Modularized spatial data processing method

Abstract

The invention discloses a modular spatial data processing method, which comprises the following steps of: 1) constructing a plurality of spatial data processing modules, and publishing the spatial data processing modules as ArcGISGP service; 2) a user submits a spatial data processing task through an interactive interface at the front end; (3) the user fills in parameters in the spatial data processing task process and transmits the filled parameters to the back end; 4) the rear end calls ArcGISGP service through a spatial data processing task flow corresponding to the spatial data processing task, executes the spatial data processing task according to the parameters transmitted by the front end, and then returns the execution result of the spatial data processing task to the front end; and 5) the user checks the processing result through the interactive interface. According to the invention, automatic cleaning, treatment and operation of spatial data can be realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a modular spatial data processing method. Background Art

[0002] With the rapid development of geographic information system technology, the acquisition, processing and analysis of spatial data are increasingly widely used in various industries.

[0003] Traditional spatial data processing methods rely heavily on manual operations, such as manual data editing, batch processing, and control. This approach is inefficient and prone to data errors due to human factors, making it difficult to meet the needs of large-scale, high-precision spatial data analysis. Summary of the Invention

[0004] The object of the present invention is to provide a modular spatial data processing method comprising the following steps:

[0005] 1) Build multiple spatial data processing modules and publish them as ArcGIS GP services; the name of each spatial data processing module is visible on the front end;

[0006] 2) Determine the type of high-frequency spatial data processing task;

[0007] For each high-frequency spatial data processing task, the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end;

[0008] The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend;

[0009] 3) Users submit spatial data processing tasks through the front-end interactive interface;

[0010] If the submitted spatial data processing task does not have a corresponding spatial data processing task flow, proceed to step 4); otherwise, proceed to step 5);

[0011] 4) For the spatial data processing task submitted in step 3), the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end;

[0012] The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend;

[0013] 5) The user fills in the parameters in the spatial data processing task flow and transmits the filled parameters to the backend;

[0014] 6) The backend calls the ArcGIS GP service through the spatial data processing task flow corresponding to the spatial data processing task, executes the spatial data processing task according to the parameters transmitted by the front end, and then returns the spatial data processing task execution result to the front end;

[0015] 7) The user views the processing results through the interactive interface.

[0016] Furthermore, the spatial data processing module is constructed by using a Python script tool or an ArcGIS model builder.

[0017] Furthermore, the steps of building a spatial data processing module through Python script tools include:

[0018] s1) Perform data cleaning, including removing duplicate data, handling missing values, correcting geometric errors, unifying coordinate systems, and standardizing fields;

[0019] s2) Perform data governance, including data format conversion, data clipping, merging, splicing, data projection conversion, and data topology inspection and repair;

[0020] s3) Perform data operations, including spatial statistical analysis, spatial interpolation analysis, and spatial overlay analysis.

[0021] Furthermore, the front-end provides task management functions, including task list display, task details display, new task creation, task deletion, stop, execution, and task monitoring;

[0022] The task list display is implemented through Vue.js+SpringBoot;

[0023] Task details are displayed through RESTful API; task details include task configuration files, script paths, scheduler information, and log records;

[0024] During the backend's spatial data processing task execution, the frontend provides task status tracking functionality; task status includes monitoring and display of tasks such as queued, executing, completed, and failed.

[0025] Task status tracking is implemented through WebSocket;

[0026] Task monitoring is achieved through Grafana or Kibana.

[0027] Furthermore, the front-end is developed and rendered using the Vue3 framework and JavaScript language;

[0028] The front-end interactive interface is designed using the ElementPlusUI framework;

[0029] The front-end implements HTTP requests to the back-end interface through the Axios library, thereby loading the data provided by the back-end into the interactive interface;

[0030] Furthermore, the front end determines the execution order of the spatial data processing modules by connecting the modules.

[0031] Furthermore, the backend is developed using Java language combined with SpringBoot framework;

[0032] The backend is equipped with the Zip4j library to support file compression and decompression operations;

[0033] The backend uses the SnailJob open source tool to implement multi-space data processing task process construction and distributed scheduling.

[0034] The backend integrates MyBatis-Plus as an ORM tool to implement interaction with the database.

[0035] Furthermore, each ArcGIS GP service is composed of multiple functions or ArcToolbox tools, including arcpy.envoutputCoordinateSystem, arcpy.env.overwriteOutput, arcpy.Split_arc, and arcpy.Intersect_arc.

[0036] Furthermore, the ArcGISGP service configuration includes service name, execution mode, message level, number of pools, maximum usable time, and service function description.

[0037] Furthermore, the front-end interactive interface supports the simultaneous input of multiple spatial data processing tasks and supports execution time configuration.

[0038] Furthermore, the spatial data processing tasks include assigning area codes and managing data before storage.

[0039] The technical effect of the present invention is unquestionable. The present invention uses Python script tools or ArcGIS model builders to build various spatial data processing modules and publish them as ArcGIS GP services. At the front end, users can compile a variety of efficient and reusable spatial data processing processes for different users or applications to call, thereby realizing the automated cleaning, management and calculation of spatial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a modular spatial data processing system architecture. DETAILED DESCRIPTION

[0041] The present invention will be further described below with reference to the following examples, but it should not be understood that the scope of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications can be made according to common technical knowledge and customary means in the art, and all of these should be included in the scope of protection of the present invention.

[0042] Example 1:

[0043] See also Figure 1 , a modular spatial data processing method, comprising the following steps:

[0044] 1) Build multiple spatial data processing modules and publish them as ArcGIS GP services; the name of each spatial data processing module is visible on the front end;

[0045] 2) Determine the type of high-frequency spatial data processing task;

[0046] For each high-frequency spatial data processing task, the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end;

[0047] The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend;

[0048] 3) Users submit spatial data processing tasks through the front-end interactive interface;

[0049] If the submitted spatial data processing task does not have a corresponding spatial data processing task flow, proceed to step 4); otherwise, proceed to step 5);

[0050] 4) For the spatial data processing task submitted in step 3), the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end;

[0051] The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend;

[0052] 5) The user fills in the parameters in the spatial data processing task flow and transmits the filled parameters to the backend;

[0053] 6) The backend calls the ArcGIS GP service through the spatial data processing task flow corresponding to the spatial data processing task, executes the spatial data processing task according to the parameters transmitted by the front end, and then returns the spatial data processing task execution result to the front end;

[0054] 7) The user views the processing results through the interactive interface.

[0055] The spatial data processing module is constructed by using a Python script tool or an ArcGIS model builder.

[0056] The steps to build a spatial data processing module through Python script tools include:

[0057] s1) Perform data cleaning, including removing duplicate data, processing missing values, correcting geometric errors, unifying coordinate systems, and standardizing fields; the data includes spatial elements, and there may be multiple duplicate elements in a feature set. These elements have the same spatial shape and attribute values and are generated during the data production process, and redundancy needs to be removed.

[0058] s2) Perform data governance, including data format conversion, data clipping, merging, splicing, data projection conversion, and data topology inspection and repair;

[0059] s3) Perform data operations, including spatial statistical analysis, spatial interpolation analysis, and spatial overlay analysis.

[0060] The front-end provides task management functions, including task list display, task details display, new task creation, task deletion, stop, execution, and task monitoring;

[0061] The task list display is implemented through Vue.js+SpringBoot;

[0062] Task details are displayed through RESTful API; task details include task configuration files, script paths, scheduler information, and log records;

[0063] During the backend's spatial data processing task execution, the frontend provides task status tracking functionality; task status includes monitoring and display of tasks such as queued, executing, completed, and failed.

[0064] Task status tracking is implemented through WebSocket;

[0065] Task monitoring is achieved through Grafana or Kibana.

[0066] The front-end is developed and rendered using the Vue3 framework and JavaScript language;

[0067] The front-end interactive interface is designed using the ElementPlusUI framework;

[0068] The front-end implements HTTP requests to the back-end interface through the Axios library, thereby loading the data provided by the back-end into the interactive interface;

[0069] The front end determines the execution order of the spatial data processing modules by connecting the modules.

[0070] The backend is developed using Java language combined with SpringBoot framework;

[0071] The backend is equipped with the Zip4j library to support file compression and decompression operations;

[0072] The backend uses the SnailJob open source tool to implement multi-space data processing task process construction and distributed scheduling.

[0073] The backend integrates MyBatis-Plus as an ORM tool to implement interaction with the database.

[0074] Each ArcGIS GP service consists of multiple functions or ArcToolbox tools, including arcpy.envoutputCoordinateSystem, arcpy.env.overwriteOutput, arcpy.Split_arc, and arcpy.Intersect_arc.

[0075] ArcGISGP service configuration includes service name, execution mode, message level, number of pools, maximum usable time, and service function description.

[0076] The front-end interactive interface supports the simultaneous input of multiple spatial data processing tasks and supports execution time configuration.

[0077] The spatial data processing tasks include area code assignment and data management before storage.

[0078] Example 1: Assigning area codes

[0079] Assign administrative district codes to features based on their spatial locations. First, call the "Coordinate Identification Module" to check whether the data coordinate system meets the requirements. If not, call the "Coordinate Conversion Module" to automatically identify and convert the coordinates. If the coordinate system meets the requirements or after coordinate conversion, call the "Repair OBJECTID Not Primary Key Module" to repair it. Once the repair is complete, use the "Region Code Assignment Module" to associate the feature with the region code and output the results.

[0080] Example 2: Data governance before storage

[0081] The collected spatial data is managed and stored in the database. First, the "Coordinate Identification Module" is called to check whether the data coordinate system meets the requirements. If not, the "Coordinate Conversion Module" is called to automatically identify and perform coordinate conversion. If the coordinate system meets the requirements or after coordinate conversion, the "Repair OBJECTID Not Primary Key Module" is called to repair it. After the repair is completed, the "Field Chinese-English Comparison Module" is called to change the field name to the first letter of the Chinese pinyin and the field alias to the corresponding Chinese name. Then, the "Assign Corresponding Chinese by Type Code Module" is used to identify the fields with type codes and generate Chinese values according to the codes. In the database, a new feature class is created through the "New Feature Class Module". Finally, the data is stored in the database through the "Data Append Module".

[0082] Example 2:

[0083] A modular spatial data processing method comprises the following steps:

[0084] 1) Build multiple spatial data processing modules and publish them as ArcGIS GP services; the name of each spatial data processing module is visible on the front end;

[0085] 2) Determine the type of high-frequency spatial data processing task;

[0086] For each high-frequency spatial data processing task, the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end;

[0087] The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend;

[0088] 3) Users submit spatial data processing tasks through the front-end interactive interface;

[0089] If the submitted spatial data processing task does not have a corresponding spatial data processing task flow, proceed to step 4); otherwise, proceed to step 5);

[0090] 4) For the spatial data processing task submitted in step 3), the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end;

[0091] The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend;

[0092] 5) The user fills in the parameters in the spatial data processing task flow and transmits the filled parameters to the backend;

[0093] 6) The backend calls the ArcGIS GP service through the spatial data processing task flow corresponding to the spatial data processing task, executes the spatial data processing task according to the parameters transmitted by the front end, and then returns the spatial data processing task execution result to the front end;

[0094] 7) The user views the processing results through the interactive interface.

[0095] Example 3:

[0096] A modular spatial data processing method, the technical content of which is the same as that of Example 2, further, the spatial data processing module is constructed by a Python script tool or an ArcGIS model builder.

[0097] Example 4:

[0098] A modular spatial data processing method, with the same technical content as any one of Examples 2-3, further comprising the steps of constructing a spatial data processing module using a Python script tool:

[0099] s1) Perform data cleaning, including removing duplicate data, handling missing values, correcting geometric errors, unifying coordinate systems, and standardizing fields;

[0100] s2) Perform data governance, including data format conversion, data clipping, merging, splicing, data projection conversion, and data topology inspection and repair;

[0101] s3) Perform data operations, including spatial statistical analysis, spatial interpolation analysis, and spatial overlay analysis.

[0102] Example 5:

[0103] A modular spatial data processing method, the technical content of which is the same as any one of Examples 2-4, further, the front end provides task management functions, including task list display, task detail display, task creation, task deletion, stop, execution, and task monitoring;

[0104] The task list display is implemented through Vue.js+SpringBoot;

[0105] Task details are displayed through RESTful API; task details include task configuration files, script paths, scheduler information, and log records;

[0106] During the backend's spatial data processing task execution, the frontend provides task status tracking functionality; task status includes monitoring and display of tasks such as queued, executing, completed, and failed.

[0107] Task status tracking is implemented through WebSocket;

[0108] Task monitoring is achieved through Grafana or Kibana.

[0109] Example 6:

[0110] A modular spatial data processing method, with the same technical content as any one of Examples 2-5, further, the front end is developed and rendered using the Vue3 framework and JavaScript language;

[0111] The front-end interactive interface is designed using the ElementPlusUI framework;

[0112] The front-end implements HTTP requests to the back-end interface through the Axios library, thereby loading the data provided by the back-end into the interactive interface;

[0113] Example 7:

[0114] A modular spatial data processing method, the technical content of which is the same as any one of embodiments 2-6, further, the front end determines the execution order of the spatial data processing modules by connecting the modules.

[0115] Example 8:

[0116] A modular spatial data processing method, the technical content of which is the same as any one of Examples 2-7, further, the backend is developed using Java language combined with SpringBoot framework;

[0117] The backend is equipped with the Zip4j library to support file compression and decompression operations;

[0118] The backend uses the SnailJob open source tool to implement multi-space data processing task process construction and distributed scheduling.

[0119] The backend integrates MyBatis-Plus as an ORM tool to implement interaction with the database.

[0120] Example 9:

[0121] A modular spatial data processing method, the technical content of which is the same as any one of Examples 2-8, further, each ArcGIS GP service is composed of multiple functions or ArcToolbox tools.

[0122] Example 10:

[0123] A modular spatial data processing method, the technical content of which is the same as any one of Examples 2-9. Furthermore, the ArcGIS GP service configuration includes a service name, execution mode, message level, number of pools, maximum usable time, and service function description.

[0124] Example 11:

[0125] A modular spatial data processing method, the technical content of which is the same as any one of Examples 2-10, further, the front-end interactive interface supports the simultaneous input of multiple spatial data processing tasks and supports execution time configuration.

[0126] Example 12:

[0127] A modular spatial data processing method, the technical content of which is the same as any one of Examples 2-11, further, the spatial data processing tasks include area code assignment and data management before storage.

[0128] Example 1: Assigning area codes

[0129] Assign administrative district codes to features based on their spatial locations. First, call the "Coordinate Identification Module" to check whether the data coordinate system meets the requirements. If not, call the "Coordinate Conversion Module" to automatically identify and convert the coordinates. If the coordinate system meets the requirements or after coordinate conversion, call the "Repair OBJECTID Not Primary Key Module" to repair it. Once the repair is complete, use the "Region Code Assignment Module" to associate the feature with the region code and output the results.

[0130] Example 2: Data governance before storage

[0131] The collected spatial data is managed and stored in the database. First, the "Coordinate Identification Module" is called to check whether the data coordinate system meets the requirements. If not, the "Coordinate Conversion Module" is called to automatically identify and perform coordinate conversion. If the coordinate system meets the requirements or after coordinate conversion, the "Repair OBJECTID Not Primary Key Module" is called to repair it. After the repair is completed, the "Field Chinese-English Comparison Module" is called to change the field name to the first letter of the Chinese pinyin and the field alias to the corresponding Chinese name. Then, the "Assign Corresponding Chinese by Type Code Module" is used to identify the fields with type codes and generate Chinese values according to the codes. In the database, a new feature class is created through the "New Feature Class Module". Finally, the data is stored in the database through the "Data Append Module".

[0132] Example 13:

[0133] A modular approach to spatial data processing that uses a front-end and back-end separation architecture:

[0134] (1) Front-end

[0135] Provides a user interaction interface to facilitate users to submit tasks, view task status, download processing results, etc.

[0136] The front-end interface was developed and rendered using the Vue3 framework and JavaScript, primarily relying on the ElementPlusUI framework for interface design and styling. For data acquisition, the Axios library was used to implement HTTP requests to the back-end interface, successfully loading the data provided by the back-end into the front-end view.

[0137] (2) Backend

[0138] Responsible for receiving tasks submitted by the front-end, scheduling and arranging tasks, and calling GP services to execute spatial data processing tasks. It also monitors the task execution status and returns the results to the front-end.

[0139] Developed in Java with the Spring Boot framework, it integrates MyBatis-Plus as an ORM tool for efficient database interaction. The Zip4j library supports efficient compression and decompression of large files during data storage and transmission. To ensure timely data updates and correct execution of business logic, the open-source SnailJob tool enables multi-task orchestration and distributed scheduling, supporting scheduled or on-demand job configuration, providing strong technical support for large-scale concurrent processing.

[0140] (3) GP services

[0141] Responsible for the specific execution of spatial data calculation tasks. Each GP service consists of multiple functions or ArcToolbox tools, which perform relatively single tasks, making it convenient for model builders to arrange task processes.

[0142] 1. Python script tool development

[0143] Use Python tools to write scripts to build complex operation modules, or use the model builder to build operation modules through a graphical interface and a small number of code blocks. When building, please note that the output of the previous service is the input of the next service. The main functions of various scripts are as follows:

[0144] (1) Data cleaning:

[0145] ① Remove duplicate data

[0146] ②Handling missing values

[0147] ③Correct geometric errors

[0148] ④Unified coordinate system

[0149] ⑤Field standardization

[0150] (2) Data Governance:

[0151] ①Data format conversion

[0152] ②Data cutting, merging, and splicing

[0153] ③Data projection conversion

[0154] ④Data topology inspection and repair

[0155] (3) Data operations:

[0156] ①Spatial statistical analysis

[0157] ②Spatial interpolation analysis

[0158] ③Spatial overlay analysis

[0159] 2.GP service release

[0160] After the script or model is built, it is published as an ArcGIS GP service, which becomes a module that can be orchestrated and called for the task calling process to perform the corresponding data processing tasks.

[0161] (1) Configure service parameters, including the service name, execution mode, message level, number of pools, maximum usage time, etc., and specify the specific functions of the service in the description.

[0162] (2) Test the published GP service functions.

[0163] (4) Task Scheduling and Orchestration

[0164] The task scheduling function supports multiple tasks to be executed in sequence and can implement timed scheduling for the entire task sequence.

[0165] SnailJob is used as a distributed task scheduling and orchestration tool. It provides flexible, reliable, and efficient distributed task retry and task scheduling. It supports dynamic task orchestration by configuring the execution order and dependencies of tasks through a visual interface. It supports the use of Cron expressions to configure the execution time of task sequences, ensuring that tasks are executed in the scheduled order. It provides real-time task monitoring, logging, and multiple alerting methods to facilitate rapid problem identification and resolution.

[0166] (5) Task management and monitoring

[0167] Manage tasks, including displaying task details, creating new tasks, deleting tasks, stopping tasks, and executing tasks. Provide task status tracking, including monitoring and displaying task queues, executing tasks, completed tasks, and failed tasks.

[0168] The task list is displayed using Vue.js and Spring Boot, enabling paging and filtering. Task details are viewed via a RESTful API, providing detailed information including the task configuration file, script path, scheduler information, and logging. Task status tracking uses WebSocket for real-time status updates. Task monitoring uses Grafana or Kibana to create dashboards displaying key metrics.

[0169] 3. Methodology

[0170] (1) Model builders use the front-end interface to drag various spatial data processing modules into the canvas according to the task logic, and connect the modules by connecting them to determine the order in which they are executed, thereby building a spatial data processing task flow and exposing relevant parameters.

[0171] (2) Users enter through the Web page, select the corresponding workflow, fill in relevant parameters, and start executing spatial data processing tasks.

[0172] (3) The backend receives tasks, schedules tasks according to the configured task flow and parameters, and calls the corresponding GP service to perform spatial data processing.

[0173] (4) The GP service executes the task and returns the processing results to the backend.

[0174] (5) The backend returns the task status and results to the frontend.

[0175] (6) Users can view task status and download processing results through the front-end interface.

[0176] 4. Advantages of the Solution

[0177] (1) Modularity: The spatial data processing process is divided into multiple modules, which can be freely matched according to needs. The workflow is constructed in a visual way, which is convenient for combination, expansion and maintenance.

[0178] (2) Automation: Automating task scheduling, task orchestration, and spatial data processing to reduce manual operations.

[0179] (3) Reusability: The published tool services can be reused and easily applied to other similar projects.

[0180] (IV) Ease of use: The graphical model builder interface makes it easy for model builders to understand and use it.

[0181] (V) Sharability: The published GP services can be easily shared with other users to achieve collaborative work in data processing.

[0182] 5. Application Scenarios

[0183] This solution can be widely used in the following scenarios:

[0184] (1) Geographic Information System Data Management

[0185] (2) Spatial data analysis and mining

[0186] (3) Urban Planning and Land Management

[0187] (IV) Disaster early warning and emergency management

[0188] VI. Summary

[0189] This solution provides a spatial data cleaning, governance, and computation solution based on ArcGIS Python scripting tools and model builders, which can effectively improve data processing efficiency, reduce labor costs, and provide a reliable data foundation for spatial data analysis.

Claims

1. A modular spatial data processing method, characterized in that: The following steps are involved: 1) Build multiple spatial data processing modules and publish them as ArcGIS GP services; the name of each spatial data processing module is visible on the front end. 2) Determine the type of high-frequency spatial data processing task; For each high-frequency spatial data processing task, the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end; The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend; 3) Users submit spatial data processing tasks through the front-end interactive interface; If the submitted spatial data processing task does not have a corresponding spatial data processing task flow, proceed to step 4); otherwise, proceed to step 5); 4) For the spatial data processing task submitted in step 3), the front end determines the required spatial data processing modules and the execution order of these spatial data processing modules, and transmits them to the back end; The backend constructs the spatial data processing task flow based on the spatial data processing modules and execution sequence selected by the frontend; 5) The user fills in the parameters in the spatial data processing task flow and transmits the filled parameters to the backend; 6) The backend calls the ArcGIS GP service through the spatial data processing task flow corresponding to the spatial data processing task, executes the spatial data processing task according to the parameters transmitted by the front end, and then returns the spatial data processing task execution result to the front end; 7) The user views the processing results through the interactive interface.

2. A modular spatial data processing method according to claim 1, characterized in that: The spatial data processing module is constructed by using a Python script tool or an ArcGIS model builder. The steps to build a spatial data processing module through Python script tools include: 1) Perform data cleaning, including removing duplicate data, handling missing values, correcting geometric errors, unifying coordinate systems, and standardizing fields; 2) Perform data governance, including data format conversion, data clipping, merging, splicing, data projection conversion, and data topology inspection and repair; 3) Perform data operations, including spatial statistical analysis, spatial interpolation analysis, and spatial overlay analysis.

3. A modular spatial data processing method according to claim 1, characterized in that: The front-end provides task management functions, including task list display, task details display, new task creation, task deletion, stop, execution, and task monitoring; The task list display is implemented through Vue.js+SpringBoot; Task details are displayed through RESTful API; task details include task configuration files, script paths, scheduler information, and log records; During the backend's spatial data processing task execution, the frontend provides task status tracking functionality; task status includes monitoring and display of tasks such as queued, executing, completed, and failed. Task status tracking is implemented through WebSocket; Task monitoring is achieved through Grafana or Kibana.

4. A modular spatial data processing method according to claim 1, characterized in that: The front-end is developed and rendered using the Vue3 framework and JavaScript language; The front-end interactive interface is designed using the ElementPlusUI framework; The front-end implements HTTP requests to the back-end interface through the Axios library, thereby loading the data provided by the back-end into the interactive interface.

5. A modular spatial data processing method according to claim 1, characterized in that: The front end determines the execution order of the spatial data processing modules by connecting the modules.

6. A modular spatial data processing method according to claim 1, characterized in that: The backend is developed using Java language combined with SpringBoot framework; The backend is equipped with the Zip4j library to support file compression and decompression operations; The backend uses the SnailJob open source tool to implement multi-space data processing task process construction and distributed scheduling. The backend integrates MyBatis-Plus as an ORM tool to implement interaction with the database.

7. A modular spatial data processing method according to claim 1, characterized in that: Each ArcGISGP service consists of multiple functions or ArcToolbox tools; the functions include arcpy.envoutputCoordinateSystem, arcpy.env.overwriteOutput, arcpy.Split_arc, and arcpy.Intersect_arc.

8. A modular spatial data processing method according to claim 1, characterized in that: ArcGISGP service configuration includes service name, execution mode, message level, number of pools, maximum usable time, and service function description.

9. A modular spatial data processing method according to claim 1, characterized in that: The front-end interactive interface supports the simultaneous input of multiple spatial data processing tasks and supports execution time configuration.

10. A modular spatial data processing method according to claim 1, characterized in that: The spatial data processing tasks include area code assignment and data management before storage.

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