Abnormal trajectory identification method, device, electronic device and storage medium

Abstract

The present application provides an abnormal trajectory identification method, device, electronic device and storage medium, which relates to the field of information processing technology and can accurately identify abnormal driving trajectories. The specific scheme includes: obtaining the travel data of the vehicle; the travel data includes: the collection position and collection time of multiple trajectory points in the driving trajectory; obtaining the target driving distance of the vehicle within a collection time; for each trajectory point in the multiple trajectory points, according to the collection position of the multiple trajectory points, determining the distance between the other trajectory points in the multiple trajectory points and the trajectory point; according to the distance and the target driving distance, determining whether the driving trajectory is abnormal.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to an abnormal trajectory recognition method, device, electronic device and storage medium. Background Art

[0002] At present, every time an online ride-hailing service completes an order, it can report travel data such as the driving trajectory and the passenger's boarding and alighting locations to the online ride-hailing service platform. Then, the online ride-hailing service platform can determine the mileage based on the driving trajectory, and then calculate the driving fee of the order based on the driving mileage. Among them, some online ride-hailing services will tamper with the driving trajectory of the travel data in order to increase the driving fee. The online ride-hailing service then reports a false driving trajectory. The mileage determined by the online ride-hailing service platform based on the false mileage is also inconsistent with the actual mileage, so the driving fee calculated based on the mileage exceeds the actual driving fee.

[0003] The relevant scheme obtains the recommended driving path corresponding to each travel data for each trip data; then compares the driving trajectory reported by the online car-hailing vehicle with the recommended driving path to determine whether the driving trajectory is abnormal. However, in some cases, such as poor road conditions and passenger-specified routes, there is a deviation between the actual driving trajectory of the online car-hailing vehicle and the recommended driving path. Therefore, it is impossible to accurately determine whether the driving trajectory of the online car-hailing vehicle is abnormal using the recommended driving path. Summary of the invention

[0004] The present application provides a method, device, electronic device and storage medium for identifying an abnormal trajectory, which can accurately identify an abnormal driving trajectory.

[0005] In order to achieve the above technical objectives, this application adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for identifying an abnormal trajectory, the method comprising: first obtaining travel data of a vehicle and obtaining a target driving distance of the vehicle within a collection period; then, for each trajectory point in a plurality of trajectory points, determining the distance between other trajectory points in the plurality of trajectory points and the trajectory point according to the collection positions of the plurality of trajectory points; finally, determining whether the driving trajectory is abnormal according to the distance and the target driving distance.

[0007] The travel data includes: the collection positions and collection times of multiple track points in the driving track.

[0008] It is understandable that a generally abnormal driving trajectory may include a recurring trajectory. For each trajectory point in this recurring trajectory, a trajectory point close to the collection position of the trajectory point can be collected again after a period of time. Therefore, in the embodiment of the present application, for each trajectory point in the driving trajectory of the vehicle, the distance between the trajectory point and multiple other trajectory points other than the trajectory point can be determined. The electronic device can then determine whether there are other trajectory points (which can be referred to as the target other trajectory points corresponding to the trajectory point) that are close to the collection position of each trajectory point and have a collection time interval of multiple collection time lengths in the multiple other trajectory points based on the determined distance and the target driving distance. If there are target other trajectory points corresponding to the trajectory point, it means that the driving trajectory is likely to include recurring trajectories. Moreover, the more target other trajectory points corresponding to the trajectory point, the longer the recurring trajectory is, that is, the higher the possibility that the driving trajectory is abnormal. Therefore, the electronic device can accurately determine whether the driving trajectory is abnormal based on whether the number of target other trajectory points corresponding to each trajectory point in the multiple trajectory points is large.

[0009] Secondly, by using the method provided in the embodiment of the present application, it is not necessary to use the recommended driving route, and it is possible to determine whether the driving trajectory in the travel data is abnormal based only on the travel data. The implementation requirements of this method are simpler.

[0010] In a possible implementation, determining whether the driving trajectory is abnormal based on the distance and the target driving distance includes: for each trajectory point, counting a first number; the first number is the number of other trajectory points whose distance is less than or equal to the target driving distance; if the first number is greater than a preset number, determining that the trajectory point is an abnormal trajectory point; and determining whether the driving trajectory is abnormal based on the abnormal trajectory point.

[0011] In this design method, based on the above analysis of the idea that "if among the multiple other trajectory points corresponding to each trajectory point, the more other trajectory points are close to the collection position of the trajectory point and the collection time interval with the trajectory point is multiple collection time lengths, it means that the repeated trajectories in the driving trajectory are longer, that is, the possibility that the driving trajectory is abnormal is higher", a specific implementation method is described in which the electronic device determines the abnormal trajectory point according to the distance and the target driving distance; and then determines whether the driving trajectory is abnormal according to the abnormal trajectory point.

[0012] In another possible implementation, the determining whether the driving trajectory is abnormal based on the abnormal trajectory points includes: counting a second number; the second number is the number of abnormal trajectory points among the multiple trajectory points; and determining whether the driving trajectory is abnormal based on the second number.

[0013] In this design method, it is described that the electronic device can determine whether the driving trajectory is abnormal based on the number of abnormal trajectory points. For example, the electronic device can determine whether the driving trajectory is abnormal based on whether the second number exceeds a certain threshold; for another example, the electronic device can determine whether the driving trajectory is abnormal based on the second number and the total number of multiple trajectory points.

[0014] In another possible implementation, the counting of the first number includes: if the other trajectory points whose distance is less than or equal to the target driving distance include multiple first trajectory points, and the collection time of the multiple first trajectory points is continuous, then the number of the multiple first trajectory points is counted as 1.

[0015] It is understandable that the vehicle may slow down or drop to 0 during driving due to waiting for red lights, traffic jams, etc., then the following first situation may occur: the vehicle stops at a trajectory point for a while, and the distance between each two adjacent trajectory points of more than 3 trajectory points that the vehicle has traveled continuously is less than the target driving distance. If a trajectory point meets the first situation above, then not only the distance between the trajectory point and its two adjacent points is less than the target driving distance, but also the distance between the adjacent points of the adjacent points of the trajectory point is less than the target driving distance. This will cause the first number of the trajectory point to increase by multiples. Secondly, there is also a second situation, that is, the target driving distance is set too small, which may also cause the distance between 3 or more trajectory points in a row in the acquisition time to be less than the target driving distance. This will also cause the first number of a trajectory point to increase by multiples.

[0016] In summary, the normal first situation during the driving process and the artificially caused second situation may cause the first number to increase by multiples. After the first number increases by multiples, it may exceed the preset number, causing the trajectory point to be mistakenly confirmed as an abnormal trajectory point. Therefore, when the electronic device counts the first number of each trajectory point, it needs to exclude the increase in the first number caused by the first and second situations.

[0017] Specifically, a plurality of first trajectory points whose distance is less than or equal to the target driving distance and whose collection time is continuous are likely to be caused by the first situation or the second situation. Therefore, the electronic device counts the number of the plurality of first trajectory points as 1 (i.e., records 1 in the first number) for the plurality of first trajectory points. In this way, it is possible to avoid the first number being increased by multiple numbers due to the first situation and the second situation.

[0018] In another possible implementation, for each trajectory point among the multiple trajectory points, the distance between the trajectory point and its adjacent point is less than or equal to the target driving distance; the adjacent point refers to a trajectory point among the multiple trajectory points whose collection time is adjacent to the collection time of the trajectory point; and the preset number is greater than 2.

[0019] It can be understood that the target driving distance can refer to the distance that the vehicle normally travels within a collection time, and the distance between each trajectory point and its adjacent point is not greater than (i.e., less than or equal to) the target driving distance. And one trajectory point corresponds to 2 adjacent points, that is, the distance between each trajectory point and its corresponding 2 adjacent points in the actual driving trajectory is not greater than the target driving distance. Then, if the distance between a trajectory point in a driving trajectory and more than 2 other trajectory points is not greater than the target driving distance, it can be determined that the trajectory point may be abnormal. Therefore, the preset number can be set to be greater than 2.

[0020] In another possible implementation, the travel data also includes driver information. The method further includes: when determining that the driving trajectory is abnormal, counting the number of abnormal driving trajectories of the driver indicated by the driver information within a preset time period according to the driver information; then, if the number of abnormal driving trajectories is greater than or equal to a preset number, issuing a prompt message. The prompt message indicates that the driving trajectory of the driver indicated by the driver information is abnormal.

[0021] It is understandable that in order to exclude that a driver's abnormal driving trajectory occurs once or several times by chance, a preset number of abnormal driving trajectories within a preset time period can be set. If a driver's abnormal driving trajectory occurs more than the preset number of times within a preset time period, it can be confirmed that the driver intentionally tampered with the driving trajectory, and the electronic device can issue a prompt message.

[0022] In a second aspect, the present application provides an abnormal trajectory recognition device, which includes modules for executing the method described in the first aspect or any possible design of the first aspect.

[0023] In a third aspect, the present application provides an electronic device, the electronic device comprising a memory and a processor. The memory and the processor are coupled. The memory is used to store a computer program code, the computer program code comprising a computer instruction. When the processor executes the computer instruction, the electronic device performs the method for identifying an abnormal trajectory as described in the first aspect and any possible design thereof.

[0024] In a fourth aspect, the present application provides a chip system, which is applied to an abnormal trajectory recognition device; the chip system includes one or more interface circuits and one or more processors. The interface circuit and the processor are interconnected through a line; the interface circuit is used to receive a signal from the memory of the abnormal trajectory recognition device and send a signal to the processor, the signal including a computer instruction stored in the memory. When the processor executes the computer instruction, the electronic device executes the abnormal trajectory recognition method as described in the first aspect and any possible design thereof.

[0025] In a fifth aspect, the present application provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the abnormal trajectory identification method as described in the first aspect and any possible design method thereof.

[0026] In a sixth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the abnormal trajectory identification method as described in the first aspect and any possible design method thereof.

[0027] For the specific description of the second to sixth aspects and their various implementations in the present application, reference may be made to the detailed description in the first aspect and its various implementations; and for the beneficial effects of the second to sixth aspects and their various implementations, reference may be made to the beneficial effects analysis in the first aspect and its various implementations, which will not be repeated here.

[0028] These and other aspects of the present application will become more apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1A A schematic diagram 1 of an abnormal driving trajectory provided in an embodiment of the present application;

[0030] Figure 1B An example of an abnormal driving trajectory provided in an embodiment of the present application Figure 2 ;

[0031] Figure 1C An example of an abnormal driving trajectory provided in an embodiment of the present application Figure 3 ;

[0032] Figure 2 Schematic diagram 1 of an implementation environment involved in a method for identifying abnormal trajectories provided in an embodiment of the present application;

[0033] Figure 3 Schematic diagram of the implementation environment involved in the abnormal trajectory identification method provided in the embodiment of the present application Figure 2 ;

[0034] Figure 4 Schematic diagram of the implementation environment involved in the abnormal trajectory identification method provided in the embodiment of the present application Figure 3 ;

[0035] Figure 5 A flowchart of a method for identifying abnormal trajectories provided in an embodiment of the present application;

[0036] Figure 6A schematic diagram of a driving trajectory provided in an embodiment of the present application;

[0037] Figure 7 A schematic diagram of the structure of an abnormal trajectory identification device provided in an embodiment of the present application;

[0038] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In the following, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", or "third", etc. may explicitly or implicitly include one or more of the features.

[0040] At present, the rise and promotion of online ride-hailing has brought great convenience to users' travel. However, there are some online ride-hailing vehicles that will tamper with the driving trajectory in the travel data to increase the mileage determined based on the false driving trajectory. Thereby, the travel expenses calculated based on the mileage can be increased. In order to identify the tampered false driving trajectory (which can be called an abnormal driving trajectory), the relevant scheme uses the recommended driving trajectory corresponding to the travel data to determine whether the reported driving trajectory is abnormal. However, in some cases, such as poor road conditions, passengers specifying routes, etc., there is a deviation between the actual driving trajectory of the online ride-hailing vehicle and the recommended driving path itself. In view of these situations, it is impossible to accurately determine whether the driving trajectory of the online ride-hailing vehicle is abnormal using the recommended driving path.

[0041] In the embodiment of the present application, by analyzing some abnormal driving trajectories, it can be found that when the online car-hailing service tampered with the driving trajectory, in order to ensure that the tampered driving trajectory looks difficult to be discovered and the difference between the tampered driving trajectory and the real driving trajectory is within a certain range, a certain section of the real driving trajectory is usually replayed. In other words, the abnormal driving trajectory may include a repeated trajectory.

[0042] Among them, the abnormal driving trajectory of the replay trajectory can be divided into multiple trajectory types. The multiple trajectory types may include: trajectory sudden change replay type, trajectory non-sudden change replay type, and return to the starting point type. The trajectory sudden change replay type may mean that the vehicle in the driving trajectory jumps to a trajectory point that has been passed during driving, and then re-walks the previously passed path from the passed trajectory point. The trajectory non-sudden change replay type may mean that a section of the driving trajectory is repeated multiple times. The return to the starting point type may mean that the driving trajectory includes not only the trajectory from the starting point to the terminal, but also the trajectory from the terminal to the starting point.

[0043] For example, Figure 1AThe track mutation replay type driving track shown in the figure, in which the vehicle starts from the starting point 111, passes through the track point 112, the track point 113, the track point 114, and arrives at the track point 115. Then, the vehicle drives back from the track point 115 to the track point 112, and then retraces the path previously traveled from the track point 112, passes through the track point 112, the track point 113, the track point 114, and arrives at the track point 115. Finally, the vehicle drives from the track point 115 to the end point 116.

[0044] It can be seen that a section of the track (i.e., the first track including track points 112, 113, 114, and 115) in the track mutation replay type driving track appears repeatedly, and track point 112 also appears repeatedly. The first track that appears for the second time in the track mutation replay type driving track is fictitious by the online car-hailing service.

[0045] For example, Figure 1B The illustrated driving trajectory is a non-mutational replay type driving trajectory, in which the vehicle starts from the starting point 121, passes through the trajectory point 122 and the trajectory point 123, and arrives at the end point 124. The vehicle turns around from the end point 124 and drives to the trajectory point 123, and then drives from the trajectory point 123 to the trajectory point 122 again. Finally, the vehicle passes through the trajectory point 123 from the trajectory point 122 and arrives at the end point 124.

[0046] It can be seen that after the vehicle travels from trajectory point 122 to trajectory point 123, it returns to trajectory point 123 and travels from trajectory point 123 to trajectory point 122. Then, the vehicle travels from trajectory point 122 to end point 124. A section of the trajectory in the non-mutation replay type driving trajectory (i.e., the second trajectory from trajectory point 122 to trajectory point 123) is repeated many times, and trajectory point 122, trajectory point 123 and end point 124 are also repeated. The second and third appearances of the second trajectory in the non-mutation replay type driving trajectory are fictitious online ride-hailing vehicles.

[0047] For example, Figure 1C The driving trajectory of the return-to-starting point type shown in FIG. 1 is a driving trajectory of the return-to-starting point type, in which the vehicle starts from the starting point 131, passes through the trajectory point 132 and the trajectory point 133, and arrives at the end point 134. Then, the vehicle travels to the starting point 131 from the end point 134 along the same trajectory.

[0048] It can be seen that after the vehicle arrives at the terminal 134, it travels back to the starting point 131 along the same trajectory from the end point 134. A section of the trajectory (i.e., the third trajectory from the starting point 131 to the end point 134) in the driving trajectory of the return-to-starting point type is repeated, and the starting point 131 is also repeated. The third trajectory of the second trip in the driving trajectory of the return-to-starting point type is fictitious by the online car-hailing service.

[0049] In summary Figure 1A , Figure 1B and Figure 1C It can be seen from the several abnormal driving trajectories shown that these abnormal driving trajectories all include a repetitive trajectory. For each trajectory point in this repetitive trajectory, a trajectory point close to the collection position of the trajectory point can be collected again after a period of time. Therefore, the embodiment of the present application provides a driving method for an abnormal trajectory, which can determine the distance between the trajectory point and multiple other trajectory points other than the trajectory point for each trajectory point in the driving trajectory of the vehicle; and then determine whether there are other trajectory points in the multiple other trajectory points that are close to the collection position of the trajectory point and have a collection time interval of multiple collection time lengths with the collection time of the trajectory point. If there are other trajectory points that are close to the collection position of the trajectory point and have a collection time interval of multiple collection time lengths with the collection time of the trajectory point, it means that the driving trajectory is likely to include a repetitive trajectory; and the more other trajectory points that are close to the collection position of the trajectory point and have a collection time interval of multiple collection time lengths with the collection time of the trajectory point, the longer the repetitive trajectory is. Therefore, if other trajectory points that are close to the collection position of the trajectory point and have a collection time interval of multiple collection time lengths with the collection time of the trajectory point are determined, it can be determined that the driving trajectory is abnormal.

[0050] The implementation of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0051] Please refer to Figure 2 , which shows a schematic diagram of the implementation environment involved in a method for identifying abnormal trajectories provided in an embodiment of the present application. Figure 2 As shown, the implementation environment may include: a server 200, a terminal 210, and a plurality of collection devices 220 for collecting vehicle travel data. The plurality of collection devices 220 are respectively installed in different vehicles.

[0052] Exemplarily, each acquisition device 220 may include a GPS module, a timing module, and an input module (eg, a touch screen). Figure 2The GPS module, timing module and input module in the acquisition module 220 are not shown. The GPS module is used to collect the longitude and latitude of multiple track points in the driving track, as well as the longitude and latitude of passengers getting on and off the bus. The timing module is used to record the collection time of multiple track points, as well as the time when passengers get on and off the bus. The input module is used to collect driver information, vehicle information, etc. input by the driver. Among them, the driving track is composed of multiple track points, and the collection positions of multiple track points in the driving track can represent the complete driving track.

[0053] The server 200 may receive the travel data of each vehicle from the collection device 220 in each vehicle. The travel data may include: the longitude and latitude of the passengers boarding and alighting, the boarding and alighting time, the longitude and latitude of multiple track points in the driving track and the collection time, the driver information, and the vehicle information, etc. The server 200 may also determine the mileage according to the longitude and latitude of the multiple track points.

[0054] Specifically, Figure 3 As shown, the terminal 210 can receive a first operation input by the user, and the first operation is used to indicate a target area and a target vehicle type (such as an online car-hailing vehicle, a taxi), etc. Then, in response to the first operation, the terminal 210 can obtain travel data of vehicles of the target vehicle type traveling in the target area from the server 200. The terminal 210 then judges the distance between multiple trajectory points in the travel data to determine whether the driving trajectory in the travel data is abnormal. If the driving trajectory is abnormal, a prompt message can be issued, and the prompt message is used to characterize that the driving trajectory of the vehicle is abnormal. For example, the terminal 210 can display the prompt message through a display screen.

[0055] Or, if Figure 4 As shown, in response to the first operation, the terminal 210 may send target information to the server 200. The target information includes a target area and a target vehicle type, etc. The server 200 may obtain travel data of vehicles of the target vehicle type traveling in the target area. The server 200 then determines the distances between multiple trajectory points in the travel data to determine whether the travel trajectory in the travel data is abnormal. If the travel trajectory is abnormal, the above-mentioned prompt information may be sent to the terminal 210. The terminal 210 may issue the prompt information.

[0056] Exemplarily, the terminal 210 in the embodiment of the present application may be a mobile phone, a tablet computer, a desktop, a laptop, a notebook computer, a netbook, etc. The embodiment of the present application does not impose any special limitation on the specific form of the terminal 210.

[0057] It should be noted that the abnormal trajectory identification method provided in the embodiment of the present application can be applied to the above-mentioned server 200, can also be applied to the above-mentioned terminal 210, and can also be applied to the above-mentioned server 200 and terminal 210. The server 200 and the terminal 210 can be collectively referred to as electronic devices. The execution subject of the abnormal trajectory identification method provided in the embodiment of the present application can also be an abnormal trajectory identification device. The identification device can be an electronic device; or, the identification device can be an application (application, APP) installed in the electronic device that provides an abnormal trajectory identification function; or, the identification device can be a central processing unit (Central Processing Unit, CPU) in the electronic device; or, the identification device can be a control module in the electronic device for executing the abnormal trajectory identification method.

[0058] The following describes in detail the abnormal trajectory recognition method provided by the embodiment of the present application by taking an electronic device as an example.

[0059] Please refer to Figure 5 , is a flow chart of a method for identifying abnormal trajectories provided in an embodiment of the present application. Figure 5 As shown, the method may include S501-S504.

[0060] S501. The electronic device obtains travel data of the vehicle; the travel data includes: collection positions and collection times of multiple track points in the driving track.

[0061] The electronic device can obtain the travel data of any vehicle, or obtain the travel data of vehicles belonging to the target vehicle type. The target vehicle type may include: online car-hailing and / or taxi.

[0062] The collection location of each track point may be the longitude and latitude of the track point. The travel data may also include: the order ID corresponding to the travel data, the passenger's boarding and alighting location (such as boarding and alighting longitude and latitude), boarding and alighting time, driver information, and vehicle information, etc. The vehicle information may be a vehicle ID, such as a vehicle number, a license plate number, etc.

[0063] S502: The electronic device obtains a target driving distance of the vehicle within a collection time period.

[0064] The electronic device may calculate the target driving distance according to a collection duration and the target driving speed of the vehicle, and save the target driving distance. Alternatively, the electronic device may directly obtain the target driving distance, for example, the target driving distance may be indicated by the first operation.

[0065] The collection duration may refer to the collection period of the trajectory point, that is, the interval between two adjacent collection times. The target driving speed may be the maximum driving speed of the vehicle or the maximum speed limit of the area where the vehicle is located.

[0066] For example, the unit of the collection time T may be seconds (s), and the unit of the target driving speed V may be kilometers per hour (km / h). Then, the electronic device may substitute the collection time T and the target driving speed V of the vehicle into the distance calculation formula shown in the following formula (1) to obtain the target driving distance D. The unit of the target driving distance D is meters (m).

[0067] D=T*V*10 3 / 3600 (1)

[0068] For example, the collection time T may be 5 seconds, the target driving speed V may be 120 km / h, and the electronic device may calculate the target driving distance D to be 167 meters.

[0069] In the embodiment of the present application, the distance between each of the multiple trajectory points and its adjacent point may be less than or equal to the target driving distance. The adjacent point of each trajectory point refers to a trajectory point whose collection time is adjacent to the collection time of the trajectory point in the multiple trajectory points.

[0070] For example, the plurality of trajectory points arranged in the order of acquisition time may include: trajectory point P1, trajectory point P2, trajectory point P3, trajectory point P4, etc. The adjacent points corresponding to trajectory point P1 include trajectory point P2. The adjacent points corresponding to trajectory point P2 include trajectory point P1 and trajectory point P3. The adjacent points corresponding to trajectory point P3 include trajectory point P2 and trajectory point P4.

[0071] In some embodiments, due to the serious traffic congestion in the morning and afternoon periods every day, the vehicle's driving speed is relatively slow. Therefore, the target driving speed is variable, and different time periods correspond to different target driving speeds. In this case, the electronic device can determine the target time period to which the passenger's boarding and alighting time in the vehicle's travel data belongs. Then, the electronic device can determine the target driving speed corresponding to the target time period from the corresponding relationship between the time period and the driving speed.

[0072] If the passenger's boarding time belongs to the first time period and the passenger's getting off time belongs to the second time period, the electronic device can use the maximum driving speed of the driving speed corresponding to the first time period and the driving speed corresponding to the second time period as the target driving speed. The first time period and the second time period are different time periods.

[0073] For example, the correspondence between time periods and driving speeds may include: 7:00-9:00 and 17:00-19:00 correspond to driving speed V1, and 19:00-24:00 and 0:00-7:00 correspond to driving speed V2. Driving speed V1 is less than driving speed V2.

[0074] It should be noted that the target driving speeds corresponding to different areas may also be different. The specific process of the electronic device determining the target driving speed according to the area where the driving track in the travel data is located can be referred to the detailed description of the electronic device determining the target driving speed according to the boarding and alighting time of passengers in the travel data, which will not be described in detail in the embodiments of the present application.

[0075] In addition, the electronic device may execute S501 first and then execute S502, or execute S502 first and then execute S501. The embodiment of the present application does not limit the order of S501 and S502.

[0076] S503: The electronic device determines, for each trajectory point among the multiple trajectory points, a distance between the trajectory point and other trajectory points among the multiple trajectory points except the trajectory point according to the collection positions of the multiple trajectory points.

[0077] There may be multiple other trajectory points corresponding to each trajectory point, and the multiple other trajectory points may include adjacent points of the trajectory point. The electronic device may calculate the distance between the trajectory point and the other trajectory points according to the acquisition position of each trajectory point and the acquisition position of each other trajectory point corresponding to the trajectory point. Furthermore, the electronic device may determine the distance between each of the multiple trajectory points and the other trajectory points corresponding to them.

[0078] For example, Figure 6 In the driving trajectory shown, a plurality of track points arranged in the order of collection time in the driving trajectory may include: track point P1, track point P2, track point P3, track point P4, track point P5, track point P6, track point P7, track point P8, track point P9, track point P10, track point P11, track point P12, track point P13, track point P14, track point P15, track point P16, track point P17, track point P18, track point P19, track point P20, track point P21, track point P22. Among them, the collection position of track point P1 is the passenger boarding position, and the collection position of track point P22 is the passenger getting off position.

[0079] by Figure 6 Taking the trajectory point P9 in FIG. 1 as an example, the multiple other trajectory points corresponding to the trajectory point P9 include: trajectory points P1 to P8, and trajectory points P10 to P22. The electronic device can calculate the distance between the trajectory point P9 and each of the other corresponding trajectory points.

[0080] In some embodiments, the collection positions of the multiple track points may be longitude and latitude, and the electronic device may use a distance calculation formula to calculate the distance between each track point and each other corresponding track point. The distance calculation formula may include a haversine equation or a Vincenty equation.

[0081] For example, the collection position of a trajectory point may include the longitude lon1 and latitude lat1 of the trajectory point. The collection position of another trajectory point corresponding to the trajectory point may include the longitude lon2 and latitude lat2 of the other trajectory point. The electronic device may calculate the distance d between the trajectory point and the other trajectory point using the haversine formula. haversine The haversine formula is shown in equation (2):

[0082]

[0083] Where r is the radius of the earth in meters. arcsin is the inverse sine function.

[0084] S504: The electronic device determines whether the driving trajectory is abnormal based on the distance and the target driving distance.

[0085] After determining the distances between each of the plurality of trajectory points and other corresponding trajectory points, the electronic device can judge whether each distance is greater than the target driving distance; and then determine whether the driving trajectory is abnormal based on the judgment result.

[0086] In some embodiments, the electronic device may count a first number for each trajectory point. The first number is the number of other trajectory points whose distance is less than or equal to the target driving distance. If the first number is greater than a preset number, the electronic device may determine that the trajectory point is an abnormal trajectory point. Then, the electronic device may determine whether the driving trajectory is abnormal based on the abnormal trajectory point.

[0087] The electronic device may count the first number for each of the plurality of trajectory points. The electronic device then determines whether the first number of each trajectory point is greater than a preset number. If the first number of a trajectory point is greater than the preset number, the electronic device may determine that the trajectory point is an abnormal trajectory point. If the first number of a trajectory point is not greater than the preset number, the electronic device may determine that the trajectory point is not an abnormal trajectory point.

[0088] Among them, the target driving distance may refer to the distance that the vehicle normally drives within a collection time, and the distance between each trajectory point and its adjacent point is not greater than (i.e., less than or equal to) the target driving distance. And one trajectory point corresponds to 2 adjacent points, that is, the distance between each trajectory point and its corresponding 2 adjacent points in the real driving trajectory is not greater than the target driving distance. Then, if the distance between a trajectory point in a driving trajectory and more than 2 other trajectory points is not greater than the target driving distance, it can be determined that the trajectory point may be abnormal. Therefore, the preset number can be set to be greater than 2. The preset number can be indicated by the above-mentioned first operation.

[0089] For example, the preset number is 3, 4, or 5.

[0090] Furthermore, during the driving process, the vehicle may slow down or reduce its driving speed to 0 due to waiting for red lights, traffic jams, etc., and the following first situation may occur: the vehicle stops at a trajectory point for a while, and the distance between each two adjacent trajectory points of more than three trajectory points that the vehicle has traveled continuously is less than the target driving distance. If a trajectory point meets the above first situation, then not only the distance between the trajectory point and its two adjacent points is less than the target driving distance, but also the distance between the adjacent points of the adjacent points of the trajectory point is less than the target driving distance. This will cause the first number of the trajectory point to increase by multiple.

[0091] Secondly, there is also a second situation, that is, the target driving distance is set too small, which may also cause the distances between three or more consecutive trajectory points in the collection time to be less than the target driving distance. This will also cause the first number of a trajectory point to increase by multiples.

[0092] In summary, the normal first situation during the driving process and the artificially caused second situation may cause the first number to increase by multiples. After the first number increases by multiples, it may exceed the preset number, causing the trajectory point to be mistakenly confirmed as an abnormal trajectory point. Therefore, when the electronic device counts the first number of each trajectory point, it needs to exclude the increase in the first number caused by the first and second situations.

[0093] Specifically, if the other trajectory points whose distance is less than or equal to the target driving distance include multiple first trajectory points, and the collection time of the multiple first trajectory points is continuous, the electronic device can count the number of the multiple first trajectory points as 1 (that is, the multiple first trajectory points are recorded as 1 in the first number). If the other trajectory points whose distance is less than or equal to the target driving distance include multiple second trajectory points, and the collection time of the multiple second trajectory points is not continuous, the number of the multiple second trajectory points is counted in the first number.

[0094] For example, Figure 6Taking the track point P9 among the multiple track points shown and the target driving distance D1 as an example, the distances between the track point P9 and other corresponding track points that can be calculated by the electronic device may include: the distance d[8,9] between the track point P9 and the track point P8, the distance d[9,10] between the track point P9 and the track point P10, the distance d[9,15] between the track point P9 and the track point P15, the distance d[9,16] between the track point P9 and the track point P16, the distance d[9,18] between the track point P9 and the track point P18, etc. The electronic device may determine that d[8,9], d[9,10], d[9,15], d[9,16], and d[9,18] are all less than the target driving distance D1.

[0095] Among them, if the acquisition time of trajectory point P15 and trajectory point P16 is continuous, then trajectory point P15 and trajectory point P16 are multiple first trajectory points, and the electronic device can record 1 of trajectory point P15 and trajectory point P16 in the first number of trajectory point P9. Trajectory point P8, trajectory point P10 and trajectory point P18 are all second trajectory points, and the electronic device can count the number of trajectory point P8, trajectory point P10 and trajectory point P18 (i.e., 3) in the first number of trajectory point P9.

[0096] In some embodiments, after the electronic device determines whether each of the plurality of trajectory points is an abnormal trajectory point, it may count a second number. The second number is the number of abnormal trajectory points in the plurality of trajectory points. Then, the electronic device may determine whether the driving trajectory is abnormal based on the second number.

[0097] For example, the electronic device can determine whether the second number is greater than a preset value to determine whether the driving trajectory is abnormal. If the second number is greater than the preset value, it is determined that the driving trajectory is abnormal.

[0098] The preset value may be the value indicated by the first operation. For example, the preset value may be 2, 3 or 4.

[0099] For another example, the electronic device may determine the proportion of the second number in the total number of multiple trajectory points, and then determine whether the driving trajectory is abnormal based on the proportion. If the proportion is greater than or equal to a preset ratio threshold, the driving trajectory is determined to be abnormal.

[0100] The electronic device can divide the second number by the total number of the multiple trajectory points to obtain the proportion. The proportion is the proportion of abnormal trajectory points in the driving trajectory among the multiple trajectory points. The proportion can reflect the proportion of repeated trajectories in the driving trajectory. The larger the proportion, the greater the proportion of repeated trajectories in the driving trajectory. Therefore, it can be seen that if the proportion is large, it indicates that the driving trajectory is abnormal.

[0101] The preset ratio threshold may be set to 20% or 30%, etc. The preset ratio threshold may be indicated by the first operation.

[0102] It can be understood that the proportion can represent the proportion of abnormal trajectory points in the driving trajectory among multiple trajectory points. The proportion can reflect the proportion of repeated trajectories in the driving trajectory. The larger the proportion, the larger the proportion of repeated trajectories in the driving trajectory. Therefore, it can be seen that if the proportion is large, the driving trajectory can be accurately determined to be abnormal. For example, the preset ratio threshold for determining the proportion can be set to 30%.

[0103] In the embodiment of the present application, after determining that the driving trajectory in the travel data is abnormal, the electronic device can also count the number of abnormal driving trajectories of the driver in the travel data. If the total number of abnormal driving trajectories is greater than or equal to the preset number, it means that the driver has a large number of abnormal driving trajectories and needs to be paid special attention to. Furthermore, the electronic device can issue a prompt message to prompt the driver to pay special attention to the driver.

[0104] Specifically, the travel data also includes driver information. When determining that the driving trajectory is abnormal, the electronic device can count the number of abnormal driving trajectories of the driver indicated by the driver information within a preset time period based on the driver information. Then, if the number of abnormal driving trajectories is greater than or equal to the preset number of times, the electronic device can issue a prompt message. The prompt message indicates that the driving trajectory of the driver indicated by the driver information is abnormal.

[0105] The electronic device may display the prompt information through a display screen, or play the prompt information through voice, etc.

[0106] The preset number of times and the preset duration are related. The longer the preset duration, the greater the preset number of times. Both the preset duration and the preset number of times can be indicated by the above-mentioned first operation.

[0107] The prompt information may include the specific content of the driver's travel data with abnormal driving trajectory within a preset time period, the number of travel data with abnormal driving trajectory, etc.

[0108] It is understandable that in order to exclude that a driver's abnormal driving trajectory occurs once or several times by chance, a preset number of abnormal driving trajectories within a preset time period can be set. If a driver's abnormal driving trajectory occurs more than the preset number of times within a preset time period, it can be confirmed that the driver intentionally tampered with the driving trajectory, and the electronic device can issue a prompt message.

[0109] It should be noted that, in addition to counting the number of abnormal driving trajectories based on the driver as the dimension, the electronic device can also count the number of abnormal driving trajectories based on the vehicle as the dimension. The process of the electronic device counting the number of abnormal driving trajectories based on the vehicle as the dimension can refer to the specific introduction of counting the number of abnormal driving trajectories based on the driver as the dimension, which will not be repeated in the embodiments of this application.

[0110] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy to realize that the technical goals in this field are combined with the units and algorithm steps of each example described in the embodiments disclosed in this article, and the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical goals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] The present application also provides a device for identifying abnormal trajectories. Figure 7 , which is a schematic diagram of the structure of an abnormal trajectory recognition device 700 provided in an embodiment of the present application. The recognition device 700 may include: a data acquisition module 701, a distance determination module 702 and an abnormality judgment module 703.

[0112] The data acquisition module 701 is used to acquire the travel data of the vehicle; the travel data includes: the collection position and collection time of multiple track points in the driving trajectory; and the target driving distance of the vehicle within a collection time period. The distance determination module 702 is used to determine the distance between other track points and the track points other than the track point in the multiple track points according to the collection position of the multiple track points for each track point in the multiple track points. The abnormality judgment module 703 is used to determine whether the driving trajectory is abnormal according to the distance and the target driving distance.

[0113] In a possible implementation, the abnormality judgment module 703 is specifically used to: for each trajectory point, count a first number; the first number is the number of other trajectory points whose distance is less than or equal to the target driving distance; if the first number is greater than a preset number, determine that the trajectory point is an abnormal trajectory point; count a second number; the second number is the number of abnormal trajectory points among multiple trajectory points; determine whether the driving trajectory is abnormal based on the second number and the total number of multiple trajectory points.

[0114] In another possible implementation, the abnormality judgment module 703 is specifically used to: if the other trajectory points whose distance is less than or equal to the target driving distance include multiple first trajectory points, and the collection time of the multiple first trajectory points is continuous, then the multiple first trajectory points are recorded as 1 in the first number; if the other trajectory points whose distance is less than or equal to the target driving distance include multiple second trajectory points, and the collection time of the multiple second trajectory points is not continuous, then the number of the multiple second trajectory points is counted in the first number.

[0115] In another possible implementation, the distance between each trajectory point and its adjacent point is less than or equal to the target driving distance; the adjacent point refers to a trajectory point whose collection time is adjacent to the collection time of each trajectory point among multiple trajectory points; and the preset number is greater than 2.

[0116] In another possible implementation, the travel data also includes driver information. The identification device 700 also includes: an abnormal statistics module 704 and a prompt module 705 .

[0117] The abnormality statistics module 704 is used to count the number of abnormal driving trajectories of the driver indicated by the driver information within a preset time period according to the driver information when the driving trajectory is determined to be abnormal. The prompt module 705 is used to issue a prompt message if the number of abnormal driving trajectories is greater than or equal to a preset number of times; the prompt message indicates that the driving trajectory of the driver indicated by the driver information is abnormal.

[0118] Of course, the abnormal trajectory identification device 700 provided in the embodiment of the present application includes but is not limited to the above modules.

[0119] Another embodiment of the present application also provides an electronic device. Figure 8 As shown, the electronic device 800 includes a memory 801 and a processor 802; the memory 801 and the processor 802 are coupled; the memory 801 is used to store computer program codes, and the computer program codes include computer instructions. When the processor 802 executes the computer instructions, the electronic device 800 executes each step executed by the electronic device in the method flow shown in the above method embodiment.

[0120] In actual implementation, the data acquisition module 701, the distance determination module 702, the abnormality judgment module 703, the abnormality statistics module 704 and the prompt module 705 can be composed of Figure 8 The processor 802 shown is implemented by calling the computer program code in the memory 801. The specific execution process can refer to the description of the above-mentioned abnormal trajectory identification method part, which will not be repeated here.

[0121] Another embodiment of the present application further provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes each step executed by the electronic device in the method flow shown in the above method embodiment.

[0122] Another embodiment of the present application also provides a chip system, which is applied to an electronic device. The chip system includes one or more interface circuits and one or more processors. The interface circuit and the processor are interconnected by a line. The interface circuit is used to receive a signal from a memory of the electronic device and send the signal to the processor, wherein the signal includes a computer instruction stored in the memory. When the processor of the electronic device executes the computer instruction, the electronic device executes each step performed by the electronic device in the method flow shown in the above method embodiment.

[0123] In another embodiment of the present application, a computer program product is provided. The computer program product includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes each step executed by the electronic device in the method flow shown in the above method embodiment.

[0124] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer execution instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more servers that can be integrated with a medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0125] The above is only a specific implementation of the present application. Those skilled in the art may conceive of changes or substitutions based on the specific implementation provided by the present application, which should all be included in the protection scope of the present application.

Claims

1. A method for identifying abnormal trajectories, characterized in that: The method comprises: Acquire travel data of the vehicle; the travel data includes: collection positions and collection times of multiple track points in the driving track; Obtaining a target driving distance of the vehicle within a collection time period; For each trajectory point among the plurality of trajectory points, determining, according to the collection positions of the plurality of trajectory points, the distance between the trajectory point and other trajectory points among the plurality of trajectory points except the trajectory point; Whether the driving trajectory is abnormal is determined according to the distance and the target driving distance.

2. The method according to claim 1, characterized in that The determining whether the driving trajectory is abnormal according to the distance and the target driving distance includes: For each of the track points, a first number is counted; the first number is the number of other track points whose distance is less than or equal to the target driving distance; If the first number is greater than a preset number, determining that the trajectory point is an abnormal trajectory point; According to the abnormal trajectory point, it is determined whether the driving trajectory is abnormal.

3. The method according to claim 2, characterized in that The step of determining whether the driving trajectory is abnormal according to the abnormal trajectory point includes: Counting a second number; the second number is the number of abnormal trajectory points among the plurality of trajectory points; Whether the driving trajectory is abnormal is determined based on the second number.

4. The method according to claim 2, characterized in that: The first statistical number includes: If the other trajectory points whose distance is less than or equal to the target driving distance include multiple first trajectory points, and the collection time of the multiple first trajectory points is continuous, the number of the multiple first trajectory points is counted as 1.

5. The method according to claim 2, characterized in that: For each trajectory point among the multiple trajectory points, the distance between the trajectory point and its adjacent point is less than or equal to the target driving distance; the adjacent point refers to a trajectory point among the multiple trajectory points whose collection time is adjacent to the collection time of the trajectory point; and the preset number is greater than 2.

6. The method according to any one of claims 1 to 5, characterized in that The travel data also includes driver information; the method also includes: When it is determined that the driving trajectory is abnormal, counting the number of abnormal driving trajectories of the driver indicated by the driver information within a preset time period according to the driver information; If the number of abnormal driving trajectories is greater than or equal to a preset number, a prompt message is issued.

7. An abnormal trajectory recognition device, characterized in that: The identification device comprises: A data acquisition module is used to acquire the travel data of the vehicle; the travel data includes: the collection positions and collection times of multiple track points in the driving track; and to acquire the target driving distance of the vehicle within a collection time period; a distance determination module, configured to determine, for each of the plurality of trajectory points, a distance between the trajectory point and other trajectory points in the plurality of trajectory points except the trajectory point according to a collection position of the plurality of trajectory points; The abnormality judgment module is used to determine whether the driving trajectory is abnormal according to the distance and the target driving distance.

8. The device according to claim 7, characterized in that The abnormality judgment module is specifically used to: for each trajectory point, count a first number; the first number is the number of other trajectory points whose distance is less than or equal to the target driving distance; if the first number is greater than a preset number, determine that the trajectory point is an abnormal trajectory point; determine whether the driving trajectory is abnormal according to the abnormal trajectory point; The abnormality judgment module is specifically used to: count a second number; the second number is the number of abnormal trajectory points among the multiple trajectory points; and determine whether the driving trajectory is abnormal according to the second number; The abnormality judgment module is specifically configured to: if the other trajectory points whose distance is less than or equal to the target driving distance include a plurality of first trajectory points, and the collection time of the plurality of first trajectory points is continuous, then the number of the plurality of first trajectory points is counted as 1; For each trajectory point among the multiple trajectory points, the distance between the trajectory point and its adjacent point is less than or equal to the target driving distance; the adjacent point refers to a trajectory point among the multiple trajectory points whose collection time is adjacent to the collection time of the trajectory point; The preset number is greater than 2; The travel data also includes driver information; The identification device further comprises: an abnormal statistics module and a prompt module; The abnormality statistics module is used to count the number of abnormal driving trajectories of the driver indicated by the driver information within a preset time period according to the driver information when determining that the driving trajectory is abnormal; The prompt module is used to issue a prompt message if the number of abnormal driving trajectories is greater than or equal to a preset number.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor; the memory and the processor are coupled; the memory is used to store computer program code, and the computer program code comprises computer instructions; When the processor executes the computer instructions, the electronic device executes the abnormal trajectory identification method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the abnormal trajectory recognition method according to any one of claims 1 to 6.

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