Vehicle false trajectory identification method, device and equipment and storage medium
By analyzing vehicle trajectory data, calculating stop and mileage data, filtering abnormal trajectory segments and calculating similarity, identifying fake trajectories, the problem of vehicle trajectory forgery is solved, and precise monitoring of vehicle trajectories is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING ZHONGJIAOXING ROAD INTERNET OF VEHICLES TECH CO LTD
- Filing Date
- 2022-11-01
- Publication Date
- 2026-05-29
AI Technical Summary
How to effectively identify false vehicle trajectories and prevent sales personnel, scheduled vehicles, and freight vehicles from falsifying trajectories to evade supervision.
By analyzing vehicle trajectory data, calculating stop data and mileage data, filtering abnormal stop data, segmenting trajectories and calculating similarity, identifying trajectory anomalies, and determining falsely reported trajectories.
It enables accurate identification of vehicle trajectories, helping freight platforms monitor the normal operation of vehicles, reduce the occurrence of false trajectories, and improve monitoring efficiency.
Smart Images
Figure CN115935056B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent transportation technology, and in particular to a method, apparatus, device, and storage medium for identifying false vehicle trajectories. Background Technology
[0002] BeiDou / GPS mobile positioning devices continuously upload the location of vehicles, ships, or people to a central platform via wireless communication networks, enabling monitoring of their location or trajectory. However, in some industry applications, users wish to evade monitoring. For example, sales personnel may falsify location data to create fake customer visit records; scheduled shuttle buses may falsify trajectories to evade safety supervision; and freight vehicles may upload false trajectory reports to improve the quality of their vehicle trajectories and avoid supervision.
[0003] Therefore, how to effectively identify trajectory fraud is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0004] This disclosure provides a method, apparatus, device, and storage medium for identifying false vehicle trajectories. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general description, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.
[0005] In a first aspect, embodiments of this application provide a method for identifying false vehicle trajectories, including:
[0006] The vehicle's parking data and mileage data are determined based on the vehicle's trajectory data.
[0007] Abnormal parking data of vehicles is calculated based on parking data and mileage data;
[0008] Determine the abnormal trajectory segments of vehicles based on abnormal parking data;
[0009] Obtain the comparison trajectory segment of the abnormal trajectory segment, calculate the similarity between the abnormal trajectory segment and the comparison trajectory segment, determine the vehicle trajectory abnormality based on the similarity, and obtain the number of days of vehicle trajectory abnormality. When the number of days of trajectory abnormality is greater than the preset number of days threshold, it is determined that the vehicle falsely reported trajectory.
[0010] In an optional embodiment, abnormal parking data of the vehicle is calculated based on parking data and mileage data, including:
[0011] Stops with the same stopping time and the same latitude and longitude are considered as the same stops. The number of times the current vehicle has the same stops as other vehicles is calculated. Vehicles that exceed the preset threshold for the number of the same stops are selected as the first abnormal vehicles.
[0012] Based on the vehicle's mileage data, determine the mileage of vehicles with the same stop date as the first abnormal vehicle, and filter vehicles with mileage greater than a preset mileage threshold as the second abnormal vehicle.
[0013] Calculate the ratio of the number of times the second abnormal vehicle had the same stop on the same day to the total number of stops. Vehicles with a ratio greater than a preset threshold are considered abnormal vehicles, and the same stop corresponding to the abnormal vehicles are considered abnormal stop data.
[0014] In an optional embodiment, determining the abnormal trajectory segments of a vehicle based on abnormal parking data includes:
[0015] The abnormal parking data is matched with the full parking data of the vehicles to obtain the continuous abnormal parking intervals of the vehicles. The start and end times of the abnormal trajectory are determined according to the start and end times of the abnormal parking intervals. The vehicle trajectory is then segmented according to the start and end times of the abnormal trajectory to obtain the abnormal trajectory segments of the vehicles.
[0016] In an optional embodiment, obtaining the comparison trajectory segments of the abnormal trajectory segments includes:
[0017] Obtain the trajectory segments of other abnormal vehicles that have the same stopping start point as the abnormal trajectory segment of the current abnormal vehicle, and use them as comparison trajectory segments.
[0018] In an optional embodiment, calculating the similarity between the abnormal trajectory segment and the comparison trajectory segment, and determining the vehicle trajectory abnormality based on the similarity, includes:
[0019] Calculate the similarity between all abnormal trajectory segments of the current abnormal vehicle and its compared trajectory segments;
[0020] Calculate the average similarity and maximum similarity of all abnormal trajectory segments of the current abnormal vehicle with their comparison trajectory segments;
[0021] When the average similarity is greater than the preset first similarity threshold and the maximum similarity is greater than the preset second similarity threshold, the vehicle trajectory is determined to be abnormal.
[0022] In an optional embodiment, calculating the similarity between the abnormal trajectory segment and the comparison trajectory segment includes:
[0023] Calculate the number of reporting points where the latitude, longitude, and north angle of the abnormal trajectory segment and the comparison trajectory segment are consistent;
[0024] Calculate the total number of reported points for the abnormal trajectory segment and the total number of reported points for the comparison trajectory segment, and take the total number of reported points with the smaller value as the target total number of reported points;
[0025] Calculate the ratio of consistent reported points to the total number of reported points, and use this ratio as the similarity between the abnormal trajectory segment and the comparison trajectory segment.
[0026] In an optional embodiment, after obtaining the abnormal trajectory segments of the vehicle, the method further includes:
[0027] Obtain the model of the terminal device used to upload reports of vehicle abnormal trajectory segments;
[0028] Obtain the terminal device model of the previous and next reporting points for each segment of the vehicle's abnormal trajectory;
[0029] Compare the terminal device models of the reporting points before, during, and after the abnormal trajectory of the vehicle to see if they are consistent; vehicles with a consistency rate lower than a preset threshold are identified as vehicles with abnormal terminal devices.
[0030] Secondly, embodiments of this application provide a device for identifying false vehicle trajectories, comprising:
[0031] The first calculation module is used to determine the vehicle's parking data and mileage data based on the vehicle's trajectory data.
[0032] The second calculation module is used to calculate abnormal parking data of the vehicle based on parking data and mileage data.
[0033] The trajectory segmentation module is used to determine the abnormal trajectory segments of vehicles based on abnormal parking data;
[0034] The identification module is used to obtain the comparison trajectory segment of the abnormal trajectory segment, calculate the similarity between the abnormal trajectory segment and the comparison trajectory segment, determine the vehicle trajectory abnormality based on the similarity, and obtain the number of days of vehicle trajectory abnormality. When the number of days of trajectory abnormality is greater than the preset number of days threshold, it is determined that the vehicle falsely reported trajectory.
[0035] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing program instructions, wherein the processor is configured to execute the vehicle false trajectory identification method provided in the above embodiments when executing the program instructions.
[0036] Fourthly, embodiments of this application provide a computer-readable medium having computer-readable instructions stored thereon, which can be executed by a processor to implement a method for identifying false vehicle trajectories provided in the above embodiments.
[0037] The technical solutions provided in this disclosure may have the following beneficial effects:
[0038] The method for identifying false vehicle trajectories provided in this application calculates the reported trajectories of all vehicles within a certain time period to obtain the vehicle's stop points and mileage. Based on the stop data, it filters out related abnormal vehicles with identical stops. The trajectories are segmented according to the same stop points, and the similarity between the trajectories of each segment and other vehicles is calculated. Finally, vehicles with falsely reported trajectories are filtered out based on the trajectory similarity. This method can be applied to all vehicles on the current freight platform, effectively identifying vehicles with falsified trajectories. It assists freight platforms and service providers in effectively monitoring the normal operation of freight vehicles and helps insurance companies analyze and assess vehicle risk before insurance coverage.
[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description
[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0041] Figure 1 This is a flowchart illustrating a method for identifying false vehicle trajectories according to an exemplary embodiment;
[0042] Figure 2 This is a flowchart illustrating another method for identifying false vehicle trajectories according to an exemplary embodiment;
[0043] Figure 3 This is a schematic diagram of a vehicle false trajectory identification device according to an exemplary embodiment;
[0044] Figure 4 This is a schematic diagram of an electronic device structure according to an exemplary embodiment;
[0045] Figure 5 This is a schematic diagram illustrating a computer storage medium according to an exemplary embodiment. Detailed Implementation
[0046] The following description and accompanying drawings fully illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
[0047] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0048] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with some aspects of the invention as detailed in the appended claims.
[0049] In the description of this invention, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0050] Figure 1 This is a flowchart illustrating a method for identifying false vehicle trajectories according to an exemplary embodiment. See also... Figure 1 The method specifically includes the following steps.
[0051] S101 determines the vehicle's parking data and mileage data based on the vehicle's trajectory data.
[0052] In an exemplary scenario, the trajectory data of vehicles on a freight platform is monitored. First, the vehicle trajectory data is acquired. This can be done through the GPS (Global Positioning System) installed on the vehicle, which provides trajectory data for a preset time period, or through the BeiDou navigation and positioning system installed on the vehicle.
[0053] Furthermore, vehicle parking data is determined based on vehicle trajectory data. Taking a calendar day as the dimension, the parking points of all freight platform vehicles for that day are calculated based on vehicle driving trajectories, and the trajectory data of the day and the parking data that was not completed the previous day are read in.
[0054] First, the acquired trajectory data for the day is preprocessed, removing trajectory points with errors and those not located. Then, the trajectory points are sorted in ascending order by GPS time. Next, vehicle stop data is located. The starting stop is the first point with a GPS speed of 0. Once the starting point is determined and the current point's speed is 0, the current point is added to the stop point sequence `stop_seq`. Simultaneously, the center point `pCener` and the maximum radius `r` of `stop_seq` are calculated. If `r` <= 500 meters, the search continues until a point with a speed > 0 or `r` > 500 meters is found, at which point the stop information is output.
[0055] Cross-day stop rules: Each day, incomplete stops (meaning the speed of the last trajectory point of the day is 0) are marked and used as one of the input sources for the next day's stop determination. Each day, incomplete stops from the previous day are read, matched with the trajectory points of the current day, and the output of completed stops is updated. Stops that are still incomplete are stored in the current day's data. Finally, the latitude and longitude of each stop and the stop duration are calculated.
[0056] Furthermore, the daily mileage of a vehicle can be determined based on its trajectory data.
[0057] Specifically, the system reads in a day's trajectory data, preprocesses it by removing trajectory points marked with errors and those without GPS location information, etc. Then, it sorts the trajectory points in ascending order of GPS time. If there are two points on the trajectory with a distance greater than 2 kilometers between them and a total distance greater than 5 kilometers, it calls the pm service to perform route planning and returns the traveled mileage; otherwise, it calculates the vehicle's mileage by accumulating the distances between adjacent points.
[0058] S102 calculates abnormal parking data of vehicles based on parking data and mileage data.
[0059] In one possible implementation, abnormal vehicles are identified based on parking data and mileage data. First, abnormal vehicles are initially screened based on parking data, then based on vehicle mileage data, and finally based on the proportion of vehicles with abnormal parking.
[0060] Specifically, stops with the same stop duration and latitude / longitude are considered as identical stops. The number of times the current vehicle shares the same stop with other vehicles is calculated. If the number of times the current vehicle shares the same stop with other vehicles exceeds a preset threshold, the current vehicle is selected as a first abnormal vehicle. Following this step, vehicles exceeding the preset threshold are also selected as first abnormal vehicles. This embodiment does not limit the specific value of the preset threshold; those skilled in the art can set it according to actual conditions. For example, if the threshold is 8, and the number of times the vehicle shares the same stop with other vehicles exceeds the threshold of 8, then the vehicle is selected as a first abnormal vehicle.
[0061] Furthermore, based on the vehicle's mileage data, the mileage of vehicles with the same stop date as the first abnormal vehicle is determined, and vehicles with mileage greater than a preset mileage threshold are selected as the second abnormal vehicles.
[0062] In one possible implementation, for the initial set of first abnormal vehicles, the mileage data of vehicles with the same stop date must be greater than or equal to a preset mileage threshold to ensure that the vehicles are driving normally on that day rather than continuously parked. If the mileage of vehicles with the same stop date is less than the preset mileage threshold, the vehicle is removed, and the second set of abnormal vehicle data is finally obtained. This application embodiment does not limit the specific value of the preset mileage threshold; those skilled in the art can set it according to actual conditions. For example, the mileage data of vehicles with the same stop date must be greater than or equal to 5km to ensure that the vehicles are driving normally on that day.
[0063] Furthermore, the ratio of the number of times the second abnormal vehicle had the same stop on that day to the total number of stops is calculated. Vehicles with a ratio greater than a preset threshold are considered abnormal vehicles, and the same stop corresponding to the abnormal vehicles are considered abnormal stop data.
[0064] Based on the second set of abnormal vehicles obtained in the previous step, and the vehicles' shared stop locations, abnormal vehicles are filtered according to the abnormal stopping ratio. The ratio of the number of times a vehicle shares a stop location with the total number of stops on a given day is calculated. Stop and vehicle data where the ratio is less than a preset threshold are filtered out. Vehicles with a ratio greater than or equal to the preset threshold are considered abnormal vehicles, avoiding accidental identical stops due to vehicles operating on the same route. This results in the filtered set of abnormal vehicles. The shared stop locations corresponding to abnormal vehicles are considered abnormal stopping data. The preset threshold can be set according to actual conditions, for example, a preset threshold of 0.5.
[0065] S103 determines the abnormal trajectory segments of the vehicle based on the abnormal parking data.
[0066] In an optional embodiment, determining the abnormal trajectory segments of a vehicle based on abnormal parking data includes: matching the abnormal parking data with the full parking data of the vehicle to obtain continuous abnormal parking intervals of the vehicle; determining the start and end times of the abnormal trajectory based on the start and end times of the abnormal parking intervals; and segmenting the vehicle trajectory based on the start and end times of the abnormal trajectory to obtain the abnormal trajectory segments of the vehicle.
[0067] Specifically, the process involves acquiring filtered abnormal parking data, matching this abnormal parking data with the full parking data of all vehicles to obtain the start and end intervals of continuous abnormal parking segments. The start and end times of these abnormal parking segments are then used as the start and end times of the abnormal trajectories. Based on these start and end times, the vehicle trajectories are segmented to obtain abnormal trajectory segments. Finally, all abnormal trajectory segments for all abnormal vehicles are obtained.
[0068] S104 obtains the comparison trajectory segment of the abnormal trajectory segment, calculates the similarity between the abnormal trajectory segment and the comparison trajectory segment, determines the vehicle trajectory abnormality based on the similarity, and obtains the number of days of vehicle trajectory abnormality. When the number of days of trajectory abnormality is greater than the preset number of days threshold, it is determined that the vehicle falsely reported trajectory.
[0069] In one possible implementation, the comparison trajectory segments of the abnormal trajectory segments are first obtained. This includes: obtaining the trajectory segments of other abnormal vehicles that have the same stopping start point as the abnormal trajectory segment of the current abnormal vehicle, and using them as comparison trajectory segments.
[0070] Specifically, the abnormal trajectory segment of the current vehicle is obtained, and the trajectory segments of other abnormal vehicles with the same stopping start point as the abnormal trajectory segment are found in the filtered abnormal vehicles. These are used as the comparison trajectory segments of the abnormal trajectory segment.
[0071] Further, the similarity between the abnormal trajectory segment and the comparison trajectory segment is calculated. In an optional embodiment, calculating the similarity between the abnormal trajectory segment and the comparison trajectory segment includes: obtaining the latitude and longitude and the angle of due north of the vehicle trajectory reporting points; calculating the number of reporting points where the latitude and longitude and the angle of due north are consistent between the abnormal trajectory segment and the comparison trajectory segment; calculating the total number of reporting points for the abnormal trajectory segment and the total number of reporting points for the comparison trajectory segment, and taking the smaller total number of reporting points as the target total number of reporting points; calculating the ratio of the consistent number of reporting points to the target total number of reporting points, and using the ratio as the similarity between the abnormal trajectory segment and the comparison trajectory segment.
[0072] Optionally, each trajectory segment may have more than two comparison trajectory segments, and the maximum similarity of the current trajectory segment is taken as the similarity of the abnormal trajectory segment.
[0073] Further, determining vehicle trajectory anomalies based on similarity includes: calculating the similarity between all abnormal trajectory segments of the current abnormal vehicle and its comparative trajectory segments; calculating the average similarity and maximum similarity of all abnormal trajectory segments of the current abnormal vehicle based on the similarity between all abnormal trajectory segments of the current abnormal vehicle and its comparative trajectory segments; determining vehicle trajectory anomalies when the average similarity is greater than a preset first similarity threshold and the maximum similarity is greater than a preset second similarity threshold. The specific values of the preset first and second similarity thresholds are not limited in this embodiment and can be set according to actual conditions.
[0074] Furthermore, the number of days with abnormal vehicle trajectories is obtained. When the number of days with abnormal trajectories exceeds a preset threshold, the vehicle is determined to have falsely reported trajectories. The preset threshold is not specifically limited in this embodiment and can be set according to actual circumstances. For example, if the preset threshold is 5 days, and the number of days with abnormal vehicle trajectories within a preset time period, such as one month, is greater than or equal to 5 days, then the vehicle is determined to have falsely reported trajectories.
[0075] This step allows for the precise identification of whether a vehicle has falsely reported its trajectory, based on data such as abnormal parking and mileage.
[0076] In some alternative embodiments, abnormalities in vehicle terminal equipment can also be identified and false trajectories reported by vehicles can be determined by analyzing changes in vehicle terminal equipment before, during, and after the segmented trajectory.
[0077] Specifically, the system obtains the model of the terminal device that uploaded the vehicle's reporting point when it is in an abnormal trajectory segment; it also obtains the model of the terminal device that reported the vehicle before and after the abnormal trajectory segment; it compares whether the terminal device models of the reporting points before, during, and after the abnormal trajectory segment are consistent, and calculates the consistency rate of the vehicles within a preset time period. For example, it calculates the ratio of the number of inconsistent to the number of consistent events within a month to obtain the reporting device consistency rate within a month. Vehicles with a consistency rate lower than a preset threshold are considered vehicles with abnormal terminal devices. The specific value of the preset threshold can be set according to the actual situation. Vehicles with abnormal terminal devices are considered vehicles that may falsely report trajectories.
[0078] Figure 2 This is a flowchart illustrating another method for identifying false vehicle trajectories according to an exemplary embodiment; as shown below. Figure 2As shown, the full trajectory data of the vehicle is acquired, and the vehicle's stop data and mileage data are obtained based on the acquired full trajectory data. Identifying common stop points based on the stop data, and filtering for initial abnormal vehicles based on the number of identified common stop points, further filtering based on the vehicle's mileage data yields abnormal vehicles and abnormal stop data. The vehicle's trajectory is segmented based on the abnormal stop data, and segmented comparison trajectory data is obtained based on the starting stop of each segment. The similarity data of the abnormal trajectory for each segment is calculated, and the average and maximum similarity of the overall trajectory for all segments are calculated. When the average similarity is greater than a preset first similarity threshold and the maximum similarity is greater than a preset second similarity threshold, the vehicle trajectory is determined to be abnormal. The number of days of abnormal trajectory is obtained. When the number of days of abnormal trajectory is greater than a preset number of days threshold, the vehicle is determined to have falsely reported a trajectory. The preset number of days threshold is not specifically limited in this embodiment and can be set according to actual conditions. For example, if the preset number of days threshold is 5 days, and the number of days with abnormal vehicle trajectory within a preset time period, such as within a month, is greater than or equal to 5 days, then it is determined that the vehicle has falsely reported its trajectory.
[0079] This method can be used to calculate the risks of all current freight platform vehicles, effectively identify falsified vehicle trajectories, assist freight platforms and service providers in effectively monitoring the normal operation of freight vehicles, and help insurance companies analyze and assess the risk of vehicles before taking out insurance.
[0080] This disclosure also provides a device for identifying false vehicle trajectories, which is used to execute the method for identifying false vehicle trajectories described in the above embodiments, such as... Figure 3 As shown, the device includes:
[0081] The first calculation module 301 is used to determine the vehicle's parking data and mileage data based on the vehicle's trajectory data.
[0082] The second calculation module 302 is used to calculate abnormal parking data of the vehicle based on the parking data and the mileage data.
[0083] The trajectory segmentation module 303 is used to determine the abnormal trajectory segments of the vehicle based on the abnormal parking data.
[0084] The identification module 304 is used to obtain the comparison trajectory segment of the abnormal trajectory segment, calculate the similarity between the abnormal trajectory segment and the comparison trajectory segment, determine the vehicle trajectory abnormality based on the similarity, and obtain the number of days of vehicle trajectory abnormality. When the number of days of trajectory abnormality is greater than a preset number of days threshold, it is determined that the vehicle falsely reported trajectory.
[0085] It should be noted that the vehicle false trajectory identification device provided in the above embodiments is only illustrated by the division of the above functional modules in the vehicle false trajectory identification method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle false trajectory identification device and the vehicle false trajectory identification method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.
[0086] This disclosure also provides an electronic device corresponding to the vehicle false trajectory identification method provided in the foregoing embodiments, to execute the vehicle false trajectory identification method described above.
[0087] Please refer to Figure 4 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 4 As shown, the electronic device includes: a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, the communication interface 403, and the memory 401 are connected via the bus 402. The memory 401 stores a computer program that can run on the processor 400. When the processor 400 runs the computer program, it executes the vehicle false trajectory identification method provided in any of the foregoing embodiments of this application.
[0088] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 403 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.
[0089] Bus 402 can be an ISA bus, PCI bus, or EISA bus, etc. Buses can be divided into address buses, data buses, control buses, etc. Memory 401 is used to store programs. After receiving execution instructions, processor 400 executes the programs. The vehicle false trajectory identification method disclosed in any of the aforementioned embodiments of this application can be applied to processor 400, or implemented by processor 400.
[0090] The processor 400 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 400 or by instructions in software form. The processor 400 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 401. The processor 400 reads the information in memory 401 and, in conjunction with its hardware, completes the steps of the above method.
[0091] The electronic device provided in this application embodiment and the vehicle false trajectory identification method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.
[0092] This application also provides a computer-readable storage medium corresponding to the vehicle false trajectory identification method provided in the foregoing embodiments. Please refer to... Figure 5 The computer-readable storage medium shown is an optical disc 500, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the vehicle false trajectory identification method provided in any of the foregoing embodiments.
[0093] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.
[0094] The computer-readable storage medium provided in the above embodiments of this application and the vehicle false trajectory identification method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.
[0095] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0096] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.
Claims
1. A method for identifying false vehicle trajectories, characterized in that, include: The vehicle's parking data and mileage data are determined based on the vehicle's trajectory data. Abnormal parking data of vehicles is calculated based on the parking data and mileage data; including: identifying parking points with the same parking duration and parking latitude and longitude as common parking points, calculating the number of times the current vehicle shares common parking points with other vehicles, and filtering vehicles with more than a preset threshold for the number of common parking points as first abnormal vehicles; determining the mileage of vehicles on the dates when the first abnormal vehicle shares common parking points based on the vehicle's mileage data, and filtering vehicles with mileage greater than a preset mileage threshold as second abnormal vehicles; calculating the ratio of the number of times the second abnormal vehicle shares common parking points on that day to the total number of parking times, and identifying vehicles with the ratio greater than a preset threshold as abnormal vehicles, and the common parking points corresponding to the abnormal vehicles are abnormal parking data. The abnormal trajectory segments of the vehicle are determined based on the abnormal parking data; Obtain the comparison trajectory segment of the abnormal trajectory segment, calculate the similarity between the abnormal trajectory segment and the comparison trajectory segment, determine the vehicle trajectory abnormality based on the similarity, and obtain the number of days of vehicle trajectory abnormality. When the number of days of trajectory abnormality is greater than a preset number of days threshold, determine that the vehicle falsely reported trajectory.
2. The method according to claim 1, characterized in that, Based on the abnormal parking data, abnormal trajectory segments of the vehicle are determined, including: The abnormal parking data is matched with the full parking data of the vehicles to obtain continuous abnormal parking intervals. The start and end times of the abnormal trajectory are determined according to the start and end times of the abnormal parking intervals. The vehicle trajectory is segmented according to the start and end times of the abnormal trajectory to obtain the abnormal trajectory segments of the vehicles.
3. The method according to claim 1, characterized in that, Obtaining the comparison trajectory segment of the abnormal trajectory segment includes: Obtain the trajectory segments of other abnormal vehicles that have the same stopping start point as the abnormal trajectory segment of the current abnormal vehicle, and use them as comparison trajectory segments.
4. The method according to claim 1, characterized in that, Calculating the similarity between the abnormal trajectory segment and the comparison trajectory segment, and determining the vehicle trajectory abnormality based on the similarity, includes: Calculate the similarity between all abnormal trajectory segments of the current abnormal vehicle and its compared trajectory segments; Calculate the average similarity and maximum similarity of all abnormal trajectory segments of the current abnormal vehicle with their comparison trajectory segments; When the average similarity is greater than a preset first similarity threshold and the maximum similarity is greater than a preset second similarity threshold, the vehicle trajectory is determined to be abnormal.
5. The method according to claim 4, characterized in that, Calculating the similarity between the abnormal trajectory segment and the comparison trajectory segment includes: Calculate the number of reporting points where the latitude, longitude, and north angle of the abnormal trajectory segment are consistent with those of the comparison trajectory segment; Calculate the total number of reported points for the abnormal trajectory segment and the total number of reported points for the comparison trajectory segment, and take the total number of reported points with the smaller value as the target total number of reported points; Calculate the ratio of the number of consistent reported points to the total number of reported points, and use the ratio as the similarity between the abnormal trajectory segment and the comparison trajectory segment.
6. The method according to claim 2, characterized in that, After obtaining the abnormal trajectory segments of the vehicle, the following is also included: Obtain the model of the terminal device used to upload reports of vehicle abnormal trajectory segments; Obtain the terminal device model of the previous and next reporting points for each segment of the vehicle's abnormal trajectory; Compare whether the terminal device models reporting the abnormal vehicle trajectory before, during, and after the segmentation are consistent; vehicles with a consistency rate lower than a preset threshold are identified as vehicles with abnormal terminal devices.
7. A device for identifying false vehicle trajectories, characterized in that, include: The first calculation module is used to determine the vehicle's parking data and mileage data based on the vehicle's trajectory data. The second calculation module is used to calculate abnormal parking data of vehicles based on the parking data and mileage data; including: identifying parking points with the same parking duration and parking latitude and longitude as common parking points, calculating the number of times the current vehicle shares common parking points with other vehicles, and filtering vehicles with more than a preset threshold for the number of common parking points as first abnormal vehicles; determining the mileage of vehicles on the dates when the first abnormal vehicles share common parking points based on the vehicle's mileage data, and filtering vehicles with mileage greater than a preset mileage threshold as second abnormal vehicles; calculating the ratio of the number of times the second abnormal vehicle shares common parking points on that day to the total number of parking times, and identifying vehicles with the ratio greater than a preset threshold as abnormal vehicles, and the common parking points corresponding to the abnormal vehicles are abnormal parking data. The trajectory segmentation module is used to determine the abnormal trajectory segments of the vehicle based on the abnormal parking data. The identification module is used to obtain the comparison trajectory segment of the abnormal trajectory segment, calculate the similarity between the abnormal trajectory segment and the comparison trajectory segment, determine the vehicle trajectory abnormality based on the similarity, and obtain the number of days of vehicle trajectory abnormality. When the number of days of trajectory abnormality is greater than a preset number of days threshold, it is determined that the vehicle falsely reported trajectory.
8. An electronic device, characterized in that, It includes a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the method for identifying false vehicle trajectories as described in any one of claims 1 to 6.
9. A computer-readable medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to implement a method for identifying false vehicle trajectories as described in any one of claims 1 to 6.