Display method, electronic device, and computer program product

Abstract

This disclosure provides a display method, an electronic device, and a computer program product. The display method includes: in response to query information, determining a target component that matches the query information, the query information including query text submitted by a user and user attribute information; and displaying a navigation control regarding the target component, the navigation control including a matching score between the target component and the query information.

Description

Technical Field

[0001] This disclosure relates to the technical fields of data processing, and in particular to a display method, electronic device, and computer program product. Background Technology

[0002] Feature recommendations are a crucial step in improving system application efficiency and reducing user cognitive load. However, most systems primarily provide users with query steps for each functional component through static help documentation. This method is inefficient and struggles to keep pace with system version iterations.

[0003] Of course, some systems offer a solution to search through search engine components, but traditional keyword-based search engines are limited by literal matching mechanisms, making it difficult to accurately capture users' true search intent, let alone combine personalized features such as user role attributes for intelligent recommendations, thus leading to problems of "not being able to find" or "not being able to find accurately". Summary of the Invention

[0004] This disclosure provides a display method, an electronic device, and a computer program product.

[0005] According to one aspect of this disclosure, a display method is provided, comprising: in response to query information, determining a target component that matches the query information, the query information including query text submitted by a user and attribute information of the user; and displaying a navigation control about the target component, the navigation control including a matching score between the target component and the query information.

[0006] According to one technical solution, by combining attribute information with query text to locate target components and intuitively displaying the matching score in the navigation control, not only is the retrieval and operation cost for users in complex systems significantly reduced, but the transparency of interaction and decision-making efficiency are also improved by quantifying the matching degree, thereby significantly optimizing the user experience.

[0007] In some implementations, a first component area is also displayed on the first interface displaying the navigation control. The first component area contains a plurality of first component controls, wherein the first component controls can display a first component when triggered. The first component is an optional component whose popularity value corresponding to the attribute information exceeds a popularity threshold in a first period. The popularity value is the ratio of the number of times the user with the attribute information triggers the optional component in the first period to the total number of times the user with the attribute information triggers all optional components in the first period.

[0008] In some implementations, determining a target component that matches the query information includes: calling a first database to analyze the query information and having the first database determine the target component, wherein the target component is an optional component in the first database that satisfies the query information, wherein the first database records the component description text of the optional component, the vector value of the component description text, and the associated attribute information, wherein the associated attribute information is the attribute information of the user who triggered the optional component in a first period.

[0009] In some implementations, determining a target component that matches the query information includes: determining the semantic similarity between the query text and the optional component; determining the number of times a user with the attribute information triggers the optional component in a first period; performing a weighted summation of the semantic similarity and the number of triggers to determine a matching score for the optional component; and selecting optional components whose matching scores exceed a first score threshold as the target component.

[0010] In some implementations, determining the semantic similarity between the query text and the optional component includes: calculating the similarity between the query text and the component description text of the optional component to determine a first similarity between the query text and the optional component; calculating the similarity between the vector value of the query text and the vector value of the component description text to determine a second similarity between the query text and the optional component; and performing a weighted summation of the first similarity and the second similarity to determine the semantic similarity.

[0011] In some implementations, before weighted summing of the semantic similarity and the number of triggers, the method further includes: determining whether the associated attribute information of the optional component has the same attribute feature as the attribute information; and when the same attribute feature exists, increasing the weight corresponding to the number of triggers.

[0012] In some implementations, before determining the target component that matches the query information, the process includes: determining the component description text of the optional component based on the component name and the field names of the business fields it contains; vectorizing the component description text to obtain vector values ​​of the component description text; using the attribute information of the user who triggered the optional component in the first cycle as the associated attribute information; and forming a component index table containing the component description text, the vector values ​​of the component description text, and the associated attribute information, and maintaining the component index table in the first database.

[0013] In some implementations, before determining the target component matching the query information, the method further includes: calling a data tracking tool to determine the page image information and page text information of the functional page to which the optional component belongs; performing desensitization processing on the page image information and the page text information respectively to obtain target image information and target text information; and calling a first model to analyze the target image information and the target text information to determine the page index table of the functional page, and maintaining the page index table in a second database, wherein the page index table records the path of the functional page, the component name of the optional component contained in the functional page, and the field name of the business field contained in the optional component.

[0014] In some implementations, the process of calling a tracking tool to determine the page text information of the functional page to which the optional component belongs includes: using interactive page elements in the functional page as the optional component; and determining the component text content filled in the optional component, the position coordinates of the optional component, and the interactive attributes of the optional component to form the page text information.

[0015] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, causing the processor to perform a display method according to any embodiment of this disclosure.

[0016] According to another aspect of this disclosure, a readable storage medium is provided, wherein executable instructions are stored therein, which, when executed by a processor, are used to implement a display method according to any embodiment of this disclosure.

[0017] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the display method described in any embodiment of this disclosure. Attached Figure Description

[0018] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0019] Figure 1 This is a schematic diagram illustrating an application scenario of the display method according to an embodiment of the present disclosure.

[0020] Figure 2 This is a schematic flowchart of a display method according to an embodiment of the present disclosure.

[0021] Figure 3 This is a schematic diagram of a first interface according to an embodiment of the present disclosure.

[0022] Figure 4 This is a schematic diagram of another first interface according to an embodiment of the present disclosure.

[0023] Figure 5 This is a flowchart illustrating the process of determining the target component according to an embodiment of the present disclosure.

[0024] Figure 6 This is a flowchart of the semantic similarity determination process according to the embodiments of this disclosure.

[0025] Figure 7 This is a flowchart illustrating the maintenance process of the component index table according to an embodiment of this disclosure.

[0026] Figure 8 This is a flowchart illustrating the maintenance process of the page index table according to an embodiment of this disclosure.

[0027] Figure 9 This is a schematic block diagram of the structure of a display device according to an embodiment of the present disclosure.

[0028] Figure 10 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0029] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0030] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] As enterprise systems become increasingly feature-rich, helping users efficiently discover and use the functions they need has become a key challenge in improving user experience and operational efficiency. Traditional function discovery mechanisms mainly rely on static help documentation and keyword-based search engines: the former provides users with a mapping between function names and search steps through pre-defined directories and user manuals; the latter allows users to search by entering keywords. In addition, some systems have introduced preset shortcuts such as keyboard shortcuts and favorites to improve the access efficiency of frequently used functions.

[0032] However, these traditional solutions face significant limitations in practical applications. Static help documentation relies heavily on manual writing and maintenance, making it difficult to keep up with the rapid iteration of the system, often resulting in outdated or even invalid content. On the other hand, keyword search engines are limited by literal matching mechanisms, lacking the semantic understanding of users' true intentions and unable to provide personalized recommendations, leading to frequent problems of "not being able to find" or "inaccurate search results."

[0033] Therefore, this disclosure proposes a display method.

[0034] Figure 1 This is a schematic diagram illustrating an application scenario of the display method according to embodiments of this disclosure. For example... Figure 1 As shown, in this application scenario, a server 100 and a terminal device 200 may be included. The server 100 and the terminal device 200 can interact with each other via a network connection. The server 100 can be a cloud server or a physical server, and the terminal device 200 can be a smart device such as a computer, television, projector, mobile phone, or tablet. The server 100 can receive requests from the terminal device 200 and execute the display method disclosed herein.

[0035] Figure 2 This is a schematic flowchart illustrating a display method according to an embodiment of this disclosure. Figure 2 As shown, this disclosure proposes a display method M200, which uses attribute information and query text to determine the target component; and when recommending the target component to the user, displays a navigation control containing a matching score, which retains interpretability while providing personalized recommendations and provides users with a basis for decision-making.

[0036] Step S210: In response to the query information, determine the target component that matches the query information.

[0037] The query information includes the user's attribute information and the query text submitted by the user. This information is used to provide multi-dimensional and quantifiable matching criteria for determining the target component, in order to identify the target component that matches the user's personalized characteristics and query needs from a large number of optional components.

[0038] Attribute information refers to the personalized information of the user corresponding to the query information, such as the user's role, location, and device characteristics. In real-world scenarios, individual users have low query frequency and a low probability of repeatedly querying the same function, making it difficult to characterize each user's personalized preferences. To overcome the problem of sparse single-user behavior data, this disclosure proposes using the group behavior characteristics of other users with the same attribute information (e.g., click-through rate of a component) as the basis for inferring the user's compatibility with the component within a monitoring period. Therefore, the query information in this solution includes attribute information to compensate for the semantic ambiguity and recommendation bias caused by single-dimensional query text.

[0039] Query text is natural language information used to express a user's needs for functions, content, or services. It is content proposed by the user based on actual query requirements, such as "query sales figures for East China in the first quarter". Its content provides semantic support for component matching.

[0040] The target component is one or more optional components that best match the query information. Optional components are page elements in the business system that support user interaction, including various forms such as tables, operation buttons, filter controls, and statistical icons. Their supported functions can be determined semantically through the component name, the name of the corresponding functional page, and the business fields they contain (e.g., order amount field, customer name field, etc.). Of course, by monitoring the behavioral characteristics of groups with different attribute information over a monitoring period, the associated attribute information of optional components can be determined. Furthermore, by combining these two dimensions—the query text and attribute information recorded in the query information—the target component that best suits the user can be accurately determined.

[0041] Step S220: Display navigation controls for the target component.

[0042] Navigation controls are interactive UI elements that serve as access points to target components. They record the path information of the functional pages to which the target component belongs and automatically navigate to the corresponding functional page when triggered by the user to display the target component. Navigation controls provide users with a quick channel to access target components, improving the efficiency of discovering the functions supported by the target component and the convenience of operation.

[0043] The navigation control also includes a matching score. The matching score is a numerical result quantifying the degree of match between the target component and the query information. In the previous step, the target component was determined by calculating the matching score between each optional component and the query information; the specific calculation method will be described later. In this step, when the target component is pushed to the user through the navigation control, the matching score obtained when determining the target component is used as part of the visualization information of the navigation control. This not only provides interpretability for the recommendation structure but also serves as decision-making assistance information to help users quickly filter out the optimal option from multiple candidate target components, thereby enhancing user trust in the recommendation results and the user experience.

[0044] Of course, navigation controls can also include visual information such as descriptions of the target component to provide users with decision-making support. The descriptions in the navigation controls can be a summary of the target component's functionality, such as a control that navigates to a selected order data list by region; or they can be previews of the business fields contained in the target component, such as user name, order amount, and interaction status. This visual description, along with the matching score, helps users understand the functionality and matching degree of the target component, reducing their decision-making costs.

[0045] The style and layout of the navigation control can be configured according to the actual situation. For example, the navigation control can be set as a floating layer on the first interface, using a semi-transparent background, etc., to ensure the visibility of the underlying page of the first interface; it can also be displayed as a drop-down menu of the query box on the first interface, without restriction. The first interface is the display interface of the navigation control, and it can be the same interface as the query text input interface.

[0046] In some implementations, the target component is displayed in response to a navigation control being triggered.

[0047] The navigation control supports multiple triggering methods. For example, the navigation control can be triggered by touch screen click, mouse click, voice control, etc., to meet the interaction habits of users with various needs, or to adapt to different terminal devices (such as terminal device 200).

[0048] This solution utilizes the path information contained in the navigation control to directly jump to the function page containing the target component after the user triggers the navigation control. This avoids the tedious operation of manually searching for the target component within the system and shortens the function access path. Furthermore, this solution determines the target component based on query text and attribute information, ensuring that the target component can simultaneously meet the user's query needs and personalized characteristics.

[0049] Figure 3 This is a schematic diagram of a first interface according to an embodiment of the present disclosure. For example... Figure 3 As shown, the first interface displayed on the screen of the terminal device is provided.

[0050] As can be seen, this implementation adopts a human-computer dialogue interaction to achieve intelligent query and push of functions. The first interface includes at least query text and navigation controls, where the query text is a query request initiated by the user in natural language, such as "view the 7-day transfer rate". After determining the target component, AI (Artificial Intelligence) generates a semantic response "Click the following components to view the 7-day transfer rate", and displays multiple navigation controls below the response dialog box. Each navigation control corresponds to a unique target component, forming a conversational interaction link of "query-response-recommendation", which not only conforms to the user's natural language communication habits, but also achieves an intuitive presentation of function recommendations through visual navigation controls.

[0051] Specifically, based on the determined number of target components, a corresponding number of navigation controls are dynamically generated. Each navigation control is bound to a unique target component, and when triggered by the user, it directly jumps to the corresponding function page. This embodiment demonstrates two navigation controls, each containing a component name, component description information, and matching score, providing users with a visual basis for decision-making.

[0052] For example, the first navigation control's component name is "Business Opportunities Module," and its description is "Records core data such as 7-day transfer rate," with a matching score of 95. The second navigation control's component name is "Store Data Module," and its description is "Records detailed transfer data for each store," with a matching score of 90. Considering that users may not express their queries clearly, or that errors may occur during the semantic similarity calculation between the query and available components, the navigation controls, by displaying multi-dimensional information such as component names, descriptions, and matching scores, help users quickly understand the functions of each target component. This allows them to identify and select the target component that meets their actual needs, effectively avoiding recommendation bias caused by ambiguous query expressions and reducing the user's query costs.

[0053] In some implementations, a first component area is also displayed on the first interface that displays the navigation controls.

[0054] As discussed above, individual users typically query infrequently and rarely query the same function repeatedly, making it difficult to maintain separate optional components tailored to each user's preferences. Therefore, this solution first obtains query information containing attribute information. Then, based on the number of times other users with the same attribute information trigger each optional component, it calculates the popularity value of each component. Next, optional components with popularity values ​​higher than a popularity threshold corresponding to the attribute information are defined as first components. Even if these first components are not directly related to the current query text, they are highly likely to be optional components that the user may need later, thus reducing the time users spend repeatedly entering query text.

[0055] The first component area contains multiple first component controls. Each first component control records the path information of the function page where the first component is located. When triggered, it can jump to the corresponding function page to display the first component. Depending on the determined number of first components, the first component area can contain one or more first component controls; the number is limited here.

[0056] The first component is the optional component whose corresponding attribute information's popularity value exceeds a popularity threshold within the first period. In other words, the first component is the optional component frequently triggered by other users with the same attribute information as the user within the first period. Taking Beijing real estate agents as an example, if a large number of Beijing real estate agents frequently clicked the "Property Viewing Statistics" component in the first quarter, it indicates that this component has a high usage preference among the "Beijing" and "real estate agent" attribute information group. When a user's attribute information includes personalized information such as "Beijing" and "real estate agent," the probability of them clicking on the "Property Viewing Statistics" component is relatively high, even if the current query text does not involve this component, there may be potential demand for clicking or viewing it later.

[0057] Furthermore, the fact that other users with the same attribute information can frequently trigger optional components within the first period indicates that users with that attribute information have the permission to use those optional components. If the "real estate agent" does not have permission to use the "property viewing statistics" component, then it is unlikely that a large number of real estate agents would trigger that component.

[0058] The first cycle refers to a complete monitoring cycle, the time span of which can be a natural day, a quarter, or a year, and can be determined according to the actual situation.

[0059] The popularity value is the ratio of the number of times a user with attribute information triggers an optional component in the first period to the total number of times a user with attribute information triggers all optional components in the first period.

[0060] It should be noted that the trigger count here is based on the number of users. If the same user triggers the same optional component multiple times within the first period, it is only counted as one time. This avoids inflating the popularity value due to repeated operations by a single user, and thus more objectively reflects the actual popularity value of the optional component among groups with attribute information.

[0061] For example, if 100 users with the attribute information "Sales Manager - Beijing - Mini Program" trigger the "Business Opportunities Module" component a total of 80 times in the first period, and the total number of times users with this attribute information trigger all optional components is 90, then the popularity value of the attribute information "Sales Manager - Beijing - Mini Program" corresponding to the "Business Opportunities Module" is 80 / 90=0.89. This value can intuitively represent the preference strength of this component in the corresponding group.

[0062] Figure 4 This is another schematic diagram of a first interface according to an embodiment of the present disclosure. For example... Figure 4 As shown, another first interface is provided for the screen of the terminal device.

[0063] As can be seen, this implementation method still uses a human-computer dialogue interaction to intelligently query and push target components. Figure 3 The difference lies in the fact that, in addition to the query text and navigation controls, the first interface also provides a first component area. This first component area lists several first component controls, such as controls to jump to the relevant function page of "First Component A" and controls to jump to the relevant function page of "First Component B," etc.

[0064] In the process of introducing this implementation method, and Figure 3 The same parts will not be repeated. Regarding the number of navigation controls displayed, the diagram only shows a single navigation control as an example. In actual application scenarios, multiple navigation controls can be dynamically rendered and displayed based on the final determined number of target components. If the number of navigation controls is large and exceeds the single-screen display range of the mobile phone, a sliding control (such as a vertical scroll bar or a horizontal sliding area) can be set to allow users to view the remaining navigation controls by swiping down or sideways, ensuring that the navigation controls of all target components can be easily accessed.

[0065] Furthermore, the layout of the first component area can be flexibly adjusted according to actual usage needs. For example, it can be placed above, below, or to the side of the navigation control to balance information hierarchy and ease of operation, achieving efficient simultaneous display of the navigation control and the first component control on the same screen. It should be noted that the layout shown in the figure is only one feasible implementation scheme and is not the only limitation on the layout method of this scheme.

[0066] When triggered by the user, the first component control can navigate to the corresponding function page and display the first component. Normally, the user can only trigger one of the first component control and the navigation control; however, if the function pages corresponding to the first component and the navigation control both support being presented as overlays, then with the support of multi-overlay rendering capabilities on the terminal device, multiple function pages can be responded to and overlaid simultaneously. This solution does not impose any restrictions on this.

[0067] In some implementations, determining the target component that matches the query information includes: calling a first database to analyze the query information and having the first database determine the target component, wherein the target component is an optional component in the first database that satisfies the query information, wherein the first database records the component description text, the vector value of the component description text, and the associated attribute information of the optional component, and the associated attribute information is the attribute information of the user who triggered the optional component in the first period.

[0068] In the first database, a component index table is maintained for each optional component. The component index table records the component description text, the vector value of the component description text, and associated attribute information for the corresponding optional component. The component index table provides a basis for multi-dimensional semantic similarity calculations, including scalar and vector values, for querying information; and it records the associated attribute information of optional components that have been triggered, providing a basis for attribute matching.

[0069] The first database can be an Elasticsearch (ES) database, capable of determining the semantic similarity between the input query information and each optional component. The query text, a lower limit for semantic similarity, and an upper limit for the number of component index tables to be retrieved can be used as input. The first database can filter component index tables containing multiple optional components with semantic similarity greater than or equal to the lower limit, where the number of selected optional components is less than or equal to the upper limit. The specific determination process will be described later and will not be elaborated here.

[0070] The component description text is the text content used to describe the optional component, including the page name of the functional page to which the optional component belongs, the component name, the names of the business fields contained in the component, etc. By parsing the component description text, the functions that the component can perform can be determined.

[0071] The vector values ​​of component description text are the result of vectorizing the component description text. Converting the semantic information of the component description text into numerical vectors in a high-dimensional space can more effectively capture the functional information in the component description text and provide a quantifiable control over the similarity between the query text and the component description text. This serves as the data foundation for semantic-based target component identification. Vectorization can be performed using natural language processing models, etc., and is not limited here.

[0072] The associated attribute information refers to the attribute information of users who triggered the optional component in the first cycle. Maintaining the attribute information of all users who triggered the optional component in the first cycle in the component index table allows for the construction of a relationship between optional components and attribute information, providing data support for quickly locating preferred components based on attribute information.

[0073] Figure 5This is a flowchart illustrating the process of determining the target component according to embodiments of this disclosure. For example... Figure 5 As shown, this disclosure provides a process for determining a target component.

[0074] In this embodiment, steps 501 to 505 are proposed, namely: determining the semantic similarity between the query text and the optional component; determining the number of times the user with attribute information triggers the optional component in the first period; performing a weighted summation of the semantic similarity and the number of triggers to determine the matching score of the optional component; determining whether the matching score exceeds a first score threshold, and using the optional component whose matching score exceeds the first score threshold as the target component.

[0075] Semantic similarity is a quantitative result of the degree of semantic similarity between query text and optional components. It is a multi-dimensional calculation result of query text, component description text of optional components, and vector values ​​of component description text. Here, multi-dimensionality refers to the scalar similarity between query text and component description text, and the vector similarity between the vector values ​​of query text and component description text. The specific determination process will be introduced later.

[0076] The trigger count is the number of times a user with attribute information triggers an optional component in the first period. If the same user triggers an optional component multiple times in the first period, the trigger count is counted as 1. To reduce the need for multiple and repeated readings of historical behavior data, the trigger count for each associated attribute can be maintained in the component index table.

[0077] In some implementations, after acquiring the historical behavior data of users with attribute information in the first period, the historical behavior data is deduplicated, and the trigger count for the same user is set to 1.

[0078] The matching score is a weighted sum of semantic similarity and trigger count. Specifically, based on the difference in importance between the query text and attribute information in the matching calculation, corresponding weights are assigned to semantic similarity and trigger count. For example, the weight value for semantic similarity can be set to 0.7, and the weight value for trigger count can be set to 0.3. By using a weighted summation method, the influence of the user's explicit query intent and implicit personalized preferences on the determination of target components is balanced. This ensures that the final matching result not only matches the user's current query needs but also takes into account the historical behavioral characteristics of groups with similar attributes, thereby improving the accuracy and adaptability of component recommendations.

[0079] The first score threshold is a standard used to measure whether an optional component is a target component. Optional components that are higher than the first score threshold can be used as target components, while optional components that are lower than the threshold are not used as target components.

[0080] The determination of the first score threshold can be flexibly configured according to specific application scenarios. For example, the matching scores of all optional components can be sorted in descending order. If it is necessary to select the top 20 optional components with the highest suitability as target components, then the matching score corresponding to the 20th ranked optional component is set as the first score threshold. At this time, all optional components with matching scores not lower than this threshold are determined as target components. In addition, the first score threshold can also be defined based on statistical analysis of historical matching data (such as taking the mean and standard deviation of matching scores) or business needs (such as setting a fixed score value), ensuring that the selected target components not only meet the quantity requirements, but also accurately match the user's query intent and personalized needs.

[0081] Figure 6 This is a flowchart of the semantic similarity determination process according to embodiments of this disclosure. For example... Figure 6 As shown, this disclosure proposes a process for determining semantic similarity.

[0082] In this embodiment, steps 601 to 603 are provided, namely: calculating the similarity between the query text and the component description text of the optional component to determine the first similarity between the query text and the optional component; calculating the similarity between the vector value of the query text and the vector value of the component description text to determine the second similarity between the query text and the optional component; and performing a weighted summation of the first similarity and the second similarity to determine the semantic similarity.

[0083] The first similarity value represents the degree of keyword matching between the query text and the component description text, and is a similarity calculation result on a scalar dimension. By determining the degree of overlap between the keywords in the query text and the keywords in the component description text, the semantic relevance between the two at the keyword level is determined.

[0084] The second similarity is the degree of matching between the vector values ​​of the query text and the vector values ​​of the component description text; it is a similarity calculation result along the vector dimension. Before calculation, the vector values ​​of the query text need to be determined; then, methods such as the cosine similarity algorithm can be used to calculate the cosine value of the angle between the two vector values, which is used as the second similarity.

[0085] Based on the importance of both scalar and vector dimensions, weights are assigned to the first and second similarities, respectively. For example, the weight of the first similarity is set to 0.2, and the weight of the second similarity is set to 0.8, which is higher than the weight of the first similarity. Then, the first and second similarities are weighted and summed to obtain the semantic similarity representing the query text and the optional components.

[0086] It should be noted that the above process can be implemented using a first database capable of performing the above steps. After determining the semantic similarity, the first database outputs a component index table of optional components that can be used for subsequent matching score calculations.

[0087] In some implementations, after determining semantic similarity, the method further includes: filtering optional components whose semantic similarity meets the initial screening criteria.

[0088] Specifically, a lower limit for semantic similarity and an upper limit for the number of component index tables to be retrieved are determined. All optional components are sorted in descending order of semantic similarity, and the number of optional components to be retrieved is determined by the upper limit. Then, among the retrieved optional components, those with semantic similarity greater than or equal to the lower limit are selected as those meeting the initial screening criteria and proceed to the subsequent matching score calculation stage. This initial screening mechanism ensures the semantic adaptability of the target components while avoiding the resource consumption caused by calculating all components, achieving a balance between efficiency and accuracy.

[0089] For example, if the maximum number of component index tables to be retrieved is 20, then the top 20 optional components are retrieved based on their semantic similarity. If the lower limit of semantic similarity is set to 0.6, then optional components with a semantic similarity greater than or equal to 0.6 need to be selected from the retrieved 20 optional components to proceed to the subsequent matching score calculation stage.

[0090] In some implementations, before weighting and summing the semantic similarity and the number of triggers, the method further includes: determining whether the associated attribute information and attribute information of the optional components have the same attribute features; and when the same attribute features exist, increasing the weight corresponding to the number of triggers.

[0091] Typically, semantic similarity and trigger frequency are weighted according to the importance of query needs and personalized information. In functional query scenarios, semantic similarity directly represents the user's explicit query needs and is therefore assigned a higher weight, such as 0.7; while trigger frequency, as auxiliary information reflecting group behavior characteristics, is usually assigned a relatively lower weight, such as 0.3. However, if the associated attribute information recorded in the component index table of the optional component is the same as or partially overlaps with the attribute information, it indicates that the optional component has a stronger personalized fit with the current user. In this case, the weight related to trigger frequency can be appropriately increased, for example, by adjusting the weight to 0.5, to strengthen the influence of attribute information on the matching results, making the final selected target component more in line with the user's actual needs and usage habits.

[0092] In some implementations, different weighting strategies can be configured based on attribute features that overlap with the attribute information. For example, when the attribute information and the associated attribute information of the optional component have the same region, the trigger count weight can be increased by 1.2 times; when the attribute information and the associated attribute information of the optional component have the same role, the trigger count weight can be increased by 1.1 times; when the attribute information and the associated attribute information of the optional component have the same terminal feature, the trigger count weight can be increased by 1.05 times, and so on.

[0093] Figure 7 This is a flowchart illustrating the maintenance process of the component index table according to an embodiment of this disclosure. For example... Figure 7 The diagram illustrates the maintenance process of the component index table.

[0094] In this embodiment, steps 701 to 704 are proposed, namely: determining the component description text of the optional component based on the component name of the optional component and the field names of the business fields it contains; vectorizing the component description text to obtain the vector value of the component description text; using the attribute information of the user who triggered the optional component in the first cycle as the associated attribute information; and forming a component index table containing the component description text, the vector value of the component description text, and the associated attribute information, and maintaining the component index table in the first database.

[0095] This implementation achieves structured storage of the semantic multi-dimensional features of optional components and their association with user behavior, providing a complete data foundation for subsequent component matching based on query text and attribute information, and effectively improving the efficiency and adaptability of component retrieval and recommendation.

[0096] Figure 8 This is a flowchart illustrating the maintenance process of the page index table according to an embodiment of this disclosure. For example... Figure 8 The image shows the process of maintaining the page index table.

[0097] In this embodiment, steps 801 to 803 are proposed, namely: calling the tracking tool to determine the page image information and page text information of the functional page to which the optional component belongs; performing desensitization processing on the page image information and page text information respectively to obtain target image information and target text information; and calling the first model to analyze the target image information and target text information to determine the page index table of the functional page, and maintaining the page index table in the second database.

[0098] The event tracking tool can be an SDK (Software Development Kit). This tool can automatically capture all page information, including page image information and page text information, through DOM (Document Object Model) traversal algorithms such as depth-first search. This reduces the cost of manual event tracking and also ensures the comprehensiveness and accuracy of optional component identification.

[0099] The way the tracking tool determines the page text information is as follows: interactive page elements in the functional page are used as optional components; and the component text content, position coordinates and interactive attributes of the optional components are determined to form the page text information.

[0100] The way the tracking tool determines the page image information is as follows: it uses the webpage's built-in print function to obtain the PDF data of the functional page; then it converts the PDF data into an image format to obtain the page image information.

[0101] When tracking tools read page text information, they automatically de-identify sensitive fields. Therefore, the obtained page text information is usually the result after de-identification, i.e., the target text information.

[0102] For page image information, an OCR (Optical Character Recognition) engine can be called to identify the field parts in the image and determine the coordinates of the sensitive field. Then, the OpenCV (Open Source Computer Vision Library) library is used to perform Gaussian blur processing on the location of the sensitive field to obtain the desensitized target image information.

[0103] The page index table records the paths of functional pages, the names of optional components contained in the functional pages, and the names of business fields contained in the optional components. It is a structured data list. After the target component is determined, the path of the functional page containing the target component can be determined based on the component name of the target component, thus supporting the navigation control's jumpability.

[0104] The second database can be a MySQL database, which supports cross-platform deployment and can efficiently handle high-concurrency access and large-scale data storage. It is suitable for a large number of functional page index tables in enterprise-level systems, as well as high-frequency, high-concurrency queries and calls to target components.

[0105] Figure 9 This is a schematic block diagram of the structure of a display device according to an embodiment of the present disclosure. Figure 9As shown, this disclosure provides a display device 900, including: a target component determination module 910, which, in response to query information, determines a target component that matches the query information, the query information including query text submitted by a user and user attribute information; and a display module 920, which displays a navigation control about the target component, the navigation control including a matching score between the target component and the query information.

[0106] The display device 900 disclosed herein may be in the form of computer software, and each module of the display device 900 may be in the form of computer software modules.

[0107] The various modules of the display device 900 disclosed herein are set up to implement the various steps of the display method. The execution principle and steps can be referred to the above text and will not be repeated here.

[0108] Figure 10 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure. Figure 10 As shown, this disclosure also provides an electronic device 1000, including: a processor 1200 and a memory 1300, the memory 1300 storing execution instructions; the processor 1200 executes the execution instructions stored in the memory 1300, causing the processor 1200 to execute a display method.

[0109] The hardware architecture of the electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application of the hardware and overall design constraints. Bus 1100 connects various circuits, including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400, such as peripheral devices, voltage regulators, power management circuits, external antennas, etc.

[0110] Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, this diagram uses only one connection line, but this does not imply that there is only one bus or one type of bus.

[0111] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. The readable storage medium can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of readable storage media include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0112] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0113] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0114] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, electronic devices, readable storage media, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] In the description of this specification, the references to the terms "an embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, or characteristic described in connection with that embodiment / mode or example is included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Furthermore, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0119] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0120] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

[0121] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0122] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0123] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0124] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0125] At the same time, it is understood that the data involved in this disclosed technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

Claims

1. A display method characterized by comprising: include: In response to query information, a target component matching the query information is determined, the query information including the query text submitted by the user and the user's attribute information; as well as A navigation control is displayed for the target component, the navigation control containing a match score between the target component and the query information.

2. The display method according to claim 1, wherein The first interface displaying the navigation controls also includes a first component area, which contains multiple first component controls. Wherein, the first component control can display the first component when it is triggered. The first component is an optional component whose popularity value corresponding to the attribute information exceeds the popularity threshold in the first period. The popularity value is the ratio of the number of times the user with the attribute information triggers the optional component in the first period to the total number of times the user with the attribute information triggers all optional components in the first period.

3. The display method according to claim 1, wherein Determining the target component that matches the query information includes: The query information is analyzed by calling a first database, and the target component is determined by the first database. The target component is an optional component in the first database that satisfies the query information. The first database records the component description text of the optional component, the vector value of the component description text, and the associated attribute information, wherein the associated attribute information is the attribute information of the user who triggered the optional component in the first cycle.

4. The display method according to claim 1 or 3, wherein Determining the target component that matches the query information includes: Determine the semantic similarity between the query text and the optional components; Determine the number of times a user with the attribute information triggers the optional component in the first cycle; The matching score of the optional component is determined by weighted summation of the semantic similarity and the number of triggers; and The optional component whose matching score exceeds a first score threshold is used as the target component.

5. The display method according to claim 4, characterized in that, Determining the semantic similarity between the query text and the optional components includes: The similarity between the query text and the component description text of the optional component is calculated to determine the first similarity between the query text and the optional component; The similarity between the vector value of the query text and the vector value of the component description text is calculated to determine a second similarity between the query text and the optional component; and The semantic similarity is determined by weighted summation of the first similarity and the second similarity.

6. The display method according to claim 4, characterized in that, Before performing a weighted summation of the semantic similarity and the number of triggers, the method further includes: Determine whether the associated attribute information of the optional component has the same attribute characteristics as the attribute information; and When the same attribute features exist, increase the weight corresponding to the number of triggers.

7. The display method according to claim 3, characterized in that, Before determining the target component that matches the query information, the process includes: The component description text of the optional component is determined based on the component name of the optional component and the field names of the business fields it contains; The component description text is vectorized to obtain the vector value of the component description text; The attribute information of the user who triggered the optional component in the first cycle will be used as the associated attribute information; and A component index table is formed, which includes the component description text, the vector value of the component description text, and the associated attribute information, and the component index table is maintained in the first database.

8. The display method according to claim 1, characterized in that, Before determining the target component that matches the query information, the process also includes: Use the event tracking tool to determine the page image and text information of the functional page to which the optional component belongs; The page image information and the page text information are respectively de-identified to obtain target image information and target text information; and The first model is invoked to analyze the target image information and the target text information to determine the page index table of the function page, and the page index table is maintained in the second database. The page index table records the path of the function page, the component name of the optional component contained in the function page, and the field name of the business field contained in the optional component.

9. An electronic device, characterized in that, include: The memory stores execution instructions; as well as A processor that executes execution instructions stored in the memory, causing the processor to perform the display method according to any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the display method according to any one of claims 1 to 8.

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