An interactive meteorological data display system and method

Abstract

This invention discloses an interactive meteorological data display system and method, relating to the field of meteorological information processing technology. The system includes: acquiring raw meteorological data and preprocessing it to generate meteorological grid data; performing binary decoding on the meteorological grid data to output standard meteorological data; performing color scalar mapping on the standard meteorological data according to a preset color mapping relationship to generate a meteorological heat map; performing a global scan of the grid point numerical matrix using a parallel reduction algorithm to generate an extreme value point data list; overlaying the meteorological heat map and the extreme value point data list and drawing extreme value symbols to generate annotation symbols; and monitoring user interaction behavior through a user event listening mechanism and performing interactive display of meteorological data. This invention achieves real-time accurate positioning and interactive dynamic display of meteorological extreme values ​​through the collaborative mechanism of global scanning of the grid point numerical matrix and the construction of a meteorological grid data cache pool.

Description

Technical Field

[0001] This invention relates to the field of meteorological information processing technology, and in particular to an interactive meteorological data display system and method. Background Technology

[0002] In recent years, interactive visualization technology for meteorological data has rapidly evolved from static display to dynamic real-time rendering. Mainstream technical solutions deeply integrate high-performance computing and graphics rendering architectures to improve data processing efficiency. Parallel computing frameworks based on web-based graphics libraries / general-purpose web-based computing graphics architectures are widely used in meteorological heat map generation, supporting real-time interpolation and rendering of large-scale gridded data. Multi-level caching mechanisms and time-series indexing strategies have initially enabled rapid switching of historical data. Interactive components support dynamic exploration of spatiotemporal data by users through event-driven models. Deep learning-assisted feature extraction technology is gradually being applied to the intelligent identification of meteorological extreme points, providing a technical foundation for the visualization and analysis of high-resolution meteorological data.

[0003] However, existing technologies still have limitations in terms of real-time interactive granularity and display efficiency. Traditional solutions rely on server-side pre-computation and static data push, resulting in extreme point annotations not being dynamically updated with user operations. Time-series data loading often uses a frame-by-frame request mechanism, lacking client-side caching pooling and multi-dimensional indexing capabilities, making it difficult to achieve millisecond-level cross-time period data switching. Meteorological element rendering and extreme point identification have not established a collaborative computing pipeline, resulting in delays in the spatial registration of heatmaps and annotation symbols, which restricts the real-time exploration experience of high-precision meteorological data. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an interactive display method for meteorological data to address the limitations in real-time interaction granularity and display efficiency.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an interactive meteorological data display method, comprising: acquiring raw meteorological data and preprocessing it; performing gridding processing on the preprocessed raw meteorological data to generate meteorological grid data; performing binary decoding on the meteorological grid data to obtain raw data blocks; extracting meteorological elements from the raw data blocks to generate a grid point numerical matrix; simultaneously performing metadata parsing on the meteorological grid data to obtain the geographical location and timestamp corresponding to the meteorological grid data; integrating the grid point numerical matrix, geographical location, and timestamp to output standard meteorological data; performing color scalar mapping on the standard meteorological data according to a preset color mapping relationship, and performing parallel interpolation and rendering on the standard meteorological data to generate a meteorological heat map; and performing full reduction on the grid point numerical matrix using a parallel reduction algorithm. The system scans and acquires meteorological extreme points, recording their latitude and longitude coordinates and meteorological element values. It then integrates and compiles these coordinates to generate an extreme point data list. Based on the latitude and longitude coordinates in the extreme point data list, it overlays the meteorological heat map with the extreme point data list and draws extreme value symbols to generate annotation symbols. It stores and indexes meteorological grid data from different time points, along with corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols, forming a meteorological grid data cache pool. Finally, it monitors user interactions through a user event listening mechanism, extracting meteorological heat maps, extreme point data lists, and annotation symbols from the meteorological grid data cache pool according to the interaction type, and then interactively displays the meteorological data.

[0008] As a preferred embodiment of the interactive meteorological data display method of the present invention, the specific steps for generating meteorological grid data are as follows:

[0009] The preprocessed raw meteorological data is divided into grid points to generate the grid point numerical distribution;

[0010] Spatial interpolation algorithms are used to fill in missing values ​​in the distribution of grid point values ​​to obtain complete grid data;

[0011] The complete grid data is processed to standardize the data format and generate meteorological grid data.

[0012] In a preferred embodiment of the interactive meteorological data display method of the present invention, the steps of performing binary decoding on the meteorological grid data to obtain raw data blocks, extracting meteorological elements from the raw data blocks, and generating a grid point numerical matrix are as follows:

[0013] Decode the meteorological grid data in binary format to obtain a compressed data byte stream;

[0014] The compressed data byte stream is decompressed to obtain the original data blocks;

[0015] The meteorological elements in the original data block are parsed and data extracted to generate an element set.

[0016] The set of elements is restructured into a matrix according to the latitude and longitude coordinates of the meteorological grid data to generate a grid point numerical matrix.

[0017] As a preferred embodiment of the interactive meteorological data display method of the present invention, the specific steps of parsing the meteorological grid data to obtain the geographical location and timestamp corresponding to the meteorological grid data are as follows:

[0018] The header information of the meteorological grid data is read by the metadata parser, and the header information is structured and decoded to obtain the raw metadata block containing geographic parameters and time identifiers.

[0019] Geographic parameters are parsed from the original metadata block to obtain the geographical locations of each meteorological element;

[0020] Extract the time stamp from the original metadata block to obtain the timestamps of each meteorological element.

[0021] As a preferred embodiment of the interactive meteorological data display method of the present invention, the specific steps for generating the meteorological heat map are as follows:

[0022] Based on a preset color mapping relationship, a scalar-to-color conversion is performed on standard meteorological data to generate a raster data array;

[0023] Parallel bilinear interpolation is performed on the raster data array to generate a pixel color array, and the pixel color array is rasterized and rendered to output a color-filled image.

[0024] A meteorological heat map is generated by spatially registering and overlaying the color-filled image with the geographic location in standard meteorological data.

[0025] As a preferred embodiment of the interactive meteorological data display method of the present invention, the step of performing a global scan of the grid point numerical matrix using a parallel reduction algorithm refers to dividing the grid point numerical matrix into multiple data blocks, performing extreme value search on multiple data blocks simultaneously, integrating the results of the extreme value search to form a data block extreme value set, and performing recursive extreme value retrieval on the data block extreme value set to obtain the meteorological extreme value points in the grid point numerical matrix.

[0026] As a preferred embodiment of the interactive meteorological data display method of the present invention, the specific steps for integrating and compiling latitude and longitude coordinates and meteorological element values ​​to generate an extreme point data list are as follows:

[0027] The latitude and longitude coordinates of meteorological extreme points are paired and associated with meteorological element values ​​to generate extreme point data pairs;

[0028] The extreme point data pairs are structured and transformed using key-value pair conversion to generate an extreme point data list.

[0029] As a preferred embodiment of the interactive meteorological data display method of the present invention, the specific steps for generating annotation symbols are as follows:

[0030] Based on the latitude and longitude coordinates in the extreme point data list, the latitude and longitude coordinates are mapped to the corresponding pixel positions in the meteorological heat map to form a pixel coordinate set;

[0031] Assign corresponding symbol style parameters to each meteorological extreme point to form a symbol description set;

[0032] Based on the pixel coordinate set and symbol description set, symbol drawing is performed on the meteorological heat map to generate annotation symbols.

[0033] As a preferred embodiment of the interactive display method for meteorological data described in this invention, the step of monitoring user interaction behavior through a user event monitoring mechanism refers to capturing user clicks on meteorological grid data in real time via a user event monitoring interface, and parsing the operation type and parameter information contained in the click selection.

[0034] Secondly, the present invention provides an interactive meteorological data display system, comprising,

[0035] The data module is used to acquire raw meteorological data, perform preprocessing, and then perform gridding on the preprocessed raw meteorological data to generate meteorological grid data.

[0036] The integration module is used to perform binary decoding on meteorological grid data to obtain raw data blocks, extract meteorological elements from the raw data blocks to generate a grid point numerical matrix; at the same time, it performs metadata parsing on the meteorological grid data to obtain the geographical location and timestamp corresponding to the meteorological grid data; and integrates the grid point numerical matrix, geographical location and timestamp to output standard meteorological data.

[0037] The image module is used to perform color scalar mapping on standard meteorological data according to a preset color mapping relationship, and to perform parallel interpolation and rendering on the standard meteorological data to generate a meteorological heat map.

[0038] The list module is used to perform a global scan of the grid point numerical matrix using a parallel reduction algorithm to obtain meteorological extreme points and record the latitude and longitude coordinates and meteorological element values ​​of the meteorological extreme points; the latitude and longitude coordinates and meteorological element values ​​are integrated and compiled to generate an extreme point data list;

[0039] The annotation module is used to overlay meteorological heat maps and extreme value data lists based on latitude and longitude coordinates in the extreme value data list, and draw extreme value symbols to generate annotation symbols;

[0040] The interactive module stores and indexes meteorological grid data at different time points, along with corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols, forming a meteorological grid data cache pool. It monitors user interaction behavior through a user event listening mechanism, extracts meteorological heat maps, extreme point data lists, and annotation symbols from the meteorological grid data cache pool according to the interaction type, and then displays the meteorological data interactively.

[0041] The beneficial effects of this invention are as follows: By employing a dual-mechanism approach of global scanning of the grid point numerical matrix and the construction of a meteorological grid data cache pool, real-time accurate positioning and interactive dynamic display of meteorological extreme points are achieved. By dividing the grid point numerical matrix into multiple data blocks and performing extreme value searches in parallel, and recursively integrating the extreme value sets of the data blocks to obtain meteorological extreme points, the efficiency of extreme point detection is significantly improved. A cache pool is formed by storing and indexing meteorological grid data from different time points, along with corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols. Furthermore, a user event monitoring mechanism is used to capture interactive behaviors in real time, enabling instantaneous dynamic extraction and visualization of meteorological data, providing low-latency, high-precision interactive decision support for meteorological analysis. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0043] Figure 1 This is a flowchart of a method for interactively displaying meteorological data.

[0044] Figure 2 This is a schematic diagram of an interactive meteorological data display system.

[0045] Figure 3 This is a flowchart for outputting standard meteorological data.

[0046] Figure 4 A flowchart for generating a list of extreme point data. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides an interactive display method for meteorological data, comprising the following steps:

[0051] S1. Obtain raw meteorological data and preprocess it. Then, perform gridding on the preprocessed raw meteorological data to generate meteorological grid data.

[0052] Specifically, raw meteorological data of the target area is acquired through data collection equipment such as weather radar or meteorological satellites. This raw meteorological data includes various meteorological elements such as temperature, humidity, air pressure, wind speed, wind direction, and rainfall, and is typically stored in raw formats such as CSV, NetCDF, or GRIB. During the acquisition process, it is ensured that the timestamps and geographic location information of the data are recorded completely. Preprocessing is performed on the acquired raw meteorological data, specifically including data cleaning and format standardization: missing values, outliers, and duplicate values ​​are checked in the raw meteorological data. Missing values ​​are filled using a method based on the average of neighboring values, and outliers are removed, such as temperature data exceeding physical reality. To address inconsistencies in the raw meteorological data formats, raw meteorological data from different sources are converted to a unified intermediate format, such as a unified time unit of UTC and a unified spatial coordinate format of WGS84 latitude and longitude. Preprocessing also includes data denoising, using a sliding window averaging method to smooth data fluctuations and ensure stable data quality. The preprocessed raw meteorological data is output, containing standardized meteorological element values, timestamps, and geographic location information, and stored as a structured dataset.

[0053] The preprocessed raw meteorological data is divided into grid points to generate the grid point numerical distribution;

[0054] Specifically, based on the geographical scope of the target area, the resolution of the meteorological grid is defined, for example, one grid point for every 0.1 degrees of latitude and longitude, forming a regular two-dimensional grid framework. For each meteorological element in the preprocessed raw meteorological data, such as temperature and humidity, the element value of the meteorological element is mapped to the corresponding grid point according to the geographical location information (latitude and longitude coordinates) of each meteorological element. If there is a grid point without a directly corresponding raw meteorological data, it is temporarily marked as a missing value, forming a preliminary grid point value distribution. The grid point value distribution includes the meteorological element value of each grid point, as well as the corresponding latitude and longitude coordinates, and retains timestamp information to ensure time consistency.

[0055] Spatial interpolation algorithms are used to fill in missing values ​​in the distribution of grid point values ​​to obtain complete grid data;

[0056] Based on the location of missing values ​​in the grid point numerical distribution, the neighboring grid points of each missing grid point are identified, such as selecting the nearest 8 or 16 non-empty grid points as references. The inverse distance weighted interpolation (IDW) algorithm is used to obtain the meteorological element values ​​of the missing grid points by weighted averaging based on the meteorological element values ​​and distance of the neighboring grid points. The closer the grid point is, the higher its weight. During the interpolation process, the spatial continuity characteristics of meteorological elements are considered. For example, the spatial variation of temperature is relatively smooth, while rainfall may have local abrupt changes. Therefore, the interpolation parameters are adjusted for different meteorological elements to improve the filling accuracy and output complete grid data. The complete grid data is represented as a two-dimensional array without missing values. Each grid point contains complete meteorological element values, latitude and longitude coordinates, and a timestamp, providing continuous and reliable data for subsequent data format standardization processing.

[0057] The complete grid data is standardized to generate meteorological grid data.

[0058] Specifically, the two-dimensional arrays in the complete grid data are converted into a standardized data format, such as NetCDF or JSON. This includes organizing the meteorological element values, latitude and longitude coordinates, and timestamps of each grid point into a unified field structure; defining unified data metadata specifications during standardization, such as using the WGS84 coordinate system to represent latitude and longitude, and unifying the timestamp format to the ISO 8601 standard; checking the consistency of data units for meteorological element values, such as unifying temperature to degrees Celsius and wind speed to meters per second; performing data compression optimization, reducing data storage volume through lossless compression algorithms (such as zlib) while retaining all information; and verifying the integrity of the standardized data to ensure that the meteorological element values, latitude and longitude coordinates, and timestamps of each grid point are not lost or incorrect, thus generating meteorological grid data.

[0059] S2. Perform binary decoding on the meteorological grid data to obtain the original data block. Extract meteorological elements from the original data block to generate a grid point numerical matrix. At the same time, perform metadata parsing on the meteorological grid data to obtain the geographical location and timestamp corresponding to the meteorological grid data. Integrate the grid point numerical matrix, geographical location and timestamp to output standard meteorological data.

[0060] Decode the meteorological grid data in binary format to obtain a compressed data byte stream;

[0061] Specifically, the process involves reading the binary file of meteorological grid data, identifying the file's encoding format, parsing the file header to obtain metadata about the compressed data, such as data block size and compression algorithm type, based on the data structure of the binary file, and then using the corresponding decoding algorithm (such as zlib or Huffman) to decode the binary data, converting the compressed meteorological grid data's binary bitstream into a readable byte sequence. During decoding, the integrity of the byte sequence is verified, checking for data corruption or loss. If errors are found, they are recorded and problematic data segments are marked. Finally, the decoded data is output as a compressed data byte stream, which is represented as a continuous byte sequence containing all the compressed information of the meteorological grid data, providing direct input for subsequent decompression processing.

[0062] The compressed data byte stream is decompressed to obtain the original data blocks;

[0063] The compression algorithm is applied to parse the compression format of the compressed data byte stream, identify the compression header information and data segment structure, obtain the content of the compressed data byte stream, remove the compression encoding and padding bits of the compressed data byte stream, and restore the compressed data byte stream to a continuous uncompressed byte sequence to generate the original data block. The decompression process requires the allocation of corresponding memory space to store the decompressed data to ensure that no data is lost due to memory overflow during the process. After decompression is completed, the integrity of the original data block is verified. By comparing the size and metadata of the data before and after decompression, it is ensured that the decompression result is consistent with the meteorological grid data before compression.

[0064] The meteorological elements in the original data block are parsed and data extracted to generate an element set.

[0065] Specifically, the structure of the original data block is analyzed to identify the fields contained within it, such as temperature (floating-point value), humidity (percentage value), and air pressure (hPa unit). For each meteorological element, the content of each field corresponding to that meteorological element is parsed one by one, and the numerical data of each meteorological element is extracted. For example, for an original data block containing temperature and humidity, the values ​​of the temperature field (e.g., 25.3 degrees Celsius) and the humidity field (e.g., 65%) are extracted. After completing the field parsing, the extracted numerical data of each meteorological element are organized into an element set. The element set is represented as a data structure containing multiple meteorological elements. Each element in the element set contains the numerical data of the meteorological element and the corresponding field identifier, ensuring that the element set retains all valid meteorological element information from the original data block.

[0066] The element set is restructured into a matrix structure according to the latitude and longitude coordinates of the meteorological grid data to generate a grid point numerical matrix;

[0067] Specifically, the system reads the values ​​of each meteorological element (including temperature, humidity, and air pressure) from the element set, as well as the corresponding latitude and longitude coordinates from the meteorological grid data. Based on the two-dimensional grid framework of the meteorological grid data, the values ​​of the meteorological elements in the element set are mapped to the corresponding grid point locations. For each grid point, the system organizes the multidimensional meteorological element values ​​according to a predefined element order (such as temperature, humidity, and air pressure arranged in sequence). The predefined element order is set according to the physical priority of the elements output by the GRIB standard coding table or numerical weather prediction model (such as the fixed arrangement specifications of core elements such as temperature, humidity, air pressure, and wind field). This forms a grid point value matrix, which is represented as a two-dimensional array. Each array element contains the multidimensional meteorological element values ​​and latitude and longitude coordinates of the corresponding grid point.

[0068] The header information of the meteorological grid data is read by the metadata parser, and the header information is structured and decoded to obtain the raw metadata block containing geographic parameters and time identifiers.

[0069] Specifically, the metadata parser is invoked to read the header information of the meteorological grid data. The header information includes geographic parameters (such as latitude and longitude coordinates and grid resolution) and time identifiers (such as data acquisition time). The metadata parser analyzes the encoding format of the header, such as confirming whether it is an XML structure or a custom binary format. The header information is then subjected to structured decoding, extracting the content containing geographic parameters and time identifiers field by field, and outputting the raw metadata block. The raw metadata block represents a structured data set containing geographic parameters and time identifiers, providing direct input for subsequent extraction of geographic parameters and timestamps.

[0070] Geographic parameters are parsed from the original metadata block to obtain the geographical locations of each meteorological element;

[0071] The data lookup method identifies fields containing geographic parameters, such as latitude and longitude range, grid resolution, and coordinate system information, from the original metadata block. For each field, specific geographic parameter values ​​are extracted, such as starting longitude, ending longitude, starting latitude, ending latitude, and grid point resolution (e.g., 0.1 degrees). During the parsing process, it is ensured that the geographic parameters are consistent with the latitude and longitude coordinates of the meteorological grid data. If the original metadata block contains geographic location information for multiple meteorological elements, the latitude and longitude data of each meteorological element are extracted one by one to obtain the geographic location of each meteorological element.

[0072] Extract the time stamp from the original metadata block to obtain the timestamps of each meteorological element;

[0073] Identify fields containing time identifiers, such as data acquisition time or observation timestamps, from the structure of the original metadata block; extract specific time values ​​for the time identifier fields, such as UTC time represented in ISO 8601 standard format; during the extraction process, check the consistency of the timestamp format, and if a non-standard format (such as local time) is found, convert it to the UTC standard format; for original metadata blocks containing multiple meteorological elements, extract the timestamp corresponding to each element one by one to ensure that the time information corresponds to the meteorological element values, and output the timestamps of each meteorological element.

[0074] Integrate grid point numerical matrix, geographic location and timestamp to output standard meteorological data;

[0075] Specifically, the meteorological element values ​​in the grid point numerical matrix are associated and matched with geographical locations (latitude and longitude coordinates) and timestamps (ISO 8601 format). According to the two-dimensional grid framework of the grid point numerical matrix, the meteorological element values ​​of each grid point are bound with the corresponding latitude and longitude coordinates and timestamps to form a unified data structure, ensuring that the meteorological element values, latitude and longitude coordinates, and timestamps of each grid point correspond one-to-one, avoiding data misalignment or omission. The unified data structure is then encoded according to the NetCDF or JSON format specifications to form standard meteorological data containing complete grid point information (such as meteorological element values, latitude and longitude coordinates, and timestamps).

[0076] S3. Based on the preset color mapping relationship, perform color scalar mapping on the standard meteorological data, and perform parallel interpolation and rendering on the standard meteorological data to generate a meteorological heat map.

[0077] Based on a preset color mapping relationship, a scalar-to-color conversion is performed on standard meteorological data to generate a raster data array;

[0078] Specifically, standard meteorological data includes meteorological element values ​​(such as temperature and humidity) and their corresponding latitude and longitude coordinates for grid points. Based on a preset color mapping relationship, which defines the correspondence between meteorological element values ​​and color values ​​(e.g., temperature values ​​are mapped from low to high as a gradient from blue to red), a scalar-to-color conversion operation is performed on the meteorological element values ​​of each grid point in the standard meteorological data. By looking up the color mapping table, each meteorological element value is converted into its corresponding color value. The conversion process ensures that the color values ​​strictly match the meteorological element values ​​to avoid color distortion or offset, generating a raster data array. This raster data array is a two-dimensional color matrix, where each element represents the color value of the corresponding grid point, retaining latitude and longitude coordinate information to provide input for subsequent visualization rendering.

[0079] Furthermore, the preset color mapping relationship is set by defining the correspondence rules between meteorological element values ​​and color spectra based on the physical characteristics of meteorological elements and the requirements of scientific visualization. Specifically, a continuous color gradient mapping relationship is adopted (e.g., temperature uses a blue-white-red spectrum, with low temperature corresponding to blue and high temperature corresponding to red). The parameters of the continuous color gradient mapping relationship (e.g., color nodes, gradient ranges, and transparency) and industry-standard color band requirements (e.g., rainbow color bands or grayscale color bands) are configured to ensure that the color mapping can not only intuitively reflect the numerical distribution characteristics of meteorological elements, but also conform to human visual perception habits.

[0080] Parallel bilinear interpolation is performed on the raster data array to generate a pixel color array, and the pixel color array is rasterized and rendered to output a color-filled image.

[0081] Specifically, the raster data array is a discrete grid point color matrix. Parallel bilinear interpolation is performed on the raster data array. Through GPU parallel computing architecture (such as WebGL fragment shader), the color values ​​of adjacent grid points around each pixel region in the raster data array are weighted and averaged to fill the color transition between grid points, generating a high-resolution pixel color array. The interpolation process maintains the continuity of the color space, ensuring smooth gradients without jagged edges. The pixel color array is then rasterized and rendered. Through the graphics rendering pipeline, the pixel color array is converted into a color-filled image, that is, the color value of each pixel is converted into a bitmap pixel in the frame buffer, generating a continuous color-filled image.

[0082] The color-filled image is spatially registered and overlaid with the geographic location in standard meteorological data to generate a meteorological heat map;

[0083] Standard meteorological data contains the latitude and longitude coordinates of each grid point. The color-filled image is spatially registered and overlaid with the geographic location in the standard meteorological data. Through coordinate transformation algorithms (such as affine transformation or projection transformation), the pixel coordinates of the color-filled image are aligned with the latitude and longitude coordinates in the standard meteorological data to ensure that each pixel is mapped to the correct geographic location. The overlay process preserves the color information of the color-filled image and embeds the corresponding geographic location information to generate a meteorological heat map. The meteorological heat map is a meteorological data layer that combines visual color and geospatial information and can be directly used for interactive display.

[0084] S4. Perform a global scan of the grid point numerical matrix using a parallel reduction algorithm to obtain meteorological extreme points, and record the latitude and longitude coordinates and meteorological element values ​​of the meteorological extreme points; integrate and compile the latitude and longitude coordinates and meteorological element values ​​to generate an extreme point data list.

[0085] Global scanning of the grid point numerical matrix using a parallel reduction algorithm involves dividing the grid point numerical matrix into multiple data blocks, performing extreme value searches on multiple data blocks simultaneously, integrating the results of the extreme value searches to form a data block extreme value set, and performing recursive extreme value retrieval on the data block extreme value set to obtain the meteorological extreme value points in the grid point numerical matrix.

[0086] Specifically, the grid point numerical matrix is ​​divided into multiple data blocks, each containing a contiguous sub-matrix region. Multiple computation threads are simultaneously launched on a GPU parallel computing architecture (such as WebGL / WebGPU), each independently responsible for its corresponding data block. Within each data block, extremum searches are performed to locate local maxima and minima, i.e., local extrema. The local extremum results of all data blocks are integrated to form a data block extremum set, containing the local maximum and minimum values ​​of each data block along with their row and column indices in the matrix. Recursive extremum retrieval is performed on the data block extremum set, using multi-level reduction comparisons (such as tree-like recursion) to progressively filter out the global maximum and global minimum values, thus obtaining the meteorological extremum points in the grid point numerical matrix. Each meteorological extremum point contains its extremum value and its precise row and column position within the grid point numerical matrix.

[0087] The latitude and longitude coordinates of meteorological extreme points are paired and associated with meteorological element values ​​to generate extreme point data pairs;

[0088] Specifically, the latitude and longitude coordinates (longitude and latitude values) of meteorological extreme points are extracted from the latitude and longitude coordinate array corresponding to the grid point value matrix based on the row and column index; the latitude and longitude coordinates of the meteorological extreme points are paired and associated with the meteorological element values ​​to establish a one-to-one correspondence between the latitude and longitude coordinates and the meteorological element values, generating extreme point data pairs. Each extreme point data pair contains complete information about the corresponding meteorological extreme point: longitude value, latitude value, and meteorological element value;

[0089] The extreme point data pairs are structured and key-value pairs are transformed to generate an extreme point data list;

[0090] Each extreme point data pair is converted into a key-value pair data structure: using latitude and longitude coordinates as the key and meteorological element values ​​and related attributes (such as extreme value type) as the value. Through data format conversion, all key-value pairs are integrated into an ordered list structure. That is, the key (latitude and longitude coordinate combination) and value (meteorological element value and related attributes) of each extreme point data pair are encapsulated into a unified structured object, such as a JSON object. All structured objects are sorted based on the numerical order of the keys (such as longitude priority sorting), and all structured objects are stored sequentially in a list to form an extreme point data list. The extreme point data list is a standardized data structure that supports quick retrieval of meteorological element values ​​by latitude and longitude key, providing a directly callable data source for subsequent annotation symbol generation.

[0091] S5. Based on the latitude and longitude coordinates in the extreme point data list, overlay the meteorological heat map with the extreme point data list and draw extreme value symbols to generate annotation symbols.

[0092] Based on the latitude and longitude coordinates in the extreme point data list, the latitude and longitude coordinates are mapped to the corresponding pixel positions in the meteorological heat map to form a pixel coordinate set;

[0093] Specifically, the extreme point data list contains the latitude and longitude coordinates of meteorological extreme points, stored in the form of longitude and latitude values. The meteorological heat map is a heat image with geographic reference information. There is a mapping relationship between the pixel positions of the meteorological heat map and geographic coordinates. Through a coordinate transformation algorithm, each latitude and longitude coordinate in the extreme point data list is converted into the corresponding pixel position on the meteorological heat map. The transformation process is based on the projection parameters and spatial resolution of the meteorological heat map, calculating the pixel coordinates (x and y values) corresponding to each latitude and longitude coordinate to ensure accurate transformation and avoid positional deviation. A pixel coordinate set is generated, which contains the pixel position coordinates of all extreme points on the heat image, providing a positioning basis for symbol drawing.

[0094] The formula for calculating pixel coordinates is:

[0095] ;

[0096] ;

[0097] in, The x-coordinate of the pixel coordinates in the meteorological heat map. The vertical axis represents the pixel coordinates of a meteorological heat map. This represents the longitude coordinates of the meteorological extreme point. This represents the latitude coordinates of the meteorological extreme point. This indicates the minimum longitude covered by the meteorological heat map. This indicates the maximum longitude covered by the meteorological heat map. Indicates the width of the meteorological heat map. This indicates the smallest latitude covered by the meteorological heat map. This indicates the maximum latitude covered by the meteorological heat map. This indicates the altitude of the meteorological heat map.

[0098] Assign corresponding symbol style parameters to each meteorological extreme point to form a symbol description set;

[0099] Specifically, meteorological extreme points include extreme value types and meteorological element values. Symbol style parameters are assigned to meteorological extreme points based on the extreme value type and the magnitude of the meteorological element values. The symbol style parameters include shape, color, size, and transparency. For example, the maximum value point is assigned a red circular symbol, and the minimum value point is assigned a blue triangle symbol. The symbol size is dynamically adjusted according to the value. The assignment process ensures style consistency and visualization effect, and generates a symbol description set. The symbol description set contains a complete symbol definition for each extreme point, such as shape type, color value, size, and transparency level, providing a style for symbol drawing.

[0100] Based on the pixel coordinate set and symbol description set, symbol drawing is performed on the meteorological heat map to generate annotation symbols;

[0101] Using graphics drawing tools (such as the graphics programming interface provided by WebGL or Canvas2D), symbols are drawn at specified pixel positions on the meteorological heat map. For each extreme point, the drawing center point is obtained based on the pixel coordinate set, and the style parameters are obtained based on the symbol description set. The graphics API is called to draw the corresponding symbol, such as a circle or triangle, and the color is filled and the transparency is set. The drawing process uses an alpha blending method to ensure that the symbols blend with the heat map background, preserve the original image data, and generate annotation symbols. The annotation symbols are visual markers that, combined with the heat map, retain the geographic coordinate correlation and support user interactive operation.

[0102] S6. Store and index meteorological grid data at different time points, along with corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols, to form a meteorological grid data cache pool. Monitor user interaction behavior through a user event listening mechanism, extract meteorological heat maps, extreme point data lists, and annotation symbols at different time points from the meteorological grid data cache pool according to the interaction type in the interaction behavior, and perform interactive display of meteorological data.

[0103] Specifically, meteorological grid data generated at different time points, along with corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols, are stored by establishing a mapping relationship based on timestamps. The storage process includes data serialization and index construction: the meteorological grid data, standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols for each time point are converted into standardized data blocks (such as binary streams or JSON strings), and a fast retrieval index is established using timestamps as primary keys. The index structure adopts B+ trees or hash mapping to ensure low complexity of timestamp-based queries, forming a meteorological grid data cache pool containing a complete spatiotemporal data chain, supporting millisecond-level data extraction.

[0104] Furthermore, the user event monitoring mechanism captures user interaction behavior in real time through the user event monitoring interface of the meteorological heat map. Interaction behavior includes time selection operations (such as dragging a slider) and label click operations. The monitoring process parses interaction events: extracting the target timestamp parameter from the time selection operation or the symbol identifier from the label click event, and executing branch responses according to the interaction type. Branch responses include time selection responses and label click responses. The time selection response extracts the meteorological heat map, extreme point data list, and label symbols for the corresponding time point from the meteorological grid data cache pool according to the target timestamp, and replaces the currently displayed content as a whole, achieving time-series switching. The label click response extracts the meteorological heat map, extreme point data list, and label symbols for the currently displayed extreme point... Detailed meteorological parameters can be retrieved by symbol identifier in the data list and displayed in a pop-up floating window. By constructing a meteorological grid data cache pool and integrating efficient indexes, rapid storage and retrieval of meteorological data at multiple time points can be achieved, significantly improving data access efficiency. Combined with user event monitoring and branch response logic, user interaction operations (such as time selection or annotation clicks) can be captured and processed in real time, and corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols can be instantly extracted from the cache pool to complete the visualization update. This ensures the smoothness and real-time nature of meteorological data interaction display, effectively supporting users' seamless exploration and analysis of historical and real-time meteorological data, and enhancing overall responsiveness and user experience.

[0105] This embodiment also provides an interactive meteorological data display system, including:

[0106] The data module is used to acquire raw meteorological data, perform preprocessing, and then perform gridding on the preprocessed raw meteorological data to generate meteorological grid data.

[0107] The integration module is used to perform binary decoding on meteorological grid data to obtain raw data blocks, extract meteorological elements from the raw data blocks to generate a grid point numerical matrix; at the same time, it performs metadata parsing on the meteorological grid data to obtain the geographical location and timestamp corresponding to the meteorological grid data; and integrates the grid point numerical matrix, geographical location and timestamp to output standard meteorological data.

[0108] The image module is used to perform color scalar mapping on standard meteorological data according to a preset color mapping relationship, and to perform parallel interpolation and rendering on the standard meteorological data to generate a meteorological heat map.

[0109] The list module is used to perform a global scan of the grid point numerical matrix using a parallel reduction algorithm to obtain meteorological extreme points and record the latitude and longitude coordinates and meteorological element values ​​of the meteorological extreme points; the latitude and longitude coordinates and meteorological element values ​​are integrated and compiled to generate an extreme point data list;

[0110] The annotation module is used to overlay meteorological heat maps and extreme value data lists based on latitude and longitude coordinates in the extreme value data list, and draw extreme value symbols to generate annotation symbols;

[0111] The interactive module stores and indexes meteorological grid data at different time points, along with corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols, forming a meteorological grid data cache pool. It monitors user interaction behavior through a user event listening mechanism, extracts meteorological heat maps, extreme point data lists, and annotation symbols from the meteorological grid data cache pool according to the interaction type, and then displays the meteorological data interactively.

[0112] This embodiment also provides a computer device suitable for the interactive display method of meteorological data, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the interactive display method of meteorological data as proposed in the above embodiment.

[0113] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0114] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the interactive meteorological data display method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0115] In summary, this invention achieves real-time, accurate positioning and interactive dynamic display of meteorological extreme points through a dual-mechanism approach: global scanning of the grid point numerical matrix and the construction of a meteorological grid data cache pool. By dividing the grid point numerical matrix into multiple data blocks and performing extreme value searches in parallel, and recursively integrating the extreme value sets of the data blocks to obtain meteorological extreme points, the efficiency of extreme point detection is significantly improved. The cache pool stores and indexes meteorological grid data from different time points, along with corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and annotation symbols. Furthermore, a user event monitoring mechanism is used to capture interactive behaviors in real time, enabling instantaneous dynamic extraction and visualization of meteorological data. This provides low-latency, high-precision interactive decision support for meteorological analysis.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for interactive display of meteorological data, characterized by: The application relates to a method for generating a weather heat map, and belongs to the technical field of weather data processing. The method comprises the following steps: acquiring original weather data and preprocessing the original weather data, carrying out grid processing on the preprocessed original weather data, and generating weather grid data; carrying out binary decoding on the weather grid data to obtain an original data block, carrying out weather element extraction on the original data block, and generating a grid point numerical matrix; meanwhile, carrying out meta information analysis on the weather grid data to obtain geographical positions and time stamps corresponding to the weather grid data; integrating the grid point numerical matrix, the geographical positions and the time stamps, and outputting standard weather data; carrying out color scalar mapping on the standard weather data according to a preset color mapping relationship, and carrying out parallel interpolation and rendering on the standard weather data to generate a weather heat map; carrying out global scanning on the grid point numerical matrix through a parallel reduction algorithm to obtain weather extreme points, and recording the longitude and latitude coordinates and weather element values of the weather extreme points; integrating and compiling the longitude and latitude coordinates and the weather element values to generate an extreme point data list; according to the longitude and latitude coordinates in the extreme point data list, carrying out information superposition and extreme symbol drawing on the weather heat map and the extreme point data list to generate a marked symbol; storing and indexing the weather grid data at different time points and corresponding standard weather data, weather heat maps, extreme point data lists and marked symbols to form a weather grid data cache pool; 2. The weather data interactive display method of claim 1, wherein: listening to the interactive behavior of a user through a user event listening mechanism, extracting the weather heat maps, extreme point data lists and marked symbols at different time points from the weather grid data cache pool according to the interactive type in the interactive behavior, and carrying out interactive display of weather data. The method for generating weather grid data comprises the following steps: carrying out grid point division on the preprocessed original weather data to generate a grid point numerical distribution; filling in missing values in the grid point numerical distribution through a spatial interpolation algorithm to obtain complete grid data; 3. The weather data interactive display method of claim 1, wherein: carrying out data format standardization processing on the complete grid data to generate weather grid data. The method for carrying out binary decoding on the weather grid data to obtain an original data block and carrying out weather element extraction on the original data block to generate a grid point numerical matrix comprises the following steps: carrying out binary format decoding on the weather grid data to obtain a compressed data byte stream; carrying out decompression processing on the compressed data byte stream to obtain an original data block; carrying out field analysis and data extraction on each weather element in the original data block to generate an element set; 4. The weather data interactive display method of claim 1, wherein: carrying out matrix structural reorganization on the element set according to the longitude and latitude coordinates of the weather grid data to generate a grid point numerical matrix. The method for carrying out meta information analysis on the weather grid data to obtain geographical positions and time stamps corresponding to the weather grid data comprises the following steps: reading file header information of the weather grid data through a metadata parser, carrying out structured decoding on the file header information, and obtaining an original metadata block containing geographical parameters and time identifiers; carrying out geographical parameter analysis on the original metadata block to obtain the geographical positions of each weather element; 5. The weather data interactive display method of claim 1, wherein: carrying out time identifier extraction on the original metadata block to obtain the time stamps of each weather element. The method for generating a weather heat map comprises the following steps: carrying out scalar-color conversion on the standard weather data according to a preset color mapping relationship to generate a raster data array; Parallel bilinear interpolation is performed on the grid data array to generate a pixel color array, and the pixel color array is rasterized and rendered to output a color-filled image; The color-filled image is spatially registered and overlaid with the geographical locations in the standard meteorological data to generate a meteorological heat map.

6. The weather data interactive display method of claim 1, wherein: The global scanning of the grid point numerical matrix by the parallel reduction algorithm refers to dividing the grid point numerical matrix into multiple data blocks, simultaneously performing extreme value searching on the multiple data blocks, integrating the results of the extreme value searching to form a data block extreme value set, and performing recursive extreme value retrieval on the data block extreme value set to obtain the meteorological extreme value points in the grid point numerical matrix.

7. The weather data interactive display method of claim 1, wherein: The integration and assembly of the latitude and longitude coordinates and the meteorological element numerical values to generate an extreme point data list includes the following specific steps: The latitude and longitude coordinates and the meteorological element numerical values of the meteorological extreme value points are paired and associated to generate an extreme point data pair; The extreme point data pair is structured and data-assembled and key-value pair converted to generate an extreme point data list.

8. The weather data interactive display method of claim 1, wherein: The generation of the labeled symbol includes the following specific steps: The latitude and longitude coordinates in the extreme point data list are mapped to the corresponding pixel positions of the meteorological heat map to form a pixel coordinate set; Each meteorological extreme value point is assigned a corresponding symbol style parameter to form a symbol description set; Based on the pixel coordinate set and the symbol description set, symbol drawing is performed on the meteorological heat map to generate a labeled symbol.

9. The weather data interactive display method of claim 1, wherein: The user interaction behavior is monitored through a user event listening mechanism, which captures user clicks on the meteorological grid data in time and the labeled symbol through a user event listening interface, and analyzes the operation type and parameter information contained in the clicks.

10. A weather data interactive display system based on the weather data interactive display method according to any one of claims 1 to 9, characterized by: It includes, A data module is configured to obtain and preprocess original meteorological data, and perform grid processing on the preprocessed original meteorological data to generate meteorological grid data; An integration module is configured to decode the meteorological grid data to obtain original data blocks, extract meteorological elements from the original data blocks to generate a grid point numerical matrix, and analyze meta-information of the meteorological grid data to obtain geographical locations and time stamps corresponding to the meteorological grid data; The grid point numerical matrix, geographical locations, and time stamps are integrated to output standard meteorological data; An image module is configured to perform color scalar mapping on the standard meteorological data according to a preset color mapping relationship, and perform parallel interpolation and rendering on the standard meteorological data to generate a meteorological heat map; A list module is configured to perform global scanning on the grid point numerical matrix by a parallel reduction algorithm to obtain meteorological extreme value points, and record latitude and longitude coordinates and meteorological element numerical values of the meteorological extreme value points, and integrate and assemble the latitude and longitude coordinates and the meteorological element numerical values to generate an extreme point data list; A labeling module is configured to perform information overlaying and extreme symbol drawing on the meteorological heat map and the extreme point data list according to the latitude and longitude coordinates in the extreme point data list to generate a labeled symbol; An interactive module is configured to store and index meteorological grid data at different time points, corresponding standard meteorological data, meteorological heat maps, extreme point data lists, and labeled symbols to form a meteorological grid data cache pool. The user's interactive behavior is monitored through a user event monitoring mechanism, and meteorological thermal maps, extreme point data lists and marked symbols at different time points are extracted from a meteorological grid data cache pool according to the interactive type in the interactive behavior, and meteorological data is interactively displayed.

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