Vehicle trip division method, device, electronic device and storage medium

Abstract

The present application provides a method, device, electronic device and storage medium for dividing a vehicle's itinerary. Specifically, in the method for dividing a vehicle's itinerary, the driving data of the vehicle within a preset time period is first obtained, and based on preset standards, it is determined whether the driving data has any abnormalities. If there are no abnormalities in the driving data, the vehicle's itinerary is divided based on the timestamp, wherein each itinerary includes the driving data corresponding to the current itinerary. If there are abnormalities in the driving data, the driving data is corrected to obtain the corrected driving data, and the vehicle's itinerary is divided based on the timestamp in the corrected driving data. By utilizing the method of the present application, the vehicle's itinerary can be specifically divided according to actual needs through the driving data of the vehicle obtained within the preset time period, thereby better analyzing the user's driving behavior in detail.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle data processing, and in particular to a method, device, electronic device and storage medium for dividing a vehicle trip. Background Art

[0002] With the development of technology, cars are becoming increasingly common in people's lives, and the number of vehicles owned by residents is increasing day by day. Consequently, there is a growing number of car-related businesses. In order for business personnel to design different products or implement different marketing strategies for different user markets and achieve targeted marketing, they need to understand user driving behavior. Vehicle driving data can reflect user driving behavior.

[0003] In existing technologies, user driving behavior analysis can only be performed based on macro-indicators of the vehicle. For example, easily quantified data such as total distance traveled and total driving time can be used to reflect the user's driving behavior. Specific data such as whether the vehicle exceeded the speed limit or experienced excessive acceleration during a particular trip cannot be counted, making it difficult to accurately analyze the user's driving behavior. Summary of the Invention

[0004] In view of this, the present application provides a method, device, electronic device and storage medium for dividing a vehicle trip to solve the problem in the prior art that when analyzing a user's driving behavior, the user can only be analyzed through the vehicle's macro indicators and cannot accurately analyze the user's driving behavior.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] The first aspect of the present application discloses a method for dividing a vehicle journey, comprising:

[0007] Acquire driving data of the vehicle within a preset time period; wherein the driving data includes mileage data and timestamp data, and each mileage corresponds to a timestamp;

[0008] Determining whether the driving data is abnormal based on a preset standard;

[0009] If the driving data has no anomaly, dividing the trips of the vehicle based on the timestamp, wherein each trip includes driving data corresponding to the current trip;

[0010] If the driving data is abnormal, the driving data is corrected to obtain corrected driving data, and the vehicle's travel is divided based on the timestamp in the corrected driving data.

[0011] Optionally, in the above method, dividing the vehicle's itinerary based on the timestamp includes:

[0012] Arrange the timestamps within the preset time period in chronological order, and calculate the difference between every two adjacent timestamps;

[0013] If the difference between the adjacent timestamps is greater than a preset threshold, the adjacent timestamps are divided into different trips; if the difference between the adjacent timestamps is not greater than the preset threshold, the adjacent timestamps are divided into the same trip.

[0014] Optionally, in the above method, if the driving data is abnormal, correcting the driving data includes:

[0015] If the data of the timestamp is abnormal, the proportion of the data of the abnormal timestamp in the data of the timestamp is calculated;

[0016] Correcting the data of the timestamp according to the data proportion;

[0017] If the mileage data is abnormal, all the mileage data are sorted in the order of the timestamps;

[0018] Calculating the difference between every two adjacent mileages in the sorted mileage data;

[0019] Screening out abnormal differences among each of the calculated mileage differences, and correcting the screened abnormal differences;

[0020] The mileage data is corrected using the corrected abnormal difference.

[0021] Optionally, in the above method, the step of correcting the timestamp data according to the data ratio includes:

[0022] If the data proportion of the abnormal timestamp is less than a preset first threshold, the abnormal timestamp is deleted;

[0023] If the data proportion of the abnormal timestamp is not less than the first threshold, the data storage time of the abnormal timestamp is obtained, and the abnormal timestamp is modified according to the data storage time.

[0024] Optionally, in the above method, screening out abnormal differences in each of the calculated mileage differences and correcting the screened abnormal differences includes:

[0025] For each calculated difference, determine whether the difference is less than a preset second threshold or greater than a preset third threshold; wherein the second threshold is less than the third threshold;

[0026] If it is determined that the difference is less than the second threshold or greater than the third threshold, the difference is determined to be an abnormal difference;

[0027] Each of the abnormal difference values ​​is assigned a preset fourth threshold.

[0028] A second aspect of the present application discloses a vehicle travel division device, comprising:

[0029] An acquisition unit, configured to acquire driving data of a vehicle within a preset time period; wherein the driving data includes mileage data and timestamp data, and each mileage corresponds to a timestamp;

[0030] a determination unit, configured to determine whether the driving data is abnormal according to a preset standard;

[0031] a trip division unit, configured to divide the trips of the vehicle based on the timestamp if the driving data is normal, wherein each trip includes driving data corresponding to the current trip;

[0032] A data correction unit is used to correct the driving data if there is an abnormality in the driving data to obtain corrected driving data, and to divide the vehicle's travel based on the timestamp in the corrected driving data.

[0033] Optionally, in the above-mentioned device, the travel division unit includes:

[0034] A first calculation subunit, configured to arrange the timestamps within the preset time period in chronological order and calculate the difference between every two adjacent timestamps;

[0035] The trip division subunit is configured to divide the adjacent timestamps into different trips if the difference between the adjacent timestamps is greater than a preset threshold, and to divide the adjacent timestamps into the same trip if the difference between the adjacent timestamps is not greater than the preset threshold.

[0036] Optionally, in the above-mentioned device, the data correction unit includes:

[0037] A second calculation subunit is configured to calculate a proportion of data with abnormal timestamps in the data with the timestamps if the data with the timestamps is abnormal;

[0038] A first data correction subunit, configured to correct the data of the timestamp according to the data ratio;

[0039] a sorting subunit, configured to sort all the mileage data in the order of the timestamps if there is an abnormality in the mileage data;

[0040] a third calculation subunit, configured to calculate a difference between every two adjacent mileages in the sorted mileage data;

[0041] a second data correction subunit, configured to screen out abnormal differences in each of the calculated mileage differences, and correct the screened abnormal differences;

[0042] The third data correction unit is used to correct the mileage data using the corrected abnormal difference.

[0043] Optionally, in the above device, the first data correction subunit includes:

[0044] a deleting subunit, configured to delete the abnormal timestamp if the data proportion of the abnormal timestamp is less than a preset first threshold;

[0045] The modification subunit is used to obtain the data storage time of the abnormal timestamp if the data proportion of the abnormal timestamp is not less than the first threshold, and modify the abnormal timestamp according to the data storage time.

[0046] Optionally, in the above device, the second data correction subunit includes:

[0047] a judging subunit, configured to judge, for each calculated difference, whether the difference is less than a preset second threshold or greater than a preset third threshold; wherein the second threshold is less than the third threshold;

[0048] a determination subunit, configured to determine that the difference is an abnormal difference if it is determined that the difference is less than the second threshold or greater than the third threshold;

[0049] The assignment subunit is used to assign each of the abnormal differences to a preset fourth threshold.

[0050] A third aspect of the present application discloses an electronic device, comprising:

[0051] one or more processors;

[0052] a storage device having one or more programs stored thereon;

[0053] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of the first aspects of the present application.

[0054] A fourth aspect of the present application discloses a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any one of the first aspects of the present application is implemented.

[0055] As can be seen from the above technical solution, the method for segmenting vehicle trips provided by this application first obtains vehicle driving data within a preset time period and, based on preset criteria, determines whether the driving data contains any anomalies. The driving data includes mileage data and timestamp data, with each mileage corresponding to a timestamp. If the driving data contains no anomalies, the vehicle's trips are segmented based on the timestamps, with each trip including the driving data corresponding to the current trip. If the driving data contains anomalies, the driving data is corrected to obtain corrected driving data, and the vehicle's trips are segmented based on the timestamps in the corrected driving data. Using the method of this application, by checking the timestamps for anomalies, the data used for trip segmentation is ensured to be valid. Furthermore, based on the obtained driving data within the preset time period, the vehicle's trips are segmented specifically according to actual needs, improving the effectiveness of trip segmentation and enabling a more detailed analysis of the user's driving behavior. This method addresses the problem in existing technologies that analyze user driving behavior based solely on macroscopic vehicle indicators, failing to accurately analyze the user's driving behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0057] Figure 1 This is a flow chart of a method for dividing a vehicle journey disclosed in an embodiment of the present application;

[0058] Figure 2 This is a flowchart of an implementation of step S103 disclosed in another embodiment of the present application;

[0059] Figure 3 This is a flowchart of an implementation of step S104 disclosed in another embodiment of the present application;

[0060] Figure 4 A schematic diagram of a vehicle travel division device disclosed in another embodiment of the present application;

[0061] Figure 5 This is a schematic diagram of an electronic device disclosed in another embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0063] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0064] Furthermore, in this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0065] As can be seen from the background, conventional technologies can only analyze a user's driving behavior through macro-indicators of the vehicle. For example, easily quantified data such as total distance traveled and total driving time can be used to reflect the user's driving behavior. However, it is impossible to calculate specific data such as whether the vehicle exceeded the speed limit or the number of sudden accelerations during a particular trip, resulting in an inability to accurately analyze the user's driving behavior.

[0066] In view of this, the present application provides a method, device, electronic device and storage medium for dividing a vehicle trip to solve the problem in the prior art that when analyzing a user's driving behavior, the user can only be analyzed through the vehicle's macro indicators and cannot accurately analyze the user's driving behavior.

[0067] The embodiment of the present application discloses a method for dividing a vehicle journey, specifically as follows: Figure 1 As shown, the following steps are included:

[0068] S101. Acquire driving data of a vehicle within a preset time period; wherein the driving data includes mileage data and timestamp data, and each mileage corresponds to a timestamp.

[0069] It should be noted that when analyzing a user's driving behavior for a specific vehicle, the first step is to obtain the vehicle's driving data within a preset time period. This data can be obtained from the vehicle's connected vehicle data. Driving data includes mileage data and timestamp data, with each mileage corresponding to a timestamp. For example, if the vehicle's driving data is obtained from 9:00 AM to 10:00 AM on a specific day, the timestamps of the vehicle's driving during that time period and the mileage corresponding to the timestamps can be obtained.

[0070] S102: Determine whether the driving data is abnormal based on a preset standard.

[0071] It should be noted that while a vehicle is in motion, the collected data may exhibit anomalies due to system failures. For example, the accumulated mileage may suddenly change during driving, such as from 100 to 0, or from 100 to 200, in a 2-minute interval. Alternatively, a timestamp may appear that is inconsistent with the current time, such as 1970. Therefore, the captured driving data needs to be tested to determine if there are any anomalies within a preset time range and mileage range. If the acquired timestamp does not fall within the preset time range, the timestamp is considered abnormal. For example, if the preset time range is 9:00 AM to 10:00 AM on September 1, 2021, then if the acquired timestamp does not fall within this range, then the acquired timestamp is considered abnormal. Similarly, if the mileage does not fall within the preset mileage range, then the mileage is considered abnormal.

[0072] S103: If the driving data has no abnormality, the vehicle trips are divided based on the timestamp, wherein each trip includes driving data corresponding to the current trip.

[0073] It's important to note that if the acquired driving data is normal, the difference between each two adjacent timestamps can be calculated. This difference can then be used to categorize the vehicle's trips. The categorization criteria can be customized based on actual needs. For example, if the time interval between two trips exceeds 15 minutes, the trips are considered two separate trips. The corresponding mileage and timestamps are then generated. To distinguish between trips, each trip can be assigned a unique trip number in the format: vin_trip_number.

[0074] Optionally, in another embodiment of the present application, an implementation of the above step S103 is as follows: Figure 2 As shown, specifically including:

[0075] S201: Arrange the timestamps within a preset time period in chronological order, and calculate the difference between every two adjacent timestamps.

[0076] It should be noted that when dividing the vehicle's trip, the timestamps within the preset time period are first sorted in ascending order. Of course, the timestamps within the preset time period can also be sorted in descending order according to actual needs. Then, for the sorted timestamps, the difference between each two adjacent timestamps is calculated.

[0077] S202: If the difference between adjacent timestamps is greater than a preset threshold, the adjacent timestamps are divided into different trips; if the difference between adjacent timestamps is not greater than the preset threshold, the adjacent timestamps are divided into the same trip.

[0078] It should be noted that after calculating the difference between each two adjacent timestamps, each difference is compared with a preset threshold, which can be set according to actual conditions, such as 15 minutes. If the difference between two adjacent timestamps is greater than the preset threshold, the adjacent timestamps are divided into different trips. If the difference between adjacent timestamps is not greater than the preset threshold, the adjacent timestamps are divided into the same trip. For example, September 1, 2021, 9:01 and September 1, 2021, 9:05 belong to the same trip, and the next timestamp is September 1, 2021, 9:23, so it is divided into another trip.

[0079] S104: If the driving data is abnormal, the driving data is corrected to obtain corrected driving data, and the vehicle's travel is divided based on the timestamp in the corrected driving data.

[0080] It should be noted that if the acquired driving data contains abnormal data, to avoid interference from the abnormal data, the acquired driving data is corrected to obtain corrected driving data. The vehicle's itinerary is then segmented based on the differences between the timestamps in the corrected driving data. This allows for a more detailed analysis of the user's driving behavior, facilitating business personnel designing different products or promoting different marketing strategies for different user markets, thereby achieving precision marketing. The segmentation of the vehicle's itinerary based on the differences between the timestamps in the corrected driving data can be seen in the example of segmenting the itinerary in step S103 and will not be further described here.

[0081] Optionally, in another embodiment of the present application, if the driving data is abnormal in the above step S104, an implementation method of correcting the driving data is as follows: Figure 3 As shown, specifically including:

[0082] S301: If the timestamp data is abnormal, calculate the proportion of the timestamp data with abnormal timestamps in the timestamp data.

[0083] It should be noted that if the acquired vehicle driving data within the preset time period has abnormal timestamp data, such as a timestamp from 1970, then the proportion of the abnormal timestamp data to the total timestamp data is calculated.

[0084] S302: Modify the timestamp data according to the data ratio.

[0085] It should be noted that after calculating the proportion of abnormal timestamp data to all timestamp data, a preset method is selected to process the abnormal timestamp according to the proportion of abnormal timestamp data to all timestamp data to correct the timestamp data.

[0086] Optionally, in another embodiment of the present application, an implementation of the above step S302 specifically includes:

[0087] If the data percentage of the abnormal timestamp is less than the preset first threshold, the abnormal timestamp will be deleted. If the data percentage of the abnormal timestamp is not less than the first threshold, the data storage time of the abnormal timestamp will be obtained and the abnormal timestamp will be modified according to the data storage time.

[0088] It should be noted that if the proportion of abnormal timestamp data to all timestamp data is less than a preset first threshold (this threshold can be set according to actual needs, for example, 30%), the abnormal timestamp data is judged to account for a small proportion, and the abnormal timestamps can be directly deleted with little impact on the overall data. If the proportion of abnormal timestamp data to all timestamp data is not less than the preset first threshold, the data storage time of these abnormal timestamps needs to be obtained and modified to the corresponding data storage time.

[0089] S303: If the mileage data is abnormal, all mileage data are sorted in the order of timestamps.

[0090] It should be noted that if there is an anomaly in the mileage data, such as a sudden change in mileage, all mileage data should be sorted in the order of timestamps.

[0091] S304: Calculate the difference between every two adjacent mileages in the sorted mileage data.

[0092] It should be noted that after all the mileage data are sorted in the order of timestamps, a difference calculation is performed on the sorted mileage data to obtain the difference between every two adjacent mileages.

[0093] S305: Screen out abnormal differences among the calculated differences of each mileage, and correct the screened abnormal differences.

[0094] It should be noted that after calculating the difference between every two adjacent mileages in the sorted mileage data, abnormal differences are screened out and the screened abnormal differences are corrected.

[0095] Optionally, in another embodiment of the present application, an implementation of the above step S305 specifically includes:

[0096] For each calculated difference, determine whether the difference is less than a preset second threshold or greater than a preset third threshold; wherein the second threshold is less than the third threshold; if it is determined that the difference is less than the second threshold or greater than the third threshold, then the difference is determined to be an abnormal difference; each abnormal difference is assigned a preset fourth threshold.

[0097] It should be noted that for each calculated difference, a determination is made as to whether the current difference is less than a preset second threshold or greater than a preset third threshold, wherein the second threshold is less than the third threshold, the second threshold represents the minimum range of a normal difference, and the third threshold represents the maximum range of a normal difference. The second and third thresholds can be set according to actual needs, for example, the second threshold is set to 0 and the third threshold is set to 5. If a certain difference is determined to be less than the preset second threshold or greater than the preset third threshold, the difference is determined to be an abnormal difference, and the abnormal difference is assigned a preset fourth threshold, wherein the fourth threshold is a preset difference correction value used to correct the abnormal difference. The fourth threshold can be set according to actual conditions, for example, the fourth threshold is set to 0.

[0098] S306: Correct the mileage data using the corrected abnormal difference.

[0099] It should be noted that after correcting the abnormal differences that have been screened out, all corrected abnormal differences are accumulated with the normal differences calculated above to obtain an accumulated difference value. The mileage value corresponding to the first timestamp within the preset time period is then summed with the accumulated difference value to obtain the corrected mileage data for the vehicle.

[0100] In a method for dividing a vehicle's itinerary provided in an embodiment of the present application, the driving data of the vehicle within a preset time period is first obtained, and the driving data is determined to be abnormal based on preset standards. The driving data includes mileage data and timestamp data, and each mileage corresponds to a timestamp. If there is no abnormality in the driving data, the vehicle's itinerary is divided based on the timestamp, wherein each itinerary includes the driving data corresponding to the current itinerary. If there is an abnormality in the driving data, the driving data is corrected to obtain the corrected driving data, and the vehicle's itinerary is divided based on the timestamp in the corrected driving data. By utilizing the method of the present application, the vehicle's itinerary can be specifically divided according to actual needs through the driving data of the vehicle obtained within the preset time period, thereby better performing a detailed analysis of the user's driving behavior. This solves the problem in the prior art that when analyzing the user's driving behavior, it can only be analyzed through the vehicle's macro indicators, and the user's driving behavior cannot be accurately analyzed.

[0101] Another embodiment of the present application also discloses a device for dividing a vehicle journey, such as Figure 4 Shown, including:

[0102] The acquisition unit 401 is used to acquire the driving data of the vehicle within a preset time period; wherein the driving data includes mileage data and timestamp data, and each mileage corresponds to a timestamp.

[0103] The determination unit 402 is configured to determine whether the driving data is abnormal based on a preset standard.

[0104] The trip division unit 403 is configured to divide the vehicle's trips based on timestamps if the driving data is normal, wherein each trip includes driving data corresponding to the current trip.

[0105] The data correction unit 404 is configured to correct the driving data if there is an abnormality in the driving data to obtain corrected driving data, and divide the vehicle's travel based on the difference between the timestamps in the corrected driving data.

[0106] In this embodiment, the specific execution process of the acquisition unit 401, the determination unit 402, the trip division unit 403 and the data correction unit 404 can be found in the corresponding Figure 1 The content of the method embodiment will not be repeated here.

[0107] In a vehicle trip segmentation device provided in an embodiment of the present application, an acquisition unit 401 first acquires vehicle driving data within a preset time period, and a determination unit 402 determines whether the driving data is abnormal based on preset criteria. The driving data includes mileage data and timestamp data, with each mileage corresponding to a timestamp. If the driving data is normal, a trip segmentation unit 403 segments the vehicle's trips based on the differences between the timestamps, where each trip includes the driving data corresponding to the current trip. If the driving data is abnormal, a data correction unit 404 corrects the driving data to obtain corrected driving data, and segments the vehicle's trips based on the differences between the timestamps in the corrected driving data. Using the method of the present application, the vehicle's trips can be specifically segmented according to actual needs using the acquired driving data within the preset time period, thereby enabling a more detailed analysis of the user's driving behavior. This solves the problem in the prior art of analyzing user driving behavior, which can only be analyzed based on macroscopic vehicle indicators, failing to accurately analyze the user's driving behavior.

[0108] Optionally, in another embodiment of the present application, an implementation of the trip division unit 403 includes:

[0109] The first calculation subunit is configured to arrange the timestamps within a preset time period in chronological order and calculate the difference between every two adjacent timestamps.

[0110] The trip division subunit is configured to divide adjacent timestamps into different trips if the difference between adjacent timestamps is greater than a preset threshold, and to divide adjacent timestamps into the same trip if the difference between adjacent timestamps is not greater than the preset threshold.

[0111] In this embodiment, the specific execution process of the first calculation subunit and the trip division subunit can be found in the corresponding Figure 2 The content of the method embodiment will not be repeated here.

[0112] Optionally, in another embodiment of the present application, an implementation of the data correction unit 404 includes:

[0113] The third calculation subunit is configured to calculate a ratio of data with abnormal timestamps in the data with abnormal timestamps if the data with the timestamps is abnormal.

[0114] The first data correction subunit is used to correct the timestamp data according to the data ratio.

[0115] The sorting subunit is used to sort all mileage data in the order of timestamps if there is an abnormality in the mileage data.

[0116] The fourth calculation subunit is configured to calculate the difference between every two adjacent mileages in the sorted mileage data.

[0117] The second data correction subunit is used to filter out abnormal differences in the calculated differences of each mileage, and correct the filtered abnormal differences.

[0118] The third data correction unit is used to correct the mileage data using the corrected abnormal difference.

[0119] In this embodiment, the specific execution process of the third calculation subunit, the first data correction subunit, the sorting subunit, the fourth calculation subunit, the second data correction subunit and the third data correction unit can be found in the corresponding Figure 3 The content of the method embodiment will not be repeated here.

[0120] Optionally, in another embodiment of the present application, an implementation of the first data correction subunit includes:

[0121] The deletion subunit is configured to delete the abnormal timestamp if the data proportion of the abnormal timestamp is less than a preset first threshold.

[0122] The modification subunit is used to obtain the data storage time of the abnormal timestamp if the data proportion of the abnormal timestamp is not less than the first threshold, and modify the abnormal timestamp according to the data storage time.

[0123] In this embodiment, the specific execution process of deleting and modifying subunits can be found in the corresponding method embodiments described above and will not be repeated here.

[0124] Optionally, in another embodiment of the present application, an implementation of the second data correction subunit includes:

[0125] The judgment subunit is used to judge whether each calculated difference is less than a preset second threshold or greater than a preset third threshold; wherein the second threshold is less than the third threshold.

[0126] The determination subunit is configured to determine that the difference is an abnormal difference if it is determined that the difference is less than the second threshold or greater than the third threshold.

[0127] The assignment subunit is used to assign each abnormal difference value to a preset fourth threshold.

[0128] In this embodiment, the specific execution process of the judgment subunit, the determination subunit and the assignment subunit can be found in the corresponding method embodiments above and will not be repeated here.

[0129] Another embodiment of the present application further provides an electronic device, such as Figure 5As shown, specifically including:

[0130] One or more processors 501 .

[0131] The storage device 502 stores one or more programs.

[0132] When one or more programs are executed by one or more processors 501 , the one or more processors 501 implement any one of the methods in the above embodiments.

[0133] Another embodiment of the present application further provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, any one of the methods in the above embodiments is implemented.

[0134] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0135] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0136] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dividing a vehicle journey, characterized in that: include: Acquire driving data of the vehicle within a preset time period; wherein the driving data includes mileage data and timestamp data, and each mileage corresponds to a timestamp; Determining whether the driving data is abnormal based on a preset time range and a preset mileage range; If the driving data has no anomaly, dividing the trips of the vehicle based on the timestamp, wherein each trip includes driving data corresponding to the current trip; If the driving data is abnormal, the driving data is corrected to obtain corrected driving data, and the vehicle's itinerary is divided based on the timestamps in the corrected driving data; wherein, if the timestamp data is abnormal, the timestamp data is corrected according to the data proportion of the abnormal timestamps; if the mileage data is abnormal, the mileage data is corrected according to the abnormal difference between adjacent mileages.

2. The method according to claim 1, characterized in that The dividing the vehicle's itinerary based on the timestamp includes: Arrange the timestamps within the preset time period in chronological order, and calculate the difference between every two adjacent timestamps; If the difference between the adjacent timestamps is greater than a preset threshold, the adjacent timestamps are divided into different trips; if the difference between the adjacent timestamps is not greater than the preset threshold, the adjacent timestamps are divided into the same trip.

3. The method according to claim 1, characterized in that If the driving data is abnormal, correcting the driving data includes: If the data of the timestamp is abnormal, the proportion of the data of the abnormal timestamp in the data of the timestamp is calculated; Correcting the data of the timestamp according to the data proportion; If the mileage data is abnormal, all the mileage data are sorted in the order of the timestamps; Calculating the difference between every two adjacent mileages in the sorted mileage data; Screening out abnormal differences among each of the calculated mileage differences, and correcting the screened abnormal differences; The mileage data is corrected using the corrected abnormal difference.

4. The method according to claim 3, characterized in that The modifying of the timestamp data according to the data proportion includes: If the data proportion of the abnormal timestamp is less than a preset first threshold, the abnormal timestamp is deleted; If the data proportion of the abnormal timestamp is not less than the first threshold, the data storage time of the abnormal timestamp is obtained, and the abnormal timestamp is modified according to the data storage time.

5. The method according to claim 3, characterized in that The step of screening out abnormal differences among each of the calculated mileage differences and correcting the screened abnormal differences includes: For each calculated difference, determine whether the difference is less than a preset second threshold or greater than a preset third threshold; wherein the second threshold is less than the third threshold; If it is determined that the difference is less than the second threshold or greater than the third threshold, the difference is determined to be an abnormal difference; Each of the abnormal difference values ​​is assigned a preset fourth threshold.

6. A vehicle travel division device, characterized in that: include: An acquisition unit, configured to acquire driving data of a vehicle within a preset time period; wherein the driving data includes mileage data and timestamp data, and each mileage corresponds to a timestamp; a determination unit, configured to determine whether the driving data is abnormal based on a preset time range and a preset mileage range; a trip division unit, configured to divide the trips of the vehicle based on the timestamp if the driving data is normal, wherein each trip includes driving data corresponding to the current trip; A data correction unit is used to correct the driving data if there is an abnormality in the driving data to obtain corrected driving data, and divide the vehicle's travel based on the timestamps in the corrected driving data; wherein, if there is an abnormality in the timestamp data, the timestamp data is corrected according to the data proportion of the abnormal timestamps; if there is an abnormality in the mileage data, the mileage data is corrected according to the abnormal difference between adjacent mileages.

7. The device according to claim 6, characterized in that The trip division unit includes: A first calculation subunit, configured to arrange the timestamps within the preset time period in chronological order and calculate the difference between every two adjacent timestamps; The trip division subunit is configured to divide the adjacent timestamps into different trips if the difference between the adjacent timestamps is greater than a preset threshold, and to divide the adjacent timestamps into the same trip if the difference between the adjacent timestamps is not greater than the preset threshold.

8. The device according to claim 6, characterized in that The data correction unit includes: A second calculation subunit is configured to calculate a proportion of data with abnormal timestamps in the data with the timestamps if the data with the timestamps is abnormal; A first data correction subunit, configured to correct the data of the timestamp according to the data ratio; a sorting subunit, configured to sort all the mileage data in the order of the timestamps if there is an abnormality in the mileage data; a third calculation subunit, configured to calculate a difference between every two adjacent mileages in the sorted mileage data; a second data correction subunit, configured to screen out abnormal differences in each of the calculated mileage differences, and correct the screened abnormal differences; The third data correction unit is used to correct the mileage data using the corrected abnormal difference.

9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method according to any one of claims 1 to 5.

10. A computer storage medium, characterized in that A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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