An insurance method, medium and device based on new energy vehicle configuration parameters

Abstract

The application provides an insurance method based on new energy vehicle configuration parameters, a medium and equipment, wherein the method comprises the following steps: obtaining historical driving behavior data of a target driver in a plurality of continuous set time periods before a 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; obtaining configuration parameters of a target vehicle, and existing depreciation degrees of each key equipment on the target vehicle; calculating expected depreciation degrees of each key equipment in the target time period according to the expected driving behavior data and the current depreciation degrees; and using a UBI insurance optimization model to calculate an 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 depreciation degrees. The technical scheme of the application can solve the problem that the insurance pricing strategy obtained by the prior art deviates from the actual situation of the vehicle, and achieve the purpose of improving user experience.

Description

Technical Field

[0001] This invention relates to the field of vehicle insurance technology, and in particular to an insurance method, medium, and device based on configuration parameters of new energy vehicles. Background Technology

[0002] UBI (Usage-Based Insurance) is an insurance model that determines premiums based on usage. It leverages connected devices such as vehicle networks, smartphones, and OBD (On-Board Diagnostics) systems to integrate data on driver habits, driving skills, vehicle information, and the surrounding environment to create a multi-dimensional model for pricing, encompassing the driver, vehicle, and environment. The core concept of the UBI insurance optimization model is to offer premium discounts to drivers with safe driving behavior. Its promotion not only strengthens insurance companies' pricing capabilities but also generates positive personal and social effects, guiding drivers towards good driving habits.

[0003] Existing UBI insurance optimization models formulate insurance pricing strategies based on drivers' past driving skills and habits. For new energy vehicles, drivers' habits not only affect driving safety but can also cause damage to critical equipment such as the vehicle's drive motor. However, current UBI insurance optimization models only consider the impact of driving habits on driving safety, neglecting the damage caused to critical equipment in new energy vehicles. This leads to a discrepancy between the insurance pricing strategy derived from the UBI model and the driver's actual situation, resulting in a poor user experience. Summary of the Invention

[0004] This invention provides an insurance method, medium, and device based on the configuration parameters of new energy vehicles, which is used to solve the problem that the insurance pricing strategy obtained by using the UBI insurance model in the prior art deviates from the actual situation of the vehicle, thereby improving the user experience.

[0005] Specifically, in order to at least solve the above-mentioned technical problems, in a first aspect, the present invention provides an insurance method based on configuration parameters of new energy vehicles, comprising:

[0006] Acquire historical driving behavior data of a target driver within multiple consecutive set time periods prior to a target time period, and predict the expected driving behavior data of the target driver during the target time period based on the historical driving behavior data;

[0007] Obtain the configuration parameters of the target vehicle, as well as the current depreciation rate of each key device on the target vehicle;

[0008] Based on the expected driving behavior data and the current depreciation rate, calculate the expected depreciation rate of each of the key devices in the target time period;

[0009] Using a UBI insurance optimization model, the insurance pricing strategy for the target vehicle during the target time period is calculated based on the configuration parameters, expected driving behavior data, and expected depreciation rate.

[0010] Further, the step of calculating the expected depreciation rate of each of the key devices in the target time period based on the expected driving data and the current depreciation rate includes:

[0011] Obtain the depreciation calculation model for each of the key equipment;

[0012] The expected driving data and the existing depreciation of each of the key devices are input into the corresponding depreciation calculation model to obtain the expected depreciation.

[0013] Furthermore, the method for obtaining the depreciation calculation model for each of the key equipment includes:

[0014] Obtain an initial calculation model for the depreciation rate of each key device based on a neural network;

[0015] Obtain the training dataset corresponding to each of the key devices, and use each of the training datasets to train the corresponding initial depreciation calculation model to obtain each of the depreciation calculation models.

[0016] Furthermore, the step of predicting the target driver's expected driving behavior data within a target time period based on the historical driving behavior data includes:

[0017] Based on the aforementioned historical driving behavior data, the target driver's historical driving habits and historical driving skill level for each of the aforementioned set time periods are obtained;

[0018] If the historical driving techniques do not meet the preset technical stability conditions, predictions are made based on the historical driving technique levels and historical driving habit data to obtain the expected driving behavior data.

[0019] Furthermore, the step of predicting the expected driving behavior data based on the historical driving skill level and historical driving habit data includes:

[0020] Based on the historical driving skill level and historical driving habit data, obtain the reference user of the target driver;

[0021] Based on the driving behavior data of the reference user, the expected driving behavior data of the target driver is obtained.

[0022] Furthermore, after obtaining the target driver's historical driving habit data and historical driving skill level for each of the set time periods, the method further includes:

[0023] If each of the historical driving skill levels meets the preset technical stability conditions, the average value of each of the historical driving skill levels will be used as the expected driving skill level.

[0024] If the expected driving skill level is less than or equal to the preset skill level, then the expected driving habit data of the target driver in the target time period is predicted based on the historical driving habit data.

[0025] Furthermore, after stating that the expected driving skill level is based on the average of each of the historical driving skill levels, the method further includes:

[0026] If the expected driving skill level is greater than the preset skill level, the driving habits of the target driver are determined to be stable based on the historical driving habit data.

[0027] If stable, statistical analysis is performed on the historical driving habit data to obtain the expected driving habit data of the target driver in the target time period.

[0028] Furthermore, after determining whether the target driver's driving habits are stable based on the historical driving habit data, the method further includes:

[0029] If unstable, the expected driving habits of the target driver in the target time period are predicted based on the historical driving habit data.

[0030] Secondly, the present invention also provides a machine-readable storage medium having a machine-executable program stored thereon, wherein when the machine-executable program is executed by a processor, it implements any of the above-mentioned insurance methods based on new energy vehicle configuration parameters.

[0031] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a machine-executable program stored in the memory and running on the processor, wherein when the processor executes the machine-executable program, it implements any of the above-mentioned insurance methods based on new energy vehicle configuration parameters.

[0032] The technical solution provided by this invention can calculate the expected depreciation of each key component of the target vehicle during the target time period based on the expected driving behavior data of the target driver during the target time period and the current depreciation of key components on the target vehicle. Then, using a UBI insurance optimization model, it calculates the insurance pricing strategy for the target vehicle during the target time period based on the expected driving behavior data of the target driver during the target time period and the expected depreciation of each key component on the target vehicle. Because the expected depreciation of each key component on the target vehicle is used for correction during the calculation of the insurance pricing strategy using the UBI insurance optimization model in this invention, the deviation between the obtained insurance pricing strategy and the actual vehicle situation can be reduced, thereby improving the user experience.

[0033] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0034] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0035] Figure 1 This is a schematic flowchart of an insurance method based on configuration parameters of new energy vehicles according to an embodiment of the present invention;

[0036] Figure 2 This is a schematic flowchart of a method for calculating the expected depreciation rate of each key device on a target vehicle during a target time period according to an embodiment of the present invention;

[0037] Figure 3 This is a schematic flowchart of a method for obtaining a depreciation calculation model of each key device on a target vehicle according to an embodiment of the present invention;

[0038] Figure 4 This is a schematic flowchart of a method for predicting the expected driving behavior data of a target driver during a target time period according to an embodiment of the present invention;

[0039] Figure 5 This is a schematic diagram of a machine-readable storage medium according to an embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0041] The following reference Figures 1 to 6This invention describes an insurance method, medium, and device based on configuration parameters of new energy vehicles according to embodiments of the present invention. In this description, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature, that is, include one or more of that feature. In the description of the present invention, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. When a feature "includes or contains" one or more of the features it encompasses, unless otherwise specifically described, this indicates that other features are not excluded and may be further included.

[0042] In the description of this embodiment, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0043] Please see Figure 1 , Figure 1 The diagram shown is a schematic flowchart of an insurance method based on new energy vehicle configuration parameters according to an embodiment of the present invention. The method includes the following steps:

[0044] Step S101: Obtain historical driving behavior data of the target driver within multiple consecutive set time periods prior to the target time period;

[0045] Step S102: Based on the target driver's historical driving behavior data, predict the target driver's expected driving behavior data for the target time period;

[0046] Step S103: Obtain the configuration parameters of the target vehicle and the current depreciation rate of each key device on the target vehicle;

[0047] Step S104: Based on the target driver's expected driving behavior data and the current wear and tear of each key piece of equipment on the target vehicle, calculate the expected wear and tear of each key piece of equipment on the target vehicle during the target time period.

[0048] Step S105: Using the UBI insurance optimization model, calculate the insurance pricing strategy for the target vehicle during the target time period based on the target vehicle's configuration parameters, expected driving behavior data, and expected depreciation of each key device.

[0049] In step S101 above, an OBD (On-Board Diagnostics) device can be installed on the target vehicle. During multiple consecutive set time periods before the target time period, the OBD device collects the target driver's driving behavior data, i.e., the target driver's historical driving behavior data.

[0050] In this embodiment, the target driver's historical driving behavior data includes, but is not limited to, average driving speed, driving frequency, emergency braking frequency, mileage, charging frequency, rapid acceleration frequency, rapid deceleration frequency, audio device usage frequency, navigation device usage frequency, and overtaking frequency.

[0051] In step S102 above, the driving behavior change pattern of the target driver can be analyzed based on the historical driving behavior data of the target driver in each set time period, and then the expected driving behavior data of the target driver in the target time period can be predicted based on the change pattern.

[0052] In this embodiment, historical driving behavior data of the target driver within each set time period can be statistically analyzed to obtain historical driving habit data for that time period. The historical driving skill level of the target driver within each set time period can then be obtained by evaluating this historical driving behavior data. Based on the target driver's historical driving habit data and historical driving skill level within each set time period, the expected driving habit data and expected driving skill level of the target driver within the target time period, i.e., expected driving behavior data, can then be predicted.

[0053] In step S103 above, the vehicle attribute information of the target vehicle, such as the brand and model of the target vehicle, can be obtained first. Then, the configuration parameters of each key device on the target vehicle can be read from the vehicle database, which is a database that stores the configuration parameters of key devices of various vehicles.

[0054] In this embodiment, the current depreciation level of the target vehicle can be determined by inspecting the target vehicle, or it can be determined based on the target vehicle's usage records, such as mileage and years of use.

[0055] In step S104 above, since the driving behavior of the target driver will cause damage to the key equipment of the target vehicle, the expected damage of the target vehicle in the target time period can be predicted based on the expected driving behavior of the target driver in the target time period.

[0056] In step S105 above, the UBI insurance optimization model is as follows:

[0057] Y = α1X1Z + α2X2 + α3X3 + μ

[0058] Where Y represents the insurance price of the target vehicle during the target time period, X1 represents the expected driving habit index of the target driver during the target time period, and α1 is the driving habit coefficient; X2 represents the expected driving skill level of the target driver during the target time period, and α2 is the driving skill coefficient; X3 represents the vehicle index of the target vehicle, and α3 is the vehicle coefficient; u represents a constant term, and Z is the wear and tear coefficient of the target vehicle.

[0059]

[0060] Where M is the number of critical devices on the target vehicle, β j H represents the weight of the j-th critical device on the target vehicle. j Let be the expected depreciation rate of the j-th critical equipment on the target vehicle during the target time period.

[0061] In this embodiment, a vehicle information table is first obtained, which stores vehicle indicators corresponding to various vehicle attribute information. After obtaining the vehicle attribute information of the target vehicle, the vehicle information table can be queried based on the target vehicle's vehicle attribute information to obtain the target vehicle's vehicle indicators. The predicted expected driving behavior data of the target driver during the target time period includes the target user's expected driving habits data and expected driving skill level during the target time period. The expected driving habit data can be queried based on the expected driving habit data to obtain the target user's expected driving habit indicators during the target time period. The target driver's expected driving habit indicators, expected driving skill level, and target vehicle indicators are then input into the aforementioned UBI insurance optimization model to obtain the insurance pricing for the target vehicle during the target time period.

[0062] In summary, the technical solution of this embodiment can calculate the expected depreciation of each key component of the target vehicle during the target time period based on the expected driving behavior data of the target driver during the target time period and the current depreciation of key components on the target vehicle. Then, using a UBI insurance optimization model, the insurance pricing strategy for the target vehicle during the target time period is calculated based on the expected driving behavior data of the target driver during the target time period and the expected depreciation of each key component on the target vehicle. Because the expected depreciation of each key component on the target vehicle is used for correction during the calculation of the insurance pricing strategy for the target vehicle during the target time period using the UBI insurance optimization model in this embodiment, the deviation between the obtained insurance pricing strategy and the actual vehicle situation can be reduced, thereby improving the user experience.

[0063] In some embodiments of the present invention, the method for calculating the expected wear and tear of each key component of the target vehicle during the target time period based on the expected driving behavior data of the target driver and the current wear and tear of each key component of the target vehicle in step S104 is as follows: Figure 2 As shown, it includes the following steps:

[0064] Step S201: Obtain the depreciation calculation model for the key equipment of the target vehicle;

[0065] Step S202: Input the expected driving behavior data of the target driver in the target time period and the existing depreciation of each key equipment into the corresponding depreciation calculation model to obtain the expected depreciation of each key equipment in the target time period.

[0066] By using the setup method in this embodiment, the depreciation rate calculation model of each key component of the target vehicle can be adopted to accurately and quickly obtain the expected depreciation rate of each key component within the target time period.

[0067] In some embodiments of the present invention, the method for obtaining the depreciation calculation model of each key device on the target vehicle in step S201 above is as follows: Figure 3 As shown, it includes the following steps:

[0068] Step S301: Obtain the initial calculation model for the depreciation degree of each key device on the target vehicle;

[0069] Step S302: Obtain the training dataset corresponding to each key device on the target vehicle;

[0070] Step S303: Train the corresponding initial depreciation calculation model using each training dataset to obtain the depreciation calculation model for each key device.

[0071] In step S301 above, the initial calculation model for each loss degree is a neural network model, such as the BP (backpropagation) neural network model.

[0072] In step S302 above, a calibration test can be performed on the target vehicle under a standard environment to detect the wear and tear of each key device on the target vehicle under various driving habit data, so as to establish a training dataset corresponding to each key device on the target vehicle.

[0073] In step S303 above, driving habit data in the training dataset can be used as input, and the depreciation degree of the corresponding key equipment in the training dataset can be used as output. The initial calculation model of each depreciation degree can be trained according to the mean squared error loss function. When the accuracy of each initial calculation model of depreciation degree is greater than the set accuracy, each initial calculation model of depreciation degree can be used as the corresponding depreciation degree calculation model.

[0074] In some embodiments of the present invention, the method for predicting the expected driving behavior data of the target driver in the target time period based on the target driver's historical driving behavior data in step S102 is as follows: Figure 4 As shown, it includes the following steps:

[0075] Step S401: Based on the target driver's historical driving behavior data in each set time period, obtain the target driver's historical driving habit data and historical driving skill level in each set time period;

[0076] Step S402: Determine whether each historical driving skill level meets the preset technical stability conditions;

[0077] If not, proceed to step S403;

[0078] Step S403: Based on the historical driving skill level and historical driving habit data, predict the expected driving behavior data of the target driver in the target time period. The expected driving behavior data includes at least the expected driving habit data and the expected driving skill level.

[0079] In step S401 above, the historical driving behavior data of the target driver in each set time period can be statistically analyzed to obtain the historical driving habit data of the target driver in each set time period; the historical driving skill level of the target driver in each set time period can be obtained by evaluating the historical driving behavior data of the target driver in each set time period.

[0080] In this embodiment, the method for obtaining the target driver's historical driving habit data for each set time period based on the target driver's historical driving behavior data for each set time period includes:

[0081] Based on the target driver’s historical driving behavior data in each set time period, obtain the target driver’s first historical driving operation data under multiple first preset working conditions;

[0082] Statistical analysis was performed on the above-mentioned historical driving operation data to obtain the target driver's historical driving habit data for each set time period.

[0083] The first preset driving conditions can include nighttime, left turn, right turn, red light, green light, intersection, and one-way street. The first historical driving operation data includes the target driver's operation information on the target vehicle under each first preset driving condition, such as operation information on headlights, turn signals, brakes, accelerator, multimedia, and electronic devices. Then, statistical analysis is performed on each first historical driving operation data based on each first preset driving condition to obtain the target driver's driving habits under each first preset condition, i.e., the target driver's historical driving habit data for each set time period.

[0084] In this embodiment, the method for obtaining the target driver's historical driving skill level in each set time period based on the target driver's historical driving behavior data in each set time period includes:

[0085] Based on the target driver’s historical driving behavior data within a set time period, obtain the target driver’s second historical driving operation data under multiple second preset working conditions;

[0086] The driving skills of the target driver are evaluated based on the second set of historical driving operation data to obtain the historical driving skill level of the target driver in each set time period.

[0087] In this embodiment, the second preset driving conditions may include highway driving conditions, traffic jam driving conditions, rain and snow driving conditions, night driving conditions, and mountain road driving conditions. The second historical driving operation data includes driving speed, acceleration frequency, deceleration frequency, street light status, and distance to surrounding obstacles. Then, a score can be obtained for each second preset driving condition based on the second historical driving operation data. Then, based on the weight of each second preset driving condition, the driving skill level of the target driver within each set time period is calculated. This driving skill level is the target driver's historical driving skill level for the corresponding set time period.

[0088] In step S402 above, the variance of each historical driving skill level can be calculated first. If the variance is less than or equal to a set threshold, it is determined that each historical driving skill meets the preset technical stability conditions; otherwise, if the variance is greater than the set threshold, it is determined that each historical driving skill level does not meet the preset technical stability conditions.

[0089] By employing the configuration method described in this embodiment, the expected driving behavior data of the target driver during a target time period can be predicted when the target driver's driving skills are stable, i.e., when each historical driving skill level meets the preset stability conditions. Since the target driver's driving behavior is relatively stable when their driving skills are stable, this embodiment can accurately predict the target driver's expected driving behavior data during the target time period.

[0090] In some embodiments of the present invention, step S102 above, which predicts the target driver's expected driving behavior data for a target time period based on the target driver's historical driving behavior data, includes:

[0091] First, reference users for the target driver are obtained based on the target driver's historical driving skill level and driving habits data for each set time period; then, the target driver's expected driving behavior data for the target time period is obtained based on the reference users' driving behavior data.

[0092] In this embodiment, the detected user driving skill level and driving habit data can be stored in a database. During the execution of step S104, the user with the highest similarity to the target driver can be selected from the database based on the target driver's historical driving habit data and historical driving skill level, and this user can be used as the target driver's reference user. Then, the driving habit data and driving skill level of the reference user in the database during the target time period are used to obtain the target driver's expected driving habit data and expected driving skill level during the target time period.

[0093] In the process of filtering reference users for the target driver from the database, it is necessary to calculate the similarity between each user in the database and the target driver. The calculation methods include:

[0094] First, a driving habit data table is obtained. This driving habit database stores driving habit indicators corresponding to various driving habits. Then, the driving habit data table is queried based on the target driver's historical driving habit data for each set time period to obtain the target driver's driving habit indicators for each set time period. Let N be the number of set time periods, and P be the historical driving habit indicator for the i-th set time period. i Historical driving skill level is Q i In the database, the driving habit index of one user in the i-th set time period is P' i Driving skill level is Q' i Then the similarity γ between the target driver and the user is

[0095]

[0096] Where a1 is the driving habit weight and a2 is the driving skill weight.

[0097] The configuration method in this embodiment allows for the rapid acquisition of the target driver's expected driving habits and expected driving skill level within a target time period, based on the driving habit data of the target driver's reference user. This improves the efficiency and speed of predicting the target driver's expected driving habits and expected driving skill level.

[0098] In some embodiments of the present invention, such as Figure 4 As shown, after step S401 determines whether each historical driving technology level meets the preset technical stability conditions, the following steps are also included:

[0099] If each historical driving skill level meets the preset technical stability conditions, then proceed to step S404;

[0100] Step S404: The average of each historical driving skill level will be used as the expected driving skill level of the target driver in the target time period;

[0101] Step S405: Determine whether the target driver's actual driving skill level is less than or equal to the preset skill level;

[0102] If so, proceed to step S406;

[0103] Step S406: Based on the target driver's historical driving habit data for each set time period, predict the target driver's expected driving habit data for the target time period.

[0104] Since the target driver's driving skill level will not change significantly during the target time period if all historical driving skills meet the preset technical stability conditions, but if the target driver's actual driving skill level is less than or equal to the preset skill level, the target driver's driving habits may change. Therefore, this method obtains the target driver's actual driving skill level based on each historical driving skill level, and predicts the target driver's expected driving habits during the target time period if the actual driving skill level is less than or equal to the preset skill level. Then, a UBI (Usage-Based Insurance) auto insurance pricing model is used to calculate the target driver's insurance price during the target time period based on the actual driving skill level and expected driving habit data, thereby improving the rationality of the target vehicle insurance pricing.

[0105] In some embodiments of the present invention, such as Figure 4 As shown, after determining whether the expected driving skill level is less than or equal to the preset skill level in step S405 above, the method further includes:

[0106] If the target driver's expected driving skill level during the target time period is greater than the preset skill level, then proceed to step S407.

[0107] Step S407: Based on the historical driving habit data of the target driver for each set time period, determine whether the target driver's driving habits are stable;

[0108] If stable, proceed to step S408;

[0109] Step S408: Perform statistical analysis on the target driver's historical driving habit data for each set time period to obtain the target driver's expected driving habit data for the target time period.

[0110] In step S407 above, the driving habit type of the target driver in each set time period can be obtained first based on the historical driving habit data of each set time period. Then, the variance between each driving habit type is calculated to determine whether the target driver's driving habits are stable.

[0111] In step S408 above, the average value of driving habit types for each set time period can be calculated, and then the average value can be used as the expected driving habit type of the set time period in the target time period. The driving habit data corresponding to the expected driving habit type can be used as the preset driving habit data of the target driver in the target time period.

[0112] Since the target driver's actual driving skill level is greater than the preset skill level, if the target driver's driving habits are stable, it is only necessary to use the historical driving habit data of each set time period as the expected driving habit data of the target time period, thereby improving the speed of obtaining the target driver's expected driving habit data.

[0113] In some embodiments of the present invention, after determining whether the target driver's driving habits are stable based on the historical driving habit data of the target driver for each set time period in step S407, the method further includes:

[0114] If the target driver's driving habits are unstable, proceed to step S409;

[0115] Step S409: Predict the target driver's expected driving habits data for the target time period based on historical driving habit data.

[0116] In this embodiment, curve fitting can be performed based on the driving habit type of the target driver in each set time period to obtain the driving habit change curve of the target driver, and the expected driving habit data of the target driver in the target time period can be predicted based on the change curve.

[0117] The technical solution provided in this embodiment can improve the reliability of obtaining data on the expected driving behavior of the target driver during the target time period.

[0118] An embodiment of the present invention also provides a machine-readable storage medium and a computer device. Figure 5 This is a schematic diagram of a machine-readable storage medium 830 according to an embodiment of the present invention; Figure 6 This is a schematic diagram of a computer device 900 according to an embodiment of the present invention. A machine-readable storage medium 830 stores a machine-executable program 840 thereon, which, when executed by a processor, implements the insurance method based on new energy vehicle configuration parameters of any of the above embodiments.

[0119] 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. When the processor 910 executes the machine-executable program 840, it implements the insurance method based on new energy vehicle configuration parameters of any of the above embodiments.

[0120] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any machine-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-based system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0121] For the purposes of this embodiment, the machine-readable storage medium 830 can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the machine-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0122] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0123] Computer device 900 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 900 can be a cloud computing node. Computer device 900 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 900 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.

[0124] Computer device 900 may include a processor 910 adapted to execute stored instructions and a memory 920 that provides temporary storage space for the operation of said instructions during operation. The processor 910 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 920 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0125] The processor 910 can be connected via a system interconnect (e.g., PCI, PCI-Express, etc.) to an I / O interface (input / output interface) suitable for connecting the computer device 900 to one or more I / O devices (input / output devices). I / O devices may include, for example, a keyboard and indicating devices, where indicating devices may include a touchpad or touchscreen, etc. I / O devices may be built into the computer device 900 or may be external devices connected to the computing device.

[0126] The processor 910 can also be linked via 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 an external computer monitor, television, or projector connected to the computer device 900. Furthermore, a network interface controller (NIC) may be adapted to connect the computer device 900 to a network via a system interconnect. In some embodiments, the NIC may use any suitable interface or protocol (such as an Internet Minicomputer System Interface) to transmit data. The network may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, etc. Remote devices can connect to the computing device via the network.

[0127] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for configuring parameters of an insurance based on a new energy vehicle, characterized in that, The method comprises the following steps: acquiring historical driving behavior data of a target driver in a plurality of set time periods before a 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, comprising: obtaining historical driving behavior data of the target driver in each of the set time periods according to each of the historical driving behavior data, which comprises historical driving habit data and historical driving skill level; in the case that each of the historical driving skill levels meets preset skill stability conditions, taking an average value of each of the historical driving skill levels as an expected driving skill level; if the expected driving skill level is less than or equal to a preset skill level, predicting expected driving behavior data of the target driver in the target time period according to each of the historical driving habit data; in the case that the expected driving skill level is greater than the preset skill level, judging whether the driving habit of the target driver is stable according to each of the historical driving habit data; if stable, statistically analyzing each of the historical driving habit data to obtain the expected driving behavior data of the target driver in the target time period; if not stable, predicting the expected driving behavior data of the target driver in the target time period according to each of the historical driving habit data; In a case where each of the historical driving skill levels does not satisfy the preset skill stability condition, a user with the highest similarity to the target driver is selected from the database according to the historical driving habit data and the historical driving skill levels, and the user is taken as a reference user of the target driver, wherein the similarity between the target driver and a user in the database is calculated comprises: ； wherein is a driving habit weight, is a driving skill weight, N is the number of the set time periods, is a historical driving habit indicator of the target driver in the i-th set time period, is a historical driving skill level of the target driver, is a driving habit indicator of one of the users in the i-th set time period, is a driving skill level of the user in the i-th set time period; obtaining the expected driving behavior data according to driving habit data and driving skill level of a reference user in the target time period; acquiring configuration parameters of a target vehicle and existing depreciation degrees of each key device on the target vehicle; calculating expected depreciation degrees of each of the key devices in the target time period according to the expected driving behavior data and the current depreciation degrees; using a UBI insurance optimization model to calculate an 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 depreciation degrees. 2.The method of claim 1, wherein, The method of calculating the expected depreciation degrees of each of the key devices in the target time period according to the expected driving data and the current depreciation degrees comprises: acquiring a depreciation degree calculation model of each of the key devices; inputting the expected driving data and the existing depreciation degrees of each of the key devices into the corresponding depreciation degree calculation model to obtain each of the expected depreciation degrees. 3.The method of claim 2, wherein, The method of acquiring the depreciation degree calculation model of each of the key devices comprises: acquiring a neural network-based initial depreciation degree calculation model of each of the key devices; acquiring corresponding training data sets of each of the key devices, and training the corresponding initial depreciation degree calculation model using each of the training data sets to obtain each of the depreciation degree calculation models.

4. A machine-readable storage medium having stored thereon a machine executable program, which, when executed by a processor, implements the insurance method based on configuration parameters of a new energy vehicle according to any one of claims 1 to 3. 5.A computer device comprising a memory, a processor, and a machine executable program stored on the memory and running on the processor, and the processor implements the insurance method based on the configuration parameters of the new energy vehicle according to any one of claims 1 to 3 when executing the machine executable program.

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