Non-inductive charging system and method based on electronic license plate and ETC technology

Abstract

The invention discloses a non-inductive toll collection system based on an electronic license plate and an ETC technology. The system comprises the electronic license plate installed on a vehicle, a data acquisition device realized by adopting the ETC technology, a data center platform used for data processing and a client providing a user interface. The electronic license plate stores basic information of the vehicle; when monitoring that a vehicle enters a preset electronic fence, the data acquisition equipment establishes communication connection with the electronic vehicle so as to read basic information of the vehicle from an electronic license plate, generate driving information of the vehicle and send the basic information and the driving information to the data center platform; and the data center platform calculates the toll amount of the vehicle according to the basic information and the driving information sent by the data acquisition equipment, and sends the toll amount to the client for payment reminding. Therefore, by adopting the non-inductive toll collection system, the vehicle can realize payment without parking when passing through the toll station, so that the passing efficiency of the expressway is remarkably improved, and the congestion caused by parking payment is reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a contactless toll collection system and method based on electronic license plates and ETC technology. Background Art

[0002] In modern traffic management, the toll collection system of highways is a key component to ensure the rationality and economic benefits of road use. Typical application scenarios include the passage of vehicles on highways, where vehicles need to collect a pass card at the entrance and pay the corresponding fee at the exit. This traditional toll collection model often causes congestion at toll stations during peak hours, affecting vehicle traffic efficiency and increasing the complexity of operation management. Summary of the invention

[0003] The embodiments of the present application provide a non-contact toll collection system based on electronic license plates and ETC technology. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0004] In a first aspect, an embodiment of the present application provides a non-contact toll collection system based on electronic license plates and ETC technology, the system comprising:

[0005] The electronic license plate installed on the vehicle, the data collection equipment implemented by ETC technology, the data center platform for data processing and the client providing the user interface; among them,

[0006] The data collection equipment is connected to the electronic license plate and the data center platform respectively. The data collection equipment is set on both sides of the road or at preset locations on the road. The data center platform is connected to the client; wherein,

[0007] Electronic license plate, used to store basic vehicle information;

[0008] The data acquisition device is used to establish a communication connection with the electronic vehicle when monitoring that the vehicle enters the preset electronic fence, so as to read the basic information of the vehicle from the electronic license plate, generate the driving information of the vehicle, and send the basic information and the driving information to the data center platform; wherein the preset electronic fence is obtained by marking a preset position on the road on the map using the GPS coordinate system and generating a closed virtual fence area at the preset position, and the preset electronic fence carries a fence identifier, and the fence identifier is used to distinguish different charging intervals;

[0009] The data center platform is used to calculate the toll amount of the vehicle based on the basic information and driving information sent by the data collection equipment, and send it to the client for payment reminder;

[0010] The client is used to generate a payment order based on the toll amount sent by the data center platform and display the payment order.

[0011] Optionally, the electronic license plate is embedded with an RFID chip and an anti-disassembly component, and the RFID chip is an RFID chip of ISO / IEC 14443A or B standard;

[0012] Among them, the RFID chip is used to store basic information including the vehicle's license plate number, vehicle type, and owner information;

[0013] The anti-disassembly component is used to generate the first violation event of the vehicle when it is detected that the electronic license plate is illegally disassembled; record the first violation event of the vehicle and send it to the client of the early warning personnel.

[0014] Optionally, generate vehicle driving information, including:

[0015] Record the time, target location and speed of the vehicle entering the preset electronic fence;

[0016] The time, target position and speed are used as the driving information of the vehicle.

[0017] Optionally, the data acquisition device is also used to:

[0018] According to the speed at which the vehicle enters the preset electronic fence, detect whether the vehicle has violated the rules in the preset electronic fence;

[0019] When the vehicle has violated the rules in the preset electronic fence, a second violation event of the vehicle is generated;

[0020] The second violation event of the vehicle is recorded and sent to the client of the warning personnel.

[0021] Optionally, the data center platform is also used for:

[0022] Statistics of traffic volume and toll amount of each preset electronic fence according to the preset period;

[0023] Generate reports based on traffic volume, toll amount and preset parameters and send them to the manager's client for analysis and decision-making;

[0024] The preset parameters are custom time period and area range.

[0025] Optionally, the data center platform is also used for:

[0026] Receive a query request from a client, the query request carrying a target vehicle ID and / or a target time period;

[0027] Based on the target vehicle ID and / or target time period, obtain the driving trajectory and fee details within the fenced area;

[0028] The driving trajectory and cost details are sent to the client for display.

[0029] Optionally, the toll amount of the vehicle is calculated based on the basic information and driving information sent by the data collection device, including:

[0030] Extract the time when the vehicle enters the preset electronic fence from the driving information sent by the data acquisition device;

[0031] Determine whether the vehicle has paid the toll based on the time when the vehicle enters the preset electronic fence and the basic information sent by the data collection device;

[0032] In the case where the vehicle has not paid the toll, the vehicle's travel trajectory is drawn based on the target location where the vehicle enters the preset electronic fence;

[0033] Based on the vehicle's driving trajectory, calculate the vehicle's actual mileage;

[0034] Determine the fence identifiers of all preset geo-fences where the vehicle has entered;

[0035] According to the fence identifier, the corresponding road section charging standard is obtained from the pre-established mapping relationship between the fence identifier and the road section charging standard;

[0036] The toll amount for the vehicle is obtained by multiplying the actual mileage by the charging standard for the road section.

[0037] Optionally, based on the vehicle entering the target position of the preset electronic fence, the vehicle's driving trajectory is drawn, including:

[0038] The target position of the vehicle entering the preset electronic fence is input into the pre-trained driving trajectory prediction model; wherein,

[0039] The pre-trained driving trajectory prediction model is a mathematical model that can predict the driving trajectory of the vehicle based on the target position of the vehicle entering the preset electronic fence;

[0040] Output the vehicle's trajectory.

[0041] Optionally, a pre-trained driving trajectory prediction model is generated by following the steps below, including:

[0042] Collect the historical driving data of each historical vehicle, where the historical driving data is the historical position sequence of each historical vehicle entering the preset electronic fence;

[0043] Get the historical driving trajectory of each historical vehicle;

[0044] For each historical position sequence of a vehicle entering a preset electronic fence, mark the historical driving trajectory of each historical vehicle to obtain a model training sample;

[0045] A driving trajectory prediction model is created using a machine learning algorithm, where the machine learning algorithm is a random forest network or a support vector machine network;

[0046] Input the model training samples into the driving trajectory prediction model and output the model loss value;

[0047] When the model loss value reaches the minimum, a pre-trained driving trajectory prediction model is generated.

[0048] In a second aspect, the embodiment of the present application provides a non-contact charging method based on electronic license plates and ETC technology, the method comprising:

[0049] The electronic license plate stores the basic information of the vehicle;

[0050] When the data collection device detects that a vehicle has entered a preset electronic fence, it establishes a communication connection with the electronic vehicle to read the basic information of the vehicle from the electronic license plate, generate the driving information of the vehicle, and send the basic information and driving information to the data center platform; wherein, the preset electronic fence is obtained by marking a preset position on the road on the map using the GPS coordinate system and generating a closed virtual fence area at the preset position, and the preset electronic fence carries a fence identifier, which is used to distinguish different charging intervals;

[0051] The data center platform calculates the toll amount of the vehicle based on the basic information and driving information sent by the data collection device, and sends it to the client for payment reminder;

[0052] The client generates a payment order based on the toll amount sent by the data center platform and displays the payment order.

[0053] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0054] In this application, electronic license plates are installed on vehicles and data collection equipment using ETC technology is deployed at key locations on the road to achieve automatic identification of vehicles when they enter the preset electronic fence area. Due to the automatic collection and identification of vehicle information, toll stations can quickly release vehicles, significantly reducing the waiting time of vehicles at toll stations and improving traffic efficiency.

[0055] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0057] Figure 1 This is a system schematic diagram of a non-contact toll collection system based on electronic license plates and ETC technology provided in an embodiment of the present application;

[0058] Figure 2 It is a flowchart of a model training method provided in an embodiment of the present application;

[0059] Figure 3 It is a flow chart of a contactless charging method based on electronic license plates and ETC technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The following description and the accompanying drawings sufficiently illustrate specific embodiments of the present application to enable those skilled in the art to practice them.

[0061] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0062] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of systems and methods consistent with some aspects of the present application as detailed in the attached claims.

[0063] In this application, by installing electronic license plates on vehicles and deploying data collection equipment using ETC technology at key locations on the road, automatic identification of vehicles when they enter a preset electronic fence area is achieved. Due to the automatic collection and identification of vehicle information, toll stations can quickly release vehicles, significantly reducing the waiting time of vehicles at toll stations and improving traffic efficiency. The following is a detailed description using an exemplary embodiment.

[0064] See also Figure 1 , Figure 1This is a system structure diagram of a contactless toll collection system based on electronic license plates and ETC technology provided in an embodiment of the present application. The system includes: an electronic license plate installed on a vehicle, a data acquisition device implemented using ETC technology, a data center platform for data processing, and a client providing a user interface; wherein the data acquisition device is respectively communicated with the electronic license plate and the data center platform, the data acquisition device is arranged on both sides of the road or at preset locations on the road, and the data center platform is communicated with the client.

[0065] Among them, the electronic license plate is a device installed on the vehicle, usually embedded with an RFID (radio frequency identification) chip or other similar wireless communication technology. It is used to store basic information of the vehicle, such as license plate number, vehicle type, owner information, etc., and can be read remotely. Data collection equipment refers to hardware installed on both sides of the road or at key nodes. They use ETC technology to communicate with electronic equipment on the vehicle, automatically read vehicle information, and record vehicle traffic data, such as time, location and speed. The data center platform is a back-end system responsible for receiving, storing, processing and analyzing data from data collection equipment. The data center platform uses big data processing technology to conduct in-depth mining and analysis of vehicle location information and calculate the amount of tolls. The client is a software or application that provides a user interface, usually installed on the user's mobile device or computer. The client is used to display the vehicle's driving information, fee details, generate payment orders, and allow users to query, pay, and other operations. Preset location points refer to specific locations pre-set on the road, such as the entrance and exit of a highway. Data collection equipment is set at these locations to collect data when the vehicle passes. The two sides of the road or the preset locations of the road are the specific installation locations of the data collection equipment. They can be installed on both sides of the road, or at specific locations on the road, such as near toll booths, to ensure that the data collection needs of all passing vehicles are covered.

[0066] In some embodiments of the present application, the electronic license plate is used to store basic information of the vehicle; the data acquisition device is used to establish a communication connection with the electronic vehicle when monitoring the vehicle entering a preset electronic fence, so as to read the basic information of the vehicle from the electronic license plate, generate the driving information of the vehicle, and send the basic information and the driving information to the data center platform; wherein, the preset electronic fence is obtained by marking a preset position on the road on the map using the GPS coordinate system and generating a closed virtual fence area at the preset position, and the preset electronic fence carries a fence identifier, and the fence identifier is used to distinguish different charging intervals; the data center platform is used to calculate the toll amount of the vehicle according to the basic information and driving information sent by the data acquisition device, and send it to the client for payment reminder; the client is used to generate a payment order according to the toll amount sent by the data center platform, and display the payment order.

[0067] Among them, the preset electronic fences are preset locations on the road marked on the map using the GPS coordinate system, and closed virtual fence areas are generated at these locations. These fences carry unique fence identifiers to distinguish different toll intervals.

[0068] In the embodiment of the present application, through this automated toll collection system, the driver does not need to stop or perform payment operations when passing through a toll station, which significantly improves the driving experience and reduces congestion and environmental pollution caused by parking fees.

[0069] In some embodiments, assuming that a vehicle is equipped with an electronic license plate, when it enters a preset electronic fence (such as the entrance of a highway), the data acquisition device installed at the location will read the information of the electronic license plate and record the vehicle's entry time, location and speed. This information is sent to the data center, and the data center calculates the cost of the vehicle traveling in a specific section. When the vehicle leaves the section (such as the exit of the highway), the data center will send a payment notification to the bound client, and the user can complete the payment directly on the client.

[0070] In some embodiments of the present application, the electronic license plate is embedded with an RFID chip and an anti-disassembly component, and the RFID chip is an ISO / IEC 14443A or B standard RFID chip; wherein the RFID chip is used to store basic information including the vehicle's license plate number, vehicle type, and owner information; the anti-disassembly component is used to generate a first violation event for the vehicle when it is detected that the electronic license plate is illegally disassembled; and the first violation event of the vehicle is recorded and sent to the client of the early warning personnel.

[0071] Among them, RFID chip: Radio Frequency Identification, which is a wireless communication technology that can identify and track items with RFID chips through radio waves. In electronic license plates, RFID chips are used to store basic vehicle information such as license plate number, vehicle type, and owner information. This chip complies with international standards ISO / IEC14443A or B, which define parameters for short-range wireless communication, such as frequency, signal modulation method, etc. The anti-disassembly component is a device used to prevent electronic license plates from being illegally disassembled. Once the electronic license plate is illegally disassembled, the anti-disassembly component will trigger a mechanism (such as generating a violation event signal), which can be used to warn or notify relevant departments. This design increases the security of electronic license plates and ensures the protection of vehicle information and the enforcement of traffic regulations. The first violation event refers to an event record generated by the anti-disassembly component when the electronic license plate is illegally disassembled. This record can be used as evidence of the violation and can be sent to the client of the early warning personnel so that corresponding measures can be taken.

[0072] In the embodiment of the present application, the anti-disassembly component enhances the security of the electronic license plate, prevents the illegal disassembly of the electronic license plate and the theft of information, and protects the information security of the vehicle and the owner.

[0073] For example, suppose a car is equipped with an electronic license plate containing an RFID chip and an anti-disassembly component. When the vehicle passes through a road section equipped with a data collection device, the data collection device reads the information on the electronic license plate and generates driving information to send to the data center platform. If someone attempts to illegally dismantle the electronic license plate, the anti-disassembly component will detect this behavior and generate the first violation event, which will be sent to the client of the traffic management department to remind them to take action.

[0074] In some embodiments of the present application, generating vehicle driving information specifically includes: recording the time, target position and speed of the vehicle entering a preset electronic fence; and using the time, target position and speed as the vehicle driving information.

[0075] In some embodiments of the present application, the data acquisition device is also used to: detect whether the vehicle has committed a violation in the preset electronic fence based on the speed at which the vehicle enters the preset electronic fence; generate a second violation event for the vehicle if the vehicle has committed a violation in the preset electronic fence; record the second violation event of the vehicle and send it to the client of the early warning personnel.

[0076] Among them, the illegal behavior of the illegal vehicle in the preset electronic fence, such as speeding or driving in the wrong direction. The second violation event refers to the second violation detected by the system, usually another violation after the first violation. Each violation will be recorded by the system and a corresponding violation event will be generated. The client of the early warning personnel is a software interface or application for receiving and displaying the violation events generated by the system in real time. Early warning personnel, which may include traffic police, traffic management personnel or system monitoring personnel, can obtain violation information through this client and take corresponding measures.

[0077] In the embodiments of the present application, by real-time monitoring and recording of violations, timely measures can be taken to reduce the occurrence of traffic accidents and improve road safety.

[0078] For example, if a car is speeding on a highway and is driving the wrong way while waiting inside an electronic fence, information about this event will be sent to the early warning personnel's client, such as a monitoring screen at a traffic management center, showing the vehicle's violation and specific location. The early warning personnel can take immediate action, such as sending a patrol car to deal with the situation.

[0079] In some embodiments of the present application, the data center platform is also used to: count the traffic volume and charging amount of each preset electronic fence according to a preset period; generate reports based on the traffic volume, charging amount and preset parameters, and send them to the manager's client for the manager to analyze and make decisions; wherein the preset parameters are custom time periods and area ranges.

[0080] Traffic volume refers to the number of vehicles passing through a certain area or road section within a specific time. The toll amount refers to the total fee that vehicles need to pay to pass through the electronic fence. Preset parameters refer to the settings that managers can customize to generate specific reports. These parameters may include specific time periods (such as morning or evening rush hour), specific area ranges (such as a city or a road section), or other specific conditions (such as a specific type of vehicle).

[0081] In the embodiment of the present application, by regularly generating reports, managers can quickly obtain key data and improve the efficiency and quality of decision-making.

[0082] For example, suppose the traffic management department wants to analyze the traffic flow and toll collection at a highway entrance during the morning and evening peak hours. The data center platform will count the traffic flow and toll collection at the entrance according to a preset period (such as daily). Then, based on this data and preset parameters (such as the time period of the morning and evening peak hours), a report is generated. This report will be sent to the manager's client, who can view the report, analyze the traffic flow and toll collection, and make corresponding management and decisions.

[0083] In some embodiments of the present application, the data center platform is also used to: receive a query request from a client, the query request carries a target vehicle ID and / or a target time period; based on the target vehicle ID and / or the target time period, obtain the driving trajectory and cost details within the fenced area; and send the driving trajectory and cost details to the client for display.

[0084] Among them, the query request is a request sent by the client to the data center platform to retrieve or obtain specific information. The target vehicle ID is an identifier used to uniquely identify a vehicle, usually associated with an electronic license plate. The target vehicle ID is specified in the query request in order to retrieve data related to that specific vehicle. The target time period is a time range specified by the client to query events or data that occurred during this time period. For example, the client may want to query the driving trajectory and fee details of a vehicle on a specific date or time period. The driving trajectory refers to the movement path of a vehicle over a period of time. In the contactless toll collection system, the driving trajectory data can be used to calculate the vehicle's driving distance, time, and other related information.

[0085] In the embodiment of the present application, users can conveniently query and view their vehicle travel and cost information through the client, thereby improving the user experience.

[0086] In this embodiment of the application, suppose a user wants to query the driving trajectory and expense details of their vehicle last month. They send a query request through the client and provide the target vehicle ID and the specific date range of last month as the target time period. After the data center platform receives this request, it retrieves the data related to the vehicle ID and time period, including the driving trajectory and expense details, and then sends this information back to the client. After the client receives the data, it will display the driving trajectory in a graphical way and list the expense details, so that the user can clearly see the driving conditions and related expenses of their vehicle last month.

[0087] In some embodiments of the present application, the specific process of calculating the toll amount of a vehicle based on the basic information and driving information sent by a data acquisition device includes: extracting the time when the vehicle enters a preset electronic fence from the driving information sent by the data acquisition device; judging whether the vehicle has paid the toll based on the time when the vehicle enters the preset electronic fence and the basic information sent by the data acquisition device; if the vehicle has not paid the toll, drawing the vehicle's driving trajectory based on the target position of the vehicle entering the preset electronic fence; calculating the vehicle's actual mileage based on the vehicle's driving trajectory; determining the fence identifiers of all preset electronic fences that the vehicle enters; according to the fence identifiers, obtaining the corresponding road section charging standard from the pre-established mapping relationship between the fence identifiers and the road section charging standard; and obtaining the toll amount of the vehicle by multiplying the actual mileage by the road section charging standard.

[0088] The mapping relationship refers to the correspondence between the fence identifier and the road section charging standard. The data center platform will determine the charging standard corresponding to the road section where the vehicle passes based on this mapping relationship.

[0089] For example, a vehicle departs from Zhangjiakou, Hebei Province to Beijing, and the total journey is expected to be 184 kilometers. In the traditional mode, the vehicle needs to get a card in Zhangjiakou, pay the fee and get a card at the Donghuayuan toll station, pass the Kangzhuang checkpoint, and finally complete the payment at the Qinghe toll station. With the present invention, geographic fences are marked at these key nodes. When the vehicle enters these fenced areas, the system automatically records the vehicle information.

[0090] The actual mileage is accurately calculated based on the vehicle's driving trajectory, and the toll amount is calculated in combination with the toll standards of the Ministry of Transport for that section of the road. The specific calculation method for highway tolls is as follows:

[0091] For small vehicles (trucks of 2 tons or less, buses with less than 20 seats), the rate is 0.45 yuan / km;

[0092] For medium-sized vehicles (trucks from 2 tons to 7 tons, buses from 21 to 50 seats), the rate is 0.90 yuan / km;

[0093] For large vehicles (trucks over 7 to 20 tons, buses with more than 51 seats, and sleeper buses), the rate is 1.462 yuan per kilometer;

[0094] For extra-large vehicles (trucks and container trucks over 20 tons), the rate is 2.138 yuan per kilometer.

[0095] The calculation formula for the toll amount is: Toll amount = distance × rate

[0096] For the trip from Zhangjiakou to Beijing, the toll amount is calculated as follows:

[0097] The toll from Zhangjiakou to Beijing = 184 kilometers × 0.45 yuan / kilometer = 82.8 yuan.

[0098] In some embodiments of the present application, the specific process of drawing the vehicle's driving trajectory based on the target position of the vehicle entering the preset electronic fence includes: inputting the target position of the vehicle entering the preset electronic fence into a pre-trained driving trajectory prediction model; wherein the pre-trained driving trajectory prediction model is a mathematical model that can predict the vehicle's driving trajectory based on the target position of the vehicle entering the preset electronic fence; and outputting the vehicle's driving trajectory.

[0099] In some embodiments of the present application, for example Figure 2 As shown, a pre-trained driving trajectory prediction model can be generated according to the following steps, including: collecting historical driving data of each historical vehicle, the historical driving data is a historical position sequence of each historical vehicle entering a preset electronic fence; obtaining the historical driving trajectory of each historical vehicle; for each historical position sequence of each historical vehicle entering the preset electronic fence, marking the historical driving trajectory of each historical vehicle to obtain a model training sample; using a machine learning algorithm to create a driving trajectory prediction model, the machine learning algorithm is a random forest network or a support vector machine network; inputting the model training sample into the driving trajectory prediction model, and outputting the model loss value; when the model loss value reaches the minimum, generating a pre-trained driving trajectory prediction model.

[0100] Among them, historical driving data refers to the data collected during the driving process of the vehicle in the past, including the location and time when the vehicle enters the preset electronic fence. These data are usually used for analysis and modeling to understand the driving behavior of the vehicle. The historical driving trajectory refers to the driving path of the vehicle in the past period of time derived from the historical driving data. The random forest network is an integrated learning method that improves the accuracy and robustness of the prediction by building multiple decision trees and outputting the average results. Random forests can handle classification and regression problems, and are also effective for feature selection and outlier detection. The support vector machine network is a supervised learning algorithm used for classification and regression analysis. The support vector machine attempts to find the optimal boundary between different categories, that is, to maximize the category interval.

[0101] In an embodiment of the present application, the accuracy of driving trajectory prediction can be improved by using a machine learning algorithm and historical driving data.

[0102] In this application, electronic license plates are installed on vehicles and data collection equipment using ETC technology is deployed at key locations on the road to achieve automatic identification of vehicles when they enter the preset electronic fence area. Due to the automatic collection and identification of vehicle information, toll stations can quickly release vehicles, significantly reducing the waiting time of vehicles at toll stations and improving traffic efficiency.

[0103] See also Figure 3 , provides a flow chart of a non-contact toll collection method based on electronic license plates and ETC technology, which is applied to a non-contact toll collection system based on electronic license plates and ETC technology, for an embodiment of the present application. Figure 3 As shown, the detection method of the embodiment of the present application may include the following steps:

[0104] S101, the electronic license plate stores basic information of the vehicle;

[0105] S102, when the data acquisition device detects that a vehicle enters a preset electronic fence, it establishes a communication connection with the electronic vehicle to read the basic information of the vehicle from the electronic license plate, generates the driving information of the vehicle, and sends the basic information and the driving information to the data center platform; wherein the preset electronic fence is obtained by marking a preset position on the road on a map using a GPS coordinate system and generating a closed virtual fence area at the preset position, and the preset electronic fence carries a fence identifier, and the fence identifier is used to distinguish different charging intervals;

[0106] S103, the data center platform calculates the toll amount of the vehicle based on the basic information and driving information sent by the data collection device, and sends it to the client for payment reminder;

[0107] S104, the client generates a payment order according to the toll amount sent by the data center platform, and displays the payment order.

[0108] For detailed explanations of the contents of steps S101 to S104, please refer to the system embodiment, which will not be repeated here.

[0109] In this application, electronic license plates are installed on vehicles and data collection equipment using ETC technology is deployed at key locations on the road to achieve automatic identification of vehicles when they enter the preset electronic fence area. Due to the automatic collection and identification of vehicle information, toll stations can quickly release vehicles, significantly reducing the waiting time of vehicles at toll stations and improving traffic efficiency.

[0110] The present application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the contactless charging method based on electronic license plates and ETC technology provided by the above-mentioned various method embodiments.

[0111] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the contactless charging method based on electronic license plates and ETC technology of the above-mentioned various method embodiments.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program for the senseless toll collection based on the electronic license plate and ETC technology can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium for the senseless toll collection based on the electronic license plate and ETC technology can be a disk, an optical disk, a read-only storage memory or a random access storage memory, etc.

[0113] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A non-contact toll collection system based on electronic license plates and ETC technology, characterized in that: The system comprises: The electronic license plate installed on the vehicle, the data collection equipment implemented by ETC technology, the data center platform for data processing and the client providing the user interface; among them, The data acquisition device is respectively connected to the electronic license plate and the data center platform in communication, the data acquisition device is set on both sides of the road or at a preset position of the road, and the data center platform is connected to the client in communication; wherein, The electronic license plate is used to store basic information of the vehicle; The data acquisition device is used to establish a communication connection with the electronic vehicle when monitoring that the vehicle enters a preset electronic fence, so as to read the basic information of the vehicle from the electronic license plate, generate the driving information of the vehicle, and send the basic information and the driving information to the data center platform; wherein the preset electronic fence is obtained by marking a preset position on the road on a map using a GPS coordinate system and generating a closed virtual fence area at the preset position, and the preset electronic fence carries a fence identifier, and the fence identifier is used to distinguish different charging intervals; The data center platform is used to calculate the toll amount of the vehicle according to the basic information and the driving information sent by the data acquisition device, and send it to the client for payment reminder; The client is used to generate a payment order according to the toll amount sent by the data center platform, and display the payment order.

2. The system according to claim 1, characterized in that The electronic license plate is embedded with an RFID chip and an anti-disassembly component, and the RFID chip is an RFID chip of ISO / IEC 14443A or B standard; The RFID chip is used to store basic information including the vehicle license plate number, vehicle type and vehicle owner information; The anti-disassembly component is used to generate a first violation event of the vehicle when it is detected that the electronic license plate is illegally disassembled; record the first violation event of the vehicle and send it to the client of the early warning personnel.

3. The system according to claim 1, characterized in that The generating the driving information of the vehicle comprises: Recording the time, target location and speed of the vehicle entering the preset electronic fence; The time, target position and speed are used as the driving information of the vehicle.

4. The system according to claim 3, characterized in that The data acquisition device is also used for: According to the speed at which the vehicle enters the preset electronic fence, detecting whether the vehicle has violated any regulations in the preset electronic fence; In the case where the vehicle has violated the rules in the preset electronic fence, generating a second violation event of the vehicle; The second violation event of the vehicle is recorded and sent to a client of the early warning personnel.

5. The system according to claim 1, characterized in that The data center platform is also used for: Statistics of traffic volume and toll amount of each preset electronic fence according to the preset period; Based on the traffic volume, the toll amount and preset parameters, a report is generated and sent to the manager's client for the manager to analyze and make decisions; wherein, The preset parameters are a custom time period and area range.

6. The system according to claim 1, characterized in that The data center platform is also used for: Receiving a query request from a client, wherein the query request carries a target vehicle ID and / or a target time period; Based on the target vehicle ID and / or target time period, obtaining the driving track and fee details within the fenced area; The driving trajectory and cost details are sent to the client for display.

7. The system according to claim 1, characterized in that The step of calculating the toll amount of the vehicle according to the basic information and the driving information sent by the data acquisition device comprises: Extracting the time when the vehicle enters the preset electronic fence from the driving information sent by the data acquisition device; Determining whether the vehicle has paid a toll based on the time when the vehicle enters the preset electronic fence and the basic information sent by the data acquisition device; In the case where the vehicle has not paid the toll, drawing a driving trajectory of the vehicle based on the target position where the vehicle enters the preset electronic fence; Calculating the actual mileage of the vehicle based on the driving trajectory of the vehicle; Determining fence identifiers of all preset electronic fences entered by the vehicle; According to the fence identifier, obtaining the corresponding road section charging standard from a pre-established mapping relationship between the fence identifier and the road section charging standard; The actual mileage is multiplied by the road section charging standard to obtain the toll amount of the vehicle.

8. The system according to claim 7, characterized in that Drawing the driving trajectory of the vehicle based on the target position where the vehicle enters the preset electronic fence includes: The target position of the vehicle entering the preset electronic fence is input into a pre-trained driving trajectory prediction model; wherein, The pre-trained driving trajectory prediction model is a mathematical model that can predict the driving trajectory of the vehicle based on the target position of the vehicle entering the preset electronic fence; The driving trajectory of the vehicle is output.

9. The method according to claim 8, characterized in that The pre-trained driving trajectory prediction model is generated according to the following steps, including: Collecting historical driving data of each historical vehicle, wherein the historical driving data is a historical position sequence of each historical vehicle entering a preset electronic fence; Get the historical driving trajectory of each historical vehicle; For each historical position sequence of a vehicle entering a preset electronic fence, annotate the historical driving trajectory of each historical vehicle to obtain a model training sample; A driving trajectory prediction model is created using a machine learning algorithm, wherein the machine learning algorithm is a random forest network or a support vector machine network; Inputting the model training sample into the driving trajectory prediction model, and outputting the model loss value; When the model loss value reaches the minimum, a pre-trained driving trajectory prediction model is generated.

10. A non-contact toll collection method based on electronic license plates and ETC technology, characterized in that: The method comprises: The electronic license plate stores the basic information of the vehicle; When the data acquisition device detects that the vehicle enters the preset electronic fence, it establishes a communication connection with the electronic vehicle to read the basic information of the vehicle from the electronic license plate, generate the driving information of the vehicle, and send the basic information and the driving information to the data center platform; wherein the preset electronic fence is obtained by marking a preset position on the road on a map using a GPS coordinate system and generating a closed virtual fence area at the preset position, and the preset electronic fence carries a fence identifier, and the fence identifier is used to distinguish different charging intervals; The data center platform calculates the toll amount of the vehicle according to the basic information and the driving information sent by the data acquisition device, and sends it to the client for payment reminder; The client generates a payment order according to the toll amount sent by the data center platform, and displays the payment order.

Images