Vehicle insurance premium evaluation method and device, vehicle and storage medium

Abstract

The invention relates to a vehicle insurance premium evaluation method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring vehicle driving data and battery state data of a vehicle in a previous preset period; according to the vehicle driving data, determining a vehicle purpose coefficient of the vehicle, according to the battery state data, determining a standing duration coefficient of a battery and determining a charging habit coefficient of a user, and according to the vehicle driving data and the battery state data, determining a driving style coefficient of the user; calculating a comprehensive coefficient by adopting the vehicle purpose coefficient, the standing duration coefficient, the charging habit coefficient and the driving style coefficient; the comprehensive coefficient is used for evaluating the use risk of the vehicle and the health state of the battery; and based on the comprehensive coefficient, evaluating the vehicle insurance premium required by the user for insurance. According to the scheme provided by the invention, the use risk of the vehicle and the health state of the battery can be comprehensively and accurately evaluated, so that the evaluation accuracy of the vehicle premium is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle insurance technology, and in particular to a vehicle insurance cost assessment method, device, vehicle and storage medium. Background Art

[0002] Electric vehicles are a new energy vehicle that has developed rapidly in recent years. They are powered by electricity and have the advantages of zero emissions, low noise, and high energy efficiency. However, the battery cost of electric vehicles is high, and battery life and performance are the focus of users. Therefore, how to reduce battery loss and extend the battery life of electric vehicles has become an important research direction in the field of electric vehicles.

[0003] In the related art, the use risk of electric vehicles is assessed based on the user's driving habits, and the health status of the battery is assessed by monitoring the number of charge and discharge times and charging time of the battery. Then, corresponding insurance premiums are formulated based on the use risk of the electric vehicle and the health status of the battery. The vehicle insurance cost required by the user is obtained through the insurance premium assessment. However, the user behavior and battery usage of electric vehicles are very complicated, and there are many other factors that affect the use risk of electric vehicles and the health status of the battery. Therefore, the related art cannot comprehensively and accurately assess the use risk of electric vehicles and the health status of the battery, resulting in low accuracy in the assessment of vehicle insurance costs. Summary of the invention

[0004] In order to solve or partially solve the problems existing in the related technology, the present application provides a vehicle insurance cost assessment method, device, vehicle and storage medium, which can comprehensively and accurately assess the vehicle's usage risks and battery health status, thereby improving the accuracy of vehicle insurance cost assessment.

[0005] The first aspect of the present application provides a vehicle insurance cost assessment method, comprising: Obtaining vehicle driving data and battery status data of the vehicle in the last preset cycle; Determining a vehicle usage coefficient of the vehicle based on the vehicle driving data, determining a battery rest time coefficient and a user charging habit coefficient based on the battery status data, and determining a user driving style coefficient based on the vehicle driving data and the battery status data; The vehicle usage coefficient, the static time coefficient, the charging habit coefficient and the driving style coefficient are used to calculate a comprehensive coefficient; the comprehensive coefficient is used to evaluate the use risk of the vehicle and the health status of the battery; Based on the comprehensive coefficient, the vehicle insurance cost required for the user to purchase insurance is evaluated.

[0006] In one embodiment, the vehicle driving data includes a vehicle mileage parameter; and determining the vehicle usage coefficient of the vehicle according to the vehicle driving data includes: Using the vehicle mileage parameter, calculate the average daily mileage of the vehicle in each month within the previous preset period; The vehicle usage mode corresponding to the month in which the average daily mileage is greater than the first mileage threshold is determined as the operational vehicle usage mode; Based on the number of months in the operational vehicle mode and / or the average daily mileage in each month corresponding to the operational vehicle mode, the vehicle usage coefficient of the vehicle is determined; wherein the vehicle usage coefficient is positively correlated with the number of months in the operational vehicle mode, and the vehicle usage coefficient is positively correlated with the average daily mileage in each month corresponding to the operational vehicle mode.

[0007] In one embodiment, the battery status data includes a battery current variation parameter; and determining the battery rest time coefficient according to the battery status data includes: Accumulating a first continuous storage time length when the battery is in a state where the battery current change parameter is less than or equal to a first current threshold; Based on the first continuous storage time, a static time coefficient of the battery is determined; wherein the static time coefficient is positively correlated with the first continuous storage time.

[0008] In one embodiment, the battery status data includes a fast charge capacity parameter, a slow charge capacity parameter, and a fast charge cut-off SOC parameter; and determining the user's charging habit coefficient based on the battery status data includes: The fast charge capacity parameter is used to accumulate the total fast charge capacity of the battery in the previous preset cycle; and the fast charge capacity parameter and the slow charge capacity parameter are used to accumulate the total charge capacity of the battery in the previous preset cycle; and the high SOC cut-off charging times of the battery when the fast charge cut-off SOC parameter is greater than the first SOC threshold are accumulated; Based on the ratio of the total fast charge capacity to the total charge capacity, determining the user's fast charge habit coefficient; and based on the high SOC cut-off charge times, determining the user's high SOC cut-off charge habit coefficient; wherein the fast charge habit coefficient is positively correlated with the ratio, and the high SOC cut-off charge habit coefficient is positively correlated with the high SOC cut-off charge times; The charging habit coefficient of the user is determined according to the fast charging habit coefficient and the high SOC cut-off charging habit coefficient.

[0009] In one embodiment, the vehicle driving data includes a vehicle driving speed parameter and a vehicle driving acceleration parameter, and the battery status data includes a battery SOC parameter; and determining the user's driving style coefficient according to the vehicle driving data and the battery status data includes: Accumulate the number of low-battery driving times of the vehicle when the battery SOC parameter is less than the second SOC threshold and the vehicle speed parameter is greater than the first speed threshold; and accumulate the ultra-high-speed driving time of the vehicle when the vehicle speed parameter is greater than the second speed threshold; and accumulate the number of rapid acceleration driving times of the vehicle when the vehicle acceleration parameter is greater than a preset acceleration threshold; Based on the number of low-battery driving times, determining a low-battery driving style coefficient of the user; and based on the ultra-high-speed driving duration, determining an ultra-high-speed driving coefficient of the vehicle; wherein the low-battery driving style coefficient is positively correlated with the number of low-battery driving times, and the ultra-high-speed driving coefficient is positively correlated with the ultra-high-speed driving duration; determining the user's aggressive driving style coefficient according to the ultra-high speed driving coefficient and the number of rapid acceleration driving times; The driving style coefficient of the user is determined according to the low-battery driving style coefficient and the aggressive driving style coefficient.

[0010] In one embodiment, the calculating of the comprehensive coefficient by using the vehicle usage coefficient, the stationary time coefficient, the charging habit coefficient and the driving style coefficient includes: When the battery has any preset risk situation in the previous preset period, obtaining a corresponding penalty coefficient; When any two item coefficients are less than or equal to the normal coefficient threshold, and / or the battery does not have any preset risk situation in the previous preset period, the corresponding reward coefficient is obtained; wherein the sub-coefficients include the vehicle use coefficient, the static time coefficient, the charging habit coefficient and the driving style coefficient; Calculate the comprehensive coefficient by using the sub-coefficients, the penalty coefficient and the reward coefficient of all items; Among them, the preset risk conditions include at least one of a long-term low-power static condition, a long-term low-temperature static condition, a long-term high-temperature static condition, and a frequent overcharging condition.

[0011] In one embodiment, it further comprises: Monitoring whether each of the sub-coefficients is greater than or equal to an abnormal coefficient threshold, and monitoring whether the battery has any of the preset risk conditions; When a sub-coefficient greater than or equal to the abnormal coefficient threshold is monitored, a reminder message matching the monitored sub-coefficient is pushed to the user; and / or, When any of the preset risk conditions is detected in the battery, reminder information matching the monitored preset risk condition is pushed to the user.

[0012] A second aspect of the present application provides a vehicle insurance cost assessment device, comprising: A historical vehicle data acquisition module is used to acquire vehicle driving data and battery status data of the vehicle in the last preset period; a module for determining each sub-coefficient, for determining a vehicle usage coefficient of the vehicle according to the vehicle driving data, a battery rest time coefficient and a user charging habit coefficient according to the battery status data, and a user driving style coefficient according to the vehicle driving data and the battery status data; a comprehensive coefficient calculation module, configured to calculate a comprehensive coefficient using the vehicle usage coefficient, the static time coefficient, the charging habit coefficient, and the driving style coefficient; the comprehensive coefficient is used to evaluate the use risk of the vehicle and the health status of the battery; The vehicle insurance cost assessment module is used to assess the vehicle insurance cost required for the user to take out insurance based on the comprehensive coefficient.

[0013] A third aspect of the present application provides a vehicle, comprising: Processor; and The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0014] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of a vehicle, the processor is caused to execute the method as described above.

[0015] The technical solution provided by this application may have the following beneficial effects: The solution provided by the present application obtains the vehicle driving data and battery status data of the vehicle in the last preset period; determines the vehicle usage coefficient of the vehicle according to the vehicle driving data, and determines the battery rest time coefficient and the user's charging habit coefficient according to the battery status data, and determines the user's driving style coefficient according to the vehicle driving data and battery status data; uses the vehicle usage coefficient, rest time coefficient, charging habit coefficient and driving style coefficient to calculate the comprehensive coefficient; the comprehensive coefficient is used to evaluate the vehicle's use risk and the battery's health status; based on the comprehensive coefficient, the vehicle insurance premium required for the user's insurance is evaluated. The present application comprehensively and accurately evaluates the vehicle's use risk and the battery's health status by comprehensively considering multiple dimensions of user behavior and multiple dimensions of battery usage, thereby improving the evaluation accuracy of vehicle insurance premiums.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail the exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0018] Figure 1 It is a flow chart of a vehicle insurance expense assessment method shown in an embodiment of the present application; Figure 2 is another flow chart of the vehicle insurance expense assessment method shown in the embodiment of the present application; Figure 3 is a schematic diagram of the structure of a vehicle insurance expense assessment device shown in an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of a vehicle shown in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0020] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0022] Electric vehicles are new energy vehicles that have developed rapidly in recent years. They are powered by electricity and have the advantages of zero emissions, low noise, and high energy efficiency. However, the battery cost of electric vehicles is high, and battery life and performance are the focus of users. Therefore, how to reduce battery loss to extend the battery life of electric vehicles has become an important research direction in the field of electric vehicles. The field of insurance mathematics mainly studies how to evaluate and predict risks through mathematical models and algorithms, so as to formulate reasonable insurance premiums. The field of user behavior analysis uses data mining and analysis technology to understand user behavior patterns and habits in order to provide more personalized services and products.

[0023] The related technology attempts to evaluate the use risk of electric vehicles through user behavior analysis. For example, some insurance companies will evaluate the use risk of electric vehicles based on the user's driving habits, such as average driving speed, mileage, etc. However, the user behavior of electric vehicles is very complex. In addition to driving habits, there are many other factors that affect the use risk of electric vehicles. Therefore, the related technology cannot comprehensively and accurately evaluate the use risk of electric vehicles.

[0024] In addition, related technologies attempt to evaluate the health of batteries by monitoring the battery usage of electric vehicles. For example, some insurance companies monitor parameters such as the number of battery charge and discharge cycles and charging time to evaluate the health of batteries. However, the battery usage of electric vehicles is very complicated. In addition to the number of charge and discharge cycles and charging time, there are many other factors that affect the health of batteries. Therefore, related technologies cannot comprehensively and accurately evaluate the health of batteries.

[0025] The above reasons lead to low accuracy in the assessment of vehicle insurance costs in related technologies.

[0026] In response to the above problems, an embodiment of the present application provides a vehicle insurance cost assessment method, which comprehensively and accurately assesses the vehicle usage risk and battery health status by comprehensively considering multiple dimensions of user behavior and multiple dimensions of battery usage, thereby improving the accuracy of vehicle insurance cost assessment.

[0027] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0028] Figure 1 It is a flow chart of the vehicle insurance expense assessment method shown in the embodiment of the present application.

[0029] See also Figure 1 The vehicle insurance cost assessment method of the present application includes: S110, obtaining vehicle driving data and battery status data of the vehicle in the last preset period.

[0030] In the embodiment of the present application, it can be applied to a risk assessment system. The risk assessment system can be deployed on the vehicle side or on the cloud side. The cloud side can be the business side of the insurance company.

[0031] Among them, the preset period may refer to the insurance year, which refers to the annual period for the insurance company to calculate insurance business, usually in units of one year, starting from a specific date, such as January 1st of each year to December 31st of the same year. During this period, the insurance company may assume the corresponding insurance liability based on the vehicle insurance product purchased by the user, and may use the risk insurance system provided in the embodiment of the present application to quickly and accurately assess the vehicle insurance premiums required for the user to take out insurance.

[0032] In actual applications, after the risk assessment system receives an assessment instruction from an insurance company for a certain vehicle, it can first obtain the vehicle driving data and battery status data of the vehicle in the previous preset period, for example, obtaining the vehicle driving data and battery status data of the vehicle every day in the previous year, wherein the vehicle driving data may include at least one of a vehicle mileage parameter, a vehicle driving speed parameter and a vehicle driving acceleration parameter, and the battery status data may include at least one of a battery current change parameter, a current charging parameter, a battery SOC (State of Charge) parameter and a battery temperature parameter, wherein the battery charging parameter may include at least one of a fast charging capacity parameter, a slow charging capacity parameter, a fast charging cut-off SOC parameter and a charging current parameter.

[0033] S120, determining the vehicle usage coefficient of the vehicle based on the vehicle driving data, determining the battery rest time coefficient and the user's charging habit coefficient based on the battery status data, and determining the user's driving style coefficient based on the vehicle driving data and the battery status data.

[0034] Different uses of vehicles will lead to different vehicle usage intensities and usage environments, which will affect the vehicle's wear and tear and the rate of renewal, as well as the health of the vehicle's battery. In this regard, the risk assessment system can determine the vehicle's usage coefficient based on the vehicle's driving data, so that the vehicle's usage risk and battery health status can be assessed based on the vehicle's usage coefficient.

[0035] Long-term storage of batteries will affect the health of the batteries and may also cause deformation, aging, failure, and other problems in other vehicle components. In this regard, the risk assessment system can determine the battery's static time coefficient based on the battery status data, so that the vehicle's use risk and battery health status can be assessed based on the static time coefficient.

[0036] The user's charging habits directly affect the health of the battery and also affect the energy consumption of the vehicle. In this regard, the risk assessment system can determine the user's charging habit coefficient based on the battery status data, so as to subsequently assess the vehicle's use risk and battery health based on the charging habit coefficient.

[0037] The user's driving style has a significant impact on the vehicle's energy consumption. For example, an aggressive driving style requires the motor to provide more power to accelerate the vehicle due to frequent sudden acceleration and braking. At the same time, the energy recovery efficiency during braking is reduced, thereby increasing the vehicle's energy consumption and battery loss. Another example is a low-power driving style. Since the battery is in a low-power state, such as the battery's SOC <5%, but the user is still driving the vehicle, in order to maintain the vehicle's operation, the battery needs to consume more energy, thereby increasing the vehicle's energy consumption and battery loss. In this regard, the risk assessment system can determine the user's driving style coefficient based on the vehicle's driving data and battery status data, so that the vehicle's use risk and battery health status can be assessed based on the driving style coefficient.

[0038] S130, using the vehicle usage coefficient, the static time coefficient, the charging habit coefficient and the driving style coefficient to calculate a comprehensive coefficient; the comprehensive coefficient is used to evaluate the vehicle usage risk and the battery health status.

[0039] After obtaining the vehicle usage coefficient, stationary time coefficient, charging habit coefficient and driving style coefficient, the risk assessment system can calculate the product value of the vehicle usage coefficient, stationary time coefficient, charging habit coefficient and driving style coefficient. The product value can be output as a comprehensive coefficient. The comprehensive coefficient can be used to assess the vehicle's usage risk and the battery's health status. For example, the larger the comprehensive coefficient, the greater the assessed vehicle usage risk and the worse the battery's health status. Conversely, the smaller the assessed vehicle usage risk and the better the battery's health status.

[0040] S140, based on the comprehensive coefficient, evaluate the vehicle insurance cost required by the user.

[0041] After obtaining the comprehensive coefficient, the risk assessment system can obtain the initial insurance cost, where the initial insurance cost can refer to the basic cost of the vehicle insurance cost determined when the new car is delivered. The risk assessment system can calculate the product value of the initial insurance cost and the comprehensive coefficient to obtain the final insurance cost, which is the vehicle insurance cost required for the current user to insure.

[0042] As can be seen from this example, the solution provided by this application obtains the vehicle driving data and battery status data of the vehicle in the previous preset period; determines the vehicle usage coefficient of the vehicle based on the vehicle driving data, and determines the battery rest time coefficient and the user's charging habit coefficient based on the battery status data, and determines the user's driving style coefficient based on the vehicle driving data and battery status data; uses the vehicle usage coefficient, rest time coefficient, charging habit coefficient and driving style coefficient to calculate the comprehensive coefficient; the comprehensive coefficient is used to evaluate the vehicle's use risk and the battery's health status; based on the comprehensive coefficient, the vehicle insurance premium required for the user's insurance is evaluated. This application comprehensively and accurately evaluates the vehicle's use risk and the battery's health status by comprehensively considering multiple dimensions of user behavior and multiple dimensions of battery usage, thereby improving the accuracy of vehicle insurance premium evaluation.

[0043] Figure 2 It is another flowchart of the vehicle insurance cost assessment method shown in the present application.

[0044] See also Figure 2 The vehicle insurance cost assessment method of the present application includes: S210, obtaining vehicle driving data and battery status data of the vehicle in the last preset period.

[0045] The preset period may refer to an insurance year, which refers to the annual period for an insurance company to conduct insurance business accounting, usually in units of one year.

[0046] Among them, the vehicle driving data may include at least one of a vehicle mileage parameter, a vehicle speed parameter and a vehicle acceleration parameter, the battery status data may include at least one of a battery current change parameter, a current charging parameter, a battery SOC parameter and a battery temperature parameter, and the battery charging parameter may include at least one of a fast charging capacity parameter, a slow charging capacity parameter, a fast charging cut-off SOC parameter and a charging current parameter.

[0047] S220, determining the vehicle usage coefficient of the vehicle based on the vehicle driving data, determining the battery rest time coefficient and the user's charging habit coefficient based on the battery status data, and determining the user's driving style coefficient based on the vehicle driving data and the battery status data.

[0048] Different uses of vehicles will lead to different vehicle usage intensities and usage environments, which will affect the vehicle's wear and tear and the rate of renewal, as well as the health of the vehicle's battery. In this regard, the risk assessment system can determine the vehicle's usage coefficient based on the vehicle's driving data, so that the vehicle's usage risk and battery health status can be assessed based on the vehicle's usage coefficient.

[0049] Long-term storage of batteries will affect the health of the batteries and may also cause deformation, aging, failure, and other problems in other vehicle components. In this regard, the risk assessment system can determine the battery's static time coefficient based on the battery status data, so that the vehicle's use risk and battery health status can be assessed based on the static time coefficient.

[0050] The user's charging habits directly affect the health of the battery and also affect the energy consumption of the vehicle. In this regard, the risk assessment system can determine the user's charging habit coefficient based on the battery status data, so as to subsequently assess the vehicle's use risk and battery health based on the charging habit coefficient.

[0051] The user's driving style has a significant impact on the vehicle's energy consumption. For example, aggressive driving style and low-power driving style will increase the vehicle's energy consumption and battery loss. In this regard, the risk assessment system can determine the user's driving style coefficient based on the vehicle's driving data and battery status data, so that the vehicle's use risk and battery health status can be assessed based on the driving style coefficient.

[0052] In one embodiment, the vehicle driving data includes a vehicle mileage parameter; and determining the vehicle usage coefficient of the vehicle according to the vehicle driving data may include: The vehicle mileage parameters are used to calculate the average daily mileage of the vehicle in each month within the previous preset period; the vehicle usage mode corresponding to the month in which the average daily mileage is greater than the first mileage threshold is determined as the operational vehicle mode; based on the number of months in the operational vehicle mode and / or the average daily mileage of each month corresponding to the operational vehicle mode, the vehicle use coefficient of the vehicle is determined; wherein, the vehicle use coefficient is positively correlated with the number of months in the operational vehicle mode, and the vehicle use coefficient is positively correlated with the average daily mileage of each month corresponding to the operational vehicle mode.

[0053] The vehicle driving data may include the vehicle mileage parameters of each day in the last preset period. The risk assessment system may use the vehicle mileage parameters to calculate the average daily mileage of the vehicle in each month in the last preset period, such as accumulating the vehicle mileage parameters of each day in a certain month and dividing it by the actual number of days in that month to obtain the average daily mileage of that month. The risk assessment system may compare the average daily mileage of each month with the first mileage threshold to determine the vehicle usage mode for each month. Among them, the vehicle usage mode may include a personal vehicle usage mode and an operational vehicle usage mode. The personal vehicle usage mode is mainly used to meet personal life needs and is not for profit purposes, such as daily commuting, picking up relatives and friends, etc., while the operational vehicle usage mode is mainly used to engage in road transport operations for profit purposes, that is, the operational vehicle usage mode is a mode of obtaining fees by providing transportation services, such as taxis, online car-hailing, etc.

[0054] Generally, the average daily mileage of the operational vehicle mode is higher than the average daily mileage of the personal vehicle mode. Therefore, the risk assessment system can determine the vehicle mode corresponding to the month with the average daily mileage less than or equal to the first mileage threshold as the personal vehicle mode, and determine the vehicle mode corresponding to the month with the average daily mileage greater than the first mileage threshold as the operational vehicle mode. Exemplarily, the first mileage threshold can be set to 200km, and the risk assessment system can compare the average daily mileage from January to December with the first mileage threshold (200km) respectively. If the average daily mileage of a certain month is ≤200km, the vehicle mode of that month can be determined to be the personal vehicle mode; if the average daily mileage of a certain month is >200km, the vehicle mode of that month can be determined to be the operational vehicle mode, thereby obtaining the vehicle modes from January to December respectively.

[0055] The user may have a high usage intensity in a certain month within the previous preset period. For example, the user may go on a self-driving tour for a month, which causes the vehicle mileage parameter of that month to exceed the first mileage threshold. The risk assessment system determines that the vehicle usage mode of that month is the operational vehicle mode. However, the vehicle usage mode of that month is actually the personal vehicle mode. In order to improve the assessment accuracy, the risk assessment system can count the number of months in the operational vehicle mode, so as to determine the vehicle use coefficient of the vehicle based on the number of months in the operational vehicle mode, where the vehicle use coefficient is positively correlated with the number of months in the operational vehicle mode.

[0056] In addition, even if the vehicle usage patterns in different months are all operational vehicle usage patterns, their usage intensities will vary greatly. For example, the passenger volume of online ride-hailing vehicles during the Spring Festival is greater than that in other months. Therefore, the risk assessment system can obtain the average daily mileage of each month corresponding to the operational vehicle usage pattern, so as to determine the vehicle usage coefficient of the vehicle based on the average daily mileage of each month corresponding to the operational vehicle usage pattern, where the vehicle usage coefficient is positively correlated with the average daily mileage of each month corresponding to the operational vehicle usage pattern.

[0057] In one embodiment, determining the vehicle usage coefficient of the vehicle based on the number of months in the operational vehicle mode and / or the average daily mileage of each month corresponding to the operational vehicle mode may include: If the number of months in the operational vehicle mode is less than the first duration threshold, the vehicle use coefficient of the vehicle is determined to be the first use coefficient; or, if the number of months in the operational vehicle mode is greater than or equal to the first duration threshold, and the average daily mileage of each month corresponding to the operational vehicle mode is less than the second mileage threshold, the vehicle use coefficient of the vehicle is determined to be the second use coefficient; or, if the number of months in the operational vehicle mode is greater than or equal to the first duration threshold, and the average daily mileage of each month corresponding to the operational vehicle mode is greater than or equal to the second mileage threshold, the vehicle use coefficient of the vehicle is determined to be the third use coefficient; wherein, the first mileage threshold < the second mileage threshold, and the first use coefficient < the second use coefficient < the third use coefficient.

[0058] In one example, the first duration threshold can be set to 5 months, and the second mileage threshold can be set to 250km. The risk assessment system first determines whether the number of months in the operational vehicle mode is greater than or equal to 5 months. If the number of months in the operational vehicle mode is less than 5 months, it means that the vehicle is often used to meet personal life needs in the previous preset period, and the risk assessment system can determine that the vehicle use coefficient of the vehicle is the first use coefficient; if the number of months in the operational vehicle mode is ≥5 months, it means that the vehicle is often used to engage in road transport operations for profit in the previous preset period, and the risk assessment system can further determine whether the average daily mileage of each month corresponding to the operational vehicle mode is greater than or equal to 250km. If 200km<the average daily mileage of each month corresponding to the operational vehicle mode<250km, it means that the vehicle is not used intensively in road transport operations, and the risk assessment system can determine that the vehicle use coefficient of the vehicle is the second use coefficient; if the average daily mileage of each month corresponding to the operational vehicle mode is ≥250km, it means that the vehicle is used intensively in road transport operations, and the risk assessment system can determine that the vehicle use coefficient of the vehicle is the third use coefficient. Among them, the first purpose coefficient can be set to 1.0, the second purpose coefficient can be set to 1.1, and the third purpose coefficient can be set to 1.2.

[0059] It should be noted that the embodiment of the present application can expand the vehicle use coefficient according to actual conditions. For example, in the multiple months corresponding to the operational vehicle mode, the average daily mileage in some months is <250km, and the average daily mileage in some months is ≥250km. In this case, the vehicle use coefficient of the vehicle can be determined as the fourth use coefficient, wherein the second use coefficient < the fourth use coefficient < the third use coefficient. For example, if the second use coefficient is 1.1 and the third use coefficient is 1.2, the fourth use coefficient can be set to 1.15. The embodiment of the present application does not limit this.

[0060] In one embodiment, the battery status data includes a battery current variation parameter; and determining the battery rest time coefficient according to the battery status data may include: Accumulate the first continuous storage time when the battery is in a state where the battery current change parameter is less than or equal to the first current threshold; determine the battery rest time coefficient based on the first continuous storage time; wherein the rest time coefficient is positively correlated with the first continuous storage time.

[0061] The battery status data may include the battery current change parameters of the battery every day within the last preset cycle. The risk assessment system may accumulate the first continuous storage time of the battery in the static state according to the battery current change parameters, wherein the static state may refer to the state in which the battery current change parameter is less than or equal to the first current threshold value, and the first continuous storage time may refer to the duration of the battery in the static state. Exemplarily, the first current threshold value may be set to 1A, and when the battery current change parameter is monitored to be ≤1A, it is determined that the battery enters the static state, and then the risk assessment system may accumulate the first continuous storage time of the battery in the static state, so as to determine the static time coefficient of the battery based on the first continuous storage time, wherein the static time coefficient is positively correlated with the first continuous storage time.

[0062] In one implementation, determining the static duration coefficient of the battery based on the first continuous storage duration may include: If the first continuous storage time is less than the second time threshold, the battery's rest time coefficient is determined to be the first time coefficient; or, if the first continuous storage time is greater than or equal to the second time threshold and less than the third time threshold, the battery's rest time coefficient is determined to be the second time coefficient; or, if the first continuous storage time is greater than or equal to the third time threshold, the battery's rest time coefficient is determined to be the third time coefficient; wherein, the second time threshold < the third time threshold, and the first time coefficient < the second time coefficient < the third time coefficient.

[0063] In one example, the second duration threshold can be set to 3 months, and the third duration threshold can be set to 6 months. The risk assessment system can compare the first continuous storage duration with the second duration threshold and the third duration threshold respectively. If the first continuous storage duration is less than 3 months, it means that the battery has not been used for nearly 3 months in the last preset period, and the risk assessment system can determine that the static duration coefficient of the battery is the first duration coefficient; if 3 months ≤ the first continuous storage duration < 6 months, it means that the battery has not been used for 3 to 6 months in the last preset period, and the risk assessment system can determine that the static duration coefficient of the battery is the second duration coefficient; if the first continuous storage duration is greater than or equal to 6 months, it means that the battery has not been used for more than 6 months in the last preset period, and the risk assessment system can determine that the static duration coefficient of the battery is the third duration coefficient. Among them, the first duration coefficient can be set to 1.0, the second duration coefficient can be set to 1.1, and the third duration coefficient can be set to 1.2.

[0064] It should be noted that the embodiment of the present application can expand the static time coefficient according to actual conditions, and the embodiment of the present application does not limit this.

[0065] In one embodiment, the battery status data includes a fast charge capacity parameter, a slow charge capacity parameter, and a fast charge cut-off SOC parameter; determining the user's charging habit coefficient based on the battery status data may include: Using fast charge capacity parameters, the total fast charge capacity of the battery in the last preset cycle is accumulated; and, using fast charge capacity parameters and slow charge capacity parameters, the total charge capacity of the battery in the last preset cycle is accumulated; and, the number of high SOC cut-off charging times when the battery is in a state where the fast charge cut-off SOC parameter is greater than a first SOC threshold is accumulated; based on the ratio of the total fast charge capacity to the total charge capacity, the user's fast charge habit coefficient is determined; and based on the high SOC cut-off charging times, the user's high SOC cut-off charging habit coefficient is determined; wherein, the fast charge habit coefficient is positively correlated with the ratio, and the high SOC cut-off charging habit coefficient is positively correlated with the high SOC cut-off charging times; the user's charging habit coefficient is determined based on the fast charge habit coefficient and the high SOC cut-off charging habit coefficient.

[0066] The battery status data may include battery charging parameters of each charging of the battery in the last preset cycle, and the battery charging parameters may include at least one of a fast charge capacity parameter, a slow charge capacity parameter, and a fast charge cut-off SOC parameter.

[0067] The risk assessment system may use fast charge capacity parameters to accumulate the total fast charge capacity of the battery in the last preset cycle, and may use slow charge capacity parameters to accumulate the total slow charge capacity of the battery in the last preset cycle, and then add the total fast charge capacity and the total slow charge capacity to obtain the total charge capacity of the battery in the last preset cycle. Among them, the total fast charge capacity may be the annual cumulative total fast charge capacity, the total slow charge capacity may be the annual cumulative total slow charge capacity, and the total charge capacity may be the annual cumulative total charge capacity. The risk assessment system may calculate the ratio of the total fast charge capacity to the total charge capacity (i.e., the annual cumulative total fast charge capacity / the annual cumulative total charge capacity) so as to determine the user's fast charge habit coefficient based on the ratio, wherein the fast charge habit coefficient is positively correlated with the ratio.

[0068] In one embodiment, determining the user's fast charging habit coefficient based on the ratio of the total fast charging power to the total charging power may include: If the ratio of the total fast-charging power to the total charged power is less than the first fast-charging ratio, the user's fast-charging habit coefficient is determined to be the first fast-charging coefficient; if the ratio of the total fast-charging power to the total charged power is greater than or equal to the first fast-charging ratio, and less than the second fast-charging ratio, the user's fast-charging habit coefficient is determined to be the second fast-charging coefficient; if the ratio of the total fast-charging power to the total charged power is greater than or equal to the second fast-charging ratio, and less than or equal to the third fast-charging ratio, the user's fast-charging habit coefficient is determined to be the third fast-charging coefficient; if the ratio of the total fast-charging power to the total charged power is greater than the third fast-charging ratio, the user's fast-charging habit coefficient is determined to be the fourth fast-charging coefficient; wherein, the first fast-charging ratio < the second fast-charging ratio < the third fast-charging ratio, and the first fast-charging coefficient < the second fast-charging coefficient < the third fast-charging coefficient < the fourth fast-charging coefficient.

[0069] In one example, the first fast charging ratio can be set to 0.6, the second fast charging ratio can be set to 0.8, and the third fast charging ratio can be set to 0.95. The risk assessment system can compare the ratio of the total fast charging power to the total charging power with the first fast charging ratio, the second fast charging ratio, and the third fast charging ratio, respectively. If the ratio of the total fast-charge power to the total charged power is <0.6, it means that the user did not tend to use the fast-charge method for charging the battery in the previous preset period, and the risk assessment system can determine the user's fast-charging habit coefficient as the first fast-charging coefficient; if 0.6≤the ratio of the total fast-charge power to the total charged power is <0.8, it means that the user tends to use the fast-charging method for charging the battery in the previous preset period, and the risk assessment system can determine the user's fast-charging habit coefficient as the second fast-charging coefficient; if 0.8≤the ratio of the total fast-charge power to the total charged power is ≤0.95, it means that the user often tends to use the fast-charging method for charging the battery in the previous preset period, and the risk assessment system can determine the user's fast-charging habit coefficient as the third fast-charging coefficient; if the ratio of the total fast-charge power to the total charged power is >0.95, it means that the user has always used the fast-charging method to charge the battery in the previous preset period, and the risk assessment system can determine the user's fast-charging habit coefficient as the fourth fast-charging coefficient. Among them, the first fast charging coefficient can be set to 0.95, the second fast charging coefficient can be set to 1.0, the third fast charging coefficient can be set to 1.05, and the fourth fast charging coefficient can be set to 1.1.

[0070] It should be noted that the embodiment of the present application can expand the fast charging habit coefficient according to actual conditions, and the embodiment of the present application does not limit this.

[0071] In addition, the user's charging habit coefficient is related not only to the fast charging habit coefficient, but also to the high SOC cutoff charging habit coefficient.

[0072] Specifically, the risk assessment system may use the fast charge cut-off SOC parameter to accumulate the number of high SOC cut-off charges when the battery is in a high SOC cut-off charging state, wherein the high SOC cut-off charging state may refer to a state where the fast charge cut-off SOC parameter is greater than the first SOC threshold. Exemplarily, the first SOC threshold may be set to 90%. When 90% < the fast charge cut-off SOC parameter is monitored, it is determined that the battery enters a high SOC cut-off charging state. Whenever the battery enters a high SOC cut-off charging state, the risk assessment system may accumulate the number of high SOC cut-off charges once to mark the overcharge of the battery during the fast charge process, so as to determine the user's high SOC cut-off charging habit coefficient based on the number of high SOC cut-off charges, wherein the high SOC cut-off charging habit coefficient is positively correlated with the number of high SOC cut-off charges.

[0073] In one embodiment, determining the user's high SOC cutoff charging habit coefficient based on the high SOC cutoff charging times may include: If the high SOC cut-off charging number is less than or equal to the first number threshold, the user's high SOC cut-off charging habit coefficient is determined to be the first cut-off coefficient; if the high SOC cut-off charging number is greater than the first number threshold and less than or equal to the second number threshold, the user's high SOC cut-off charging habit coefficient is determined to be the second cut-off coefficient; if the high SOC cut-off charging number is greater than the second number threshold and less than or equal to the third number threshold, the user's high SOC cut-off charging habit coefficient is determined to be the third cut-off coefficient; if the high SOC cut-off charging number is greater than the third number threshold, the user's high SOC cut-off charging habit coefficient is determined to be the fourth cut-off coefficient; wherein, the first number threshold < the second number threshold < the third number threshold, and the first cut-off coefficient < the second cut-off coefficient < the third cut-off coefficient < the fourth cut-off coefficient.

[0074] In one example, the first count threshold may be set to 8 times, the second count threshold may be set to 30 times, and the third count threshold may be set to 60 times, and the risk assessment system may compare the high SOC cutoff charging times with the first count threshold, the second count threshold, and the third count threshold, respectively. If the number of high SOC cutoff charging times is ≤8 times, it means that the user rarely overcharges the battery during fast charging in the previous preset cycle, and the risk assessment system can determine the user's high SOC cutoff charging habit coefficient as the first cutoff coefficient; if 8 times < the number of high SOC cutoff charging times ≤30 times, it means that the user sometimes overcharges the battery during fast charging in the previous preset cycle, and the risk assessment system can determine the user's high SOC cutoff charging habit coefficient as the second cutoff coefficient; if 30 times < the number of high SOC cutoff charging times ≤60 times, it means that the user often overcharges the battery during fast charging in the previous preset cycle, and the risk assessment system can determine the user's high SOC cutoff charging habit coefficient as the third cutoff coefficient; if the number of high SOC cutoff charging times is >60 times, it means that the user always overcharges the battery during fast charging in the previous preset cycle, and the risk assessment system can determine the user's high SOC cutoff charging habit coefficient as the fourth cutoff coefficient. The first cutoff coefficient may be set to 0.95, the second cutoff coefficient may be set to 1.0, the third cutoff coefficient may be set to 1.15, and the fourth cutoff coefficient may be set to 1.2.

[0075] It should be noted that the embodiment of the present application can expand the high SOC cut-off charging habit coefficient according to actual conditions, and the embodiment of the present application does not limit this.

[0076] After obtaining the fast charging habit coefficient and the high SOC cutoff charging habit coefficient, the risk assessment system can calculate the user's charging habit coefficient through a first calculation formula: charging habit coefficient = 0.7 × fast charging habit coefficient + 0.3 × high SOC cutoff charging habit coefficient.

[0077] In one embodiment, the vehicle driving data includes a vehicle driving speed parameter and a vehicle driving acceleration parameter, and the battery status data includes a battery SOC parameter; determining the user's driving style coefficient based on the vehicle driving data and the battery status data may include: The number of low-battery driving times when the vehicle is in a state where the battery SOC parameter is less than the second SOC threshold and the vehicle speed parameter is greater than the first speed threshold is accumulated; and the ultra-high-speed driving time when the vehicle is in a state where the vehicle speed parameter is greater than the second speed threshold is accumulated; and the number of sudden acceleration driving times when the vehicle is in a state where the vehicle acceleration parameter is greater than a preset acceleration threshold is accumulated; based on the number of low-battery driving times, a low-battery driving style coefficient of the user is determined; and based on the ultra-high-speed driving time, an ultra-high-speed driving coefficient of the vehicle is determined; wherein the low-battery driving style coefficient is positively correlated with the number of low-battery driving times, and the ultra-high-speed driving coefficient is positively correlated with the ultra-high-speed driving time; based on the ultra-high-speed driving coefficient and the number of sudden acceleration times, an aggressive driving style coefficient of the user is determined; and based on the low-battery driving style coefficient and the aggressive driving style coefficient, a driving style coefficient of the user is determined.

[0078] The vehicle driving data may include the vehicle driving speed parameters and vehicle driving acceleration parameters of the vehicle every day in the last preset cycle, and the battery status data may include the battery SOC parameters of the battery every day in the last preset cycle.

[0079] The risk assessment system may use the battery SOC parameter and the vehicle speed parameter to accumulate the number of low-battery driving times when the vehicle is in a low-battery driving state, wherein the low-battery driving state may refer to a state where the battery SOC parameter is less than the second SOC threshold and the vehicle speed parameter is greater than the first speed threshold. Exemplarily, the second SOC threshold may be set to 5%, and the first speed threshold may be set to 0. When the battery SOC parameter is monitored to be less than 5%, and 0 is less than the vehicle speed parameter, it indicates that the battery SOC is less than 5% on that day but the user is still driving the vehicle. In this case, the risk assessment system may accumulate the number of low-battery driving times once, so as to determine the user's low-battery driving style coefficient based on the number of low-battery driving times, wherein the low-battery driving style coefficient is positively correlated with the number of low-battery driving times.

[0080] In one embodiment, determining the low-battery driving style coefficient of the user based on the number of low-battery driving times may include: If the number of low-battery driving times is less than or equal to the fourth times threshold, the user's low-battery driving style coefficient is determined to be the first low-battery coefficient; if the number of low-battery driving times is greater than the fourth times threshold and less than or equal to the fifth times threshold, the user's low-battery driving style coefficient is determined to be the second low-battery coefficient; wherein the fourth times threshold < the fifth times threshold, and the first low-battery coefficient < the second low-battery coefficient.

[0081] In one example, the fourth number threshold can be set to 30 times, the fifth number threshold can be set to 60 times, and the risk assessment system can compare the number of low-battery driving times with the fourth number threshold and the fifth number threshold, respectively. If the number of low-battery driving times is ≤30 times, it means that the user occasionally encounters low-battery driving in the previous preset period, and the risk assessment system can determine that the user's low-battery driving style coefficient is the first low-battery coefficient; if 30 times < low-battery driving times ≤60 times, it means that the user often encounters low-battery driving in the previous preset period, and the risk assessment system can determine that the user's low-battery driving style coefficient is the second low-battery coefficient. Among them, the first low-battery coefficient can be set to 1.0 or 1.1, and the second low-battery coefficient can be set to 1.15 or 1.2.

[0082] It should be noted that the embodiment of the present application can expand the low-battery driving style coefficient according to actual conditions, and the embodiment of the present application does not limit this.

[0083] In addition, the user's driving style coefficient is not only related to the low-battery driving style coefficient, but also to the aggressive driving style coefficient, wherein the relevant factors of the aggressive driving style coefficient may include ultra-high-speed driving time and the number of sudden acceleration driving times.

[0084] Specifically, the risk assessment system may use the vehicle speed parameter to accumulate the ultra-high speed driving time of the vehicle in the ultra-high speed driving state, wherein the ultra-high speed driving state may refer to a state in which the vehicle speed parameter is greater than a second vehicle speed threshold. Exemplarily, the second vehicle speed threshold may be set to 120 km / h, and when 120 km / h < the vehicle speed parameter, it is determined that the vehicle enters the ultra-high speed driving state, and then the ultra-high speed driving time of the vehicle in the ultra-high speed driving state may be accumulated, so as to determine the ultra-high speed driving coefficient of the vehicle based on the ultra-high speed driving time, wherein the ultra-high speed driving coefficient is positively correlated with the ultra-high speed driving time.

[0085] In one embodiment, determining the ultra-high-speed driving coefficient of the vehicle based on the ultra-high-speed driving duration may include: If the ultra-high-speed driving duration is less than or equal to the fourth duration threshold, the vehicle's ultra-high-speed driving coefficient is determined to be the first ultra-high-speed coefficient; if the ultra-high-speed driving duration is greater than the fourth duration threshold and is less than or equal to the fifth duration threshold, the vehicle's ultra-high-speed driving coefficient is determined to be the second ultra-high-speed coefficient; wherein, the fourth duration threshold < the fifth duration threshold, and the first ultra-high-speed coefficient < the second ultra-high-speed coefficient.

[0086] In one example, the fourth duration threshold can be set to 30 hours, the fifth duration threshold can be set to 60 hours, and the risk assessment system can compare the ultra-high-speed driving duration with the fourth duration threshold and the fifth duration threshold respectively. If the ultra-high-speed driving duration is ≤30 hours, it means that the user occasionally drives the vehicle at an ultra-high speed during the previous preset period, and the risk assessment system can determine that the user's ultra-high-speed driving coefficient is the first ultra-high-speed coefficient; if 30 hours < ultra-high-speed driving duration ≤60 hours, it means that the user often drives the vehicle at an ultra-high speed during the previous preset period, and the risk assessment system can determine that the user's ultra-high-speed driving coefficient is the second ultra-high-speed coefficient. Among them, the first ultra-high-speed coefficient can be set to 1.0 or 1.1, and the second ultra-high-speed coefficient can be set to 1.15 or 1.2.

[0087] It should be noted that the embodiment of the present application can expand the ultra-high-speed driving coefficient according to actual conditions, and the embodiment of the present application does not limit this.

[0088] At the same time, the risk assessment system can use the vehicle driving acceleration parameter to accumulate the number of times the vehicle is in a sudden acceleration driving state, wherein the sudden acceleration driving state may refer to a state where the vehicle driving acceleration parameter is greater than a preset acceleration threshold. For example, generally, the acceleration value of a sudden acceleration of a vehicle is usually between 0.8G and 1.0G, which is considered to be a normal sudden acceleration range. Therefore, the embodiment of the present application can set the preset acceleration threshold range to [0.8G, 1.0G]. When 0.8 < vehicle driving speed parameter < 1.0G, it is determined that the vehicle has entered a sudden acceleration driving state, and then the number of times the vehicle is in a sudden acceleration driving state can be accumulated.

[0089] After obtaining the ultra-high speed driving coefficient and the number of sudden acceleration times, the risk assessment system can calculate the user's aggressive driving style coefficient through a second calculation formula: aggressive driving style coefficient = 0.7 × ultra-high speed driving coefficient + 0.3 × number of sudden acceleration times.

[0090] After obtaining the low-battery driving style coefficient and the aggressive driving style coefficient, the risk assessment system may calculate the user's driving style coefficient through a third calculation formula: driving style coefficient = 0.3 × low-battery driving style coefficient + 0.7 × aggressive driving style coefficient.

[0091] S230, when the battery encounters any preset risk situation in the previous preset cycle, obtain a corresponding penalty coefficient.

[0092] In the related art, after evaluating the use risk of electric vehicles and the health status of batteries, risks are mainly managed by adjusting insurance premiums. However, this method cannot effectively reduce the battery loss of electric vehicles, nor can it help users improve their insurance costs.

[0093] In this regard, the embodiments of the present application can induce users to use the vehicle better by introducing penalty coefficients and reward coefficients, thereby effectively reducing the battery loss of electric vehicles and helping users improve their vehicle insurance costs. Specifically, the embodiments of the present application can pre-configure a variety of preset risk situations, wherein the preset risk situation can include at least one of a long-term low-power static situation, a long-term low-temperature static situation, a long-term high-temperature static situation, and a frequent overcharging situation, and then configure a corresponding penalty coefficient for each preset risk situation.

[0094] In one example, the battery status data may include the battery SOC parameters of each day in the last preset cycle, and the risk assessment system may determine whether the battery has been in a long-term low-power static state based on the battery SOC parameters. In a specific implementation, when the battery SOC parameter is monitored to be less than the third SOC threshold, it is determined that the battery has entered a low-power state, and then the risk assessment system may accumulate the second continuous storage time of the battery in the low-power state, which may refer to the duration of the battery in the low-power state, so as to determine whether the battery has been in a long-term low-power static state based on the second continuous storage time. If the second continuous storage time is less than the sixth time threshold, it means that the battery has not been in a low-power state for a long time, and the battery is determined to be in a short-term low-power state. Since the short-term low-power state can reduce the number of charge and discharge cycles of the battery, it is beneficial to extend the service life of the battery, so the risk assessment system does not need to introduce a penalty coefficient; if the second continuous storage time is greater than or equal to the sixth time threshold, it means that the battery has been in a low-power state for too long, and the battery is determined to be in a long-term low-power state. Since the long-term low-power state will cause battery capacity attenuation, battery performance degradation, changes in the internal structure of the battery, and battery damage, the risk assessment system can obtain the corresponding penalty coefficient. Exemplarily, the third SOC threshold can be set to 3%, and the sixth time threshold can be set to 1 month. When the battery SOC parameter is monitored to be less than 3%, it is determined that the battery enters a low-power state. Then the risk assessment system can accumulate the second continuous storage time of the battery in a low-power state. If the second continuous storage time is ≥ 1 month, it is determined that the battery has been in a long-term low-power state, so the corresponding penalty coefficient is obtained. The penalty coefficient corresponding to the long-term low-power state can be set to 0.1.

[0095] In another example, the battery status data may include the battery temperature parameters of each day in the last preset cycle, and the risk assessment system may determine whether the battery has been left at a low temperature for a long time based on the battery temperature parameters. In a specific implementation, when the battery temperature parameter is monitored to be less than the first temperature threshold, it is determined that the battery has entered a low temperature state, and then the risk assessment system may accumulate the third continuous storage time of the battery in the low temperature state, which may refer to the duration of the battery in the low temperature state, so as to determine whether the battery has been left at a low temperature for a long time based on the third continuous storage time. If the third continuous storage time is less than the seventh time threshold, it means that the battery has not been in a low temperature state for a long time, and the battery is determined to have a short-term low temperature standstill. Since the short-term low temperature standstill can slow down the chemical reaction inside the battery, thereby reducing battery loss and helping to extend the battery life, the risk assessment system does not need to introduce a penalty coefficient. If the third continuous storage time is greater than or equal to the seventh time threshold, it means that the battery has been in a low temperature state for too long, and the battery is determined to have a long-term low temperature standstill. Since the long-term low temperature standstill can cause the battery performance to become unstable, the charging speed to slow down, and the cruising range to decrease, the risk assessment system can obtain the corresponding penalty coefficient. Exemplarily, the first temperature threshold can be set to -30°, and the seventh time threshold can be set to 1 month. When the battery temperature parameter is monitored to be less than -30°, it is determined that the battery has entered a low temperature state. The risk assessment system can then accumulate the third continuous storage time of the battery in the low temperature state. If the third continuous storage time is ≥ 1 month, it is determined that the battery has been in a long-term low-temperature static state, and the corresponding penalty coefficient is obtained. The penalty coefficient corresponding to the long-term low-temperature static state can be set to 0.1.

[0096] In another example, the battery status data may include the battery temperature parameters of each day in the last preset cycle, and the risk assessment system may determine whether the battery has been left at a high temperature for a long time based on the battery temperature parameters. In a specific implementation, when the battery temperature parameter is monitored to be greater than the second temperature threshold, it is determined that the battery has entered a high temperature state, wherein the second temperature threshold is greater than the first temperature threshold, and the risk assessment system may accumulate the fourth continuous storage time of the battery in the high temperature state, and the fourth continuous storage time may refer to the duration of the battery in the high temperature state, so as to determine whether the battery has been left at a high temperature for a long time based on the fourth continuous storage time. If the fourth continuous storage time is less than the eighth time threshold, it means that the battery has not been in a high temperature state for a long time, and it is determined that the battery has a short-term high temperature static state. Since the short-term high temperature static state may be caused by the vehicle being exposed to the sun in a hot environment, it is a normal phenomenon, so the risk assessment system does not need to introduce a penalty coefficient; if the fourth continuous storage time is greater than or equal to the eighth time threshold, it means that the battery has been in a high temperature state for too long, and it is determined that the battery has a long-term high temperature static state. Since the long-term high temperature static state will cause the battery's performance to deteriorate, the internal structure to change, and there are safety hazards, the risk assessment system can obtain the corresponding penalty coefficient. Exemplarily, the second temperature threshold can be set to 60°, and the eighth time threshold can be set to 1 month. When the battery temperature parameter is monitored to be greater than 60°, it is determined that the battery has entered a high temperature state. Then the risk assessment system can accumulate the fourth continuous storage time of the battery in the high temperature state. If the fourth continuous storage time is ≥1 month, it is determined that the battery has been in a long-term high temperature static state, so the corresponding penalty coefficient is obtained. The penalty coefficient corresponding to the long-term high temperature static state can be set to 0.1.

[0097] In another example, the battery status data may include the battery charging parameters of each charge of the battery in the last preset cycle, and the battery charging parameters may include the charging current parameters. The risk assessment system may determine whether the battery is frequently overcharged based on the charging current parameters. In a specific implementation, when it is monitored that the charging current parameter is greater than the charging request current, it is determined that the battery enters an overcharge state, and the risk assessment system may accumulate the overcharge current of the battery in the overcharge state, so as to determine whether the battery is frequently overcharged based on the overcharge current. If the overcharge current is less than the second current threshold, it means that the battery is rarely overcharged, and it will not cause great damage to the battery, so the risk assessment system may not need to introduce a penalty coefficient; if the overcharge current is greater than or equal to the second current threshold, it means that the battery is often overcharged, that is, the battery is frequently overcharged. Since frequent overcharging will cause the battery's performance to degrade, the charging efficiency to decrease, and there are safety hazards, the risk assessment system can obtain the corresponding penalty coefficient. Exemplarily, the second current threshold can be set to 100Ah. When the charging current parameter is monitored to be greater than the charging request current, it is determined that the battery has entered an overcharge state. The risk assessment system can then accumulate the overcharge current of the battery in the overcharge state. If the overcharge current is ≥100Ah, it is determined that the battery is frequently overcharged, and the corresponding penalty coefficient is obtained. The penalty coefficient corresponding to the frequent overcharge situation can be set to 0.1.

[0098] S240, when any two sub-coefficients are less than or equal to the normal coefficient threshold, and / or the battery has not experienced any preset risk situation in the previous preset cycle, obtain the corresponding reward coefficient; wherein the sub-coefficients include vehicle usage coefficient, stationary time coefficient, charging habit coefficient and driving style coefficient.

[0099] If the user has good vehicle usage habits, a reward coefficient can be introduced to encourage the user to continue to maintain good vehicle usage habits. Specifically, the risk assessment system can use the vehicle usage coefficient, the static time coefficient, the charging habit coefficient and the driving style coefficient as sub-coefficients, and then determine whether each sub-coefficient is less than or equal to the normal coefficient threshold. If a sub-coefficient is less than or equal to the normal coefficient threshold, it means that the user has good vehicle usage habits in the dimension where the sub-coefficient is located; if a sub-coefficient is greater than the normal coefficient threshold, it means that the user has bad vehicle usage habits in the dimension where the sub-coefficient is located. The risk assessment system counts the number of sub-coefficients that are less than or equal to the normal coefficient threshold. If any two sub-coefficients are less than or equal to the normal coefficient threshold, the corresponding reward coefficient can be obtained. Exemplarily, the normal coefficient threshold can be set to 1.0. Assuming that the vehicle usage coefficient is 1.0, the static time coefficient is 1.0, the charging habit coefficient is 0.985, and the driving style coefficient is 2.5, there are 3 sub-coefficients that are ≤1.0, so the corresponding reward coefficient is obtained, and the reward coefficient can be set to 0.1.

[0100] If the battery does not experience any of the preset risk situations in the last preset cycle, it can also be said that the user has good vehicle usage habits, so the corresponding reward coefficient can be obtained, and the reward coefficient can be set to 0.1.

[0101] Of the above two conditions, as long as one condition is met, the corresponding reward coefficient can be obtained. In addition, in order to effectively reduce the risk, the embodiment of the present application can also set the above two conditions to be met at the same time to obtain the corresponding reward coefficient, that is, when any two sub-coefficients are less than or equal to the normal coefficient threshold, and the battery has not experienced any preset risk situation in the previous preset cycle, the risk assessment system will obtain the corresponding reward coefficient, and the reward coefficient can be set to 0.1.

[0102] Furthermore, in order to minimize the risk, the embodiment of the present application can also set all items to be less than or equal to the normal coefficient threshold, that is, when all item sub-coefficients are less than or equal to the normal coefficient threshold, and the battery has not experienced any preset risk situation in the previous preset period, the risk assessment system obtains the corresponding reward coefficient, and the reward coefficient can be set to 0.1.

[0103] S250 uses all item sub-coefficients, penalty coefficients and reward coefficients to calculate a comprehensive coefficient; the comprehensive coefficient is used to evaluate the vehicle's use risk and the battery's health status.

[0104] After obtaining all sub-coefficients, penalty coefficients and reward coefficients, that is, after obtaining the vehicle usage coefficient, stationary time coefficient, charging habit coefficient, driving style coefficient, penalty coefficient and reward coefficient, the risk assessment system can calculate the product value of the vehicle usage coefficient, stationary time coefficient, charging habit coefficient and driving style coefficient, and then use the product value, penalty coefficient and reward coefficient to calculate the comprehensive coefficient.

[0105] Specifically, the risk assessment system can calculate the comprehensive coefficient through the fourth calculation formula: Comprehensive coefficient = vehicle usage coefficient × stationary time coefficient × charging habit coefficient × driving style coefficient + penalty coefficient - reward coefficient.

[0106] The comprehensive coefficient can be used to evaluate the vehicle's usage risk and the battery's health status. For example, the larger the comprehensive coefficient, the greater the vehicle's usage risk and the worse the battery's health status. Conversely, the smaller the vehicle's usage risk and the better the battery's health status.

[0107] S260, based on the comprehensive coefficient, evaluates the vehicle insurance cost required by the user.

[0108] After obtaining the comprehensive coefficient, the risk assessment system can obtain the initial insurance cost, and then calculate the product of the initial insurance cost and the comprehensive coefficient to obtain the final insurance cost, which is the vehicle insurance cost required for the current user.

[0109] Specifically, the risk assessment system can calculate the final insurance cost through the fifth calculation formula: initial insurance cost × comprehensive coefficient = final insurance cost.

[0110] In one embodiment, the method may further include: Monitor whether each sub-coefficient is greater than or equal to the abnormal coefficient threshold, and monitor whether the battery has any preset risk situation; when a sub-coefficient greater than or equal to the abnormal coefficient threshold is monitored, push reminder information matching the monitored sub-coefficient to the user; and / or, when any preset risk situation of the battery is monitored, push reminder information matching the monitored preset risk situation to the user.

[0111] In the embodiment of the present application, each sub-coefficient, penalty coefficient and reward coefficient can be updated regularly, for example, each sub-coefficient, penalty coefficient and reward coefficient can be updated daily / weekly / monthly. Users can check the specific situation at any time by logging into the APP (Application) interface of the risk assessment system.

[0112] Since the dimensions of each sub-coefficient and penalty coefficient are related to the service life of the vehicle and battery, the embodiment of the present application can provide risk warning services to assist insurance companies in better managing risks and improving user satisfaction. Specifically, the risk assessment system can monitor in real time whether each sub-coefficient is greater than or equal to the abnormal coefficient threshold. When a sub-coefficient greater than or equal to the abnormal coefficient threshold is detected, the risk assessment system can push reminder information matching the monitored sub-coefficient to the user to remind the user of the bad vehicle usage habits in the relevant dimensions, thereby preventing the user from continuing such bad vehicle usage habits and avoiding excessive vehicle insurance costs.

[0113] As an example, the abnormal coefficient threshold can be set to 1.1. Assuming the charging habit coefficient is 1.115, the risk assessment system monitors that the charging habit coefficient is greater than 1.1. In order to prevent users from continuing this bad vehicle usage habit and avoid excessive vehicle insurance premiums due to the charging habit coefficient, the risk assessment system can push reminder information that matches the charging habit coefficient to users to remind users of their bad charging habits. In addition, the risk assessment system can also push charging suggestions that match the charging habit coefficient to help users optimize their charging habits, thereby further improving the battery life and performance of electric vehicles.

[0114] As another example, the abnormal coefficient threshold can be set to 1.1. Assuming the driving style coefficient is 2.5, the risk assessment system monitors that the driving style coefficient is greater than 1.1. In order to prevent users from continuing this bad vehicle usage habit and avoid excessive vehicle insurance premiums due to the driving style coefficient, the risk assessment system can push reminder information that matches the driving style coefficient to users to remind them of their bad driving habits. In addition, the risk assessment system can also push driving suggestions that match the driving style coefficient to help users optimize their driving habits, thereby further improving the battery life and performance of electric vehicles.

[0115] In addition, the risk assessment system can also monitor in real time whether the battery has any preset risk conditions. When any preset risk condition is detected in the battery, the risk assessment system can push reminder information matching the monitored preset risk condition to the user, thereby reminding the user of bad vehicle usage habits in relevant dimensions, thereby preventing the user from continuing such bad vehicle usage habits and avoiding excessive vehicle insurance costs.

[0116] As an example, when it is monitored that the battery has been idle for a long time with low power, in order to prevent users from continuing this bad vehicle usage habit and to avoid excessive vehicle insurance premiums due to the penalty coefficient introduced by the long-term low-power static situation, the risk assessment system can push reminder information matching the long-term low-power static situation to the user, thereby reminding the user to charge the battery as soon as possible.

[0117] As another example, when it is monitored that the battery has been left at a low temperature for a long time, in order to prevent the user from continuing this bad vehicle usage habit and to avoid excessive vehicle insurance costs due to the penalty factor introduced by the long-term low-temperature static condition, the risk assessment system can push reminder information matching the long-term low-temperature static condition to the user, thereby reminding the user to start the vehicle regularly.

[0118] Among them, the push method of reminder information can include at least one of Xiao P voice push, APP SMS push, customer service phone push, and email push.

[0119] It should be noted that the embodiments of the present application can expand other sub-coefficients according to actual conditions. For example, the health status of the battery is also affected by factors such as the battery manufacturing process and charging time. Therefore, the embodiments of the present application can introduce sub-coefficients such as the manufacturing process coefficient and the charging time coefficient. The embodiments of the present application do not limit this.

[0120] As can be seen from this example, the solution provided by this application comprehensively considers multiple dimensions of user behavior and multiple dimensions of battery usage, thereby comprehensively and accurately evaluating the vehicle's usage risks and battery health status, thereby improving the accuracy of vehicle insurance premium assessment.

[0121] Furthermore, the solution provided in the present application can induce users to use vehicles better by introducing penalty coefficients and reward coefficients, thereby effectively reducing the battery loss of electric vehicles and helping users improve their vehicle insurance costs.

[0122] Furthermore, the solution provided in this application, by providing risk warning services, can promptly remind users of bad vehicle usage habits in relevant dimensions, thereby preventing users from continuing such bad vehicle usage habits and avoiding excessive vehicle insurance premiums. Therefore, it can effectively assist insurance companies to better manage risks and improve user satisfaction.

[0123] Furthermore, the solution provided in the present application helps users optimize their driving habits and charging habits by providing personalized driving suggestions and charging suggestions, thereby further improving the battery life and performance of electric vehicles.

[0124] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides a vehicle insurance cost assessment device, a vehicle, a computer-readable storage medium and corresponding embodiments.

[0125] Figure 3 It is a schematic diagram of the structure of a vehicle insurance expense assessment device shown in an embodiment of the present application.

[0126] See also Figure 3 The present application provides a vehicle insurance cost assessment device, which may include: The historical vehicle data acquisition module 310 is used to acquire the vehicle driving data and battery status data of the vehicle in the last preset period; The sub-coefficient determination module 320 is used to determine the vehicle usage coefficient of the vehicle according to the vehicle driving data, determine the battery rest time coefficient and determine the user's charging habit coefficient according to the battery status data, and determine the user's driving style coefficient according to the vehicle driving data and the battery status data; A comprehensive coefficient calculation module 330 is used to calculate a comprehensive coefficient using a vehicle usage coefficient, a static time coefficient, a charging habit coefficient, and a driving style coefficient; the comprehensive coefficient is used to evaluate the use risk of the vehicle and the health status of the battery; The vehicle insurance cost assessment module 340 is used to assess the vehicle insurance cost required by the user based on the comprehensive coefficient.

[0127] In one embodiment, the vehicle driving data includes a vehicle mileage parameter; the sub-coefficient determination module 320 may include: The daily average mileage calculation submodule is used to calculate the daily average mileage of the vehicle in each month within the previous preset period using the vehicle mileage parameters; An operational vehicle mode determination submodule, used to determine the vehicle mode corresponding to the month in which the average daily mileage is greater than the first mileage threshold as an operational vehicle mode; The vehicle usage coefficient determination submodule is used to determine the vehicle usage coefficient of the vehicle based on the number of months in the operating vehicle mode and / or the average daily mileage in each month corresponding to the operating vehicle mode; wherein the vehicle usage coefficient is positively correlated with the number of months in the operating vehicle mode, and the vehicle usage coefficient is positively correlated with the average daily mileage in each month corresponding to the operating vehicle mode.

[0128] In one embodiment, the battery status data includes a battery current variation parameter; the sub-coefficient determination module 320 may include: A first continuous storage duration accumulation submodule, used for accumulating a first continuous storage duration when the battery is in a state where the battery current variation parameter is less than or equal to a first current threshold; The rest time coefficient determination submodule is used to determine the rest time coefficient of the battery based on the first continuous storage time; wherein the rest time coefficient is positively correlated with the first continuous storage time.

[0129] In one embodiment, the battery status data includes a fast charge capacity parameter, a slow charge capacity parameter, and a fast charge cutoff SOC parameter; each sub-coefficient determination module 320 may include: The fast charge total power accumulation submodule is used to use the fast charge power parameters to accumulate the total fast charge power of the battery in the previous preset cycle, and A total charging power accumulation submodule is used to accumulate the total charging power of the battery in the last preset cycle using a fast charging power parameter and a slow charging power parameter; and A high SOC cut-off charging times accumulation submodule, used to accumulate the high SOC cut-off charging times when the battery is in a state where the fast charging cut-off SOC parameter is greater than a first SOC threshold; a fast charging habit coefficient determination submodule, used to determine the user's fast charging habit coefficient based on the ratio of the total fast charging power to the total charging power; and A high SOC cut-off charging habit coefficient determination submodule is used to determine the user's high SOC cut-off charging habit coefficient based on the high SOC cut-off charging times; wherein the fast charging habit coefficient is positively correlated with the ratio, and the high SOC cut-off charging habit coefficient is positively correlated with the high SOC cut-off charging times; The charging habit coefficient determination submodule is used to determine the user's charging habit coefficient based on the fast charging habit coefficient and the high SOC cut-off charging habit coefficient.

[0130] In one embodiment, the vehicle driving data includes a vehicle driving speed parameter and a vehicle driving acceleration parameter, and the battery state data includes a battery SOC parameter; each sub-coefficient determination module 320 may include: A low-battery driving times accumulation submodule, used to accumulate the low-battery driving times of the vehicle when the battery SOC parameter is less than the second SOC threshold and the vehicle driving speed parameter is greater than the first vehicle speed threshold; and An ultra-high-speed driving duration accumulation submodule, used to accumulate the ultra-high-speed driving duration of the vehicle when the vehicle driving speed parameter is greater than a second vehicle speed threshold; and A sudden acceleration running times accumulation submodule, used to accumulate the sudden acceleration running times of the vehicle when the vehicle running acceleration parameter is greater than a preset acceleration threshold; A low-battery driving style coefficient determination submodule, used to determine a user's low-battery driving style coefficient based on the number of low-battery driving times; and The ultra-high-speed driving coefficient determination submodule is used to determine the ultra-high-speed driving coefficient of the vehicle based on the ultra-high-speed driving duration; wherein the low-battery driving style coefficient is positively correlated with the number of low-battery driving times, and the ultra-high-speed driving coefficient is positively correlated with the ultra-high-speed driving duration; The aggressive driving style coefficient determination submodule is used to determine the aggressive driving style coefficient of the user according to the ultra-high speed driving coefficient and the number of rapid acceleration driving times; The driving style coefficient determination submodule is used to determine the user's driving style coefficient according to the low-battery driving style coefficient and the intense driving style coefficient.

[0131] In one embodiment, the comprehensive coefficient calculation module 330 may include: The penalty coefficient acquisition submodule is used to obtain the corresponding penalty coefficient when the battery encounters any preset risk situation in the previous preset cycle; A reward coefficient acquisition submodule is used to obtain a corresponding reward coefficient when any two sub-coefficients are less than or equal to a normal coefficient threshold, and / or the battery has not experienced any preset risk situation in the previous preset cycle; wherein the sub-coefficients include a vehicle use coefficient, a static time coefficient, a charging habit coefficient, and a driving style coefficient; A comprehensive coefficient calculation submodule is used to calculate the comprehensive coefficient using all item sub-coefficients and penalty coefficients and reward coefficients; Among them, the preset risk conditions include at least one of long-term low-power static conditions, long-term low-temperature static conditions, long-term high-temperature static conditions, and frequent overcharging conditions.

[0132] In one embodiment, the device may further include: The abnormality monitoring module is used to monitor whether each sub-coefficient is greater than or equal to the abnormal coefficient threshold, and to monitor whether the battery has any preset risk conditions; A first reminder module is used to push reminder information matching the monitored sub-coefficient to the user when a sub-coefficient greater than or equal to the abnormal coefficient threshold is monitored; and / or, The second reminder module is used to push reminder information matching the monitored preset risk situation to the user when any preset risk situation of the battery is monitored.

[0133] As can be seen from this example, the solution provided by this application obtains the vehicle driving data and battery status data of the vehicle in the previous preset period; determines the vehicle usage coefficient of the vehicle based on the vehicle driving data, and determines the battery rest time coefficient and the user's charging habit coefficient based on the battery status data, and determines the user's driving style coefficient based on the vehicle driving data and battery status data; uses the vehicle usage coefficient, rest time coefficient, charging habit coefficient and driving style coefficient to calculate the comprehensive coefficient; the comprehensive coefficient is used to evaluate the vehicle's use risk and the battery's health status; based on the comprehensive coefficient, the vehicle insurance premium required for the user's insurance is evaluated. This application comprehensively and accurately evaluates the vehicle's use risk and the battery's health status by comprehensively considering multiple dimensions of user behavior and multiple dimensions of battery usage, thereby improving the accuracy of vehicle insurance premium evaluation.

[0134] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0135] Figure 4 It is a schematic diagram of the structure of a vehicle shown in an embodiment of the present application.

[0136] See also Figure 4 , the vehicle 400 includes a memory 410 and a processor 420 .

[0137] The processor 420 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0138] The memory 410 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 420 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at runtime. In addition, the memory 410 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 410 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0139] The memory 410 stores executable codes, and when the executable codes are processed by the processor 420 , the processor 420 can execute part or all of the methods described above.

[0140] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0141] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by the vehicle's processor, the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0142] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A vehicle insurance cost assessment method, characterized in that: include: Obtaining vehicle driving data and battery status data of the vehicle in the last preset cycle; Determining a vehicle usage coefficient of the vehicle based on the vehicle driving data, determining a battery rest time coefficient and a user charging habit coefficient based on the battery status data, and determining a user driving style coefficient based on the vehicle driving data and the battery status data; The vehicle usage coefficient, the static time coefficient, the charging habit coefficient and the driving style coefficient are used to calculate a comprehensive coefficient; the comprehensive coefficient is used to evaluate the use risk of the vehicle and the health status of the battery; Based on the comprehensive coefficient, the vehicle insurance cost required for the user to purchase insurance is evaluated.

2. The method according to claim 1, characterized in that The vehicle driving data includes a vehicle mileage parameter; and determining the vehicle usage coefficient of the vehicle according to the vehicle driving data includes: Using the vehicle mileage parameter, calculate the average daily mileage of the vehicle in each month within the previous preset period; The vehicle usage mode corresponding to the month in which the average daily mileage is greater than the first mileage threshold is determined as the operational vehicle usage mode; Based on the number of months in the operational vehicle mode and / or the average daily mileage in each month corresponding to the operational vehicle mode, the vehicle usage coefficient of the vehicle is determined; wherein the vehicle usage coefficient is positively correlated with the number of months in the operational vehicle mode, and the vehicle usage coefficient is positively correlated with the average daily mileage in each month corresponding to the operational vehicle mode.

3. The method according to claim 1, characterized in that The battery status data includes a battery current variation parameter; and determining the battery rest time coefficient according to the battery status data includes: Accumulating a first continuous storage time length when the battery is in a state where the battery current change parameter is less than or equal to a first current threshold; Based on the first continuous storage time, a static time coefficient of the battery is determined; wherein the static time coefficient is positively correlated with the first continuous storage time.

4. The method according to claim 1, characterized in that The battery status data includes a fast charge capacity parameter, a slow charge capacity parameter and a fast charge cut-off SOC parameter; and determining the user's charging habit coefficient according to the battery status data includes: The fast charge capacity parameter is used to accumulate the total fast charge capacity of the battery in the previous preset cycle; and the fast charge capacity parameter and the slow charge capacity parameter are used to accumulate the total charge capacity of the battery in the previous preset cycle; and the high SOC cut-off charging times of the battery when the fast charge cut-off SOC parameter is greater than the first SOC threshold are accumulated; Based on the ratio of the total fast charge capacity to the total charge capacity, determining the user's fast charge habit coefficient; and based on the high SOC cut-off charge times, determining the user's high SOC cut-off charge habit coefficient; wherein the fast charge habit coefficient is positively correlated with the ratio, and the high SOC cut-off charge habit coefficient is positively correlated with the high SOC cut-off charge times; The charging habit coefficient of the user is determined according to the fast charging habit coefficient and the high SOC cut-off charging habit coefficient.

5. The method according to claim 1, characterized in that The vehicle driving data includes a vehicle driving speed parameter and a vehicle driving acceleration parameter, and the battery status data includes a battery SOC parameter; and determining the user's driving style coefficient according to the vehicle driving data and the battery status data includes: Accumulate the number of low-battery driving times of the vehicle when the battery SOC parameter is less than the second SOC threshold and the vehicle speed parameter is greater than the first speed threshold; and accumulate the ultra-high-speed driving time of the vehicle when the vehicle speed parameter is greater than the second speed threshold; and accumulate the number of rapid acceleration driving times of the vehicle when the vehicle acceleration parameter is greater than a preset acceleration threshold; Based on the number of low-battery driving times, determining a low-battery driving style coefficient of the user; and based on the ultra-high-speed driving duration, determining an ultra-high-speed driving coefficient of the vehicle; wherein the low-battery driving style coefficient is positively correlated with the number of low-battery driving times, and the ultra-high-speed driving coefficient is positively correlated with the ultra-high-speed driving duration; determining the user's aggressive driving style coefficient according to the ultra-high speed driving coefficient and the number of rapid acceleration driving times; The driving style coefficient of the user is determined according to the low-battery driving style coefficient and the aggressive driving style coefficient.

6. The method according to claim 1, characterized in that The calculating of the comprehensive coefficient by using the vehicle usage coefficient, the stationary time coefficient, the charging habit coefficient and the driving style coefficient includes: When the battery has any preset risk situation in the previous preset period, obtaining a corresponding penalty coefficient; When any two sub-coefficients are less than or equal to the normal coefficient threshold, and / or the battery does not have any preset risk situation in the previous preset period, the corresponding reward coefficient is obtained; wherein the sub-coefficients include the vehicle use coefficient, the static time coefficient, the charging habit coefficient and the driving style coefficient; Calculate the comprehensive coefficient by using the sub-coefficients, the penalty coefficient and the reward coefficient of all items; Among them, the preset risk conditions include at least one of a long-term low-power static condition, a long-term low-temperature static condition, a long-term high-temperature static condition, and a frequent overcharging condition.

7. The method according to claim 6, characterized in that Also includes: Monitoring whether each of the sub-coefficients is greater than or equal to an abnormal coefficient threshold, and monitoring whether the battery has any of the preset risk conditions; When a sub-coefficient greater than or equal to the abnormal coefficient threshold is monitored, a reminder message matching the monitored sub-coefficient is pushed to the user; and / or, When any of the preset risk conditions is detected in the battery, reminder information matching the monitored preset risk condition is pushed to the user.

8. A vehicle insurance cost assessment device, characterized in that: include: A historical vehicle data acquisition module is used to acquire vehicle driving data and battery status data of the vehicle in the last preset period; a module for determining each sub-coefficient, for determining a vehicle usage coefficient of the vehicle according to the vehicle driving data, a battery rest time coefficient and a user charging habit coefficient according to the battery status data, and a user driving style coefficient according to the vehicle driving data and the battery status data; a comprehensive coefficient calculation module, configured to calculate a comprehensive coefficient using the vehicle usage coefficient, the static time coefficient, the charging habit coefficient, and the driving style coefficient; the comprehensive coefficient is used to evaluate the use risk of the vehicle and the health status of the battery; The vehicle insurance cost assessment module is used to assess the vehicle insurance cost required for the user to take out insurance based on the comprehensive coefficient.

9. A vehicle, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable codes stored thereon, which, when executed by a processor of a vehicle, causes the processor to execute the method according to any one of claims 1 to 7.

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