Financial data display method and system based on big data processing

Abstract

The invention provides a financial data display method and system based on big data processing, and relates to the technical field of financial data display, and the method comprises the steps: employing a consumption information determination model to determine a plurality of pieces of to-be-displayed financial consumption data on the same day; generating a plurality of simulation display layouts by using a generative adversarial network based on the user information, the plurality of pieces of financial consumption data of the user on the day and the plurality of pieces of financial consumption data to be displayed on the day; obtaining a target display layout; according to the method, financial data display is performed on multiple pieces of to-be-displayed financial consumption data on the same day based on the target display layout, the financial consumption data concerned by the user can be accurately displayed, and the displayed content is ensured to be closer to the actual demand of the user.

Description

Technical Field

[0001] The present invention relates to the field of financial data display technology, and in particular to a financial data display method and system based on big data processing. Background Art

[0002] With the rapid development of Internet finance, personal financial management has become an important part of modern life. Users conduct daily financial transactions such as shopping, transferring money, and investing through various online platforms and mobile applications. These transactions not only generate a large amount of financial consumption data, but also involve frequent data changes. Traditional methods usually send all financial data change information directly to the user's mobile phone, regardless of whether the information is useful to the user. Due to the frequent financial activities of users, a large amount of data change information may be generated every day, resulting in excessive amount of information received by users. Too much information not only increases the reading burden of users, but also may cause users to miss really important information, affecting user experience.

[0003] Therefore, how to accurately display the financial consumption data that users care about and ensure that the displayed content is closer to the users' actual needs is an issue that needs to be urgently addressed. Summary of the invention

[0004] The main technical problem solved by the present invention is how to accurately display the financial consumption data that users are concerned about, ensuring that the displayed content is closer to the actual needs of users.

[0005] According to a first aspect, the present invention provides a financial data display method based on big data processing, including: obtaining user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history; using a consumption information determination model to determine multiple financial consumption data to be displayed on the day based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data of the user in history; using a generative adversarial network to generate multiple simulated display layouts based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and displaying the multiple simulated display layouts on a software interface, wherein the number of display boxes in each simulated display layout is equal to the number of financial consumption data to be displayed on the day; obtaining a target display layout, wherein the target display layout is selected by the user from the multiple simulated display layouts; and displaying financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

[0006] In a possible implementation, the displaying of financial data based on the target display layout and the plurality of financial consumption data to be displayed on the day includes: Constructing a financial graph structure, wherein the financial graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, each node represents a display frame in a target display layout, node features of each node include user information, display frame information, and a plurality of financial consumption data to be displayed on the day, and features of the edges between the nodes include a distance and a direction between the display frames; Processing the financial graph structure based on a graph attention network to determine the user attention of each display frame and the financial consumption data to be displayed corresponding to each display frame; Determining the number of displayed words in each display frame in the target display layout based on the user attention of each display frame and the target display layout; Based on the number of displayed words in each display frame in the target display layout and the to-be-displayed financial consumption data corresponding to each display frame, a word number cutting model is used to cut the number of words and obtain the target financial data corresponding to each display frame; The financial data is displayed based on the target financial data corresponding to each display frame.

[0007] In one possible implementation, the consumption information determination model is a long-short term neural network model, the input of the consumption information determination model is the user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history, and the output of the consumption information determination model is multiple financial consumption data to be displayed on the day.

[0008] In a possible implementation, the input of the generative adversarial network is the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and the output of the generative adversarial network is multiple simulated display layouts.

[0009] According to a second aspect, the present invention provides a financial data display system based on big data processing, comprising: The first acquisition module is used to acquire user information, multiple financial consumption data of the user on the same day, and multiple financial consumption data of the user in history; A determination module, configured to determine the plurality of financial consumption data to be displayed on the day using a consumption information determination model based on the user information, the plurality of financial consumption data of the user on the day, and the plurality of financial consumption data of the user in history; A generation module, configured to generate multiple simulated display layouts using a generative adversarial network based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and display the multiple simulated display layouts on a software interface, wherein the number of display frames in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; A second acquisition module is used to acquire a target display layout, where the target display layout is selected by a user from the multiple simulated display layouts; The display module is used to display financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

[0010] In a possible implementation, the display module is further used for: Constructing a financial graph structure, wherein the financial graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, each node represents a display frame in a target display layout, node features of each node include user information, display frame information, and a plurality of financial consumption data to be displayed on the day, and features of the edges between the nodes include a distance and a direction between the display frames; Processing the financial graph structure based on a graph attention network to determine the user attention of each display frame and the financial consumption data to be displayed corresponding to each display frame; Determining the number of displayed words in each display frame in the target display layout based on the user attention of each display frame and the target display layout; Based on the number of displayed words in each display frame in the target display layout and the to-be-displayed financial consumption data corresponding to each display frame, a word number cutting model is used to cut the number of words and obtain the target financial data corresponding to each display frame; The financial data is displayed based on the target financial data corresponding to each display frame.

[0011] In one possible implementation, the consumption information determination model is a long-short term neural network model, the input of the consumption information determination model is the user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history, and the output of the consumption information determination model is multiple financial consumption data to be displayed on the day.

[0012] In a possible implementation, the input of the generative adversarial network is the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and the output of the generative adversarial network is multiple simulated display layouts.

[0013] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method as described above, the method comprising: obtaining user information, multiple financial consumption data of the user on the day, and multiple financial consumption data in the user's history; using a consumption information determination model to determine multiple financial consumption data to be displayed on the day based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data in the user's history; using a generative adversarial network to generate multiple simulated display layouts based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and displaying the multiple simulated display layouts on a software interface, the number of display boxes in each simulated display layout being equal to the number of the multiple financial consumption data to be displayed on the day; obtaining a target display layout, the target display layout being selected by the user from the multiple simulated display layouts; and displaying financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

[0014] According to the fourth aspect, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned financial data display method based on big data processing, the method comprising: obtaining user information, multiple financial consumption data of the user on the day, and multiple financial consumption data in the user's history; using a consumption information determination model to determine multiple financial consumption data to be displayed on the day based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data in the user's history; using a generative adversarial network to generate multiple simulated display layouts based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and displaying the multiple simulated display layouts on a software interface, the number of display boxes in each simulated display layout being equal to the number of the multiple financial consumption data to be displayed on the day; obtaining a target display layout, the target display layout being selected by the user from the multiple simulated display layouts; and displaying financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

[0015] The present invention provides a financial data display method and system based on big data processing, the method comprising obtaining user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history; using a consumption information determination model to determine multiple financial consumption data to be displayed on the day based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data of the user in history; using a generative adversarial network to generate multiple simulated display layouts based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and displaying the multiple simulated display layouts on a software interface, wherein the number of display frames in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; obtaining a target display layout, wherein the target display layout is selected by the user from the multiple simulated display layouts; and displaying financial data based on the target display layout and the multiple financial consumption data to be displayed on the day. The method can accurately display the financial consumption data that the user is concerned about, and ensure that the displayed content is closer to the actual needs of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an application scenario of a financial data display method based on big data processing provided by an embodiment of the present invention; Figure 2 A flowchart of a financial data display method based on big data processing provided by an embodiment of the present invention; Figure 3 A schematic diagram of a process for displaying financial data based on a target display layout and multiple financial consumption data to be displayed on the same day provided by an embodiment of the present invention; Figure 4 A schematic diagram of a financial data display system based on big data processing provided by an embodiment of the present invention; Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present invention are not shown or described in the specification, this is to avoid the core part of the present invention being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0018] Figure 1 A schematic diagram of an application scenario of a financial data display method based on big data processing provided by an embodiment of the present invention. Figure 1 The application scenario of the financial data display method based on big data processing may include a server 11, a network 12, a terminal 13 and a storage device 14.

[0019] In some embodiments, the server 11 may be a single server or a server group. The server 11 may access information and / or data stored in the terminal 13 or the storage device 14 through the network 12. In some embodiments, the server 11 may be used to execute Figure 2 The financial data display method based on big data processing shown in.

[0020] The network 12 may facilitate the exchange of information and / or data. In some embodiments, the network 12 may be any form of wired or wireless network, or any combination thereof.

[0021] Terminal 13 may refer to one or more terminal devices used by a user. In some embodiments, terminal 13 may include one or more combinations of a mobile device, a tablet computer, a laptop computer, and the like.

[0022] The storage device 14 may store data and / or instructions. For example, the storage device 14 may store data instructions of a financial data display method based on big data processing.

[0023] In an embodiment of the present invention, there is provided Figure 2 A financial data display method based on big data processing is shown, and the financial data display method based on big data processing includes steps S1 to S5: Step S1, obtaining user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history; User information includes the user's personal information, such as name, age, gender, occupation, income level, consumption preferences, etc. For example, user information includes Zhang San, 28 years old, male, software engineer, monthly income of 10,000 yuan, and likes traveling and food.

[0024] The multiple financial consumption data of the user on the same day include multiple financial consumption records of the user on the current date, including consumption amount, consumption time, consumption location, consumption category, etc. For example, the multiple financial consumption data of the user on the same day include that on October 15, 2024, Zhang San spent 30 yuan in a coffee shop, 60 yuan in a restaurant at noon, and 50 yuan in a movie theater at night.

[0025] The multiple pieces of financial consumption data in the user's history include multiple financial consumption records of the user in the past period of time, including consumption amount, consumption time, consumption location, consumption category, etc. For example, the multiple pieces of financial consumption data in the user's history include that from September 1, 2024 to October 15, 2024, Zhang San spent a total of 300 yuan in a coffee shop, 1,200 yuan in a restaurant, 200 yuan in a cinema, and 500 yuan in a supermarket.

[0026] Step S2, determining multiple financial consumption data to be displayed on the current day using a consumption information determination model based on the user information, multiple financial consumption data of the user on the current day, and multiple financial consumption data of the user in history; The number of financial consumption data to be displayed is a plurality of financial consumption data to be displayed on the day that the user is most concerned about.

[0027] The consumption information determination model is a long-short term neural network model, the input of the consumption information determination model is the user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history, and the output of the consumption information determination model is multiple financial consumption data to be displayed on the day.

[0028] The long-term and short-term neural network model includes a long-term and short-term neural network (LSTM, Long Short-Term Memory). The long-term and short-term neural network model can process the multiple financial consumption data of the user on the same day and the multiple financial consumption data of the user in the past in a continuous time period, can better capture the relationship in the time series of the multiple financial consumption data of the user on the same day and the multiple financial consumption data of the user in the past, and can output the features that comprehensively consider the correlation relationship between the multiple financial consumption data of the user on the same day and the multiple financial consumption data of the user in the past at each time point, so that the output features are more accurate and comprehensive.

[0029] User information, multiple pieces of financial consumption data of the user on the same day, and multiple pieces of financial consumption data of the user in history have obvious time series and correlation. These data not only reflect the user's current consumption behavior, but also include the user's historical consumption patterns and preferences. The long short-term neural network (LSTM) model is particularly good at processing time series data and can capture long-term dependencies in time series.

[0030] The long-term and short-term neural network model can more accurately filter out the financial consumption data that users are most concerned about and reduce information overload. For example, the long-term and short-term neural network can identify that users are more concerned about large-amount consumption and specific types of consumption, and thus display these data first.

[0031] Step S3, generating multiple simulated display layouts using a generative adversarial network based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and displaying the multiple simulated display layouts on a software interface, wherein the number of display frames in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; The simulated display layout is a layout scheme generated by the generative adversarial network. Each simulated display layout contains multiple display frames, each of which is used to display a piece of financial consumption data.

[0032] For example, layout 1: Display box 1: 10:00 coffee shop 30 yuan, display box 2: 12:00 restaurant 60 yuan, display box 3: 19:00 cinema 50 yuan.

[0033] Layout 2: Display frame 1: 12:00 restaurant 60 yuan, display frame 2: 19:00 cinema 50 yuan, display frame 3: 10:00 coffee shop 30 yuan.

[0034] Layout 3: Display frame 1: 19:00 Cinema 50 yuan, Display frame 2: 12:00 Restaurant 60 yuan, Display frame 3: 10:00 Coffee shop 30 yuan.

[0035] The input of the generative adversarial network is the user information, multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and the output of the generative adversarial network is multiple simulated display layouts.

[0036] The Generative Adversarial Network (GAN) consists of a generator and a discriminator. These two parts compete with each other and learn from each other to promote the training of the model. The generator is responsible for generating simulated display layouts. The generator generates a variety of different display layouts by learning the characteristics of user information, multiple financial consumption data of the user on the same day, and multiple financial consumption data to be displayed on the same day. The discriminator is responsible for evaluating the authenticity of the generated display layout. The discriminator provides feedback by comparing the generated layout with the real layout to help the generator continuously optimize the quality of the generated layout.

[0037] User information, multiple financial consumption data of the user on the same day, and multiple financial consumption data to be displayed on the same day provide rich information, which includes not only the user's background and preferences, but also current and historical consumption behaviors. Generative adversarial networks (GANs) can use this rich information to generate a variety of simulated display layouts to meet the personalized needs of different users. The number of display boxes in each simulated display layout generated by the generator is equal to the number of multiple financial consumption data to be displayed on the same day. This is because each display box needs to display a specific financial consumption data to ensure the integrity and readability of the information. The generator will generate a corresponding number of display boxes based on the number of financial consumption data input. The generator will generate multiple different layout schemes for users to choose from.

[0038] Step S4, obtaining a target display layout, wherein the target display layout is selected by the user from the multiple simulated display layouts.

[0039] Multiple simulated display layouts generated by the generative adversarial network are displayed on the software interface. Each layout includes multiple display frames, each of which is used to display a piece of financial consumption data. The user browses multiple simulated display layouts and selects the most satisfactory layout according to his or her preferences and needs. In some embodiments, the system can record the target display layout selected by the user for subsequent use.

[0040] Step S5, displaying financial data based on the target display layout and the plurality of financial consumption data to be displayed on the day.

[0041] In some embodiments, Figure 3 A schematic diagram of a process for displaying financial data based on a target display layout and multiple financial consumption data to be displayed on the same day provided in an embodiment of the present invention, wherein the process for displaying financial data based on a target display layout and multiple financial consumption data to be displayed on the same day includes steps S21 to S25: Step S21, constructing a financial graph structure, wherein the financial graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, each node represents a display frame in the target display layout, the node features of each node include user information, display frame information, and a plurality of financial consumption data to be displayed on the day, and the features of the edges between the nodes include the distance and direction between the display frames; Construct a graph data structure, where each node represents a display box in the target display layout, the node features of each node include user information, and the features of the edges between nodes include the distance and direction between the display boxes. For example: the nodes include display box 1, display box 2, and display box 3, and the edges include the distance from display box 1 to display box 2 is 50px, the direction is right, and the distance from display box 2 to display box 3 is 30px, and the direction is down. The display layout is structured as a graph to facilitate subsequent graph attention network processing.

[0042] The display frame information includes the display frame size and position.

[0043] Step S22, processing the financial graph structure based on the graph attention network to determine the user attention of each display frame and the financial consumption data to be displayed corresponding to each display frame; Graph Attention Network (GAT) is used to process graph structure data. Graph Attention Network learns the feature representation of nodes through the attention mechanism. Graph Attention Network can capture the relationship between nodes and assign a weight to each node to indicate its importance.

[0044] The financial graph structure is a graph data structure in which each node represents a display frame. The features of each node include user information, display frame information, and multiple financial consumption data to be displayed on the day. The features of the edges between nodes include the distance and direction between display frames. This structure can effectively represent the relationship and information between display frames. Each display frame, as a node, contains user information, display frame information, and multiple financial consumption data to be displayed on the day. These features can reflect the importance and content of the display frame. The distance and direction between display frames, as edge features, can reflect the spatial relationship between display frames. These relationships are very important for determining user attention, because users may pay more attention to display frames that are more prominent or more reasonably arranged. The graph attention network assigns a weight to each node through the attention mechanism to indicate its importance. These weights reflect the user's attention to each display frame. The graph attention network can dynamically adjust the weight of each display frame based on the node features and edge features to ensure that the information that users care about most is displayed first.

[0045] User attention reflects the attractiveness of each display frame to the user, which is calculated by the graph attention network. User attention reflects the user's attention to a certain display frame.

[0046] Step S23, determining the number of displayed words in each display frame in the target display layout based on the user attention of each display frame and the target display layout; In some embodiments, the number of displayed characters in each display frame can be determined by querying a preset display character count table, wherein the preset display character count table includes the user attention of each display frame, the target display layout, and the corresponding number of displayed characters in each display frame, and the preset display character count table can be artificially constructed based on historical data.

[0047] In some embodiments, the number of displayed words for each display box in the target display layout can be determined by a display word count determination model. The display word count determination model is a deep neural network model. The input of the display word count determination model is the user attention of each display box and the target display layout, and the output of the display word count determination model is the number of displayed words for each display box in the target display layout. Generally, the higher the user attention, the more displayed words. The display word count determination model can dynamically adjust the number of displayed words according to the user attention to ensure that the information that the user cares about most is displayed in more detail. The deep neural network model includes a deep neural network (DNN).

[0048] Step S24, based on the number of displayed characters of each display frame in the target display layout and the to-be-displayed financial consumption data corresponding to each display frame, a character number cutting model is used to cut the characters and obtain the target financial data corresponding to each display frame; In some embodiments, the word count trimming model is a deep neural network model. The input of the word count trimming model is the number of displayed words in each display frame in the target display layout, and the financial consumption data to be displayed corresponding to each display frame. The output of the word count trimming model is the target financial data corresponding to each display frame. Assuming that the number of displayed words in a display frame is 10, and the corresponding financial consumption data is "spending 35 yuan at a coffee shop at the gate of Peking University at 10:30", the trimmed data is "35 yuan at a coffee shop at 10:30".

[0049] In some embodiments, the word count cutting model includes an amount determination layer, a key information segment determination layer, a key information segment relevance determination layer, and a cutting layer. The amount determination layer, the key information segment determination layer, the key information segment relevance determination layer, and the cutting layer all include a deep neural network. The input of the amount determination layer is the financial consumption data to be displayed corresponding to each display frame, the output of the amount determination layer is the consumption amount, the input of the key information segment determination layer is the financial consumption data to be displayed corresponding to each display frame, the output of the key information segment relevance determination layer is multiple key information segments of each financial consumption data to be displayed, the input of the key information segment relevance determination layer is multiple key information segments and consumption amount of each financial consumption data to be displayed, the output of the key information segment determination layer is the relevance between each key information segment and the consumption amount, the input of the cutting layer is the relevance between each key information segment and the consumption amount, multiple key information segments and consumption amount of each financial consumption data to be displayed, and the output of the cutting layer is the target financial data corresponding to each display frame.

[0050] Step S25, displaying financial data based on the target financial data corresponding to each display frame.

[0051] In the target display layout selected by the user, each display frame displays the cropped target financial data.

[0052] Based on the same inventive concept, Figure 4 A schematic diagram of a financial data display system based on big data processing provided by an embodiment of the present invention, wherein the financial data display system based on big data processing includes: The first acquisition module 41 is used to acquire user information, multiple financial consumption data of the user on the same day, and multiple financial consumption data of the user in history; A determination module 42, configured to determine the plurality of financial consumption data to be displayed on the day using a consumption information determination model based on the user information, the plurality of financial consumption data of the user on the day, and the plurality of financial consumption data of the user in history; A generating module 43 is used to generate multiple simulated display layouts using a generative adversarial network based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and display the multiple simulated display layouts on the software interface, wherein the number of display frames in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; A second acquisition module 44 is used to acquire a target display layout, where the target display layout is selected by a user from the multiple simulated display layouts; The display module 45 is used to display financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

[0053] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 5 As shown, including: The invention comprises: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and is configured to be executed by the processor 51 to implement the financial data display method based on big data processing as provided above, the method comprising: obtaining user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user's history; determining multiple financial consumption data to be displayed on the day using a consumption information determination model based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data of the user's history; generating multiple simulated display layouts using a generative adversarial network based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and displaying the multiple simulated display layouts on a software interface, wherein the number of display boxes in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; obtaining a target display layout, wherein the target display layout is selected by the user from the multiple simulated display layouts; and displaying financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

[0054] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor 51, implements the aforementioned financial data display method based on big data processing, the method comprising: obtaining user information, multiple financial consumption data of the user on the day, and multiple financial consumption data in the user's history; using a consumption information determination model to determine multiple financial consumption data to be displayed on the day based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data in the user's history; using a generative adversarial network to generate multiple simulated display layouts based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and displaying the multiple simulated display layouts on a software interface, wherein the number of display boxes in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; obtaining a target display layout, the target display layout being selected by the user from the multiple simulated display layouts; and displaying financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

[0055] The financial data display method based on big data processing provided in the embodiments of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR) and virtual reality (VR) devices, smart home devices, car computers and other electronic devices, and the embodiments of the present application do not impose any restrictions on this.

[0056] Taking the mobile phone 100 as an example of the electronic device, Figure 6 A schematic structural diagram of the mobile phone 100 is shown.

[0057] like Figure 6 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0058] The processing module 110 may include one or more processing units, for example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0059] The processing module 110 can be used to: obtain user information, multiple financial consumption data of the user on the current day, and multiple financial consumption data of the user in history; determine multiple financial consumption data to be displayed on the current day using a consumption information determination model based on the user information, multiple financial consumption data of the user on the current day, and multiple financial consumption data of the user in history; generate multiple simulated display layouts using a generative adversarial network based on the user information, multiple financial consumption data of the user on the current day, and multiple financial consumption data to be displayed on the current day, and display the multiple simulated display layouts on a software interface, wherein the number of display boxes in each simulated display layout is equal to the number of financial consumption data to be displayed on the current day; obtain a target display layout, wherein the target display layout is selected by the user from the multiple simulated display layouts; and display financial data based on the target display layout and the multiple financial consumption data to be displayed on the current day.

[0060] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0061] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0062] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0063] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0064] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A financial data display method based on big data processing, characterized in that: include: Obtain user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history; Determine the multiple financial consumption data to be displayed on the current day using a consumption information determination model based on the user information, the multiple financial consumption data of the user on the current day, and the multiple financial consumption data of the user in history; Based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, a plurality of simulated display layouts are generated using a generative adversarial network, and the multiple simulated display layouts are displayed on a software interface, wherein the number of display frames in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; Acquire a target display layout, where the target display layout is selected by a user from among the multiple simulated display layouts; Financial data is displayed based on the target display layout and the multiple financial consumption data to be displayed on the day.

2. The financial data display method based on big data processing according to claim 1, characterized in that: The display of financial data based on the target display layout and the plurality of financial consumption data to be displayed on the day includes: Constructing a financial graph structure, wherein the financial graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, each node represents a display frame in a target display layout, node features of each node include user information, display frame information, and a plurality of financial consumption data to be displayed on the day, and features of the edges between the nodes include a distance and a direction between the display frames; Processing the financial graph structure based on a graph attention network to determine the user attention of each display frame and the financial consumption data to be displayed corresponding to each display frame; Determining the number of displayed words in each display frame in the target display layout based on the user attention of each display frame and the target display layout; Based on the number of displayed words in each display frame in the target display layout and the to-be-displayed financial consumption data corresponding to each display frame, a word number cutting model is used to cut the number of words and obtain the target financial data corresponding to each display frame; The financial data is displayed based on the target financial data corresponding to each display frame.

3. The financial data display method based on big data processing according to claim 1, characterized in that: The consumption information determination model is a long-short term neural network model, the input of the consumption information determination model is the user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history, and the output of the consumption information determination model is multiple financial consumption data to be displayed on the day.

4. The financial data display method based on big data processing according to claim 1, characterized in that: The input of the generative adversarial network is the user information, multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and the output of the generative adversarial network is multiple simulated display layouts.

5. A financial data display system based on big data processing, characterized in that: include: The first acquisition module is used to acquire user information, multiple financial consumption data of the user on the same day, and multiple financial consumption data of the user in history; A determination module, configured to determine the plurality of financial consumption data to be displayed on the day using a consumption information determination model based on the user information, the plurality of financial consumption data of the user on the day, and the plurality of financial consumption data of the user in history; A generation module, configured to generate multiple simulated display layouts using a generative adversarial network based on the user information, the multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and display the multiple simulated display layouts on a software interface, wherein the number of display frames in each simulated display layout is equal to the number of the multiple financial consumption data to be displayed on the day; A second acquisition module is used to acquire a target display layout, where the target display layout is selected by a user from the multiple simulated display layouts; The display module is used to display financial data based on the target display layout and the multiple financial consumption data to be displayed on the day.

6. The financial data display system based on big data processing according to claim 5, characterized in that: The display module is also used for: Constructing a financial graph structure, wherein the financial graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, each node represents a display frame in a target display layout, node features of each node include user information, display frame information, and a plurality of financial consumption data to be displayed on the day, and features of the edges between the nodes include a distance and a direction between the display frames; Processing the financial graph structure based on a graph attention network to determine the user attention of each display frame and the financial consumption data to be displayed corresponding to each display frame; Determining the number of displayed words in each display frame in the target display layout based on the user attention of each display frame and the target display layout; Based on the number of displayed words in each display frame in the target display layout and the to-be-displayed financial consumption data corresponding to each display frame, a word number cutting model is used to cut the number of words and obtain the target financial data corresponding to each display frame; The financial data is displayed based on the target financial data corresponding to each display frame.

7. The financial data display system based on big data processing according to claim 5, characterized in that: The consumption information determination model is a long-short term neural network model, the input of the consumption information determination model is the user information, multiple financial consumption data of the user on the day, and multiple financial consumption data of the user in history, and the output of the consumption information determination model is multiple financial consumption data to be displayed on the day.

8. The financial data display system based on big data processing according to claim 5, characterized in that: The input of the generative adversarial network is the user information, multiple financial consumption data of the user on the day, and the multiple financial consumption data to be displayed on the day, and the output of the generative adversarial network is multiple simulated display layouts.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the financial data display method based on big data processing as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the financial data display method based on big data processing as described in any one of claims 1 to 4 is implemented.

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