Low-altitude aircraft three-dimensional route design method based on Beidou grid

Abstract

The invention relates to the technical field of aircraft route design, in particular to a Beidou grid-based low-altitude aircraft three-dimensional route design method, which comprises the following steps of: performing airspace type subdivision processing on a target flight area in a flight space model; judging the airspace available state of the airspace type; dividing an initial route coverage space of the target low-altitude aircraft; carrying out conflict investigation on the airspace use condition in the initial route range; when the conflict checking result is a conflict-free state, performing arc fitting on the air route space trajectory of the target low-altitude aircraft; and verifying the fitted route trajectory, and if the current airspace state is changed, adjusting the fitted route trajectory to obtain a three-dimensional route design scheme. According to the method, the airline can be planned by combining airspace type division and a dynamic available state, the airline can be changed and adjusted according to the real-time airspace, and the flexibility and safety of airline planning are improved.

Description

Technical Field

[0001] This invention relates to the field of aircraft flight path design technology, and in particular to a three-dimensional flight path design method for low-altitude aircraft based on the BeiDou grid. Background Technology

[0002] With the widespread application of low-altitude aircraft in logistics transportation, emergency rescue, and aerial inspection, the limitations of traditional route planning methods in complex airspace environments are becoming increasingly apparent. Existing technologies mainly suffer from three problems: First, the classification of airspace types fails to effectively integrate with dynamic availability status. Traditional planning is largely based on static airspace classifications such as no-fly zones and controlled areas, without establishing a dynamic connection with a three-dimensional grid. This results in routes being unable to adapt to real-time changes such as the establishment of temporary no-fly zones or sudden weather conditions, leading to a mismatch between routes and actual airspace rules. Second, the ability to identify airspace use conflicts is insufficient. Existing conflict detection relies on post-event simulation or manual verification, lacking a three-dimensional spatial conflict prediction model based on the BeiDou grid. This makes it difficult to automatically identify potential risks such as overlapping flight paths during the planning stage, especially in multi-aircraft collaborative scenarios, which can easily lead to safety hazards. Third, a dynamic adjustment mechanism is lacking. Traditional routes are fixed after generation and cannot be adjusted in real time according to airspace changes such as temporary airspace openings or route closures. This forces aircraft to detour or apply for changes, reducing operational efficiency and increasing the risk of violations. In addition, the application of existing technologies to the BeiDou grid is mostly limited to the positioning level, and its spatial grid characteristics are not fully utilized to achieve refined management and dynamic adjustment of airspace resources, making it difficult to meet the needs of high-dynamic airspace use in multiple scenarios in the low-altitude economy era.

[0003] Chinese Patent Publication No. CN118762134A discloses a method and program product for generating terrain flight paths based on 3D reality. The method includes: obtaining reality model data; generating a safety fence based on the reality model data and a safety distance, wherein the safety fence is used to cover obstacles in the reality model to prevent drones from colliding with the obstacles; obtaining a quantity and direction based on preset parameters; obtaining a vertical plane corresponding to the safety fence based on the quantity and direction, wherein the preset parameters are used to configure the drone; and obtaining a flight segment based on the intersection line of the vertical plane and the safety fence, thereby obtaining a terrain flight path based on the flight segment. This solution only generates a safety fence based on reality model data and a safety distance, and generates a terrain flight path by obtaining a flight segment through the intersection of vertical planes. It can only avoid static obstacles and cannot combine airspace type division and dynamic availability status to plan flight paths. It is difficult to identify airspace usage conflicts and cannot adjust flight paths according to real-time airspace changes, thus reducing the flexibility and safety of flight path planning. Summary of the Invention

[0004] To address this, the present invention provides a three-dimensional flight path design method for low-altitude aircraft based on the BeiDou grid, which overcomes the problems in existing technologies that cannot combine airspace type classification and dynamic availability status to plan flight paths, make it difficult to identify airspace usage conflicts, and cannot adjust flight paths according to real-time airspace changes, thus reducing the flexibility and safety of flight path planning.

[0005] To achieve the above objectives, this invention provides a three-dimensional flight path design method for low-altitude aircraft based on the BeiDou grid, comprising the following steps: S1. Based on the BeiDou grid system, a model is constructed for the flight scenario of the target low-altitude aircraft to obtain a flight space model. The target flight area in the flight space model is then segmented into airspace types according to preset spatial reference information to obtain airspace types. The airspace types include no-fly zones, restricted zones, and suitable flight zones. The preset spatial reference information includes a three-dimensional coordinate frame and grid coding rules. S2. The availability status of airspace types is determined by dynamically updating airspace information to obtain airspace availability status data, which includes the availability time period and altitude range information of each airspace. S3. Divide the initial flight path coverage space of the target low-altitude aircraft according to the airspace availability status data and the flight requirement parameters of the target low-altitude aircraft to obtain the initial flight path range data. The flight requirement parameters include the coordinates of the flight start point, the coordinates of the flight end point, and the flight mission parameters. S4. Based on the conflict detection algorithm, the airspace usage within the initial route range is checked for conflicts, and the conflict checking results are obtained. The conflict checking is based on the airspace usage status, route parameters and airport restriction time information. S5. When the conflict resolution result is a conflict-free state, the spatial trajectory of the target low-altitude aircraft is fitted with an arc shape based on the great circle route calculation rule to obtain the fitted route trajectory data. The arc shape fitting is based on the spatial geometric features of the great circle route. S6. Verify the fitted flight path based on the real-time updated airspace dynamic information. If the current airspace status changes, adjust the fitted flight path based on the latest airspace availability to obtain a three-dimensional flight path design scheme.

[0006] Compared with the prior art, the beneficial effects of this application are as follows: By constructing a flight space model based on the BeiDou grid system, and classifying the target flight area into no-fly zones, restricted zones, and suitable flight zones according to preset spatial reference information, the available time periods and altitude ranges of each airspace are determined based on dynamic airspace update information. Then, the initial flight path range is delineated by combining the flight requirement parameters of the target low-altitude aircraft. Finally, based on a conflict detection algorithm, conflicts are checked against the initial flight path range according to airspace usage status, flight path parameters, and airport restriction time information. This effectively solves the problems of existing technologies that cannot combine airspace type classification and dynamic availability status for flight path planning, and that make it difficult to check airspace usage conflicts. This application can accurately integrate static airspace classification with dynamic availability status, and comprehensively identify potential conflicts through conflict detection algorithms, avoiding flight safety hazards caused by ambiguous airspace attributes or unchecked conflicts. It ensures that flight path planning strictly conforms to actual airspace usage conditions, significantly improving the safety of flight path planning.

[0007] After conflict resolution and elimination, the spatial trajectory of the target low-altitude aircraft is fitted with an arc shape based on the great circle-like route calculation rules. The fitted trajectory is then verified based on real-time updated airspace dynamic information. If the current airspace status changes, the trajectory is adjusted according to the latest airspace availability, ultimately forming a three-dimensional route design scheme. This effectively solves the problem that existing technologies cannot adjust routes according to real-time airspace changes, reducing the flexibility of route planning. In this application, the arc fitting of the great circle-like route ensures the rationality and efficiency of the route trajectory, while the real-time airspace dynamic verification and adjustment mechanism enables the route to respond quickly to changes in airspace status, breaking the limitations of fixed routes and enabling route planning to dynamically adapt to airspace changes. This ensures that the flight process always follows the latest airspace availability requirements, avoiding route failure due to changes in airspace status, and greatly improving the flexibility and real-time adaptability of route planning.

[0008] Furthermore, S1 includes the following steps: S11. Obtain geographic information data of the flight scene, and divide the geographic information data into grids according to the grid coding rules to obtain scene grid data. Then, perform coordinate mapping on the scene grid data in a three-dimensional coordinate frame based on preset flight constraints to obtain a flight space model. The flight space model includes the three-dimensional position information and grid coding of each grid unit. S12. In the flight space model, the spatial range of the target flight area is defined according to the airspace use planning information to obtain the target area range data, and the spatial contour is extracted from the target area range data to obtain the target spatial contour data. S13. Based on the grid coding rules, attribute identification is performed on the grid cells corresponding to the target spatial contour data to obtain the corresponding spatial domain type.

[0009] In this solution, geographic information data of the flight scenario is divided into grids according to grid coding rules. In a three-dimensional coordinate framework, the gridded data of the scenario is mapped to obtain a flight space model based on preset flight constraints. Combined with airspace use planning information, the target flight area is defined and the spatial contour is extracted. Then, based on the grid coding rules, the grid cells corresponding to the target spatial contour data are attribute-identified to obtain the airspace type. This solution can accurately construct a flight environment model containing the three-dimensional position information and grid coding of grid cells, clarify the spatial boundary and airspace attributes of the target flight area, and provide accurate and standardized spatial data support for flight path planning and airspace resource scheduling, ensuring the rationality of airspace use and flight safety.

[0010] Furthermore, S2 includes the following steps: S21. Obtain the dynamic airspace update information released by the airspace management system, and perform spatial coding consistency verification between the dynamic airspace update information and the airspace type to generate an airspace status matching table. The dynamic airspace update information includes airspace status change instructions, temporary flight restriction notices and meteorological monitoring data. S22. Based on the airspace status matching table, identify the dynamic status of airspace types to obtain the dynamic status identifier of each airspace area, and map the dynamic status identifier to the static attributes of airspace types to generate airspace status fusion data. The dynamic status identifier includes no-fly status, restricted-fly status, and suitable-fly status. S23. Extract the available time periods of each airspace area based on the airspace status fusion data to obtain the available time period information that the airspace is allowed to use, and perform time consistency matching between the available time period information and the time interval of the aircraft's planned flight to generate time matching data. S24. Based on the time matching data, the altitude range of the airspace area is analyzed to obtain altitude range information, and the altitude range information is matched with the flight altitude parameters of the aircraft to generate altitude matching data. The altitude range information includes the minimum and maximum altitudes that the airspace allows for flight. S25. Integrate the time matching data and altitude matching data to obtain airspace availability status data, which includes the available time period and available altitude range corresponding to the airspace type.

[0011] This solution acquires dynamically updated airspace information and performs spatial coding consistency verification with airspace types. It combines dynamic status identifiers with static airspace attributes, and then matches the availability time period with the aircraft's planned flight time interval, as well as the altitude interval information with flight altitude parameters. This enables accurate identification of airspace availability status, ensuring precise adaptation of airspace use in the spatiotemporal and altitude dimensions. It significantly improves the reliability of airspace availability status data, effectively supports the scientific formulation of aircraft flight plans, and avoids waste of airspace resources and flight conflict risks.

[0012] Furthermore, S3 includes the following steps: S31. Calculate the straight-line distance between the coordinates of the flight start point and the coordinates of the flight end point in the three-dimensional coordinate frame, and calculate the flight time corresponding to the straight-line distance based on the straight-line distance and the flight speed in the flight mission parameters. Compare the flight time with the available time period in the airspace availability status data to determine the flight time window. S32. Construct a three-dimensional spatial region in a three-dimensional coordinate framework based on the flight time window, the coordinates of the flight start point, and the coordinates of the flight end point. Exclude the spatial regions corresponding to the no-fly zone and the restricted zone during the unavailable time period in the three-dimensional spatial region to obtain the initial boundary range. The three-dimensional spatial region extends to both sides on the horizontal plane with the line connecting the flight start point and the flight end point as the reference. The three-dimensional spatial region covers the available altitude range in the vertical direction. S33. Divide the initial boundary range according to the grid coding rules to generate multiple grid cells, and filter each grid cell according to the airspace type and available time period, retaining the grid cells in the flight-safe area that are available time periods to form a candidate grid set. S34. Spatial encoding is performed on the candidate grid set according to the grid encoding rules to generate a grid encoding sequence. The grid encoding sequence is then spatially clustered to merge adjacent grid cells, forming a coherent spatial region. Boundary point sets are extracted from the coherent spatial region, and the boundary point sets are smoothed using a curve fitting algorithm to generate initial flight path range data. The initial flight path range data includes boundary point sequences and vertical height range information.

[0013] In this scheme, a three-dimensional spatial region is constructed in a three-dimensional coordinate framework based on the flight time window, the coordinates of the flight start point, and the coordinates of the flight end point. The spatial region corresponding to the no-fly zone and restricted zone during the unavailable time period is excluded to obtain the initial boundary range. The initial boundary range is divided into grid cells, and grid cells with available time periods in the suitable flight zone are selected to form a candidate grid set. The candidate grid set is spatially encoded and clustered to merge adjacent grid cells. The boundary point set is smoothed by a curve fitting algorithm to generate the initial route range data, ensuring that the initial route range is within the available time period and the suitable flight zone, and forming a coherent and smooth spatial region. This accurately matches the flight mission parameters and airspace availability status data, improving the pertinence and reliability of route planning.

[0014] Furthermore, S4 includes the following steps: S41. Extract horizontal boundary points and vertical altitude range information from the initial flight path range data, and obtain preset airspace usage status data from the airspace dynamic update information. The preset airspace usage status data includes flight path parameters of other aircraft and airport restriction time information. S42. Within the horizontal boundary points of the initial flight path range, a path generation algorithm is used to connect the coordinates of the flight start point and the flight end point to generate multiple candidate flight paths. The candidate flight paths are represented by a sequence of three-dimensional coordinate points, and the height of the coordinate points is transformed within the vertical height range information. S43. Calculate the spatial distance between the coordinate point sequence of each candidate flight path and the flight path coordinate points of other aircraft in the preset airspace usage status data, and compare the spatial distance with the preset safety distance. If the spatial distance is less than the preset safety distance, output the same spatial point between the target low-altitude aircraft and other aircraft, calculate the expected time difference of the same spatial point, and mark the same spatial point with the expected time difference exceeding the preset safety time as a spatial conflict or time conflict. S44. Compare the estimated flight time of each candidate route with the airport restriction period information. If the flight time overlaps with the restriction period, mark the candidate route as having an airport conflict. S45. Combine the conflict markers of all candidate routes to generate conflict statistics results. If at least one candidate route has no conflict marker, the conflict resolution result is a conflict-free state; otherwise, it is a conflict state. The conflict resolution result includes the conflict type and conflict location coordinates.

[0015] This solution extracts horizontal boundary points and vertical altitude ranges from initial route range data to obtain preset airspace usage status data. Multiple candidate routes are generated using a path generation algorithm. The spatial distance between candidate routes and other aircraft routes is calculated and compared with preset safety distances. Overlap with airport restriction periods is checked, and comprehensive conflict statistics are generated. This improves the comprehensiveness and accuracy of route conflict investigation, effectively identifying spatial, temporal, and airport conflicts. It ensures that candidate routes maintain safe compatibility with other aircraft and airport restrictions during flight, reducing flight risks.

[0016] Furthermore, S5 includes the following steps: S51. When the conflict resolution result is a conflict-free state, obtain the coordinates of the flight start point and the flight end point from the flight demand parameters, and obtain the vertical altitude range from the initial route range data. S52. In the three-dimensional coordinate frame, project the coordinates of the flight start point and the flight end point onto the reference ellipsoid, calculate the shortest path curve between the coordinates of the flight start point and the flight end point, and adjust the elevation of the shortest path curve according to the vertical altitude range to form a three-dimensional curve model of a great circle route. The shortest path curve is determined based on the Earth's geometric model. S53. Sample multiple interpolation points uniformly along the three-dimensional curve model to form an interpolation point sequence. The number of interpolation points is determined based on flight mission parameters. The distribution of interpolation points in three-dimensional space follows the geometric characteristics of the three-dimensional curve model. The height of the interpolation points changes smoothly within the vertical height range. S54. Use spline interpolation algorithm to fit the interpolation point sequence to generate a smooth arc-shaped flight path trajectory, which is represented by a parametric equation; S55. Check whether each coordinate point of the arc-shaped flight path is within the preset flight path range boundary of the initial flight path range data. If the coordinate point exceeds the preset flight path range boundary, adjust the spatial position of the coordinate point to within the preset flight path range boundary to obtain the fitted flight path trajectory data. The fitted flight path trajectory data includes the trajectory equation and the list of coordinate points.

[0017] In this scheme, the coordinates of the flight start point and the flight end point are projected onto a reference ellipsoid. The shortest path curve is determined based on the Earth's geometric model. Combined with the vertical altitude range adjustment, a three-dimensional curve model of a great circle-like flight path is formed. The interpolation point sequence is fitted by a spline interpolation algorithm to generate a smooth arc-shaped flight path trajectory. The coordinate points that exceed the preset flight path range boundary are checked and adjusted. This ensures that the flight path trajectory conforms to the Earth's geometric characteristics, has both the shortest path attribute and elevation smoothness, and ensures that the trajectory always stays within the preset range boundary, significantly improving the accuracy and applicability of flight path planning.

[0018] Furthermore, S6 includes the following steps: S61. Obtain real-time airspace dynamic information from the airspace dynamic update system and obtain fitted flight path data. The real-time airspace dynamic information includes airspace type change information, available time period update information, and altitude range adjustment information. S62. Based on preset spatial matching rules and preset time synchronization rules, each coordinate point in the fitted flight trajectory data is matched one by one with the latest airspace availability status data, and it is checked whether the spatial position and flight time corresponding to each coordinate point overlap with the designated range of the no-fly zone and the unavailable time period of the restricted area. S63. If conflicting coordinate points are detected, the continuous coordinate point correlation analysis method is used to identify the conflict segment composed of continuous conflicting coordinate points, and the spatial range and temporal range of the conflict segment are calculated. The conflict segment includes the starting point, the ending point and the conflict type. S64. Based on the latest airspace availability data, search for flyable airspace areas within the spatial range of the conflict segment and adjacent airspaces of the conflict segment, and generate a new path segment to bypass the conflict segment according to the path generation algorithm. The new path segment is connected to the start and end points of the conflict segment. The flyable airspace areas include airspace areas of the airspace type being a suitable flight zone, and the available time period of the suitable flight zone matching the planned flight time period of the target low-altitude aircraft. S65. The new path segment is fused with the non-conflicting segments in the fitted route trajectory data using a trajectory stitching algorithm to obtain a three-dimensional route design scheme.

[0019] In this solution, by acquiring real-time airspace dynamic information and fitted flight path data, and based on preset spatial matching rules and preset time synchronization rules, the system checks for conflicts between coordinate points and no-fly zones and restricted areas. It uses continuous coordinate point correlation analysis to identify conflict segments, searches for flyable airspace areas to generate new path segments, and fuses the new path segments with non-conflicting segments using a trajectory splicing algorithm. This accurately avoids airspace usage conflicts, ensures the compliance and efficiency of target low-altitude aircraft flights, achieves dynamic optimization of three-dimensional flight path design, and improves the flexibility and reliability of flight path adaptation to real-time airspace changes. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the three-dimensional flight path design method for low-altitude aircraft based on the BeiDou grid according to an embodiment of the present invention. Detailed Implementation

[0021] The following detailed description illustrates the specific implementation method: like Figure 1 The diagram shown is a flowchart illustrating the three-dimensional flight path design method for low-altitude aircraft based on the BeiDou grid according to an embodiment of the present invention, including the following steps: S1. Based on the BeiDou grid system, a model is constructed for the flight scenario of the target low-altitude aircraft to obtain a flight space model. The target flight area in the flight space model is then segmented into airspace types according to preset spatial reference information to obtain airspace types. The airspace types include no-fly zones, restricted zones, and suitable flight zones. The preset spatial reference information includes a three-dimensional coordinate frame and grid coding rules. S2. The availability status of airspace types is determined by dynamically updating airspace information to obtain airspace availability status data, which includes the availability time period and altitude range information of each airspace. S3. Divide the initial flight path coverage space of the target low-altitude aircraft according to the airspace availability status data and the flight requirement parameters of the target low-altitude aircraft to obtain the initial flight path range data. The flight requirement parameters include the coordinates of the flight start point, the coordinates of the flight end point, and the flight mission parameters. S4. Based on the conflict detection algorithm, the airspace usage within the initial route range is checked for conflicts, and the conflict checking results are obtained. The conflict checking is based on the airspace usage status, route parameters and airport restriction time information. S5. When the conflict resolution result is a conflict-free state, the spatial trajectory of the target low-altitude aircraft is fitted with an arc shape based on the great circle route calculation rule to obtain the fitted route trajectory data. The arc shape fitting is based on the spatial geometric features of the great circle route. S6. Verify the fitted flight path based on the real-time updated airspace dynamic information. If the current airspace status changes, adjust the fitted flight path based on the latest airspace availability to obtain a three-dimensional flight path design scheme.

[0022] Specifically, S1 includes the following steps: S11. Obtain geographic information data of the flight scene, and divide the geographic information data into grids according to the grid coding rules to obtain scene grid data. Then, perform coordinate mapping on the scene grid data in a three-dimensional coordinate frame based on preset flight constraints to obtain a flight space model. The flight space model includes the three-dimensional position information and grid coding of each grid unit. S12. In the flight space model, the spatial range of the target flight area is defined according to the airspace use planning information to obtain the target area range data, and the spatial contour is extracted from the target area range data to obtain the target spatial contour data. S13. Based on the grid coding rules, attribute identification is performed on the grid cells corresponding to the target spatial contour data to obtain the corresponding spatial domain type.

[0023] In this embodiment, grid coding rules refer to a rule system for standardizing and encoding geographic space. Typically, a grid division method based on latitude and longitude coordinates is used. For example, longitude and latitude are divided into rectangular grids at fixed intervals (e.g., 0.01 degrees), and each grid is assigned a unique code (e.g., "N34.1234_E116.5678"). For instance, a region with a longitude range of 116.0-117.0 and a latitude range of 34.0-35.0, divided at 0.01-degree intervals, forms 100×100 grid units. Each unit's code includes its center point coordinates. When dividing the geographic information data into grids according to the grid coding rules, geographic information data (such as terrain elevation and obstacle locations) is first acquired. The grid size and coding format are then determined according to the grid coding rules. The geographic data is mapped to the corresponding grid units to form scene gridded data, ensuring that each grid contains three-dimensional location information (longitude, latitude, and altitude) and a unique code. Preset flight constraints refer to the physical and regulatory restrictions that must be followed during flight, including altitude restrictions (such as a minimum flight altitude of 50 meters and a maximum of 1,000 meters), speed restrictions (such as a maximum horizontal speed of 200 km / h), obstacle avoidance rules (such as maintaining a safe distance of 50 meters from buildings), and airspace type restrictions (such as no-fly zones and restricted-fly zones). When mapping scene gridded data to coordinates within a 3D coordinate framework based on preset flight constraints, the basic geographic information (longitude, latitude, and terrain elevation) of each grid cell in the scene gridded data is first mapped to the latitude and longitude coordinates of the 3D coordinate framework. Then, based on the height restrictions (minimum 50 meters, maximum 1000 meters) in the preset flight constraints, the height coordinates of the grid cells are adjusted in conjunction with terrain elevation and obstacle location data to ensure that the height is within the constraint range. At the same time, according to the obstacle avoidance rules (maintaining a safe distance of 50 meters from buildings), the 3D position of grid cells with obstacles is checked and corrected to avoid conflicts with obstacles. It is also necessary to refer to the spatial adaptation requirements corresponding to the speed limit to ensure that the spatial channel formed after the grid cell coordinate mapping conforms to the physical constraints. Finally, each grid cell obtains 3D position information (longitude, latitude, and altitude) and a unique grid code that conforms to all preset flight constraints, and integrates them into a flight space model.

[0024] Airspace usage planning information refers to planning data that divides flight areas by function and allocates time, including the flight area range (e.g., latitude and longitude boundaries), airspace type (e.g., training area, transportation area, emergency area), usage time (e.g., open daily from 8:00 to 18:00), and flight altitude layer division (e.g., low altitude below 300 meters, mid-altitude 300-600 meters). In the flight space model, when defining the spatial range of the target flight area based on airspace usage planning information, the latitude and longitude boundaries in the planning information need to be converted into three-dimensional coordinate ranges. The target area range data (e.g., longitude 116.2-116.8, latitude 34.2-34.6, altitude 50-500 meters) is obtained through spatial range definition. When extracting the spatial contour of the target area range data, the geometric shape of the area boundary (e.g., polygon, circle) needs to be identified, and the area contour is determined through the boundary point coordinates to form the target area data. For example, the convex hull algorithm is used to extract the boundary points of the target area, and connecting the boundary points forms a closed contour, ensuring that the contour data contains the three-dimensional position information and mesh encoding of all target mesh cells.

[0025] The attribute identification of grid cells corresponding to the target spatial contour data based on grid coding rules includes: First, clarifying the coding parsing logic related to airspace type in the preset grid coding rules, determining the fields in the coding structure used to distinguish airspace types (such as the last two digits of the code, specific segment characters), and the correspondence between these fields and airspace types (such as "01" at the end of the code corresponding to controlled airspace, "02" corresponding to monitored airspace, "03" corresponding to operational airspace, and "04" corresponding to no-fly zone). Extracting the grid code of each grid cell in the target area data, and according to the parsing method of the coding rules, splitting the airspace feature fields in the code to obtain the coding feature information of each grid cell. Simultaneously, retrieving the airspace type definitions for different sub-regions within the target flight area from the airspace usage planning information, and matching the coding features of the grid cells with the airspace types defined in the planning. For each grid cell, adding a clear airspace type identifier to its attribute field based on the matching results, ensuring that the identifier information is consistent with the grid code and three-dimensional position information. If some grid cell encoding features are unclear, supplement the identification by combining their three-dimensional coordinates with the spatial correspondence of the planned spatial type, and finally complete the attribute identification of all grid cells in the target area, clarifying the spatial type corresponding to each grid cell.

[0026] Specifically, S2 includes the following steps: S21. Obtain the dynamic airspace update information released by the airspace management system, and perform spatial coding consistency verification between the dynamic airspace update information and the airspace type to generate an airspace status matching table. The dynamic airspace update information includes airspace status change instructions, temporary flight restriction notices and meteorological monitoring data. S22. Based on the airspace status matching table, identify the dynamic status of airspace types to obtain the dynamic status identifier of each airspace area, and map the dynamic status identifier to the static attributes of airspace types to generate airspace status fusion data. The dynamic status identifier includes no-fly status, restricted-fly status, and suitable-fly status. S23. Extract the available time periods of each airspace area based on the airspace status fusion data to obtain the available time period information that the airspace is allowed to use, and perform time consistency matching between the available time period information and the time interval of the aircraft's planned flight to generate time matching data. S24. Based on the time matching data, the altitude range of the airspace area is analyzed to obtain altitude range information, and the altitude range information is matched with the flight altitude parameters of the aircraft to generate altitude matching data. The altitude range information includes the minimum and maximum altitudes that the airspace allows for flight. S25. Integrate the time matching data and altitude matching data to obtain airspace availability status data, which includes the available time period and available altitude range corresponding to the airspace type.

[0027] In this embodiment, in step S21, the system receives real-time airspace dynamic update information from the airspace management system via a data interaction channel established with the airspace management system. This airspace dynamic update information includes three core categories: airspace status change instructions, temporary flight restriction notices, and meteorological monitoring data. Subsequently, a preset airspace type database is retrieved, and spatial codes (including airspace geographic range codes and type attribute codes) corresponding to each airspace type are extracted. For each piece of airspace dynamic update information, the associated coding information, such as airspace range and control attributes, is extracted. A spatial coding comparison algorithm is used for consistency verification. For example, it verifies whether the airspace code marked in the temporary flight restriction notice completely corresponds to the spatial code of the preset airspace type, and whether the airspace geographic code corresponding to the meteorological monitoring data matches the code of the target airspace type, ensuring that the airspace dynamic update information and the corresponding airspace type are without deviation at the spatial coding level. After verification, the corresponding airspace dynamic update information and verification results are integrated according to airspace type, generating an airspace status matching table containing airspace type, airspace status change instruction details, temporary flight restriction content, meteorological monitoring data, and coding verification results.

[0028] In S22, based on the airspace status matching table, the dynamic update information of each airspace area is analyzed one by one. Combined with preset status determination rules, dynamic status identifiers are identified: if the airspace status change instruction is a no-fly order or a temporary flight restriction notice explicitly restricts the entire airspace at all times, it is determined to be a no-fly state; if a temporary flight restriction notice sets specific conditions or the airspace status change instruction is restricted flight, it is determined to be a restricted-fly state; if there are no no-fly or restricted instructions and meteorological monitoring data meets flight standards, it is determined to be a flyable state, thus completing the assignment of dynamic status identifiers for each airspace area. Then, static attributes of the airspace type (including basic airspace purpose, inherent control level, designed flight attributes, etc.) are extracted, and mapping rules between dynamic status identifiers and static attributes are established. The no-fly, restricted-fly, or flyable status identifiers of each airspace area are associated and matched with the corresponding static attributes of the airspace type. For example, the restricted-fly status identifier is bound to the static attribute of airspace with a control level of level two, integrating them to form fused airspace status data containing airspace type, dynamic status identifiers, static attributes, and mapping relationships.

[0029] In S23, information related to available airspace time slots is filtered from the airspace status fusion data. Available time slots for each airspace region are extracted based on dynamic status identifiers and static attributes: airspaces in a no-fly zone are directly marked as having no available time slots; airspaces in a restricted-fly zone have their specific permitted flight time slots extracted from temporary flight restriction notices; and airspaces in a flyable zone have their available time slots determined according to their static attributes, forming clear information on available airspace time slots. Simultaneously, the planned flight time intervals (including takeoff time, landing time, and flight duration) are acquired. A time axis overlap analysis method is used for time consistency matching, comparing the overlap between available time slots in each airspace region and the planned flight time intervals. This confirms whether the duration of overlapping time slots meets flight requirements and whether there are time conflicts. The matching results (e.g., complete overlap, partial overlap, no overlap), details of overlapping time slots, and conflict explanations are then compiled and integrated to generate time matching data.

[0030] In S24, based on airspace status fusion data, the altitude control requirements corresponding to each airspace area are analyzed to clarify the minimum and maximum altitudes allowed for flight, forming complete altitude range information. Subsequently, the flight altitude capability parameters of the aircraft (including core parameters such as the aircraft's designed minimum safe flight altitude and maximum service ceiling) are collected to establish altitude matching verification standards. Altitude matching is performed through numerical comparison: the minimum allowed flight altitude is compared with the aircraft's minimum safe flight altitude to confirm whether the aircraft meets the minimum airspace flight requirements; the maximum allowed flight altitude is compared with the aircraft's maximum service ceiling to determine whether the aircraft can fly within the maximum altitude range of the airspace. Simultaneously, it is verified whether any altitude range exceeds the aircraft's capability range. The comparison results, details of met / unmet altitude parameters, and adaptability assessments are summarized to generate altitude matching data.

[0031] S25 clarifies the correlation dimensions between time-matching and altitude-matching data, using airspace type as the core index to integrate and correlate them. For each airspace type, the corresponding matching results and available time periods are extracted from the time-matching data, while the corresponding matching results and available altitude ranges (including the minimum and maximum permitted flight altitudes) are extracted from the altitude-matching data. Invalid information with time or altitude mismatches is removed, retaining only valid data that meets both time and altitude matching requirements. Following a unified data format specification, the valid data is structured to ensure that each airspace type clearly corresponds to a unique available time period and available altitude range, without data conflicts or duplication. The final result is airspace availability status data containing airspace type, corresponding available time periods (with clearly defined start and end times and durations), and available altitude ranges (with clearly defined minimum and maximum altitudes).

[0032] Specifically, S3 includes the following steps: S31. Calculate the straight-line distance between the coordinates of the flight start point and the coordinates of the flight end point in the three-dimensional coordinate frame, and calculate the flight time corresponding to the straight-line distance based on the straight-line distance and the flight speed in the flight mission parameters. Compare the flight time with the available time period in the airspace availability status data to determine the flight time window. S32. Construct a three-dimensional spatial region in a three-dimensional coordinate framework based on the flight time window, the coordinates of the flight start point, and the coordinates of the flight end point. Exclude the spatial regions corresponding to the no-fly zone and the restricted zone during the unavailable time period in the three-dimensional spatial region to obtain the initial boundary range. The three-dimensional spatial region extends to both sides on the horizontal plane with the line connecting the flight start point and the flight end point as the reference. The three-dimensional spatial region covers the available altitude range in the vertical direction. S33. Divide the initial boundary range according to the grid coding rules to generate multiple grid cells, and filter each grid cell according to the airspace type and available time period, retaining the grid cells in the flight-safe area that are available time periods to form a candidate grid set. S34. Spatial encoding is performed on the candidate grid set according to the grid encoding rules to generate a grid encoding sequence. The grid encoding sequence is then spatially clustered to merge adjacent grid cells, forming a coherent spatial region. Boundary point sets are extracted from the coherent spatial region, and the boundary point sets are smoothed using a curve fitting algorithm to generate initial flight path range data. The initial flight path range data includes boundary point sequences and vertical height range information.

[0033] In this embodiment, the curve fitting algorithm refers to an algorithm that, based on a set of boundary points in a continuous spatial region, constructs a continuous and smooth curve through a mathematical model, so that the curve accurately fits the spatial distribution characteristics of the boundary point set, while eliminating discrete deviations and irregular abrupt changes in the boundary point set, forming a regular boundary that meets the requirements of airspace route planning safety and smoothness. The curve fitting algorithms include: least squares curve fitting, which constructs the optimal fitting curve by minimizing the sum of squared errors between the boundary point set and the fitted curve, suitable for scenarios where the boundary point set is relatively concentrated and has low dispersion; B-spline curve fitting, which generates a piecewise continuous and smooth curve by setting a series of control points, which can flexibly adapt to complex and irregular boundary point sets and adapt to situations with diverse airspace boundary shapes; and Bézier curve fitting, which determines the starting point, ending point, and intermediate control parameters of the curve based on the boundary point set, forming a smoothly transitioning curve, suitable for route planning scenarios where precise control of the boundary direction is required.

[0034] In S31, within a preset three-dimensional coordinate framework, the three-dimensional values ​​(including specific coordinate data for the x, y, and z axes) corresponding to the coordinates of the flight start and end points are first obtained. Using a three-dimensional spatial straight-line distance calculation formula, the corresponding axis values ​​of the start and end point coordinates are substituted into the formula to calculate the straight-line distance between the two points. Then, the specific flight speed is extracted from the flight mission parameters, ensuring that the units of measurement for straight-line distance and flight speed are consistent (e.g., distance in kilometers, speed in kilometers per hour). The flight time corresponding to this straight-line distance is obtained by dividing the straight-line distance by the flight speed. The available time periods recorded in the airspace availability status data are retrieved, and the calculated flight time is compared one by one with the available time periods for each airspace. It is determined whether the flight time falls entirely within a certain available time period, or whether there are available time period segments that can completely cover the flight time. All available time periods that meet the flight time coverage requirements are filtered out and finally determined as the flight time window.

[0035] In S32, based on a three-dimensional coordinate framework, the established flight time window, flight start coordinates, and flight end coordinates are imported to initiate the construction of a three-dimensional spatial region. On the horizontal plane, the line connecting the flight start and end coordinates is used as the central axis, and the region expands evenly to both sides of the line according to a preset safe expansion distance (such as a fixed expansion distance set according to aircraft type and flight mission requirements), forming a complete horizontal spatial range. Vertically, it completely covers the clearly defined available altitude range in the airspace availability data, ensuring that the entire range from the minimum to the maximum permissible flight altitude is included, thus completing the construction of the three-dimensional spatial region. Subsequently, all no-fly zones and restricted areas within this three-dimensional spatial region are located using the airspace database. The entire spatial range of the no-fly zones and the specific spatial range of the restricted areas during unavailable periods are extracted. Using spatial overlay analysis technology, these prohibited or restricted spatial areas are precisely removed from the constructed three-dimensional spatial region, and the remaining spatial range becomes the initial boundary range.

[0036] In S33, a preset grid coding rule is retrieved. This rule clearly defines the size, spatial coding logic, and division criteria of the 3D grid (such as the spatial meaning of the grid side length and the number of coding bits). The initial boundary range is divided according to this rule into multiple independent 3D grid units with unique spatial location identifiers. The airspace type information corresponding to each generated grid unit is extracted from the airspace attribute database. First, grid units with the airspace type of "flyable zone" are selected. Then, the available time period corresponding to the grid unit in the flyable zone is checked to determine whether the available time period completely matches or partially overlaps with the determined flight time window and meets the flight requirements. Grid units that simultaneously meet both conditions—"airspace type is flyable zone" and "available time period matches flight time window"—are retained. All grid units that meet the conditions are summarized and organized in spatial order to form a candidate grid set.

[0037] In step S34, based on preset grid coding rules, each grid cell in the candidate grid set undergoes spatial coding. The coding information must include core attributes such as the grid cell's three-dimensional spatial location and dimensions. The coding results are arranged according to the distribution order of the grid cells in three-dimensional space, generating a continuous and ordered grid coding sequence. A spatial clustering algorithm is used to analyze and process the grid coding sequence. Based on the spatial positional relationship corresponding to the coding, adjacent grid cells with consistent attributes are grouped into the same cluster. By merging grid cells within the same cluster, a seamless, continuous, and complete coherent spatial region is formed. Using spatial boundary extraction technology, all three-dimensional coordinate points on the outer contour of the coherent spatial region are collected, forming a complete boundary point set. A suitable curve fitting algorithm is selected (in this embodiment, the B-spline curve fitting algorithm is used). The boundary point set is input into the B-spline curve fitting algorithm, and the curve parameters are iteratively calculated and adjusted to ensure that the generated curve smoothly conforms to the distribution pattern of the boundary point set, eliminating discrete deviations and irregular abrupt changes. Finally, initial flight path range data containing the smoothed boundary point sequence and corresponding available altitude range information is generated.

[0038] Specifically, S4 includes the following steps: S41. Extract horizontal boundary points and vertical altitude range information from the initial flight path range data, and obtain preset airspace usage status data from the airspace dynamic update information. The preset airspace usage status data includes flight path parameters of other aircraft and airport restriction time information. S42. Within the horizontal boundary points of the initial flight path range, a path generation algorithm is used to connect the coordinates of the flight start point and the flight end point to generate multiple candidate flight paths. The candidate flight paths are represented by a sequence of three-dimensional coordinate points, and the height of the coordinate points is transformed within the vertical height range information. S43. Calculate the spatial distance between the coordinate point sequence of each candidate flight path and the flight path coordinate points of other aircraft in the preset airspace usage status data, and compare the spatial distance with the preset safety distance. If the spatial distance is less than the preset safety distance, output the same spatial point between the target low-altitude aircraft and other aircraft, calculate the expected time difference of the same spatial point, and mark the same spatial point with the expected time difference exceeding the preset safety time as a spatial conflict or time conflict. S44. Compare the estimated flight time of each candidate route with the airport restriction period information. If the flight time overlaps with the restriction period, mark the candidate route as having an airport conflict. S45. Combine the conflict markers of all candidate routes to generate conflict statistics results. If at least one candidate route has no conflict marker, the conflict resolution result is a conflict-free state; otherwise, it is a conflict state. The conflict resolution result includes the conflict type and conflict location coordinates.

[0039] In this embodiment, the path generation algorithm refers to an algorithm that searches and connects multiple feasible three-dimensional flight paths within the area defined by the horizontal boundary points of the initial flight path range, based on the coordinates of the flight start point, the coordinates of the flight end point, and the vertical height range constraints. For example, a simplified A* algorithm can be used for small low-altitude UAVs, searching for paths connecting the start and end points within the latitude and longitude boundaries with a height step of 10 meters. The preset safety distance refers to the minimum safe spatial distance threshold between the target low-altitude aircraft and other aircraft, set in advance to avoid spatial collisions. The preset safety distance is determined based on the aircraft's physical dimensions (wingspan, fuselage length), the range of low-altitude airflow disturbance, the aircraft's maneuverability, and industry safety standards. For example, for a small multi-rotor UAV (wingspan 0.5 meters), considering a 5-meter airflow deviation and adding a 1.5 times safety factor, the preset safety distance is set to 50 meters; for large low-altitude operational aircraft (wingspan 5 meters), which require more maneuverability, the preset safety distance can be set to 200 meters. The preset safety time refers to the minimum safe time interval threshold set beforehand to prevent collisions between target low-altitude aircraft and other aircraft at the same spatial point due to excessively short time intervals. The preset safety time is determined based on the aircraft's flight speed, maneuver response time (acceleration, deceleration, and turning reaction time), low-altitude airspace traffic flow, and airspace management regulations. For example, if the average speed of a low-altitude aircraft is 10 m / s, the maneuver response time is 3 seconds, and an emergency response buffer is added, the preset safety time is set to 10 seconds; when airspace traffic flow is high, this can be increased to 20 seconds.

[0040] The process of generating multiple candidate flight routes includes: defining the latitude and longitude region (e.g., 116.3°-116.4°E, 39.9°-40.0°N) formed by the horizontal boundary points of the initial flight route range; determining the coordinates of the flight start point (116.32°E, 39.92°N, 120m), the coordinates of the flight end point (116.38°E, 39.98°N, 180m), and the vertical altitude range (100m-200m). Using the A* algorithm, within the two-dimensional plane defined by the horizontal boundary, the flight start point and flight end point are first used as the starting and target nodes, and a 100m × 100m grid is created. The A* algorithm calculates the estimated cost from each node to the end point, filters low-cost path nodes, and forms 3-5 two-dimensional paths with different orientations. For each horizontal path, combined with the aircraft's maximum climb rate of 5m / s, a gradient climb and segmented level flight method is used to assign altitude values ​​(10m steps) to each grid node. For example, the first half of a path climbs from 120m to 180m, and the second half is level flight. In the end, each path consists of three-dimensional coordinate points arranged in flight order, forming multiple candidate routes. All coordinate points are within the horizontal boundary and their altitudes meet the constraints.

[0041] The process of spatial distance calculation and conflict marking includes: extracting the three-dimensional coordinate point sequence of candidate flight paths and their corresponding estimated arrival times (100-meter distance between adjacent coordinate points, 10 m / s speed, and 10-second time interval); simultaneously extracting the three-dimensional coordinate point sequence and estimated arrival times of other aircraft's flight paths. Using the three-dimensional Euclidean distance formula, the spatial distance (latitude, longitude, and altitude straight-line distance) between each coordinate point of the candidate flight path of the target low-altitude aircraft and each coordinate point of the flight path of other aircraft is calculated. The calculation results are compared one by one with a preset safety distance. If the spatial distance of a set of coordinate points is less than the preset safety distance, it is determined to be a common spatial point. The estimated arrival times of the target low-altitude aircraft and other aircraft corresponding to this common spatial point are extracted, and the time difference (absolute value) is calculated. If the time difference exceeds the preset safety time, it is marked as a spatial conflict (insufficient spatial distance) or a time conflict (insufficient time interval) according to the judgment rules, and the three-dimensional coordinates of the common spatial point are recorded.

[0042] The process of comparing flight time with airport restricted time periods is as follows: Based on the sequence of three-dimensional coordinate points of the candidate route, the spatial distance between adjacent coordinate points is calculated and accumulated to obtain the total length. Combined with a preset cruise speed (e.g., 36 km / h), the estimated flight time (estimated total flight time ÷ cruise speed) is calculated. Based on the planned takeoff time of the target low-altitude aircraft, the flight time interval (planned takeoff time to planned takeoff time + estimated flight time) is derived. For example, if the total route length is 5 km, the cruise speed is 36 km / h, the total estimated flight time is 50 minutes, and the planned takeoff time is 9:00, the flight time interval is 9:00-9:50. Airport restricted time period information (e.g., low-altitude aircraft are prohibited from flying over from 9:20 to 10:00) is extracted from the airspace usage status data. The start and end times of the two time intervals are compared. If there is partial or complete overlap (e.g., overlap between 9:20 and 9:50), the candidate route is determined to conflict with the airport restricted time period and is marked as an airport conflict.

[0043] Specifically, S5 includes the following steps: S51. When the conflict resolution result is a conflict-free state, obtain the coordinates of the flight start point and the flight end point from the flight demand parameters, and obtain the vertical altitude range from the initial route range data. S52. In the three-dimensional coordinate frame, project the coordinates of the flight start point and the flight end point onto the reference ellipsoid, calculate the shortest path curve between the coordinates of the flight start point and the flight end point, and adjust the elevation of the shortest path curve according to the vertical altitude range to form a three-dimensional curve model of a great circle route. The shortest path curve is determined based on the Earth's geometric model. S53. Sample multiple interpolation points uniformly along the three-dimensional curve model to form an interpolation point sequence. The number of interpolation points is determined based on flight mission parameters. The distribution of interpolation points in three-dimensional space follows the geometric characteristics of the three-dimensional curve model. The height of the interpolation points changes smoothly within the vertical height range. S54. Use spline interpolation algorithm to fit the interpolation point sequence to generate a smooth arc-shaped flight path trajectory, which is represented by a parametric equation; S55. Check whether each coordinate point of the arc-shaped flight path is within the preset flight path range boundary of the initial flight path range data. If the coordinate point exceeds the preset flight path range boundary, adjust the spatial position of the coordinate point to within the preset flight path range boundary to obtain the fitted flight path trajectory data. The fitted flight path trajectory data includes the trajectory equation and the list of coordinate points.

[0044] In this embodiment, spline interpolation is a numerical calculation method that fits discrete data points using a piecewise smooth function. In the flight path generation scenario, this spline interpolation algorithm is used to fit the interpolation point sequence so that the final flight path trajectory meets the smoothness requirements of flight and avoids flight risks caused by sudden trajectory changes. The determination of the spline interpolation algorithm mainly includes navigation accuracy requirements, flight speed, trajectory curvature constraints, etc. in the flight mission parameters: if the navigation accuracy requirement is high, a higher-order spline (such as a cubic spline) needs to be selected to improve the fitting accuracy; if the flight speed is fast, the algorithm parameters need to be adjusted to ensure a smooth trajectory transition and reduce the frequency of attitude adjustment; at the same time, the number of interpolation points is combined to reasonably divide the segmented intervals and balance the computational efficiency and trajectory smoothness.

[0045] The preset flight path boundary is a pre-defined three-dimensional spatial boundary based on airspace management rules, flight mission permits, obstacle distribution data, and geographical constraints. It constrains the legal range of the flight path trajectory. The preset flight path boundary has horizontal and vertical dimensions: the horizontal boundary is typically defined by a polygonal area formed by latitude and longitude coordinates, covering the legal airspace range from the flight start point to the destination; the vertical boundary is the vertical altitude range in the initial flight path data, clearly defining the minimum safe altitude and maximum restricted altitude for flight. Determining this preset flight path boundary requires integrating multiple types of data: no-fly / restricted-fly zone data designated by flight control authorities, the scope definition in airspace use permit documents, terrain elevation data, and vertex coordinate data of obstacles (such as tall buildings, towers, and mountains). These constraints are transformed into explicit three-dimensional boundary parameters using spatial modeling methods to ensure that the flight path does not infringe on restricted airspace or collide with obstacles.

[0046] The Earth geometry model is a mathematical model describing the shape of the Earth. Its core structure is based on a reference ellipsoid (commonly the WGS84 reference ellipsoid), including key parameters such as the ellipsoid's semi-major axis, semi-minor axis, oblateness, and first eccentricity. These parameters are used to construct an approximate geometric shape of the Earth, replacing the ideal sphere to improve the accuracy of flight path calculations. The input data for the Earth geometry model consists of the latitude and longitude coordinates (geocentric coordinates) of the flight start and end points, and the vertical altitude range from the initial flight path range data. The output data includes two parts: first, the three-dimensional coordinates (spatial rectangular coordinates) of the start and end points on the reference ellipsoid; and second, the parameters of the shortest path curve calculated based on the ellipsoid's geometric features (such as azimuth, arc length, and spatial coordinates of each point). The core function of this Earth geometry model is to provide the mathematical basis for the Earth's shape, ensuring that the calculation of the shortest path curve conforms to the actual geometric characteristics of the Earth, making the generated flight path more closely resemble real flight scenarios.

[0047] The process of constructing the three-dimensional curve model includes: First, within a three-dimensional coordinate framework (a spatial rectangular coordinate framework based on the geodetic coordinate system, with the X-axis pointing to the intersection of the Prime Meridian and the equator, the Z-axis pointing to the Earth's North Pole, and the Y-axis forming a right-handed coordinate system), the latitude and longitude coordinates of the flight start and end points are converted into three-dimensional coordinates on a reference ellipsoid. Using the geodetic coordinate inverse calculation formula, the semi-major and semi-minor axis parameters of the reference ellipsoid are substituted to convert the latitude and longitude values ​​into spatial rectangular coordinates (X, Y, Z), completing the projection onto the reference ellipsoid. Then, the shortest path curve is calculated: based on the ellipsoidal parameters of the Earth's geometric model, the geodesic (the core of the great circle-like flight path) of the start and end points on the reference ellipsoid is calculated using the principles of spherical trigonometry. By solving for the azimuth and geodetic distance between the two points, the trajectory equation of the geodesic is determined; this curve represents the shortest path on the Earth's surface. Finally, the elevation is adjusted according to the vertical height range: the upper and lower limits of the elevation are extracted from the initial flight path range data, and the linear smooth interpolation method is used to assign an elevation value that conforms to the height range to each point on the geodesic line according to the arc length distribution of the geodesic line. This ensures that the elevation smoothly transitions from the starting elevation to the ending elevation and does not exceed the vertical height limit throughout the entire process, ultimately forming a three-dimensional curve model that integrates the horizontal trajectory and the vertical elevation.

[0048] The process of generating the interpolation point sequence includes: First, determining the number of interpolation points based on flight mission parameters: considering flight speed, navigation update frequency, and trajectory control accuracy requirements, if the flight speed is high and navigation accuracy requirements are high, the number of interpolation points is increased to improve trajectory detail; if the flight distance is short and the environment is simple, the number is appropriately reduced to improve computational efficiency. Then, uniform sampling is performed along the 3D curve model: using the total arc length of the 3D curve as a reference, the arc length is divided equally according to the number of interpolation points to obtain the arc length position of each sampling point; by substituting the arc length values ​​of each equally divided arc length into the parametric equation of the curve, the 3D coordinates (X, Y, Z) corresponding to each sampling point are calculated. During the sampling process, it is necessary to ensure that the interpolation points follow the geometric characteristics of the curve model: by verifying the curvature value of the sampling points in real time, the curvature value is kept consistent with the overall curvature of the three-dimensional curve to avoid local bulges or depressions; at the same time, the height change is guaranteed to be smooth: the elevation value of each sampling point is continuously verified, and if the height difference between adjacent points exceeds the preset elevation threshold, the elevation is corrected by secondary interpolation to ensure that the rate of change of height meets the requirements of flight mechanics, and finally an orderly sequence of interpolation points is formed, with the coordinates of each point strictly conforming to the three-dimensional curve model.

[0049] The implementation process of spline interpolation fitting includes: selecting a cubic spline interpolation algorithm (balancing smoothness and computational efficiency), and clarifying the core constraints of the fitting: the three-dimensional coordinates of each point in the interpolation point sequence must be passed through by the fitted curve (interpolation condition), and the first and second derivatives of adjacent piecewise curves at the interpolation points must be continuous (smoothness condition). Then, a system of fitting equations is constructed: the interpolation point sequence is numbered sequentially, and independent cubic spline functions are constructed for the X, Y, and Z components of the three-dimensional coordinates, using arc length as the parameter t; for each component, a system of linear equations containing unknown coefficients is established based on the interpolation and smoothness conditions (e.g., for n interpolation points, each component corresponds to n-1 piecewise polynomials, each polynomial containing 4 coefficients; redundant parameters are eliminated through constraints). The system of equations is solved using Gaussian elimination to obtain the coefficients of each piecewise polynomial, which are then integrated into a complete parametric spline curve equation. After fitting, the smoothness of the trajectory is verified: the first and second derivatives of the fitted curve are calculated to ensure that there are no abrupt changes and that the curvature changes are consistent with the flight attitude adjustment capability. Finally, the arc-shaped flight path trajectory equation (X(t), Y(t), Z(t)) represented by the arc length parameter t is generated to achieve smooth fitting of the interpolation point sequence.

[0050] The implementation process of trajectory verification, adjustment, and output includes: Defining the verification criteria for the preset route range boundaries: In the horizontal direction, the latitude and longitude of the trajectory coordinate points are converted to planar coordinates using a polygon inclusion method to verify whether the trajectory coordinate points are located inside the horizontal boundary polygon; in the vertical direction, the elevation values ​​of the coordinate points are directly compared to see if they are within the vertical height range. Subsequently, a full-point verification is performed on the arc-shaped route trajectory: Sufficiently dense discrete coordinate points are generated using parametric equations (density higher than the interpolation point sequence to ensure no omissions), and the horizontal position and vertical height of each discrete point are compared one by one to see if they conform to the preset route range boundaries. If a coordinate point is found to exceed the preset route range boundaries, adjustments are made according to the following rules: When exceeding in the horizontal direction, the elevation and curve curvature of the trajectory coordinate point remain unchanged, and the horizontal position of the trajectory coordinate point is shifted to the nearest point inside the boundary. Simultaneously, the horizontal coordinates of adjacent interpolation points are corrected to ensure that the distance between adjacent points meets the smoothing requirements after adjustment; when exceeding in the vertical direction, while maintaining the horizontal position, the elevation value is adjusted to the nearest boundary value of the height range, or the elevation of the interpolation points in this area is redistributed using interpolation to smoothly return the height to within the range. After adjustment, spline fitting is performed again, and the verification-adjustment-fitting process is repeated until all discrete points are within the boundaries of the preset flight path range. Finally, the fitted flight path trajectory data is output, including the parameterized trajectory equation (for the flight control system to calculate the position in real time) and the adjusted coordinate point list (for the navigation equipment to store and retrieve), ensuring that the data meets the flight execution requirements.

[0051] Specifically, S6 includes the following steps: S61. Obtain real-time airspace dynamic information from the airspace dynamic update system and obtain fitted flight path data. The real-time airspace dynamic information includes airspace type change information, available time period update information, and altitude range adjustment information. S62. Based on preset spatial matching rules and preset time synchronization rules, each coordinate point in the fitted flight trajectory data is matched one by one with the latest airspace availability status data, and it is checked whether the spatial position and flight time corresponding to each coordinate point overlap with the designated range of the no-fly zone and the unavailable time period of the restricted area. S63. If conflicting coordinate points are detected, the continuous coordinate point correlation analysis method is used to identify the conflict segment composed of continuous conflicting coordinate points, and the spatial range and temporal range of the conflict segment are calculated. The conflict segment includes the starting point, the ending point and the conflict type. S64. Based on the latest airspace availability data, search for flyable airspace areas within the spatial range of the conflict segment and adjacent airspaces of the conflict segment, and generate a new path segment to bypass the conflict segment according to the path generation algorithm. The new path segment is connected to the start and end points of the conflict segment. The flyable airspace areas include airspace areas of the airspace type being a suitable flight zone, and the available time period of the suitable flight zone matching the planned flight time period of the target low-altitude aircraft. S65. The new path segment is fused with the non-conflicting segments in the fitted route trajectory data using a trajectory stitching algorithm to obtain a three-dimensional route design scheme.

[0052] In this embodiment, the preset spatial matching rules are a standardized logical system used to determine the spatial correlation between flight path coordinates and airspace availability data. It includes three key elements: a unified coordinate system specification, a spatial inclusion relationship determination method, and a position matching accuracy threshold. The unified coordinate system specification requires that the flight path coordinates (three-dimensional rectangular coordinates) and airspace boundary coordinates use the same reference ellipsoid (e.g., WGS84) and coordinate frame to eliminate matching deviations caused by reference differences. The spatial inclusion relationship determination method uses a "point-3D volume" inclusion algorithm, which calculates whether the spatial distance and vertical height between the coordinates and the horizontal polygon boundary of the no-fly zone / restricted zone are within the specified range to determine if the area is in restricted airspace. The position matching accuracy threshold is set according to the airspace control level (e.g., 5 meters for core no-fly zones and 10 meters for ordinary restricted zones) to handle boundary ambiguity. The determination of the preset spatial matching rules requires aligning with the coordinate reference generated by the flight path, combining the airspace type (no-fly zones use "strict inclusion" logic, and restricted zones use "buffer inclusion" logic) to set the judgment strictness, and also referencing the positioning accuracy of navigation equipment to balance the risks of conflict misjudgment and omission, ensuring that the spatial matching results meet airspace management requirements.

[0053] The preset time synchronization rules are a set of rules that unify the data benchmarks for flight times and available airspace periods, and clarify the criteria for determining time period overlap. These rules include a standardized timestamp format, a unified time benchmark specification, and an algorithm for determining time period overlap. The standardized timestamp format uses UTC time (accurate to the second), converting the flight time and available airspace periods corresponding to flight line coordinates to this format. The unified time benchmark specification requires all time data to be based on the same time system (avoiding confusion between local time and UTC time), ensuring benchmark consistency through time offset correction. The algorithm for determining time period overlap uses "interval intersection detection" logic to determine whether there is an overlap between the flight time of a coordinate point (or the time interval corresponding to consecutive coordinate points) and the unavailable airspace period. The determination of the preset time synchronization rules must be compatible with the time data format of the airspace dynamic update system (such as ISO 8601), and the time granularity is set in conjunction with the flight mission time planning accuracy (seconds / minutes). The overlap determination logic is adjusted according to the type of airspace restricted periods (continuous periods / discrete periods) to ensure the accuracy and timeliness of time matching.

[0054] The continuous coordinate point correlation analysis method is a systematic analysis technique for identifying conflict segments composed of consecutive conflicting coordinate points. It includes adjacent point correlation determination criteria, continuous conflict thresholds, and conflict segment boundary delineation rules. The adjacent point correlation determination criteria are based on the natural order of the flight path interpolation point sequence, judging whether the conflict status (conflict / non-conflict) of adjacent coordinate points is consistent, and whether the spacing and time interval conform to normal flight logic. The continuous conflict threshold is a preset minimum number of consecutive conflicting points (e.g., 3). When the number of consecutive conflicting points reaches this number, a conflict segment is determined to have formed. The conflict segment boundary delineation rules use the first conflicting point as the starting point of the conflict segment and the point preceding the first non-conflicting point as the ending point of the conflict segment, while also recording the conflict type (airspace type change / available time period update / altitude range adjustment). The determination of the continuous coordinate point correlation analysis method needs to be based on the interpolation point density and flight speed to calculate the time interval between adjacent points (e.g., if the interpolation point spacing is 500 meters and the speed is 100 meters / second, then the time interval is 5 seconds). When the interpolation points are dense and the flight speed is fast, a smaller threshold (2 points) should be set, and when the spacing is large, a larger threshold (3-5 points) should be set. The judgment priority should be adjusted according to the severity of the conflict type. The conflict threshold in the no-fly zone is lower than that in the restricted zone to ensure that high-risk conflicts are dealt with first.

[0055] The trajectory stitching algorithm is an algorithm that merges the non-conflicting parts of the new path segment with the original route into a continuous and smooth three-dimensional route. It requires the coordinates, first derivative (velocity direction), and second derivative (acceleration) of the stitching nodes (start and end points of the conflict segment) to be continuous. The trajectory stitching algorithm steps include: stitching node preprocessing (extracting node coordinates and derivative data), parameter alignment (using arc length as a unified parameter to unify the parameterization benchmark between the original route and the new path segment), and smooth transition processing (local spline fitting of trajectory segments near the stitching nodes). The determination of the trajectory stitching algorithm should match the fitting algorithm of the original route (e.g., if the original route uses cubic spline interpolation, the stitching algorithm should also use cubic splines) to ensure consistent overall trajectory smoothness; the fitting range should be adjusted according to the curvature of the stitching nodes (expanding the fitting interval to 3-5 interpolation points before and after when the curvature is large); and the continuity requirements should be set according to the flight scenario (precision navigation / normal flight), requiring continuity of the second derivative, which can be relaxed to continuity of the first derivative for normal flight, balancing smoothness and computational efficiency.

[0056] In S61, through the standardized data interface (such as RESTAPI) of the airspace dynamic update system, real-time airspace dynamic information is subscribed to and obtained at a preset frequency (such as once every minute). This includes airspace type change records (such as ordinary airspace being converted to a no-fly zone), available time period update data (such as the extension of the unavailable time period in the restricted area), and altitude interval adjustment parameters (such as the increase of the lower limit of the allowed flight altitude). The generated fitted flight path trajectory data is read, including the parameterized trajectory equation, the list of interpolation point coordinates, and the flight timestamp corresponding to each coordinate point. The airspace boundary coordinates in the airspace dynamic information are converted into three-dimensional rectangular coordinates consistent with the flight path, and the available time periods and flight timestamps are uniformly converted into UTC time format to ensure data consistency.

[0057] In S62, firstly, a preset spatial matching rule is invoked to perform spatial matching for each coordinate point. The three-dimensional coordinates of each coordinate point are substituted into the "point-3D volume" inclusion algorithm to calculate the shortest spatial distance between each coordinate point and the horizontal polygon of each no-fly zone / restricted zone. At the same time, it is checked whether the vertical height is within the restricted range. If the shortest spatial distance of a coordinate point is less than the matching accuracy threshold and the height is within the range, spatial overlap is determined. Then, a preset time synchronization rule is invoked to extract the UTC timestamp corresponding to the coordinate point. The "interval intersection detection" algorithm is used to determine whether the timestamp is within the airspace unavailable period. If there is an intersection, time overlap is determined. The spatial matching result and the time matching result of each coordinate point are logically ANDed. If both the spatial matching result and the time matching result overlap, it is marked as a conflicting coordinate point, and the conflict type (airspace type change / available period update / altitude range adjustment) is recorded. This completes the matching and verification of all coordinate points one by one. For example, suppose the 3D coordinates of a point on the fitted flight path are in the WGS84 coordinate system (X=3985200m, Y=11723100m, Z=145m), with a corresponding UTC timestamp of 13:42:28. Using a preset spatial matching rule, the coordinates are substituted into the "point-3D volume" inclusion algorithm. The shortest spatial distance to a horizontal polygon within a restricted area is calculated to be 8 meters. The matching accuracy threshold for this restricted area is 10 meters, and Z=145m falls within its 120m-160m restricted interval, indicating spatial overlap. Using a preset time synchronization rule and the "interval intersection detection" algorithm, the timestamp is found to be within the unavailable time period of 13:40:00-13:45:00 within the restricted area, indicating time overlap. A logical AND operation is then performed to mark this point as a conflicting coordinate point, and the conflict type is recorded as an available time period update.

[0058] In S63, all marked conflict coordinate points are sorted according to the original flight path interpolation point sequence. The continuous coordinate point correlation analysis method is called to first determine whether the distance and time interval between adjacent conflict coordinate points conform to normal flight logic (e.g., the distance does not exceed 1.5 times the average distance of interpolation points), and then the number of consecutive conflict points is counted. When the number of consecutive conflict points reaches the preset consecutive conflict point number threshold (e.g., 3), the first conflict point is taken as the starting point of the conflict segment, and the point before the first non-conflict point is taken as the ending point of the conflict segment. The spatial range of the conflict segment (three-dimensional coordinates of the starting and ending points, horizontal coverage area, and vertical altitude range) is calculated. The conflict time range is determined by the flight timestamps of the starting and ending points. The conflict type (airspace type change / available time period update / altitude range adjustment) is determined by combining the matching records, forming complete conflict segment information. For example, all marked conflict coordinate points are sorted according to the original route interpolation point sequence (P1-P15), where P5-P9 are conflict coordinate points. The continuous coordinate point correlation analysis method is called to find that the distance between adjacent points P5-P9 is 450 meters (1.5 times the average distance of 500 meters for interpolation points), the time interval is 4.5 seconds, and the number of consecutive conflict points reaches the preset threshold of 3 out of 5. P5 is the starting point of the conflict segment (X=3986500m, Y=11724300m, Z=145m, UTC13:44:10), and P9 is the ending point (X=3988300m, Y=11726100m, Z=145m, UTC13:46:30). The spatial range is the three-dimensional area between the two points, the time range is 13:44:10-13:46:30, and the conflict type is altitude range adjustment.

[0059] In S64, based on the latest airspace availability data, a search area (e.g., within a 5km radius) is defined centered on the spatial range of the conflict segment. Flyable airspace areas within this search area are selected (meeting the requirements of being a flyable zone, having available time periods matching flight route times, and meeting altitude range requirements), thus constructing a flyable zone spatial database. Constraints are set for the path generation algorithm, with the conflict segment's starting point as the start point and the conflict segment's ending point as the end point. Constraint parameters include the flyable zone boundary (which must not be exceeded), maximum curvature (meeting the turning capability of flight equipment), and maximum altitude change rate (meeting climb / descent performance requirements). A path generation algorithm (e.g., the A* algorithm) is invoked, using the flyable zone database as the search space and a cost function combining path length and curvature change rate as the optimization objective, generating multiple candidate new path segments. The cost function value for each candidate path is calculated, and the optimal solution is selected as the final new path segment. For example, the spatial range of a conflict segment is (X=3990100m-Y=11728500m to X=3992100m-Y=11730500m, Z=150m), with a planned flight time of 14:05:00-14:07:00 UTC. A 5-kilometer search area is delineated centered on this conflict segment spatial range, and the airspace type to the east is selected as a suitable flight zone, with an available time of 14:00:00-14:10:00 UTC and an altitude range of 130m-170m. A suitable flight zone spatial database is then constructed. Constraints are set as follows: the starting point is the beginning point of the conflict segment (X=3990100m, Y=11728500m, Z=150m), and the ending point is the end point (X=3992100m, Y=11730500m, Z=150m). The maximum curvature is 5° / km, and the maximum rate of change of height is 0.5m / s. The A* algorithm is used, with path length + rate of change of curvature as the cost function, to generate three candidate paths. The path with the lowest cost is selected as the new path segment, with its starting and ending point coordinates consistent with the conflict segment.

[0060] In S65, the non-conflicting portion of the fitted flight path data is extracted and divided into the preceding trajectory before the conflict segment and the following trajectory after the conflict segment. The coordinates, first derivative (velocity direction), and second derivative (acceleration) of the preceding trajectory at the starting point of the conflict segment, and the corresponding data of the following trajectory at the ending point of the conflict segment are obtained respectively. The splicing nodes (starting and ending points) of the new path segment with the preceding and following trajectories are aligned with parameters, using arc length as a unified parameter. The trajectory splicing algorithm is called to perform local cubic spline fitting on the end of the preceding trajectory (3-5 interpolation points before and after) near the starting point and the beginning of the new path segment to ensure the continuity of coordinates, first derivative, and second derivative at the splicing point. The same fitting operation is performed on the end of the new path segment near the ending point and the beginning of the following trajectory. After the fitting is completed, the smoothness of the spliced ​​flight path is verified (the curvature and altitude change rate of each point are calculated to ensure no abrupt changes), and all coordinate points are checked to be within the flight-safe area. Finally, the fused parameterized trajectory equation and the updated list of coordinate points are output to form a complete three-dimensional flight path design scheme. For example, the non-conflict segments of the fitted route are divided into the first segment (P1-P4) and the second segment (P10-P15). The coordinates, first derivative (southeast direction), and second derivative (0.2) of the first segment at the starting point A (X=3990100m, Y=11728500m, Z=150m) of the conflict segment are given. The corresponding data for the latter segment at the end point B (X=3992100m, Y=11730500m, Z=150m) is (northeast direction, 0.18). Using arc length as a unified parameter, the new path segment is aligned with the preceding and following segments. A trajectory stitching algorithm is invoked to perform local cubic spline fitting on the three interpolation points before and after point A, and the three interpolation points before and after point B. After fitting, verification shows that the curvature of each point is ≤5° / km, the altitude change rate is ≤0.5m / s, there are no abrupt changes, and all coordinate points are within the suitable flight zone. The parameterized trajectory equation and coordinate point list are output, forming a three-dimensional flight path design scheme. A non-conflicting segment refers to the portion of the fitted flight path data that, after being checked one by one in step S62 based on preset spatial matching rules and preset time synchronization rules, has coordinate points whose spatial location does not overlap with the designated area of ​​the no-fly zone, whose flight time does not overlap with the unavailable time period of the restricted area, and which is not identified as a conflict segment by step S63. For example, the fitted flight path of the target low-altitude aircraft contains interpolation points P1-P10. After verification in step S62, the spatial positions of P1-P2 and P8-P10 are both within the flight-safe zone, the flight time does not overlap with the restricted time periods of various airspaces, and they are not included in the conflict segment P3-P7 identified in step S63. Therefore, the trajectory segments corresponding to P1-P2 and P8-P10 are non-conflict segments. The coordinate values, flight time, trajectory curvature, and other data of the non-conflict segments remain in their original fitted state and serve as the basis for trajectory splicing.

[0061] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A three-dimensional flight path design method for low-altitude aircraft based on the BeiDou grid, characterized in that: Includes the following steps: S1. Based on the BeiDou grid system, a model is constructed for the flight scenario of the target low-altitude aircraft to obtain a flight space model. The target flight area in the flight space model is then segmented into airspace types according to preset spatial reference information to obtain airspace types. The airspace types include no-fly zones, restricted zones, and suitable flight zones. The preset spatial reference information includes a three-dimensional coordinate frame and grid coding rules. S2. The availability status of airspace types is determined by dynamically updating airspace information to obtain airspace availability status data, which includes the availability time period and altitude range information of each airspace. S3. Divide the initial flight path coverage space of the target low-altitude aircraft according to the airspace availability status data and the flight requirement parameters of the target low-altitude aircraft to obtain the initial flight path range data. The flight requirement parameters include the coordinates of the flight start point, the coordinates of the flight end point, and the flight mission parameters. S4. Based on the conflict detection algorithm, the airspace usage within the initial route range is checked for conflicts, and the conflict checking results are obtained. The conflict checking is based on the airspace usage status, route parameters and airport restriction time information. S5. When the conflict resolution result is a conflict-free state, the spatial trajectory of the target low-altitude aircraft is fitted with an arc shape based on the great circle route calculation rule to obtain the fitted route trajectory data. The arc shape fitting is based on the spatial geometric features of the great circle route. S6. Verify the fitted flight path based on the real-time updated airspace dynamic information. If the current airspace status changes, adjust the fitted flight path based on the latest airspace availability to obtain a three-dimensional flight path design scheme.

2. The method for designing three-dimensional flight paths of low-altitude aircraft based on the BeiDou grid according to claim 1, characterized in that: S1 includes the following steps: S11. Obtain geographic information data of the flight scene, and divide the geographic information data into grids according to the grid coding rules to obtain scene grid data. Then, perform coordinate mapping on the scene grid data in a three-dimensional coordinate frame based on preset flight constraints to obtain a flight space model. The flight space model includes the three-dimensional position information and grid coding of each grid unit. S12. In the flight space model, the spatial range of the target flight area is defined according to the airspace use planning information to obtain the target area range data, and the spatial contour is extracted from the target area range data to obtain the target spatial contour data. S13. Based on the grid coding rules, attribute identification is performed on the grid cells corresponding to the target spatial contour data to obtain the corresponding spatial domain type.

3. The method for designing three-dimensional flight paths of low-altitude aircraft based on the BeiDou grid according to claim 1, characterized in that: S2 includes the following steps: S21. Obtain the dynamic airspace update information released by the airspace management system, and perform spatial coding consistency verification between the dynamic airspace update information and the airspace type to generate an airspace status matching table. The dynamic airspace update information includes airspace status change instructions, temporary flight restriction notices and meteorological monitoring data. S22. Based on the airspace status matching table, identify the dynamic status of airspace types to obtain the dynamic status identifier of each airspace area, and map the dynamic status identifier to the static attributes of airspace types to generate airspace status fusion data. The dynamic status identifier includes no-fly status, restricted-fly status, and suitable-fly status. S23. Extract the available time periods of each airspace area based on the airspace status fusion data to obtain the available time period information that the airspace is allowed to use, and perform time consistency matching between the available time period information and the time interval of the aircraft's planned flight to generate time matching data. S24. Based on the time matching data, the altitude range of the airspace area is analyzed to obtain altitude range information, and the altitude range information is matched with the flight altitude parameters of the aircraft to generate altitude matching data. The altitude range information includes the minimum and maximum altitudes that the airspace allows for flight. S25. Integrate the time matching data and altitude matching data to obtain airspace availability status data, which includes the available time period and available altitude range corresponding to the airspace type.

4. The three-dimensional flight path design method for low-altitude aircraft based on BeiDou grid according to claim 1, characterized in that: S3 includes the following steps: S31. Calculate the straight-line distance between the coordinates of the flight start point and the coordinates of the flight end point in the three-dimensional coordinate frame, and calculate the flight time corresponding to the straight-line distance based on the straight-line distance and the flight speed in the flight mission parameters. Compare the flight time with the available time period in the airspace availability status data to determine the flight time window. S32. Construct a three-dimensional spatial region in a three-dimensional coordinate framework based on the flight time window, the coordinates of the flight start point, and the coordinates of the flight end point. Exclude the spatial regions corresponding to the no-fly zone and the restricted zone during the unavailable time period in the three-dimensional spatial region to obtain the initial boundary range. The three-dimensional spatial region extends to both sides on the horizontal plane with the line connecting the flight start point and the flight end point as the reference. The three-dimensional spatial region covers the available altitude range in the vertical direction. S33. Divide the initial boundary range according to the grid coding rules to generate multiple grid cells, and filter each grid cell according to the airspace type and available time period, retaining the grid cells in the flight-safe area that are available time periods to form a candidate grid set. S34. Spatial encoding is performed on the candidate grid set according to the grid encoding rules to generate a grid encoding sequence. The grid encoding sequence is then spatially clustered to merge adjacent grid cells, forming a coherent spatial region. Boundary point sets are extracted from the coherent spatial region, and the boundary point sets are smoothed using a curve fitting algorithm to generate initial flight path range data. The initial flight path range data includes boundary point sequences and vertical height range information.

5. The method for designing three-dimensional flight paths of low-altitude aircraft based on the BeiDou grid according to claim 1, characterized in that: S4 includes the following steps: S41. Extract horizontal boundary points and vertical altitude range information from the initial flight path range data, and obtain preset airspace usage status data from the airspace dynamic update information. The preset airspace usage status data includes flight path parameters of other aircraft and airport restriction time information. S42. Within the horizontal boundary points of the initial flight path range, a path generation algorithm is used to connect the coordinates of the flight start point and the flight end point to generate multiple candidate flight paths. The candidate flight paths are represented by a sequence of three-dimensional coordinate points, and the height of the coordinate points is transformed within the vertical height range information. S43. Calculate the spatial distance between the coordinate point sequence of each candidate flight path and the flight path coordinate points of other aircraft in the preset airspace usage status data, and compare the spatial distance with the preset safety distance. If the spatial distance is less than the preset safety distance, output the same spatial point between the target low-altitude aircraft and other aircraft, calculate the expected time difference of the same spatial point, and mark the same spatial point with the expected time difference exceeding the preset safety time as a spatial conflict or time conflict. S44. Compare the estimated flight time of each candidate route with the airport restriction period information. If the flight time overlaps with the restriction period, mark the candidate route as having an airport conflict. S45. Combine the conflict markers of all candidate routes to generate conflict statistics results. If at least one candidate route has no conflict marker, the conflict resolution result is a conflict-free state; otherwise, it is a conflict state. The conflict resolution result includes the conflict type and conflict location coordinates.

6. The three-dimensional flight path design method for low-altitude aircraft based on BeiDou grid according to claim 1, characterized in that: S5 includes the following steps: S51. When the conflict resolution result is a conflict-free state, obtain the coordinates of the flight start point and the flight end point from the flight demand parameters, and obtain the vertical altitude range from the initial route range data. S52. In the three-dimensional coordinate frame, project the coordinates of the flight start point and the flight end point onto the reference ellipsoid, calculate the shortest path curve between the coordinates of the flight start point and the flight end point, and adjust the elevation of the shortest path curve according to the vertical altitude range to form a three-dimensional curve model of a great circle route. The shortest path curve is determined based on the Earth's geometric model. S53. Sample multiple interpolation points uniformly along the three-dimensional curve model to form an interpolation point sequence. The number of interpolation points is determined based on flight mission parameters. The distribution of interpolation points in three-dimensional space follows the geometric characteristics of the three-dimensional curve model. The height of the interpolation points changes smoothly within the vertical height range. S54. Use spline interpolation algorithm to fit the interpolation point sequence to generate a smooth arc-shaped flight path trajectory, which is represented by a parametric equation; S55. Check whether each coordinate point of the arc-shaped flight path is within the preset flight path range boundary of the initial flight path range data. If the coordinate point exceeds the preset flight path range boundary, adjust the spatial position of the coordinate point to within the preset flight path range boundary to obtain the fitted flight path trajectory data. The fitted flight path trajectory data includes the trajectory equation and the list of coordinate points.

7. The method for designing three-dimensional flight paths of low-altitude aircraft based on the BeiDou grid according to claim 1, characterized in that: S6 includes the following steps: S61. Obtain real-time airspace dynamic information from the airspace dynamic update system and obtain fitted flight path data. The real-time airspace dynamic information includes airspace type change information, available time period update information, and altitude range adjustment information. S62. Based on preset spatial matching rules and preset time synchronization rules, each coordinate point in the fitted flight trajectory data is matched one by one with the latest airspace availability status data, and it is checked whether the spatial position and flight time corresponding to each coordinate point overlap with the designated range of the no-fly zone and the unavailable time period of the restricted area. S63. If conflicting coordinate points are detected, the continuous coordinate point correlation analysis method is used to identify the conflict segment composed of continuous conflicting coordinate points, and the spatial range and temporal range of the conflict segment are calculated. The conflict segment includes the starting point, the ending point and the conflict type. S64. Based on the latest airspace availability data, search for flyable airspace areas within the spatial range of the conflict segment and adjacent airspaces of the conflict segment, and generate a new path segment to bypass the conflict segment according to the path generation algorithm. The new path segment is connected to the start and end points of the conflict segment. The flyable airspace areas include airspace areas of the airspace type being a suitable flight zone, and the available time period of the suitable flight zone matching the planned flight time period of the target low-altitude aircraft. S65. The new path segment is fused with the non-conflicting segments in the fitted route trajectory data using a trajectory stitching algorithm to obtain a three-dimensional route design scheme.

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