Big Data-Based Card Surface Generation Method, Device, Computer Equipment and Medium

Abstract

The present application relates to a method, device, computer device and medium for generating a card surface based on big data. The method obtains a card surface image of an electronic card selected by a user, recognizes the card surface image, determines the card tail number and the card surface number on the electronic card, determines the complete card number of the electronic card from a card information database according to the card tail number and the card surface number, and uses the complete card number to obtain consumption data within a target time period from a big data platform. According to the current location of the user, the target consumption scenario where the user is located is determined. According to the consumption amount of each consumption scenario in the consumption data, the consumption ratio corresponding to the target consumption scenario is determined. When it is detected that the consumption ratio corresponding to the target consumption scenario is greater than a first threshold, a first pattern is generated and the first pattern is used to cover the card surface image, thereby realizing intelligent personalized card surface generation for the user and prompting the user's current consumption through consumption historical data so as to achieve the purpose of reminding the user to pay attention.

Description

Technical Field

[0001] This application is applicable to the field of big data technology, and particularly relates to a method, device, computer device and medium for generating a card surface based on big data. Background Art

[0002] Currently, traditional bank cards, including debit cards and credit cards, are all physical cards with fixed card surfaces. In mobile banking or online banking, the user's bank card will be displayed in the form of an electronic card on the corresponding interface, but it is still a fixed card surface and cannot be open for customization by users. During the user's consumption process, there may be scenarios where the electronic card needs to be presented. If the card surface of the electronic card is fixed, it cannot prompt the user's consumption status, resulting in low user attention to their own consumption, poor user experience, and inability to arouse the user's interest in using it. Therefore, how to intelligently generate a card surface for the user's electronic card to prompt the user to pay attention to the consumption status is an urgent problem to be solved. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, device, computer device and medium for generating a card surface based on big data to solve the problem of how to intelligently generate a card surface for the user's electronic card to prompt the user to pay attention to the consumption status.

[0004] In a first aspect, an embodiment of this application provides a method for generating a card surface based on big data. The method for generating a card surface includes:

[0005] Obtain a card surface image of the electronic card selected by the user, recognize the card surface image, and determine the card tail number and card surface number on the electronic card;

[0006] According to the card tail number and card surface number, determine the complete card number of the electronic card from the card information database, and use the complete card number to obtain consumption data within a target time period from the big data platform;

[0007] Determine the target consumption scenario where the user is located according to the user's current location;

[0008] Determine the consumption proportion corresponding to the target consumption scenario according to the consumption amount of each consumption scenario in the consumption data;

[0009] When it is detected that the consumption proportion corresponding to the target consumption scenario is greater than a first threshold, generate a first pattern and use the first pattern to cover the card surface image.

[0010] In an embodiment, determining the target consumption scenario where the user is located according to the user's current location includes:

[0011] Obtain the text description information of the current location according to the current location of the user;

[0012] Identify the text description information, determine the keywords corresponding to the current location, and use the keywords to match the corresponding scenario from the mapping relationship table as the target consumption scenario where the user is located.

[0013] In one embodiment, identifying the card surface image and determining the card tail number and card surface number on the electronic card includes:

[0014] Perform grayscale processing on the card surface image to obtain a grayscale image;

[0015] Use optical character recognition technology to recognize the grayscale image, determine that the number in the first position area obtained by recognition is the card tail number on the electronic card, and determine that the number in the second area obtained by recognition is the card surface number on the electronic card.

[0016] In one embodiment, after determining the consumption ratio corresponding to the target consumption scenario, it further includes:

[0017] Determine the consumption ratio of each consumption scenario according to the consumption amount of each consumption scenario in the consumption data;

[0018] Sort the consumption ratios of each consumption scenario from largest to smallest. If the consumption ratio corresponding to the target consumption scenario is among the top N in the sorting, generate a second pattern and use the second pattern to cover the card surface image of the electronic card.

[0019] In one embodiment, after determining the consumption ratio corresponding to the target consumption scenario, it further includes:

[0020] When it is detected that the consumption ratio corresponding to the target consumption scenario is not greater than the first threshold, obtain the consumption ratio of each consumption scenario;

[0021] Generate a basic card surface, take the center point of the basic card surface as the origin, divide the basic card surface by angle according to the consumption ratio of each consumption scenario, and fill different patterns for each divided area to obtain a third pattern;

[0022] Use the third pattern to cover the card surface image.

[0023] In one embodiment, after determining the consumption ratio corresponding to the target consumption scenario, it further includes:

[0024] Obtain the target threshold corresponding to the target consumption scenario configured by the user;

[0025] Take the target threshold as the first threshold.

[0026] In one embodiment, after covering the card surface image with any pattern, the following steps are further included:

[0027] Monitor whether there is a trigger action in the card surface area of the electronic card;

[0028] If the trigger action is detected in the card surface area of the electronic card, select any preset pattern from a preset pattern library and use the preset pattern to cover the card surface image.

[0029] In a second aspect, an embodiment of the present application provides a card surface generation device based on big data. The card surface generation device includes:

[0030] A card surface recognition module, configured to obtain the card surface image of the electronic card selected by the user, recognize the card surface image, and determine the card tail number and card surface number on the electronic card;

[0031] A consumption data acquisition module, configured to determine the complete card number of the electronic card from a card information database according to the card tail number and card surface number, and use the complete card number to obtain consumption data within a target time period from a big data platform;

[0032] A consumption scenario determination module, configured to determine the target consumption scenario where the user is located according to the current location of the user;

[0033] A consumption ratio determination module, configured to determine the consumption ratio corresponding to the target consumption scenario according to the consumption amount of each consumption scenario in the consumption data;

[0034] A card surface generation module, configured to generate a first pattern when it is detected that the consumption ratio corresponding to the target consumption scenario is greater than a first threshold, and use the first pattern to cover the card surface image.

[0035] In one embodiment, the above consumption scenario determination module includes:

[0036] A description information acquisition unit, configured to obtain the text description information of the current location according to the current location of the user;

[0037] A consumption scenario determination unit, configured to recognize the text description information, determine the keyword corresponding to the current location, and use the keyword to match the corresponding scenario from a mapping relation table as the target consumption scenario where the user is located.

[0038] In one embodiment, the above card surface recognition module includes:

[0039] A grayscale processing unit, configured to perform grayscale processing on the card surface image to obtain a grayscale image;

[0040] A card surface recognition unit, which is used to recognize the grayscale image by using optical character recognition technology, determine that the number in the recognized first position area is the card tail number on the electronic card, and determine that the number in the recognized second area is the card surface number on the electronic card.

[0041] In one embodiment, the card surface generation device further includes:

[0042] A first proportion determination module, which is used to determine the consumption proportion of each consumption scenario according to the consumption amount of each consumption scenario in the consumption data after determining the consumption proportion corresponding to the target consumption scenario;

[0043] A second card surface generation module, which is used to sort the consumption proportions of each consumption scenario from large to small. If the consumption proportion corresponding to the target consumption scenario is among the top N in the sorting, generate a second pattern and use the second pattern to cover the card surface image of the electronic card, where N is an integer greater than zero.

[0044] In one embodiment, the card surface generation device further includes:

[0045] A second proportion determination module, which is used to obtain the consumption proportion of each consumption scenario after determining the consumption proportion corresponding to the target consumption scenario when it is detected that the consumption proportion corresponding to the target consumption scenario is not greater than the first threshold;

[0046] A pattern generation module, which is used to generate a basic card surface, take the center point of the basic card surface as the origin, divide the basic card surface by angles according to the consumption proportion of each consumption scenario, and fill different patterns for each divided area to obtain a third pattern;

[0047] A third card surface generation module, which is used to cover the card surface image with the third pattern.

[0048] In one embodiment, the card surface generation device further includes:

[0049] A threshold acquisition module, which is used to obtain the target threshold corresponding to the target consumption scenario configured by the user after determining the consumption proportion corresponding to the target consumption scenario;

[0050] A threshold determination module, which is used to use the target threshold as the first threshold.

[0051] In one embodiment, the card surface generation device further includes:

[0052] A monitoring module, which is used to monitor whether there is a triggering action in the card surface area of the electronic card after covering the card surface image with any pattern;

[0053] The fourth card surface generation module is configured to, if the triggering action is detected within the card surface area of the electronic card, select any preset pattern from a preset pattern library and use the any preset pattern to cover the card surface image.

[0054] In a third aspect, an embodiment of the present application provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for generating a card surface as described in the first aspect is implemented.

[0055] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method for generating a card surface as described in the first aspect is implemented.

[0056] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: The present application obtains the card surface image of the electronic card selected by the user, identifies the card surface image, determines the card tail number and the card surface number on the electronic card, determines the complete card number of the electronic card from the card information database according to the card tail number and the card surface number, and uses the complete card number to obtain the consumption data within the target time period from the big data platform. According to the current location of the user, the target consumption scenario where the user is located is determined. According to the consumption amount of each consumption scenario in the consumption data, the consumption ratio corresponding to the target consumption scenario is determined. When it is detected that the consumption ratio corresponding to the target consumption scenario is greater than the first threshold, a first pattern is generated and used to cover the card surface image, thereby realizing intelligent personalized card surface generation for the user, and prompting the user's current consumption through consumption historical data to achieve the purpose of reminding the user to pay attention and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1 FIG. 18 is a schematic diagram of an application environment of a method for generating a card surface based on big data provided in Embodiment 1 of the present application;

[0059] Figure 2 FIG. 22 is a schematic flowchart of a method for generating a card surface based on big data provided in Embodiment 2 of the present application;

[0060] Figure 3 FIG. 26 is a schematic flowchart of a method for generating a card surface based on big data provided in Embodiment 3 of the present application;

[0061] Figure 4 It is a schematic structural diagram of a card surface generation device based on big data provided in the fourth embodiment of the present application;

[0062] Figure 5 It is a schematic structural diagram of a computer device provided in the fifth embodiment of the present application. Detailed implementation manners

[0063] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are set forth in order to provide a thorough understanding of the embodiments of the present application. However, those skilled in the art should understand 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 so as not to obscure the description of the present application with unnecessary details.

[0064] It should be understood that when used in the specification and claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0065] It should also be understood that the term "and / or" as used in the specification and claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0066] As used in the specification and claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" depending on the context.

[0067] In addition, in the description of the specification and claims of the present application, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0068] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that specific features, structures, or characteristics described in connection with that embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0069] Embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0070] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0071] It should be understood that the magnitudes of the sequence numbers of the steps in the following embodiments do not mean the order of execution is prior or subsequent. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0072] To illustrate the technical solution of this application, the following will be described through specific embodiments.

[0073] A method for generating a card surface based on big data provided by Embodiment 1 of this application can be applied in an application environment such as Figure 1 , in which the client communicates with the server. Among them, the client includes but is not limited to computer devices such as palm computers, desktop computers, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, cloud computer devices, personal digital assistants (PDAs), etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0074] See Figure 2, which is a schematic flowchart of a method for generating a card surface based on big data provided in the second embodiment of this application. The above method for generating a card surface can be applied to Figure 1 the client in. The computer device corresponding to the client is connected to data sources such as corresponding servers and databases to obtain corresponding data. A client software is set in the above computer device, and the client software provides a corresponding software interface, and the software interface is used to display corresponding information to be displayed. As Figure 2 shown, the method for generating a card surface may include the following steps:

[0075] Step S201, obtain the card surface image of the electronic card selected by the user, identify the card surface image, and determine the card tail number and the card surface number on the electronic card.

[0076] In this application, after the user logs in to the client software on the client, the user can enter the corresponding software interface. The user selects an electronic card in the software interface. The card surface image of the electronic card is the card surface image set by the service provider providing the client software service for the electronic card. At this time, the card surface image is an inherent image, that is, the basic state of the card surface without operations such as generation or display.

[0077] After the user logs in to the client software, there may be at least two electronic cards in the software interface. Therefore, it is necessary to obtain the electronic card selected by the user. Operations such as the user clicking on the electronic card can be used as the user's selection operation.

[0078] The card surface image of the electronic card is an inherent image provided by the service provider. The card surface image contains digital identifiers, patterns, other identifiers, etc. By identifying the card surface image, the card tail number and the card surface number of the electronic card can be determined. For example, the card tail number of the electronic card is fixedly displayed in the central area of the card surface image of the electronic card, and the card surface number of the electronic card is fixedly displayed in the upper left corner area of the card surface image of the electronic card.

[0079] Optionally, identifying the card surface image and determining the card tail number and the card surface number on the electronic card includes:

[0080] Perform grayscale processing on the card surface image to obtain a grayscale image;

[0081] Use optical character recognition technology to identify the grayscale image, determine that the number in the first position area obtained by recognition is the card tail number on the electronic card, and determine that the number in the second area obtained by recognition is the card surface number on the electronic card.

[0082] Among them, when recognizing the card surface image, for the sake of recognition accuracy, the card surface image is first subjected to grayscale processing to obtain the corresponding grayscale image, and then the optical character recognition (OCR) technology is used on the grayscale image to recognize the card surface information, so as to extract the numbers in the first position area and the numbers in the second position area. If the first position area is defined as the area where the card tail number is located, the corresponding number is the card tail number; if the second position area is defined as the area where the card surface number is located, the corresponding number is the card surface number.

[0083] Step S202: According to the card tail number and the card surface number, determine the complete card number of the electronic card from the card information database, and use the complete card number to obtain the consumption data within the target time period from the big data platform.

[0084] In this application, the card information database stores the mapping relationship between the complete card number and the card surface number. Using the card surface number, at least one complete card number can be determined from the card information database, and then the complete card number corresponding to the tail number is matched from at least one complete card number, which is the complete card number of the electronic card selected by the user.

[0085] If the electronic card is a debit card, credit card, bus card or other cards with uniqueness, each consumption made with the electronic card is recorded in the corresponding big data platform. The data recorded in the big data platform includes consumption time, consumption amount, consumption scenario, refund details, etc. The target time period is a time period set according to requirements, such as one day, one week, one month, etc. Analyzing the data within the target time period can improve the processing efficiency and conform to general consumption habits.

[0086] Generally, the big data platform will set up corresponding firewalls, and only with the authorization of the big data platform can the consumption data be obtained from the big data platform. In one implementation manner, when the client logs in to the big data platform, it needs to obtain the complete card number. In addition, it also obtains the user information of the user who logs in to the client software, and then sends the user information to the big data platform, so that the big data platform can obtain the user information of this user. Before sending the user information of the user to the big data platform, the user can be asked whether to allow its client to send the user information to the big data platform. Only with the user's permission can the user information be provided to the big data platform, otherwise the current consumption data acquisition service is ended. If the user does not allow its client to send the user information to the big data platform, a reminder for granting permission can also be output to remind the user that it is necessary to allow its client to send the user information to the big data platform to obtain the consumption record. For example, a dialog box of "reminder for granting permission" pops up on the interface of the application program of the above-mentioned client, and the user can perform selection operations in the dialog box to realize granting permission or refusing to grant permission.

[0087] Step S203: Determine the target consumption scenario where the user is located based on the user's current location.

[0088] In this application, the current location may refer to the location where the user is currently located, and this current location can be achieved based on Beidou navigation, GPS navigation, or communication base station positioning. For the client software used by the user on their client, it is necessary to have the permission to obtain the client location information. If the permission to obtain the location information is not available, a reminder for granting permission needs to be output to remind the user that authorization is required to perform subsequent steps.

[0089] The current location coincides with marked points such as merchants. Therefore, the information actually displayed in the current location is the merchant name, and based on the analysis of the merchant name, it is possible to confirm the consumption scenario where the user is currently located, that is, the target consumption scenario.

[0090] Optionally, determining the target consumption scenario where the user is located based on the user's current location includes:

[0091] Obtain the text description information of the current location based on the user's current location;

[0092] Identify the text description information, determine the corresponding keywords for the current location, and use the keywords to match the corresponding scenario from the mapping relationship table as the target consumption scenario where the user is located.

[0093] Among them, the text description information can be information that describes the current location in text form, such as **Street** Shop (XX Store). Identifying the text description information is to identify and extract the keywords in the text description information to obtain the corresponding keywords. For example, for the text description information of the location “**Street** Shop (XX Store)”, “**Street** Shop” is the description of the store address, and “XX Store” is the description of the store name. The store name is related to its consumption scenario. Therefore, the store name is extracted as the keyword.

[0094] The mapping relationship table is a preset mapping relationship between keywords and consumption scenarios. One keyword can correspond to at least one scenario. Therefore, based on the keywords, the consumption scenario where the user is located can be determined.

[0095] Step S204: Determine the consumption proportion corresponding to the target consumption scenario according to the consumption amount of each consumption scenario in the consumption data.

[0096] In this application, after obtaining the consumption data, the consumption amounts of each consumption scenario can be statistically classified to determine the consumption amount corresponding to each consumption scenario. Match the target consumption scenario with each consumption scenario in the consumption data. If the target consumption scenario is not included in all consumption scenarios in the consumption data, the consumption proportion corresponding to the target consumption scenario is zero.

[0097] In one embodiment, the consumption amounts with the consumption scenario being the target consumption scenario are screened out from the above-mentioned consumption data, and the consumption amounts of the target consumption scenario are compared with all the consumption amounts in the consumption data to determine the consumption proportion corresponding to the target consumption scenario as the comparison result.

[0098] Step S205: When it is detected that the consumption proportion corresponding to the target consumption scenario is greater than the first threshold, generate a first pattern and use the first pattern to cover the card surface image.

[0099] In this application, the first threshold is a preset proportion value, which is greater than or equal to zero and less than or equal to 1. By comparing the consumption proportion with the first threshold, it can be determined whether the consumption proportion is greater than the first threshold. If the consumption proportion is greater than the first threshold, it indicates that the consumption proportion of the target consumption scenario in the target time period is relatively high. Therefore, generate a first pattern and use the first pattern to cover the card surface image, so as to achieve a prompt for the user. The first pattern can be red to play a warning and reminder role.

[0100] Optionally, after determining the consumption proportion corresponding to the target consumption scenario, it further includes:

[0101] Obtain the target threshold corresponding to the target consumption scenario configured by the user;

[0102] Use the target threshold as the first threshold.

[0103] Among them, different thresholds can be set for different consumption scenarios. Therefore, after determining the target consumption scenario, obtain the target threshold corresponding to the target consumption scenario and use it as the first threshold.

[0104] The target threshold can be pre-configured by the user in the client software and stored corresponding to the user's account, and can be directly called from the storage when in use. In one embodiment, the target threshold can also be configured by the user instantaneously. During the instant configuration process, a corresponding configuration interface needs to pop up in the software interface of the client software to obtain the target threshold configured by the user.

[0105] Optionally, after using any pattern to cover the card surface image, it further includes:

[0106] Monitor whether there is a triggering action in the card surface area of the electronic card;

[0107] If a triggering action is detected in the card surface area of the electronic card, select any preset pattern from the preset pattern library and use the preset pattern to cover the card surface image.

[0108] After using a pattern to cover the card surface image, continuously monitor whether there is a triggering action in the card surface area where the electronic card is located in the software interface of the client software. If there is, randomly select a pattern from the pattern library to cover the card surface image again. The user can selectively cover the card surface image. If the user is not satisfied with the pattern generated based on big data or does not want to display it, the user can click on the electronic card to switch the pattern.

[0109] In addition, if the user believes that data such as the proportion of consumption scenarios is relatively sensitive and does not want to display the proportion based on consumption scenarios, the user can switch the pattern by the above-mentioned method of triggering the card surface area.

[0110] The embodiment of the present application obtains the card surface image of the electronic card selected by the user, identifies the card surface image, determines the card tail number and the card surface number on the electronic card. According to the card tail number and the card surface number, the complete card number of the electronic card is determined from the card information database, and the consumption data within the target time period is obtained from the big data platform using the complete card number. According to the user's current location, the target consumption scenario where the user is located is determined. According to the consumption amount of each consumption scenario in the consumption data, the consumption proportion corresponding to the target consumption scenario is determined. When it is detected that the consumption proportion corresponding to the target consumption scenario is greater than the first threshold, a first pattern is generated and used to cover the card surface image, thus realizing intelligent personalized card surface generation for the user, and prompting the user's current consumption through consumption history data to achieve the purpose of reminding the user to pay attention and improving the user experience.

[0111] See Figure 3 FIG. Figure 3 shown, the card surface generation method may include the following steps:

[0112] Step S301, obtain the card surface image of the electronic card selected by the user, identify the card surface image, and determine the card tail number and the card surface number on the electronic card.

[0113] Step S302, according to the card tail number and the card surface number, determine the complete card number of the electronic card from the card information database, and obtain the consumption data within the target time period from the big data platform using the complete card number.

[0114] Step S303, determine the target consumption scenario where the user is located according to the user's current location.

[0115] Step S304, determine the consumption proportion corresponding to the target consumption scenario according to the consumption amount of each consumption scenario in the consumption data.

[0116] Among them, steps S301 to S304 are the same as the content of steps S201 to S204 above. For the description of steps S201 to S204, please refer to the above, and it will not be repeated here.

[0117] Step S305: Determine the consumption proportion of each consumption scenario according to the consumption amount of each consumption scenario in the consumption data.

[0118] In this application, each consumption scenario in the consumption data is statistically analyzed to determine the consumption proportion corresponding to each consumption scenario.

[0119] Step S306: Sort the consumption proportions of each consumption scenario from largest to smallest. If the consumption proportion corresponding to the target consumption scenario is among the top N in the sorting, generate a second pattern and use the second pattern to cover the card surface image of the electronic card.

[0120] In this application, according to the consumption proportion of the consumption scenario, the consumption scenarios are sorted from largest to smallest, and the top N consumption scenarios are extracted from the sorting results.

[0121] Detect whether the target consumption scenario belongs to one of the top N consumption scenarios. If not, it means that the target consumption scenario may not need to be prompted. If so, it means that the consumption proportion of the target consumption scenario is relatively high, and the card surface image needs to be covered with a different pattern to achieve the reminder effect.

[0122] Optionally, after determining the consumption proportion corresponding to the target consumption scenario, it further includes:

[0123] When it is detected that the consumption proportion corresponding to the target consumption scenario is not greater than the first threshold, obtain the consumption proportion of each consumption scenario;

[0124] Generate a basic card surface. Taking the center point of the basic card surface as the origin, divide the basic card surface by angles according to the consumption proportion of each consumption scenario, and fill different patterns for each divided area to obtain a third pattern;

[0125] Use the third pattern to cover the card surface image.

[0126] Among them, the basic card face can be a card face with a basic pattern. This card face has a fixed length and width, and the basic pattern can be white. In view of the relatively small consumption proportion corresponding to the above target consumption scenarios, the consumption proportion of each consumption scenario in the above consumption data is statistically analyzed, and a segmentation diagram is generated based on the statistical results. The origin of the segmentation diagram is the center point of the card face. According to the consumption proportion of each consumption scenario in the statistical results, 360° is divided into angles corresponding to the proportion. Different patterns can be filled in the divided areas, and then a mixed pattern is obtained. Using this pattern to cover the card face image, the card face is generated. For example, the segmentation diagram is a pie chart. The pie chart is segmented according to the consumption proportion, and different patterns are filled in each area to obtain a pie chart of the basic card face.

[0127] In the embodiment of the present application, the card face image of the electronic card selected by the user is obtained, the card face image is recognized, the card tail number and the card face number on the electronic card are determined. According to the card tail number and the card face number, the complete card number of the electronic card is determined from the card information database, and the consumption data within the target time period is obtained from the big data platform using the complete card number. According to the user's current location, the target consumption scenario where the user is located is determined. According to the consumption amount of each consumption scenario in the consumption data, the consumption proportion corresponding to the target consumption scenario is determined. According to the consumption amount of each consumption scenario in the consumption data, the consumption proportion of each consumption scenario is determined, and the consumption proportions of each consumption scenario are sorted from large to small. If the consumption proportion corresponding to the target consumption scenario is among the top N in the sorting, a second pattern is generated and used to cover the card face image of the electronic card, thereby providing another way to generate the card face, realizing intelligent personalized card face generation for users, and prompting the user's current consumption through consumption history data to achieve the purpose of reminding the user to pay attention and improving the user experience.

[0128] Corresponding to the card face generation method in the above embodiment Figure 4 The structural block diagram of the card face generation device based on big data provided in the fourth embodiment of the present application is shown. The above card face generation device can be applied to Figure 1 the server in, and the computer device corresponding to the server is connected to the corresponding database to obtain the corresponding data. The above computer device can also be connected to the client to collect the data sent by the user at the client. For the convenience of description, only the parts related to the embodiment of the present application are shown.

[0129] See Figure 4 This card face generation device includes:

[0130] A card face recognition module 41, configured to obtain the card face image of the electronic card selected by the user, recognize the card face image, and determine the card tail number and the card face number on the electronic card;

[0131] A consumption data acquisition module 42, configured to determine the complete card number of the electronic card from the card information database according to the card tail number and the card surface number, and use the complete card number to obtain the consumption data within the target time period from the big data platform;

[0132] A consumption scenario determination module 43, configured to determine the target consumption scenario where the user is located according to the user's current location;

[0133] A consumption ratio determination module 44, configured to determine the consumption ratio corresponding to the target consumption scenario according to the consumption amount of each consumption scenario in the consumption data;

[0134] A first card surface generation module 45, configured to generate a first pattern and use the first pattern to cover the card surface image when it is detected that the consumption ratio corresponding to the target consumption scenario is greater than the first threshold.

[0135] Optionally, the above consumption scenario determination module 43 includes:

[0136] A description information acquisition unit, configured to acquire the text description information of the current location according to the user's current location;

[0137] A consumption scenario determination unit, configured to identify the text description information, determine the keywords corresponding to the current location, and use the keywords to match the corresponding scenario from the mapping relationship table as the target consumption scenario where the user is located.

[0138] Optionally, the above card surface recognition module 41 includes:

[0139] A grayscale processing unit, configured to perform grayscale processing on the card surface image to obtain a grayscale image;

[0140] A card surface recognition unit, configured to use optical character recognition technology to recognize the grayscale image, determine that the number in the recognized first position area is the card tail number on the electronic card, and determine that the number in the recognized second area is the card surface number on the electronic card.

[0141] Optionally, the card surface generation device further includes:

[0142] A first ratio determination module, configured to determine the consumption ratio of each consumption scenario according to the consumption amount of each consumption scenario in the consumption data after determining the consumption ratio corresponding to the target consumption scenario;

[0143] A second card surface generation module, configured to sort the consumption ratios of each consumption scenario from largest to smallest. If the consumption ratio corresponding to the target consumption scenario is among the top N in the sorting, generate a second pattern and use the second pattern to cover the card surface image of the electronic card, where N is an integer greater than zero.

[0144] Optionally, the card surface generation device further includes:

[0145] The second proportion determination module is configured to, after determining the consumption proportion corresponding to the target consumption scenario, when detecting that the consumption proportion corresponding to the target consumption scenario is not greater than the first threshold, obtain the consumption proportion of each consumption scenario;

[0146] The pattern generation module is configured to generate a basic card face, take the center point of the basic card face as the origin, divide the basic card face by angles according to the consumption proportion of each consumption scenario, and fill different patterns in each divided area to obtain a third pattern;

[0147] The third card face generation module is configured to use the third pattern to cover the card face image.

[0148] Optionally, the card face generation device further includes:

[0149] The threshold acquisition module is configured to, after determining the consumption proportion corresponding to the target consumption scenario, obtain the target threshold corresponding to the target consumption scenario configured by the user;

[0150] The threshold determination module is configured to use the target threshold as the first threshold.

[0151] Optionally, the card face generation device further includes:

[0152] The monitoring module is configured to monitor whether there is a trigger action in the card face area of the electronic card after using any pattern to cover the card face image;

[0153] The fourth card face generation module is configured to, if a trigger action is detected in the card face area of the electronic card, select any preset pattern from a preset pattern library and use the any preset pattern to cover the card face image.

[0154] It should be noted that for the information interaction, execution process, etc. between the above modules, since they are based on the same concept as the method embodiments of this application, their specific functions and the technical effects brought, for details, please refer to the method embodiment part, and will not be elaborated here.

[0155] Figure 5 This is a schematic structural diagram of a computer device provided in Embodiment 5 of this application. As Figure 5 shown, the computer device of this embodiment includes: at least one processor ( Figure 5 only one is shown in

[0156] ), a memory, and a computer program stored in the memory and executable on at least one processor. When the processor executes the computer program, it implements the steps in any of the above-mentioned card face generation method embodiments. Figure 5The above are merely examples of computer devices and do not constitute limitations thereto. A computer device may include more or fewer components than those shown in the figures, or combine certain components, or have different components. For example, it may also include a network interface, a display screen, an input device, etc.

[0157] The so-called processor may be a CPU, and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0158] The memory includes a readable storage medium, an internal memory, etc. Among them, the internal memory may be the memory of the computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be the hard disk of the computer device, and in some other embodiments, it may also be an external storage device of the computer device. For example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the memory may also include both the internal storage unit and the external storage device of the computer device. The memory is used to store the operating system, application programs, a boot loader, data, and other programs, such as the program code of a computer program. The memory may also be used to temporarily store data that has been output or is to be output.

[0159] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above device can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here. 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 such an understanding, to implement all or part of the processes in the above method embodiments of this application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0160] All or part of the processes in the above method embodiments of this application can also be completed by a computer program product. When the computer program product runs on a computer device, it enables the computer device to execute and implement the steps in the above method embodiments.

[0161] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0162] Those of ordinary skill in the art can realize that the units and algorithm steps of each example 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 in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0163] In the embodiments provided in this application, it should be understood that the disclosed device / computer device and method can be implemented in other ways. For example, the device / computer device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0164] 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 can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] The above embodiments are only used to illustrate the technical solutions of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A method for generating a card surface based on big data, characterized in that, The described card face generation method includes: Obtain the card face image of the electronic card selected by the user, identify the card face image, and determine the card tail number and card face number on the electronic card; According to the card tail number and card face number, determine the complete card number of the electronic card from the card information database, and use the complete card number to obtain the consumption data within the target time period from the big data platform; According to the current location of the user, determine the target consumption scenario where the user is located, and the current location refers to the location where the user is currently located; According to the consumption amount of each consumption scenario in the consumption data, determine the consumption proportion corresponding to the target consumption scenario; When it is detected that the consumption proportion corresponding to the target consumption scenario is greater than the first threshold, generate a first pattern and use the first pattern to cover the card face image; When it is detected that the consumption proportion corresponding to the target consumption scenario is not greater than the first threshold, obtain the consumption proportion of each consumption scenario; Generate a basic card face, use the center point of the basic card face as the origin, divide the basic card face by the consumption proportion of each consumption scenario at an angle, and fill different patterns in each divided area to obtain a third pattern; Use the third pattern to cover the card face image.

2. A card surface generation method based on big data, characterized in that, The described card face generation method includes: Obtain the card face image of the electronic card selected by the user, identify the card face image, and determine the card tail number and card face number on the electronic card; According to the card tail number and card face number, determine the complete card number of the electronic card from the card information database, and use the complete card number to obtain the consumption data within the target time period from the big data platform; According to the current location of the user, determine the target consumption scenario where the user is located, and the current location refers to the location where the user is currently located; According to the consumption amount of each consumption scenario in the consumption data, determine the consumption proportion corresponding to the target consumption scenario; According to the consumption amount of each consumption scenario in the consumption data, determine the consumption proportion of each consumption scenario; Sort the consumption proportions of each consumption scenario from largest to smallest. If the consumption proportion corresponding to the target consumption scenario is among the top N in the sorting, generate a second pattern and use the second pattern to cover the card face image of the electronic card, where N is an integer greater than zero.

3. The card surface generation method according to claim 1, wherein Determining the target consumption scenario where the user is located according to the current location of the user includes: According to the current location of the user, obtain the text description information of the current location; Identify the text description information, determine the keywords corresponding to the current location, and use the keywords to match the corresponding scenario from the mapping relationship table as the target consumption scenario where the user is located.

4. The card surface generation method according to claim 1, wherein Identifying the card face image and determining the card tail number and card face number on the electronic card includes: Perform grayscale processing on the card face image to obtain a grayscale image; Use optical character recognition technology to identify the grayscale image, and determine that the number in the first position area obtained by recognition is the card tail number on the electronic card, and determine that the number in the second area obtained by recognition is the card face number on the electronic card.

5. The card surface generation method according to claim 1, characterized in that After determining the consumption proportion corresponding to the target consumption scenario, it further includes: Obtain the target threshold corresponding to the target consumption scenario configured by the user; Use the target threshold as the first threshold.

6. The card surface generation method according to any one of claims 1 to 5, characterized in that, After covering the card surface image with any pattern, it further includes: Monitor whether there is a trigger action in the card surface area of the electronic card; If the trigger action is detected in the card surface area of the electronic card, select any preset pattern from the preset pattern library and use the preset pattern to cover the card surface image.

7. A card surface generation device based on big data, characterized in that, The card surface generation device includes: A card surface recognition module, configured to obtain the card surface image of the electronic card selected by the user, recognize the card surface image, and determine the card tail number and card surface number on the electronic card; A consumption data acquisition module, configured to determine the complete card number of the electronic card from the card information database according to the card tail number and card surface number, and use the complete card number to obtain consumption data within a target time period from the big data platform; A consumption scenario determination module, configured to determine the target consumption scenario where the user is located according to the current location of the user, and the current location refers to the location where the user is currently located; A consumption ratio determination module, configured to determine the consumption ratio corresponding to the target consumption scenario according to the consumption amount of each consumption scenario in the consumption data; A first card surface generation module, configured to generate a first pattern when detecting that the consumption ratio corresponding to the target consumption scenario is greater than the first threshold, and use the first pattern to cover the card surface image; A second ratio determination module, configured to obtain the consumption ratio of each consumption scenario when detecting that the consumption ratio corresponding to the target consumption scenario is not greater than the first threshold; A pattern generation module, configured to generate a basic card surface, use the center point of the basic card surface as the origin, divide the basic card surface by the consumption ratio of each consumption scenario at an angle, and fill different patterns for each divided area to obtain a third pattern; A third card surface generation module, configured to use the third pattern to cover the card surface image.

8. A card surface generation device based on big data, characterized in that, The card surface generation device includes: A card surface recognition module, configured to obtain the card surface image of the electronic card selected by the user, recognize the card surface image, and determine the card tail number and card surface number on the electronic card; A consumption data acquisition module, configured to determine the complete card number of the electronic card from the card information database according to the card tail number and card surface number, and use the complete card number to obtain consumption data within a target time period from the big data platform; A consumption scenario determination module, configured to determine the target consumption scenario where the user is located according to the current location of the user, and the current location refers to the location where the user is currently located; A consumption ratio determination module, configured to determine the consumption ratio corresponding to the target consumption scenario according to the consumption amount of each consumption scenario in the consumption data; A first ratio determination module, configured to determine the consumption ratio of each consumption scenario according to the consumption amount of each consumption scenario in the consumption data; The second card face generation module is used to sort the consumption ratios of each consumption scenario from largest to smallest. If the consumption ratio corresponding to the target consumption scenario is among the top N in the sorting, a second pattern is generated, and the card face image of the electronic card is covered with the second pattern, where N is an integer greater than zero.

9. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the card face generation method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the card face generation method according to any one of claims 1 to 6 is implemented.

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