Large-scale two-dimensional label map aggregation node optimization method

Abstract

The application discloses a large-scale two-dimensional label map aggregation node optimization method, relates to the technical field of computers, and comprises the following steps: when a map interactive operation or label node data changes, the zoom level and the viewport range of a current prompt map are acquired; based on the viewport range, target label nodes in the viewport range are screened from the label node data; according to the zoom level, the basic aggregation parameters for label node aggregation are determined; and combined with the node density of the target label nodes in the viewport range, the basic aggregation parameters are dynamically adjusted to obtain the aggregation parameters for the current viewport range. The large-scale two-dimensional label map aggregation node optimization method significantly reduces the memory occupation and the calculation burden caused by repeated creation of node objects, improves the overall operation stability and the resource utilization efficiency of the system, and thus better meets the comprehensive requirements of performance, real-time performance and display effect of a large-scale two-dimensional label map application scene.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to a method for optimizing aggregation nodes in large-scale two-dimensional label maps. Background Technology

[0002] With the continuous development of Geographic Information Systems (GIS), the Internet of Things (IoT), and big data technologies, two-dimensional label maps are widely used in scenarios such as urban traffic monitoring, logistics network distribution display, and population and facility statistical analysis. These applications typically require the simultaneous display of a large number of label nodes on the map. These label nodes not only contain spatial location information such as latitude and longitude but may also contain multi-dimensional attribute information such as category, priority, and status. In practical use, users frequently perform interactive operations such as zooming and panning the map. The requirements for label display density and information granularity differ significantly at different zoom levels: at low zoom levels, a large number of nodes displayed together can easily cause label overlap and interface clutter; at high zoom levels, it is necessary to accurately display the detailed information of individual nodes. Therefore, how to reasonably control the number of labels and information density while ensuring smooth map interaction has become an urgent problem to be solved in the field of two-dimensional label map technology.

[0003] To address the aforementioned needs, existing technologies typically employ tag node aggregation to reduce map display load. Common solutions include static aggregation based on fixed grid division and dynamic aggregation based on hierarchical clustering. The former divides the space using a pre-defined grid and aggregates nodes within the same grid. However, this method is limited by fixed boundaries, easily leading to boundary fragmentation and unreasonable aggregation. Furthermore, in large-scale node scenarios, it requires frequent full-scale computation, resulting in low computational efficiency and resource utilization. The latter constructs a multi-level aggregation structure to achieve display switching at different zoom levels. While this improves the spatial rationality of aggregation to some extent, the aggregation structure is usually pre-built static data, making it difficult to update in a timely manner when node data changes. Additionally, the aggregation hierarchy lacks flexibility in adapting to zoom levels, and storage and maintenance costs are high. In summary, existing technologies generally suffer from low aggregation efficiency, high resource consumption, and insufficient dynamic adaptability when dealing with large-scale 2D tag map scenarios, failing to meet the comprehensive performance and accuracy requirements of practical applications. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing aggregation nodes in large-scale two-dimensional label maps, thereby solving the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing aggregation nodes in a large-scale two-dimensional label map, comprising:

[0006] When map interaction operations or label node data change, obtain the zoom level and viewport range of the current tooltip map;

[0007] Based on the viewport range, filter the target label nodes that are within the viewport range from the label node data;

[0008] The basic aggregation parameters for tag node aggregation are determined based on the scaling level, and then dynamically adjusted based on the node density of the target tag nodes within the viewport range to obtain the aggregation parameters for the current viewport range.

[0009] Based on the aggregation parameters, the target tag nodes are aggregated and calculated to generate the display results of aggregated nodes or single nodes;

[0010] During the process of generating and displaying results, existing node objects are reused to complete the creation and updating of aggregate nodes or individual nodes, thereby reducing the repeated creation of node objects.

[0011] Preferably, the step of dynamically adjusting the basic aggregation parameters based on the node density of the target label nodes within the viewport range includes: calculating the node density based on the number of target label nodes within the current viewport range and the spatial area corresponding to the viewport range; and amplifying or reducing the basic aggregation parameters based on the node density, so that the aggregation range is increased when the node density is higher than a preset threshold, and the aggregation range is decreased when the node density is lower than a preset threshold.

[0012] Preferably, the node density is calculated by the ratio of the number of target label nodes within the viewport range to the area of ​​the viewport range.

[0013] Preferably, the aggregation calculation of target tag nodes based on aggregation parameters includes: when the spatial distance between target tag nodes meets the aggregation parameters, further determining whether at least one attribute information of the target tag node meets a preset consistency condition; and only when both the spatial condition and the attribute condition are met, the corresponding target tag nodes are aggregated into the same aggregation node.

[0014] Preferably, the step of reusing existing node objects to complete the creation and updating of aggregate nodes or individual nodes includes: pre-constructing a node pool for storing node objects; retrieving idle node objects from the node pool for reuse when generating aggregate nodes or individual nodes; and resetting and recycling node objects back into the node pool when they are no longer used for the current display results.

[0015] Preferably, when the map zoom level changes, the viewport range changes, or label node data is added, deleted, or its attributes change, the aggregation calculation is re-performed only on the target label nodes affected by the changes.

[0016] Preferably, when generating an aggregate node, the display attributes of the aggregate node are determined based on the target tag nodes constituting the aggregate node, and the display attributes include at least the position of the aggregate node and the number of target tag nodes it contains.

[0017] Preferably, the aggregation parameters include at least a spatial distance threshold for limiting whether a target label node participates in the aggregation. The spatial distance threshold varies with the map zoom level, and the lower the map zoom level, the larger the corresponding spatial distance threshold.

[0018] Preferably, the attribute information of the target tag node includes at least one of category attribute, priority attribute, or status attribute, and during the aggregation calculation process, only target tag nodes whose attribute information meets the preset similarity condition are aggregated into the same aggregate node.

[0019] Preferably, the node pool sets a usage status flag for each node object. When a node object is used for the current display result, it is marked as being in use. When a node object is no longer used for the current display result, it is switched to an idle state.

[0020] As can be seen from the above technical solution, the present invention has the following beneficial effects:

[0021] This method for optimizing aggregation nodes in large-scale 2D label maps dynamically adjusts aggregation parameters based on map zoom level, viewport range, and node density within the viewport during map interaction or changes in label node data. It also reuses existing node objects during aggregation calculations, ensuring smooth response to map zooming and panning while achieving efficient aggregation and splitting of massive numbers of label nodes. This method avoids boundary fragmentation and unreasonable aggregation issues caused by fixed grid partitioning and eliminates the need to build and maintain static multi-level aggregation structures, effectively reducing the overall computational overhead and storage costs in large-scale node scenarios. Furthermore, by adaptively adjusting the aggregation scale according to node density, the aggregation results maintain high accuracy and reasonableness across different zoom levels and node distributions, reducing label overlap and information loss. In addition, the node object reuse mechanism significantly reduces memory consumption and computational burden caused by repeated node creation, improving overall system stability and resource utilization efficiency, thus better meeting the comprehensive requirements of performance, real-time performance, and display effects in large-scale 2D label map applications. Attached Figure Description

[0022] Figure 1 This is a flowchart of the two-dimensional label map aggregation node optimization method of the present invention;

[0023] Figure 2This is a schematic diagram of a single node set after filtering according to the present invention;

[0024] Figure 3 This is a schematic diagram of the system module connections of the present invention;

[0025] Figure 4 This is a schematic diagram of the node pool structure of the present invention;

[0026] Figure 5 This is a flowchart illustrating the execution process of the method of the present invention. Detailed Implementation

[0027] 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.

[0028] like Figures 1-3 As shown, this invention provides a technical solution: a method for optimizing aggregation nodes in a large-scale two-dimensional label map, comprising:

[0029] When map interaction operations or label node data change, obtain the zoom level and viewport range of the current tooltip map;

[0030] Based on the viewport range, filter the target label nodes that are within the viewport range from the label node data;

[0031] The basic aggregation parameters for tag node aggregation are determined based on the scaling level, and then dynamically adjusted based on the node density of the target tag nodes within the viewport range to obtain the aggregation parameters for the current viewport range.

[0032] Based on the aggregation parameters, the target tag nodes are aggregated and calculated to generate the display results of aggregated nodes or single nodes;

[0033] During the process of generating and displaying results, existing node objects are reused to complete the creation and updating of aggregate nodes or individual nodes, thereby reducing the repeated creation of node objects.

[0034] This method is applied to the display and management of large-scale 2D map labels, and is triggered when map interaction operations or label node data changes. The system first obtains the current map zoom level and corresponding viewport extent from the map rendering component. The zoom level characterizes the map's display scale, and the viewport extent defines the visible geographical area within the current screen. By obtaining these parameters in real time, the system provides the foundation for subsequent label node filtering and aggregation calculations.

[0035] After obtaining the viewport range, the system performs spatial filtering on the tag node data based on the spatial location information of the tag nodes, retaining only the tag nodes located within the current viewport range as target tag nodes for subsequent calculations. This method avoids aggregating and rendering invisible tag nodes outside the viewport range, effectively reducing the computational scale and improving the system's processing efficiency in large-scale data scenarios.

[0036] After filtering the target tag nodes, the system determines the basic aggregation parameters for tag node aggregation based on the current map zoom level. These basic aggregation parameters limit the spatial scale during tag node aggregation calculations, and their values ​​vary with the zoom level to ensure that the aggregation effect of tag nodes remains consistent with the user's visual perception at different map scales.

[0037] like Figures 1-5 As shown, based on the above, the system further performs statistical analysis on the node density of target label nodes within the current viewport range and dynamically adjusts the basic aggregation parameters based on the node density. When the target label nodes are densely distributed within the viewport range, the aggregation parameters are increased to improve the aggregation degree, thereby reducing the number of displayed nodes; when the target label nodes are sparsely distributed, the aggregation parameters are decreased to reduce the aggregation degree, retaining more individual label nodes for display. By obtaining aggregation parameters suitable for the current viewport range in this way, the aggregation strategy can adapt to both changes in zoom level and changes in node distribution.

[0038] After determining the aggregation parameters, the system performs aggregation calculations on the target tag nodes based on these parameters, dividing them into different aggregation units according to the spatial relationship between the tag nodes. For each aggregation unit, if it contains only one target tag node, a corresponding single node display result is generated; if it contains multiple target tag nodes, a corresponding aggregation node is generated, and the number of tag nodes contained in the aggregation node and related attribute information are recorded, thus forming the final node result to be displayed within the current viewport.

[0039] During the generation of aggregated nodes or individual node display results, the system reuses existing node objects to complete node creation and updates. Specifically, the system prioritizes selecting reusable objects from existing node objects and updates their attributes such as location, display style, and number of aggregates. A new node object is only created if no reusable node object exists. This node object reuse mechanism reduces the repeated creation and destruction of node objects, lowers memory usage and system overhead, thereby improving the map's operational stability and response speed in scenarios with high-frequency interactions and large-scale labeled nodes.

[0040] The basic aggregation parameters are dynamically adjusted based on the node density of the target label nodes within the viewport range. This includes: calculating the node density based on the number of target label nodes within the current viewport range and the corresponding spatial area of ​​the viewport range; and amplifying or reducing the basic aggregation parameters based on the node density, so that the aggregation range is increased when the node density is higher than a preset threshold, and the aggregation range is decreased when the node density is lower than a preset threshold.

[0041] In the above implementation, after completing the screening of target label nodes within the viewport and determining the basic aggregation parameters, the system further introduces node density as an adjustment factor to adaptively adjust the basic aggregation parameters. The node density is used to quantitatively reflect the distribution of label nodes within the current viewport and is an important basis for achieving dynamic aggregation.

[0042] In practice, the system first counts the number of target label nodes within the current viewport range. Simultaneously, based on the geographical area covered by the current viewport range, it obtains the spatial area corresponding to that viewport range. This spatial area can be calculated based on geographical coordinates or converted from the planar area projected onto a map; any area that can represent the spatial scale covered by the viewport range is acceptable.

[0043] After obtaining the number of target tag nodes and the spatial area, the system calculates the node density within the current viewport range by dividing the number of target tag nodes by the spatial area. This node density reflects the concentration of tag nodes within a unit of space, providing a quantitative basis for subsequent aggregation parameter adjustments.

[0044] After calculating the node density, the system compares the node density with a preset threshold. When the node density is higher than the preset threshold, it indicates that the label nodes are densely distributed within the current viewport. In this case, the system amplifies the basic aggregation parameters to expand the aggregation range, making it easier to aggregate multiple spatially adjacent label nodes into the same aggregation node, thereby reducing the number of nodes displayed on the screen.

[0045] When the node density is lower than the preset threshold, it indicates that the distribution of label nodes within the current viewport is relatively sparse. In this case, the system reduces the basic aggregation parameters to decrease the aggregation range, so that more label nodes are displayed as individual nodes, avoiding information loss due to excessive aggregation.

[0046] In this way, the system can dynamically amplify or reduce the basic aggregation parameters according to the real-time distribution of tag nodes within the viewport, so that the final aggregation range matches the current node density.

[0047] Node density is calculated by dividing the number of target label nodes within the viewport area by the area of ​​the viewport area.

[0048] In the above implementation, node density, as an important quantitative indicator for adjusting tag node aggregation parameters, is explicitly calculated as the ratio of the number of target tag nodes to the viewport area. This calculation method can intuitively reflect the spatial concentration of tag nodes within the current viewport area.

[0049] In the specific implementation process, after filtering the target label nodes within the viewport range, the system first counts the number of target label nodes within the viewport range to obtain the total number of target label nodes. Subsequently, the system obtains the spatial area corresponding to the current viewport range. The spatial area is used to characterize the size of the area covered by the viewport range in the map coordinate system. It can be calculated based on the geographic coordinate boundary of the viewport range or calculated through the planar area after map projection transformation.

[0050] After obtaining the number of target label nodes and the viewport area, the system calculates the ratio of the number of target label nodes to the viewport area to obtain a node density value. This node density value represents the number of target label nodes per unit area and is used for dynamic adjustment of subsequent aggregation parameters, enabling the aggregation range to adaptively change according to the node distribution.

[0051] By adopting the above-mentioned node density calculation method, the node distribution under different viewport ranges and scaling levels can be measured with a unified and quantifiable standard, thereby providing a stable and reliable input basis for the dynamic adjustment of aggregation parameters.

[0052] Based on the aggregation parameters, the aggregation calculation of the target tag nodes includes: when the spatial distance between the target tag nodes meets the aggregation parameters, further determining whether at least one attribute information of the target tag node meets the preset consistency condition. Only when both the spatial condition and the attribute condition are met will the corresponding target tag nodes be aggregated into the same aggregate node.

[0053] In the above implementation, the aggregation calculation of tag nodes is not only based on spatial location relationships, but also further combined with the attribute information of tag nodes for comprehensive judgment, so as to avoid semantic inconsistency caused by aggregation based solely on spatial distance.

[0054] In the specific implementation, after obtaining the aggregation parameters applicable to the current viewport range, the system first determines the spatial distance between target tag nodes based on the aggregation parameters. The system calculates the spatial distance between any two target tag nodes according to their spatial coordinates and compares this spatial distance with the aggregation parameters. When the spatial distance meets the aggregation conditions defined by the aggregation parameters, it is determined that the corresponding target tag node has the potential to be aggregated in space.

[0055] Based on the satisfied spatial conditions, the system further performs a consistency judgment on the attribute information of the target tag node. The attribute information may be category information, business type identifier, status identifier, or other attribute fields used to distinguish the semantics of the node associated with it. According to preset consistency conditions, the system compares at least one attribute information. When the compared attribute information meets the consistency conditions, it determines that the corresponding target tag node has the semantic conditions to be aggregated.

[0056] The system will only assign target tag nodes to the same aggregation unit and generate corresponding aggregation nodes if the spatial distance between them meets the spatial conditions defined by the aggregation parameters, and at least one of their attribute information meets the preset consistency conditions. If either the spatial condition or the attribute condition is not met, the corresponding target tag node will not be aggregated; instead, it will be treated as an independent node or assigned to another aggregation unit that meets the conditions.

[0057] Through the dual judgment mechanism of spatial and attribute conditions, fine-grained aggregation control of target tag nodes is achieved, so that the aggregation results can maintain the consistency of business semantics on the basis of spatial rationality.

[0058] Creating and updating aggregate nodes or individual nodes by reusing existing node objects includes: pre-building a node pool for storing node objects; retrieving idle node objects from the node pool for reuse when generating aggregate nodes or individual nodes; and resetting and recycling node objects back into the node pool when they are no longer used for the current display results.

[0059] In the above implementation, in order to reduce the performance overhead caused by the frequent creation and destruction of node objects, the system adopts a node object reuse mechanism and manages node objects in a unified manner through a node pool.

[0060] In the specific implementation process, the system pre-constructs a node pool for storing node objects during the initialization phase or at a preset time. The node pool is used to centrally store reusable node objects and identify the usage status of node objects to distinguish between idle node objects and node objects that are in use.

[0061] When generating the display results for aggregate nodes or individual nodes, the system first searches for idle node objects in the node pool and assigns them to the aggregate node or individual node that needs to be displayed. Subsequently, the relevant attributes of the node object are updated, including but not limited to spatial location, display style, aggregation quantity identifier, and associated data, to ensure they meet the display requirements of the current aggregation calculation result. By reusing idle node objects in the node pool, the system avoids creating new node objects for each display result.

[0062] When a node object is no longer used for the current display result, such as when the corresponding display node needs to be removed due to changes in viewport range, zoom level, or updates to aggregation results, the system unbinds the node object from the current display list and resets its state. The reset process includes clearing the attribute information related to the current display result from the node object, restoring it to a reusable initial state.

[0063] After the reset process is complete, the system reclaims the node object into the node pool and marks it as available for use, so that it can be retrieved and reused when new aggregate nodes or individual nodes are generated subsequently. Through the above node pool management process, the cyclical utilization of node objects between different display cycles is realized.

[0064] When the map zoom level changes, the viewport range changes, or label node data is added, deleted, or its attributes change, the aggregation calculation is re-performed only on the target label nodes affected by the changes.

[0065] In the above implementation, in order to further reduce the overall overhead of aggregation calculation, when the system detects that the map status or label node data has changed, it does not re-execute aggregation calculation for all target label nodes within the current viewport range. Instead, it introduces a change impact range judgment mechanism and only performs aggregation update for target label nodes affected by the change.

[0066] In practice, the system monitors in real time events such as changes in map zoom level, viewport range, and the addition, deletion, or attribute changes of label node data. When any of these events is detected, the system first analyzes the scope of the event's impact to determine which target label nodes might be affected.

[0067] When the map zoom level changes, the system determines whether the aggregation parameters have been adjusted with the zoom level and determines which original aggregation units may have changed their aggregation relationships. Only the target label nodes contained in these aggregation units are re-aggregated, while the unaffected aggregation units retain their original calculation results.

[0068] When the viewport range changes, the system determines whether a target label node has newly entered or moved out of the viewport range. It performs aggregation calculations only on the target label nodes that have newly entered the viewport range, and releases or reclaims the aggregation results corresponding to the target label nodes that have moved out of the viewport range. It does not repeat the calculations on the target label nodes that are still in the viewport range and have not changed.

[0069] When tag node data is added, deleted, or its attributes change, the system only marks the changed tag node and its spatial neighborhood target tag nodes as the set of nodes that need to be recalculated, and performs local aggregation update based on this set of nodes, without triggering the re-aggregation of all target tag nodes.

[0070] Through the aforementioned change impact judgment and local update mechanism, incremental execution of aggregate computation is achieved.

[0071] When generating an aggregate node, the display attributes of the aggregate node are determined based on the target tag nodes that constitute the aggregate node. The display attributes include at least the position of the aggregate node and the number of target tag nodes it contains.

[0072] In the above implementation, after the system completes the aggregation calculation of the target tag nodes based on the aggregation parameters, it generates corresponding aggregation nodes for multiple target tag nodes that are divided into the same aggregation unit. In order for the aggregation node to accurately reflect the set of tag nodes it represents, the system determines its display attributes based on the target tag nodes that constitute the aggregation node during the generation process.

[0073] In the specific implementation process, the system first obtains the spatial location information of all target tag nodes that constitute the same aggregation node, and calculates the location of the aggregation node based on the spatial location information. The location of the aggregation node can be determined by averaging, weighted averaging, or using the location of preset representative nodes of the geographic coordinates of the target tag nodes, as long as it can reflect the overall location of the aggregation node on the map.

[0074] While determining the location of the aggregation node, the system counts the number of target tag nodes that make up the aggregation node to determine the total number of target tag nodes contained in the aggregation node. The number of target tag nodes is used to characterize the scale of the aggregation node and serves as one of the important display attributes of the aggregation node.

[0075] After determining the location and quantity, the system writes the location of the aggregation node and the number of target tag nodes it contains into the display attributes of the aggregation node, and completes the display configuration of the aggregation node accordingly, so that the aggregation node can intuitively reflect its spatial location and the number of tag nodes it represents on the map.

[0076] The aggregation parameters include at least a spatial distance threshold used to limit whether the target label node participates in the aggregation. The spatial distance threshold varies with the map zoom level, and the lower the map zoom level, the larger the corresponding spatial distance threshold.

[0077] In the above implementation, a spatial distance threshold is introduced into the aggregation parameters as an important spatial constraint for determining whether a target tag node participates in the aggregation calculation. The spatial distance threshold limits the spatial proximity between target tag nodes; only when the spatial distance between target tag nodes meets this threshold condition is it determined that they have the potential to participate in the aggregation.

[0078] In practice, after obtaining the current map zoom level, the system determines the corresponding spatial distance threshold based on the zoom level. There is a mapping relationship between the spatial distance threshold and the map zoom level; when the map zoom level changes, the system synchronously adjusts the value of the spatial distance threshold.

[0079] When the map zoom level is low, the map is displayed at a larger scale, covering a wider spatial area within the viewport. In this case, the system sets a larger spatial distance threshold, allowing target label nodes that are relatively far away to still meet the aggregation conditions, thereby increasing the aggregation level and reducing the number of nodes displayed on the screen at the same time.

[0080] When the map zoom level is high, the map display scale is small, and the spatial area covered within the viewport is relatively reduced. In this case, the system sets the spatial distance threshold to a smaller value, making the aggregation judgment more refined, and only aggregating target label nodes that are very close in spatial location, in order to retain more display details of individual nodes.

[0081] After determining the spatial distance threshold, the system judges the spatial distance between target tag nodes based on the spatial distance threshold when performing the aggregation calculation of target tag nodes, which serves as one of the basic conditions for whether the target tag node participates in the aggregation calculation.

[0082] The attribute information of the target tag node includes at least one of the following: category attribute, priority attribute, or status attribute. During the aggregation calculation process, only target tag nodes whose attribute information meets the preset similarity condition are aggregated into the same aggregate node.

[0083] In the above implementation, in order to further improve the rationality of the tag node aggregation results at the business semantic level, the similarity judgment of attribute information is introduced as an important constraint condition for aggregation calculation, based on the aggregation judgment based on spatial distance.

[0084] In the specific implementation process, when performing aggregation calculations on target tag nodes, the system first obtains the attribute information associated with each target tag node. The attribute information includes at least one or more of the following: category attribute, priority attribute, or status attribute, used to characterize the business type, importance, or current status of the target tag node.

[0085] Provided the spatial distance condition is met, the system further performs similarity judgment on the attribute information of the target tag nodes. For category attributes, the system can determine whether the target tag nodes belong to the same category; for priority attributes, the system can determine whether the priority of the target tag nodes is the same or within a preset similar level range; for state attributes, the system can determine whether the target tag nodes are in the same or compatible state.

[0086] The system comprehensively judges the aforementioned attribute information based on preset similarity conditions. Only when the attribute information of a target tag node meets the preset similarity conditions will the system group the corresponding target tag node into the same aggregation unit and generate a corresponding aggregation node; if the attribute information does not meet the similarity conditions, even if the spatial distance conditions are met, no aggregation operation will be performed. Through this method, the aggregation nodes are not only spatially reasonable but also consistent at the business attribute level.

[0087] At least one node object in the node pool must be marked as being in use. When a node object is used for the current display result, it is marked as being in use. When a node object is no longer used for the current display result, it is switched to an idle state.

[0088] In the above implementation, in order to achieve efficient management and reuse of node objects in the node pool, a usage status flag is set for each node object in the node pool to indicate whether the node object is currently occupied.

[0089] In the specific implementation process, the system maintains a corresponding usage status identifier for each node object in the node pool. The usage status identifier is used to distinguish whether the node object is in a used state or an idle state. When generating the display results of an aggregate node or a single node, the system retrieves an idle node object from the node pool, and immediately switches its usage status identifier to a used state after allocating the node object for the current display result, in order to avoid the node object being reassigned.

[0090] When a node object is no longer used in the current display result, such as when the corresponding display node needs to be removed due to changes in viewport range, zoom level, or updates to aggregation results, the system removes the node object from the current display result and switches its usage status flag from the used state to the idle state, so that the node object can be retrieved and reused again.

[0091] Through the above-mentioned setting and switching mechanism of usage status indicators, the system can accurately control the occupancy of node objects, ensure that node objects flow in an orderly manner between different display cycles, and avoid the problem of node objects being reused or resources being wasted.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing aggregation nodes in large-scale two-dimensional label maps, characterized in that, include: When map interaction operations or label node data change, obtain the current map zoom level and viewport range; Based on the viewport range, filter the target label nodes that are within the viewport range from the label node data; The basic aggregation parameters for tag node aggregation are determined based on the scaling level, and then dynamically adjusted based on the node density of the target tag nodes within the viewport range to obtain the aggregation parameters for the current viewport range. Based on the aggregation parameters, the target tag nodes are aggregated and calculated to generate the display results of aggregated nodes or single nodes; In the process of generating and displaying results, existing node objects are reused to complete the creation and updating of aggregate nodes or individual nodes, thereby reducing the repeated creation of node objects; The method of dynamically adjusting the basic aggregation parameters based on the node density of target label nodes within the viewport range includes calculating the node density based on the number of target label nodes within the current viewport range and the corresponding spatial area of ​​the viewport range; and amplifying or reducing the basic aggregation parameters based on the node density, so that the aggregation range is increased when the node density is higher than a preset threshold, and the aggregation range is decreased when the node density is lower than a preset threshold.

2. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 1, characterized in that: The node density is calculated by dividing the number of target label nodes within the viewport range by the area of ​​the viewport range.

3. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 1, characterized in that: The aggregation calculation of target tag nodes based on aggregation parameters includes: when the spatial distance between target tag nodes meets the aggregation parameters, further determining whether at least one attribute information of the target tag node meets a preset consistency condition; only when both the spatial condition and the attribute condition are met will the corresponding target tag nodes be aggregated into the same aggregate node.

4. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 1, characterized in that: The process of creating and updating aggregate nodes or individual nodes by reusing existing node objects includes: pre-constructing a node pool for storing node objects; retrieving idle node objects from the node pool for reuse when generating aggregate nodes or individual nodes; and resetting and recycling node objects back into the node pool when they are no longer used for the current display result.

5. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 1, characterized in that, When the map zoom level changes, the viewport range changes, or label node data is added, deleted, or its attributes change, the aggregation calculation is re-performed only on the target label nodes affected by the changes.

6. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 1, characterized in that, When generating an aggregate node, the display attributes of the aggregate node are determined based on the target tag nodes that constitute the aggregate node. The display attributes include at least the position of the aggregate node and the number of target tag nodes it contains.

7. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 1, characterized in that: The aggregation parameters include at least a spatial distance threshold for limiting whether a target label node participates in the aggregation. The spatial distance threshold varies with the map zoom level, and the lower the map zoom level, the larger the corresponding spatial distance threshold.

8. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 3, characterized in that: The attribute information of the target tag node includes at least one of the following: category attribute, priority attribute, or status attribute. During the aggregation calculation process, only target tag nodes whose attribute information meets the preset similarity condition are aggregated into the same aggregation node.

9. The method for optimizing large-scale two-dimensional label map aggregation nodes according to claim 4, characterized in that: The node pool sets a usage status flag for each node object. When a node object is used for the current display result, it is marked as being in use. When a node object is no longer used for the current display result, it is switched to an idle state.

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