CN112885105B - Commuting vehicle identification method and device based on high-definition checkpoint data and storage medium - Google Patents

Commuting vehicle identification method and device based on high-definition checkpoint data and storage medium Download PDF

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CN112885105B
CN112885105B CN202110051846.8A CN202110051846A CN112885105B CN 112885105 B CN112885105 B CN 112885105B CN 202110051846 A CN202110051846 A CN 202110051846A CN 112885105 B CN112885105 B CN 112885105B
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bayonet
vehicle
frequent
data
sequence
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CN112885105A (en
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宁平华
王蓓
何则干
叶声远
司徒惠源
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Guangzhou Municipal Engineering Design & Research Institute Co Ltd
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Guangzhou Municipal Engineering Design & Research Institute Co Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules

Abstract

The invention discloses a commuting vehicle identification method, a device and a storage medium based on high-definition bayonet data, wherein the method comprises the steps of obtaining an n-day bayonet sequence set of a first vehicle, carrying out frequent sequence mining to obtain a frequent bayonet subsequence set and a maximum value of the relative length of the frequent bayonet subsequence set, obtaining a maximum value set of the relative length of frequent bayonet subsequences of M vehicles, obtaining a maximum value threshold value of the relative length of the frequent bayonet subsequence of the commuting vehicle by adopting a merging algorithm of hierarchical clustering, and determining whether each vehicle in the M vehicles is the commuting vehicle according to the maximum value threshold value. Because units such as a traffic department and the like easily obtain or master a large amount of urban checkpoint data, the traffic department and the like can identify commuting vehicles at low cost, high efficiency and high reliability according to the checkpoint data, and can be used as a data basis for traffic management of the traffic department, case investigation of the case handling department, road construction research of the capital construction department and the like. The invention is widely applied to the technical field of traffic informatization.

Description

Commuting vehicle identification method and device based on high-definition checkpoint data and storage medium
Technical Field
The invention relates to the technical field of traffic informatization, in particular to a commuting vehicle identification method and device based on high-definition checkpoint data and a storage medium.
Background
Vehicles traveling on roads have relatively fixed travel attributes, for example, a vehicle owned by a citizen is mainly used for commuting and has a relatively fixed travel path, and a commercial vehicle is mainly used for carrying passengers or cargo on roads and has an unfixed travel path. The travel attribute of the vehicle is identified, and the vehicle can be used as a data base for traffic management of a traffic department, case investigation of a case handling department, road construction research of a capital construction department and the like. The travel attributes of the vehicles are mainly determined according to data such as registration information of the vehicles in a traffic department in the prior art, the method is low in accuracy, and some of the prior art determines the travel attributes of the vehicles through tracking investigation of individual vehicles, but the method is difficult to popularize into identification of a plurality of vehicles in a city or even a larger range.
Disclosure of Invention
In view of at least one of the above technical problems, an object of the present invention is to provide a commuting vehicle identification method, apparatus and storage medium based on high definition checkpoint data.
On one hand, the embodiment of the invention comprises a commuting vehicle identification method based on high-definition bayonet data, which comprises the following steps:
s1, acquiring all bayonet data of a first vehicle in n days;
s2, determining an n-day bayonet sequence set of the first vehicle according to all bayonet data of the first vehicle within n days;
s3, performing frequent sequence mining on the n-day bayonet sequence set of the first vehicle to obtain a frequent bayonet subsequence set of the first vehicle;
s4, determining the maximum value of the relative length of the frequent bayonet subsequences in the frequent bayonet subsequence set of the first vehicle;
s5, determining the maximum value of the relative length of the frequent bayonet sub-sequence in the frequent bayonet sub-sequence set of the Mth vehicle to form the maximum value set of the relative length of the frequent bayonet sub-sequence of the M vehicles;
s6, clustering a maximum value set of the relative lengths of the frequent bayonet subsequences of the M vehicles by adopting a merging algorithm of hierarchical clustering to obtain a maximum value threshold of the relative lengths of the frequent bayonet subsequences of the commuter vehicles;
and S7, determining whether the first vehicle, the second vehicle and the Mth vehicle are commuting vehicles according to the maximum threshold value of the relative length of the frequent bayonet subsequences of the commuting vehicles.
Further, the bayonet data includes:
ith gate data d of first vehiclei(ai,ti) Involving passing bayonets aiAnd a time t of shooting at the gatei；
Bayonet aiSubject to bayonet sets A, tiThe unit of (a) is year, month, day, hour, minute and second.
Further, the step S2 specifically includes:
s21, dividing all gate data in n days of a first vehicle into n data sets according to the year, month and day of gate shooting time, namely forming a single data set by the gate data in the same day to obtain n day data sets;
s22, arranging all data of the day data set according to the morning and the evening of the bayonet shooting time, extracting the bayonet information in each piece of data, and forming a single-day bayonet sequence s-s of the first vehicle1s2…，s1s2…∈A；
S23, forming the n-day bayonet sequence into an n-day bayonet sequence set Dn＝{s1,s2,…sn}。
Further, the step S3 specifically includes:
s31, acquiring a candidate bayonet sequence set psi of a first layer(1)＝{r＝a|a∈A}；
S32, according to the n-day bayonet sequence set Dn＝{s1,s2...snComputing a candidate bayonet sequence set psi of the first layer(1)Each candidate bayonet sequence in the set D [ r ═ a ∈ A ]nThe candidate bayonet sequences with the occurrence rate smaller than the occurrence rate threshold value beta are selected from the candidate bayonet sequence set psi of the first layer(1)Deleting the sequence in the { r ═ a ∈ A }, thereby obtaining a frequent bayonet subsequence set phi of the first layer(1)＝{r∈ψ(1)|rsupDnr≥β}；
S33. according to the formula psi(2)＝{r＝rh+rz|rh∈Φ(1),rz∈Φ(1)Acquiring a candidate bayonet sequence set psi of a second layer(2)；
S34, calculating a candidate bayonet sequence set psi of the second layer(2)The candidate bayonet sequences with the occurrence rate smaller than the threshold value beta of the occurrence rate are selected from the candidate bayonet sequence set psi of the second layer(2)Thereby obtaining the frequent bayonet of the second layerSet of subsequences
Figure GDA0003399106160000021
Set of frequent bayonet subsequences at the second layer
Figure GDA0003399106160000022
If the current set is empty, stopping, otherwise, setting k to 2 and executing step S35;
s35. passing the formula psi(k+1)＝{r＝rh+rz[k]|rh[1:k-1]＝rz[1:k-1],rh∈Φk,rz∈ΦkAcquiring a candidate bayonet sequence set psi of the k +1 th layer(k+1)(ii) a Wherein r ish[1:k-1]Represents the sequence rhFrom position 1 to position k-1;
s36, calculating a candidate bayonet sequence set of the (k + 1) th layer
Figure GDA0003399106160000023
The candidate bayonet sequences with the occurrence rate smaller than the threshold value beta of the occurrence rate are selected from the candidate bayonet sequence set psi of the k +1 layer(k+1)So as to obtain the frequent sub-sequence set of the bayonet at the k +1 layer
Figure GDA0003399106160000024
Set of frequent bayonet subsequences at the k +1 th layer
Figure GDA0003399106160000031
If the current value is an empty set, stopping, otherwise setting k to k +1 and jumping back to execute the step S35, and returning to the frequent bayonet subsequence set until k reaches an upper limit value;
s37. in the step S32,
Figure GDA0003399106160000032
representing the sequence r in the set DnThe frequency of (3) is calculated as follows:
inspection set DnEach piece of data s inh(ii) a If the sequence shIncluding sequenceRow r, adding one to the occurrence frequency of the sequence r; the number of occurrences of the sequence r divided by n equals the occurrence.
Further, the step S4 specifically includes:
s41, recording the k value when the iteration of the frequent bayonet sub-sequence stops in the step S35;
s42, calculating DnThe average length L of all sequences in (a); dividing k by L yields the maximum value of the relative length of the frequent bayonet subsequences.
Further, the step S6 specifically includes:
s61, recording a set of maximum values of relative lengths of the frequent bayonet subsequences of the M vehicles as Q ═ Q1,q2,…qM}；
S62, regarding each point in the set Q as a class, generating M classes, and calculating the distance between any two classes to obtain an M multiplied by M distance matrix; merging the two classes with the minimum distance into one class; changing original M classes into M-1 classes;
s63, calculating the distance between any two classes to obtain a new distance matrix; merging the two classes with the minimum distance into one class; the total number of classes is reduced by 1;
s64, repeating the step S63 until the number of the classes is 2, and stopping iteration;
s65, taking the larger value of the minimum values in the two classes as the maximum value threshold value of the relative length of the frequent checkpoint subsequences of the commuter vehicle;
s66. the distance between any two classes of the steps S62 and S63 is equal to the minimum value of the difference between the data in the first class and the data in the second class.
Further, the step S7 specifically includes:
s71, if the maximum value of the relative length of the frequent bayonet sub-sequence of the mth vehicle (M is 1,2, … M) exceeds the maximum value threshold of the relative length of the frequent bayonet sub-sequence of the commuter vehicle obtained in the step S6, the mth vehicle is the commuter vehicle; otherwise, the mth vehicle is not the commuter vehicle.
Further, one piece of gate data of the first vehicle includes a gate number character string, year, month, day, hour, minute and second when the first vehicle passes through the gate, a license plate number character string of the first vehicle, and a vehicle type character string of the first vehicle.
In another aspect, the present invention further includes a computer device, including a memory for storing at least one program and a processor for loading the at least one program to execute the commuting vehicle identification method based on high definition bayonet data in the embodiment.
In another aspect, the present invention further includes a storage medium in which a processor-executable program is stored, the processor-executable program being configured to perform the commuting vehicle identification method based on high definition bayonet data in the embodiment when executed by a processor.
The invention has the beneficial effects that: since units such as a transportation department can easily obtain or master a large amount of city gate data, the commuting vehicle identification method based on the high-definition gate data in the embodiment can identify whether vehicles belong to commuting vehicles with low cost, high efficiency and high reliability according to the gate data, and can be used as a data basis for the transportation department to perform traffic management, the case work department to perform case investigation, the infrastructure department to perform road construction research and the like.
Drawings
Fig. 1 is a flowchart of a method for transmitting data in a satellite network according to an embodiment.
Detailed Description
In this embodiment, referring to fig. 1, the commuting vehicle identification method based on the high definition bayonet data includes the following steps:
s1, acquiring all bayonet data of a first vehicle in n days; wherein the gate data includes ith gate data d of the first vehiclei(ai,ti) Involving passing bayonets aiAnd a time t of shooting at the gateiBayonet aiSubject to bayonet sets A, tiFor example, a piece of gate data of a first vehicle includes a gate number character string, a year, month, day, hour, minute and second when the first vehicle passes through the gate, and a license plate number character of the first vehicleA string and a vehicle type string of the first vehicle;
s2, determining an n-day bayonet sequence set of the first vehicle according to all bayonet data of the first vehicle within n days;
s3, performing frequent sequence mining on the n-day bayonet sequence set of the first vehicle to obtain a frequent bayonet subsequence set of the first vehicle;
s4, determining the maximum value of the relative length of the frequent bayonet subsequences in the frequent bayonet subsequence set of the first vehicle;
s5, determining the maximum value of the relative length of the frequent bayonet sub-sequence in the frequent bayonet sub-sequence set of the Mth vehicle to form the maximum value set of the relative length of the frequent bayonet sub-sequence of the M vehicles;
s6, clustering a maximum value set of the relative lengths of the frequent bayonet subsequences of the M vehicles by adopting a merging algorithm of hierarchical clustering to obtain a maximum value threshold of the relative lengths of the frequent bayonet subsequences of the commuter vehicles;
and S7, determining whether the first vehicle, the second vehicle and the Mth vehicle are commuting vehicles according to the maximum threshold value of the relative length of the frequent bayonet subsequences of the commuting vehicles.
In this embodiment, the step S2, that is, the step of determining the n-day bayonet sequence set of the first vehicle according to all the bayonet data in the n-day of the first vehicle, specifically includes the following steps S21-S23:
s21, dividing all gate data in n days of a first vehicle into n data sets according to the year, month and day of gate shooting time, namely forming a single data set by the gate data in the same day to obtain n day data sets;
s22, arranging all data of the day data set according to the morning and the evening of the bayonet shooting time, extracting the bayonet information in each piece of data, and forming a single-day bayonet sequence s-s of the first vehicle1s2…，s1s2…∈A；
S23, forming the n-day bayonet sequence into an n-day bayonet sequence set Dn＝{s1,s2,…sn}。
In this embodiment, the step S3, that is, the step of performing frequent sequence mining on the n-day bayonet sequence set of the first vehicle to obtain a frequent bayonet subsequence set of the first vehicle specifically includes:
s31, acquiring a candidate bayonet sequence set psi of a first layer(1)＝{r＝a|a∈A}；
S32, according to the n-day bayonet sequence set Dn＝{s1,s2...snComputing a candidate bayonet sequence set psi of the first layer(1)Each candidate bayonet sequence in the set D [ r ═ a ∈ A ]nThe candidate bayonet sequences with the occurrence rate smaller than the occurrence rate threshold value beta are selected from the candidate bayonet sequence set psi of the first layer(1)Deleting in { r ═ a ∈ a }, thereby obtaining a frequent bayonet subsequence set of the first layer
Figure GDA0003399106160000055
S33, passing through the formula
Figure GDA0003399106160000056
Obtaining a candidate bayonet sequence set psi of a second layer(2)；
S34, calculating a candidate bayonet sequence set psi of the second layer(2)The candidate bayonet sequences with the occurrence rate smaller than the threshold value beta of the occurrence rate are selected from the candidate bayonet sequence set psi of the second layer(2)Thereby obtaining a frequent bayonet subsequence set of the second layer
Figure GDA0003399106160000051
Set of frequent bayonet subsequences at the second layer
Figure GDA0003399106160000052
If the current set is empty, stopping, otherwise, setting k to 2 and executing step S35;
s35. passing the formula psi(k+1)＝{r＝rh+rz[k]|rh[1:k-1]＝rz[1:k-1],rh∈Φk,rz∈ΦkAcquiring a candidate bayonet sequence set psi of the k +1 th layer(k+1)(ii) a Wherein r ish[1:k-1]Represents the sequence rhFrom position 1 to position k-1;
s36, calculating a candidate bayonet sequence set of the (k + 1) th layer
Figure GDA0003399106160000053
The candidate bayonet sequences with the occurrence rate smaller than the threshold value beta of the occurrence rate are selected from the candidate bayonet sequence set psi of the k +1 layer(k+1)So as to obtain the frequent card port subsequence set phi of the k +1 layer(k+1)＝{r∈ψ(k+1)|rsupDnr is more than or equal to beta, and when the frequent bayonet subsequence set phi of the k +1 th layer(k+1)＝{r∈ψ(k+1)|rsupDnr is more than or equal to beta, stopping the operation if the value is an empty set, otherwise, setting k to be k +1 and jumping back to execute the step S35 until k reaches an upper limit value, and returning to the frequent bayonet subsequence set;
s37. in the step S32,
Figure GDA0003399106160000054
representing the sequence r in the set DnThe frequency of (3) is calculated as follows:
inspection set DnEach piece of data s inh(ii) a If the sequence shIf the sequence r is included, adding one to the occurrence frequency of the sequence r; the number of occurrences of the sequence r divided by n equals the occurrence;
s38, in the step S32, the occurrence rate threshold value beta is self-defined by an analyst, and the recommended value of the method is 0.03;
s39, in the step S35, rh[1:k-1]Represents the sequence rhFrom position 1 to position k-1.
In this embodiment, the method for calculating the maximum value of the relative length of the frequent bayonet sub-sequence in the frequent bayonet sub-sequence set in step S4 is as follows:
s41, recording the k value when the iteration of the frequent bayonet sub-sequence stops in the step S35;
s42, calculating DnThe average length L of all sequences in (a); dividing k by L yields the maximum value of the relative length of the frequent bayonet subsequences.
In this embodiment, in step S6, the step of calculating the maximum threshold value of the relative length of the frequent bayonet sub-sequence of the commuter car specifically includes:
s61, recording a set of maximum values of relative lengths of the frequent bayonet subsequences of the M vehicles as Q ═ Q1,q2,…qM}；
S62, regarding each point in the set Q as a class, generating M classes, and calculating the distance between any two classes to obtain an M multiplied by M distance matrix; merging the two classes with the minimum distance into one class; changing original M classes into M-1 classes;
s63, calculating the distance between any two classes to obtain a new distance matrix; merging the two classes with the minimum distance into one class; the total number of classes is reduced by 1;
s64, repeating the step S63 until the number of the classes is 2, and stopping iteration;
s65, taking the larger value of the minimum values in the two classes as the maximum value threshold value of the relative length of the frequent checkpoint subsequences of the commuter vehicle;
s66. the distance between any two classes of the steps S62 and S63 is equal to the minimum value of the difference between the data in the first class and the data in the second class.
In the present embodiment, in step S7, the conditions for determining whether the first vehicle and the second vehicle … … and the mth vehicle are commuter vehicles are as follows:
s71, if the maximum value of the relative length of the frequent bayonet sub-sequence of the mth vehicle (M is 1,2, … M) exceeds the maximum value threshold of the relative length of the frequent bayonet sub-sequence of the commuter vehicle obtained in the step S6, the mth vehicle is the commuter vehicle; otherwise, the mth vehicle is not the commuter vehicle.
In the present embodiment, the principle of executing steps S1-S7 is: if a vehicle belongs to a commuter vehicle, the daily path of the vehicle is relatively fixed, so that the relative length of the corresponding frequent bayonet sub-sequence is larger, and if the vehicle belongs to a patrol vehicle, the path of the vehicle is specified by passengers or consignors, and the path of the vehicle is not fixed, so that the relative length of the corresponding frequent bayonet sub-sequence is smaller, the frequent bayonet sub-sequence set obtained according to the n-day bayonet sequence set can reflect the fixed degree of the path of the vehicle by executing the steps S1-S7, thereby providing reliable reference for determining whether the vehicle belongs to the commuter vehicle or the patrol vehicle.
Since units such as a transportation department can easily obtain or master a large amount of city gate data, the commuting vehicle identification method based on the high-definition gate data in the embodiment can identify whether vehicles belong to commuting vehicles with low cost, high efficiency and high reliability according to the gate data, and can be used as a data basis for the transportation department to perform traffic management, the case work department to perform case investigation, the infrastructure department to perform road construction research and the like.
The commuting vehicle identification method based on the high-definition checkpoint data in the present embodiment may be implemented by writing a computer program for implementing the commuting vehicle identification method based on the high-definition checkpoint data in the present embodiment, writing the computer program into a computer device or a storage medium, and when the computer program is read out and executed. It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.

Claims (9)

1. A commuting vehicle identification method based on high-definition bayonet data is characterized by comprising the following steps:
s1, acquiring all bayonet data of a first vehicle in n days;
s2, determining an n-day bayonet sequence set of the first vehicle according to all bayonet data of the first vehicle within n days;
s3, performing frequent sequence mining on the n-day bayonet sequence set of the first vehicle to obtain a frequent bayonet subsequence set of the first vehicle;
s4, determining the maximum value of the relative length of the frequent bayonet subsequences in the frequent bayonet subsequence set of the first vehicle;
s5, determining the maximum value of the relative length of the frequent bayonet sub-sequence in the frequent bayonet sub-sequence set of the Mth vehicle to form the maximum value set of the relative length of the frequent bayonet sub-sequence of the M vehicles;
s6, clustering a maximum value set of the relative lengths of the frequent bayonet subsequences of the M vehicles by adopting a merging algorithm of hierarchical clustering to obtain a maximum value threshold of the relative lengths of the frequent bayonet subsequences of the commuter vehicles;
s7, determining whether the first vehicle, the second vehicle and the Mth vehicle are commuting vehicles according to a maximum threshold value of the relative length of the frequent bayonet sub-sequence of the commuting vehicles;
the step S3 specifically includes:
s31, acquiring a candidate bayonet sequence set psi of a first layer(1)＝{r＝a|a∈A}；
S32, according to the n-day bayonet sequence set Dn＝{s1,s2...snComputing a candidate bayonet sequence set psi of the first layer(1)Each candidate bayonet sequence in the set D [ r ═ a ∈ A ]nThe candidate bayonet sequences with the occurrence rate smaller than the occurrence rate threshold value beta are selected from the candidate bayonet sequence set psi of the first layer(1)Deleting the (r ═ a ∈ A), thereby obtaining the frequent bayonet of the first layerSet of subsequences
Figure FDA0003399106150000011
S33. according to the formula psi(2)＝{r＝rh+rz|rh∈Φ(1),rz∈Φ(1)Acquiring a candidate bayonet sequence set psi of a second layer(2)；
S34, calculating a candidate bayonet sequence set psi of the second layer(2)The candidate bayonet sequences with the occurrence rate smaller than the threshold value beta of the occurrence rate are selected from the candidate bayonet sequence set psi of the second layer(2)Thereby obtaining a frequent bayonet subsequence set of the second layer
Figure FDA0003399106150000012
Set of frequent bayonet subsequences at the second layer
Figure FDA0003399106150000013
If the current set is empty, stopping, otherwise, setting k to 2 and executing step S35;
s35. passing the formula psi(k+1)＝{r＝rh+rz[k]|rh[1:k-1]＝rz[1:k-1],rh∈Φk,rz∈ΦkAcquiring a candidate bayonet sequence set psi of the k +1 th layer(k+1)(ii) a Wherein r ish[1:k-1]Represents the sequence rhFrom position 1 to position k-1;
s36, calculating a candidate bayonet sequence set of the (k + 1) th layer
Figure FDA0003399106150000014
The candidate bayonet sequences with the occurrence rate smaller than the threshold value beta of the occurrence rate are selected from the candidate bayonet sequence set psi of the k +1 layer(k+1)So as to obtain the frequent sub-sequence set of the bayonet at the k +1 layer
Figure FDA0003399106150000021
Set of frequent bayonet subsequences at the k +1 th layer
Figure FDA0003399106150000022
If the current value is an empty set, stopping, otherwise setting k to k +1 and jumping back to execute the step S35, and returning to the frequent bayonet subsequence set until k reaches an upper limit value;
s37. in the step S32,
Figure FDA0003399106150000023
representing the sequence r in the set DnThe frequency of (3) is calculated as follows:
inspection set DnEach piece of data s inh(ii) a If the sequence shIf the sequence r is included, adding one to the occurrence frequency of the sequence r; the number of occurrences of the sequence r divided by n equals the occurrence.
2. The high-definition bayonet data-based commuter vehicle identification method of claim 1, wherein the bayonet data comprises:
ith gate data d of first vehiclei(ai,ti) Involving passing bayonets aiAnd a time t of shooting at the gatei；
Bayonet aiSubject to bayonet sets A, tiThe unit of (a) is year, month, day, hour, minute and second.
3. The commuter vehicle identification method based on high-definition bayonet data as claimed in claim 1, wherein said step S2 specifically comprises:
s21, dividing all gate data in n days of a first vehicle into n data sets according to the year, month and day of gate shooting time, namely forming a single data set by the gate data in the same day to obtain n day data sets;
s22, arranging all data of the day data set according to the morning and the evening of the bayonet shooting time, extracting the bayonet information in each piece of data, and forming a single-day bayonet sequence s-s of the first vehicle1s2…，s1s2…∈A；
S23, forming the n-day bayonet sequence into an n-day bayonet sequence set Dn＝{s1,s2,…sn}。
4. The commuter vehicle identification method based on high-definition bayonet data as claimed in claim 1, wherein said step S4 specifically comprises:
s41, recording the k value when the iteration of the frequent bayonet sub-sequence stops in the step S35;
s42, calculating DnThe average length L of all sequences in (a); dividing k by L yields the maximum value of the relative length of the frequent bayonet subsequences.
5. The commuter vehicle identification method based on high-definition bayonet data as claimed in claim 1, wherein said step S6 specifically comprises:
s61, recording a set of maximum values of relative lengths of the frequent bayonet subsequences of the M vehicles as Q ═ Q1,q2,…qM}；
S62, regarding each point in the set Q as a class, generating M classes, and calculating the distance between any two classes to obtain an M multiplied by M distance matrix;
s63, calculating the distance between any two classes to obtain a new distance matrix; merging the two classes with the minimum distance into one class; the total number of classes is reduced by 1;
s64, repeating the step S63 until the number of the classes is 2, and stopping iteration;
s65, taking the larger value of the minimum values in the two classes as the maximum value threshold value of the relative length of the frequent checkpoint subsequences of the commuter vehicle;
s66. the distance between any two classes of the steps S62 and S63 is equal to the minimum value of the difference between the data in the first class and the data in the second class.
6. The commuter vehicle identification method based on high-definition bayonet data as claimed in claim 1, wherein said step S7 specifically comprises:
s71, if the maximum value of the relative length of the frequent bayonet sub-sequence of the mth vehicle (M is 1,2, … M) exceeds the maximum value threshold of the relative length of the frequent bayonet sub-sequence of the commuter vehicle obtained in the step S6, the mth vehicle is the commuter vehicle; otherwise, the mth vehicle is not the commuter vehicle.
7. The high-definition-checkpoint-data-based commuting vehicle identification method according to any one of claims 1 to 6, wherein one checkpoint data of the first vehicle comprises a checkpoint number character string, a year, month, hour, minute and second when the first vehicle passes through a checkpoint, a license plate number character string of the first vehicle and a vehicle type character string of the first vehicle.
8. A computer device comprising a memory for storing at least one program and a processor for loading the at least one program to perform the method of any one of claims 1 to 7.
9. A storage medium having stored therein a processor-executable program, wherein the processor-executable program, when executed by a processor, is configured to perform the method for commuting vehicle identification based on high definition bayonet data as claimed in any one of claims 1 to 7.
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