Code scanning payment method

Abstract

The invention discloses a code scanning payment method, and relates to the technical field of mobile payment, and the method comprises the steps: receiving an image frame transmitted by a glasses end through a vehicle terminal; marking the two-dimensional code to be decoded in the image frame, and transmitting the marked image frame to a display module; after a two-dimensional code selection instruction input for the displayed image frame is received, determining a target two-dimensional code, and stacking the target two-dimensional code to a preset scanning area; and decoding the target two-dimensional code in the preset scanning area to obtain the to-be-verified payment information, and calling a payment application for payment after receiving a payment confirmation instruction. According to the invention, a user can visually observe and accurately select the target two-dimensional code through the vehicle-mounted terminal display module in a multi-two-dimensional code scene, the risk of scanning irrelevant codes by mistake is avoided, and the visual feedback defect of a glasses terminal without a light machine display module is made up by using the interactive display capability of the vehicle-mounted terminal. The accuracy and efficiency of code scanning payment through the AI glasses in the driving scene are improved.

Description

Technical Field

[0001] This application relates to the field of mobile payment technology, and in particular to a QR code payment method. Background Technology

[0002] With the widespread application of AI (Artificial Intelligence) glasses, scanning QR codes for payment via AI glasses has become a common need in driving scenarios such as parking fees and toll fees. However, existing AI glasses scanning technology still has significant limitations in practical applications. On the one hand, most AI glasses, for the sake of lightweight design, are not equipped with an optomechanical display module, making it impossible to present the visual information of the QR code image captured by the camera to the user in real time. This makes it difficult for users to directly observe and locate the QR code target during the scanning process. On the other hand, in complex scenarios where multiple QR codes appear simultaneously, such as when multiple parking space payment codes are side by side in a parking lot, existing recognition systems can usually only automatically recognize a single QR code, thus increasing the risk of accidentally scanning irrelevant codes. Therefore, in driving environments, users find it difficult to quickly and accurately complete the recognition and payment of a specified QR code in dynamic scenarios, seriously affecting the efficiency of users using AI glasses for QR code payment. Summary of the Invention

[0003] The main purpose of this application is to provide a QR code payment method, which aims to solve the technical problem of low efficiency in QR code payment through AI glasses in current driving scenarios.

[0004] To achieve the above objectives, this application proposes a QR code payment method applied to an in-vehicle infotainment system, the QR code payment method comprising: Receive image frames transmitted from the glasses, wherein the image frames are captured by the glasses upon receiving an image frame acquisition command; The identified QR code to be decoded in the image frame is labeled, and the labeled image frame is transmitted to the display module for display. After receiving a QR code selection instruction input to the displayed image frame, the target QR code in the QR code to be decoded is determined according to the QR code selection instruction, and the target QR code is enlarged to the preset scanning area of ​​the display module; The target QR code in the preset scanning area is decoded to obtain the payment information to be verified, and after receiving the payment confirmation instruction for the payment information to be verified, the payment application is invoked to make the payment.

[0005] In one embodiment, the step of decoding the target QR code in the preset scanning area to obtain the payment information to be verified includes: Decode the target QR code in the preset scanning area; If the target QR code is successfully decoded, the payment information to be verified is obtained; If the target QR code fails to be decoded, the shooting angle of the target QR code is identified, a perspective adjustment prompt is generated based on the shooting angle, and the perspective adjustment prompt is output to the glasses and / or the display module to prompt adjustment of the shooting angle of the image frame.

[0006] In one embodiment, the viewing angle adjustment prompt information includes viewing angle adjustment audio prompt information and / or viewing angle adjustment visual prompt information. The step of generating viewing angle adjustment prompt information based on the shooting angle and outputting the viewing angle adjustment prompt information to the glasses and / or the display module includes: An audio prompt message for adjusting the viewing angle is generated based on the shooting angle, and the audio prompt message for adjusting the viewing angle is output to the glasses and / or the display module; and / or Based on the shooting angle, a perspective adjustment visual prompt is generated, and the perspective adjustment visual prompt is output to the display module.

[0007] In one embodiment, after the QR code to be decoded marked on the display module is touched, a QR code selection instruction is generated. Before the step of determining the target QR code among the QR codes to be decoded according to the QR code selection instruction, the method further includes: Monitor the duration of continuous input of the QR code selection command; If the duration of continuous input exceeds a preset duration threshold, the QR code selection instruction is determined to be a selection instruction to be determined, and a confirmation prompt message is generated and output to the display module according to the QR code selection instruction. Upon receiving the selection confirmation instruction input into the displayed confirmation prompt information, the QR code selection instruction is determined to be a valid selection instruction, and the step of determining the target QR code in the QR code to be decoded is executed based on the QR code selection instruction.

[0008] In one embodiment, after the step of receiving the image frame transmitted from the glasses, the method further includes: A preset QR code detection algorithm is invoked to detect QR codes in the image frame and identify the QR codes to be decoded in the image frame. The preset QR code detection algorithm includes a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner point refinement algorithm, and a decoding verification algorithm executed sequentially.

[0009] In one embodiment, the image frame acquisition command includes a video stream acquisition command and an image capture command. Before the step of annotating the identified QR code to be decoded in the image frame, the method further includes: When the image frame is a video stream composed of multiple image frames, the video stream is used as the image frame to perform the step of annotating the QR code to be decoded identified in the image frame based on the video stream, wherein the video stream is captured by the glasses when a video stream acquisition command is received; In the case where the image frame is a captured image consisting of a single image frame, the captured image is used as the image frame to perform the step of annotating the QR code to be decoded identified in the image frame based on the captured image, wherein the captured image is captured by the glasses end upon receiving an image capturing command.

[0010] In one embodiment, prior to the step of using the video stream as the image frame, the method further includes: The system receives viewing angle offset information transmitted from the glasses, wherein the viewing angle offset information is transmitted in association with the video stream, and the viewing angle offset information is determined by the glasses based on the angle difference between the calibrated zero angle and the actual deflection angle, wherein the calibrated zero angle represents the angle at which the glasses face the front of the vehicle when they are worn. When the viewing angle offset information is greater than a preset angle threshold, the target image frame in the video stream is extracted and the target image frame is used as the image frame. The step of annotating the QR code to be decoded in the image frame is performed based on the target image frame. The target image frame is any one of the multiple adjacent image frames in the video stream. The angle change of the viewing angle offset information corresponding to the multiple adjacent image frames is less than the preset angle change threshold, and each of the multiple adjacent images includes a suspected QR code image area.

[0011] Furthermore, to achieve the above objectives, this application also proposes a QR code payment method applied to glasses, the QR code payment method comprising: Receive image frame acquisition instructions, and call the camera module to capture image frames according to the image frame acquisition instructions; The identified QR code to be decoded in the image frame is labeled, and the labeled image frame is transmitted to the display module of the vehicle terminal for display. After receiving the QR code selection instruction input into the displayed image frame, the vehicle terminal determines the target QR code in the QR code to be decoded according to the QR code selection instruction, and enlarges the target QR code to the preset scanning area of ​​the display module for decoding to obtain the payment information to be verified. Upon receiving a payment confirmation instruction for the payment information to be verified, the payment application is invoked to perform the payment.

[0012] In one embodiment, the glasses include a WiFi module and a Bluetooth module. The image frame is transmitted to the vehicle-mounted system via the WiFi module. After the step of transmitting the labeled image frame to the display module of the vehicle-mounted system for display, the system further includes: The system receives a selection confirmation instruction and invokes the Bluetooth module to transmit the selection confirmation instruction to the vehicle-mounted terminal. The selection confirmation instruction is used by the vehicle-mounted terminal to confirm the QR code selection instruction after receiving the QR code selection instruction input for the displayed image frame, so as to perform the step of determining the target QR code in the QR code to be decoded according to the QR code selection instruction on the vehicle-mounted terminal based on the confirmed QR code selection instruction.

[0013] In one embodiment, before the step of annotating the identified QR code to be decoded in the image frame, the method further includes: A preset QR code detection algorithm is invoked to detect QR codes in the image frame and identify the QR codes to be decoded in the image frame. The preset QR code detection algorithm includes a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner point refinement algorithm, and a decoding verification algorithm executed sequentially.

[0014] In one embodiment, the image frame acquisition instruction includes a video stream acquisition instruction, the image frame includes a video stream composed of multiple image frames, and the video stream is captured by the glasses when the video stream acquisition instruction is received; Before the step of annotating the identified QR code to be decoded in the image frame, the method further includes: The viewing angle offset angle information is determined based on the angle difference between the calibrated zero angle and the actual deflection angle, wherein the calibrated zero angle represents the angle at which the glasses end faces the front of the vehicle when it is worn. When the image frame is the video stream and the viewpoint offset angle information is greater than a preset angle threshold, a target image frame is extracted from the video stream and used as the image frame. The step of annotating the QR code to be decoded identified in the image frame is then performed based on the target image frame. The target image frame is any one of multiple adjacent image frames in the video stream. The angle change of the viewpoint offset angle information corresponding to the multiple adjacent image frames is less than a preset angle change threshold, and each of the multiple adjacent image frames includes a suspected QR code image region.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application receives image frames transmitted from the glasses on the vehicle's infotainment system, marks the QR codes to be decoded in the image frames, and displays them on the display module. Then, based on the user's input selection command, it determines the target QR code and enlarges it to the preset scanning area of ​​the display module. Finally, it decodes the target QR code and calls the payment application to complete the payment after confirmation. This enables the transmission of image frames captured by AI glasses to the display module on the vehicle's infotainment system in driving scenarios to provide a visual interaction mechanism. It effectively solves the problem of QR code positioning difficulties caused by the lack of a photoelectric display module in AI glasses and allows users to accurately select the target QR code in multiple QR code scenarios by touching the display module on the vehicle's infotainment system.

[0016] Therefore, this application enables users to intuitively observe and accurately select the target QR code through the vehicle-mounted display module in multi-QR code scenarios, effectively avoiding the risk of accidentally scanning irrelevant codes. At the same time, it utilizes the interactive display capabilities of the vehicle-mounted device to compensate for the visual feedback deficiency of the glasses without an optical display module, significantly improving the accuracy and efficiency of QR code payment through AI glasses in driving scenarios. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the QR code payment method of this application in Implementation Example 1. Figure 2 This is a flowchart illustrating Embodiment 2 of the QR code payment method of this application; Figure 3 This is a flowchart illustrating Embodiment 3 of the QR code payment method of this application; Figure 4 This is a flowchart illustrating Embodiment 4 of the QR code payment method of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the QR code payment method in this application embodiment.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: The vehicle-mounted terminal receives image frames transmitted from the glasses terminal, wherein the image frames are captured by the glasses terminal upon receiving an image frame acquisition command; the identified QR codes to be decoded in the image frames are labeled, and the labeled image frames are transmitted to the display module for display; upon receiving a QR code selection command input to the displayed image frame, the target QR code in the QR code to be decoded is determined according to the QR code selection command, and the target QR code is enlarged to a preset scanning area of ​​the display module; the target QR code in the preset scanning area is decoded to obtain payment information to be verified, and upon receiving a payment confirmation command for the payment information to be verified, the payment application is invoked to perform the payment.

[0024] Existing AI glasses scanning technology still has significant limitations in practical applications. Firstly, most AI glasses, for lightweight considerations, lack an optomechanical display module, making it impossible to present the real-time visual information of the QR code image captured by the camera. This makes it difficult for users to directly observe and locate the QR code target during scanning. Secondly, in complex scenarios where multiple QR codes appear simultaneously, such as when multiple parking space payment codes are side-by-side in a parking lot, existing recognition systems typically can only automatically recognize a single QR code, increasing the risk of mistakenly scanning irrelevant codes. Therefore, in driving environments, users struggle to quickly and accurately complete the recognition and payment of a specific QR code in dynamic scenarios, severely impacting the efficiency of using AI glasses for payment.

[0025] This application provides a solution whereby the vehicle-mounted infotainment system receives image frames transmitted from the glasses, marks the QR codes to be decoded within these frames, and displays them on a display module. Subsequently, based on user input, the target QR code is determined and enlarged to a preset scanning area on the display module. Finally, the target QR code is decoded, and upon confirmation, a payment application is invoked to complete the payment. This enables a visual interaction mechanism by transmitting image frames captured by AI glasses to the vehicle-mounted infotainment system's display module in a driving scenario. This effectively solves the problem of QR code positioning difficulties caused by the lack of a photoelectric display module in AI glasses, and allows users to accurately select target QR codes in multi-QR code scenarios by touching the vehicle-mounted infotainment system's display module. Therefore, this application enables users to intuitively observe and accurately select target QR codes in multi-QR code scenarios through the vehicle-mounted infotainment system's display module, effectively avoiding the risk of accidentally scanning irrelevant codes. Furthermore, the interactive display capabilities of the vehicle-mounted infotainment system compensate for the visual feedback deficiencies of the glasses' lack of a photoelectric display module, significantly improving the accuracy and efficiency of QR code payment via AI glasses in driving scenarios.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or QR code payment system capable of performing the above functions. The following description uses a QR code payment system as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, the embodiments of this application provide a QR code payment method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the QR code payment method of this application.

[0028] In this embodiment, the QR code payment method is applied to the vehicle-mounted terminal, and the QR code payment method includes steps S10~S40: Step S10: Receive image frames transmitted from the glasses, wherein the image frames are captured by the glasses upon receiving an image frame acquisition command; It should be noted that an image frame is static image or dynamic video stream data captured by the glasses' camera module after receiving an image frame acquisition command. This image frame contains visual information about the shooting environment, especially multiple QR code patterns, serving as the basic data unit for subsequent recognition and processing by the vehicle-mounted system. The image frame acquisition command is the control signal that triggers the glasses to start the image acquisition operation. This command can be issued by the user via voice commands (e.g., "Start scanning") or by pressing a physical button on the vehicle-mounted system. Voice commands can be received by the microphone configured on the glasses. In this embodiment, the image frame acquisition command is the initial trigger condition for starting the entire QR code payment process, instructing the glasses to call its camera module to capture an image frame of the current environment and transmit it to the vehicle-mounted system.

[0029] Step S20: Mark the QR code to be decoded identified in the image frame, and transmit the marked image frame to the display module for display; It should be noted that the QR code to be decoded is a QR code image initially detected by the vehicle's recognition processing in the image frame, and has not yet undergone decoding. These QR codes may include one or more candidate codes present in the image frame, requiring further selection and confirmation by the user.

[0030] The display module is a screen component integrated into the vehicle's infotainment system for displaying images and providing a user interface. It can be, but is not limited to, a touchscreen display. This display module receives annotated image frames transmitted from the vehicle's infotainment system and presents them to the user in a visual format, supporting user interaction via touch.

[0031] Understandably, due to lightweight design considerations, AI glasses often lack an optical-mechanical display module, preventing users from directly observing the images captured by the camera. This makes it difficult to quickly locate and perceive multiple QR codes in the environment during the initial scanning phase, hindering effective selection. Therefore, this embodiment analyzes the received image frames in real time on either the vehicle's infotainment system or the glasses themselves, identifying all potential QR codes to be decoded. These QR codes are then annotated using visual elements (such as highlighted frames and numbers). This annotated, semantically rich image frame is then sent to the display module on the vehicle's infotainment system for visualization. This avoids the lack of visual perception caused by the AI ​​glasses' lack of display functionality, allowing users to clearly see all the QR codes captured by the glasses on the vehicle's screen. It transforms the invisible perspective of the glasses into visible, interactive image information on the vehicle's screen, providing the necessary visual foundation and interactive prerequisite for users to make accurate selections later.

[0032] Step S30: After receiving the QR code selection instruction input to the displayed image frame, determine the target QR code in the QR code to be decoded according to the QR code selection instruction, and enlarge the target QR code to the preset scanning area of ​​the display module. It should be noted that the QR code selection command is a command signal generated after the user selects the QR code to be decoded marked in the displayed image frame on the display module through touch or voice operation. This command is used to specify the specific QR code that the user intends to decode, i.e., the target QR code.

[0033] The preset scanning area is a fixed area pre-defined on the display module. It is used to enlarge and display the target QR code after the user selects it, providing a clearer and more stable image for decoding. This area can be, but is not limited to, located in the center of the display module or in a position easily observed by the user.

[0034] Understandably, in scenarios where multiple QR codes coexist, existing systems typically automatically recognize a single QR code, lacking a mechanism for users to actively select it. This easily leads to the accidental scanning of non-target QR codes, affecting the accuracy and security of payments. Therefore, this embodiment utilizes the interactive capabilities of the vehicle's display module to receive a QR code selection command triggered by the user based on the displayed labeled image frame. This command explicitly specifies the target QR code the user intends to operate on. Subsequently, the system does not immediately decode all QR codes, but accurately locates the target QR code according to the user's command and enlarges its image to display within a specially optimized preset scanning area. This effectively avoids the problem of automatic misidentification by the system in multi-QR code scenarios, causing users to passively accept incorrect results. By introducing two precise positioning methods—user selection and area enlargement—the accuracy of the subsequent decoding operation is ensured. At the same time, the enlargement operation provides a clearer and more stable image source for the decoding algorithm, thereby significantly improving the accuracy of intention and reliability of QR code payment in complex scenarios.

[0035] Step S40: Decode the target QR code in the preset scanning area to obtain the payment information to be verified, and after receiving the payment confirmation instruction for the payment information to be verified, call the payment application to make the payment.

[0036] It's important to note that the payment information to be verified is the raw payment data obtained after decoding the target QR code, such as the payment amount, merchant identifier, or transaction code. This information requires user confirmation before the payment operation can be executed. Therefore, the payment confirmation command is the instruction signal entered by the user after final confirmation of the payment information to be verified, authorizing the vehicle's infotainment system to call the local payment application or the payment application bound to the glasses to complete the payment transaction. This command can be generated through user touch display modules or voice input. Voice input can be received by either the vehicle's infotainment system or the glasses.

[0037] For example, when a user wearing AI glasses is in a parking lot, they can trigger an image frame acquisition command on the glasses by saying the voice command "Start scanning". The camera module on the glasses then captures an image frame containing multiple parking space payment QR codes and transmits it to the vehicle's infotainment system via WiFi. The infotainment system uses its built-in QR code detection algorithm to identify three QR codes to be decoded in the image frame. It then marks these QR codes with a highlighted border with a serial number on the image frame and outputs the marked image frame to the vehicle's touch screen for display. After observing the screen, the user selects QR code number 2 by touching the screen, generating a QR code selection command. The infotainment system then determines that QR code number 2 is the target QR code, enlarges its image, and centers it in the preset scanning area in the center of the screen. The decoding module on the infotainment system then decodes the enlarged target QR code, parsing out the payment information to be verified, including the parking lot ID and the amount. After the user confirms the payment by touching the screen, the system calls the payment application installed in the vehicle to complete the payment transaction.

[0038] This embodiment provides a QR code payment method. The vehicle's infotainment system receives image frames transmitted from the glasses, identifies the QR code to be decoded within the image frame, marks it, and displays it on a display module. Then, based on user input, the target QR code is determined and enlarged to a preset scanning area on the display module. Finally, the target QR code is decoded, and upon confirmation, the payment application is invoked to complete the payment. This achieves a visual interaction mechanism by transmitting image frames captured by AI glasses to the vehicle's infotainment system's display module in a driving scenario. This effectively solves the problem of QR code positioning difficulties caused by the lack of a photoelectric display module in AI glasses, and allows users to accurately select the target QR code in multi-QR code scenarios by touching the vehicle's infotainment system's display module. Therefore, this embodiment enables users to intuitively observe and accurately select the target QR code in multi-QR code scenarios through the vehicle's infotainment system's display module, effectively avoiding the risk of accidentally scanning irrelevant codes. Simultaneously, the interactive display capabilities of the vehicle's infotainment system compensate for the visual feedback deficiencies of the glasses' lack of a photoelectric display module, significantly improving the accuracy and efficiency of QR code payments via AI glasses in driving scenarios.

[0039] In one feasible implementation, after the QR code to be decoded marked on the display module is touched, a QR code selection instruction is generated. Before the step of determining the target QR code in the QR code to be decoded according to the QR code selection instruction in step S30, steps S301 to S303 may also be included: Step S301: Monitor the continuous input duration of the QR code selection command; Step S302: If the continuous input duration exceeds a preset duration threshold, determine that the QR code selection instruction is a selection instruction to be determined, and generate and output confirmation prompt information to the display module according to the QR code selection instruction. It should be noted that the pending selection instruction refers to the situation where the system detects that the user is touching the QR code to be decoded marked on the display module, and the duration of the operation exceeds a preset duration threshold (e.g., 0.5 seconds). The system initially judges this touch operation as an intermediate state instruction that has not yet been finalized and requires secondary confirmation from the user. This instruction indicates that the user may be intentionally making a selection, but the system will not execute the subsequent operation of confirming the target QR code to avoid accidental touches.

[0040] The confirmation prompt message is a prompt message generated and output to the display module after the system generates a selection instruction to be confirmed, in order to request final confirmation from the user. This message is usually, but not limited to, presented in a visual form, such as a pop-up dialog box displaying "Confirm selection of this QR code?", and can be further supplemented by an auditory form, such as a voice broadcast on the vehicle's infotainment system or glasses, "Confirm selection of this QR code?". The purpose is to clearly inform the user that the current selection is in a pending confirmation state and guide the user to the next confirmation step.

[0041] Step S303: After receiving the selection confirmation instruction input into the displayed confirmation prompt information, determine that the QR code selection instruction is a valid selection instruction, and execute the step of determining the target QR code in the QR code to be decoded based on the QR code selection instruction.

[0042] It should be noted that the "Confirm" command is a confirmation instruction entered by the user in response to the system's confirmation prompt message, either upon seeing or simultaneously hearing it. For example, a user tapping the "Confirm" button in a prompt dialog box. This instruction represents the user's final acceptance and confirmation of the aforementioned selection, indicating that the user indeed wishes to select the QR code.

[0043] A valid selection instruction is a QR code selection instruction that the system determines is legal, valid, and allows execution after receiving a selection confirmation instruction from the user in response to the confirmation prompt. Only after this instruction takes effect will the system execute subsequent steps to determine the target QR code from the QR code to be decoded based on the initially triggered selection operation.

[0044] It is understandable that accidental touches during touch operations in a driving environment can easily lead to the mis-triggered QR code selection command, resulting in incorrect target QR code selection. Therefore, this implementation monitors the duration of continuous input of the QR code selection command. When the duration exceeds a preset threshold, it is initially classified as a pending selection command rather than being executed immediately. Simultaneously, a confirmation prompt is generated, requiring the user to confirm again. Only after receiving the confirmation command is the command ultimately recognized as a valid selection command, and subsequent steps are executed. This solution effectively avoids erroneous selections and unintended payments caused by accidental touches in a driving environment. By introducing a dual interaction mechanism of time delay and secondary confirmation, it significantly improves the accuracy and clarity of user selection operations, enhancing the robustness and reliability of the system in complex in-vehicle environments.

[0045] For example, in a driving scenario, after a user sees multiple marked QR codes to be decoded on the vehicle's infotainment display module, and long-presses the thumbnail of one of the QR codes for more than a preset 0.5-second duration threshold, the vehicle's infotainment system detects this long-press operation but does not immediately treat it as the final selection. Instead, it determines it as a selection instruction to be confirmed and displays a confirmation prompt message next to the QR code on the screen, saying "Confirm selection of this QR code?" The user then clicks the "Yes" button in the prompt message to enter the selection confirmation instruction. Only after receiving this instruction does the system finally recognize the long-press operation as a valid selection instruction and execute the subsequent operation of confirming the target QR code and enlarging it to the preset scanning area.

[0046] In this embodiment, by monitoring the continuous input duration of the QR code selection command, it is converted into a pending selection command after the timeout and a confirmation prompt message is output. It is only recognized as a valid selection command after receiving the user's confirmation instruction for the prompt message. This avoids the problem of erroneous selection caused by accidental touch due to careless vehicle operation, and realizes the clarity of the user's selection intention through a secondary confirmation mechanism, thereby improving the accuracy and reliability of the system's interactive operation in complex in-vehicle environments.

[0047] In one possible implementation, step S101 may be included after step S10: Step S101: Call a preset QR code detection algorithm to perform QR code detection on the image frame and identify the QR code to be decoded in the image frame. The preset QR code detection algorithm includes a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner point refinement algorithm, and a decoding verification algorithm executed sequentially.

[0048] It should be noted that the preset QR code detection algorithm is a complete image processing and recognition process consisting of four sub-algorithms executed sequentially: a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner point refinement algorithm, and a decoding verification algorithm. This algorithm is a standard processing procedure called by the vehicle-mounted system to systematically identify the QR code to be decoded from the image frame. Its purpose is to ensure efficient and accurate localization and preliminary screening of the QR code area under different image quality and complex backgrounds through multi-stage collaboration.

[0049] The preprocessing enhancement algorithm is the first stage of the preset QR code detection algorithm. Its function is to optimize the image quality of the original image frame, such as performing operations like grayscale conversion, contrast enhancement, noise filtering, or geometric distortion correction. The purpose of this algorithm is to eliminate interference caused by uneven ambient lighting, image blurring, or jitter, improve image quality, and lay a clear image foundation for subsequent feature localization steps.

[0050] The coarse feature localization algorithm is the second stage following the preprocessing enhancement algorithm. Its function is to quickly scan the enhanced image and initially filter out candidate image regions that may be QR codes based on the typical visual features of the QR code. This stage focuses on retrieval speed, aiming to quickly narrow down the detection range and avoid pixel-level fine analysis of the entire image, thereby improving the overall detection efficiency.

[0051] The corner refinement algorithm is the third stage. Its function is to perform more refined pixel-level analysis within the candidate region provided by the coarse feature localization algorithm. By identifying and accurately fitting the corners or boundaries of the QR code, it precisely locates the outline and position of each QR code. The purpose of this algorithm is to correct any deviations that may exist in the coarse localization and obtain the precise geometric coordinates of each QR code to be decoded, thus preparing for subsequent decoding steps.

[0052] The decoding verification algorithm is the final stage of the process. Its function is to attempt to decode and read the QR code area that has been precisely located at the corner points, and to verify whether the read data format conforms to the predetermined specifications or is complete and valid. This step not only performs a decoding attempt, but also undertakes a preliminary verification function to ensure that the identified QR code to be decoded is truly readable and valid, thereby avoiding sending meaningless patterns or damaged QR codes into subsequent interaction processes.

[0053] Understandably, directly performing global decoding on image frames suffers from high computational load, significant response latency, and a tendency to miss or misidentify QR codes in complex backgrounds. To address this issue, this implementation employs a pre-defined QR code detection algorithm—comprising a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner refinement algorithm, and a decoding verification algorithm—to systematically detect image frames. This avoids QR code recognition failures or low efficiency caused by poor image quality, background interference, or inaccurate localization. Through a multi-stage progressive image processing flow, it significantly improves the detection rate and reliability of quickly and accurately identifying all potential QR codes in complex environments during real-world driving scenarios, providing a reliable data foundation for subsequent user interaction.

[0054] For example, after receiving the image frame transmitted from the glasses, the vehicle-mounted system calls a preset QR code detection algorithm. First, a preprocessing enhancement algorithm is used to perform grayscale conversion and histogram equalization on the image to improve contrast. Then, a feature coarse localization algorithm uses morphological operations based on the features of the QR code positioning pattern to quickly filter out three candidate regions. Next, a corner refinement algorithm performs sub-pixel-level edge detection on each candidate region to accurately fit the coordinates of the four corner points of the QR code. Finally, a decoding verification algorithm performs Reed-Solomon error correction decoding on the refined QR code region and verifies whether the decoded data conforms to the predefined URL (Uniform Resource Locator) format. Finally, the two regions that pass the verification are identified as the QR code to be decoded and output to the interactive interface for annotation and display.

[0055] In this embodiment, a pre-defined QR code detection algorithm, consisting of a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner point refinement algorithm, and a decoding verification algorithm, is used to systematically detect image frames. This avoids problems such as missed QR code recognition, misrecognition, and decoding failure caused by poor image quality, background interference, or inaccurate rapid localization. Through a multi-stage progressive image processing and verification process, the ability to quickly, accurately, and robustly identify all potential QR codes from complex visual scenes in a real in-vehicle environment is significantly improved, providing reliable technical support for subsequent user selection and payment processes.

[0056] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 The step S40, which involves decoding the target QR code in the preset scanning area to obtain the payment information to be verified, may further include steps S41 to S43: Step S41: Decode the target QR code in the preset scanning area; Step S42: If the target QR code is successfully decoded, the payment information to be verified is obtained; Step S43: If the target QR code fails to be decoded, identify the shooting angle of the target QR code, generate a perspective adjustment prompt based on the shooting angle, and output the perspective adjustment prompt to the glasses and / or the display module to prompt adjustment of the shooting angle of the image frame.

[0057] It should be noted that the shooting angle is the spatial angle between the QR code plane and the optical central axis of the camera module at the glasses end, identified by analyzing the image of the QR code when decoding of the target QR code in the preset scanning area fails. This shooting angle reflects the degree of distortion of the QR code image (such as whether it exhibits trapezoidal distortion) and the visibility of key features (such as positioning patterns), and is a key technical parameter for determining whether decoding failure is caused by an improper shooting angle.

[0058] The perspective adjustment prompt is generated and output to the glasses and / or display module after the system identifies a shooting perspective that is causing decoding failure. It guides the user to adjust the shooting angle of the camera module on the glasses. The content of this information is based on the analysis of the shooting perspective, and its purpose is to assist the user in changing the direction of the glasses by moving their head or body, thereby placing the target QR code in a more easily decoded, frontal or centered position in subsequent image frames.

[0059] Understandably, when decoding of a target QR code fails due to an improper shooting angle, the system will stall, and the user will be unable to know the reason for the failure or how to correct it, leading to interruption of the operation process and reduced payment efficiency. To solve this problem, this embodiment identifies the shooting angle of the target QR code when decoding fails and generates angle adjustment prompts based on these angles. These prompts are then output to the glasses and / or display module to guide the user to adjust the shooting angle. This effectively avoids the problem of the entire scanning process terminating due to decoding failure at a single angle. It achieves user interaction and collaboration guided by real-time visual or auditory feedback, dynamically optimizes image acquisition conditions, and thus significantly improves the system's fault tolerance and final decoding success rate in complex real-world scenarios.

[0060] In specific implementation, the perspective adjustment prompt information includes perspective adjustment audio prompt information and / or perspective adjustment visual prompt information. The step S43, which generates perspective adjustment prompt information based on the shooting perspective and outputs the perspective adjustment prompt information to the glasses and / or the display module, may include steps S431 to S432: Step S431: Generate viewing angle adjustment audio prompt information based on the shooting angle, and output the viewing angle adjustment audio prompt information to the glasses and / or the display module; and / or Step S432: Generate visual cues for adjusting the viewing angle based on the shooting angle, and output the visual cues for adjusting the viewing angle to the display module.

[0061] It should be noted that the viewing angle adjustment audio prompts are carried by audio signals with specific frequencies, rhythms, or stereo channel variations, used to guide users in adjusting the shooting angle of the glasses. For example, when the user needs to move their head to the right, the system will output a stronger or higher-frequency prompt tone (such as a buzzer sound at 800-1000Hz) through the speakers on the glasses or the vehicle's infotainment system, directly guiding the user to perform the correct viewing angle adjustment action through auditory perception.

[0062] Visual cues for angle adjustment are dynamic graphics or symbolic guidance displayed on the vehicle's infotainment system. These visual cues guide users in adjusting the viewing angle of their glasses. For example, a directional arrow (e.g., a left arrow indicating the need to turn the head to the left) or color-coded cues (e.g., red for large angle deviation, green for a suitable angle) may be displayed next to the target QR code on the screen. This visual aid helps users understand and perform angle correction.

[0063] Understandably, a single prompt mode may lead to inefficient transmission of perspective adjustment guidance due to driving environment interference (such as ambient noise and screen glare) or user perception preferences. Therefore, by concretizing perspective adjustment prompts into audio and / or visual prompts and providing multimodal feedback through corresponding output channels, user perception delays, misunderstandings, or operational errors caused by a single prompt method or incompatibility with the environment are avoided. This achieves complementary auditory and visual dual-channel information transmission, significantly enhancing the anti-interference and intuitiveness of guidance information, enabling users to understand the adjustment direction and complete perspective correction more quickly and accurately.

[0064] In this embodiment, by identifying the shooting angle and generating an angle adjustment prompt message to the user when decoding fails, process stagnation and operation failure caused by insufficient image quality in a single acquisition are avoided. This achieves an effective improvement in system fault tolerance and final decoding success rate through a dynamic adjustment mechanism involving human-machine collaboration.

[0065] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3The image frame acquisition command includes a video stream acquisition command and an image capture command. Before step S20, steps S201 to S202 may also be included: Step S201: When the image frame is a video stream composed of multiple image frames, the video stream is used as the image frame, and the step of annotating the QR code to be decoded identified in the image frame is performed based on the video stream, wherein the video stream is captured by the glasses when a video stream acquisition command is received. It should be noted that the video stream is a data sequence consisting of multiple image frames that are continuous in time and dynamically change in content, continuously captured by the camera module of the glasses after receiving a video stream acquisition command. This data format allows the vehicle's infotainment system to receive continuous visual feedback when the driver slightly tilts their head to adjust their viewing angle to capture a target, thus enabling real-time observation and dynamic adjustment. The video stream acquisition command is a specific control signal that triggers the glasses to enter continuous shooting mode and begin outputting the video stream. This command is suitable for scenarios where the driver needs to slightly tilt their head to find and track QR codes while simultaneously observing the vehicle's infotainment screen for real-time feedback. It can be activated by, but is not limited to, pressing and holding the vehicle's interactive button or issuing a specific voice command.

[0066] Step S202: If the image frame is a captured image consisting of a single image frame, the captured image is used as the image frame to perform the step of annotating the QR code to be decoded identified in the image frame based on the captured image, wherein the captured image is captured by the glasses end upon receiving an image capturing command.

[0067] It should be noted that the captured image is a single, independent still image frame output by the camera module of the glasses after receiving the image capture command, performing a single acquisition operation. This method is suitable for scenarios where the driver needs to make a significant head turn to align with a QR code on the side, and then return their head to the center to view the vehicle's screen for further operations. The image capture command is a specific control signal that triggers the glasses to perform a single image acquisition and output a still image. This command is suitable for scenarios where the target is relatively stationary and the driver can complete the framing by making a single, significant head turn. It is typically triggered by a short button press or a specific voice command distinct from the voice command corresponding to the video stream acquisition command, to achieve rapid capture and efficient processing.

[0068] Understandably, drivers need to perform varying degrees of head rotation to capture QR codes in different scenarios, making a single image acquisition mode unsuitable for diverse operational situations. Therefore, this embodiment defines two triggering mechanisms: a video stream acquisition command and an image capture command. These correspond to scenarios requiring a slight head tilt to observe the vehicle's screen (corresponding to continuous video stream transmission) and scenarios requiring a large head turn followed by a return to the center to observe the screen (corresponding to triggering a single image capture). This effectively avoids safety hazards such as limited shooting angles, interrupted operation processes, or driver distraction caused by a mismatch between the acquisition mode and the head movement range. It supports different image frame acquisition modes, ensuring real-time acquisition capabilities under slight movements while optimizing operational efficiency and safety in scenarios with large-scale rotations, significantly improving the system's adaptability to different driving habits and scenarios.

[0069] For example, when a driver needs to scan a QR code on a parking pillar at the side of a parking lot, if the target is at the edge of their field of vision, the driver can issue a video stream capture command by saying "video capture". Upon receiving the command, the microphone of the AI ​​glasses will start continuously capturing a video stream and transmitting it to the vehicle's infotainment system in real time via WiFi. The driver can then observe the dynamically updated image on the vehicle's screen while keeping their head slightly turned until the target QR code enters their field of vision. If the target is located directly to the left of the driver's seat and requires a large head turn, the driver can issue an image capture command by saying "take a photo". Upon receiving the command, the microphone of the AI ​​glasses will immediately capture a single image and transmit it to the vehicle's infotainment system. The driver can then straighten their head and observe the static image displayed on the vehicle's screen to make subsequent selections.

[0070] In this embodiment, by defining and distinguishing two triggering mechanisms—video stream acquisition command and image capture command—the transmission of a video stream consisting of multiple frames of images is initiated or a single image capture is performed, respectively. This avoids the safety hazards of limited shooting angle, interrupted operation process, and driver distraction caused by a single acquisition mode being unable to adapt to different operating scenarios. It achieves support for different image frame acquisition modes, ensuring real-time interaction capabilities in dynamic scenarios and optimizing operating efficiency and driving safety in static scenarios, significantly improving the system's adaptability to different driving operation needs.

[0071] In one feasible implementation, before the step of using the captured image as the image frame in step S202, steps S01-S02 may also be included: Step S01: Receive the viewing angle offset information transmitted by the glasses, wherein the viewing angle offset information is transmitted in association with the video stream, and the viewing angle offset information is determined by the glasses based on the angle difference between the calibrated zero angle and the actual deflection angle, wherein the calibrated zero angle represents the angle at which the glasses face the front of the vehicle when they are worn. It should be noted that the viewing angle offset information is a parameter data calculated and transmitted by the glasses, used to quantify the degree of deviation of its optical central axis relative to the reference direction directly in front of the vehicle. This information is calculated from the angle difference between the calibrated zero-position angle and the actual deflection angle, representing the amplitude of the user's head rotation, and is a key basis for the system to judge the stability of the glasses' viewing angle during image frame acquisition. Among them, the calibrated zero-position angle is a reference angle value that is pre-calibrated and stored during the system initialization phase, representing the optical central axis of its camera module facing directly in front of the vehicle when the glasses are correctly worn. This angle serves as the zero-position reference in the spatial coordinate system and is used to subsequently calculate the relative offset between the actual shooting direction and the vehicle's forward direction. The actual deflection angle refers to the specific orientation angle of its camera module's optical central axis relative to the spatial reference coordinate system at the current moment, measured in real time by the inertial measurement unit built into the glasses during image frame acquisition. This angle dynamically reflects the real-time rotation state of the user's head.

[0072] Step S02: When the viewing angle offset information is greater than a preset angle threshold, extract the target image frame in the video stream and use the target image frame as the image frame. Then, perform the step of annotating the QR code to be decoded identified in the image frame based on the target image frame. The target image frame is any one of the multiple adjacent image frames in the video stream. The angle change of the viewing angle offset information corresponding to the multiple adjacent image frames is less than a preset angle change threshold, and each of the multiple adjacent image frames includes a suspected QR code image area.

[0073] It should be noted that the target image frame is a single-frame image selected from the video stream based on stability criteria for subsequent QR code recognition and interactive processing. The selection criteria for this frame are: the angular change in the viewpoint offset information of its multiple adjacent image frames is less than a preset threshold, and all of these frames contain suspected QR code image regions, thus ensuring high viewpoint stability and content validity. The multiple adjacent image frames are a temporally continuous sequence of image frames in the video stream. The system analyzes the magnitude of the change in viewpoint offset information (i.e., the amount of angular change) of all frames within this sequence to determine whether the user maintained a sufficiently stable head posture during the acquisition process, thereby evaluating the overall stability of the image sequence.

[0074] The angle change of the viewpoint offset angle information is a statistical measure such as the maximum difference or standard deviation between the viewpoint offset angle values ​​of all frames in a specified multi-frame adjacent image frame sequence. This change is used to quantify the amplitude of the user's head shaking in a short period of time. If the change is less than the preset angle change threshold, the viewpoint is considered to be stable in that time period and suitable for image frame extraction and recognition.

[0075] A suspected QR code image region refers to an image region in an image frame that possesses typical QR code characteristics (such as a position detection graphic of a specific ratio) and is identified through preliminary image analysis (such as contour detection and texture analysis). This region has not yet undergone complete decoding verification, but its existence indicates that the image frame is likely to contain a valid QR code target, which is an important condition for triggering subsequent stability judgment and frame screening.

[0076] Understandably, if the video stream mode is used when the driver's head is turned at an excessive angle, the driver will be unable to simultaneously capture images while observing the vehicle's screen on the other side, leading to operational interruptions and safety hazards. To address this issue, this implementation monitors the viewing angle offset information associated with the video stream in real time. When this angle exceeds a preset threshold, it automatically filters out stable target image frames from the video stream, switching the acquisition mode from real-time video stream to an equivalent static image processing flow. This avoids visual interruptions, operational disruptions, and potential safety risks caused by conflicts between the driver's viewing angle and the screen's orientation. The system automatically selects the optimal processing strategy based on the head tilt angle, ensuring both the effectiveness of image acquisition and the continuity and safety of driving interaction, significantly improving the system's intelligent adaptability.

[0077] For example, when the driver turns their head sharply to the left to scan a QR code outside the left window, the glasses detect that the difference between the actual deflection angle and the calibrated zero angle reaches 55°, exceeding the preset threshold of 45° angle change. At this time, the system automatically selects five consecutive adjacent image frames from the continuously collected video stream, where the angle change of the viewing angle is less than 3° and all of them detect the suspected QR code image area. The third frame is then transmitted to the vehicle's display as the target image frame. This intelligently terminates the video stream mode that requires continuous observation, allowing the driver to directly complete the QR code selection and payment operation on a clear static image after straightening their head.

[0078] The system can run a preliminary QR code feature detection algorithm in real time on each image frame in the video stream: First, the image is converted to grayscale and Gaussian blur is applied for noise reduction. Then, an edge detection algorithm is used to extract all contours in the image. Next, based on the geometric features of the contours (including closure, convex hull properties, and area filtering), initial filtering is performed to retain candidate regions that conform to the typical proportions of QR code positioning graphics (such as a width ratio of 1:1:3:1:1). Finally, the radial symmetry feature unique to QR codes is verified by calculating the consistency of gradient distribution in a specific direction within the candidate regions. When candidate regions that have passed the above verification process and have relatively stable spatial positions exist in 5 consecutive image frames, the group of adjacent image frames is determined to be "adjacent image frames of a suspected QR code image region". At this time, the system automatically selects any one of the adjacent image frames in the group whose angular offset is less than a preset angular change threshold as the target image frame. This frame can be the first frame, the middle frame, or the last frame in the group, completing the intelligent switching from dynamic video stream to static keyframe.

[0079] In this embodiment, the system monitors the viewing angle offset information associated with the video stream in real time, calculated from the calibrated zero angle and the actual deflection angle. When this information exceeds a preset threshold, the system automatically filters out target image frames that meet stability requirements from the video stream to replace the continuous video stream for processing. This avoids visual conflicts, operational interruptions, and potential safety risks caused by the driver having to simultaneously observe the video stream and the vehicle's infotainment screen located in different directions when the driver's head deflection angle is too large. The system automatically switches to an equivalent static image processing mode based on the head posture, ensuring the effective acquisition of key information and ensuring that the driver can safely interact by returning their head to the correct position. This significantly improves the system's practicality and safety in extreme viewing angle scenarios.

[0080] Based on the first and / or second and / or third embodiments of this application, in the fourth embodiment of this application, the content that is the same as or similar to the above-described embodiments one, two, and three can be referred to the above description and will not be repeated hereafter. Based on this, please refer to... Figure 4 The QR code payment method is applied to the glasses device, and the QR code payment method includes steps A10 to A30: Step A10: Receive an image frame acquisition instruction, and call the camera module to capture image frames according to the image frame acquisition instruction; Step A20: Mark the QR code to be decoded identified in the image frame, and transmit the marked image frame to the display module of the vehicle terminal for display, so that after the vehicle terminal receives the QR code selection instruction input into the displayed image frame, it determines the target QR code in the QR code to be decoded according to the QR code selection instruction, and enlarges the target QR code to the preset scanning area of ​​the display module for decoding to obtain the payment information to be verified; Step A30: After receiving the payment confirmation instruction for the payment information to be verified, the payment application is invoked to make the payment.

[0081] Understandably, in order to fully utilize the local computing power of the glasses to achieve system load balancing and adapt to the differences in hardware performance of different in-vehicle systems, while providing an execution foundation for the glasses to directly call the bound personal payment application, this embodiment transfers the execution of the two core processing steps—annotating the QR code to be decoded and calling the payment application for payment—from the in-vehicle system to the glasses. The in-vehicle system only retains the functions of image display, receiving QR code selection instructions, and decoding the target QR code. This avoids the processing bottlenecks, increased system latency, and payment process inconvenience caused by the imperfect in-vehicle payment ecosystem that may result from concentrating all computing tasks on the in-vehicle system. It achieves a reasonable division of labor and load optimization between devices, ensuring the same visual interaction and accurate selection effect as the in-vehicle system, while improving payment convenience and security by utilizing the local payment capabilities of the glasses, and enhancing the flexibility and adaptability of the system architecture.

[0082] For example, when a user wearing AI glasses triggers a QR code scan in a parking lot, the glasses first call the camera module to capture an image frame containing multiple QR codes. Then, a QR code detection algorithm is run locally to identify and label all the QR codes to be decoded. The labeled image frame is then wirelessly transmitted to the vehicle's display module. After the user selects the target QR code on the vehicle's screen, the vehicle sends the corresponding QR code selection command back to the glasses. The glasses then determine the target QR code and call the locally integrated Alipay application to complete the payment authorization and transaction execution. Finally, the payment result is displayed on the vehicle's screen or output through the speaker.

[0083] In this embodiment, by transferring the execution of the two core processing steps—annotating the QR code to be decoded and calling the payment application for payment—from the vehicle-mounted device to the glasses, the problems of excessive system load, increased processing latency, and payment process interruption caused by the limitations of the vehicle-mounted payment ecosystem, which would otherwise result in concentrating all computing and payment tasks on the vehicle-mounted device, are avoided. This achieves the goal of maintaining the advantages of visual interaction on the vehicle-mounted device while effectively sharing the system load and reducing interaction latency through the local processing capabilities of the glasses. Furthermore, it ensures the convenience and security of the payment process by directly utilizing the personal payment application bound to the glasses, thus achieving efficient collaboration between devices and optimization of the system architecture.

[0084] In one feasible implementation, the glasses include a WiFi module and a Bluetooth module, and the image frame is transmitted to the vehicle-mounted system by the WiFi module. After step A20, step A21 may also be included: Step A21: Receive the selection confirmation instruction and call the Bluetooth module to transmit the selection confirmation instruction to the vehicle terminal. The selection confirmation instruction is used by the vehicle terminal to confirm the QR code selection instruction after receiving the QR code selection instruction input for the displayed image frame, so as to perform the step of determining the target QR code in the QR code to be decoded according to the QR code selection instruction on the vehicle terminal based on the confirmed QR code selection instruction.

[0085] It is understandable that relying on a single wireless communication mode (such as WiFi or Bluetooth) to simultaneously transmit large-bandwidth image data and small-volume control commands poses risks of channel contention, transmission delay, and insufficient stability. Therefore, to address this issue, this implementation constructs a dual-mode redundant communication architecture of "WiFi + Bluetooth." The WiFi module is dedicated to high-bandwidth, labeled image frame transmission, while the Bluetooth module is dedicated to low-power, high-real-time selection and determination command transmission. This effectively avoids delays or loss of critical control commands due to bandwidth consumption by image data, as well as communication interruptions caused by single-channel interference. Through channel separation and functional specialization, it ensures both smooth image transmission and the immediacy and reliability of control commands, thereby comprehensively improving the system's communication stability and real-time interaction in real-world driving environments.

[0086] For example, when a user selects a target QR code by touching it on the vehicle's display module, they can say "confirm" in voice. The microphone on the glasses will pick up the voice and generate a selection confirmation command, which will then be transmitted to the vehicle's display module via Bluetooth. After receiving the command via Bluetooth, the vehicle's display module will confirm the validity of the currently received QR code selection command, thereby triggering the subsequent process of confirming the target QR code. By combining voice interaction with Bluetooth Low Energy transmission, the dedicated command channel and low-latency confirmation are achieved while maintaining WiFi image transmission.

[0087] In one possible implementation, step A201 may be included before step A20: Step A201: Call the preset QR code detection algorithm to perform QR code detection on the image frame and identify the QR code to be decoded in the image frame; The preset QR code detection algorithm includes a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner point refinement algorithm, and a decoding verification algorithm executed sequentially.

[0088] Understandably, relying entirely on the vehicle's infotainment system for QR code recognition would easily lead to excessive computational load, affecting its real-time response. Therefore, by calling a pre-defined QR code detection algorithm on the glasses to recognize the QR code in the original image frame and determine the QR code to be decoded, and then synchronously transmitting the recognition result (i.e., the annotation information) along with the original image frame to the vehicle's infotainment system, the computational pressure and processing delays caused by image recognition are avoided on the vehicle's infotainment system. This achieves a reasonable distribution of computational load to the glasses through front-end preprocessing, significantly reducing the computational burden on the vehicle's infotainment system while maintaining the original data transmission, and improving the overall system response speed and multi-tasking capabilities.

[0089] For example, after the glasses acquire an original image frame containing multiple QR codes, they immediately call the local preset QR code detection algorithm to perform preprocessing enhancement, coarse feature localization, corner point refinement, and decoding verification processes. They identify three QR codes to be decoded and generate a highlighted annotation layer with serial numbers on the original image frame. Then, the annotated complete image frame is transmitted to the vehicle display module via WiFi. The vehicle display module directly receives and displays the image containing the recognition results for the user to select, thereby moving the computationally intensive recognition process to the glasses.

[0090] In one feasible implementation, the image frame acquisition instruction includes a video stream acquisition instruction, the image frame includes a video stream composed of multiple image frames, and the video stream is captured by the glasses end upon receiving the video stream acquisition instruction; Before step A20, steps A01 to A02 may also be included: Step A01: Determine the viewing angle offset information based on the angle difference between the calibrated zero angle and the actual deflection angle, wherein the calibrated zero angle represents the angle at which the glasses end faces the front of the vehicle when it is worn. Step A02: When the image frame is the video stream and the viewpoint offset angle information is greater than a preset angle threshold, extract the target image frame from the video stream and use the target image frame as the image frame. Then, perform the step of annotating the QR code to be decoded identified in the image frame based on the target image frame. The target image frame is any one of the multiple adjacent image frames in the video stream. The angle change of the viewpoint offset angle information corresponding to the multiple adjacent image frames is less than a preset angle change threshold, and each of the multiple adjacent image frames includes a suspected QR code image area.

[0091] Understandably, when video stream acquisition is accompanied by significant head rotation, continuously transmitting the complete video stream to the vehicle's infotainment system would waste transmission resources, and the system would also waste computational resources by processing a large number of invalid frames. Therefore, this implementation calculates and judges the viewing angle information locally on the glasses. Only when the angle exceeds a threshold is a stable target image frame automatically selected from the video stream, and this single target image frame is transmitted to the vehicle's infotainment system instead of the original video stream. This effectively avoids the continuous transmission of invalid video data and the repeated processing of redundant frames by the vehicle's infotainment system. It achieves effective reduction of data transmission volume through intelligent pre-screening on the glasses, significantly saving communication bandwidth and vehicle's computing resources, while ensuring that the interactive system always operates based on valid image frames.

[0092] For example, during the video stream acquisition process, the glasses use a built-in inertial measurement unit to calculate the viewing angle offset information between the calibrated zero angle and the actual deflection angle in real time. When the angle value exceeds the 45° threshold, the glasses automatically extract five consecutive adjacent image frames from the video stream with a viewing angle change of less than 3° and all of them containing suspected QR code areas. The third frame with the highest clarity is selected as the target image frame, the video stream transmission is terminated, and this single target image frame is sent to the vehicle's infotainment system via WiFi. Thus, after autonomously completing the viewing angle judgment and frame selection, only the highest quality static image is transmitted to the vehicle's infotainment system for subsequent interactive operations.

[0093] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the QR code payment method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0094] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the QR code payment method in Embodiment 1 above.

[0095] The following is for reference. Figure 5 It shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of this application. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0096] like Figure 5 As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0097] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0098] The electronic device provided in this application, employing the QR code payment method described in the above embodiments, can solve the technical problem of low efficiency in QR code payment via AI glasses in current driving scenarios. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the QR code payment method provided in the above embodiments, and other technical features of this electronic device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0099] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0101] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A QR code payment method, characterized in that, When applied to in-vehicle infotainment systems, the QR code payment method includes: Receive image frames transmitted from the glasses, wherein the image frames are captured by the glasses upon receiving an image frame acquisition command; The identified QR code to be decoded in the image frame is labeled, and the labeled image frame is transmitted to the display module for display. After receiving a QR code selection instruction input to the displayed image frame, the target QR code in the QR code to be decoded is determined according to the QR code selection instruction, and the target QR code is enlarged to the preset scanning area of ​​the display module; The target QR code in the preset scanning area is decoded to obtain the payment information to be verified, and after receiving the payment confirmation instruction for the payment information to be verified, the payment application is invoked to make the payment.

2. The QR code payment method as described in claim 1, characterized in that, The step of decoding the target QR code in the preset scanning area to obtain the payment information to be verified includes: Decode the target QR code in the preset scanning area; If the target QR code is successfully decoded, the payment information to be verified is obtained; If the target QR code fails to be decoded, the shooting angle of the target QR code is identified, a perspective adjustment prompt is generated based on the shooting angle, and the perspective adjustment prompt is output to the glasses and / or the display module to prompt adjustment of the shooting angle of the image frame.

3. The QR code payment method as described in claim 2, characterized in that, The perspective adjustment prompt information includes audio prompt information and / or visual prompt information for perspective adjustment. The step of generating perspective adjustment prompt information based on the shooting perspective and outputting the perspective adjustment prompt information to the glasses and / or the display module includes: Based on the shooting angle, generate an audio prompt message for angle adjustment and output the audio prompt message to the glasses and / or the display module; and / or Based on the shooting angle, a perspective adjustment visual prompt is generated, and the perspective adjustment visual prompt is output to the display module.

4. The QR code payment method as described in claim 1, characterized in that, After the QR code to be decoded marked on the display module is touched, a QR code selection instruction is generated. Before the step of determining the target QR code in the QR code to be decoded according to the QR code selection instruction, the method further includes: Monitor the duration of continuous input of the QR code selection command; If the duration of continuous input exceeds a preset duration threshold, the QR code selection instruction is determined to be a selection instruction to be determined, and a confirmation prompt message is generated and output to the display module according to the QR code selection instruction. Upon receiving the selection confirmation instruction input into the displayed confirmation prompt information, the QR code selection instruction is determined to be a valid selection instruction, and the step of determining the target QR code in the QR code to be decoded is executed based on the QR code selection instruction.

5. The QR code payment method as described in claim 1, characterized in that, After the step of receiving the image frame transmitted from the glasses, the method further includes: A preset QR code detection algorithm is invoked to detect QR codes in the image frame and identify the QR codes to be decoded in the image frame. The preset QR code detection algorithm includes a preprocessing enhancement algorithm, a coarse feature localization algorithm, a corner point refinement algorithm, and a decoding verification algorithm executed sequentially.

6. The QR code payment method as described in claim 1, characterized in that, The image frame acquisition command includes a video stream acquisition command and an image capture command. Before the step of annotating the identified QR code to be decoded in the image frame, the method further includes: When the image frame is a video stream composed of multiple image frames, the video stream is used as the image frame to perform the step of annotating the QR code to be decoded identified in the image frame based on the video stream, wherein the video stream is captured by the glasses when a video stream acquisition command is received; In the case where the image frame is a captured image consisting of a single image frame, the captured image is used as the image frame to perform the step of annotating the QR code to be decoded identified in the image frame based on the captured image, wherein the captured image is captured by the glasses end upon receiving an image capturing command.

7. The QR code payment method as described in claim 6, characterized in that, Before the step of using the video stream as the image frame, the method further includes: The system receives viewing angle offset information transmitted from the glasses, wherein the viewing angle offset information is transmitted in association with the video stream, and the viewing angle offset information is determined by the glasses based on the angle difference between the calibrated zero angle and the actual deflection angle, wherein the calibrated zero angle represents the angle at which the glasses face the front of the vehicle when they are worn. When the viewing angle offset information is greater than a preset angle threshold, the target image frame in the video stream is extracted and the target image frame is used as the image frame. The step of annotating the QR code to be decoded in the image frame is performed based on the target image frame. The target image frame is any one of the multiple adjacent image frames in the video stream. The angle change of the viewing angle offset information corresponding to the multiple adjacent image frames is less than the preset angle change threshold, and each of the multiple adjacent images includes a suspected QR code image area.

8. A QR code payment method, characterized in that, When applied to eyeglasses, the QR code payment method includes: Receive image frame acquisition instructions, and call the camera module to capture image frames according to the image frame acquisition instructions; The identified QR code to be decoded in the image frame is labeled, and the labeled image frame is transmitted to the display module of the vehicle terminal for display. After receiving the QR code selection instruction input into the displayed image frame, the vehicle terminal determines the target QR code in the QR code to be decoded according to the QR code selection instruction, and enlarges the target QR code to the preset scanning area of ​​the display module for decoding to obtain the payment information to be verified. Upon receiving a payment confirmation instruction for the payment information to be verified, the payment application is invoked to perform the payment.

9. The QR code payment method as described in claim 8, characterized in that, The glasses include a WiFi module and a Bluetooth module. The image frame is transmitted from the WiFi module to the vehicle-mounted system. After the step of transmitting the labeled image frame to the display module of the vehicle-mounted system for display, the system further includes: The system receives a selection confirmation instruction and invokes the Bluetooth module to transmit the selection confirmation instruction to the vehicle-mounted terminal. The selection confirmation instruction is used by the vehicle-mounted terminal to confirm the QR code selection instruction after receiving the QR code selection instruction input for the displayed image frame, so as to perform the step of determining the target QR code in the QR code to be decoded according to the QR code selection instruction on the vehicle-mounted terminal based on the confirmed QR code selection instruction.

10. The QR code payment method as described in claim 8, characterized in that, The image frame acquisition instruction includes a video stream acquisition instruction, and the image frame includes a video stream composed of multiple image frames. The video stream is captured by the glasses when the video stream acquisition instruction is received. Before the step of annotating the identified QR code to be decoded in the image frame, the method further includes: The viewing angle offset angle information is determined based on the angle difference between the calibrated zero angle and the actual deflection angle, wherein the calibrated zero angle represents the angle at which the glasses end faces the front of the vehicle when it is worn. When the image frame is the video stream and the viewpoint offset angle information is greater than a preset angle threshold, a target image frame is extracted from the video stream and used as the image frame. The step of annotating the QR code to be decoded identified in the image frame is then performed based on the target image frame. The target image frame is any one of multiple adjacent image frames in the video stream. The angle change of the viewpoint offset angle information corresponding to the multiple adjacent image frames is less than a preset angle change threshold, and each of the multiple adjacent image frames includes a suspected QR code image region.

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