Sky-ground integrated data fusion management system and method thereof

Abstract

The invention discloses a sky-ground integrated data fusion management system and a method thereof, relates to the technical field of data processing, and aims to generate high-precision virtual space data by efficiently integrating and processing multi-source data and provide real-time decision support and data sharing functions for users. The system comprises a data access module, a data preprocessing module, a space generation module, a data adaptation module, a space-time alignment module, a space-time coordinate embedding module, a data fusion module, a global supervision module and a data service module. According to the method, accurate alignment and efficient fusion of data of different sources in time and space can be ensured, the system optimizes the data fusion accuracy by utilizing dynamic subdivision and cross-modal data association technologies, and meanwhile, the operation state of each module is monitored in real time through a global supervision mechanism and is dynamically optimized, so that efficient and stable operation of the system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a sky-ground-air integrated data fusion management system and method thereof. Background Art

[0002] With the rapid development of technologies such as remote sensing technology, unmanned aerial vehicles, and ground sensors, the types of environmental data obtained are numerous, covering multi-source data such as satellites, the air, and the ground. However, due to problems such as the spatio-temporal differences in these data sources and the diverse data formats, how to efficiently and accurately fuse them and apply them to decision-making support has become a challenge for current technologies.

[0003] In the prior art, Chinese invention patent CN110068655B proposed a sky-ground-air integrated air monitoring system, including data acquisition, storage, processing, and monitoring modules, in which the accuracy of air monitoring is improved through multi-source data fusion. This system realizes large-scale and all-weather environmental monitoring. However, in the data processing process, there is a lack of an efficient spatio-temporal alignment mechanism, which may lead to insufficient accuracy of the fused data. Especially when processing high-frequency data with dynamic changes, errors may occur.

[0004] In addition, Chinese invention patent application CN111028096A discloses a system for integrating sky, air, and ground data, which combines satellite, air, and ground monitoring data with Internet of Things technology for fusion, and can achieve a balance between large-scale and small-scale data. However, although its method has advantages in agricultural monitoring, the processing of spatio-temporal alignment and fusion accuracy of multi-source data is still not fine enough. Especially when dealing with dynamic scenarios and high-dynamic regions, it may not be able to ensure high-precision spatio-temporal consistency and data accuracy. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and propose a sky-ground-air integrated data fusion management system and method thereof to solve the above-mentioned existing problems.

[0006] The present invention solves the problems of insufficient data fusion accuracy and spatio-temporal synchronization existing in the prior art by introducing a multi-head attention mechanism, spatio-temporal alignment technology, and dynamic segmentation algorithm. Specifically, the system is improved in the following aspects: Multi-head attention mechanism: The multi-head attention mechanism is used to perform weighted matching on the triangle vertices from data sources such as satellites, unmanned aerial vehicles, and ground sensors to ensure the accuracy of spatio-temporal alignment. This technology makes up for the deficiencies in the spatio-temporal synchronization processing of high-frequency updated data (such as meteorological sensors) in the prior art.

[0007] Spatial-Temporal Alignment and Embedding: After data adaptation and spatial-temporal alignment, the present invention uses the Beidou satellite positioning system and timestamps to accurately embed the spatial-temporal coordinates of each data point, forming a spatial-temporal quadruple (x, y, z, t), providing precise spatial positioning for subsequent data fusion.

[0008] Dynamic Subdivision and Cross-Modal Fusion: Through dynamic subdivision units, the high-dynamic regions are carefully processed according to data error driving, ensuring the precise fusion of data in dynamically changing regions, thus overcoming the problem of inaccurate processing of high-dynamic scenarios in the prior art.

[0009] The object of the present invention is achieved by the following technical solutions: A sky-ground integrated data fusion management system, characterized in that it includes a data access module, a data preprocessing module, a space generation module, a data adaptation module, a spatial-temporal alignment module, a spatial-temporal coordinate embedding module, a data fusion module, and a global supervision module; Space Generation Module: Based on the target area, a three-dimensional virtual space is established, the virtual space is divided into basic grids according to a preset granularity, and Delaunay method is used to triangulate each basic grid to generate virtual grids; The data adaptation module is used to adapt data from different sources to the virtual grids, specifically including: a satellite data adaptation unit, an aerial video adaptation unit, and a ground data adaptation unit; The spatial-temporal alignment module aligns the spatial-temporal coordinates of the data. The spatial-temporal alignment of the data is adjusted through the multi-head attention mechanism, the spatial attention and the time attention sub-modules, specifically including: a multi-head attention mapping unit, a spatial attention unit, and a time attention unit; The spatial-temporal coordinate embedding module is used to, after completing data adaptation and spatial-temporal alignment, use the Beidou satellite positioning system and timestamps to embed the spatial-temporal coordinates of each triangle vertex, forming a spatial-temporal quadruple (x, y, z, t); Data Fusion Module: The multi-head attention mechanism is used to perform weighted fusion on the data to achieve deep fusion and real-time update, specifically including: a dynamic subdivision unit and a cross-modal data association unit; The global supervision module is used to monitor the running status of each module in real time and perform dynamic optimization on the system, specifically including: a global multi-head attention supervision unit and a feedback regulation unit; The data service module is used to display and share the fused data, supporting decision-making and collaborative operations.

[0010] The data access module is used to receive and integrate multi-source data from satellites, drones, ground sensors, and point clouds, and uniformly manage each data source through the data transmission unit and achieve real-time and efficient access.

[0011] The data preprocessing module is used to process the incoming data to ensure data quality and spatio-temporal consistency. Specifically, it includes: The spatio-temporal synchronization correction unit is used to correct the incoming data in terms of space and time to ensure the spatio-temporal consistency of multi-source data; the data format standardization unit is used to convert various types of data into a unified format to ensure compatibility for subsequent processing; the noise suppression unit is used to filter the noise in the original data, eliminate invalid information, and improve data quality.

[0012] The satellite data adaptation unit cuts the satellite line-scan images according to the virtual grid size, performs triangulation on each strip, and dynamically calibrates the spatio-temporal coordinates of the triangle vertices through satellite orbit parameters; the aerial video adaptation unit splits the video frames into continuous strips, performs triangulation according to the same rules, and adaptively performs secondary subdivision on high-dynamic regions; the ground data adaptation unit maps the point cloud or sensor data to the virtual grid, constructs a local triangular network, and performs secondary subdivision on the local network based on curvature or density thresholds.

[0013] The multi-head attention mapping unit uses the multi-head attention mechanism to perform weighted matching on the triangle vertices of different data sources to ensure the alignment of spatio-temporal coordinates; the spatial attention unit adjusts the triangulation density according to terrain features (such as ridges and rivers) to ensure the precise alignment of data in high-dynamic regions; the temporal attention unit analyzes the temporal characteristics of high-frequency updated data through the self-attention mechanism, automatically adjusts the time weights, and suppresses the noise of historical data.

[0014] The dynamic subdivision unit recursively subdivides the regions exceeding the threshold according to the regional errors in data fusion to achieve refined processing of high-dynamic regions; the cross-modal data association unit optimizes the information complementarity between different data sources through cross-modal data association technology to improve the accuracy of data fusion.

[0015] The global multi-head attention supervision unit monitors the status and quality of each module in data acquisition, preprocessing, virtual space modeling, and data fusion in real time; the feedback regulation unit dynamically adjusts the operating parameters and processing strategies of each module according to the global supervision feedback information to ensure the efficient coordination of the overall system.

[0016] The data service module includes: a data visualization unit, which is used to display the fused data in a graphical form to support the intuitive display of spatial information; a decision support unit, which is used to provide real-time decision support for users based on data analysis; a data sharing unit, which is used to provide a standardized data interface to support cross-platform data sharing and collaboration.

[0017] A sky-ground integrated data fusion management method includes the following steps: S1: Receive and integrate multi-source data from satellites, drones, ground sensors, and point clouds, and achieve unified management and real-time and efficient access to the data through the data transmission unit; S2: Preprocess the accessed data, including spatio-temporal synchronization correction, data format standardization, and noise suppression, to ensure data quality and spatio-temporal consistency; S3: Establish a three-dimensional virtual space based on the target area, divide the virtual space into basic grids according to a preset granularity, and use the Delaunay method to triangulate the basic grids to generate virtual grids; S4: Adapt data from different sources to the virtual grids, including satellite data adaptation, aerial video adaptation, and ground data adaptation; S5: Align the spatio-temporal coordinates of the sub-data through the multi-head attention mechanism, spatial attention, and temporal attention to ensure the spatio-temporal consistency of multi-source data; S6: After completing data adaptation and spatio-temporal alignment, use the Beidou satellite positioning system and timestamps to embed spatio-temporal coordinates for each triangle vertex to form a spatio-temporal quadruple (x, y, z, t); S7: Perform weighted fusion on the data after spatio-temporal coordinate embedding, including dynamic subdivision and cross-modal data association, to achieve in-depth fusion and real-time update of the data; S8: Monitor the running status of each module in real time, and adjust the running parameters of each module through the global multi-head attention supervision mechanism and feedback regulation to ensure the efficient coordination of the overall system; S9: Visualize, provide decision support, and share the fused data for users to make real-time decisions and collaborate.

[0018] In the process of receiving and integrating multi-source data in step S1, the data transmission unit is used to access satellite, drone, ground sensor, and point cloud data in real time, and the synchronous access of each data source is achieved through a unified interface protocol.

[0019] The beneficial effects of the present invention are: 1. Through the multi-source data access module, it is possible to access data from different sources such as satellites, drones, ground sensors, and point clouds in real time and efficiently. Through the unified management of the data transmission unit, the synchronous access and stable transmission of various data sources are ensured. The system can automatically adapt according to different data types and sources, greatly improving the data integration efficiency. For application scenarios with high real-time requirements (such as disaster warning, traffic management, etc.), the system can provide real-time data streams to ensure rapid response and processing, can ensure the real-time access of multiple data sources, meet the requirements for data real-time in different fields, and improve the data access efficiency through the unified management of the data transmission unit, simplifying the integration process of multi-source data.

[0020] 2. The data preprocessing module ensures the fusion of data from different data sources within the same time and space framework through steps such as spatio-temporal synchronization correction, data format standardization, and noise suppression. Spatio-temporal synchronization correction can eliminate the time errors of data from different sources. Data format standardization guarantees seamless connection of data. Noise suppression improves data quality, removes invalid information, and ensures data accuracy in subsequent processing. Through spatio-temporal synchronization correction, the consistency of data in time and space is ensured, providing a reliable data basis for subsequent analysis.

[0021] 3. The spatial generation module adopted in the present invention divides the virtual space into basic grids in detail through the Delaunay triangulation method, and realizes the precise adaptation of different data sources through the data adaptation module. This process ensures that different data types (such as satellite images, aerial video, ground sensor data, etc.) can be accurately mapped into a unified virtual space, ensuring the consistency and high precision of data in the virtual grid. The virtual grid generated by the Delaunay triangulation method ensures a refined representation of the virtual space, which is applicable to geographic information systems (GIS) and spatial analysis.

[0022] 4. The present invention realizes the efficient operation of the spatio-temporal alignment module through technologies such as multi-head attention mechanism, spatial attention, and temporal attention. The system can accurately align the spatio-temporal coordinates from different data sources, and perform weighted fusion on multi-source data through the data fusion module. This process can eliminate the errors between data from different sources, ensure the precise fusion of multi-source data within the same spatio-temporal framework, and optimize the complementarity between different data sources through cross-modal data association technology to further improve the fusion accuracy. Through weighted fusion and dynamic subdivision processing, the system can achieve high-precision data fusion and adapt to complex dynamic environments.

[0023] 5. The global supervision module of the system can monitor the running status of modules such as data collection, preprocessing, virtual space modeling, and data fusion in real time, evaluate the status and quality of each module in real time through the global multi-head attention supervision mechanism, and dynamically adjust the running parameters of the system according to the feedback information to ensure the collaborative work of each module. Through this adaptive optimization, the system can cope with changing environments and requirements, ensure overall efficiency and accuracy. The real-time monitoring and feedback mechanism ensures that the system maintains high efficiency and stability during long-term operation, reducing possible errors and delays.

[0024] 6. The data service module provides data visualization, decision support, and data sharing functions. It can display the fused data in a graphical form to help users intuitively understand complex data. In addition, the decision support unit provides real-time decision-making suggestions based on the fused data to assist users in making scientific decisions. The data sharing function supports cross-platform data sharing through standardized interfaces, promoting data circulation and collaboration.

[0025] 7. Through multiple technologies such as spatio-temporal synchronization, data adaptation, spatio-temporal alignment, and high-precision fusion, the present invention ensures that the system can process massive data from multiple platforms and devices in real time, providing timely decision support and real-time feedback. The system is particularly suitable for application scenarios that require real-time response and processing, such as intelligent transportation management, environmental monitoring, urban planning, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the system architecture diagram of the present invention; Figure 2 is the system interaction of the present invention Figure 1 ; Figure 3 is the system interaction of the present invention Figure 2 . DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0028] Here, it should be noted that the orientation concepts of "left", "right", "up", "down", "front", "back", "inside", and "outside" in the following solutions are all relative directions, and will not be listed one by one here.

[0029] Embodiment 1: As Figures 1 to 3 shown, this embodiment provides a sky-earth-air integrated data fusion management system, aiming to generate high-precision virtual space data through the integration and processing of multi-source data, and provide decision support and data sharing functions for users. The architecture of this system includes a data access module, a data preprocessing module, a space generation module, a data adaptation module, a spatio-temporal alignment module, a spatio-temporal coordinate embedding module, a data fusion module, a global supervision module, and a data service module.

[0030] The data access module is responsible for receiving multi-source data from satellites, drones, ground sensors, point clouds, etc., and uniformly managing it through the data transmission unit to ensure the real-time and efficient access of data. During this process, the system can perform appropriate preprocessing according to the characteristics and data formats of different data sources to adapt to the subsequent processing modules.

[0031] The accessed data first passes through the data preprocessing module for spatio-temporal synchronization correction, data format standardization, and noise suppression. The spatio-temporal synchronization correction unit ensures the consistency of multi-source data in the spatio-temporal dimension by using spatio-temporal calibration algorithms. The data format standardization unit converts the formats from different sources into a unified standard format to ensure seamless connection during data processing. The noise suppression unit filters the original data to remove invalid information and improve the data quality.

[0032] The space generation module constructs a three-dimensional virtual space based on the target area (such as a specific city, region, or county). This module uses the Delaunay triangulation algorithm to divide the virtual space into grids, generates basic grids through the set granularity (such as 1km×1km×altitude layer), and generates three-dimensional virtual grids on this basis. This process ensures that the structure of the virtual space is detailed and highly adaptable, and can accurately represent the spatial characteristics of the target area.

[0033] The data adaptation module adapts data from different sources to the generated virtual grids, specifically including: Satellite data adaptation unit: Cuts the linear scanning images of the satellite according to the size of the virtual grid, performs triangulation on each strip, and dynamically calibrates through satellite orbit parameters to ensure the precise alignment of the data.

[0034] Aerial video adaptation unit: Splits the aerial video frames into continuous strips, uses triangulation rules similar to satellite data, and adaptively encrypts and subdivides high-dynamic areas.

[0035] Ground data adaptation unit: Maps ground sensor or point cloud data to the virtual grid, constructs a local triangular network, and performs secondary subdivision on high-density or complex areas to ensure the fine adaptation of the data.

[0036] The spatio-temporal alignment module is responsible for aligning the spatio-temporal coordinates of the data. Through the multi-head attention mechanism, the spatial attention and time attention sub-modules are combined to ensure that multi-source data can be aligned in space and time, specifically including: Multi-head attention mapping unit: Uses the multi-head attention mechanism to perform weighted matching on the triangular vertices from different data sources (such as satellite data, aerial video, ground data) to ensure the accurate alignment of spatio-temporal coordinates.

[0037] Spatial Attention Unit: Automatically adjusts the triangulation density according to terrain features (such as ridges, rivers) to ensure the precise alignment of data in high-dynamic regions.

[0038] Temporal Attention Unit: Analyzes the temporal characteristics of high-frequency updated data through the self-attention mechanism, automatically adjusts the weights, and suppresses historical noise.

[0039] To achieve the spatio-temporal precise alignment of multi-source data, the following spatio-temporal dynamic alignment attention formula is used: Where: and are the query vector of data i and the key vector of data j in the h-th attention head respectively; and represent the temporal and spatial differences between data i and data j respectively; are learnable parameters; denotes element-wise multiplication, is the L1 norm.

[0040] After completing data adaptation and spatio-temporal alignment, the spatio-temporal coordinate embedding module uses the Beidou satellite positioning system and accurate timestamps to perform spatio-temporal coordinate embedding on each triangle vertex, generating spatio-temporal quadruples (x, y, z, t), providing accurate spatial information for subsequent data fusion and accuracy enhancement.

[0041] To achieve precise spatio-temporal positioning of each triangle vertex, the following embedding formula is proposed: Where: (x, y, z) are spatial coordinates and t is the timestamp; W is the weight matrix and b is the bias; Λ and are the scale factors of space and time respectively; Adopts sine and cosine functions for periodic encoding, and maps to the high-dimensional feature space through the non-linear activation function tanh to achieve the fused expression of spatio-temporal information; The data fusion module uses the multi-head attention mechanism to perform weighted fusion on the data to ensure the deep fusion and real-time update of multi-source data, specifically including: Dynamic Subdivision Unit: According to the regional errors that occur during the data fusion process, recursively subdivides the regions that exceed the threshold to ensure that high-dynamic regions are carefully processed.

[0042] To adaptively subdivide high-dynamic regions during the data fusion process, the following recursive subdivision formula is proposed: Where: represents a triangular region, is the sub-region after its subdivision; is the preset error threshold; Regional error is defined as follows: Where: is the original value of a certain data source, is the value after fusion; is a hyperparameter, is the number of data points within the region; Cross-modal data association unit: Optimize the information complementarity between different data sources through cross-modal data association technology to further improve the accuracy of data fusion.

[0043] The global supervision module is responsible for real-time monitoring of the operating status of each module and dynamically optimizing the system performance according to the feedback information, specifically including: Global multi-head attention supervision unit: Monitor the operating status and quality of the data acquisition, preprocessing, virtual space modeling, and data fusion modules to ensure the consistency and efficiency of the system.

[0044] Feedback regulation unit: Adjust the operating parameters of each module according to the global supervision feedback to ensure the coordinated operation and optimization of each part.

[0045] The data service module visualizes the fused data, provides real-time decision support for users, and supports cross-platform data sharing and collaboration. This module includes: Data visualization unit: Graphically present the fused data to help users intuitively understand the data.

[0046] Decision support unit: Provide real-time decision support based on the fused data to assist users in making scientific decisions.

[0047] Data sharing unit: Provide a standardized data interface to support cross-platform data sharing and collaboration.

[0048] The system of this embodiment can generate high-precision virtual space data through the precise fusion of multi-source data, which is widely used in fields such as geographic information systems (GIS), intelligent transportation, and environmental monitoring. Through the spatio-temporal alignment and data fusion process, errors can be effectively reduced, and the accuracy and practicality of the data can be improved. This system can not only perform efficient data processing and dynamic optimization, but also support users to make real-time decisions and share data, enhancing the functions of collaboration and decision support.

[0049] In addition, through the multi-head attention mechanism and spatio-temporal coordinate embedding technology, this embodiment can ensure the consistency of various types of data in the multi-dimensional space, providing a reliable data basis for subsequent data analysis and processing.

[0050] Embodiment 2: As Figures 1 to 3 shown, this embodiment details the sky-ground integrated data fusion management system based on Embodiment 1, further emphasizing the specific implementation methods of the data access module, data preprocessing module, and data adaptation module. The functions of these modules are to ensure the efficient access and high-quality processing of multi-source data, and to provide accurate inputs for subsequent data fusion and spatio-temporal alignment. This system is also applicable to the integrated processing of multi-source data such as satellites, aerial photography, and ground sensors, constructing a precise virtual space model, and providing decision support and data sharing functions.

[0051] The main task of the data access module is to receive data from different data sources, including satellite data, UAV data, ground sensor data, and point cloud data. The working process of this module is as follows: Data collection: Obtain multi-source data, including images, videos, sensor data, etc., from devices such as satellites and UAVs through wireless communication technology or other communication means.

[0052] Data integration: Through the data transmission unit, all data sources (satellites, UAVs, ground sensors, and point cloud data) are uniformly managed to ensure the real-time access and efficient synchronization of each data source.

[0053] Real-time processing: This module ensures that the real-time collected data can quickly enter the preprocessing module, providing a quick response for subsequent data processing.

[0054] This module ensures the efficient and stable access of data, providing real-time and complete multi-source data input for the system, adapting to the access requirements of different types of data, and ensuring the efficient operation of the system.

[0055] The data preprocessing module is used to process the accessed multi-source data to ensure the quality and spatio-temporal consistency of the data. The specific implementation process includes the following sub-modules: Space-time synchronization and calibration unit: This unit ensures the space-time consistency of different data sources by comparing the timestamps and geographical coordinate information of multiple data sources. Through space-time calibration algorithms, all the accessed data can be aligned within the same time and space framework.

[0056] Data format standardization unit: This unit converts data in different formats into a unified standard format to ensure that the system can efficiently process various data from different sources.

[0057] Noise suppression unit: Filters the noise in the original data, removes invalid or incorrect information, to improve the data quality and provide a clean data source for subsequent data fusion and analysis.

[0058] By synchronizing and calibrating the space-time, unifying the format, and suppressing the noise of the data, this module greatly improves the data quality, ensures seamless docking of the data in the subsequent processing, and reduces the calculation errors caused by data problems.

[0059] The main function of the data adaptation module is to adapt data from different sources to the virtual grid. Specifically, this module includes: Satellite data adaptation unit: The satellite data adaptation unit is responsible for cutting the line-scan images obtained by the satellite according to the size of the virtual grid, and triangulating each strip. During this process, the space-time coordinates of the vertices of each triangle are dynamically calibrated through satellite orbit parameters to ensure the accurate alignment of the data.

[0060] The formula of the satellite data adaptation unit is as follows: Let the satellite scan image be , and the preset virtual grid size be and . Cut the image according to the grid size to obtain strip s, and triangulate the strip. Suppose a triangle T = { } is obtained within a certain strip. For each vertex , use the satellite orbit parameter function (where is the acquisition time) for dynamic calibration, and calculate the calibrated vertex coordinates: , i = 1, 2, 3 Among them, is the correction coefficient, is the adjustment parameter. This formula combines the satellite orbit information with the vertex distance to achieve dynamic calibration.

[0061] Aerial video adaptation unit: This unit splits each frame of the aerial video into continuous strips, triangulates them according to similar rules as satellite data, and performs adaptive secondary subdivision for high-dynamic regions to ensure that the data in these regions can be accurately adapted to the virtual grid.

[0062] The formula for the aerial video adaptation unit is as follows: Let the aerial video frame be V(t, x, y). After splitting each frame into continuous strips a preliminary grid is obtained using a triangulation method similar to that for satellite data . To adapt to high-dynamic regions, a region adaptation factor D is defined: where is the local pixel change degree (or motion estimation value) within the strip, is the threshold value, is the scaling parameter, is the basic subdivision factor. Subsequently, the preliminary grid is recursively subdivided: This formula adaptively increases the grid density within high-dynamic regions to achieve refined adaptation.

[0063] Ground data adaptation unit: This unit is responsible for mapping ground sensor or point cloud data to a virtual grid, constructing a local triangular mesh, and performing secondary subdivision of the grid based on curvature or density thresholds to ensure that the ground data can be precisely matched with the virtual space.

[0064] The formula for the ground data adaptation unit is as follows: Let the ground sensor or point cloud data be G(x, y, z). It is assigned to the virtual grid through a mapping function: Preliminary meshes are obtained by performing local triangulation on the data within each grid cell . To perform secondary subdivision for high-density or complex regions, a subdivision factor is defined: where is the basic subdivision factor, is the scaling parameter. Then is secondarily subdivided: This formula achieves adaptive encrypted subdivision of complex regions through local density or curvature information to ensure fine adaptation of the ground data.

[0065] This module enables data from different sources to seamlessly interface with the virtual space, ensuring that various types of data can be adapted to a unified grid with the same standard. This process greatly improves the accuracy of data integration, providing a reliable foundation for subsequent steps such as spatio-temporal alignment and data fusion.

[0066] The spatio-temporal alignment module uses the multi-head attention mechanism, spatial attention, and temporal attention sub-modules to achieve precise alignment of different data sources in space and time. Through this precise alignment, the system can ensure that multi-source data is analyzed and fused in the same coordinate system.

[0067] After completing data adaptation and spatio-temporal alignment, the spatio-temporal coordinate embedding module uses the Beidou satellite positioning system and timestamps to perform spatio-temporal coordinate embedding on each triangle vertex, providing precise spatial positioning for subsequent data fusion.

[0068] The data fusion module uses the multi-head attention mechanism, combined with dynamic segmentation and cross-modal data association technologies, to ensure deep fusion and real-time update of data.

[0069] The global supervision module monitors the running status of each module in real time, and dynamically optimizes the system through the global multi-head attention supervision unit and feedback regulation unit to ensure the efficiency and accuracy of data processing.

[0070] The data service module provides data visualization, decision support, and data sharing functions, enabling users to intuitively understand data, obtain real-time decision support, and share and collaborate on data with other systems or users.

[0071] In the process of data access, preprocessing, adaptation, and spatio-temporal alignment in this embodiment, it is ensured that the system can efficiently process data from different sources and provide high-quality input for subsequent data fusion. Especially in the process of spatio-temporal synchronization and data adaptation, through fine algorithms and technologies, it is ensured that various types of data can be seamlessly fused, providing strong support for the final space generation and decision support.

[0072] In addition, the global supervision function of the system ensures the collaborative work of each module, greatly improving the data quality and timeliness. Through these optimizations, the system can be widely applied to multiple fields such as urban management, environmental monitoring, and intelligent transportation, helping users to perform efficient data analysis and decision-making.

[0073] Embodiment 3: Sky-ground integrated data fusion management method As Figures 1 to 3 shown, on the basis of Embodiments 1 and 2, this embodiment details a sky-ground integrated data fusion management method, which realizes the efficient access, preprocessing, virtual space modeling, data adaptation, spatio-temporal alignment, coordinate embedding, weighted fusion, global supervision, and data service of multi-source data through multiple steps. The specific steps are as follows: S1: Receive and integrate multi-source data Using a high-performance data transmission unit, through a unified interface protocol, data is collected in real time from satellites, drones, ground sensors, and point cloud devices. Each data source collects information in its own specific format, and the data transmission unit manages them uniformly, realizing the synchronous access and stable transmission of data, laying a foundation for subsequent processing.

[0074] S2: Data preprocessing Perform spatio-temporal synchronization correction, data format standardization, and noise suppression on the accessed raw data. The spatio-temporal synchronization correction unit eliminates the differences in device location and acquisition time by comparing the timestamps and geographical coordinates of different data sources; the format standardization unit converts different format data into a unified standard; the noise suppression unit uses filtering and outlier rejection techniques to improve data quality and consistency.

[0075] S3: Virtual space construction Based on the target area, construct a three-dimensional virtual space, and divide this space into basic grids according to a preset granularity (such as 1km×1km×altitude layer). Use the Delaunay triangulation algorithm to finely divide each basic grid to generate virtual grids with fine structures, providing an accurate spatial basis for data mapping.

[0076] S4: Data adaptation Adapt data from different sources into the virtual grids: The satellite data adaptation unit cuts the satellite line-scan images according to the grid size, performs triangulation on each strip, and dynamically calibrates the spatio-temporal coordinates of the triangle vertices using satellite orbit parameters; The aerial video adaptation unit splits the video frames into continuous strips, uses the same triangulation rules, and performs adaptive secondary subdivision on high-dynamic regions; The ground data adaptation unit maps the point cloud or sensor data to the virtual grids, constructs local triangulations, and performs secondary subdivision according to curvature or density thresholds.

[0077] S5: Spatio-temporal alignment Use the multi-head attention mechanism to perform weighted matching on the triangle vertices generated by each data source to ensure the precise spatial correspondence of each data; at the same time, the spatial attention unit automatically adjusts the local grid density according to terrain features (such as ridges, rivers), and the temporal attention unit analyzes the temporal characteristics of the data and adjusts the temporal weights to effectively suppress historical noise, thereby achieving precise spatio-temporal alignment.

[0078] S6: Spatio-temporal coordinate embedding After data adaptation and alignment are completed, use the Beidou satellite positioning system combined with precise timestamps to perform spatio-temporal coordinate embedding on each triangle vertex, generating spatio-temporal quadruples (x, y, z, t), forming an accurate spatio-temporal data mapping, providing a solid foundation for subsequent fusion.

[0079] S7: Data Weighted Fusion The multi-head attention mechanism is adopted to perform weighted fusion on the embedded data, realizing deep data fusion and real-time update. A dynamic subdivision unit is set in the fusion module to recursively subdivide the high-dynamic regions according to the regional error, and the cross-modal data association unit is used to optimize the information complementarity between different data sources, improving the fusion accuracy.

[0080] S8: Global Supervision and Feedback Regulation The global multi-head attention supervision unit is used to monitor the operation status of the data acquisition, preprocessing, virtual space construction, and data fusion modules in real time; the feedback regulation unit dynamically adjusts the operation parameters of each module according to the monitoring results to ensure the coordinated and efficient operation of all links of the system.

[0081] S9: Data Visualization and Decision Support The fused data is intuitively displayed through the data visualization platform. At the same time, the decision support unit provides real-time decision-making suggestions, and cross-platform data sharing is realized through the standardized data interface to support user collaborative operation and real-time decision-making.

[0082] S10: Data Synchronous Access (Additional Step) In step S1, the data transmission unit uses a unified interface protocol to realize the synchronous access of satellite, drone, ground sensor, and point cloud data, ensuring the real-time and consistency of data access.

[0083] Using the unified interface and high-performance data transmission unit, it ensures the real-time acquisition and synchronous management of multi-source data and is applicable to dynamic environment monitoring.

[0084] The spatio-temporal synchronization correction, format standardization, and noise suppression technologies significantly improve the data quality and provide an accurate data basis for subsequent processing.

[0085] The virtual grid constructed by Delaunay triangulation ensures the high-precision mapping of spatial data and adapts to the requirements of complex terrains.

[0086] The application of the multi-head attention mechanism and spatial and temporal attention realizes the accurate alignment and embedding of data in space and time, providing a reliable basis for data fusion.

[0087] The dynamic subdivision and cross-modal data association technologies effectively solve the problems of regional error and multi-source data complementarity, realizing deep fusion and real-time update.

[0088] The real-time monitoring and feedback regulation mechanism ensure the stable and efficient operation of the system, reduce errors, and improve the overall processing efficiency.

[0089] The data visualization and decision support module provides users with intuitive data display and real-time decision-making basis, while supporting cross-platform data sharing to promote collaborative operations.

[0090] In summary, this embodiment significantly improves the accuracy and real-time performance of multi-source data fusion, providing a powerful data analysis and decision support platform for fields such as intelligent city management, environmental monitoring, and traffic management.

[0091] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein, and should not be regarded as excluding other embodiments. Instead, it can be used in various other combinations, modifications, and environments, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. Any changes and variations made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. An integrated space-air-ground data fusion management system, characterized in that, It includes a data access module, a data preprocessing module, a space generation module, a data adaptation module, a spatio-temporal alignment module, a spatio-temporal coordinate embedding module, a data fusion module, and a global supervision module; The space generation module: Based on the target area, a three-dimensional virtual space is established, the virtual space is divided into basic grids according to a preset granularity, and Delaunay method is used to triangulate each basic grid to generate virtual grids; The data adaptation module is used to adapt data from different sources to the virtual grids, specifically including: a satellite data adaptation unit, an aerial video adaptation unit, and a ground data adaptation unit; The spatio-temporal alignment module aligns the spatio-temporal coordinates of the data. The spatio-temporal alignment of the data is adjusted through the multi-head attention mechanism, the spatial attention and the time attention sub-modules, specifically including: a multi-head attention mapping unit, a spatial attention unit, and a time attention unit; The spatio-temporal coordinate embedding module is used to perform spatio-temporal coordinate embedding on each triangle vertex using the Beidou satellite positioning system and time stamps after the data adaptation and spatio-temporal alignment are completed, forming a spatio-temporal quadruple (x, y, z, t); The data fusion module: uses the multi-head attention mechanism to perform weighted fusion on the data to achieve deep fusion and real-time update, specifically including: a dynamic subdivision unit and a cross-modal data association unit; The global supervision module is used to monitor the running status of each module in real time and perform dynamic optimization on the system, specifically including: a global multi-head attention supervision unit and a feedback regulation unit; The data service module is used to display and share the fused data, and support decision-making and collaborative operations.

2. The integrated sky-ground data fusion management system according to claim 1, wherein: The data access module is used to receive and integrate multi-source data from satellites, drones, ground sensors, and point clouds, and uniformly manage each data source through the data transmission unit and achieve real-time and efficient access.

3. The integrated sky-earth data fusion management system according to claim 1, characterized in that: The data preprocessing module is used to process the accessed data to ensure data quality and spatio-temporal consistency. Specifically, it includes: a spatio-temporal synchronization correction unit for correcting the accessed data in terms of space and time to ensure the spatio-temporal consistency of multi-source data; a data format standardization unit for converting various types of data into a unified format to ensure compatibility for subsequent processing; a noise suppression unit for filtering noise from the original data, removing invalid information, and improving data quality.

4. The integrated sky-earth data fusion management system according to claim 1, characterized in that: The satellite data adaptation unit cuts the satellite line-scan image according to the virtual grid size, triangulates each strip, and dynamically calibrates the spatio-temporal coordinates of the triangle vertices through satellite orbit parameters; the aerial video adaptation unit splits the video frames into continuous strips, triangulates them according to the same rules, and adaptively performs secondary subdivision on the high-dynamic regions; the ground data adaptation unit maps the point cloud or sensor data to the virtual grid, constructs a local triangular network, and performs secondary subdivision on the local network based on curvature or density thresholds.

5. The integrated sky-earth data fusion management system according to claim 2, wherein: The multi-head attention mapping unit uses the multi-head attention mechanism to perform weighted matching on the triangular vertices of different data sources to ensure spatio-temporal coordinate alignment; the spatial attention unit adjusts the triangulation density according to the terrain features to ensure the precise alignment of data in high-dynamic regions; the temporal attention unit analyzes the temporal characteristics of high-frequency updated data through the self-attention mechanism, automatically adjusts the temporal weights, and suppresses the noise of historical data.

6. The integrated sky-earth data fusion management system according to claim 5, characterized in that: The dynamic subdivision unit recursively subdivides the regions exceeding the threshold according to the regional errors in data fusion to achieve refined processing of high-dynamic regions; the cross-modal data association unit optimizes the information complementarity between different data sources through cross-modal data association technology to improve the accuracy of data fusion.

7. The integrated sky-ground data fusion management system according to claim 6, characterized in that: The global multi-head attention supervision unit monitors the status and quality of each module of data acquisition, preprocessing, virtual space modeling, and data fusion in real time; the feedback regulation unit dynamically adjusts the operation parameters and processing strategies of each module according to the global supervision feedback information to ensure the efficient cooperation of the overall system.

8. The integrated sky-ground data fusion management system according to claim 7, characterized in that: The data service module includes: a data visualization unit for presenting the fused data in a graphical form to support the intuitive display of spatial information; a decision support unit for providing real-time decision support for users based on data analysis; and a data sharing unit for providing a standardized data interface to support cross-platform data sharing and collaboration.

9. A method for integrated sky-earth data fusion management, characterized in that: It includes the following steps: S1: Receive and integrate multi-source data from satellites, drones, ground sensors, and point clouds, and achieve unified management and real-time and efficient access to the data through the data transmission unit; S2: Preprocess the accessed data, including spatio-temporal synchronization correction, data format standardization, and noise suppression, to ensure data quality and spatio-temporal consistency; S3: Establish a three-dimensional virtual space based on the target area, divide the virtual space into basic grids according to the preset granularity, and use the Delaunay method to triangulate the basic grids to generate virtual grids; S4: Adapt the data from different sources to the virtual grids, including satellite data adaptation, aerial video adaptation, and ground data adaptation; S5: Align the spatio-temporal coordinates of the sub-data through the multi-head attention mechanism, spatial attention, and temporal attention to ensure the spatio-temporal consistency of multi-source data; S6: After completing data adaptation and spatio-temporal alignment, use the Beidou satellite positioning system and timestamp to embed the spatio-temporal coordinates of each triangular vertex to form a spatio-temporal quadruple (x, y, z, t); S7: Perform weighted fusion on the data after spatio-temporal coordinate embedding, including dynamic subdivision and cross-modal data association, to achieve deep fusion and real-time update of the data; S8: Monitor the running status of each module in real time, and adjust the operation parameters of each module through the global multi-head attention supervision mechanism and feedback regulation to ensure the efficient cooperation of the overall system; S9: Visualize, provide decision support, and share the fused data for users to make real-time decisions and collaborate.

10. A sky-ground integrated data fusion management method according to claim 9, characterized in that: In the process of receiving and integrating multi-source data in step S1, a data transmission unit is used to access satellite, drone, ground sensor, and point cloud data in real time, and the synchronous access of each data source is realized through a unified interface protocol.

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