A method, device and storage medium for determining a suspected fee evasion

Abstract

The application discloses a method, device and equipment for determining fee evasion suspicion and a storage medium. The method comprises the following steps: determining a first time when a current green-pass vehicle drives into a target section and a second time when the green-pass vehicle drives away from the target section; determining a first target position data set from a first vehicle position data set and a second target position data set from a second vehicle position data set; determining stay information of the current green-pass vehicle by using the first target position data set and the second target position data set, and determining whether the current green-pass vehicle has fee evasion suspicion according to the stay information. The technical scheme of the embodiment of the application can obtain the stay information of the green-pass vehicle by analyzing the driving track of the green-pass vehicle in a period of time before driving into the target section and the driving track of the green-pass vehicle in a period of time after driving away from the target section, so that whether the green-pass vehicle abnormally stays in a suspicious area, that is, whether the green-pass vehicle has fee evasion suspicion, can be accurately determined.

Description

Technical Field

[0001] This invention relates to the field of highway management technology, and in particular to a method, apparatus, equipment, and storage medium for determining suspected toll evasion. Background Technology

[0002] With the formulation and implementation of service standards, relevant agencies operating and managing expressways can use data related to expressway toll collection, such as expressway entrance and exit transaction records, gantry toll collection records, gantry license plate recognition records, vehicle type recognition records, images, and videos, as the basis for charging vehicles.

[0003] Currently, the annual toll exemptions for green channel vehicles on highways amount to tens of billions of yuan. If calculated based on the industry's 0.5% toll evasion rate, the potential amount of toll evasion by counterfeit green channel vehicles could reach hundreds of millions of yuan, representing a huge sum. Toll stations typically analyze and inspect green channel vehicles using X-rays for full vehicle inspection.

[0004] However, because counterfeit green channel vehicles generally evade tolls by mixing non-green channel goods, the workload of highway exit toll stations in inspecting green channel vehicles is heavy. There are often problems such as low cooperation from vehicle owners, some toll stations not conducting inspections, and inspections being merely a formality, resulting in poor inspection results. As a result, the phenomenon of counterfeit green channel vehicles has become increasingly common. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for determining suspected toll evasion, in order to solve the problem of poor detection effectiveness against counterfeit green channel vehicles.

[0006] In a first aspect, the present invention provides a method for determining suspected fare evasion, comprising:

[0007] Determine the first time when the green channel vehicle enters the target road segment and the second time when it leaves the target road segment;

[0008] A first target location dataset is determined from a first vehicle location dataset, and a second target location dataset is determined from a second vehicle location dataset, wherein the first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time;

[0009] Using the first target location dataset and the second target location dataset, the current stop information of the green channel vehicle is determined, and based on the stop information, it is determined whether the current green channel vehicle is suspected of evading tolls.

[0010] Secondly, the present invention provides a device for determining suspected fare evasion, comprising:

[0011] The time determination module is used to determine the first time when the green channel vehicle enters the target road segment and the second time when it leaves the target road segment;

[0012] The location set determination module is used to determine a first target location dataset from a first vehicle location dataset and a second target location dataset from a second vehicle location dataset, wherein the first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time.

[0013] The toll evasion suspicion judgment module is used to determine the current green channel vehicle's stopping information using the first target location dataset and the second target location dataset, and to determine whether the current green channel vehicle is suspected of toll evasion based on the stopping information.

[0014] Thirdly, the present invention provides an electronic device comprising:

[0015] At least one processor;

[0016] and memory that is communicatively connected to at least one processor;

[0017] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the method for determining the suspected evasion of fees as described in the first aspect.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute the method for determining suspected toll evasion as described in the first aspect.

[0019] The present invention provides a scheme for determining suspected toll evasion. It determines the first time a green channel vehicle enters a target road segment and the second time it leaves the target road segment. A first target location dataset is determined from a first vehicle location dataset, and a second target location dataset is determined from a second vehicle location dataset. The first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time. Using the first and second target location datasets, the stopping information of the current green channel vehicle is determined, and based on the stopping information, it is determined whether the current green channel vehicle is suspected of toll evasion. By adopting the above technical solution, and analyzing the driving trajectory of the green channel vehicle for a period of time before entering the target road segment and for a period of time after leaving the target road segment, the stopping information of the green channel vehicle can be obtained. This allows for accurate determination of whether the green channel vehicle is abnormally stopping in a suspicious area, i.e., whether it is suspected of toll evasion.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for determining suspected toll evasion according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for determining suspected toll evasion according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a green channel vehicle verification system according to Embodiment 2 of the present invention;

[0025] Figure 4 This is a schematic diagram of a device for determining suspected toll evasion according to Embodiment 3 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0029] Example 1

[0030] Figure 1 The flowchart of a method for determining suspected toll evasion is provided in Embodiment 1 of the present invention. This embodiment can be applied to determining whether a green channel vehicle is suspected of evading tolls. The method can be executed by a device for determining suspected toll evasion, which can be implemented in hardware and / or software. The device can be configured in an electronic device, which can be composed of two or more physical entities or a single physical entity.

[0031] like Figure 1 As shown, the method for determining suspected fare evasion provided in Embodiment 1 of the present invention specifically includes the following steps:

[0032] S101. Determine the first time when the green channel vehicle enters the target road segment and the second time when it leaves the target road segment.

[0033] In this embodiment, the target road segment can be a designated section of highway. When a green channel vehicle enters the target road segment, its entry time can be obtained, and when it leaves the target road segment, its departure time can be obtained. The first and second times can be obtained using electronic devices such as cameras around the target road segment.

[0034] S102. Determine a first target location dataset from the first vehicle location dataset and a second target location dataset from the second vehicle location dataset, wherein the first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time.

[0035] In this embodiment, since there are usually significant differences between the driving trajectories of counterfeit green channel vehicles and genuine green channel vehicles—for example, counterfeit green channel vehicles often stop at non-green channel vehicle loading and / or unloading points—a first target location dataset and a second target location dataset, such as a data set for a certain time period, can be selected from the first vehicle location dataset and the second vehicle location dataset of the current green channel vehicles. The location data can be obtained using the positioning device equipped on the current green channel vehicle. The first vehicle location dataset and the second vehicle location dataset contain the correspondence between time and location data.

[0036] S103. Using the first target location dataset and the second target location dataset, determine the current green channel vehicle's stopping information, and determine whether the current green channel vehicle is suspected of evading tolls based on the stopping information.

[0037] In this embodiment, the current green channel vehicle's stopping information, such as the correspondence between parking location and stopping time, can be determined from the first target location dataset and the second target location dataset. Based on this stopping information, it can be determined whether the current green channel vehicle is engaging in abnormal behavior such as prolonged stopping at non-green channel vehicle loading and / or unloading points. If so, it can be determined that the current green channel vehicle is suspected of evading tolls.

[0038] The method for determining suspected toll evasion provided in this invention determines the first time a green channel vehicle enters a target road segment and the second time it leaves the target road segment. It determines a first target location dataset from a first vehicle location dataset and a second target location dataset from a second vehicle location dataset. The first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time. Using the first and second target location datasets, it determines the stopping information of the current green channel vehicle and determines whether the current green channel vehicle is suspected of toll evasion based on the stopping information. This invention's technical solution, by analyzing the driving trajectory of a green channel vehicle for a period of time before entering the target road segment and for a period of time after leaving the target road segment, can obtain the stopping information of the green channel vehicle, thereby accurately determining whether the green channel vehicle is abnormally stopping in a suspicious area, i.e., whether it is suspected of toll evasion.

[0039] Optionally, after determining whether the current green channel vehicle is suspected of toll evasion based on the stop information, the method further includes: if it is determined that the current green channel vehicle is suspected of toll evasion, then receiving on-site verification information of the current green channel vehicle, and determining whether the current green channel vehicle has engaged in toll evasion based on the on-site verification information. The advantage of this setup is that by receiving verification information from suspected green channel vehicles, misjudgments of suspected green channel vehicles can be avoided.

[0040] Specifically, if it is determined that a green channel vehicle is suspected of toll evasion, on-site verification information can be received. This on-site verification information can be understood as the data obtained by the green channel parking auditors from conducting on-site evidence collection on the green channel vehicle. If the on-site verification information indicates that the green channel vehicle has engaged in toll evasion, then it can be definitively confirmed that the green channel vehicle has evaded tolls.

[0041] Example 2

[0042] Figure 2 This is a flowchart of a method for determining suspected toll evasion according to Embodiment 2 of the present invention. The technical solution of the present invention is further optimized based on the above-mentioned optional technical solutions, and provides a specific way to determine whether a green channel vehicle is suspected of toll evasion.

[0043] Optionally, determining the first time the current green channel vehicle enters the target road segment and the second time it leaves the target road segment includes: determining the time when the current green channel vehicle passes through the entrance toll station of the target road segment as the first time, and using the camera at the entrance toll station to determine the vehicle identity information of the current green channel vehicle; using the vehicle identity information and the camera at the exit toll station of the target road segment, identifying the current green channel vehicle from the traffic flow passing through the exit toll station, and determining the time when the current green channel vehicle passes through the exit toll station as the second time. The advantage of this setup is that by utilizing the cameras at the toll stations, the time when the current green channel vehicle enters and leaves the target road segment can be quickly and accurately identified.

[0044] Optionally, determining the current stop information of the green channel vehicle using the first target location dataset and the second target location dataset includes: determining the current stop location of the green channel vehicle using the first target location dataset and the second target location dataset; determining a first stop duration corresponding to a stop location belonging to a first preset target area, and determining a second stop duration corresponding to a stop location belonging to a second preset target area, wherein the stop information includes the first stop duration and the second stop duration, and the first preset target area is different from the second preset target area. The advantage of this setting is that by determining the current stop location of the green channel vehicle, it is possible to quickly determine whether the current green channel vehicle is staying in a suspicious area and the corresponding duration, and whether it is staying in an area where normal green channel vehicles congregate and the corresponding duration.

[0045] Optionally, determining whether the current green channel vehicle is suspected of toll evasion based on the dwelling information includes: if the dwelling time corresponding to the first preset target area in the first dwelling time and / or the dwelling time corresponding to the first preset target area in the second dwelling time is less than the first preset time, and / or if the dwelling time corresponding to the second preset target area in the first dwelling time and / or the dwelling time corresponding to the second preset target area in the second dwelling time is greater than the second preset time, then it is determined that the current green channel vehicle is suspected of toll evasion. The advantage of this setting is that, using the above method, it is possible to determine whether the current green channel vehicle has abnormally prolonged dwellings in non-green channel cargo yards, and whether its dwelling time in green channel cargo yards is too short, thereby quickly determining whether the current green channel vehicle is suspected of toll evasion.

[0046] like Figure 2 As shown in Embodiment 2 of the present invention, a method for determining suspected fare evasion specifically includes the following steps:

[0047] S201. The time when the current green channel vehicle passes through the entrance toll station of the target road section is determined as the first time, and the vehicle identity information of the current green channel vehicle is determined by the camera at the entrance toll station.

[0048] For example, if the target road segment is a section of highway, the time when the green channel vehicle passes through the entrance toll station of the highway can be determined as the first time, and the image captured by the camera at the entrance toll station can be obtained. Based on the image, the vehicle identity information of the current green channel vehicle can be determined, such as the license plate number, vehicle type and license plate color.

[0049] S202. Using vehicle identification information and cameras at the exit toll station of the target road section, identify the current green channel vehicle from the traffic flow passing through the exit toll station, and determine the time when the current green channel vehicle passes through the exit toll station as the second time.

[0050] Specifically, the system can first obtain images captured by cameras at the exit toll station, and then identify vehicles from the traffic flow in the images that match the vehicle identity information of the current green channel vehicle. This vehicle is the current vehicle. The time when the current vehicle passes through the exit toll station is then determined as the second time.

[0051] S203. Determine the first target location dataset from the first vehicle location dataset, and determine the second target location dataset from the second vehicle location dataset.

[0052] Optionally, determining the first target location dataset from the first vehicle location dataset and the second target location dataset from the second vehicle location dataset includes: obtaining the first target location dataset within a first preset time period before the first time from the first vehicle location dataset, and obtaining the second target location dataset within a second preset time period after the second time from the second vehicle location dataset. The advantage of this setting is that by setting an appropriate preset time period, location data within the time period during which the current vehicle is most likely to load or unload non-green channel goods can be obtained.

[0053] For example, if both the first and second preset durations are 24 hours, a BeiDou positioning device can be pre-configured on the current green channel vehicle. After obtaining the first vehicle location dataset and the second vehicle location dataset, the first target location dataset for the 24 hours prior to the first time is obtained from the first vehicle location dataset, and the second target location dataset for the 24 hours after the second time is obtained from the second vehicle location dataset.

[0054] S204. Using the first target location dataset and the second target location dataset, determine the current stopping location of the green channel vehicle.

[0055] Specifically, the location where the green channel vehicle stops before entering the target road segment and the location where it stops after leaving the target road segment can be determined from the first target location dataset.

[0056] S205. Determine the first dwell time corresponding to the dwell position belonging to the first preset target area, and determine the second dwell time corresponding to the dwell position belonging to the second preset target area.

[0057] The dwell information includes the first dwell time and the second dwell time, and the first preset target area is different from the second preset target area.

[0058] Specifically, the first dwell time of the current vehicle in the preset target area before entering the target road segment can be determined, and the second dwell time of the current vehicle in the preset target area after leaving the target road segment can be determined. The preset target area may include loading and / or unloading points for non-green channel vehicles, as well as loading and / or unloading points for normal green channel vehicles.

[0059] Optionally, the determination of the first and second preset target areas includes: establishing a heat map of green channel vehicle convergence using the positioning device configured on the target green channel vehicle, and establishing a heat map of freight vehicle convergence using the positioning device configured on the target freight vehicle; determining the area in the green channel vehicle convergence heat map with a heat value greater than a first preset value as the first preset target area, and determining the area in the freight vehicle convergence heat map with a heat value greater than a second preset value as the second preset target area. The advantage of this setup is that by establishing the convergence heat map, the areas where normal green channel vehicles and ordinary freight vehicles converge can be accurately determined.

[0060] Specifically, a heatmap for green channel vehicles can be created first based on a large amount of historical location data of target green channel vehicles, and a heatmap for freight vehicles can be created based on a large amount of historical location data of target freight vehicles. The heat values ​​in the heatmaps are positively correlated with the number of corresponding vehicles converging. Target green channel vehicles can be understood as green channel vehicles other than the currently active ones, and target freight vehicles can be understood as non-green channel vehicles carrying ordinary goods. Then, areas in the green channel vehicle heatmaps with heat values ​​greater than a first preset value are designated as the first electronic fence area, i.e., the first preset target area. Similarly, areas in the freight vehicle heatmaps with heat values ​​greater than a second preset value are designated as the second electronic fence area, i.e., the second preset target area. Finally, the accuracy of the first and second preset target areas can be determined through manual verification. Both the first and second preset target areas must be less than a preset distance from the target road segment, meaning they should both be near the target road segment.

[0061] S206. If the stay duration corresponding to the first preset target area in the first stay duration and / or the stay duration corresponding to the first preset target area in the second stay duration is less than the first preset duration, and / or if the stay duration corresponding to the second preset target area in the first stay duration and / or the stay duration corresponding to the second preset target area in the second stay duration is greater than the second preset duration, then it is determined that the current green channel vehicle is suspected of evading tolls.

[0062] Specifically, as mentioned above, if the stay duration corresponding to the first preset target area in the first stay duration and / or the stay duration corresponding to the first preset target area in the second stay duration is less than the first preset duration, it indicates that the current green channel vehicle's stay time in the normal green channel vehicle gathering area is too short, and the current green channel vehicle is suspected of evading tolls. If the stay duration corresponding to the second preset target area in the first stay duration and / or the stay duration corresponding to the second preset target area in the second stay duration is longer than the second preset duration, it indicates that the current green channel vehicle's stay time in the ordinary truck gathering area is too long, and the current green channel vehicle is suspected of evading tolls.

[0063] S207. If it is determined that the current green channel vehicle is suspected of evading tolls, the on-site verification information of the current green channel vehicle shall be received, and the on-site verification information shall be used to determine whether the current green channel vehicle has engaged in toll evasion.

[0064] Figure 3 This is a schematic diagram of a green channel vehicle verification system. Figure 3 As shown, the system includes a business application layer, a data support layer, an infrastructure layer, a communication layer, and a perception layer. The data platform layer in the system can execute the method for determining suspected evasion of fees provided in this embodiment.

[0065] The method for determining toll evasion suspicion provided in this invention utilizes toll station cameras to quickly and accurately identify the time when a green channel vehicle enters and leaves the target road segment. Then, by determining the current location of the green channel vehicle, it can quickly determine whether the vehicle is staying in a suspicious area and for the corresponding duration, as well as whether it is staying in an area where normal green channel vehicles congregate and for the corresponding duration. Furthermore, it can determine whether the vehicle is abnormally staying for an extended period in a non-green channel freight yard, or whether its stay in a green channel freight yard is too short. Thus, it can quickly determine whether the green channel vehicle is suspected of toll evasion.

[0066] Example 3

[0067] Figure 4 This is a schematic diagram of a device for determining suspected fare evasion according to Embodiment 3 of the present invention. Figure 4 As shown, the device includes: a time determination module 301, a location set determination module 302, and a fare evasion suspicion judgment module 303, wherein:

[0068] The time determination module is used to determine the first time when the current green channel vehicle enters the target road segment and the second time when it leaves the target road segment;

[0069] The location set determination module is used to determine a first target location dataset from a first vehicle location dataset and a second target location dataset from a second vehicle location dataset, wherein the first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time.

[0070] The toll evasion suspicion judgment module is used to determine the current green channel vehicle's stopping information using the first target location dataset and the second target location dataset, and to determine whether the current green channel vehicle is suspected of toll evasion based on the stopping information.

[0071] The device for determining suspected toll evasion provided in this embodiment of the invention can obtain the stopping information of green channel vehicles by analyzing the driving trajectory of green channel vehicles for a period of time before entering the target road segment and the driving trajectory for a period of time after leaving the target road segment. In this way, it can accurately determine whether green channel vehicles are abnormally stopping in suspicious areas, that is, whether there is a suspicion of toll evasion.

[0072] Optional, the fare evasion suspicion assessment module includes:

[0073] The stopping location determination unit is used to determine the stopping location of the current green channel vehicle using the first target location dataset and the second target location dataset;

[0074] The dwell time determination unit is used to determine a first dwell time corresponding to a dwell position belonging to a first preset target area, and to determine a second dwell time corresponding to a dwell position belonging to a second preset target area, wherein the dwell information includes the first dwell time and the second dwell time, and the first preset target area is different from the second preset target area.

[0075] Optionally, the method for determining the first preset target area and the second preset target area includes: establishing a green channel vehicle convergence heat map using a positioning device configured on the target green channel vehicle, and establishing a freight vehicle convergence heat map using a positioning device configured on the target freight vehicle; determining the area in the green channel vehicle convergence heat map with a heat value greater than a first preset value as the first preset target area, and determining the area in the freight vehicle convergence heat map with a heat value greater than a second preset value as the second preset target area.

[0076] Optional, the fare evasion suspicion assessment module includes:

[0077] The toll evasion suspicion determination unit is used to determine that the current green channel vehicle is suspected of toll evasion if the stay time corresponding to the first preset target area in the first stay time and / or the stay time corresponding to the first preset target area in the second stay time is less than the first preset time, and / or if the stay time corresponding to the second preset target area in the first stay time and / or the stay time corresponding to the second preset target area in the second stay time is greater than the second preset time.

[0078] Optionally, the location set determination module is specifically used to obtain a first target location dataset within a first preset time period before the first time from the first vehicle location dataset, and to obtain a second target location dataset within a second preset time period after the second time from the second vehicle location dataset.

[0079] Optionally, the device may also include:

[0080] The toll evasion behavior determination module is used to, after determining whether the current green channel vehicle is suspected of toll evasion based on the stop information, if it is determined that the current green channel vehicle is suspected of toll evasion, receive the on-site verification information of the current green channel vehicle, and determine whether the current green channel vehicle has engaged in toll evasion behavior based on the on-site verification information.

[0081] Optional, the time determination module includes:

[0082] The time and vehicle identity determination unit is used to determine the time when the current green channel vehicle passes through the entrance toll station of the target road section as the first time, and to determine the vehicle identity information of the current green channel vehicle using the camera of the entrance toll station;

[0083] The departure time determination unit is used to determine the current green channel vehicle from the traffic flow passing through the exit toll station by using the vehicle identity information and the camera at the exit toll station of the target road segment, and to determine the time when the current green channel vehicle passes through the exit toll station as the second time.

[0084] The device for determining suspected toll evasion provided in the embodiments of the present invention can execute the method for determining suspected toll evasion provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0085] Example 4

[0086] Figure 5A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0087] like Figure 5 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0088] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0089] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as methods for determining suspected toll evasion.

[0090] In some embodiments, the method for determining suspected toll evasion may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the method for determining suspected toll evasion described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the method for determining suspected toll evasion by any other suitable means (e.g., by means of firmware).

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0093] The computer equipment provided above can be used to execute the method for determining suspected toll evasion provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0094] Example 5

[0095] In the context of this invention, the computer-readable storage medium may be a tangible medium, and the computer-executable instructions, when executed by a computer processor, are used to perform a method for determining suspected toll evasion, the method comprising:

[0096] Determine the first time when the green channel vehicle enters the target road segment and the second time when it leaves the target road segment;

[0097] A first target location dataset is determined from a first vehicle location dataset, and a second target location dataset is determined from a second vehicle location dataset, wherein the first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time;

[0098] Using the first target location dataset and the second target location dataset, the current stop information of the green channel vehicle is determined, and based on the stop information, it is determined whether the current green channel vehicle is suspected of evading tolls.

[0099] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by, or in conjunction with, an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0100] The computer equipment provided above can be used to execute the method for determining suspected toll evasion provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0101] It is worth noting that in the embodiments of the above-mentioned device for determining suspected toll evasion, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0102] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for determining suspicion of fare evasion, characterized in that, include: Determine the first time when the green channel vehicle enters the target road segment and the second time when it leaves the target road segment; A first target location dataset is determined from a first vehicle location dataset, and a second target location dataset is determined from a second vehicle location dataset, wherein the first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time; Using the first target location dataset and the second target location dataset, the current stop information of the green channel vehicle is determined, and based on the stop information, it is determined whether the current green channel vehicle is suspected of evading tolls; The step of determining the current stop information of the green channel vehicle using the first target location dataset and the second target location dataset includes: Using the first target location dataset and the second target location dataset, the current stopping location of the green channel vehicle is determined; The first dwell time corresponding to the dwell position belonging to the first preset target area is determined, and the second dwell time corresponding to the dwell position belonging to the second preset target area is determined, wherein the dwell information includes the first dwell time and the second dwell time, and the first preset target area is different from the second preset target area; The methods for determining the first preset target area and the second preset target area include: A heat map of green channel vehicles is created using the positioning devices installed on the target green channel vehicles, and a heat map of freight vehicles is created using the positioning devices installed on the target freight vehicles. The area with a heat value greater than a first preset value in the heat map of the green channel vehicles is determined as the first preset target area, and the area with a heat value greater than a second preset value in the heat map of the freight vehicles is determined as the second preset target area; The step of determining whether the current green channel vehicle is suspected of toll evasion based on the stop information includes: If the stay duration corresponding to the first preset target area in the first stay duration and / or the stay duration corresponding to the first preset target area in the second stay duration is less than the first preset duration, and / or, If the duration of stay corresponding to the second preset target area in the first stay duration and / or the duration of stay corresponding to the second preset target area in the second stay duration is greater than the second preset duration, then it is determined that the current green channel vehicle is suspected of evading tolls.

2. The method according to claim 1, characterized in that, The step of determining the first target location dataset from the first vehicle location dataset and determining the second target location dataset from the second vehicle location dataset includes: Obtain a first target location dataset within a first preset time period before the first time from the first vehicle location dataset, and obtain a second target location dataset within a second preset time period after the second time from the second vehicle location dataset.

3. The method according to any one of claims 1-2, characterized in that, After determining whether the current green channel vehicle is suspected of toll evasion based on the stop information, the method further includes: If it is determined that the current green channel vehicle is suspected of evading tolls, the on-site verification information of the current green channel vehicle is received, and the on-site verification information is used to determine whether the current green channel vehicle has engaged in toll evasion.

4. The method according to claim 1, characterized in that, Determining the first time the green channel vehicle enters the target road segment and the second time it leaves the target road segment includes: The time when the current green channel vehicle passes through the entrance toll station of the target road section is determined as the first time, and the vehicle identity information of the current green channel vehicle is determined by the camera of the entrance toll station. Using the vehicle identification information and the camera at the exit toll station of the target road section, the current green channel vehicle is identified from the traffic flow passing through the exit toll station, and the time when the current green channel vehicle passes through the exit toll station is determined as the second time.

5. A device for determining suspected fare evasion, characterized in that, include: The time determination module is used to determine the first time when the green channel vehicle enters the target road segment and the second time when it leaves the target road segment; The location set determination module is used to determine a first target location dataset from a first vehicle location dataset and a second target location dataset from a second vehicle location dataset, wherein the first vehicle location dataset includes the location data of the current green channel vehicle before the first time, and the second vehicle location dataset includes the location data of the current green channel vehicle after the second time. The toll evasion suspicion judgment module is used to determine the current green channel vehicle's stopping information using the first target location dataset and the second target location dataset, and to determine whether the current green channel vehicle is suspected of toll evasion based on the stopping information; The fare evasion suspicion determination module includes: The stopping location determination unit is used to determine the stopping location of the current green channel vehicle using the first target location dataset and the second target location dataset; A dwell time determination unit is used to determine a first dwell time corresponding to a dwell position belonging to a first preset target area, and to determine a second dwell time corresponding to a dwell position belonging to a second preset target area, wherein the dwell information includes the first dwell time and the second dwell time, and the first preset target area is different from the second preset target area; The methods for determining the first preset target area and the second preset target area include: A heat map of green channel vehicles is created using the positioning devices installed on the target green channel vehicles, and a heat map of freight vehicles is created using the positioning devices installed on the target freight vehicles. The area with a heat value greater than a first preset value in the heat map of the green channel vehicles is determined as the first preset target area, and the area with a heat value greater than a second preset value in the heat map of the freight vehicles is determined as the second preset target area; The fare evasion suspicion determination module includes: The toll evasion suspicion determination unit is used to determine that the current green channel vehicle is suspected of toll evasion if the stay time corresponding to the first preset target area in the first stay time and / or the stay time corresponding to the first preset target area in the second stay time is less than the first preset time, and / or if the stay time corresponding to the second preset target area in the first stay time and / or the stay time corresponding to the second preset target area in the second stay time is greater than the second preset time.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining suspected evasion of fees as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining suspected toll evasion as described in any one of claims 1-4.

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