An automatic map generation method and system for post-processing of a hydrodynamic model result

Abstract

The application discloses a kind of automation drawing generation methods and systems of water power model result post-processing, and it is related to hydraulic engineering technical field.The front-end interactive module, rear-end processing module, data storage module are included;The front-end interactive module is communicated with the rear-end processing module bidirectionally;The rear-end processing module is communicated with the data storage module, and original file and achievement drawing are read and written by file transfer protocol;The front-end interactive module is communicated with the data storage module by API interface, and the preview and download of the achievement drawing are realized.The application shortens the traditional manual processing process of 2 to 5 days to several minutes by standard three-step interactive process and rear-end automation processing, significantly simplifies the complex post-processing operation link, and greatly improves the generation efficiency of drawing.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, and in particular to an automated method and system for generating maps after post-processing hydrodynamic model results. Background Technology

[0002] In the field of hydrology and water conservancy engineering, river channel simulation and flood analysis are core components for ensuring project safety, assessing environmental impact, and developing disaster prevention plans. The MIKE21 model, as a mainstream international two-dimensional hydrodynamic model, has been widely used in projects such as flood evolution, water level forecasting, and inundation analysis, and its numerical calculation accuracy and reliability are widely recognized in the industry. The core technical problems of existing technologies are: 1. Fragmented post-processing workflow and extremely low efficiency: MIKE21 model post-processing requires four core steps: "data extraction from spatiotemporal variation field files → data cleaning and calculation in Excel / MATLAB → manual plotting in ArcMap / MATLAB → format adjustment and annotation," involving 4-5 independent software programs. Each step requires manual operation, taking 2-4 hours for a single case and 2-5 days for multiple cases, resulting in low efficiency; 2. Poor consistency of results and heavy reliance on manual labor: Different engineers have different plotting workflows, operating habits, and data processing methods. Differences in methodology lead to inconsistencies in legends, symbols, and scales across different batches of maps from the same project. Furthermore, manual data cleaning is prone to missing outliers, and manual mapping is susceptible to coordinate misalignment and layer overlay errors, resulting in data errors of 3%-5%. Thirdly, the process is highly specialized, incurring significant time and labor costs: post-processing requires proficiency in MIKE21 data extraction tools, Excel functions, MATLAB programming, and ArcMap mapping. Such professionals are scarce, and the intensive manual work, coupled with repetitive tasks, easily leads to fatigue-related errors. Therefore, for those skilled in the art, achieving full automation, standardization, and efficiency in the post-processing of MIKE21 model results is a pressing issue. Summary of the Invention

[0003] The purpose of this invention is to provide an automated method and system for generating maps after post-processing hydrodynamic model results, aiming to solve the drawbacks of traditional processes in the background art, which require manual operation across platforms, are inefficient, and heavily rely on the experience of technical personnel.

[0004] To achieve the above objectives, the present invention provides the following solution: On one hand, it provides an automated map generation system for post-processing hydrodynamic model results, including a front-end interaction module, a back-end processing module, and a data storage module; the front-end interaction module and the back-end processing module communicate bidirectionally via HTTPS protocol and WebSocket; the back-end processing module is communicatively connected to the data storage module, reading and writing original files and result maps via a file transfer protocol; the front-end interaction module and the data storage module are communicatively connected via an API interface to enable previewing and downloading of the result maps.

[0005] Preferably, the front-end interaction module is used to receive uploaded files and perform format verification; the front-end interaction module includes a file selection unit, a parameter configuration unit, a map visualization unit, and a verification and error handling unit connected in sequence.

[0006] Preferably, the backend processing module is used to process the files received by the frontend interaction module to generate result images; the backend processing module includes a file parsing unit, a data processing unit, an image generation unit, and a task queue management unit connected in sequence.

[0007] Preferably, the file parsing unit uses the DHI (Danish Hydraulic Institute) open-source project interface to read and convert the spatiotemporal variation field file.

[0008] Preferably, the data storage module includes an original file storage unit, a configuration parameter storage unit, a result map storage unit, and a log storage unit; wherein, the original file storage unit is used to classify and store spatiotemporal change field files, the configuration parameter storage unit is used to associate and store map files with corresponding configuration parameters, the result map storage unit is used to store output result maps, and the log storage unit is used to record user operation logs and system operation logs.

[0009] On the other hand, an automated map generation method for post-processing hydrodynamic model results is provided, characterized by generating the resulting maps based on the aforementioned automated map generation system for post-processing hydrodynamic model results. The specific steps include the following:

[0010] Users upload spatiotemporal change field files through the front-end interactive module and the format is automatically validated.

[0011] If the format is validated, the output parameters are configured. After configuration, the front end temporarily stores the parameters and transmits them to the back end processing module via HTTPS protocol with encryption.

[0012] The front-end interactive module loads a vector river map generated based on the MIKE21 model calculation domain. The user interactively delineates the analysis range based on the vector river map, and the range data is synchronized to the back-end processing module in JSON format.

[0013] The file parsing unit in the backend processing module calls the Python interface of DHI's open-source project to read the hydrological data in the spatiotemporal variation field file and convert it into structured data in a specific format;

[0014] The structured data is cleaned, subjected to secondary calculations and template adaptation. The map generation unit calls the ArcPy toolset and performs automated rendering based on the predefined rules of the mxd template to generate the resulting map.

[0015] Preferably, the steps for cleaning the structured data are as follows: outliers are identified and removed using the 3σ principle, and missing data is filled in using Kriging interpolation to ensure data continuity.

[0016] Preferably, the step of performing secondary calculations on the structured data is as follows: calculating comparison indicators before and after the project, including water level difference, flow velocity change rate, increase or decrease in submerged area, and water depth gradient; the step of template adaptation on the structured data is as follows: matching the cleaned structured data and the secondary calculation results to the corresponding layer of the selected MXD template according to the field mapping rules.

[0017] Preferably, the steps for automated rendering based on the predefined rules of the MXD template are as follows: First, load the data layer adapted to the template and overlay basic geographic features; then apply preset symbol markers, and automatically add legends, scale bars, north arrows, legend names, and generation dates; finally, export the map as a specified format map according to the size and resolution, i.e., the final map.

[0018] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0019] (1) Based on process integration and automated scheduling, a one-click system integrating "input-processing-output" is constructed. The operation is simplified through front-end graphical interaction, the back-end algorithm automates the replacement of manual labor, and the standardization of mxd templates ensures the quality of results. Ultimately, the efficiency of post-processing of MIKE21 model results is improved and the post-processing cycle is reduced.

[0020] (2) By using backend task queue management and concurrent processing technology, server hardware resources are fully utilized to improve the processing speed of multiple users and multiple tasks and optimize resource scheduling efficiency.

[0021] (3) Traditional solutions require manual data transfer between 4-5 independent software programs. This invention achieves seamless collaboration between tools through system integration, avoids repetitive work of data export / import, and outputs high-precision result maps. The structured data and standardized maps generated by automated processing can be directly connected to the AI ​​intelligent report analysis module without secondary data conversion.

[0022] (4) It solves the drawbacks of traditional processes that require manual operation across platforms, are inefficient, and heavily rely on the experience of technical personnel. The system has built-in core algorithms such as data cleaning, outlier removal, and intelligent contour filtering, which can completely eliminate the bottleneck of switching between multiple software such as Mike21 and ArcMap. This allows junior technicians to automatically generate maps that meet the requirements according to the unified specifications through simple operations without needing to be proficient in various professional software. This greatly improves processing efficiency while effectively ensuring the consistency of the quality of the results. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a system structure diagram of the present invention;

[0025] Figure 2 This is a flowchart of the method of the present invention;

[0026] Figure 3 This is a flowchart of the front-end interaction module processing of the present invention;

[0027] Figure 4 This is a flowchart of the backend processing module of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The purpose of this invention is to provide an automated map generation system for post-processing hydrodynamic model results, such as... Figure 1As shown, it includes a front-end interaction module, a back-end processing module, and a data storage module. The front-end interaction module and the back-end processing module communicate bidirectionally via HTTPS protocol and WebSocket. The front-end transmits file paths, configuration parameters, and range data, while the back-end pushes task progress and completion notifications. The back-end processing module and the data storage module communicate and connect to read and write original files and result images via file transfer protocol. The front-end interaction module and the data storage module communicate and connect via API interface to enable previewing and downloading of result images.

[0030] Furthermore, the front-end interaction module: the hardware carrier is a computer terminal or tablet device, and the software form is a graphical interface developed based on the Vue3 framework and TDesign component library, including a file selection unit, a parameter configuration unit, a map visualization unit, and a verification and error handling unit connected in sequence. Specifically, the file selection unit supports multiple files, multiple working conditions, and multiple spatiotemporal variation field files, and integrates format verification logic; the parameter configuration unit provides a visual input box and drop-down selector, restricting parameter input rules; the map visualization unit is developed based on ECharts, supports river map zooming and panning, real-time rendering of the analysis range, and specifies the final output range of the map and the calculation project's impact range; the verification and error handling unit is used to perform real-time verification of user operations, provide clear prompts and correction suggestions, and ensure the validity of input data.

[0031] Furthermore, the backend processing module processes the files received by the frontend interaction module to generate output maps. This module includes a file parsing unit, a data processing unit, a map generation unit, and a task queue management unit, connected sequentially. It's worth noting that the backend processing module uses a layered design: the API layer processes configuration information returned from the frontend interaction and generates tasks based on user configuration; the Worker layer handles all business logic from the spatiotemporal variation field file to map output, executing steps and supporting resumption of tasks after interruption or unexpected exit; the task distribution layer dynamically distributes tasks based on hardware configuration, automatically determines whether conditions allow for concurrent multi-task processing, records task steps, and returns task results to the API layer; the guardian layer ensures correct program execution and repairs unexpected exits and task interruptions. The Worker layer contains a data processing unit that verifies and analyzes the spatiotemporal variation field file.

[0032] Specifically, the file parsing unit calls the mikeio interface of DHI's open-source project for reading and format conversion of spatiotemporal variation field files, supporting batch file parallel parsing; the data processing unit includes data cleaning algorithms and secondary calculations. The data cleaning algorithm adopts a combination strategy of 3σ principle (suitable for normally distributed data) + absolute deviation method (suitable for non-normally distributed data) to automatically identify and remove outliers; missing data completion uses Kriging interpolation to achieve high-precision completion based on the spatial correlation of adjacent grid data; secondary calculations: the system has preset calculation formulas to support the automated calculation of 6 core indicators such as water level difference and flow velocity change rate, and can extend the calculation indicators through configuration files. It also includes secondary flow velocity contour cleaning to filter abnormal line segments, excessively short line segments, effective line segments outside the engineering range, and scattered line segments with 0 values.

[0033] The image generation unit includes MXD template construction and adaptation logic:

[0034] The pre-defined MXD template structure includes three types of layers: basic geographic layers (river boundaries, dikes, hydrological stations); data layers (water level, flow velocity, inundated area); and annotation layers (legend, scale bar, north arrow, map name, and working condition name). It also includes a predefined symbol system (e.g., light blue for water levels 0-5m, dark blue for 5-10m, blue arrows before and after engineering, blue for zero lines, green for negative lines, and red for positive lines), a scale bar (dynamically and automatically generated), and a legend frame (fixed on the right side of the map for clarity). The adaptation logic establishes a correspondence between .dfsu data fields and MXD template layer fields through a field mapping table (stored in the backend database) (e.g., "WaterLevel" corresponds to the "water level" field). After data processing, the data is automatically written to the template layer, and the ArcPyMapping tool of ArcMap's ArcPy module is called to apply the pre-defined symbols, completing automated rendering.

[0035] Task queue management unit: Local deployment uses a memory caching mechanism and local files to record the current task queue, while remote server deployment uses file locks and JSON storage format to manage task status. It supports task interruption recovery, historical task query, and real-time task progress query, enabling task management, supporting concurrent requests, and executing tasks in order of request time to avoid server overload; it automatically terminates timed-out tasks and provides feedback to the user to ensure system stability.

[0036] Furthermore, the local deployment of the data storage module uses local JSON binary files, while the remote server deployment uses a database. The data storage module includes raw file storage units, configuration parameter storage units, output map storage units, and log storage units. The raw file storage unit is used to categorize and store spatiotemporal change field files; the configuration parameter storage unit is used to associate map files with their corresponding configuration parameters; the output map storage unit is used to store the output output maps; and the log storage unit is used to record user operation logs and system operation logs.

[0037] This invention also provides an automated map generation method for post-processing hydrodynamic model results, which generates result maps based on an automated map generation system for post-processing hydrodynamic model results, such as... Figure 2 As shown, the specific steps include the following:

[0038] S1. Users upload spatiotemporal change field files through the front-end interaction module and the format is automatically validated.

[0039] S2. If the format validation passes, configure the output parameters. After configuration, the front end temporarily stores the parameters and transmits them to the back end processing module via HTTPS protocol with encryption.

[0040] S3. Load the vector river map generated based on the MIKE21 model calculation domain in the front-end interaction module. The user interactively delineates the analysis range based on the vector river map and synchronizes the range data to the back-end processing module in JSON format.

[0041] S4. The file parsing unit in the backend processing module calls the Python interface of the DHI open source project to read the hydrological data in the spatiotemporal variation field file and convert it into structured data in a specific format.

[0042] S5. The structured data is cleaned, recalculated, and adapted to the template. The map generation unit calls the ArcPy toolset and performs automated rendering based on the predefined rules of the mxd template to generate the resulting map.

[0043] It should be noted that the spatiotemporal variation field file is a file stored in the .dfsu format, and the .dfsu file contains the spatial field that varies over time.

[0044] Furthermore, users can upload model result files before and after the project through the system's front-end interface. For example... Figure 3 As shown, the specific steps of the front-end interaction module are as follows:

[0045] S11. The user selects the type of MXD template for this output (flow velocity, flow field, sampling point) and configures the image name for the current working condition.

[0046] S12: The user uploads files before and after the project (if the files exist on the server, the cache is used to avoid duplicate uploads). The front-end client verifies the file extension ".dfsu" and performs secondary verification on the file header. Before uploading, a unique identifier is generated by trimming the header, middle, and footer by 8 bytes each (if insufficient, the entire file is used). This unique identifier is used to communicate with the server to determine if a cache exists. If so, the cache information is retrieved directly. For new file uploads, the back-end reads the river information and returns it in GeoJSON format. Simultaneously, the user configures detailed parameters for the output type, such as the re-interpolation point precision (grid spacing), the angle unit used (radians, angles), the river background of the map (before and after the project), the time series data to be used (specifying a time series), the map output range scaling ratio, the contour value list (flow velocity), and other related configuration information.

[0047] S13. The interactive interface rendering uses the Echarts engine to render the model boundary. Users can select the actual output range in real time and automatically calculate the difference range of the river channel before and after the project for users to choose as the project range. Users can specify the final output size and accuracy of the map and generate the result map.

[0048] like Figure 4 The diagram illustrates the specific steps of the backend processing module. Upon receiving the new spatiotemporal change field file, the module first generates the basic information of the entire file, stores it in the same folder using JSON, and then performs time-limited persistent storage. The specific steps include the following:

[0049] S41. Validate parameters: Perform validity checks on the configuration parameters passed from the front end.

[0050] S42, Cache Query: Check if there are cached results in the current local calculation history. If not, start the calculation.

[0051] Furthermore, such as Figure 4 As shown, the specific steps of S5 are as follows:

[0052] S511. Based on the specified time series, read the .dfsu result file and convert it into an intermediate file in local Shapefile format;

[0053] S512, Primary Data Cleaning: Perform data anomaly cleaning on the generated Shapefile, applying the 3σ principle to identify outliers (including handling undefined values, removing data exceeding the range of "mean ± 3 times standard deviation"), to ensure the continuity and rationality of the data.

[0054] S513. Spatial Interpolation: Perform Kriging interpolation on the output files before and after processing (e.g., before and after the project). This step is completed by calling the arcpy interface to drive the ArcGIS software, and the interpolation results are stored in TIFF format.

[0055] S514, Standard Fishing Net Generation: Generates a uniform regular grid covering the calculation area based on the grid precision parameters configured by the user, and outputs it in Shapefile format.

[0056] S515, Engineering Impact Difference Calculation: Based on the newly generated regular grid, perform gridded difference calculation on the interpolation result files before and after processing (e.g., before and after the project), including: water level difference, flow velocity change rate, increase or decrease in flooded area, and water depth gradient. The calculation results are stored in a new Shapefile file.

[0057] S516. Secondary Data Cleaning and Enhancement: Perform secondary cleaning on output files such as flow velocity, filtering outliers, missing values, and unreasonable topographic values, or performing local interpolation to complete them; generate contour lines, and filter the contour line data to remove excessively short lines, zero-value outliers, and discontinuous unreasonable line segments; perform dynamic smoothing of contour lines according to the river channel range, and add corresponding attribute identifier fields to the contour line data.

[0058] S517. Data trimming: Based on the river channel range, the data processed in step S56 is trimmed and extracted a second time.

[0059] Next, the drawing and rendering of the graphics are performed. The specific steps are as follows:

[0060] S521, Automated Mapping: Using ArcPy, data is associated with an MXD template: ① Load data layers and overlay basic geographic features; ② Apply preset symbol systems (e.g., blue-red gradient color scales for water levels, arrow sizes to distinguish flow velocities); ③ Calculate the actual scale, generate a north arrow, and generate corresponding legend names, legends, drawing dates, and other specific elements for each working condition based on user front-end configurations; ④ Export the map to the specified format based on the user-configured paper size and dpi resolution.

[0061] S522, Results Packaging and Caching: Package, cache and archive all results for easy download and direct modification by users;

[0062] S523, Historical Tasks and Quick Response: Generate task history based on configuration objects. For secondary requests, such as modifying the output range or modifying simple information that does not involve result calculation, directly read the cache for quick processing to achieve graph output in seconds.

[0063] S524. Result Return: The final drawing and complete process file are returned to the front end for user interaction, display, download, and further refinement. The refined new MXD can be re-uploaded for subsequent drawing output.

[0064] Furthermore, all intermediate and final results are stored in Shapefile format to ensure consistency in spatial coordinate systems; all front-end and back-end data transmissions use GeoJSON format to guarantee the uniformity of data interaction. To ensure robustness, each computational step generates a corresponding entity Shapefile file. The overall computation task employs a file locking and phased construction mechanism. If the task is unexpectedly interrupted or terminated, the system can automatically resume execution from the breakpoint after restarting and print the task status in real time, ensuring the robustness and traceability of the computation process.

[0065] The steps for performing secondary calculations on structured data are as follows: calculate comparison indicators before and after the project, including water level difference, flow velocity change rate, increase or decrease in submerged area, and water depth gradient; the steps for template adaptation of structured data are as follows: match the cleaned structured data and the secondary calculation results to the corresponding layer of the selected MXD template according to the field mapping rules.

[0066] Furthermore, the automated rendering process based on predefined rules of the MXD template involves the following steps: First, loading the template-adapted data layer and overlaying basic geographic features; then applying preset symbols and automatically adding a legend, scale bar, north arrow, legend name, and generation date; finally, exporting the map as a specified format map according to size and resolution. The generated map is stored in the data storage module, and the backend pushes a completion notification to the frontend via WebSocket, allowing users to preview locally or download online. Information is updated to the legend and map name based on different working conditions, and a unified view is corrected according to the configured display range.

[0067] In traditional workflows, data export, data cleaning, flow field result calculation, and velocity result calculation all require engineers to be proficient in multiple tools within ArcMap software. This presents core pain points such as cumbersome operation, high technical barriers, and difficulty in standardizing map output. The fully automated intelligent processing solution proposed in this invention completely restructures the data flow and map output logic after hydrodynamic simulation. This solution, through its built-in, mature process cleaning standards and algorithms, fully automates the traditionally manual and time-consuming data preprocessing steps. Users only need to upload raw data and perform simple configurations; the backend seamlessly completes data export, data cleaning, data correction, result calculation, secondary checks, contour line generation, and map output. This fundamentally solves the problems of reliance on the experience of technical personnel and repetitive labor.

[0068] Furthermore, this solution uses a unified and manageable dynamic template mechanism to solidify national mapping standards for different map elements such as flow velocity, flow field, and sampling points into the system. It can automatically update elements such as legend names and titles based on working conditions and supports map generation at the second-level cache. This not only achieves a high degree of uniformity in mapping standards across multiple departments but also compresses manual operations that previously required hours or even days into minutes, truly realizing a leap from tedious and repetitive manual processes to intelligent and large-scale automated processing.

[0069] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An automated map generation system for post-processing hydrodynamic model results, characterized in that, It includes a front-end interaction module, a back-end processing module, and a data storage module; the front-end interaction module and the back-end processing module communicate bidirectionally via HTTPS protocol and WebSocket; the back-end processing module and the data storage module are connected to each other to read and write original files and result images via file transfer protocol; the front-end interaction module and the data storage module are connected to each other via API interface to enable previewing and downloading of the result images.

2. The automated map generation system for post-processing hydrodynamic model results according to claim 1, characterized in that, The front-end interaction module is used to receive uploaded files and perform format verification; the front-end interaction module includes a file selection unit, a parameter configuration unit, a map visualization unit, and a verification and error handling unit connected in sequence.

3. The automated map generation system for post-processing hydrodynamic model results according to claim 1, characterized in that, The backend processing module is used to process the files received by the frontend interaction module and generate the resulting images; the backend processing module includes a file parsing unit, a data processing unit, an image generation unit, and a task queue management unit connected in sequence.

4. The automated map generation system for post-processing hydrodynamic model results according to claim 3, characterized in that, The file parsing unit calls the DHI open-source project interface to read and convert the format of spatiotemporal variation field files.

5. The automated map generation system for post-processing hydrodynamic model results according to claim 1, characterized in that, The data storage module includes an original file storage unit, a configuration parameter storage unit, a result map storage unit, and a log storage unit; wherein, the original file storage unit is used to classify and store spatiotemporal change field files, the configuration parameter storage unit is used to associate and store map files with corresponding configuration parameters, the result map storage unit is used to store output result maps, and the log storage unit is used to record user operation logs and system operation logs.

6. An automated map generation method for post-processing hydrodynamic model results, characterized in that, The automated map generation system for post-processing hydrodynamic model results as described in any one of claims 1-5 generates result maps, and the specific steps include the following: Users upload spatiotemporal change field files through the front-end interactive module and the format is automatically validated. If the format is validated, the output parameters are configured. After configuration, the front end temporarily stores the parameters and transmits them to the back end processing module via HTTPS protocol with encryption. The front-end interactive module loads a vector river map generated based on the MIKE21 model calculation domain. The user interactively delineates the analysis range based on the vector river map, and the range data is synchronized to the back-end processing module in JSON format. The file parsing unit in the backend processing module calls the Python interface of the DHI open source project to read the hydrological data in the spatiotemporal variation field file and convert it into structured data in a specific format; The structured data is cleaned, subjected to secondary calculations and template adaptation. The map generation unit calls the ArcPy toolset and performs automated rendering based on the predefined rules of the mxd template to generate the resulting map.

7. The automated map generation method for post-processing hydrodynamic model results according to claim 6, characterized in that, The steps for cleaning the structured data are as follows: outliers are identified and removed using the 3σ principle, and missing data is filled in using Kriging interpolation to ensure data continuity.

8. The automated map generation method for post-processing hydrodynamic model results according to claim 6, characterized in that, The steps for performing secondary calculations on the structured data are as follows: calculate comparison indicators before and after the project, including water level difference, flow velocity change rate, increase or decrease in submerged area, and water depth gradient; the steps for template adaptation of the structured data are as follows: match the cleaned structured data and the secondary calculation results to the corresponding layer of the selected MXD template according to the field mapping rules.

9. The automated map generation method for post-processing hydrodynamic model results according to claim 6, characterized in that, The steps for automated rendering based on predefined rules of MXD templates are as follows: First, load the template-adapted data layer and overlay basic geographic features; then apply preset symbols and markers, and automatically add legends, scale bars, north arrows, legend names, and generation dates; finally, export the resulting map as a specified format map according to size and resolution.

Images