CN115935056A - Method, device and equipment for identifying false track of vehicle and storage medium - Google Patents
Method, device and equipment for identifying false track of vehicle and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for identifying a false track of a vehicle, wherein the method comprises the following steps: determining parking data and driving mileage data of the vehicle according to the trajectory data of the vehicle; calculating abnormal parking data of the vehicle according to the parking data and the driving mileage data; determining abnormal track segments of the vehicle according to the abnormal parking data; and obtaining a contrast track section of the abnormal track section, calculating the similarity between the abnormal track section and the contrast track section, determining that the track of the vehicle is abnormal according to the similarity, obtaining the number of days of abnormal track of the vehicle, and determining the false reported track of the vehicle when the number of days of abnormal track is greater than a preset number of days threshold. According to the method for identifying the false track of the vehicle, the false track of the vehicle can be effectively identified, and a freight platform and a service provider can be assisted to effectively monitor the normal operation of the freight vehicle.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a method, a device, equipment and a storage medium for identifying false tracks of vehicles.
Background
The Beidou/GPS mobile positioning equipment continuously uploads the positions of vehicles, ships or people to the central platform through a wireless communication network, and the positions or tracks of the vehicles, the ships and the people can be monitored. However, in some industrial applications, users wish to evade monitoring, for example, sales service personnel, utilize location counterfeiting to forge customer visit records; the fixed line vehicle forges a track to avoid safety supervision; and the condition that the freight vehicle uploads false track report points for avoiding, monitoring and improving the track quality of the vehicle and the like.
Therefore, how to effectively identify the track counterfeiting is a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device, equipment and a storage medium for identifying a false track of a vehicle. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a method for identifying a false trajectory of a vehicle, including:
determining parking data and driving mileage data of the vehicle according to the trajectory data of the vehicle;
calculating abnormal parking data of the vehicle according to the parking data and the driving mileage data;
determining abnormal track segments of the vehicle according to the abnormal parking data;
the method comprises the steps of obtaining a contrast track section of an abnormal track section, calculating the similarity between the abnormal track section and the contrast track section, determining that a vehicle track is abnormal according to the similarity, obtaining the number of days of the vehicle track abnormality, and determining the false reporting track of the vehicle when the number of days of the track abnormality is larger than a preset number of days threshold.
In an optional embodiment, calculating abnormal parking data of the vehicle according to the parking data and the mileage data comprises:
taking the stop points with the same stop time and the same stop longitude and latitude as the same stop points, calculating the number of the stop points of the current vehicle and other vehicles, and screening the vehicles exceeding the preset threshold value of the number of the same stop points as first abnormal vehicles;
determining the driving mileage of a vehicle with the same stop point date of a first abnormal vehicle according to the driving mileage data of the vehicle, and screening the vehicle with the driving mileage larger than a preset mileage threshold value as a second abnormal vehicle;
and calculating the ratio of the number of times that the second abnormal vehicle has the same stop point on the same day to the total stop number of times, taking the vehicle with the ratio larger than a preset threshold value as the abnormal vehicle, and taking the same stop point corresponding to the abnormal vehicle as abnormal stop data.
In an optional embodiment, determining an abnormal trajectory segment for the vehicle based on the abnormal stopping data comprises:
and matching the abnormal parking data with the vehicle total parking data to obtain a continuous abnormal parking interval of the vehicle, determining the starting and stopping time of the abnormal track according to the starting and stopping time of the abnormal parking interval, and segmenting the vehicle track according to the starting and stopping time of the abnormal track to obtain the abnormal track segmentation of the vehicle.
In an optional embodiment, obtaining a contrast track segment of the abnormal track segment includes:
and acquiring track segments of the other abnormal vehicles with the same stopping starting point as the abnormal track segment of the current abnormal vehicle as a comparison track segment.
In an optional embodiment, calculating the similarity between the abnormal track segment and the comparative track segment, and determining the vehicle track abnormality according to the similarity comprises:
calculating the similarity of all abnormal track segments of the current abnormal vehicle and the comparison track segments;
calculating the average similarity and the maximum similarity of all abnormal track segments of the current abnormal vehicle according to the similarity of all abnormal track segments of the current abnormal vehicle and the comparison track segments of the current abnormal vehicle;
and when the average similarity is greater than a preset first similarity threshold and the maximum similarity is greater than a preset second similarity threshold, determining that the vehicle track is abnormal.
In an optional embodiment, the calculating the similarity between the abnormal track segment and the comparative track segment includes:
calculating the report number of the track longitude and latitude of the abnormal track segment and the contrast track segment, which is consistent with the included angle in the positive north direction;
calculating the total report point number of the abnormal track segment and the total report point number of the comparative track segment, and taking the total report point number with smaller numerical value of the abnormal track segment and the comparative track segment as a target total report point number;
and calculating the ratio of the consistent report points to the total target report points, and taking the ratio as the similarity of the abnormal track segment and the comparative track segment.
In an optional embodiment, after obtaining the abnormal trajectory segment of the vehicle, the method further includes:
acquiring the model of uploading terminal equipment for subsection reporting of abnormal tracks of the vehicle;
acquiring the model of terminal equipment of a front report point and a rear report point of the abnormal track section of the vehicle;
comparing whether the types of the terminal equipment of the report points before, during and after the abnormal track of the vehicle is segmented are consistent; and taking the vehicle with the consistency rate lower than the preset threshold value as the abnormal vehicle of the terminal equipment.
In a second aspect, an embodiment of the present application provides an apparatus for identifying a false trajectory of a vehicle, including:
the first calculation module is used for determining parking data and mileage data of the vehicle according to the track data of the vehicle;
the second calculation module is used for calculating abnormal parking data of the vehicle according to the parking data and the traveling mileage data;
the track segmentation module is used for determining abnormal track segmentation of the vehicle according to the abnormal parking data;
the identification module is used for acquiring a contrast track segment of the abnormal track segment, calculating the similarity between the abnormal track segment and the contrast track segment, determining that the vehicle track is abnormal according to the similarity, acquiring the number of days of the vehicle track abnormality, and determining the false reporting track of the vehicle when the number of days of the track abnormality is greater than a preset number of days threshold.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory storing program instructions, where the processor is configured to execute the method for identifying a false trajectory of a vehicle provided in the foregoing embodiment when executing the program instructions.
In a fourth aspect, the present application provides a computer-readable medium, on which computer-readable instructions are stored, where the computer-readable instructions are executable by a processor to implement a method for identifying a false trajectory of a vehicle provided in the foregoing embodiment.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the method for identifying the false vehicle track, the track report points of all vehicles in a certain time are calculated, the stop points and the driving mileage of the vehicles are obtained through calculation, related abnormal vehicles which are completely consistent in stop are screened according to stop data of the vehicles, track segmentation is carried out according to the same stop points, the track similarity of each segmented vehicle and other vehicles is calculated, and finally the vehicles with the false reported tracks are screened according to the track similarity of the vehicles. The method can be used for calculating all current freight platform vehicles, effectively identifying vehicle fake tracks, assisting the freight platform and a service provider to effectively supervise the normal operation of the freight vehicles, and helping a security officer to analyze and judge the risk condition of the vehicles before insuring.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic flow diagram illustrating a method for identifying false tracks on a vehicle in accordance with an exemplary embodiment;
FIG. 2 is a flow chart diagram illustrating another method of identifying a false trajectory for a vehicle, according to an exemplary embodiment;
FIG. 3 is a schematic diagram of a vehicle false track identification device according to an exemplary embodiment;
FIG. 4 is a schematic diagram illustrating an electronic device according to an exemplary embodiment;
FIG. 5 is a schematic diagram illustrating a computer storage medium in accordance with an exemplary embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following description refers to the accompanying drawings in which the same numbers in different drawings represent 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 certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Fig. 1 is a flowchart illustrating a method for identifying a vehicle false track according to an exemplary embodiment, and referring to fig. 1, the method specifically includes the following steps.
S101, determining parking data and mileage data of the vehicle according to the track data of the vehicle.
In one exemplary scenario, trajectory data for vehicles in a cargo platform is supervised. First, trajectory data of the vehicle is acquired. The track data in the preset time period of the vehicle can be acquired through a Global Positioning System (GPS) mounted on the vehicle, and the track data of the vehicle can also be acquired through a beidou navigation Positioning System mounted on the vehicle.
Further, parking data of the vehicle is determined from the trajectory data of the vehicle. And taking a natural day as a dimension, calculating the current-day stopping points of all the freight platform vehicles based on the vehicle running track, and reading track data of one day and stopping data which is not finished yesterday.
Firstly, the acquired track data of the current day is preprocessed, track point data with wrong tracks is removed, and track point data which is not positioned is removed. And then, sequencing the track points, and sequencing according to the GPS time positive sequence of the track points. Then finding the stopping data of the vehicle, taking the first point with the GPS speed of 0 at the stopping starting point, adding the current point into the stopping point sequence stop _ seq when the starting point is determined and the current point speed =0, meanwhile, calculating the center point pCener of the stop _ seq and the maximum radius r, if r < =500 m, continuing to find downwards until finding a point with the speed of the point >0 or r >500 m, and outputting the stopping point information.
The cross-day berthing rules are as follows: each day marks the incomplete stops of the day (meaning that the speeds of the last track points of the day are all 0) as one of the input sources for the next-day stop judgment. Reading uncompleted stops in the previous day every day, matching with track points in the current day, updating and outputting the completed stops, and storing the uncompleted stops into data in the current day; and finally, calculating to obtain the docking longitude and latitude and the docking duration of each docking.
Further, the daily mileage of the vehicle can be determined according to the trajectory data of the vehicle.
Specifically, track data of one day is read in, the obtained track data of the day is preprocessed, track point data of a track error mark is removed, track point data which is not located is removed, and the like. And then, sequencing the track points, and sequencing according to the GPS time positive sequence of the track points. If the distance between two points of the track is more than 2 kilometers and the total mileage is more than 5 kilometers, calling the pm service to do path planning and returning the driving mileage, otherwise, calculating the driving mileage of the vehicle by using a mode of accumulating the distances between two adjacent points.
S102, calculating abnormal parking data of the vehicle according to the parking data and the traveling mileage data.
In one possible implementation, the abnormal vehicle is screened out according to the parking data and the mileage data. Firstly, initially screening abnormal vehicles according to parking data, then screening the abnormal vehicles based on vehicle mileage data, and finally screening the abnormal vehicles according to the abnormal vehicle parking proportion.
Specifically, the stop points with the same stop duration and the same stop longitude and latitude are used as the same stop points, the number of the stop points of the current vehicle and the other vehicles is calculated, if the number of the stop points of the current vehicle and the other vehicles exceeds the preset same stop point number threshold value, the current vehicle is screened as a first abnormal vehicle, and according to the step, the vehicles exceeding the preset same stop point number threshold value are screened as first abnormal vehicles. The specific value of the preset threshold value of the number of the same stop points is not limited in the embodiment of the application, and a person skilled in the art can set the threshold value according to the actual situation. For example, if the threshold number of same stops is 8, the vehicle is screened as the first abnormal vehicle if the number of same stops of the vehicle and other vehicles exceeds the threshold 8.
And further, determining the driving mileage of the vehicle with the same stopping point date of the first abnormal vehicle according to the driving mileage data of the vehicle, and screening the vehicle with the driving mileage larger than a preset mileage threshold value as a second abnormal vehicle.
In one possible implementation manner, for the initial first abnormal vehicle set which is screened, the driving mileage data of the vehicles corresponding to the same parking date of the corresponding vehicle needs to be greater than or equal to a preset mileage threshold value, so that the vehicles are ensured to normally drive in the current day instead of being parked all the time. And if the date mileage of the vehicles with the same stopping points is smaller than the preset mileage threshold value, rejecting the vehicles, and finally screening to obtain second abnormal vehicle data. The specific value of the preset mileage threshold is not limited in the embodiment of the application, and a person skilled in the art can set the specific value according to the actual situation. For example, the mileage data of the vehicle at the same date of the vehicle at the same stop point needs to be greater than or equal to 5km, so that the vehicle can be ensured to normally run on the same day.
Further, the ratio of the number of times that the second abnormal vehicle has the same stop point on the same day to the total number of stops is calculated, the vehicle with the ratio larger than a preset threshold value is taken as the abnormal vehicle, and the same stop point corresponding to the abnormal vehicle is taken as abnormal stop data.
And screening abnormal vehicles according to the abnormal vehicle stop ratio according to the corresponding second abnormal vehicle set obtained in the last step and the same stop points of the vehicles. Calculating the ratio of the times of the vehicles having the same stopping points on the same day to the total stopping times, filtering the stopping and vehicle data of which the same stopping times/the total stopping times on the same day are smaller than a preset threshold value, and taking the vehicles of which the ratio is larger than or equal to the preset threshold value as abnormal vehicles to avoid the accidental stopping consistent condition of the vehicles caused by the same route operation. And obtaining the screened abnormal vehicle set. The same stopping point corresponding to the abnormal vehicle is abnormal stopping data. The preset threshold may be set according to actual conditions, for example, the preset threshold is 0.5.
S103, determining abnormal track segments of the vehicle according to the abnormal parking data.
In an optional embodiment, determining the abnormal trajectory segment of the vehicle from the abnormal parking data comprises: and matching the abnormal parking data with the vehicle total parking data to obtain a continuous abnormal parking interval of the vehicle, determining the starting and stopping time of the abnormal track according to the starting and stopping time of the abnormal parking interval, and segmenting the vehicle track according to the starting and stopping time of the abnormal track to obtain the abnormal track segmentation of the vehicle.
Specifically, the screened abnormal parking data are obtained, the abnormal parking data of the vehicle are matched with the total parking data of the vehicle, the starting and ending interval of the continuous abnormal parking section of the vehicle is obtained, the starting and ending interval time of the abnormal parking section is used as the starting and ending time of the abnormal track, the track of the vehicle is segmented according to the starting and ending time of the abnormal track, and the abnormal track segmentation of the vehicle is obtained. And finally obtaining all abnormal track segments of all abnormal vehicles.
S104, obtaining a contrast track segment of the abnormal track segment, calculating the similarity between the abnormal track segment and the contrast track segment, determining that the vehicle track is abnormal according to the similarity, obtaining the number of days of the vehicle track abnormality, and determining the false reporting track of the vehicle when the number of days of the track abnormality is larger than a preset number of days threshold.
In one possible implementation, the contrast trajectory segment of the abnormal trajectory segment is first obtained. The method comprises the following steps: and acquiring track segments of the other abnormal vehicles with the same stopping starting point as the abnormal track segment of the current abnormal vehicle as a comparison track segment.
Specifically, an abnormal track segment of the current vehicle is obtained, track segments of other abnormal vehicles with the same stopping starting point with the abnormal track segment are searched in the screened abnormal vehicles, and the track segments are used as comparison track segments of the abnormal track segment.
Further, the similarity of the abnormal track segment and the contrast track segment is calculated. In an optional embodiment, calculating the similarity between the abnormal track segment and the comparative track segment includes: acquiring longitude and latitude and a true north included angle of a vehicle track report point, and calculating the report point number of the track longitude and latitude and the true north included angle of the abnormal track section and the contrast track section; calculating the total report point number of the abnormal track segment and the total report point number of the comparative track segment, and taking the total report point number with smaller numerical value of the abnormal track segment and the comparative track segment as a target total report point number; and calculating the ratio of the consistent report points to the total target report points, and taking the ratio as the similarity of the abnormal track segment and the comparative track segment.
Optionally, each track segment may have more than or equal to 2 contrast track segments, and the maximum value of the similarity of the track segment of this time is taken as the similarity of the abnormal track segment.
Further, determining the vehicle track abnormality according to the similarity comprises the following steps: calculating the similarity of all abnormal track segments of the current abnormal vehicle and the comparison track segments; calculating the average similarity and the maximum similarity of all abnormal track segments of the current abnormal vehicle according to the similarity of all abnormal track segments of the current abnormal vehicle and the comparison track segments of the current abnormal vehicle; and when the average similarity is greater than a preset first similarity threshold and the maximum similarity is greater than a preset second similarity threshold, determining that the vehicle track is abnormal. The specific values of the preset first similarity threshold and the second similarity threshold are not limited in the embodiments of the present application, and can be set according to actual conditions.
Further, the number of days of track abnormity of the vehicle is obtained, and when the number of days of track abnormity is larger than a preset number of days threshold, the vehicle false reporting track is determined. The preset number of days threshold is not specifically limited in the embodiment of the present application, and can be set according to actual conditions. For example, the preset threshold of days is 5 days, and if the number of days in which the vehicle track is abnormal in one month is greater than or equal to 5 days in the preset time period, the vehicle false report track is determined.
According to the steps, whether the vehicle reports the track falsely or not can be accurately identified based on abnormal parking data, driving mileage data and the like of the vehicle.
In some optional embodiments, the vehicle terminal device abnormality can be identified by analyzing the change of the vehicle terminal devices in front of, in middle of and behind the segmented track, and the vehicle false reporting track can be determined.
Specifically, the model of uploading terminal equipment of a report point when the vehicle is in an abnormal track subsection is obtained; acquiring the model of terminal equipment of a front report point and a rear report point of a vehicle in an abnormal track segment; and comparing whether the types of the terminal equipment of the report points before, during and after the abnormal track of the vehicle is segmented are consistent, counting the consistency rate of the vehicle within a preset time period, for example, calculating the ratio of the inconsistent times to the consistent times within one month to obtain the consistency rate of the report point equipment within one month, and taking the vehicle with the consistency rate lower than a preset threshold value as the abnormal vehicle of the terminal equipment. The specific value of the preset threshold can be set according to the actual situation. And taking the vehicle with abnormal terminal equipment as a vehicle which can report the track falsely.
FIG. 2 is a schematic flow diagram illustrating another method of identifying false tracks on a vehicle, according to an exemplary embodiment; as shown in fig. 2, the full-scale trajectory data of the vehicle is acquired, and the parking data and the mileage data of the vehicle are obtained according to the acquired full-scale trajectory data of the vehicle. The method comprises the steps of identifying the same stop points according to stop data of the vehicle, filtering according to the number of the identified same stop points to obtain an initial abnormal vehicle, and filtering again according to the mileage data of the vehicle to obtain the abnormal vehicle and the abnormal stop data. The method comprises the steps of segmenting tracks of a vehicle according to abnormal stop data, stopping according to the abnormal segmented tracks and the beginning of each segment of track to obtain segmented contrast track data, calculating abnormal track similarity data of each segment of the vehicle, calculating the average value and the maximum value of the overall track similarity of all the segments of the vehicle, and determining that the track of the vehicle is abnormal when the average similarity is larger than a preset first similarity threshold and the maximum similarity is larger than a preset second similarity threshold. And acquiring the number of days of abnormal track of the vehicle, and determining the false reporting track of the vehicle when the number of days of abnormal track is greater than a preset number of days threshold. The preset number of days threshold is not specifically limited in the embodiment of the present application, and can be set according to actual conditions. For example, the preset threshold of days is 5 days, and if the number of days in which the vehicle track is abnormal in one month is greater than or equal to 5 days in the preset time period, the vehicle false report track is determined.
According to the method, calculation can be carried out on all current freight platform vehicles, fake tracks of the vehicles can be effectively identified, the freight platform and a service provider can be assisted to effectively monitor normal operation of the freight vehicles, and a security holder can be helped to analyze and judge the risk condition of the vehicles before insuring.
The disclosed embodiment further provides a device for identifying a vehicle false trajectory, which is used for executing the method for identifying a vehicle false trajectory of the above embodiment, and as shown in fig. 3, the device includes:
the first calculation module 301 is configured to determine parking data and mileage data of the vehicle according to the trajectory data of the vehicle;
a second calculating module 302, configured to calculate abnormal parking data of the vehicle according to the parking data and the mileage data;
a track segmentation module 303, configured to determine an abnormal track segment of the vehicle according to the abnormal parking data;
the identification module 304 is configured to obtain a comparison track segment of the abnormal track segment, calculate a similarity between the abnormal track segment and the comparison track segment, determine that a vehicle track is abnormal according to the similarity, obtain the number of days of vehicle track abnormality, and determine a false report track of the vehicle when the number of days of track abnormality is greater than a preset number-of-days threshold.
It should be noted that, in the identification method of the vehicle false trace provided by the above embodiment, only the division of the above functional modules is taken as an example, and in practical application, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above described functions. In addition, the device for identifying the vehicle false track and the method for identifying the vehicle false track provided by the embodiment belong to the same concept, and the detailed implementation process is shown in the method embodiment and is not described herein again.
The embodiment of the disclosure also provides an electronic device corresponding to the method for identifying the vehicle false track provided by the foregoing embodiment, so as to execute the method for identifying the vehicle false track.
Referring to fig. 4, a schematic diagram of an electronic device provided in some embodiments of the present application is shown. As shown in fig. 4, the electronic apparatus includes: a processor 400, a memory 401, a bus 402 and a communication interface 403, wherein the processor 400, the communication interface 403 and the memory 401 are connected through the bus 402; the memory 401 stores a computer program operable on the processor 400, and the processor 400 executes the computer program to perform the method for identifying a false trajectory of a vehicle according to any of the embodiments of the present application.
The Memory 401 may include a Random Access Memory (RAM) and a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The electronic equipment provided by the embodiment of the application and the method for identifying the false track of the vehicle provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
Referring to fig. 5, the computer readable storage medium is an optical disc 500, on which a computer program (i.e., a program product) is stored, and when the computer program is executed by a processor, the computer program performs the method for identifying the vehicle false trace according to any of the embodiments.
It should be noted that examples of the computer-readable storage medium 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 are not described in detail herein.
The computer-readable storage medium provided by the above embodiment of the present application and the method for identifying a false trajectory of a vehicle provided by the embodiment of the present application have the same beneficial effects as the method adopted, run or implemented by the application program stored in the computer-readable storage medium.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for identifying a false trajectory of a vehicle, comprising:
determining parking data and driving mileage data of the vehicle according to the trajectory data of the vehicle;
calculating abnormal parking data of the vehicle according to the parking data and the driving mileage data;
determining abnormal track segments of the vehicle according to the abnormal parking data;
and obtaining a contrast track segment of the abnormal track segment, calculating the similarity between the abnormal track segment and the contrast track segment, determining that the vehicle track is abnormal according to the similarity, obtaining the number of days of the vehicle track abnormality, and determining the false reporting track of the vehicle when the number of days of the track abnormality is greater than a preset number of days threshold.
2. The method of claim 1, wherein calculating the abnormal stopping data of the vehicle from the stopping data and the mileage data comprises:
taking the stop points with the same stop time and the same stop longitude and latitude as the same stop points, calculating the number of the stop points of the current vehicle and other vehicles, and screening the vehicles exceeding the preset threshold value of the number of the same stop points as first abnormal vehicles;
determining the driving mileage of a vehicle with the same stop point date of a first abnormal vehicle according to the driving mileage data of the vehicle, and screening the vehicle with the driving mileage larger than a preset mileage threshold value as a second abnormal vehicle;
and calculating the ratio of the number of times that the second abnormal vehicle has the same stop point on the same day to the total stop number of times, and taking the vehicle with the ratio larger than a preset threshold value as the abnormal vehicle, wherein the same stop point corresponding to the abnormal vehicle is abnormal stop data.
3. The method of claim 1, wherein determining an abnormal trajectory segment for a vehicle from the abnormal parking data comprises:
and matching the abnormal parking data with the vehicle total parking data to obtain a continuous abnormal parking interval of the vehicle, determining the starting and stopping time of the abnormal track according to the starting and stopping time of the abnormal parking interval, and segmenting the vehicle track according to the starting and stopping time of the abnormal track to obtain the abnormal track segmentation of the vehicle.
4. The method of claim 1, wherein obtaining a contrast trajectory segment of the abnormal trajectory segments comprises:
and acquiring track segments of the other abnormal vehicles with the same stopping starting point as the abnormal track segment of the current abnormal vehicle as a comparison track segment.
5. The method according to claim 1, wherein calculating the similarity of the abnormal track segment and the contrast track segment, and determining the vehicle track abnormality according to the similarity comprises:
calculating the similarity of all abnormal track segments of the current abnormal vehicle and the comparison track segments;
calculating the average similarity and the maximum similarity of all abnormal track segments of the current abnormal vehicle according to the similarity of all abnormal track segments of the current abnormal vehicle and the compared track segments;
and when the average similarity is greater than a preset first similarity threshold and the maximum similarity is greater than a preset second similarity threshold, determining that the vehicle track is abnormal.
6. The method of claim 5, wherein calculating the similarity of the abnormal trajectory segment and the comparative trajectory segment comprises:
calculating the report number of the track longitude and latitude and the north included angle of the abnormal track segment and the comparison track segment;
calculating the total report point number of the abnormal track segment and the total report point number of the comparative track segment, and taking the total report point number with smaller numerical value of the abnormal track segment and the comparative track segment as a target total report point number;
and calculating the ratio of the consistent report points to the total target report points, and taking the ratio as the similarity of the abnormal track segment and the comparison track segment.
7. The method of claim 3, after obtaining the abnormal trajectory segment of the vehicle, further comprising:
acquiring the model of uploading terminal equipment for subsection reporting of abnormal tracks of the vehicle;
acquiring the model of terminal equipment of a front report point and a rear report point of the abnormal track section of the vehicle;
comparing whether the types of the terminal equipment of the report points before, during and after the vehicle abnormal track is segmented are consistent; and taking the vehicle with the consistency rate lower than the preset threshold value as the abnormal vehicle of the terminal equipment.
8. An apparatus for identifying a false trajectory of a vehicle, comprising:
the first calculation module is used for determining parking data and mileage data of the vehicle according to the track data of the vehicle;
the second calculation module is used for calculating abnormal parking data of the vehicle according to the parking data and the mileage data;
the track segmentation module is used for determining abnormal track segmentation of the vehicle according to the abnormal parking data;
the identification module is used for acquiring a contrast track segment of the abnormal track segment, calculating the similarity between the abnormal track segment and the contrast track segment, determining that the vehicle track is abnormal according to the similarity, acquiring the number of days of the vehicle track abnormality, and determining the false reporting track of the vehicle when the number of days of the track abnormality is greater than a preset number of days threshold.
9. An electronic device comprising a processor and a memory storing program instructions, the processor being configured to perform the method of identifying false tracks of a vehicle as claimed in any one of claims 1 to 7 when executing the program instructions.
10. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement a method of identifying false tracks on a vehicle as claimed in any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116485301A (en) * | 2023-05-30 | 2023-07-25 | 佛山众陶联供应链服务有限公司 | Service authenticity judging method and system based on service information and logistics information |
| CN116644373A (en) * | 2023-07-27 | 2023-08-25 | 深圳恒邦新创科技有限公司 | Automobile flow data analysis management system based on artificial intelligence |
| CN118444348A (en) * | 2024-06-28 | 2024-08-06 | 广州悦跑信息科技有限公司 | Track positioning data analysis and correction system and method |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116485301A (en) * | 2023-05-30 | 2023-07-25 | 佛山众陶联供应链服务有限公司 | Service authenticity judging method and system based on service information and logistics information |
| CN116485301B (en) * | 2023-05-30 | 2023-12-05 | 佛山众陶联供应链服务有限公司 | Service authenticity judging method and system based on service information and logistics information |
| CN116644373A (en) * | 2023-07-27 | 2023-08-25 | 深圳恒邦新创科技有限公司 | Automobile flow data analysis management system based on artificial intelligence |
| CN116644373B (en) * | 2023-07-27 | 2023-10-31 | 广东小途汽车科技有限公司 | Automobile flow data analysis management system based on artificial intelligence |
| CN118444348A (en) * | 2024-06-28 | 2024-08-06 | 广州悦跑信息科技有限公司 | Track positioning data analysis and correction system and method |
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