A method, device and medium for intelligent toll collection assistance based on highways

Abstract

The present invention discloses a method, device, and medium for intelligent toll collection assistance based on highways, belonging to the fields of big data analysis and highway toll collection technology. The method is used to address the technical issues of existing highway toll stations, such as the difficulty in auditing suspected vehicles and combating toll evasion, as well as the difficulty in accurately identifying vehicle information. The method comprises: obtaining first vehicle information of vehicles traveling on the highway; screening the first vehicle information for toll credit information using a preset audit model to obtain screening results; and, based on the screening results, performing data forensics processing on the suspected vehicle to determine abnormal passage results of the suspected vehicle; identifying second vehicle information; and, based on the abnormal passage results corresponding to the second vehicle information, determining an abnormally passing vehicle; and issuing a system alert to the abnormally passing vehicle to assist toll collectors in completing the work of combating toll evasion by vehicles on the scene.

Description

Technical Field

[0001] The present application relates to the fields of big data analysis and highway toll collection, and in particular to an intelligent toll collection assistance method and device based on highways. Background Art

[0002] With technological advancements and social development, provincial toll booths on expressways have been gradually eliminated, resulting in a nationwide "one network" of expressways. This network features extensive ETC coverage, long vehicle mileage, and complex road conditions, leading to significant changes in toll collection models. This "one network" operation has ushered in a new era for public transportation.

[0003] After the provincial border toll stations are dismantled, vehicles can pass through other provinces and travel longer distances. At the same time, for toll collectors, the means and channels for obtaining vehicle information have become fewer. It is more difficult to handle special vehicles such as those with no entrance and abnormal billing. It is also more difficult to judge the types of vehicles entering and exiting the entrance, which often leads to misjudgment by toll collectors. In addition, it is difficult to identify vehicles suspected of evading tolls, which brings certain difficulties to the work of toll collection and anti-evasion. Summary of the Invention

[0004] The embodiments of the present application provide a highway-based intelligent toll collection assistance method, device and medium for solving the following technical problems: it is difficult for existing highway toll stations to audit and crack down on suspected vehicles for toll evasion, and it is difficult to accurately identify vehicle information.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] On the one hand, an embodiment of the present application provides an intelligent toll collection assistance method based on a highway, the method comprising: obtaining first vehicle information of a vehicle traveling on the highway; screening the toll credit information of the first vehicle information through a preset audit model to obtain a screening result; and based on the screening result, performing data forensics processing on the suspected vehicle to determine the abnormal passage result of the suspected vehicle; identifying second vehicle information; and determining the abnormal passing vehicle based on the abnormal passage result corresponding to the second vehicle information; issuing a system alert to the abnormal passing vehicle to assist toll collectors in completing the work of combating toll evasion by on-site vehicles.

[0007] The embodiment of the present application, through identification and monitoring of vehicles on the highway and through advance audit analysis of vehicles in the exit lanes, can promptly complete the collection of evidence of vehicles evading tolls while in motion when the vehicles leave the highway toll exit, thereby achieving on-site audits of vehicles that evade tolls such as "large vehicles with small labels" and "cargo vehicles converted into passenger vehicles". It is also beneficial for toll collectors at the exit gate. When a suspected vehicle leaves the station, the system will automatically warn the toll collectors to crack down on the toll-evading vehicles, thereby improving the accuracy of identifying suspected vehicles, making it more convenient for toll collectors to collect tolls, reducing the cost of manual identification of suspected vehicles, and reducing the number of vehicles evading tolls.

[0008] In a feasible implementation, before obtaining the first vehicle information of a vehicle traveling on a highway, the method further includes: obtaining historical archive information of the vehicle through the vehicle management cloud center of the traffic management department; wherein the historical archive information includes at least vehicle model information, vehicle usage information, vehicle owner information, owner contact information, vehicle violation information, vehicle annual inspection information and vehicle highway passage information; associating the vehicle model information, vehicle usage information, vehicle owner information and owner contact information with the basic attributes of the vehicle to obtain basic vehicle dynamic information; associating the vehicle violation information, vehicle annual inspection information and vehicle highway passage information with the vehicle operation attributes to obtain operation vehicle dynamic information; establishing a dynamic data structure based on the basic vehicle dynamic information and the operation vehicle dynamic information; and inputting the dynamic data structure into a preset ElasticSearch database to obtain a vehicle dynamic database.

[0009] In a feasible embodiment, the first vehicle information includes vehicle model information and license plate information; the acquisition of the first vehicle information of vehicles traveling on the highway specifically includes: segmenting the preset vehicle image according to a preset collaborative segmentation algorithm to obtain a target vehicle image; marking the vehicle contour vertices and vehicle contour edges of the target vehicle image to obtain a vertex set and an edge set of the target vehicle image respectively; and generating a weighted connected graph of the relevant vehicle contour vertices based on the vertex set and the edge set; traversing the vertex set and the edge set in the weighted connected graph, and constructing the optimal image of the target vehicle based on the traversed weighted connected graph. A minimum spanning tree is formed; based on the minimum spanning tree, regions with similar contour boundaries in the target vehicle image are aligned to obtain local key regions; features are extracted from the local key regions to obtain local features of the vehicle; an SVM classifier is trained using the local features of the vehicle to obtain a vehicle classification model; an image acquisition device is installed at the entrance of a highway toll station to capture images of traveling vehicles to obtain an image of a vehicle to be identified; the vehicle type is identified on the image of the vehicle to be identified using the vehicle classification model to obtain the vehicle model information in the image of the vehicle to be identified; the vehicle license plate in the image of the vehicle to be identified is identified to obtain the license plate information.

[0010] In a feasible implementation, after using a preset audit model, the first vehicle information also includes vehicle weight information; before screening the toll credit information of the first vehicle information and obtaining the screening result, the method also includes: weighing the vehicle traveling in the overtaking lane through a preset overtaking lane control system in the overtaking lane to obtain the weighing information of the vehicle; identifying the vehicle license plate of the vehicle through a surveillance camera in the overtaking lane to obtain the weighed license plate information; and making a one-to-one correspondence between the weighed license plate information and the weighing information of the vehicle to obtain the vehicle weight information.

[0011] In a feasible implementation, the toll credit information of the first vehicle information is screened through a preset audit model to obtain a screening result, which specifically includes: configuring the audit model accordingly according to the vehicle charging rules of the expressway to obtain the preset audit model; wherein the vehicle charging rules are that the same vehicle model corresponds to the same charging index; obtaining the vehicle weight information and the first vehicle information of the vehicle to be screened; matching the vehicle model information and license plate information in the first vehicle information with the vehicle model information in the vehicle dynamic database to obtain a first matching result; wherein the first matching result includes a consistent match or an inconsistent match; according to the preset audit model, matching the vehicle weight information and the corresponding vehicle model information in the first vehicle information with the vehicle charging rules to obtain a second matching result; according to the first matching result and the second matching result, screening the toll credit information of the first vehicle information and the vehicle weight information to obtain the screening result.

[0012] In a feasible implementation manner, the first vehicle information and the vehicle weight information are screened for toll credit information based on the first matching result and the second matching result to obtain the screening result, which specifically includes: if the first matching result is a consistent match and the second matching result is also a consistent match, then the toll credit information of the vehicle to be screened is good toll information; if the first matching result is a consistent match and the second matching result is an inconsistent match, then the vehicle to be screened has evaded vehicle weight tolls, and the toll credit information of the vehicle to be screened is weighing credit difference information; if the first matching result is an inconsistent match and the second matching result is a consistent match, then The vehicle to be screened has been secretly modified, and the toll credit information of the vehicle to be screened is vehicle model credit difference information; if the first matching result is a matching inconsistency and the second matching result is a matching inconsistency, then the vehicle to be screened has been violated, and the toll credit information of the vehicle to be screened is serious violation credit information; the vehicles to be screened corresponding to the weighing credit difference information, the vehicle model credit difference information and the serious violation credit information are screened to obtain screened vehicles; and the screening results corresponding to the screened vehicles are obtained; wherein, the screening results at least include the toll evasion, toll credit information, the first matching result information and the second matching result information of the screened vehicle.

[0013] In a feasible implementation, based on the screening results, data forensics processing is performed on the suspected vehicle to determine the abnormal passage result of the suspected vehicle, specifically including: comparing the screening result information of the screened vehicle with the vehicle high-speed passage information of the corresponding suspected vehicle in the vehicle dynamic database to obtain a comparison result; wherein, the comparison result is consistent or inconsistent; if the comparison result is consistent, the screened vehicle is determined to be the suspected vehicle; based on the vehicle violation information in the vehicle dynamic database, it is determined whether the suspected vehicle has historical illegal passage information; if the historical illegal passage information exists, If there is any information, the screening result of the suspected vehicle and the historical violation information are subjected to data compression processing to obtain a historical proof data packet; if there is no historical violation information, the screening result of the suspected vehicle is subjected to data compression processing to obtain a current proof data packet; the historical proof data packet and the current proof data packet are input into the toll collection system of the highway exit toll station; and the suspected vehicle is marked for abnormal passage through the evidence content information after parsing the historical proof data packet and the current proof data packet to obtain the abnormal passage result of the suspected vehicle; wherein, the abnormal passage result is the marked suspected vehicle.

[0014] In a feasible implementation, the abnormally passing vehicle is determined based on the abnormal passage result corresponding to the second vehicle information, specifically including: collecting the second vehicle information of the vehicle traveling at the highway toll station exit through the data acquisition equipment at the highway toll station exit; wherein the second vehicle information includes vehicle model information and license plate information; comparing the second vehicle information with the vehicle information of the suspected vehicle; if the second vehicle information is consistent with the vehicle information of the suspected vehicle, then associating the abnormal passage result of the suspected vehicle with the second vehicle information to obtain the abnormally passing vehicle corresponding to the second vehicle information.

[0015] The embodiment of the present application assists toll collectors in completing the task of cracking down on toll-evading vehicles on the spot by marking abnormally passing vehicles and providing key reminders to toll collectors and displaying the system front end.

[0016] In a second aspect, an embodiment of the present application also provides an intelligent toll collection assistance device based on a highway, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor so that the at least one processor can execute an intelligent toll collection assistance method based on a highway as described in any of the above embodiments.

[0017] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium, characterized in that the storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by the terminal, the terminal executes a smart toll collection assistance method based on a highway as described in any of the above embodiments.

[0018] The embodiments of the present application provide an intelligent toll collection assistance method, equipment and medium based on highways. By conducting an audit and analysis of vehicles in the exit lanes in advance, the evidence of vehicles evading tolls while in motion is collected in a timely manner when the vehicles leave the highway toll exit, thereby enabling on-site inspections of vehicles evading tolls such as "large vehicles with small labels" and "cargo vehicles converted to passenger vehicles". It is also beneficial for toll collectors at the exit gate. When a suspicious vehicle leaves the station, the system will automatically warn the toll collectors to crack down on the toll-evading vehicles, thereby improving the accuracy of identifying suspicious vehicles, making it more convenient for toll collectors to collect tolls, reducing the cost of manual identification of suspicious vehicles, and reducing the number of vehicles evading tolls. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 A flow chart of an intelligent toll collection assistance method based on highways provided in an embodiment of the present application;

[0021] Figure 2 A vehicle weighing and charging flow chart provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of the structure of an intelligent toll collection auxiliary device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0024] The embodiment of the present application provides an intelligent toll collection assistance method based on highways, such as Figure 1 As shown, the intelligent toll collection assistance method based on highways specifically includes steps S101-S104:

[0025] S101: Acquire first vehicle information of a vehicle traveling on a highway.

[0026] Specifically, according to a preset collaborative segmentation algorithm, the preset vehicle image is segmented to obtain a target vehicle image. The vehicle contour vertices and vehicle contour edges of the target vehicle image are marked to obtain a vertex set and an edge set of the target vehicle image respectively. Based on the vertex set and the edge set, a weighted connected graph of the relevant vehicle contour vertices is generated. The vertex set and the edge set in the weighted connected graph are traversed, and a minimum spanning tree of the target vehicle image is constructed based on the traversed weighted connected graph. Based on the minimum spanning tree, regions with similar contour boundaries in the target vehicle image are aligned to obtain local key regions. Features of the local key regions are extracted to obtain local features of the vehicle. An SVM classifier is trained using the local features of the vehicle, and a vehicle classification model is obtained based on the trained SVM classifier.

[0027] Furthermore, an image capture device installed at the entrance of a highway toll station captures images of traveling vehicles to obtain images of the vehicle to be identified. The vehicle classification model is used to identify the vehicle type in the image to be identified, obtaining vehicle model information in the image to be identified. The license plate in the image to be identified is then recognized to obtain license plate information. The first vehicle information includes vehicle model information, license plate information, and vehicle weight.

[0028] As a feasible implementation method, a weighted connected graph of the vertices and edges of the vehicle contour is established through a preset collaborative segmentation algorithm, and then a minimum spanning tree of the target vehicle image is constructed. The local key features of the target vehicle image are extracted based on the minimum spanning tree, and then the SVM classifier is trained to obtain a trained vehicle classification model. The obtained vehicle image to be identified is used to identify the vehicle type and obtain the corresponding license plate information, and finally the first vehicle information of the vehicle to be identified is obtained.

[0029] S102. Screen the toll credit information of the first vehicle using a preset audit model to obtain a screening result; and based on the screening result, perform data forensics processing on the suspected vehicle to determine the abnormal passage result of the suspected vehicle.

[0030] Specifically, a pre-set overweight control system in the overweight control lane weighs vehicles traveling in the lane to obtain vehicle weight information. A surveillance camera in the lane then identifies the vehicle's license plate and obtains weighed license plate information. The weighed license plate information is then matched one-to-one with the vehicle's weight information to obtain vehicle weight information.

[0031] In one embodiment, Figure 2 A vehicle weighing and charging flow chart is provided for the application embodiment, such as Figure 2 As shown, the weight of vehicles traveling in the overtaking lane is captured, and the vehicle weighing data is then transmitted to the toll lane of the toll collection system. The license plate information and corresponding vehicle model are captured using the surveillance cameras in the overtaking lane, and then tolls are collected. Furthermore, historical vehicle information is obtained through the traffic management department's vehicle management cloud center. This historical information includes at least vehicle model information, vehicle usage information, vehicle owner information, owner contact information, vehicle violation information, vehicle annual inspection information, and vehicle highway clearance information.

[0032] Furthermore, the vehicle model information, vehicle usage information, vehicle owner information, and owner contact information are correlated with the basic vehicle attributes to obtain basic vehicle dynamic information. Vehicle violation information, vehicle annual inspection information, and vehicle highway clearance information are correlated with vehicle operation attributes to obtain operating vehicle dynamic information.

[0033] Furthermore, a dynamic data structure is established based on the basic vehicle dynamic information and the running vehicle dynamic information, and the dynamic data structure is input into a preset ElasticSearch database to obtain a vehicle dynamic database.

[0034] Furthermore, the audit model is configured accordingly based on the vehicle toll collection rules of the expressway to obtain a preset audit model. The vehicle toll collection rules stipulate that the same vehicle model corresponds to the same toll index. Vehicle weight information and first vehicle information are obtained for the vehicle to be screened. The vehicle model information and license plate information in the first vehicle information are matched with the vehicle model information in the vehicle dynamic database to obtain a first matching result. The first matching result may include a consistent match or an inconsistent match. Based on the preset audit model, the vehicle weight information and corresponding vehicle model information in the first vehicle information are matched with the vehicle toll collection rules to obtain a second matching result.

[0035] Furthermore, based on the first matching result and the second matching result, the first vehicle information and the vehicle weight information are screened for toll credit information to obtain a screening result.

[0036] If the first matching result is a consistent match and the second matching result is also a consistent match, the toll credit information of the vehicle to be screened is good toll information. If the first matching result is a consistent match and the second matching result is a inconsistent match, the vehicle to be screened has evaded vehicle weight tolls, and the toll credit information of the vehicle to be screened is weighing credit difference information. If the first matching result is inconsistent match and the second matching result is consistent match, the vehicle to be screened has been secretly modified, and the toll credit information of the vehicle to be screened is vehicle model credit difference information. If the first matching result is inconsistent match and the second matching result is inconsistent match, the vehicle to be screened has violated the vehicle regulations, and the toll credit information of the vehicle to be screened is serious violation credit information.

[0037] The vehicles to be screened corresponding to the weight credit difference information, vehicle type credit difference information, and serious violation credit information are then screened to obtain screened vehicles. A screening result corresponding to the screened vehicles is then obtained. The screening result includes at least the toll evasion information, toll credit information, first matching result information, and second matching result information of the screened vehicles.

[0038] Furthermore, the screening result information of the screened vehicle is compared with the corresponding high-speed traffic information of the suspected vehicle in the vehicle dynamic database to obtain a comparison result. The comparison result may be a match or a disagreement. If the comparison result is a match, the screened vehicle is determined to be a suspected vehicle. Based on the vehicle violation information in the vehicle dynamic database, it is determined whether the suspected vehicle has any historical traffic violation information. If so, the screening result and historical violation information of the suspected vehicle are compressed to obtain a historical verification data package. If no historical traffic violation information is found, the screening result of the suspected vehicle is compressed to obtain a current verification data package.

[0039] As a feasible implementation method, by comparing the screening result information of the screened vehicles with the vehicle highway passage information of the corresponding suspected vehicle in the vehicle dynamic database, it is possible to better query from the vehicle dynamic database to determine whether it is the same vehicle. If it is the same vehicle, then the vehicle is the suspected vehicle, and the historical illegal passage information of the suspected vehicle and the illegal passage information under the screening results are uniformly collected to obtain the historical proof data package and the current proof data package of the vehicle. When the vehicle arrives at the highway toll exit, the proof materials can be prepared in advance, which will help the toll collectors to complete the on-site inspection of the vehicle and realize the recovery of the toll evasion by the vehicle.

[0040] Furthermore, the historical and current proof packets are input into the toll collection system at the highway exit toll station. The suspected vehicle is then flagged as having abnormal traffic based on the parsed evidence from the historical and current proof packets, resulting in an abnormal traffic result for the suspected vehicle. The abnormal traffic result represents the flagged suspected vehicle.

[0041] In one embodiment, Figure 2 As shown, the screening result information of the screened vehicles is obtained through the toll collection assistant and compared with the vehicle weight data obtained from the vehicle dynamic database in the cloud platform. Then, the historical proof data packets such as the vehicle's inspection information, driving information, historical violation information, and the current proof data packet are summarized and sent to the toll collection assistant, and queried and displayed on the front-end display page of the toll collection assistant.

[0042] In one embodiment, a pre-configured vehicle dynamic database and a preset audit model are used to screen and compare the first vehicle information to determine whether the vehicle in the first vehicle information is a suspect vehicle. The screening results and historical violation information of the suspect vehicle are then processed to obtain abnormal traffic results for the suspect vehicle. Furthermore, the audit analysis platform based on the audit model can also audit and match the toll collection rules with the first vehicle information, including the current traffic characteristics, suspected evasion information, and historical evasion information. This platform can identify key factors influencing toll collection, such as the route, vehicle type, and special circumstances, and verify the toll amount, thus helping to combat various toll evasion behaviors.

[0043] S103: Identify the second vehicle information; and determine the abnormally passing vehicle based on the abnormal passing result corresponding to the second vehicle information.

[0044] Specifically, data collection equipment at the highway toll station exit collects second vehicle information from vehicles traveling through the toll station exit. This second vehicle information includes vehicle model and license plate information. This second vehicle information is compared with the vehicle information of the suspected vehicle. If the second vehicle information matches the suspected vehicle information, the abnormal passage result of the suspected vehicle is linked to the second vehicle information, and the vehicle corresponding to the abnormal passage of the second vehicle information is obtained.

[0045] As a feasible implementation method, the second vehicle information identified at the highway toll station exit, namely the vehicle model information and license plate information, is compared with the suspected vehicles screened out by the screening results to see whether they are the same vehicle, and the abnormal passage result information of the suspected vehicle is associated with the second vehicle information that is consistent with the comparison, and the vehicle identified at the highway toll station exit is determined to be an abnormal passage vehicle.

[0046] S104. Issue system alerts to vehicles that pass abnormally, so as to assist toll collectors in combating toll evasion by vehicles on site.

[0047] Specifically, based on the abnormally passing vehicles that have been marked in the toll collector's system, the toll collector will be reminded of key vehicles in a timely manner through the front-end display interface, and then the abnormal passing result information corresponding to the abnormally passing vehicle will also be displayed accordingly, so that the toll collector can assist the toll collector in completing on-site inspections of vehicles that evade tolls based on the evidence information and historical violation information provided by the abnormal passing result information.

[0048] As a feasible implementation method, by marking abnormal passing vehicles, giving key reminders to toll collectors and displaying them on the front end of the system, it is possible to assist toll collectors in completing the work of cracking down on toll-evading vehicles on the spot and recovering the evaded tolls.

[0049] In addition, the embodiment of the present application also provides an intelligent toll collection auxiliary device based on highways, such as Figure 3 As shown, the intelligent toll collection auxiliary device 300 specifically includes: at least one processor 301; and a memory 302 in communication with the at least one processor 301; wherein the memory 302 stores instructions that can be executed by the at least one processor 301, so that the at least one processor 301 can execute:

[0050] Obtain the first vehicle information of vehicles traveling on the highway; screen the toll credit information of the first vehicle information through a preset audit model to obtain a screening result; and based on the screening result, perform data forensics processing on the suspected vehicle to determine the abnormal passage result of the suspected vehicle; identify the second vehicle information; and determine the abnormal passage vehicle based on the abnormal passage result corresponding to the second vehicle information; issue a system warning to the abnormal passage vehicle to assist toll collectors in completing the work of combating toll evasion by vehicles on site.

[0051] The embodiment of the present application provides an intelligent toll collection assistance method, equipment and medium based on highways. By conducting an audit and analysis of vehicles in the exit lanes in advance, when the vehicles leave the highway toll exit, the evidence of vehicles evading tolls while in motion is collected in a timely manner, and on-site inspections of vehicles evading tolls such as "large vehicles with small labels" and "cargo vehicles converted into passenger vehicles" are carried out. It is also beneficial for toll collectors at the exit gate. When a suspicious vehicle leaves the station, the system will automatically warn the toll collectors, prompting them to crack down on the toll-evading vehicles, thereby improving the accuracy of identifying suspicious vehicles, making it more convenient for toll collectors to collect tolls, reducing the cost of manual identification of suspicious vehicles, and reducing the number of vehicles evading tolls.

[0052] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant parts, refer to the descriptions of the method embodiments.

[0053] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0054] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0055] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0058] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0059] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0060] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0061] The foregoing description describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included within the scope of the claims of the present application.

Claims

1. An intelligent toll collection assistance method based on highways, characterized in that: The method comprises: Acquire first vehicle information of vehicles traveling on the highway; The toll credit information of the first vehicle information is screened using a preset audit model to obtain a screening result, specifically including: According to the vehicle charging rules of the expressway, the audit model is configured accordingly to obtain the preset audit model; wherein the vehicle charging rules are that the same vehicle model corresponds to the same charging index; Obtaining vehicle weight information and first vehicle information of the vehicle to be screened; Matching the vehicle model information and license plate information in the first vehicle information with the vehicle model information in the vehicle dynamic database to obtain a first matching result; wherein the first matching result includes a consistent match or an inconsistent match; According to the preset audit model, the vehicle weight information and the corresponding vehicle model information in the first vehicle information are matched with the vehicle charging rules to obtain a second matching result; Based on the first matching result and the second matching result, the first vehicle information and the vehicle weight information are screened for toll credit information to obtain the screening result; and based on the screening result, data forensics processing is performed on the suspected vehicle to determine the abnormal passage result of the suspected vehicle; Identifying the second vehicle information; and determining the abnormally passing vehicle according to the abnormal passing result corresponding to the second vehicle information; The system will issue warnings to the abnormally passing vehicles to assist toll collectors in combating toll evasion by vehicles on site.

2. The intelligent toll collection assistance method based on highway according to claim 1, characterized in that: Before obtaining first vehicle information of vehicles traveling on the highway, the method further includes: Obtaining historical vehicle information through the traffic management department's vehicle management cloud center; wherein the historical information includes at least vehicle model information, vehicle usage information, vehicle owner information, owner contact information, vehicle violation information, vehicle annual inspection information, and vehicle highway clearance information; Associating the vehicle model information, vehicle usage information, vehicle owner information, and owner contact information with vehicle basic attributes to obtain basic vehicle dynamic information; Associating the vehicle violation information, vehicle annual inspection information, and vehicle highway pass information with vehicle operation attributes to obtain operating vehicle dynamic information; A dynamic data structure is established according to the basic vehicle dynamic information and the running vehicle dynamic information; and the dynamic data structure is input into a preset ElasticSearch database to obtain a vehicle dynamic database.

3. The intelligent toll collection assistance method based on highway according to claim 1, characterized in that: The first vehicle information includes vehicle model information and license plate information; The obtaining of first vehicle information of vehicles traveling on the highway specifically includes: According to the preset collaborative segmentation algorithm, the preset vehicle image is segmented to obtain the target vehicle image; Labeling the vehicle contour vertices and the vehicle contour edges of the target vehicle image to obtain a vertex set and an edge set of the target vehicle image, respectively; and generating a weighted connected graph of the vehicle contour vertices based on the vertex set and the edge set; Traversing the vertex set and edge set in the weighted connected graph to construct a minimum spanning tree of the target vehicle image; According to the minimum spanning tree, regions with similar contour boundaries in the target vehicle image are aligned to obtain local key regions; Extracting features from the local key area to obtain local features of the vehicle; Using the local features of the vehicle, an SVM classifier is trained to obtain a vehicle classification model; The image acquisition device installed at the entrance of the highway toll station is used to capture images of the moving vehicles to obtain the image of the vehicle to be identified; Identify the vehicle type of the vehicle image to be identified by using the vehicle classification model to obtain the vehicle type information in the vehicle image to be identified; The vehicle license plate in the to-be-identified vehicle image is identified to obtain the license plate information.

4. The intelligent toll collection assistance method based on highway according to claim 1, characterized in that: The first vehicle information also includes vehicle weight information; Before screening the toll credit information of the first vehicle information using a preset audit model to obtain a screening result, the method further includes: Weighing vehicles traveling in the overweight control lane through a preset overweight control system in the overweight control lane to obtain weighing information of the vehicles; The vehicle license plate of the vehicle is recognized by the monitoring camera in the overweight lane to obtain the weighed license plate information; The weighed license plate information is matched one-to-one with the weighing information of the vehicle to obtain the vehicle weight information.

5. The intelligent toll collection assistance method based on highway according to claim 1, characterized in that: Screening the first vehicle information and the vehicle weight information for toll credit information based on the first matching result and the second matching result to obtain the screening result specifically includes: If the first matching result is a consistent match and the second matching result is also a consistent match, then the toll credit information of the vehicle to be screened is good toll credit information; If the first matching result is a consistent match and the second matching result is a inconsistent match, then the vehicle to be screened has evaded vehicle weight tolls, and the toll credit information of the vehicle to be screened is weight credit difference information; If the first matching result is inconsistent and the second matching result is consistent, then the vehicle to be screened has been tampered with, and the toll credit information of the vehicle to be screened is vehicle type credit difference information; If the first matching result is a mismatch and the second matching result is a mismatch, the vehicle to be screened has a vehicle violation, and the toll credit information of the vehicle to be screened is serious violation credit information; The vehicles to be screened corresponding to the weighing credit difference information, the vehicle type credit difference information and the serious violation credit information are screened to obtain screened vehicles; and the screening results corresponding to the screened vehicles are obtained; wherein the screening results at least include the toll evasion, toll credit information, first matching result information and second matching result information of the screened vehicles.

6. The intelligent toll collection assistance method based on highway according to claim 5, characterized in that: Based on the screening results, data forensics processing is performed on the suspected vehicle to determine the abnormal traffic results of the suspected vehicle, including: Comparing the screening result information of the screened vehicles with the vehicle highway passing information of the corresponding suspect vehicles in the vehicle dynamic database to obtain a comparison result; wherein the comparison result is a consistent comparison or an inconsistent comparison; If the comparison result is a match, the screened vehicle is determined as the suspected vehicle; Determine whether the suspected vehicle has any history of traffic violation information based on the vehicle violation information in the vehicle dynamic database; If the historical traffic violation information exists, the screening result of the suspected vehicle and the historical violation information are compressed to obtain a historical proof data packet; If the historical traffic violation information does not exist, the screening result of the suspected vehicle is subjected to data compression processing to obtain a current certification data packet; The historical proof data packet and the current proof data packet are input into the toll collection system of the highway exit toll station; and the suspected vehicle is marked as having passed abnormally based on the evidence content information after parsing the historical proof data packet and the current proof data packet, to obtain the abnormal passage result of the suspected vehicle; wherein the abnormal passage result is the marked suspected vehicle.

7. The intelligent toll collection assistance method based on highway according to claim 1, characterized in that: Determining an abnormally passing vehicle according to the abnormal passing result corresponding to the second vehicle information specifically includes: collecting the second vehicle information of vehicles traveling at the highway toll station exit through a data collection device at the highway toll station exit; wherein the second vehicle information includes vehicle model information and license plate information; The second vehicle information is compared with the vehicle information of the suspected vehicle; if the second vehicle information is consistent with the vehicle information of the suspected vehicle, the abnormal passage result of the suspected vehicle is associated with the second vehicle information to obtain the abnormal passage vehicle corresponding to the second vehicle information.

8. An intelligent toll collection auxiliary device based on highway, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the highway-based intelligent toll collection assistance method according to any one of claims 1-7.

9. A non-volatile computer storage medium, characterized in that The storage medium is a non-volatile computer-readable storage medium, which stores at least one program. Each of the programs includes instructions. When the instructions are executed by the terminal, the terminal executes the intelligent toll collection assistance method based on highways according to any one of claims 1 to 7.

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