Car insurance assessment method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle insurance assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining first data, the first data comprising information of multiple dimensions of interaction between a vehicle and network equipment in a driving process; determining a multi-dimensional feature based on the first data; and vehicle insurance assessment is carried out based on the multi-dimensional features. According to the method provided by the invention, the evaluation quality of the vehicle insurance can be improved, and the vehicle insurance evaluation result can reflect the actual risk level of the vehicle more truly.

Description

Technical Field

[0001] This application relates to the field of vehicle insurance, and more particularly to a vehicle insurance assessment method, apparatus, electronic device, and storage medium. Background Technology

[0002] In traditional auto insurance assessments, insurance companies primarily rely on the insured vehicle's accident history and the amount of each claim. However, vehicle attributes such as model, age, and features, as well as the owner's personal attributes like age, driving experience, and traffic violation record, are also important assessment factors. For example, some high-end luxury vehicles are often at a disadvantage in insurance assessments due to their high maintenance costs; older vehicles, with their aging mechanical parts and increased risk of malfunction, will also be affected. Younger drivers, due to their lack of experience, have a relatively higher probability of causing accidents, which also puts them at a disadvantage in insurance assessments.

[0003] It is evident that using such single-dimensional data in car insurance assessments results in poor quality and fails to meet user needs. Summary of the Invention

[0004] This application provides a vehicle insurance assessment method, apparatus, electronic device, and storage medium, which helps to improve the quality of vehicle insurance assessments and enable the assessment results to more accurately reflect the actual risk level of the vehicle.

[0005] In a first aspect, embodiments of this application provide a vehicle insurance assessment method, comprising: acquiring first data, the first data including information on multiple dimensions of vehicle interaction with network devices during driving; determining multi-dimensional features based on the first data; and performing vehicle insurance assessment based on the multi-dimensional features.

[0006] In one possible implementation, the multi-dimensional features include at least the user's driving behavior-related features at different times.

[0007] In one possible implementation, acquiring the first data includes: periodically acquiring the first data; and assessing the vehicle insurance based on the multi-dimensional features includes: assessing the vehicle insurance based on periodically determined multi-dimensional features, wherein the periodically determined multi-dimensional features are determined by the periodically acquired first data.

[0008] In one possible implementation, the vehicle insurance assessment based on the multi-dimensional features includes: inputting the multi-dimensional features into a vehicle insurance assessment model for vehicle insurance assessment; wherein, the vehicle insurance model is used to characterize the mapping relationship between the vehicle insurance assessment value and the multi-dimensional features, and the mapping relationship includes the linear relationship between the vehicle insurance assessment value and the multi-dimensional features.

[0009] In one possible implementation, the mapping relationship further includes an intercept term and a random error term.

[0010] In one possible implementation, the linear relationship includes coefficients corresponding to the multi-dimensional features, and the vehicle insurance assessment model updates the coefficients through training.

[0011] In one possible implementation, the first data includes at least the International Mobile Subscriber Identity (IMSI), vehicle location time, vehicle location, and network device identification number.

[0012] Secondly, embodiments of this application provide a vehicle insurance assessment device, including one or more functional modules, which are used to perform the vehicle insurance assessment method as described in the first aspect.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory, wherein the memory is used to store a program; and the processor is used to run the program to implement the vehicle insurance assessment method as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a readable storage medium storing a program that, when run on an electronic device, causes the electronic device to implement the vehicle insurance assessment method as described in the first aspect.

[0015] Fifthly, embodiments of this application provide a program that, when run on the processor of an electronic device, causes the electronic device to perform the vehicle insurance assessment method as described in the first aspect.

[0016] In one possible design, the program in the fifth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application; Figure 2 A flowchart illustrating an embodiment of the vehicle insurance assessment method provided in this application; Figure 3 This is a schematic diagram of the structure of the vehicle insurance assessment device provided in the embodiments of this application. Detailed Implementation

[0018] In this embodiment of the application, unless otherwise stated, the character " / " indicates that the preceding and following objects are in an OR relationship. For example, A / B can represent A or B. "AND / OR" describes the relationship between the associated objects, indicating that three relationships can exist. For example, A AND / OR B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0019] It should be noted that the terms "first" and "second" used in the embodiments of this application are used only for distinguishing descriptive purposes and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor should they be construed as indicating or implying order.

[0020] In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more. Furthermore, "at least one of the following" or similar expressions refer to any combination of these items, which may include any combination of a single item or a plurality of items. For example, at least one of A, B, or C can represent: A, B, C, A and B, A and C, B and C, or A, B, and C. Each of A, B, and C can be an element itself or a set containing one or more elements.

[0021] In this application, terms such as "exemplary," "in some embodiments," and "in another embodiment" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the term "exemplary" is intended to present the concept in a concrete manner.

[0022] In the embodiments of this application, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. Similarly, in the embodiments of this application, "communication" and "transmission" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction, their meanings are consistent. For example, transmission can include sending and / or receiving, and can be a noun or a verb.

[0023] In the embodiments of this application, the term "equal to" can be used in conjunction with "greater than" to apply to technical solutions employing the condition of "greater than", and can also be used in conjunction with "less than" to apply to technical solutions employing the condition of "less than". It should be noted that when "equal to" is used with "greater than", it cannot be used with "less than"; and when "equal to" is used with "less than", it cannot be used with "greater than".

[0024] In traditional auto insurance assessments, insurance companies primarily rely on the insured vehicle's accident history and the amount of each claim. However, vehicle attributes such as model, age, and features, as well as the owner's personal attributes like age, driving experience, and traffic violation record, are also important assessment factors. For example, some high-end luxury vehicles are often at a disadvantage in insurance assessments due to their high maintenance costs; older vehicles, with their aging mechanical parts and increased risk of malfunction, will also be affected. Younger drivers, due to their lack of experience, have a relatively higher probability of causing accidents, which also puts them at a disadvantage in insurance assessments.

[0025] It is evident that this traditional car insurance assessment, based on data from a single dimension, results in poor quality assessments that fail to meet user needs.

[0026] Furthermore, for new energy vehicles, insurance companies conduct low-speed collision tests (15 km / h) when the new car is launched to assess the damage and repair costs. This, combined with vehicle technology and safety performance data provided by the automaker, categorizes the vehicle into risk levels, directly linking insurance rates to these levels. This method accurately predicts claims costs, avoiding the traditional "one-size-fits-all" pricing approach for different risk levels, and also forces automakers to optimize vehicle safety and repair cost-effectiveness designs.

[0027] However, this type of car insurance assessment only focuses on the vehicle's static attributes, such as repair costs obtained from low-speed collision tests and technical and safety performance data provided by the automaker. This results in a uniform risk level and rate for the same model. But the actual risks of the same model can vary drastically among different owners due to differences in driving habits and usage scenarios. This inability to distinguish these differences leads to inaccurate assessment results.

[0028] Furthermore, both the traditional auto insurance assessment models and those for newer insured vehicle models mentioned above are based on static calculations using historical data, and cannot be adjusted promptly according to real-time or recent changes in driver behavior. Driver behavior is dynamic and may change due to factors such as changes in lifestyle and driving environment. The aforementioned assessment models cannot react quickly enough to adjust insurance premiums, resulting in a lag in risk assessment and pricing for insurance companies, making it impossible to effectively address the dynamic nature of driving risks.

[0029] To address the aforementioned issues, this application provides a vehicle insurance assessment method that helps improve the quality of vehicle insurance assessments, enabling the assessment results to more accurately reflect the actual risk level of a vehicle.

[0030] The vehicle insurance assessment method shown in this application can be applied to electronic devices.

[0031] The electronic device may be a desktop computer, a server, or a server cluster consisting of multiple servers. This application does not impose any special limitation on the type of electronic device.

[0032] Figure 1 First, the hardware structure of the electronic device 100 is shown as an example.

[0033] The aforementioned electronic device 100 may include: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the methods provided in the embodiments shown herein by calling the program instructions.

[0034] Figure 1 A block diagram is shown that is suitable for implementing the embodiments described herein. Figure 1 The electronic device 100 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments described herein.

[0035] like Figure 1 As shown, the components of the electronic device 100 may include, but are not limited to: one or more processors 110, memory 120, communication bus 140 connecting different system components (including memory 120 and processor 110), and communication interface 130.

[0036] Communication bus 140 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MAC) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, and Peripheral Component Interconnect (PCI) buses.

[0037] Electronic device 100 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the device, including volatile and non-volatile media, removable and non-removable media.

[0038] Memory 120 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Although Figure 1 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the communication bus 140 via one or more data media interfaces. The memory 120 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments herein.

[0039] A program / utility having a set (at least one) of program modules may be stored in memory 120. Such program modules include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments herein.

[0040] Electronic device 100 can also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), and with one or more devices that enable a user to interact with the device, and / or with any device that enables the device to communicate with one or more other devices (e.g., network card, modem, etc.). This communication can be performed through communication interface 130. Furthermore, electronic device 100 can also communicate through a network adapter (… Figure 1 (Not shown) communicates with one or more networks (e.g., Local Area Network (LAN), Wide Area Network (WAN), and / or public networks, such as the Internet). The aforementioned network adapter can communicate with other modules of the device via communication bus 140. It should be understood that, although... Figure 1As not shown, other hardware and / or software modules may be used in conjunction with electronic device 100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, Redundant Arrays of Independent Drives (RAID) systems, tape drives, and data backup storage systems.

[0041] The processor 110 executes various functional applications and data processing by running programs stored in the memory 120, such as implementing the methods provided in the embodiments herein.

[0042] It is understood that the interface connection relationships between the modules illustrated in the embodiments herein are merely illustrative and do not constitute a structural limitation on the electronic device 100. In other embodiments herein, the electronic device 100 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0043] Figure 2 A flowchart illustrating an embodiment of the vehicle insurance assessment method provided in this application includes the following steps: Step 201: Obtain first data, which includes various information exchanged between the vehicle and network devices during the driving process.

[0044] Specifically, the first data may be signaling data, which can be collected by the on-board unit (OBD) in the vehicle.

[0045] In some alternative embodiments, the aforementioned first data can also be collected through a vehicle positioning system or a smart cockpit system, and this application does not impose any special limitations on this.

[0046] The first data may include various information that the vehicle interacts with network devices during its operation; the network devices may be, for example, base stations, satellites, or other types of access network devices, and this application embodiment does not impose any special limitations on them.

[0047] For example, the first data may include, but is not limited to, International Mobile Subscriber Identity (IMSI), vehicle location time (time corresponding to the current location of the vehicle), vehicle location, network device identification number, and other related information.

[0048] Among them, the International Mobile Subscriber Identity (IMSI) can be used to identify the vehicle terminal, the vehicle location time can be used to identify the system time of the vehicle at its current location, the vehicle location can be represented by information such as longitude and latitude, and the network device identification number can be represented by the base station ID or cell ID.

[0049] It is understandable that after obtaining the first data, there may be some missing data, noisy data, or invalid data records in the first data. In order to solve these problems, the embodiments of this application adopt methods such as noise reduction and filling missing values ​​for data preprocessing. At the same time, in order to eliminate the difference in units between different dimensions of data, the Min-Max scaling method is used to normalize the data of all dimensions to improve the efficiency of subsequent processing.

[0050] Step 202: Determine multi-dimensional features based on the first data.

[0051] Specifically, methods for determining multi-dimensional features based on the first data may include: using a road network fitting algorithm and a dimension label model to calculate multi-dimensional features.

[0052] For example, the multidimensional feature may include 16 dimensions.

[0053] The 16 dimensions of features may include driving days, mileage, total driving time, number of night driving sessions, night driving time, night driving kilometers, number of fatigued driving sessions, fatigued driving time, fatigued driving kilometers, number of morning peak driving sessions, morning peak driving time, morning peak driving kilometers, number of evening peak driving sessions, evening peak driving time, evening peak driving kilometers, and permanent residence area.

[0054] Understandably, the number of driving days can be obtained by statistically analyzing the dates appearing in the first set of data and removing duplicate dates.

[0055] The mileage feature can be obtained by combining the vehicle's latitude and longitude information in the first data with the Earth's surface distance calculation formula to calculate the distance between adjacent locations, and then summing up the distances in all trips to get the mileage.

[0056] The total travel time feature can be calculated by taking the start and end times of each trip from the first data, calculating the time difference, and then summing up the time differences of all trips to obtain the total travel time.

[0057] The nighttime driving frequency characteristics, nighttime driving duration characteristics, and nighttime driving mileage characteristics can be used to filter driving records within a specific nighttime period from the first data. The number of nighttime drivings, duration, and mileage can be calculated according to the calculation methods of total trips, total driving time, and mileage, respectively.

[0058] For example, the nighttime period can be from 22:00 on the night of the day to 4:00 the next day. It is understood that the above time period is only an example and does not constitute a limitation on the embodiments of this application. In some embodiments, other time periods can also be used as the nighttime period.

[0059] The characteristics of fatigued driving frequency, fatigued driving duration, and fatigued driving mileage can be determined by analyzing the continuity of vehicle driving time in the first set of data. When the continuous driving time exceeds a set threshold, it is judged as a fatigued driving incident. The frequency, duration, and mileage of fatigued driving are then statistically analyzed using the above method.

[0060] The characteristics of driving frequency, driving duration, and driving mileage during the morning rush hour can be used to filter driving records for the corresponding time period from the first data, and then the number of driving times, duration, and mileage during the morning rush hour can be statistically calculated.

[0061] For example, the morning peak period can be the time period from 7:00 to 9:00. It should be understood that the above time period is only an example and does not constitute a limitation on the embodiments of this application. In some embodiments, other time periods can also be used as the morning peak period.

[0062] The characteristics of driving frequency, driving duration, and driving mileage during the evening peak hours can be used to filter driving records for the corresponding time periods from the first data, and then the number of driving times, duration, and mileage during the evening peak hours can be statistically calculated.

[0063] For example, the evening peak period can be the time period from 17:00 to 19:00. It should be understood that the above time period is only an example and does not constitute a limitation on the embodiments of this application. In some embodiments, other time periods can also be used as the evening peak period.

[0064] The location of a vehicle's permanent residence can be determined by analyzing the location information where the vehicle appears most frequently in the first set of data.

[0065] As can be seen, by comprehensively capturing the driving behavior characteristics of drivers in different scenarios and at different times, this application embodiment can reflect the frequency of vehicle use and the intensity of driving activities from different perspectives. It can comprehensively and deeply depict the driver's driving behavior and the vehicle's usage status, more accurately assess its potential driving risks, provide a richer and more accurate information basis for vehicle insurance assessment, significantly improve the accuracy and reliability of the assessment, and enable vehicle insurance assessment to more realistically reflect the driver's actual risk level.

[0066] In some optional embodiments, step 201 may also involve periodically acquiring the first data. By dynamically collecting and updating the first data, the vehicle insurance assessment can dynamically change with driving behavior, promptly reflecting changes in risk. This allows for timely updates to the vehicle insurance assessment value based on real-time or recent changes in the driver's driving behavior. This dynamic assessment mechanism effectively solves the problem of traditional vehicle insurance assessments lacking dynamic adjustment, making insurance pricing fairer and more reasonable, while also improving the risk management capabilities and market competitiveness of insurance companies.

[0067] For example, when a driver's driving behavior changes significantly, such as frequent fatigue driving or increased driving during peak hours, the assessment system can respond quickly, recalculate the assessment value, ensure that the insurance premium matches the driver's current risk status, effectively reduce the insurance company's payout risk, and also provide drivers with fairer and more reasonable insurance pricing.

[0068] Step 203: Conduct vehicle insurance assessment based on multi-dimensional features.

[0069] In some optional embodiments, assessing vehicle insurance based on multi-dimensional features may include: inputting multi-dimensional features into a vehicle insurance assessment model to conduct a vehicle insurance assessment in order to obtain assessment results.

[0070] The vehicle insurance assessment model can be characterized by a multiple linear regression formula.

[0071] For example, the formula for multiple linear regression can be expressed as follows: Y=b0+b1 x1+b2 x2 + … + bn xn+c; Where Y is the evaluation result, b0 is the intercept term, the n parameters b1-bn are used to characterize the coefficients of each dimension feature, c is the random error term, used to characterize the unexplained part of the model, n is the number of dimensional features, and n is an integer greater than 1.

[0072] Understandably, these coefficients b1-bn reflect the degree and direction of influence of each dimension feature on auto insurance assessment.

[0073] For example, assuming x1 is the driving days feature, if b1 is positive, it means that the more driving days, the higher the car insurance assessment value, that is, the driving days and the car insurance assessment value are positively correlated; conversely, if b1 is negative, it means that the driving days and the car insurance assessment value are negatively correlated.

[0074] It should be noted that the assessment value is a numerical value derived from a comprehensive assessment of the vehicle's risk status. Its range can be set according to actual business needs. The higher the assessment value, the higher the risk of the vehicle.

[0075] Insurance companies can determine vehicle insurance costs based on calculated assessment values ​​and their own premium pricing strategies. For vehicles with higher assessed values, due to their relatively higher risk, insurance companies will increase premiums accordingly; conversely, for vehicles with lower assessed values, insurance companies will offer premium discounts. This method achieves a precise match between insurance costs and vehicle risk, improving insurance companies' risk management capabilities and market competitiveness.

[0076] As can be seen, the embodiments of this application construct a car insurance assessment model using a multiple linear regression algorithm, which can accurately determine the linear relationship between data in each dimension and the car insurance assessment value, thus achieving quantitative calculation of the assessment value. By transforming complex multi-dimensional data into a quantified car insurance assessment value through the multiple linear regression algorithm, insurance companies are provided with a scientific and objective risk assessment tool. Compared with traditional assessment methods, this assessment model based on the multiple linear regression algorithm can more accurately measure the risk status of vehicles, making insurance premium pricing more reasonable, protecting the interests of insurance companies while providing fair insurance pricing for car owners.

[0077] In some alternative embodiments, the vehicle insurance assessment model can also be optimized.

[0078] For example, optimizing the auto insurance assessment model may include optimizing the auto insurance assessment model by minimizing a loss function or a mean squared error loss function.

[0079] It is understood that other loss functions can also be used to optimize the car insurance assessment model, and this application does not impose any special limitations on this.

[0080] Taking the mean squared error loss function as an example, the mean squared error loss function can be characterized by the following formula: ; Where m is the sample size. This is the actual car insurance assessment value. This is the predicted car insurance assessment value.

[0081] It is understandable that the gradient descent algorithm is used to continuously update the parameters of the car insurance assessment model, such as b0, b1, ..., bn, so that the car insurance assessment model can better fit the training data.

[0082] In some optional embodiments, to prevent overfitting of the auto insurance assessment model, L2 regularization (ridge regression) can be used, adding a regularization term to the loss function. This regularization term can include a regularization coefficient λ. By adjusting the size of the regularization coefficient λ, the fitting ability and complexity of the auto insurance assessment model can be balanced. When λ is too large, the auto insurance assessment model may underfit, resulting in poor fitting of the training data; when λ is too small, the auto insurance assessment model may not be able to effectively prevent overfitting. By experimenting on the validation set, an appropriate value of λ can be selected to minimize the mean squared error of the auto insurance assessment model on the validation set.

[0083] In some optional embodiments, the performance of the vehicle insurance assessment model can also be evaluated.

[0084] For example, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) can be used. 2 The performance of the vehicle insurance assessment model on the validation and test sets is evaluated using metrics such as (e.g., [missing information]).

[0085] The root mean square error (RMSE) reflects the square root of the average error between the model's predicted and actual values. The mean absolute error (MAE) reflects the average absolute error between the model's predicted and actual values. The coefficient of determination (COD) measures the goodness of fit of the model to the data, ranging from 0 to 1, with values ​​closer to 1 indicating a better fit. The car insurance assessment model is continuously optimized until its performance on the test set meets the expected requirements, such as the RMSE falling below a certain threshold or the COD exceeding a certain threshold.

[0086] Figure 3 This is a schematic diagram of the structure of the vehicle insurance assessment device provided in the embodiments of this application, as shown below. Figure 3 As shown, the aforementioned vehicle insurance assessment device 30 includes: an acquisition module 31, a determination module 32, and an assessment module 33; wherein, The acquisition module 31 is used to acquire first data, which includes information from multiple dimensions of the vehicle's interaction with network devices during driving. The determination module 32 is used to determine multi-dimensional features based on the first data; Evaluation module 33 is used to evaluate vehicle insurance based on the multi-dimensional features.

[0087] In one possible implementation, the multi-dimensional features include at least the user's driving behavior-related features at different times.

[0088] In one possible implementation, the acquisition module 31 is further configured to periodically acquire the first data; The evaluation module 33 is also used to evaluate vehicle insurance based on periodically determined multi-dimensional features, wherein the periodically determined multi-dimensional features are determined by the periodically acquired first data.

[0089] In one possible implementation, the evaluation module 33 is further used to input the multi-dimensional features into the vehicle insurance evaluation model for vehicle insurance evaluation; The vehicle insurance model is used to represent the mapping relationship between the vehicle insurance assessment value and the multi-dimensional features, and the mapping relationship includes the linear relationship between the vehicle insurance assessment value and the multi-dimensional features.

[0090] In one possible implementation, the mapping relationship further includes an intercept term and a random error term.

[0091] In one possible implementation, the linear relationship includes coefficients corresponding to the multi-dimensional features, and the vehicle insurance assessment model updates the coefficients through training.

[0092] In one possible implementation, the first data includes at least the International Mobile Subscriber Identity (IMSI), vehicle location time, vehicle location, and network device identification number.

[0093] Figure 3 The vehicle insurance assessment device 30 provided in the embodiment can be used to execute the technical solution of the method embodiment shown in this application, and its implementation principle and technical effect can be further referred to the relevant description in the method embodiment.

[0094] It should be understood that the division of the various modules in the above-described vehicle insurance assessment device 30 is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented in software via processing element calls, while others are implemented in hardware. For example, the detection module can be a separate processing element, or it can be integrated into a chip in the terminal device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0095] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs). Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0096] In the above embodiments, the processor may include, for example, a CPU, DSP, microcontroller, or digital signal processor, and may also include a GPU, embedded neural network processing unit (NPU), and image signal processor (ISP). The processor may also include necessary hardware accelerators or logic processing hardware circuits, such as an ASIC, or one or more integrated circuits for controlling the execution of the program in this application. Furthermore, the processor may have the function of operating one or more software programs, which may be stored in a storage medium.

[0097] This application also provides a readable storage medium storing a program that, when run on an electronic device, causes the electronic device to execute the method provided in the embodiments shown in this application.

[0098] This application also provides a program product, which includes a program that, when run on an electronic device, causes the electronic device to perform the method provided in the embodiments shown in this application.

[0099] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0100] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for assessing vehicle insurance, characterized in that, The method includes: Acquire first data, which includes information from multiple dimensions of the vehicle's interaction with network devices during operation; Multidimensional features are determined based on the first data; Auto insurance assessment is based on the aforementioned multi-dimensional features.

2. The method according to claim 1, characterized in that, The multi-dimensional features include at least the features related to the user's driving behavior at different times.

3. The method according to claim 1, characterized in that, The acquisition of the first data includes: Periodically acquire the first data; The vehicle insurance assessment based on the aforementioned multi-dimensional features includes: Auto insurance assessment is based on periodically determined multi-dimensional features, wherein the periodically determined multi-dimensional features are determined by the first data acquired periodically.

4. The method according to claim 1, characterized in that, The vehicle insurance assessment based on the aforementioned multi-dimensional features includes: The multi-dimensional features are input into the vehicle insurance assessment model to conduct vehicle insurance assessment; The vehicle insurance model is used to represent the mapping relationship between the vehicle insurance assessment value and the multi-dimensional features, and the mapping relationship includes the linear relationship between the vehicle insurance assessment value and the multi-dimensional features.

5. The method according to claim 4, characterized in that, The mapping relationship also includes an intercept term and a random error term.

6. The method according to claim 4, characterized in that, The linear relationship includes coefficients corresponding to the multi-dimensional features, and the vehicle insurance assessment model updates the coefficients through training.

7. The method according to any one of claims 1-6, characterized in that, The first data includes at least the International Mobile Subscriber Identity (IMSI), vehicle location time, vehicle location, and network device identification number.

8. A vehicle insurance assessment device, characterized in that, The device includes: The acquisition module is used to acquire first data, which includes information from multiple dimensions of the vehicle's interaction with network devices during driving. A determination module is used to determine multi-dimensional features based on the first data; The assessment module is used to assess vehicle insurance based on the aforementioned multi-dimensional features.

9. An electronic device, characterized in that, include: A processor and a memory, wherein the memory is used to store a program; the processor is used to run the program to implement the vehicle insurance assessment method as described in any one of claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program that, when run on an electronic device, implements the vehicle insurance assessment method as described in any one of claims 1-7.

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