WO2021185174A1

WO2021185174A1 - Electronic card selection method and apparatus, terminal, and storage medium

Info

Publication number: WO2021185174A1
Application number: PCT/CN2021/080488
Authority: WO
Inventors: 万磊; 王强
Original assignee: 华为技术有限公司
Priority date: 2020-03-17
Filing date: 2021-03-12
Publication date: 2021-09-23
Also published as: CN113409041A; CN113409041B

Abstract

An electronic card selection method and apparatus, a terminal, and a storage medium. The method comprises: obtaining current scene information, and determining a scene type according to the scene information (S301); and selecting a candidate electronic card matching the scene type as a target electronic card (S302). According to the method, when there is a need to call an electronic card for an operation such as authentication and payment, current scene information is acquired by a terminal device, the scene type is determined according to scene objects comprised in the scene information, and an electronic card associated with the scene type is selected from all candidate electronic cards as a target electronic card; thus, automatic electronic card selection is implemented, and electronic card operation efficiency and response speed are improved.

Description

Method, device, terminal and storage medium for selecting electronic card

This application requires the priority of a Chinese patent application filed with the State Intellectual Property Office, the application number is 202010187020.X, and the application title is "A method, device, terminal, and storage medium for selecting an electronic card" on March 17, 2020. The entire content is incorporated into this application by reference.

Technical field

This application relates to the field of information processing technology, and in particular to an electronic card selection method, device, terminal, and storage medium.

Background technique

In daily life, users can use physical cards for payment, authentication and other operations, but as the types of services continue to increase, the number of corresponding physical cards also increases, and thanks to the development of electronic technology, physical cards can be converted It is an electronic card and is bound to a smart terminal to perform related payment and authentication operations. However, with the existing electronic card technology, users need to manually select the electronic card associated with the current operation when performing operations such as authentication and payment, which increases the difficulty of operation and lowers the operation efficiency.

Summary of the invention

The embodiments of the application provide an electronic card selection method, device, terminal, and storage medium, which can solve the existing electronic card technology and need to manually select the electronic card associated with the current operation, thereby increasing the operation difficulty and lower operation efficiency. The problem.

In the first aspect, an embodiment of the present application provides a method for selecting an electronic card, including:

Acquiring current scene information, and determining a scene type according to the scene information;

The candidate electronic card matching the scene type is selected as the target electronic card.

In a possible implementation manner of the first aspect, the acquiring current scene information and determining the scene type according to the scene information includes:

Receive scene images fed back by smart glasses;

Identifying the shooting subject contained in the scene image;

The scene type is determined according to all the photographing subjects.

Collect the ambient sound in the current scene;

Acquiring a frequency domain spectrum of the environmental sound, and determining a sounding subject contained in the current scene according to the frequency value contained in the frequency domain spectrum;

The scene type is determined according to all the speaking subjects.

Acquiring current location information, and extracting scene keywords contained in the location information;

Calculate the confidence probability of each candidate scene according to the confidence of all candidate scenes associated with the scene keywords;

The candidate scene with the highest confidence probability is selected as the scene type corresponding to the location information.

In a possible implementation manner of the first aspect, the selecting a candidate electronic card matching the scene type as a target electronic card includes:

Respectively calculating the matching degree between each of the candidate electronic cards and the scene type;

The candidate electronic card with the highest matching degree is selected as the target electronic card.

In a possible implementation manner of the first aspect, after the selecting the candidate electronic card matching the scene type as the target electronic card, the method further includes:

Perform a card swiping authentication operation through the target electronic card and the card swiping device;

If the credit card authentication fails, from all the candidate electronic cards except the target electronic card, select the candidate electronic card with the highest matching degree as the new target electronic card, and return to execute the pass The target electronic card and the card swiping device perform a card swiping operation until the swiping authentication is successful.

Acquiring the standard scene of each candidate electronic card;

The scene type is matched with each of the standard scenes, and the target electronic card is determined according to the matching result.

In the second aspect, an embodiment of the present application provides an electronic card selection device, including:

A scene type determining unit, configured to obtain current scene information, and determine the scene type according to the scene information;

The electronic card selection unit is used to select a candidate electronic card matching the scene type as the target electronic card.

In a third aspect, the embodiments of the present application provide a terminal device, a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the The computer program implements the method for selecting the electronic card described in any one of the above-mentioned first aspects.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium that stores a computer program, and is characterized in that, when the computer program is executed by a processor, any of the above-mentioned aspects of the first aspect is implemented. One method for selecting the electronic card.

In a fifth aspect, the embodiments of the present application provide a computer program product that, when the computer program product runs on a terminal device, causes the terminal device to execute the method for selecting an electronic card in any one of the above-mentioned first aspects.

It is understandable that, for the beneficial effects of the second aspect to the fifth aspect described above, reference may be made to the relevant description in the first aspect described above, and details are not repeated here.

When an electronic card needs to be invoked for authentication, payment and other operations, the embodiment of the application collects current scene information through the terminal device, determines the scene type according to the scene object contained in the scene information, and selects the scene type from all candidate electronic cards. The type-associated electronic card is used as the target electronic card, which realizes the purpose of automatically selecting the electronic card, and improves the operation efficiency and response speed of the electronic card.

Description of the drawings

FIG. 1 is a block diagram of a part of the structure of a mobile phone provided by an embodiment of the present application;

FIG. 2 is a schematic diagram of the software structure of a mobile phone according to an embodiment of the present application;

FIG. 3 is an implementation flowchart of an electronic card selection method provided by the first embodiment of the present application;

4 is a schematic diagram of scene type recognition based on scene images provided by an embodiment of the present application;

FIG. 5 is a schematic diagram of selecting an electronic card provided by an embodiment of the present application;

6 is a specific implementation flowchart of an electronic card selection method S301 provided by the second embodiment of the present application;

FIG. 7 is a schematic diagram of a shooting scene range of a terminal device during a card swiping process according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a photographing range of smart glasses during a card swiping process according to another embodiment of the present application;

9 is a specific implementation flowchart of an electronic card selection method S301 provided by the third embodiment of the present application;

10 is a specific implementation flowchart of an electronic card selection method S301 provided by the fourth embodiment of the present application;

11 is a specific implementation flowchart of an electronic card selection method S302 provided by the fifth embodiment of the present application;

12 is a specific implementation flowchart of an electronic card selection method S302 provided by the sixth embodiment of the present application;

FIG. 13 is a schematic structural diagram of an electronic card selection system provided by an embodiment of the present application;

14 is a structural block diagram of an electronic card selection device provided by an embodiment of the present application;

FIG. 15 is a schematic diagram of a terminal device according to another embodiment of the present application.

Detailed ways

In the following description, for the purpose of illustration rather than limitation, specific details such as a specific system structure and technology are proposed for a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from obstructing the description of this application.

It should be understood that when used in the specification and appended claims of this application, the term "comprising" indicates the existence of the described features, wholes, steps, operations, elements and/or components, but does not exclude one or more other The existence or addition of features, wholes, steps, operations, elements, components, and/or collections thereof.

It should also be understood that the term "and/or" used in the specification and appended claims of this application refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

As used in the description of this application and the appended claims, the term "if" can be construed as "when" or "once" or "in response to determination" or "in response to detecting ". Similarly, the phrase "if determined" or "if detected [described condition or event]" can be interpreted as meaning "once determined" or "in response to determination" or "once detected [described condition or event]" depending on the context ]" or "in response to detection of [condition or event described]".

In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

Reference to "one embodiment" or "some embodiments" described in the specification of this application means that one or more embodiments of this application include a specific feature, structure, or characteristic described in combination with the embodiment. Therefore, the sentences "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. appearing in different places in this specification are not necessarily All refer to the same embodiment, but mean "one or more but not all embodiments" unless it is specifically emphasized otherwise. The terms "including", "including", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

In view of the current electronic card technology, it is necessary to manually select the electronic card associated with the current operation, which increases the difficulty of the operation and the problem of low operation efficiency. The embodiments of the present application provide an electronic card selection method, device, equipment and storage medium. When you need to call an electronic card for authentication, payment, etc., collect the current scene information through the terminal device, determine the scene type according to the scene object contained in the scene information, and select the electronic card associated with the scene type from all candidate electronic cards As the target electronic card, the purpose of automatically selecting the electronic card is realized, and the operation efficiency and response speed of the electronic card are improved.

The technical solution of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

The method for selecting an electronic card provided by the embodiments of this application can be applied to mobile phones, tablet computers, wearable devices, in-vehicle devices, augmented reality (AR)/virtual reality (VR) devices, notebook computers, and ultra mobile devices. Personal computers (ultra-mobile personal computers, UMPC), netbooks, personal digital assistants (personal digital assistants, PDAs) and other terminal devices can also be applied to databases, servers, and service response systems based on terminal artificial intelligence. Examples of this application There are no restrictions on the specific types of terminal equipment.

For example, the terminal device may be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a wireless local loop (Wireless Local Loop, WLL) station, Personal Digital Assistant (PDA) devices, handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, computers, laptops, handheld communication devices, handheld computing devices, and /Or other devices used to communicate on the wireless system and next-generation communication systems, for example, mobile terminals in 5G networks or mobile terminals in the future evolved Public Land Mobile Network (PLMN) network, etc.

As an example and not a limitation, when the terminal device is a wearable device, the wearable device can also be a general term for the application of wearable technology to intelligently design daily wear and develop wearable devices, such as near-field devices. Gloves, watches, etc. for communication modules. A wearable device is a portable device that is directly worn on the body or integrated into the user's clothes or accessories. It is attached to the user's body and performs operations such as payment and authentication through a pre-bound electronic card. Wearable devices are not only a kind of hardware device, but also realize powerful functions through software support, data interaction, and cloud interaction. In a broad sense, wearable smart devices include full-featured, large-sized, complete or partial functions that can be implemented without relying on smart phones, such as smart watches or smart glasses, and only focus on a certain type of application function, and need to be used in conjunction with other devices such as smart phones. , Such as various types of smart watches with display screens, smart bracelets, etc.

In this embodiment, the aforementioned terminal device may be a mobile phone 100 having a hardware structure as shown in FIG. 1. As shown in FIG. 1, the mobile phone 100 may specifically include: a radio frequency (RF) circuit 110, a memory 120, and an input The unit 130, the display unit 140, the sensor 150, the audio circuit 160, the short-range wireless communication module 170, the processor 180, and the power supply 190 and other components. Those skilled in the art can understand that the structure of the mobile phone 100 shown in FIG. 1 does not constitute a limitation on the terminal device. The terminal device may include more or less components than those shown in the figure, or a combination of certain components, or different components. Component arrangement.

The following describes the components of the mobile phone in detail with reference to Figure 1:

The RF circuit 110 can be used for receiving and sending signals during information transmission or communication. In particular, after receiving the downlink information of the base station, it is processed by the processor 180; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, and the like. In addition, the RF circuit 110 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile Communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (Code Division) Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), Email, Short Messaging Service (SMS), etc.

The memory 120 may be used to store software programs and modules. The processor 180 executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 120. The memory 120 may mainly include a program storage area and a data storage area. The program storage area may store an operating system, an application program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; Data created by the use of mobile phones (such as audio data, phone book, etc.), etc. In addition, the memory 120 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Specifically, the memory 120 may store the card information of the electronic card and the corresponding relationship between each electronic card and the associated scene type. The mobile phone may determine the target electronic card associated with the current scene through the memory 120.

The input unit 130 may be used to receive inputted numeric or character information, and generate key signal input related to user settings and function control of the mobile phone 100. Specifically, the input unit 130 may include a touch panel 131 and other input devices 132. The touch panel 131, also known as a touch screen, can collect user touch operations on or near it (for example, the user uses any suitable objects or accessories such as fingers, stylus, etc.) on the touch panel 131 or near the touch panel 131. Operation), and drive the corresponding connection device according to the preset program. Optionally, the touch panel 131 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch position, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it To the processor 180, and can receive and execute the commands sent by the processor 180. In addition, the touch panel 131 can be implemented in multiple types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 131, the input unit 130 may also include other input devices 132. Specifically, the other input device 132 may include, but is not limited to, one or more of a physical keyboard, function keys (such as volume control buttons, switch buttons, etc.), trackball, mouse, and joystick.

The display unit 140 may be used to display information input by the user or information provided to the user and various menus of the mobile phone. The display unit 140 may include a display panel 141. Optionally, the display panel 141 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch panel 131 can cover the display panel 141. When the touch panel 131 detects a touch operation on or near it, it transmits it to the processor 180 to determine the type of the touch event, and then the processor 180 responds to the touch event. The type provides corresponding visual output on the display panel 141. Although in FIG. 1, the touch panel 131 and the display panel 141 are used as two independent components to realize the input and input functions of the mobile phone, but in some embodiments, the touch panel 131 and the display panel 141 can be integrated. Realize the input and output functions of the mobile phone.

The mobile phone 100 may also include at least one sensor 150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 141 according to the brightness of the ambient light. The proximity sensor can close the display panel 141 and/or when the mobile phone is moved to the ear. Or backlight. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in various directions (usually three-axis), and can detect the magnitude and direction of gravity when it is stationary. It can be used to identify mobile phone posture applications (such as horizontal and vertical screen switching, related Games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, percussion), etc.; as for other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which can also be configured in mobile phones, I will not here Go into details. Optionally, the mobile phone can use the learning algorithm to obtain the measured value of each sensor when the user performs the card swiping action, so as to determine in advance whether the user needs to perform the card swiping operation before the mobile phone approaches the card swiping device, and collect the current scene information to determine Scene type, thereby further improving the selection efficiency of electronic cards.

The audio circuit 160, the speaker 161, and the microphone 162 can provide an audio interface between the user and the mobile phone. The audio circuit 160 can transmit the electrical signal converted from the received audio data to the speaker 161, which is converted into a sound signal for output by the speaker 161; on the other hand, the microphone 162 converts the collected sound signal into an electrical signal, and the audio circuit 160 After being received, it is converted into audio data, and then processed by the audio data output processor 180, and then sent to, for example, another mobile phone via the RF circuit 110, or the audio data is output to the memory 120 for further processing.

Communication technologies such as WiFi, Bluetooth, and Near Field Communication (NFC) are short-range wireless transmission technologies. The mobile phone can help users send and receive emails, browse web pages, and access streaming media through the short-range wireless module 170. The user provides wireless broadband Internet access. The aforementioned short-range wireless module 170 may include a WiFi chip, a Bluetooth chip, and an NFC chip. Through the WiFi chip, the function of the mobile phone 100 to perform WiFi Direct connection with other terminal devices can also be realized, and the mobile phone 100 can also work to provide wireless access services. The AP mode that allows other wireless devices to access (Access Point mode) or the STA mode that can connect to the AP and does not accept wireless device access (Station mode) to establish point-to-point communication between the mobile phone 100 and other WiFi devices; the mobile phone can use The NFC chip establishes a short-distance communication link with the card swiping device, and sends the pre-written card information of the electronic card to the card swiping device according to the above-mentioned short-distance communication link, performs subsequent swiping operations, and feeds back the swiping result to the mobile phone , Output the credit card result through the display module of the mobile phone.

The processor 180 is the control center of the mobile phone. It uses various interfaces and lines to connect various parts of the entire mobile phone. Various functions and processing data of the mobile phone can be used to monitor the mobile phone as a whole. Optionally, the processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor and a modem processor, where the application processor mainly processes the operating system, user interface, application programs, etc. , The modem processor mainly deals with wireless communication. It can be understood that the foregoing modem processor may not be integrated into the processor 180.

The mobile phone 100 also includes a power source 190 (such as a battery) for supplying power to various components. Preferably, the power source can be logically connected to the processor 180 through a power management system, so that functions such as charging, discharging, and power consumption management can be managed through the power management system.

The mobile phone 100 may also include a camera. Optionally, the position of the camera on the mobile phone may be front-mounted or rear-mounted, which is not limited in the embodiment of the present application. Among them, the mobile phone can collect the scene image of the current scene through the camera, and determine the scene information and the scene type by analyzing the scene image.

The software system of the mobile phone 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. The embodiment of the present invention takes the layered Android system as an example to illustrate the software structure of the mobile phone 100.

FIG. 2 is a block diagram of the software structure of the mobile phone 100 according to an embodiment of the present application. The Android system is divided into four layers, which are application layer, application framework layer (framework, FWK), system layer, and hardware abstraction layer. The layers communicate through software interfaces.

The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Communication between layers through software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.

The application layer can include a series of application packages.

As shown in Figure 2, the application package can include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.

The application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

As shown in Figure 2, the application framework layer can include a window manager, a content provider, a view system, a phone manager, a resource manager, and a notification manager.

The window manager is used to manage window programs. The window manager can obtain the size of the display screen, determine whether there is a status bar, lock the screen, take a screenshot, etc.

The content provider is used to store and retrieve data and make these data accessible to applications. The data may include videos, images, audios, phone calls made and received, browsing history and bookmarks, phone book, etc.

The view system includes visual controls, such as controls that display text, controls that display pictures, and so on. The view system can be used to build applications. The display interface can be composed of one or more views. For example, a display interface that includes a short message notification icon may include a view that displays text and a view that displays pictures.

The phone manager is used to provide the communication function of the electronic device 100. For example, the management of the call status (including connecting, hanging up, etc.).

The resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and so on.

The notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and it can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify download completion, message reminders, and so on. The notification manager can also be a notification that appears in the status bar at the top of the system in the form of a chart or a scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, text messages are prompted in the status bar, prompt sounds, electronic devices vibrate, and indicator lights flash.

Android Runtime includes core libraries and virtual machines. Android runtime is responsible for the scheduling and management of the Android system.

The core library consists of two parts: one part is the function functions that the java language needs to call, and the other part is the core library of Android.

The application layer and application framework layer run in a virtual machine. The virtual machine executes the java files of the application layer and the application framework layer as binary files. The virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.

The system library can include multiple functional modules. For example: surface manager (surface manager), media library (Media Libraries), three-dimensional graphics processing library (for example: OpenGL ES), 2D graphics engine (for example: SGL), etc.

The surface manager is used to manage the display subsystem, and provides a combination of 2D and 3D graphics for multiple applications.

The media library supports playback and recording of a variety of commonly used audio and video formats, as well as still image files. The media library can support multiple audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, synthesis, and layer processing.

The 2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is the layer between hardware and software. The kernel layer contains at least display driver, camera driver, audio driver, and sensor driver. In some embodiments, the above-mentioned kernel layer further includes a PCIE driver.

In the embodiment of the present application, the execution subject of the process is a device equipped with a near field communication module. As an example and not a limitation, the above-mentioned device equipped with a near field communication module may specifically be a terminal device, and the terminal device may be a mobile terminal such as a smart phone, a tablet computer, and a laptop used by the user. Fig. 3 shows the implementation flow chart of the method for selecting an electronic card provided by the first embodiment of the present application, and the details are as follows:

In S301, the current scene information is acquired, and the scene type is determined according to the scene information.

In this embodiment, the terminal device can obtain current scene information through built-in sensors and other information collection modules, or it can establish a data link with an external information collection device to receive scene information collected by other information collection devices.

In a possible implementation, the terminal device has a built-in camera module. The camera module can be a front camera module and/or a rear camera module. The camera module collects the scene image of the current scene and recognizes the above scene image as the scene. Information, the scene information is analyzed to determine the scene type; the terminal device has a built-in microphone module, which can collect the scene audio of the current scene, and recognize the above scene audio as scene information, perform audio analysis on the scene audio, and determine the scene type; The terminal device has a built-in positioning module, and obtains positioning information through the positioning module, uses the positioning information as scene information, and determines the associated scene type according to the positioning information.

In a possible implementation manner, the specific implementation manner for the terminal device to determine the scene type through the scene image may be: the terminal device may be configured with corresponding standard images for different scene types. The terminal device can match the currently acquired scene image with each standard image, and determine the scene type associated with the scene image according to the matching result. Specifically, the process of matching the scene image with the standard image can specifically be as follows: the terminal device can perform grayscale processing on the scene image, convert the scene image into a monochrome image, and generate and The image array corresponding to the monochrome image is imported into the preset convolutional neural network, and the above-mentioned convolutional array is pooled and reduced by the preset convolution kernel to generate the image feature vector corresponding to the above-mentioned image array, and Calculate the vector distance between the image feature vector and the standard feature vector corresponding to each standard image, use the vector distance as the matching probability value between each standard image, and select the scene type associated with the standard image with the largest probability value as the The scene type of the scene image. Among them, the standard feature vector of the above-mentioned standard image can be collected by a self-learning algorithm. The implementation of the above-mentioned self-learning algorithm can be as follows: when each electronic card is initially bound, the terminal device collects information about the corresponding use scenario of the electronic card. Standard image, based on the above standard image to generate the above standard feature vector, in the process of subsequent use, every time the electronic card is used to perform a card swiping operation, the scene image corresponding to the card swiping operation is imported into the above-mentioned neural network to adjust the generated standard The feature vector, so that the configured standard feature vector can be adjusted posteriorly during each use, thereby improving the accuracy of the standard feature vector.

In a possible implementation, each electronic card corresponds to a cloud server. The cloud server can be used to store the operation record of the electronic card and the scene image associated with each operation record. The cloud server extracts historical scene images from each operation record, and generates the aforementioned standard feature vector from all historical scene images. The cloud server can send the aforementioned standard feature vector to each terminal device at a preset update cycle. The aforementioned standard feature vector can be associated with an electronic card identifier. The terminal device stores the received electronic card identifier and standard feature vector in the storage unit. In the subsequent matching operation, the above-mentioned standard feature vector can be extracted to perform the matching operation.

Exemplarily, FIG. 4 shows a schematic diagram of scene type recognition based on scene images provided by an embodiment of the present application. As shown in FIG. 4, a terminal device 41 and a card swiping device 42 are included. The mobile terminal 41 is configured with a camera module 411 and a near field communication module 412. When the terminal device 41 approaches the card swiping device 42, the near field communication module 412 will detect the near field communication signal sent by the swiping device 42 and establish a communication connection with the card swiping device 42. At this time, the terminal device can activate the camera module 411 through the camera The module 411 collects a scene image in the current scene, and determines the scene type according to the scene image.

In a possible implementation manner, the terminal device collects multiple different types of scene information, and determines the current scene type according to the different types of scene information. Specifically, the terminal device can collect scene images and scene audio of the current scene, identify multiple candidate object types from the scene images, filter out the target object types from the candidate object types according to the scene audio, and determine the scene type according to the target object types. Screening invalid candidate object types through scene audio can calibrate the recognition process of scene types, thereby improving the recognition efficiency. Of course, you can also use scene audio to identify candidate vocal objects in the current scene and utter the candidates according to the scene image. The target utterance object is filtered out of the objects, and the scene type is determined according to the target utterance object. For example, the terminal device obtains a scene image through the camera module. Due to factors such as too far a shooting distance or obstruction by obstacles, some scene objects cannot be recognized through the scene image, thereby reducing the accuracy of scene type recognition. In order to solve the above problems, the terminal device can collect the ambient sound in the current scene through the microphone module while acquiring the scene image, determine the sound subject through the ambient sound, and determine the subject through image recognition of the scene image, through the sound subject and shooting The object determines the type of scene.

Specifically, in a possible implementation manner, the method of determining the scene type by the sounding subject and the shooting object may be: the terminal device determines the first confidence level of each candidate scene type through all the sounding subjects, and determining each scene type by all the shooting objects The second confidence of the candidate scene type is weighted based on the voice weight, the first confidence is weighted based on the image weight, and the second confidence is weighted based on the image weight, and calculations about each candidate are calculated based on the weighted first confidence and second confidence For the matching degree of the scene type, the candidate scene type with the highest matching degree is selected as the scene type of the current scene.

For example, according to the scene type associated with the electronic card stored in the terminal device, it can be divided into three different scene types: bank type, bus type, and access control type. When the terminal device detects that the electronic card needs to be called, it can obtain the scene image of the current scene through the camera module. Through image recognition technology, it is determined that the scene image contains three photographic objects of the teller machine, the bank sign and the screen door, thus three candidates The first confidence level corresponding to the scene type is: (bank type, 80%), (access control type, 50%), (traffic type, 20%), and by collecting environmental sounds, it is determined that the sounding subjects included in the scene include Case, mechanical operation sound, so that the second confidence level corresponding to the three candidate scene types are: (bank type, 60%), (access control type, 50%), (traffic type, 60%), and the preset image weight The value is 1, and the voice weight value is 0.8. Therefore, the corresponding matching degrees of the above three candidate scene types are respectively, (bank type, 80%*1+60*0.8=1.28), (access control type, 50%*1+50 *0.8=0.9), (traffic type, 20%*1+60*0.8=0.68), therefore, the candidate scene with the highest matching degree is the bank type, so the bank type is taken as the current scene type.

It should be noted that the above uses the combination of voice type and image type of scene information to determine the scene type as an example for illustration. In actual use, more than two types of scene information can be used, or other scenes that are not limited to the above two types of information can be used. The information is combined to determine the scene type, so I won't repeat them here.

In a possible implementation, the user can trigger the selection process of the electronic card by clicking the electronic card activation control or opening the electronic card application. The terminal device can also use the near field communication module to detect a near field communication signal, Trigger the selection process of the electronic card.

In a possible implementation, the terminal device can obtain the user's movement trajectory when performing a card swiping operation through the terminal device through a built-in learning algorithm, so that when it is detected that the current terminal's movement trajectory is consistent with the aforementioned learned movement trajectory , Automatically activate the electronic card selection process, so as to achieve the purpose of selecting the electronic card in advance and improve the subsequent response speed. The specific implementation process is as follows: the terminal device will continue to obtain the parameter value of the motion sensor, and according to the sequence of the collection time, store the parameter value corresponding to each collection time in the motion parameter queue, and continuously update it according to the first-in-first-out order The above mentioned motion parameter queue. If the terminal device detects that the user performs a card swiping operation, it acquires all the parameter values in the motion parameter queue at the time of the card swiping operation, and generates a motion trajectory corresponding to the motion parameter queue at the time of the card swipe. The terminal device can import the movement trajectory corresponding to the historical card swiping operation into the machine learning model, so that a recognition model for the card swiping operation can be generated. When the terminal device is in use, it will import each parameter value in the parameter motion queue into the above-mentioned swiping operation recognition model to determine whether there is a swiping action, if it is, then execute the electronic card selection process; otherwise, continue to collect the motion sensor And update the above-mentioned parameter motion queue. It should be noted that each time the terminal device performs a card swiping operation, it can update the above-mentioned card swiping operation recognition model, thereby improving the accuracy of recognition.

In S302, a candidate electronic card matching the scene type is selected as the target electronic card.

In this embodiment, the user can bind multiple electronic cards to the terminal device, and each bound electronic card is the aforementioned candidate electronic card. The method of binding the electronic card may be: the user can input the identification of the physical card into the terminal device, and send the authorization information of the physical card to the cloud server of the entity card owner through the electronic card control of the terminal device, for example, input The bound mobile phone number or user identity information, etc., after the cloud server detects that the authorization information is legal, it can feed back the corresponding authorization code to the terminal device, and the terminal device sends the authorization code to the electronic card corresponding to the physical card generated in the terminal device. The card is associated, so that an electronic card corresponding to the physical card is created in the terminal device.

In this embodiment, the terminal device can configure associated scene types for different candidate electronic cards. After the scene type corresponding to the scene information is determined, it can be judged whether the current scene type matches the scene type of each candidate electronic card, that is, whether the scene type associated with the electronic card is consistent with the current scene type, and the candidate electronic cards with the same scene type The card is used as the target electronic card, and subsequent swiping operations are performed.

In a possible implementation manner, if the scene types associated with multiple candidate electronic cards are the same, the candidate electronic card with the highest priority can be selected as the target electronic card according to the priority of each candidate electronic card. For example, FIG. 5 shows a schematic diagram of selecting an electronic card provided by an embodiment of the present application. The terminal equipment is bound with four electronic cards: bank card A, bank card B, bus card, and access card. The terminal device determines that the current scene type is the bank type by collecting the current scene information, and the scene types associated with bank card A and bank card B are all bank types, that is, the scene types of the above two electronic cards are the same as the current scene type In this case, the priority corresponding to the above two bank cards can be obtained. If the priority of bank card A is higher than the priority of bank card B, bank card A can be selected as the target electronic card.

In a possible implementation, if multiple candidate electronic cards are associated with the same scene type, the matching degree of multiple candidate electronic cards with the same scene type can be calculated according to the current card swiping time and location, and the matching degree can be selected The highest candidate electronic card is used as the target electronic card. Specifically, different electronic cards have corresponding usage habits. For example, a user uses electronic card A for swiping operations in the morning, and electronic card B for swiping operations in the afternoon. The terminal device can use the historical time in the history of each electronic card to swipe the card. As well as the historical location, the matching degree with the current scene is calculated, and a candidate electronic card with a higher matching degree is selected as the target electronic card.

It can be seen from the above that the method for selecting an electronic card provided by the embodiment of this application collects current scene information through the terminal device when the electronic card needs to be invoked for authentication, payment, and other operations, and determines according to the scene objects contained in the scene information. Scene type, and select the electronic card associated with the scene type from all candidate electronic cards as the target electronic card, which realizes the purpose of automatically selecting the electronic card and improves the operation efficiency and response speed of the electronic card.

FIG. 6 shows a specific implementation flowchart of an electronic card selection method S301 provided by the second embodiment of the present application. Referring to FIG. 6, compared with the embodiment described in FIG. 3, S301 in an electronic card selection method provided in this embodiment includes: S601 to S603, which are detailed as follows:

Further, the acquiring current scene information and determining the scene type according to the scene information includes:

In S601, a scene image fed back by the smart glasses is received.

In this embodiment, the terminal device establishes a communication connection with an external smart glasses, and collects scene images in the current scene through the camera module built into the smart glasses. Since smart glasses are worn near the user's eyes, compared with using the camera module built in the terminal device to capture scene images, the line of sight is clearer, and the consistency between the scene viewed by the user is higher, and the main scene subjects are reduced. It is blocked by other objects during shooting, which improves the accuracy of scene type recognition. For some scenes, such as the type of traffic scenes, when users use an electronic card to take a bus, they will often take out their mobile phone from the bag of clothes or trousers, and then directly perform the card swipe operation, in the moving path from the pocket to the card machine. , The camera module built into the terminal device is likely to be unable to collect scene images containing the card reader.

Exemplarily, FIG. 7 shows a schematic diagram of a shooting scene range of a terminal device during a card swiping process according to an embodiment of the present application. As shown in Figure 7, the initial position of the terminal device is in the pocket. When the card needs to be swiped, it needs to be taken out of the pocket and close to the credit card machine, that is, the target location is near the credit card machine. During the movement, the captured area is shown as the fan-shaped area in Fig. 7. It can be seen that only when the terminal device is close to the card swiping machine, the captured scene image will contain the card swiping device, and only part of the image of the swiping device, so the recognition accuracy is low.

Exemplarily, FIG. 8 shows a schematic diagram of a photographing range of smart glasses provided by another embodiment of the present application during a card swiping process. As shown in Figure 8, because the smart glasses are worn on the user's eye area, their photographable range is basically the same as the visual range of the human eye, and the user swipes the card while the user is moving forward, that is, when the user approaches the card swiping device The camera can be continuously recorded by the smart glasses, so that compared to using the built-in camera module of the terminal device, the collection of environmental images through the smart glasses has a better recognition effect.

In a possible implementation, when the terminal device detects that the preset scene information collection conditions are met, it can send a collection instruction to the smart glasses, and the smart glasses can perform image collection operations after receiving the collection instruction, and The collected images are fed back to the terminal device, so that the terminal device can obtain the above-mentioned scene image. Specifically, the aforementioned scene information collection condition may be: when the terminal device detects that the current scene contains a near field communication signal, it recognizes that the scene information collection condition is satisfied; or, the terminal device records multiple card swiping operations based on historical card swiping operations Location, when it is detected that the current location reaches the above-mentioned stored card swiping location, it is recognized that the scene information collection condition is satisfied.

In a possible implementation, the smart glasses can acquire the current scene image in a preset collection period, and feed the collected scene image back to the terminal device, and the terminal device can recognize the subject in the scene image, It is determined whether the photographing subject contains a target subject related to the card swiping operation, and if it exists, the operation of S603 is executed.

In this embodiment, wireless communication can be established between the terminal device and the smart glasses. Specifically, the smart glasses have built-in wireless communication modules, such as a WiFi module, a Bluetooth module, and a ZigBee module. Correspondingly, the terminal device can also Corresponding wireless communication module is built-in. The terminal device searches for the wireless network of the smart glasses and joins the wireless network, thereby establishing a wireless communication link with the smart glasses.

In S602, the subject included in the scene image is identified.

In this embodiment, the terminal device can analyze the shooting subject contained in the scene image through an image analysis algorithm. The method of determining the shooting subject can be specifically as follows: by identifying the contour lines contained in the scene image, the scene image is divided into multiple For each subject area, the subject type of the shooting subject corresponding to each subject area is determined according to the contour shape and color characteristics of the subject area.

In a possible implementation manner, the terminal device may be configured with a list of subject types, and a corresponding subject model is associated with each subject type. The terminal device may match each subject area with each subject model, and select the subject type of the subject model with the highest matching degree as the photographing subject corresponding to the subject area.

In a possible implementation manner, the terminal device may perform a preprocessing operation on the scene image before analyzing the scene image, so that the accuracy of the subject identification can be improved. Specifically, the preprocessing operation may be as follows: the terminal device performs grayscale processing on the scene image, that is, converts the color image into a monochrome image, and adjusts the monochrome image through the filter and the actual light intensity of the shooting scene. For example, increase the pixel value of the highlight area and reduce the pixel value of the shadow area, and use the contour recognition algorithm to determine the contour line contained in the scene image, and deepen the contour area, so as to facilitate the separation of each subject and determine each shot The contour feature of the main body.

In S603, the scene type is determined according to all the photographing subjects.

In this embodiment, the terminal device may calculate the matching factors of each candidate type according to the identified photographing subject, and superimpose the matching factors of all photographing subjects to determine the matching degree of each candidate type. The candidate scene with the highest matching degree is selected as the scene type corresponding to the scene image.

In a possible implementation manner, the terminal device may determine the weight value corresponding to each photographic subject according to the area occupied by each photographic subject in the scene image. Among them, the larger the area occupied by the subject in the scene image, the higher the corresponding weight value; conversely, the smaller the occupied area, the lower the corresponding weight value, and is based on the relationship between each subject and the candidate type. The matching factor and weight value are weighted and superimposed to determine the matching degree of each candidate type.

For example, the subjects captured in a scene image include: cash machines, screen doors, bank signs, and physical persons, and the above-mentioned subjects occupy 25%, 30%, and 8% of the entire scene image. And 15%, the terminal device can convert the above-mentioned area area ratio into the corresponding weight value, respectively: 2, 2, 1, and 1.5. The matching factors between the above four shooting subjects and the bank scene type are: 100%, 80%, 100%, and 30%, so the matching degree between the scene image and the bank scene type is specifically: 2*100%+2 *80%+1*100%+1.5*30%=5.05.

In the embodiments of the present application, the scene image is collected through smart glasses, and the subject contained in the scene image is analyzed to determine the current scene type, realize the automatic recognition of the scene type, and further improve the accuracy of the scene type recognition Therefore, the accuracy of electronic card selection is improved.

FIG. 9 shows a specific implementation flowchart of an electronic card selection method S301 provided by the third embodiment of the present application. Referring to FIG. 9, compared with the embodiment described in FIG. 3, S301 in an electronic card selection method provided in this embodiment includes: S901 to S903, and the details are as follows:

In S901, the ambient sound in the current scene is collected.

In this embodiment, the terminal device can collect the ambient sound of the current scene through a built-in or external microphone module. Specifically, when the terminal device detects that a preset scene information collection condition is met, it can send a scene information collection instruction to the microphone module. The process of triggering the scene type recognition operation based on the scene information collection condition can refer to the related description of the previous embodiment, which is not repeated here.

In a possible implementation manner, the user wears a headset control, the headset control includes a first microphone module, and a communication link is established between the terminal device and the headset control. In this case, the terminal device can control the first microphone module and the built-in second microphone module of the headset control to collect ambient sound, and determine the ambient sound in the current scene based on the ambient sound collected by the two microphone modules. Specifically, the method for determining the environmental sound of the current scene based on the two environmental sounds may be: the terminal device detects the first signal-to-noise ratio of the first environmental sound collected by the first microphone module, and determines the second environment collected by the second microphone module The second signal-to-noise ratio of the sound, and the magnitude of the two signal-to-noise ratios are judged, and the environmental sound with the larger signal-to-noise ratio is selected as the environmental sound in the current scene. As the signal-to-noise ratio is larger, it means that the influence of the noise signal is smaller when collecting the environmental sound, so that the accuracy rate is higher in the subsequent process of determining the main body of the sound.

In S902, the frequency domain spectrum of the environmental sound is acquired, and the sounding subject contained in the current scene is determined according to the frequency value contained in the frequency domain spectrum.

In this embodiment, the terminal device can perform Fourier transform on the environmental sound, convert the time domain signal into the frequency domain signal, and obtain the frequency domain spectrum corresponding to the environmental sound, and based on the frequency value contained in the spectrum spectrum and each frequency value The corresponding frequency domain amplitude determines the vocal subjects contained in the scene. Since different objects have fixed sounding frequencies, the terminal device can determine different sounding subjects through different frequency values. For example, the sound frequency of the human body is 8-10KHz, while the sound frequency of the buzzer is fixed at 2KHz. Therefore, by converting the environmental sound into a frequency domain signal, the sounding subject corresponding to the environmental sound can be determined.

In a possible implementation manner, the terminal device can determine the weight value corresponding to each vocal subject, wherein the method of determining the weight value may be: the terminal device recognizes the amplitude of each vocal subject in the frequency domain spectrum, and determines based on the amplitude The weight value of each vocal subject.

In S903, the scene type is determined according to all the speaking subjects.

In this embodiment, after determining the utterance subject, the terminal device may calculate the matching factor of each candidate type according to the identified utterance subject, and superimpose the matching factors of all the utterance subjects to determine the matching degree of each candidate type. . The candidate scene with the highest matching degree is selected as the scene type corresponding to the environmental sound.

In the embodiment of the present application, the environmental sound is collected by the microphone, and the vocalization subject contained in the environmental sound is analyzed to determine the current scene type, which realizes the automatic recognition of the scene type and improves the accuracy of electronic card selection.

FIG. 10 shows a specific implementation flowchart of an electronic card selection method S301 provided by the fourth embodiment of the present application. Referring to FIG. 10, with respect to the embodiment described in FIG. 3, S301 in an electronic card selection method provided in this embodiment includes: S1001 to S1003, which are detailed as follows:

In S1001, current location information is acquired, and scene keywords contained in the location information are extracted.

In this embodiment, the terminal device has a built-in positioning module, through which the current positioning coordinates of the terminal device can be determined, and the location information associated with the positioning coordinates can be obtained through a third-party map server or map application. For example, if the current location coordinates obtained by the terminal device are (113.300562, 23.143292), it can be input into the corresponding map application to obtain the location information associated with the location coordinates, such as the location information corresponding to the aforementioned location coordinates. It is: Bank A in area B of city A, so that the current scene type can be determined by the location information containing the text content.

In this embodiment, the terminal device can extract scene keywords from the location information through a semantic recognition algorithm. In a possible implementation manner, the terminal device may delete the characters related to the area, retain the characters related to the scene, and use the characters related to the scene as the aforementioned scene keywords. For example, the above determined location information is: Bank G, No. XX, Street C, Area A, City B, through a semantic recognition algorithm, it can be determined that "No. XX No. C Street, Area A, City B" is a region-related character, and then delete it. The remaining characters related to the scene, namely "Bank G", use "Bank G" as the key word of the scene in Mulberry.

In S1002, the confidence probability of each candidate scene is calculated according to the confidence of all candidate scenes associated with the scene keywords.

In this embodiment, the terminal device may calculate the confidence levels between each scene keyword and each candidate scene respectively, and calculate the confidence probability between the location information and each candidate scene according to the confidence levels of all the scene keywords. For example, if the location information contains scene keyword A and scene keyword B, the confidence level with the first candidate scene is divided into 80% and 60%, and the terminal device can superimpose the above two confidence levels , It is also possible to calculate the mean value between the above two confidence levels, and use the calculation result as the confidence probability of the first candidate scene.

In a possible implementation manner, the terminal device may be configured with corresponding keyword lists for different candidate scenes. The terminal device may determine whether the above scene keywords are in the candidate scene keyword list, and determine the above based on the judgment result. Confidence level. Specifically, if the scene keyword is in the keyword list of the candidate scene, the confidence between the scene keyword and the candidate scene identified above is 100%; otherwise, it is judged whether there is any character in the scene keyword In the keyword list of the candidate scene, and based on the number of characters contained, determine the confidence level with the candidate scene.

In S1003, the candidate scene with the highest confidence probability is selected as the scene type corresponding to the location information.

In this embodiment, the terminal device may select a candidate scene with the highest confidence as the scene type matching the location information.

In the embodiment of the present application, by determining the location information and performing semantic analysis on the location information, the scene keywords are determined, and the confidence probability of each candidate scene is determined based on the scene keywords, thereby determining the current scene type, and realizing the scene type Automatic recognition improves the accuracy of electronic card selection.

FIG. 11 shows a specific implementation flowchart of an electronic card selection method S302 provided by the fifth embodiment of the present application. Referring to FIG. 11, with respect to any one of the embodiments described in FIG. 3, FIG. 6, FIG. 9 and FIG.

Further, the selecting the candidate electronic card matching the scene type as the target electronic card includes:

In S1101, the matching degree between each candidate electronic card and the scene type is calculated respectively.

In this embodiment, after the terminal device determines the scene type, the matching degree between each existing candidate electronic card in the terminal device can be calculated separately. Specifically, the terminal device may store standard scenes of each candidate electronic card, and each standard scene may correspond to at least one scene tag, and a corresponding tag tree is established based on the range of the scene tag. For example, for a certain transportation electronic card, the following scene tags are associated: "District Bus", "Bus", "Bus" and "Traffic". According to the scope of the scene tag, it can be determined, "Bus" It covers a variety of regional bus types such as "district-level buses" and "city-level buses". It is a collective term for bus types, that is, "buses" are larger than "district-level buses", so "buses" are "district-level buses" The parent node of, and so on, can be constructed into a tag tree. The terminal device can configure the corresponding matching degree according to the size of the range, where the smaller the range, the higher the corresponding matching degree. The terminal device can determine whether the current scene type matches any scene tag of the candidate electronic card, and use the matching degree associated with the matched scene tag as the matching degree between the scene type and the candidate electronic card.

In S1102, the candidate electronic card with the highest matching degree is selected as the target electronic card.

In this embodiment, since the matching degree is used to identify the association relationship between each candidate electronic card and the current scene, the higher the matching degree, the stronger the association relationship between the candidate electronic card and the current scene; on the contrary, The lower the matching degree, the weaker the association relationship between the candidate electronic card and the current scene. Based on this, the terminal device can select the candidate electronic card with the highest matching degree as the target electronic card, realizing the automatic selection of the electronic card.

In the embodiment of the present application, by calculating the matching degree between each candidate electronic card and the scene type, and selecting the candidate electronic card with the highest matching degree as the target electronic card, the accuracy of selecting the target electronic card is improved.

Further, as another embodiment of the present application, after S302, S1103 and S1104 may also be included:

In S1103, a card swiping authentication operation is performed through the target electronic card and the card swiping device.

In this embodiment, after the terminal device determines the target electronic card, it can send the card information of the target electronic card to the card-swiping device through the near field communication link with the card-swiping device to perform card-swiping authentication on the target electronic card To determine whether the target electronic card matches the card swiping device. If the matching is successful, perform subsequent operations such as authentication, authorization, and deduction, where the subsequent operations are related to the type of operation initiated by the user. For example, if the target electronic card is a transportation electronic card, the transportation electronic card can be used to pay for transportation; If the target electronic card is an access control type electronic card, the door opening authorization can be performed through the access control electronic card. If it is detected that the card swiping authentication fails, the operation of S1104 is executed.

In S1104, if the credit card authentication fails, select the candidate electronic card with the highest matching degree from all the candidate electronic cards except the target electronic card as the new target electronic card, and return Perform the card swiping operation through the target electronic card and the card swiping device until the card swiping authentication is successful.

In this embodiment, if the terminal device receives the authentication failure information fed back by the card swiping device, it means that the currently selected target electronic card does not match the current scene type, so it is necessary to re-determine the target electronic card from the candidate electronic cards. Therefore, the terminal device can select the candidate electronic card with the second highest matching degree value as the target electronic card, and re-execute the card swipe authentication operation until the swipe authentication succeeds.

In the embodiment of the present application, when the card swiping fails, the candidate electronic card with the second highest matching degree is automatically selected as the target electronic card, thereby achieving the purpose of automatically replacing the electronic card and reducing user operations.

FIG. 12 shows a specific implementation flowchart of an electronic card selection method S302 provided by the sixth embodiment of the present application. Referring to FIG. 12, with respect to any one of the embodiments described in FIG. 3, FIG. 6, FIG. 9 and FIG.

In S1201, the standard scene of each candidate electronic card is acquired.

In this embodiment, when storing each candidate electronic card, the terminal device can determine the associated standard scene according to user settings or based on the electronic card type, and establish a standard scene index table, and after determining the scene type of the current scene, Based on the above-mentioned standard scene index table, the standard scenes pre-associated with each candidate electronic card are obtained.

In S1202, the scene type is matched with each of the standard scenes, and the target electronic card is determined according to the matching result.

In this embodiment, the terminal device can match the currently recognized scene type with each standard scene, determine whether there is any candidate electronic card whose standard scene is consistent with the current scene type, and if so, identify the candidate electronic card For the target electronic card.

In the embodiment of this application, by associating standard scenes for different candidate electronic cards, matching the standard scenes with the scene types, and determining the target electronic card, the automatic selection of the target electronic card is realized, which reduces the user's operational difficulty and improves Swipe efficiency.

Illustratively, FIG. 13 shows a schematic structural diagram of an electronic card selection system provided by an embodiment of the present application. As shown in FIG. 13, the electronic card selection system includes a mobile terminal 131, smart glasses 132, an external microphone 133, and a card swiping device 134, wherein communication is established between the mobile terminal 131, the smart glasses 132 and the external microphone 133 Connected, the mobile terminal 131 and the card swiping device 134 establish a communication connection through the near field communication module. The mobile terminal 131 has a camera module 1311, a positioning module 1312, and a built-in microphone module 1313. The mobile terminal 131 can collect different types of scene information through the above multiple modules. It should be noted that the mobile terminal 131 can call any module or external device. To collect one scene information, it is also possible to collect multiple scene information through two or more modules and external devices, determine the scene type based on the scene information, and select the target electronic card based on the scene type.

It should be understood that the size of the sequence number of each step in the foregoing embodiment does not mean the order of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

Corresponding to the electronic card selection method described in the above embodiment, FIG. 14 shows a structural block diagram of the electronic card selection device provided in the embodiment of the present application. For ease of description, only the information related to the embodiment of the present application is shown part.

Referring to Figure 14, the selection device of the electronic card includes:

The scene type determining unit 141 is configured to obtain current scene information, and determine the scene type according to the scene information;

The electronic card selection unit 142 is configured to select a candidate electronic card matching the scene type as the target electronic card.

Optionally, the scene type determining unit 141 includes:

The scene image acquisition unit is used to receive the scene image fed back by the smart glasses;

A scene image analysis unit for identifying the shooting subject contained in the scene image;

The photographing subject analysis unit is configured to determine the scene type according to all the photographing subjects.

Optionally, the scene type determining unit 141 includes:

The environmental sound collection unit is used to collect the environmental sound in the current scene;

The utterance subject determining unit is configured to obtain the frequency domain spectrum of the environmental sound, and determine the utterance subject contained in the current scene according to the frequency value contained in the frequency domain spectrum;

The utterance subject analysis unit is configured to determine the scene type according to all the utterance subjects.

Optionally, the scene type determining unit 141 includes:

The scene keyword extraction unit is used to obtain the current location information and extract the scene keywords contained in the location information;

A confidence probability calculation unit, configured to calculate the confidence probability of each candidate scene according to the confidence of all candidate scenes associated with the scene keywords;

The scene type selection unit is configured to select the candidate scene with the highest confidence probability as the scene type corresponding to the location information.

Optionally, the electronic card selection unit 142 includes:

A matching degree calculation unit, configured to calculate the matching degree between each candidate electronic card and the scene type;

The matching degree selecting unit is configured to select the candidate electronic card with the highest matching degree as the target electronic card.

Optionally, the electronic card selection device further includes:

A card swiping authentication unit, configured to perform a card swiping authentication operation through the target electronic card and the card swiping device;

The authentication failure response unit is configured to, if the card swiping authentication fails, select the candidate electronic card with the highest matching degree as the new target electronic card from all the candidate electronic cards except the target electronic card , And return to execute the card swiping operation through the target electronic card and the card swiping device until the card swiping authentication is successful.

Optionally, the electronic card selection unit 142 includes:

A standard scene acquiring unit, configured to acquire the standard scene of each candidate electronic card;

The standard scene matching unit is configured to match the scene type with each of the standard scenes, and determine the target electronic card according to the matching result.

Therefore, the electronic card selection device provided by the embodiment of the present application can also determine the quantization accuracy corresponding to different network levels by acquiring the network information of the target neural network before generating the target neural network, and based on the quantization accuracy of the current level and the previous one. Hierarchical quantization precision, configure the preprocessing function used to convert the data format between different precisions, and generate the target neural network according to the preprocessing function, so as to realize the processing of different precision data in the same target neural network, and solve the problem of mixed precision The compatibility problem of neural network improves the efficiency of calculation.

FIG. 15 is a schematic structural diagram of a terminal device provided by an embodiment of this application. As shown in FIG. 15, the terminal device 15 of this embodiment includes: at least one processor 150 (only one is shown in FIG. 15), a processor, a memory 151, and a processor stored in the memory 151 and capable of being processed in the at least one processor. The computer program 152 running on the processor 150, when the processor 150 executes the computer program 152, implements the steps in any of the above-mentioned electronic card selection method embodiments.

The terminal device 15 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The terminal device may include, but is not limited to, a processor 150 and a memory 151. Those skilled in the art can understand that FIG. 15 is only an example of the terminal device 15 and does not constitute a limitation on the terminal device 15. It may include more or less components than shown in the figure, or a combination of certain components, or different components. , For example, can also include input and output devices, network access devices, and so on.

The so-called processor 150 may be a central processing unit (Central Processing Unit, CPU), and the processor 150 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSPs), and application specific integrated circuits (Application Specific Integrated Circuits). , ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.

The memory 151 may be an internal storage unit of the terminal device 15 in some embodiments, such as a hard disk or a memory of the terminal device 15. In other embodiments, the memory 151 may also be an external storage device of the terminal device 15, for example, a plug-in hard disk equipped on the terminal device 15, a smart media card (SMC), a secure digital (Secure Digital, SD) card, Flash Card, etc. Further, the memory 151 may also include both an internal storage unit of the terminal device 15 and an external storage device. The memory 151 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 151 can also be used to temporarily store data that has been output or will be output.

It should be noted that the information interaction and execution process between the above-mentioned devices/units are based on the same concept as the method embodiment of this application, and its specific functions and technical effects can be found in the method embodiment section. I won't repeat it here.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In practical applications, the above functions can be allocated to different functional units and modules as needed. Module completion, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist alone physically, or two or more units can be integrated into one unit. The above-mentioned integrated units can be hardware-based Formal realization can also be realized in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present application. For the specific working process of the units and modules in the foregoing system, reference may be made to the corresponding process in the foregoing method embodiment, which will not be repeated here.

An embodiment of the present application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and running on the at least one processor, and the processor executes The computer program implements the steps in any of the foregoing method embodiments.

The embodiments of the present application also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each of the foregoing method embodiments can be realized.

The embodiments of the present application provide a computer program product. When the computer program product runs on a mobile terminal, the steps in the foregoing method embodiments can be realized when the mobile terminal is executed.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the implementation of all or part of the processes in the above-mentioned embodiment methods in the present application can be accomplished by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the photographing device/terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), and random access memory (RAM, Random Access Memory), electric carrier signal, telecommunications signal and software distribution medium. For example, U disk, mobile hard disk, floppy disk or CD-ROM, etc. In some jurisdictions, according to legislation and patent practices, computer-readable media cannot be electrical carrier signals and telecommunication signals.

In the above-mentioned embodiments, the description of each embodiment has its own focus. For parts that are not described in detail or recorded in an embodiment, reference may be made to related descriptions of other embodiments.

A person of ordinary skill in the art may realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/network equipment and method may be implemented in other ways. For example, the device/network device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units. Or components can be combined or integrated into another system, or some features can be omitted or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of protection of this application.

Claims

A method for selecting an electronic card, which is characterized in that it includes:

Acquiring current scene information, and determining a scene type according to the scene information;

The candidate electronic card matching the scene type is selected as the target electronic card.
The selection method according to claim 1, wherein the acquiring current scene information and determining the scene type according to the scene information comprises:

Receive scene images fed back by smart glasses;

Identifying the shooting subject contained in the scene image;

The scene type is determined according to all the photographing subjects.
The selection method according to claim 1, wherein the acquiring current scene information and determining the scene type according to the scene information comprises:

Collect the ambient sound in the current scene;

Acquiring a frequency domain spectrum of the environmental sound, and determining a sounding subject contained in the current scene according to the frequency value contained in the frequency domain spectrum;

The scene type is determined according to all the speaking subjects.
The selection method according to claim 1, wherein the acquiring current scene information and determining the scene type according to the scene information comprises:

Acquiring current location information, and extracting scene keywords contained in the location information;

Calculate the confidence probability of each candidate scene according to the confidence of all candidate scenes associated with the scene keywords;

The candidate scene with the highest confidence probability is selected as the scene type corresponding to the location information.
According to the selection method of any one of claims 1-4, the selection of a candidate electronic card matching the scene type as a target electronic card comprises:

Respectively calculating the matching degree between each of the candidate electronic cards and the scene type;

The candidate electronic card with the highest matching degree is selected as the target electronic card.
The selection method according to claim 5, wherein after the selection of a candidate electronic card matching the scene type as a target electronic card, the method further comprises:

Perform a card swiping authentication operation through the target electronic card and the card swiping device;

If the credit card authentication fails, from all the candidate electronic cards except the target electronic card, select the candidate electronic card with the highest matching degree as the new target electronic card, and return to execute the pass The target electronic card and the card swiping device perform a card swiping operation until the swiping authentication is successful.
The selection method according to any one of claims 1 to 4, wherein the selection of a candidate electronic card matching the scene type as a target electronic card comprises:

Acquiring the standard scene of each candidate electronic card;

The scene type is matched with each of the standard scenes, and the target electronic card is determined according to the matching result.
An electronic card selection device, characterized in that it comprises:

A scene type determining unit, configured to obtain current scene information, and determine the scene type according to the scene information;

The electronic card selection unit is used to select a candidate electronic card matching the scene type as the target electronic card.
A terminal device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program as claimed in claims 1 to 7. The method of any one of.
A computer-readable storage medium storing a computer program, wherein the computer program implements the method according to any one of claims 1 to 7 when the computer program is executed by a processor.