Intelligent display and management method and system of map information anchor points in a limited area

Abstract

This invention proposes an intelligent display and management method and system for map information anchors within a limited area. The method includes acquiring multiple information anchors within the limited area, classifying the anchors in multiple dimensions based on attribute data, generating corresponding visual labels (including color, shape, and size), and managing the validity period of the anchors based on timeliness data. This invention achieves intelligent classification, dynamic aggregation, and personalized display of map information within a limited area by constructing multi-dimensional information anchors, significantly improving the organization of information display and user experience. It uses attribute, timeliness, and privacy data for refined management of information anchors, preventing invalid information from occupying display resources for extended periods; it balances information density and visualization effects through dynamic aggregation and scaling linkage mechanisms; and it introduces AI-assisted classification and recommendation models to enhance the intelligence of information matching.

Description

Technical Field

[0001] This invention relates to the field of map information processing technology, and in particular to a method and system for intelligent display and management of map information anchor points within a limited area. Background Technology

[0002] Existing map information display systems generally suffer from problems such as information overload, chaotic display, and poor timeliness when processing user-generated content (UGC) within a limited area. This is especially true in cities or business districts with high information density, where there is a lack of mechanisms for effectively classifying information anchors, controlling timeliness, managing privacy, and dynamically aggregating them. As a result, users find it difficult to quickly obtain valuable information, which affects the practicality of map services and user experience. Summary of the Invention

[0003] In view of this, in order to solve the problems existing in the technical background, the present invention proposes an intelligent display and management method and system for map information anchor points within a limited area. Specifically, it includes the following: A method for intelligent display and management of map information anchor points within a limited area includes the following steps: S1, obtain multiple information anchor points within a limited area, each information anchor point is associated with attribute data, timeliness data and privacy permission data; S2, classify the information anchor points in multiple dimensions based on the attribute data and generate corresponding visual labels. The visual labels include at least one differentiating feature among color, shape, size or transparency. S3. Based on the timeliness data, the information anchor point is managed for validity period. When the preset validity period is exceeded, the information anchor point is automatically moved to the historical archive state and is no longer displayed in the main map view. S4. Based on the privacy permission data, the visibility of the information anchor is controlled, including at least one or more of the following: visible to individuals, visible to friends, or visible to the public. S5, in response to map zooming operation, dynamically adjust the display granularity of the information anchor points according to the current zoom level, so that only the core anchor points are displayed at the city-level zoom level, and all anchor point details are expanded at the street-level zoom level; and in response to the information anchor point density in the area exceeding a preset threshold, automatically aggregate adjacent or similar anchor points into a block label for display, and expand to display all anchor point information contained therein when the user triggers the block label.

[0004] In one embodiment of the present invention, the step of classifying information anchors in multiple dimensions using attribute data further includes: analyzing user-uploaded images or text content using a preset semantic recognition model, automatically matching information anchors to preset categories, and generating corresponding personalized tags; the semantic recognition model is optimized based on user behavior data to achieve dynamic improvement in classification accuracy.

[0005] In one embodiment of the present invention, the timeliness data includes a user-defined validity period range. The system automatically triggers anchor point sinking or archiving logic based on the comparison result between the current time and the validity period. For anchor points that have exceeded their validity period and have no user interaction, the system automatically marks them as invalid information and hides them. For anchor points marked as "invalid information" by the user, the system further verifies them through an audit mechanism and performs sinking or deletion operations.

[0006] In one embodiment of the present invention, the step of information anchor aggregation further includes: dynamically adjusting the aggregation threshold based on at least one of real-time pedestrian flow data, regional heat or user behavior data; when the regional information density exceeds the dynamic threshold, automatically generating an aggregation block label, and expanding it into a list or sub-anchor set after the user clicks on the block.

[0007] In one embodiment of the present invention, step S5 further includes a personalized filtering and priority sorting step: based on at least one of user profile, historical interaction data or current geographical location, a priority prediction model is constructed to automatically display information anchors with high relevance or high potential popularity at the top; at the same time, users can manually set filtering conditions to filter the displayed content.

[0008] A method for intelligent display and management of map information anchor points within a limited area, wherein the priority prediction model calculates the recommendation weight of information anchor points using the following formula:

[0009] in, Pi Indicates information anchor point i For users u Recommendation priority f `interact` is an interaction function that calculates based on the number of times a user has clicked, liked, or viewed an anchor point in the past. f The profile is a user profile matching function, which is calculated based on the degree of matching between user preference categories and anchor categories; f geo is a geographic proximity function, calculated based on the distance between the user's current location and the anchor point; α , β , γ These are the weighting coefficients, obtained through optimization using training data.

[0010] A map information anchor point intelligent display and management system within a limited area includes a data acquisition module for acquiring anchor points and their attributes, timeliness, and privacy data; a classification and tagging module for generating visual tags with differentiated features; a timeliness management module for automatically archiving expired anchor points based on their validity period; a privacy control module for controlling visibility according to permissions; and a scaling and aggregation module for adjusting the display granularity according to the scaling level and aggregating them into block tags for users to click and expand when the density exceeds a threshold. These modules are connected sequentially and work collaboratively.

[0011] The above technical solution has the following beneficial effects: This invention achieves intelligent classification, dynamic aggregation, and personalized display of map information within a limited area by constructing multi-dimensional information anchors, significantly improving the organization of information display and user experience. Its beneficial effects include: refined management of information anchors based on attributes, timeliness, and privacy data, preventing invalid information from occupying display resources for extended periods; balancing information density and visualization effects through dynamic aggregation and scaling linkage mechanisms; introducing AI-assisted classification and recommendation models to enhance the intelligence level of information matching; supporting an information cleanup mechanism that combines user interaction and system review to continuously maintain the quality of the information pool; and enhancing the system's scalability and technical barriers to adapt to the deployment needs of various application scenarios. Attached Figure Description

[0012] Picture 1 The flowchart is a method and system for intelligent display and management of map information anchor points within a limited area according to the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Example 1, see Picture 1 The method for intelligent display and management of map information anchor points within a limited area, as shown, includes the following steps: S1, obtain multiple information anchor points within a limited area, each information anchor point is associated with attribute data, timeliness data and privacy permission data; S2, classify the information anchor points in multiple dimensions based on the attribute data and generate corresponding visual labels. The visual labels include at least one differentiating feature among color, shape, size or transparency. S3. Based on the timeliness data, the information anchor point is managed for validity period. When the preset validity period is exceeded, the information anchor point is automatically moved to the historical archive state and is no longer displayed in the main map view. S4. Based on the privacy permission data, the visibility of the information anchor is controlled, including at least one or more of the following: visible to individuals, visible to friends, or visible to the public. S5, in response to map zooming operation, dynamically adjust the display granularity of the information anchor points according to the current zoom level, so that only the core anchor points are displayed at the city-level zoom level, and all anchor point details are expanded at the street-level zoom level; and in response to the information anchor point density in the area exceeding a preset threshold, automatically aggregate adjacent or similar anchor points into a block label for display, and expand to display all anchor point information contained therein when the user triggers the block label.

[0015] Example 2, based on Example 1, further includes the step of classifying information anchors in multiple dimensions using attribute data: analyzing user-uploaded images or text content using a preset semantic recognition model, automatically matching information anchors to preset categories, and generating corresponding personalized tags; the semantic recognition model is optimized based on user behavior data to dynamically improve classification accuracy.

[0016] The timeliness data includes a user-defined validity period range. The system automatically triggers anchor point sinking or archiving logic based on the comparison result between the current time and the validity period. For anchor points that have exceeded their validity period and have no user interaction, the system automatically marks them as invalid information and hides them. For anchor points marked as "invalid information" by users, the system further verifies them through an audit mechanism and performs sinking or deletion operations.

[0017] The information anchor aggregation step further includes: dynamically adjusting the aggregation threshold based on at least one of real-time pedestrian flow data, regional heat or user behavior data; when the regional information density exceeds the dynamic threshold, automatically generating an aggregation block label, and expanding it into a list or sub-anchor set after the user clicks on the block.

[0018] Example 3, based on Example 1, further includes a personalized filtering and priority sorting step in step S5: a priority prediction model is constructed based on at least one of user profile, historical interaction data, or current geographic location, and information anchors with high relevance or high potential popularity are automatically displayed at the top; at the same time, users can manually set filtering conditions to filter the displayed content.

[0019] A method for intelligent display and management of map information anchor points within a limited area, wherein the priority prediction model calculates the recommendation weight of information anchor points using the following formula:

[0020] in, Pi Indicates information anchor point i For users u Recommendation priority f `interact` is an interaction function that calculates based on the number of times a user has clicked, liked, or viewed an anchor point in the past. f The profile is a user profile matching function, which is calculated based on the degree of matching between user preference categories and anchor categories; f geo is a geographic proximity function, calculated based on the distance between the user's current location and the anchor point; α , β , γ These are the weighting coefficients, obtained through optimization using training data.

[0021] The system first collects various information anchors posted by users through a data acquisition module, including store information, user shares, and event announcements. Each anchor carries attribute data, timeliness data, and privacy permission data. Based on this data, the system categorizes the anchors in multiple dimensions, generating visual labels with differentiated features such as color, shape, size, or transparency. For example, store information can be represented by a blue circular label, event announcements by an orange square label, and user shares by a green star label, making it easy for users to quickly identify the information type on the map.

[0022] Users can choose a validity period ranging from 1 to 30 days when publishing anchors, and the system automatically monitors the anchor's validity and expiration status. For expired anchors, the system moves them down from the main view to the history archive area, no longer interfering with the current information display. If an anchor is frequently viewed or interacted with by users after its expiration, the system can retain its display permissions according to preset rules, reflecting the flexibility of management. Simultaneously, users can mark invalid information, and the system will delete or move it down after confirmation through an approval mechanism, forming a continuous maintenance mechanism for information quality.

[0023] In this embodiment, users can set visibility permissions for each anchor point, including personal visibility, friend visibility, or public visibility. The system dynamically filters the displayed content based on the current user's identity and permissions, ensuring that information display meets user privacy expectations. Future versions may also integrate social relationship chains to achieve more granular permission control.

[0024] This invention dynamically adjusts the display granularity based on the current zoom level. For example, at the city-level zoom, the system only displays core anchor points or aggregated block labels; at the street-level zoom, the system expands all anchor point details to ensure a balance between information density and visualization. When the number of anchor points in a certain area exceeds a preset threshold, the system automatically triggers aggregation logic, merging adjacent or similar anchor points into block labels, such as "XX Business District - 5 Restaurants / 3 Events". After a user clicks on a block, the system expands to display all the anchor point information it contains, which the user can choose to view in list format or click on to view them one by one on the map.

[0025] This invention constructs a priority prediction model based on user historical interaction data, profile tags, and current location, automatically displaying highly relevant anchor points at the top. For example, the system can prioritize recommending anchor points matching a user's interests based on frequently followed store types or activity categories. Simultaneously, the system supports users manually setting filter conditions, such as "Only view today's activities," "Food recommendations," and "Visible to friends," further enhancing the flexibility of information filtering. A filter panel can be set on the map interface, allowing users to adjust the displayed content in real time by selecting options such as "Social," "Business," "Store," "Visible to individuals," "Visible to friends," and "Visible to the public," meeting diverse needs.

[0026] In this application example, located in a core business district of a first-tier city, the system provides intelligent information anchor point display and management services to surrounding users. This area has extremely high information density, containing a large amount of user-generated content such as store reviews, limited-time event announcements, and personal sharing points. Using traditional map display methods would easily lead to information overload and visual confusion. This system effectively improves the intelligence level of information organization and display by introducing multi-dimensional classification, timeliness management, privacy control, and dynamic aggregation mechanisms. In this application example, the system first collects all information anchor points within the area through a data acquisition module. Each anchor point is associated with attribute data, timeliness data, and privacy permission data. Attribute data includes text descriptions or images uploaded by users. The system automatically analyzes the content using a preset semantic recognition model, matching it to preset categories such as "food recommendations," "limited-time events," and "personal check-ins," and generates visual labels with differentiated features such as color, shape, and size, allowing users to easily identify the information type on the map. Timeliness data is customized by users when publishing. For example, a coffee shop might set the validity period of its "Weekend Limited-Time Discount" activity anchor to 48 hours. The system automatically triggers management logic based on the comparison between the current time and the validity period. If the activity ends, the anchor is moved from the main view to the historical archive to prevent expired information from occupying display resources for a long time. Privacy permissions data allows users to set "visible to personal," "visible to friends," or "visible to the public." The system dynamically filters the displayed content based on the currently logged-in user's identity and permissions to ensure that information display meets user privacy expectations. Regarding zoom and aggregation management, when users browse at the city-level zoom level, the system only displays core anchors or aggregation block tags, such as "XX Business District · 8 Restaurants / 3 Activities," to avoid information overload. When users zoom to the street level, the system automatically expands all anchor details, displaying the specific location and tag information of each anchor. The aggregation logic is not only based on static density thresholds, but also dynamically adjusts the aggregation thresholds by incorporating real-time pedestrian flow data and regional popularity. For example, during peak pedestrian traffic periods on weekend evenings, the system automatically increases the aggregation threshold, merging more anchor points into block labels to ensure a clear and readable map interface. Regarding personalized display, the system builds a priority prediction model based on user profiles, historical interaction data, and current geographic location, using a formula...

[0027] Calculate the recommendation weight of each anchor point for the current user. Taking Mr. Zhang as an example, he frequently views coffee shop-related anchor points and participates in related activities within the system. The system assigns him a high interaction function value by analyzing his historical clicks and likes. Simultaneously, his user profile shows a high match between his preferred categories and coffee shops, resulting in a high profile matching function value. Currently, Mr. Zhang is located near a coffee shop within this business district, further increasing the weight of surrounding anchor points through the geographical proximity function. Assume the system optimizes through training data... α , β, γ With values ​​of 0.5, 0.3, and 0.2 respectively, a newly opened boutique coffee shop anchor point, due to its high match with Mr. Zhang's historical interaction data and its location within 200 meters of his current location, ultimately received a significantly higher recommendation priority Pi value than other anchor points. The system automatically prioritized it and highlighted it on the map, greatly improving information reach efficiency. Simultaneously, the system allows users to manually set filtering conditions; for example, Mr. Zhang could choose "Only see today's activities" or "Visible to friends" to further filter the displayed content and meet his personalized needs. Throughout the information maintenance process, the system also incorporates user interaction and review mechanisms. For instance, if a user discovers a closed shop anchor point still displayed on the map, they can mark it as "invalid information." After confirmation through the review mechanism, the system automatically moves it down or deletes it, continuously maintaining the quality of the information pool. This application example fully demonstrates the technical advantages of this invention in the intelligent display and management of map information anchor points within a limited area. By combining multiple mechanisms such as semantic recognition, dynamic aggregation, personalized recommendation, and privacy control, it significantly improves the organization of information display and user experience, adapting to the complex and ever-changing information management needs of high-density urban areas.

[0028] In other embodiments, AI technology is introduced to enhance the system's intelligence. In the classification stage, the system automatically analyzes user-uploaded images or text content using a semantic recognition model, matching them to preset categories and generating personalized tags. In the recommendation stage, the system trains a priority prediction model based on user behavior data, dynamically adjusting the display order of anchor points. In the aggregation stage, the system dynamically adjusts aggregation thresholds based on real-time pedestrian flow data and regional popularity information to avoid information overload. In the content review stage, the system automatically identifies illegal content and triggers a cleanup process, reducing manual intervention costs and improving the quality of the information pool.

[0029] The basic principles and main features of the present invention have been described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention. All such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent display and management of map information anchor points within a limited area, characterized in that, Includes the following steps: S1, obtain multiple information anchor points within a limited area, each information anchor point is associated with attribute data, timeliness data and privacy permission data; S2, classify the information anchor points in multiple dimensions based on the attribute data and generate corresponding visual labels. The visual labels include at least one differentiating feature among color, shape, size or transparency. S3. Based on the timeliness data, the information anchor point is managed for validity period. When the preset validity period is exceeded, the information anchor point is automatically moved to the historical archive state and is no longer displayed in the main map view. S4. Based on the privacy permission data, the visibility of the information anchor is controlled, including at least one or more of the following: visible to individuals, visible to friends, or visible to the public. S5, in response to map zooming operation, dynamically adjust the display granularity of the information anchor points according to the current zoom level, so that only the core anchor points are displayed at the city-level zoom level, and all anchor point details are expanded at the street-level zoom level; and in response to the information anchor point density in the area exceeding a preset threshold, automatically aggregate adjacent or similar anchor points into a block label for display, and expand to display all anchor point information contained therein when the user triggers the block label.

2. The intelligent display and management method for map information anchor points within a limited area according to claim 1, characterized in that, The step of classifying information anchors using attribute data in multiple dimensions further includes: analyzing user-uploaded images or text content using a preset semantic recognition model, automatically matching information anchors to preset categories, and generating corresponding personalized tags; the semantic recognition model is optimized based on user behavior data to dynamically improve classification accuracy.

3. The intelligent display and management method for map information anchor points within a limited area according to claim 1, characterized in that, The timeliness data includes a user-defined validity period range. The system automatically triggers anchor point sinking or archiving logic based on the comparison result between the current time and the validity period. For anchor points that have exceeded their validity period and have no user interaction, the system automatically marks them as invalid information and hides them. For anchor points marked as "invalid information" by users, the system further verifies them through an audit mechanism and performs sinking or deletion operations.

4. The intelligent display and management method for map information anchor points within a limited area according to claim 1, characterized in that, The information anchor aggregation step further includes: dynamically adjusting the aggregation threshold based on at least one of real-time pedestrian flow data, regional heat or user behavior data; when the regional information density exceeds the dynamic threshold, automatically generating an aggregation block label, and expanding it into a list or sub-anchor set after the user clicks on the block.

5. The intelligent display and management method for map information anchor points within a limited area according to claim 1, characterized in that, Step S5 also includes a personalized filtering and priority sorting step: based on at least one of user profile, historical interaction data or current geographical location, a priority prediction model is constructed to automatically display information anchors with high relevance or high potential popularity at the top; at the same time, users can manually set filtering conditions to filter the displayed content.

6. The intelligent display and management method for map information anchor points within a limited area according to claim 1, characterized in that, The priority prediction model uses the following formula to calculate the recommendation weight of information anchor points:

7. Among them, Pi Indicates information anchor point i For users u Recommendation priority f `interact` is an interaction function that calculates based on the number of times a user has clicked, liked, or viewed an anchor point in the past. f The profile is a user profile matching function, which is calculated based on the degree of matching between user preference categories and anchor categories; f geo is a geographic proximity function, calculated based on the distance between the user's current location and the anchor point; α , β , γ These are the weighting coefficients, obtained through optimization using training data.

8. A map information anchor point intelligent display and management system within a limited area, characterized in that, This includes a data acquisition module, used to acquire anchor points and their attributes, as well as timeliness and privacy data; The classification and tagging module generates visual tags with differentiated features; the expiration management module automatically archives expired anchors based on their validity period; and the privacy control module controls visibility according to permissions. The scaling and aggregation module is used to adjust the display granularity according to the scaling level, and aggregate into block labels when the density exceeds the threshold for users to click and expand; the modules are connected in sequence and work together.

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