A method and apparatus for processing vehicle network data

Abstract

This invention discloses a method and apparatus for processing vehicle-to-everything (V2X) data, relating to the field of data processing technology. Its main objective is to reduce the probability of abnormal V2X data occurrence while simultaneously reducing the volume of V2X data. The main technical solution includes: acquiring a V2X dataset containing multiple V2X data entries, each with a corresponding timestamp and target vehicle parameter values; processing the V2X dataset using a quartile algorithm to obtain upper and lower limit parameter values ​​for the target vehicle parameters; filtering the V2X data in the dataset based on the upper and lower limit parameter values ​​to form a target dataset; and sampling the target dataset based on the sampling quantity, sampling time interval, target vehicle parameter data type, and the timestamps and parameter values ​​corresponding to all V2X data entries in the target dataset.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for processing vehicle network data. Background Technology

[0002] Vehicle-to-everything (V2X) data refers to the vehicle operating status data collected by V2X systems. In today's automotive industry, V2X data is crucial, as it allows for the analysis of vehicle performance and the identification of potential problems.

[0003] The amount of vehicle-to-everything (V2X) data generated during vehicle operation is enormous, and this data also contains anomalies. Performing vehicle performance analysis based on this massive volume of abnormal V2X data not only results in low efficiency but also leads to low-quality analysis.

[0004] Therefore, there is an urgent need for a method to process vehicle network data in order to reduce the probability of abnormal vehicle network data in the vehicle network data used for vehicle performance analysis, while also reducing the volume of vehicle network data used for vehicle performance analysis. Summary of the Invention

[0005] In view of this, the present invention proposes a method and apparatus for processing vehicle network data, the main purpose of which is to reduce the probability of abnormal vehicle network data occurrence while reducing the volume of vehicle network data.

[0006] To achieve the above objectives, the present invention mainly provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for processing vehicle network data, the method comprising:

[0008] Obtain a vehicle network dataset, wherein the vehicle network dataset includes multiple vehicle network data entries, and each vehicle network data entry has a corresponding timestamp and parameter values ​​of the target vehicle parameters;

[0009] The vehicle network dataset is processed using the quartile algorithm to obtain the upper and lower limit parameter values ​​of the target vehicle parameters;

[0010] Based on the upper limit parameter value and the lower limit parameter value, filter the vehicle network data in the vehicle network dataset to form a target dataset;

[0011] The target dataset is sampled based on the number of samples, the sampling time interval, the data type of the target vehicle parameters, and the timestamps and parameter values ​​corresponding to all vehicle-to-everything (V2X) data in the target dataset.

[0012] In a second aspect, the present invention provides a vehicle network data processing device, the device comprising:

[0013] The acquisition unit is used to acquire a vehicle network dataset, wherein the vehicle network dataset includes multiple vehicle network data, and each of the vehicle network data has a corresponding timestamp and parameter values ​​of the target vehicle parameters;

[0014] The processing unit is used to process the vehicle network dataset using the quartile algorithm to obtain the upper limit parameter value and the lower limit parameter value of the target vehicle parameter;

[0015] The filtering unit is used to filter the vehicle network data in the vehicle network dataset based on the upper limit parameter value and the lower limit parameter value to form a target dataset;

[0016] The sampling unit is used to sample the target dataset based on the number of samples, the sampling time interval, the data type of the target vehicle parameters, and the timestamps and parameter values ​​corresponding to all vehicle-to-everything (V2X) data in the target dataset.

[0017] Thirdly, the present invention provides a computer-readable storage medium comprising a stored program, wherein, when the program is executed, the device on which the storage medium is located executes the vehicle network data processing method described in the first aspect.

[0018] Fourthly, the present invention provides an electronic device comprising: at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other via the bus; and the processor is configured to call program instructions in the memory to execute the vehicle network data processing method described in the first aspect.

[0019] By employing the above technical solution, the method and apparatus for processing vehicle network data provided by this invention, when there is a need for vehicle network data processing, acquires a vehicle network dataset including multiple vehicle network data points. Each vehicle network data point in the dataset has a corresponding timestamp and a parameter value of the target vehicle parameter. Then, the vehicle network dataset is processed using a quartile algorithm to obtain the upper and lower limit parameter values ​​of the target vehicle parameter. Based on the upper and lower limit parameter values, the vehicle network data in the dataset is filtered to form a target dataset. Finally, the target dataset is sampled based on the sampling quantity, sampling time interval, data type of the target vehicle parameter, and the timestamps and parameter values ​​corresponding to all vehicle network data in the target dataset. It can be seen that the solution provided by this invention uses a quartile algorithm to process the vehicle network dataset to obtain the upper and lower limit parameter values ​​of the target vehicle parameter, and uses these obtained upper and lower limit parameter values ​​to filter abnormal vehicle network data in the dataset, thus reducing the probability of abnormal vehicle network data occurrence. Furthermore, it can sample the dataset obtained after filtering abnormal vehicle network data, thereby reducing the volume of vehicle network data used in vehicle performance analysis. Therefore, the solution provided by the embodiments of the present invention can reduce the probability of abnormal vehicle network data in the vehicle network data used for vehicle performance analysis, while reducing the volume of vehicle network data used for vehicle performance analysis, thereby improving the efficiency and quality of vehicle performance analysis when performing vehicle performance analysis based on vehicle network data.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of a method for processing vehicle network data according to an embodiment of the present invention is shown;

[0023] Figure 2 This diagram illustrates the structure of a vehicle network data processing device according to an embodiment of the present invention.

[0024] Figure 3This diagram illustrates the structure of a vehicle network data processing device according to another embodiment of the present invention;

[0025] Figure 4 A schematic diagram of the structure of an electronic device provided in one embodiment of the present invention is shown. Detailed Implementation

[0026] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0027] Vehicle-to-everything (V2X) data refers to the vehicle operating status data collected by V2X systems. In today's automotive industry, V2X data is crucial, as it allows for the analysis of vehicle performance and the identification of potential problems.

[0028] Especially after a vehicle is developed, it needs to undergo extensive benchmark and road tests. During these tests, each mileage traveled by the test vehicle generates a corresponding Measurement Data Format (MDF) file. These MDF files are subsequently parsed and analyzed to obtain different signal data from the vehicle at different points in time. This data within the MDF file constitutes the vehicle-to-everything (V2X) data. By analyzing V2X data, automakers can gain a clearer understanding of the vehicle's operating status and identify potential problems.

[0029] The amount of vehicle-to-everything (V2X) data generated during vehicle operation is enormous, and this data also contains anomalous entries. Analyzing vehicle performance based on this massive volume of abnormal V2X data is not only inefficient but also results in low-quality analysis.

[0030] Therefore, there is an urgent need for a method to process vehicle network data in order to reduce the probability of abnormal vehicle network data in the vehicle network data used for vehicle performance analysis, while also reducing the volume of vehicle network data used for vehicle performance analysis.

[0031] This invention provides a method and apparatus for processing vehicle network data, thereby reducing the probability of abnormal vehicle network data in vehicle performance analysis and reducing the volume of vehicle network data used for vehicle performance analysis. This improves the efficiency and quality of vehicle performance analysis based on vehicle network data. The method and apparatus for processing vehicle network data provided in this invention will be described in detail below.

[0032] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for processing vehicle network data, which mainly includes:

[0033] 101. Obtain the vehicle network dataset.

[0034] In practical applications, when there is a need to perform vehicle performance analysis using any target vehicle parameter, a vehicle-to-everything (V2X) dataset targeting the target vehicle parameter is acquired to analyze vehicle performance. The V2X dataset includes multiple V2X data entries, each with a corresponding timestamp and the parameter value of the target vehicle parameter. The specific type of the target vehicle parameter can be determined based on business requirements; this embodiment does not impose specific limitations. Optionally, the target vehicle parameter can be any parameter related to vehicle performance. For example, the target vehicle parameter can be, but is not limited to, any of the following: vehicle speed, interior temperature, exterior temperature, engine speed, etc.

[0035] There are two ways to obtain vehicle-to-everything (V2X) datasets:

[0036] The first method involves reading a vehicle network dataset for the target vehicle parameters from a preset storage area. The preset storage area pre-stores a vehicle network dataset for vehicle performance analysis, which is a dataset of the target vehicle parameters.

[0037] The second method involves acquiring a Measurement Data Format (MDF) file containing vehicle measurement data for the target vehicle parameters, reading all vehicle-to-everything (V2X) data from this file, and then assembling the collected V2X data into a V2X dataset. During vehicle operation, the vehicle's driving data acquisition equipment records a measurement data file, which primarily stores the signal data generated during vehicle movement; this signal data constitutes the V2X data. For example, if an MDF file for vehicle speed is acquired, and 180,000 signal data points are obtained from the MDF file, then these 180,000 signal data points are identified as 180,000 V2X data points, and these 180,000 V2X data points are then combined to form a V2X dataset.

[0038] For example, the vehicle speed changes constantly during driving, so the MDF file records the data in Table-1 below.

[0039] Table 1

[0040] Target vehicle parameters (speed) Parameter value (vehicle speed value) Timestamp VehicleSpd 1.0125 0.433215948 VehicleSpd 1.06875 0.452784713 VehicleSpd 1.18125 0.472821473 VehicleSpd 1.18125 0.492781233 VehicleSpd 1.29375 0.512962491

[0041] Obtain the MDF file shown in Table-1. If you get 6 signal data from the MDF file, then identify the 6 signal data as 6 vehicle-to-everything (V2X) data and combine the 6 V2X data into a V2X dataset.

[0042] 102. The quartile algorithm is used to process the vehicle network dataset to obtain the upper and lower limit parameter values ​​of the target vehicle.

[0043] In practical applications, to ensure the accuracy of vehicle performance analysis, it is necessary to remove abnormal vehicle network data from the vehicle network dataset. The key to removing abnormal vehicle network data lies in determining the upper and lower limit parameter values ​​of the target vehicle. The following describes the specific process of processing the vehicle network dataset using the quartile algorithm to obtain the upper and lower limit parameter values ​​of the target vehicle, which includes steps 102A to 102C:

[0044] Step 102A: Sort the vehicle network data in the vehicle network dataset in ascending order based on the timestamp to form a vehicle network data sequence.

[0045] To clarify the changing trends of parameter values ​​corresponding to vehicle-to-everything (V2X) data over time, the V2X data in the dataset needs to be sorted in ascending order according to their timestamps, forming a V2X data sequence. In this sequence, for any two adjacent V2X data points, the timestamp of the earlier data point precedes the timestamp of the later data point. For example, as shown in Table 1, in the V2X data sequence, the data with timestamp "0.433215948" precedes the data with timestamp "0.452784713".

[0046] Step 102B: Based on the total amount of vehicle network data in the vehicle network data sequence, determine the first position value corresponding to the upper quartile and the second position value corresponding to the lower quartile.

[0047] The values ​​at the first and second positions corresponding to the upper quartiles are both derived from the total amount of vehicle-to-everything (V2X) data in the V2X data sequence. The specific determination process for the first and second position values ​​is explained below:

[0048] The process of determining the first position value is as follows: The first position value corresponding to the upper quartile is determined using the formula w1 = (n+1) / 4, where w1 is the first position value and n is the total amount of vehicle-to-everything (V2X) data in the V2X data sequence. The first position value represents the sorting position of the upper quartile in the V2X data sequence.

[0049] For example, if the total amount of vehicle network data in the vehicle network data sequence is 99, then the value at the first position w1 = (n+1) / 4 = (99+1) / 4 = 25. For example, if the total amount of vehicle network data in the vehicle network data sequence is 100, then the value at the first position w1 = (n+1) / 4 = (100+1) / 4 = 25.25.

[0050] The process of determining the second position value is as follows: The second position value corresponding to the lower quartile is determined using the formula w3 = 3 × (n + 1) / 4, where w3 is the second position value and n is the total amount of vehicle-to-everything (V2X) data in the V2X data sequence. The second position value indicates the sorting position of the lower quartile within the V2X data sequence.

[0051] For example, if the total amount of vehicle network data in the vehicle network data sequence is 99, then the value at the second position w3 = 3 × (n+1) / 4 = 3 × (99+1) / 4 = 75. For example, if the total amount of vehicle network data in the vehicle network data sequence is 100, then the value at the second position w3 = 3 × (n+1) / 4 = 3 × (100+1) / 4 = 75.75.

[0052] Step 102C: Determine the upper quartile based on the sorting of the vehicle network data in the vehicle network data sequence and the value of the first position.

[0053] Based on the sorting of vehicle network data in the vehicle network data sequence and the value of the first position, the specific process of determining the upper quartile is related to the specific value of the first position. Therefore, the specific situations for determining the upper quartile include the following two:

[0054] The first approach is to determine the upper quartile of the parameter value corresponding to the vehicle network data with the sequence number of the first position when the value of the first position is an integer.

[0055] For example, if the value at the first position is w1, then the parameter value s1 corresponding to the vehicle network data with sequence number w1 in the vehicle network data sequence is determined as the upper quartile.

[0056] The second method involves rounding down the first position value to obtain the first value and rounding up the first position value to obtain the second value when the first position value is a decimal. Then, the parameter values ​​corresponding to the vehicle network data with the first value and the second value are obtained from the vehicle network data sequence. The upper quartile is determined based on the two obtained parameter values.

[0057] If the value in the first position is a decimal, it means that there is no sequence number with the same value in the vehicle network data sequence. In this case, the following operations are required: round down the value w1 in the first position to obtain the first value w1.1, and round up the value w1 in the first position to obtain the second value w1.2. Obtain the parameter value s1.1 corresponding to the vehicle network data with the sequence number w1.1 and the parameter value s1.2 corresponding to the vehicle network data with the sequence number w1.2. Determine the upper quartile based on the two obtained parameter values ​​s1.1 and s1.2. The process of determining the upper quartile based on s1.1 and s1.2 is as follows: the upper quartile is determined using the following formula: Q1 = 0.25 × s1.1 + 0.75 × s1.2, where Q1 is the upper quartile.

[0058] Step 102D: Determine the lower quartile based on the sorting of the vehicle network data in the vehicle network data sequence and the value of the second position.

[0059] Based on the sorting of vehicle network data in the vehicle network data sequence and the value of the second position, the specific process of determining the lower quartile is related to the specific value of the second position. Therefore, the specific situations for determining the lower quartile include the following two:

[0060] The first approach is to determine the lower quartile of the parameter value corresponding to the vehicle network data with the sequence number of the second position when the value of the second position is an integer.

[0061] For example, if the value at the second position is w3, then the parameter value s3 corresponding to the vehicle network data with sequence number w3 in the vehicle network data sequence is determined as the lower quartile.

[0062] The second method involves rounding down the value in the second position to obtain the third value and rounding up the value in the second position to obtain the fourth value. Then, the parameter values ​​corresponding to the vehicle network data with the third and fourth serial numbers are obtained from the vehicle network data sequence. Based on the two obtained parameter values, the lower quartile is determined.

[0063] If the value in the second position is a decimal, it indicates that there is no identical sequence number in the vehicle network data sequence. In this case, the following operations are required: round down the value w3 in the second position to obtain the third value w3.1, and round up the value w3 in the second position to obtain the fourth value w3.2. Obtain the parameter value s3.1 corresponding to the vehicle network data with the sequence number w3.1 and the parameter value s3.2 corresponding to the vehicle network data with the sequence number w3.2. Determine the lower quartile based on the two obtained parameter values ​​s3.1 and s3.2. The process of determining the lower quartile based on s3.1 and s3.2 is as follows: use the formula Q3 = 0.75 × s3.1 + 0.25 × s3.2 to determine the lower quartile, where Q3 is the lower quartile.

[0064] Step 102E: Determine the upper and lower limit parameter values ​​of the target vehicle parameters based on the upper and lower quartiles.

[0065] The specific process for determining the upper and lower limit parameter values ​​of the target vehicle based on the upper and lower quartiles is as follows: determine the interquartile range based on the upper and lower quartiles, determine the upper limit parameter value based on the interquartile range and the upper quartile, and determine the lower limit parameter value based on the interquartile range and the lower quartile.

[0066] The interquartile range (IQR) represents the width of the middle 50% of a vehicle-to-everything (V2X) data sequence, measuring the dispersion of parameter values ​​within the V2X data sequence. The IQR is determined by the following formula: IQR = Q3 - Q1, where IQR is the interquartile range, Q3 is the lower quartile, and Q1 is the upper quartile.

[0067] The upper limit parameter value is determined by the following formula: Upper limit parameter value = Q3 + 1.5IQR. The upper limit parameter value represents the upper limit of the target vehicle parameters in the vehicle-to-everything (V2X) data sequence. V2X data with corresponding parameter values ​​higher than the upper limit parameter value are considered abnormal V2X data.

[0068] The lower limit parameter value is determined by the following formula: Lower limit parameter value = Q1 - 1.5IQR. The lower limit parameter value represents the lower limit value of the target vehicle parameter in the vehicle-to-everything (V2X) data sequence. V2X data with corresponding parameter values ​​lower than the lower limit parameter value are considered abnormal V2X data.

[0069] 103. Filter the vehicle network data in the vehicle network dataset based on the upper limit parameter value and the lower limit parameter value to form the target dataset.

[0070] The upper and lower limit parameter values ​​define the range of parameter values. Therefore, the vehicle network data in the vehicle network dataset is filtered based on these values ​​to remove abnormal data. The specific process of filtering the vehicle network data in the dataset based on the upper and lower limit parameter values ​​to form the target dataset is as follows: remove vehicle network data whose corresponding parameter values ​​are less than the lower limit or greater than the upper limit; and integrate the remaining data from the vehicle network dataset into the target dataset.

[0071] Since the target dataset is formed after removing abnormal vehicle network data, it is less likely to include abnormal vehicle network data. Therefore, analyzing vehicle performance based on the target dataset can improve the accuracy of vehicle performance analysis.

[0072] 104. Based on the number of samples, the sampling time interval, the data type of the target vehicle parameters, and the timestamps and parameter values ​​corresponding to all vehicle network data in the target dataset, the target dataset is sampled.

[0073] Although the target dataset filters out abnormal vehicle network data, it contains a large amount of vehicle network data. In order to reduce the amount of vehicle network data used when analyzing vehicle performance, the target dataset needs to be sampled after it is formed.

[0074] Based on the number of samples, the sampling time interval, the data type of the target vehicle parameters, and the timestamps and parameter values ​​corresponding to all vehicle-to-everything (V2X) data in the target dataset, the specific process of sampling the target dataset includes the following steps 104A to 104B:

[0075] Step 104A: Set the value range based on the number of samples, where the number of samples is the upper limit of the value range.

[0076] In practical applications, the sampling quantity is determined by the volume of vehicle network data required for vehicle performance analysis. The sampling quantity should be less than the total amount of vehicle network data in the target dataset. When the sampling quantity is equal to the total amount of vehicle network data in the target dataset, there is no need to perform sampling processing on the target dataset; the target dataset can be used directly. When setting the value range based on the sampling quantity, set 1 as the lower limit of the value range and set the sampling quantity as the upper limit of the value range.

[0077] For example, the target dataset includes 180,000 vehicle-to-everything (V2X) data points, and the sampling quantity is set to 18,000. This means that 18,000 V2X data points need to be sampled from the 180,000 V2X data points.

[0078] After determining the number of samples, a value range needs to be set based on the number of samples, where 1 is the lower limit of the value range and the number of samples is the upper limit of the value range. For example, if the number of samples is set to 18000, then the value range is [1, 18000].

[0079] Step 104B: Starting from the lower limit of the value range, sequentially select values ​​from the range, and execute steps 104B1 to 104B4 for each selected value:

[0080] 104B1. Set the target timestamp corresponding to the current sample based on the sampling time interval and the current value.

[0081] In practical applications, the sampling time interval is determined based on specific business requirements and is related to the number of samples. For example, if the target dataset includes 180,000 vehicle network data entries, with a timestamp interval of 0.01 seconds between each data entry, and the sampling quantity is set to 18,000, then the sampling time interval is 0.1 seconds.

[0082] The currently selected value represents the number of times the current sample is taken. For example, if the currently selected value is 2, it means that this is the second sample. The product of the sampling time interval and the currently selected value is set as the target timestamp for the current sample. For example, if the currently selected value is 2 and the sampling time interval is 0.1 seconds, then the target timestamp for the current sample is 0.2 seconds.

[0083] 104B2. When there is vehicle network data in the target dataset with the same timestamp as the target timestamp, the parameter values ​​of the vehicle network data shall be determined as the parameter values ​​corresponding to the target timestamp.

[0084] After determining the target timestamp, it is compared with the timestamps of each vehicle-to-everything (V2X) data point in the target dataset. If a V2X data point in the target dataset has the same timestamp as the target timestamp, then the parameter value of that V2X data point is determined as the parameter value corresponding to the target timestamp. In other words, it is recorded as a single V2X data point, with the timestamp corresponding to this record being the "target timestamp," and the corresponding parameter value being the parameter value mentioned above for the target timestamp. This recorded V2X data point is thus a single sampled V2X data point.

[0085] 104B3. When there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameters is a continuous data type, extract the parameter values ​​of the vehicle network data corresponding to the first timestamp and the second timestamp respectively, and determine the parameter value corresponding to the target timestamp based on the two extracted parameter values.

[0086] The target timestamp is located between the first timestamp and the second timestamp. The first timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are after the target timestamp, and the second timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are before the target timestamp.

[0087] For example, the data type of the target vehicle parameter is a continuous data type. The product between the sampling time interval and the currently taken value is 0.1×m. This product is set as the target timestamp corresponding to the current sampling. The target timestamp "0.1×m" is located between the first timestamp t1 and the second timestamp t2, and the parameter values ​​corresponding to t1 and t2 are s1 and s2, respectively. Then the parameter value corresponding to the target timestamp "0.1×m" is (s1+s2) / 2. The record corresponding to the parameter value "(s1+s2) / 2" and the target timestamp "0.1×m" is a vehicle network data record. The timestamp corresponding to this vehicle network data record is "0.1×m", and the corresponding parameter value is "(s1+s2) / 2". The recorded vehicle network data is a vehicle network data record formed after sampling.

[0088] In practical applications, the data type of the target vehicle parameters can be determined using the following two methods:

[0089] The first method involves reading the data type of the target vehicle parameters from the data type table corresponding to the vehicle-to-everything (V2X) dataset. The data type table records the data type of the target vehicle parameters corresponding to the V2X dataset.

[0090] The second method involves extracting parameter values ​​from all vehicle network data in the vehicle network dataset and deduplicating the extracted parameter values. When the total number of deduplicated parameter values ​​reaches a preset value, the data type of the target vehicle parameter is determined to be a continuous data type. When the total number of deduplicated parameter values ​​does not reach the preset value, the data type of the target vehicle parameter is determined to be a discrete data type. The preset value is set based on the total number of all vehicle network data in the vehicle network dataset.

[0091] Continuous data type target vehicle parameters have values ​​that continuously increase or decrease over time during vehicle operation. For example, vehicle speed and engine speed are continuous data type vehicle parameters. Discrete data type target vehicle parameters have values ​​that vary between several values. For example, fan status, air conditioning compressor status, and brake pedal status are turned on or off over time during vehicle operation, with corresponding parameter values ​​of 0 or 1. Similarly, for gears, the corresponding parameter values ​​would vary between 1st, 2nd, 3rd, and NP gears.

[0092] When determining the type of target vehicle parameters, a preset value is first set based on the total number of all vehicle-to-everything (V2X) data in the V2X dataset. For example, the preset value could be 60% of the total number. Then, parameter values ​​are extracted from all V2X data in the V2X dataset. For instance, if the target vehicle parameter is vehicle speed, the speed of each V2X data point in the dataset is extracted. The extracted parameter values ​​are then deduplicated, retaining only one duplicate value. If the total number of deduplicated parameter values ​​reaches the preset value, the data type of the target vehicle parameter is determined to be a continuous data type. If the total number of deduplicated parameter values ​​does not reach the preset value, the data type of the target vehicle parameter is determined to be a discrete data type.

[0093] 104B4. When there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameters is a discrete data type, the parameter value of the vehicle network data corresponding to the third timestamp shall be determined as the parameter value corresponding to the target timestamp.

[0094] The third timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are located after the target timestamp.

[0095] For example, the data type of the target vehicle parameters is a discrete data type. The product of the sampling time interval and the currently taken value, 0.1×m, is set as the target timestamp corresponding to the current sample. The timestamp with the smallest difference from the target timestamp among all timestamps after the target timestamp in the target dataset is designated as the third timestamp, t3. The parameter value corresponding to t3 is then designated as s3. Therefore, the parameter value corresponding to the target timestamp "0.1×m" is s3. The parameter value "s3" and the target timestamp "0.1×m" are recorded as a single piece of vehicle network data. The timestamp corresponding to this recorded vehicle network data is "0.1×m", and the corresponding parameter value is "s3". The recorded vehicle network data is thus a single piece of vehicle network data formed after sampling.

[0096] After sampling the target dataset, a new dataset is formed, which includes all the vehicle network data obtained from the sampling process. Once the new dataset is obtained, corresponding vehicle performance analysis can be performed based on it. It should be noted that when performing vehicle performance analysis, datasets obtained from sampling two or more target vehicle parameters can be used in combination.

[0097] For example, after performing steps 101-104 above on the vehicle network datasets corresponding to vehicle speed, engine speed, and fuel consumption, separate datasets were formed after sampling and processing. Then, by combining the datasets formed after sampling and processing of the three datasets, the vehicle network data shown in Table-2 was obtained.

[0098] Table 2

[0099] Timestamp Vehicle speed value Engine speed value fuel consumption 0.1 0 100 1 0.2 0 200 1 0.3 0 300 1 0.4 0.95625 400 3 0.5 1.2375 500 3 0.6 1.63125 600 3 0.7 2.109375 700 3 … … … … 1799.9 … … …

[0100] Based on Table 2 above, the following vehicle performance analysis can be performed: From 0.1 to 0.3 seconds, the vehicle is in an idling state. The total idling fuel consumption can be calculated by integrating the fuel consumption data. After 0.4 seconds, the vehicle is in a normal driving state. The total fuel consumption for normal driving can be obtained by integrating the fuel consumption data.

[0101] For example, after performing steps 101-104 above on the vehicle network datasets corresponding to the in-vehicle temperature and the outside temperature, separate datasets were formed after sampling and processing. Then, by combining the two datasets after sampling and processing, the vehicle network data shown in Table-3 was obtained.

[0102] Table 3

[0103]

[0104]

[0105] Based on Table 3 above, the following vehicle performance analysis can be performed: changes in vehicle interior temperature with changes in exterior temperature at different times, and the temperature difference between the vehicle interior and exterior.

[0106] The vehicle network data processing method provided in this embodiment of the invention, when there is a need for vehicle network data processing, acquires a vehicle network dataset including multiple vehicle network data points. Each vehicle network data point in the dataset has a corresponding timestamp and a parameter value of the target vehicle parameter. Then, the vehicle network dataset is processed using a quartile algorithm to obtain upper and lower limit parameter values ​​of the target vehicle parameter. Based on the upper and lower limit parameter values, the vehicle network data in the dataset is filtered to form a target dataset. Finally, the target dataset is sampled based on the number of samples, the sampling time interval, the data type of the target vehicle parameter, and the timestamps and parameter values ​​corresponding to all vehicle network data in the target dataset. It can be seen that the solution provided in this embodiment of the invention uses the quartile algorithm to process the vehicle network dataset to obtain the upper and lower limit parameter values ​​of the target vehicle parameter, and uses the obtained upper and lower limit parameter values ​​to filter abnormal vehicle network data in the dataset, thus reducing the probability of abnormal vehicle network data. Furthermore, it can sample the dataset obtained after filtering abnormal vehicle network data, thereby reducing the volume of vehicle network data used in vehicle performance analysis. Therefore, the solution provided by the embodiments of the present invention can reduce the probability of abnormal vehicle network data in the vehicle network data used for vehicle performance analysis, while reducing the volume of vehicle network data used for vehicle performance analysis, thereby improving the efficiency and quality of vehicle performance analysis when performing vehicle performance analysis based on vehicle network data.

[0107] Furthermore, based on the above method embodiments, another embodiment of the present invention also provides a vehicle network data processing device, such as... Figure 2 As shown, the device includes:

[0108] The acquisition unit 21 is used to acquire a vehicle network dataset, wherein the vehicle network dataset includes multiple vehicle network data, and each of the vehicle network data has a corresponding timestamp and parameter values ​​of the target vehicle parameters;

[0109] Processing unit 22 is used to process the vehicle network dataset using the quartile algorithm to obtain the upper limit parameter value and lower limit parameter value of the target vehicle parameter;

[0110] Filtering unit 23 is used to filter the vehicle network data in the vehicle network dataset based on the upper limit parameter value and the lower limit parameter value to form a target dataset;

[0111] The sampling unit 24 is used to sample the target dataset based on the number of samples, the sampling time interval, the data type of the target vehicle parameters, and the timestamps and parameter values ​​corresponding to all vehicle network data in the target dataset.

[0112] The vehicle network data processing device provided in this embodiment of the invention, when there is a need for vehicle network data processing, acquires a vehicle network dataset including multiple vehicle network data points. Each vehicle network data point in the dataset has a corresponding timestamp and a parameter value of the target vehicle parameter. Then, the vehicle network dataset is processed using a quartile algorithm to obtain upper and lower limit parameter values ​​for the target vehicle parameter. Based on these upper and lower limit parameter values, the vehicle network data in the dataset is filtered to form a target dataset. Finally, the target dataset is sampled based on the sampling quantity, sampling time interval, data type of the target vehicle parameter, and the timestamps and parameter values ​​corresponding to all vehicle network data points in the target dataset. It can be seen that the solution provided in this embodiment of the invention uses a quartile algorithm to process the vehicle network dataset to obtain upper and lower limit parameter values ​​for the target vehicle parameter, and uses these upper and lower limit parameter values ​​to filter abnormal vehicle network data in the dataset, thus reducing the probability of abnormal vehicle network data occurrence. Furthermore, it can perform sampling processing on the dataset obtained after filtering abnormal vehicle network data, thereby reducing the volume of vehicle network data used in vehicle performance analysis. Therefore, the solution provided by the embodiments of the present invention can reduce the probability of abnormal vehicle network data in the vehicle network data used for vehicle performance analysis, while reducing the volume of vehicle network data used for vehicle performance analysis, thereby improving the efficiency and quality of vehicle performance analysis when performing vehicle performance analysis based on vehicle network data.

[0113] Optional, such as Figure 3 As shown, the processing unit 22 includes:

[0114] The sorting module 221 is used to sort the vehicle network data in the vehicle network dataset in ascending order based on the timestamps to form a vehicle network data sequence;

[0115] The first determining module 222 is used to determine the first position value corresponding to the upper quartile and the second position value corresponding to the lower quartile based on the total amount of vehicle network data in the vehicle network data sequence.

[0116] The second determining module 223 is used to determine the upper quartile based on the sorting of the vehicle network data in the vehicle network data sequence and the first position value;

[0117] The third determining module 224 is used to determine the lower quartile based on the sorting of the vehicle network data in the vehicle network data sequence and the second position value;

[0118] The fourth determining module 225 is used to determine the upper limit parameter value and the lower limit parameter value of the target vehicle parameter based on the upper quartile and the lower quartile.

[0119] Optional, such as Figure 3 As shown, the second determining module 223 is specifically used to determine the parameter value corresponding to the vehicle network data with the sequence number of the first position value in the vehicle network data sequence as the upper quartile when the value of the first position is an integer; when the value of the first position is a decimal, the module rounds down the value of the first position to obtain a first value and rounds up the value of the first position to obtain a second value, obtains the parameter value corresponding to the vehicle network data with the sequence number of the first value and the parameter value corresponding to the vehicle network data with the sequence number of the second value in the vehicle network data sequence, and determines the upper quartile based on the two obtained parameter values.

[0120] Optional, such as Figure 3 As shown, the third determining module 224 is specifically used to determine the parameter value corresponding to the vehicle network data with the sequence number of the second position value in the vehicle network data sequence as the lower quartile when the value of the second position is an integer; when the value of the second position is a decimal, the module rounds down the value of the second position to obtain a third value and rounds up the value of the second position to obtain a fourth value, obtains the parameter value corresponding to the vehicle network data with the sequence number of the third value and the parameter value corresponding to the vehicle network data with the fourth value in the vehicle network data sequence, and determines the lower quartile based on the two obtained parameter values.

[0121] Optional, such as Figure 3 As shown, the fourth determining module 225 is specifically used to determine the interquartile range based on the upper quartile and the lower quartile; determine the upper limit parameter value based on the interquartile range and the upper quartile; and determine the lower limit parameter value based on the interquartile range and the lower quartile.

[0122] Optional, such as Figure 3 As shown, the filter unit 23 includes:

[0123] The elimination module 231 is used to eliminate vehicle network data in the vehicle network data set whose corresponding parameter values ​​are less than the lower limit parameter value and whose corresponding parameter values ​​are greater than the upper limit parameter value.

[0124] Integration module 232 is used to integrate the data that has not been removed from the vehicle network dataset into the target dataset.

[0125] Optional, such as Figure 3As shown, sampling unit 24 is specifically used to set a value range based on the sampling quantity, wherein the sampling quantity is the upper limit of the value range; starting from the lower limit of the value range, values ​​are sequentially taken from the value range, and for each value taken, the following is performed: setting the target timestamp corresponding to the current sample based on the sampling time interval and the currently taken value; when there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameter is a continuous data type, the parameter values ​​of the vehicle network data corresponding to the first timestamp and the second timestamp are extracted respectively, and the parameter value corresponding to the target timestamp is determined based on the two extracted parameter values, wherein the target timestamp is located between the first timestamp and the second timestamp, and the first timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are after the target timestamp, and the second timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are before the target timestamp.

[0126] Optional, such as Figure 3 As shown, the sampling unit 24 is further configured to, when there is vehicle network data in the target dataset with the same timestamp as the target timestamp, determine the parameter value of the vehicle network data as the parameter value corresponding to the target timestamp; or, when there is no vehicle network data in the target dataset with the same timestamp as the target timestamp, and the data type of the target vehicle parameter is a discrete data type, determine the parameter value of the vehicle network data corresponding to the third timestamp as the parameter value corresponding to the target timestamp, wherein the third timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are located after the target timestamp.

[0127] Optional, such as Figure 3 As shown, the device further includes:

[0128] The determining unit 25 is used to extract parameter values ​​of all vehicle network data in the vehicle network dataset and perform deduplication processing on the extracted parameter values; when the total number of parameter values ​​after deduplication reaches a preset value, the data type of the target vehicle parameter is determined to be a continuous data type; when the total number of parameter values ​​after deduplication does not reach the preset value, the data type of the target vehicle parameter is determined to be a discrete data type; wherein, the preset value is set based on the total number of all vehicle network data in the vehicle network dataset.

[0129] For a detailed explanation of the methods used in the operation of each functional module in the vehicle network data processing device provided in this embodiment of the invention, please refer to the corresponding method details in the above method embodiments, which will not be repeated here.

[0130] The vehicle network data processing device provided in this embodiment of the invention includes a processor and a memory. The aforementioned acquisition unit, processing unit, filtering unit, sampling unit, and determination unit are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0131] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the probability of abnormal vehicle-to-everything (V2X) data occurrences can be reduced, while simultaneously decreasing the volume of V2X data.

[0132] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device where the storage medium is located to execute the above-described method for processing vehicle network data.

[0133] This invention provides a processor for running a program, wherein the program executes the above-described method for processing vehicle network data.

[0134] This invention provides an electronic device, such as... Figure 4 As shown, the electronic device 30 includes at least one processor 301, at least one memory 302 connected to the processor 301, and a bus 303; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call program instructions in the memory to execute the above-mentioned vehicle network data processing method. The electronic device in this article can be a server, PC, PAD, mobile phone, etc.

[0135] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps:

[0136] A method for processing vehicle network data includes:

[0137] Obtain a vehicle network dataset, wherein the vehicle network dataset includes multiple vehicle network data entries, and each vehicle network data entry has a corresponding timestamp and parameter values ​​of the target vehicle parameters;

[0138] The vehicle network dataset is processed using the quartile algorithm to obtain the upper and lower limit parameter values ​​of the target vehicle parameters;

[0139] Based on the upper limit parameter value and the lower limit parameter value, filter the vehicle network data in the vehicle network dataset to form a target dataset;

[0140] The target dataset is sampled based on the number of samples, the sampling time interval, the data type of the target vehicle parameters, and the timestamps and parameter values ​​corresponding to all vehicle-to-everything (V2X) data in the target dataset.

[0141] Optionally, the vehicle network dataset is processed using a quartile algorithm to obtain the upper and lower bound parameter values ​​of the target vehicle parameters, including:

[0142] The vehicle network data in the vehicle network dataset are sorted in ascending order based on the timestamps to form a vehicle network data sequence;

[0143] Based on the total amount of vehicle network data in the vehicle network data sequence, determine the first position value corresponding to the upper quartile and the second position value corresponding to the lower quartile.

[0144] Based on the sorting of the vehicle network data in the vehicle network data sequence and the value of the first position, the upper quartile is determined;

[0145] Based on the sorting of the vehicle network data in the vehicle network data sequence and the second position value, the lower quartile is determined;

[0146] Based on the upper quartile and the lower quartile, the upper limit parameter value and the lower limit parameter value of the target vehicle parameter are determined.

[0147] Optionally, the upper quartile is determined based on the sorting of the vehicle network data in the vehicle network data sequence and the value at the first position, including:

[0148] When the value at the first position is an integer, the parameter value corresponding to the vehicle network data with the sequence number at the first position in the vehicle network data sequence is determined as the upper quartile;

[0149] When the value at the first position is a decimal, the value at the first position is rounded down to obtain a first value and the value at the first position is rounded up to obtain a second value. The parameter values ​​corresponding to the vehicle network data with the sequence number of the first value and the parameter values ​​corresponding to the vehicle network data with the sequence number of the second value are obtained in the vehicle network data sequence. The upper quartile is determined based on the two obtained parameter values.

[0150] Optionally, based on the sorting of the vehicle network data in the vehicle network data sequence and the second position value, the lower quartile is determined, including:

[0151] When the value at the second position is an integer, the parameter value corresponding to the vehicle network data with the sequence number at the second position in the vehicle network data sequence is determined as the lower quartile;

[0152] When the value at the second position is a decimal, the value at the second position is rounded down to obtain the third value and the value at the second position is rounded up to obtain the fourth value. The parameter value corresponding to the vehicle network data with the sequence number of the third value and the parameter value corresponding to the vehicle network data with the fourth value are obtained in the vehicle network data sequence. The lower quartile is determined based on the two obtained parameter values.

[0153] Optionally, the upper and lower limit parameter values ​​of the target vehicle parameters are determined based on the upper quartile and the lower quartile, including:

[0154] The interquartile range is determined based on the upper quartile and the lower quartile;

[0155] The upper limit parameter value is determined based on the interquartile range and the upper quartile.

[0156] The lower limit parameter value is determined based on the interquartile range and the lower quartile.

[0157] Optionally, the vehicle network data in the vehicle network dataset is filtered based on the upper limit parameter value and the lower limit parameter value to form a target dataset, including:

[0158] Remove vehicle network data from the vehicle network dataset whose corresponding parameter values ​​are less than the lower limit parameter value or whose corresponding parameter values ​​are greater than the upper limit parameter value;

[0159] The data that was not removed from the vehicle network dataset is integrated into the target dataset.

[0160] Optionally, the target dataset is sampled based on the number of samples, the sampling time interval, the data type of the target vehicle parameters, and the timestamps and parameter values ​​corresponding to all vehicle-to-everything (V2X) data in the target dataset, including:

[0161] A value range is set based on the number of samples, wherein the number of samples is the upper limit of the value range;

[0162] Starting from the lower limit of the range, values ​​are sequentially selected from the range, and the following steps are performed for each selected value:

[0163] Set the target timestamp corresponding to the current sample based on the sampling time interval and the current value;

[0164] If there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameter is a continuous data type, the parameter values ​​of the vehicle network data corresponding to the first timestamp and the second timestamp are extracted respectively. Based on the two extracted parameter values, the parameter value corresponding to the target timestamp is determined. The target timestamp is located between the first timestamp and the second timestamp, and the first timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are after the target timestamp. The second timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are before the target timestamp.

[0165] Optionally, after setting the target timestamp corresponding to the current sample based on the sampling time interval and the currently taken value, the method further includes:

[0166] When there is vehicle network data in the target dataset with the same timestamp as the target timestamp, the parameter value of the vehicle network data is determined as the parameter value corresponding to the target timestamp.

[0167] Optionally, after setting the target timestamp corresponding to the current sample based on the sampling time interval and the currently taken value, the method further includes:

[0168] When there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameter is a discrete data type, the parameter value of the vehicle network data corresponding to the third timestamp is determined as the parameter value corresponding to the target timestamp. The third timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are located after the target timestamp.

[0169] Optionally, the method further includes:

[0170] Extract the parameter values ​​of all vehicle network data in the vehicle network dataset, and perform deduplication on the extracted parameter values;

[0171] When the total number of parameter values ​​after deduplication reaches a preset value, the data type of the target vehicle parameter is determined to be a continuous data type.

[0172] If the total number of parameter values ​​after deduplication does not reach a preset value, the data type of the target vehicle parameter is determined to be a discrete data type.

[0173] The preset value is set based on the total number of all vehicle network data in the vehicle network dataset.

[0174] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0175] In a typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input / output interfaces, network interfaces, etc.

[0176] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM, and memory includes at least one memory chip. Memory is an example of computer-readable media.

[0177] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0178] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0179] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for processing vehicle network data, characterized in that, The method includes: Obtain a vehicle network dataset, wherein the vehicle network dataset includes multiple vehicle network data entries, and each vehicle network data entry has a corresponding timestamp and parameter values ​​of the target vehicle parameters; The vehicle network dataset is processed using the quartile algorithm to obtain the upper and lower limit parameter values ​​of the target vehicle parameters; Based on the upper limit parameter value and the lower limit parameter value, filter the vehicle network data in the vehicle network dataset to form a target dataset; The value range is set based on the number of samples, wherein the number of samples is the upper limit of the value range; Starting from the lower limit of the range, values ​​are sequentially selected from the range, and the following steps are performed for each selected value: Set the target timestamp corresponding to the current sample based on the sampling time interval and the current value; If there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameter is a continuous data type, the parameter values ​​of the vehicle network data corresponding to the first timestamp and the second timestamp are extracted respectively. Based on the two extracted parameter values, the parameter value corresponding to the target timestamp is determined. The target timestamp is located between the first timestamp and the second timestamp, and the first timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are after the target timestamp. The second timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are before the target timestamp.

2. The method according to claim 1, characterized in that, The vehicle network dataset is processed using the quartile algorithm to obtain the upper and lower bound parameter values ​​of the target vehicle parameters, including: The vehicle network data in the vehicle network dataset are sorted in ascending order based on the timestamps to form a vehicle network data sequence; Based on the total amount of vehicle network data in the vehicle network data sequence, determine the first position value corresponding to the upper quartile and the second position value corresponding to the lower quartile. Based on the sorting of the vehicle network data in the vehicle network data sequence and the value of the first position, the upper quartile is determined; Based on the sorting of the vehicle network data in the vehicle network data sequence and the second position value, the lower quartile is determined; Based on the upper quartile and the lower quartile, the upper limit parameter value and the lower limit parameter value of the target vehicle parameter are determined.

3. The method according to claim 2, characterized in that, Based on the sorting of the vehicle network data in the vehicle network data sequence and the value of the first position, the upper quartile is determined, including: When the value at the first position is an integer, the parameter value corresponding to the vehicle network data with the sequence number at the first position in the vehicle network data sequence is determined as the upper quartile; When the value at the first position is a decimal, the value at the first position is rounded down to obtain a first value and the value at the first position is rounded up to obtain a second value. The parameter values ​​corresponding to the vehicle network data with the sequence number of the first value and the parameter values ​​corresponding to the vehicle network data with the sequence number of the second value are obtained in the vehicle network data sequence. The upper quartile is determined based on the two obtained parameter values. and / or, Based on the sorting of the vehicle network data in the vehicle network data sequence and the second position value, the lower quartile is determined, including: When the value at the second position is an integer, the parameter value corresponding to the vehicle network data with the sequence number at the second position in the vehicle network data sequence is determined as the lower quartile; When the value at the second position is a decimal, the value at the second position is rounded down to obtain the third value and the value at the second position is rounded up to obtain the fourth value. The parameter value corresponding to the vehicle network data with the sequence number of the third value and the parameter value corresponding to the vehicle network data with the fourth value are obtained in the vehicle network data sequence. The lower quartile is determined based on the two obtained parameter values. and / or, Based on the upper quartile and the lower quartile, the upper limit parameter value and the lower limit parameter value of the target vehicle are determined, including: The interquartile range is determined based on the upper quartile and the lower quartile; The upper limit parameter value is determined based on the interquartile range and the upper quartile. The lower limit parameter value is determined based on the interquartile range and the lower quartile.

4. The method according to claim 1, characterized in that, Based on the upper limit parameter value and the lower limit parameter value, the vehicle network data in the vehicle network dataset is filtered to form a target dataset, including: Remove vehicle network data from the vehicle network dataset whose corresponding parameter values ​​are less than the lower limit parameter value or whose corresponding parameter values ​​are greater than the upper limit parameter value; The data that was not removed from the vehicle network dataset is integrated into the target dataset.

5. The method according to claim 1, characterized in that, After setting the target timestamp corresponding to the current sample based on the sampling time interval and the currently taken value, the method further includes: When there is vehicle network data in the target dataset with the same timestamp as the target timestamp, the parameter value of the vehicle network data is determined as the parameter value corresponding to the target timestamp; or, When there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameter is a discrete data type, the parameter value of the vehicle network data corresponding to the third timestamp is determined as the parameter value corresponding to the target timestamp. The third timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are located after the target timestamp.

6. The method according to claim 1, characterized in that, The method further includes: Extract the parameter values ​​of all vehicle network data in the vehicle network dataset, and perform deduplication on the extracted parameter values; When the total number of parameter values ​​after deduplication reaches a preset value, the data type of the target vehicle parameter is determined to be a continuous data type. If the total number of parameter values ​​after deduplication does not reach a preset value, the data type of the target vehicle parameter is determined to be a discrete data type. The preset value is set based on the total number of all vehicle network data in the vehicle network dataset.

7. A device for processing vehicle network data, characterized in that, The device comprises: The acquisition unit is used to acquire a vehicle network dataset, wherein the vehicle network dataset includes multiple vehicle network data, and each of the vehicle network data has a corresponding timestamp and parameter values ​​of the target vehicle parameters; The processing unit is used to process the vehicle network dataset using the quartile algorithm to obtain the upper limit parameter value and the lower limit parameter value of the target vehicle parameter; The filtering unit is used to filter the vehicle network data in the vehicle network dataset based on the upper limit parameter value and the lower limit parameter value to form a target dataset; A sampling unit is used to set a value range based on the number of samples, wherein the number of samples is the upper limit of the value range; starting from the lower limit of the value range, values ​​are sequentially taken from the value range, and for each value taken, the following is performed: setting the target timestamp corresponding to the current sample based on the sampling time interval and the currently taken value; when there is no vehicle network data with the same timestamp as the target timestamp in the target dataset, and the data type of the target vehicle parameter is a continuous data type, the parameter values ​​of the vehicle network data corresponding to the first timestamp and the second timestamp are extracted respectively, and the parameter value corresponding to the target timestamp is determined based on the two extracted parameter values, wherein the target timestamp is located between the first timestamp and the second timestamp, and the first timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are after the target timestamp, and the second timestamp is the timestamp with the smallest difference from the target timestamp among all timestamps in the target dataset that are before the target timestamp.

8. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to perform the vehicle network data processing method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: At least one processor, and at least one memory and bus connected to the processor; wherein the processor and the memory communicate with each other via the bus; The processor is used to call program instructions in the memory to execute the vehicle network data processing method according to any one of claims 1 to 6.

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