Expressway vehicle identification method and system

Abstract

According to the expressway vehicle identification method and system disclosed by the invention, the monitoring points are arranged on the expressway, the vehicle images on the expressway are obtained, the vehicle features in the vehicle images and the corresponding vehicle license plate data are extracted, and the expressway vehicles are identified by taking the vehicle license plate data as the main and the vehicle features as the auxiliary. The expressway vehicle identification accuracy is improved; in addition, the speed of the identified vehicle is measured, the occurrence condition is compared and analyzed, an alarm is triggered in time, and the driving safety of the expressway is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and more specifically, to a highway vehicle identification method and system. Background Art

[0002] Highway vehicle identification technology is a key component of the intelligent transportation system. With the increase in highway traffic volume, highway vehicle driving safety has become an issue that cannot be ignored. Currently, highway vehicle identification is mainly used in traffic management, auxiliary law enforcement, etc., and lacks application in traffic safety, such as the safety warning of highway vehicle disappearance caused by the collapse of highway bridges.

[0003] Therefore, the prior art has defects and needs to be improved urgently. Summary of the invention

[0004] In view of the above problems, an object of the present invention is to provide a highway vehicle identification method and system, which can improve the accuracy of highway vehicle identification.

[0005] A first aspect of the present invention provides a method for identifying vehicles on a highway, comprising:

[0006] Based on the preset monitoring points, obtain the vehicle image information on the highway;

[0007] Extracting vehicle features and corresponding vehicle license plate data from vehicle image information;

[0008] Comparing and analyzing the vehicle license plate data with the license plate data in a preset license plate database to obtain a first similarity value;

[0009] If the first similarity value is greater than a preset first similarity threshold and there is a first similarity value, determining the highway vehicle according to the vehicle license plate data corresponding to the first similarity value;

[0010] If the first similarity value is greater than a preset first similarity threshold and there are multiple first similarity values, extract the vehicle information corresponding to the license plate data in the preset license plate database corresponding to the first similarity value and set it as the first similar vehicle information;

[0011] extracting vehicle features of the first similar vehicle;

[0012] Comparing and analyzing the vehicle feature of the first similar vehicle and the vehicle feature in the vehicle image information to obtain a second similarity value;

[0013] If there is a second similarity value greater than a preset second similarity threshold, extracting the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value, and determining the license plate data corresponding to the first similar vehicle according to the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value;

[0014] The highway vehicle is determined based on license plate data corresponding to a first similar vehicle.

[0015] In this solution, the step of obtaining license plate data in the preset license plate library includes:

[0016] Obtain the location information of the monitoring point;

[0017] According to the location information of the monitoring point, determine the high-speed line where the corresponding monitoring point is located, as well as all the entrances and exits on the corresponding high-speed line;

[0018] Extract the license plate data of vehicles at all entrances and exits on the corresponding expressway;

[0019] The license plate data of vehicles entering the corresponding high-speed line is subtracted from the license plate data of vehicles exiting the station to obtain the license plate data in the preset license plate database.

[0020] In this solution, the step of obtaining the license plate data in the preset license plate library also includes:

[0021] Obtain license plate data for each entrance or exit;

[0022] Extract the distance value from the monitoring point to the entrance or exit;

[0023] Determine the distance level from the monitoring point to the station entrance or exit according to the preset distance value range within which the distance value from the monitoring point to the station entrance or exit falls;

[0024] The distance level from the monitoring point to the entrance or exit is multiplied by the preset update cycle weight coefficient to obtain the time period for the license plate data of the entrance or exit to be updated to the preset license plate database.

[0025] In this solution, after obtaining the first similarity value, the following steps are also included:

[0026] The monitoring points are numbered in sequence according to the order of their positions to obtain the number of each monitoring point;

[0027] Extracting the adjacent previous monitoring point of the current monitoring point and the first similarity value corresponding to the adjacent previous monitoring point;

[0028] If the license plate data in the license plate database corresponding to the first similarity value corresponding to the adjacent previous monitoring point is the same as the license plate data in the license plate database corresponding to the first similarity value corresponding to the current monitoring point, then the first similarity value corresponding to the adjacent previous monitoring point is multiplied by a preset revision weight coefficient to obtain the first similarity revision value corresponding to the current monitoring point;

[0029] The first similarity revised value corresponding to the current monitoring point is added to the first similarity value corresponding to the current monitoring point to obtain the revised first similarity value of the current monitoring point.

[0030] This plan also includes:

[0031] If all first similarity values ​​are less than or equal to a preset first similarity threshold, the corresponding monitoring point is marked once;

[0032] Based on a preset first time period, obtaining a total number of times that a corresponding monitoring point is marked;

[0033] If the total number of times the corresponding monitoring point is marked is greater than the preset first number threshold, an error prompt message of the corresponding monitoring point is triggered;

[0034] The monitoring point error prompt information and the number of the monitoring point are sent to a preset management terminal for prompting.

[0035] This plan also includes:

[0036] If there are any two monitoring points that determine the same highway vehicle, then the distance values ​​corresponding to the two monitoring points are extracted and the two time nodes of the highway vehicle are determined;

[0037] Calculate the difference between two time nodes of the highway vehicles to obtain the time difference;

[0038] Divide the distance value between the two monitoring points by the time difference to obtain the first speed value of the vehicle on the corresponding highway;

[0039] If the first speed value is greater than the preset first speed threshold, an overspeed warning message is triggered;

[0040] If the first speed value is greater than the preset second speed threshold, a duplicate card warning message is triggered;

[0041] Sending the speeding reminder information or license plate counterfeiting warning information to a preset management terminal for prompting;

[0042] The second speed threshold is greater than the preset first speed threshold.

[0043] This plan also includes:

[0044] When a highway vehicle is identified by any monitoring point, the corresponding identification time node is extracted and set as the first identification time node;

[0045] Get the distance value from any monitoring point to the next adjacent monitoring point;

[0046] Divide the distance value from any monitoring point to the next adjacent monitoring point by the preset third speed value to obtain the monitoring time period of the next adjacent monitoring point for the vehicle on the highway;

[0047] If the highway vehicle is not identified by the next adjacent monitoring point within the corresponding monitoring time period, the next adjacent monitoring point is marked as an error once;

[0048] If there is an exit between any monitoring point and the next adjacent monitoring point, and the monitoring point at the exit identifies the highway vehicle, the marking error of the next adjacent monitoring point will be deleted once;

[0049] Based on the preset second time period, obtain the total number of times that an adjacent next monitoring point is marked with errors;

[0050] If the total number of times the next adjacent monitoring point is marked incorrectly is greater than the preset second number threshold, a warning message is triggered;

[0051] The warning information is sent to a preset management terminal for prompting.

[0052] A second aspect of the present invention provides a highway vehicle identification system, comprising a memory and a processor, wherein the memory stores a highway vehicle identification method program, and when the highway vehicle identification method program is executed by the processor, the following steps are implemented:

[0053] Based on the preset monitoring points, obtain the vehicle image information on the highway;

[0054] Extracting vehicle features and corresponding vehicle license plate data from vehicle image information;

[0055] Comparing and analyzing the vehicle license plate data with the license plate data in a preset license plate database to obtain a first similarity value;

[0056] If the first similarity value is greater than a preset first similarity threshold and there is a first similarity value, determining the highway vehicle according to the vehicle license plate data corresponding to the first similarity value;

[0057] If the first similarity value is greater than a preset first similarity threshold and there are multiple first similarity values, extract the vehicle information corresponding to the license plate data in the preset license plate database corresponding to the first similarity value and set it as the first similar vehicle information;

[0058] extracting vehicle features of the first similar vehicle;

[0059] Comparing and analyzing the vehicle feature of the first similar vehicle and the vehicle feature in the vehicle image information to obtain a second similarity value;

[0060] If there is a second similarity value greater than a preset second similarity threshold, extracting the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value, and determining the license plate data corresponding to the first similar vehicle according to the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value;

[0061] The highway vehicle is determined based on license plate data corresponding to a first similar vehicle.

[0062] In this solution, the step of obtaining license plate data in the preset license plate library includes:

[0063] Obtain the location information of the monitoring point;

[0064] According to the location information of the monitoring point, determine the high-speed line where the corresponding monitoring point is located, as well as all the entrances and exits on the corresponding high-speed line;

[0065] Extract the license plate data of vehicles at all entrances and exits on the corresponding expressway;

[0066] The license plate data of vehicles entering the corresponding high-speed line is subtracted from the license plate data of vehicles exiting the station to obtain the license plate data in the preset license plate database.

[0067] In this solution, the step of obtaining the license plate data in the preset license plate library also includes:

[0068] Obtain license plate data for each entrance or exit;

[0069] Extract the distance value from the monitoring point to the entrance or exit;

[0070] Determine the distance level from the monitoring point to the station entrance or exit according to the preset distance value range within which the distance value from the monitoring point to the station entrance or exit falls;

[0071] The distance level from the monitoring point to the entrance or exit is multiplied by the preset update cycle weight coefficient to obtain the time period for the license plate data of the entrance or exit to be updated to the preset license plate database.

[0072] The present invention discloses a highway vehicle identification method and system, which sets monitoring points on the highway, obtains vehicle images on the highway, and extracts vehicle features and corresponding vehicle license plate data in the vehicle images, and identifies highway vehicles mainly based on the vehicle license plate data and supplemented by vehicle features, thereby improving the accuracy of highway vehicle identification; in addition, by measuring the speed of the identified vehicles and comparing and analyzing the situations that occur, an alarm is triggered in time, thereby improving highway driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1A flow chart of a highway vehicle identification method of the present invention is shown;

[0074] Figure 2 A block diagram of a highway vehicle identification system of the present invention is shown. DETAILED DESCRIPTION

[0075] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0076] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0077] Figure 1 A flow chart of a highway vehicle identification method of the present invention is shown.

[0078] like Figure 1 As shown, the present invention discloses a highway vehicle identification method, comprising:

[0079] S101, obtaining vehicle image information on the highway based on preset monitoring points;

[0080] S102, extracting vehicle features and corresponding vehicle license plate data from the vehicle image information;

[0081] S103, comparing and analyzing the vehicle license plate data with the license plate data in a preset license plate database to obtain a first similarity value;

[0082] S104, if the first similarity value is greater than a preset first similarity threshold and there is a first similarity value, determining the highway vehicle according to the vehicle license plate data corresponding to the first similarity value;

[0083] S105, if the first similarity value is greater than a preset first similarity threshold and there are multiple first similarity values, extract the vehicle information corresponding to the license plate data in the preset license plate database corresponding to the first similarity value and set it as the first similar vehicle information;

[0084] S106, extracting vehicle features of the first similar vehicle;

[0085] S107, comparing and analyzing the vehicle feature of the first similar vehicle and the vehicle feature in the vehicle image information to obtain a second similarity value;

[0086] S108, if there is a second similarity value greater than the preset second similarity threshold, extracting the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value, and determining the license plate data corresponding to the first similar vehicle according to the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value;

[0087] S109: Determine the highway vehicle based on the license plate data corresponding to the first similar vehicle.

[0088] According to an embodiment of the present invention, monitoring points are pre-set on the highway, and each monitoring point is provided with a shooting device. The shooting device is used to obtain images of vehicles passing through the current monitoring point. The vehicle features include the color, model and other features of the vehicle, and the vehicle license plate data includes the numbers, Chinese characters and license plate color on the license plate. If the second similarity values ​​are less than or equal to the preset second similarity threshold, the corresponding monitoring point is marked once.

[0089] According to an embodiment of the present invention, the step of acquiring license plate data in the preset license plate database includes:

[0090] Obtain the location information of the monitoring point;

[0091] According to the location information of the monitoring point, determine the high-speed line where the corresponding monitoring point is located, as well as all the entrances and exits on the corresponding high-speed line;

[0092] Extract the license plate data of vehicles at all entrances and exits on the corresponding expressway;

[0093] The license plate data of vehicles entering the corresponding high-speed line is subtracted from the license plate data of vehicles exiting the station to obtain the license plate data in the preset license plate database.

[0094] It should be noted that when a vehicle enters the expressway from the entrance, the license plate data and vehicle characteristics of the corresponding vehicle will be registered and associated, and uploaded to the server for storage for subsequent updating of the license plate database; when a vehicle exits the expressway from the exit, the license plate data and vehicle characteristics of the corresponding vehicle will be registered and associated, and the license plate data and vehicle characteristics of the corresponding vehicle will be identified for subsequent updating and deletion of the vehicle information from the license plate database.

[0095] According to an embodiment of the present invention, the step of acquiring license plate data from the preset license plate database further includes:

[0096] Obtain license plate data for each entrance or exit;

[0097] Extract the distance value from the monitoring point to the entrance or exit;

[0098] Determine the distance level from the monitoring point to the station entrance or exit according to the preset distance value range within which the distance value from the monitoring point to the station entrance or exit falls;

[0099] The distance level from the monitoring point to the entrance or exit is multiplied by the preset update cycle weight coefficient to obtain the time period for the license plate data of the entrance or exit to be updated to the preset license plate database.

[0100] It should be noted that multiple preset distance value ranges are divided based on a preset distance base. For example, if 10 kilometers is a distance base, a distance value range is preset every 10 kilometers. Each distance value range corresponds to a distance level, and the larger the value in the distance value range, the higher the distance level. The higher the distance level, the license plate data of the entrance or exit will be updated to the license plate library preset at the corresponding monitoring point according to the update time period.

[0101] According to an embodiment of the present invention, after obtaining the first similarity value, the method further includes:

[0102] The monitoring points are numbered in sequence according to the order of their positions to obtain the number of each monitoring point;

[0103] Extracting the adjacent previous monitoring point of the current monitoring point and the first similarity value corresponding to the adjacent previous monitoring point;

[0104] If the license plate data in the license plate database corresponding to the first similarity value corresponding to the adjacent previous monitoring point is the same as the license plate data in the license plate database corresponding to the first similarity value corresponding to the current monitoring point, then the first similarity value corresponding to the adjacent previous monitoring point is multiplied by a preset revision weight coefficient to obtain the first similarity revision value corresponding to the current monitoring point;

[0105] The first similarity revised value corresponding to the current monitoring point is added to the first similarity value corresponding to the current monitoring point to obtain the revised first similarity value of the current monitoring point.

[0106] It should be noted that the order of the positions is based on the direction of vehicle travel. When the vehicle is traveling, the numbers of the monitoring points passed by the vehicle are increasing. For example, if the current monitoring point is numbered 05, then the adjacent previous monitoring point is numbered 04. If the license plate data in the license plate library corresponding to the first similarity values ​​in two adjacent monitoring points are the same, the corresponding first similarity value in the adjacent next monitoring point is revised, and the adjacent next monitoring point is the current monitoring point.

[0107] According to an embodiment of the present invention, it also includes:

[0108] If all first similarity values ​​are less than or equal to a preset first similarity threshold, the corresponding monitoring point is marked once;

[0109] Based on a preset first time period, obtaining a total number of times that a corresponding monitoring point is marked;

[0110] If the total number of times the corresponding monitoring point is marked is greater than the preset first number threshold, an error prompt message of the corresponding monitoring point is triggered;

[0111] The monitoring point error prompt information and the number of the monitoring point are sent to a preset management terminal for prompting.

[0112] It should be noted that the number of each monitoring point and the location of the corresponding monitoring point are recorded in the preset management terminal. The preset management terminal can find the location of the corresponding monitoring point according to the number of the monitoring point, thereby finding the location of the erroneous monitoring point.

[0113] According to an embodiment of the present invention, it also includes:

[0114] If there are any two monitoring points that determine the same highway vehicle, then the distance values ​​corresponding to the two monitoring points are extracted and the two time nodes of the highway vehicle are determined;

[0115] Calculate the difference between two time nodes of the highway vehicles to obtain the time difference;

[0116] Divide the distance value between the two monitoring points by the time difference to obtain the first speed value of the vehicle on the corresponding highway;

[0117] If the first speed value is greater than the preset first speed threshold, an overspeed warning message is triggered;

[0118] If the first speed value is greater than the preset second speed threshold, a duplicate card warning message is triggered;

[0119] Sending the speeding reminder information or license plate counterfeiting warning information to a preset management terminal for prompting;

[0120] The second speed threshold is greater than the preset first speed threshold.

[0121] It should be noted that through highway vehicle identification, vehicle management can be improved and highway vehicles can be prevented from using fake license plates and not complying with traffic regulations while driving.

[0122] According to an embodiment of the present invention, it also includes:

[0123] When a highway vehicle is identified by any monitoring point, the corresponding identification time node is extracted and set as the first identification time node;

[0124] Get the distance value from any monitoring point to the next adjacent monitoring point;

[0125] Divide the distance value from any monitoring point to the next adjacent monitoring point by the preset third speed value to obtain the monitoring time period of the next adjacent monitoring point for the vehicle on the highway;

[0126] If the highway vehicle is not identified by the next adjacent monitoring point within the corresponding monitoring time period, the next adjacent monitoring point is marked as an error once;

[0127] If there is an exit between any monitoring point and the next adjacent monitoring point, and the monitoring point at the exit identifies the highway vehicle, the marking error of the next adjacent monitoring point will be deleted once;

[0128] Based on the preset second time period, obtain the total number of times that an adjacent next monitoring point is marked with errors;

[0129] If the total number of times the next adjacent monitoring point is marked incorrectly is greater than the preset second number threshold, a warning message is triggered;

[0130] The warning information is sent to a preset management terminal for prompting.

[0131] It should be noted that when there is no exit or service area between adjacent monitoring points, a vehicle passing the previous monitoring point will inevitably pass the next monitoring point. Therefore, this feature can be used to determine whether a fault or accident occurs between adjacent monitoring points, thereby preventing secondary accidents from occurring after a traffic accident.

[0132] Figure 2 A block diagram of a highway vehicle identification system of the present invention is shown.

[0133] like Figure 2 The second aspect of the present invention provides a highway vehicle identification system 2, comprising a memory 21 and a processor 22, wherein the memory stores a highway vehicle identification method program, and when the highway vehicle identification method program is executed by the processor, the following steps are implemented:

[0134] Based on the preset monitoring points, obtain the vehicle image information on the highway;

[0135] Extracting vehicle features and corresponding vehicle license plate data from vehicle image information;

[0136] Comparing and analyzing the vehicle license plate data with the license plate data in a preset license plate database to obtain a first similarity value;

[0137] If the first similarity value is greater than a preset first similarity threshold and there is a first similarity value, determining the highway vehicle according to the vehicle license plate data corresponding to the first similarity value;

[0138] If the first similarity value is greater than a preset first similarity threshold and there are multiple first similarity values, extract the vehicle information corresponding to the license plate data in the preset license plate database corresponding to the first similarity value and set it as the first similar vehicle information;

[0139] extracting vehicle features of the first similar vehicle;

[0140] Comparing and analyzing the vehicle feature of the first similar vehicle and the vehicle feature in the vehicle image information to obtain a second similarity value;

[0141] If there is a second similarity value greater than a preset second similarity threshold, extracting the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value, and determining the license plate data corresponding to the first similar vehicle according to the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value;

[0142] The highway vehicle is determined based on license plate data corresponding to a first similar vehicle.

[0143] According to an embodiment of the present invention, the step of acquiring license plate data in the preset license plate database includes:

[0144] Obtain the location information of the monitoring point;

[0145] According to the location information of the monitoring point, determine the high-speed line where the corresponding monitoring point is located, as well as all the entrances and exits on the corresponding high-speed line;

[0146] Extract the license plate data of vehicles at all entrances and exits on the corresponding expressway;

[0147] The license plate data of vehicles entering the corresponding high-speed line is subtracted from the license plate data of vehicles exiting the station to obtain the license plate data in the preset license plate database.

[0148] According to an embodiment of the present invention, the step of acquiring license plate data from the preset license plate database further includes:

[0149] Obtain license plate data for each entrance or exit;

[0150] Extract the distance value from the monitoring point to the entrance or exit;

[0151] Determine the distance level from the monitoring point to the station entrance or exit according to the preset distance value range within which the distance value from the monitoring point to the station entrance or exit falls;

[0152] The distance level from the monitoring point to the entrance or exit is multiplied by the preset update cycle weight coefficient to obtain the time period for the license plate data of the entrance or exit to be updated to the preset license plate database.

[0153] The present invention discloses a highway vehicle identification method and system, which sets monitoring points on the highway, obtains vehicle images on the highway, and extracts vehicle features and corresponding vehicle license plate data in the vehicle images, and identifies highway vehicles mainly based on the vehicle license plate data and supplemented by vehicle features, thereby improving the accuracy of highway vehicle identification; in addition, by measuring the speed of the identified vehicles and comparing and analyzing the situations that occur, an alarm is triggered in time, thereby improving highway driving safety.

[0154] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0155] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0156] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0157] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0158] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for identifying vehicles on a highway, characterized in that: include: Based on the preset monitoring points, obtain the vehicle image information on the highway; Extracting vehicle features and corresponding vehicle license plate data from vehicle image information; Comparing and analyzing the vehicle license plate data with the license plate data in a preset license plate database to obtain a first similarity value; If the first similarity value is greater than a preset first similarity threshold and there is a first similarity value, determining the highway vehicle according to the vehicle license plate data corresponding to the first similarity value; If the first similarity value is greater than a preset first similarity threshold and there are multiple first similarity values, extract the vehicle information corresponding to the license plate data in the preset license plate database corresponding to the first similarity value and set it as the first similar vehicle information; extracting vehicle features of the first similar vehicle; Comparing and analyzing the vehicle feature of the first similar vehicle and the vehicle feature in the vehicle image information to obtain a second similarity value; If there is a second similarity value greater than a preset second similarity threshold, extracting the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value, and determining the license plate data corresponding to the first similar vehicle according to the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value; The highway vehicle is determined based on license plate data corresponding to a first similar vehicle.

2. A highway vehicle identification method according to claim 1, characterized in that: The step of acquiring the license plate data in the preset license plate library includes: Obtain the location information of the monitoring point; According to the location information of the monitoring point, determine the high-speed line where the corresponding monitoring point is located, as well as all the entrances and exits on the corresponding high-speed line; Extract the license plate data of vehicles at all entrances and exits on the corresponding expressway; The license plate data of vehicles entering the corresponding high-speed line is subtracted from the license plate data of vehicles exiting the station to obtain the license plate data in the preset license plate database.

3. A highway vehicle identification method according to claim 2, characterized in that: The step of acquiring the license plate data in the preset license plate library also includes: Obtain license plate data for each entrance or exit; Extract the distance value from the monitoring point to the entrance or exit; Determine the distance level from the monitoring point to the station entrance or exit according to the preset distance value range within which the distance value from the monitoring point to the station entrance or exit falls; The distance level from the monitoring point to the entrance or exit is multiplied by the preset update cycle weight coefficient to obtain the time period for the license plate data of the entrance or exit to be updated to the preset license plate database.

4. A highway vehicle identification method according to claim 1, characterized in that: After obtaining the first similarity value, the following steps are also included: The monitoring points are numbered in sequence according to the order of their positions to obtain the number of each monitoring point; Extracting the adjacent previous monitoring point of the current monitoring point and the first similarity value corresponding to the adjacent previous monitoring point; If the license plate data in the license plate database corresponding to the first similarity value corresponding to the adjacent previous monitoring point is the same as the license plate data in the license plate database corresponding to the first similarity value corresponding to the current monitoring point, then the first similarity value corresponding to the adjacent previous monitoring point is multiplied by a preset revision weight coefficient to obtain the first similarity revision value corresponding to the current monitoring point; The first similarity revised value corresponding to the current monitoring point is added to the first similarity value corresponding to the current monitoring point to obtain the revised first similarity value of the current monitoring point.

5. A highway vehicle identification method according to claim 1, characterized in that: Also includes: If all first similarity values ​​are less than or equal to a preset first similarity threshold, the corresponding monitoring point is marked once; Based on a preset first time period, obtaining a total number of times that a corresponding monitoring point is marked; If the total number of times the corresponding monitoring point is marked is greater than the preset first number threshold, an error prompt message of the corresponding monitoring point is triggered; The monitoring point error prompt information and the number of the monitoring point are sent to a preset management terminal for prompting.

6. A highway vehicle identification method according to claim 1, characterized in that: Also includes: If there are any two monitoring points that determine the same highway vehicle, then the distance values ​​corresponding to the two monitoring points are extracted and the two time nodes of the highway vehicle are determined; Calculate the difference between two time nodes of the highway vehicles to obtain the time difference; Divide the distance value between the two monitoring points by the time difference to obtain the first speed value of the vehicle on the corresponding highway; If the first speed value is greater than the preset first speed threshold, an overspeed warning message is triggered; If the first speed value is greater than the preset second speed threshold, a duplicate card warning message is triggered; Sending the speeding reminder information or license plate counterfeiting warning information to a preset management terminal for prompting; The second speed threshold is greater than the preset first speed threshold.

7. A highway vehicle identification method according to claim 1, characterized in that: Also includes: When a highway vehicle is identified by any monitoring point, the corresponding identification time node is extracted and set as the first identification time node; Get the distance value from any monitoring point to the next adjacent monitoring point; Divide the distance value from any monitoring point to the next adjacent monitoring point by the preset third speed value to obtain the monitoring time period of the next adjacent monitoring point for the vehicle on the highway; If the highway vehicle is not identified by the next adjacent monitoring point within the corresponding monitoring time period, the next adjacent monitoring point is marked as an error once; If there is an exit between any monitoring point and the next adjacent monitoring point, and the monitoring point at the exit identifies the highway vehicle, the marking error of the next adjacent monitoring point will be deleted once; Based on the preset second time period, obtain the total number of times that an adjacent next monitoring point is marked with errors; If the total number of times the next adjacent monitoring point is marked incorrectly is greater than the preset second number threshold, a warning message is triggered; The warning information is sent to a preset management terminal for prompting.

8. A highway vehicle identification system, characterized in that: The invention comprises a memory and a processor, wherein a highway vehicle identification method program is stored in the memory, and the highway vehicle identification method program is executed by the processor to implement the following steps: Based on the preset monitoring points, obtain the vehicle image information on the highway; Extracting vehicle features and corresponding vehicle license plate data from vehicle image information; Comparing and analyzing the vehicle license plate data with the license plate data in a preset license plate database to obtain a first similarity value; If the first similarity value is greater than a preset first similarity threshold and there is a first similarity value, determining the highway vehicle according to the vehicle license plate data corresponding to the first similarity value; If the first similarity value is greater than a preset first similarity threshold and there are multiple first similarity values, extract the vehicle information corresponding to the license plate data in the preset license plate database corresponding to the first similarity value and set it as the first similar vehicle information; extracting vehicle features of the first similar vehicle; Comparing and analyzing the vehicle feature of the first similar vehicle and the vehicle feature in the vehicle image information to obtain a second similarity value; If there is a second similarity value greater than a preset second similarity threshold, extracting the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value, and determining the license plate data corresponding to the first similar vehicle according to the vehicle feature of the first similar vehicle corresponding to the maximum second similarity value; The highway vehicle is determined based on license plate data corresponding to a first similar vehicle.

9. A highway vehicle identification system according to claim 8, characterized in that: The step of acquiring the license plate data in the preset license plate library includes: Obtain the location information of the monitoring point; According to the location information of the monitoring point, determine the high-speed line where the corresponding monitoring point is located, as well as all the entrances and exits on the corresponding high-speed line; Extract the license plate data of vehicles at all entrances and exits on the corresponding expressway; The license plate data of vehicles entering the corresponding high-speed line is subtracted from the license plate data of vehicles exiting the station to obtain the license plate data in the preset license plate database.

10. A highway vehicle identification system according to claim 9, characterized in that: The step of acquiring the license plate data in the preset license plate library also includes: Obtain license plate data for each entrance or exit; Extract the distance value from the monitoring point to the entrance or exit; Determine the distance level from the monitoring point to the station entrance or exit according to the preset distance value range within which the distance value from the monitoring point to the station entrance or exit falls; The distance level from the monitoring point to the entrance or exit is multiplied by the preset update cycle weight coefficient to obtain the time period for the license plate data of the entrance or exit to be updated to the preset license plate database.

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