A screen layout determination method, device, apparatus and storage medium

Abstract

Embodiments of the present application disclose a screen layout determination method and device, equipment and a storage medium, and relate to the technical field of computers. The method comprises: performing canvas feature recognition on a large-screen design file corresponding to a target screen to determine canvas feature information; matching the canvas feature information with template feature information of each layout template in a preset layout knowledge base to determine matching data; selecting a target layout template from the preset layout knowledge base according to the matching data and heat data of each layout template; and performing layout presentation on the target screen based on the target layout template. The above scheme can automatically match a screen layout suitable for presentation on the target screen according to the large-screen design file, thereby realizing dynamic adaptive adjustment of the layout, adapting to current scene presentation, meeting the needs and preferences of users, and improving the visual experience and personalized experience of users.

Description

Technical Field

[0001] This application relates to the field of data visualization and intelligent interaction technology, and in particular to a screen layout determination method, apparatus, device and storage medium. Background Technology

[0002] Intelligent large screen layout refers to the methods used in data visualization large screen design to optimize information display through reasonable structural arrangement and intelligent technology. Its core objective is to ensure that key data is clearly highlighted, secondary information is presented in an orderly manner, and it adapts to different screen sizes and display environments, thereby improving information delivery efficiency and user experience.

[0003] Currently, in screen displays, grid layout systems (such as Bootstrap grid) are based on a mechanical layout with fixed rows and columns, making it difficult to achieve intelligent automatic adjustments. If layout adjustments are made using drag-and-drop free layout tools (such as DataV), users have to manually adjust the position and size of components, which makes it difficult to quickly respond to user needs. Summary of the Invention

[0004] This application provides a screen layout determination method, apparatus, device, and storage medium to adaptively select a target layout template suitable for the current scene and present it on the target screen, quickly responding to user needs and presenting a suitable screen layout.

[0005] According to one aspect of this application, a screen layout determination method is provided, the method comprising:

[0006] Perform canvas feature recognition on the large screen design file corresponding to the target screen to determine the canvas feature information;

[0007] The canvas feature information is matched with the template feature information of each layout template in the preset layout knowledge base to determine the matching data;

[0008] Based on the matching data and the popularity data of each layout template, a target layout template is selected from the preset layout knowledge base;

[0009] The target screen is laid out and presented based on the target layout template.

[0010] According to one aspect of this application, a screen layout determination apparatus is provided, the apparatus comprising:

[0011] The canvas feature information recognition module is used to identify canvas features in the large-screen design file corresponding to the target screen and determine the canvas feature information.

[0012] The matching module is used to match the canvas feature information with the template feature information of each layout template in the preset layout knowledge base to determine the matching data;

[0013] The target layout template selection module is used to select a target layout template from the preset layout knowledge base based on the matching data and the popularity data of each layout template.

[0014] The layout presentation module is used to present the layout of the target screen based on the target layout template.

[0015] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and,

[0017] A memory that is communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor can perform the screen layout determination method of any embodiment of the present application.

[0019] According to another aspect of this application, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the screen layout determination method of any embodiment of this application.

[0020] According to another aspect of this application, a computer program product is provided, which includes a computer program that, when executed by a processor, implements the screen layout determination method of any embodiment of this application.

[0021] The technical solution of this application embodiment involves identifying canvas features in the large-screen design file corresponding to the target screen to determine canvas feature information; matching the canvas feature information with the template feature information of each layout template in a preset layout knowledge base to determine matching data; selecting a target layout template from the preset layout knowledge base based on the matching data and the popularity data of each layout template; and presenting the layout of the target screen based on the target layout template. This solution achieves automated adaptation and optimization of large-screen design through an intelligent canvas feature recognition and template matching mechanism. By accurately identifying the canvas features of the target screen and matching them with the feature information of templates in the preset layout knowledge base, candidate templates that highly match the screen characteristics can be quickly selected, significantly reducing manual adjustment time and improving design efficiency. Introducing popularity data as a selection criterion and referring to user behavior and preference data enhances the user adaptability of the layout template. Automatic layout presentation based on the target template can efficiently adapt to the display needs of different target screens, significantly improving the automation level and presentation effect of large-screen design.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a screen layout determination method provided in an embodiment of this application;

[0025] Figure 2 A flowchart illustrating a screen layout determination method provided in another embodiment of this application;

[0026] Figure 3 A flowchart illustrating a screen layout determination method provided in another embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of a screen layout determination device provided in an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," "third," "fourth," "actual," "preset," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] The acquisition, storage, use, and processing of data in this application comply with relevant national laws and regulations. The acquired data is obtained with authorization and will not be disclosed without permission, used for illegal purposes, purposes detrimental to the interests of others, or for personalized analysis or product promotion. It should be noted that certain software, components, models, and other existing industry solutions may be mentioned in the embodiments of this application. These should be considered exemplary and intended only to illustrate the feasibility of implementing the technical solution of this application, but do not imply that the applicant has already used or necessarily used the relevant content of such solutions.

[0032] Figure 1 This is a flowchart illustrating a screen layout determination method provided in an embodiment of this application. This embodiment is applicable to situations where a target layout template is dynamically and adaptively selected for display on a target screen. The method can be executed by a screen layout determination device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0033] S110. Perform canvas feature recognition on the large screen design file corresponding to the target screen to determine the canvas feature information.

[0034] The target screen is the screen that requires adaptive layout selection for presentation. This target screen can be an intelligent screen to meet the needs of adaptive layout selection and presentation changes. The large-screen design file can be the source file used to guide the design of the large-screen visualization interface, containing all design elements and rules such as layout, styles, and chart configurations. The large-screen design file may include PSD source files, chart configuration files, or style files, used to define the data display method, visual style, and interaction logic.

[0035] In this embodiment, canvas feature recognition can be performed on the large-screen design file corresponding to the target screen to identify the canvas feature information of the layout to be presented on the target screen. The method for recognizing the large-screen design file can include using a pre-trained ResNet-50 model to identify the scene to be presented on the target screen, such as a sales data analysis scene or a market trend monitoring scene. The main color value and proportion can be extracted based on K-means clustering, and entities can be extracted from the canvas description text using a BERT model to determine the components and text to be presented on the target screen.

[0036] For example, the feature dimension table extracted from the large screen design file is shown in Table 1:

[0037] Table 1

[0038]

[0039] S120. Match the canvas feature information with the template feature information of each layout template in the preset layout knowledge base to determine the matching data.

[0040] The preset layout knowledge base is a pre-built knowledge base containing various layout templates. This base includes multiple layout templates and their corresponding template feature information. Template feature information includes, for example, the layout template's scenario tags, included components, and popularity data. Scenario tags describe the applicable business scenario for the layout template, such as "sales data analysis" or "market trend monitoring." Scenario tags are used to quickly filter layout templates relevant to the current business scenario. Component rules define the required component types and associated component pairs in the layout template. For example, some layout templates may require "map" and "bar chart" components. Popularity value indicates the frequency and popularity of the layout template's use. The initial popularity value can be set to a fixed value, such as 100. The popularity value will dynamically adjust based on the layout template's usage. It may also include the last usage time: recording the timestamp of the last time the layout template was used to calculate popularity data.

[0041] For example, the canvas feature information can be matched with the template feature information of each layout template in the preset layout knowledge base. That is, the preset layout knowledge base is traversed, and the template feature information of each layout template in the preset layout knowledge base is matched with the canvas feature information to determine the matching data between each template feature information and the canvas feature information. In other words, the matching data between the layout template and the target screen is determined, which reflects the adaptability of each layout template to the target screen and the degree of adaptation of each layout template on the target screen.

[0042] S130. Based on the matching data and the popularity data of each layout template, select a target layout template from the preset layout knowledge base.

[0043] For example, the popularity data is the data in the template feature information of the layout template, reflecting the popularity of the layout template among users. The popularity includes the degree to which users tend to select, adjust, and set it.

[0044] Among them, the matching data objectively reflects the degree of adaptation of each layout template to the current target screen, and the popularity data reflects the user's preference. Based on the matching data and popularity data, the target layout template can be selected from the preset layout knowledge base. That is, the target layout template suitable for presentation on the target screen is selected by considering both the matching data and popularity data.

[0045] S140. The target screen is laid out and presented based on the target layout template.

[0046] For example, after selecting a target layout template, the target screen can be presented based on the target layout template, thereby presenting a layout on the target screen that matches the requirements of the large screen design file, and taking into account the user's preferences to meet the user's needs.

[0047] The technical solution of this application embodiment involves identifying canvas features in the large-screen design file corresponding to the target screen to determine canvas feature information; matching the canvas feature information with the template feature information of each layout template in a preset layout knowledge base to determine matching data; selecting a target layout template from the preset layout knowledge base based on the matching data and the popularity data of each layout template; and presenting the layout of the target screen based on the target layout template. This solution achieves automated adaptation and optimization of large-screen design through an intelligent canvas feature recognition and template matching mechanism. By accurately identifying the canvas features of the target screen and matching them with the feature information of templates in the preset layout knowledge base, candidate templates that highly match the screen characteristics can be quickly selected, significantly reducing manual adjustment time and improving design efficiency. Introducing popularity data as a selection criterion and referring to user behavior and preference data enhances the user adaptability of the layout template. Automatic layout presentation based on the target template can efficiently adapt to the display needs of different target screens, significantly improving the automation level and presentation effect of large-screen design.

[0048] Figure 2 This is a flowchart illustrating a screen layout determination method according to another embodiment of this application. This embodiment is an optimization based on the above embodiment; solutions not described in detail in this embodiment are found in the above embodiment. Figure 2 As shown, the method in this embodiment of the application specifically includes the following steps:

[0049] S210. Perform canvas feature recognition on the large screen design file corresponding to the target screen to determine the canvas feature information.

[0050] S220. Match the canvas scene in the canvas feature information with the scene tags in the template feature information of each layout template in the preset layout knowledge base, and filter out candidate layout templates with a scene matching degree higher than the matching degree threshold.

[0051] In this embodiment, canvas feature information and template feature information can be matched globally to select candidate layout templates that are considered a good match. Specifically, the canvas scene in the canvas feature information can be matched with the scene tags in the template feature information of each layout template in a preset layout knowledge base to determine the matching degree between the scene tags in the template feature information of each layout template and the canvas scene in the canvas feature information. Specifically, the canvas scene text in the canvas feature information can be matched with the scene tag text in the template feature information of each layout template to determine the text similarity between the canvas scene and the scene tag, representing the scene matching degree. A matching degree threshold is preset, for example, 70%. Layout templates with a scene matching degree higher than the matching degree threshold are selected as candidate layout templates, thereby achieving the first round of screening of layout templates based on the scene and narrowing down the scope.

[0052] S230. For each candidate layout template, match the template feature information of the candidate layout template with the canvas feature information to determine the matching data.

[0053] For example, for the selected candidate layout templates, the template feature information of each candidate layout template is matched with the canvas feature information. Unlike single scene matching, the process of matching the template feature information with the canvas feature information involves matching as many feature information as possible to determine the matching data.

[0054] In this embodiment of the application, matching the template feature information of the candidate layout template with the canvas feature information to determine the matching data includes:

[0055] Each component in the template feature information of the candidate layout template is matched with each component in the canvas feature information, and the ratio of the number of matched components to the total number of components in the canvas feature information is used as the component matching parameter.

[0056] The matching data is determined based on the component matching parameters and the scene matching degree.

[0057] For example, in the process of matching the template feature information of the candidate layout template with the canvas feature information to determine the matching data, each component in the template feature information of the candidate layout template can be matched with each component in the canvas feature information to determine the number of matching components. The ratio of the number of matching components to the total number of components in the canvas feature information is used as the component matching parameter, reflecting the degree of component matching. Component matching can be based on features such as component name and component shape. The matching data can be determined according to the component matching parameter and the scene matching degree. The scene matching degree is the matching degree between the scene label in the template feature information determined in the above embodiment and the canvas scene in the canvas feature information. Determining the matching data according to the component matching parameter and the scene matching degree can be achieved by weighted summation of the component matching parameter and the scene matching degree. The matching data can be component matching parameter × w1 + scene matching degree × w2. Where w1 is the weight corresponding to the component matching parameter, w1 can be 0.6, and w2 is the weight corresponding to the scene matching degree, w2 can be 0.4.

[0058] S240. Based on the matching data and the popularity data of each layout template, select a target layout template from the preset layout knowledge base.

[0059] S250. The target screen is laid out and presented based on the target layout template.

[0060] The solution in this embodiment matches the canvas scene in the canvas feature information with the scene tags in the template feature information of each layout template in the preset layout knowledge base, filtering out candidate layout templates with a scene matching degree higher than the matching degree threshold; for each candidate layout template, the template feature information of the candidate layout template is matched with the canvas feature information to determine the matching data. This solution achieves rapid and accurate recommendation of design layouts through intelligent matching of canvas scenes and the preset layout knowledge base, significantly improving design efficiency and personalized adaptation capabilities. A "scene-template" two-dimensional matching mechanism is adopted: preliminary screening is performed based on canvas scene tags and layout template scene tags, and candidate templates are quickly locked by setting a matching degree threshold, avoiding computational redundancy from full database traversal; then, for candidate templates, feature-level matching is used to further quantify the adaptation degree, generating matching data containing details such as matching scores and similar areas. The hierarchical matching strategy ensures both the targeting of recommendations and improves the accuracy of recommendations through feature-level analysis.

[0061] Figure 3 This is a flowchart illustrating a screen layout determination method according to another embodiment of this application. This embodiment is an optimization based on the above embodiments; solutions not described in detail in this embodiment are found in the above embodiments. Figure 3As shown, the method in this embodiment of the application specifically includes the following steps:

[0062] S310. Perform canvas feature recognition on the large screen design file corresponding to the target screen to determine the canvas feature information.

[0063] S320. Match the canvas feature information with the template feature information of each layout template in the preset layout knowledge base to determine the matching data.

[0064] S330. The matching data and the popularity data are weighted and summed to determine the template score corresponding to the layout template; wherein the weight of the matching data is higher than the weight of the popularity data.

[0065] For example, after determining the matching data and popularity data, a weighted sum can be performed on the matching data and popularity data to obtain the template score corresponding to each layout template. The template score is expressed as: Matching data × W1 + Popularity data × W2. W1 is the weight corresponding to the matching data, and W2 is the weight corresponding to the popularity data. Based on the above embodiment, when the matching data is split into component matching parameters and scene matching degree, the template score can be expressed as: Component matching parameters × α + Scene matching degree × β + Popularity data × γ. α is the weight corresponding to the component matching parameters, which can be 0.5; β is the weight corresponding to the scene matching degree, which can be 0.3; and γ is the weight corresponding to the popularity data, which can be 0.2. In this embodiment, since the matching data is an objective matching degree obtained by directly matching the content in the canvas feature information and the template feature information, and the popularity data is a score based on user habits, the direct matching data has higher reference value, and the weight of the matching data can be set higher than the weight of the popularity data.

[0066] S340. The layout template with the highest template score is taken as the target layout template.

[0067] For example, the layout template with the highest template score can be selected as the target layout template, reflecting that the target layout template has the highest degree of adaptation to the screen display layout required by the large screen design file.

[0068] In this embodiment, the matching data and the popularity data are weighted and summed to determine the template score corresponding to the layout template; wherein the weight of the matching data is higher than the weight of the popularity data. The layout template with the highest template score is selected as the target layout template. This scheme achieves intelligent selection and accurate recommendation of layout templates through weighted fusion and dynamic weight allocation of matching data and popularity data, significantly improving design efficiency and user satisfaction. A two-dimensional evaluation system of "adaptability-popularity" is constructed: matching data is used as the main weight to ensure that the recommendation results closely match the actual needs of users; popularity data is used as an auxiliary weight to take into account design trends and public preferences. The template score generated by weighted summation avoids "homogenized recommendations" caused by relying solely on popularity data and overcomes the shortcomings of template practicality that may be overlooked by relying solely on matching data. Finally, the layout template with the highest score is selected as the target template, which can quickly output design schemes that meet personalized needs and have popular potential, reduce user screening costs, and improve the intelligence level of design tools.

[0069] In this embodiment of the application, the process of determining the popularity data of the layout template includes:

[0070] The time decay parameter is determined based on the time difference between the last time the layout template was used and the current time.

[0071] Based on the user's selection and / or setting operations of the layout template, a user preference parameter is determined; wherein, the user preference parameter is positively correlated with the number of times the user selects and / or sets the layout template;

[0072] Based on the popularity data of the layout template before the update, as well as the time decay parameter and the user preference parameter, the updated popularity data is determined.

[0073] In this embodiment, the process of determining the task data for each layout template in the preset layout knowledge base includes recording the last time the layout template was used by the user. For each layout template, the time difference between the last time the layout template was used by the user and the current time is detected and calculated in real time, and a time decay parameter is determined based on the time difference. The time decay parameter decreases as the time difference increases, and can be specifically expressed as: Time decay parameter = 'a' is an adjustment factor, which is usually an integer. Due to time difference.

[0074] If a user selects and / or sets a layout template, it indicates that the user approves of or is interested in that layout template. Therefore, user preference parameters can be determined based on the user's selection and / or setting actions. Specifically, user preference parameters can be positively correlated with the number of times the user selects and / or sets a layout template. For example, if a user selects a layout template as the target layout template, a first preset value is added to the user preference parameter for that layout template as a bonus. If the user sets a layout template, a second preset value is added to the user preference parameter for that layout template as a bonus. The first preset value can be greater than the second preset value, meaning that the user's approval of the layout template reflected by selecting it is greater than the user's approval reflected by setting it.

[0075] Based on the popularity data before the layout template update, combined with the time decay parameter and user preference parameter, the popularity data after the update can be determined. Specifically, it can be expressed as: Popular data after update = Popular data before update × Time decay parameter + User preference parameter.

[0076] This solution achieves intelligent updating and personalized adaptation of layout template popularity data by introducing a dual dynamic adjustment mechanism of time decay parameters and user preference parameters, significantly improving the timeliness and user satisfaction of the recommendation system. The time decay parameter is generated by calculating the time difference between the last time a layout template was used and the current time, effectively avoiding the recommendation lag problem caused by the "historical data accumulation effect" in traditional popularity statistics, ensuring that template popularity dynamically reflects its recent activity. The user preference parameter is generated based on the number of times users select and set templates, incorporating individual preferences into the popularity calculation, making the recommendation results more aligned with actual user habits. By fusing the popularity data before the update, the time decay parameter, and the user preference parameter to generate updated popularity data, the solution retains the basic trend of template popularity, suppresses the recommendation probability of outdated templates through time decay, and strengthens personalized adaptation capabilities through user preference.

[0077] In this embodiment of the application, after selecting a target layout template from the preset layout knowledge base, the method further includes:

[0078] If a user's adjustment operation on the target layout template is detected, a new layout template is generated based on the user's adjustment operation on the target layout template and added to the preset layout knowledge base;

[0079] If a user rating and / or commenting on the target layout template is detected, the popularity data of the target layout template is adjusted based on the rating value and / or rating trend data.

[0080] For example, users can provide feedback when using a smart screen in the following ways:

[0081] Manual Layout Adjustment: Users can manually adjust the position, size, and other attributes of components. Ratings and Reviews: Users can rate the current layout or leave reviews. Preference Selection: Users can choose one of several layout templates recommended by the system. The system collects user feedback and converts it into specific feature vector adjustment instructions. For example: Manual Adjustment: If a user moves a component from one position to another, the system records this operation and converts it into a feature vector adjustment. Ratings and Reviews: If a user gives a layout template a high score, the system increases the popularity data for that layout; if a user gives a low score or a negative review, the system decreases the popularity data for that layout template. Preference Selection: If a user selects a system-recommended layout template, the system records this selection and increases the popularity data for that layout.

[0082] This solution constructs a dynamic, self-optimizing layout knowledge base update mechanism by capturing users' adjustments, ratings, and comments on target layout templates in real time, significantly improving the intelligence and user engagement of the recommendation system. When a user's adjustment to a template is detected, the system automatically generates a new version template and incorporates it into the knowledge base, achieving a closed loop of "user creation feeding back into the recommendation library," enabling the knowledge base to continuously evolve and cover richer design styles. By analyzing user ratings and comment tendencies, the system dynamically adjusts template popularity data, ensuring that recommendation priorities not only reflect template popularity but also incorporate genuine user feedback. This mechanism enhances the timeliness and diversity of recommendation results, avoids knowledge base stagnation, and improves the system's alignment with individual needs through user behavior-driven optimization, ultimately forming a positive cycle of "use-feedback-optimization-re-recommendation." It is particularly suitable for scenarios requiring rapid iteration of design content, such as e-commerce posters and social media templates.

[0083] In this embodiment of the application, after selecting a target layout template from the preset layout knowledge base, the method further includes:

[0084] The target layout template is used as the parent, and a child layout template is generated based on the parent through crossover and mutation operations.

[0085] Add the child layout template to the preset layout knowledge base.

[0086] For example, the target layout template with the highest template score can be selected as the parent, or the layout template with the highest current popularity data, or the layout template with the highest preset quantity can be selected as the parent. New child layouts are generated through crossover and mutation operations. For example, the "map" component in the parent layout can be swapped with the "bar chart" component in another layout to generate a new layout. The generated new layout templates are evaluated, and their matching degree and popularity value are calculated. The child layout templates are then added to the preset layout knowledge base for selection.

[0087] This solution, by introducing a genetic algorithm mechanism, achieves intelligent evolution of layout templates and continuous enrichment of the knowledge base, significantly enhancing the innovation and diversity of the design system. Using the target layout template as the parent, offspring templates are generated through cross-operations (such as element combination and exchange, color scheme mixing) and mutation operations (such as random spacing adjustment, graphic element replacement). This retains the core design style of the parent while introducing new creative elements through genetic recombination, thus preventing the knowledge base from becoming homogenized. Simultaneously, the offspring templates are automatically added to the preset knowledge base, forming a closed loop of "design-generation-optimization-reapplication," enabling the recommendation system to continuously output layout schemes that combine popular trends with innovative potential.

[0088] To ensure the efficient operation and data quality of the pre-defined knowledge base layout, regular maintenance is required.

[0089] Template cleanup: Regularly check the layout templates in the preset layout knowledge base and remove those with low popularity data and those that have not been used for a long time. This helps keep the preset layout knowledge base streamlined and efficient.

[0090] Template Updates: Based on changes in business needs and user feedback, promptly update the component rules and scene tags in the layout template. For example, if the analysis requirements for a particular business scenario change, the corresponding layout template will also need to be adjusted.

[0091] Performance optimization: Optimize the storage and retrieval mechanisms of the preset layout knowledge base to ensure the system can quickly respond to user layout requests. This may involve technical means such as optimizing database indexes and introducing caching mechanisms.

[0092] Figure 4 This is a schematic diagram of a screen layout determination device provided in an embodiment of this application. This device can execute the screen layout determination method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the method. Figure 4 As shown, the device includes:

[0093] The canvas feature information recognition module 410 is used to perform canvas feature recognition on the large screen design file corresponding to the target screen and determine the canvas feature information.

[0094] The matching module 420 is used to match the canvas feature information with the template feature information of each layout template in the preset layout knowledge base to determine the matching data;

[0095] The target layout template selection module 430 is used to select a target layout template from the preset layout knowledge base based on the matching data and the popularity data of each layout template.

[0096] The layout presentation module 440 is used to present the layout of the target screen based on the target layout template.

[0097] In this embodiment, the matching module 420 matches the canvas feature information with the template feature information of each layout template in the preset layout knowledge base to determine the matching data, including:

[0098] The canvas scene in the canvas feature information is matched with the scene tag in the template feature information of each layout template in the preset layout knowledge base, and candidate layout templates with a scene matching degree higher than the matching degree threshold are selected.

[0099] For each candidate layout template, the template feature information of the candidate layout template is matched with the canvas feature information to determine the matching data.

[0100] In this embodiment, the matching module 420 matches the template feature information of the candidate layout template with the canvas feature information to determine the matching data, including:

[0101] Each component in the template feature information of the candidate layout template is matched with each component in the canvas feature information, and the ratio of the number of matched components to the total number of components in the canvas feature information is used as the component matching parameter.

[0102] The matching data is determined based on the component matching parameters and the scene matching degree.

[0103] In this embodiment of the application, the target layout template selection module 430 selects a target layout template from the preset layout knowledge base based on the matching data and the popularity data of each layout template, including:

[0104] The matching data and the popularity data are weighted and summed to determine the template score corresponding to the layout template; wherein the weight of the matching data is higher than the weight of the popularity data.

[0105] The layout template with the highest template score is selected as the target layout template.

[0106] In this embodiment of the application, the device further includes: a heat data determination module, used for:

[0107] The time decay parameter is determined based on the time difference between the last time the layout template was used and the current time.

[0108] Based on the user's selection and / or setting operations of the layout template, a user preference parameter is determined; wherein, the user preference parameter is positively correlated with the number of times the user selects and / or sets the layout template;

[0109] Based on the popularity data of the layout template before the update, as well as the time decay parameter and the user preference parameter, the updated popularity data is determined.

[0110] In this embodiment of the application, after selecting a target layout template from the preset layout knowledge base, the device further includes an adjustment module, used for:

[0111] If a user's adjustment operation on the target layout template is detected, a new layout template is generated based on the user's adjustment operation on the target layout template and added to the preset layout knowledge base;

[0112] If a user rating and / or commenting on the target layout template is detected, the popularity data of the target layout template is adjusted based on the rating value and / or rating trend data.

[0113] In this embodiment of the application, after selecting a target layout template from the preset layout knowledge base, the device further includes an adding module, used for:

[0114] The target layout template is used as the parent, and a child layout template is generated based on the parent through crossover and mutation operations.

[0115] Add the child layout template to the preset layout knowledge base.

[0116] The screen layout determination device provided in this application embodiment can execute a screen layout determination method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.

[0117] Figure 5A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital processors, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0118] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0119] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0120] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the screen layout determination method.

[0121] In some embodiments, the screen layout determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the screen layout determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the screen layout determination method by any other suitable means (e.g., by means of firmware).

[0122] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable screen layout determination device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0126] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0127] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0128] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the screen layout determination method as provided in any embodiment of this application.

[0129] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired information of the technical solution of this application can be achieved, and this is not limited herein.

[0131] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining screen layout, characterized in that, The method includes: Perform canvas feature recognition on the large screen design file corresponding to the target screen to determine the canvas feature information; The canvas feature information is matched with the template feature information of each layout template in the preset layout knowledge base to determine the matching data; Based on the matching data and the popularity data of each layout template, a target layout template is selected from the preset layout knowledge base; The target screen is laid out and presented based on the target layout template.

2. The method according to claim 1, characterized in that, The canvas feature information is matched with the template feature information of each layout template in the preset layout knowledge base to determine the matching data, including: The canvas scene in the canvas feature information is matched with the scene tag in the template feature information of each layout template in the preset layout knowledge base, and candidate layout templates with a scene matching degree higher than the matching degree threshold are selected. For each candidate layout template, the template feature information of the candidate layout template is matched with the canvas feature information to determine the matching data.

3. The method according to claim 2, characterized in that, Matching the template feature information of the candidate layout template with the canvas feature information to determine the matching data includes: Each component in the template feature information of the candidate layout template is matched with each component in the canvas feature information, and the ratio of the number of matched components to the total number of components in the canvas feature information is used as the component matching parameter. The matching data is determined based on the component matching parameters and the scene matching degree.

4. The method according to claim 1, characterized in that, Based on the matching data and the popularity data of each layout template, a target layout template is selected from the preset layout knowledge base, including: The matching data and the popularity data are weighted and summed to determine the template score corresponding to the layout template; wherein the weight of the matching data is higher than the weight of the popularity data. The layout template with the highest template score is selected as the target layout template.

5. The method according to claim 1, characterized in that, The process of determining the popularity data of the layout template includes: The time decay parameter is determined based on the time difference between the last time the layout template was used and the current time. Based on the user's selection and / or setting operations of the layout template, a user preference parameter is determined; wherein, the user preference parameter is positively correlated with the number of times the user selects and / or sets the layout template; Based on the popularity data of the layout template before the update, as well as the time decay parameter and the user preference parameter, the updated popularity data is determined.

6. The method according to claim 1, characterized in that, After selecting a target layout template from the preset layout knowledge base, the method further includes: If a user's adjustment operation on the target layout template is detected, a new layout template is generated based on the user's adjustment operation on the target layout template and added to the preset layout knowledge base; If a user rating and / or commenting on the target layout template is detected, the popularity data of the target layout template is adjusted based on the rating value and / or rating trend data.

7. The method according to claim 1, characterized in that, After selecting a target layout template from the preset layout knowledge base, the method further includes: The target layout template is used as the parent, and a child layout template is generated based on the parent through crossover and mutation operations. Add the child layout template to the preset layout knowledge base.

8. A screen layout determining device, characterized in that, The screen layout determination device includes: The canvas feature information recognition module is used to identify canvas features in the large-screen design file corresponding to the target screen and determine the canvas feature information. The matching module is used to match the canvas feature information with the template feature information of each layout template in the preset layout knowledge base to determine the matching data; The target layout template selection module is used to select a target layout template from the preset layout knowledge base based on the matching data and the popularity data of each layout template. The layout presentation module is used to present the layout of the target screen based on the target layout template.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the screen layout determination method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the screen layout determination method according to any one of claims 1-7.

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