and the like, and build a multi-dimensional model of people, vehicles and environment for pricing. The core concept of the UBI insurance optimization model is to give premium benefits to drivers with safe driving behaviors, and the popularization of the 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 optimization model is used for making insurance pricing strategies according to the previous driving technical information and driving habit information of a driver. For the new energy vehicle, the driving habit of the driver not only affects the driving safety, but also causes certain damage to key equipment such as a driving motor of the vehicle, but the UBI insurance optimization model in the prior art only considers the influence of the driving habit of the driver on the driving safety, and does not consider the damage to the key equipment of the new energy vehicle, so that the deviation between the insurance pricing strategy obtained by the UBI insurance model and the actual situation of the driver is caused, and the problem of poor user experience is caused.
Disclosure of Invention
The invention provides an insurance method, medium and equipment based on new energy vehicle configuration parameters, 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 condition of a vehicle, and achieving the purpose of improving user experience.
Specifically, in order to solve at least the above technical problems, in a first aspect, the present invention provides an insurance method based on new energy vehicle configuration parameters, including:
Acquiring historical driving behavior data of a target driver in a plurality of continuous set time periods before the target time period, and predicting expected driving behavior data of the target driver in the target time period according to the historical driving behavior data;
acquiring configuration parameters of a target vehicle and the existing damage degree of each key device on the target vehicle;
calculating the expected damage degree of each key device in the target time period according to the expected driving behavior data and the current damage degree;
and calculating the insurance pricing strategy of the target vehicle in the target time period according to the configuration parameters, the expected driving behavior data and the expected damage degree by adopting a UBI insurance optimization model.
Further, the calculating the expected damage degree of each key device in the target time period according to the expected driving data and the current damage degree includes:
Obtaining a damage degree calculation model of each key device;
And inputting the expected driving data and the existing damage degree of each key device into the corresponding damage degree calculation model to obtain each expected damage degree.
Further, the obtaining the damage degree calculation model of each key device includes:
acquiring an initial calculation model of the damage degree of each key device based on the neural network;
and acquiring training data sets corresponding to the key devices, and training the corresponding initial calculation model of the damage degree by adopting the training data sets to obtain the calculation model of the damage degree.
Further, the predicting the expected driving behavior data of the target driver in the target period according to the historical driving behavior data includes:
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;
and under the condition that each historical driving technology does not meet the preset technical stability condition, predicting according to each historical driving technology grade and each historical driving habit data to obtain the expected driving behavior data.
Further, the predicting according to each of the historical driving skill levels and each of the historical driving habit data to obtain the expected driving behavior data includes:
Acquiring a reference user of the target driver according to each historical driving technology grade and each historical driving habit data;
And acquiring expected driving behavior data of the target driver according to the driving behavior data of the reference user.
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:
taking an average value according to each historical driving technology grade as the expected driving technology grade under the condition that each historical driving technology grade meets the preset technology stability condition;
If the expected driving skill level is smaller than or equal to a preset skill level, predicting expected driving habit data of the target driver in the target time period according to each historical driving habit data;
Further, after the average value according to each of the historical driving skill levels is taken as the expected driving 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 expected driving skill level is larger than the preset skill level;
And 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 insurance methods based on new energy vehicle configuration parameters.
In a third aspect, the present invention further provides a computer device, including a memory, a processor, and a machine executable program stored in the memory and running on the processor, where the processor implements any one of the above insurance methods based on the new energy vehicle configuration parameters when executing the machine executable program.
According to the technical scheme provided by the invention, the expected damage degree of each key device on the target vehicle in the target time period can be calculated according to the expected driving behavior data of the target driver in the target time period and the current damage degree of the key device on the target vehicle; and then, calculating the insurance pricing strategy of the target vehicle in the target time period according to the expected driving behavior data of the target driver in the target time period and the expected damage degree of each key device on the target vehicle by adopting a UBI insurance optimization model. In the technical scheme of the invention, in the process of calculating the insurance pricing strategy of the target vehicle in the target time period by adopting the UBI insurance optimization model, the expected damage degree of each key device on the target vehicle is adopted for correction, so that the deviation between the obtained insurance pricing strategy and the actual condition of the vehicle can be reduced, and the aim of improving the user experience is achieved.
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 an insurance method based on new energy vehicle configuration parameters in accordance with one embodiment of the invention;
FIG. 2 is a schematic flow chart diagram of a method of calculating an expected damage to critical devices on a target vehicle over a target time period in accordance with one embodiment of the invention;
FIG. 3 is a schematic flow chart diagram of a method of obtaining a damage calculation model for each critical device on a target vehicle in accordance with one embodiment of the invention;
FIG. 4 is a schematic flow chart of a method of predicting expected driving behavior data of a target driver over a target period of time in accordance with one embodiment of the invention;
FIG. 5 is a schematic diagram of a machine-readable storage medium according to one embodiment of the invention;
FIG. 6 is a schematic diagram of a computer device according to one embodiment of the invention.
Detailed Description
An insurance method, medium and apparatus based on configuration parameters of a new energy vehicle according to an embodiment of the present invention will be described with reference to fig. 1 to 6. 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.
Referring to fig. 1, fig. 1 is a schematic flow chart of a new energy vehicle configuration parameter-based insurance method according to an embodiment of the present invention, 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: predicting expected driving behavior data of the target driver in a target time period according to the historical driving behavior data of the target driver;
Step S103: acquiring configuration parameters of a target vehicle and the existing damage degree of each key device on the target vehicle;
Step S104: according to the expected driving behavior data of the target driver and the current damage degree of each key device of the target vehicle, calculating the expected damage degree of each key device on the target vehicle in a target time period;
step S105: and calculating the insurance pricing strategy of the target vehicle in the target time period according to the configuration parameters, the expected driving behavior data and the expected damage degree of each key device of the target vehicle by adopting a UBI insurance optimization 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, a change rule of the driving behavior of the target driver may be analyzed according to the historical driving behavior data of the target driver in each set period, and then the expected driving behavior data of the target driver in the target period may be predicted according to the change rule.
In this embodiment, the historical driving habit data of the target driver in each set period of time may be obtained by performing statistical analysis on the historical driving behavior data of the target driver in each set period of time; 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. And then, according to the historical driving habit data and the historical driving skill grade of the target driver in each set time period, predicting the expected driving habit data and the expected driving skill grade, namely the expected driving behavior data, of the target driver in the target time period.
In the above step S103, the vehicle attribute information of the target vehicle, such as the brand and model of the target vehicle, may be acquired first, and then the configuration parameters of each key device on the target vehicle may be read from a vehicle database, where the vehicle database is a database storing the configuration parameters of key devices of a plurality of vehicles.
In this embodiment, the existing damage degree of the target vehicle may be determined by detecting the target vehicle, or may be determined according to a usage record of the target vehicle, such as a driving range and a service life.
In the above step S104, since the driving behavior of the target driver may damage the key devices of the target vehicle, the expected damage degree of the target vehicle in the target period may be predicted according to the expected driving behavior of the target driver in the target period.
In the above step S105, the UBI insurance optimization model is:
Y＝α1X1Z+α2X2+α3X3+μ
Wherein Y represents insurance pricing of the target vehicle in the target time period, X 1 represents expected driving habit indexes of the target driver in the target time period, and alpha 1 is driving habit coefficient; x 2 represents the expected driving skill level of the target driver in the target period, and α 2 is the driving skill coefficient; x 3 represents a vehicle index of the target vehicle, and α 3 represents a vehicle coefficient; u represents a constant term, Z is the loss factor of the target vehicle, and
Where M is the number of critical devices on the target vehicle, β j is the weight of the jth critical device on the target vehicle, and H j is the expected damage of the jth critical device on the target vehicle in the target time period.
In this embodiment, a vehicle information table may be acquired first, where vehicle indexes corresponding to various kinds of 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. The predicted expected driving behavior data of the target driver in the target time period, including expected driving habit data and expected driving technical grade of the target user in the target time period, can query a driving habit data table according to the expected driving habit data so as to obtain an expected driving habit index of the target user in the target time period. 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 optimization model to obtain insurance pricing of the target vehicle in the target time period.
In summary, according to the technical solution of the present embodiment, the expected damage degree of each key device on the target vehicle in the target time period may be calculated according to the expected driving behavior data of the target driver in the target time period and the current damage degree of the key device on the target vehicle; and then, calculating the insurance pricing strategy of the target vehicle in the target time period according to the expected driving behavior data of the target driver in the target time period and the expected damage degree of each key device on the target vehicle by adopting a UBI insurance optimization model. In the technical scheme of the embodiment, in the process of calculating the insurance pricing strategy of the target vehicle in the target time period by adopting the UBI insurance optimization model, the expected damage degree of each key device on the target vehicle is adopted for correction, so that the deviation between the obtained insurance pricing strategy and the actual condition of the vehicle can be reduced, and the aim of improving the user experience is achieved.
In some embodiments of the present invention, the method for calculating the expected damage degree of each key device on the target vehicle in the target time period according to the expected driving behavior data of the target driver and the current damage degree of each key device on the target vehicle in the step S104 is shown in fig. 2, and includes the following steps:
step S201: obtaining a damage degree calculation model of key equipment of a target vehicle;
Step S202: and inputting expected driving behavior data of the target driver in the target time period and the existing damage degree of each key device into a corresponding damage degree calculation model to obtain the expected damage degree of each key device in the target time period.
By the arrangement mode of the embodiment, the damage degree calculation model of each key device of the target vehicle can be adopted, and the expected damage degree of each key device in the target time period can be accurately and rapidly obtained.
In some embodiments of the present invention, the method for obtaining the damage degree calculation model of each key device on the target vehicle in step S201 is shown in fig. 3, and includes the following steps:
step S301: obtaining an initial calculation model of the damage degree of each key device on a target vehicle;
step S302: acquiring a training data set corresponding to each key device on a target vehicle;
step S303: and training the corresponding initial calculation model of the damage degree by adopting each training data set so as to obtain the calculation model of the damage degree of each key device.
In the step S301, each of the initial calculation models of the degree of fracture is a neural network model, for example, BP (backpropagation) neural network models.
In the step S302, a calibration test may be performed on the target vehicle in a standard environment to detect the damage degree of each key device on the target vehicle according to each driving habit data, so as to establish a training data set corresponding to each key device on the target vehicle.
In the step S303, driving habit data in the training data set may be used as input, the damage degree of the corresponding key device in the training data set may be used as output, the initial calculation models of the damage degree may be trained according to the mean square error loss function, and when the accuracy of the initial calculation model of the damage degree is greater than the set accuracy, the initial calculation model of the damage degree may be used as the corresponding calculation model of the damage degree.
In some embodiments of the present invention, the method for predicting the expected driving behavior data of the target driver in the target period according to the historical driving behavior data of the target driver in the step S102 is shown in fig. 4, and includes the following steps:
Step S401: 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 S402: judging whether each historical driving technology grade meets a preset technology stability condition;
If not, executing step S403;
Step S403: and predicting expected driving behavior data of the target driver in the target time period according to each historical driving technology grade and each historical driving habit data, wherein the expected driving behavior data at least comprises the expected driving habit data and the expected driving technology grade.
In the step S401, the historical driving habit data of the target driver in each set period may be obtained by performing a 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 this embodiment, the method for obtaining the historical driving habit data of the target driver in each set time period according to the historical driving behavior data of the target driver in each set time period includes:
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;
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.
The first preset working conditions can comprise night, left turn, right turn, red light, green light, intersection and 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. And then carrying out statistical analysis on each first historical driving operation data according to each first preset working condition to obtain the driving habit of the target driver under each first preset working condition, namely the historical driving habit data of the target driver in each set time period.
In this embodiment, 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 includes:
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;
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 this embodiment, the second preset operating condition 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. Then, scoring under each second preset working condition can 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 the step S402, 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.
By the setting manner of the embodiment, the expected driving behavior data of the target driver in the target time period can be predicted under the condition that the driving technology of the target driver is stable, that is, under the condition that each historical driving technology level meets the preset technology stability condition. Under the condition that the driving technology of the target driver is stable, the driving behavior of the target driver is stable, so that the expected driving behavior data of the target driver in the target time period can be accurately predicted.
In some embodiments of the present invention, the predicting the expected driving behavior data of the target driver in the target period according to the historical driving behavior data of the target driver in the step S102 includes:
Firstly, acquiring a reference user of a target driver according to historical driving technology grade and historical driving habit data of the target driver in each set time period; and then according to the driving behavior data of the reference user, acquiring the expected driving behavior data of the target driver in the target time period.
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, the historical driving skill grade is Q i, the driving habit index of one user in the database in the ith set time period is P 'i, the driving skill grade is Q' i, and the similarity gamma between the target driver and the user is
Wherein a 1 is driving habit weight, and a 2 is driving skill weight.
According to the setting mode of the embodiment, expected driving habit data and expected driving technical grade of the target driver in a target time period can be rapidly obtained according to driving habit data of a reference user of the target driver, so that the working efficiency and the speed for predicting the expected driving habit data and the expected driving technical grade of the target driver are improved.
In some embodiments of the present invention, as shown in fig. 4, after determining in step S401 whether each historical driving technology level meets the preset technology stability condition, the method further includes:
If each historical driving technology grade meets the preset technology stability condition, executing step S404;
Step S404: taking the average value according to each historical driving technology grade as the expected driving technology grade of a target driver in a target time period;
step S405: 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 S406;
Step S406: and predicting expected driving habit data of the target driver in the target time period according to the historical driving habit data of the target driver in each set time period.
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, expected driving habit data of the target driver in a target time period is predicted under the condition that the actual driving technology grade of the target driver is smaller than or equal to a preset technology grade, and then the UBI vehicle insurance pricing model is adopted, and 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 rationality of insurance pricing of the target vehicle is improved.
In some embodiments of the present invention, as shown in fig. 4, after determining whether the expected driving skill level is less than or equal to the preset skill level in step S405, the method further includes:
If the expected driving skill level of the target driver in the target time period is greater than the preset skill level, executing step S407;
Step S407: judging whether the driving habit of the target driver is stable or not according to the historical driving habit data of the target driver in each set time period;
If so, executing step S408;
Step S408: and carrying out statistical analysis on the historical driving habit data of the target driver in each set time period to obtain the expected driving habit data of the target driver in the target time period.
In the step S407, the driving habit type of the target driver in 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 is calculated to determine whether the driving habit of the target driver is stable.
In the above step S408, 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, after determining whether the driving habit of the target driver is stable according to the historical driving habit data of each set period of time of the target driver in the step S407, the method further includes:
if the driving habit of the target driver is unstable, step S409 is executed;
Step S409: and predicting expected driving habit data of the target driver in the target time period according to the historical driving habit data.
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 behavior data 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. 5 is a schematic diagram of a machine-readable storage medium 830 according to one embodiment of the invention; fig. 6 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 configuration parameter-based insurance method of any of the above embodiments.
Computer device 900 may include a memory 920, a processor 910, and a machine executable program 840 stored on memory 920 and running on processor 910, and processor 910 implements the new energy vehicle configuration parameter-based insurance method of any of the above embodiments when executing 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. In addition, 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. An insurance method based on new energy vehicle configuration parameters, comprising:
Acquiring historical driving behavior data of a target driver in a plurality of continuous set time periods before the target time period, and predicting expected driving behavior data of the target driver in the target time period according to the historical driving behavior data;
acquiring configuration parameters of a target vehicle and the existing damage degree of each key device on the target vehicle;
calculating the expected damage degree of each key device in the target time period according to the expected driving behavior data and the current damage degree;
and calculating the insurance pricing strategy of the target vehicle in the target time period according to the configuration parameters, the expected driving behavior data and the expected damage degree by adopting a UBI insurance optimization model.
2. The method for protecting a new energy vehicle according to claim 1, wherein calculating the expected damage degree of each key device in the target period according to the expected driving data and the current damage degree comprises:
Obtaining a damage degree calculation model of each key device;
And inputting the expected driving data and the existing damage degree of each key device into the corresponding damage degree calculation model to obtain each expected damage degree.
3. The method for protecting a new energy vehicle according to claim 2, wherein the obtaining the calculation model of the damage degree of each key device comprises:
acquiring an initial calculation model of the damage degree of each key device based on the neural network;
and acquiring training data sets corresponding to the key devices, and training the corresponding initial calculation model of the damage degree by adopting the training data sets to obtain the calculation model of the damage degree.
4. The method of claim 1, wherein predicting the expected driving behavior data of the target driver for the target period based on the historical driving behavior data comprises:
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;
and under the condition that each historical driving technology does not meet the preset technical stability condition, predicting according to each historical driving technology grade and each historical driving habit data to obtain the expected driving behavior data.
5. The method of claim 4, wherein predicting based on each of the historical driving skill levels and each of the historical driving habit data to obtain the expected driving behavior data comprises:
Acquiring a reference user of the target driver according to each historical driving technology grade and each historical driving habit data;
And acquiring expected driving behavior data of the target driver according to the driving behavior data of the reference user.
6. The method according to claim 4, further comprising, after said obtaining the historical driving habit data and the historical driving skill level of the target driver for each of the set time periods:
taking an average value according to each historical driving technology grade as the expected driving technology grade under the condition that each historical driving technology grade meets the preset technology stability condition;
and if the expected driving skill level is smaller than or equal to a preset skill level, predicting expected driving habit data of the target driver in the target time period according to each historical driving habit data.
7. The method according to claim 6, further comprising, after said taking as said expected driving skill level an average value according to each of said historical driving skill levels:
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 expected driving skill level is larger than the preset skill level;
And 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 according to claim 7, further comprising, after said determining whether the driving habit of the target driver is stable based on each of the historical driving habit data:
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 configuration parameter-based insurance method 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 new energy vehicle configuration parameter based insurance method according to any one of claims 1-8.
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