CN111126773A - Abnormal vehicle identification method and device and electronic equipment - Google Patents

Abnormal vehicle identification method and device and electronic equipment Download PDF

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Publication number
CN111126773A
CN111126773A CN201911176921.2A CN201911176921A CN111126773A CN 111126773 A CN111126773 A CN 111126773A CN 201911176921 A CN201911176921 A CN 201911176921A CN 111126773 A CN111126773 A CN 111126773A
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target vehicle
vehicle
probability
abnormal
nodes
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CN111126773B (en
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朱俊辉
梅俊杰
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Hanhai Information Technology Shanghai Co Ltd
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Beijing Mobike Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06Q50/40—
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
    • Y02W90/00—Enabling technologies or technologies with a potential or indirect contribution to greenhouse gas [GHG] emissions mitigation

Abstract

The invention discloses a method, a device and electronic equipment for identifying abnormal vehicles, wherein the method comprises the following steps: for the positioning information of a target vehicle, obtaining each position node reached by the target vehicle and transfer information among the position nodes; obtaining the probability of the target vehicle reaching each position node according to the transfer information among the position nodes; identifying whether the target vehicle is in an abnormal use state or not according to the probability distribution condition; when the device is in the abnormal use state, the set abnormal processing is performed.

Description

Abnormal vehicle identification method and device and electronic equipment
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an abnormal vehicle identification method, an abnormal vehicle identification device, and an electronic device.
Background
At present, the shared vehicle trip becomes a emerging trip mode in a city, and the trip demand of urban people can be effectively solved.
Due to the characteristic that the shared vehicle can be shared and used by each user, the parking position of the shared vehicle can be changed continuously according to the use condition of the user, and therefore, in an area with low vehicle aggregation density, the user may need to walk for a distance to search for the vehicle when going out. Therefore, for the reasons of convenient use, cost saving and the like, some users may destroy the electronic lock of the shared vehicle to take the shared vehicle as used without paying any use cost to the operator, which not only causes that other users cannot normally use the occupied vehicle, violates the sharing concept of the shared vehicle, but also brings great economic loss to the operator.
Disclosure of Invention
It is an object of an embodiment of the present invention to provide an abnormal vehicle identification method to identify abnormal vehicle use behavior with respect to a private vehicle.
According to a first aspect of the present invention, there is provided an abnormal vehicle identification method including:
according to the positioning information of the target vehicle, obtaining each position node reached by the target vehicle and transfer information among the position nodes;
obtaining the probability of the target vehicle reaching each position node according to the transfer information among the position nodes;
identifying whether the target vehicle is in an abnormal use state or not according to the probability distribution condition;
when the device is in the abnormal use state, the set abnormal processing is performed.
Optionally, in the obtaining, according to the transfer information between the location nodes, a probability that the target vehicle reaches each of the location nodes, obtaining the probability that the target vehicle reaches any of the location nodes includes:
according to the transfer relation, obtaining the transfer probability that the target vehicle respectively reaches each position node in the position nodes from the position nodes;
and acquiring a convergence value of the probability of the target vehicle reaching the position node according to the transition probability and the set initial value of the probability of the target vehicle reaching the position node.
Optionally, the identifying whether the target vehicle is in an abnormal use state according to the distribution condition of the probability includes:
acquiring the number of position nodes of which the probability is greater than or equal to a set probability threshold;
in the case where the number is within a set range, it is recognized that the target vehicle is in an abnormal use state.
Optionally, the method further includes a step of obtaining the probability threshold, including:
obtaining a known vehicle in an abnormal use state as a training sample;
according to the positioning information of the training sample, obtaining position nodes reached by the training sample and transfer information among the position nodes of the training sample;
obtaining the probability of the training sample reaching each corresponding position node according to the transfer information among the position nodes of the training sample;
and determining the probability threshold according to the distribution condition of the probability of the training sample reaching each corresponding position node.
Optionally, the method further comprises the step of acquiring the target vehicle, including at least one of:
the first item: searching a vehicle with a position changed without a vehicle using request as the target vehicle;
the second term is: in response to receiving a reporting event regarding an abnormal vehicle, acquiring a vehicle to which the reporting event is directed as the target vehicle.
Optionally, the obtaining, according to the positioning information about the target vehicle, each location node that the target vehicle has reached includes:
acquiring each position area divided in advance, wherein one position area corresponds to one position node;
obtaining a position area corresponding to each piece of positioning information according to the positioning information of the target vehicle;
and acquiring each position node reached by the target vehicle according to the position area corresponding to each piece of positioning information.
Optionally, the performing set exception handling includes:
and sending the vehicle information of the target vehicle to an account number of an operator for retrieval intervention.
Optionally, after identifying that the target vehicle is in an abnormal use state, the method further includes:
sending the vehicle information of the target vehicle to an account of an operator for rechecking;
obtaining a rechecking result returned by the account of the operator after rechecking;
and a step of performing the set exception processing again when the rechecking result indicates that the target vehicle is privately occupied.
According to a second aspect of the present invention, there is also provided an abnormal vehicle recognition apparatus including:
the information acquisition module is used for acquiring each position node reached by the target vehicle and transfer information among the position nodes according to the positioning information of the target vehicle;
the calculation module is used for obtaining the probability of the target vehicle reaching each position node according to the transfer information among the position nodes;
the abnormal recognition module is used for recognizing whether the target vehicle is in an abnormal use state or not according to the distribution condition of the probability; and the number of the first and second groups,
and the exception handling module is used for carrying out set exception handling under the condition of an abnormal use state.
According to a third aspect of the present invention, there is also provided an electronic apparatus including the abnormal vehicle recognition device according to the second aspect of the present invention; alternatively, the electronic device comprises a memory for storing an executable computer program and a processor; a processor for operating the electronic device under control of the computer program to perform the method according to the first aspect of the invention.
The method for identifying the abnormal vehicle has the advantages that the position nodes of the target vehicle and the transfer relationship among the position nodes are extracted according to the positioning information of the target vehicle, whether the target vehicle has the characteristics representing the private use state is analyzed according to the transfer relationship among the position nodes, and then whether the target vehicle is in the abnormal use state is automatically identified, so that the target vehicle in the abnormal use state can be timely processed, the risk control of a shared vehicle is realized, and the loss of operators is reduced. In addition, the abnormal vehicle identification method of the embodiment does not depend on user data for abnormal identification, so the method of the embodiment can be applied to abnormal identification in which a user privately uses a vehicle by destroying a lock mechanism of the vehicle, and has a wider application range.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a functional block diagram showing a hardware configuration of a shared vehicle system that may be used to implement an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of an abnormal vehicle identification method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing location nodes and a transition relationship between the location nodes of a target vehicle according to an example of the present invention;
FIG. 4 is a directed connectivity graph of the branching relationships of the example shown in FIG. 3;
fig. 5 is a functional block diagram of an abnormal vehicle recognition apparatus according to an embodiment of the present invention;
FIG. 6 is a functional block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
< hardware configuration >
FIG. 1 is a block diagram of a hardware configuration of a shared vehicle system 100 that may be used to implement an embodiment of the invention.
As shown in fig. 1, the shared vehicle system 100 includes a server 1000, a mobile terminal 2000, and a vehicle 3000.
The server 1000 provides a service point for processes, databases, and communications facilities. The server 1000 may be a unitary server or a distributed server across multiple computers or computer data centers. The server may be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interaction server, a database server, or a proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported or implemented by the server. For example, a server, such as a blade server, a cloud server, etc., or may be a server group consisting of a plurality of servers, which may include one or more of the above types of servers, etc.
In one embodiment, as shown in fig. 1, the server 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600.
The processor 1100 is used to execute computer programs. The computer program may be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, various bus interfaces such as a serial bus interface (including a USB interface), a parallel bus interface, and the like. Communication device 1400 is capable of wired or wireless communication, for example. The display device 1150 is, for example, a liquid crystal display panel, an LED display panel touch display panel, or the like. Input devices 1160 may include, for example, a touch screen, a keyboard, and the like.
In this embodiment, the memory 1200 of the server 1000 is used to store computer instructions for controlling the processor 1100 to operate to implement or support the implementation of the abnormal vehicle identification method according to any embodiment of the present invention. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
Although a plurality of devices of the server 1000 are shown in fig. 1, the present invention may only relate to some of the devices, for example, the server 1000 only relates to the memory 1200, the processor 1100, the communication device 1400, and the like.
In this embodiment, the mobile terminal 2000 is, for example, a mobile phone, a laptop, a tablet computer, a palmtop computer, a wearable device, and the like.
As shown in fig. 1, the mobile terminal 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, a speaker 2700, a microphone 2800, and the like.
The processor 2100 may be a mobile version processor. The memory 2200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 2300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 2400 can perform wired or wireless communication, for example, the communication device 2400 may include a short-range communication device, such as any device that performs short-range wireless communication based on a short-range wireless communication protocol, such as a Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, LiFi, and the like, and the communication device 2400 may also include a remote communication device, such as any device that performs WLAN, GPRS, 2G/3G/4G/5G remote communication. The display device 2500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 2600 may include, for example, a touch screen, a keyboard, and the like. A user can input/output voice information through the speaker 2700 and the microphone 2800.
In this embodiment, the mobile terminal 2000 may be configured to initiate a vehicle using request for the vehicle 3000 to the server 1000, where the vehicle using request carries the unique identifier of the requested vehicle 3000.
The user may scan the two-dimensional code of the vehicle 3000 through the mobile terminal 2000 to trigger the car using request, or the user may input the unique code of the vehicle 3000 through the mobile terminal 2000 to trigger the car using request.
In this embodiment, the memory 2200 of the mobile terminal 2000 is configured to store computer instructions for controlling the processor 2100 to operate in support of implementing an abnormal vehicle identification method according to any embodiment of the present invention, for example, at least including: the unique identification of the vehicle 3000 is obtained, a vehicle usage request for a specific vehicle is formed and sent to the server, a vehicle usage request record is formed, and the like. The skilled person can design the computer instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
Although a plurality of devices of the mobile terminal 2000 are illustrated in fig. 1, the present invention may only relate to some of the devices, and is not limited thereto.
The vehicle 3000 may be a bicycle shown in fig. 1, and may be various types such as a tricycle, an electric scooter, a motorcycle, and a four-wheeled passenger vehicle, and is not limited thereto.
As shown in fig. 1, vehicle 3000 may include a processor 3100, a memory 3200, an interface device 3300, a communication device 3400, a display device 3500, an input device 3600, a speaker 3700, a microphone 3800, and so forth. The processor 3100 may be a microprocessor MCU or the like. The memory 3200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface 3300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 3400 is capable of wired or wireless communication, for example, and also capable of short-range and long-range communication, for example. The output device 2500 may be, for example, a device that outputs a signal, may be a display device such as a liquid crystal display panel or a touch panel, or may be a speaker or the like that outputs voice information or the like. The input device 2600 may include, for example, a touch panel, a keyboard, or a microphone for inputting voice information.
Although a plurality of devices of the vehicle 3000 are shown in fig. 1, the present invention may relate only to some of the devices, for example, the vehicle 3000 relates only to the communication device 3400, the memory 3200, and the processor 3100.
In addition, the vehicle 3000 includes a lock mechanism or the like controlled by the processor 3100 to effect unlocking of the vehicle 3000.
In this embodiment, the vehicle 3000 may report its own position information to the server 1000, and report its own use state information to the server 1000, for example, when it is detected that the user has completed the lock operation, a lock notification signal may be reported to the server 1000.
In this embodiment, memory 3200 of vehicle 3000 is used to store computer instructions that control processor 3100 to operate to perform information interactions with server 1000. The skilled person can design the computer instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
The network 4000 may be a wireless communication network or a wired communication network, and may be a local area network or a wide area network. In the information push system 100 shown in fig. 1, the vehicle 3000 and the server 1000, and the mobile terminal 2000 and the server 1000 can communicate with each other through the network 4000. The vehicle 3000 may be the same as the server 1000, and the network 4000 through which the mobile terminal 2000 communicates with the server 1000 may be different from each other.
It should be understood that although fig. 1 shows only one server 1000, mobile terminal 2000, vehicle 3000, there is no intention to limit the respective numbers, and multiple servers 1000, multiple mobile terminals 2000, multiple vehicles 3000, etc. may be included in the shared vehicle system 100.
< method examples >
The abnormal vehicle recognition method of the embodiment is used for recognizing the behavior of a user who privately uses a certain vehicle.
The first way of this private use may be: the user breaks the lock mechanism of the vehicle so that the vehicle remains unlocked at all times, but the vehicle's positioning device can still be used properly. The first mode is characterized by comprising the following steps: the user can use the vehicle at will without sending a vehicle using request to the server, and without paying any fee.
The second way of the private use may be: the user adds private lock to the vehicle or hides the vehicle, so that other users cannot normally use the vehicle. The characteristics of this second mode include: the user uses the vehicle through a normal vehicle using process, and the method comprises the steps of scanning the two-dimensional code of the vehicle through the mobile terminal to send a vehicle using request to the server, so that the server unlocks the vehicle according to the vehicle using request; and, after each end of the use of the vehicle, settling the fee in accordance with the settlement request from the server.
Fig. 2 is a flowchart illustrating an abnormal vehicle identification method according to an embodiment of the present invention, which may be implemented by, for example, the server 1000 shown in fig. 1 or the vehicle 3000 shown in fig. 1.
As shown in fig. 2, the method of the present embodiment may include the following steps S2100 to S2400.
In step S2100, transfer information between the position nodes and the position nodes that the target vehicle has reached is obtained based on the positioning information for the target vehicle.
In this embodiment, each vehicle has a unique identifier, and different target vehicles can be distinguished according to the identifier.
Each positioning information includes position information and time information corresponding to the position information, that is, each positioning information reflects a position of the target vehicle at the corresponding time.
The location information may be represented by latitude and longitude, for example.
In the positioning information, at least the position information is provided by a positioning device of the target vehicle, and the time information may be determined by the positioning device, or may be determined by a processor of the target vehicle when receiving the position information provided by the positioning device, or may be determined by a server when receiving the position information reported by the target vehicle, and the like, which is not limited herein.
In examples where the method is implemented by the server 1000, the server may obtain at least location information of the target vehicle from the target vehicle to form location information for the target vehicle; alternatively, the positioning information may be acquired directly from the target vehicle.
The target vehicle may report location information or positioning information to the server at set time intervals.
In this embodiment, the abnormal vehicle recognition may be performed by analyzing the positioning information for the target vehicle within a set time period.
For example, the set time period is the last 30 days, the last 60 days, and the like, and is not limited herein.
In this embodiment, each position node that the target vehicle has reached may be determined according to the position information in the positioning information, and the position nodes form a position node set corresponding to the target vehicle. Different vehicles have different position node sets, and the embodiment identifies whether the target vehicle is in an abnormal use state within the range of the position node set corresponding to the target vehicle.
In this embodiment, the same position in different positioning information corresponds to the same position node, and different positions correspond to different position nodes.
Since the user randomly parks the vehicle to a position near the destination when using the vehicle to reach the destination, the parking positions of the vehicle are likely to be different when the user uses the vehicle many times to reach the same destination, but the parking positions are all directed to the same destination in practice. In order to identify whether a target vehicle is used by a user between individual concentrated places, for example, between a home and a company, based on the positioning information of the target vehicle, an operation area of the target vehicle, for example, city, may be divided into a plurality of location areas, and one location area corresponds to one location node, so that different location information corresponding to the same location area belongs to the same location and corresponds to the same location node.
For example, if one piece of location information on the subject vehicle shows that the subject vehicle is located at the position P1 and the other piece of location information on the subject vehicle shows that the subject vehicle is located at the position P2, but the position P1 and the position P2 belong to a location area divided in advance, the position P1 and the position P2 belong to the same position.
In this embodiment, the step S2100 of obtaining each location node reached by the target vehicle according to the positioning information about the target vehicle may include the following steps S2110 to S2130:
step S2110, obtaining each position area divided in advance, wherein one position area corresponds to one position node.
In this embodiment, when dividing the location area, the operation area may be divided into 100m × 100m grids, that is, each grid is square, the side length of the grid is 100m, and one grid corresponds to one location area.
Step S2120, according to the positioning information of the target vehicle, a position area corresponding to each positioning information is obtained.
Step S2130 is to obtain, according to the location area corresponding to each piece of positioning information, each location node that the target vehicle has reached.
According to the steps S2110 to S2130, the number of location nodes of the target vehicle can be effectively reduced by performing normalization processing on the location of the target vehicle by dividing the location area, which not only reduces the amount of data processing, but also is beneficial to improving the identification accuracy.
In this embodiment, according to the time information in each piece of positioning information, the sequence between each piece of positioning information can be determined, and then, according to whether the target vehicle transfers between two pieces of positioning information that are adjacent in time, transfer information between each position node can be determined.
For example, as shown in fig. 3, 15 pieces of positioning information L1 to L15 for the target vehicle are acquired in step S2100, and a total of 4 position nodes, which are position nodes A, B, C, D in fig. 3 and 4, are extracted from the position information in the positioning information. From the time information in these positioning information, the arrangement order of the positioning information as shown in fig. 3 can be obtained. If two positioning information adjacent in time correspond to the same position, i.e., correspond to the same position node, referring to fig. 3, two positioning information L1, L2 adjacent in time correspond to the same position node a, it is considered that the target vehicle has not been transferred within the time interval of the two positioning information. If two positioning nodes adjacent in time correspond to different positions, that is, correspond to different position nodes, referring to fig. 3, the positioning information L2 corresponds to the position node a, and the positioning information L3 corresponds to the position node B, it is considered that the target vehicle has moved in the time interval of the two positioning information, and the moving direction is from the position node a to the position node B, which indicates that the target vehicle has a driving route from the position node a to the position node B, and then a directional moving relationship can be established between the position node a and the position node B. Thus, from the time information in the 15 pieces of positioning information, it can be determined that the transition relationship exists between the location nodes A, B, C, D as shown in fig. 3 and 4.
In step S2200, the probability that the target vehicle reaches each position node is obtained according to the transition information obtained in step S2100.
In this embodiment, by analyzing the probability of the target vehicle reaching each location node, it can be seen whether the use of the target vehicle by the user is concentrated on a small number of specific nodes or is uniformly distributed on each location node. For the shared vehicle which is normally used, the same vehicle can be used by a great number of users, the probability of being used by the same user for multiple times is very low, and different users have different driving routes, namely the shared vehicle corresponds to different position nodes, which shows that in the normal use state, the shared vehicle is transferred randomly, the probability distribution of the probability of reaching each corresponding position node is relatively balanced, and the situation of one or two position nodes with high probability cannot occur, so that whether the target vehicle is in the private abnormal use state or not can be analyzed according to the distribution situation of the probability of reaching each position node by the target vehicle.
For example, a location node with an occurrence probability greater than or equal to a set probability threshold indicates that the target vehicle is frequently driven to the location node; for another example, if two position nodes with occurrence probabilities greater than or equal to the set probability threshold value indicate that the target vehicle has a feature of wandering among more concentrated position nodes, which both coincide with the feature that the target vehicle is occupied by private, it may be determined whether the target vehicle is in an abnormal use state, and the accuracy is high.
In one embodiment, the transfer information may include the above transfer relationships and the number of transfers corresponding to each transfer relationship. For example, as shown in fig. 3, the number of transitions corresponding to the transition relationship from location node a to location node B is 3, and so on. In this embodiment, the arrival probability of the position node forming the transition relationship may be determined according to the ratio of the transition number corresponding to each transition relationship to the total transition number, and the arrival probability of each position node may be further determined.
In another embodiment, the transfer information may include the above transfer relationships, for example, the transfer relationships between the location nodes as shown in fig. 4 are obtained based on the positioning information for the target vehicle.
In this embodiment, the position nodes reached by the target vehicle and the transition relationships between the position nodes may be represented by drawing a directed connected graph G (P, E), where P represents a position node and E represents a transition relationship, as shown in fig. 4, for example.
In this embodiment, for any location node, the obtaining of the probability that the target vehicle reaches the location node in step S2200 based on the transition information obtained in step S2100 may include the following steps S2210 and S2220:
step S2210, obtaining the transition probability that the target vehicle respectively reaches each position node of all the position nodes from the position node according to the transition relation among the position nodes of the target vehicle.
Continuing with the above example, for location node a, the transition probability of the target vehicle arriving at location node A, B, C, D at location node a is obtained. For location node B, the transition probability of the target vehicle arriving at location node A, B, C, D at location node B is obtained. For location node C, the transition probability of the target vehicle reaching location node A, B, C, D at location node C is obtained. For location node D, the transition probability of the target vehicle arriving at location node A, B, C, D at location node D is obtained.
The transition probability of the node arriving at the node at any position is 0. The transition probability of an arbitrary position node to reach other position nodes depends on the number of other position nodes having roll-out relation with the position node.
According to fig. 4, location node a has a transition relationship pointing to location node B, C, D, so the transition probabilities from location node a to location node B, C, D are all 1/3 and the transition probability to location node a is 0. Position node B has a transition relationship pointing to position node A, C, so the probabilities from position node B to position node A, B, C, D are 1/2, 0, 1/2, 0, respectively. Position node C has a transition relationship pointing to position node a, and therefore the transition probabilities from position node C to position node A, B, C, D are 1, 0, respectively. Position node D has only a transition relationship to position node a, and therefore the transition probabilities from position node D to position node A, B, C, D are 1, 0, respectively.
In this step, after obtaining these transition probabilities, a transition matrix T of the target vehicle may be generated. In the above example, a 4 x 4 transition matrix T may be generated.
In step S2220, a convergence value of the probability that the target vehicle reaches the position node is obtained based on the initial value of the probability that the set target vehicle reaches the position node and the transition probability obtained in step S2210.
In this embodiment, according to the transfer relationship between the position nodes of the target vehicle, the probability that the target vehicle reaches any position node at any position node may be determined, and on the basis of setting an initial value of the probability that the target vehicle reaches any position node, a convergence value that tends to be stable of the probability that the target vehicle reaches the position node may be obtained through multiple iterations, thereby improving the accuracy of the obtained probability.
In the above iterative process, a convergence condition for stopping the iteration may be set. For example, the convergence condition may be: for each position node, the difference value between the probability value of the current iteration and the probability value of the last iteration is smaller than or equal to a set threshold value. For another example, the convergence condition may be: the iteration times reach the set times, and the like. For example, the convergence condition may be a condition that the iteration is stopped when any one of the above conditions is satisfied.
In this embodiment, the initial value of the probability that the target vehicle reaches each position node may be set to be the same, that is, 1/4.
In one example, the probability V (p) of the target vehicle reaching each location node may be obtained by iterating through equation (1) belowi)：
Figure BDA0002290200560000131
In the above formula (1), piRepresents a location node i; p is a radical ofjRepresents a location node j; m (p)i) Indicating the ability to transfer to a location node piA set of location nodes; v (p)i(ii) a t +1) represents the probability that the target vehicle reaches the position node i at the time of the t +1 th iteration; v (p)j(ii) a t) represents the probability that the target vehicle reaches the position node j at the time of the tth iteration; e (p)j) The number of connections transferred from the position node j, that is, the number of transfer nodes corresponding to the position node j when the position node j is used as a transfer node.
Continuing with the example shown in FIG. 4, taking location node A as an example, M (p)A) Including location nodesB、C、D；E(pB) Equal to 2.
In another example, for the position nodes reached by the target vehicle, although there is no transfer relationship between some position nodes at present, for example, in the example shown in fig. 4, the target vehicle does not have a transfer relationship from the position node B to the position node D, but since the user may directly transfer from the position node B to the position node D, considering that the target vehicle can directly transfer from a certain position node to any other position node, the following formula (2) may be adopted to perform iteration to obtain the probability V (p) that the target vehicle reaches each position nodei)：
Figure BDA0002290200560000141
Compared with the difference of the formula (1), the difference of the formula (2) is that a damping factor d is added, the damping factor d represents the probability of transferring the position node j to other position nodes with a transfer relation, the damping factor d is usually greater than 0.5, for example, the value is within a range of 0.8-0.95, and N represents the number of the position nodes.
According to the above steps S2210 and S2220, the method of this embodiment can obtain the convergence value of the probability of the target vehicle for each location node by means of iteration and the like, which can also obtain an accurate probability value with less positioning information, which is beneficial to providing accuracy of abnormality identification.
In step S2300, it is identified whether the target vehicle is in an abnormal use state or not according to the distribution of the probability that the target vehicle reaches each position node obtained in step S2300.
Because the distribution situation of the probability of the target vehicle reaching each position node is balanced under the normal use state, and individual obvious high probability is avoided, whether the target vehicle is in the private abnormal use state or not can be analyzed according to the distribution situation of the probability of the target vehicle reaching each position node.
In one embodiment, the step S2300 of distributing the probability of the target vehicle reaching each location node, and the step of identifying whether the target vehicle is in the abnormal use state may include steps S2310 and S2320:
in step S2310, the number of location nodes for which the probability is greater than or equal to the set first probability threshold is obtained.
The first probability threshold may be set empirically, and may be set within a range of 0.5 to 0.8, for example.
The first probability threshold may also be determined based on a standard deviation of the probability of the target vehicle reaching each location node.
The first probability threshold may also be trained from the location information of known anomalous samples (vehicles in anomalous usage). For example, for an abnormal sample, the positions nodes reached by the abnormal sample and the transition relationship between the positions nodes are obtained in the manner of the above steps S2100 and S2200, and further the probability of the abnormal sample reaching each position node is obtained, so that an appropriate probability threshold can be determined according to the convergence condition of the abnormal sample or multiple abnormal samples to a specific position node.
In step S2320, if the number is within the set range, it is recognized that the target vehicle is in an abnormal use state.
Correspondingly, in the case that the number does not belong to the set range, the target vehicle is identified as being in a normal use state.
The setting range may be empirically set, and may be set to be greater than 0 and less than or equal to 3, for example.
The set range may also be proportionally determined based on the number of position nodes that the target vehicle has reached.
The setting range may also be obtained by training according to known positioning information of the abnormal sample, which is not limited herein.
According to the steps S2310 and S2320, the method of this embodiment may identify whether the target vehicle is in an abnormal state according to the absolute number of the position nodes exceeding the probability threshold, without considering the proportion of the absolute number to the total number of the position nodes, may quickly give an abnormal result, may effectively identify the feature that the vehicle lingers among the specific position nodes in the vehicle occupancy, and may improve the identification accuracy.
In one embodiment, the step S2300 of determining the distribution of the probability of the target vehicle reaching each position node may further include: and if the maximum value of the obtained probabilities is greater than or equal to a set second probability threshold, whether the target vehicle is in an abnormal use state.
The second probability threshold may be the same as or different from the first probability threshold.
The second probability threshold may be set within a range of 0.5 to 0.8, for example, but is not limited thereto.
The second probability threshold may be set with reference to the setting manner of the first probability threshold, including setting based on experience, setting based on a standard deviation of a probability that the target vehicle reaches each position node, or training based on positioning information of a known abnormal sample (a vehicle in an abnormal use state), and the like, which are not described herein again.
According to the method of the embodiment, whether the target vehicle is in the abnormal use state or not can be identified by comparing the maximum probability with the set probability threshold value without considering the overall situation of the probability that the target vehicle reaches each position node, and then the abnormal identification result can be rapidly and effectively given.
In step S2400, if the target vehicle is in an abnormal use state, a set abnormality process is performed.
The set exception handling may include at least one of: the first item: sending warning information to the mobile terminal bound by the target account, wherein the warning information comprises information that the target account is found to occupy a vehicle privately; the second term is: providing a penalty value to the target account number; the third item: closing the target account; the fourth item: forbidding a vehicle using request from the target account; the fifth item: and sending the vehicle information of the target vehicle to an account number of an operator for retrieval intervention.
The above first to fifth items are suitable for the case where the user privately uses the vehicle through a normal flow. For such a case, the user account causing the abnormal use state may be searched for, so as to perform any one of the first item to the fourth item on the user account.
Under the private use condition that the user does not need to use the target vehicle through the account by destroying the lock mechanism of the target vehicle, the vehicle information of the target vehicle can be sent to the account of the operator for retrieval intervention according to the fifth item.
Regarding the first item, the target user can be informed of the behavior that the target user finds the vehicle in private use by sending warning information to the mobile terminal bound to the target account, so that the user can timely recover the normal use of the vehicle in private use.
The warning message may also include a penalty event to be applied to the target account number, the penalty event including, for example, at least one of the second, third, and fourth terms.
The second term may be a penalty, a reduced preference amount, an increased vehicle cost, or the like.
Regarding the third item, closing the target account is to remove the account in the database, and prohibit the mobile phone number bound by the account from registering a new account again.
In the fourth item, after the vehicle using request from the target account is prohibited, the server prohibits unlocking the requested vehicle according to the vehicle using request when receiving the vehicle using request from the target account again.
The fourth item may also be a request for forbidding to pass the vehicle using request from the target account within a set time length, for example, after the target vehicle is found to belong to an abnormal vehicle, the request for using the vehicle using request from the target account within the next month is forbidden.
Regarding the fifth item, since the target vehicle is privately occupied by the target account, the vehicle information of the target vehicle, which includes the unique identifier of the vehicle and also includes the location information of the vehicle, may be sent to the account of the operator, so that the operator may retrieve the target vehicle according to the vehicle information, thereby reducing the loss.
According to the above steps S2100 to S2400, the abnormal vehicle identification method of this embodiment extracts the position nodes of the target vehicle and the transfer relationship between the position nodes according to the positioning information of the target vehicle, and analyzes whether the target vehicle has a feature representing a private use state, that is, a feature highly converging on an individual position node, according to the transfer relationship between the position nodes, thereby implementing automatic identification of whether the target vehicle is in an abnormal use state, so that the target vehicle in the abnormal use state can be processed in time, risk control of the shared vehicle is implemented, and loss of the operator is reduced.
According to the above steps S2100 to S2400, the abnormal vehicle recognition method of the present embodiment performs the abnormal recognition based on the positioning information of the target vehicle, and does not depend on the user data, and therefore, the method of the present embodiment can be applied to the abnormal recognition in which the user privies the use of the vehicle by breaking the lock mechanism of the vehicle.
In one embodiment, the method may further comprise the step of acquiring the target vehicle.
In this embodiment, abnormality recognition may be performed with an arbitrary vehicle as a target vehicle.
In this embodiment, the target vehicle may also be determined based on the abnormal report, so that on one hand, the investigation range may be narrowed, and on the other hand, the accuracy of the abnormal report may also be determined. In this regard, the step of acquiring the target vehicle may include: in response to receiving a reporting event regarding an abnormal vehicle, a vehicle to which the reporting event is directed is acquired as a target vehicle.
In this embodiment, a vehicle whose position is to be shifted without a vehicle use request may be searched in the database as a target vehicle. In this way, the scope of the investigation can be reduced on the one hand.
In one embodiment, after identifying that the target vehicle is in the abnormal use state in step S2300, the method of the present invention may further include the steps of:
step S3100, the vehicle information of the target vehicle is transmitted to an account of the operator for rechecking.
The vehicle information includes at least a unique identification of the target vehicle and may also include current location information of the target vehicle.
According to the step S3100, after receiving the rechecking task, the operator may lock the current position of the vehicle according to the vehicle information of the vehicle to be rechecked, and arrive at the current position of the vehicle to recheck whether the vehicle is actually occupied. And after rechecking, the operator can feed back a rechecking result to the server through the mobile terminal of the operator.
Step S3200, obtaining a rechecking result returned by the account of the operator after rechecking.
In step S3300, when the check result indicates that the target vehicle is privately occupied, the operation of performing the abnormality processing set in step S2400 is executed again.
In this embodiment, when the rechecking result indicates that the target vehicle is not privately occupied, the current identification processing of the target vehicle is ended.
According to the method of the embodiment, the high accuracy of exception handling can be ensured by adding the rechecking link, and the situations of error identification and error handling can not occur.
In addition, according to the method of the embodiment, the target vehicle combination after being reviewed by the operator can be used as a new training sample to participate in self-learning training for updating the probability threshold and/or the setting range of the quantity, so that the accuracy of abnormality identification is improved.
< apparatus embodiment >
In the present embodiment, there is also provided an abnormal vehicle recognition apparatus, and as shown in fig. 5, the abnormal vehicle recognition apparatus 5000 may include an information acquisition module 5100, a calculation module 5200, an abnormality recognition module 5300, and an abnormality processing module 5400.
The information obtaining module 5100 is configured to obtain, according to the positioning information about the target vehicle, each location node that the target vehicle has reached and transfer information between the location nodes.
The calculation module 5200 is configured to obtain, according to the transfer information between the location nodes, a probability that the target vehicle reaches each of the location nodes.
The abnormal recognition module 5300 is configured to recognize whether the target vehicle is in an abnormal use state according to the distribution of the probability.
The exception handling module 5400 is used for performing set exception handling when the abnormal use state is present.
In one embodiment, the transfer information includes a transfer relationship between location nodes, and the calculation module 5200, when obtaining the probability that the target vehicle reaches an arbitrary location node according to the transfer information between the location nodes, may be configured to: according to the transfer relation, obtaining the transfer probability that the target vehicle respectively reaches each position node in the obtained position nodes from the position node; and acquiring a convergence value of the probability that the target vehicle reaches the position node according to the transition probability and the set initial value of the probability that the target vehicle reaches the position node.
In one embodiment, the anomaly identification module 5300, when identifying whether the target vehicle is in an abnormal use state according to the distribution of the probabilities, may be configured to: acquiring the number of position nodes of which the probability is greater than or equal to a set probability threshold; in the case where the number is within the set range, it is recognized that the target vehicle is in an abnormal use state.
In one embodiment, the abnormal vehicle identification apparatus 5000 may further include a parameter determination module, which when acquiring the probability threshold, may be configured to: obtaining a known vehicle in an abnormal use state as a training sample; according to the positioning information of the training sample, obtaining position nodes reached by the training sample and transfer information among the position nodes of the training sample; obtaining the probability of the training sample reaching each corresponding position node according to the transfer information among the position nodes of the training sample; and determining the probability threshold according to the distribution condition of the probability of the training sample reaching each corresponding position node.
In one embodiment, the abnormal vehicle identification apparatus 5000 may further include a target vehicle acquisition module, which may be configured to perform at least one of the following when acquiring the target vehicle: the first item: searching a vehicle with a position changed without a vehicle using request as the target vehicle; the second term is: in response to receiving a reporting event regarding an abnormal vehicle, acquiring a vehicle to which the reporting event is directed as the target vehicle.
In one embodiment, the information obtaining module 5100, when obtaining the location nodes reached by the target vehicle according to the positioning information about the target vehicle, may be configured to: acquiring each position area divided in advance, wherein one position area corresponds to one position node; obtaining a position area corresponding to each piece of positioning information according to the positioning information of the target vehicle; and acquiring each position node reached by the target vehicle according to the position area corresponding to each piece of positioning information.
In one embodiment, the exception handling module 5400, when performing the set exception handling, may be configured to: and sending the vehicle information of the target vehicle to an account number of an operator for retrieval intervention.
In one embodiment, the abnormal vehicle identification apparatus 5000 may further include a checking module, configured to send the vehicle information of the target vehicle to an account of an operator for rechecking after the abnormal identification module 5300 identifies that the target vehicle is in the abnormal use state; obtaining a rechecking result returned by the account of the operator after rechecking; and a step of notifying the abnormality processing module 5400 of execution of the set abnormality processing when the rechecking result is that the target vehicle is privately occupied.
< apparatus embodiment >
In the present embodiment, there is also provided an electronic device 6000, as shown in fig. 6, which may include an abnormal vehicle recognition apparatus 6000 according to any embodiment of the present invention for implementing the abnormal vehicle recognition method according to any embodiment of the present invention.
In further embodiments, the electronic device 6000 may also include a processor 6100 and a memory 6200, the memory 6200 for storing executable computer instructions; the processor 6200 is configured to operate the electronic device 6000 according to the control of the instruction to execute the abnormal vehicle identification method according to any embodiment of the present invention.
The electronic device 6000 may be, for example, the server 1000 in fig. 1, or may be another terminal device, which is not limited herein.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims (10)

1. An abnormal vehicle identification method comprising:
according to the positioning information of the target vehicle, obtaining each position node reached by the target vehicle and transfer information among the position nodes;
obtaining the probability of the target vehicle reaching each position node according to the transfer information among the position nodes;
identifying whether the target vehicle is in an abnormal use state or not according to the probability distribution condition;
when the device is in the abnormal use state, the set abnormal processing is performed.
2. The method according to claim 1, wherein the transition information includes a transition relationship between the location nodes, and in the obtaining of the probability that the target vehicle reaches each of the location nodes according to the transition information between the location nodes, obtaining the probability that the target vehicle reaches any of the location nodes includes:
according to the transfer relation, obtaining the transfer probability that the target vehicle respectively reaches each position node in the position nodes from the position nodes;
and acquiring a convergence value of the probability of the target vehicle reaching the position node according to the transition probability and the set initial value of the probability of the target vehicle reaching the position node.
3. The method of claim 1, wherein the identifying whether the target vehicle is in an abnormal use state according to the distribution of the probabilities comprises:
acquiring the number of position nodes of which the probability is greater than or equal to a set probability threshold;
in the case where the number is within a set range, it is recognized that the target vehicle is in an abnormal use state.
4. The method of claim 3, wherein the method further comprises the step of obtaining the probability threshold, comprising:
obtaining a known vehicle in an abnormal use state as a training sample;
according to the positioning information of the training sample, obtaining position nodes reached by the training sample and transfer information among the position nodes of the training sample;
obtaining the probability of the training sample reaching each corresponding position node according to the transfer information among the position nodes of the training sample;
and determining the probability threshold according to the distribution condition of the probability of the training sample reaching each corresponding position node.
5. The method of claim 1, wherein the method further comprises the step of acquiring the target vehicle comprising at least one of:
the first item: searching a vehicle with a position changed without a vehicle using request as the target vehicle;
the second term is: in response to receiving a reporting event regarding an abnormal vehicle, acquiring a vehicle to which the reporting event is directed as the target vehicle.
6. The method of claim 1, wherein the obtaining location nodes reached by a target vehicle according to positioning information for the target vehicle comprises:
acquiring each position area divided in advance, wherein one position area corresponds to one position node;
obtaining a position area corresponding to each piece of positioning information according to the positioning information of the target vehicle;
and acquiring each position node reached by the target vehicle according to the position area corresponding to each piece of positioning information.
7. The method of claim 1, wherein the set exception handling comprises:
and sending the vehicle information of the target vehicle to an account number of an operator for retrieval intervention.
8. The method of any of claims 1-7, wherein after identifying that the target vehicle is in an abnormal use state, the method further comprises:
sending the vehicle information of the target vehicle to an account of an operator for rechecking;
obtaining a rechecking result returned by the account of the operator after rechecking;
and a step of performing the set exception processing again when the rechecking result indicates that the target vehicle is privately occupied.
9. An abnormal vehicle recognition device comprising:
the information acquisition module is used for acquiring each position node reached by the target vehicle and transfer information among the position nodes according to the positioning information of the target vehicle;
the calculation module is used for obtaining the probability of the target vehicle reaching each position node according to the transfer information among the position nodes;
the abnormal recognition module is used for recognizing whether the target vehicle is in an abnormal use state or not according to the distribution condition of the probability; and the number of the first and second groups,
and the exception handling module is used for carrying out set exception handling under the condition of an abnormal use state.
10. An electronic device comprising the abnormal vehicle recognition apparatus of claim 9; alternatively, the first and second electrodes may be,
the electronic device comprises a memory for storing an executable computer program and a processor; a processor for operating the electronic device under control of the computer program to perform the method of any one of claims 1 to 8.
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CN111126774A (en) * 2019-11-26 2020-05-08 北京摩拜科技有限公司 Abnormal vehicle identification method and device and electronic equipment
CN111091393B (en) * 2019-11-26 2023-09-05 汉海信息技术(上海)有限公司 Abnormal account identification method and device and electronic equipment
CN112929816A (en) * 2021-02-01 2021-06-08 北京嘀嘀无限科技发展有限公司 Vehicle abnormal behavior recognition method, device, medium, and computer program product
CN112929816B (en) * 2021-02-01 2023-06-30 北京嘀嘀无限科技发展有限公司 Vehicle abnormal behavior recognition method, device, medium and computer program product
CN113434616A (en) * 2021-06-18 2021-09-24 上海连尚网络科技有限公司 Method, apparatus, medium, and program product for managing shared vehicles
CN115424443A (en) * 2022-09-20 2022-12-02 安徽江淮汽车集团股份有限公司 Vehicle abnormity monitoring method based on driving data

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