Abnormal travel correction method and system and electronic equipment

Abstract

The invention relates to the technical field of Internet of Vehicles, in particular to an abnormal travel correction method and system and electronic equipment, and the method comprises the steps: obtaining a travel data set, the travel data set comprises travel data uploaded by at least one vehicle, the travel state in the travel data is in travel, and the travel data comprises travel duration; if the travel duration is greater than a preset duration threshold, marking the travel data corresponding to the current travel duration as abnormal travel data; on the basis of a preset journey ending judgment rule, journey ending verification is carried out on the abnormal journey data, and the journey ending judgment rule is determined on the basis of historical driving data of the target user corresponding to the abnormal journey data; if the journey corresponding to the abnormal journey data is determined to be an ended journey, ending the journey corresponding to the current abnormal journey data so as to complete abnormal journey correction; according to the method, abnormal travel identification and correction can be accurately completed, the flexibility is high, and the cost is low.

Description

Technical Field

[0001] The present application relates to the field of vehicle networking technology, and in particular to an abnormal stroke correction method, system and electronic equipment. Background Art

[0002] With the rapid development of Internet of Vehicles technology, communication between vehicles and the cloud has become an important part of the intelligent transportation system. In the Internet of Vehicles environment, vehicles can not only upload travel data to the cloud in real time for analysis and management, but also upload a trip end signal at the end of the trip to mark the official end of the trip. This process is usually automated, ensuring that the vehicle's trip information is updated in the cloud in a timely manner and providing accurate information support for subsequent scheduling, data analysis, and billing services.

[0003] However, in actual applications, the vehicle may fail to upload the trip end signal within the scheduled time. This problem may be caused by a variety of reasons, such as network failure, communication interruption between the vehicle and the cloud, and vehicle equipment failure. When the vehicle fails to upload the trip end signal in time at the end of the trip, the cloud will not be able to correctly identify the end time of the trip, causing a series of problems. For example: because the previous trip has not ended, multiple trips occur concurrently. Summary of the invention

[0004] The present application provides an abnormal trip correction method, system and electronic device to solve the technical problem in the related art that when a vehicle fails to upload a trip end signal in time at the end of a trip, it is easy to cause the trip in the cloud to fail to end normally, thereby causing unnecessary impact on subsequent trip display, scheduling, and data analysis.

[0005] The present application provides an abnormal trip correction method, the method comprising: obtaining a trip data set, the trip data set comprising trip data uploaded by at least one vehicle, the trip status in the trip data being trip in progress, and the trip data comprising trip duration;

[0006] If the travel duration is greater than a preset duration threshold, the travel data corresponding to the current travel duration is marked as abnormal travel data;

[0007] Based on a preset trip end determination rule, performing a trip end check on the abnormal trip data, wherein the trip end determination rule is determined based on historical driving data of a target user corresponding to the abnormal trip data;

[0008] If it is determined that the trip corresponding to the abnormal trip data is a completed trip, the trip corresponding to the current abnormal trip data is completed to complete the abnormal trip correction.

[0009] In one embodiment of the present application, before performing a trip end check on the abnormal trip data based on a preset trip end determination rule, the method further includes:

[0010] determining the plurality of abnormal travel data as an abnormal travel data set;

[0011] Based on the travel data uploaded in real time by the target vehicle, the abnormal travel data in the abnormal travel data set is updated, and the target vehicle refers to the vehicle corresponding to the abnormal travel data;

[0012] The updated abnormal trip data is checked for a trip end signal. If no trip end signal is detected, a trip end check is performed on the abnormal trip data based on the trip end determination rule.

[0013] In one embodiment of the present application, based on a preset trip end determination rule, the abnormal trip data is subjected to a trip end check, including:

[0014] Performing a trip detection and a state detection on the target vehicle corresponding to the abnormal trip data, if the target vehicle has two or more trips, or the state of the target vehicle is a charging state, determining that the trip corresponding to the current abnormal trip data is a completed trip;

[0015] If the target vehicle has only one trip and the status of the target vehicle is in progress, the vehicle position signal uploaded by the target vehicle for the last time is obtained from the abnormal trip data; if the target vehicle is located in a preset target area, a suspected end mark is added to the current abnormal trip data, and the target area includes: an area where a preset common parking point is located, an area where a common parking point of the target user is located, and an area where a target parking point is located, the common parking point is obtained based on the historical driving data of the target user, and the target parking point is obtained by statistically analyzing the historical parking points of multiple users;

[0016] Based on the suspected end mark, a trip end check of the abnormal trip data is completed.

[0017] In one embodiment of the present application, based on a preset trip end determination rule, performing a trip end check on the abnormal trip data further includes:

[0018] If the target vehicle has only one trip and the status of the target vehicle is in progress, the time when the target vehicle last reported a signal is obtained from the abnormal trip data;

[0019] If the time when the target vehicle reported the signal for the last time is within a preset target time period, the suspected end mark is added to the current abnormal travel data, and the target time period is obtained by statistically analyzing the parking time of multiple users.

[0020] In one embodiment of the present application, based on the suspected end mark, completing the trip end check of the abnormal trip data includes:

[0021] Acquiring the electronic parking gear position, the time of the last uploaded vehicle position signal, the vehicle speed signal, and the vehicle power mode in the abnormal travel data;

[0022] If the electronic parking gear position of the target vehicle is the parking gear and the duration is greater than a preset first threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the difference between the current time and the time of the last uploaded vehicle position signal is greater than a preset second threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip;

[0023] If the speed of the target vehicle is less than a preset speed threshold and the duration is greater than a preset third threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the vehicle power mode is an off mode or a sleep mode, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the vehicle power mode is an off mode and the duration is greater than a preset fourth threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip.

[0024] In one embodiment of the present application, if it is determined that the trip corresponding to the abnormal trip data is a completed trip, then the trip corresponding to the current abnormal trip data is ended to complete the abnormal trip correction, including:

[0025] If it is determined that the trip corresponding to the abnormal trip data is a completed trip, a type identifier is added to the current abnormal trip data, where the type identifier is used to refer to a determination condition used when determining that the current trip is a completed trip, or is used to refer to a determination condition used when determining that the current trip is suspected to be completed, where the determination condition is in the trip completion determination rule;

[0026] When the type identification is added, the trip corresponding to the current abnormal trip data is ended to complete the abnormal trip correction.

[0027] In one embodiment of the present application, the plurality of abnormal travel data are determined as an abnormal travel data set, including:

[0028] If a new travel data set is received, performing travel anomaly analysis on the travel data in the new travel data set to obtain abnormal travel data in the new travel data set;

[0029] The abnormal travel data in the new travel data set and the abnormal travel data that has not been corrected in the last abnormal travel correction process are determined as the abnormal travel data set.

[0030] In one embodiment of the present application, it further includes:

[0031] When the current travel data set is obtained, if there is a travel data set collected in the previous travel collection cycle, the travel data in the current travel data set is matched with the travel data in the travel data set collected in the previous travel collection cycle;

[0032] If the match is successful, the trip data are spliced ​​to obtain spliced ​​trip data, wherein the successful match means that the two trip data belong to the same trip;

[0033] The spliced ​​travel data is stored in the current travel data set for use when a new travel data set is collected in the next travel collection cycle.

[0034] The present application also provides an abnormal trip correction system, the system comprising: a data acquisition module, for acquiring a trip data set, the trip data set comprising trip data uploaded by at least one vehicle, the trip status in the trip data being trip in progress, and the trip data comprising trip duration;

[0035] An abnormal trip marking module, configured to mark the trip data corresponding to the current trip duration as abnormal trip data if the trip duration is greater than a preset duration threshold;

[0036] a trip end verification module, configured to perform a trip end verification on the abnormal trip data based on a preset trip end determination rule, wherein the trip end determination rule is determined based on historical driving data of a target user corresponding to the abnormal trip data;

[0037] The abnormal trip correction module is used to terminate the trip corresponding to the current abnormal trip data if it is determined that the trip corresponding to the abnormal trip data is a completed trip, so as to complete the abnormal trip correction.

[0038] The present application also provides an electronic device, comprising a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement an abnormal stroke correction method provided in any of the above embodiments.

[0039] Beneficial effects of the embodiments of the present application: The abnormal trip correction method, system and electronic device provided by the embodiments of the present application, the method obtains a trip data set, the trip data set includes trip data uploaded by at least one vehicle, the trip status in the trip data is trip in progress, and the trip data includes trip duration; if the trip duration is greater than a preset duration threshold, the trip data corresponding to the current trip duration is marked as abnormal trip data; based on a preset trip end determination rule, the abnormal trip data is checked for trip end, the trip end determination rule is determined based on the historical driving data of the target user corresponding to the abnormal trip data; if it is determined that the trip corresponding to the abnormal trip data is a terminated trip, the trip corresponding to the current abnormal trip data is terminated to complete the abnormal trip correction. The method can complete the abnormal trip identification and correction more accurately, avoid a series of problems caused by the vehicle's failure to upload the trip end signal in time at the end of the trip, and has high flexibility and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of a flow chart of an abnormal stroke correction method provided in one embodiment of the present application;

[0041] Figure 2 A schematic diagram of a flow chart of abnormal stroke judgment and correction in an abnormal stroke correction method provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of a flow chart of fusion condition judgment in an abnormal stroke correction method provided in an embodiment of the present application;

[0043] Figure 4 A schematic diagram of iterative correction of full data and incremental data in an abnormal stroke correction method provided in an embodiment of the present application;

[0044] Figure 5 An exemplary schematic diagram of traditional trip splicing provided in one embodiment of the present application;

[0045] Figure 6 An exemplary schematic diagram of stroke splicing in an abnormal stroke correction method provided in an embodiment of the present application;

[0046] Figure 7 A flowchart of an exemplary embodiment of an abnormal stroke correction method provided in one embodiment of the present application;

[0047] Figure 8 A schematic diagram of the structure of an abnormal stroke correction system provided in one embodiment of the present application;

[0048] Fig. 9 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0049] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0050] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0051] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0052] Combine the following Figures 1 to 9 , the abnormal stroke correction method, system and electronic equipment provided by this application are explained.

[0053] See also Figure 1 , Figure 1 A flow chart of an abnormal stroke correction method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0054] S110: Acquire a travel data set, where the travel data set includes travel data uploaded by at least one vehicle, where the travel status in the travel data is that the travel is in progress, and the travel data includes a travel duration.

[0055] In some examples of this embodiment, the travel data also includes: travel status, electronic parking gear position, time of the last uploaded vehicle position signal, vehicle speed signal, and vehicle power mode, etc.

[0056] It can be understood that the trip data in this embodiment does not include a trip end signal sent by the vehicle.

[0057] S120: If the travel duration is greater than a preset duration threshold, the travel data corresponding to the current travel duration is marked as abnormal travel data.

[0058] In some embodiments, if there is a manual abnormal mark for the trip corresponding to the trip data, the trip data is determined to be abnormal trip data, and the manual abnormal mark is a mark manually uploaded or added. It can be understood that the determination method for abnormal trip data in the above step S120 is a passive method, while the method of manually uploading or adding marks by the user is an active method.

[0059] In some examples of this embodiment, the duration threshold can be set according to actual conditions, such as 24 hours.

[0060] Through the above steps S110 and S120, abnormal trip data has been identified, but the abnormal trip data does not mean that the trip has necessarily ended. Therefore, a trip end check is performed on the abnormal trip data to determine whether the trip corresponding to the abnormal trip data has ended and whether trip end processing is required.

[0061] S130: Performing a trip end check on the abnormal trip data based on a preset trip end determination rule, wherein the trip end determination rule is determined based on the historical driving data of the target user corresponding to the abnormal trip data.

[0062] In some examples of this embodiment, the target user refers to the user associated with the target vehicle that uploaded the abnormal travel data. The historical driving data may include the target user's vehicle speed information, driving behavior data, driving time, road condition information, and vehicle status.

[0063] In some examples of this embodiment, determining a trip end judgment rule based on the historical driving data of the target user corresponding to the abnormal trip data may include: integrating the historical driving data to obtain an integrated data set; extracting features from the data in the data set to obtain vehicle speed features, acceleration behavior features, emergency braking behavior features, and parking time features, etc.; determining a trip end judgment rule based on the extracted features, for example: if emergency braking and other behaviors occur frequently in a short period of time and normal driving is not resumed, it may mean that the driver foresees an emergency and thus determines that the trip has ended, etc.

[0064] S140: If it is determined that the trip corresponding to the abnormal trip data is a completed trip, the trip corresponding to the current abnormal trip data is completed to complete the abnormal trip correction.

[0065] Through the abnormal trip correction process described above, it is possible to effectively avoid the occurrence of multiple trips in parallel, and avoid unnecessary trouble and panic for users. It is understandable that if the trip is abnormal because the vehicle fails to upload the trip end signal in time at the end of the trip, it may appear that the target vehicle has been powered off or is being charged, but the cloud still shows that the vehicle is still driving. This will cause unnecessary trouble and trouble to the user. The abnormal trip correction method in the above embodiment can better solve this problem.

[0066] In order to reduce the burden of trip end verification, in some embodiments, before performing trip end verification on the abnormal trip data based on a preset trip end determination rule, the method further includes:

[0067] 1. Determine the plurality of abnormal travel data as an abnormal travel data set.

[0068] 2. Based on the travel data uploaded by the target vehicle in real time, the abnormal travel data in the abnormal travel data set is updated, and the target vehicle refers to the vehicle corresponding to the abnormal travel data.

[0069] In some examples of this embodiment, since the above determination of abnormal trip data is a real-time or instantaneous behavior, it is still possible to receive a delayed trip end signal due to network delays and other issues before performing the trip end verification. Therefore, this embodiment updates the abnormal trip data before performing the trip end verification, that is, collects the signal uploaded by the target vehicle corresponding to the abnormal trip data, so as to facilitate determining whether the target vehicle uploads a trip end signal during the time period. If a trip end signal is received, the corresponding trip can be ended directly according to the trip end signal, thereby reducing the pressure and burden of subsequent trip end verification.

[0070] 3. Performing a trip end signal check on the updated abnormal trip data. If no trip end signal is detected, performing a trip end check on the abnormal trip data based on the trip end determination rule.

[0071] In some embodiments, based on a preset trip end determination rule, performing a trip end check on the abnormal trip data includes:

[0072] 1. Performing a trip detection and a state detection on the target vehicle corresponding to the abnormal trip data. If the target vehicle has two or more trips, or the state of the target vehicle is a charging state, determining that the trip corresponding to the current abnormal trip data is a completed trip.

[0073] It is understandable that the same vehicle usually does not have two parallel trips, that is, the same vehicle usually does not have two ongoing trips. This obvious opposition and mutual exclusion indicates that the current trip is a completed trip. Therefore, if the target vehicle has two or more trips, the trip corresponding to the current abnormal trip data can be determined as a completed trip and subsequent corrections can be made.

[0074] In addition, the same vehicle is usually not in a state of both charging and driving. Since the trip status corresponding to the abnormal trip data is in progress, if the target vehicle corresponding to the abnormal trip data is in the charging state at this time, then the two states are mutually exclusive. In this case, the trip corresponding to the current abnormal trip data can be determined as a completed trip and subsequent corrections can be made.

[0075] The above-mentioned judgment method of mutually exclusive situations has the advantages of clear logic, simple implementation, and high accuracy. In order to better cope with more complex scenarios, such as the vehicle not making mutually exclusive actions after a trip abnormality, the following further judgment is made in combination with the historical driving data of the target user and multiple other users.

[0076] 2. If the target vehicle has only one trip and the status of the target vehicle is in progress, the vehicle position signal uploaded by the target vehicle for the last time is obtained from the abnormal trip data; if the target vehicle is located in a preset target area, a suspected end mark is added to the current abnormal trip data, and the target area includes: an area where a preset common parking point is located, an area where the target user's common parking point is located, and an area where the target parking point is located. The common parking point is obtained based on the historical driving data of the target user, and the target parking point is obtained by statistically analyzing the historical parking points of multiple users.

[0077] In some examples of this embodiment, common parking spots include companies, charging stations, residential areas, commercial areas, and other common parking locations. The area where the parking spot is located is the area covered by a circle with the parking spot as the center and a preset distance (such as 100 meters, etc.) as the radius. The target user's common parking spots can be obtained by counting the number of parking times at different parking spots in the target user's historical driving data.

[0078] In some examples of this embodiment, the target parking point can be obtained by collecting statistics on the historical parking point information of multiple users in the cloud. For example, if multiple users have parked at a certain parking point for many times, the parking point can be determined as the target parking point. With the target parking point as the center and the preset distance as the radius, the circular range formed is determined as the area where the target parking point is located.

[0079] By obtaining the area where the target parking point is located, it is possible to avoid the situation where there are fewer common parking points due to the target user being a new user or having less historical driving data. It is understandable that even if the target user has fewer common parking points, the target parking point can be used for judgment and marking.

[0080] 3. Based on the suspected end mark, complete the trip end verification of the abnormal trip data.

[0081] In some examples of this embodiment, abnormal travel data with a suspected end mark can be directly determined as travel data to be ended, and the travel data to be ended can be processed to end the travel.

[0082] In some embodiments, based on a preset trip end determination rule, performing a trip end check on the abnormal trip data further includes:

[0083] 1. If the target vehicle has only one trip and the status of the target vehicle is in progress, the time when the target vehicle last reported a signal is obtained from the abnormal trip data.

[0084] 2. If the time when the target vehicle last reported a signal is within a preset target time period, the suspected end mark is added to the current abnormal travel data, and the target time period is obtained by statistically analyzing the parking time of multiple users.

[0085] In some examples of this embodiment, the parking time of multiple users can be obtained and statistically analyzed to obtain the common parking time period of most users, such as 8:00-10:00, 11:00-13:00, and 18:00-21:00, etc. The common parking time period is determined as the above target time period. The above determination can help improve the accuracy of the trip end verification.

[0086] In order to further improve the accuracy of the trip end verification, in some embodiments, based on the suspected end mark, completing the trip end verification of the abnormal trip data includes:

[0087] A. Obtain the electronic parking gear position, the time of the last uploaded vehicle position signal, the vehicle speed signal, and the vehicle power mode in the abnormal travel data.

[0088] B. If the electronic parking gear position of the target vehicle is the parking gear and the duration is greater than a preset first threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the difference between the current time and the time of the last uploaded vehicle position signal is greater than a preset second threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip.

[0089] C. If the speed of the target vehicle is less than a preset speed threshold and the duration is greater than a preset third threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the vehicle power mode is an off mode or a sleep mode, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the vehicle power mode is an off mode and the duration is greater than a preset fourth threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip.

[0090] It is understandable that if any one of the conditions in the above steps B and C is met, the trip corresponding to the abnormal trip data can be determined as a completed trip, and subsequent trip correction processing is performed, that is, the trip is ended. The above method can help improve the accuracy of abnormal trip correction.

[0091] In addition, in this embodiment, regardless of whether the abnormal trip data carries a suspected end mark, the abnormal trip data needs to be subjected to the conditional judgment in the above steps B and C. This results in three situations: situation one, the abnormal trip data carries a suspected end mark and meets at least one of the conditions defined in the above steps B and C; situation two, the abnormal trip data does not carry a suspected end mark and meets at least one of the conditions defined in the above steps B and C; situation three, the abnormal trip data does not meet at least one of the conditions defined in the above steps B and C. Among these three situations, situation one has the highest accuracy, followed by situation two. In situation three, in order to improve the accuracy of the trip correction, if the abnormal trip data does not meet at least one of the conditions defined in the above steps B and C, regardless of whether it carries a suspected end mark, the corresponding trip is determined as an unfinished trip, that is, this correction fails.

[0092] In some examples of this embodiment, the first threshold, the second threshold, the third threshold, and the third threshold can all be set according to actual conditions, such as 1 hour, 2 hours, etc.

[0093] In some embodiments, if it is determined that the trip corresponding to the abnormal trip data is a completed trip, then the trip corresponding to the current abnormal trip data is ended to complete the abnormal trip correction, including:

[0094] 1. If it is determined that the trip corresponding to the abnormal trip data is a completed trip, a type identifier is added to the current abnormal trip data, and the type identifier is used to refer to the determination condition used when determining that the current trip is a completed trip, or is used to refer to the determination condition used when determining that the current trip is suspected to be completed, and the determination condition is in the trip completion determination rule.

[0095] The following table 1 shows an example of the type identification, as follows:

[0096] Table 1 Type identification examples

[0097]

[0098] Table 2 below provides an exemplary description of the advantages and disadvantages of the above four types of identification, as follows:

[0099] Table 2 Advantages and disadvantages of type identification

[0100]

[0101] 2. When the type identification is added, the trip corresponding to the current abnormal trip data is ended to complete the abnormal trip correction.

[0102] It can be understood that by adding the above-mentioned type identification, it is possible for subsequent relevant personnel to identify the judgment conditions used to end the trip, which facilitates subsequent tracing and condition correction.

[0103] Figure 2 This is a flow chart of abnormal stroke judgment and correction in the abnormal stroke correction method provided in one embodiment of the present application. Please refer to Figure 2 , S210: Acquire a travel data set.

[0104] S220: Determine whether the travel data is abnormal travel data, that is, determine whether the travel data in the travel data set is abnormal travel data.

[0105] S230: Acquire an abnormal trip data set. Specifically, the abnormal trip data in the current trip data set and the abnormal trip data that could not be corrected in the last correction process (abnormal trip data that was not ended in the last correction process, i.e., the trip was not ended) are combined into an abnormal trip data set.

[0106] S240: Check whether there is a trip end signal. If yes, execute step S280; if no, execute step S250.

[0107] S250: Mutually exclusive condition determination: If the mutually exclusive condition is met, then step S280 is executed; if the mutually exclusive condition is not met, then step S260 is executed.

[0108] S260: Fusion condition judgment. Specifically, judge whether the following fusion conditions are met: the target vehicle is located in the preset target area, or the time when the target vehicle last reported a signal is within the preset target time period. If so, add a suspected end mark and execute step S270; if not, directly execute step S270.

[0109] S270: Time and signal judgment, that is, judging based on the judgment conditions in the above steps B and C. If the judgment conditions in the above steps B and C are met, step S280 is executed; if the judgment conditions in the above steps B and C are not met, step S230 is executed.

[0110] S280: Add type identification.

[0111] S290: End the trip. Specifically, end the trip corresponding to the current abnormal trip data.

[0112] Figure 3 Please refer to the flowchart of the fusion condition judgment in the abnormal stroke correction method provided in one embodiment of the present application. Figure 3 , S310: judging the common parking spot condition, that is, judging whether the target vehicle is in the area where the common parking spot is located; if the condition is met, executing step S350; if not, executing step S320.

[0113] S320: Single user analysis and judgment. Specifically, based on the historical driving data of the target user, the target user's common parking spots are obtained, and it is judged whether the target vehicle is in the area where the common parking spots are located; if the condition is met, step S350 is executed; if not, step S330 is executed.

[0114] S330: Multi-user analysis and judgment. Specifically, based on the historical driving data of multiple users, the target parking point is obtained. It is determined whether the target vehicle is in the area where the target parking point is located. If the condition is met, step S350 is executed; if not, step S340 is executed.

[0115] S340: Time feature judgment. Specifically, it is judged whether the time when the target vehicle last reported a signal is within the preset target time period. If this condition is met, step S350 is executed; if not, the time and signal judgment in the above step S270 is performed.

[0116] S350: Add a suspected end tag.

[0117] In some embodiments, determining the plurality of abnormal travel data as an abnormal travel data set includes:

[0118] 1. If a new travel data set is received, performing travel anomaly analysis on the travel data in the new travel data set to obtain abnormal travel data in the new travel data set;

[0119] 2. The abnormal travel data in the new travel data set and the abnormal travel data that has not been corrected in the last abnormal travel correction process are determined as the abnormal travel data set.

[0120] It can be understood that, through the above steps, it is possible to avoid the need to judge and correct all travel data every time a correction is made, thereby reducing the difficulty and complexity of data processing.

[0121] Figure 4 A schematic diagram of iterative correction of full data and incremental data in an abnormal trip correction method provided for an embodiment of the present application, wherein full data refers to a data set that has not undergone abnormal trip correction. For example: the trip data set collected in the first trip collection cycle has not undergone abnormal trip correction. Assuming that the first trip collection cycle is T-3, and assuming that the trip data set collected in the T-3 cycle is the trip data from January 1, 2024 to November 30, 2024, then, by performing abnormal trip identification in the above step S120, the abnormal trip data in the trip data set can be obtained. The multiple abnormal trip data obtained here combine the abnormal trip data set.

[0122] Incremental data refers to the travel data collected in the next or future travel collection cycle. Assume that the next cycle of the T-3 cycle is T-2, and assume that a batch of new travel data is collected in the T-2 cycle. Then, by screening the batch of new travel data, multiple abnormal travel data are obtained. The multiple screened abnormal travel data are combined with the abnormal travel data that cannot be corrected in the abnormal travel data set corresponding to the T-3 cycle to obtain a new abnormal travel data set.

[0123] For example, assuming that the travel data collected in the T-2 cycle is the travel data on December 1, 2024, after screening the abnormal travel data, the abnormal travel data in the batch of travel data is obtained. On this basis, the batch of abnormal travel data is combined with the abnormal travel data that has not been corrected in the abnormal travel data set corresponding to the T-3 cycle to obtain a new abnormal travel data set.

[0124] Assume that the next cycle of the T-2 cycle is T-1, and assume that a batch of new travel data is collected in the T-1 cycle. Then, by screening the batch of new travel data, multiple abnormal travel data are obtained. The multiple screened abnormal travel data are combined with the abnormal travel data that cannot be corrected in the abnormal travel data set corresponding to the T-2 cycle to obtain a new abnormal travel data set.

[0125] Similarly, assuming that the next cycle of the T-1 cycle is T, and assuming that a batch of new travel data is collected in the T cycle. Then, by screening the batch of new travel data, multiple abnormal travel data are obtained. The screened multiple abnormal travel data are combined with the abnormal travel data that cannot be corrected in the abnormal travel data set corresponding to the T-1 cycle to obtain a new abnormal travel data set.

[0126] like Figure 4As shown, the trip data set collected in the T-3 cycle is the full amount of data. Based on the trip data set, abnormal trip data screening, trip end verification and correction are performed. If the correction is successful, it is determined that the abnormal trip data has been corrected. If the correction fails, that is, the abnormal trip data does not meet the multiple judgment conditions set in the above embodiment, the abnormal trip data is combined with the new abnormal trip data in the T-2 cycle, and the next trip end verification and correction are performed together. The new abnormal trip data in the T-2 cycle is obtained by screening the new trip data set collected in the T-2 cycle for abnormal trip data, and the same is true for the T-1 cycle and the T cycle. If the correction is successful, it is determined that the abnormal trip data has been corrected. If the correction fails, the abnormal trip data that failed to be corrected is combined with the new abnormal trip data of the T-1 cycle, and the next trip end verification and correction are performed together, and so on, and the iteration continues until there is no new abnormal trip data and abnormal trip data that has not been successfully corrected.

[0127] The traditional stroke abnormality correction method needs to recalculate the full data + incremental data every time the stroke is corrected. This method greatly increases the difficulty of calculation and reduces the calculation efficiency. Therefore, through the iterative method in the above embodiment, while greatly reducing the amount of calculation, it can also improve the calculation efficiency and correction efficiency.

[0128] For example: Assume that there are 1 million trip data from January 1, 2024 to November 30, 2024, 10,000 trip data are added on December 1, 2024, and 10,000 trip data are added on December 2, 2024. The traditional method is to calculate the 1 million data once, and then calculate the 1.01 million trip data once when the trip data is added for the first time, and calculate the 1.02 million data once when the trip data is added for the second time. The method in the above embodiment is to screen the trip data from January 1, 2024 to November 30, 2024 for abnormal trip data, and perform trip end verification and correction. When the trip data is added for the first time, the 10,000 trip data added on December 1, 2024 are screened for abnormal trip data, and the screened abnormal trip data are combined with the abnormal trip data that failed to be corrected successfully last time to obtain a new abnormal trip data set, and the new abnormal trip data set is checked and corrected for the trip end, and it is iterated in this way.

[0129] A single trip may be a multi-day trip or a non-multi-day trip. A multi-day trip means that the start time of a single trip is from the same day and does not end on the same day. A non-multi-day trip means that the start time of a single trip is from the same day and ends on the same day. If the trip corresponding to the abnormal trip data is a multi-day trip or a multi-cycle (trip collection cycle) trip, it is necessary to splice the trip data. The following is an explanation of the method of splicing the trip data. In some embodiments, it also includes:

[0130] 1. When the current travel data set is obtained, if there is a travel data set collected in the previous travel collection cycle, the travel data in the current travel data set is matched with the travel data in the travel data set collected in the previous travel collection cycle.

[0131] 2. If the match is successful, the itinerary data are spliced ​​to obtain spliced ​​itinerary data. The successful match means that the two itinerary data belong to the same itinerary.

[0132] 3. The spliced ​​itinerary data is stored in the current itinerary data set for use when a new itinerary data set is collected in the next itinerary collection cycle.

[0133] In some examples of this embodiment, when the travel data collected in each travel collection cycle is stored, the travel data in that cycle is spliced ​​with the travel data in all previous cycles, so that when travel data is collected in a new travel collection cycle later, the travel data stored in the previous cycle can be directly accessed or queried, and the travel data splicing can be completed without accessing and querying the travel data stored in each travel collection cycle one by one.

[0134] Figure 5 For an exemplary schematic diagram of a conventional stroke splicing provided in an embodiment of the present application, please refer to Figure 5 , the traditional itinerary splicing method, assuming that the current itinerary collection cycle is T-1, and the itinerary data collected in the T-1 cycle includes the itinerary data of itinerary F, then, before screening abnormal itinerary data, it is necessary to determine that the itinerary data of itinerary F collected in the T-1 cycle is complete itinerary data. Therefore, the traditional method needs to query the itinerary data collected in the T-2 cycle. If there is itinerary data of itinerary F in the T-2 cycle, it is necessary to splice the itinerary data of itinerary F collected in the T-1 cycle with the itinerary data of itinerary F in the T-2 cycle. When the splicing is completed, the itinerary data collected in the T-3 cycle is queried until there is no itinerary data of itinerary F in the itinerary data in a certain cycle. This method requires multiple iterations, that is, it is necessary to access the previous cycle multiple times to complete the itinerary splicing. When the amount of itinerary data is large, such as when there are tens of millions of itineraries, the splicing efficiency will be reduced, and the amount of data to be accessed and queried will be huge. TN represents the Nth itinerary collection cycle.

[0135] Figure 6 For an exemplary schematic diagram of stroke splicing in the abnormal stroke correction method provided in an embodiment of the present application, please refer to Figure 6In this embodiment, the storage method of the travel data collected in each travel collection cycle includes: splicing the travel data collected in the current travel collection cycle with the travel data stored in the previous travel collection cycle, and storing the spliced ​​travel data. Figure 6 As shown in , assuming that there is currently a trip F, and assuming that T-5 is the first trip collection cycle, then the trip data stored in the T-5 cycle is all the trip data collected in this cycle. T-4 is the next trip collection cycle of T-5, then the trip data stored in the T-4 cycle is the trip data after T-4 and T-5 are spliced ​​together. T-3 is the next trip collection cycle of T-4, then the trip data stored in the T-3 cycle is the trip data after T-3, T-4 and T-5 are spliced ​​together. T-2 is the next trip collection cycle of T-3, then the trip data stored in the T-2 cycle is the trip data after T-2, T-3, T-4 and T-5 are spliced ​​together. T-1 is the next trip collection cycle of T-2, then the trip data stored in the T-1 cycle is the trip data after T-1, T-2, T-3, T-4 and T-5 are spliced ​​together. By adopting this method, the efficiency of trip data splicing can be effectively improved. It can be understood that, assuming that the current trip collection cycle is T-1, and assuming that the trip data collected in T-1 includes the trip data of trip F, then, it is only necessary to access the trip data stored in the T-2 cycle to complete the trip data splicing of trip F, without having to access the trip data stored in each cycle one by one.

[0136] In some embodiments, the method also includes: obtaining the real-time status of the target vehicle, and if the real-time status of the target vehicle is a parked status, determining that the judgment result of the current abnormal travel data is correct; if the real-time status of the target vehicle is a driving status, determining that the judgment result of the current abnormal travel data is wrong and feeding back, so as to adjust the trip end determination rules.

[0137] In some embodiments, the method further includes: receiving vehicle status information of the target vehicle fed back by the user, determining whether the judgment result of the current abnormal trip data is correct or wrong based on the vehicle status information, and if the judgment result is wrong, providing error feedback to adjust the trip end judgment rule. It can be understood that the accuracy of abnormal trip correction can be further improved through the above method.

[0138] The abnormal stroke correction method in the above embodiment is explained below using an exemplary embodiment.

[0139] Please refer to Figure 7 , S710: Determine whether the current trip data set is full data or incremental data. If it is full data, execute step S730; if it is incremental data, execute step S720.

[0140] S720: Determine whether the incremental data spans multiple days. If it spans multiple days, obtain the incremental data spanning multiple days; if it does not span multiple days, obtain the incremental data spanning multiple days. It can be understood that by determining the type of the itinerary data set, subsequent recording can be facilitated.

[0141] S730: Screening of abnormal trip data, trip end verification and correction.

[0142] S740: Correction result accuracy check. Specifically, based on the real-time status of the target vehicle or the vehicle status information of the target vehicle fed back by the user, determine whether the correction result is incorrect. If it is incorrect, optimize the rules used in the trip end verification process; if it is correct, end the correction.

[0143] The abnormal stroke correction system provided by the present application is described below. The abnormal stroke correction system described below and the abnormal stroke correction method described above can be referenced to each other.

[0144] Please refer to Figure 8 , the abnormal stroke correction system provided in this embodiment includes:

[0145] A data collection module 810 is used to obtain a travel data set, wherein the travel data set includes travel data uploaded by at least one vehicle, the travel status in the travel data is travel in progress, and the travel data includes a travel duration;

[0146] The abnormal trip marking module 820 is used to mark the trip data corresponding to the current trip duration as abnormal trip data if the trip duration is greater than a preset duration threshold;

[0147] A trip end verification module 830, configured to perform a trip end verification on the abnormal trip data based on a preset trip end determination rule, wherein the trip end determination rule is determined based on the historical driving data of the target user corresponding to the abnormal trip data;

[0148] The abnormal trip correction module 840 is used to end the trip corresponding to the current abnormal trip data if it is determined that the trip corresponding to the abnormal trip data is a completed trip, so as to complete the abnormal trip correction. The data acquisition module 810, the abnormal trip marking module 820, the trip end verification module 830, and the abnormal trip correction module 840 are connected. The abnormal trip correction system in this embodiment can complete the abnormal trip identification and correction more accurately, avoiding a series of problems caused by the vehicle's failure to upload the trip end signal in time at the end of the trip, with high flexibility and strong feasibility.

[0149] It should be noted that the abnormal stroke correction method and the abnormal stroke correction system provided in the above embodiment belong to the same concept, and the specific manner in which each module performs the operation has been described in detail in the method embodiment, which will not be repeated here. In actual application, the abnormal stroke correction system provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0150] In some embodiments, an electronic device is also provided, which may be a server, and its internal structure is shown in FIG. Fig. 9 As shown. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, the functions or steps on the server side of the above method are implemented.

[0151] In some embodiments, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: a trip data set is obtained, the trip data set includes trip data uploaded by at least one vehicle, the trip status in the trip data is trip in progress, and the trip data includes trip duration; if the trip duration is greater than a preset duration threshold, the trip data corresponding to the current trip duration is marked as abnormal trip data; based on a preset trip end determination rule, a trip end check is performed on the abnormal trip data, the trip end determination rule is determined based on the historical driving data of the target user corresponding to the abnormal trip data; if it is determined that the trip corresponding to the abnormal trip data is a terminated trip, the trip corresponding to the current abnormal trip data is terminated to complete the abnormal trip correction.

[0152] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: obtaining a travel data set, the travel data set including travel data uploaded by at least one vehicle, the travel status in the travel data is travel in progress, and the travel data includes travel duration; if the travel duration is greater than a preset duration threshold, the travel data corresponding to the current travel duration is marked as abnormal travel data; based on a preset travel end determination rule, a travel end check is performed on the abnormal travel data, and the travel end determination rule is determined based on the historical driving data of the target user corresponding to the abnormal travel data; if it is determined that the travel corresponding to the abnormal travel data is a completed travel, the travel corresponding to the current abnormal travel data is ended to complete the abnormal travel correction.

[0153] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or electronic device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0154] The flow chart and block diagram in the accompanying drawings illustrate the possible implementation architecture, function and operation of the method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0155] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for correcting abnormal stroke, characterized in that: include: Acquire a travel data set, the travel data set including travel data uploaded by at least one vehicle, the travel status in the travel data is travel in progress, and the travel data includes travel duration; If the travel duration is greater than a preset duration threshold, the travel data corresponding to the current travel duration is marked as abnormal travel data; Based on a preset trip end determination rule, performing a trip end check on the abnormal trip data, wherein the trip end determination rule is determined based on historical driving data of a target user corresponding to the abnormal trip data; If it is determined that the trip corresponding to the abnormal trip data is a completed trip, the trip corresponding to the current abnormal trip data is completed to complete the abnormal trip correction.

2. The abnormal stroke correction method according to claim 1, characterized in that: Before performing a trip end check on the abnormal trip data based on a preset trip end judgment rule, the method further includes: determining the plurality of abnormal travel data as an abnormal travel data set; Based on the travel data uploaded in real time by the target vehicle, the abnormal travel data in the abnormal travel data set is updated, and the target vehicle refers to the vehicle corresponding to the abnormal travel data; The updated abnormal trip data is checked for a trip end signal. If no trip end signal is detected, a trip end check is performed on the abnormal trip data based on the trip end determination rule.

3. The abnormal stroke correction method according to claim 1 or 2, characterized in that: Based on the preset trip end judgment rule, the abnormal trip data is subjected to trip end verification, including: Performing a trip detection and a state detection on the target vehicle corresponding to the abnormal trip data, if the target vehicle has two or more trips, or the state of the target vehicle is a charging state, determining that the trip corresponding to the current abnormal trip data is a completed trip; If the target vehicle has only one trip and the status of the target vehicle is in progress, the vehicle position signal uploaded by the target vehicle for the last time is obtained from the abnormal trip data; if the target vehicle is located in a preset target area, a suspected end mark is added to the current abnormal trip data, and the target area includes: an area where a preset common parking point is located, an area where a common parking point of the target user is located, and an area where a target parking point is located, the common parking point is obtained based on the historical driving data of the target user, and the target parking point is obtained by statistically analyzing the historical parking points of multiple users; Based on the suspected end mark, a trip end check of the abnormal trip data is completed.

4. The abnormal stroke correction method according to claim 3, characterized in that: Based on a preset trip end determination rule, performing a trip end check on the abnormal trip data, further comprising: If the target vehicle has only one trip and the status of the target vehicle is in progress, the time when the target vehicle last reported a signal is obtained from the abnormal trip data; If the time when the target vehicle reported the signal for the last time is within a preset target time period, the suspected end mark is added to the current abnormal travel data, and the target time period is obtained by statistically analyzing the parking time of multiple users.

5. The abnormal stroke correction method according to claim 3, characterized in that: Based on the suspected end mark, completing the trip end check of the abnormal trip data includes: Acquiring the electronic parking gear position, the time of the last uploaded vehicle position signal, the vehicle speed signal, and the vehicle power mode in the abnormal travel data; If the electronic parking gear position of the target vehicle is the parking gear and the duration is greater than a preset first threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the difference between the current time and the time of the last uploaded vehicle position signal is greater than a preset second threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip; If the speed of the target vehicle is less than a preset speed threshold and the duration is greater than a preset third threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the vehicle power mode is an off mode or a sleep mode, the trip corresponding to the abnormal trip data is determined to be a completed trip; if the vehicle power mode is an off mode and the duration is greater than a preset fourth threshold, the trip corresponding to the abnormal trip data is determined to be a completed trip.

6. The abnormal stroke correction method according to claim 1, characterized in that: If it is determined that the trip corresponding to the abnormal trip data is a completed trip, then the trip corresponding to the current abnormal trip data is completed to complete the abnormal trip correction, including: If it is determined that the trip corresponding to the abnormal trip data is a completed trip, a type identifier is added to the current abnormal trip data, where the type identifier is used to refer to a determination condition used when determining that the current trip is a completed trip, or is used to refer to a determination condition used when determining that the current trip is suspected to be completed, where the determination condition is in the trip completion determination rule; When the type identification is added, the trip corresponding to the current abnormal trip data is ended to complete the abnormal trip correction.

7. The abnormal stroke correction method according to claim 2, characterized in that: Determining the plurality of abnormal travel data as an abnormal travel data set includes: If a new travel data set is received, performing travel anomaly analysis on the travel data in the new travel data set to obtain abnormal travel data in the new travel data set; The abnormal travel data in the new travel data set and the abnormal travel data that has not been corrected in the last abnormal travel correction process are determined as the abnormal travel data set.

8. The abnormal stroke correction method according to claim 1, characterized in that: Also includes: When the current travel data set is obtained, if there is a travel data set collected in the previous travel collection cycle, the travel data in the current travel data set is matched with the travel data in the travel data set collected in the previous travel collection cycle; If the match is successful, the trip data are spliced ​​to obtain spliced ​​trip data, wherein the successful match means that the two trip data belong to the same trip; The spliced ​​travel data is stored in the current travel data set for use when a new travel data set is collected in the next travel collection cycle.

9. An abnormal stroke correction system, characterized in that: include: A data collection module is used to obtain a travel data set, wherein the travel data set includes travel data uploaded by at least one vehicle, the travel status in the travel data is that the travel is in progress, and the travel data includes a travel duration; An abnormal trip marking module, configured to mark the trip data corresponding to the current trip duration as abnormal trip data if the trip duration is greater than a preset duration threshold; a trip end verification module, configured to perform a trip end verification on the abnormal trip data based on a preset trip end determination rule, wherein the trip end determination rule is determined based on historical driving data of a target user corresponding to the abnormal trip data; The abnormal trip correction module is used to terminate the trip corresponding to the current abnormal trip data if it is determined that the trip corresponding to the abnormal trip data is a completed trip, so as to complete the abnormal trip correction.

10. An electronic device, characterized in that: It comprises a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the abnormal stroke correction method as described in any one of claims 1 to 8.

Images