CN114331477A - Abnormal track identification method and device, electronic equipment and storage medium - Google Patents

Abstract

The application provides an abnormal track identification method, an abnormal track identification device, electronic equipment and a storage medium, relates to the technical field of information processing, and can effectively identify abnormal driving mileage. The specific scheme comprises the following steps: acquiring travel data of a vehicle; the travel data comprise a boarding position, a alighting position and a driving mileage; determining a first area to which the getting-on position belongs and a second area to which the getting-off position belongs; acquiring mileage upper bound values corresponding to trip data by taking the first area and the second area as starting and stopping points; and if the travel distance is greater than or equal to the upper limit value of the travel distance corresponding to the travel data, determining that the row data is abnormal travel data.

Description

Abnormal track identification method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of information processing technologies, and in particular, to an abnormal trajectory identification method and apparatus, an electronic device, and a storage medium.

Background

At present, every time an order is finished by a network appointment car, trip data such as driving mileage, getting-on/off positions of passengers and the like can be reported to a network appointment platform. Then, the network appointment platform can calculate the driving cost of the order according to the driving mileage. In order to increase the running cost, some network appointment vehicles generate a virtual high running mileage through tampering and report the driving mileage. The running cost calculated by the network appointment platform according to the virtual high running mileage exceeds the real running cost.

Due to some characteristics of the net appointment vehicle, for example, the getting-on and getting-off positions of passengers are flexible and variable and are not fixed; the driving path of the networked taxi is influenced by road conditions, the designated route of passengers and the like, and changes. These all result in the mileage in different trip data being thousands of differences. Furthermore, for each trip datum, no standard mileage can be referenced. That is, the problem of the online car contracted lie-reporting of the driving distance cannot be recognized at present.

Disclosure of Invention

The application provides an abnormal track identification method, an abnormal track identification device, electronic equipment and a storage medium, which can effectively identify abnormal driving mileage.

In order to achieve the technical purpose, the following technical scheme is adopted in the application:

in a first aspect, an embodiment of the present application provides an abnormal trajectory identification method, where the method includes: firstly, acquiring travel data of a vehicle; the travel data comprise a boarding position, a alighting position and a driving mileage; determining a first area to which the getting-on position belongs and a second area to which the getting-off position belongs; acquiring mileage upper bound values corresponding to the trip data by taking the first area and the second area as starting and stopping points; and if the travel mileage is greater than or equal to the mileage upper bound value corresponding to the travel data, determining that the travel data is abnormal travel data.

It is understood that the electronic device may determine and store at least one upper limit mileage value according to a plurality of historical trip data within the target time period. Then, the electronic device may determine whether the travel distance in the travel data of one vehicle is abnormal by using the saved upper limit value of the travel distance.

The electronic device may determine a first area to which the getting-on position belongs and a second area to which the getting-off position belongs in the line data. Thus, the classification of the ever-changing positions of loading and unloading is realized. Then, the electronic device may acquire a mileage upper bound value corresponding to the travel data with the first area and the second area as the start and stop points. Since the getting-on position in the travel data is in the first area and the getting-off position in the travel data is in the second area, it is known that the driving range from the getting-on position to the getting-off position is almost the same as the upper limit value of the range using the first area and the second area as the starting and stopping points. Therefore, it is possible to determine whether the mileage from the boarding position to the alighting position is too high by using the mileage upper limit value with the first area and the second area as the start point and the stop point. If the travel distance is greater than or equal to the upper mileage threshold value, which indicates that the travel distance is too high, it may be determined that the travel distance in the travel data is abnormal, i.e., the travel data is abnormal travel data. Namely, the method provided by the embodiment of the application realizes the travel data for identifying the abnormal travel mileage.

In one possible implementation, the travel data further includes driver information. The method further comprises the following steps: counting the number of abnormal travel data of a driver within a preset time length, which are indicated by the driver information, according to the driver information; and if the number of the abnormal trip data is greater than or equal to the preset times, sending prompt information.

It can be understood that, in order to exclude the driver from accidentally occurring abnormal trip data for one or more times, a preset number of times of abnormal trip data occurring within a preset time period may be set. Furthermore, the electronic device may count the number of abnormal travel data of the driver in the abnormal travel data. If the total number of the abnormal travel data is greater than or equal to the preset number, the possibility that the driver intentionally tampers with the traveled mileage is high, and important attention needs to be paid. Therefore, the electronic device may issue a prompt message for prompting the driver to pay attention to the emphasis.

In another possible implementation manner, the method further includes: acquiring a plurality of historical travel data of vehicles in a target area; determining respective position information of at least one of the target areas; then, determining at least one group of areas and target historical travel data corresponding to each group of areas in the at least one group of areas according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of the at least one area; and finally, determining the upper limit value of the mileage corresponding to each group of areas according to the driving mileage in the target historical trip data corresponding to each group of areas.

Wherein, historical trip data includes: the getting-on position, the getting-off position and the driving mileage. One area and the other area in each group of areas respectively comprise an getting-on position and a getting-off position in the target historical travel data. The mileage upper bound value corresponding to each group of areas is the mileage upper bound value taking two areas in each group of areas as starting and stopping points.

It can be understood that, in the embodiment of the present application, the target area is firstly divided into at least one area, and historical travel data of a vehicle traveling between any two areas is acquired. The target area is divided into at least one area, so that historical trip data with different upper and lower positions are classified. For example, the historical travel data of the getting-on/off position in two areas included in any group of areas can be regarded as the historical travel data of the vehicle traveling between the group of areas, namely, the historical travel data of the target corresponding to the group of areas. Therefore, a plurality of target historical travel data of the upper and lower parking positions belonging to the group of areas can be obtained. Then, according to a plurality of target historical trip data of the upper and lower vehicle positions belonging to the group of areas, the upper bound value of the driving mileage taking two areas in the group of areas as the starting and stopping points (i.e. the upper bound value of the mileage corresponding to the group of areas) can be determined. By utilizing the mileage upper bound value corresponding to the group of areas, whether the driving mileage of the vehicle with the getting-on/off position belonging to the group of areas is too high can be judged, namely, the abnormal driving mileage is identified.

In another possible implementation manner, the determining the respective location information of at least one of the target areas includes: the method comprises the steps of dividing a target area into at least one area with the same size by adopting a preset coding algorithm, and determining first coded data of each area in the at least one area. Wherein the first encoded data of each region is position information of each region. The preset encoding algorithm may include a GeoHash algorithm.

The determining at least one group of regions and the target historical travel data corresponding to each group of regions in the at least one group of regions according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of the at least one region includes: for each historical trip data in the plurality of historical trip data, a preset coding algorithm is adopted to respectively convert the getting-on position and the getting-off position in the historical trip data to obtain a pair of second coded data; determining a group of regions from the at least one region according to the pair of second coded data, and determining that the historical trip data belongs to target historical trip data corresponding to the group of regions; the first encoded data for a set of regions includes a pair of second encoded data.

In this design, an implementation is described in which the target region is divided into at least one region.

In another possible implementation manner, the dividing the target region into at least one region with the same size by using a preset coding algorithm, and determining first coded data of each region in the at least one region includes: converting the longitude and latitude in the target area by adopting a preset coding algorithm, and extracting GeoHash characters with preset digits in front of the converted coded data to obtain first coded data of at least one area and each area; the first encoded data of each region is a GeoHash character of a preset number of bits before in the converted encoded data in each region.

The determining a set of regions from the at least one region based on a pair of second encoded data comprises: from among the at least one region, it is determined that a region of the first encoded data equal to a character of a first preset number of digits of any one of a pair of second encoded data belongs to the group of regions.

In this design, a specific implementation of dividing the target region into at least one region is described.

In another possible implementation manner, the determining, for each group of regions, the upper limit value of the mileage corresponding to each group of regions according to the mileage in the target historical trip data corresponding to each group of regions includes: under the condition that the total number of the target historical trip data corresponding to each group of areas is greater than or equal to a preset confidence threshold, sequencing the driving mileage in the target historical trip data corresponding to each group of areas to obtain sequenced driving mileage; determining a first numerical value and a second numerical value from the sorted driving mileage; wherein the first numerical value is smaller than the median of the sorted driving mileage; the second numerical value is greater than the median of the sorted driving mileage; and multiplying the difference of the second numerical value minus the first numerical value by a preset multiple, and then summing the difference and the second numerical value to obtain the mileage upper bound value corresponding to each group of areas.

It will be appreciated that although the mileage in the plurality of target historical travel data corresponding to any one set of areas is the mileage of the vehicle between two areas in that set of areas. However, the upper and lower vehicle positions in different target historical travel data may be different; the driving paths in the historical target travel data with the similar positions of the upper and lower vehicles are influenced by the factors such as the designated routes of the passengers, road conditions and the like, and may be different. These all contribute to some gap in mileage in different trip data between two zones in the set of zones. Then, if the target historical travel data is more, the distribution of the traveled mileage between two areas in the group of areas can be reflected more accurately. If the target historical travel data is less, the route between the two areas in the group of areas is a cold route, and the distribution of the mileage between the two areas in the group of areas reflected by the less target historical travel data is also inaccurate. Therefore, the electronic device determines the upper bound value of the driving range between two areas in the set of areas (i.e. the upper bound value of the range corresponding to the set of areas) with higher accuracy according to more target historical travel data.

Furthermore, the electronic device may determine the upper limit value of the mileage corresponding to the group of areas according to the traveled mileage in all the target historical travel data when the total number of the target historical travel data corresponding to the group of areas is greater than or equal to the preset confidence threshold. If the total number is smaller than the preset confidence threshold, the electronic device may not determine the upper limit value of the mileage corresponding to the group of areas.

In a second aspect, the present application provides an abnormal trajectory recognition apparatus. The abnormal trajectory recognition apparatus comprises various modules for performing the method of the first aspect or any one of the possible design manners of the first aspect.

In a third aspect, the present application provides an electronic device comprising a memory and a processor. The memory is coupled to the processor. The memory is for storing computer program code comprising computer instructions. The computer instructions, when executed by a processor, cause an electronic device to perform the method for abnormal trajectory identification as set forth in the first aspect and any one of its possible designs.

In a fourth aspect, the present application provides a chip system, which is applied to an abnormal trajectory recognition apparatus; 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 configured to receive signals from a memory of the abnormal trajectory recognition device and send signals to the processor, the signals including computer instructions stored in the memory. The computer instructions, when executed by the processor, cause the electronic device to perform the method for identifying abnormal trajectories as set forth in the first aspect and any one of its possible designs.

In a fifth aspect, the present application provides a computer-readable storage medium storing computer instructions, which, when executed on an electronic device, cause the electronic device to perform the method for identifying an abnormal trajectory according to the first aspect and any one of the possible design manners thereof.

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, cause the electronic device to execute the abnormal trajectory identification method according to the first aspect and any possible design manner thereof.

Reference may be made in detail to the second to sixth aspects and various implementations of the first aspect in this application; moreover, for the beneficial effects of the second aspect to the sixth aspect and various implementation manners thereof, reference may be made to beneficial effect analysis in the first aspect and various implementation manners thereof, and details are not described here.

These and other aspects of the present application will be more readily apparent from the following description.

Drawings

Fig. 1 is a first schematic view of an implementation environment related to an abnormal trajectory identification method according to an embodiment of the present application;

fig. 2 is a schematic diagram of an implementation environment related to an abnormal trajectory identification method according to an embodiment of the present application;

fig. 3 is a schematic view of an implementation environment related to an abnormal trajectory identification method according to an embodiment of the present application;

fig. 4 is a first flowchart of an abnormal trajectory identification method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of dividing a target area into a plurality of areas with the same size according to an embodiment of the present disclosure;

FIG. 6 is a first box diagram of a quartile according to an embodiment of the present disclosure;

fig. 7 is a data step chart based on the three-sigma criterion according to an embodiment of the present application;

FIG. 8 is a second box-type diagram of a quartile according to an embodiment of the present disclosure;

fig. 9 is a second flowchart of an abnormal trajectory identification method according to an embodiment of the present application;

fig. 10 is a flowchart three of an abnormal trajectory identification method according to an embodiment of the present application;

fig. 11 is a fourth flowchart of an abnormal trajectory identification method according to an embodiment of the present application;

fig. 12 is a schematic structural diagram of an abnormal trajectory recognition apparatus according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

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

At present, the rise and the popularization of the net appointment vehicle bring great convenience for the trip of a user. Some network appointments falsify the driving distance in the travel data so as to improve the driving cost calculated according to the tampered driving distance. Since the driving mileage in different trip data varies from one trip to another, no standard driving mileage can be referred to for each driving mileage. That is, the problem of the online car contracted lie-reporting of the driving distance cannot be recognized at present.

In order to solve the problem, an embodiment of the present application provides an abnormal trajectory identification method, which divides a target area into at least one area; then, historical travel data of the vehicle traveling between any two areas is acquired. The target area is divided into at least one area, so that historical trip data with different upper and lower positions are classified. For example, the historical travel data of the getting-on/off position in any two areas can be regarded as the historical travel data of the vehicle traveling between the two areas, namely, the historical travel data of the target corresponding to the two areas. Therefore, a plurality of target historical travel data of the two areas belonging to the upper and lower parking positions can be obtained. Then, according to a plurality of target historical trip data of the upper and lower vehicle positions belonging to the two areas, the upper limit value of the mileage between the two areas can be determined. By utilizing the upper limit value of the mileage between the two areas, whether the driving mileage of the vehicle with one getting-on/off position belonging to the two areas is too high can be judged, namely, the abnormal driving mileage is identified.

Embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Please refer to fig. 1, which illustrates an implementation environment diagram related to an abnormal trajectory identification method according to an embodiment of the present application. As shown in FIG. 1, the implementation environment may include: the system comprises a server 100, a terminal 110, and a plurality of collection devices 120 for collecting travel data of a vehicle. Wherein the plurality of collecting apparatuses 120 are installed in the plurality of vehicles, respectively.

Illustratively, each acquisition device 120 may include a GPS module, a timing module, and an input module (e.g., a touch screen). The GPS module, the timing module, and the input module in the acquisition module 120 are not shown in fig. 1. The GPS module is used for collecting a driving path and the longitude and latitude of passengers getting on and off; and also for determining the mileage. The timing module is used for collecting the time of getting on or off the vehicle of passengers. The input module is used for collecting driver information, vehicle information and the like input by a driver.

The server 100 may receive travel data of the vehicle from the plurality of collecting devices 120. The travel data may include: the passenger's longitude and latitude, the time of getting on and off the bus, the driving path, the driving mileage, the driver's information, and the vehicle information, etc.

As shown in fig. 2, the terminal 110 may receive a first operation input by the user, where the first operation is used to indicate a target area, a target vehicle type (e.g., net appointment, taxi), a target duration, a zone division parameter, and the like. Then, the terminal 110 may acquire, from the server 100, historical travel data of vehicles belonging to the target vehicle type traveling in the target area for the target duration in response to the first operation. The terminal 110 may also divide the target area into at least one area according to the area division parameter. The terminal 110 determines the upper limit value of the mileage between two areas in at least one area according to the obtained historical trip data. After obtaining the upper limit value of the mileage, the terminal 110 may obtain trip data of one vehicle from the server 100, and determine whether the traveled mileage in the trip data is abnormal according to the upper limit value of the mileage. If the travel mileage in the travel data is abnormal, prompt information can be sent out, and the prompt information is used for representing that the travel mileage of the vehicle is abnormal. For example, the terminal 110 may display the prompt message through a display screen.

Alternatively, as shown in fig. 3, the terminal 110 may transmit the information including the target to the server 100 in response to the first operation. The target information includes a target area, a target vehicle type, a target time length, an area division parameter, and the like. Then, the terminal 110 may divide the target area into at least one area according to the area division parameter. The server 100 also acquires historical travel data of vehicles within the target time period, traveling within the target area, belonging to the target vehicle type. The server 100 determines the upper limit value of the mileage between two areas in at least one area according to the acquired historical trip data. After obtaining the upper limit value of the mileage, the server 100 may receive trip data of a vehicle sent by one collection device 120, and determine whether the traveled mileage in the trip data is abnormal according to the upper limit value of the mileage. If the travel distance in the travel data is abnormal, the prompt information may be sent to the terminal 110. The terminal 110 may issue the prompt.

Wherein the target duration may be one month, one quarter, etc. The region partitioning parameter may characterize a size of at least one region, e.g., 1.2 kilometers (km) × 0.6 km.

For example, the terminal 110 in the embodiment of the present application may be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a notebook computer, a netbook, and the like, and the embodiment of the present application does not particularly limit the specific form of the terminal 110.

It should be noted that the abnormal trajectory recognition method provided in the embodiment of the present application may be applied to the server 100, the terminal 110, and the server 100 and the terminal 110. The server 100 and the terminal 110 may be collectively referred to as an electronic device. The execution main body of the abnormal track identification method provided by the embodiment of the application can also be an abnormal track identification device. The apparatus may be an electronic device; alternatively, the apparatus may be an Application (APP) installed in the electronic device and providing an abnormal trajectory recognition function; alternatively, the apparatus may be a Central Processing Unit (CPU) in the electronic device; still alternatively, the apparatus may be a control module in the electronic device for executing the abnormal trajectory recognition method. The following describes the abnormal trajectory recognition provided by the embodiment of the present application in detail by taking an electronic device as an example.

Please refer to fig. 4, which is a flowchart illustrating an abnormal trajectory recognition method according to an embodiment of the present application. As shown in fig. 4, the method may include S401-S404.

S401, the electronic equipment acquires a plurality of historical travel data of vehicles in a target area; the historical trip data includes: the getting-on position, the getting-off position and the driving mileage.

The electronic device can acquire all historical travel data within the target duration. Each historical travel data can include the getting-on position (such as the getting-on longitude and latitude), the getting-off position (such as the getting-off longitude and latitude) and the driving mileage, and can also include the getting-on time, the getting-off time, the driver information, the vehicle information and the like.

The target region may be any region, for example, shanxi province, beijing city, and the like.

S402, the electronic equipment determines the position information of at least one area in the target area.

The electronic device may divide the target area into at least one area and acquire location information of each of the at least one area.

In some embodiments, the electronic device may employ a preset encoding algorithm to divide the target region into at least one region having the same size and determine first encoded data for each of the at least one region.

Wherein the first encoded data of each region is position information of each region. The first coded data for each region may be indicative of the position of any point within the region.

The preset encoding algorithm may include a GeoHash algorithm. The GeoHash algorithm is an address coding method, which can code a longitude and latitude in a two-dimensional space into a character string, and the longer the character string is, the smaller the represented range is, and the more accurate the position is. That is, the first few characters in a string may indicate that the string includes all of the positions of the first few characters. For example, converting the latitude and longitude of a point to wx4g0ec1, the prefix wx4g0e of wx4g0ec1 may represent a larger range encompassing wx4g0ec 1.

In some embodiments, the electronic device may convert the longitude and latitude of the target area by using a preset encoding algorithm (e.g., a GeoHash algorithm), and extract a GeoHash character with a preset number m before the converted encoded data to obtain the first encoded data of at least one area and each area. The first coded data of each region is a GeoHash character with a preset number m in the converted coded data in each region.

The electronic equipment extracts the GeoHash characters of the first m bits of the coded data after conversion, and divides the target area into grids with the same size. A grid is an area. Wherein the smaller m, the smaller the size of the region. For example, when n is 6, the size of each region is 1.2km by 0.6km, and the first encoded data of each region is a 6-bit GeoHash character.

For example, taking the target area shown in fig. 5 as xu state area in shanghai city as an example, and m equals 6, the electronic device may convert the longitude and latitude of the target area by using a GeoHash algorithm, and extract the first 6 bits of GeoHash characters from the converted encoded data to obtain 9 areas in the target area and the first encoded data of the 9 areas. When n is 6, the size of each region is 1.2km by 0.6km, and the first coded data of each region is a 6-bit GeoHash character. The first coded data for the 9 regions are wtw37p, wtw37r, wtw37x, wtw37n, wtw37q, wtw37w, wtw37j, wtw37m, wtw37t respectively.

Taking a point in the area 55, whose longitude and latitude coordinates are (39.923201,116.390705) as an example, a process of converting the longitude and latitude of the point by using the Geohash algorithm will be described.

(1) The electronic device may first convert the latitude and longitude of the point to binary. The method specifically comprises the following steps: the latitude ranges from (-90, 90) to (-90, 90) with a median of 0. Since the latitude 39.923201 at this point is greater than 0, a 1 is obtained first; the median value of (0, 90) is 45, and the latitude 39.923201 is less than 45; thus, a 0 is obtained; and then calculating sequentially, so as to obtain a binary representation corresponding to the latitude 39.923201. Similarly, a binary representation of longitude 116.390705 may be obtained.

(2) The electronic device may then merge the binary representations of the longitude and latitude. Where longitude is in even digits and latitude is in odd digits. The electronic device may obtain 111001100111100000110011110110 as the combined binary number for the point.

(3) The electronic device may encode the merged binary number using Base32 encoding. Base32 encoding refers to encoding using 32 characters 0-9 and b-z (minus a, i, l, o). The method specifically comprises the following steps: the electronic device may convert the merged binary number into a decimal number; and generating a character string corresponding to the decimal number according to the characters corresponding to the 32 characters. For example, the electronic device converts the merged binary number to a decimal number, which is 2825283722; then, looking up the character corresponding to the 32 characters can obtain the character string corresponding to the decimal number as wtw37 q.

As can be seen, the first encoded data for region 55 is wtw37q, and the first 6 characters of a dot in region 55 are also wtw37 q. The first 6 GeoHash characters in the converted coded data of all points in each region are the same as the first coded data of the region.

S403, the electronic equipment determines at least one group of regions and target historical travel data corresponding to each group of regions in the at least one group of regions according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of the at least one region; one region and the other region in each group of regions respectively comprise an getting-on position and a getting-off position in the target historical travel data corresponding to each group of regions.

The electronic device may determine one area including the getting-on position and another area including the getting-off position from the at least one area according to the getting-on position and the getting-off position in each historical travel data. One area including the getting-on position and another area including the getting-off position constitute a set of areas. Further, the electronic device may obtain multiple sets of regions, each set of regions including two regions.

In the embodiment of the application, after the electronic device obtains the first coded data of each region by using the preset coding algorithm, for each historical trip data in the plurality of historical trip data, the electronic device may also convert the getting-on position and the getting-off position in the historical trip data by using the preset coding algorithm to obtain a pair of second coded data. The electronic equipment further determines a group of regions from at least one region according to the pair of second coded data, and determines that the historical travel data belongs to target historical travel data corresponding to the group of regions.

The getting-on position can be a getting-on longitude and latitude, and the getting-off position can be a getting-off longitude and latitude. The pair of second encoded data includes: the second coded data corresponding to the getting-on position and the second coded data corresponding to the getting-off position.

Wherein, the first encoded data of a group of regions including a pair of second encoded data may refer to: the first coded data of one of the set of zones includes the second coded data corresponding to the getting-on position, and the first coded data of another of the set of zones includes the second coded data corresponding to the getting-off position.

In some embodiments, the electronic device may determine, from among the at least one region, that a region of the first encoded data equal to a preset number of first-digit characters of any one of a pair of second encoded data belongs to the group of regions.

Illustratively, the first encoded data for region 52 in FIG. 5 is wtw37r, and the first encoded data for region 55 is wtw37 q. The second coded data corresponding to the getting-on position in the historical trip data is wtw37qd2, and the second coded data corresponding to the getting-off position in the historical trip data is wtw37re 1. At this time, the electronic device may determine that the first encoded data of the region 52 includes the second encoded data corresponding to the getting-off position in the history trip data, and the first encoded data of the region 55 includes the second encoded data corresponding to the getting-on position in the history trip data. Further, the electronic device may determine that the area 52 and the area 55 constitute a set of areas, and the historical travel data belongs to the target historical travel data corresponding to the area 52 and the area 55.

S404, aiming at each group of areas, the electronic equipment determines the upper limit value of the mileage corresponding to each group of areas according to the driving mileage in the target historical trip data corresponding to each group of areas; the mileage upper bound value corresponding to each group of areas is the mileage upper bound value taking two areas in each group of areas as starting and stopping points.

The electronic device may determine, for each of the at least one group of regions, a mileage upper bound value corresponding to each group of regions according to the mileage in the target historical trip data corresponding thereto. Furthermore, the electronic device may determine at least one mileage upper bound value, and the regions corresponding to different mileage upper bound values are not identical. Then, the electronic device may determine whether the traveled mileage in the trip data of one vehicle is abnormal or not by using the determined mileage upper bound value.

In the embodiment of the present application, although the driving mileage in the plurality of target historical travel data corresponding to any one group of areas is the driving mileage of the vehicle between two areas in the group of areas. However, the upper and lower vehicle positions in different target historical travel data may be different; the driving paths in the historical target travel data with the similar positions of the upper and lower vehicles are influenced by the factors such as the designated routes of the passengers, road conditions and the like, and may be different. These all contribute to some gap in mileage in different trip data between two zones in the set of zones. Then, if the target historical travel data is more, the distribution of the traveled mileage between two areas in the group of areas can be reflected more accurately. If the target historical travel data is less, the route between the two areas in the group of areas is a cold route, and the distribution of the mileage between the two areas in the group of areas reflected by the less target historical travel data is also inaccurate. Therefore, the electronic device determines the upper bound value of the driving range between two areas in the set of areas (i.e. the upper bound value of the range corresponding to the set of areas) with higher accuracy according to more target historical travel data.

Furthermore, the electronic device may determine the upper limit value of the mileage corresponding to any group of areas according to the traveled mileage in all the target historical travel data when the total number of the target historical travel data corresponding to the group of areas is greater than or equal to the preset confidence threshold. If the total number is smaller than the preset confidence threshold, the electronic device may not determine the upper limit value of the mileage corresponding to the group of areas; or the electronic equipment determines the upper limit value of the mileage corresponding to the group of areas according to the traveled mileage in all the target historical travel data, and takes the total number as the confidence coefficient of the upper limit value of the mileage corresponding to the group of areas.

The preset confidence threshold may be indicated by the first operation input by the user. The confidence level of the mileage upper bound value may characterize the number of miles driven that are used to determine the mileage upper bound value.

In some embodiments, the electronic device may determine the upper limit value of the mileage corresponding to each group of the areas according to the traveled mileage in all the target historical travel data corresponding to each group of the areas by using a quartering method, a three-sigma method, or the like.

Wherein, the quartering method is to quartet a group of data to obtain the numerical value (namely the quartile) at each quantile point; and determining an upper bound value corresponding to the group of data by using the quartile.

Illustratively, as can be seen from the box plot of quartiles shown in FIG. 6, there are three quartiles. The first quartile Q1, which may also be referred to as the smaller quartile or lower quartile, refers to the number that is 25% of all data after the ascending arrangement. The second quartile Q2, which may also be referred to as a "median," refers to the number that is 50% of all data after the ascending sequence. The third quartile Q3, which may also be referred to as the "larger quartile," refers to the number that is 75% of all data after the ascending sequence.

The electronic device may substitute the first quartile Q1 and the third quartile Q3 into the following equation (1) to calculate the upper bound value Z:

Z＝Q3+k*(Q3-Q1)＝Q3+k*IQR (1)

the difference between the third Quartile Q3 and the first Quartile Q1 may be referred to as an interquartile Range (IQR). The preset multiple k is a value within a certain range, for example, k may be 1.5 or 3.

It is understood that if a value exceeds the upper bound value Z, or is less than Q1-k IQR, the value may be confirmed to be an abnormal value. The box type graph for representing the quartile is drawn according to real data, and the box type graph can really and intuitively represent all the real data without any requirement on the data.

Second, some outliers are generally too large or too small, too small being farther from the first quartile Q1, and too large being farther from the third quartile Q1. That is, outliers are generally farther away from the quartile and interfere less with the quartile. Or, the interference resistance of the quartile is stronger. Then, the upper bound value is calculated according to the quartile and the quartile distance, and the quartile distance is also calculated according to the quartile, so that the upper bound value is calculated according to the quartile. Then, the interference of the abnormal value to the quartile is small, and the interference to the upper bound value calculated according to the quartile is also small. The upper bound value obtained by the quartering method can more accurately detect a larger abnormal value.

Wherein the three-sigma method determines an upper bound value of a set of data according to a three-sigma criterion. As shown in fig. 7, the three sigma criterion indicates that statistically, if the distribution of a set of data is approximately normal step, about 68% of the data in the set of data will be within one standard deviation Std of the mean of the set of data, about 95% of the data will be within two standard deviations Std of the mean of the set of data, and about 99.7% of the data will be within three standard deviations Std of the mean of the set of data. Therefore, if any one data exceeds 3 times the standard deviation Std compared to the mean of the set of data, the data is highly likely to be an abnormal value.

For example, the electronic device may substitute the mean of a set of data and the standard deviation Std of the set of data into the following formula (2), and calculate the upper bound value Z:

Z＝mean+3*Std (2)

it can be known that the accuracy of the electronic device for judging the abnormal value by using the upper bound value calculated by the formula (2) is high.

In some embodiments, the electronic device may first rank the traveled mileage in the target historical travel data corresponding to each group of regions, to obtain the ranked traveled mileage. The electronic device may then determine a first numerical value and a second numerical value from the ranked miles driven. And finally, the electronic equipment can multiply the difference of the second numerical value minus the first numerical value by a preset multiple k, and then sum the difference and the second numerical value to obtain the mileage upper bound value corresponding to each group of areas.

Wherein the first numerical value is less than the median of the ranked driving range. The second value is greater than the median of the ranked driving range. For example, the first value is the first quartile Q1, the second value is the third quartile Q3, and the predetermined multiple may be 1.5 or 3.

For example, taking the example that the electronic device determines the upper limit value of the mileage corresponding to each group of areas by using a quartering method, the electronic device may first sort the traveled mileage in all the target historical travel data corresponding to each group of areas in an ascending order to obtain the ascending traveled mileage. The total number of the traveled mileage in all the target historical travel data corresponding to each group of the regions is assumed to be n. The position of the first quartile Q1 in the ascending range may be equal to (n +1) × 0.25, and the position of the third quartile Q3 in the ascending range may be equal to (n +1) × 0.75.

Wherein, if (n +1) × 0.25 is an integer, the first quartile Q1 is equal to the (n +1) × 0.25 data in the ascending mileage. If (n +1) × 0.25 is a decimal, then rounding (e.g., rounding off the decimal point, etc.) may be performed on (n +1) × 0.25 to obtain h. The first quartile Q1 is equal to the h-th data in ascending mileage. Similarly, the electronic device may determine a third quartile Q3.

For example, 11 miles driven include: 6,47, 49, 15, 42, 41,7, 39, 43, 40, 36. The electronic equipment performs ascending sequencing on the 11 driving miles, and the obtained ascending driving mileage is as follows: 6,7, 15, 36, 39, 40, 41, 42, 43, 47, 49. (11+1) × 0.25 ═ 3, then the first quartile Q1 is 15. (11+1) × 0.75 ═ 6, then the third quartile Q3 is 43.

For another example, where n is 9, the ascending mileage is: 6,7, 15, 36, 39, 40, 41, 42, 43. (9+1) × 0.25 ═ 2.5, then the electronic device rounding 2.5 can yield 2 or 3, and the first quartile Q1 is 7 or 15. (9+1) × 0.75 ═ 7.5, then the electronic device rounded 7.5 to yield 7 or 8, and the third quartile Q3 was 41 or 42.

In some embodiments, the electronic device may first calculate the mean and the standard deviation Std for the traveled mileage in the target historical travel data corresponding to each group of regions. Then, the electronic device may take the sum of 3 times the standard deviation Std and the mean as the upper limit value of the mileage corresponding to each group of the areas.

Illustratively, taking a group of regions in Hangzhou city as an example, the first encoded data of one region in the group of regions is wtmsh8, and the first encoded data of another region in the group of regions is wtmk 72. The electronic device may obtain all of the target historical travel data corresponding to the set of regions. The electronic device may then calculate a mean equal to 58.542279 and a standard deviation Std equal to 9.675643 for the miles driven in all of the target historical travel data. And the electronic equipment substitutes the average mean and the standard deviation Std into the formula (2) to obtain the upper limit value of the mileage corresponding to the group of areas equal to 87.5692.

Alternatively, the electronic device may determine that the first quartile Q1 equals 52km, the second quartile Q2 equals 53.1km, and the third quartile Q3 equals 65.6km, based on the miles driven in all of the target historical travel data, as shown in fig. 8. Assuming that the preset multiple k is 1.5, the electronic device may substitute 52km, 65.6km, and 1.5 into the above formula (1), so as to obtain an upper limit value of the mileage corresponding to the group of areas equal to 86.

In this embodiment of the application, the electronic device may update the upper limit value of the mileage corresponding to each group of areas according to a preset update period, or receive and respond to a request operation for triggering update, and update the upper limit value of the mileage corresponding to each group of areas. The preset update period may be indicated by the first operation.

For example, the preset update period may be one quarter, and the electronic device may determine the upper limit value of the mileage corresponding to each group of the regions according to the historical trip data from 1 month in 2021 to 3 months in 2021. Then, the electronic device may determine a new upper limit value of the mileage again according to the historical trip data from 4 months in 2021 to 6 months in 2021.

In the embodiment of the application, the electronic device can determine the upper limit value of the mileage corresponding to each group of the at least one group of the regions according to a plurality of historical trip data within the target time length, and store the upper limit value of the mileage. Then, the electronic device may determine whether the travel distance in the travel data of one vehicle is abnormal by using the saved upper limit value of the travel distance.

Specifically, as shown in fig. 9, the abnormal trajectory identification method provided in the embodiment of the present application may further include S601-S604.

S601, the electronic equipment acquires travel data of vehicles in a target area; the travel data comprise a boarding position, a alighting position and a driving mileage.

The electronic device may obtain the trip data of the vehicle after the target duration. For example, the electronic device may acquire travel data of vehicles within the target area.

S602, the electronic equipment determines a first area to which the getting-on position belongs and a second area to which the getting-off position belongs.

The electronic device may determine the first area and the second area according to the getting-on position (e.g., the getting-on longitude and latitude) and the getting-off position (e.g., the getting-off longitude and latitude) in the travel data. The first area comprises a boarding position, and the second area comprises a disembarking position.

It should be noted that, the electronic device may determine the specific process of the first area and the second area according to the getting-on position and the getting-off position, refer to the detailed description of determining a group of areas including the getting-on position and the getting-off position by the electronic device, and this embodiment of the present application is not described herein again.

S603, the electronic equipment acquires the mileage upper bound value corresponding to the trip data by taking the first area and the second area as the starting and stopping points.

The electronic device may determine, from at least one mileage upper bound value corresponding to at least one group of stored areas, a mileage upper bound value taking the first area and the second area as starting and stopping points as a mileage upper bound value corresponding to the trip data.

In some embodiments, the confidence of the mileage upper bound value corresponding to the trip data is greater than or equal to a preset confidence threshold. If the determined confidence level of the mileage upper bound value taking the first area and the second area as the starting and stopping points is less than the preset confidence level threshold value from the at least one mileage upper bound value, the electronic device may determine that there is no mileage upper bound value corresponding to the trip data.

And S604, if the driving mileage is greater than or equal to the upper limit value of the mileage corresponding to the travel data, the electronic equipment determines that the row data is abnormal travel data.

When the travel distance in the travel data is greater than or equal to the upper limit value of the travel distance corresponding to the travel data, the electronic equipment determines that the travel data is abnormal travel data with abnormal travel distance, marks the abnormality of the travel data and stores the abnormal travel data. When the travel mileage in the travel data is smaller than the upper limit value of the mileage corresponding to the travel data, the electronic device determines that the travel data is not abnormal travel data, and can also store the travel data, namely the travel data is new historical travel data.

In the embodiment of the application, after the electronic device determines that the row data is the abnormal travel data, the electronic device can also count the number of the abnormal travel data of the driver aiming at the travel data. If the total number of the abnormal travel data is greater than or equal to the preset number, the number of the abnormal travel data of the driver is more, and the driver needs to be focused. Further, the electronic device may send a prompt message for prompting the driver to pay attention to the key point.

Specifically, as shown in fig. 10, after S604, the method may further include S605-S606.

S605, the electronic equipment counts the number of abnormal travel data of the driver within a preset time length, which are indicated by the driver information, according to the driver information in the travel data.

The electronic device may store the determined abnormal trip data. Then, the electronic device may determine all abnormal travel data of the driver indicated by the driver information from the saved abnormal travel data, and count the number of the abnormal travel data.

The preset duration may refer to a period of time before the trip data is acquired. For example, one month, one quarter, etc. before the trip data is acquired.

S606, if the number of the abnormal travel data is larger than or equal to the preset number, the electronic equipment sends prompt information; the prompt information represents that the driving mileage of the driver indicated by the driver information is abnormal.

The electronic device can display the prompt message through a display screen, or play the prompt message through voice, and the like.

Wherein the preset number of times and the preset duration are related. The longer the preset time is, the larger the preset times are. Both the preset time period and the preset number of times may be indicated by the above-described first operation.

The prompt information may include specific content of abnormal travel data of the driver within a preset time, the number of the abnormal travel data, and the like.

It can be understood that, in order to exclude the driver from accidentally occurring abnormal trip data for one or more times, a preset number of times of abnormal trip data occurring within a preset time period may be set. If the number of times of abnormal trip data of a driver in a preset time exceeds the preset number of times, the driver can be confirmed to intentionally tamper the driving mileage, and then the electronic equipment can send out prompt information.

In S605 to S606, the electronic device counts the number of abnormal travel data with the driver as a dimension. In addition, the electronic device may also count the number of abnormal travel data by using the vehicle as a dimension. The process of counting the number of abnormal trip data by the electronic device with the vehicle as a dimension may be referred to in the specific description of S605-S606, and details of the embodiment of the present application are not repeated herein.

In this embodiment of the present application, a group of areas may include an area a and an area B, and the target historical travel data corresponding to the group of areas may include: the first target historical travel data of the vehicle from the area A to the area B can also comprise second target historical travel data of the vehicle from the area B to the area A. The first target historical travel data and the second target historical travel data correspond to the same set of regions (namely, a region A and a region B). Then, the electronic device may represent the set of regions by one key (which may be referred to as a first key), and whether the first target historical travel data or the second target historical travel data, the corresponding set of regions may be represented by the first key.

The electronic device may use the first encoded data of the two regions in the group of regions to form a first keyword key uniquely characterizing the group of regions according to a preset sequence of characters. Specifically, if the characters in the first encoded data all belong to 32 characters, i.e., 0-9 and b-z (a, i, l, o is removed), the precedence order of the characters may include: the precedence order from 0 to 9, and the precedence order from b to z (with a, i, l, o removed). The sequence of the characters may further include: the sequence of 0-9 precedes b-z (a, i, l, o are removed). Then, the electronic device may compare the precedence order of every two characters from the first character of the first encoding data of the region a and the first character of the first encoding data of the region B based on the precedence order of the characters, and splice the first encoding data with the first sorted character before the first encoding data with the second sorted character to obtain the first keyword key.

Illustratively, the first encoded data for region A is wtmsh8 and the first encoded data for region B is wtmk 72. Wherein the first 3 characters in wtmsh8 are the same as the first 3 characters in wtmk72, and the order of the fourth character s in wtmsh8 is after the fourth character k in wtmk72, the electronic device may obtain that the first keyword key representing area a and area B is wtmk72+ wtmsh 8. Furthermore, a set of regions corresponding to the first target historical trip data and the second target historical trip data can be represented by wtmk72+ wttmsh 8.

In some embodiments, the electronic device may determine a first key consisting of the first encoded data for two regions of each set of regions. Further, the electronic device may obtain a plurality of first keywords. And then, the electronic equipment correspondingly stores the plurality of first keywords and the mileage upper bound value corresponding to each keyword in a database. Furthermore, after the electronic device acquires travel data of one vehicle, a keyword (which may be referred to as a second keyword) corresponding to the travel data may be determined. The second keyword represents a first region and a second region to which the upper and lower vehicle positions in the travel data belong respectively. And the electronic equipment searches the mileage upper bound value corresponding to the second keyword from the database by using the second keyword.

Specifically, as shown in fig. 11, S402 in the method may include S701, S403 may include S702, and S404 may include S703-S704. The method may further include S601-S606. Wherein S602 may include S705, S603 may include S706, S604 may include S709, and S606 may include S711.

S701, the electronic equipment determines respective first coded data of at least one region in the target region; the first encoded data is used to characterize the location of the region.

It should be noted that, for a specific process of S701, reference may be made to the detailed description of the first encoded data in S402, which is not described herein again in this embodiment of the application.

S702, the electronic equipment determines a plurality of first keywords and target historical travel data corresponding to each first keyword in the plurality of first keywords according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of at least one region; wherein each first key is used to characterize a set of regions.

The first keyword corresponding to each group of regions may be formed by splicing the first encoded data of the two regions in each group of regions according to the preset sequence of the characters.

It should be noted that, for a specific process of S702, reference may be made to detailed descriptions of determining each group of regions and target historical trip data corresponding to the group of regions in S403, and detailed descriptions of generating the first keyword by the electronic device, which are not described herein again in this embodiment of the present application.

And S703, the electronic equipment determines the upper limit value of the mileage and the confidence coefficient of the upper limit value of the mileage corresponding to each keyword according to the traveled mileage in the target historical travel data corresponding to each keyword.

The confidence of the mileage upper bound value corresponding to each keyword may be the number of the target historical trip data corresponding to each keyword.

And S704, the electronic equipment stores the mileage upper bound value corresponding to each keyword.

It should be noted that, for a specific process of S703-S704, reference may be made to the detailed description of determining the upper limit value of the mileage and the confidence of the upper limit value of the mileage corresponding to each group of regions in S404, and details of the embodiment of the present application are not repeated herein.

S705, the electronic equipment determines a first area to which the getting-on position belongs and a second area to which the getting-off position belongs from the at least one area, and determines second keywords corresponding to the first area and the second area.

The electronic device may first determine the second encoded data of the first area to which the getting-on position belongs and the second encoded data of the two second areas to which the getting-off position belongs. And the electronic equipment obtains a second keyword according to the second coded data of the first area and the second coded data of the second area.

The second keywords corresponding to the first region and the second region may be formed by splicing characters with a preset digit number in the second encoded data of the first region and characters with a preset digit number in the second encoded data of the second region according to the preset sequence of the characters.

For example, the preset number m is 6, and the second encoded data of the two second regions includes: wtw37qd2, wtw37re 1. The electronic equipment splices wtw37q in wtw37qd2 and wtw37r in wtw37re1 according to the preset sequence of the characters. Wherein the first 5 characters in wtw37q are the same as the first 5 characters in wtw37 q. wtw37q after the 6 th character r in wtw37r, the electronic device may stitch wtw37q and wtw37r, and may get wtw37r + wtw37q as the second key.

S706, the electronic equipment acquires the mileage upper bound value corresponding to the second keyword and the confidence coefficient of the mileage upper bound value from the mileage upper bound values corresponding to the first keywords.

The mileage upper bound value corresponding to the second keyword is the mileage upper bound value with the first area and the second area as the starting and stopping points in the mileage upper bound values corresponding to the first keywords.

It should be noted that, for a specific process of S706, reference may be made to the detailed description of determining the upper limit value of the mileage and the confidence of the upper limit value of the mileage corresponding to each group of regions in S603, and details of the embodiment of the present application are omitted here.

And S707, the electronic device judges whether the confidence of the mileage upper bound value corresponding to the second keyword is not less than a preset confidence threshold.

If the confidence of the mileage upper bound value corresponding to the second keyword is not less than (i.e., greater than or equal to) the preset confidence threshold, the electronic device performs S708. If the confidence of the mileage upper bound value corresponding to the second keyword is less than the preset confidence threshold, the electronic device may end the recognition process.

And S708, the electronic equipment judges whether the travel mileage in the travel data is not less than the mileage upper bound value corresponding to the second keyword.

If the driving mileage in the travel data is not less than (i.e., greater than or equal to) the upper limit value of the mileage corresponding to the second keyword, the electronic device executes S709. If the travel mileage in the travel data is less than the upper limit value of the mileage corresponding to the second keyword, the electronic device may end the identification process.

And S709, the electronic equipment determines that the row data is abnormal travel data, and stores the travel data as abnormal data.

It should be noted that, for a specific process in S709, reference may be made to the detailed description of determining that the row data is the abnormal row data in S604, and details of the embodiment of the present application are not described herein.

And S710, the electronic equipment judges whether the number of the abnormal travel data is not less than the preset number.

If the number of abnormal trip data is not less than (i.e., greater than or equal to) the preset number of times, the electronic device executes S711. If the number of the abnormal trip data is less than the preset number, the electronic device may end the identification process.

And S711, the electronic equipment sends out prompt information.

It should be noted that, for a specific process of S711, reference may be made to the detailed description of sending the prompt information in S606, and details of this embodiment are not described herein.

The scheme provided by the embodiment of the application is mainly introduced from the perspective of a method. To implement the above functions, it includes hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The embodiment of the application also provides an abnormal track recognition device. Fig. 12 is a schematic structural diagram of an abnormal trajectory recognition apparatus 800 according to an embodiment of the present application. The apparatus 800 may include: a data acquisition module 801, a location analysis module 802, and an anomaly identification module 803.

The data acquisition module 801 is used for acquiring travel data of a vehicle; the travel data comprise a boarding position, a alighting position and a driving mileage. The position analysis module 802 is further configured to determine a first area to which the getting-on position belongs and a second area to which the getting-off position belongs. The anomaly identification module 803 is configured to obtain an upper limit value of the mileage corresponding to the trip data, where the first area and the second area are used as start and stop points; and if the travel distance is greater than or equal to the upper limit value of the travel distance corresponding to the travel data, determining that the row data is abnormal travel data.

In another possible implementation, the travel data further includes driver information.

An anomaly identification module 803, further configured to: counting the number of abnormal travel data of a driver within a preset time length, which are indicated by the driver information, according to the driver information; and if the number of the abnormal trip data is greater than or equal to the preset times, sending prompt information.

In one possible implementation, the apparatus 800 further includes an upper bound value determination module 804.

The data acquisition module 801 is further configured to acquire a plurality of historical travel data of the vehicle in the target area; the historical trip data includes: the getting-on position, the getting-off position and the driving mileage. A location analysis module 802 further configured to: determining respective location information of at least one of the target regions; and determining at least one group of areas and target historical travel data corresponding to each group of areas in the at least one group of areas according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of the at least one area. And an upper bound value determining module 804, configured to determine, for each group of regions, a mileage upper bound value corresponding to each group of regions according to the traveled mileage in the target historical travel data corresponding to each group of regions.

Wherein, one area and the other area in each group of areas respectively comprise the getting-on position and the getting-off position in the target historical travel data. The mileage upper bound value corresponding to each group of areas is the mileage upper bound value taking two areas in each group of areas as starting and stopping points.

In another possible implementation manner, the location analysis module 802 is specifically configured to: dividing the target area into at least one area with the same size by adopting a preset coding algorithm, and determining first coded data of each area in the at least one area; wherein the first encoded data of each region is position information of each region; the preset coding algorithm comprises a GeoHash algorithm; for each historical trip data in the plurality of historical trip data, a preset coding algorithm is adopted to respectively convert the getting-on position and the getting-off position in the historical trip data to obtain a pair of second coded data; determining a group of regions from at least one region according to a pair of second coded data, and determining that the historical trip data belongs to target historical trip data corresponding to the group of regions; the first encoded data for a set of regions includes a pair of second encoded data.

In another possible implementation manner, the location analysis module 802 is specifically configured to: converting the longitude and latitude in the target area by adopting a preset coding algorithm, and extracting GeoHash characters with preset digits in front of the converted coded data to obtain first coded data of at least one area and each area; the first coded data of each region is GeoHash characters with preset digits in the converted coded data in each region; from among the at least one region, it is determined that a region of the first encoded data equal to a character of a first preset number of digits of any one of a pair of second encoded data belongs to a group of regions.

In another possible implementation manner, the upper bound value determining module 804 is specifically configured to: under the condition that the total number of the target historical trip data corresponding to each group of areas is greater than or equal to a preset confidence threshold, sequencing the driving mileage in the target historical trip data corresponding to each group of areas to obtain sequenced driving mileage; determining a first numerical value and a second numerical value from the sorted driving mileage; wherein the first numerical value is smaller than the median of the sorted driving mileage; the second numerical value is greater than the median of the sorted driving mileage; and multiplying the difference of the second numerical value minus the first numerical value by a preset multiple, and then summing the difference and the second numerical value to obtain the mileage upper bound value corresponding to each group of areas.

Of course, the abnormal trajectory recognition apparatus 800 provided in the embodiment of the present application includes, but is not limited to, the above modules.

Another embodiment of the present application further provides an electronic device. As shown in fig. 13, the electronic device 900 includes a memory 901 and a processor 902; the memory 901 is coupled to the processor 902; the memory 901 is used to store computer program code, which includes computer instructions. Wherein the computer instructions, when executed by the processor 902, cause the electronic device 900 to perform the steps performed by the electronic device in the method flow illustrated in the above-described method embodiments.

In actual implementation, the data acquisition module 801, the location analysis module 802, the anomaly identification module 803, and the upper bound value determination module 804 may be implemented by the processor 902 shown in fig. 9 calling computer program code in the memory 901. The specific implementation process may refer to the description of the above abnormal trajectory identification method, and is not described herein again.

Another embodiment of the present application further provides a computer-readable storage medium, in which computer instructions are stored, and when the computer instructions are executed on an electronic device, the electronic device is caused to perform the steps performed by the electronic device in the method flow shown in the foregoing method embodiment.

Another embodiment of the present application further provides a chip system, which is applied to an electronic device. The system-on-chip 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 configured to receive signals from a memory of the electronic device and to send the signals to the processor, the signals including computer instructions stored in the memory. When the processor of the electronic device executes the computer instructions, the electronic device performs the steps performed by the electronic device in the method flow illustrated in the above-described method embodiments.

There is also provided in another embodiment of the present application a computer program product, which includes computer instructions that, when executed on an electronic device, cause the electronic device to perform the steps performed by the electronic device in the method flows shown in the above-mentioned method embodiments.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may 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. The processes or functions according to the embodiments of the present application are generated in whole or in part when the computer-executable instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that can be accessed by a computer or can comprise one or more data storage devices, such as servers, data centers, and the like, that can be integrated with the media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

The foregoing is only illustrative of the present application. Those skilled in the art can conceive of changes or substitutions based on the specific embodiments provided in the present application, and all such changes or substitutions are intended to be included within the scope of the present application.

Claims (10)

1. An abnormal track identification method is characterized by comprising the following steps:

acquiring travel data of a vehicle; the travel data comprise an getting-on position, a getting-off position and a driving mileage;

determining a first area to which the getting-on position belongs and a second area to which the getting-off position belongs;

acquiring mileage upper bound values corresponding to the trip data by taking the first area and the second area as starting and stopping points;

and if the travel mileage is greater than or equal to the mileage upper bound value corresponding to the travel data, determining that the travel data is abnormal travel data.

2. The method of claim 1, wherein the travel data further comprises driver information;

the method further comprises the following steps:

counting the number of abnormal travel data of a driver within a preset time length, which are indicated by the driver information, according to the driver information;

and if the number of the abnormal travel data is greater than or equal to the preset number, sending prompt information.

3. The method according to claim 1 or 2, characterized in that the method further comprises:

acquiring a plurality of historical travel data of vehicles in a target area; the historical trip data comprises: the getting-on position, the getting-off position and the driving mileage;

determining respective location information of at least one of the target regions;

determining at least one group of areas and target historical travel data corresponding to each group of areas in the at least one group of areas according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of the at least one area; one region and the other region in each group of regions respectively comprise an getting-on position and a getting-off position in the target historical travel data corresponding to each group of regions;

for each group of regions, determining a mileage upper bound value corresponding to each group of regions according to the driving mileage in the target historical trip data corresponding to each group of regions; the mileage upper bound value corresponding to each group of areas is the mileage upper bound value taking two areas in each group of areas as starting and stopping points.

4. The method of claim 3, wherein determining respective location information for at least one of the target regions comprises:

dividing the target area into the at least one area with the same size by adopting a preset coding algorithm, and determining first coded data of each area in the at least one area; wherein the first encoded data of each region is position information of each region;

determining at least one group of regions and target historical travel data corresponding to each group of regions in the at least one group of regions according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of the at least one region, including:

for each historical trip data in the plurality of historical trip data, adopting the preset coding algorithm to respectively convert the getting-on position and the getting-off position in the historical trip data to obtain a pair of second coded data;

determining a group of regions from the at least one region according to the pair of second encoding data, and determining that the historical travel data belongs to target historical travel data corresponding to the group of regions; the first encoded data for the set of regions includes the pair of second encoded data.

5. The method according to claim 4, wherein the dividing the target region into the at least one region with the same size by using a preset encoding algorithm and determining the first encoding data of each region of the at least one region comprises:

converting the longitude and latitude in the target area by adopting the preset coding algorithm, and extracting GeoHash characters with preset digits in front of the converted coded data to obtain first coded data of the at least one area and each area; the first coded data of each region is GeoHash characters with preset digits in the converted coded data in each region;

said determining a set of regions from said at least one region based on said pair of second encoded data comprises:

determining, from the at least one region, that a region of the first encoded data equal to a character of a preset number of leading digits of any one of the pair of second encoded data belongs to the group of regions.

6. The method according to claim 3, wherein the determining, for each group of regions, the upper limit value of the mileage corresponding to each group of regions according to the mileage in the target historical travel data corresponding to each group of regions comprises:

under the condition that the total number of the target historical trip data corresponding to each group of regions is greater than or equal to a preset confidence threshold, sequencing the driving mileage in the target historical trip data corresponding to each group of regions to obtain sequenced driving mileage;

determining a first numerical value and a second numerical value from the ranked driving mileage; wherein the first numerical value is less than the median of the ranked driving range; the second value is greater than the median of the ranked driving range;

and multiplying the difference obtained by subtracting the first numerical value from the second numerical value by a preset multiple, and then summing the difference and the second numerical value to obtain the mileage upper bound value corresponding to each group of areas.

7. An abnormal trajectory recognition apparatus, characterized in that the apparatus comprises:

the data acquisition module is used for acquiring travel data of the vehicle; the travel data comprise an getting-on position, a getting-off position and a driving mileage;

the position analysis module is used for determining a first area to which the getting-on position belongs and a second area to which the getting-off position belongs;

an anomaly identification module to: acquiring mileage upper bound values corresponding to the trip data by taking the first area and the second area as starting and stopping points; and if the travel mileage is greater than or equal to the mileage upper bound value corresponding to the travel data, determining that the travel data is abnormal travel data.

8. The apparatus of claim 7,

the travel data further comprises driver information; the anomaly identification module is further configured to: counting the number of abnormal travel data of a driver within a preset time length, which are indicated by the driver information, according to the driver information; if the number of the abnormal travel data is larger than or equal to the preset number, sending prompt information;

the identification device further comprises an upper bound value determination module;

the data acquisition module is further used for acquiring a plurality of historical travel data of the vehicles in the target area; the historical trip data comprises: the getting-on position, the getting-off position and the driving mileage;

the location analysis module is further configured to: determining respective location information of at least one of the target regions; determining at least one group of areas and target historical travel data corresponding to each group of areas in the at least one group of areas according to the getting-on position and the getting-off position in the plurality of historical travel data and the respective position information of the at least one area; one region and the other region in each group of regions respectively comprise an getting-on position and a getting-off position in the target historical travel data corresponding to each group of regions;

the abnormality identification module is used for determining a mileage upper bound value corresponding to each group of regions according to the driving mileage in the target historical trip data corresponding to each group of regions; the mileage upper bound value corresponding to each group of areas is a mileage upper bound value taking two areas in each group of areas as starting and stopping points;

the position analysis module is specifically configured to:

dividing the target area into the at least one area with the same size by adopting a preset coding algorithm, and determining first coded data of each area in the at least one area; wherein the first encoded data of each region is position information of each region;

for each historical trip data in the plurality of historical trip data, adopting the preset coding algorithm to respectively convert the getting-on position and the getting-off position in the historical trip data to obtain a pair of second coded data;

determining a group of regions from the at least one region according to the pair of second encoding data, and determining that the historical travel data belongs to target historical travel data corresponding to the group of regions; the first encoded data for the set of regions comprises the pair of second encoded data;

the position analysis module is specifically configured to:

converting the longitude and latitude in the target area by adopting the preset coding algorithm, and extracting GeoHash characters with preset digits in front of the converted coded data to obtain first coded data of the at least one area and each area; the first coded data of each region is GeoHash characters with preset digits in the converted coded data in each region;

determining, from the at least one region, that a region of the first encoded data equal to a preset number of first-digit characters of any one of the pair of second encoded data belongs to the group of regions;

the upper bound value determining module is specifically configured to:

under the condition that the total number of the target historical trip data corresponding to each group of regions is greater than or equal to a preset confidence threshold, sequencing the driving mileage in the target historical trip data corresponding to each group of regions to obtain sequenced driving mileage;

determining a first numerical value and a second numerical value from the ranked driving mileage; wherein the first numerical value is less than the median of the ranked driving range; the second value is greater than the median of the ranked driving range;

and multiplying the difference obtained by subtracting the first numerical value from the second numerical value by a preset multiple, and then summing the difference and the second numerical value to obtain the mileage upper bound value corresponding to each group of areas.

9. An electronic device, comprising a memory and a processor; the memory and the processor are coupled; the memory for storing computer program code, the computer program code comprising computer instructions;

wherein the computer instructions, when executed by the processor, cause the electronic device to perform the method of abnormal trajectory identification of any of claims 1-6.

10. A computer-readable storage medium storing computer instructions which, when executed on an electronic device, cause the electronic device to perform the method of identifying abnormal trajectories of any one of claims 1 to 6.

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