Method and system for automatically splicing and calibrating massive map slice data
Through data chunking and caching, simplified symbolization, progressive rendering and performance monitoring, the problem of slow rendering of big data maps is solved, achieving faster data loading and better user experience.
Patent Information
- Application Number
- CN202510710057.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-29
- Publication Date
- 2025-08-26
AI Technical Summary
When processing big data, there are problems in the prior art that data is slowly loaded and rendered for a long time, especially when rendering large amounts of point data in geographic information systems, the system resources are exhausted or rendered at a very slow speed.
The automatic splicing calibration system for massive map slicing data is adopted, including big data processing module, front-end automatic drawing module and style application module. Through data chunking and caching, simplified symbolization, progressive rendering, performance monitoring and adjustment, the map loading and rendering process is optimized.
It effectively alleviates the problem of insufficient performance, improves data loading speed and rendering time, provides faster data loading and a better user experience, and reduces repeated data reading and rendering.
Smart Images
Figure CN120543372A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of map slice data splicing and calibration, and in particular to a method and system for automatic splicing and calibration of massive map slice data. Background Art
[0002] SLD is an XML-based language used to define the visual style of map layers in a geographic information system (GIS). It can specify style attributes such as symbolization, color, and transparency. For example, for point features, you can define the shape (e.g., circle, square, etc.), size, and color of the point; for line features, you can define the line width, color, and style (solid, dashed, etc.); and for area features, you can define the fill color, outline style, and more.
[0003] Currently, processing big data has become a challenge. When processing big data, you will face performance issues such as slow data loading and long rendering times. For example, when there is a large amount of point data, rendering all points at the same time may cause system resources to be exhausted or the rendering speed to be extremely slow.
[0004] To address the above problems, a method and system for automatic splicing and calibration of massive map slice data are proposed. Summary of the Invention
[0005] The purpose of the present invention is to provide a method and system for automatically stitching and calibrating massive map slice data. By adopting the present invention, the problems of slow data loading and long rendering time when processing big data mentioned in the above background are solved.
[0006] To achieve the above objectives, the present invention provides the following technical solutions: a system for automatically stitching and calibrating massive map slice data, comprising a big data processing module, a front-end automatic mapping module, and a style application module, wherein the big data processing module, the front-end automatic mapping module, and the style application module transmit structured data via an API interface;
[0007] The big data processing module specifically includes the following steps:
[0008] S11: data segmentation and caching;
[0009] S12: Simplified symbolization;
[0010] S13: Progressive rendering;
[0011] S14: Performance monitoring and adjustment.
[0012] Furthermore, the data segmentation and caching described in S11 includes the following steps:
[0013] Split large data sets into smaller blocks, load and render the corresponding data blocks according to the user's view range, and set up an appropriate caching mechanism to avoid repeatedly loading the same data.
[0014] Furthermore, the simplified symbolization described in S12 specifically includes the following steps:
[0015] For layers with large amounts of data, simplify the symbolization settings. For line and surface features, it is also necessary to reduce the complexity of the style.
[0016] Furthermore, the progressive rendering described in S13 includes the following steps:
[0017] First, large data sets are quickly rendered in low resolution or simplified form, allowing users to quickly see the general map content. Then, based on user interaction, more detailed styles and data are gradually loaded to improve the clarity and details of the map.
[0018] Furthermore, the performance monitoring and adjustment described in S14 are as follows:
[0019] Use performance monitoring tools to monitor map loading and rendering time, and then further adjust parameter settings and optimization strategies based on the monitoring results.
[0020] Furthermore, the front-end automatic image matching module specifically includes the following steps:
[0021] S21: Automatically match images with data standards.
[0022] Furthermore, the data standard automatic mapping described in S21 specifically includes the following steps:
[0023] S211: Standard keyword extraction, extracting key words or topics from data standards;
[0024] S212: Standard mapping, building a mapping relationship between data standard information and rotation styles, and finding the corresponding rotation style based on the data standard information;
[0025] S213: Front-end display, showing the completed rotation diagram to the user's attention range.
[0026] Furthermore, the style application module is as follows:
[0027] Upload the layer to which you want to apply the style, and select the layer you just uploaded on the layer editing page to apply the style.
[0028] The present invention also proposes another technical solution: a method for automatically aligning and calibrating massive map slice data, comprising the following steps:
[0029] S1: Split the large data set into smaller blocks, load and render the corresponding data blocks according to the user's view range, and set up an appropriate caching mechanism;
[0030] S2: For layers with large amounts of data, simplify the symbolization settings;
[0031] S3: First, quickly render large data sets in low resolution or simplified form, allowing users to quickly see the general map content, and then gradually load more detailed styles and data based on user interaction;
[0032] S4: Use performance monitoring tools to monitor map loading and rendering time, and then further adjust parameter settings and optimization strategies based on the monitoring results;
[0033] S5: Automatically assign pictures to the front end;
[0034] S6: Apply the layer.
[0035] Compared with the prior art, the present invention has the following beneficial effects:
[0036] 1. In the process of processing big data, it alleviates the problem of insufficient performance, making data loading speed faster and rendering time shorter.
[0037] 2. Use performance monitoring tools to monitor map loading and rendering time. Based on the monitoring results, further adjust SLD settings and optimization strategies to improve the efficiency of big data mapping and user experience.
[0038] 3. The present invention can cache commonly used map slices to reduce repeated reading and rendering of data. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] Figure 1 is a flow chart of the overall system of the present invention;
[0040] Figure 2 This is a specific flow chart of the big data processing module of the present invention;
[0041] Figure 3 This is a specific flow chart of the front-end automatic image matching module of the present invention;
[0042] Figure 4 The following is a specific flow chart of the data standard automatic mapping of the present invention. DETAILED DESCRIPTION
[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0044] In order to solve the technical problems of slow data loading and long rendering time when processing big data, such as Figures 1-4 As shown, the following preferred technical solutions are provided:
[0045] A massive map slice data automatic splicing and calibration system includes a big data processing module 1, a front-end automatic map matching module 2, and a style application module 3, wherein the big data processing module 1, the front-end automatic map matching module 2, and the style application module 3 transmit structured data through an API interface;
[0046] The big data processing module 1 specifically includes the following steps:
[0047] S11: data segmentation and caching;
[0048] S12: Simplified symbolization;
[0049] S13: Progressive rendering;
[0050] S14: Performance monitoring and adjustment.
[0051] The front-end automatic image matching module 2 specifically includes the following steps:
[0052] S21: Automatically match images with data standards.
[0053] The data standard automatic mapping mentioned in S21 specifically includes the following steps:
[0054] S211: Standard keyword extraction, extracting key words or topics from data standards;
[0055] S212: Standard mapping, building a mapping relationship between data standard information and rotation styles, and finding the corresponding rotation style based on the data standard information;
[0056] S213: Front-end display, showing the completed rotation diagram to the user's attention range.
[0057] 1. Data segmentation and caching:
[0058] Split large datasets into smaller chunks, loading and rendering the appropriate chunks based on the user's viewport (e.g., the current display area of a map). Also, set up appropriate caching mechanisms to avoid repeatedly loading the same data. For example, use the browser's local storage or a server-side cache to store previously loaded chunks.
[0059] 2. Simplified symbolization:
[0060] For layers with large amounts of data, simplify the symbology. For example, for large amounts of point data, use simple, uniform symbols (such as points of the same color and size) rather than complex symbol styles. For line and area features, you can also reduce the complexity of the style, such as using a single color and simple outlines.
[0061] 3. Progressive rendering:
[0062] First, large datasets are quickly rendered in low resolution or simplified form, allowing users to quickly see the general map content. Then, based on user interaction (such as map zoom), more detailed styles and data are gradually loaded to improve the clarity and details of the map.
[0063] 4. Implementation example (taking GeoServer as an example):
[0064] 1. Create SLD file:
[0065] Write an SLDXML file that complies with the OGC standard to define the style of the layer. For example, the following is a simple point feature SLD example:
[0066]
[0067]
[0068] 2. Apply SLD in GeoServer:
[0069] Upload the created SLD file to GeoServer and associate it with the corresponding layer. In the GeoServer management interface, find the layer style setting option and select the uploaded SLD file to apply to the layer.
[0070] 5. Performance monitoring and adjustment:
[0071] Use performance monitoring tools (such as the performance panel in your browser's developer tools) to monitor map loading and rendering times. Based on the monitoring results, further adjust SLD settings and optimization strategies to improve the efficiency of big data mapping and user experience.
[0072] Through the above methods and strategies, OGCSLD can be effectively used to process big data illustrations and balance the relationship between visualization effects and performance.
[0073] The following is a specific example of using SLD to process large data maps in GeoServer:
[0074] Case Background:
[0075] A dataset contains a large amount of weather station data. This data is stored as point features in a PostGIS database. Each point represents a weather station and contains multiple attribute fields such as temperature, humidity, and wind speed. This weather station data needs to be published as a map service and visualized based on different meteorological elements to meet the needs of weather data analysis and monitoring.
[0076] Processing process:
[0077] 1. Data preparation: Import weather station data into the PostGIS database and ensure the accuracy and completeness of the data;
[0078] 2. Create SLD file:
[0079] Use tools such as uDig to create SLD files3.
[0080] For example, to visualize weather stations based on temperature, you can define the following styles in the SLD:
[0081]
[0082]
[0083]
[0084]
[0085] 3. Publish data and apply SLD in GeoServer:
[0086] a: Publish weather station data in PostGIS as a layer in GeoServer;
[0087] b: In the GeoServer management interface, go to the style settings page, upload the created SLD file, and associate it with the weather station layer.
[0088] 4. Performance optimization:
[0089] a: Enable the cache function of GeoServer to cache commonly used map slices to reduce repeated reading and rendering of data;
[0090] b: The weather station data is processed in blocks. According to the user's map view range, only the weather station data within the currently visible area is loaded and rendered.
[0091] Case effect:
[0092] With the above configuration, users can access the weather station map service published by GeoServer in a client (such as an OpenLayers map client) and quickly and intuitively view the distribution of weather stations under different temperature conditions based on the style defined by the SLD. For example, during high summer temperatures, it is possible to quickly locate areas where the temperature at weather stations exceeds 30 degrees Celsius; during cold winters, it is possible to clearly see areas where the temperature is below 10 degrees Celsius. At the same time, due to the adoption of performance optimization measures, even with large amounts of weather station data, the map loading and rendering speeds can meet user needs and provide a good user experience.
[0093] 6. Automatic front-end image matching:
[0094] First, extract standard keywords and key words or topics from the data standards; then build a mapping relationship between data standard information and rotation styles, and find the corresponding rotation style based on the data standard information; finally, perform front-end display and display the completed rotation diagram to the user's attention range.
[0095] 7. SLD large batch automatic mapping:
[0096] In Geographic Information Systems (GIS), SLD (StyledLayerDescriptor) is used for large-scale automatic mapping, usually to set styles for a large number of geographic features according to certain rules. The following uses GeoServer as an example to introduce the specific steps and sample code:
[0097] 1. Overall idea:
[0098] a: Data preparation: ensure that there is a large amount of geographic feature data stored in a database that supports GeoServer (such as PostGIS);
[0099] b: rule formulation, formulating SLD style rules based on the attributes of geographic features (such as numerical range, category, etc.);
[0100] c: Style application: Apply the SLD file to the layer in GeoServer.
[0101] 2. Detailed steps and examples:
[0102] a: Assume there is a PostGIS database containing a large number of weather station data, each of which has attributes such as temperature and humidity.
[0103] b: rule formulation and creation of SLD files;
[0104] Here's an example SLD that symbolizes weather stations with different colors and sizes based on their temperature values:
[0105]
[0106]
[0107]
[0108]
[0109] The above SLD file divides the weather station into three temperature ranges based on the temperature attributes of the weather station, and represents them with circular symbols of different colors and sizes.
[0110] c: Style application:
[0111] Save the above SLD file as weather_stations.sld and apply the style in GeoServer as follows:
[0112] Log in to the GeoServer management interface; navigate to the "Styles" tab and click "Add a new style"; upload the weather_stations.sld file and click "Publish"; find the layer to which you want to apply the style (such as weather_stations) and select the style you just uploaded in the "Styles" section of the layer editing page; all the above steps will complete the application of the style.
[0113] The present invention also proposes another embodiment: a method for automatically aligning and calibrating massive map slice data, comprising the following steps:
[0114] Step 1: Split the large data set into smaller chunks, load and render the corresponding chunks based on the user's view range, and set up an appropriate caching mechanism;
[0115] Step 2: For layers with large amounts of data, simplify the symbolization settings;
[0116] Step 3: First, quickly render the large dataset in a low-resolution or simplified manner so that users can quickly see the general map content. Then, based on user interaction, gradually load more detailed styles and data.
[0117] Step 4: Use performance monitoring tools to monitor map loading and rendering time, and then further adjust parameter settings and optimization strategies based on the monitoring results;
[0118] Step 5: Automatically assign pictures to the front end;
[0119] Step 6: Apply the layer.
[0120] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.
[0121] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. An automated stitching and calibration system for massive map slice data, characterized in that: The system comprises a big data processing module (1), a front-end automatic picture matching module (2) and a style application module (3), wherein the big data processing module (1), the front-end automatic picture matching module (2) and the style application module (3) transmit structured data via an API interface; The big data processing module (1) specifically comprises the following steps: S11: data segmentation and caching; S12: Simplified symbolization; S13: Progressive rendering; S14: Performance monitoring and adjustment.
2. The automated stitching and calibration system for massive map slice data according to claim 1, characterized in that: The data segmentation and caching described in S11 includes the following steps: Split large data sets into smaller blocks, load and render the corresponding data blocks according to the user's view range, and set up an appropriate caching mechanism to avoid repeatedly loading the same data.
3. The automatic stitching and calibration system for massive map slice data according to claim 2 is characterized in that: The simplified symbolization described in S12 specifically includes the following steps: For layers with large amounts of data, simplify the symbolization settings. For line and surface features, it is also necessary to reduce the complexity of the style.
4. The automatic stitching and calibration system for massive map slice data according to claim 3 is characterized in that: The progressive rendering described in S13 includes the following steps: First, large data sets are quickly rendered in low resolution or simplified form, allowing users to quickly see the general map content. Then, based on user interaction, more detailed styles and data are gradually loaded to improve the clarity and details of the map.
5. The automatic stitching and calibration system for massive map slice data according to claim 4 is characterized in that: The performance monitoring and adjustment described in S14 are as follows: Use performance monitoring tools to monitor map loading and rendering time, and then further adjust parameter settings and optimization strategies based on the monitoring results.
6. The automated stitching and calibration system for massive map slice data according to claim 5, characterized in that: The front-end automatic image matching module (2) specifically includes the following steps: S21: Automatically match images with data standards.
7. The automatic stitching and calibration system for massive map slice data according to claim 6, characterized in that: The data standard automatic mapping described in S21 specifically includes the following steps: S211: Standard keyword extraction, extracting key words or topics from data standards; S212: Standard mapping, building a mapping relationship between data standard information and rotation styles, and finding the corresponding rotation style based on the data standard information; S213: Front-end display, showing the completed rotation diagram to the user's attention range.
8. The automatic stitching and calibration system for massive map slice data according to claim 7, characterized in that: The style application module (3) is as follows: Upload the layer to which you want to apply the style, and select the layer you just uploaded on the layer editing page to apply the style.
9. The method for automatic stitching and calibration of massive map slice data according to claim 8, characterized in that: The steps include: S1: Split the large data set into smaller blocks, load and render the corresponding data blocks according to the user's view range, and set up an appropriate caching mechanism; S2: For layers with large amounts of data, simplify the symbolization settings; S3: First, quickly render large data sets in low resolution or simplified form, allowing users to quickly see the general map content, and then gradually load more detailed styles and data based on user interaction; S4: Use performance monitoring tools to monitor map loading and rendering time, and then further adjust parameter settings and optimization strategies based on the monitoring results; S5: Automatically assign pictures to the front end; S6: Apply the layer.