Electric vehicle condition performance evaluation method and device based on Internet of Vehicles

Abstract

The invention relates to the technical field of vehicle condition performance evaluation, and discloses an electric vehicle condition performance evaluation method and device based on the Internet of Vehicles. The method comprises the following steps: performing data acquisition on a target vehicle by adopting an Internet of Vehicles technology, and obtaining vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension; based on a preset standard, aggregating the vehicle operation data into at least one preset state type, and performing data cleaning on the vehicle operation data of the at least one preset state type to obtain a preset data set; and performing vehicle condition performance evaluation on the preset data set by adopting a preset performance evaluation algorithm corresponding to each preset performance evaluation dimension, and determining a vehicle condition performance evaluation result corresponding to the target vehicle. The detailed information of the target vehicle can be obtained through the vehicle network technology, and the algorithms corresponding to the plurality of performance evaluation dimensions are adopted to carry out comprehensive and multi-dimensional performance evaluation on the vehicle, so that the transaction process is more transparent and professional, and the second-hand transaction risk of the new energy vehicle is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle condition performance evaluation, and in particular to a method and device for evaluating the vehicle condition performance of an electric vehicle based on an Internet of Vehicles. Background Art

[0002] As the new energy vehicle market continues to expand, people's acceptance of new energy vehicles is gradually increasing. Therefore, the new energy used vehicle market has broad market prospects.

[0003] However, there is currently a lack of professional second-hand car inspection standards in the second-hand car trading market for new energy electric vehicles, which makes it difficult for buyers to accurately assess the actual condition of the vehicle during the second-hand car transaction process, thereby increasing the risk of second-hand transactions of new energy vehicles. A better solution is needed. Summary of the invention

[0004] In view of this, the present invention provides a method and device for evaluating the condition and performance of an electric vehicle based on the Internet of Vehicles, so as to solve the problem that it is difficult to accurately evaluate the condition and performance of a vehicle during the second-hand transaction of new energy vehicles, thereby increasing transaction risks.

[0005] On the one hand, the present invention provides a method for evaluating the condition performance of an electric vehicle based on the Internet of Vehicles. The method uses the Internet of Vehicles technology to collect data from a target vehicle to obtain vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension; based on a preset standard, the vehicle operation data is aggregated into at least one preset state type, and the vehicle operation data of the at least one preset state type is cleaned to obtain a preset data set; a preset performance evaluation algorithm corresponding to each preset performance evaluation dimension is used to perform a vehicle condition performance evaluation on the preset data set to determine the vehicle condition performance evaluation result corresponding to the target vehicle.

[0006] On the other hand, the present invention also provides an electric vehicle condition performance evaluation device based on the Internet of Vehicles, the device comprising: a collection module, used to adopt the Internet of Vehicles technology to collect data from a target vehicle, and obtain vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension; an aggregation and cleaning module, used to aggregate the vehicle operation data into at least one preset state type based on a preset standard, perform data cleaning on the vehicle operation data of the at least one preset state type, and obtain a preset data set; an evaluation module, used to adopt a preset performance evaluation algorithm corresponding to each preset performance evaluation dimension, perform vehicle condition performance evaluation on the preset data set, and determine the vehicle condition performance evaluation result corresponding to the target vehicle.

[0007] On the other hand, the present invention also provides a computer device, which includes: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor implements the above-mentioned electric vehicle condition performance evaluation method based on the Internet of Vehicles by executing the computer instructions.

[0008] On the other hand, the present invention further provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to implement the above-mentioned electric vehicle condition performance evaluation method based on the Internet of Vehicles.

[0009] On the other hand, the present invention also provides a computer program product, including computer instructions, which are used to enable a computer to execute the electric vehicle condition performance evaluation method based on the Internet of Vehicles according to the above-mentioned first aspect or any corresponding embodiment.

[0010] During this process, data is collected from the target vehicle through vehicle network technology, the vehicle operation data of the target vehicle is obtained, the vehicle operation data is aggregated and cleaned, the preset data set is obtained, and algorithms corresponding to multiple performance evaluation dimensions are used to conduct a comprehensive and multi-dimensional performance evaluation of the vehicle, making the transaction process more transparent and professional, and improving buyers' understanding of the condition of used cars, thereby reducing the risks in the second-hand transaction of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 It is a flow chart of a method for evaluating the condition and performance of a tram based on the Internet of Vehicles provided by an embodiment of the present invention;

[0013] Figure 2 This is a specific flow chart of a method for evaluating the condition and performance of a tram based on the Internet of Vehicles provided by an embodiment of the present invention.

[0014] Figure 3 It is a structural schematic diagram of a vehicle condition performance evaluation device based on vehicle networking provided by an embodiment of the present invention;

[0015] Figure 4 It is another structural schematic diagram of the electric vehicle condition performance evaluation device based on the Internet of Vehicles provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In recent years, with the continuous expansion of the new energy vehicle market and the rapid growth of electric vehicle ownership, people's awareness and acceptance of new energy vehicles are gradually increasing, and a huge second-hand electric vehicle market will emerge. Compared with the traditional second-hand car market, the new energy second-hand car market is showing great potential, not only with a high growth rate, but also showing a significant increase in the value retention rate, indicating its broad prospects in the future.

[0017] However, the following problems may exist in the current second-hand car transaction process:

[0018] 1. Currently, the information on used new energy vehicles in the market is not transparent. There is a lack of a unified vehicle information database and a standardized vehicle evaluation system, which makes it difficult for buyers to obtain sufficient information to make decisions, thereby increasing transaction risks;

[0019] 2. As the technical characteristics of new energy vehicles are different from those of fuel vehicles, there is a lack of professional testing standards and methods in the second-hand car transaction process, which makes it difficult for buyers to accurately assess the actual condition of the vehicle when purchasing a second-hand car, further increasing the risk of the transaction;

[0020] 3. The battery performance of new energy vehicles directly affects the value of the vehicle. As a consumer rather than a technician, it is difficult for the buyer to accurately know the battery performance of the intended vehicle, which increases the uncertainty of the transaction and further reduces the buyer's purchasing experience.

[0021] In order to solve at least one of the above problems, various embodiments of the present invention provide a method for evaluating the condition performance of an electric vehicle based on the Internet of Vehicles, including: using the Internet of Vehicles technology to collect data on a target vehicle, and obtaining vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension; based on preset standards, aggregating the vehicle operation data into at least one preset state type, performing data cleaning on the vehicle operation data of at least one preset state type, and obtaining a preset data set; using a preset performance evaluation algorithm corresponding to each preset performance evaluation dimension, respectively, to perform a vehicle condition performance evaluation on the preset data set, and determine a vehicle condition performance evaluation result corresponding to the target vehicle.

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] According to an embodiment of the present invention, a method for evaluating the condition of a tram based on the Internet of Vehicles is provided. Figure 1 is a flow chart of a method for evaluating the condition and performance of a tram based on the Internet of Vehicles provided by an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0024] Step S101, using the Internet of Vehicles technology to collect data from a target vehicle, and obtaining vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension;

[0025] Step S102, based on a preset standard, aggregating the vehicle operation data into at least one preset state type, performing data cleaning on the vehicle operation data of at least one preset state type, and obtaining a preset data set;

[0026] Step S103, using the preset performance evaluation algorithm corresponding to each preset performance evaluation dimension, perform vehicle condition performance evaluation on the preset data set to determine the vehicle condition performance evaluation result corresponding to the target vehicle.

[0027] In a possible implementation, the Internet of Vehicles technology is used to collect data from the target vehicle, including:

[0028] Through the Internet of Vehicles system connected to the target vehicle, the vehicle operation data of the target vehicle is collected and acquired in real time.

[0029] Here, vehicle operation data may refer to the full life cycle operation monitoring data of the target vehicle; vehicle operation data includes data information corresponding to each preset performance evaluation dimension; for example, vehicle operation data may include but is not limited to: vehicle basic information, vehicle driving time, vehicle battery charging times, etc.; preset performance evaluation dimensions may include but are not limited to: vehicle status, battery status, mileage, etc.

[0030] In a possible implementation, aggregation may refer to a process of aggregating multiple data sets into one data set.

[0031] For example, the vehicle operation data corresponding to each preset performance evaluation dimension is aggregated and determined as at least one preset state type; wherein the preset state type is used to characterize the operation state of the vehicle.

[0032] Furthermore, the data cleaning process may include but is not limited to: deduplication, outlier processing and missing value filling.

[0033] Among them, deduplication may refer to removing duplicate records in vehicle operation data, outlier processing may refer to identifying and correcting abnormal data in vehicle operation data, and missing value filling may refer to estimating and filling missing vehicle operation data.

[0034] Furthermore, the cleaned data is thinned to obtain representative data points in the vehicle operation data, so as to reduce the amount of data while retaining necessary information.

[0035] In a possible implementation, a preset performance evaluation algorithm corresponding to each preset performance evaluation dimension is used to perform vehicle condition performance evaluation on a preset data set to determine a vehicle condition performance evaluation result corresponding to the target vehicle, including:

[0036] Obtain the preset performance evaluation dimensions that need to be evaluated for the target vehicle, determine the preset evaluation algorithm corresponding to each preset performance evaluation dimension, input the thinned preset data set into the corresponding preset evaluation algorithm, determine the intermediate results, and integrate the intermediate results to form a comprehensive evaluation result.

[0037] The way of integrating the intermediate results may include but is not limited to weighted average, decision tree analysis, etc.

[0038] Here, the weighted average is used to determine the vehicle condition performance evaluation result, which can be achieved based on the following steps:

[0039] According to the importance of each preset performance evaluation dimension in the vehicle condition performance evaluation, a corresponding weight value is assigned;

[0040] Normalize each intermediate result to determine the standardized form result corresponding to the intermediate result;

[0041] The standardized form results of each preset performance evaluation dimension are multiplied by the corresponding weight and added together to determine the comprehensive evaluation result of the target vehicle.

[0042] Through the above method, through the Internet of Vehicles technology, the vehicle operation data of the target vehicle can be collected in real time to improve the timeliness and accuracy of the data; by obtaining the vehicle operation data corresponding to each preset performance evaluation dimension, the vehicle condition and performance of the target vehicle can be fully reflected to assist buyers in transactions, thereby reducing the risks of used car transactions and improving transparency and credibility.

[0043] In a specific embodiment, the preset performance evaluation dimensions include at least one of the following: vehicle nominal information dimension, battery health information dimension, vehicle usage intensity information dimension, driving information dimension, charging information dimension, historical alarm information dimension and owner credit information dimension.

[0044] In a possible implementation, the vehicle nominal information dimension may refer to the default factory setting parameters of the target vehicle.

[0045] Specifically, the vehicle nominal information dimensions of the target vehicle may include, but are not limited to: vehicle brand, vehicle battery nominal information and vehicle nominal cruising range.

[0046] Among them, the vehicle battery nominal information may include at least one of the following: battery type, nominal capacity, rated voltage, cycle life, etc.

[0047] In one possible implementation, the battery health information dimension may refer to the current energy storage status of the battery of the target vehicle.

[0048] Specifically, the battery health information dimensions may include, but are not limited to: a battery power state (State of Charge, SOC) index and a battery capacity state (State of Health, SOH) index.

[0049] Among them, the battery power available state index may refer to the current power state of the battery, which is used to characterize the ratio of the energy stored in the battery in the current charging state to its maximum energy storage capacity; the battery capacity available state index may refer to the health status and effective capacity of the battery compared with the factory state, which can be used to evaluate the aging degree and remaining service life of the battery.

[0050] In a possible implementation, the vehicle usage intensity information dimension may refer to the usage and usage intensity of the target vehicle.

[0051] Specifically, the dimensions of vehicle usage intensity information may include, but are not limited to: the last vehicle driving time, the last vehicle charging time, the proportion of comfortable temperature operation and the vehicle usage time.

[0052] Among them, the last vehicle driving time represents the last time the target vehicle was used, which can represent the frequency of vehicle use and the level of activeness of use; the last vehicle charging time represents the last time the target vehicle was charged, which can represent the battery usage and charging habits; the comfortable temperature operation ratio represents the proportion of the time the target vehicle operates within the comfortable temperature range to the total operating time; the vehicle usage time represents the overall usage intensity of the target vehicle.

[0053] Here, the comfortable temperature may refer to a temperature range corresponding to the optimal working state of the battery of the target vehicle; the operation of the vehicle within the comfortable temperature range may improve the battery charging and discharging efficiency of the vehicle.

[0054] In a possible implementation, the driving information dimension may refer to the operating performance and usage of the target vehicle.

[0055] Specifically, the dimensions of vehicle usage intensity information may include but are not limited to: displayed mileage, average annual mileage, average daily driving time, average SOC consumption range and low-battery driving frequency score.

[0056] Among them, the last vehicle driving time represents the total mileage displayed on the dashboard of the target vehicle, representing the vehicle's usage history; the average annual mileage represents the average distance traveled by the target vehicle each year, representing the vehicle's usage frequency and activity level; the average daily driving time represents the average daily driving time of the target vehicle, reflecting the vehicle's usage habits; the average SOC consumption range represents the average consumption range of the battery status of the target vehicle during each driving; the low-power driving frequency score represents the vehicle's driving frequency when the power is lower than the preset threshold, representing the user's attention to battery power management.

[0057] In a possible implementation, the charging information dimension may refer to the charging status of the target vehicle.

[0058] Specifically, the dimensions of charging information may include but are not limited to: total charging times, proportion of fast charging times, average charging SOC range per time, and overvoltage charging score.

[0059] Among them, the total number of charging times represents the total number of charging times of the target vehicle, and represents the degree of dependence of the vehicle on electricity; the proportion of fast charging times represents the ratio of the number of charging times using fast charging method to the total number of charging times; the average daily driving time represents the average daily driving time of the target vehicle, reflecting the usage habits of the vehicle; the average charging SOC range represents the average value range of the SOC charged during each charging process; the overvoltage charging score represents the score of the overvoltage situation experienced by the target vehicle during the charging process and its impact.

[0060] In a possible implementation, the historical warning information dimension may refer to the historical fault warning conditions of the target vehicle.

[0061] Specifically, the historical alarm information dimension may refer to unconventional information; wherein, unconventional information may include but is not limited to: first-level unconventional information, second-level unconventional information and third-level unconventional information.

[0062] Exemplarily, level one unconventional information may specifically refer to a fault warning of lower severity, which will not cause a significant impact on the operation of the target vehicle, for example, level one unconventional information may refer to a tire pressure warning; level two unconventional information may specifically refer to a fault warning of medium severity, which will not immediately cause a significant impact on the operation of the target vehicle, for example, level two unconventional information may refer to an oil refilling warning; level three unconventional information may specifically refer to a fault warning of high severity, which requires understanding and measures to be taken, for example, level three unconventional information may refer to brake failure.

[0063] In a possible implementation, the vehicle owner's credit information dimension may refer to whether the target vehicle has tampered with the meter.

[0064] Specifically, the dimensions of the car owner's credit information may include but are not limited to: whether there has been any meter adjustment, the time of the most recent meter adjustment, the mileage before the most recent meter adjustment, and the mileage after the most recent meter adjustment.

[0065] Through the above method, through the evaluation of the above seven preset performance evaluation dimensions, the overall condition of the target vehicle can be fully reflected, and a more accurate vehicle condition performance evaluation result can be provided; a clear indicator system can help buyers obtain transparent information, thereby reducing the information asymmetry problem in used car transactions and improving the trust and security of transactions; through the evaluation of the historical alarm information dimension, the potential fault hazards of the vehicle can be identified in time, thereby helping buyers to perform preventive maintenance.

[0066] In a specific embodiment, aggregating vehicle operation data into at least one preset state type is achieved based on the following steps:

[0067] Determine that data in the vehicle operation data where the speed of the target vehicle is greater than 0 is first operation data, and aggregate the first operation data into a vehicle driving state;

[0068] Determine that data in the vehicle operation data in which the speed of the target vehicle is equal to 0 and the charging current is equal to 0 is second operation data, and aggregate the second operation data into a vehicle shutdown parking state;

[0069] Determine that data in the vehicle operation data in which the speed of the target vehicle is equal to 0 and the charging current is greater than 0 is the third operation data, and aggregate the third operation data into a vehicle parking charging state.

[0070] Specifically, based on the Internet of Vehicles technology, the vehicle driving data of the target vehicle under the driving information dimension and the vehicle charging data under the charging information dimension are obtained: the data in which the speed in the vehicle driving data is greater than 0 is determined to be the first operating data, the data in which the speed in the vehicle driving data is equal to 0 and the charging current in the vehicle charging data is equal to 0 is determined to be the second operating data, and the data in which the speed in the vehicle driving data is equal to 0 and the charging current in the vehicle charging data is greater than 0 is determined to be the third operating data; the first operating data is aggregated into the vehicle driving status, the second operating data is aggregated into the vehicle engine-off and parking status, and the third operating data is aggregated into the vehicle parking and charging status.

[0071] Through the above method, the vehicle operation data can be efficiently aggregated into three preset state types, thereby providing data support for subsequent vehicle condition performance analysis.

[0072] In a specific embodiment, a preset performance evaluation algorithm corresponding to each preset performance evaluation dimension is used to perform vehicle condition performance evaluation on a preset data set to determine a vehicle condition performance evaluation result corresponding to the target vehicle, including:

[0073] Obtaining a vehicle operating temperature of the target vehicle determined based on a preset rule, and using a first preset performance evaluation algorithm corresponding to the vehicle use intensity information dimension to determine a comfortable temperature operation ratio in a vehicle condition performance evaluation result of the target vehicle based on the vehicle operating temperature;

[0074] Obtaining first unconventional information corresponding to the model of the target vehicle and second unconventional information corresponding to the target vehicle itself, using a second preset performance evaluation algorithm corresponding to the historical warning information dimension, and determining historical warning level information in the vehicle condition performance evaluation result of the target vehicle based on the first unconventional information and the second unconventional information;

[0075] A third preset performance evaluation algorithm corresponding to the vehicle owner credit information dimension is used to determine the vehicle owner credit information of the target vehicle; the vehicle owner credit information is used to characterize whether the target vehicle has odometer tampering behavior.

[0076] Among them, the first unconventional information corresponding to the model of the target vehicle may refer to the first-level unconventional information, second-level unconventional information and third-level unconventional information of the model corresponding to the target vehicle, and the second unconventional information corresponding to the target vehicle itself may refer to the first-level unconventional information, second-level unconventional information and third-level unconventional information of the target vehicle.

[0077] In a possible implementation, the method further includes:

[0078] The battery performance of the target vehicle is evaluated using the battery performance evaluation algorithm corresponding to the battery health information dimension.

[0079] Specifically, the battery power available state E is calculated using the following formula (1): s :

[0080] E s =(E c -E end ) / (E r -E end ) (1)

[0081] Among them, E s Represents the actual power, that is, the current battery power of the target vehicle; E r Indicates the rated capacity, i.e. the nominal maximum capacity of the battery; E end Characterizes the end-of-life capacity of the battery, that is, the minimum capacity standard when the battery reaches the end of its service life.

[0082] The available capacity state C of the battery is calculated using the following formula (2): s :

[0083] C s =(C c -C end ) / (C r -Cend ) (2)

[0084] Among them, C s Characterizes the available capacity of the battery, that is, the current available storage capacity of the battery; C r Characterizes the rated capacity, that is, the maximum storage capacity of the battery; C end Characterizes the end-of-life capacity, which is the minimum capacity standard when the battery reaches the end of its service life.

[0085] The following formula (3) is used to express the available state of battery power E c :

[0086] E c =E cha / ΔSOC×100 (3)

[0087] Among them, formula (3) represents the charging power E of the target vehicle at an appropriate temperature (20℃-30℃) in the past year: cha and SOC change ΔSOC, calculate the available power state E c .

[0088] The following formula (4) is used to express the available state of battery power C c :

[0089] C c =C cha / ΔSOC×100 (4)

[0090] Among them, formula (4) represents the charging capacity C of the target vehicle at an appropriate temperature in the past year: cha and SOC change ΔSOC, calculate the available power state C c .

[0091] The vehicle model level is aggregated according to the announcement number corresponding to the vehicle, and the following formula (5) is used to express the vehicle model level power availability state E s,model :

[0092] E s,model =avg(E s ) (5)

[0093] Among them, formula (5) represents all the available power states E of the same vehicle model. s Average to determine the vehicle type level power availability E s,model .

[0094] The following formula (6) is used to express the available capacity status of vehicle type:

[0095] C s,model =avg(C s ) (6)

[0096] Wherein, formula (6) represents the capacity availability state C of all vehicles of the same type. s Average and determine the available power status C of the vehicle type s,model .

[0097] The available power state E of the target vehicle c Perform exponential conversion to determine the battery power available state index E as shown in formula (7) index :

[0098] E index =E s / E s,model (7)

[0099] Among them, the available power status of the target vehicle is compared with the average value of the corresponding vehicle model to obtain the indexed status index E index , which is used to reflect the performance of the target vehicle's battery power availability status among vehicles of the same type.

[0100] The available power state of the target vehicle C s Perform index conversion to determine the battery capacity available state index C as shown in formula (8): index :

[0101] C index =C s / C s,model (8)

[0102] Among them, the capacity availability status of the target vehicle is compared with the average value of the corresponding vehicle model to obtain the indexed status index C index , which is used to reflect the performance of the available battery capacity of the target vehicle among vehicles of the same type.

[0103] In a possible implementation, the method further includes:

[0104] The driving information of the target vehicle is evaluated using a driving evaluation algorithm corresponding to the driving information dimension.

[0105] Specifically, the vehicle networking technology is used to obtain the displayed mileage of the target vehicle, and the annual average mileage M is calculated using the following formula (9): avg,y :

[0106] M avg,y =Sum(M i ) / n y (9)

[0107] Among them, Sum(M i ) represents the sum of each section of the mileage in the displayed mileage, n y Represents the total number of years the target vehicle has been in operation.

[0108] The average daily driving time t is calculated using the following formula (10): d :

[0109] t d =sum(M i ) / n d (10)

[0110] Among them, n d Represents the total number of days the target vehicle is in operation.

[0111] The average consumption SOC interval SOC is calculated using the following formula (11): intern :

[0112]

[0113] Among them, SOC end,i Characterizes the battery SOC state at the end of each driving segment, SOC begin,i Characterizes the battery SOC state at the beginning of each driving segment, and k represents the number of driving segments calculated.

[0114] The following formula (12) is used to calculate the average low-power driving frequency score:

[0115] R d,lowV =1-sum(t d,lowV ) / sum(t d )*100% (12)

[0116] Among them, t d,lowV Indicates the driving time of the target vehicle in low battery state, t d The total driving time of the vehicle is represented. Formula (12) is used to evaluate the frequency of the target vehicle running in a low-battery state. The ratio of the low-battery driving time to the total driving time is calculated, and the ratio is subtracted from 1 and multiplied by 100%. The higher the score, the lower the frequency of the target vehicle running in a low-battery state.

[0117] In a possible implementation, the method further includes:

[0118] The charging information of the target vehicle is evaluated using the charging evaluation algorithm corresponding to the charging information dimension.

[0119] Specifically, the total number of charging times N of the target vehicle is calculated using the following formula (13): cha :

[0120] N cha = count(Ncha,i) t begin ≤t i ≤t last (13)

[0121] Among them, Ncha,i represents the record of each charging of the target vehicle, b egin Characterizes the start time of data analysis, t last Characterizing the end time of data analysis, formula (13) is used to determine the total number of charging times within the preset time period by counting the charging events within the preset time period.

[0122] The following formula (14) is used to calculate the proportion of fast charging times of the target vehicle:

[0123] R qcha =N qcha / N cha (14)

[0124] Among them, N qcha Characterizes the number of fast charges, N cha Indicates the total number of charging times.

[0125] The average charging SOC range of the target vehicle is calculated using the following formula (15):

[0126]

[0127] Among them, SOC chaend,i Characterizes the SOC at the end of the i-th charge, SOC chabe,i represents the battery state at the beginning of the i-th charge, and k represents the total number of charge times; formula (15) is used to calculate the change amplitude of the battery SOC in each charge and find its average value.

[0128] Through the above method, the exponential conversion in formula (7) and formula (8) can be used to compare the battery status of the target vehicle with that of vehicles of the same model, so that buyers can intuitively understand the battery performance of the target vehicle; through the evaluation of historical alarm information, the overall health status of the target vehicle can be analyzed for buyers; the use of a multi-dimensional performance evaluation method combined with vehicle networking technology can achieve a comprehensive and detailed performance evaluation of the vehicle.

[0129] In a specific embodiment, obtaining the vehicle operating temperature of the target vehicle determined based on a preset rule is achieved based on the following steps:

[0130] Obtaining running segment data in a preset data set of the target vehicle, parsing a timestamp in the running segment data, and determining a month and a date corresponding to the timestamp; the running segment data includes at least one of the following: a timestamp, a driving state, and a battery temperature;

[0131] Determine the parking time corresponding to the beginning of each running segment in the running segment data of the target vehicle based on the timestamp and the driving status;

[0132] When the parking time is greater than or equal to the preset time, it is determined that the target vehicle is in the first parking state when the running segment starts, and the battery temperature corresponding to the timestamp when the running segment starts is used as the vehicle running temperature;

[0133] When the parking time is less than the preset time, it is determined that the target vehicle is in the second parking state at the beginning of the running segment, the city where the target vehicle is located and the month and date corresponding to the timestamp at the beginning of the running segment are determined, and the median charging temperature corresponding to the city, month and date is used as the vehicle operating temperature.

[0134] In a possible implementation, the first parking state may refer to a long-term parking state, and the second parking state may refer to a short-term parking state; the preset duration may refer to 30 minutes, which can be set according to actual needs and is not specifically limited here.

[0135] Specifically, the parking time before the start of each running segment in the running segment data is determined. When the parking time is greater than or equal to 30 minutes, it is determined that the target vehicle is in a long-term parking state at the start of the running segment, and the battery temperature corresponding to the timestamp at the start of the running segment is used as the vehicle operating temperature; when the parking time is less than 30 minutes, it is determined that the target vehicle is in a short-time parking state at the start of the running segment, and the median charging temperature corresponding to the current month and date of the city where the target vehicle is located is used as the vehicle operating temperature.

[0136] Here, when the vehicle is parked for a long time, the vehicle battery is also in a static state for a long time. At this time, the battery temperature changes little. The battery temperature at the beginning of the running segment is more accurate and can reflect the actual temperature of the battery. When the vehicle is parked for a short time, the battery temperature is greatly affected by the surrounding environment or recent operation. By using the median temperature of the target vehicle charging in the same time period in the city as a substitute, a more reasonable temperature estimate is provided.

[0137] In one possible implementation, the first operating time during which the temperature rise is less than a preset temperature during the operation of the target vehicle is calculated, the total operating time of the vehicle within the time interval is counted, and the ratio of the first operating time to the total operating time is determined as the comfortable temperature operation ratio in the vehicle condition performance evaluation result of the target vehicle.

[0138] Specifically, the following formula (16) is used to calculate the elevated temperature during the operation of the target vehicle:

[0139] T rise =T max,i -T begin,e (16)

[0140] Among them, T max,i Characterizes the maximum battery temperature during the current operation, T begin,eCharacterizes the temperature at the start of a run.

[0141] The first operating time (i.e., the comfortable temperature operating time) is calculated using the following formula (17):

[0142] t su =t i s T rise ≤x (17)

[0143] Here, x represents the preset temperature, which is also the uncomfortable temperature.

[0144] The comfortable temperature operation ratio is calculated using the following formula (18):

[0145] R conf =t su / t all (18)

[0146] Among them, t su Characterizes the comfortable temperature operation time, t all The total operation time of the vehicle is characterized, and the comfortable temperature operation ratio is calculated by the ratio of the comfortable temperature operation time to the total operation time of the vehicle.

[0147] Here, the total vehicle operation time can be calculated based on the timestamp from the first record in the vehicle odometer to the end of the last operation, and the number of days in this period can be counted.

[0148] Through the above method, by analyzing the comfortable temperature operation ratio of the target vehicle's battery, the actual health status of the used car battery can be effectively evaluated, helping consumers understand the battery's service life and performance, and reducing the risk of buyers in used car transactions.

[0149] In a specific embodiment, a second preset performance evaluation algorithm corresponding to the historical warning information dimension is used to determine the historical warning level information in the vehicle condition performance evaluation result of the target vehicle based on the first unconventional information and the second unconventional information, which is implemented based on the following steps:

[0150] Determine, based on the first unconventional information, the number of accident reports of the model of the target vehicle and the number of accident reports of the brand of the target vehicle;

[0151] Determine the relative performance of the model of the target vehicle in the accident category represented by the first unconventional information based on the ratio of the number of accident reports of the model of the target vehicle to the number of accident reports of the brand of the target vehicle;

[0152] Determine the number of accident reports of the target vehicle itself based on the second unconventional information, and determine the relative performance of the target vehicle itself in the accident category represented by the second unconventional information according to the ratio of the number of accident reports of the target vehicle itself to the number of accident reports of the model to which the target vehicle belongs;

[0153] Based on the relative performance in the accident category represented by the first unconventional information and the relative performance in the accident category represented by the second unconventional information, historical warning level information in the vehicle condition performance evaluation result of the target vehicle is determined.

[0154] In a possible implementation, the first unconventional information and the second unconventional information of the vehicle model both include: primary unconventional information, secondary unconventional information and tertiary unconventional information.

[0155] Specifically, the following formula (19) can be used to determine the relative performance of the model to which the target vehicle belongs:

[0156] R vehmod,class =N vehmod,class / N brand,class (19)

[0157] Among them, N vehmod,class The total number of reported accident vehicles of the target vehicle type, N brand,class The total number of reported accident vehicles representing the brand of the target vehicle.

[0158] Furthermore, the relative performance of the target vehicle can be determined using the following formula (20):

[0159] R veh,class =N veh,class / N vehmod,class (20)

[0160] Among them, N veh,class The total number of accident reports of target vehicles, N vehmod,class The total number of accident vehicles reported that represent the model of the target vehicle.

[0161] Through the above method, by analyzing the accident reporting data of the model and brand of the target vehicle and comparing it with the historical warning information of the target vehicle, it is possible to more comprehensively evaluate the safety of the vehicle from the overall perspective of the brand and model, and reflect the potential risk level of the vehicle; comparing the historical warning information of the target vehicle with its model and brand not only takes into account the vehicle's own accident record, but also the performance of similar vehicles, thereby avoiding the limitations of a single vehicle data and improving the accuracy of the evaluation.

[0162] In a specific embodiment, the third preset performance evaluation algorithm corresponding to the vehicle owner credit information dimension is used to determine the vehicle owner credit information of the target vehicle, including:

[0163] Determine the adjacent first and second movement segments in the running segment data of the target vehicle, obtain the first vehicle longitude and latitude information of the end mileage of the first movement segment, and the start mileage of the second movement segment and the second vehicle longitude and latitude information;

[0164] When the longitude and latitude information of the first vehicle is the same as the longitude and latitude information of the second vehicle and the mileage at the end of the journey is less than the mileage at the beginning of the journey, it is determined that the odometer of the target vehicle has been adjusted and the odometer has been adjusted up;

[0165] When the longitude and latitude information of the first vehicle is the same as the longitude and latitude information of the second vehicle and the mileage at the end of the journey is greater than the mileage at the beginning of the journey, it is determined that the odometer of the target vehicle has been adjusted and the odometer has been adjusted down;

[0166] When the longitude and latitude information of the first vehicle is the same as the longitude and latitude information of the second vehicle and the end mileage of the driving is equal to the start mileage of the driving, it is determined that there is no tampering behavior on the odometer of the target vehicle;

[0167] When the longitude and latitude information of the first vehicle is different from the longitude and latitude information of the second vehicle and the mileage at the end of the journey is less than the mileage at the start of the journey, it is determined that there is no tampering behavior of the target vehicle;

[0168] When the longitude and latitude information of the first vehicle is different from the longitude and latitude information of the second vehicle and the mileage at the end of the journey is greater than the mileage at the beginning of the journey, it is determined that the odometer of the target vehicle has been adjusted and the odometer has been adjusted down;

[0169] When the first vehicle longitude and latitude information is different from the second vehicle longitude and latitude information and the travel end mileage is equal to the travel start mileage, it is determined that the target vehicle has an odometer adjustment behavior and the odometer is adjusted down.

[0170] In a possible implementation, determining adjacent first motion segments and second motion segments in the running segment data of the target vehicle includes:

[0171] Obtain the driving segment history records of the vehicle, and sort the driving segments in the driving segment set according to timestamps;

[0172] The segment with a smaller start timestamp in the sorted driving segment set is taken as the first motion segment, and the segment with a larger start timestamp is taken as the second motion segment.

[0173] Here, the first motion segment and the second motion segment are two adjacent driving segments in the driving segment set.

[0174] Furthermore, the end mileage of the first movement segment is recorded as D b,e , the starting mileage of the first movement segment is recorded as D a,b .

[0175] In a possible implementation, the third preset performance evaluation algorithm for determining whether there is an odometer rollback behavior in the target vehicle can be as shown in Table 1 below:

[0176]

[0177] Table 1

[0178] Wherein, when the longitude and latitude information of the first vehicle is the same as that of the second vehicle and Db,e < Da,b, it is determined that the target vehicle has an odometer rollback behavior and the odometer has been rolled up, or the target vehicle has a situation of missing driving segments;

[0179] When the longitude and latitude information of the first vehicle is the same as that of the second vehicle and Db,e > Da,b, it is determined that the target vehicle has an odometer rollback behavior and the odometer has been rolled down;

[0180] When the longitude and latitude information of the first vehicle is the same as that of the second vehicle and Db,e = Da,b, it is determined that the odometer of the target vehicle does not have an odometer rollback behavior;

[0181] When the longitude and latitude information of the first vehicle is different from that of the second vehicle and Db,e < Da,b, it is determined that the target vehicle does not have an odometer rollback behavior;

[0182] When the longitude and latitude information of the first vehicle is different from that of the second vehicle and Db,e > Da,b, it is determined that the target vehicle has an odometer rollback behavior and the odometer has been rolled down;

[0183] When the longitude and latitude information of the first vehicle is different from that of the second vehicle and Db,e = Da,b, it is determined that the target vehicle has an odometer rollback behavior, the odometer has been rolled down and there is a situation of missing driving segments.

[0184] In a possible implementation, to determine whether there is an odometer rollback behavior in the target vehicle, the detection result of the owner's credit information is recorded and stored in tabular form. The tabular form can be as shown in Table 2 below:

[0185] Field Name Field value Is the vehicle suspected of tampering? Yes / No / null Last adjustment time YYYY-MM-DD HH:SS:ss Mileage before the last adjustment Mileage Value Mileage after the last adjustment Mileage Value

[0186] Table 2

[0187] Through the above method, by comparing the longitude and latitude information and driving mileage of adjacent driving segments, it is possible to effectively detect whether there is an odometer rollback behavior in the vehicle, thereby improving the accuracy of detection; based on historical odometer rollback behaviors, it is possible to provide an important reference basis for the owner's credit information, thereby improving the transparency of used car transactions and reducing the risks of used car transactions.

[0188] Exemplarily, Figure 2 is the specific process schematic diagram of the electric vehicle condition performance evaluation method based on the vehicle networking provided by the embodiment of the present invention, as shown in Figure 2As shown, where:

[0189] Obtain the Internet of Vehicles data of the target vehicle, clean the Internet of Vehicles data, evaluate the vehicle condition and performance of the Internet of Vehicles data, and output the evaluation results of the target vehicle.

[0190] Here, the vehicle performance evaluation includes: vehicle nominal information evaluation, vehicle performance evaluation, comprehensive vehicle usage intensity evaluation, vehicle driving information evaluation, vehicle charging information evaluation, historical alarm information evaluation and vehicle owner credit information evaluation.

[0191] Figure 3 is a schematic diagram of the structure of a vehicle condition performance evaluation device based on the Internet of Vehicles provided by an embodiment of the present invention, such as Figure 3 As shown, the device can be applied to intelligent electronic devices such as servers and computers; the device includes: a collection module 301, an aggregation cleaning module 302 and an evaluation module 303;

[0192] The acquisition module 301 is used to collect data from the target vehicle using the Internet of Vehicles technology to obtain vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension;

[0193] An aggregation and cleaning module 302 is used to aggregate the vehicle operation data into at least one preset state type based on a preset standard, and perform data cleaning on the vehicle operation data of at least one preset state type to obtain a preset data set;

[0194] The evaluation module 303 is used to use the preset performance evaluation algorithm corresponding to each preset performance evaluation dimension to perform vehicle condition performance evaluation on the preset data set and determine the vehicle condition performance evaluation result corresponding to the target vehicle.

[0195] In a specific embodiment, the preset performance evaluation dimensions include at least one of the following: vehicle nominal information dimension, battery health information dimension, vehicle usage intensity information dimension, driving information dimension, charging information dimension, historical alarm information dimension and owner credit information dimension.

[0196] In a specific embodiment, the aggregation cleaning module 302 is used to determine that data of the target vehicle having a speed greater than 0 in the vehicle operation data is the first operation data, and aggregate the first operation data into the vehicle driving state;

[0197] Determine that data in the vehicle operation data in which the speed of the target vehicle is equal to 0 and the charging current is equal to 0 is second operation data, and aggregate the second operation data into a vehicle shutdown parking state;

[0198] Determine that data in which the speed of the target vehicle is equal to 0 and the charging current is greater than 0 in the vehicle operation data is the third operation data, and aggregate the third operation data into the vehicle parking charging state.

[0199] In a specific embodiment, the evaluation module 303 is used to obtain the vehicle operating temperature of the target vehicle based on the preset rules, and use the first preset performance evaluation algorithm corresponding to the vehicle intensity information dimension to determine the comfortable temperature operation ratio in the vehicle condition performance evaluation result of the target vehicle based on the vehicle operating temperature;

[0200] Obtaining first unconventional information corresponding to the model of the target vehicle and second unconventional information corresponding to the target vehicle itself, using a second preset performance evaluation algorithm corresponding to the historical warning information dimension, and determining historical warning level information in the vehicle condition performance evaluation result of the target vehicle based on the first unconventional information and the second unconventional information;

[0201] A third preset performance evaluation algorithm corresponding to the vehicle owner credit information dimension is used to determine the vehicle owner credit information of the target vehicle; the vehicle owner credit information is used to characterize whether the target vehicle has odometer tampering behavior.

[0202] In a specific embodiment, the evaluation module 303 is used to obtain the running segment data in the preset data set of the target vehicle, parse the timestamp in the running segment data, and determine the month and date corresponding to the timestamp; the running segment data includes at least one of the following: timestamp, driving status, battery temperature;

[0203] Determine the parking time corresponding to the beginning of each running segment in the running segment data of the target vehicle based on the timestamp and the driving status;

[0204] When the parking time is greater than or equal to the preset time, it is determined that the target vehicle is in the first parking state when the running segment starts, and the battery temperature corresponding to the timestamp when the running segment starts is used as the vehicle running temperature;

[0205] When the parking time is less than the preset time, it is determined that the target vehicle is in the second parking state at the beginning of the running segment, the city where the target vehicle is located and the month and date corresponding to the timestamp at the beginning of the running segment are determined, and the median charging temperature corresponding to the city, month and date is used as the vehicle operating temperature.

[0206] In a specific embodiment, the evaluation module 303 is used to determine the number of accident reports of the model of the target vehicle and the number of accident reports of the brand of the target vehicle based on the first unconventional information;

[0207] Determine the relative performance of the model of the target vehicle in the accident category represented by the first unconventional information based on the ratio of the number of accident reports of the model of the target vehicle to the number of accident reports of the brand of the target vehicle;

[0208] Determine the number of accident reports of the target vehicle itself based on the second unconventional information, and determine the relative performance of the target vehicle itself in the accident category represented by the second unconventional information according to the ratio of the number of accident reports of the target vehicle itself to the number of accident reports of the model to which the target vehicle belongs;

[0209] Based on the relative performance in the accident category represented by the first unconventional information and the relative performance in the accident category represented by the second unconventional information, historical warning level information in the vehicle condition performance evaluation result of the target vehicle is determined.

[0210] In a specific embodiment, the evaluation module 303 is used to determine the adjacent first motion segment and the second motion segment in the running segment data of the target vehicle, obtain the first vehicle longitude and latitude information of the driving end mileage of the first motion segment, and the driving start mileage and the second vehicle longitude and latitude information of the second motion segment;

[0211] When the longitude and latitude information of the first vehicle is the same as the longitude and latitude information of the second vehicle and the mileage at the end of the journey is less than the mileage at the beginning of the journey, it is determined that the odometer of the target vehicle has been adjusted and the odometer has been adjusted up;

[0212] When the longitude and latitude information of the first vehicle is the same as the longitude and latitude information of the second vehicle and the mileage at the end of the journey is greater than the mileage at the beginning of the journey, it is determined that the odometer of the target vehicle has been adjusted and the odometer has been adjusted down;

[0213] When the longitude and latitude information of the first vehicle is the same as the longitude and latitude information of the second vehicle and the end mileage of the driving is equal to the start mileage of the driving, it is determined that there is no tampering behavior on the odometer of the target vehicle;

[0214] When the longitude and latitude information of the first vehicle is different from the longitude and latitude information of the second vehicle and the mileage at the end of the journey is less than the mileage at the start of the journey, it is determined that there is no tampering behavior of the target vehicle;

[0215] When the longitude and latitude information of the first vehicle is different from the longitude and latitude information of the second vehicle and the mileage at the end of the journey is greater than the mileage at the beginning of the journey, it is determined that the odometer of the target vehicle has been adjusted and the odometer has been adjusted down;

[0216] When the first vehicle longitude and latitude information is different from the second vehicle longitude and latitude information and the travel end mileage is equal to the travel start mileage, it is determined that the target vehicle has an odometer adjustment behavior and the odometer is adjusted down.

[0217] It should be noted that: the electric vehicle condition performance evaluation device based on the Internet of Vehicles provided in the above embodiment only uses the division of the above program modules as an example to illustrate when implementing the corresponding electric vehicle condition performance evaluation method based on the Internet of Vehicles. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the above-described processing. In addition, the device provided in the above embodiment and the corresponding Figure 1 The embodiments of the method shown belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0218] The embodiment of the present invention also provides a computer device having the above Figure 3 The electric vehicle condition performance evaluation device based on the Internet of Vehicles is shown.

[0219] See also Figure 4 , Figure 4 is another structural schematic diagram of a vehicle condition performance evaluation device based on the Internet of Vehicles provided by an embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0220] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0221] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0222] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0223] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0224] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0225] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0226] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0227] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0228] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0229] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for evaluating the condition and performance of electric vehicles based on Internet of Vehicles, characterized in that: The method comprises: Using Internet of Vehicles technology, data collection is performed on a target vehicle to obtain vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension; Based on a preset standard, the vehicle operation data is aggregated into at least one preset state type, and data cleaning is performed on the vehicle operation data of the at least one preset state type to obtain a preset data set; The preset performance evaluation algorithm corresponding to each preset performance evaluation dimension is used to perform vehicle condition performance evaluation on the preset data set to determine the vehicle condition performance evaluation result corresponding to the target vehicle.

2. The method according to claim 1, characterized in that The preset performance evaluation dimensions include at least one of the following: vehicle nominal information dimension, battery health information dimension, vehicle usage intensity information dimension, driving information dimension, charging information dimension, historical alarm information dimension and vehicle owner credit information dimension.

3. The method according to claim 2, characterized in that Based on a preset standard, the vehicle operation data is aggregated into at least one preset state type, which is achieved based on the following steps: Determine that data in the vehicle operation data where the speed of the target vehicle is greater than 0 is first operation data, and aggregate the first operation data into a vehicle driving state; Determine that data in the vehicle operation data in which the speed of the target vehicle is equal to 0 and the charging current is equal to 0 is second operation data, and aggregate the second operation data into a vehicle shutdown state; Determine that data in which the speed of the target vehicle is equal to 0 and the charging current is greater than 0 in the vehicle operation data is third operation data, and aggregate the third operation data into a vehicle parking charging state.

4. The method according to claim 2, characterized in that: Using the preset performance evaluation algorithm corresponding to each preset performance evaluation dimension, the vehicle condition performance evaluation is performed on the preset data set to determine the vehicle condition performance evaluation result corresponding to the target vehicle, including: Obtaining a vehicle operating temperature of a target vehicle determined based on a preset rule, and using a first preset performance evaluation algorithm corresponding to the vehicle use intensity information dimension to determine a comfortable temperature operation ratio in a vehicle condition performance evaluation result of the target vehicle based on the vehicle operating temperature; Acquire first unconventional information corresponding to the vehicle model of the target vehicle and second unconventional information corresponding to the target vehicle itself, and use a second preset performance evaluation algorithm corresponding to the historical warning information dimension to determine the historical warning level information in the vehicle condition performance evaluation result of the target vehicle based on the first unconventional information and the second unconventional information; A third preset performance evaluation algorithm corresponding to the vehicle owner credit information dimension is used to determine the vehicle owner credit information of the target vehicle; the vehicle owner credit information is used to characterize whether the target vehicle has odometer tampering behavior.

5. The method according to claim 4, characterized in that Obtaining the vehicle operating temperature of the target vehicle based on a preset rule is achieved based on the following steps: Acquire running segment data in a preset data set of the target vehicle, parse the timestamp in the running segment data, and determine the month and date corresponding to the timestamp; the running segment data includes at least one of the following: timestamp, driving status, and battery temperature; Determine, based on the timestamp and the driving state, a parking time corresponding to the start of each running segment in the running segment data of the target vehicle; When the parking time is greater than or equal to a preset time, determining that the target vehicle is in a first parking state when the running segment starts, and using a battery temperature corresponding to a timestamp when the running segment starts as a vehicle running temperature; When the parking time is less than a preset duration, it is determined that the target vehicle is in the second parking state at the beginning of the running segment, the city where the target vehicle is located and the month and date corresponding to the timestamp at the beginning of the running segment are determined, and the median charging temperature corresponding to the city, month and date is used as the vehicle operating temperature.

6. The method according to claim 5, characterized in that Using a second preset performance evaluation algorithm corresponding to the historical warning information dimension, based on the first unconventional information and the second unconventional information, determining the historical warning level information in the vehicle condition performance evaluation result of the target vehicle is implemented based on the following steps: Determining the number of accident reports of the model of the target vehicle and the number of accident reports of the brand of the target vehicle based on the first unconventional information; Determining the relative performance of the model of the target vehicle in the accident category represented by the first unconventional information according to the ratio of the number of accident reports of the model of the target vehicle to the number of accident reports of the brand of the target vehicle; Determine the number of accident reports of the target vehicle itself based on the second unconventional information, and determine the relative performance of the target vehicle itself in the accident category represented by the second unconventional information according to the ratio of the number of accident reports of the target vehicle itself to the number of accident reports of the model of the target vehicle; Based on the relative performance in the accident category represented by the first unconventional information and the relative performance in the accident category represented by the second unconventional information, the historical warning level information in the vehicle condition performance evaluation result of the target vehicle is determined.

7. The method according to claim 5, characterized in that The adopting of a third preset performance evaluation algorithm corresponding to the vehicle owner credit information dimension to determine the vehicle owner credit information of the target vehicle includes: Determine a first motion segment and a second motion segment adjacent to each other in the running segment data of the target vehicle, and obtain the first vehicle longitude and latitude information of the driving end mileage of the first motion segment, and the second vehicle longitude and latitude information of the driving start mileage of the second motion segment; When the first vehicle longitude and latitude information is the same as the second vehicle longitude and latitude information and the travel end mileage is less than the travel start mileage, it is determined that the target vehicle has odometer adjustment behavior and the odometer is adjusted up; When the first vehicle longitude and latitude information is the same as the second vehicle longitude and latitude information and the travel end mileage is greater than the travel start mileage, it is determined that the target vehicle has odometer adjustment behavior and the odometer is adjusted down; When the first vehicle longitude and latitude information is the same as the second vehicle longitude and latitude information and the travel end mileage is equal to the travel start mileage, determining that there is no tampering behavior on the odometer of the target vehicle; When the first vehicle longitude and latitude information is different from the second vehicle longitude and latitude information and the end mileage is less than the start mileage, it is determined that the target vehicle has no tampering behavior; When the first vehicle longitude and latitude information is different from the second vehicle longitude and latitude information and the travel end mileage is greater than the travel start mileage, it is determined that the target vehicle has a tampering behavior and the odometer is adjusted down; When the first vehicle longitude and latitude information is different from the second vehicle longitude and latitude information and the travel end mileage is equal to the travel start mileage, it is determined that the target vehicle has an odometer adjustment behavior and the odometer is adjusted down.

8. A vehicle condition performance evaluation device based on the Internet of Vehicles, characterized in that: The device comprises: A collection module, used to collect data from a target vehicle using Internet of Vehicles technology, and obtain vehicle operation data of the target vehicle determined based on at least one preset performance evaluation dimension; An aggregation and cleaning module, configured to aggregate the vehicle operation data into at least one preset state type based on a preset standard, and perform data cleaning on the vehicle operation data of the at least one preset state type to obtain a preset data set; The evaluation module is used to use the preset performance evaluation algorithm corresponding to each preset performance evaluation dimension to perform vehicle condition performance evaluation on the preset data set and determine the vehicle condition performance evaluation result corresponding to the target vehicle.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the electric vehicle condition performance evaluation method based on the Internet of Vehicles as described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the electric vehicle condition performance evaluation method based on the vehicle network as described in any one of claims 1 to 7.

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