Map interest point preloading method and system based on user focus
By using a user focus analysis model to filter and sort points of interest, the problems of map loading delay and high resource consumption were solved, enabling fast and personalized loading of points of interest, thus improving user experience and data quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-11
- Publication Date
- 2026-04-03
AI Technical Summary
Existing methods for loading map points of interest suffer from latency, insufficient data quality, and high device resource consumption, especially in poor network environments or with large data volumes, which negatively impacts user experience.
By collecting user behavior and environmental data, a user focus analysis model is built to identify core and potential focus areas, filter and sort points of interest, prioritize loading high-priority points of interest, dynamically update pre-loaded content, and reduce data loading in non-focus areas.
Significantly reduces loading latency for points of interest, improves data quality, reduces network transmission and device resource consumption, adapts to diverse user scenarios, and provides personalized services.
Smart Images
Figure CN121786086A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of computer software and geographic information system technology, specifically to a method and system for preloading map points of interest based on user focus. Background Technology
[0002] As a key component of smart city construction, the intelligent emergency command and dispatch system aims to manage the entire process—before, during, and after an incident—enabling rapid emergency response and improving the city's emergency handling capabilities. The system utilizes electronic maps, where incidents, emergency personnel, monitoring equipment, and resource reserves are represented by corresponding points of interest (POIs). Users typically require quick access to information about the surrounding entities, such as emergency personnel, monitoring equipment, and resource reserves. This places high demands on the loading speed and accuracy of POIs on the map.
[0003] Currently, there are several main methods for loading Points of Interest (POIs) on maps: real-time loading, preloading based on fixed areas, and preloading based on simple path prediction. Real-time loading loads all PPOs at once. This method can easily lead to delays in PPO display, especially in poor network conditions or with large amounts of PPO data, requiring users to wait for extended periods and impacting the user experience. Preloading based on fixed areas preloads PPOs within the current field of view and a certain surrounding area. This method may load a large number of PPOs that the user is not interested in, and may also fail to meet sudden viewing needs due to insufficient preloading range. Preloading based on simple path prediction predicts the areas the user is likely to reach based on their movement trajectory and preloads PPOs for that area. This method has a relatively simple prediction method, relying solely on the navigation path, and the accuracy of preloading still needs improvement.
[0004] Reducing the latency of map point of interest loading, improving data quality, and reducing network transmission quality and device resource consumption are technical problems that need to be solved. Summary of the Invention
[0005] The technical objective of this invention is to address the above-mentioned shortcomings by providing a method and system for preloading map points of interest based on user focus, thereby solving the technical problems of reducing the delay in loading map points of interest, improving data quality, reducing network transmission quality, and minimizing device resource consumption.
[0006] In a first aspect, the present invention provides a method for preloading map points of interest based on user focus, comprising the following steps:
[0007] Data collection: Collect user behavior data during map application usage and user's current environment data;
[0008] User focus area analysis: Construct a user focus analysis model. Based on behavioral data and environmental data, the user focus analysis model identifies the user's current focus area. The focus area includes core focus area and potential focus area. The core focus area is the area that the user is currently paying attention to and is most likely to view. The potential focus area is the area that the user may switch to in the short term.
[0009] Filtering of points of interest within the focus area: Extract points of interest within the focus area and filter them. The filtering criteria include expired points of interest, points of interest with invalid periods, and points of interest that the user has explicitly set as uninteresting.
[0010] Sorting of points of interest within the focus area: Prioritizing the filtered points of interest within the focus area;
[0011] Points of interest (POIs) loading: For POIs filtered within the focus area, POIs are preloaded according to priority ranking. User behavior data and environmental data are monitored in real time. The focus area, POI priority ranking, and preloaded content are dynamically updated based on changes in user behavior data and environmental data. Further loading of unused POI data in non-focus areas where the user has left is stopped.
[0012] As a preferred option, user behavior data includes the current map view center coordinates, map zoom level, map location clicked by the user, browsing dwell time, user mouse movement trajectory, user operations, types of historical browsing points of interest, and status of historical browsing points of interest.
[0013] Environmental data includes current weather, current time, network environment, network speed, and device performance.
[0014] As a preferred method, the sorting rule for sorting points of interest within the focal area is as follows:
[0015] Points of interest within the core focus area have a higher priority than points of interest within the potential focus area, and the closer to the focus center, the higher the priority.
[0016] Based on the user's previous actions, clicked points of interest, and status, determine the types of points of interest the user may need, and assign higher priority to points of interest that match the user's needs.
[0017] Priority is set based on the attributes of the points of interest themselves and the current environment.
[0018] As a preferred approach, the preloading strategy for loading points of interest is as follows: prioritize the preloading of high-priority points of interest within the core focus area; for low-priority points of interest within the core focus area and high-priority points of interest within the potential focus area, perform asynchronous preloading based on network conditions and device resources; low-priority points of interest within the potential focus area may not be preloaded temporarily or may only have basic brief information preloaded.
[0019] Secondly, the present invention provides a map point of interest preloading system based on user focus, comprising a data acquisition module, a user focus area analysis module, a point of interest filtering module within the focus area, a point of interest sorting module within the focus area, and a point of interest loading module;
[0020] The data acquisition module is used to perform the following tasks: collect user behavior data during the use of the map application and the user's current environmental data;
[0021] The user focus area analysis module is used to perform the following: build a user focus analysis model, and based on behavioral data and environmental data, identify the user's current focus area through the user focus analysis model. The focus area includes core focus areas and potential focus areas. The core focus area is the area that the user is currently paying attention to and is most likely to view, while the potential focus area is the area that the user may switch to in the short term.
[0022] The point of interest filtering module within the focus area is used to perform the following: extract points of interest within the focus area and filter them. The filtering content includes expired points of interest, points of interest with invalid periods, and points of interest that the user has explicitly set as uninteresting.
[0023] The interest point sorting module within the focus area is used to perform the following: prioritize the filtered interest points within the focus area;
[0024] The Point of Interest (POI) loading module performs the following: For POIs filtered within the focus area, it preloads POIs according to the priority ranking result, and monitors user behavior data and environmental data in real time. Based on changes in user behavior data and environmental data, it dynamically updates the focus area, POI priority ranking, and preloaded content, and stops further loading of unused POI data in non-focus areas where the user has left.
[0025] As a preferred option, user behavior data includes the current map view center coordinates, map zoom level, map location clicked by the user, browsing dwell time, user mouse movement trajectory, user operations, types of historical browsing points of interest, and status of historical browsing points of interest.
[0026] Environmental data includes current weather, current time, network environment, network speed, and device performance.
[0027] As a preferred method, the sorting rule for sorting points of interest within the focal area is as follows:
[0028] Points of interest within the core focus area have a higher priority than points of interest within the potential focus area, and the closer to the focus center, the higher the priority.
[0029] Based on the user's previous actions, clicked points of interest, and status, determine the types of points of interest the user may need, and assign higher priority to points of interest that match the user's needs.
[0030] Priority is set based on the attributes of the points of interest themselves and the current environment.
[0031] As a preferred approach, the preloading strategy for loading points of interest is as follows: prioritize the preloading of high-priority points of interest within the core focus area; for low-priority points of interest within the core focus area and high-priority points of interest within the potential focus area, perform asynchronous preloading based on network conditions and device resources; low-priority points of interest within the potential focus area may not be preloaded temporarily or may only have basic brief information preloaded.
[0032] The map point of interest preloading method and system based on user focus of the present invention has the following advantages:
[0033] 1. Improve the timeliness of loading points of interest and user experience: By accurately identifying the user's focus and preloading high-priority points of interest, the loading delay of points of interest after user operation is significantly reduced, making map interaction smoother and allowing users to quickly obtain the information they need;
[0034] 2. Reduce invalid data loading and resource consumption: Targeted preloading is performed only for points of interest within the user's focus area, avoiding redundant loading of points of interest in non-focus areas, reducing network data transmission volume, and reducing device memory usage and processor consumption;
[0035] 3. Adapt to diverse user scenarios and dynamic needs: By comprehensively analyzing user behavior and environmental data to determine the focus, it can adapt to various user scenarios such as browsing, searching, and emergency response, and can adjust the preloading strategy in real time according to the dynamic changes in user focus, thus improving the flexibility and accuracy of preloading.
[0036] 4. Enhanced personalized service capabilities: Prioritizing points of interest makes pre-loaded points of interest more aligned with users' individual needs, thus improving the personalization level of map services. Attached Figure Description
[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0038] The invention will be further described below with reference to the accompanying drawings.
[0039] Figure 1This is a flowchart of a map point of interest preloading method based on user focus, as described in Example 1. Detailed Implementation
[0040] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.
[0041] This invention provides a method and system for preloading map points of interest based on user focus, which addresses the technical problem.
[0042] Example 1:
[0043] This invention discloses a method for preloading map points of interest based on user focus, comprising five steps: data collection, analysis of user focus area, filtering of points of interest within the focus area, sorting of points of interest within the focus area, and loading of points of interest.
[0044] Step S100 Data Collection: Collect user behavior data and current environmental data during the use of the map application.
[0045] The data includes the current map view center coordinates, map zoom level, map location clicked by the user, browsing dwell time, user mouse movement trajectory, user actions, types of historical browsing points of interest, and status of historical browsing points of interest. Environmental data includes current weather (such as rainstorms, smog, etc.), current time (such as weekdays / weekends, time periods), network environment (such as 4G / 5G / WIFI), network speed, and device performance (such as memory and processor speed).
[0046] Step S200 User Focus Area Analysis: Construct a user focus analysis model. Based on behavioral data and environmental data, identify the user's current focus area through the user focus analysis model. The focus area includes core focus areas and potential focus areas. The core focus area is the area that the user is currently paying attention to and is most likely to view. The potential focus area is the area that the user may switch to in the short term.
[0047] This embodiment constructs a user focus analysis model in this step to identify the user's current core focus area and potential focus areas. The size of the focus area can be dynamically adjusted based on factors such as map zoom level and the urgency of the event. For example, the higher the zoom level (the more detailed the map), the smaller the focus area; the more urgent the event, the larger the potential focus area. The core focus area refers to the area that the user is currently paying attention to and is most likely to view. For example, if the user clicks on a point on the map, a certain range centered on that point (dynamically adjusted according to the zoom level) is the core focus area; the potential focus area refers to the area that the user may switch to focusing on in the short term. For example, based on the user's mouse direction and speed, the areas they may focus on in the future can be predicted.
[0048] Step S300: Filtering points of interest within the focus area: Extract points of interest within the focus area and filter them. The filtering criteria include expired points of interest, points of interest with invalid periods, and points of interest that the user has explicitly set as uninteresting.
[0049] Step S400: Sort points of interest within the focus area: Sort the filtered points of interest within the focus area by priority.
[0050] As a specific implementation of sorting points of interest within the focal area, priority sorting includes:
[0051] (1) The priority of items within the core focus area is higher than that within the potential focus area, and the closer to the focus center, the higher the priority;
[0052] (2) Determine the type of interest the user may need based on the user's previous operations, clicked interest content and status, and assign higher priority to interest that matches the user's needs.
[0053] (3) Interest point attributes: such as expert skills (rescue, firefighting), emergency stock type (firefighting, medical), equipment type (ambulance, fire truck), etc.;
[0054] (4) Current environment: For example, in rainy weather, the priority of interest points related to flood control and drainage is increased, and in continuous drought weather, the priority of interest points related to fire prevention and firefighting is increased.
[0055] Step S500: Loading Points of Interest (POIs): For POIs within the focus area that have been filtered, preload POIs according to the priority ranking results, and monitor user behavior data and environmental data in real time. Dynamically update the focus area, POI priority ranking, and preloaded content based on changes in user behavior data and environmental data, and stop further loading of unused POI data in non-focus areas where the user has left.
[0056] In this embodiment, changes in user behavior and environmental data (such as user dragging the map, changing the zoom level, weather changes, etc.) are monitored in real time. Steps 3 to 5 are repeated to dynamically update the user's focus area, the priority sorting of points of interest, and the preloaded content. Further loading of unused point of interest data in non-focus areas where the user has left is stopped, and some low-priority cached data can be cleared.
[0057] When loading points of interest (POIs), the preloading strategy is as follows: prioritize the preloading of high-priority POIs within the core focus area. For low-priority POIs within the core focus area and high-priority POIs within potential focus areas, asynchronous preloading is performed based on network conditions and device resources. Low-priority POIs within potential focus areas may not be preloaded temporarily or may only have basic brief information preloaded.
[0058] The method in this embodiment first collects user behavior data and environmental data during map usage; then, by analyzing the user's behavior data and environmental data, it determines the user's current focus area and potential focus areas; next, it filters and prioritizes the points of interest (POIs) within the focus and potential focus areas; finally, it returns the corresponding map POIs based on the ranking results. This method significantly improves the loading speed of map POIs, prioritizes returning POIs that users are more interested in, reduces the time spent by users, improves the accuracy of loaded data, optimizes the user experience, and reduces device resource consumption.
[0059] Example 2:
[0060] The present invention discloses a map point of interest preloading system based on user focus, comprising a data acquisition module, a user focus area analysis module, a point of interest filtering module within the focus area, a point of interest sorting module within the focus area, and a point of interest loading module.
[0061] The data acquisition module is used to perform the following tasks: collect user behavior data during the use of the map application and the user's current environmental data.
[0062] The data includes the current map view center coordinates, map zoom level, map location clicked by the user, browsing dwell time, user mouse movement trajectory, user actions, types of historical browsing points of interest, and status of historical browsing points of interest. Environmental data includes current weather (such as rainstorms, smog, etc.), current time (such as weekdays / weekends, time periods), network environment (such as 4G / 5G / WIFI), network speed, and device performance (such as memory and processor speed).
[0063] The user focus area analysis module is used to perform the following: build a user focus analysis model, and based on behavioral data and environmental data, identify the user's current focus area through the user focus analysis model. The focus area includes core focus areas and potential focus areas. The core focus area is the area that the user is currently paying attention to and is most likely to view, while the potential focus area is the area that the user may switch to in the short term.
[0064] In this embodiment, this module constructs a user focus analysis model to identify the user's current core focus area and potential focus areas. The size of the focus area can be dynamically adjusted based on factors such as map zoom level and the urgency of the event. For example, the higher the zoom level (the more detailed the map), the smaller the focus area; the more urgent the event, the larger the potential focus area. The core focus area refers to the area that the user is currently paying attention to and is most likely to view. For example, if the user clicks on a point on the map, a certain area centered on that point (dynamically adjusted according to the zoom level) is the core focus area; the potential focus area refers to the area that the user may switch to focusing on in the short term. For example, based on the user's mouse direction and speed, the areas they may focus on in the future can be predicted.
[0065] The point of interest filtering module within the focus area is used to perform the following: extract points of interest within the focus area and filter them. The filtering content includes expired points of interest, points of interest with invalid periods, and points of interest that the user has explicitly set as uninteresting.
[0066] The interest point sorting module within the focus area is used to perform the following: prioritize the filtered interest points within the focus area.
[0067] As a specific implementation of the point-of-interest ranking module within the focal area, priority ranking includes:
[0068] (1) The priority of items within the core focus area is higher than that within the potential focus area, and the closer to the focus center, the higher the priority;
[0069] (2) Determine the type of interest the user may need based on the user's previous operations, clicked interest content and status, and assign higher priority to interest that matches the user's needs.
[0070] (3) Interest point attributes: such as expert skills (rescue, firefighting), emergency stock type (firefighting, medical), equipment type (ambulance, fire truck), etc.;
[0071] (4) Current environment: For example, in rainy weather, the priority of interest points related to flood control and drainage is increased, and in continuous drought weather, the priority of interest points related to fire prevention and firefighting is increased.
[0072] The Point of Interest (POI) loading module performs the following: For POIs filtered within the focus area, it preloads POIs according to the priority ranking result, and monitors user behavior data and environmental data in real time. Based on changes in user behavior data and environmental data, it dynamically updates the focus area, POI priority ranking, and preloaded content, and stops further loading of unused POI data in non-focus areas where the user has left.
[0073] In this embodiment, the point of interest loading module monitors changes in user behavior and environmental data in real time (such as users dragging the map, changing the zoom level, weather changes, etc.), repeats steps 3 to 5, dynamically updates the user's focus area, the priority order of points of interest, and the preloaded content, stops further loading of unused point of interest data in non-focus areas where the user has left, and can clear some low-priority cached data.
[0074] When loading points of interest (POIs), the preloading strategy is as follows: prioritize the preloading of high-priority POIs within the core focus area. For low-priority POIs within the core focus area and high-priority POIs within potential focus areas, asynchronous preloading is performed based on network conditions and device resources. Low-priority POIs within potential focus areas may not be preloaded temporarily or may only have basic brief information preloaded.
[0075] The system in this embodiment can execute the method disclosed in Embodiment 1 to preload map points of interest.
[0076] The above provides a detailed description of the map interest point preloading method and system based on user focus provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for preloading map points of interest based on user focus, characterized in that, Includes the following steps: Data collection: Collect user behavior data during map application usage and user's current environment data; User focus area analysis: Construct a user focus analysis model. Based on behavioral data and environmental data, the user focus analysis model identifies the user's current focus area. The focus area includes core focus area and potential focus area. The core focus area is the area that the user is currently paying attention to and is most likely to view. The potential focus area is the area that the user may switch to in the short term. Filtering of points of interest within the focus area: Extract points of interest within the focus area and filter them. The filtering criteria include expired points of interest, points of interest with invalid periods, and points of interest that the user has explicitly set as uninteresting. Sorting of points of interest within the focus area: Prioritizing the filtered points of interest within the focus area; Points of interest (POIs) loading: For POIs filtered within the focus area, POIs are preloaded according to priority ranking. User behavior data and environmental data are monitored in real time. The focus area, POI priority ranking, and preloaded content are dynamically updated based on changes in user behavior data and environmental data. Further loading of unused POI data in non-focus areas where the user has left is stopped.
2. The map interest point preloading method based on user focus according to claim 1, characterized in that, User behavior data includes the current map view center coordinates, map zoom level, map location clicked by the user, browsing dwell time, user mouse movement trajectory, user actions, types of historical browsing points of interest, and status of historical browsing points of interest. Environmental data includes current weather, current time, network environment, network speed, and device performance.
3. The map interest point preloading method based on user focus according to claim 1, characterized in that, When sorting points of interest within the focal area, the sorting rules are as follows: Points of interest within the core focus area have a higher priority than points of interest within the potential focus area, and the closer to the focus center, the higher the priority. Based on the user's previous actions, clicked points of interest, and status, determine the types of points of interest the user may need, and assign higher priority to points of interest that match the user's needs. Priority is set based on the attributes of the points of interest themselves and the current environment.
4. The map interest point preloading method based on user focus according to claim 1, characterized in that, When loading points of interest (POIs), the preloading strategy is as follows: prioritize the preloading of high-priority POIs within the core focus area. For low-priority POIs within the core focus area and high-priority POIs within potential focus areas, asynchronous preloading is performed based on network conditions and device resources. Low-priority POIs within potential focus areas may not be preloaded temporarily or may only have basic brief information preloaded.
5. A map point of interest preloading system based on user focus, characterized in that, It includes a data acquisition module, a user focus area analysis module, a focus area interest point filtering module, a focus area interest point sorting module, and an interest point loading module; The data acquisition module is used to perform the following tasks: collect user behavior data during the use of the map application and the user's current environmental data; The user focus area analysis module is used to perform the following: build a user focus analysis model, and based on behavioral data and environmental data, identify the user's current focus area through the user focus analysis model. The focus area includes core focus areas and potential focus areas. The core focus area is the area that the user is currently paying attention to and is most likely to view, while the potential focus area is the area that the user may switch to in the short term. The point of interest filtering module within the focus area is used to perform the following: extract points of interest within the focus area and filter them. The filtering content includes expired points of interest, points of interest with invalid periods, and points of interest that the user has explicitly set as uninteresting. The interest point sorting module within the focus area is used to perform the following: prioritize the filtered interest points within the focus area; The Point of Interest (POI) loading module performs the following: For POIs filtered within the focus area, it preloads POIs according to the priority ranking result, and monitors user behavior data and environmental data in real time. Based on changes in user behavior data and environmental data, it dynamically updates the focus area, POI priority ranking, and preloaded content, and stops further loading of unused POI data in non-focus areas where the user has left.
6. The map point of interest preloading system based on user focus according to claim 5, characterized in that, User behavior data includes the current map view center coordinates, map zoom level, map location clicked by the user, browsing dwell time, user mouse movement trajectory, user actions, types of historical browsing points of interest, and status of historical browsing points of interest. Environmental data includes current weather, current time, network environment, network speed, and device performance.
7. The map point of interest preloading system based on user focus according to claim 5, characterized in that, When sorting points of interest within the focal area, the sorting rules are as follows: Points of interest within the core focus area have a higher priority than points of interest within the potential focus area, and the closer to the focus center, the higher the priority. Based on the user's previous actions, clicked points of interest, and status, determine the types of points of interest the user may need, and assign higher priority to points of interest that match the user's needs. Priority is set based on the attributes of the points of interest themselves and the current environment.
8. The map point of interest preloading system based on user focus according to claim 5, characterized in that, When loading points of interest (POIs), the preloading strategy is as follows: prioritize the preloading of high-priority POIs within the core focus area. For low-priority POIs within the core focus area and high-priority POIs within potential focus areas, asynchronous preloading is performed based on network conditions and device resources. Low-priority POIs within potential focus areas may not be preloaded temporarily or may only have basic brief information preloaded.