Weather forecast visualization method and device based on multi-mode comparison and linkage analysis

Abstract

The invention discloses a weather forecast visualization method and device based on multi-mode comparison and linkage analysis, and the method comprises the steps: determining a plurality of weather forecast modes which are selected by a user and need to be compared in response to a selection instruction, and reading the forecast data of each weather forecast mode; according to the forecast data, generating and displaying a multi-mode comparison view; performing linkage analysis on the multi-mode comparison view to obtain a meteorological chart linkage group needing linkage; in response to an operation instruction of a user for a target weather chart in the weather chart linkage group, a current operation event is captured, and the operation event carries view state change parameters; and carrying out weather forecast visualization on the weather chart linkage group according to the view state change parameters to obtain a displayed weather forecast visualization result. According to the invention, the working efficiency is improved, and a predictor can carry out weather forecast analysis and adjustment more efficiently. Meanwhile, the risk of inaccurate analysis results caused by manual misoperation is reduced, and the reliability of the analysis results is improved.

Description

Technical Field

[0001] This application relates to the field of smart weather forecasting technology, and in particular to a weather forecasting visualization method and device based on multi-model comparison and linkage analysis. Background Technology

[0002] In the field of meteorological forecasting, forecasters need to integrate forecast data from multiple meteorological forecasting models to make more accurate weather predictions. For example, in typhoon warnings, forecasters need to compare various forecast data to accurately identify the typhoon's path, intensity, and the areas it may affect.

[0003] Currently, weather forecasting relies on multiple weather forecasting models. In this model, forecasters need to view and compare the forecast data from these models on multiple different interfaces. For example, they use specialized mapping software to view sea level pressure fields and precipitation forecast maps from different models.

[0004] However, this method makes it difficult to intuitively and efficiently compare and adjust forecast data from different models within a single interface, forcing forecasters to frequently switch between multiple software programs or interfaces, increasing operational complexity and time costs. Secondly, forecasters need to manually retrieve and compare different forecast data, which is not only inefficient but also prone to human error affecting the accuracy of the analysis results. Summary of the Invention

[0005] This application provides a weather forecast visualization method and apparatus based on multi-mode comparison and linkage analysis. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0006] In a first aspect, embodiments of this application provide a weather forecast visualization method based on multi-model comparison and linkage analysis, the method comprising:

[0007] In response to the user's selection command for the list of weather forecast visualization modes, the system determines the multiple weather forecast modes to be compared selected by the user and reads the forecast data for each weather forecast mode; the list of weather forecast visualization modes is used to display all available weather forecast mode options. Based on the forecast data from each weather forecast model, a multi-model comparison view is generated and displayed; the multi-model comparison view is used to simultaneously display the forecast data from multiple weather forecast models. Perform linkage analysis on multi-mode comparison views to obtain the meteorological map linkage group that needs to be linked; In response to user commands to operate on a target weather map in a linked weather map group, capture the current operation event, which carries view state change parameters. Based on the parameters of view status change, the meteorological map linkage group is used to visualize the meteorological forecast, and the displayed meteorological forecast visualization results are obtained.

[0008] Secondly, embodiments of this application provide a weather forecast visualization device based on multi-mode comparison and linkage analysis, the device comprising: The weather forecast model determination module is used to respond to the user's selection command for the weather forecast visualization model list, determine the multiple weather forecast models to be compared selected by the user, and read the forecast data of each weather forecast model; the weather forecast visualization model list is used to display all available weather forecast model options. The multi-model comparison view display module is used to generate and display a multi-model comparison view based on the forecast data of each weather forecast model; the multi-model comparison view is used to display the forecast data of multiple weather forecast models simultaneously. The linkage analysis module is used to perform linkage analysis on multi-mode comparison views to obtain the linkage group of meteorological maps that need to be linked. The operation event capture module is used to respond to user operation commands for the target weather map in the weather map linkage group, capture the current operation event, and the operation event carries view state change parameters; The weather forecast visualization results display module is used to visualize weather forecasts for linked weather map groups based on the parameters of view status changes, and to display the weather forecast visualization results.

[0009] The technical solutions provided in this application embodiment may include the following beneficial effects: In this embodiment, on the one hand, by generating and displaying a multi-mode comparison view within a single interface, forecasters no longer need to frequently switch between multiple software programs or interfaces. All meteorological forecast model data that need to be compared can be intuitively displayed on one interface, greatly reducing operational complexity and saving time wasted due to interface switching, thereby improving work efficiency and enabling forecasters to perform meteorological forecast analysis and adjustments more efficiently. On the other hand, by reading the forecast data of the meteorological forecast model selected by the user and performing linked analysis based on the multi-mode comparison view, the system automatically obtains the linked meteorological map group that needs to be linked. When the user operates on the target meteorological map, the system can automatically capture the operation event and visualize the meteorological forecast for the linked meteorological map group according to the view state change parameters. This process reduces the workload of forecasters manually retrieving and comparing data, reduces the risk of inaccurate analysis results due to human error, and improves the reliability of the analysis results.

[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0011] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0012] Figure 1 This is a schematic diagram of the method flow of a weather forecast visualization method based on multi-mode comparison and linkage analysis provided in an embodiment of this application; Figure 2 This is a schematic diagram of an interactive component provided in an embodiment of this application; Figure 3 This is a schematic diagram showing the display result of a multi-mode comparison view provided in an embodiment of this application; Figure 4 This is a schematic flowchart illustrating the process of generating and displaying a multi-mode comparison view according to an embodiment of this application. Figure 5 This is a schematic block diagram of a linkage analysis process provided in an embodiment of this application; Figure 6 This is a schematic flowchart illustrating a process for displaying weather forecast visualization results, as provided in an embodiment of this application. Figure 7 This is a schematic diagram of the structure of a weather forecast visualization device based on multi-mode comparison and linkage analysis provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] The following description and accompanying drawings fully illustrate specific embodiments of this application to enable those skilled in the art to practice them.

[0014] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0015] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0016] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0017] Currently, weather forecasting relies on multiple weather forecasting models. In this model, forecasters need to view and compare the forecast data from these models on multiple different interfaces. For example, they use specialized mapping software to view sea level pressure fields and precipitation forecast maps from different models.

[0018] The inventors realized that this method made it difficult to intuitively and efficiently compare and adjust forecast data from different models within a single interface, forcing forecasters to frequently switch between multiple software programs or interfaces, increasing operational complexity and time costs. Secondly, forecasters needed to manually retrieve and compare different forecast data, which was not only inefficient but also prone to human error affecting the accuracy of the analysis results.

[0019] To address the existing technical problems, this application provides a weather forecast visualization method and apparatus based on multi-mode comparison and linkage analysis, thereby resolving the issues mentioned above. In this application's embodiments, on the one hand, by generating and displaying a multi-mode comparison view within a single interface, forecasters no longer need to frequently switch between multiple software programs or interfaces. All weather forecast model data requiring comparison can be intuitively displayed on a single interface, significantly reducing operational complexity and saving time wasted on interface switching, thus improving work efficiency and enabling forecasters to perform weather forecast analysis and adjustments more efficiently. On the other hand, by reading the forecast data of the weather forecast model selected by the user and performing linkage analysis based on the multi-mode comparison view, the system automatically obtains the linked weather map groups that need to be linked. When the user operates on the target weather map, the system can automatically capture the operation event and visualize the weather forecast for the linked weather map groups based on the view state change parameters. This process reduces the workload of forecasters manually retrieving and comparing data, lowers the risk of inaccurate analysis results due to human error, and improves the reliability of the analysis results. Exemplary embodiments are described in detail below.

[0020] The following will be combined with the appendix Figure 1 -Appendix Figure 6This application provides a detailed description of the weather forecast visualization method based on multi-model comparison and linkage analysis provided in its embodiments. This method can be implemented using a computer program and can run on a weather forecast visualization device based on the von Neumann architecture and multi-model comparison and linkage analysis. This computer program can be integrated into applications or run as a standalone tool application.

[0021] Please see Figure 1 This is a flowchart illustrating a weather forecast visualization method based on multi-mode comparison and linkage analysis, provided in this application embodiment. Figure 1 As shown, the method in this application embodiment includes the following steps: S101, in response to the user's selection instruction for the list of weather forecast visualization modes, determines the multiple weather forecast modes to be compared selected by the user, and reads the forecast data of each weather forecast mode; the list of weather forecast visualization modes is used to display all available weather forecast mode options; Users are the operators of this weather forecast visualization system, such as weather forecasters, weather analysts, and researchers. Weather forecast models are numerical computational models used to simulate and predict future atmospheric conditions. Different models differ in their physical process parameterization, mathematical methods, initial data, and spatial resolution. Examples include global models ECMWF-IFS (European Centre for Medium-Range Weather Forecasts-IFS), GFS (Global Forecast System), and regional models WRF and GRAPES. Selection commands are actions taken by users, such as clicking with a mouse, clicking on a touchscreen, or pressing Enter on the keyboard, to check one or more options in the "Weather Forecast Visualization Model List." Forecast data is a structured result file output by the weather forecast model after calculation. This file contains predicted values ​​of various meteorological elements at multiple forecast times and on a three-dimensional spatial grid.

[0022] In some embodiments of this application, when a user logs into the system and the system loads a page, it obtains metadata about all available weather forecast models from a configuration file or by requesting from the backend. Based on this metadata, it renders interactive components, such as... Figure 2 As shown, when the user clicks to check or decheck a mode, the front-end framework triggers an onChange event to obtain the unique identifiers of all currently selected modes. For each mode identifier, it finds the corresponding forecast data file path and reads these data files from the meteorological data server and object storage using the forecast data file path.

[0023] In one possible implementation, Xiaoming opens the system and sees a "Weather Forecast Model Selection" area on the left side of the interface. This area contains a multi-select list showing models such as the ECMWF European Centre model, the US GFS model, and the China GRAPES model. Xiaoming clicks the mouse to select the ECMWF global model and the US GFS model, and selects precipitation as the relevant meteorological element. The system's front-end pre-built JavaScript function detects the change in the checkbox state and encapsulates the selected values—the model identifiers of the ECMWF European Centre and the US GFS models, and sea level pressure—to obtain a command, such as "Please give me the ECMWF and GFS models for precipitation (variable=precipitation) in East Asia over the next 24 hours (forecast_hour=24)." The system then locates the corresponding file in the data warehouse, extracts the data for the "24-hour cumulative precipitation" variable, and obtains the forecast data for each weather forecast model.

[0024] S102, Generate and display a multi-model comparison view based on the forecast data of each weather forecast model; the multi-model comparison view is used to display the forecast data of multiple weather forecast models simultaneously; Among them, the multi-model comparison view is a visualization interface specifically designed for comparison. This interface organizes the results from different forecast models in the same screen area, allowing users to intuitively see the similarities and differences between different models in predicting meteorological elements for the same region and time point, thereby assessing the uncertainty or reliability of the forecast.

[0025] For example, when a user selects the ECMWF European Centre, the US GFS model, the Chinese GRAPES model, and the Japanese (JMA) model, and selects precipitation, the multi-model comparison view will display the following results: Figure 3 As shown.

[0026] In some embodiments of this application, the specific process of generating and displaying a multi-model comparison view based on the forecast data of each weather forecast model includes: calculating the optimal grid layout of the current display interface based on the number of multiple weather forecast models, and generating grid cells; preprocessing the forecast data of each weather forecast model to generate a weather forecast map for each weather forecast model; rendering the weather forecast map of each weather forecast model into the grid cells to generate and display the multi-model comparison view.

[0027] Specifically, based on the number of multiple weather forecast models, the optimal grid layout for the current display interface is calculated. The specific process for generating grid cells includes: when the number of multiple weather forecast models is 1, a 1x1 grid layout is used; or when the number of models is greater than 1 and less than or equal to 4, a 2x2 grid layout is used; or when the number of models is greater than 4, a square grid layout is calculated, where the product of the number of rows and columns in the grid layout is greater than or equal to the number of models and the difference between the number of rows and columns in the grid layout is minimized; the number of grid rows and columns in the grid layout is obtained; based on the number of grid rows and columns, the available width and height of the current display interface, and the preset cell spacing, the standard width and standard height of each grid cell are calculated; and based on the standard width and standard height of each grid cell, grid cells are defined in the current display interface.

[0028] Grid layout is a UI layout method that divides a parent container into several equally sized rectangular grids (i.e., grid cells), and then places child elements into these cells. Grid layout is used to organize multiple weather maps on the screen. A 1x1 grid layout is a 1x1 grid, essentially a single view occupying the entire area. When only one mode is selected, no comparison is needed, so a large view can be displayed full screen. A 2x2 grid layout is a 2x2 grid, with a total of 4 cells. It is suitable for displaying 2 to 4 modes simultaneously. A square-shaped grid layout refers to a grid where the number of rows and columns are as close as possible, forming an approximately square rectangular array. For example, for 5 modes, 2x3 (6 cells) is more "square" than 1x5 (5 cells). The number of grid rows R and the number of grid columns C are the two dimensions of a grid layout. R is the number of rows, and C is the number of columns. The total number of cells is R × C. The available width W and available height H of the currently displayed interface are the sizes of the area that can be used to display the weather map after excluding non-content areas such as the browser border, menu bar, and system taskbar. The preset cell spacing is the blank distance between two adjacent grid cells.

[0029] It should be noted that because each grid cell must be complete, it cannot be drawn as half. If there are 5 patterns, a grid with 2.5 columns cannot be created. Therefore, a layout that can generate at least 5 cells must be selected, such as 2 x 3 = 6 cells. Thus, the product of the number of rows and columns of the grid layout must be greater than or equal to the number of patterns.

[0030] The standard width of each grid cell and standard height The calculation expression is:

[0031]

[0032] in, This is the available width of the currently displayed interface. It is the number of columns in the grid layout. It is the preset cell spacing. This is the available height of the currently displayed interface. It represents the number of rows in the grid layout.

[0033] In one possible implementation, consider a browser window with a usable width (W) of 1600px, a usable height (H) of 1000px, and a preset cell spacing (P) of 10px. The number of patterns is 3. Since the number of patterns is greater than 1 and less than or equal to 4, a 2x2 layout is used. The calculation process is: R=2, C=2. =(1600-(2+1)×10) / 2=(1600-30) / 2=785px; =(1000-(2+1)×10) / 2=(1000-30) / 2=485px; Ultimately, the system creates a 2x2 grid. Although there are only 3 charts, there are 4 cells. The fourth cell may be empty or filled with a message. Each weather chart is 785x485 pixels in size.

[0034] Specifically, the process of preprocessing the forecast data for each weather forecasting model and generating the weather forecast map for each model includes: parsing the forecast data for each weather forecasting model to extract meteorological element field data, latitude and longitude coordinate information, and forecast time information, as a structured meteorological dataset; determining the meteorological element type and target forecast time selected by the user in real time through the visualization interface; filtering out two-dimensional grid data that matches the meteorological element type and target forecast time from the structured meteorological dataset; removing invalid values ​​from the two-dimensional grid data and filling in missing values, and converting the filled two-dimensional grid data to a preset standard latitude and longitude grid using spatial interpolation methods to obtain standardized grid data with unified spatial resolution and coordinate reference system; rendering the standardized grid data to generate visual graphic elements used to represent the spatial distribution of meteorological elements; overlaying the visual graphic elements onto a preloaded geographic base map layer to form a combined layer, and adding graphic embellishment elements to the combined layer to obtain the weather forecast map for each weather forecasting model.

[0035] The graphic embellishment elements include at least one of the following: the name of each weather forecast model, the target forecast time, the legend, the color scale, and the scale bar.

[0036] The meteorological element field data is a collection of all values ​​for a specific meteorological element (such as temperature, humidity, and wind speed) within the entire forecast area. Each number represents the predicted value of that element in a small grid on the map. Latitude and longitude coordinates are used to describe the grid on the Earth's surface, specifically representing the location on Earth corresponding to each value in the meteorological element field data. Forecast time information indicates the future point in time or time period that the forecast data corresponds to. Meteorological element type indicates the specific weather phenomenon the user wants to view, such as "2-meter temperature," "precipitation," and "wind direction and speed." Visualization elements are the process of converting numerical data into visual symbols. For example, temperature data can be converted into color blocks of different colors, or wind speed data into arrows. The geographic base map layer is the basic map serving as the background, containing geographic information such as coastlines, national borders, rivers, and cities.

[0037] In one possible implementation, the system first opens the original meteorological data file, extracts various information contained within, such as the values ​​of various meteorological elements, the corresponding latitude and longitude coordinates for each value, and the forecast time, and organizes this information into a structured dataset. The user makes a selection on the interface, such as checking "24-hour cumulative precipitation." The system determines the user's current needs: the element type is "precipitation," and the time is "the next 24 hours." Based on the user's current needs, the system finds a set of "precipitation" data for the "next 24 hours" from the structured dataset, obtaining two-dimensional grid data. The system marks erroneous data in the two-dimensional grid data, then uses the average of surrounding good data or other statistical methods to estimate and fill in these gaps and errors, completing the data cleaning process. It then converts the data to a preset standard latitude and longitude grid using spatial interpolation methods, obtaining standardized grid data with a unified spatial resolution and coordinate reference system. Based on the value of each grid point, the system converts it into visual elements according to preset rules. For example, it specifies that "0-10mm is light blue, 10-25mm is medium blue..." The rendered color precipitation map is overlaid on the already loaded geographic base map to form a complete map. To make the map understandable to users, graphic embellishments need to be added in appropriate locations, such as a legend in the lower right corner, a title at the bottom "24-hour cumulative precipitation forecast," and a scale bar in the lower left corner. This results in the weather forecast map for each weather forecast mode.

[0038] For example Figure 4 As shown, Figure 4It is a process schematic block diagram of the process of generating and displaying a multi-mode comparison view provided by this application. The system receives the forecast data of multiple weather forecast models and determines the total number N of models. If N = 1, there is only one model at this time, and no comparison is needed, and a full-screen 1x1 grid layout is adopted. If 1 < N ≤ 4, the number of models is small, and a 2x2 grid layout is adopted, which is regular and has high space utilization. If N > 4 and the number of models is large, it is necessary to calculate a grid layout that is as square as possible. At this time, the number of rows x the number of columns ≥ N, and the difference between the number of rows and the number of columns is the smallest. According to the above decision, the specific grid row number (R) and column number (C) are determined. Combining the available width (W) and height (H) of the current screen, and the preset cell spacing (P), the standard width and height of each grid cell are calculated. On the web page or application interface, according to the calculated R, C and cell sizes, the corresponding number of equal-sized blank containers (grid cells) are dynamically created. The system reads the original forecast data files of each model, extracts the core meteorological element field data, longitude and latitude coordinate information, and forecast time information from them, and forms a structured data set. Obtain the meteorological element types (such as: temperature, precipitation, wind field) and target forecast time (such as: next 24 hours) selected by the user in real time through the interface. According to the user's selection, the two-dimensional grid data corresponding to the elements and time are accurately located and extracted from the structured data set. The two-dimensional grid data is cleaned and transformed to be standardized into standardized grid data. The standardized grid data is used to generate visual graphic elements representing the spatial distribution of meteorological elements by means of coloring, drawing contour lines, drawing arrows (wind field), etc. The generated graphic elements (meteorological data layer) are superimposed on the pre-loaded geographic base map layer (administrative divisions, coastline, rivers, etc.) to form a combined layer, so that the meteorological information has a geographical reference. Necessary graphic decoration elements, such as model names, forecast times, legends, color scales, scales, etc. are added to the map, so as to obtain a single meteorological forecast map with complete information and direct comprehensibility. Finally, the single meteorological forecast maps are placed one by one into the corresponding created grid cells to generate and display the final multi-mode comparison view. At this time, the user can intuitively compare the forecast differences of different models for the same element and the same time on one interface.

[0039] S103. Perform linkage analysis on the multi-mode comparison view to obtain a group of meteorological maps to be linked; In some embodiments of this application, the specific process of performing linkage analysis on multi-mode comparison views to obtain the meteorological map linkage group that needs to be linked includes: acquiring metadata, image status, and user interaction history associated with the target meteorological forecast map; the target meteorological forecast map is each meteorological forecast map in the multi-mode comparison view; extracting multi-dimensional linkage analysis features from the metadata, image status, and user interaction history to obtain the feature vector of the target meteorological forecast map; calculating the linkage similarity score between any two meteorological forecast maps in the multi-mode comparison view using the feature vector of the target meteorological forecast map; merging the linkage similarity scores between any two meteorological forecast maps to obtain the linkage similarity score matrix; and identifying multiple map clusters with dense linkage similarity based on the linkage similarity score matrix to obtain the meteorological map linkage group that needs to be linked.

[0040] The target weather forecast map is an independent weather forecast map within the multi-model comparison view, serving as the basic unit for linkage analysis. Metadata describes the attributes of the weather map itself, including weather forecast model identifiers (e.g., ECMWF, GFS), meteorological element types (e.g., temperature, precipitation), forecast time, and spatial extent. Image status indicates the current display state of the weather map, including map projection type, display range (latitude and longitude boundaries), zoom level, and center point coordinates. User interaction history records user actions on the weather map, such as panning, zooming, and selection time, as well as the frequency of simultaneous selection or comparison operations between different maps. Linkage analysis features are key indicators extracted from metadata, image status, and user interaction history, used to measure the similarity between weather maps. Feature vectors are vectors composed of numericalized multi-dimensional linkage analysis features, used to quantify the characteristics of a weather map. The linkage similarity score matrix is ​​a matrix composed of pairwise linkage similarity scores between all weather maps, used for overall analysis of the relationships between maps. Map clusters are groups of weather maps with dense linkage similarity scores in the similarity matrix, indicating high feature similarity.

[0041] In one possible implementation, weather forecaster Xiao Wang is analyzing a heavy precipitation event. He has selected precipitation forecast maps from four models (ECMWF, GFS, WRF, and CMA) and set the same forecast time (next 24 hours) and display area (North China). The system acquires metadata (model identifier, feature type, forecast time), image status (same display area), and user interaction history (Xiao Wang frequently operates these four maps simultaneously) for each map. A feature vector is generated for each map, containing dimensions such as model identifier, feature type, display area, forecast time, and historical co-occurrence frequency. The linkage similarity score between any two maps is calculated. Since these four maps are highly similar in terms of model identifier, feature type, display area, and forecast time, and have a high historical co-occurrence frequency, the similarity scores between each pair are very high. A 4x4 linkage similarity score matrix is ​​formed, where all elements are close to 1, indicating high similarity between the four maps. Through cluster analysis, a map cluster containing all four maps is identified as the meteorological map linkage group that needs to be linked.

[0042] The feature vector of the target weather forecast map includes weather forecast model identifier, weather element type, current display spatial range, current forecast time, and frequency of simultaneous selection or comparison with other weather forecast maps in the view within a preset historical period.

[0043] Specifically, the process of calculating the linkage similarity score between any two weather forecast maps in the multi-model comparison view using the feature vector of the target weather forecast map includes: calculating the feature matching degree or distance in the dimensions of weather forecast model identifier, meteorological element type, display spatial range overlap, forecast time, and historical co-occurrence frequency, based on the weather forecast model identifier, meteorological element type, current display spatial range, current forecast time, and frequency; and performing weighted fusion based on the feature matching degree or distance and preset weights to generate the linkage similarity score between any two weather forecast maps in the multi-model comparison view.

[0044] Using the embodiments of this application, when Xiao Wang performs operations such as panning, zooming, or switching time on any one of the maps, the system will automatically apply the same operation to the other three maps, realizing synchronous comparative analysis of multi-mode forecasts and greatly improving work efficiency.

[0045] For example Figure 5 As shown, Figure 5It is a process schematic block diagram of a linkage analysis process provided by this application. In linkage analysis, the system first obtains the association information of each meteorological map, including metadata, image status, and user interaction history. The metadata includes data sources (such as ECMWF, GFS models), element types (temperature, precipitation, etc.), and forecast time. The image status includes the geographical range, zoom level, and map projection of the current view. The user interaction history is the frequency of this map and other maps being selected or compared by the user simultaneously in past usage. From the vast amount of association information, 5 most crucial dimensions for measuring the linkage relationship are refined. The dimensions include model, element, range, time, and frequency. Model: Whether the forecast models are the same. Element: Whether the meteorological elements shown (such as temperature, wind) are the same. Range: Whether the currently displayed geographical areas overlap. Time: Whether the forecast time points are consistent. Frequency: The number of times used together in history. Since the importance of different dimensions for "whether linkage should be performed" is different (for example, "display range" is usually more important than "forecast model"), the system assigns a preset weight to the matching degree of each dimension, and then performs a weighted average calculation to finally obtain a single, comprehensive linkage similarity score (usually a value between 0 and 1). The higher this score, the more similar the two maps are and the more they should be linked. The linkage similarity scores between all meteorological maps in the view are filled into an NxN table (matrix), where N is the total number of maps. The system uses a clustering algorithm to analyze the similarity matrix obtained in the previous step. The algorithm automatically finds the "score-dense" areas in the matrix and groups the meteorological maps with high similarity scores and close connections into the same set as the meteorological map linkage group, and outputs the meteorological map linkage group.

[0046] S104, in response to the operation instruction of the user for the target meteorological map in the meteorological map linkage group, capture the current operation event, and the operation event carries view state change parameters; Among them, the operation instruction is the specific interaction behavior of the user on the target meteorological map. The core types include: Pan / roam: Drag the map to change the displayed center position. Zoom: Scroll the mouse wheel or use the zoom tool to change the display level (scale) of the map. Switch forecast time: Slide the time axis or select the drop-down menu to change the displayed forecast period. Switch meteorological element: Select different element layers, such as switching from "precipitation" to "wind speed". When the user interacts on the interface (such as clicking, dragging), the browser or application framework will generate a data object representing this interaction, that is, an "event". It contains all the information about this operation. The key data embedded in the "operation event" quantitatively describes the change in the view state before and after the operation. This is the core basis for implementing linkage, mainly including: Spatial parameters: The new map center point coordinates (longitude, latitude), the new zoom level. Time parameter: The newly selected forecast time. Element parameter: The newly selected meteorological element type.

[0047] In some embodiments of this application, during system initialization, an event listener is bound to each weather map (typically a Canvas or map container div) within the linked group. These listeners continuously monitor user actions. When the user performs an action on the target weather map, the bound listener is triggered, and the browser generates a native Event object. Meaningful view state change parameters are extracted from the native Event object. The extracted new view state parameters are encapsulated into a custom, structured data object (e.g., a JSON object {type: 'pan', center: [lng,lat], zoom: 10}), thus obtaining the view state change parameters.

[0048] S105, based on the view status change parameters, perform weather forecast visualization on the meteorological map linkage group to obtain the displayed weather forecast visualization results.

[0049] In some embodiments of this application, the specific process of visualizing weather forecasts in a linked group of weather maps based on view state change parameters to obtain the displayed weather forecast visualization results includes: calculating the display parameters of other weather maps in the linked group besides the target weather map based on the view state change parameters; re-rendering the other weather maps based on their display parameters to ensure that their display range is consistent with the target weather map, thus obtaining the final weather map group; obtaining the target forecast time input by the user on the client; and visualizing the weather forecast based on the final weather map group and the target forecast time to obtain the displayed weather forecast visualization results.

[0050] Display parameters are specific configuration data that control how weather maps are displayed on the interface. These mainly include the coordinates of the map's center point, zoom level, and the geographical area displayed; these parameters determine the map view seen by the user. Weather forecast visualization is the process of presenting weather forecast data (such as temperature, precipitation, and wind fields) graphically. It typically includes steps such as data parsing, grid processing, color mapping, and graphic rendering, ultimately generating a weather map that the user can see.

[0051] Specifically, the process of calculating the display parameters of other meteorological maps in the meteorological map linkage group, excluding the target meteorological map, based on the view state change parameters includes: extracting the changed display range of the target meteorological map from the view state change parameters; obtaining the map projection type currently used by the first meteorological map; the first meteorological map being each of the other meteorological maps in the meteorological map linkage group, excluding the target meteorological map; determining whether the map projection type of the first meteorological map is consistent with the map projection type of the target meteorological map; if so, using the four boundary coordinate values ​​of the changed display range of the target meteorological map as the display parameters of the first meteorological map; if not, converting the four boundary coordinate values ​​of the display range from the map projection type of the target meteorological map to the map projection type of the first meteorological map, obtaining the four boundary coordinate values ​​of the converted display range, and using them as the display parameters of the first meteorological map.

[0052] The displayed area after the target weather map changes represents the boundary of the geographical region visible on the current screen after user interaction with the target weather map. This can be represented by four boundary coordinate values: west longitude, east longitude, south latitude, and north latitude. Map projection type is the mathematical transformation method used to represent the three-dimensional Earth's surface on a two-dimensional planar map. Projection types include: Mercator projection, Lambert projection, and polar projection.

[0053] In one possible implementation, a weather forecaster is comparing a storm forecast for the North Atlantic. The linked group contains two maps: Target Weather Map A is a GFS global model using an isotropic projection (geographic coordinates, undistorted). First Weather Map B is a HWRF regional high-resolution model using a Lambert conformal projection for more accurate representation of mid-latitude regions. The forecaster zooms in on Target Weather Map A, focusing the view on the storm's center area. The new display range is: 60°W, 30°N, 50°W, 40°N. The system extracts the new range [-60, 30, -50, 40] of Target Weather Map A and knows its map projection type is EPSG:4326. Simultaneously, it obtains the map projection type of First Weather Map B, which is Lambert Conformal Conic. EPSG:4326 is not equal to Lambert Conformal Conic; the projections are inconsistent, and the conversion process begins. The system uses the Proj4js library to transform the coordinates of the four corner points (-60, 30), (-50, 30), (-50, 40), and (-60, 40) of the target weather map A from EPSG:4326 to the Lambert Conformal Conic projection. After the transformation, these points become a new set of (x, y) planar coordinates, with values ​​in meters, and their shape may no longer be a standard rectangle due to projection distortion. The system finds the minimum / maximum values ​​of x and y in this new set of coordinates, assuming the result is [x_min, y_min, x_max, y_max]. This rectangle, on the Lambert projection plane, encloses a geographical area that is exactly the same as the area enclosed by [-60, 30, -50, 40] in the target weather map A. The system sets [x_min, y_min, x_max, y_max] as the new display parameters for the first weather map B using the Lambert projection.

[0054] Specifically, the process of visualizing weather forecasts based on the final meteorological map set and target forecast time to obtain the displayed weather forecast visualization results includes: parsing the corresponding weather forecast model identifier from the second meteorological map; the second meteorological map is each meteorological map in the final meteorological map set; obtaining the original structured meteorological dataset corresponding to the weather forecast model identifier; the original structured meteorological dataset contains meteorological element field data for multiple forecast times; selecting meteorological element field data that matches the target forecast time from the meteorological element field data as the target time data; rendering the target time data graphically according to the current display parameters of the second meteorological map to generate a weather forecast sub-map of the weather forecast model identifier at the target forecast time; arranging and combining all the generated weather forecast sub-maps according to the layout of the multi-model comparison view to generate an updated multi-model comparison view; and displaying the updated multi-model comparison view to obtain the displayed weather forecast visualization results.

[0055] For example Figure 6 As shown, Figure 6 This application provides a schematic flowchart of a meteorological forecast visualization process. The system captures view state change parameters from user operation events, traverses each first meteorological map in the linked meteorological map group except for the target meteorological map, extracts the changed display range of the target meteorological map from the view state change parameters, and obtains the map projection type currently used by the first meteorological map and the map projection type of the target meteorological map. It checks if the projection types are consistent; if so, it directly uses the target map's display range as the first map's display parameter; otherwise, it transforms the display range coordinates to the first map's projection coordinate system to obtain the transformed display range coordinates, and re-renders the first meteorological map to match the target map's display range, thus obtaining the final meteorological map group. When all first meteorological maps have been traversed, it obtains the target forecast time input by the user on the client side, and performs meteorological forecast visualization based on the final meteorological map group and the target forecast time, obtaining the displayed meteorological forecast visualization result.

[0056] In this embodiment, on the one hand, by generating and displaying a multi-mode comparison view within a single interface, forecasters no longer need to frequently switch between multiple software programs or interfaces. All meteorological forecast model data that need to be compared can be intuitively displayed on one interface, greatly reducing operational complexity and saving time wasted due to interface switching, thereby improving work efficiency and enabling forecasters to perform meteorological forecast analysis and adjustments more efficiently. On the other hand, by reading the forecast data of the meteorological forecast model selected by the user and performing linked analysis based on the multi-mode comparison view, the system automatically obtains the linked meteorological map group that needs to be linked. When the user operates on the target meteorological map, the system can automatically capture the operation event and visualize the meteorological forecast for the linked meteorological map group according to the view state change parameters. This process reduces the workload of forecasters manually retrieving and comparing data, reduces the risk of inaccurate analysis results due to human error, and improves the reliability of the analysis results.

[0057] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0058] Please see Figure 7 This illustration shows a schematic diagram of a weather forecast visualization device based on multi-mode comparison and linkage analysis, provided in an exemplary embodiment of this application. This weather forecast visualization device based on multi-mode comparison and linkage analysis can be implemented as all or part of an electronic device through software, hardware, or a combination of both. The device 1 includes a weather forecast model determination module 10, a multi-mode comparison view display module 20, a linkage analysis module 30, an operation event capture module 40, and a weather forecast visualization result display module 50.

[0059] The weather forecast model determination module 10 is used to respond to the user's selection instruction for the weather forecast visualization model list, determine the multiple weather forecast models to be compared selected by the user, and read the forecast data of each weather forecast model; the weather forecast visualization model list is used to display all available weather forecast model options. The multi-model comparison view display module 20 is used to generate and display a multi-model comparison view based on the forecast data of each weather forecast model; the multi-model comparison view is used to display the forecast data of multiple weather forecast models simultaneously. The linkage analysis module 30 is used to perform linkage analysis on multi-mode comparison views to obtain the linkage group of meteorological maps that need to be linked. The operation event capture module 40 is used to capture the current operation event in response to the user's operation command for the target meteorological map in the meteorological map linkage group. The operation event carries the view state change parameters. The weather forecast visualization result display module 50 is used to visualize the weather forecast based on the view status change parameters of the weather map linkage group and obtain the displayed weather forecast visualization results.

[0060] It should be noted that the meteorological forecast visualization device based on multi-model comparison and linkage analysis provided in the above embodiments is only illustrated by the division of the above functional modules when executing the meteorological forecast visualization method based on multi-model comparison and linkage analysis. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the meteorological forecast visualization device based on multi-model comparison and linkage analysis and the meteorological forecast visualization method embodiment based on multi-model comparison and linkage analysis provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be repeated here.

[0061] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0062] In this embodiment, on the one hand, by generating and displaying a multi-mode comparison view within a single interface, forecasters no longer need to frequently switch between multiple software programs or interfaces. All meteorological forecast model data that need to be compared can be intuitively displayed on one interface, greatly reducing operational complexity and saving time wasted due to interface switching, thereby improving work efficiency and enabling forecasters to perform meteorological forecast analysis and adjustments more efficiently. On the other hand, by reading the forecast data of the meteorological forecast model selected by the user and performing linked analysis based on the multi-mode comparison view, the system automatically obtains the linked meteorological map group that needs to be linked. When the user operates on the target meteorological map, the system can automatically capture the operation event and visualize the meteorological forecast for the linked meteorological map group according to the view state change parameters. This process reduces the workload of forecasters manually retrieving and comparing data, reduces the risk of inaccurate analysis results due to human error, and improves the reliability of the analysis results.

[0063] This application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the weather forecast visualization method based on multi-mode comparison and linkage analysis provided in the above-described method embodiments.

[0064] This application also provides a computer program product containing instructions that, when run on a computer, causes the computer to execute the weather forecast visualization method based on multi-mode comparison and linkage analysis of the above-described method embodiments.

[0065] Please see Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 8As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0066] The communication bus 1002 is used to realize the connection and communication between these components.

[0067] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0068] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0069] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the electronic device 1000 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, without being integrated into the processor 1001.

[0070] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage system located remotely from the aforementioned processor 1001. Figure 8 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a weather forecast visualization application based on multi-mode comparison and linkage analysis.

[0071] exist Figure 8 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 1001 can be used to call the weather forecast visualization application based on multi-mode comparison and linkage analysis stored in the memory 1005, and specifically perform the following operations: In response to the user's selection command for the list of weather forecast visualization modes, the system determines the multiple weather forecast modes to be compared selected by the user and reads the forecast data for each weather forecast mode; the list of weather forecast visualization modes is used to display all available weather forecast mode options. Based on the forecast data from each weather forecast model, a multi-model comparison view is generated and displayed; the multi-model comparison view is used to simultaneously display the forecast data from multiple weather forecast models. Perform linkage analysis on multi-mode comparison views to obtain the meteorological map linkage group that needs to be linked; In response to user commands to operate on a target weather map in a linked weather map group, capture the current operation event, which carries view state change parameters. Based on the parameters of view status change, the meteorological map linkage group is used to visualize the meteorological forecast, and the displayed meteorological forecast visualization results are obtained.

[0072] In one embodiment, when the processor 1001 generates and displays a multi-model comparison view based on forecast data from each weather forecast model, it specifically performs the following operations: Based on the number of multiple weather forecast models, calculate the optimal grid layout for the current display interface and generate grid cells; Preprocess the forecast data for each weather forecast model to generate a weather forecast map for each model; The weather forecast map for each weather forecast model is rendered into a grid cell to generate and display a multi-model comparison view.

[0073] In one embodiment, when the processor 1001 calculates the optimal grid layout for the current display interface based on the number of multiple weather forecast patterns and generates grid cells, it specifically performs the following operations: When the number of multiple weather forecast models is 1, a 1x1 grid layout is used; or when the number of models is greater than 1 and less than or equal to 4, a 2x2 grid layout is used; or when the number of models is greater than 4, a square grid layout is calculated, where the product of the number of rows and columns of the grid layout is greater than or equal to the number of models and the difference between the number of rows and columns of the grid layout is minimized. Get the number of grid rows and columns in the grid layout; Calculate the standard width and standard height of each grid cell based on the number of grid rows and columns, the available width and height of the currently displayed interface, and the preset cell spacing. Define grid cells in the current display interface based on the standard width and standard height of each grid cell.

[0074] In one embodiment, when the processor 1001 performs preprocessing of the forecast data for each weather forecast model and generates a weather forecast map for each weather forecast model, it specifically performs the following operations: The forecast data of each weather forecast model is analyzed to extract meteorological element field data, latitude and longitude coordinate information, and forecast time information, which are used as a structured meteorological dataset. Determine the type of meteorological element and target forecast time selected by the user in real time through the visual interface; Two-dimensional grid data matching meteorological element types and target forecast times are selected from structured meteorological datasets. Invalid values ​​are removed from the two-dimensional grid data, missing values ​​are filled in, and the filled two-dimensional grid data is converted to a preset standard latitude and longitude grid through spatial interpolation method to obtain standardized grid data with unified spatial resolution and coordinate reference system; Standardized grid data is rendered graphically to generate visual graphic elements that characterize the spatial distribution of meteorological elements; Visual graphic elements are overlaid on a preloaded geographic base map layer to form a combined layer, and graphic embellishment elements are added to the combined layer to obtain the weather forecast map for each weather forecast model. The graphic embellishment elements include at least one of the following: the name of each weather forecast model, the target forecast time, the legend, the color scale, and the scale bar.

[0075] In one embodiment, when processor 1001 performs a linkage analysis on multi-mode comparison views to obtain the meteorological map linkage group that needs to be linked, it specifically performs the following operations: Obtain metadata, image status, and user interaction history associated with the target weather forecast map; the target weather forecast map is each weather forecast map in the multi-model comparison view; Multi-dimensional linkage analysis features are extracted from metadata, image status, and user interaction history to obtain the feature vector of the target weather forecast map; Calculate the linkage similarity score between any two weather forecast maps in the multi-model comparison view using the feature vector of the target weather forecast map; The linkage similarity scores between any two weather forecast maps are merged to obtain a linkage similarity score matrix. Based on the linkage similarity score matrix, multiple map clusters with dense linkage similarity are identified, and the linkage groups of meteorological maps that need to be linked are obtained.

[0076] In one embodiment, when the processor 1001 calculates the linkage similarity score between any two weather forecast maps in the multi-model comparison view using the feature vector of the target weather forecast map, it specifically performs the following operations: The feature matching degree or distance is calculated in the dimensions of weather forecast model identifier, weather element type, current display spatial range, current forecast time, and frequency, in the dimensions of weather forecast model identifier, weather element type, display spatial range overlap, forecast time, and historical co-occurrence frequency. Based on feature matching degree or distance and preset weights, a weighted fusion is performed to generate a linkage similarity score between any two weather forecast maps in the multi-mode comparison view.

[0077] In one embodiment, when the processor 1001 performs weather forecast visualization on the meteorological map linkage group based on the view state change parameters and obtains the displayed weather forecast visualization result, it specifically performs the following operations: Based on the view status change parameters, calculate the display parameters of other meteorological maps in the meteorological map linkage group, excluding the target meteorological map; Based on the display parameters of other meteorological maps, re-render the other meteorological maps to ensure that the display range of the other meteorological maps is consistent with that of the target meteorological map, thus obtaining the final meteorological map set; Obtain the target forecast time input by the user on the client; Based on the final meteorological map set and target forecast time, the meteorological forecast is visualized to obtain the displayed meteorological forecast visualization results.

[0078] In one embodiment, when the processor 1001 calculates the display parameters of other meteorological maps in the meteorological map linkage group besides the target meteorological map based on the view state change parameters, it specifically performs the following operations: Extract the display range of the target weather map after the change from the view status change parameters; Get the map projection type currently used by the first meteorological map; the first meteorological map is every other meteorological map in the meteorological map linkage group except for the target meteorological map; Determine whether the map projection type of the first meteorological map is consistent with the map projection type of the target meteorological map; If so, use the coordinates of the four boundaries of the target weather map after the change in the display range as the display parameters of the first weather map; If not, the four boundary coordinates of the display range will be converted from the map projection type of the target meteorological map to the map projection type of the first meteorological map, and the converted four boundary coordinates of the display range will be used as the display parameters of the first meteorological map.

[0079] In one embodiment, when the processor 1001 performs weather forecast visualization based on the final weather map set and the target forecast time to obtain the displayed weather forecast visualization result, it specifically performs the following operations: The corresponding weather forecast model identifier is extracted from the second weather map; the second weather map is each weather map in the final weather map set. Obtain the original structured meteorological dataset corresponding to the weather forecast model identifier; the original structured meteorological dataset contains meteorological element field data for multiple forecast times; Meteorological element field data that matches the target forecast time are selected from the meteorological element field data and used as the target time data; Based on the current display parameters of the second meteorological map, the data at the target time is graphically rendered to generate a meteorological forecast sub-map with the meteorological forecast model identifier at the target forecast time. All generated weather forecast sub-maps are arranged and combined according to the layout of the multi-model comparison view to generate an updated multi-model comparison view. The updated multi-mode comparison view is displayed, providing a visual representation of the weather forecast.

[0080] In this embodiment, on the one hand, by generating and displaying a multi-mode comparison view within a single interface, forecasters no longer need to frequently switch between multiple software programs or interfaces. All meteorological forecast model data that need to be compared can be intuitively displayed on one interface, greatly reducing operational complexity and saving time wasted due to interface switching, thereby improving work efficiency and enabling forecasters to perform meteorological forecast analysis and adjustments more efficiently. On the other hand, by reading the forecast data of the meteorological forecast model selected by the user and performing linked analysis based on the multi-mode comparison view, the system automatically obtains the linked meteorological map group that needs to be linked. When the user operates on the target meteorological map, the system can automatically capture the operation event and visualize the meteorological forecast for the linked meteorological map group according to the view state change parameters. This process reduces the workload of forecasters manually retrieving and comparing data, reduces the risk of inaccurate analysis results due to human error, and improves the reliability of the analysis results.

[0081] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The weather forecast visualization program based on multi-mode comparison and linkage analysis can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the weather forecast visualization program based on multi-mode comparison and linkage analysis can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0082] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A weather forecast visualization method based on multi-model comparison and linkage analysis, characterized in that, The method includes: In response to the user's selection instruction for the list of weather forecast visualization modes, the system determines the multiple weather forecast modes to be compared selected by the user and reads the forecast data for each weather forecast mode; the list of weather forecast visualization modes is used to display all available weather forecast mode options. Based on the forecast data of each weather forecast model, a multi-model comparison view is generated and displayed; the multi-model comparison view is used to simultaneously display the forecast data of multiple weather forecast models. Perform a linkage analysis on the multi-mode comparison view to obtain the meteorological map linkage group that needs to be linked. In response to the user's operation command for the target weather map in the weather map linkage group, the current operation event is captured, and the operation event carries view state change parameters; Based on the view state change parameters, the meteorological map linkage group is used to visualize the meteorological forecast, and the displayed meteorological forecast visualization results are obtained.

2. The method according to claim 1, characterized in that, The step of generating and displaying a multi-model comparison view based on the forecast data of each weather forecast model includes: Based on the number of weather forecast models, calculate the optimal grid layout for the current display interface and generate grid cells. Preprocess the forecast data of each weather forecast model to generate a weather forecast map for each weather forecast model; The weather forecast map of each weather forecast model is rendered into the grid cell to generate and display a multi-model comparison view.

3. The method according to claim 2, characterized in that, The step of calculating the optimal grid layout for the current display interface and generating grid cells based on the number of weather forecast models includes: When the number of weather forecast models is 1, a 1x1 grid layout is used; or when the number of models is greater than 1 and less than or equal to 4, a 2x2 grid layout is used; or when the number of models is greater than 4, a square grid layout is calculated, wherein the product of the number of rows and columns of the grid layout is greater than or equal to the number of models and the difference between the number of rows and columns of the grid layout is minimized. Obtain the number of grid rows and columns of the grid layout; Calculate the standard width and standard height of each grid cell based on the number of grid rows and columns, the available width and available height of the currently displayed interface, and the preset cell spacing. Based on the standard width and standard height of each grid cell, a grid cell is defined in the current display interface; wherein, the standard width of each grid cell... and standard height The calculation expression is: in, This is the available width of the currently displayed interface. It is the number of columns in the grid layout. It is the preset cell spacing. This is the available height of the currently displayed interface. It represents the number of rows in the grid layout.

4. The method according to claim 2, characterized in that, The preprocessing of forecast data from each weather forecast model to generate a weather forecast map for each model includes: The forecast data of each weather forecast model is analyzed to extract meteorological element field data, latitude and longitude coordinate information, and forecast time information, which are used as a structured meteorological dataset. Determine the type of meteorological element and the target forecast time selected by the user in real time through the visual interface; From the structured meteorological dataset, two-dimensional grid data that matches the meteorological element type and target forecast time are selected; Invalid values ​​are removed from the two-dimensional grid data, missing values ​​are filled in, and the filled two-dimensional grid data is converted to a preset standard latitude and longitude grid through spatial interpolation method to obtain standardized grid data with unified spatial resolution and coordinate reference system; The standardized grid data is rendered graphically to generate visual graphic elements that characterize the spatial distribution of meteorological elements. The visualization graphic elements are overlaid on the preloaded geographic base map layer to form a combined layer, and graphic embellishment elements are added to the combined layer to obtain the weather forecast map for each weather forecast model. The graphic embellishment elements include at least one of the following: the name of each weather forecast model, the target forecast time, legend, color code, and scale.

5. The method according to claim 1, characterized in that, The step of performing a linkage analysis on the multi-mode comparison views to obtain the meteorological map linkage group that needs to be linked includes: Obtain metadata, image status, and user interaction history associated with the target weather forecast map; the target weather forecast map is each weather forecast map in the multi-mode comparison view; From the metadata, image status, and user interaction history, multi-dimensional linkage analysis features are extracted to obtain the feature vector of the target weather forecast map; The linkage similarity score between any two weather forecast maps in the multi-model comparison view is calculated using the feature vector of the target weather forecast map. The linkage similarity scores between any two weather forecast maps are merged to obtain a linkage similarity score matrix. Based on the linkage similarity score matrix, multiple map clusters with dense linkage similarity are identified, and the meteorological map linkage groups that need to be linked are obtained.

6. The method according to claim 5, characterized in that, The feature vector of the target weather forecast map includes weather forecast mode identifier, weather element type, current display spatial range, current forecast time, and frequency of simultaneous selection or comparison operations by the user with other weather forecast maps in the view within a preset historical period. The step of calculating the linkage similarity score between any two weather forecast maps in the multi-model comparison view using the feature vector of the target weather forecast map includes: Based on the weather forecast model identifier, weather element type, current display spatial range, current forecast time, and frequency, calculate the feature matching degree or distance in the dimensions of weather forecast model identifier, weather element type, display spatial range overlap, forecast time, and historical co-occurrence frequency. Based on the feature matching degree or distance and preset weights, a weighted fusion is performed to generate a linkage similarity score between any two weather forecast maps in the multi-mode comparison view.

7. The method according to claim 1, characterized in that, The step of visualizing weather forecasts on the linked weather map group based on the view state change parameters, and obtaining the displayed weather forecast visualization results, includes: Based on the view status change parameters, calculate the display parameters of other meteorological maps in the meteorological map linkage group, excluding the target meteorological map; Based on the display parameters of the other meteorological maps, the other meteorological maps are re-rendered to ensure that the display range of the other meteorological maps is consistent with that of the target meteorological map, thus obtaining the final meteorological map set; Obtain the target forecast time input by the user on the client; Based on the final meteorological map set and the target forecast time, the meteorological forecast is visualized to obtain the displayed meteorological forecast visualization results.

8. The method according to claim 7, characterized in that, The step of calculating the display parameters of other meteorological maps in the meteorological map linkage group, excluding the target meteorological map, based on the view state change parameters includes: Extract the display range of the target weather map after the change from the view state change parameters; Obtain the map projection type currently used by the first meteorological map; the first meteorological map is every other meteorological map in the meteorological map linkage group except for the target meteorological map; Determine whether the map projection type of the first meteorological map is consistent with the map projection type of the target meteorological map; If so, the four boundary coordinate values ​​of the changed display range of the target meteorological map are used as the display parameters of the first meteorological map; If not, the four boundary coordinate values ​​of the display range are converted from the map projection type of the target meteorological map to the map projection type of the first meteorological map to obtain the four boundary coordinate values ​​of the converted display range, which are used as the display parameters of the first meteorological map.

9. The method according to claim 7, characterized in that, The process of visualizing the weather forecast based on the final meteorological map set and the target forecast time to obtain the displayed weather forecast visualization results includes: The corresponding weather forecast model identifier is extracted from the second weather map; the second weather map is each weather map in the final weather map set. Obtain the original structured meteorological dataset corresponding to the weather forecast model identifier; the original structured meteorological dataset contains meteorological element field data for multiple forecast times; From the meteorological element field data, meteorological element field data that matches the target forecast time are selected as the target time data; Based on the current display parameters of the second meteorological map, the target time data is graphically rendered to generate a meteorological forecast sub-map with the meteorological forecast mode identified at the target forecast time; All generated weather forecast sub-maps are arranged and combined according to the layout of the multi-mode comparison view to generate an updated multi-mode comparison view. The updated multi-mode comparison view is displayed, resulting in a visualized weather forecast.

10. A weather forecast visualization device based on multi-mode comparison and linkage analysis, characterized in that, The device includes: The weather forecast model determination module is used to respond to the user's selection instruction for the weather forecast visualization model list, determine the multiple weather forecast models to be compared selected by the user, and read the forecast data of each weather forecast model; the weather forecast visualization model list is used to display all available weather forecast model options. The multi-mode comparison view display module is used to generate and display a multi-mode comparison view based on the forecast data of each weather forecast model; the multi-mode comparison view is used to display the forecast data of multiple weather forecast models simultaneously. The linkage analysis module is used to perform linkage analysis on the multi-mode comparison view to obtain the meteorological map linkage group that needs to be linked. The operation event capture module is used to capture the current operation event in response to the user's operation command for the target weather map in the weather map linkage group. The operation event carries view state change parameters. The weather forecast visualization result display module is used to visualize the weather forecast based on the view status change parameters and obtain the displayed weather forecast visualization results.

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