Spatiotemporal data integrated governance system, methods, electronic devices and storage media

Abstract

This invention provides an integrated spatiotemporal data governance system, method, electronic device, and storage medium, belonging to the field of geographic information systems and spatiotemporal data processing technology. The method includes: loading and visualizing multi-source spatiotemporal data through a unified graphical interface; receiving user-triggered process instructions covering cleaning, classification, transformation, association, quality inspection, and data storage; dynamically invoking corresponding functional plugins through an extensible function scheduling mechanism, executing the processes sequentially in independent background threads; providing real-time, non-blocking feedback on task status; and finally updating the view and outputting the results. This invention significantly improves the standardization, processing efficiency, and system scalability of multi-source spatiotemporal data governance.

Description

Technical Field

[0001] This invention relates to the field of geographic information systems and spatiotemporal data technology, specifically to an integrated spatiotemporal data governance system, method, electronic device, and storage medium. Background Technology

[0002] Spatiotemporal data, as a crucial data carrier simultaneously carrying geospatial location and temporal information, plays an irreplaceable role in numerous fields such as surveying and mapping, natural resource management, urban planning, transportation, and emergency command. With the rapid development of information technology, the sources of spatiotemporal data are becoming increasingly diversified, generally exhibiting the characteristics of "multi-source heterogeneity," meaning it simultaneously contains data in various formats and structures, including vector data (such as Shapefile, GeoJSON), raster data (such as remote sensing imagery DOM), point cloud data (such as LAS), and various attribute tables. This raw data must undergo effective governance, including a series of steps such as data cleaning, classification, transformation, association, quality inspection, and warehousing, before it can be transformed into high-quality, standardized data assets usable for analysis and decision-making. This process, known as "multi-source heterogeneous spatiotemporal data governance," is a key prerequisite for unlocking the value of data.

[0003] However, current technological practices face several significant shortcomings in implementing the aforementioned governance processes. First, there is a heavy reliance on tools and fragmented workflows: data processors are forced to frequently switch between multiple independent professional software programs (such as ArcGIS / QGIS for vector data, ENVI / ERDAS for raster data, and CloudCompare for point clouds), resulting in repeated data imports and exports. This process is cumbersome, inefficient, and prone to information loss or errors during format conversion. Second, the technical barriers are high, and the process is disconnected from business needs: while programming languages ​​like Python combined with libraries such as GDAL and Pandas can flexibly process data, this requires operators to possess high levels of programming skills, excluding many frontline business personnel (such as planners and analysts), creating a significant technical gap. Third, the processing lacks standardization, leading to inconsistent output quality: the widespread reliance on individually written, fragmented scripts for workshop-style processing, coupled with a lack of unified process specifications and quality control mechanisms, makes it difficult to guarantee the consistency and standardization of the processing results, directly impacting the reliability of the final data product.

[0004] In summary, existing technologies have not yet been able to effectively address the problems of low governance efficiency and uncontrollable quality of results caused by the fragmentation of tools, complexity of operation, high technical threshold, and non-standard processes when dealing with the governance needs of multi-source heterogeneous spatiotemporal data. Summary of the Invention

[0005] In view of this, it is necessary to provide an integrated spatiotemporal data governance system, method, electronic device and storage medium to solve the technical problems of low efficiency and uncontrollable quality of multi-source heterogeneous spatiotemporal data governance caused by the reliance on different tools and lack of standardization in the processing process in existing methods.

[0006] To address the aforementioned technical problems, in a first aspect, the present invention provides an integrated spatiotemporal data governance system, comprising: The front-end interaction module is used to load and visualize multi-source heterogeneous spatiotemporal data through a unified graphical user interface, and provides a standardized full-process governance function entry point for data cleaning, classification, transformation, association, quality inspection and storage. The data processing engine, which is connected to the front-end interaction module, is configured to sequentially call and execute multiple functional sub-modules according to a preset spatiotemporal data governance process. The functional sub-modules include a data cleaning sub-module, a data classification sub-module, a data transformation sub-module, a data association sub-module, a data quality inspection sub-module, and a data storage sub-module, and each sub-module is integrated in a loosely coupled manner. The plug-in management module is used to encapsulate each functional sub-module in the data processing engine as an independent plug-in, and supports dynamic loading, unloading or updating of the plug-in during system operation to achieve hot expansion of data processing functions. The task status monitoring module is used to start an independent background processing thread to execute the task when the data processing engine is executing the task, and to provide real-time, non-blocking feedback of task progress and status information to the front-end interaction module through a signal mechanism.

[0007] In one possible implementation, the front-end interaction module includes: Layer management tree, used to organize and manage loaded vector data, raster data, and point cloud data in a hierarchical structure; The map view window is used for interactive zooming, panning, and spatial querying of the spatiotemporal data.

[0008] On the other hand, the present invention also provides a spatiotemporal data integrated governance method, comprising: Through a unified graphical user interface, load and merge multi-source heterogeneous spatiotemporal data for visualization; Receive governance process instructions triggered by the user through the graphical user interface. The governance process instructions include complete instructions or combinations of some of these process instructions, from data cleaning, classification, transformation, association, quality inspection to warehousing. In response to the governance process instructions, the corresponding functional plugins are dynamically scheduled through an extensible function scheduling mechanism, and the governance process is executed sequentially in an independent background thread; During task execution, the graphical user interface provides real-time, non-blocking feedback on task progress and status to the user. After the task is completed, the processing results are updated to the unified view in the graphical user interface and output to the specified storage location.

[0009] In one possible implementation, the sequential execution of the governance process in a separate background thread includes: Perform data cleaning steps to repair and filter topological errors, attribute anomalies, or noise in the input data to obtain cleaned data; Perform a data classification step to classify and extract the cleaned data based on its spatial or attribute features to obtain classified data; Perform a data conversion step to uniformly convert the coordinate system or file format of the classified data to obtain standardized data; Perform a data association step to establish spatial or attribute association relationships between different data layers of the standardized data and obtain the associated data. Perform a data quality inspection step, and check the quality of the associated data according to preset rules; Perform the data entry step, writing the data that has passed the quality inspection step into the target spatial database or standard file.

[0010] In one possible implementation, the data cleaning step includes: Identify the data type of the input data, wherein the data type includes at least vector data and point cloud data; Based on the data type, the corresponding data cleaning method is scheduled for data cleaning; wherein... If the data type is vector data, the scheduling topology repair algorithm will repair the topology errors in it; If the data type is point cloud data, the scheduling noise filtering algorithm filters out noise points and outliers.

[0011] In one possible implementation, the step of classifying and extracting the cleaned data based on its spatial or attribute features to obtain classified data includes: Call the preset machine learning model library; The supervised learning classification algorithm in the model library is used to automatically classify ground features based on the spatial and / or attribute features of the cleaned data.

[0012] In one possible implementation, an index building step is included before performing the data association step: For the spatial elements in the standardized data, an RTree spatial index is pre-constructed, wherein the data association step accelerates spatial retrieval based on the RTree spatial index to establish spatial association relationships between different data layers.

[0013] In one possible implementation, providing real-time, non-blocking feedback on task progress and status to the user via the graphical user interface includes: Create and start a separate background execution thread for each governance task; An asynchronous event communication channel is established between the background execution thread and the main thread of the graphical user interface; The progress and status information generated during the execution of the governance task are transmitted to the graphical user interface for display in real time through the asynchronous event communication channel.

[0014] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the spatiotemporal data integrated governance method described in any of the above implementations.

[0015] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps of the spatiotemporal data integrated governance method described in any of the above implementations.

[0016] The beneficial effects of this invention are as follows: The spatiotemporal data integrated governance system provided by this invention offers a unified graphical interface and standardized full-process governance function entry point in its front-end interaction module, reducing the need for users to switch between multiple professional software programs and improving operational continuity and convenience. The data processing engine sequentially calls multiple loosely coupled and integrated functional sub-modules according to a preset process, achieving automated connection between cleaning, classification, transformation, association, quality inspection, and warehousing stages, improving the standardization and overall efficiency of the processing flow. A plug-in management module encapsulates each functional sub-module as a dynamically loadable independent plug-in, thereby enabling flexible expansion and updating of specific processing functions, enhancing the system's adaptability and maintainability. A task status monitoring module starts independent background threads to execute tasks and provides real-time non-blocking status feedback through a signal mechanism, ensuring smooth user interface response and user experience during time-consuming task execution. The collaborative technical architecture formed by the cooperation of these modules effectively improves the integrated governance efficiency of multi-source heterogeneous spatiotemporal data from processing to management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0018] Figure 1 A schematic flowchart of an embodiment of the spatiotemporal data integrated governance system provided by the present invention; Figure 2 A schematic flowchart of an embodiment of the spatiotemporal data integrated governance method provided by the present invention; Figure 3 For the present invention Figure 2 A schematic diagram of an embodiment of S203; Figure 4 For the present invention Figure 3 A schematic diagram of an embodiment of S301; Figure 5 For the present invention Figure 3 A schematic diagram of an embodiment of S302; Figure 6 For the present invention Figure 1 A schematic diagram of an embodiment of S204; Figure 7 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0019] 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 a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0021] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] This invention provides an integrated spatiotemporal data governance system, method, electronic device, and storage medium. The technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] Figure 1 This is a schematic diagram of an embodiment of the spatiotemporal data integrated governance system 100 provided by the present invention, as shown below. Figure 1 As shown, the spatiotemporal data integrated governance system 100 includes: The front-end interaction module 101 is used to load and visualize multi-source heterogeneous spatiotemporal data through a unified graphical user interface, and provides a standardized full-process governance function entry point for data cleaning, classification, transformation, association, quality inspection and storage. The data processing engine 102 is connected to the front-end interaction module 101 and is configured to call and execute multiple functional sub-modules in sequence according to the preset spatiotemporal data governance process. The functional sub-modules include a data cleaning sub-module, a data classification sub-module, a data transformation sub-module, a data association sub-module, a data quality inspection sub-module, and a data storage sub-module, and each sub-module is integrated in a loosely coupled manner. The plug-in management module 103 is used to encapsulate each functional sub-module in the data processing engine 102 as an independent plug-in, and supports dynamic loading, unloading or updating of the plug-in during system operation to realize hot expansion of data processing functions. The task status monitoring module 104 is used to start an independent background processing thread to execute the task when the data processing engine 102 is executing the task, and to provide real-time, non-blocking feedback of task progress and status information to the front-end interaction module 101 through a signal mechanism.

[0025] "Loosely coupled integration" means that functional sub-modules do not directly depend on each other's internal implementation details, but interact through clearly defined interfaces, dependency injection, or middleware, thereby reducing the correlation between modules and improving the maintainability and replaceability of the system.

[0026] Hot expansion refers to the ability to dynamically add, remove, or update functional components during software operation without stopping or restarting the main program. Common implementation methods include dynamic link libraries (DLLs), script plugins, OSGi frameworks, or microservice architectures.

[0027] Signaling is a software design pattern used for communication between objects. It is usually implemented as the observer pattern or the publish-subscribe model and is particularly common in graphical user interface programming. It allows an object (sender) to asynchronously notify one or more other objects (receivers) when a specific event occurs, without the sender needing to know the specific information of the receiver.

[0028] Specifically, this embodiment provides a spatiotemporal data integrated governance system 100. This system can be deployed on general-purpose computer equipment. Its core lies in the collaborative operation of four core modules—a front-end interaction module 101, a data processing engine 102, a plug-in management module 103, and a task status monitoring module 104—forming a specific software architecture that integrates and automates the entire process of multi-source spatiotemporal data governance.

[0029] The front-end interaction module 101 is an application interface developed based on graphical interface frameworks such as PySide6. It provides users with a unified data operation entry point and visualization window. Users can use the module's import function to load multi-source heterogeneous data at once, such as vector road networks in Shapefile format, remote sensing imagery in GeoTIFF format, and laser point clouds in LAS format. This data is organized hierarchically through the layer management tree within the module and displayed in a unified map view window.

[0030] More importantly, this module transforms complex governance processes into intuitive graphical buttons and menus, such as providing standardized function entry points for "data cleaning," "coordinate transformation," and "data import." Users can trigger the corresponding governance commands simply by clicking, without having to worry about the complex implementation logic in the backend.

[0031] The data processing engine 102 is the computational core of the system, typically composed of a series of Python scripts or libraries that conform to specific interface specifications. This engine 102 consists of a workflow set of loosely coupled functional sub-modules. Upon receiving instructions from the front-end interaction module 101, the engine sequentially calls and executes the corresponding sub-modules according to a pre-defined, optimal spatiotemporal data governance process (e.g., cleaning, classification, and transformation). These sub-modules include a data cleaning sub-module specifically responsible for correcting topological errors, a data classification sub-module for intelligent feature identification, and a data transformation sub-module for coordinate system transformation. Each sub-module is relatively independent, but they are scheduled and connected through the engine to collectively complete the transformation from raw data to standardized results.

[0032] The plug-in management module 103 is key to achieving system functional scalability. This module encapsulates each functional sub-module in the data processing engine 102 (such as a specific point cloud denoising algorithm) into an independent plug-in unit. The plug-ins follow predefined input, output, and interface specifications.

[0033] Based on this, the plug-in management module 103 can dynamically load new functional plug-ins or update existing plug-ins during system operation without restarting the main program or modifying the core architecture.

[0034] For example, when a new data quality inspection function for oblique photogrammetry models is needed, developers only need to write a new quality inspection plugin script according to the specifications. The plugin management module 103 can automatically discover and integrate this plugin, and then the corresponding new function button will dynamically appear on the interface of the front-end interaction module 101, thereby realizing hot expansion of functions.

[0035] The task status monitoring module 104 is responsible for ensuring a smooth user interface response and interactive experience when the system is performing time-consuming governance tasks. When the data processing engine 102 starts executing a long-running task (such as filtering hundreds of millions of point clouds), this module immediately intervenes and takes over the task execution process. Its core mechanism is to start an independent background processing thread (e.g., through PySide6's QThread) to actually run the data processing code, thereby avoiding blocking the main thread responsible for interface rendering and response. At the same time, it establishes a communication mechanism (such as PyQt's signal and slot mechanism) so that the background thread can periodically and securely transmit task execution progress, current status, or exception information back to the front-end interaction module 101 in real time. The front-end interface dynamically updates the progress bar and status bar text based on the received information, enabling users to have a transparent perception of task progress.

[0036] The spatiotemporal data integrated governance system provided in this embodiment offers a unified graphical interface and standardized full-process governance function entry point in its front-end interaction module, reducing the need for users to switch between multiple professional software programs and improving operational continuity and convenience. The data processing engine sequentially calls multiple loosely coupled and integrated functional sub-modules according to a preset process, achieving automated connection between cleaning, classification, transformation, association, quality inspection, and warehousing stages, improving the standardization and overall efficiency of the processing flow. A plug-in management module encapsulates each functional sub-module as a dynamically loadable independent plug-in, thereby enabling flexible expansion and updating of specific processing functions, enhancing the system's adaptability and maintainability. A task status monitoring module starts independent background threads to execute tasks and provides real-time non-blocking status feedback through a signal mechanism, ensuring smooth user interface response and user experience during time-consuming task execution. The collaborative technical architecture formed by the cooperation of these modules effectively improves the integrated governance efficiency of multi-source heterogeneous spatiotemporal data from processing to management.

[0037] In one possible implementation, the front-end interaction module 101 includes: Layer management tree, used to organize and manage loaded vector data, raster data, and point cloud data in a hierarchical structure; The map view window is used for interactive zooming, panning, and spatial querying of the spatiotemporal data.

[0038] In Geographic Information Systems (GIS), a layer is a core concept, referring to a single data set that shares the same spatial reference and can be displayed, managed, and analyzed as an independent unit. A layer typically corresponds to a type of land cover or a data source.

[0039] Spatial query refers to retrieval operations based on geographic location and topological relationships. Common types include point selection query (Identify), rectangular selection query, polygon area query, and queries based on spatial relationships (such as finding all roads that intersect with a certain river).

[0040] In this embodiment, the layer management tree organizes and manages all the spatiotemporal data loaded into the system with a clear tree-like hierarchical structure. It abstracts different types of spatial data resources (such as vector surface data, road line data, remote sensing image raster data, laser point cloud data, etc.) into independent layer nodes, and allows users to expand, collapse, rename, and change the stacking order of these layers as if they were files or directories.

[0041] For example, when processing data for a city area, users can see layers named "Planned Land Use Red Line (Area)", "Main Road Network (Line)", "2023 Satellite Imagery (Raster)" and "Airborne Laser Point Cloud (Point Cloud)" displayed side by side in this tree.

[0042] It should be noted that each layer node in this embodiment usually has a checkbox next to it. Users can control the visibility and hiding of the layer in the map view window by checking or unchecking the checkbox, thereby achieving flexible data comparison and viewing.

[0043] The map view window is responsible for merging, rendering, and graphically displaying the data from all visible layers in the layer management tree. This window not only provides basic interactive navigation functions such as zooming and panning, but more importantly, it supports location-based spatial queries. Users can trigger a query by clicking on a location on the map. The system will convert the screen coordinates of that location into geographic coordinates and automatically perform a "penetration-based" search of all visible layers of different types at that location.

[0044] For example, clicking on a building on the map may simultaneously return the vector attributes of the land parcel where the building is located (such as land use and area), its corresponding image pixel value, and the point cloud elevation information covering it, and integrate this information into a query results panel.

[0045] This embodiment effectively unifies heterogeneous spatial data from different sources and formats at both the logical (tree-based management) and visual (map fusion) levels by combining a layer management tree with a map view window. This approach allows users to freely control the display combination of various data within a unified interface and perform intuitive spatial exploration and analysis without having to switch between multiple software windows, thus significantly reducing the operational complexity of collaborative browsing and analysis of multi-source data.

[0046] Figure 2 A schematic flowchart of an embodiment of the spatiotemporal data integrated governance method provided by the present invention is shown below. Figure 2 As shown, the spatiotemporal data integrated governance method includes: S201. Load and merge multi-source heterogeneous spatiotemporal data through a unified graphical user interface; S202. Receive governance process instructions triggered by the user through the graphical user interface. The governance process instructions include complete instructions or combinations of some of the process instructions from data cleaning, classification, transformation, association, quality inspection to warehousing. S203. In response to governance process instructions, the corresponding functional plugins are dynamically scheduled through an extensible function scheduling mechanism, and the governance process is executed sequentially in an independent background thread. S204. During task execution, provide real-time, non-blocking feedback on task progress and status to the user through a graphical user interface; S205. After the task is completed, update the processing results to the unified view in the graphical user interface and output them to the specified storage location.

[0047] This embodiment provides an integrated spatiotemporal data governance method, which realizes unified loading, collaborative processing and result output of multi-source heterogeneous spatiotemporal data through an integrated graphical user interface.

[0048] Firstly, a graphical user interface developed based on the PySide6 framework is used to simultaneously import and visualize spatiotemporal data in various formats, including vector road network data (such as Shapefile format), remote sensing image raster data (such as GeoTIFF format), and laser point cloud data (such as LAS format). This interface integrates standardized function entry points covering data cleaning, classification, coordinate transformation, data association, quality inspection, and data storage. When users discover topological errors, coordinate system inconsistencies, or format compatibility issues during visualization, they can directly select the corresponding remediation operation within the interface without switching to other software tools.

[0049] It should be noted that the core of this embodiment lies in the adoption of a scalable function scheduling mechanism, which is implemented based on a plug-in architecture. Each governance function—such as topology repair, coordinate system transformation, attribute normalization, or point cloud filtering—is encapsulated as an independent Python module or script and follows a unified interface specification.

[0050] When a user triggers a governance command, the system dynamically scans, loads, and instantiates one or more functional plugins corresponding to that command.

[0051] For example, if a user selects "Perform data cleaning and coordinate transformation", the system will call the "data cleaning plugin" and the "data transformation plugin" in sequence and execute the governance process according to the preset logical order.

[0052] All plugin tasks run in background worker threads created by QThread or other thread management mechanisms to avoid blocking the main thread of the graphical user interface. During task execution, the system transmits task status information back to the main thread in real time through asynchronous communication mechanisms (such as the signal and slot mechanism in PyQt / PySide6).

[0053] The task status information includes the current execution step (e.g., "Coordinate transformation in progress"), processing progress (e.g., "50% of elements processed"), and error messages. Upon receiving this information, the main thread immediately updates the progress bar, status bar, or log window on the interface to ensure a smooth and responsive user experience.

[0054] Once all the remediation processes are completed, the system will automatically refresh the processed results onto the map view for visualization, such as displaying the overlay effect of the topology-repaired road network and the remote sensing image with unified coordinates. Simultaneously, according to the user-configured output strategy, the system will write the results to a specified spatial database (such as PostGIS) or export them as a standard geospatial format file (such as GeoPackage).

[0055] This embodiment integrates previously scattered and isolated spatiotemporal data governance tools into a coherent, configurable, and scalable automated process, enabling users to efficiently complete the entire governance task from problem identification and process execution to output without writing code or frequently switching applications, significantly improving the professionalism, efficiency, and ease of use of spatiotemporal data processing.

[0056] In some embodiments of the present invention, such as Figure 3 As shown, step S203, which executes the governance process sequentially in an independent background thread, includes: S301. Perform the data cleaning step to repair and filter the topological errors, attribute anomalies or noise in the input data to obtain cleaned data. S302. Perform the data classification step to classify and extract the cleaned data based on its spatial or attribute features to obtain the classified data. S303. Perform the data conversion step to uniformly convert the coordinate system or file format of the classified data to obtain standardized data. S304. Perform the data association step to establish spatial or attribute association relationships between different data layers of standardized data and obtain the associated data. S305. Perform data quality inspection steps and check the quality of the associated data according to preset rules; S306. Perform the data entry step, and write the data that has been confirmed as qualified by the quality inspection step into the target space database or standard file.

[0057] In some embodiments of the present invention, such as Figure 3 As shown, step S203, executed sequentially in an independent background thread, represents a pre-defined, standardized, multi-step data processing chain. This chain integrates discrete data governance tasks into an ordered, automated overall workflow, comprising steps S301 (data cleaning), S302 (data classification), S303 (data transformation), S304 (data association), S305 (data quality inspection), and S306 (data import).

[0058] In step S301, the system identifies and corrects fundamental quality problems in the original spatiotemporal data.

[0059] For vector-based digital line drawing (DLG) data, this step primarily corrects geometric topology errors, including eliminating dangling lines, redundant nodes, and overlapping surface features. For lidar point cloud data, noise filtering is performed to remove outliers introduced by moving objects such as birds and vehicles, as well as outliers that are significantly deviated from the ground surface.

[0060] The data output after this step is the cleaned data with initial quality improvement.

[0061] Then, in step S302, the system performs semantic classification of ground features in the cleaned data based on attributes such as spatial distribution, geometric shape, spectral characteristics, or echo intensity.

[0062] For example, point cloud data can be divided into categories such as ground points, vegetation points, and building points, or pixels in remote sensing images can be clustered into patches such as water bodies, farmland, and construction land.

[0063] This step outputs categorized data with clear category labels, providing a semantic basis for subsequent processing.

[0064] In step S303, the system unifies the spatial reference system and storage format for multi-source data. Specifically, this includes: Transform the classified data from different projected coordinate systems to a unified standard coordinate system (such as CGCS2000 National Geodetic Coordinate System). At the same time, heterogeneous formats (such as Shapefile, IMG, etc.) are converted into the standard format required by the target application (such as GeoJSON, GeoTIFF, etc.), generating standardized data with consistent structure and coordinates.

[0065] Then, step S304 is executed to establish logical or spatial relationships between different data layers.

[0066] For example, spatial overlay can be used to align the centerline of a vector road with high-resolution remote sensing imagery to verify road timeliness; or key fields can be used to link the vector boundaries of land parcels with their attribute tables to achieve integrated management of graphics and attributes. This step outputs correlated data with cross-source relationships.

[0067] Next, in step S305, the system performs automated batch checks on the associated data based on a preset set of quality rules—covering dimensions such as completeness, logical consistency, location accuracy, and attribute accuracy—and generates a structured quality inspection report. Only after the data passes all rule checks can the final step S306 be performed.

[0068] In step S306, qualified governance results are written into a designated spatial database (such as PostGIS) or exported as a standard geospatial data package (such as GeoPackage) according to specifications, completing the full cycle of governance from original input to usable results.

[0069] The above six steps are executed automatically in sequence in a background thread, forming a solidified and reusable data governance pipeline. This process transforms the traditional discrete operations that rely on manual intervention and experience-based judgment into standardized, traceable automated operations, ensuring that different operators follow consistent processing logic and quality benchmarks when executing at different times, thereby significantly improving the standardization, reliability, and productivity of spatiotemporal data results.

[0070] In some embodiments of the present invention, such as Figure 4 As shown, step S301, which involves data cleaning, includes: S401. Identify the data type of the input data, which includes at least vector data and point cloud data; S402. Based on the data type, schedule the corresponding data cleaning method to perform data cleaning; whereby... If the data type is vector data, the scheduling topology repair algorithm will repair the topology errors in it; If the data type is point cloud data, the scheduling noise filtering algorithm filters out noise points and outliers.

[0071] In some embodiments of the present invention, such as Figure 4 As shown, step S301, which involves performing data cleaning, is concretized as an intelligent scheduling process with data type awareness capabilities. The first step in this process is to identify the data type of the input data (step S401). Among them, the data types at least cover two core data forms with very different structures in spatiotemporal data governance: vector data and point cloud data.

[0072] The system automatically identifies the data type based on its file extension, internal structure identifier, or metadata information. For example, it identifies .shp and .geojson files as vector data, while .las and .laz files are point cloud data.

[0073] Based on the accurately identified data type, the corresponding dedicated data cleaning method is then scheduled for processing (step S402). The topology repair algorithm and the noise filtering algorithm are both specific implementation methods for achieving the corresponding cleaning function.

[0074] If the data is identified as vector data, a topology repair algorithm is invoked. This algorithm primarily addresses spatial relationship errors between vector graphic features. For example, it can call geometric operation functions from libraries such as Geopadas to merge overlapping areas of polygon data (unary_union), capture or extend dangling endpoints of line features to close gaps, or eliminate redundant pseudo-nodes.

[0075] If the data is identified as point cloud data, a noise filtering algorithm is invoked. This algorithm aims to remove discrete noise points that are unrelated to real ground features. It can be implemented using point cloud processing libraries such as LASpy and PDAL, employing statistical outlier analysis (e.g., Statistical Outlier Removal) or spatial density-based filtering methods (e.g., Radius Outlier Removal) to distinguish and remove outliers that are floating above buildings or deeply embedded below the ground.

[0076] By employing the aforementioned automated identification-scheduling approach, this embodiment changes the traditional data cleaning process that relies on manual judgment of data problems and manual tool selection. This method automatically distinguishes data types and invokes the most suitable toolchain, thereby ensuring the possibility of efficient and accurate repair of different types of data quality problems at the source, significantly improving the automation level of the data cleaning process and the reliability of the processing results.

[0077] In some embodiments of the present invention, such as Figure 5 As shown, step S302 involves classifying and extracting the cleaned data based on its spatial or attribute features, resulting in classified data including: S501, Call the preset machine learning model library; S502. Using supervised learning classification algorithms from the model library, the feature elements are automatically classified based on the spatial and / or attribute features of the cleaned data.

[0078] Specifically, step S302 involves classifying and extracting the cleaned data based on its spatial or attribute characteristics to obtain classified data. The core of this step is the introduction and integration of intelligent automatic recognition technology.

[0079] This step first calls the pre-defined machine learning model library (step S501). This machine learning model library is a collection of software resources that pre-integrates and encapsulates various classification algorithm implementations and tools, providing a basic computational framework and model resources for automatic classification tasks.

[0080] Subsequently, the supervised learning classification algorithm in the model library is used to perform the specific classification task (step S502).

[0081] Supervised learning classification algorithms refer to a class of machine learning algorithms that need to learn the mapping relationship between features and categories based on labeled training samples.

[0082] In the context of this embodiment, such algorithms are used to automatically analyze the spatial features (such as the three-dimensional coordinates, intensity, and echo count of point clouds; the spectral values ​​and texture of image pixels; and the shape, area, and perimeter of vectors) and / or attribute features (such as the name, type code, and other descriptive fields of ground features) contained in the cleaned data, thereby classifying the ground feature elements in the data into predefined semantic categories.

[0083] For example, classifying laser point cloud data can automatically categorize points into types such as ground, vegetation, buildings, and power lines; analyzing high-resolution remote sensing images can extract patches such as water bodies, forest land, farmland, roads, and buildings.

[0084] Machine learning model libraries include, but are not limited to, software libraries that provide classic machine learning algorithms or deep learning frameworks, such as Scikit-learn, TensorFlow, and PyTorch.

[0085] Supervised learning classification algorithms include, but are not limited to: Support Vector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Network (CNN) based on deep learning.

[0086] By employing the methods described above, this embodiment transforms the traditional data classification work, which relies on manual visual interpretation or the writing of complex rules, into an automated model calculation process. This approach can handle massive amounts of data with complex features, significantly improving the processing efficiency and consistency of land cover classification and extraction results while reducing subjective human intervention.

[0087] In some embodiments of the present invention, an index building step is included before performing the data association step: To standardize spatial elements in the data, an RTree spatial index is pre-built. The data association step accelerates spatial retrieval based on the RTree spatial index to establish spatial relationships between different data layers.

[0088] Specifically, this embodiment employs an efficient and accurate spatial relationship matching process to implement the data association step. Before executing this step, the system pre-executes a crucial index building step: pre-building an RTree spatial index for spatial elements in standardized data that has undergone format and coordinate system unification processing. This step is necessary data preprocessing for subsequent efficient spatial calculations.

[0089] RTree spatial indexing is a tree-like data structure specifically designed for efficiently managing multidimensional spatial data (such as geographic coordinates). RTree spatial indexing can be implemented using mature spatial computing libraries, including but not limited to: the libspatialindex library (which GeoPandas relies on), the GiST index built into the PostGIS database, or the rtree library implemented entirely in Python. Its core principle is to group the bounding boxes of adjacent features in space and aggregate them layer by layer upwards to form a hierarchical search tree. When spatial queries are needed (such as finding other features that intersect, are adjacent to, or contain a given feature), this index allows the system to quickly eliminate a large number of obviously irrelevant features, significantly narrowing the calculation scope to at least a few candidate feature sets that may have spatial relationships, thus avoiding brute-force traversal that calculates every single feature.

[0090] In the subsequent data association steps, the system uses this RTree spatial index to accelerate spatial retrieval operations.

[0091] For example, when performing spatial overlay analysis of "points and surfaces" to establish relationships, the system does not need to calculate the geometric relationship between each facility point and all planned block surfaces. Instead, it uses the RTree index to quickly locate a few candidate block surfaces that each facility point may fall into, and then performs precise geometric relationship judgments (such as whether the point is inside the surface) only on these candidate surfaces.

[0092] By employing this two-stage approach of rapid indexing and precise calculation for confirmation, this embodiment reduces the time complexity of spatial association calculations from a potentially high-order polynomial level to an approximate logarithmic level. This approach is particularly effective in governance scenarios involving massive amounts of spatiotemporal data (such as millions of elements), transforming what would otherwise be time-consuming or even impossible spatial association tasks into efficient operations that can be completed within actual business hours. This significantly improves the feasibility of establishing complex spatial relationships and the timeliness of the entire governance process.

[0093] In some embodiments of the present invention, such as Figure 6 As shown, step S204 provides real-time, non-blocking feedback on task progress and status to the user through a graphical user interface, including: S601. Create and start an independent background execution thread for each governance task; S602. Establish an asynchronous event communication channel between the background execution thread and the main thread of the graphical user interface; S603. Through the asynchronous event communication channel, the progress and status information generated during the execution of the governance task are transmitted to the graphical user interface for display in real time.

[0094] Asynchronous event communication channels are a technique for achieving loosely coupled communication between threads. Specific implementations include, but are not limited to, the "Signal & Slot" mechanism used in the PyQt / PySide framework, and patterns such as message queues or event buses used in other GUI frameworks or general programming. These implementations allow one thread to send notifications or data to another thread without waiting or blocking.

[0095] Specifically, this embodiment provides real-time, non-blocking feedback on task progress and status to the user through a graphical user interface. This is achieved through an architecture that decouples long-running task execution from user interface interaction and executes concurrently.

[0096] This approach first creates and starts an independent background execution thread for each triggered governance task (e.g., denoising and filtering a large point cloud dataset) (step S601). This thread runs independently of the main thread of the graphical user interface (GUI) responsible for interface rendering and event response, thereby ensuring that time-consuming data calculation processes do not block the main thread, allowing users to smoothly perform interface interaction operations such as window scaling and layer switching during task execution.

[0097] To synchronize the background processing status with the foreground interface, this embodiment establishes an asynchronous event communication channel between the background execution thread and the GUI main thread (step S602). This channel is not used to directly transmit large amounts of management data itself, but is specifically used to transmit control and message data describing the task execution status.

[0098] Finally, through this asynchronous event communication channel, the progress information (such as "65% of the point cloud data has been processed") and status information (such as "Noise filtering is being performed", "Task completed", or "An exception has occurred") generated in real time during the execution of the governance task are transmitted to the GUI main thread (step S603). After the main thread safely receives this information, it immediately drives the interface components to update, such as refreshing the progress bar value, displaying the current step in the status bar, or appending a record to the log window, thereby providing users with continuous and transparent visual feedback on task execution.

[0099] By employing the aforementioned "thread separation and asynchronous communication" approach, this embodiment ensures that the front-end user interface remains responsive regardless of the workload of the back-end data processing tasks, and clearly presents the execution flow of the tasks to the user, thereby significantly improving the smoothness of user interaction and operational controllability during long-running governance tasks.

[0100] like Figure 7 As shown, the present invention also provides an electronic device 700. The electronic device 700 includes a processor 701, a memory 702, and a display 703. Figure 7 Only some components of the electronic device 700 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0101] In some embodiments, processor 701 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 702 or process data, such as the spatiotemporal data integrated governance method of the present invention.

[0102] In some embodiments, processor 701 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 701 may be local or remote. In some embodiments, processor 701 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.

[0103] In some embodiments, memory 702 may be an internal storage unit of electronic device 700, such as a hard disk or memory of electronic device 700. In other embodiments, memory 702 may also be an external storage device of electronic device 700, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 700.

[0104] Furthermore, the memory 702 may include both internal storage units of the electronic device 700 and external storage devices. The memory 702 is used to store application software and various types of data installed on the electronic device 700.

[0105] In some embodiments, display 703 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 703 is used to display information from electronic device 700 and to display a visual user interface. Components 701-703 of electronic device 700 communicate with each other via a system bus.

[0106] In one embodiment, when the processor 701 executes the spatiotemporal data integration governance program in the memory 702, the following steps can be implemented: Through a unified graphical user interface, load and merge multi-source heterogeneous spatiotemporal data for visualization; Receive governance process instructions triggered by users through the graphical user interface. Governance process instructions include complete instructions or combinations of some of these processes, from data cleaning, classification, transformation, association, quality inspection to warehousing. In response to governance process instructions, the corresponding functional plugins are dynamically scheduled through an extensible function scheduling mechanism, and the governance process is executed sequentially in an independent background thread. During task execution, the task progress and status are provided to the user in real time and non-blocking through a graphical user interface; After the task is completed, the processing results are updated to the unified view in the graphical user interface and output to the specified storage location.

[0107] It should be understood that when the processor 701 executes the spatiotemporal data integrated governance program in the memory 702, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0108] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 700 mentioned. Electronic device 700 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, electronic device 700 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0109] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the spatiotemporal data integrated governance method provided in the above-described method embodiments.

[0110] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0111] The spatiotemporal data integrated governance method, apparatus, electronic device, and storage medium provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A spatiotemporal data integrated governance system, characterized in that, include: The front-end interaction module is used to load and visualize multi-source heterogeneous spatiotemporal data through a unified graphical user interface, and provides a standardized full-process governance function entry point for data cleaning, classification, transformation, association, quality inspection and storage. The data processing engine, which is connected to the front-end interaction module, is configured to sequentially call and execute multiple functional sub-modules according to a preset spatiotemporal data governance process. The functional sub-modules include a data cleaning sub-module, a data classification sub-module, a data transformation sub-module, a data association sub-module, a data quality inspection sub-module, and a data storage sub-module, and each sub-module is integrated in a loosely coupled manner. The plug-in management module is used to encapsulate each functional sub-module in the data processing engine as an independent plug-in, and supports dynamic loading, unloading or updating of the plug-in during system operation to achieve hot expansion of data processing functions. The task status monitoring module is used to start an independent background processing thread to execute the task when the data processing engine is executing the task, and to provide real-time, non-blocking feedback of task progress and status information to the front-end interaction module through a signal mechanism.

2. The spatiotemporal data integrated governance system according to claim 1, characterized in that, The front-end interaction module includes: Layer management tree, used to organize and manage loaded vector data, raster data, and point cloud data in a hierarchical structure; The map view window is used for interactive zooming, panning, and spatial querying of the spatiotemporal data.

3. A spatiotemporal data integrated governance method, characterized in that, include: Through a unified graphical user interface, load and merge multi-source heterogeneous spatiotemporal data for visualization; Receive governance process instructions triggered by the user through the graphical user interface. The governance process instructions include complete instructions or combinations of some of these process instructions, from data cleaning, classification, transformation, association, quality inspection to warehousing. In response to the governance process instructions, the corresponding functional plugins are dynamically scheduled through an extensible function scheduling mechanism, and the governance process is executed sequentially in an independent background thread; During task execution, the graphical user interface provides real-time, non-blocking feedback on task progress and status to the user. After the task is completed, the processing results are updated to the unified view in the graphical user interface and output to the specified storage location.

4. The spatiotemporal data integrated governance method according to claim 3, characterized in that, The process of executing the governance procedure sequentially in an independent background thread includes: Perform data cleaning steps to repair and filter topological errors, attribute anomalies, or noise in the input data to obtain cleaned data; Perform a data classification step to classify and extract the cleaned data based on its spatial or attribute features to obtain classified data; Perform a data conversion step to uniformly convert the coordinate system or file format of the classified data to obtain standardized data; Perform a data association step to establish spatial or attribute association relationships between different data layers of the standardized data and obtain the associated data. Perform a data quality inspection step, and check the quality of the associated data according to preset rules; Perform the data entry step, writing the data that has passed the quality inspection step into the target spatial database or standard file.

5. The spatiotemporal data integrated governance method according to claim 4, characterized in that, The data cleaning steps include: Identify the data type of the input data, wherein the data type includes at least vector data and point cloud data; Based on the data type, the corresponding data cleaning method is scheduled for data cleaning; wherein... If the data type is vector data, then the topology repair algorithm is scheduled to repair topology errors; If the data type is point cloud data, the scheduling noise filtering algorithm filters out noise points and outliers.

6. The spatiotemporal data integrated governance method according to claim 4, characterized in that, The execution data classification step includes: Call the preset machine learning model library; The supervised learning classification algorithm in the model library is used to automatically classify ground features based on the spatial and / or attribute features of the cleaned data.

7. The spatiotemporal data integrated governance method according to claim 4, characterized in that, Before performing the data association step, the spatiotemporal data integrated governance method includes an index building step, which includes: For the spatial elements in the standardized data, an RTree spatial index is pre-constructed, wherein the data association step accelerates spatial retrieval based on the RTree spatial index to establish spatial association relationships between different data layers.

8. The spatiotemporal data integrated governance method according to claim 3 or 4, characterized in that, The provision of real-time, non-blocking feedback on task progress and status to the user through the graphical user interface includes: Create and start a separate background execution thread for each governance task; An asynchronous event communication channel is established between the background execution thread and the main thread of the graphical user interface; The progress and status information generated during the execution of the governance task are transmitted to the graphical user interface in real time for display through the asynchronous event communication channel.

9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the spatiotemporal data integrated governance method according to any one of claims 3 to 8.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the spatiotemporal data integrated governance method according to any one of claims 3 to 8.

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