Software development method and system based on big data processing

Abstract

The invention provides a software development method and system based on big data processing, and relates to the technical field of software development, and the method comprises the steps: obtaining a screen recording operation video and software development description characters of user preference software; determining an interactive interface and user interface preference information of the user preference software by using the analysis model based on the screen recording operation video of the user preference software; determining the lowest user demand module number and the highest user demand module number based on the software development description text; generating a plurality of simulation software interaction interfaces by using a generative adversarial network based on the minimum user demand module number, the maximum user demand module number, the interaction interfaces of the user preference software and user interface preference information; the target software interaction interface is determined based on the multiple simulation software interaction interfaces. The method can efficiently and accurately develop the software interaction interface meeting user preferences and requirements.

Description

Technical Field

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

[0002] In the field of software development, as user needs become increasingly diversified and personalized, how to quickly and accurately develop software products that meet user expectations based on user preferences and needs has become a major challenge facing software developers. In the modern software development process, the design of the user interface is crucial to the user experience and the success of the software. Traditional software interface design methods usually rely on the experience and intuition of designers. The personal experience and preferences of designers may not match the preferences and needs of actual users, resulting in an unsatisfactory designed interface. Traditional design methods require multiple iterations and user feedback to gradually optimize the interface, which is time-consuming.

[0003] Therefore, how to efficiently and accurately develop a software interaction interface that meets user preferences and needs is a problem that needs to be solved urgently. Summary of the invention

[0004] The main technical problem solved by the present invention is how to efficiently and accurately develop a software interaction interface that meets user preferences and needs.

[0005] According to a first aspect, the present invention provides a software development method based on big data processing, including: obtaining a screen recording operation video of a user's favorite software and a software development description text; using an analysis model based on the screen recording operation video of the user's favorite software to determine the interactive interface and user interface preference information of the user's favorite software; determining the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text; using a generative adversarial network to generate multiple simulated software interactive interfaces based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interactive interface of the user's favorite software, and the user interface preference information; and determining a target software interactive interface based on the multiple simulated software interactive interfaces.

[0006] In a possible implementation, determining the target software interaction interface based on the multiple simulated software interaction interfaces includes: Acquire a simulation software interaction interface selected by a user, wherein the simulation software interaction interface selected by the user is selected by the user from the multiple simulation software interaction interfaces; acquire multiple pre-stored template software interaction interfaces; construct a graph structure, wherein the graph structure includes multiple nodes and multiple edges between the multiple nodes, wherein the multiple nodes include the simulation software interaction interface node selected by the user and multiple template software interaction interface nodes, wherein the simulation software interaction interface node selected by the user is a central node, and each template software interaction interface node respectively establishes an edge with the simulation software interaction interface node selected by the user, and the node features of the simulation software interaction interface node selected by the user include the simulation software interaction interface selected by the user, and the node features of the template software interaction interface node include the template software interaction interface and the user interface preference information, and the features of each template software interaction interface node respectively establishing an edge with the simulation software interaction interface node selected by the user include the similarity between the template software interaction interface and the simulation software interaction interface selected by the user; and process the graph structure based on a graph convolutional network to determine a target software interaction interface, wherein the target software interaction interface is one of the multiple template software interaction interfaces.

[0007] In one possible implementation, the analysis model is a long-short term neural network model, the input of the analysis model is a screen recording running video of the user's favorite software, and the output of the analysis model is the interactive interface and user interface preference information of the user's favorite software.

[0008] In a possible implementation, the user interface preference information includes interface color, interface layout, interface complexity, and font size.

[0009] According to a second aspect, the present invention provides a software development system based on big data processing, comprising: The acquisition module is used to obtain the screen recording operation video and software development description text of the user's favorite software; An analysis module, for determining the interactive interface and user interface preference information of the user's favorite software based on the screen recording operation video of the user's favorite software using an analysis model; A module quantity determination module, used to determine the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text; A generation module, configured to generate a plurality of simulated software interaction interfaces using a generative adversarial network based on the number of modules required by the user at the minimum, the number of modules required by the user at the maximum, the interaction interface of the software preferred by the user, and the user interface preference information; The interface determination module is used to determine the target software interaction interface based on the multiple simulated software interaction interfaces.

[0010] In a possible implementation, the interface determination module is also used to: obtain a simulation software interaction interface selected by a user, wherein the simulation software interaction interface selected by the user is selected by the user from the multiple simulation software interaction interfaces; obtain multiple pre-stored template software interaction interfaces; construct a graph structure, wherein the graph structure includes multiple nodes and multiple edges between the multiple nodes, wherein the multiple nodes include a simulation software interaction interface node selected by the user and multiple template software interaction interface nodes, wherein the simulation software interaction interface node selected by the user is a central node, and each template software interaction interface node establishes an edge with the simulation software interaction interface node selected by the user, and the node features of the simulation software interaction interface node selected by the user include the simulation software interaction interface selected by the user, and the node features of the template software interaction interface node include the template software interaction interface and the user interface preference information, and the features of each template software interaction interface node establishing an edge with the simulation software interaction interface node selected by the user include the similarity between the template software interaction interface and the simulation software interaction interface selected by the user; and process the graph structure based on a graph convolutional network to determine a target software interaction interface, wherein the target software interaction interface is one of the multiple template software interaction interfaces.

[0011] In one possible implementation, the analysis model is a long-short term neural network model, the input of the analysis model is a screen recording running video of the user's favorite software, and the output of the analysis model is the interactive interface and user interface preference information of the user's favorite software.

[0012] In a possible implementation, the user interface preference information includes interface color, interface layout, interface complexity, and font size.

[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 is configured to be executed by the processor to implement a method as described above, the method comprising: obtaining a screen recording running video of a user's favorite software and a software development description text; determining an interactive interface and user interface preference information of the user's favorite software based on an analysis model of the screen recording running video of the user's favorite software; determining a minimum number of modules required by the user and a maximum number of modules required by the user based on the software development description text; generating multiple simulated software interactive interfaces using a generative adversarial network based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interactive interface of the user's favorite software, and the user interface preference information; and determining a target software interactive interface based on the multiple simulated software interactive interfaces.

[0014] According to the fourth aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned big data-based software development method, the method comprising: obtaining a screen recording running video of a user's preferred software and a software development description text; determining an interactive interface and user interface preference information of the user's preferred software based on an analysis model based on the screen recording running video of the user's preferred software; determining a minimum number of modules required by the user and a maximum number of modules required by the user based on the software development description text; generating multiple simulated software interactive interfaces using a generative adversarial network based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interactive interface of the user's preferred software, and the user interface preference information; and determining a target software interactive interface based on the multiple simulated software interactive interfaces.

[0015] The present invention provides a software development method and system based on big data processing, the method comprising obtaining a screen recording operation video of a user's favorite software and a software development description text; using an analysis model based on the screen recording operation video of the user's favorite software to determine an interactive interface and user interface preference information of the user's favorite software; determining the number of modules required by the user at the minimum and the number of modules required by the user at the maximum based on the software development description text; using a generative adversarial network to generate multiple simulated software interactive interfaces based on the number of modules required by the user at the minimum, the number of modules required by the user at the maximum, the interactive interface of the user's favorite software, and the user interface preference information; and determining a target software interactive interface based on the multiple simulated software interactive interfaces. The method can efficiently and accurately develop a software interactive interface that meets the user's preferences and needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an application scenario of a software development method based on big data processing provided by an embodiment of the present invention; Figure 2 A flowchart of a software development method based on big data processing provided by an embodiment of the present invention; Figure 3 A schematic diagram of a process for determining a target software interactive interface provided by an embodiment of the present invention; Figure 4 A schematic diagram of a software development 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 2 A schematic diagram of an application scenario of a software development method based on big data processing provided in an embodiment of the present invention. Figure 2 The application scenario of the software development method based on big data 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 software development method based on big data 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 software development method based on big data.

[0023] In an embodiment of the present invention, there is provided Figure 1 A software development method based on big data processing is shown, and the software development method based on big data includes steps S1 to S5: Step S1, obtaining screen recording operation video and software development description text of the user's favorite software.

[0024] The user-favorite software is the software that the user likes. The software interface of the user-favorite software shows the user's preferences.

[0025] Software development description text is textual material that describes software development requirements. Software development description text includes functional requirements, user requirements, etc.

[0026] The screen recording operation video of the user's favorite software is a screen recording video of the user's favorite software when it is running. The screen recording operation video of the user's favorite software records the interface layout of the software and the interface changes of the software.

[0027] In some embodiments, the screen recording software can be used to record the user's favorite software to obtain a screen recording running video of the user's favorite software.

[0028] Step S2, determining the interactive interface and user interface preference information of the user's favorite software based on the screen recording operation video of the user's favorite software using an analysis model.

[0029] The analysis model is a long-short term neural network model, the input of the analysis model is a screen recording running video of the user's favorite software, and the output of the analysis model is the interactive interface and user interface preference information of the user's favorite software.

[0030] 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 relationship in the time series of the screen recording and running video of the user's favorite software in continuous time periods, and can output the features of the screen recording and running video of the user's favorite software that comprehensively considers each time point, so that the output features are more accurate and comprehensive.

[0031] The interactive interface of the user's favorite software is the actual interface layout and functions of the user when using the favorite software.

[0032] The user interface preference information indicates the user's preference for interface color, layout, complexity, font size, etc. The user interface preference information includes interface color, interface layout, interface complexity, and font size.

[0033] Interface color refers to the color theme that the user prefers, such as dark theme or light theme.

[0034] Interface layout refers to the interface layout that users prefer, such as simple layout or complex layout.

[0035] Interface complexity refers to the user's preference for interface complexity, such as preferring a simple interface or a feature-rich interface.

[0036] Font size refers to the user's preference for font size, such as preference for large or small fonts.

[0037] In some embodiments, the analysis model includes an interface change detection layer, an interactive interface determination layer, and a preference information determination layer. The interface change detection layer, the interactive interface determination layer, and the preference information determination layer all include long-term and short-term neural networks. The input of the interface change detection layer is the screen recording running video of the user's favorite software, the output of the interface change detection layer is the multiple interfaces of the user's favorite software and the dwell time of each interface of the user's favorite software, the input of the interactive interface determination layer is the multiple interfaces of the user's favorite software and the dwell time of each interface of the user's favorite software, the output of the interactive interface determination layer is the interactive interface of the user's favorite software, the input of the preference information determination layer is the interactive interface of the user's favorite software, the multiple interfaces of the user's favorite software, and the dwell time of each interface of the user's favorite software, and the output of the preference information determination layer is the user interface preference information.

[0038] The interface change detection layer can accurately identify interface changes after user operations, and output multiple interfaces of the user's favorite software and the dwell time of each interface of the user's favorite software.

[0039] The interactive interface determination layer combines multiple interfaces and dwell time, comprehensively analyzes the user's interactive behavior, and determines the interface layout and functions that users use most frequently.

[0040] The preference information determination layer is used to determine the user's preferences for interface color, layout, complexity, font size, etc. By dividing the analysis model into multiple layers, each layer focuses on a specific task, which improves the accuracy and efficiency of the analysis.

[0041] Step S3, determining the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text.

[0042] In some embodiments, the software development description text can be processed through a module quantity determination model to determine the minimum number of modules required by the user and the maximum number of modules required by the user.

[0043] In some embodiments, the module quantity determination model is a deep neural network model. The input of the module quantity determination model is the software development description text, and the output of the module quantity determination model is the minimum number of modules required by the user and the maximum number of modules required by the user.

[0044] The minimum number of modules required by users and the maximum number of modules required by users are the minimum number of modules required by users and the maximum number of modules required by users predicted by the model.

[0045] For example, the software development description may include "users need a document editing function that supports text input, formatting, and image insertion" and "users also need a file saving function that supports local saving and cloud synchronization." The deep neural network model can understand and process this rich semantic information, extract key functional modules from it, and ultimately determine the minimum number of modules required by users and the maximum number of modules required by users.

[0046] The minimum number of modules required by users refers to the number of basic functional modules that users must have, while the maximum number of modules required refers to the number of all functional modules that users hope to have.

[0047] By clarifying these needs, resources can be allocated rationally, with priority given to the development of core functions, thus avoiding resource dispersion and waste.

[0048] Step S4, using a generative adversarial network to generate multiple simulated software interaction interfaces based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interaction interface of the software preferred by the user, and the user interface preference information.

[0049] The input of the generative adversarial network is the minimum number of modules required by the user, the maximum number of modules required by the user, the interactive interface of the software preferred by the user, and the user interface preference information, and the output of the generative adversarial network is multiple simulated software interactive interfaces.

[0050] The Generative Adversarial Network (GAN) consists of a generator and a discriminator. These two parts compete with each other, learn from each other, and jointly promote the training of the model.

[0051] As an example, the minimum user requirements include document editing and file saving functions, the highest user requirements include document editing, file saving, version control, cloud synchronization, and online collaboration functions, the user's preferred software interface includes menu bar, toolbar, and editing area, and the user interface preference information includes dark theme, simple layout, and large font. The generative adversarial network generates multiple simulated interfaces with different layouts, colors, and font sizes based on the above information.

[0052] By generating multiple simulation software interaction interfaces through generative adversarial networks, more design options can be provided, improving the diversity and innovation of the design for subsequent user selection.

[0053] Step S5, determining a target software interaction interface based on the multiple simulated software interaction interfaces.

[0054] In some embodiments, Figure 3A flow chart of determining a target software interaction interface provided by an embodiment of the present invention, wherein determining the target garbage disposal time includes steps S21 to S24: Step S21, obtaining a simulation software interaction interface selected by a user, wherein the simulation software interaction interface selected by the user is selected by the user from the plurality of simulation software interaction interfaces; The user can select one interface from multiple simulation software interaction interfaces as the simulation software interaction interface selected by the user.

[0055] The simulation software interaction interface selected by the user is the simulation interface that the user is most interested in. By determining the simulation interface that the user is most interested in, it can be ensured that subsequent processing is based on the user's real preferences, thereby improving user satisfaction with the design.

[0056] Step S22, obtaining a plurality of pre-stored template software interaction interfaces; The pre-stored multiple template software interaction interfaces are already designed software interaction interface templates.

[0057] Step S23, constructing a graph structure, wherein the graph structure includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein the plurality of nodes include a simulation software interaction interface node selected by the user and a plurality of template software interaction interface nodes, wherein the simulation software interaction interface node selected by the user is a central node, and each template software interaction interface node respectively establishes an edge with the simulation software interaction interface node selected by the user, and the node feature of the simulation software interaction interface node selected by the user includes the simulation software interaction interface selected by the user, and the node feature of the template software interaction interface node includes the template software interaction interface and the user interface preference information, and the feature of each template software interaction interface node respectively establishing an edge with the simulation software interaction interface node selected by the user includes the similarity between the template software interaction interface and the simulation software interaction interface selected by the user; A graph structure is a data structure consisting of nodes (vertices) and edges (edges) to represent the relationship between nodes. The simulation software interaction interface node selected by the user is the central node, and multiple template software interaction interface nodes establish edges with the central node respectively.

[0058] The graph structure provides a systematic and structured way to represent the relationship between the simulated interface selected by the user and the template interface. By constructing the graph structure, the similarity between the template interface and the simulated interface selected by the user can be easily calculated.

[0059] In some embodiments, a deep neural network model may be used to calculate the similarity between the template software interaction interface and the simulation software interaction interface selected by the user.

[0060] The node features in the graph structure provide rich information, which is helpful for subsequent feature extraction and processing. The node features include the simulation interface, template interface and user interface preference information selected by the user, providing a comprehensive design reference. The edge features in the graph structure can represent the similarity between the template interface and the simulation interface selected by the user.

[0061] Step S24, processing the graph structure based on a graph convolutional network to determine a target software interaction interface, where the target software interaction interface is one of a plurality of template software interaction interfaces.

[0062] The target software interaction interface is one of the multiple template software interaction interfaces. The graph convolution network is a deep learning model specifically used to process graph structure data. The input of the graph convolution network is the graph structure, and the output of the graph convolution network is the target software interaction interface.

[0063] The graph convolution layer extracts the features of nodes and edges, updates the features of each node by aggregating the information of neighboring nodes, and further processes the features using a fully connected layer or a convolutional layer to generate the target software interaction interface.

[0064] The graph convolutional network can automatically process complex graph structure data and finally determine the target software interaction interface. The graph convolutional network can process the graph structure and effectively select the template software interaction interface that best meets user needs as the target software interaction interface.

[0065] Based on the same inventive concept, Figure 4 A schematic diagram of a software development system based on big data processing provided by an embodiment of the present invention, the software development system based on big data includes: An acquisition module 41 is used to acquire screen recording operation videos and software development description texts of the user's favorite software; An analysis module 42 is used to determine the interactive interface and user interface preference information of the user's favorite software based on the screen recording operation video of the user's favorite software using an analysis model; A module quantity determination module 43, used to determine the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text; A generation module 44, configured to generate a plurality of simulated software interaction interfaces using a generative adversarial network based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interaction interface of the software preferred by the user, and the user interface preference information; The interface determination module 45 is used to determine the target software interaction interface based on the multiple simulated software interaction interfaces.

[0066] 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 software development method based on big data as provided above, the method comprising: obtaining a screen recording operation video of a user's favorite software and a software development description text; determining an interactive interface and user interface preference information of the user's favorite software based on an analysis model of the screen recording operation video of the user's favorite software; determining a minimum number of modules required by the user and a maximum number of modules required by the user based on the software development description text; generating multiple simulated software interactive interfaces using a generative adversarial network based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interactive interface of the user's favorite software, and the user interface preference information; and determining a target software interactive interface based on the multiple simulated software interactive interfaces.

[0067] 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 big data-based software development method, the method comprising: obtaining a screen recording running video of a user's preferred software and a software development description text; determining an interactive interface and user interface preference information of the user's preferred software based on an analysis model of the screen recording running video of the user's preferred software; determining the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text; generating multiple simulated software interactive interfaces using a generative adversarial network based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interactive interface of the user's preferred software, and the user interface preference information; and determining a target software interactive interface based on the multiple simulated software interactive interfaces.

[0068] The big data-based software development method 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.

[0069] 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.

[0070] like Figure 6As 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.

[0071] 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.

[0072] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that commands the various components of the mobile phone 100 to work in coordination according to the instructions. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0073] The application processor may be installed with an operating system of the mobile phone 100, which is used to manage the hardware and software resources of the mobile phone 100. For example, it may manage and configure memory, determine the priority of system resource supply and demand, manage file systems, manage drivers, etc. The operating system may also be used to provide an operating interface for users to interact with the system. Various types of software may be installed in the operating system, such as drivers, applications (applications, apps), etc. Exemplarily, the operating system of the mobile phone 100 may be an Android system, a Linux system, etc.

[0074] A memory may also be provided in the processing module 110 for storing instructions and data. In some embodiments, the memory in the processing module 110 is a cache memory. The memory may store instructions or data that have just been used or are cyclically used by the processing module 110. If the processing module 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processing module 110, and thus improves the efficiency of the system.

[0075] In some embodiments, the processing module 110 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuits sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0076] The processing module 110 can be used to: obtain a screen recording operation video of a user's favorite software and a software development description text; determine the interactive interface and user interface preference information of the user's favorite software based on the screen recording operation video of the user's favorite software using an analysis model; determine the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text; generate multiple simulated software interactive interfaces using a generative adversarial network based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interactive interface of the user's favorite software, and the user interface preference information; and determine a target software interactive interface based on the multiple simulated software interactive interfaces.

[0077] The charging management module 140 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger through the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive wireless charging input through a wireless charging coil of the mobile phone 100. While the charging management module 140 is charging the battery 142, it may also power the electronic device through the power management module 141.

[0078] The power management module 141 is used to connect the battery 142, the charging management module 140 and the processing module 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, and supplies power to the processing module 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle number, battery health status (leakage, impedance), etc. In some other embodiments, the power management module 141 can also be set in the processing module 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set in the same device.

[0079] The wireless communication function of the mobile phone 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.

[0080] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve the utilization of antennas. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0081] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the same device as at least some of the modules of the processing module 110.

[0082] The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processing module 110 and be set in the same device as the mobile communication module 150 or other functional modules.

[0083] The wireless communication module 160 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the mobile phone 100. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the frequency of the electromagnetic wave signal and performs filtering, and sends the processed signal to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, modulate the frequency of the signal, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0084] In some embodiments, the antenna 1 of the mobile phone 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the mobile phone 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0085] The mobile phone 100 implements the display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, which is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processing module 110 may include one or more GPUs, which execute program instructions to generate or change display information.

[0086] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.

[0087] The mobile phone 100 can realize the shooting function through the ISP, the camera 193, the video codec, the GPU, the display screen 194 and the application processor. In some embodiments, the mobile phone 100 can realize the video communication function through the ISP, the camera 193, the video codec, the GPU and the application processor.

[0088] ISP is used to process the data fed back by camera 193. For example, when taking a photo, the shutter is opened, and the light is transmitted to the camera photosensitive element through the lens. The light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to ISP for processing and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on the noise, brightness, and skin color of the image. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, ISP can be set in camera 193.

[0089] The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the mobile phone 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

[0090] The digital signal processor is used to process digital signals, and can process not only digital image signals but also other digital signals. For example, when the mobile phone 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

[0091] Video codecs are used to compress or decompress digital videos. Mobile phone 100 may support one or more video codecs. Thus, mobile phone 100 may play or record videos in various coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0092] NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, the intelligent cognition of the mobile phone 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

[0093] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processing module 110 through the external memory interface 120 to implement a data storage function, such as storing music, video and other files in the external memory card.

[0094] The internal memory 121 can be used to store computer executable program codes, which include instructions. The processing module 110 executes various functional applications and data processing of the mobile phone 100 by running the instructions stored in the internal memory 121. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the mobile phone 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0095] The mobile phone 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor.

[0096] The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 can be arranged in the processing module 110, or some functional modules of the audio module 170 can be arranged in the processing module 110.

[0097] The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The mobile phone 100 can listen to music or listen to a hands-free call through the speaker 170A.

[0098] The receiver 170B, also called a "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or voice message, the voice can be received by placing the receiver 170B close to the ear.

[0099] Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the sound source, realize directional recording function, etc.

[0100] The earphone interface 170D is used to connect a wired earphone and can be a USB interface 130 or a 3.5 mm open mobile terminal platform (OMTP) standard interface or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0101] The key 190 includes a power key, a volume key, etc. The key 190 can be a mechanical key or a touch key. The mobile phone 100 can receive key input and generate key signal input related to the user settings and function control of the mobile phone 100.

[0102] Motor 191 can generate vibration prompts. Motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. For touch operations acting on different areas of the display screen 194, motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0103] Indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0104] The SIM card interface 195 is used to connect the SIM card. The SIM card can be connected to and separated from the mobile phone 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195. The mobile phone 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to realize functions such as calls and data communications. In some embodiments, the mobile phone 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 software development method based on big data processing, characterized in that: include: Obtain screen recording operation videos and software development description text of the user's favorite software; Determine the interactive interface and user interface preference information of the user's favorite software based on the screen recording operation video of the user's favorite software using the analysis model; Determine the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text; Generate multiple simulated software interaction interfaces using a generative adversarial network based on the minimum number of modules required by the user, the maximum number of modules required by the user, the interaction interface of the software preferred by the user, and the user interface preference information; A target software interaction interface is determined based on the multiple simulated software interaction interfaces.

2. The software development method based on big data according to claim 1, characterized in that: Determining the target software interaction interface based on the multiple simulated software interaction interfaces includes: Acquire a simulation software interaction interface selected by a user, wherein the simulation software interaction interface selected by the user is selected by the user from the plurality of simulation software interaction interfaces; Acquire multiple pre-stored template software interaction interfaces; Constructing a graph structure, the graph structure comprising a plurality of nodes and a plurality of edges between the plurality of nodes, the plurality of nodes comprising a simulation software interaction interface node selected by a user and a plurality of template software interaction interface nodes, wherein the simulation software interaction interface node selected by the user is a central node, each template software interaction interface node respectively establishes an edge with the simulation software interaction interface node selected by the user, the node feature of the simulation software interaction interface node selected by the user comprises the simulation software interaction interface selected by the user, the node feature of the template software interaction interface node comprises the template software interaction interface and the user interface preference information, and the feature of each template software interaction interface node respectively establishing an edge with the simulation software interaction interface node selected by the user comprises the similarity between the template software interaction interface and the simulation software interaction interface selected by the user; The graph structure is processed based on a graph convolutional network to determine a target software interaction interface, where the target software interaction interface is one of a plurality of template software interaction interfaces.

3. The software development method based on big data according to claim 1, characterized in that: The analysis model is a long-short term neural network model, the input of the analysis model is a screen recording running video of the user's favorite software, and the output of the analysis model is the interactive interface and user interface preference information of the user's favorite software.

4. The software development method based on big data according to claim 1, characterized in that: The user interface preference information includes interface color, interface layout, interface complexity, and font size.

5. A software development system based on big data processing, characterized in that: include: The acquisition module is used to obtain the screen recording operation video and software development description text of the user's favorite software; An analysis module, for determining the interactive interface and user interface preference information of the user's favorite software based on the screen recording operation video of the user's favorite software using an analysis model; A module quantity determination module, used to determine the minimum number of modules required by the user and the maximum number of modules required by the user based on the software development description text; A generation module, configured to generate a plurality of simulated software interaction interfaces using a generative adversarial network based on the number of modules required by the user at the minimum, the number of modules required by the user at the maximum, the interaction interface of the software preferred by the user, and the user interface preference information; The interface determination module is used to determine the target software interaction interface based on the multiple simulated software interaction interfaces.

6. The software development system based on big data as claimed in claim 5, characterized in that: The interface determination module is also used for: Acquire a simulation software interaction interface selected by a user, wherein the simulation software interaction interface selected by the user is selected by the user from the plurality of simulation software interaction interfaces; Acquire multiple pre-stored template software interaction interfaces; Constructing a graph structure, the graph structure comprising a plurality of nodes and a plurality of edges between the plurality of nodes, the plurality of nodes comprising a simulation software interaction interface node selected by a user and a plurality of template software interaction interface nodes, wherein the simulation software interaction interface node selected by the user is a central node, each template software interaction interface node respectively establishes an edge with the simulation software interaction interface node selected by the user, the node feature of the simulation software interaction interface node selected by the user comprises the simulation software interaction interface selected by the user, the node feature of the template software interaction interface node comprises the template software interaction interface and the user interface preference information, and the feature of each template software interaction interface node respectively establishing an edge with the simulation software interaction interface node selected by the user comprises the similarity between the template software interaction interface and the simulation software interaction interface selected by the user; The graph structure is processed based on a graph convolutional network to determine a target software interaction interface, where the target software interaction interface is one of a plurality of template software interaction interfaces.

7. The software development system based on big data as claimed in claim 5, characterized in that: The analysis model is a long-short term neural network model, the input of the analysis model is a screen recording running video of the user's favorite software, and the output of the analysis model is the interactive interface and user interface preference information of the user's favorite software.

8. The software development system based on big data as claimed in claim 5, characterized in that: The user interface preference information includes interface color, interface layout, interface complexity, and font size.

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 big data-based software development method 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 big data-based software development method as described in any one of claims 1 to 4 is implemented.

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