Travel record authentication method, electronic device, storage medium, vehicle

Abstract

The application provides a driving record authentication method, an electronic device, a storage medium and a vehicle. The method comprises: obtaining a historical driving record of a driver and obtaining an associated record of the driver; casting a non-fungible token (NFT) image of the driver based on the historical driving record and the associated record; and in response to a query request for the driver, transmitting the NFT image of the driver to a user terminal that sends the query request. The application can use the NFT image to show the overall analysis result of the past driving record of the driver, and reduce the time lag and information gap when the inquirer checks.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular to a driving record authentication method, electronic device, storage medium, and vehicle. Background Technology

[0002] With the rise of ride-hailing services, users (such as taxi passengers) often wish to access information about a driver's past driving history, including records of violations and accidents, for safety reasons. However, this process is time-consuming and impractical, requiring applications to police authorities. Without readily available records, passengers have little access to information about a driver's driving history. Summary of the Invention

[0003] In view of this, it is necessary to provide a blockchain-based method for authenticating driving records, electronic devices, storage media, and vehicles that can utilize the public and tamper-proof characteristics of blockchain to display the overall analysis results of a driver's past driving records using NFT images, thereby reducing the timeliness and information gap for inquiries and increasing the credibility of driver records.

[0004] The first aspect of this application provides a driving record authentication method applied to an electronic device acting as a node in a blockchain. The method includes: acquiring a driver's historical driving records and acquiring the driver's associated records; minting a non-fungible token (NFT) image of the driver based on the historical driving records and associated records; and transmitting the driver's NFT image to the user terminal that sent the query request in response to a query request for the driver.

[0005] In one embodiment, the step of minting the driver's non-fungible token (NFT) image based on the historical driving records and associated records includes: analyzing the acquired historical driving records and associated records to obtain the analysis result of each record in the historical driving records and associated records; obtaining multiple behavioral records of the driver based on the analysis result of each record; creating corresponding image badges for each of the multiple behavioral records to obtain multiple image badges; and minting the driver's NFT image based on the multiple image badges.

[0006] In one embodiment, minting the driver's non-fungible token (NFT) image based on the historical driving records and associated records includes: analyzing the acquired historical driving records and associated records to obtain the analysis result of each record in the historical driving records and associated records; obtaining multiple behavioral records of the driver based on the analysis result of each record; creating corresponding NFT image badges for each of the multiple behavioral records to obtain multiple NFT image badges; and using the multiple NFT image badges as the driver's NFT image.

[0007] In one embodiment, minting the driver's non-fungible token (NFT) image based on the historical driving records and associated records includes: determining the driver's level based on the historical driving records and associated records; and generating a non-fungible token (NFT) image for the driver corresponding to the level.

[0008] In one embodiment, the historical driving record includes the driver's historical location trajectory information, and the associated record includes the driver's driver's license status information; minting the driver's non-fungible token (NFT) image based on the historical driving record and associated record includes:

[0009] Based on the driver's historical location trajectory information and the historical location trajectory information of each of the other drivers, the percentage ranking of the driver's mileage records is obtained. The percentage ranking of the driver's mileage records includes the percentage ranking of the driver's total mileage, the percentage ranking of the driver's first driving area distribution, and the percentage ranking of the driver's second driving area distribution.

[0010] Cast an NFT image corresponding to the percentage ranking of the driver's total mileage, and display the percentage ranking of the driver's mileage records and the driver's license status information in the NFT image.

[0011] In one embodiment, the percentage ranking of the driver's first driving area distribution includes: the percentage ranking of total mileage in urban areas, the percentage ranking of total mileage in suburban areas, and the percentage ranking of total mileage in mountainous areas; the percentage ranking of the driver's second driving area distribution includes: the percentage ranking of total mileage corresponding to each municipal administrative region.

[0012] In one embodiment, before obtaining the driver's historical driving records, the method further includes: receiving a registration request associated with the driver, the registration request carrying the driver's personal information, the personal information including the driver's e-wallet address and identity information and identification information of the vehicle driven by the driver; responding to the registration request, assigning a user account to the driver and storing the driver's personal information, the user account being the e-wallet address.

[0013] In one embodiment, before obtaining the driver's historical driving records, the method further includes: receiving a login request associated with the driver; when the login request is determined to be a valid request, receiving driving records sent by the vehicle, associating the driving records with the current time, and storing the driving records according to the driver's personal information; when the login request is determined to be an invalid request, not receiving the driving records and issuing feedback information.

[0014] This application also provides an electronic device, including: a processor; and a memory, wherein the memory stores a plurality of program modules, the plurality of program modules being loaded by the processor and executing the driving record authentication method.

[0015] This application also provides a computer-readable storage medium storing at least one computer instruction, characterized in that the instruction is executed by a processor using the driving record authentication method.

[0016] This application also provides a vehicle, the vehicle comprising: a processor; an on-site detection device; and a wireless communication device; wherein when the on-site detection device detects that the driver is in the driving position, the processor sends a login request to an electronic device via the wireless communication device; and when the processor receives feedback information sent by the electronic device based on the login request, the processor sends the vehicle's driving records to the electronic device, thereby enabling the electronic device to cast the driver's non-fungible token (NFT) image based on the driver's driving records within a preset time period and the driver's associated records.

[0017] The implementation method of this application can leverage the public and tamper-proof nature of blockchain to utilize NFT images to display the overall analysis results of a driver's past driving records, thereby reducing the timeliness and information gap for inquiries and increasing the credibility of driver records. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 The diagram shows the architecture of the blockchain used in the driving record authentication method provided in the preferred embodiment of this application.

[0020] Figure 2 The diagram shown illustrates the application environment for driving record authentication provided by a preferred embodiment of this application.

[0021] Figure 3 The diagram shown is a structural schematic of a vehicle provided in a preferred embodiment of this application.

[0022] Figure 4 The diagram shown is a structural schematic of an electronic device provided in a preferred embodiment of this application.

[0023] Figure 5 The diagram shown is a functional block diagram of the driving record authentication system provided in a preferred embodiment of this application.

[0024] Figure 6 The diagram shown is a flowchart of the user account registration process for the driving record authentication method provided in the preferred embodiment of this application.

[0025] Figure 7 The diagram shown is a flowchart of the user login process for the driving record authentication method provided in the preferred embodiment of this application.

[0026] Figure 8 The diagram shown is a flowchart of the driving record authentication method provided in a preferred embodiment of this application.

[0027] Figure 9 Examples of various image labels are provided.

[0028] Figure 10 Here is an example of an NFT image.

[0029] Figure 11 Here is another example of an NFT image.

[0030] Figure 12 Here is an example illustrating the process of performing driving record authentication.

[0031] The following detailed description, in conjunction with the accompanying drawings, will further illustrate this application. Detailed Implementation

[0032] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0033] Numerous specific details are set forth in the following description to provide a thorough understanding of this application. The described embodiments are merely some, not all, of the embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0035] Please see Figure 1 The diagram shown is a schematic representation of the blockchain architecture used in the driving record authentication method provided in the preferred embodiment of this application.

[0036] The driving record authentication method in this application is applied to an electronic device 1, which establishes communication connections with multiple other electronic devices 1 via a network. A blockchain 2 is formed among these multiple electronic devices 1, with each electronic device 1 being a node on the blockchain 2. The network can be a wired network or a wireless network, such as radio, Wireless Fidelity (WIFI), or cellular.

[0037] Each electronic device 1 can be a device equipped with a driving record authentication system, such as a personal computer, a server, etc., wherein the server can be a single server, a server cluster, or a cloud server, etc.

[0038] Please see Figure 2 The diagram shows the application environment of a preferred embodiment of the driving record authentication method of this application. In this embodiment, the driver 4 of the vehicle 3 can first register a user account on the blockchain 2 through the vehicle 3 or the user terminal 5, thereby linking the user account with the identification information of the vehicle 3 (e.g., license plate number and / or engine number). The vehicle 3 can be a vehicle, ship, aircraft, or other equipment. The user terminal 5 can be a mobile phone, tablet computer, server, or other equipment. The vehicle 3 can upload its real-time driving records during the driving process to the electronic device 1, thereby using the blockchain to store the driving records of the vehicle 3, ensuring that the driving records cannot be tampered with and guaranteeing their authenticity. In addition, the smart contract program in the electronic device 1 (e.g., the driving record authentication system mentioned in the context of this application) can obtain the driver 4's level based on the driving records within a preset time period, and mint a non-fungible token (NFT) image corresponding to the driver 4's level. When a query request is received from the user terminal, the driver's NFT image is sent to the user terminal. Specific details will be described later.

[0039] Please see Figure 3 The diagram shown is a structural schematic of a vehicle provided in a preferred embodiment of this application.

[0040] The vehicle 3 includes, but is not limited to, a processor 401, and a vehicle speed detection device 402, a distance detection device 403, an in-vehicle camera device 404, a seat presence detection device 405, a positioning device 406, a seat belt detection device 407, an acceleration detection device 408, an external camera device 409, and a wireless communication device 414, all of which are communicatively connected to the processor 401. The vehicle 3 also includes a traffic light recognition module 410, a face recognition module 411, an electronic map module 412, and a driver status analysis module 413.

[0041] In one embodiment, the vehicle speed detection device 402 may be a vehicle speed sensor for sensing the speed of the vehicle 3. The distance detection device 403 may be used to detect the distance between the vehicle 3 and surrounding vehicles and / or objects. The in-vehicle camera device 404 may be a camera for capturing images of the interior of the vehicle 3, thereby allowing the vehicle 3 to obtain images of the driver 4 and passengers. The presence detection device 405 may be used to detect whether the driver 4 of the vehicle 3 is in the driver's seat.

[0042] In one embodiment, the positioning device 406 can locate the real-time position of the vehicle 3. The positioning device 406 can be one or a combination of multiple of the following: Global Positioning System (GPS), Assisted Global Positioning System (AGPS), BeiDou Navigation Satellite System (BDS), GLONASS, and other wireless communication devices.

[0043] In one embodiment, the seatbelt detection device 407 is used to detect the seatbelt usage status, for example, whether the driver 4 of the vehicle 3 is wearing a seatbelt. The acceleration detection device 408 is used to detect the acceleration of the vehicle 3. The external camera device 409 can be a camera used to capture images of the scene in front of or around the vehicle 3 in the direction of travel. The traffic light recognition module 410, the face recognition module 411, the electronic map module 412, and the driver status analysis module 413 can be software modules stored in the memory 415 of the vehicle 3. The traffic light recognition module 410 can identify traffic lights (e.g., red, yellow, or green lights), traffic signs (e.g., forward signs, turn signs, U-turn signs, speed limit signs, etc.), and road markings in front of the vehicle 3 in the direction of travel based on the images captured by the external camera device 409. The face recognition module 411 can identify the driver's facial information based on the image of the driver captured by the in-vehicle camera device 404. The electronic map module 412 can be a preset electronic map, such as Google Maps or Baidu Maps. The processor 401 can obtain traffic rules (e.g., going straight, turning, or making a U-turn) and traffic information (e.g., traffic congestion, average speed, and whether there are nearby traffic accidents) of the current location of the vehicle 3 based on the positioning device 406 and the electronic map module 412. The driver state analysis module 413 can analyze the driver 4's mental state based on the image of the driver 4 captured by the in-vehicle camera device 404, such as identifying whether the driver 4 is currently driving while fatigued, or whether there are irregular behaviors such as using a mobile phone or smoking.

[0044] In one embodiment, the processor 401 can transmit data from the vehicle speed detection device 402, distance detection device 403, in-vehicle camera device 404, seat presence detection device 405, positioning device 406, seat belt detection device 407, acceleration detection device 408, external camera device 409, traffic light recognition module 410, face recognition module 411, electronic map module 412, and driver status analysis module 413 to the electronic device 1 via the wireless communication device 414.

[0045] Please see Figure 4 The diagram shown is a structural schematic of a preferred embodiment of the electronic device of this application.

[0046] The electronic device 1 includes, but is not limited to, a processor 10, a memory 20, and a computer program 30 stored in the memory 20 and executable on the processor 10 (e.g., ...). Figure 5The driving record authentication system 100 shown is described. When the processor 10 executes the computer program 30, it implements the steps in the driving record authentication method, for example... Figure 6 , Figure 6 , Figure 8 The various method steps are shown. Alternatively, when the processor 10 executes the computer program 30, it implements the functions of each module / unit in the driving record authentication system, for example... Figure 5 Modules 101-103 in the document.

[0047] For example, the computer program 30 can be divided into one or more modules / units, which are stored in the memory 20 and executed by the processor 10 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, the instruction segments describing the execution process of the computer program 30 in the electronic device 1. For example, the computer program 30 can be divided into... Figure 5 The system includes a registration module 101, a response module 102, and an execution module 103. For the specific functions of each module, please refer to the functions of each module in the embodiment of the driving record authentication system.

[0048] Those skilled in the art will understand that the schematic diagram is merely an example of electronic device 1 and does not constitute a limitation on electronic device 1. It may include more or fewer components than shown in the diagram, or combine certain components, or different components. For example, electronic device 1 may also include input / output devices, network access devices, buses, etc.

[0049] The processor 10 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor 10 may be any conventional processor. The processor 10 is the control center of the electronic device 1, connecting all parts of the electronic device 1 via various interfaces and lines.

[0050] The memory 20 can be used to store the computer program 30 and / or modules / units. The processor 10 implements various functions of the electronic device 1 by running or executing the computer program and / or modules / units stored in the memory 20 and calling the data stored in the memory 20. The memory 20 may mainly include a program storage area and a data storage area. The program storage area may store the operating device and the application program required for at least one function (such as sound playback function, image playback function, etc.). The data storage area may store data created according to the use of the electronic device 1 (such as audio data, telephone book, etc.). In addition, the memory 20 may include high-speed random access memory and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0051] Please see Figure 5 The diagram shown is a functional block diagram of a preferred embodiment of the driving record authentication system of this application.

[0052] In some embodiments, the driving record authentication system 100 runs in the electronic device 1. The driving record authentication system 100 may include multiple functional modules composed of program code segments. The program code of each program segment in the driving record authentication system 100 may be stored in the memory 20 of the electronic device 1 and executed by the at least one processor 10 to implement the driving record authentication function.

[0053] In this embodiment, the driving record authentication system 100 can be divided into multiple functional modules according to the functions it performs. (See also...) Figure 5 As shown, the functional modules may include a registration module 101, a response module 102, and an execution module 103. The term "module" in this application refers to a series of computer program segments that can be executed by at least one processor and perform a fixed function, and which are stored in the memory 20. It is understood that in other embodiments, the above-mentioned modules may also be program instructions or firmware embedded in the processor 10.

[0054] The following is combined Figure 6 , Figure 7 , Figure 8 Let's introduce the functions of each module.

[0055] Please see Figure 6The diagram shown is a flowchart of the user account registration process for the driving record authentication method provided in this application. Depending on different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0056] In step S601, the registration module 101 receives a registration request associated with the driver 4. The registration request carries the personal information of the driver 4, including the driver 4's e-wallet address, identity information, and vehicle 3 identification information.

[0057] In one embodiment, the driver 4 can send the registration request through the user terminal 3. The driver 4's identity information can be biometric information such as facial features or fingerprints that can be used to uniquely authenticate the driver 4. The driver 4's identity information may also include the driver 4's name, ID card number, telephone number, etc.

[0058] In one embodiment, the identification information of the vehicle 3 may be an identification number that can be used to uniquely authenticate the vehicle 3, such as the license plate number or engine number of the vehicle 3.

[0059] In step S602, the registration module 101 responds to the registration request, assigns a user account to the driver 4, and stores the driver 4's personal information.

[0060] In one embodiment, the registration module 101 uses the driver 4's e-wallet address as the user account.

[0061] In one embodiment, the registration module 101 associates and binds the driver 4's e-wallet address, identity information, and vehicle 3 identification information, and stores the associated driver 4's e-wallet address, identity information, and vehicle 3 identification information in the memory 20 of the electronic device 1.

[0062] Please see Figure 7 The diagram shown is a flowchart of the user login process for the driving record authentication method provided in this application. Depending on different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0063] In step S701, the receiving module 102 receives a login request associated with the driver 4.

[0064] In one embodiment, when the presence detection device 405 detects that the driver 3 is in the driving position, the processor 401 of the vehicle 3 may send the login request to the electronic device via the wireless communication device 414 of the vehicle 3. The login request carries the personal information of the driver 4.

[0065] In one embodiment, the processor 401 of the vehicle 3 can send a prompt message to the user terminal 5 via the wireless communication device 414 when it detects that the driver 4 is in the driving position. The user terminal 5 can respond to the input of the driver 4 and send the driver 4's personal information to the vehicle 3. The processor 401 can then generate a login request based on the driver 4's personal information and send the login request to the electronic device 1 via the wireless communication device 414.

[0066] In one embodiment, when the driver 4's identity information is the driver 4's facial information, the processor 401 can control the in-vehicle camera 404 to capture an image of the driver 4 when the presence detection device 405 detects that the driver 4 is in the driving position. The processor 401 can execute the facial recognition module 411 to recognize the image of the driver 4 and obtain the driver 4's facial information. In other embodiments, the processor 401 can receive input from the driver 4 to obtain the driver's personal information.

[0067] In step S702, the execution module 103 determines whether the login request is a valid request. If the login request is determined to be invalid, step S703 is executed. If the login request is determined to be valid, step S704 is executed.

[0068] In one embodiment, when the personal information carried in the login request is consistent with the pre-stored personal information of the driver 4, the execution module 103 determines that the login request is a valid request.

[0069] In step S703, when it is determined that the login request is invalid, the execution module 103 does not receive the driving record sent by the vehicle 3, and sends a login failure message to the vehicle 3, such as indicating that the login is invalid.

[0070] In step S704, when the login request is determined to be a valid request, the receiving module 102 receives the driving record sent by the vehicle 3, associates the driving record with the current time, and stores the driving record according to the personal information of the driver 4.

[0071] In one embodiment, the receiving module 102 can send a successful login message to the vehicle 3 upon determining that the login request is valid. Upon receiving the successful login message, the processor 401 of the vehicle 3 can transmit real-time data from the vehicle speed detection device 402, distance detection device 403, in-vehicle camera device 404, seat presence detection device 405, positioning device 406, seatbelt detection device 407, acceleration detection device 408, external camera device 409, traffic light recognition module 410, face recognition module 411, electronic map module 412, and driver status analysis module 413 to the electronic device 1 via the wireless communication module 414. That is, upon receiving the successful login message, the processor 401 can send the real-time driving record of the vehicle 3 to the electronic device 1, thereby allowing the response module 102 to receive the driving record of the vehicle 3 sent by the vehicle 3.

[0072] In one embodiment, the driving record of the vehicle 3 includes, but is not limited to: the vehicle speed, acceleration, position, traffic rules corresponding to the position, the distance between the vehicle 3 and surrounding vehicles and / or objects, the seat belt usage status of the driver 4 of the vehicle 3, the mental state of the driver 4, and the traffic lights ahead of the vehicle 3.

[0073] In one embodiment, the receiving module 102 stores the driving record of the vehicle 3 based on the personal information of the driver 4, that is, the receiving module 102 stores the driving record after associating it with the personal information of the driver 4.

[0074] Please see Figure 8 The diagram shown is a flowchart of a driving record authentication method provided in a preferred embodiment of this application. Depending on different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0075] In step S801, the execution module 103 periodically (e.g., on the 10th of each month or once every two weeks) obtains the historical driving records of the driver 4 and the associated records of the driver 4.

[0076] It is understood that this step can be performed at any time after steps S701-S704.

[0077] In one embodiment, the driver 4's historical driving record includes the driver 4's driving record within a preset time period. The driving record includes, but is limited to: the vehicle speed, acceleration, and position of the vehicle 3, the traffic rules corresponding to that position, the distance between the vehicle 3 and surrounding vehicles and / or objects, the seat belt usage status of the driver 4 of the vehicle 3, the driver 4's mental state, and the traffic lights ahead of the vehicle 3.

[0078] In one embodiment, the driving records within the preset time period may be the driving records of driver 4 within the most recent year or month. In other embodiments, the driving records within the preset time period may include all driving records of driver 4.

[0079] The associated records of driver 4 include, but are not limited to: accident records (traffic accident records, criminal records), the time of obtaining the driver's license, the current status of the driver's license (i.e., whether the driver's license is valid), mileage records, passenger evaluation records (including positive and negative reviews) of driver 4 on preset platforms (such as Uber, Meituan Taxi, etc.), and recommendation and reward records.

[0080] In one embodiment, the execution module 103 can obtain the driver's associated records from, for example, a public account or a website address.

[0081] In step S802, the execution module 103 casts the non-fungible token (NFT) image of the driver 4 based on the historical driving records and associated records.

[0082] In this embodiment, the NFT protocol (and the way it connects to the blockchain) can use either the ERC-721 or ERC-1155 protocol. It should be noted that the ERC-721 and ERC-1155 protocols are smart contract protocols used on the Ethereum blockchain.

[0083] In the first embodiment, the step of minting the non-fungible token (NFT) image of the driver 4 based on the historical driving records and associated records includes: analyzing the acquired historical driving records and associated records to obtain the analysis result of each record in the historical driving records and associated records; obtaining multiple behavioral records of the driver 4 based on the analysis result of each record; creating corresponding image badges for each of the multiple behavioral records to obtain multiple image badges; and minting the NFT image of the driver 4 based on the multiple image badges.

[0084] In one embodiment, the multiple behavioral records include, but are not limited to, no accident records, no violation driving records, no dangerous driving records, positive review rate, and recommendation reward records.

[0085] In one embodiment, the analysis of the historical driving records and related records obtained by the analysis includes determining whether the acceleration is within a preset threshold value from the historical driving records.

[0086] Determine whether the driver 4 is wearing a seatbelt from the historical driving records;

[0087] From the historical driving records, determine whether the distance between the vehicle 3 and surrounding vehicles and / or objects is within a preset distance;

[0088] Based on the vehicle speed and location information from the historical driving records, it is determined whether the vehicle 3 is speeding;

[0089] Based on the vehicle speed and continuous position information from the historical driving records, it is determined whether the vehicle 3 made an illegal turn;

[0090] Based on the vehicle speed and continuous location information from the historical driving records, it is determined whether the vehicle 3 is illegally parked;

[0091] Based on the vehicle speed and location information and traffic light data from the historical driving records, it is determined whether the vehicle 3 violated any regulations.

[0092] Based on the historical driving records and the image of driver 4, it is determined whether driver 4 has committed a traffic safety violation.

[0093] From the associated records, determine whether the driver's license of driver 4 is valid;

[0094] From the associated records, determine whether driver 4 has an accident record;

[0095] From the associated records, determine the positive review rate of driver 4 (i.e., the ratio of positive reviews to all reviews); and

[0096] From the associated records, determine whether the driver 4 has a recommended tipping record.

[0097] In one embodiment, the execution module 103 further calculates the number of years that the driver 4 has achieved each of the multiple behavioral records based on the driver 4's driver's license issuance date. For example, the execution module 103 calculates that the driver 4 has achieved one year without an accident record, one year without a violation driving record, one year without a dangerous driving record, one year with an 80% positive review rate, and one year with a recommendation and reward record.

[0098] In one embodiment, the image tag may refer to an image including graphics. In one embodiment, the image tags created by the execution module 103 for each behavior record may be the same or different in size.

[0099] For example, see Figure 9 As shown, the execution module 103 generates image badges 91-95 corresponding to the number of years that the driver 4 has achieved each of the multiple behavioral records (i.e., no accident records, no traffic violation records, no dangerous driving records, positive review rate, and recommendation reward records). See also Figure 10 As shown, the execution module 103 casts an NFT image 900 of the driver 4 based on the plurality of image stamps. In one embodiment, the execution module 103 casts an NFT image of the driver 4 based on the plurality of image stamps, the current time, and the driver 4's personal information.

[0100] In one embodiment, the NFT image of driver 4 includes the time at which the NFT image was generated (e.g., Figure 10 The name of the driver 4 (e.g., 20220714 shown) Figure 10 The identification information of the driver 4 and the vehicle 3 being driven (e.g., JOHN DOE) and the face, as well as the vehicle identification information of the driver 4. Figure 10 The above refers to ABC-1234 and the multiple behavioral records. In the second embodiment, the minting of the non-fungible token (NFT) image of the driver 4 based on the historical driving records and associated records includes: analyzing the acquired historical driving records and associated records to obtain the analysis result of each record in the historical driving records and associated records; obtaining multiple behavioral records of the driver based on the analysis result of each record; creating corresponding NFT image badges for each of the multiple behavioral records to obtain multiple NFT image badges; and using the multiple NFT image badges as the driver's NFT image.

[0101] In the third embodiment, taking the historical driving record including the driver's historical location trajectory information and the associated record including the driver's driver's license status information and mileage records as an example, the process of minting the driver's non-fungible token (NFT) image based on the historical driving record and associated record includes: obtaining the percentage ranking of the driver's mileage records based on the driver's historical location trajectory information and the historical location trajectory information of each other driver, wherein the percentage ranking of the driver's mileage records includes the percentage ranking of the driver's total mileage, the percentage ranking of the driver's first driving area distribution, and the percentage ranking of the driver's second driving area distribution; and minting an NFT image corresponding to the percentage ranking of the driver's total mileage, and displaying the percentage ranking of the driver's mileage records and the driver's driver's license status information on the NFT image.

[0102] In one embodiment, the driver's license status information includes the issuance date and validity period of each of the driver's licenses. For example, the driver's license status information includes: the passenger car driver's license has been valid since October 10, 2001; the large bus driver's license was valid from November 11, 2005 to June 29, 2010; invalid from June 30, 2010 to October 10, 2018; and valid from October 11, 2018 to the present.

[0103] In one embodiment, the percentage ranking of the driver's first driving area distribution includes: the percentage ranking of mileage in urban areas, the percentage ranking of mileage in suburban areas, and the percentage ranking of mileage in mountainous areas; the percentage ranking of the driver's second driving area distribution includes: the percentage ranking of mileage corresponding to each municipal administrative region.

[0104] For example, based on the driver's historical location trajectory information and the historical location trajectory information of each of the other drivers, the analysis yields the driver's total mileage of 83,000 km, corresponding to a percentage ranking of 80%. The analysis also reveals that the driver's mileage in urban areas is 30,000 km (90% percentage ranking), in suburban areas is 50,000 km (50% percentage ranking), and in mountainous areas is 3,000 km (10% percentage ranking). The driver's mileage records in each municipal administrative region include: 50,000 km in Taipei, 30,000 km in Taoyuan, and 3,000 km in Taichung. The percentage ranking of the driver's mileage in each municipal administrative region is: 80% in Taipei, 40% in Taoyuan, and 10% in Taichung.

[0105] When the percentage ranking of total mileage is greater than or equal to 80%, the execution module 103 casts a first NFT image and displays the percentage ranking of the driver's mileage record and the driver's license status information in the first NFT image.

[0106] Similarly, when the percentage ranking of total mileage is between 60% and 80%, the execution module 103 casts a second NFT image, displaying the driver's mileage record percentage ranking and the driver's license status information. When the percentage ranking of total mileage is between 40% and 60%, the execution module 103 casts a third NFT image, displaying the driver's mileage record percentage ranking and the driver's license status information. When the percentage ranking of total mileage is between 20% and 40%, the execution module 103 casts a fourth NFT image, displaying the driver's mileage record percentage ranking and the driver's license status information. When the percentage ranking of total mileage is between 0% and 20%, the execution module 103 casts a fifth NFT image, displaying the driver's mileage record percentage ranking and the driver's license status information.

[0107] In the fourth embodiment, the process of minting the non-fungible token NFT image of the driver 4 based on the historical driving records and associated records includes: determining the level of the driver 4 based on the historical driving records and associated records; and generating a non-fungible token NFT image for the driver 4 corresponding to the level.

[0108] In one embodiment, the execution module 103 can predefine the driver 4's levels as: Level 1, Level 2, and Level 3, with Level 1 being the highest, Level 2 the next highest, and Level 3 the lowest. Of course, in other embodiments, the levels can be classified in other ways.

[0109] In one embodiment, determining the driver 4's level based on the acquired historical driving records and associated records includes:

[0110] The pre-trained driver level 4 recognition model is invoked to identify the driver level 4 based on the acquired historical driving records and related records.

[0111] In one embodiment, the method by which the execution module 103 obtains the driver-level recognition model includes:

[0112] Obtain a preset number of sample data corresponding to different levels. Each sample data includes driving records and associated records. Label the sample data corresponding to each level with a category so that the sample data corresponding to each level carries a category label. Use the preset number of sample data corresponding to different levels after category labeling as training samples.

[0113] The training samples are randomly divided into a training set with a first preset ratio and a validation set with a second preset ratio. A deep neural network is trained using the training set to obtain the driver-level recognition model, and the accuracy of the driver-level recognition model is verified using the validation set.

[0114] If the accuracy is greater than or equal to a preset accuracy, training ends; if the accuracy is less than the preset accuracy, the number of training samples is increased and the deep neural network is retrained until the accuracy of the driver-level recognition model is obtained again, which is greater than or equal to the preset accuracy.

[0115] In one embodiment, casting an NFT image corresponding to the driver 4 for the level includes: casting corresponding NFT image templates for different levels; when the level of the driver 4 is determined, calling the NFT image template corresponding to the level of the driver 4, and casting an NFT image of the driver 4 based on the current time and the personal information of the driver 4.

[0116] like Figure 11 As shown, in one embodiment, the NFT image may include, but is not limited to: the driver 4's name, level, valid expiration date of driver's license, identification information of the vehicle 3 driven by the driver 4, passenger rating records and recommendation reward records for the driver 4 on a preset platform (e.g., Uber, Meituan Taxi).

[0117] In other embodiments, determining the driver's level based on the acquired driving records and associated records includes: analyzing the acquired historical driving records and associated records to obtain the analysis results for each record; quantifying the analysis results for each record to obtain quantitative data; and determining the driver's level based on the quantitative data.

[0118] In one embodiment, the execution module 103 can quantify the analysis result of each record by assigning different scores to different analysis results. For example, a first score can be assigned to acceleration exceeding a threshold, and a second score higher than the first score can be assigned to acceleration not exceeding the threshold. Similarly, a third score can be assigned to using a seatbelt, and a fourth score lower than the third score can be assigned to not using a seatbelt. In this manner, the execution module 103 can assign scores to other analysis results separately, thereby achieving data quantification.

[0119] In one embodiment, the execution module 103 can predetermine different scores corresponding to different levels. The execution module 103 calculates the average value of the quantified data, and the level of the driver 4 can be determined based on this average value.

[0120] In one embodiment, the execution module 103 also stores the NFT image and obtains a link to the NFT image. The link to the NFT image is also the storage location of the NFT image in the blockchain 2. The execution module 103 can also generate a QR code corresponding to the link to the NFT image. In step S803, in response to a query request for the driver 4, the execution module 103 transmits the NFT image and the link to the NFT image to the user terminal 5 that sent the query request.

[0121] In other embodiments, the execution module 103 may also respond to a query request sent by the vehicle 3 to the driver 4 by transmitting the NFT image and a link to the NFT image to the vehicle 3 that sent the query request.

[0122] For example, when this application is applied to a ride-hailing platform, if a passenger needs to know about the driver 4 in advance, they can request the driver 4 to provide an NFT image. The driver 4 can then send a query request to the electronic device 1 via a user terminal. This query request can include the driver 4's identity information, vehicle 3 identification information, user account, etc. Upon receiving the query request, the execution module 103 can send an NFT image showing the overall situation of the driver 4 driving the vehicle 3 to the user terminal 5 or the vehicle 3.

[0123] In other embodiments, the user terminal may also refer to another user's terminal. For example, a passenger may send the query request through a personal terminal such as a mobile phone and obtain the driver's NFT image, the link corresponding to the NFT image, and / or the QR code of the link corresponding to the NFT image.

[0124] It should be noted that in this embodiment, the execution module 103 also sends the link of the NFT image to the query terminal, such as the user terminal 5 or the vehicle 3, so that the query terminal or other terminal can access the blockchain 2 through the link to obtain the NFT image of the driver 4, and can compare the NFT image of the driver 4 obtained from the blockchain 2 with the NFT image provided by the driver 4 through the user terminal 5 / vehicle 3 to determine whether the NFT image provided by the driver 4 is real, etc.

[0125] To clearly understand this application, Figure 12 The following example illustrates the process of driving record authentication. As shown in Figure 12, vehicle 3 uploads real-time driving records of driver 4 while driving vehicle 3 to the blockchain. The blockchain's smart contract periodically generates NFT images of driver 4 based on historical driving records. Users / drivers 4 can query driver 4's NFT images through terminals such as mobile phones.

[0126] In other embodiments, the execution module 103 may also perform corresponding restrictive measures on the driver 4 based on the NFT image of the driver 4.

[0127] In one embodiment, the restrictive measures include, but are not limited to, limiting or restricting the area or mode of travel that the driver 4 can operate in.

[0128] In one embodiment, the execution module 103 can restrict or limit the driver 4's drivable area and / or driving mode based on the type of the vehicle 4 driven by the driver 4 (e.g., whether the vehicle 4 is two-wheeled or four-wheeled, and whether the vehicle 4 is driven by two-wheel drive or four-wheel drive), the current status of the driver 4's driver's license (e.g., it is valid and includes accident records), the historical driving records, and road condition information obtained from electronic maps (e.g., Baidu Maps, Google Maps, etc.), thereby realizing traffic flow management.

[0129] For example, if the percentage of driving mileage recorded by driver 4 in mountainous areas is less than or equal to 20%, then driver 4 is restricted from driving in mountainous areas.

[0130] For example, it restricts driver 4 to driving only four-wheeled vehicles on highways, and restricts driver 4 with a mileage of less than 1,000 km from driving vehicles on highway sections.

[0131] For example, driver 4 is restricted to driving only four-wheeled vehicles on highways, and if driver 4's mileage does not exceed 5000km, the speed limit is 80km / h. If the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0132] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by the same unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A driving record authentication method, applied to an electronic device acting as a node in a blockchain, characterized in that, The method includes: Receive a login request associated with the driver, the login request being generated and sent by the vehicle in response to the presence detection device detecting that the driver is in the driving position; In response to the login request being valid, the vehicle sends its driving record. Periodically obtain the driver's historical driving records, which consist of the driving records, and obtain the driver's associated records; Based on the historical driving records and the associated records, a non-fungible token (NFT) image of the driver is minted on the blockchain, the NFT image containing the analysis results of the driver's historical driving records; In response to a query request for the driver, the driver's NFT image is transmitted to the user terminal that sent the query request; and Based on the NFT image, appropriate restrictions are imposed on the driver.

2. The driving record authentication method as described in claim 1, characterized in that, The process of minting the driver's non-fungible token (NFT) image on the blockchain based on the historical driving records and the associated records includes: The analysis results of each record in the historical driving records and the associated records are obtained by analyzing the historical driving records and the associated records, and multiple behavioral records of the driver are obtained based on the analysis results of each record; For each of the aforementioned behavioral records, corresponding image stamps are created to obtain multiple image stamps; and The driver's NFT image is cast based on the multiple image stamps.

3. The driving record authentication method as described in claim 1, characterized in that, The process of minting the driver's non-fungible token (NFT) image on the blockchain based on the historical driving records and the associated records includes: The analysis results of each record in the historical driving records and the associated records are obtained by analyzing the historical driving records and the associated records, and multiple behavioral records of the driver are obtained based on the analysis results of each record; For each of the aforementioned behavioral records, a corresponding NFT image stamp is created, resulting in multiple NFT image stamps; and The multiple NFT image stamps are used as the driver's NFT image.

4. The driving record authentication method as described in claim 1, characterized in that, The historical driving records include the driver's historical location trajectory information, and the associated records include the driver's driver's license status information; the process of minting the driver's non-fungible token (NFT) image on the blockchain based on the historical driving records and the associated records includes: Based on the driver's historical location trajectory information and the historical location trajectory information of each of the other drivers, the percentage ranking of the driver's mileage records is obtained. The percentage ranking of the driver's mileage records includes the percentage ranking of the driver's total mileage, the percentage ranking of the driver's first driving area distribution, and the percentage ranking of the driver's second driving area distribution. Cast an NFT image corresponding to the percentage ranking of the driver's total mileage, and display the percentage ranking of the driver's mileage records and the driver's license status information in the NFT image.

5. The driving record authentication method as described in claim 4, characterized in that, The percentage ranking of the driver's first driving area distribution includes: the percentage ranking of mileage in urban areas, the percentage ranking of mileage in suburban areas, and the percentage ranking of mileage in mountainous areas; the percentage ranking of the driver's second driving area distribution includes: the percentage ranking of mileage corresponding to each municipal administrative region.

6. The driving record authentication method as described in claim 1, characterized in that, Before obtaining the driver's historical driving records, the method further includes: Receive a registration request associated with the driver, the registration request carrying the driver's personal information, including the driver's e-wallet address and identity information, as well as the identification information of the vehicle driven by the driver; In response to the registration request, a user account is assigned to the driver and the driver's personal information is stored, wherein the user account is the e-wallet address.

7. The driving record authentication method as described in claim 1, characterized in that, The method also includes: When the login request is determined to be a valid request, the received driving record is associated with the current time, and the driving record is stored according to the driver's personal information; The method further includes: when it is determined that the login request is invalid, not receiving the driving record and issuing feedback information.

8. An electronic device, characterized in that, The electronic device, acting as a node in the blockchain, includes: Processor; and A memory containing a plurality of program modules, which are loaded and executed by the processor according to any one of claims 1 to 7.

9. A computer-readable storage medium having stored thereon at least one computer instruction, characterized in that, The instructions are loaded by the processor and executed as described in any one of claims 1 to 7 regarding the driving record authentication method.

10. A means of transportation, characterized in that, The vehicle is communicatively connected to an electronic device that acts as a blockchain node, and the vehicle includes: processor; On-site detection equipment; Wireless communication equipment; The processor is configured to send a login request to the electronic device via the wireless communication device when the presence detection device detects that the driver is in the driving position. The processor is further configured to: upon receiving feedback information sent by the electronic device based on the login request, send the vehicle's driving record to the electronic device, so that the electronic device can periodically mint the driver's non-fungible token (NFT) image on the blockchain based on the driver's historical driving record and the driver's associated records, and implement corresponding restrictive measures on the driver based on the NFT image.

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