An airport clearance area remote sensing intelligent identification and visualization management method and system

Abstract

This invention relates to the field of spatial information data processing technology. It discloses a method and system for remote sensing intelligent identification and visualization management of airport airspace. The method includes acquiring remote sensing data of the airspace and facility occlusion data; firstly, constructing a data missing topology set to explicitly label local missing data caused by dynamic occlusion; then, performing three-dimensional topological reconstruction on the airspace remote sensing data to generate incomplete area prediction information; spatially filling boundary breaks; extracting boundary spatial coordinates based on the incomplete area prediction information; constructing an initial boundary model and calculating boundary consistency parameters based on the airspace remote sensing data; generating boundary consistency constraint information; optimizing the initial boundary model based on these constraints to obtain an optimized boundary model; and calculating the aircraft flight path safety distance in real time. This improves the real-time boundary monitoring accuracy of the airspace under conditions of occlusion and limited three-dimensional boundary structure consistency.

Description

Technical Field

[0001] This invention relates to the field of spatial information data processing technology, and more specifically, to a remote sensing intelligent identification and visualization management method and system for airport airspace. Background Technology

[0002] Real-time boundary monitoring of airport airspace mainly relies on satellite remote sensing and three-dimensional spatial analysis technology. Under the constraints of limited computing power, real-time communication and spatial location monitoring accuracy, it can achieve obstacle identification and boundary determination in the airspace. Existing technologies mostly use methods such as local feature extraction, region segmentation and determination, correlation measurement, and time series smoothing and filtering to achieve real-time airspace monitoring. The applicable boundaries are usually where the remote sensing data is complete and continuous, the obstacle position changes slowly, and the observation perspective is stable.

[0003] In actual operation, the frequent dynamic movement of airport ground facilities causes local missing remote sensing data. At the same time, the spatial position and structure of three-dimensional obstacles in the airport airspace show significant dynamic changes with operational activities. These factors together reduce the consistency of the three-dimensional boundary structure based on local feature extraction and temporal smoothing methods, affecting the accuracy of real-time boundary monitoring of the airspace. Therefore, the technical problem that needs to be solved is how to ensure the accuracy of real-time boundary monitoring of the airport airspace under the conditions of frequent dynamic obstruction of remote sensing data by airport facilities and limited consistency of the three-dimensional boundary structure of the airspace.

[0004] In view of this, the present invention proposes a remote sensing intelligent identification and visualization management method and system for airport airspace to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, the present invention provides a remote sensing intelligent identification and visualization management method and system for airport airspace.

[0006] To achieve the above objectives, the present invention provides the following technical solution: Firstly, a remote sensing intelligent identification and visualization management method for airport airspace is provided, including: Acquire remote sensing data of the airspace and facility obstruction data, and construct a set of missing data topologies based on the facility obstruction data; Based on the missing data topology set, three-dimensional topological reconstruction of the airspace remote sensing data is performed to generate incomplete area prediction information. Based on the predicted information of the incomplete area, the boundary space coordinates are extracted. Based on the remote sensing data of the clearance area and the boundary space coordinates, an initial boundary model is constructed. Based on the initial boundary model, the boundary consistency parameters are calculated, and boundary consistency constraint information is generated. The initial boundary model is optimized based on the boundary consistency constraint information, an optimized boundary model is generated, and the safe distance of the aircraft trajectory is calculated in real time based on the optimized boundary model.

[0007] In some embodiments, a set of missing data topologies is constructed based on facility occlusion data, including: Obtain the spatial coordinates of the facilities in the facility occlusion data, and obtain the occlusion start time and occlusion end time. Establish a spatiotemporal occlusion unit by combining the facility location coordinates with the corresponding start and end times. Each spatiotemporal occlusion unit is mapped to the remote sensing spatial coordinates of the clearance area, and the coordinates of the occlusion spatial region corresponding to each spatiotemporal occlusion unit are marked. Calculate the spatial adjacency distance between the coordinates of each occluded spatial region, and mark the coordinate pairs of adjacent occluded spatial regions based on the spatial adjacency distance; A set of missing data topologies is generated based on the coordinate pairs of adjacent occluded spatial regions.

[0008] In some embodiments, each spatiotemporal occlusion unit is mapped to remote sensing spatial coordinates of the clearance area, and the coordinates of the occlusion spatial region corresponding to each spatiotemporal occlusion unit are marked, including: Obtain the spatial extent of remote sensing data for the airspace clearance area, and establish remote sensing spatial coordinates based on the spatial extent; The remote sensing spatial grid is divided into units based on the remote sensing spatial coordinates, and the center coordinates of each unit are marked. The spatial location coordinates of the facilities in each spatiotemporal occlusion unit are spatially matched with the center coordinates of the remote sensing spatial grid unit one by one, and the coordinates of the occlusion spatial area are output.

[0009] In some embodiments, the spatial location coordinates of the facility in each spatiotemporal occlusion unit are spatially matched with the center coordinates of the remote sensing spatial grid unit one by one to output the coordinates of the occlusion spatial region, including: Extract the spatial three-dimensional coordinates corresponding to the spatial location coordinates of the facility, and extract the three-dimensional coordinates of the center of the remote sensing spatial grid cell; Calculate the spatial position difference between the spatial location coordinates of each facility and the center coordinates of each remote sensing spatial grid cell; Based on the calculated spatial location difference, the center coordinates of the remote sensing spatial grid unit with the smallest difference are selected to establish a spatial match with the corresponding facility spatial location coordinates. Record the spatial location coordinates of all spatially matched facilities and the center coordinates of remote sensing spatial grid cells, and output the coordinates of the occlusion spatial areas of the spatially matched facilities.

[0010] In some embodiments, three-dimensional topological reconstruction of the clearance area remote sensing data is performed based on the missing data topology set to generate incomplete area prediction information, including: The missing spatial grid locations of remote sensing data are determined based on the missing topology set of data, and the missing spatial grid locations are marked to obtain the missing spatial grid location markings; Extract the locations of unlabeled valid remote sensing spatial grids and their corresponding remote sensing data to form a set of valid grid remote sensing data. Based on the spatial topological adjacency relationship between the missing spatial grid location markers and the effective grid remote sensing data set, calculate the spatial topological location deviation between the missing spatial grid location and the adjacent effective spatial grid location; Based on the calculated spatial topological position deviation, the missing spatial grid positions are reconstructed in three dimensions, and the incomplete region prediction information is output.

[0011] In some embodiments, the spatial topological position deviation between the missing spatial grid location and the adjacent valid spatial grid location is calculated based on the spatial topological adjacency relationship between the missing spatial grid location markers and the valid grid remote sensing data set, including: Extract the spatial grid position coordinates corresponding to the missing spatial grid position markers, and extract the spatial coordinates of adjacent valid spatial grid positions; Based on the spatial topological connection relationship between the missing spatial grid location and the adjacent valid spatial grid location, calculate the spatial coordinate difference between the missing grid location and the adjacent valid grid location; Based on the spatial coordinate difference, obtain the topological position deviation direction and position deviation value of the missing grid location; Record the topological position deviation direction and position deviation value corresponding to all missing spatial grid locations, and output the spatial topological position deviation.

[0012] In some embodiments, calculating the spatial coordinate difference between the missing grid location and the adjacent valid grid location includes: Obtain the three-dimensional spatial coordinates of the missing grid location and the adjacent valid grid location, and calculate the spatial coordinate difference for each; Obtain the duration of occlusion, and calculate the spatiotemporal coherence attenuation weights corresponding to each missing grid location based on the duration of occlusion. The three-dimensional spatial position coordinates are weighted and corrected according to the spatiotemporal coherence attenuation weight to obtain the corrected target position coordinate difference. The differences in target position coordinates between each missing grid location and its adjacent valid grid location are summarized, and the spatial position coordinate differences are output.

[0013] In some embodiments, optimizing the initial boundary model based on boundary consistency constraint information to generate an optimized boundary model includes: Obtain the boundary space coordinates of the initial boundary model and extract the boundary topology constraint parameters from the boundary consistency constraint information; Based on the boundary topology constraint parameters, the spatial coordinate points in the initial boundary model are marked with topology constraints to form constraint boundary coordinate points; Adjust the positions of spatial coordinate points with topological constraint differences in the initial boundary model based on the coordinate points of the constraint boundary to generate an optimized boundary model.

[0014] In some embodiments, adjusting the positions of spatial coordinate points with topological constraint differences in the initial boundary model based on the constraint boundary coordinate points to generate an optimized boundary model includes: Obtain the safety slope limit parameters of the airport airspace and map them into a gradient surface constraint model; Calculate the spatial difference between the coordinate points in the initial boundary model and the safety constraint plane based on the gradient surface constraint model; Based on spatial difference, limit smoothing is performed on spatial coordinate points with different topological constraints to output an optimized boundary model that meets the airspace safety standard.

[0015] Secondly, a remote sensing intelligent identification and visualization management system for airport airspace is provided, which is used to implement the aforementioned remote sensing intelligent identification and visualization management method for airport airspace, including: Missing Topology Module: Used to acquire remote sensing data of airspace and facility occlusion data, and to construct a set of missing topologies based on the facility occlusion data; Topology Reconstruction Module: Used to perform three-dimensional topology reconstruction on remote sensing data of the clearance area based on the missing data topology set, and generate prediction information for the incomplete area; Boundary Consistency Module: This module is used to extract boundary spatial coordinates based on incomplete area prediction information, construct an initial boundary model based on remote sensing data of the clearance area and boundary spatial coordinates, calculate boundary consistency parameters based on the initial boundary model, and generate boundary consistency constraint information. Boundary optimization module: used to optimize the initial boundary model based on boundary consistency constraint information, generate an optimized boundary model, and calculate the safe distance of the aircraft trajectory in real time based on the optimized boundary model.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention acquires remote sensing data of airspace and facility occlusion data. First, it constructs a data missing topology set, explicitly labeling and limiting the impact range of local missing data caused by dynamic occlusion. Based on this, it performs three-dimensional topological reconstruction of the airspace remote sensing data, generating predicted information for incomplete areas. At the spatial level, it fills in boundary breaks caused by missing data. Based on the predicted information for incomplete areas, it extracts boundary spatial coordinates and constructs an initial boundary model using the airspace remote sensing data. It then calculates boundary consistency parameters to generate boundary consistency constraint information, ensuring that the boundary structure converges to a unified standard under different times and perspectives. Based on the boundary consistency constraint information, it optimizes the initial boundary model to obtain an optimized boundary model, and calculates the aircraft flight path safety distance in real time. This chain of "explicit missing data, reconstruction and completion, consistency constraints, and model optimization" offsets the errors caused by dynamic occlusion and boundary inconsistencies, steadily improving the real-time boundary monitoring accuracy of the airspace. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a remote sensing intelligent identification and visualization management method for airport airspace in this invention. Figure 2 This is a schematic diagram of the structure of an airport airspace remote sensing intelligent identification and visualization management system according to the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. In the following detailed description, many specific details are set forth to provide a thorough understanding of the exemplary embodiments described. However, it will be apparent to those skilled in the art that the described embodiments may be practiced without some or all of these specific details. In other exemplary embodiments, well-known structures have not been described in detail to avoid unnecessarily obscuring the concepts of this disclosure. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention. Furthermore, the various aspects described in the embodiments may be combined arbitrarily without conflict.

[0019] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0020] Example 1 Figure 1 This disclosure illustrates a remote sensing intelligent identification and visualization management method for airport airspace provided by at least one embodiment, including: S101: Acquire remote sensing data of the airspace and facility obstruction data, and construct a set of missing data topologies based on the facility obstruction data; Construct a set of missing data topologies based on facility occlusion data, including: Obtain the spatial coordinates of the facilities in the facility occlusion data, and obtain the occlusion start time and occlusion end time. Establish a spatiotemporal occlusion unit by combining the facility location coordinates with the corresponding start and end times. Each spatiotemporal occlusion unit is mapped to the remote sensing spatial coordinates of the clearance area, and the coordinates of the occlusion spatial region corresponding to each spatiotemporal occlusion unit are marked. Calculate the spatial adjacency distance between the coordinates of each occluded spatial region, and mark the coordinate pairs of adjacent occluded spatial regions based on the spatial adjacency distance; A set of missing data topologies is generated based on the coordinate pairs of adjacent occluded spatial regions.

[0021] In this embodiment, a key issue in the remote sensing intelligent identification and visualization management of airport airspace is the lack of remote sensing data caused by the frequent movement of airport ground facilities, which in turn affects the real-time and accurate monitoring of obstacles in the airport airspace. Therefore, this embodiment first proposes a method for constructing a data missing topology set based on facility occlusion data. In the facility occlusion data, the spatial location coordinates of the facilities and the corresponding occlusion start time and occlusion end time are clearly obtained. The spatial location coordinates of the facilities are correlated with the occlusion start time and occlusion end time to form a spatiotemporal occlusion unit. The spatiotemporal occlusion unit is used to accurately represent the spatial occlusion and occlusion duration caused by the airport ground facilities at a specific spatial location to the remote sensing observation of the airport airspace.

[0022] For example, if the spatial location coordinates of a facility are (120m, 50m, 5m), the facility obstruction starts at 10:00, and the facility obstruction ends at 10:15, then the above spatiotemporal obstruction unit can be represented as (120m, 50m, 5m, 10:00, 10:15). Subsequently, each spatiotemporal obstruction unit is mapped one by one to the remote sensing spatial coordinate system of the airport airspace. Specifically, firstly, the remote sensing spatial grid is clearly divided according to the spatial range corresponding to the airport airspace remote sensing data, and the center coordinates of the remote sensing spatial grid are generated. Then, the spatial location coordinates of the facilities in the spatiotemporal obstruction unit are mapped one by one to the center coordinates of the remote sensing spatial grid, and the mapped center coordinates of the remote sensing spatial grid are marked as the obstruction spatial area coordinates corresponding to the facility obstruction.

[0023] Furthermore, in this embodiment, for the aforementioned occlusion space region coordinates, the spatial adjacency distance between each occlusion space region coordinate is calculated. Specifically, the spatial adjacency distance is used to determine the spatial proximity between occlusion space region coordinates. For example, if the spatial adjacency distance between one occlusion space region coordinate (120m, 50m, 0m) and another occlusion space region coordinate (125m, 50m, 0m) is 5m, then the two occlusion space region coordinates are determined to be adjacent to each other. Based on the determined spatial adjacency distance, all spatially adjacent occlusion space region coordinates are clearly marked in the form of coordinate pairs. Finally, a complete set of missing data topology is formed based on the clearly marked occlusion space region coordinate pairs.

[0024] The missing data topology set clearly characterizes the spatial topological relationships of areas with missing remote sensing data in airport airspace. Compared with existing methods in remote sensing data processing that directly fill in data or ignore obscured areas, the missing data topology set constructed in this embodiment clearly reflects the spatial location distribution and adjacency topological relationships of the missing data areas. This can effectively support the consistent modeling and real-time optimization of the three-dimensional spatial boundary of the airport airspace, and significantly improve the accuracy and reliability of real-time monitoring of the airport airspace boundary.

[0025] Each spatiotemporal occlusion unit is mapped to the remote sensing spatial coordinates of the clearance area, and the coordinates of the occlusion spatial region corresponding to each spatiotemporal occlusion unit are marked, including: Obtain the spatial extent of remote sensing data for the airspace clearance area, and establish remote sensing spatial coordinates based on the spatial extent; The remote sensing spatial grid is divided into units based on the remote sensing spatial coordinates, and the center coordinates of each unit are marked. The spatial location coordinates of the facilities in each spatiotemporal occlusion unit are spatially matched with the center coordinates of the remote sensing spatial grid unit one by one, and the coordinates of the occlusion spatial area are output.

[0026] In this embodiment, the purpose of mapping each spatiotemporal occlusion unit to the remote sensing spatial coordinates of the airspace is to establish a clear and computable spatial correspondence between the occlusion behavior of airport ground facilities and the remote sensing observation space, thereby providing an accurate spatial basis for the subsequent construction of the missing data topology set. Specifically, firstly, the spatial range covered by the remote sensing data of the airspace is obtained. This spatial range is usually represented by a unified coordinate reference, such as establishing a three-dimensional spatial range description with the airport reference point as the origin. On this basis, a remote sensing spatial coordinate system is constructed so that any spatial location in the remote sensing observation data can be uniquely identified. Then, the airspace is regularly divided according to the remote sensing spatial coordinate system to form multiple remote sensing spatial grid units, and the corresponding center coordinates are marked for each remote sensing spatial grid unit. These center coordinates serve as the reference point for spatial matching and spatial calculation. After completing the above spatial preparation, the spatial location coordinates of the facilities in the spatiotemporal occlusion units formed in the previous step are spatially matched one by one with the center coordinates of the remote sensing spatial grid units.

[0027] For example, when the spatial coordinates of a facility are (120m, 50m, 5m), by comparing the spatial positional differences between the facility's spatial positional coordinates and the center coordinates of each remote sensing spatial grid cell, the center coordinates of the remote sensing spatial grid cell with the smallest spatial positional difference are selected as the corresponding occlusion spatial region coordinates. Finally, a set of occlusion spatial region coordinates corresponding one-to-one with each spatiotemporal occlusion cell is output. This set of occlusion spatial region coordinates is used to clearly identify the specific location of the area occluded by the facility in the remote sensing observation space. Compared with the existing technology that only marks the occlusion area at the image level, this embodiment uses spatial coordinate level mapping processing to make the facility occlusion information and the remote sensing spatial structure form a stable and reusable correspondence, thereby providing a clear and reliable spatial input for subsequent spatial adjacency calculation and topology analysis.

[0028] For each spatiotemporal occlusion unit, the spatial location coordinates of the facility are spatially matched with the center coordinates of the remote sensing spatial grid unit one by one, and the coordinates of the occlusion spatial region are output, including: Extract the spatial three-dimensional coordinates corresponding to the spatial location coordinates of the facility, and extract the three-dimensional coordinates of the center of the remote sensing spatial grid cell; Calculate the spatial position difference between the spatial location coordinates of each facility and the center coordinates of each remote sensing spatial grid cell; Based on the calculated spatial location difference, the center coordinates of the remote sensing spatial grid unit with the smallest difference are selected to establish a spatial match with the corresponding facility spatial location coordinates. Record the spatial location coordinates of all spatially matched facilities and the center coordinates of remote sensing spatial grid cells, and output the coordinates of the occlusion spatial areas of the spatially matched facilities.

[0029] In this embodiment, the purpose of spatially matching the facility spatial location coordinates of each spatiotemporal occlusion unit with the center coordinates of the remote sensing spatial grid unit is to clarify the specific location of the airport facility occlusion within the remote sensing observation space, and thus clearly determine the spatial range of the occluded remote sensing space. Specifically, firstly, the facility spatial location coordinates corresponding to the facility occlusion are extracted from each spatiotemporal occlusion unit, for example, the facility spatial location coordinates are (120m, 50m, 5m). At the same time, the three-dimensional spatial coordinates of the center of the remote sensing spatial grid unit are also extracted, for example, the center coordinates of a certain grid unit are (120m, 50m, 0m). Then, for each facility spatial location coordinate and the center coordinates of the remote sensing spatial grid unit, the three-dimensional spatial position difference is calculated. The spatial position difference represents the specific positional deviation in three-dimensional space between the actual spatial location coordinates of the facility and the center coordinates of the remote sensing spatial grid unit.

[0030] Furthermore, based on the calculated spatial position difference results, the center coordinates of the remote sensing spatial grid cell with the smallest spatial position difference are selected as the explicit spatial matching coordinates for the facility's spatial position coordinates. For example, the spatial position difference between the facility's spatial position coordinates (120m, 50m, 5m) and the center coordinates of the remote sensing spatial grid cell (120m, 50m, 0m) is 5m, which is less than the difference with other grids. Therefore, the center coordinates of the remote sensing spatial grid cell (120m, 50m, 0m) are explicitly determined as the spatial matching point with the facility's spatial position coordinates. Finally, the facility's spatial position coordinates and the corresponding center coordinates of the remote sensing spatial grid cell for all spatial matching points are recorded to form a complete and explicit spatial matching record. Based on the explicit spatial matching record, the coordinates of the occluded spatial area are output.

[0031] Compared to the simple occlusion area determination method in the prior art, the processing method in this embodiment can more clearly establish a precise spatial correspondence between remote sensing space and facility space, providing clear and accurate spatial input data for subsequent three-dimensional spatial topology reconstruction of areas with missing remote sensing data, and significantly improving the reliability and spatial accuracy of remote sensing monitoring data processing.

[0032] S20: Perform three-dimensional topological reconstruction of the airspace remote sensing data based on the missing data topology set to generate incomplete area prediction information; Based on the missing topology set, three-dimensional topological reconstruction of the clearance area remote sensing data is performed to generate incomplete area prediction information, including: The missing spatial grid locations of remote sensing data are determined based on the missing topology set of data, and the missing spatial grid locations are marked to obtain the missing spatial grid location markings; Extract the locations of unlabeled valid remote sensing spatial grids and their corresponding remote sensing data to form a set of valid grid remote sensing data. Based on the spatial topological adjacency relationship between the missing spatial grid location markers and the effective grid remote sensing data set, calculate the spatial topological location deviation between the missing spatial grid location and the adjacent effective spatial grid location; Based on the calculated spatial topological position deviation, the missing spatial grid positions are reconstructed in three dimensions, and the incomplete region prediction information is output.

[0033] In this embodiment, the purpose of performing three-dimensional topological reconstruction of the airspace remote sensing data based on the missing data topology set is to clearly restore the spatial location and structure obscured by facilities when there are missing areas in the airport airspace remote sensing data. This provides a reliable remote sensing data foundation for accurately identifying the real-time boundary of the airport airspace. Specifically, firstly, the spatial grid locations of missing remote sensing data are clearly determined based on the missing data topology set. That is, the spatial grid locations of the remote sensing data that are obscured by facilities are marked one by one using the remote sensing spatial grid locations recorded in the missing data topology set to obtain clear missing spatial grid location marks. Furthermore, the unmarked valid remote sensing spatial grid locations are clearly extracted from the airspace remote sensing data, and the valid remote sensing data corresponding to the above valid remote sensing spatial grid locations are also clearly extracted to form a complete and clear set of valid grid remote sensing data.

[0034] For example, if the spatial grid coordinates of the missing spatial grid location markers are (120m, 50m, 0m) and (125m, 50m, 0m), then the effective remote sensing spatial grid locations can be (130m, 50m, 0m) and (135m, 50m, 0m). The remote sensing data values ​​corresponding to these effective grid locations are explicitly extracted, such as height and image features. Then, this embodiment explicitly calculates the spatial topological adjacency relationship between the missing spatial grid location markers and the effective grid remote sensing data set, that is, the spatial relationship between the missing grids and the effective grids that are clearly adjacent to each other in space, and clearly determines the specific spatial deviation direction and deviation value between each missing grid location and the adjacent effective grid location.

[0035] For example, the calculated spatial topological position deviation between the missing spatial grid location (125m, 50m, 0m) and the adjacent valid spatial grid location (130m, 50m, 0m) is 5m, with the direction defined as the positive X-axis. Finally, this embodiment explicitly reconstructs the three-dimensional spatial position of the missing spatial grid location based on the calculated spatial topological position deviation value, that is, explicitly adjusts the position of the missing spatial grid location (125m, 50m, 0m) to (127.5m, 50m, 0m), and performs similar spatial topological position adjustments on all missing spatial grid locations, thereby obtaining accurate incomplete area prediction information. Compared with the existing remote sensing data processing methods of simple interpolation, filling, or ignoring obstructed areas, this embodiment can more clearly and reliably restore the spatial position and structure of the area of ​​the airport airspace obstructed by facilities, effectively improving the accuracy and real-time reliability of remote sensing monitoring.

[0036] Based on the spatial topological adjacency relationship between the missing spatial grid location markers and the effective grid remote sensing data set, the spatial topological location deviation between the missing spatial grid location and the adjacent effective spatial grid location is calculated, including: Extract the spatial grid position coordinates corresponding to the missing spatial grid position markers, and extract the spatial coordinates of adjacent valid spatial grid positions; Based on the spatial topological connection relationship between the missing spatial grid location and the adjacent valid spatial grid location, calculate the spatial coordinate difference between the missing grid location and the adjacent valid grid location; Based on the spatial coordinate difference, obtain the topological position deviation direction and position deviation value of the missing grid location; Record the topological position deviation direction and position deviation value corresponding to all missing spatial grid locations, and output the spatial topological position deviation.

[0037] In this embodiment, the purpose of calculating the spatial topological position deviation between the missing spatial grid position and the adjacent valid spatial grid position based on the spatial topological adjacency relationship between the missing spatial grid position marker and the valid grid remote sensing data set is to clearly determine the specific offset direction and spatial position difference of the missing spatial position of the remote sensing data relative to the surrounding valid remote sensing observation spatial position, so as to achieve accurate spatial correction of the missing position of the remote sensing data in the clearance area.

[0038] Specifically, firstly, the spatial grid position coordinates corresponding to the missing spatial grid position marker are clearly extracted. For example, the missing spatial grid position marker is (125m, 50m, 0m). At the same time, the spatial coordinates of the adjacent valid spatial grid positions are also clearly extracted. For example, the coordinates of the adjacent valid spatial grid positions are (130m, 50m, 0m) and (125m, 55m, 0m). Then, according to the spatial topological connection relationship between the clearly defined missing spatial grid position coordinates and the coordinates of the adjacent valid spatial grid positions, this embodiment specifically calculates the spatial position coordinate difference. For example, the spatial coordinate difference between the missing spatial grid position (125m, 50m, 0m) and the adjacent valid spatial grid positions (130m, 50m, 0m) and (125m, 55m, 0m) are calculated, and the differences are (5m, 0m, 0m) and (0m, 5m, 0m) respectively. Based on this, the specific topological position deviation direction and specific position deviation value of each missing grid position relative to the valid grid position are obtained according to the calculated spatial position coordinate difference.

[0039] For example, for a spatial position difference (5m, 0m, 0m), the direction of the topological position deviation is clearly defined as the positive X-axis, and the specific position deviation value is 5m. For a difference (0m, 5m, 0m), the direction of the topological position deviation is defined as the positive Y-axis, and the specific position deviation value is 5m. Finally, the topological position deviation direction and specific position deviation value corresponding to all missing spatial grid positions are clearly recorded, and clear and complete spatial topological position deviation information is output. Compared with the existing technology of directly performing spatial interpolation or simple spatial repair, this embodiment achieves accurate reconstruction of missing spatial positions in remote sensing data through clear topological position deviation direction and value, effectively enhancing the accuracy of remote sensing monitoring of the three-dimensional boundary of the airport airspace and the stability of the spatial structure.

[0040] Calculate the spatial coordinate difference between the missing grid location and the adjacent valid grid location, including: Obtain the three-dimensional spatial coordinates of the missing grid location and the adjacent valid grid location, and calculate the spatial coordinate difference for each; Obtain the duration of occlusion, and calculate the spatiotemporal coherence attenuation weights corresponding to each missing grid location based on the duration of occlusion. The three-dimensional spatial position coordinates are weighted and corrected according to the spatiotemporal coherence attenuation weight to obtain the corrected target position coordinate difference. The differences in target position coordinates between each missing grid location and its adjacent valid grid location are summarized, and the spatial position coordinate differences are output.

[0041] In this embodiment, the purpose of calculating the spatial coordinate difference between the missing grid location and the adjacent valid grid location is to clarify the precise spatial difference between the missing location in the remote sensing data and the adjacent valid observation area, thereby effectively guiding the subsequent three-dimensional topology reconstruction of the remote sensing data. Specifically, firstly, the three-dimensional spatial coordinates of the missing grid location and the adjacent valid grid location are obtained. For example, the three-dimensional coordinates of the missing grid location are (125m, 50m, 0m), and the coordinates of the adjacent valid grid location are (130m, 50m, 0m). Then, the spatial coordinate difference between the missing grid location and the adjacent valid grid location is calculated separately. The result of the difference calculation between the two locations is (5m, 0m, 0m).

[0042] Simultaneously, the duration of facility occlusion at the missing grid location is determined. For example, if the occlusion duration is 15 minutes, the spatiotemporal coherence attenuation weight corresponding to the missing grid location is calculated based on the occlusion duration. Specifically, the longer the occlusion duration, the larger the spatiotemporal coherence attenuation weight, indicating a more significant impact on the spatial accuracy of the missing grid location. For instance, when the occlusion duration is 15 minutes, the calculated spatiotemporal coherence attenuation weight is 0.8. Based on this, the spatial location coordinate difference is weighted and corrected according to the calculated spatiotemporal coherence attenuation weight. For example, if the initial difference is (5m, 0m, 0m) and the weight is 0.8, the corrected target location coordinate difference is (4m, 0m, 0m). Finally, all weighted target location coordinate differences are summarized, and a complete and clear spatial location coordinate difference is output.

[0043] Compared to existing technologies that directly interpolate or average the missing locations in remote sensing data, this embodiment explicitly considers the direct impact of the duration of facility obstruction on the accuracy of reconstructing the missing spatial locations. This makes the calculation of spatial topological location deviation in the missing remote sensing data area more accurate, effectively improving the accuracy and stability of remote sensing data reconstruction and real-time monitoring of airport airspace.

[0044] S30: Extract boundary space coordinates based on incomplete area prediction information, construct an initial boundary model based on remote sensing data of the clearance area and boundary space coordinates, calculate boundary consistency parameters based on the initial boundary model, and generate boundary consistency constraint information; In this embodiment, the specific spatial range and structural characteristics of the airport airspace boundary are clarified based on the incomplete area prediction information to further improve the accuracy of real-time boundary monitoring of the airport airspace. Specifically, the boundary spatial coordinates are first extracted from the incomplete area prediction information. The boundary spatial coordinates are used to accurately represent the clear spatial boundary between the missing area and the effective area within the remote sensing observation range. For example, the boundary spatial coordinates are clearly extracted as (125m, 50m, 0m), (130m, 55m, 0m), (135m, 50m, 0m), etc. Subsequently, based on the acquired airspace remote sensing data and the clearly extracted boundary spatial coordinates, an initial boundary model of the airport airspace is established. The initial boundary model is a three-dimensional spatial topology that can accurately represent the boundary structure of the obstructed area within the airspace. The initial boundary model includes a clear boundary spatial location, boundary connection method, and boundary topological relationship, clearly reflecting the actual spatial structural characteristics of the airspace boundary.

[0045] For example, the clearly extracted boundary space coordinates (125m, 50m, 0m), (130m, 55m, 0m), and (135m, 50m, 0m) clearly constitute the node coordinates in the boundary model. Furthermore, this embodiment clearly calculates boundary consistency parameters based on the constructed initial boundary model. Specifically, the boundary consistency parameters are used to clearly quantify the clear spatial consistency of the topology between the boundary space coordinate points within the boundary model. For example, the topological spatial position difference parameter between the boundary coordinate point (125m, 50m, 0m) and the adjacent boundary coordinate point (130m, 55m, 0m) is clearly calculated. This parameter clearly characterizes the clear consistency feature of the airspace boundary topology in spatial distribution. Finally, based on the clearly calculated boundary consistency parameters, boundary consistency constraint information is clearly generated. This boundary consistency constraint information is used to clearly guide the subsequent precise optimization processing of the airport airspace boundary, thereby significantly improving the clear spatial accuracy of real-time monitoring and identification of the airspace boundary.

[0046] Compared to existing technologies that rely solely on simple boundary segmentation or spatial interpolation, this embodiment significantly enhances the spatial consistency and structural clarity of the three-dimensional boundaries of remote sensing data for airspace clearance by employing explicit boundary consistency parameters and topological constraints. This effectively improves the reliability and real-time accuracy of remote sensing boundary monitoring for airport airspace clearance.

[0047] S40: Optimize the initial boundary model based on the boundary consistency constraint information, generate the optimized boundary model, and calculate the safe distance of the aircraft trajectory in real time based on the optimized boundary model.

[0048] The initial boundary model is optimized based on the boundary consistency constraint information to generate an optimized boundary model, including: Obtain the boundary space coordinates of the initial boundary model and extract the boundary topology constraint parameters from the boundary consistency constraint information; Based on the boundary topology constraint parameters, the spatial coordinate points in the initial boundary model are marked with topology constraints to form constraint boundary coordinate points; Adjust the positions of spatial coordinate points with topological constraint differences in the initial boundary model based on the coordinate points of the constraint boundary to generate an optimized boundary model.

[0049] In this embodiment, the location structure of the airport airspace boundary is accurately determined by optimizing the initial boundary model to ensure the reliability of real-time calculation of the safe distance of the aircraft approach track. Specifically, all boundary spatial coordinate points are first obtained from the initial boundary model, and boundary topology constraint parameters are extracted from the boundary consistency constraint information. The boundary topology constraint parameters specifically represent the positional differences and topological relationship constraints between the boundary spatial coordinate points in the initial boundary model of the airspace.

[0050] For example, the topological structure difference values ​​between a certain boundary spatial coordinate point (125m, 50m, 0m) and its adjacent coordinate point (130m, 50m, 0m) are explicitly extracted. Then, based on the above-mentioned boundary topological constraint parameters, each boundary spatial coordinate point in the initial boundary model is explicitly marked with topological constraints. Specifically, the topological constraint marking is used to clearly indicate the deviation area of ​​the spatial position of the boundary coordinate point in the initial boundary model, forming a clear set of constraint boundary coordinate points. Further, in this embodiment, based on the clearly marked set of constraint boundary coordinate points, the spatial position adjustment processing of the spatial coordinate points with topological constraint differences in the initial boundary model is clearly performed. For example, if the boundary coordinate point (125m, 50m, 0m) in the initial boundary model clearly has a difference from the topological position required by the constraint, for example, the difference is 3m, then the spatial position of the coordinate point is clearly adjusted by 3m, thereby clearly generating an optimized boundary model. The above-mentioned optimized boundary model clearly represents the precise spatial position and topological structure of the airport airspace boundary after topological structure optimization. Further, in this embodiment, the safe distance of the aircraft approach track is clearly calculated in real time based on the generated optimized boundary model.

[0051] Compared to the simple interpolation or boundary correction processing of missing remote sensing data areas in existing technologies, this embodiment explicitly introduces boundary consistency constraint parameters to perform explicit structural topology optimization on the boundary model. This effectively improves the clarity of real-time monitoring of remote sensing boundaries in airport airspace, the consistency of topological structure, and the reliability and accuracy of real-time calculation of aircraft track safety distance, thus clearly ensuring that aircraft can obtain stable and reliable safety margin during approach track processes.

[0052] Based on the coordinates of the constraint boundary points, adjust the positions of the spatial coordinate points with topological constraint differences in the initial boundary model to generate an optimized boundary model, including: Obtain the safety slope limit parameters of the airport airspace and map them into a gradient surface constraint model; Calculate the spatial difference between the coordinate points in the initial boundary model and the safety constraint plane based on the gradient surface constraint model; Based on spatial difference, limit smoothing is performed on spatial coordinate points with different topological constraints to output an optimized boundary model that meets the airspace safety standard.

[0053] In this embodiment, the main purpose of adjusting the spatial coordinates of the initial boundary model based on the coordinates of the constraint boundary is to make the remote sensing spatial boundary structure of the airport airspace meet the specific requirements of airport flight safety. Specifically, the safe slope limit parameters of the airport airspace are first clearly obtained. The safe slope limit parameters clearly indicate the maximum allowable slope of obstacles in the airspace.

[0054] For example, the maximum allowable slope of an airport airspace may be explicitly defined as 1:50. The explicitly obtained safety slope limit parameters are further mapped to a specific gradient surface constraint model. This gradient surface constraint model explicitly represents the maximum allowable height limit and spatial slope constraint for each boundary coordinate point within the airspace. Based on this, this embodiment explicitly calculates the spatial difference between each spatial coordinate point in the initial boundary model and the airport airspace safety limit plane according to the aforementioned explicit gradient surface constraint model. This explicitly determines the height deviation between the position and height of each spatial coordinate point and the explicit safety limit plane. For example, if the initial height coordinates of a spatial coordinate point are (120m, 50m, 7m), and the allowable height of the explicit safety limit plane is 5m... The spatial difference between the spatial coordinate point and the safety constraint plane is 2m. Based on the calculated spatial difference, the spatial coordinate points with topological constraint differences in the initial boundary model are subjected to limit smoothing processing. The position and height of the above-mentioned coordinate points are adjusted to meet the airport airspace safety standards. For example, the spatial coordinate point (120m, 50m, 7m) is adjusted to (120m, 50m, 5m) after limit smoothing processing. Thus, the optimized boundary model that meets the airport airspace safety standards is output, and it is completely matched with the topological constraint requirements of the constraint boundary coordinate points. This avoids the problems of boundary structure height distortion and topological inconsistency caused by the lack of remote sensing monitoring data in the airspace area.

[0055] Compared to existing technologies that rely solely on simple height interpolation or boundary smoothing, this embodiment effectively ensures the clarity, accuracy, and topological consistency of the airspace remote sensing boundary optimization structure in airport flight safety monitoring applications through a well-defined gradient surface constraint model.

[0056] Example 2 Please see Figure 2 As shown, based on the same inventive concept, this embodiment discloses a remote sensing intelligent identification and visualization management system for airport airspace. For details not covered in this embodiment, please refer to the relevant sections of Embodiment 1. The system includes: Missing Topology Module: Used to acquire remote sensing data of airspace and facility occlusion data, and to construct a set of missing topologies based on the facility occlusion data; Topology Reconstruction Module: Used to perform three-dimensional topology reconstruction on remote sensing data of the clearance area based on the missing data topology set, and generate prediction information for the incomplete area; Boundary Consistency Module: This module is used to extract boundary spatial coordinates based on incomplete area prediction information, construct an initial boundary model based on remote sensing data of the clearance area and boundary spatial coordinates, calculate boundary consistency parameters based on the initial boundary model, and generate boundary consistency constraint information. Boundary optimization module: used to optimize the initial boundary model based on boundary consistency constraint information, generate an optimized boundary model, and calculate the safe distance of the aircraft trajectory in real time based on the optimized boundary model.

[0057] The detailed description above, in conjunction with the accompanying drawings, describes examples but does not represent all examples that can be implemented or fall within the scope of the claims. The terms “example” and “exemplary” are used in this specification to mean “serving as an example, instance or illustration” and do not mean “superior to or better than other examples”.

[0058] Throughout this specification, the phrase "an embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. Therefore, the use of these phrases may refer to more than one embodiment. Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0059] It should also be noted that these embodiments may be described as processes depicted as flowcharts, structural diagrams, or block diagrams. Although a flowchart may describe the operations as sequential processes, many of these operations can be performed in parallel or concurrently, and the order of these operations may be rearranged.

Claims

1. A remote sensing intelligent identification and visualization management method for airport airspace, characterized in that, include: Acquire remote sensing data of the airspace and facility obstruction data, and construct a set of missing data topologies based on the facility obstruction data; Based on the missing data topology set, three-dimensional topological reconstruction of the airspace remote sensing data is performed to generate incomplete area prediction information. Based on the predicted information of the incomplete area, the boundary space coordinates are extracted. Based on the remote sensing data of the clearance area and the boundary space coordinates, an initial boundary model is constructed. Based on the initial boundary model, the boundary consistency parameters are calculated, and boundary consistency constraint information is generated. The initial boundary model is optimized based on the boundary consistency constraint information, an optimized boundary model is generated, and the safe distance of the aircraft trajectory is calculated in real time based on the optimized boundary model.

2. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 1, characterized in that, Construct a set of missing data topologies based on facility occlusion data, including: Obtain the spatial coordinates of the facilities in the facility occlusion data, and obtain the occlusion start time and occlusion end time. Establish a spatiotemporal occlusion unit by combining the facility location coordinates with the corresponding start and end times. Each spatiotemporal occlusion unit is mapped to the remote sensing spatial coordinates of the clearance area, and the coordinates of the occlusion spatial region corresponding to each spatiotemporal occlusion unit are marked. Calculate the spatial adjacency distance between the coordinates of each occluded spatial region, and mark the coordinate pairs of adjacent occluded spatial regions based on the spatial adjacency distance; A set of missing data topologies is generated based on the coordinate pairs of adjacent occluded spatial regions.

3. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 2, characterized in that, Each spatiotemporal occlusion unit is mapped to the remote sensing spatial coordinates of the clearance area, and the coordinates of the occlusion spatial region corresponding to each spatiotemporal occlusion unit are marked, including: Obtain the spatial extent of remote sensing data for the airspace clearance area, and establish remote sensing spatial coordinates based on the spatial extent; The remote sensing spatial grid is divided into units based on the remote sensing spatial coordinates, and the center coordinates of each unit are marked. The spatial location coordinates of the facilities in each spatiotemporal occlusion unit are spatially matched with the center coordinates of the remote sensing spatial grid unit one by one, and the coordinates of the occlusion spatial area are output.

4. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 3, characterized in that, For each spatiotemporal occlusion unit, the spatial location coordinates of the facility are spatially matched with the center coordinates of the remote sensing spatial grid unit one by one, and the coordinates of the occlusion spatial region are output, including: Extract the spatial three-dimensional coordinates corresponding to the spatial location coordinates of the facility, and extract the three-dimensional coordinates of the center of the remote sensing spatial grid cell; Calculate the spatial position difference between the spatial location coordinates of each facility and the center coordinates of each remote sensing spatial grid cell; Based on the calculated spatial location difference, the center coordinates of the remote sensing spatial grid unit with the smallest difference are selected to establish a spatial match with the corresponding facility spatial location coordinates. Record the spatial location coordinates of all spatially matched facilities and the center coordinates of remote sensing spatial grid cells, and output the coordinates of the occlusion spatial areas of the spatially matched facilities.

5. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 4, characterized in that, Based on the missing topology set, three-dimensional topological reconstruction of the clearance area remote sensing data is performed to generate incomplete area prediction information, including: The missing spatial grid locations of remote sensing data are determined based on the missing topology set of data, and the missing spatial grid locations are marked to obtain the missing spatial grid location markings; Extract the locations of unlabeled valid remote sensing spatial grids and their corresponding remote sensing data to form a set of valid grid remote sensing data. Based on the spatial topological adjacency relationship between the missing spatial grid location markers and the effective grid remote sensing data set, calculate the spatial topological location deviation between the missing spatial grid location and the adjacent effective spatial grid location; Based on the calculated spatial topological position deviation, the missing spatial grid positions are reconstructed in three dimensions, and the incomplete region prediction information is output.

6. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 5, characterized in that, Based on the spatial topological adjacency relationship between the missing spatial grid location markers and the effective grid remote sensing data set, the spatial topological location deviation between the missing spatial grid location and the adjacent effective spatial grid location is calculated, including: Extract the spatial grid position coordinates corresponding to the missing spatial grid position markers, and extract the spatial coordinates of adjacent valid spatial grid positions; Based on the spatial topological connection relationship between the missing spatial grid location and the adjacent valid spatial grid location, calculate the spatial coordinate difference between the missing grid location and the adjacent valid grid location; Based on the spatial coordinate difference, obtain the topological position deviation direction and position deviation value of the missing grid location; Record the topological position deviation direction and position deviation value corresponding to all missing spatial grid locations, and output the spatial topological position deviation.

7. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 6, characterized in that, Calculate the spatial coordinate difference between the missing grid location and the adjacent valid grid location, including: Obtain the three-dimensional spatial coordinates of the missing grid location and the adjacent valid grid location, and calculate the spatial coordinate difference for each; Obtain the duration of occlusion, and calculate the spatiotemporal coherence attenuation weights corresponding to each missing grid location based on the duration of occlusion. The three-dimensional spatial position coordinates are weighted and corrected according to the spatiotemporal coherence attenuation weight to obtain the corrected target position coordinate difference. The differences in target position coordinates between each missing grid location and its adjacent valid grid location are summarized, and the spatial position coordinate differences are output.

8. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 1, characterized in that, The initial boundary model is optimized based on the boundary consistency constraint information to generate an optimized boundary model, including: Obtain the boundary space coordinates of the initial boundary model and extract the boundary topology constraint parameters from the boundary consistency constraint information; Based on the boundary topology constraint parameters, the spatial coordinate points in the initial boundary model are marked with topology constraints to form constraint boundary coordinate points; Adjust the positions of spatial coordinate points with topological constraint differences in the initial boundary model based on the coordinate points of the constraint boundary to generate an optimized boundary model.

9. The method for remote sensing intelligent identification and visualization management of airport airspace according to claim 8, characterized in that, Based on the coordinates of the constraint boundary points, adjust the positions of the spatial coordinate points with topological constraint differences in the initial boundary model to generate an optimized boundary model, including: Obtain the safety slope limit parameters of the airport airspace and map them into a gradient surface constraint model; Calculate the spatial difference between the coordinate points in the initial boundary model and the safety constraint plane based on the gradient surface constraint model; Based on spatial difference, limit smoothing is performed on spatial coordinate points with different topological constraints to output an optimized boundary model that meets the airspace safety standard.

10. An airport airspace remote sensing intelligent identification and visualization management system, used to implement the airport airspace remote sensing intelligent identification and visualization management method according to any one of claims 1-9, characterized in that, include: Missing Topology Module: Used to acquire remote sensing data of airspace and facility occlusion data, and to construct a set of missing topologies based on the facility occlusion data; Topology Reconstruction Module: Used to perform three-dimensional topology reconstruction on remote sensing data of the clearance area based on the missing data topology set, and generate prediction information for the incomplete area; Boundary Consistency Module: This module is used to extract boundary spatial coordinates based on incomplete area prediction information, construct an initial boundary model based on remote sensing data of the clearance area and boundary spatial coordinates, calculate boundary consistency parameters based on the initial boundary model, and generate boundary consistency constraint information. Boundary optimization module: used to optimize the initial boundary model based on boundary consistency constraint information, generate an optimized boundary model, and calculate the safe distance of the aircraft trajectory in real time based on the optimized boundary model.

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