CN110164137B - Method, system and medium for identifying fake-licensed vehicle based on driving time of bayonet pair - Google Patents

Method, system and medium for identifying fake-licensed vehicle based on driving time of bayonet pair Download PDF

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CN110164137B
CN110164137B CN201910412526.3A CN201910412526A CN110164137B CN 110164137 B CN110164137 B CN 110164137B CN 201910412526 A CN201910412526 A CN 201910412526A CN 110164137 B CN110164137 B CN 110164137B
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bayonet
vehicle
fake
driving time
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CN110164137A (en
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李建华
杨杰
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HUNAN CREATOR INFORMATION TECHNOLOGIES CO LTD
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HUNAN CREATOR INFORMATION TECHNOLOGIES CO LTD
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    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
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Abstract

The invention discloses a method and a system for identifying a fake-licensed vehicle based on a bayonet for driving time, a method and a system for identifying a fake-licensed vehicle based on a bayonet for deep learning of driving time, and a computer readable storage medium. According to the identification method and the identification system of the fake-licensed vehicle based on the bayonet for the driving time, the track data of the vehicle running bayonet is subjected to statistical analysis and data modeling, so that the identification accuracy is good, particularly the accuracy and the efficiency of checking the fake-licensed vehicle are powerfully improved by combining the registration information of the number plate vehicle, the vehicle operation environment is beautified, and the life and property safety of people is guaranteed. In addition, the continuous characteristics of the vehicle track can be obtained under the condition that the road network and distance data are not needed, meanwhile, missed shot data can be compatible, and compared with the method that the track continuity is determined through calculating the distance or according to the road network, the method is better in effect and lower in complexity.

Description

Method, system and medium for identifying fake-licensed vehicle based on driving time of bayonet pair
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a method and a system for identifying a fake-licensed vehicle based on driving time by a bayonet and a computer-readable storage medium.
Background
The fake-plate vehicles, commonly called clone vehicles, refer to the vehicles with the same type and color, and the fake-plates with the same number are sleeved on the vehicles with the same type and color. The fake-licensed car owner can apply the license plate of other people, so when the fake-licensed car owner drives on the road, behaviors of seriously violating traffic regulations such as randomly putting through red lights, rolling yellow lines and the like are made, serious potential safety hazards are brought to traffic safety, or mutual fake-licensed in families and friend circles are carried out, so that related inspection and cost are avoided. Because the fake-licensed vehicle has the characteristics of strong concealment, difficult investigation and evidence collection and the like, and no effective method or system for realizing automatic identification of the fake-licensed vehicle exists at present, how to accurately identify the fake-licensed vehicle becomes an urgent problem to be solved by the administrative department of transportation and management, and becomes the key point for managing and controlling the fake-licensed vehicle.
Disclosure of Invention
The invention provides a method and a system for identifying a fake-licensed vehicle based on the driving time of a bayonet and a computer readable storage medium, which aim to solve the technical problem that the prior art cannot accurately identify the fake-licensed vehicle.
According to one aspect of the invention, a method for identifying a fake-licensed vehicle based on a driving time by a bayonet comprises the following steps:
step S1: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S2:establishing a Cartesian product K of the bayonet set K, and combining all bayonet pairs to form a two-dimensional matrix K of n x n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S3: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTSequence K of the bayonet tracks of the number plateTThe two bayonets passing through the middle part and the front part form a bayonet pair so as to obtain a license plate bayonet pair track sequence KdThen, the number plate bayonet is aligned with the track sequence KdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S4: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S5: two-dimensional matrix K of driving time threshold value of bayonet pair established based on driving time of bayonet pair and minimum threshold value of driving time of bayonet pairn；
Step S6: modeling the driving time matrix by the one-dimensional bayonet to obtain a driving time matrix K of the bayonetb(ii) a And
step S7: time matrix K of card port pair driving of each number plate is countedbThe middle bayonet obtains a boundary P value of u +/-3 sigma by applying normal distribution to the probability P that the driving time is 0, and the license plates outside the u +/-3 sigma interval are fake-licensed vehicles; the number plates within the u + -3 sigma interval and near the boundary P value are suspected fake-licensed vehicles.
Further, the step S4 is specifically mentioned
According to the vehicle passing bayonet pair record in the step S3, the vehicle passes through the bayonet pair (k)i，kj) Middle bayonet kiIs recorded as tkiPassing through bayonet kjIs recorded as tkjThen bayonet pair (k)i，kj) Has a travel time of tij＝tkj－tki；
Paired bayonet (k)i，kj) All driving time tijUsing normal distribution, taking t of u-3 sigmaijAs a bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj) (ii) a Or finding the driving time t of each bayonet pair by using an outlier algorithm or a clustering algorithmijThe threshold value of the outlier is the bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj)。
Further, the step S5 is specifically:
initially, the bayonet is aligned to the two-dimensional matrix KmBayonet pair (k) ini，kj) The value is assigned to 0, and then the bayonet is paired with the two-dimensional matrix KmIn a corresponding bayonet pair (k)i，kj) Replacing with a minimum time threshold t (k) of the driving time of the bayoneti，kj) To obtain a two-dimensional matrix K of the driving time threshold value of the bayonet pairn。
Further, the step S6 is specifically:
pairing the adjacent bayonets of the number plate with the track sequence KaThe bayonet pair is replaced by the corresponding bayonet pair driving time tijThen, the track sequence K is paired by adjacent bayonets of the number plateaIn the bayonet pair search bayonet pair driving time threshold value two-dimensional matrix KnTime threshold in (1) if bayonet is to driving time tijLess than KnSetting the medium time threshold value to be 0, otherwise, keeping the medium time threshold value unchanged, and aligning the adjacent bayonets of the number plate with the track sequence KaBecome bayonet to driving time matrix Kb。
Further, the identification method of the fake-licensed vehicle further comprises the following steps:
step S8: and storing the number plate of the identified fake-licensed vehicle and the number plate of the suspected fake-licensed vehicle into a fake-licensed vehicle database.
Further, the identification method of the fake-licensed vehicle further comprises the following steps:
step S9: capturing the fake-licensed vehicles;
the step S9 includes:
step S91: obtaining card port data and card port longitude and latitude of the fake-licensed vehicle or suspected fake-licensed vehicle within a period of time, and extracting a high-frequency card port and a high-frequency period of time from the card port data and the card port longitude and latitude as a hot spot card port;
step S92: photographing a vehicle to obtain a vehicle number plate, or manually inputting the vehicle number plate, or accessing the number plate transmitted by a bayonet in real time; and
step S93: and comparing the obtained vehicle number plate with the fake-licensed vehicle database at the hot spot bayonet, if the comparison result is the fake-licensed vehicle, immediately outputting alarm information, calling out the information of the vehicle to which the number plate belongs from the traffic vehicle management data system, and assisting a traffic police to catch the fake-licensed vehicle.
The invention also provides a system for identifying the fake-licensed vehicle based on the driving time of the bayonet, which is suitable for the identification method and comprises the steps of
The data acquisition module is used for acquiring basic data required by fake-licensed vehicle identification,
the statistical analysis module is used for performing statistical analysis on the vehicle running track data and identifying the fake-licensed vehicle;
the controller is used for controlling the data acquisition module to acquire basic data and controlling the statistical analysis module to perform statistical analysis and identify the fake-licensed vehicles;
the controller is respectively connected with the data acquisition module and the statistical analysis module, and the data acquisition module is connected with the statistical analysis module.
Further, the identification system also comprises
The fake-licensed vehicle capturing terminal is connected with the controller and is used for capturing fake-licensed vehicles;
the fake-licensed car capturing terminal comprises
The hot spot bayonet acquiring unit is used for acquiring bayonet data and bayonet longitude and latitude of the fake-licensed vehicle or the suspected fake-licensed vehicle within a period of time, and extracting a high-frequency bayonet and a high-frequency period of time from the bayonet data and the bayonet longitude and latitude as the hot spot bayonet;
the license plate acquisition unit is used for photographing the vehicle to acquire a vehicle license plate or manually inputting the vehicle license plate or accessing the license plate transmitted by the card port in real time;
and the capturing unit is used for comparing the obtained vehicle number plate with the fake-licensed vehicle database at the hot spot bayonet, immediately outputting alarm information if the comparison result is the fake-licensed vehicle, calling out the information of the vehicle to which the number plate belongs from the traffic vehicle management data system, and assisting a traffic police in capturing the fake-licensed vehicle.
The invention also provides a computer readable storage medium for storing a computer program for identifying a fake-licensed vehicle based on the driving time of a card gate, wherein the computer program executes the following steps when running on a computer:
step S1: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S2: establishing a Cartesian product K of the bayonet set K, and combining all bayonet pairs to form a two-dimensional matrix K of n x n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S3: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTSequence K of the bayonet tracks of the number plateTThe two bayonets passing through the middle part and the front part form a bayonet pair so as to obtain a license plate bayonet pair track sequence KdThen, the trajectory sequence K is matched from the number plate bayonetdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S4: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S5: two-dimensional matrix K for establishing driving time threshold value of bayonet pair based on driving time of bayonet pairn；
Step S6: one-dimensional bayonet-pair traveling craneModeling time matrix to obtain driving time matrix K of bayonet pairb(ii) a And
step S7: time matrix K of card port pair driving of each number plate is countedbThe middle bayonet obtains a boundary P value of u +/-3 sigma by applying normal distribution to the probability P that the driving time is 0, and the license plates outside the u +/-3 sigma interval are fake-licensed vehicles; within the u +/-3 sigma interval, the number plate near the boundary P value is a suspected fake-licensed car.
The invention also provides a method for identifying the fake-licensed vehicle for deeply learning the driving time based on the bayonet, which comprises the following steps:
step S100: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S200: establishing a Cartesian product K of the bayonet set K, and combining all bayonet pairs to form a two-dimensional matrix K of n x n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S300: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTForming a bayonet pair by two bayonets passing through the front and the back in the license plate bayonet track sequence to obtain a license plate bayonet pair track sequence KdThen, the trajectory sequence K is matched from the number plate bayonetdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S400: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S500: two-dimensional matrix K for establishing driving time threshold value of bayonet pair based on driving time of bayonet pairn；
Step S600: modeling the driving time matrix of the one-dimensional bayonet to obtain the driving time of the bayonetMatrix Kb(ii) a And
step S700: time matrix K of card port pair driving of each number plate is countedbThe probability P of 0 occurrence of the driving time by the middle bayonet is applied to normal distribution, and P corresponding to the u value is obtaineduA value; and
step S800: and carrying out deep learning by utilizing a convolutional neural network to realize intelligent identification of the fake-licensed vehicle.
Further, the step S800 specifically includes the following steps:
step S801: selection of PuOne-dimensional bayonet pair driving time matrix K of x number plates with values close to each otherbAs a positive sample E+；
Step S802: randomly combining all the number plates into a new vehicle to generate a new temporary number plate, combining the bayonet track data of the new temporary number plate and the temporary number plate, repeating the steps S300-S700, and selecting PuOne-dimensional bayonet pair driving time matrix K of x number plates with values close to each otherbAs a negative sample E-；
Step S803: unified positive sample E+Negative sample E-And a one-dimensional bayonet pair driving time matrix KbNormalizing the length of the sample;
step S804: a positive sample E+And negative example E-Inputting a convolutional neural network, performing feature learning, and classifying the features learned by the convolutional network by using a softmax classifier to obtain a one-dimensional deep learning-based intelligent identification model R of the fake-licensed vehicleAIThen, the verification data is used for continuously adjusting the value intervals, the sample lengths and the convolutional neural network parameters of the threshold, the date, the x and the x to train so as to obtain the optimal intelligent identification model R of the fake-licensed vehicleAI(ii) a And
step S805: intelligent recognition model R for vehicle by utilizing optimal fake-licensedAIFor all vehicles, the one-dimensional bayonet pair driving time matrix KbAnd identifying to obtain a set of fake plate and number plate.
The invention also provides a system for identifying the fake-licensed vehicle for deeply learning the driving time based on the bayonet, which comprises
The data acquisition module is used for acquiring basic data required by fake-licensed vehicle identification,
the deep learning module is used for carrying out data modeling, statistical analysis, deep learning and fake-licensed vehicle identification on the vehicle running track data;
the controller is used for controlling the data acquisition module to acquire basic data and controlling the deep learning module to perform data modeling, statistical analysis, deep learning and fake-licensed vehicle identification;
the controller is respectively connected with the data acquisition module and the deep learning module, and the deep learning module is connected with the data acquisition module.
The invention also provides a computer-readable storage medium for storing a computer program for deep learning of driving time based on a gate to identify a fake-licensed vehicle, the computer program executing the following steps when running on a computer:
step S100: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S200: establishing a Cartesian product K of the bayonet set K, and combining all bayonet pairs to form a two-dimensional matrix K of n x n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S300: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTForming a bayonet pair by two bayonets passing through the front and the back in the license plate bayonet track sequence to obtain a license plate bayonet pair track sequence KdThen, the trajectory sequence K is matched from the number plate bayonetdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S400: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S500: two-dimensional matrix K for establishing driving time threshold value of bayonet pair based on driving time of bayonet pairn；
Step S600: modeling the driving time matrix by the one-dimensional bayonet to obtain a driving time matrix K of the bayonetb(ii) a And
step S700: time matrix K of card port pair driving of each number plate is countedbThe probability P of 0 occurrence of the driving time by the middle bayonet is applied to normal distribution, and P corresponding to the u value is obtaineduA value; and
step S800: and carrying out deep learning by utilizing a convolutional neural network to realize intelligent identification of the fake-licensed vehicle.
The invention has the following beneficial effects:
the identification method of the fake-licensed vehicle based on the bayonet for the driving time has good identification accuracy by carrying out statistical analysis and data modeling on the track data of the vehicle running bayonet, particularly combines the registration information of the number plate vehicle, powerfully improves the accuracy and efficiency of checking the fake-licensed vehicle, beautifies the vehicle operating environment and ensures the life and property safety of people. In addition, the continuous characteristics of the vehicle track can be obtained under the condition that the road network and distance data are not needed, meanwhile, missed shot data can be compatible, and compared with the method that the track continuity is determined through calculating the distance or according to the road network, the method is better in effect and lower in complexity.
The identification system of the fake-licensed vehicle based on the bayonet pair driving time also has the advantages.
The fake-licensed vehicle identification method for deeply learning the driving time based on the bayonet comprises the steps of carrying out statistical analysis on vehicle running bayonet track data, carrying out data modeling, learning and training by utilizing a convolutional neural network, and implicitly learning from training data by adopting the convolutional neural network, wherein the convolutional neural network avoids explicit characteristic extraction and errors introduced in threshold setting; the convolutional neural network has unique superiority in the aspect of image processing, and particularly, the characteristic that one-dimensional vectors can be directly input into the network avoids the complexity of data reconstruction in the processes of feature extraction and classification. In addition, the continuous characteristics of the vehicle track can be obtained under the condition that the road network and distance data are not needed, meanwhile, missed shot data can be compatible, and compared with the method that the track continuity is determined through calculating the distance or according to the road network, the method is better in effect and lower in complexity.
The identification system of the fake-licensed vehicle for deeply learning the driving time based on the bayonet has the advantages.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a method for identifying a fake-licensed vehicle based on a driving time by a bayonet according to a preferred embodiment of the invention.
Fig. 2 is a sub-flowchart of step S9 in fig. 1 according to the preferred embodiment of the present invention.
Fig. 3 is a schematic block diagram of a system for identifying a fake-licensed vehicle based on a card gate versus a driving time according to another embodiment of the present invention.
Fig. 4 is a block diagram of the deck truck capture terminal of fig. 3 in accordance with another embodiment of the present invention.
Fig. 5 is a schematic block diagram of the data acquisition module in fig. 3 according to another embodiment of the present invention.
FIG. 6 is a block diagram of the data cleansing module shown in FIG. 3 according to another embodiment of the present invention.
Fig. 7 is a schematic block diagram of the statistical analysis module in fig. 3 according to another embodiment of the present invention.
Fig. 8 is a flow chart of a method for identifying a fake-licensed vehicle based on a bayonet deep learning of driving time according to another embodiment of the present invention.
Fig. 9 is a sub-flowchart of step S800 in fig. 8 according to another embodiment of the present invention.
Fig. 10 is a schematic block diagram of a system for recognizing a fake-licensed vehicle based on a bayonet deep learning of driving time according to another embodiment of the present invention.
Fig. 11 is a schematic block diagram of the deep learning module in fig. 10 according to another embodiment of the present invention.
The reference numbers illustrate:
11. a controller; 12. a data acquisition module; 13. a data cleaning module; 14. a statistical analysis module; 17. a fake-licensed car capture terminal; 121. a number plate collecting unit; 122. a card port data acquisition unit; 123. a card port longitude and latitude acquisition unit; 131. a data selection unit; 132. a data cleaning unit; 133. a data conversion unit; 141. a bayonet driving time threshold matrix modeling unit; 142. the one-dimensional bayonet is a traffic time matrix modeling unit; 143. a statistical analysis recognition unit; 171. a hot spot bayonet acquisition unit; 172. a number plate collecting unit; 173. a capturing unit; 21. a controller; 22. a data acquisition module; 23. a data cleaning module; 25. a deep learning module; 27. a fake-licensed car capture terminal; 251. a bayonet driving time threshold matrix modeling unit; 252. the one-dimensional bayonet is a traffic time matrix modeling unit; 253. a sample selection unit; 254. and a deep learning unit.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the accompanying drawings, but the invention can be embodied in many different forms, which are defined and covered by the following description.
As shown in fig. 1, the preferred embodiment of the present invention provides a method for identifying a fake-licensed vehicle based on a driving time of a card gate, which is used for effectively identifying the fake-licensed vehicle. The identification method of the fake-licensed vehicle comprises the following steps:
step S1: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S2: establishing a two-dimensional matrix K of bayonet pairsmSpecifically, a cartesian product K x K of the bayonet set K itself is established, and all bayonet pair groups are poorCombining to form a two-dimensional matrix K of n-x-n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S3: establishing vehicle passing bayonet pair record and obtaining track sequence K of adjacent bayonets of license plateaSpecifically, the running track of each vehicle is obtained, and the gate track data of each vehicle is sequenced according to all gates passing through in the sequence of running time to obtain the number plate gate track sequence K of each vehicleTSequence K of the bayonet tracks of the number plateTThe two bayonets passing through the middle part and the front part form a bayonet pair so as to obtain a license plate bayonet pair track sequence KdThen, the number plate bayonet is aligned with the track sequence KdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S4: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S5: establishing a two-dimensional matrix K of a bayonet pair driving time threshold valuen；
Step S6: modeling the driving time matrix by the one-dimensional bayonet to obtain a driving time matrix K of the bayonetb(ii) a And
step S7: identifying the number plate of the fake-licensed vehicle and the number plate of the suspected fake-licensed vehicle, specifically, counting a driving time matrix K of a bayonet of each number platebThe middle bayonet obtains a boundary P value of u +/-3 sigma by applying normal distribution to the probability P that the driving time is 0, and the license plates outside the u +/-3 sigma interval are fake-licensed vehicles; within the u +/-3 sigma interval, the number plate near the boundary P value is a suspected fake-licensed car.
It is understood that, in the step S1, the bayonet number is obtained from a traffic bayonet system.
It can be understood that the two-dimensional matrix K of bayonet pairs obtained in step S2mIs composed of
Figure BDA0002063270510000091
It is understood that in step S3, vehicle gate data is obtained from the traffic gate system, and the vehicle gate data at least includes a vehicle number plate, a gate number, an elapsed time, and a gate photo, and a vehicle trajectory database is generated according to the vehicle gate data. For example, a vehicle passes A, C, F, B … … K in sequenceiBayonet, then the license plate bayonet track sequence K of the vehicleT＝＝{A，C，F，B……kiThe bayonet track sequence K of the number plateTThe vehicle driving direction is implied, namely the bayonet direction; then, the number plate is bayonet track sequence KTThe two bayonets passing through the middle and the front form a bayonet pair (k)i，kj) And obtaining a number plate bayonet pair track sequence Kd＝{(A，A)，(A，C)，(A，B)，(C，B)，(C，F)，(F，B)……(ki，kj) Wherein (a, a) represents that the vehicle turns around at the a gate, (a, C) represents that the vehicle drives from the a gate to the C gate, (a, B) represents that the vehicle drives from the a gate to the B gate, … …; then, the card ports of the number plates are aligned to the track sequence KdThe bayonet pairs in the driving track of each vehicle are extracted to form vehicle passing bayonet pair records, wherein the bayonet pairs (k) arei，kj) Indicating that the vehicle first passes through the gate kiNext pass through the bayonet kjThe format is as follows:
bayonet ki Bayonet kj Passing through a bayonet kiTime of Passing through a bayonet kjTime of License plate number
Finally, the number plate bayonet track sequence KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea＝{(A，C)，(C，F)，(F，B)……(ki，kj)}. Wherein, the bayonet track sequence K of the number plateTFor the continuous passage of vehicles, thus a sequence of bayonet-pair trajectories KdIs adjacent to the bayonet in the geographic position and the bayonet direction and is adjacent to the kiThe adjacent bayonets are usually about 4.
It is also understood that, in the step S3, the trajectory data of the corresponding vehicle operation gate may be extracted according to the date range T, the vehicle type, and the operation type, while the trajectory data of other types of vehicles and other time periods are hidden, and of course, the trajectory data of multiple types of vehicles may be selected at the same time. In addition, incomplete data, wrong data and repeated data can be filtered, and inconsistent information of the extracted vehicle running track data can be converted to be uniform.
It can be understood that step S4 specifically includes:
according to the vehicle passing bayonet pair record in the step S3, the vehicle passes through the bayonet pair (k)i，kj) Middle bayonet kiIs recorded as tkiPassing through bayonet kjIs recorded as tkjThen bayonet pair (k)i，kj) Has a travel time of tij＝tkj－tkiThe format is as follows:
bayonet ki Bayonet kj Time of passing through bayonet ki Time of passing through the bayonet kj Running time tij License plate number
Paired bayonet (k)i，kj) All driving time tijUsing normal distribution, taking t of u-3 sigmaijAs a bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj) (ii) a Or finding the driving time t of each bayonet pair by using an outlier algorithm or a clustering algorithmijThe threshold value of the outlier is the bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj)。
It is understood that, in step S5, specifically:
initially, the bayonet is aligned to the two-dimensional matrix KmBayonet pair (k) ini，kj) The value is assigned to 0, and then the bayonet is paired with the two-dimensional matrix KmIn a corresponding bayonet pair (k)i，kj) Replacing with a minimum time threshold t (k) of the driving time of the bayoneti，kj) To obtain a two-dimensional matrix K of the driving time threshold value of the bayonet pairn，KnAs shown below
Figure BDA0002063270510000101
Figure BDA0002063270510000111
Understandably, due to the number plate bayonet track sequence KTA gate for the continuous passage of vehicles, thus a sequence of card gates versus track KdThe adjacent bayonets which are adjacent in sequence have the shortest driving time, the non-adjacent bayonets have longer time, and the influence on the minimum time threshold is very small even if the bayonets are damaged, mistakenly shot, cleaned by data, fake plates and other factors influencing the driving time due to the larger sample size, so that the recognition result is very accurate.
It is understood that the step S6 is specifically embodied as
Pairing the adjacent bayonets of the number plate with the track sequence KaThe bayonet pair is replaced by the corresponding bayonet pair driving time tijThen, the track sequence K is paired by adjacent bayonets of the number plateaIn the bayonet pair search bayonet pair driving time threshold value two-dimensional matrix KnTime threshold in (1) if bayonet is to driving time tijTwo-dimensional matrix K smaller than driving time thresholdnThe time threshold value in the step (1) is set to be 0, otherwise, the time threshold value is not changed, and the number plate is adjacent to the bayonet pair track sequence KaBecome bayonet to driving time matrix KbE.g. Kb＝{t(A，C)，t(C，F)，0……t(ki，kj) A driving time of a bayonet from the F bayonet to the B bayonet is smaller than a driving time threshold two-dimensional matrix KnAnd thus t (F, B) is set to 0.
It is understood that, as a preferred method, the method for identifying the fake-licensed vehicle further comprises the following steps:
step S8: and storing the number plate of the identified fake-licensed vehicle and the number plate of the suspected fake-licensed vehicle into a fake-licensed vehicle database for subsequent capture of the fake-licensed vehicle.
It is understood that, as a preferred method, the method for identifying the fake-licensed vehicle further comprises the following steps:
step S9: the fake-licensed vehicles are captured.
As shown in fig. 2, the step S9 specifically includes:
step S91: obtaining card slot data and card slot longitude and latitude of the fake-licensed vehicle or suspected fake-licensed vehicle within a period of time, then clustering card slot moving positions of each vehicle, and extracting a high-frequency card slot in a cluster and a high-frequency period as a hot spot card slot;
step S92: photographing a vehicle to obtain a vehicle number plate, or manually inputting the vehicle number plate, or accessing the number plate transmitted by a bayonet in real time; and
step S93: and comparing the obtained vehicle number plate with the fake-licensed vehicle database at the hot spot bayonet, if the comparison result is the fake-licensed vehicle, immediately outputting alarm information, calling out the information of the vehicle to which the number plate belongs from the traffic vehicle management data system, and assisting a traffic police to catch the fake-licensed vehicle.
The identification method of the fake-licensed vehicle based on the bayonet for the driving time has good identification accuracy by carrying out statistical analysis and data modeling on the track data of the vehicle running bayonet, particularly combines the registration information of the number plate vehicle, powerfully improves the accuracy and efficiency of checking the fake-licensed vehicle, beautifies the vehicle operating environment and ensures the life and property safety of people. In addition, the continuous characteristics of the vehicle track can be obtained under the condition that the road network and distance data are not needed, meanwhile, missed shot data can be compatible, and compared with the method that the track continuity is determined through calculating the distance or according to the road network, the method is better in effect and lower in complexity.
It can be understood that, as shown in fig. 3, another embodiment of the present invention further provides a system for identifying a card-passing vehicle based on a driving time, which is preferably applied to the method for identifying a card-passing vehicle based on a driving time, as described above, and the system comprises
A data acquisition module 12 for acquiring basic data required for fake-licensed vehicle identification,
the statistical analysis module 14 is used for performing statistical analysis on the vehicle running track data and identifying the fake-licensed vehicle;
the controller 11 is used for controlling the data acquisition module 12 to acquire basic data and controlling the statistical analysis module 14 to perform statistical analysis and identify fake-licensed vehicles;
the controller 11 is respectively connected with the data acquisition module 12 and the statistical analysis module 14, and the data acquisition module 12 is connected with the statistical analysis module 14.
It will be appreciated that the identification system preferably further comprises
And the fake-licensed vehicle capturing terminal 17 is connected with the controller 11 and is used for capturing fake-licensed vehicles. The controller 11 is further configured to push information of the fake-licensed vehicle to the fake-licensed vehicle capturing terminal 17.
As shown in FIG. 4, the fake-licensed vehicle capturing terminal 17 comprises
The hot spot bayonet acquiring unit 171 is configured to acquire bayonet data and bayonet longitude and latitude of the fake-licensed vehicle or the suspected fake-licensed vehicle within a certain period of time, and extract a high-frequency bayonet and a high-frequency period of time from the bayonet data and the bayonet longitude and latitude as the hot spot bayonet;
the license plate acquisition unit 172 is used for photographing the vehicle to acquire a vehicle license plate, or manually inputting the vehicle license plate, or accessing the license plate transmitted by the gate in real time;
and the capturing unit 173 is configured to compare the obtained vehicle license plate with the fake-licensed vehicle database at the hot spot gate, and if the comparison result is a fake-licensed vehicle, immediately output warning information, call out information of the vehicle to which the license plate belongs from the traffic management data system, and assist the traffic police in capturing the fake-licensed vehicle.
As shown in FIG. 5, the data acquisition module 12 includes
The license plate acquisition unit 121 is used for acquiring vehicle license plate data from a traffic management data system, wherein the vehicle license plate data at least comprises a vehicle license plate, a vehicle type, an operation type and the like, and generating a vehicle license plate database;
the gate data acquisition unit 122 is used for acquiring vehicle gate data from a traffic gate system, wherein the vehicle gate data at least comprises a vehicle number plate, a gate number, elapsed time and a gate photo, and a vehicle running track database is generated;
and the card slot longitude and latitude acquisition unit 123 is used for acquiring the geographic position data of the card slot from the traffic card slot system, wherein the geographic position data of the card slot at least comprises a card slot number, a card slot affiliated position area and card slot longitude and latitude, and generating a card slot longitude and latitude database.
It will be appreciated that the identification system preferably further comprises
The data cleaning module 13 is respectively connected with the controller 11, the data acquisition module 12 and the statistical analysis module 14, and is used for extracting corresponding vehicle operation bayonet track data according to a date range T, a vehicle type and an operation type, simultaneously hiding other types of vehicles and operation track data in other time periods, certainly simultaneously selecting operation track data of multiple vehicle types, filtering incomplete data, wrong data and repeated data, and converting inconsistent information of the extracted vehicle operation track data to make the extracted vehicle operation track data uniform.
As shown in FIG. 6, the data cleansing module 13 includes
The data selecting unit 131 is configured to extract trajectory data of corresponding vehicle operation checkpoints according to the date range T, the vehicle type, and the operation type, and meanwhile hide trajectory data of other types of vehicles and other periods, and certainly, may also select trajectory data of multiple vehicle types at the same time.
A data cleansing unit 132 for filtering out incomplete data, erroneous data, and duplicate data;
and a data conversion unit 133 for converting and unifying the extracted inconsistent information of the vehicle trajectory data.
As shown in FIG. 7, the statistical analysis module 14 includes
The bayonet-pair driving time threshold matrix modeling unit 141 is used for establishing a cartesian product of a bayonet itself, exhausting all bayonet pairs, performing big data analysis on driving time of each bayonet pair, obtaining a minimum bayonet-pair driving time threshold, and forming a bayonet-pair driving time threshold matrix;
the one-dimensional bayonet-to-driving time matrix modeling unit 142 is used for sequencing the track data of each number plate according to time to form a track sequence of adjacent bayonets of the number plate, searching for a threshold corresponding to the bayonet-to-driving time threshold matrix, and establishing a one-dimensional bayonet-to-driving time matrix of each number plate;
the statistical analysis and recognition unit 143 is configured to perform big data analysis on the probability of the section distribution of the gate pair driving time in the track matrix of each card gate pair, thereby recognizing the fake-licensed vehicle.
The identification system of the fake-licensed vehicle based on the bayonet to the driving time has good identification accuracy by carrying out statistical analysis and data modeling on the track data of the vehicle running bayonet, particularly combines the registration information of the number plate vehicle, powerfully improves the accuracy and efficiency of checking the fake-licensed vehicle, beautifies the vehicle operating environment and ensures the life and property safety of people. In addition, the continuous characteristics of the vehicle track can be obtained under the condition that the road network and distance data are not needed, meanwhile, missed shot data can be compatible, and compared with the method that the track continuity is determined through calculating the distance or according to the road network, the method is better in effect and lower in complexity.
It will be appreciated that another embodiment of the invention also provides a computer readable storage medium for storing a computer program for identifying a fake-licensed vehicle based on a driving time at a gate, the computer program performing the following steps when run on a computer:
step S1: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S2: establishing a Cartesian product K of the bayonet set K, and combining all bayonet pairs to form a two-dimensional matrix K of n x n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S3: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTSequence K of the bayonet tracks of the number plateTThe two bayonets passing through the middle part and the front part form a bayonet pair so as to obtain a license plate bayonet pair track sequence KdThen, the trajectory sequence K is matched from the number plate bayonetdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S4: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S5: two-dimensional matrix K for establishing driving time threshold value of bayonet pair based on driving time of bayonet pairn；
Step S6: modeling the driving time matrix by the one-dimensional bayonet to obtain a driving time matrix K of the bayonetb(ii) a And
step S7: time matrix K of card port pair driving of each number plate is countedbThe middle bayonet obtains a boundary P value of u +/-3 sigma by applying normal distribution to the probability P that the driving time is 0, and the license plates outside the u +/-3 sigma interval are fake-licensed vehicles; within the u +/-3 sigma interval, the number plate near the boundary P value is a suspected fake-licensed car.
The general form of computer readable media includes: floppy disk (floppy disk), flexible disk (flexible disk), hard disk, magnetic tape, any of its magnetic media, CD-ROM, any of the other optical media, punch cards (punch cards), paper tape (paper tape), any of the other physical media with patterns of holes, Random Access Memory (RAM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), FLASH erasable programmable read only memory (FLASH-EPROM), any of the other memory chips or cartridges, or any of the other media from which a computer can read. The instructions may further be transmitted or received by a transmission medium. The term transmission medium may include any tangible or intangible medium that is operable to store, encode, or carry instructions for execution by the machine, and includes digital or analog communications signals or intangible medium that facilitates communication of the instructions. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus for transmitting a computer data signal.
As shown in fig. 8, another embodiment of the present invention further provides a method for identifying a fake-licensed vehicle based on a bayonet for deep learning driving time, including the following steps:
step S100: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S200:establishing a Cartesian product K of the bayonet set K, and combining all bayonet pairs to form a two-dimensional matrix K of n x n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S300: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTSequence K of the bayonet tracks of the number plateTThe two bayonets passing through the middle part and the front part form a bayonet pair so as to obtain a license plate bayonet pair track sequence KdThen, the trajectory sequence K is matched from the number plate bayonetdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S400: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S500: two-dimensional matrix K for establishing driving time threshold value of bayonet pair based on driving time of bayonet pairn；
Step S600: modeling the driving time matrix by the one-dimensional bayonet to obtain a driving time matrix K of the bayonetb(ii) a And
step S700: time matrix K of card port pair driving of each number plate is countedbThe probability P of 0 occurrence of the driving time by the middle bayonet is applied to normal distribution, and P corresponding to the u value is obtaineduA value; and
step S800: and carrying out deep learning by utilizing a convolutional neural network to realize intelligent identification of the fake-licensed vehicle.
It is understood that the contents of steps S100-S600 of the present embodiment are the same as the contents of steps S1-S6 of the above preferred embodiment, and the difference between the two is only in step S700 and step S800.
It can be understood that, as shown in fig. 9, the step S800 specifically includes the following steps:
step S801: selection of PuValue attachedOne-dimensional bayonet-pair driving time matrix K of nearly x number platesbAs a positive sample E+；
Step S802: randomly combining all the number plates into a new vehicle to generate a new temporary number plate, combining the bayonet track data of the new temporary number plate and the temporary number plate, repeating the steps S300-S700, and selecting PuOne-dimensional bayonet pair driving time matrix K of x number plates with values close to each otherbAs a negative sample E-；
Step S803: unified positive sample E+Negative sample E-And a one-dimensional bayonet pair driving time matrix KbNormalizing the length of the sample;
step S804: a positive sample E+And negative example E-Inputting a convolutional neural network, performing feature learning, and classifying the features learned by the convolutional network by using a softmax classifier to obtain a one-dimensional deep learning-based intelligent identification model R of the fake-licensed vehicleAIThen, the verification data is used for continuously adjusting the value intervals, the sample lengths and the convolutional neural network parameters of the threshold, the date, the x and the x to train so as to obtain the optimal intelligent identification model R of the fake-licensed vehicleAI(ii) a And
step S805: intelligent recognition model R for vehicle by utilizing optimal fake-licensedAIFor all vehicles, the one-dimensional bayonet pair driving time matrix KbAnd identifying to obtain a set of fake-licensed license plates and storing the set of fake-licensed license plates and the set of fake-licensed license plates in a fake-licensed vehicle database.
It is understood that in the step S802, the negative sample E-The card port track data of the fake-licensed license plate which is found in history is selected, the sample is more real, and the fake-licensed vehicle intelligent identification model R is adoptedAIThe recognition effect of (2) is better.
It can be understood that, in the step S803, the one-dimensional bayonet is aligned with the driving time matrix KbPositive sample E+Inner and negative sample E-The length of the track pairs in the array is different, and the track pairs need to be uniformly adjusted into a matrix with the length of L.
It can be further understood that the identification method of the fake-licensed vehicle based on the bayonet for deep learning of the driving time in the embodiment can be applied simultaneously with the identification method of the fake-licensed vehicle based on the bayonet for the driving time in the above preferred embodiment, and the identification results complement each other and are mutually proved, so that the identification accuracy is improved.
The identification method of the fake-licensed vehicle based on the bayonet for deep learning of the driving time comprises the steps of carrying out statistical analysis on vehicle running bayonet track data, carrying out data modeling, learning and training by utilizing a convolutional neural network, and using the convolutional neural network to avoid explicit feature extraction and errors introduced in threshold setting and implicitly learn from training data; the convolutional neural network has unique superiority in the aspect of image processing, and particularly, the characteristic that one-dimensional vectors can be directly input into the network avoids the complexity of data reconstruction in the processes of feature extraction and classification. In addition, the continuous characteristics of the vehicle track can be obtained under the condition that the road network and distance data are not needed, meanwhile, missed shot data can be compatible, and compared with the method that the track continuity is determined through calculating the distance or according to the road network, the method is better in effect and lower in complexity.
It can be understood that, as shown in fig. 10, another embodiment of the present invention further provides a system for identifying a fake-licensed vehicle based on a bayonet for deep learning of driving time, which is preferably applied to the method for identifying a fake-licensed vehicle based on a bayonet for deep learning of driving time as described above. The identification system for the fake-licensed vehicle for deeply learning the driving time based on the bayonet comprises
The data acquisition module 22 is used for acquiring basic data required by fake-licensed vehicle identification;
the deep learning module 25 is used for carrying out data modeling, statistical analysis, deep learning and fake-licensed vehicle identification on the vehicle running track data;
the controller 21 is used for controlling the data acquisition module 22 to acquire basic data and controlling the deep learning module 25 to perform data modeling, statistical analysis, deep learning and fake-licensed vehicle identification;
the controller 21 is respectively connected with the data acquisition module 22 and the deep learning module 25, and the deep learning module 25 is connected with the data acquisition module 22.
It is to be understood that the identification system preferably further comprises a fake-licensed vehicle capture terminal 27 and a data washing module 23, wherein the fake-licensed vehicle capture terminal 27 is connected with the controller 21, and the data washing module 23 is respectively connected with the controller 21, the data washing module 22 and the deep learning module 25. The data acquisition module 22 in the present embodiment is the same as the data acquisition module 12 in the above-described embodiment, the data cleaning module 23 is the same as the data cleaning module 13, the fake-licensed vehicle capturing terminal 27 is the same as the fake-licensed vehicle capturing terminal 17, and the controller 21 is the same as the controller 11.
As shown in FIG. 11, the deep learning module 25 includes
The bayonet-pair driving time threshold matrix modeling unit 251 is used for establishing a Cartesian product of the bayonet itself, exhausting all bayonet pairs, carrying out big data analysis on driving time of each bayonet pair, obtaining a minimum threshold value of the driving time of the bayonet pair and forming a bayonet-pair driving time threshold matrix;
the one-dimensional bayonet-to-driving time matrix modeling unit 252 is used for sequencing the track data of each number plate according to time to form a track sequence of adjacent bayonet pairs of the number plates, searching a threshold value corresponding to the bayonet-to-driving time threshold value matrix, and establishing a one-dimensional bayonet-to-driving time matrix of each number plate;
the sample selection unit 253 is used for selecting one-dimensional gate pair driving time matrix data of a certain number of normal vehicles as a positive sample for deep learning; randomly combining all the number plates into a trolley, generating a new temporary number plate, combining the track data of the two temporary number plates simultaneously to obtain a new one-dimensional bayonet-to-driving time matrix KbSelecting one-dimensional bayonet pair track matrix data with the same number as the positive samples as negative samples of deep learning;
the data modeling unit 254 is configured to make up for the differences in the lengths of the positive samples, the negative samples, and the one-dimensional trajectory matrices of all the license plates, due to the differences in the number of pass gates in the vehicle trajectory data, and the data modeling unit 254 is configured to make up for the differences in the lengths of the positive samples, the negative samples, and the one-dimensional trajectory matrices of all the license plates, and construct a one-dimensional gate-to-travel time matrix K that can be input to the deep learning unit 255, i.e., that canbCarrying out normalization processing on the positive sample and the negative sample;
a deep learning unit 255, configured to obtain the intelligent recognition model R by performing feature learning on positive samples and negative samples of fixed sizes and classifying features learned by the convolutional network by using a softmax classifierAIThen, using the verification data, continuously adjusting T and convolution neural network parameters, training to obtain the optimal intelligent recognition model RAI(ii) a Next, the optimal intelligent recognition model R is appliedAIFor all one-dimensional bayonet pair track matrixes KbAnd (5) identifying to obtain a set of fake plate and number plate set A.
The bayonet driving time threshold matrix modeling unit 251 is the same as the bayonet driving time threshold matrix modeling unit 141, and the one-dimensional bayonet driving time matrix modeling unit 252 is the same as the one-dimensional bayonet driving time matrix modeling unit 142.
The identification system of the fake-licensed vehicle for deeply learning the driving time based on the bayonet in the embodiment performs statistical analysis and data modeling on the trajectory data of the vehicle running bayonet, performs learning and training by using a convolutional neural network, and adopts the convolutional neural network to avoid explicit feature extraction and errors introduced in threshold setting and implicitly performs learning from training data; the convolutional neural network has unique superiority in the aspect of image processing, and particularly, the characteristic that one-dimensional vectors can be directly input into the network avoids the complexity of data reconstruction in the processes of feature extraction and classification. In addition, the continuous characteristics of the vehicle track can be obtained under the condition that the road network and distance data are not needed, meanwhile, missed shot data can be compatible, and compared with the method that the track continuity is determined through calculating the distance or according to the road network, the method is better in effect and lower in complexity.
It is to be understood that another embodiment of the present invention further provides a computer-readable storage medium storing a computer program for deep learning driving time based on a gate to identify a fake-licensed vehicle, the computer program executing the following steps when running on a computer:
step S100: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S200: establishing a Cartesian product K of the bayonet set K, and combining all bayonet pairs to form a two-dimensional matrix K of n x n bayonet pairsm＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S300: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTSequence K of the bayonet tracks of the number plateTThe two bayonets passing through the middle part and the front part form a bayonet pair so as to obtain a license plate bayonet pair track sequence KdThen, the trajectory sequence K is matched from the number plate bayonetdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S400: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S500: two-dimensional matrix K for establishing driving time threshold value of bayonet pair based on driving time of bayonet pairn；
Step S600: modeling the driving time matrix by the one-dimensional bayonet to obtain a driving time matrix K of the bayonetb(ii) a And
step S700: time matrix K of card port pair driving of each number plate is countedbThe probability P of 0 occurrence of the driving time by the middle bayonet is applied to normal distribution, and P corresponding to the u value is obtaineduA value; and
step S800: and carrying out deep learning by utilizing a convolutional neural network to realize intelligent identification of the fake-licensed vehicle.
The general form of computer readable media includes: floppy disk (floppy disk), flexible disk (flexible disk), hard disk, magnetic tape, any of its magnetic media, CD-ROM, any of the other optical media, punch cards (punch cards), paper tape (paper tape), any of the other physical media with patterns of holes, Random Access Memory (RAM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), FLASH erasable programmable read only memory (FLASH-EPROM), any of the other memory chips or cartridges, or any of the other media from which a computer can read. The instructions may further be transmitted or received by a transmission medium. The term transmission medium may include any tangible or intangible medium that is operable to store, encode, or carry instructions for execution by the machine, and includes digital or analog communications signals or intangible medium that facilitates communication of the instructions. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus for transmitting a computer data signal.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for identifying fake-licensed vehicles based on the driving time of a bayonet is characterized in that,
the method comprises the following steps:
step S1: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S2: building a Cartesian product K of the bayonet set K, exhausting all bayonet pair combinations to form a n bayonet pair two-dimensional matrix Km＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S3: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTSequence K of the bayonet tracks of the number plateTThe two bayonets passing through the middle part and the front part form a bayonet pair so as to obtain a license plate bayonet pair track sequence KdThen, the number plate is aligned with the track sequence from the bayonetColumn KdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S4: counting the minimum threshold value of the bayonet pair driving time and the bayonet pair driving time: according to the vehicle passing bayonet pair record in the step S3, the vehicle passes through the bayonet pair (k)i，kj) Middle bayonet kiIs recorded as tkiPassing through bayonet kjIs recorded as tkjThen bayonet pair (k)i，kj) Has a travel time of tij＝tkj－tki；
Paired bayonet (k)i，kj) All driving time tijUsing normal distribution, taking t of u-3 sigmaijAs a bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj) (ii) a Or finding the driving time t of each bayonet pair by using an outlier algorithm or a clustering algorithmijThe threshold value of the outlier is the bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj)；
Step S5: two-dimensional matrix K of driving time threshold value of bayonet pair established based on driving time of bayonet pair and minimum threshold value of driving time of bayonet pairn: initially, the bayonet is aligned to the two-dimensional matrix KmBayonet pair (k) ini，kj) The value is assigned to 0, and then the bayonet is paired with the two-dimensional matrix KmIn a corresponding bayonet pair (k)i，kj) Replacing with a minimum time threshold t (k) of the driving time of the bayoneti，kj) To obtain a two-dimensional matrix K of the driving time threshold value of the bayonet pairn；
Step S6: modeling the driving time matrix by the one-dimensional bayonet to obtain a driving time matrix K of the bayonetb: pairing the adjacent bayonets of the number plate with the track sequence KaThe bayonet pair is replaced by the corresponding bayonet pair driving time tijThen, the track sequence is aligned by adjacent bayonets of the number plateColumn KaIn the bayonet pair search bayonet pair driving time threshold value two-dimensional matrix KnTime threshold in (1) if bayonet is to driving time tijLess than KnSetting the medium time threshold value to be 0, otherwise, keeping the medium time threshold value unchanged, and aligning the adjacent bayonets of the number plate with the track sequence KaBecome bayonet to driving time matrix Kb(ii) a And
step S7: time matrix K of card port pair driving of each number plate is countedbThe middle bayonet obtains a boundary P value of u +/-3 sigma by applying normal distribution to the probability P that the driving time is 0, and the license plates outside the u +/-3 sigma interval are fake-licensed vehicles; the number plates within the u + -3 sigma interval and near the boundary P value are suspected fake-licensed vehicles.
2. The method of identifying a fake-licensed vehicle of claim 1,
the identification method of the fake-licensed vehicle further comprises the following steps:
step S8: and storing the number plate of the identified fake-licensed vehicle and the number plate of the suspected fake-licensed vehicle into a fake-licensed vehicle database.
3. The method of identifying a fake-licensed vehicle of claim 2,
the identification method of the fake-licensed vehicle further comprises the following steps:
step S9: capturing the fake-licensed vehicles;
the step S9 includes:
step S91: obtaining card port data and card port longitude and latitude of the fake-licensed vehicle or suspected fake-licensed vehicle within a period of time, and extracting a high-frequency card port and a high-frequency period of time from the card port data and the card port longitude and latitude as a hot spot card port;
step S92: photographing a vehicle to obtain a vehicle number plate, or manually inputting the vehicle number plate, or accessing the number plate transmitted by a bayonet in real time; and
step S93: and comparing the obtained vehicle number plate with the fake-licensed vehicle database at the hot spot bayonet, if the comparison result is the fake-licensed vehicle, immediately outputting alarm information, calling out the information of the vehicle to which the number plate belongs from the traffic vehicle management data system, and assisting a traffic police to catch the fake-licensed vehicle.
4. A system for identifying a fake-licensed vehicle based on a bayonet-pair driving time, which is suitable for the identification method according to any one of claims 1 to 3,
comprises that
A data acquisition module (12) for acquiring basic data required for fake-licensed vehicle identification,
the statistical analysis module (14) is used for performing statistical analysis on the vehicle running track data and identifying the fake-licensed vehicle;
the controller (11) is used for controlling the data acquisition module (12) to acquire basic data and controlling the statistical analysis module (14) to perform statistical analysis and identify the fake-licensed vehicle;
the controller (11) is respectively connected with the data acquisition module (12) and the statistical analysis module (14), and the data acquisition module (12) is connected with the statistical analysis module (14).
5. The system for identifying a fake-licensed vehicle based on a bayonet versus drive time of claim 4,
the identification system further comprises
The fake-licensed vehicle capturing terminal (17) is connected with the controller (11) and is used for capturing fake-licensed vehicles;
the fake-licensed car catching terminal (17) comprises
The hot spot bayonet acquisition unit (171) is used for acquiring bayonet data and bayonet longitude and latitude of the fake-licensed vehicle or the suspected fake-licensed vehicle within a period of time, and extracting a high-frequency bayonet and a high-frequency period of time from the bayonet data and the bayonet longitude and latitude as the hot spot bayonet;
the license plate acquisition unit (172) is used for photographing the vehicle to acquire a vehicle license plate or manually inputting the vehicle license plate or accessing the license plate transmitted by the card port in real time;
and the capturing unit (173) is used for comparing the obtained vehicle license plate with the fake-licensed vehicle database at the hot spot bayonet, immediately outputting alarm information if the comparison result is the fake-licensed vehicle, calling out the information of the vehicle to which the license plate belongs from the traffic vehicle management data system, and assisting the traffic police to capture the fake-licensed vehicle.
6. A computer-readable storage medium for storing a computer program for identifying a fake-licensed vehicle on the basis of a driving time at a gate, characterized in that the computer program performs the steps of the identification method according to claim 1 when running on a computer.
7. A method for identifying fake-licensed vehicles based on the deep learning of driving time by a bayonet is characterized in that,
the method comprises the following steps:
step S100: acquiring all bayonet numbers to form a bayonet set K ═ K1，k2，k3，k4……kn}；
Step S200: building a Cartesian product K of the bayonet set K, exhausting all bayonet pair combinations to form a n bayonet pair two-dimensional matrix Km＝[ki*kj]，i∈N+∧i≤n，j∈N+∧j≤n；
Step S300: obtaining the running track of each vehicle, and sequencing the bayonet track data of each vehicle according to all bayonets passed by the running time sequence to obtain the license plate bayonet track sequence K of each vehicleTForming a bayonet pair by two bayonets passing through the front and the back in the license plate bayonet track sequence to obtain a license plate bayonet pair track sequence KdThen, the trajectory sequence K is matched from the number plate bayonetdExtracting the bayonet pairs in the driving track of each vehicle to form a vehicle passing bayonet pair record, and sequencing the number plate bayonet tracks KTThe two bayonets which are adjacent and sequentially pass through form a bayonet pair (k)i，kj) To obtain the track sequence K of adjacent bayonets of the number platea；
Step S400: counting the bayonet-to-driving time and the minimum threshold value of the bayonet-to-driving time;
step S500: two-dimensional matrix K for establishing driving time threshold value of bayonet pair based on driving time of bayonet pairn；
Step S600: modeling the driving time matrix by a one-dimensional bayonet to obtainTime matrix K of bayonet-pair drivingb(ii) a And
step S700: time matrix K of card port pair driving of each number plate is countedbThe probability P of 0 occurrence of the driving time by the middle bayonet is applied to normal distribution, and P corresponding to the u value is obtaineduA value; and
step S800: carrying out deep learning by utilizing a convolutional neural network to realize intelligent identification of fake-licensed vehicles;
the step S400 is specifically
According to the vehicle passing bayonet pair record in the step S3, the vehicle passes through the bayonet pair (k)i，kj) Middle bayonet kiIs recorded as tkiPassing through bayonet kjIs recorded as tkjThen bayonet pair (k)i，kj) Has a travel time of tij＝tkj－tki；
Paired bayonet (k)i，kj) All driving time tijUsing normal distribution, taking t of u-3 sigmaijAs a bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj) (ii) a Or finding the driving time t of each bayonet pair by using an outlier algorithm or a clustering algorithmijThe threshold value of the outlier is the bayonet kiTo bayonet kjMinimum time threshold t (k)i，kj)；
The step S500 is specifically:
initially, the bayonet is aligned to the two-dimensional matrix KmBayonet pair (k) ini，kj) The value is assigned to 0, and then the bayonet is paired with the two-dimensional matrix KmIn a corresponding bayonet pair (k)i，kj) Replacing with a minimum time threshold t (k) of the driving time of the bayoneti，kj) To obtain a two-dimensional matrix K of the driving time threshold value of the bayonet pairn；
The step S600 specifically includes:
pairing the adjacent bayonets of the number plate with the track sequence KaThe bayonet pair is replaced by the corresponding bayonet pair driving time tijThen, the track sequence K is paired by adjacent bayonets of the number plateaIn the bayonet pair searching bayonetTo two-dimensional matrix K of driving time thresholdnTime threshold in (1) if bayonet is to driving time tijLess than KnSetting the medium time threshold value to be 0, otherwise, keeping the medium time threshold value unchanged, and aligning the adjacent bayonets of the number plate with the track sequence KaBecome bayonet to driving time matrix Kb。
8. The method of identifying a fake-licensed vehicle of claim 7,
the step S800 specifically includes the following steps:
step S801: selection of PuOne-dimensional bayonet pair driving time matrix K of x number plates with values close to each otherbAs a positive sample E+；
Step S802: randomly combining all the number plates into a new vehicle to generate a new temporary number plate, combining the bayonet track data of the new temporary number plate and the temporary number plate, repeating the steps S300-S700, and selecting PuOne-dimensional bayonet pair driving time matrix K of x number plates with values close to each otherbAs a negative sample E-；
Step S803: unified positive sample E+Negative sample E-And a one-dimensional bayonet pair driving time matrix KbNormalizing the length of the sample;
step S804: a positive sample E+And negative example E-Inputting a convolutional neural network, performing feature learning, and classifying the features learned by the convolutional network by using a softmax classifier to obtain a one-dimensional deep learning-based intelligent identification model R of the fake-licensed vehicleAIThen, the verification data is used for continuously adjusting the value intervals, the sample lengths and the convolutional neural network parameters of the threshold, the date, the x and the x to train so as to obtain the optimal intelligent identification model R of the fake-licensed vehicleAI(ii) a And
step S805: intelligent recognition model R for vehicle by utilizing optimal fake-licensedAIFor all vehicles, the one-dimensional bayonet pair driving time matrix KbAnd identifying to obtain a set of fake plate and number plate.
9. A system for recognizing a fake-licensed vehicle for deep learning of driving time based on a checkpoint, using the recognition method according to claim 7,
comprises that
A data acquisition module (22) for acquiring basic data required for fake-licensed vehicle identification,
the deep learning module (25) is used for carrying out data modeling, statistical analysis, deep learning and fake-licensed vehicle identification on the vehicle running track data;
the controller (21) is used for controlling the data acquisition module (22) to acquire basic data and controlling the deep learning module (25) to perform data modeling, statistical analysis, deep learning and fake-licensed vehicle identification;
the controller (21) is respectively connected with the data acquisition module (22) and the deep learning module (25), and the deep learning module (25) is connected with the data acquisition module (22).
10. A computer-readable storage medium for storing a computer program for deep learning of driving time on the basis of a checkpoint for identification of a fake-licensed vehicle, characterized in that the computer program performs the steps of the identification method according to claim 7 when it is run on a computer.
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