CN115601974B - Method and device for determining highway fake-licensed vehicle - Google Patents
Method and device for determining highway fake-licensed vehicle Download PDFInfo
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Abstract
The utility model discloses a highway fake-licensed vehicle determining method and device, confirm first target vehicle through obtaining access data set and portal running water set, confirm second target vehicle through time window, obtain the portal discernment license plate of second target vehicle in the target time quantum, judge whether portal discernment license plate accords with the rule of predetermineeing, if yes, confirm as unusual license plate, calculate and confirm fake-licensed vehicle, because confirm first target vehicle through access data set, portal running water set, utilize time window to confirm second target vehicle in the first target vehicle, cut apart the gate running water set with the passing sign of second target vehicle, confirm the portal discernment license plate of second target vehicle in the target time quantum, confirm unusual license plate according to the rule of predetermineeing again, calculate and confirm fake-licensed vehicle, compared with prior art, confirm fake-licensed vehicle through access data set and portal running water set and improve the rate of accuracy of detecting fake-licensed vehicle, further improve detection efficiency.
Description
Technical Field
The application relates to the field of vehicle identification, in particular to a method and a device for determining a highway fake-licensed vehicle.
Background
Along with the development of economy, expressways in China are continuously built, and expressway provincial networking and trans-provincial networking charging are widely implemented. Meanwhile, with the continuous expansion of the highway road network, some illegal owners can escape from high-speed tolls by using means of changing the license plates under the drive of economic benefits.
The existing fake-licensed vehicle detection mode mainly extracts and compares the characteristics of the pictures of the photographed vehicles, and then judges whether the fake-licensed vehicles exist or not, and due to the fact that different license plates of the same vehicle type exist and the fact that the vehicles can be camouflaged, the fake-licensed vehicles are low in accuracy through picture detection, detection efficiency is low, and how to improve the fake-licensed vehicle detection efficiency is a concern.
Disclosure of Invention
In view of the above, the present application provides a method and apparatus for determining a highway fake-licensed vehicle, which are used for improving the detection efficiency of the fake-licensed vehicle.
In order to achieve the above object, the following solutions have been proposed:
a method of determining a highway fake-licensed vehicle, comprising:
the method comprises the steps that an entrance data set D1 and a portal running water set D2 are obtained, wherein the entrance data set D1 comprises a passing identifier, an entrance license plate, an exit license plate, an entrance time and an exit time of each vehicle, and the portal running water set D2 comprises a passing identifier, a portal identification license plate and a portal identification time of each vehicle;
determining a first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same passing identifier and forming a first target set C1, wherein the first target set C1 comprises the passing identifier, the entrance time, the exit time and the portal identification time of the first target vehicle;
determining a passing identifier of a second target vehicle by using the inlet time, the outlet time, the portal identification time and the time window of the first target set C1, and forming a second target set C2;
dividing the portal running water set D2 according to the passing identification in the second target set C2 to obtain a portal identification license plate of the second target vehicle in a target time period and forming a third target set C3;
judging whether the portal identification license plate in the third target set C3 accords with a preset rule or not;
if yes, determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4;
and calculating and determining the fake-licensed vehicle by using the fourth target set C4.
Optionally, the determining the first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same traffic sign and forming a first target set C1 includes:
extracting feature matrixes of the entrance data set D1 and the portal running water set D2 according to the passing identification;
determining whether the number of the portal frames through which the vehicles with the same entrance license plate, exit license plate and portal frame identification license plate pass is smaller than a first threshold value in the entrance time and exit time ranges;
if yes, the vehicle is determined to be a first target vehicle, and a first target set C1 is formed by the traffic identification, the entry time, the exit time and the portal identification time of the first target vehicle.
Optionally, determining the first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same traffic sign and forming a first target set C1, including:
the first target set C1 is determined using the following relation:
D1 vm ＝[D1 v1 D1 v2 … D1 vm ]，D2 vn ＝[D2 v1 D2 v2 … D2 vn ]
where T1 represents the entrance time, T4 represents the exit time, D1vm represents the feature matrix of the entrance data set D1, D2vn represents the feature matrix of the gantry running water set D2, T represents the time between T1 and T4, and X1 represents the first threshold.
Optionally, determining the traffic identifier of the second target vehicle by using the entrance time, the exit time, the portal identification time and the time window of the first target set C1 and forming a second target set C2, including:
constructing a time sequence history characteristic of a time window by using the inlet time, the outlet time and the portal identification time of the first target set C1;
determining a second threshold for the time window using the time series history feature;
calculating a difference value of portal identification time of the first target set C1;
selecting a first target vehicle with the difference value larger than the second threshold value as a second target vehicle;
and forming the passing identifier of the second target vehicle into a second target set C2.
Optionally, determining a time feature value by using the entrance time, the exit time, the portal identification time and the time window of the first target set C1, and forming a second target set C2 by using the time feature value, including:
determining the second set of targets C2 using the relationship:
wherein ,
representing the previous portal identification time,/->
The latter portal identification time is represented, t2 represents the first temporal feature sequence, t3 represents the second temporal feature sequence, and X2 represents the second threshold.
Optionally, the determining whether the portal identification license plate in the third target set C3 meets a preset rule includes:
judging whether the portal recognition license plate in the third target set C3 is not in the period from the entrance time to the first portal recognition time and the period from the second portal recognition time to the next entrance time, and is not in the entrance data set D1.
Optionally, determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4 includes:
determining the abnormal license plate and composing the fourth target set C4 by using the following relation:
wherein ,Fv Representing license plates, C3 representing a third target set, C2 representing a second target set, D1 representing an entrance data set, T n Representing the nth time characteristic value.
Optionally, calculating and determining the fake-licensed vehicle by using the fourth target set C4 includes:
calculating a result set of the support, confidence and lifting degree of the abnormal license plate by using the fourth target set C4;
and determining the fake-licensed vehicle according to the result set of the support degree, the confidence degree and the lifting degree of the abnormal license plate.
Optionally, the calculating the result set of the support, the confidence and the promotion degree of the abnormal license plate includes:
and calculating a result set of the support, confidence and lifting degree of the abnormal license plate by using the following relation:
wherein support represents support, conf represents confidence, lift represents result set of promotion degree, L sample The number of terms representing the fourth target set C4, count representing the number, C1 Vm Indicating a normal license plate, C4 Vn Representing an abnormal license plate.
A highway fake-licensed vehicle determination device, comprising:
the system comprises a data acquisition unit, a portal running water collection D2 and a portal running water collection unit, wherein the data acquisition unit is used for acquiring an access data collection D1 and a portal running water collection D2, the access data collection D1 comprises a passing identifier, an access license plate, an exit license plate, an access time and an exit time of each vehicle, and the portal running water collection D2 comprises a passing identifier, a portal identification license plate and a portal identification time of each vehicle;
the first target set determining unit is used for determining a first target vehicle according to the entrance license plate, the exit license plate and the portal frame identification license plate of the same traffic identifier and forming a first target set C1, wherein the first target set C1 comprises the traffic identifier, the entrance time, the exit time and the portal frame identification time of the first target vehicle;
the second target set determining unit is used for determining a passing identifier of a second target vehicle by using the entrance time, the exit time, the portal identification time and the time window of the first target set C1 and forming a second target set C2;
the third target set determining unit is used for dividing the portal running water set D2 according to the passing identifier in the second target set C2 to obtain a portal identification license plate of the second target vehicle in the target time period and form a third target set C3;
the rule judging unit is used for judging whether the portal identification license plate in the third target set C3 accords with a preset rule;
the fourth target set determining unit is used for determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4 after the rule judging unit is executed;
and the calculation and determination unit is used for calculating and determining the fake-licensed vehicle by using the fourth target set C4.
According to the method and the device for determining the fake-licensed vehicles on the expressway, the first target vehicle is determined through the inlet data set and the portal running water set, the second target vehicle is determined from the first target vehicle through the time window, the portal running water set is divided through the passing identification of the second target vehicle, the portal identification license plate of the second target vehicle in the target time period is determined, the abnormal license plate is determined from the portal identification license plates in the target time period according to the preset rule, the fake-licensed vehicles are determined through calculating the related numerical value of the abnormal license plate, and compared with the image feature extraction comparison in the prior art, the accuracy of detecting the fake-licensed vehicles is improved through analysis and calculation of the inlet data set and the portal running water set, and the detection efficiency is further improved.
Drawings
Fig. 1 is a flowchart of a method for determining a highway fake-licensed vehicle according to an embodiment of the present application;
fig. 2 is a schematic diagram of an application scenario of a method for determining a highway fake-licensed vehicle according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a highway fake-licensed vehicle determining device according to an embodiment of the present application;
fig. 4 is a block diagram of a hardware configuration of a highway fake-licensed vehicle determination device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Fig. 1 is a schematic diagram of a method for determining a highway fake-licensed vehicle according to an embodiment of the present application, where the method may include the following steps:
step S100, an entrance data set D1 and a portal running water set D2 are obtained.
Specifically, the access data set D1 may include a passing identifier, an access license plate, an exit license plate, an access time, and an exit time of each vehicle, and the portal running water set D2 may include a passing identifier, a portal identification license plate, and a portal identification time of each vehicle, where the access data set D1 and the portal running water set D2 may be acquired through a data interface or an electronic device having a data transmission function.
Step S110, determining a first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same traffic sign and forming a first target set C1.
Specifically, the first target set C1 may include a traffic identifier, an entry time, an exit time, and a portal identification time of the first target vehicle, and may be traffic data information of the same vehicle locked from an entry license plate, an exit license plate, and a portal identification license plate of the entry data set D1 and the portal running water set D2 by the same traffic identifier, that is, the first target vehicle is determined, and the first target set C1 is formed.
Step S120, determining a traffic identifier of the second target vehicle by using the entrance time, the exit time, the portal identification time and the time window of the first target set C1, and forming a second target set C2.
Specifically, the entrance time, the exit time and the portal identification time of the first target set C1 may be utilized to determine a second target vehicle from the first target set C1 according to needs, and form the traffic identifier of the second target vehicle into a second target set C2.
And step S130, dividing the portal running water set D2 according to the traffic identification in the second target set C2 to obtain a portal identification license plate of the second target vehicle in the target time period and forming a third target set C3.
Specifically, since the portal running water set D2 includes the traffic identifier of each vehicle, the portal identification license plate and the portal identification time of the second target vehicle can be determined according to the traffic identifier in the second target set C2, and the portal identification license plate of the second target vehicle in the target time period can be selected according to the portal identification time of the second target vehicle and the third target set C3 can be formed.
And step 140, judging whether the portal identification license plate in the third target set C3 accords with a preset rule.
Specifically, the preset rule may be set according to the requirement, and whether the license plate is in accordance with the preset rule may be determined by determining the portal in the third target set C3.
And step S150, determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4.
Specifically, when the second target vehicle in the target time period accords with the preset rule, the second target vehicle may be determined as an abnormal license plate, and the abnormal license plate may be formed into a fourth target set C4.
And step S160, calculating and determining the fake-licensed vehicle by using the fourth target set C4.
Specifically, the fake-licensed vehicle may be determined by calculating the relevant data of the second target vehicle in the fourth target set C4.
In the embodiment of the application, the first target vehicle is determined through the inlet data set and the portal running water set, the second target vehicle is determined from the first target vehicle by utilizing the time window, the portal running water set is divided by utilizing the passing identification of the second target vehicle, the portal identification license plate of the second target vehicle in the target time period is determined, the abnormal license plate is determined from the portal identification license plates in the target time periods according to the preset rules, and the fake-licensed vehicle is determined by calculating the relative numerical value of the abnormal license plate.
In some embodiments of the present application, the following further describes the process of determining the first target vehicle and forming the first target set C1 according to the entrance license plate, the exit license plate and the portal identification license plate of the same traffic sign in the above step S110, where the process may include the following steps:
and S111, extracting feature matrixes of the entrance data set D1 and the portal running water set D2 according to the traffic identification.
Specifically, the license plate features in the entrance and exit data set D1 can be extracted through the passing identifier as a feature matrix, and the license plate features in the portal running water set D2 can be extracted through the passing identifier as a feature matrix.
And step S112, determining whether the number of the portals through which the vehicles with the same entrance license plate, exit license plate and portal identification license plate pass is smaller than a first threshold value in the entrance time and exit time ranges.
Specifically, the range of the vehicle can be determined through the time ranges of the entrance time and the exit time, the driving route of the vehicle is determined through the entrance license plate, the exit license plate and the portal identification license plate in the range, whether the license plate is changed or not in the driving process of the vehicle can be judged through the number of the given portal in the driving route, namely, whether the number of the portals through which the vehicle with the identical entrance license plate, the exit license plate and the portal identification license plate passes in the entrance time and the exit time ranges is smaller than a first threshold value or not is determined, and the first threshold value can be the number of the given portal in the driving route.
And step S113, if yes, determining the vehicle as a first target vehicle, and forming a first target set C1 by a traffic identifier, an entry time, an exit time and a portal identification time of the first target vehicle.
Specifically, if the number of the door frames passing through by the vehicle is smaller than the first threshold value, the vehicle can be determined to be a first target vehicle, the first target vehicle can be a suspected vehicle with a fake license vehicle problem, or the number of the door frames passing through by the vehicle is smaller than the first threshold value due to the failure of the door frame identification device, so that the traffic sign, the entry time, the exit time and the door frame identification time of the first target vehicle can be formed into a first target set C1, for example, a trip should have 15 records of the door frame number, and as a result, only 3 records are smaller than 15, and the vehicle is determined to be the first target vehicle.
Further, in some embodiments of the present application, the first target set C1 may be determined using the following relation:
D1 vm ＝[D1 v1 D1 v2 … D1 vm ]，D2 vn ＝[D2 v1 D2 v2 … D2 vn ]
wherein T1 may represent an entrance time, T4 may represent an exit time, D1vm may represent a feature matrix of the entrance data set D1, D2vn may represent a feature matrix of the gantry running water set D2, T may represent a time between T1 and T4, and X1 may represent a first threshold.
In some embodiments of the present application, a procedure for determining the traffic identifier of the second target vehicle by using the entry time, the exit time, the portal identification time and the time window of the first target set C1 and forming the second target set C2 in step S120 is described below, and the procedure may include the following steps:
step S121, constructing a time series history feature of a time window by using the entrance time, the exit time and the portal identification time of the first target set C1.
Specifically, the time window may be a sliding time window, the offset of the time window may be 1, the time series history feature may be obtained through known time training, and the time series history feature of the time window may be constructed by using the entry time, the exit time, and the portal identification time of the first target set C1.
Step S122, determining a second threshold of the time window by using the time series history feature.
Specifically, the normal running time through the road section can be determined by the time series history feature within a determined time range, and a second threshold value can be set by the time range, that is, the second threshold value of the time window can be determined by using the time series history feature.
Step S123, calculating a difference value of gantry recognition times of the first target set C1.
Specifically, the time difference between the frames may be calculated by the gantry identification time of the first target set C1, that is, the difference between the gantry identification times of the first target set C1 may be calculated, as shown in the following table:
| Id | VehclePlate | TransTime | TransTime_new | |
| 1 | guangdong | T1 | T2 | |
| 2 | Guangdong | T2 | T3 | |
| 3 | Guangdong | T3 | T4 | |
| 4 | Guangdong C | T4 | null |
Wherein Id represents the code number of each road section, vehclePalate represents a license plate, transTime represents the previous portal identification time, and TransTime_new represents the subsequent portal identification time.
Step S124, selecting the first target vehicle with the difference value greater than the second threshold value as the second target vehicle.
Specifically, after the difference value is calculated, further determination can be made on the suspected vehicle, namely the first target vehicle, and the first target vehicle with the difference value larger than the second threshold value can be selected as the second target vehicle.
Step S125, forming the traffic identifier of the second target vehicle into a second target set C2.
Specifically, after the second target vehicle is determined, a set of traffic identifiers corresponding to the second target vehicle may be made, that is, the traffic identifiers of the second target vehicle form a second target set C2.
Further, in some embodiments of the present application, the second target set C2 may be determined using the following relationship:
wherein ,
can represent the previous portal identification time, +.>
The latter portal identification time may be represented, t2 may represent a first time feature sequence, t3 may represent a second time feature sequence, X2 may represent a second threshold value, the first time feature sequence may represent that a period between a previous portal and a next portal, in which the vehicle is traveling in an abnormal period, i.e., in a certain road section, is detected to pass, exceeds the previous portal identification time in a normal traveling period, and similarly, the second time feature sequence may represent that a period between a previous portal and a next portal, in which the vehicle is traveling in an abnormal period, i.e., in a certain road section, is detected to pass, exceeds the next portal identification time in a normal traveling period.
In some embodiments of the present application, the following description will discuss a process of determining whether the portal identification license plate in the third target set C3 meets the preset rule in step S140, where the process may include:
judging whether the portal recognition license plate in the third target set C3 is not in the period from the entrance time to the first portal recognition time and the period from the second portal recognition time to the next entrance time, and is not in the entrance data set D1.
Specifically, by searching for the license plates in the time period from the entrance time to the first portal frame identification time and the time period from the second portal frame identification time to the next entrance time, it is determined whether the portal frame identification license plates in the third target set C3 are located therein and are also located in the entrance data set D1, and when the portal frame identification license plates in the third target set C3 are not located in the time period from the entrance time to the first portal frame identification time and the time period from the second portal frame identification time to the next entrance time and are not located in the entrance data set D1, it may be determined that the license plates are located in the second portal frame identification time. For example, a Vehicle running at a high speed is shown in the following table, where PassId is a traffic sign, vehicle is a license plate, entry is time t1, losttime is last portal identification vanishing time t2, apeartime is appearance time t3, extime is this exit time t4, and next is next entry time t5 of the Vehicle.
Finding out the portal identification license plate appears in the time from losttime (t 2) to Apeartime (t 3), namely 2030-07-019:00:00 to 2030-07-0121:00:00, but does not appear in the time from Entime (t 1) to Apeartime (t 3) and the time from Extime (t 4) to Nexttime (t 5), namely the abnormal license plate data without an inlet and an outlet.
Further, in some embodiments of the present application, the process of determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into the fourth target set C4 in step S150 may determine the abnormal license plate and form the fourth target set C4 by using the following relation:
wherein ,Fv Can represent license plate, C3 can represent a third target set, C2 can represent a second target set, D1 can represent an entrance and exit data set, T n The nth time characteristic value may be represented.
In some embodiments of the present application, a process for calculating and determining a fake-licensed vehicle using the fourth target set C4 is described below, and the process may include the following steps:
step S161, calculating a result set of the support, confidence and promotion of the abnormal license plate by using the fourth target set C4.
Specifically, the support degree may be a frequency representing the occurrence of a certain item set, the confidence degree may be a frequency representing the occurrence of a B item at the same time when an a item occurs, the promotion degree may be a frequency representing the occurrence of an a item and a B item at the same time, but the respective occurrence frequencies of the two items are considered at the same time, and the fourth target set C4 may be used to calculate the values of the support degree, the confidence degree and the promotion degree of the abnormal license plate and form a result set.
And S162, determining the fake-licensed vehicle according to the result set of the support degree, the confidence degree and the lifting degree of the abnormal license plate.
Specifically, after the result set of the support degree, the confidence degree and the lifting degree of the abnormal license plate is obtained, the fake-licensed vehicles can be determined according to the support degree, the confidence degree and the lifting degree in the result set, for example, the fake-licensed vehicles can be selected as the fake-licensed vehicles according to the ranking of the sum of the support degree, the confidence degree and the lifting degree multiplied by a preset weight. Examples are shown in the following table:
| PassId | normal license plate | Abnormal license plate |
| 01 | Guangdong A1 | Guangdong A2, guangdong A3, guangdong B2 and Guangdong C3 |
| 02 | Guangdong A1 | Guangdong A3 and Guangdong C4 |
| 03 | Guangdong A1 | Guangdong A3 and Guangdong C5 |
| 04 | Guangdong A1 | Guangdong B3 and Guangdong A5 |
| 05 | Guangdong B1 | Guangdong B2 and Guangdong A4 |
| License plate association | Support degree | Confidence level | Degree of elevation of |
| Guangdong A1- |
3/5 | 3/4 | 5/4 |
And carrying out normalization processing on the abnormal license plate and carrying out association rule algorithm calculation on the data item set, so as to obtain a support degree, a confidence degree and a lifting degree result set of the vehicle and the abnormal license plate, wherein the correlation can be determined according to the lifting degree, the lifting degree is larger than 1 to indicate that the positive correlation is stronger, the lifting degree is smaller than 1 to indicate that the negative correlation is higher, and the lifting degree=1 to indicate that the vehicle and the abnormal license plate are not correlated, namely are mutually independent.
Further, the following relation can be used to calculate the result set of the support, confidence and lifting degree of the abnormal license plate:
wherein support may represent support, conf may represent confidence, lift may represent a result set of lift, L sample The number of items that may represent the fourth target set C4, count may represent the number, C1 Vm Can represent the normal license plate, c4 Vn An abnormal license plate may be represented.
An application scenario of an optional method for determining a highway fake-licensed vehicle is described below, and as shown in fig. 2, the steps of the flow may be as follows:
acquiring access data D1, portal brand identification data D2 and portal transaction data D3;
acquiring a portal identification missing data set C1;
based on a time sequence sliding window algorithm and autocorrelation characteristic calculation, acquiring vehicle entrance and exit time, card recognition disappearance/appearance time and next entrance time as a set C2;
identifying data set C3 in the missing time;
the external association set D1 acquires abnormal license plate data C4;
obtaining the support degree, confidence coefficient and lifting degree of the vehicle and abnormal license plates through license plate normalization processing calculation and association rule algorithm calculation;
and analyzing the correlation degree of license plate correlation to obtain a result.
The expressway fake-licensed vehicle determination device provided in the embodiment of the present application is described below, and the expressway fake-licensed vehicle determination device described below and the expressway fake-licensed vehicle determination method described above may be referred to correspondingly to each other.
As shown in fig. 3, which discloses a schematic structure of a highway fake-licensed vehicle determination device, the highway fake-licensed vehicle determination device may include:
the data acquisition unit 11 is configured to acquire an entrance data set D1 and a portal running water set D2, where the entrance data set D1 includes a traffic identifier, an entrance license plate, an exit license plate, an entrance time, and an exit time of each vehicle, and the portal running water set D2 includes a traffic identifier, a portal identification license plate, and a portal identification time of each vehicle;
a first target set determining unit 12, configured to determine a first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same traffic identifier, and form a first target set C1, where the first target set C1 includes a traffic identifier, an entrance time, an exit time and a portal identification time of the first target vehicle;
a second target set determining unit 13, configured to determine a traffic identifier of a second target vehicle by using the entry time, the exit time, the portal identification time and the time window of the first target set C1, and form a second target set C2;
a third target set determining unit 14, configured to segment the portal running water set D2 according to the traffic identifier in the second target set C2, obtain a portal identification license plate of the second target vehicle in the target time period, and form a third target set C3;
the rule judging unit 15 is configured to judge whether the portal identification license plate in the third target set C3 meets a preset rule;
a fourth target set determining unit 16, configured to determine, after the rule determining unit is executed, the second target vehicle as an abnormal license plate and form the abnormal license plate into a fourth target set C4;
a calculation determination unit 17 for calculating and determining the fake-licensed vehicle by using the fourth target set C4.
Optionally, the first target set determining unit 12 may include:
the matrix extraction unit is used for extracting feature matrices of the entrance data set D1 and the portal running water set D2 according to the passing identifier;
the portal number determining unit is used for determining whether the number of portals, through which vehicles with the same entrance license plate, exit license plate and portal identification license plate pass, is smaller than a first threshold value or not in the entrance time and exit time range;
and the first target set forming unit is used for determining the vehicle as the first target vehicle after the portal number determining unit is executed, and forming a first target set C1 by the traffic identifier, the entry time, the exit time and the portal identification time of the first target vehicle.
Optionally, the first target set determining unit 12 may include:
a first target set calculating unit, configured to determine the first target set C1 by using the following relation:
D1 vm ＝[D1 v1 D1 v2 … D1 vm ]，D2 vn ＝[D2 v1 D2 v2 … D2 vn ]
where T1 represents the entrance time, T4 represents the exit time, D1vm represents the feature matrix of the entrance data set D1, D2vn represents the feature matrix of the gantry running water set D2, T represents the time between T1 and T4, and X1 represents the first threshold.
Optionally, the second target set determining unit 13 may include:
the feature establishing unit is used for constructing time sequence historical features of a time window by using the inlet time, the outlet time and the portal identification time of the first target set C1;
a second threshold determining unit configured to determine a second threshold of the time window using the time-series history feature;
a difference calculating unit, configured to calculate a difference of gantry identification times of the first target set C1;
a second target vehicle selecting unit, configured to select, as a second target vehicle, a first target vehicle whose difference is greater than the second threshold;
and the second target set forming unit is used for forming the passing identifier of the second target vehicle into a second target set C2.
Optionally, the second target set determining unit 13 may include:
a second target set calculating unit, configured to determine the second target set C2 by using the following relation:
wherein ,
representing the previous portal identification time,/->
The latter portal identification time is represented, t2 represents the first temporal feature sequence, t3 represents the second temporal feature sequence, and X2 represents the second threshold.
Optionally, the rule determining unit 15 may include:
the first rule judging subunit is configured to judge whether the portal identification license plate in the third target set C3 is not in the period from the entrance time to the first portal identification time and the period from the second portal identification time to the next entrance time, and is not in the entrance data set D1.
Optionally, the fourth target set determining unit 16 may include:
the fourth target set selecting unit is configured to determine the abnormal license plate by using the following relation and compose the fourth target set C4:
wherein ,Fv Representing license plates, C3 representing a third target set, C2 representing a second target set, D1 representing an entrance data set, T n Representing the nth time characteristic value.
Alternatively, the calculation determining unit 17 may include:
the first calculation and determination subunit is configured to calculate a result set of the support, the confidence and the lifting degree of the abnormal license plate by using the fourth target set C4;
and the second calculation and determination subunit is used for determining the fake-licensed vehicle according to the result set of the support degree, the confidence degree and the lifting degree of the abnormal license plate.
Alternatively, the calculation determining unit 17 may include:
the third calculation determining subunit is configured to calculate a result set of the support, the confidence and the lifting degree of the abnormal license plate by using the following relational expression:
wherein support represents support, conf represents confidence, lift represents result set of promotion degree, L sample The number of terms representing the fourth target set C4, count representing the number, C1 Vm Indicating a normal license plate, C4 Vn Representing an abnormal license plate.
The highway fake-licensed vehicle determining device provided by the embodiment of the application can be applied to highway fake-licensed vehicle determining equipment. The highway fake-licensed vehicle determination device may be a terminal. Fig. 4 shows a block diagram of a hardware structure of the road-side set-up vehicle determination apparatus, and referring to fig. 4, the hardware structure of the road-side set-up vehicle determination apparatus may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;
in the embodiment of the application, the number of the processor 1, the communication interface 2, the memory 3 and the communication bus 4 is at least one, and the processor 1, the communication interface 2 and the memory 3 complete communication with each other through the communication bus 4;
processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention, etc.;
the memory 3 may comprise a high-speed RAM memory, and may further comprise a non-volatile memory (non-volatile memory) or the like, such as at least one magnetic disk memory;
wherein the memory stores a program, the processor is operable to invoke the program stored in the memory, the program operable to:
the method comprises the steps that an entrance data set D1 and a portal running water set D2 are obtained, wherein the entrance data set D1 comprises a passing identifier, an entrance license plate, an exit license plate, an entrance time and an exit time of each vehicle, and the portal running water set D2 comprises a passing identifier, a portal identification license plate and a portal identification time of each vehicle;
determining a first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same passing identifier and forming a first target set C1, wherein the first target set C1 comprises the passing identifier, the entrance time, the exit time and the portal identification time of the first target vehicle;
determining a passing identifier of a second target vehicle by using the inlet time, the outlet time, the portal identification time and the time window of the first target set C1, and forming a second target set C2;
dividing the portal running water set D2 according to the passing identification in the second target set C2 to obtain a portal identification license plate of the second target vehicle in a target time period and forming a third target set C3;
judging whether the portal identification license plate in the third target set C3 accords with a preset rule or not;
if yes, determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4;
and calculating and determining the fake-licensed vehicle by using the fourth target set C4.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
The embodiment of the application also provides a storage medium, which may store a program adapted to be executed by a processor, the program being configured to:
the method comprises the steps that an entrance data set D1 and a portal running water set D2 are obtained, wherein the entrance data set D1 comprises a passing identifier, an entrance license plate, an exit license plate, an entrance time and an exit time of each vehicle, and the portal running water set D2 comprises a passing identifier, a portal identification license plate and a portal identification time of each vehicle;
determining a first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same passing identifier and forming a first target set C1, wherein the first target set C1 comprises the passing identifier, the entrance time, the exit time and the portal identification time of the first target vehicle;
determining a passing identifier of a second target vehicle by using the inlet time, the outlet time, the portal identification time and the time window of the first target set C1, and forming a second target set C2;
dividing the portal running water set D2 according to the passing identification in the second target set C2 to obtain a portal identification license plate of the second target vehicle in a target time period and forming a third target set C3;
judging whether the portal identification license plate in the third target set C3 accords with a preset rule or not;
if yes, determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4;
and calculating and determining the fake-licensed vehicle by using the fourth target set C4.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and each embodiment may be combined with each other, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of determining a highway fake-licensed vehicle, comprising:
the method comprises the steps that an entrance data set D1 and a portal running water set D2 are obtained, wherein the entrance data set D1 comprises a passing identifier, an entrance license plate, an exit license plate, an entrance time and an exit time of each vehicle, and the portal running water set D2 comprises a passing identifier, a portal identification license plate and a portal identification time of each vehicle;
determining a first target vehicle according to the entrance license plate, the exit license plate and the portal identification license plate of the same passing identifier and forming a first target set C1, wherein the first target set C1 comprises the passing identifier, the entrance time, the exit time and the portal identification time of the first target vehicle;
determining a passing identifier of a second target vehicle by using the inlet time, the outlet time, the portal identification time and the time window of the first target set C1, and forming a second target set C2;
dividing the portal running water set D2 according to the passing identification in the second target set C2 to obtain a portal identification license plate of the second target vehicle in a target time period and forming a third target set C3;
judging whether the portal identification license plate in the third target set C3 accords with a preset rule or not;
if yes, determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4;
and calculating and determining the fake-licensed vehicle by using the fourth target set C4.
2. The method according to claim 1, wherein said determining a first target vehicle and forming a first target set C1 from said entrance license plate, exit license plate and portal identification license plate of the same pass identification comprises:
extracting feature matrixes of the entrance data set D1 and the portal running water set D2 according to the passing identification;
determining whether the number of the portal frames through which the vehicles with the same entrance license plate, exit license plate and portal frame identification license plate pass is smaller than a first threshold value in the entrance time and exit time ranges;
if yes, the vehicle is determined to be a first target vehicle, and a first target set C1 is formed by the traffic identification, the entry time, the exit time and the portal identification time of the first target vehicle.
3. The method according to claim 1, wherein said determining a first target vehicle and forming a first target set C1 from said entrance license plate, exit license plate and portal identification license plate of the same pass identification comprises:
the first target set C1 is determined using the following relation:
D1 vm ＝[D1 v1 D1 v2 …D1 vm ]，D2 vn ＝[D2 v1 D2 v2 …D2 vn ]
where T1 represents the entrance time, T4 represents the exit time, D1vm represents the feature matrix of the entrance data set D1, D2vn represents the feature matrix of the gantry running water set D2, T represents the time between T1 and T4, and X1 represents the first threshold.
4. The method according to claim 1, wherein determining the traffic identification of the second target vehicle using the entrance time, exit time, portal identification time and time window of the first target set C1 and composing the second target set C2 comprises:
constructing a time sequence history characteristic of a time window by using the inlet time, the outlet time and the portal identification time of the first target set C1;
determining a second threshold for the time window using the time series history feature;
calculating a difference value of portal identification time of the first target set C1;
selecting a first target vehicle with the difference value larger than the second threshold value as a second target vehicle;
and forming the passing identifier of the second target vehicle into a second target set C2.
5. The method according to claim 1, wherein determining a time feature value using the entrance time, the exit time, the portal identification time, and the time window of the first target set C1, and composing the time feature value into a second target set C2, comprises:
determining the second set of targets C2 using the relationship:
wherein ,
representing the previous portal identification time,/->
The latter portal identification time is represented, t2 represents the first temporal feature sequence, t3 represents the second temporal feature sequence, and X2 represents the second threshold.
6. The method according to claim 1, wherein determining whether the portal identification license plate in the third target set C3 meets a preset rule comprises:
judging whether the portal recognition license plate in the third target set C3 is not in the period from the entrance time to the first portal recognition time and the period from the second portal recognition time to the next entrance time, and is not in the entrance data set D1.
7. The method of claim 1, wherein determining the second target vehicle as an anomalous license plate and grouping the anomalous license plate into a fourth target set C4 comprises:
determining the abnormal license plate and composing the fourth target set C4 by using the following relation:
wherein ,Fv Representing license plates, C3 representing a third target set, C2 representing a second target set, D1 representing an entrance data set, T n Representing the nth time characteristic value.
8. The method of claim 1, wherein computing a fake-licensed vehicle using the fourth target set C4 comprises:
calculating a result set of the support, confidence and lifting degree of the abnormal license plate by using the fourth target set C4;
and determining the fake-licensed vehicle according to the result set of the support degree, the confidence degree and the lifting degree of the abnormal license plate.
9. The method of claim 8, wherein the calculating the result set of support, confidence, and promotion of the abnormal license plate comprises:
and calculating a result set of the support, confidence and lifting degree of the abnormal license plate by using the following relation:
wherein support represents support, conf represents confidence, lift represents result set of promotion degree, L sample The number of terms representing the fourth target set C4, count representing the number, C1 Vm Indicating a normal license plate, C4 Vn Representing an abnormal license plate.
10. A highway fake-licensed vehicle determination device, comprising:
the system comprises a data acquisition unit, a portal running water collection D2 and a portal running water collection unit, wherein the data acquisition unit is used for acquiring an access data collection D1 and a portal running water collection D2, the access data collection D1 comprises a passing identifier, an access license plate, an exit license plate, an access time and an exit time of each vehicle, and the portal running water collection D2 comprises a passing identifier, a portal identification license plate and a portal identification time of each vehicle;
the first target set determining unit is used for determining a first target vehicle according to the entrance license plate, the exit license plate and the portal frame identification license plate of the same traffic identifier and forming a first target set C1, wherein the first target set C1 comprises the traffic identifier, the entrance time, the exit time and the portal frame identification time of the first target vehicle;
the second target set determining unit is used for determining a passing identifier of a second target vehicle by using the entrance time, the exit time, the portal identification time and the time window of the first target set C1 and forming a second target set C2;
the third target set determining unit is used for dividing the portal running water set D2 according to the passing identifier in the second target set C2 to obtain a portal identification license plate of the second target vehicle in the target time period and form a third target set C3;
the rule judging unit is used for judging whether the portal identification license plate in the third target set C3 accords with a preset rule;
the fourth target set determining unit is used for determining the second target vehicle as an abnormal license plate and forming the abnormal license plate into a fourth target set C4 after the rule judging unit is executed;
and the calculation and determination unit is used for calculating and determining the fake-licensed vehicle by using the fourth target set C4.
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| CN114495028A (en) * | 2022-01-21 | 2022-05-13 | 北京华录高诚科技有限公司 | Vehicle fake plate identification method and device, electronic equipment and storage medium |
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