A multi-end unified scheduling method and system for unmanned aerial vehicle operation

Abstract

This invention provides a multi-terminal unified orchestration method and system for unmanned aerial vehicle (UAV) operations, relating to the field of UAV intelligent technology. The method includes acquiring initial task configuration instructions issued by multi-source control terminals, binding the three-dimensional coordinates of the meteorological monitoring tower's anemometer base, the center of the firebreak calibration stakes, and the centroid of the emergency material airdrop target to obtain a multi-source environmental perception reference anchor point set; standardizing the initial task configuration instructions to obtain a cross-terminal task semantic parsing set; and obtaining an environmental constraint feature vector based on the three-dimensional topological distribution characteristics of the cross-terminal task semantic parsing set and the multi-source environmental perception reference anchor point set. This invention achieves unified parsing of multi-source control terminal instructions and adaptive calibration of UAV operations in complex mountainous environments.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) intelligent technology, and in particular to a multi-terminal unified orchestration method and system for UAV operations. Background Technology

[0002] In mountainous forest fire prevention and emergency rescue and emergency material airdrop operations, the forest area management platform, emergency command center platform and on-site mobile command terminal will simultaneously issue task instructions such as wide-area patrol and inspection, fire isolation zone marking and emergency material airdrop to multiple types of drones. Each control terminal uses different private communication protocols to transmit heterogeneous instruction data, and the operation area has a complex environment with large mountain terrain undulation and strong mountain turbulence interference.

[0003] The existing control methods can only forward instructions in a simple way, which makes it difficult to perform unified semantic parsing and protocol adaptation for multi-source heterogeneous instructions. They do not combine geographical entities such as meteorological monitoring towers, fire isolation zone marker stakes, and airdrop targets to build environmental constraint models, and they do not carry out route compensation and multi-aircraft three-dimensional airspace conflict resolution for mountain airflow disturbances.

[0004] Different drone flight control protocols are incompatible with each other, and mission logic is strongly bound to terminal hardware. This leads to problems such as drone flight path deviation, inaccurate positioning of material delivery, interference of multiple drone flight paths and timing conflicts, which affect the collaborative safety of drone emergency operations. Summary of the Invention

[0005] This invention provides a multi-terminal unified orchestration method and system for UAV operations, enabling unified parsing of commands from multiple control terminals and adaptive calibration of UAV operations in complex mountainous environments.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a multi-terminal unified orchestration method for unmanned aerial vehicle (UAV) operations, the method comprising: The initial task configuration instructions issued by the multi-source control terminal are obtained, and the three-dimensional coordinates of the meteorological monitoring tower anemometer base, the fire isolation zone calibration pile center, and the centroid of the emergency material airdrop target are bound together to obtain the multi-source environmental perception reference anchor point set. The initial task configuration instructions are standardized to obtain a cross-end task semantic parsing set; based on the three-dimensional topological distribution features of the cross-end task semantic parsing set and the multi-source environmental perception benchmark anchor point set, the environmental constraint feature vector is obtained. A virtual space fitting surface is constructed using environmental constraint feature vectors; mesh partitioning and gradient calculation are performed based on the virtual space fitting surface to obtain compensation adjustment parameters; the compensation adjustment parameters are input into the cross-task semantic parsing set to obtain the task calibration vector set; the task calibration vector set is standardized to obtain the standardized task intermediate model. Using the standardized task intermediate model as the processing object, the task calibration vector set is branched and mapped according to the fixed-wing wide-area cruise topology and material delivery pose to obtain multi-terminal task branch flow. The multi-terminal task branch flow is combined with the target UAV terminal's exclusive flight control communication protocol to recode parameters and reorganize command fields to obtain a unified operation execution sequence. The unified operation execution sequence is distributed to the corresponding UAV terminal link. The virtual space fitted surface state is updated based on the operation status feedback data returned by the terminal to obtain the updated surface state. The updated surface state is combined with compensation adjustment parameters to perform time sequence verification and three-dimensional spatial conflict resolution on the unified operation execution sequence to obtain the final orchestration instruction set.

[0007] Secondly, a multi-terminal unified orchestration system for unmanned aerial vehicle (UAV) operations includes: The acquisition module is used to acquire the initial task configuration instructions issued by the multi-source control terminal, bind the three-dimensional coordinates of the meteorological monitoring tower anemometer base, the center of the fire isolation zone calibration pile, and the centroid of the emergency material airdrop target, and obtain the multi-source environmental perception reference anchor point set; The extraction module is used to standardize the initial task configuration instructions to obtain a cross-end task semantic parsing set; based on the three-dimensional topological distribution features of the cross-end task semantic parsing set and the multi-source environmental perception benchmark anchor point set, the environmental constraint feature vector is obtained. The module is used to construct a virtual space fitting surface through environmental constraint feature vectors; perform mesh subdivision and gradient calculation based on the virtual space fitting surface to obtain compensation adjustment parameters; input the compensation adjustment parameters into the cross-end task semantic parsing set to obtain the task calibration vector set; and perform standardization processing on the task calibration vector set to obtain the standardized task intermediate model. The mapping module is used to process the standardized task intermediate model, and to branch the task calibration vector set according to the fixed-wing wide-area cruise topology and material delivery pose to obtain multi-terminal task branch flow. The multi-terminal task branch flow is combined with the target UAV terminal's exclusive flight control communication protocol to re-encode parameters and reassemble command fields to obtain a unified operation execution sequence. The verification module is used to distribute the unified job execution sequence to the corresponding UAV terminal link, update the virtual space fitted surface state based on the job status feedback data returned by the terminal, and obtain the updated surface state; combine the updated surface state with compensation adjustment parameters to perform time sequence verification and three-dimensional spatial conflict resolution on the unified job execution sequence, and obtain the final orchestration instruction set.

[0008] Thirdly, a computing device includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0009] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0010] The above-described solution of the present invention has at least the following beneficial effects: Because this invention employs technical means such as multi-source environmental perception reference anchor point binding, multi-terminal heterogeneous command protocol stack stripping and semantic standardization, virtual space fitting surface construction and mountain turbulence field gradient calculation, terminal-independent task encapsulation and logical topology reorganization, multi-task branch mapping and dedicated flight control protocol recoding, real-time status feedback iterative update and three-dimensional airspace conflict resolution, it overcomes the technical problems of heterogeneous semantic inconsistency of multi-control terminal command protocols, flight path deviation and inaccurate airdrop positioning caused by mountain terrain and turbulence interference, incompatibility of flight control protocols of multiple UAV models, and interference and timing conflicts of multiple aircraft tracks in forest fire prevention operations in mountainous areas. As a result, it achieves unified parsing and adaptation arrangement of multi-source control commands, improves the anti-interference capability and positioning accuracy of UAV operations in complex environments, reduces the adaptation cost of multi-aircraft collaborative operations, and ensures the timing consistency and airspace collaborative security of multi-aircraft emergency operations. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a multi-terminal unified orchestration method for unmanned aerial vehicle (UAV) operations provided by an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of a multi-terminal unified orchestration system for unmanned aerial vehicle (UAV) operations provided by an embodiment of the present invention. Detailed Implementation

[0013] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0014] like Figure 1 As shown, an embodiment of the present invention proposes a multi-terminal unified orchestration method for UAV operations, the method comprising the following steps: Step 1: Obtain the initial task configuration instructions issued by the multi-source control terminal, bind the three-dimensional coordinates of the meteorological monitoring tower anemometer base, the center of the fire isolation zone calibration pile, and the centroid of the emergency material airdrop target, and obtain the multi-source environmental perception reference anchor point set; Step 2: Standardize the initial task configuration instructions to obtain a cross-end task semantic parsing set; based on the three-dimensional topological distribution features of the cross-end task semantic parsing set and the multi-source environmental perception benchmark anchor point set, obtain the environmental constraint feature vector. Step 3: Construct a virtual space fitting surface using environmental constraint feature vectors; perform mesh partitioning and gradient calculation based on the virtual space fitting surface to obtain compensation adjustment parameters; input the compensation adjustment parameters into the cross-task semantic parsing set to obtain the task calibration vector set; standardize the task calibration vector set to obtain the standardized task intermediate model. Step 4: Using the standardized task intermediate model as the processing object, the task calibration vector set is branched and mapped according to the fixed-wing wide-area cruise topology and material delivery pose to obtain multi-terminal task branch flow. The multi-terminal task branch flow is combined with the exclusive flight control communication protocol of the target UAV terminal to re-encode parameters and reorganize command fields to obtain a unified operation execution sequence. Step 5: Distribute the unified operation execution sequence to the corresponding UAV terminal link, update the virtual space fitted surface state based on the operation status feedback data returned by the terminal, and obtain the updated surface state; combine the updated surface state with the compensation adjustment parameters to perform time sequence verification and three-dimensional spatial conflict resolution on the unified operation execution sequence, and obtain the final orchestration instruction set.

[0015] In this embodiment of the invention, because the invention employs the following technical means: binding multiple types of environmental reference three-dimensional coordinates to form a set of perception anchor points; performing protocol stripping and semantic standardization on multi-terminal commands; constructing a virtual space fitting surface and performing mesh subdivision and turbulent field gradient calculation; injecting environmental compensation parameters to generate calibration vectors and encapsulating them into a terminal-independent standardized task intermediate model; branching mapping according to the operation type and combining it with flight control protocol recoding; updating the surface based on terminal feedback and performing timing verification and three-dimensional airspace conflict resolution; thus overcoming the technical problems of heterogeneous and inconsistent commands from multiple source control terminals, decreased operational accuracy due to interference from complex mountainous environments, incompatibility of protocols between multiple UAV terminals, and airspace conflicts and timing errors in multi-aircraft operations; thereby achieving unified parsing and arrangement of heterogeneous commands from multiple terminals, improving the anti-interference capability of UAVs in complex mountainous environments, realizing protocol compatibility and collaborative operation of multiple UAV models, and ensuring consistent UAV operation timing and airspace safety.

[0016] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1 involves capturing initial task configuration instructions carrying heterogeneous communication frames issued by the forest management platform, emergency command center platform, and on-site mobile command terminal in real time through a multi-protocol access gateway, and parsing them to obtain a multi-terminal heterogeneous instruction stream. Specifically, this includes: enabling a multi-protocol access gateway with multi-protocol compatibility parsing capabilities; simultaneously accessing the communication transmission links of the forest management platform, emergency command center platform, and on-site mobile command terminal through the gateway's built-in multi-link parallel listening module; continuously capturing wireless and wired communication data sent out by the three types of control terminals in real time; completely obtaining the initial task configuration instructions carrying heterogeneous communication frames with different private protocol formats issued by each control terminal; performing layered parsing operations on the captured heterogeneous communication frames according to the frame structure specifications of the corresponding communication protocols; sequentially stripping the link layer encapsulation fields, network layer transmission headers, and application layer redundancy check signaling from the communication frames; retaining the core business data carrying the task information in each instruction frame; sorting and integrating the core business data of different control terminals according to the order of instruction issuance; and finally completing the parsing and generation of the multi-terminal heterogeneous instruction stream.

[0017] Step 1.2: Extract the operation area identifier from the multi-terminal heterogeneous command stream. Retrieve the measured coordinates of fixed geographic reference entities that match the operation area identifier through the geographic information database to obtain the original coordinate set of reference entities. Specifically, this includes: performing field-by-field decomposition and feature extraction on the multi-terminal heterogeneous command stream, filtering out operation area identifier information such as operation area number, operation boundary latitude and longitude range, and operation terrain zoning identifier from the command stream, using the above operation area identifier as standardized search conditions, and accessing the geographic information database with built-in geographic measured data to perform the search. The database traverses the internally stored fixed geographic reference entity coordinate data according to the search conditions, matches and filters out the measured three-dimensional coordinate data of the meteorological monitoring tower anemometer base, the center of the fire isolation zone calibration pile, and the centroid of the emergency material airdrop target within the operation area, classifies and collects all successfully matched coordinate data according to entity type, and removes redundant coordinate data that are collected repeatedly to obtain the complete original coordinate set of reference entities.

[0018] Step 1.3 involves performing a unified spatial reference transformation on the coordinates of the meteorological monitoring tower anemometer base, the center coordinates of the fire isolation zone calibration piles, and the centroid coordinates of the emergency material airdrop target from the original coordinate set of the reference entities. This transformation is then spatiotemporally linked and bound to the task identifier in the multi-terminal heterogeneous command stream to obtain a multi-source environmental perception reference anchor point set. Specifically, this includes performing a unified spatial reference transformation on the coordinates of various entities in the original coordinate set of the reference entities. Let the original three-dimensional coordinates of the reference entities be (…). , , The spatial reference conversion factors are respectively , , The coordinate system translation compensation amounts are respectively , , The transformed unified spatial reference coordinates are ( , , The specific calculation formula is as follows: , , Through the above three sets of calculation formulas, the spatial references for the coordinates of the meteorological monitoring tower anemometer base, the center coordinates of the fire isolation zone calibration piles, and the centroid coordinates of the emergency material airdrop target are uniformly transformed. This eliminates coordinate system deviations between different original coordinates, extracts the unique task identifier corresponding to each task in the multi-terminal heterogeneous instruction stream, and timestamps the three-dimensional coordinates that have completed the reference transformation and the task identifiers. Using the task issuance time as the association reference, the correspondence between the task identifiers and the transformed coordinates is established, and the spatiotemporal association matching degree is calculated to ensure the binding accuracy. All the bound task identifiers and three-dimensional coordinate data are integrated to finally obtain the multi-source environmental perception reference anchor point set.

[0019] In this embodiment of the invention, by employing a multi-protocol access gateway to listen in parallel, capture, and parse heterogeneous communication frames in layers, retrieving fixed reference entity coordinates from a geographic information database based on the work area identifier, and completing spatial benchmark unification through coordinate linear conversion and binding the converted coordinates with the task identifier in time synchronization, the technical problems of heterogeneous multi-source control terminal command protocols being unable to be uniformly parsed, inconsistent coordinate systems of work environment reference entity coordinates, and disconnection between task commands and spatial environment data are overcome. This enables efficient and standardized parsing of heterogeneous multi-terminal control commands, obtains key geographic reference coordinates of the work area, and constructs a spatiotemporally unified environmental perception benchmark anchor point, providing stable and reliable spatial data support for environmental constraint feature extraction and UAV mission orchestration.

[0020] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1 involves reverse parsing the communication protocol of the initial task configuration instructions carried by the multi-source environmental perception reference anchor point set. This involves stripping away the private link encapsulation header and redundant interactive signaling, extracting the original task payload data, and obtaining the unencapsulated task data stream. Specifically, this includes: for the initial task configuration instructions associated with the multi-source environmental perception reference anchor point set, starting the communication protocol reverse parsing module, and according to the respective private communication protocol specifications of the forest area management platform, emergency command center platform, and on-site mobile command terminal, performing layer-by-layer reverse decomposition and feature identification of the instruction transmission frame structure, thereby locating and completely parsing the data. The private link encapsulation header outside the instruction is stripped away. This header contains non-core task information such as link handshake identifier, transmission route marker, and link encryption check bit. Redundant interactive signaling such as heartbeat frames, response frames, retransmission requests, and link keep-alives attached to the instruction transmission process is removed one by one. The original task payload data in the instruction used to describe the UAV's patrol range, material delivery coordinates, operation timing requirements, and fire site marking route are retained. All payload data after removing redundant information is continuously spliced ​​and integrated according to the time sequence of instruction issuance, and finally a clean, unencapsulated task data stream without encapsulation or redundancy is obtained.

[0021] Step 2.2 involves mapping fields and normalizing dimensions of the decapsulated task data stream using a unified task ontology dictionary to obtain a structured cross-platform task semantic parsing set. Specifically, this includes: using a pre-built and fixed unified task ontology dictionary, which incorporates all standardized task fields and unified dimension specifications for UAV forest fire prevention and emergency airdrop operations, matching and mapping various heterogeneous description fields in the decapsulated task data stream with standard semantic fields within the dictionary. Differentiated task fields customized by different control terminals are uniformly converted into standardized semantic fields. For example, the cruise radius and delivery landing point fields of the forest management platform and the airdrop coordinate fields of the patrol range of the emergency command center are uniformly mapped to standardized semantic fields such as the cruise range and airdrop target coordinates in the dictionary. This ensures that the task description semantics are consistent and unambiguous across different control terminals. Simultaneously, dimension normalization is performed on physical quantities such as spatial coordinates, flight altitude, and cruise speed involved in the data stream. The unified processing method determines the unified reference range of each physical quantity through a unified task ontology dictionary. The spatial coordinates are based on the coordinate range of the geodetic reference coordinate system of the operation area, the flight altitude is based on the difference between the lowest and highest altitudes of the operation area, and the cruise speed is based on the safe operating speed range of the UAV. For each original physical quantity value, the difference between the original value and the minimum value of the corresponding physical quantity reference range is calculated. This difference is then divided by the difference between the maximum and minimum values ​​of the reference range. Through the combination of this difference operation and the division operation, all original physical quantity values ​​are uniformly converted to the standard range of 0 to 1. This eliminates the calculation deviation caused by the inconsistency of physical quantities and the large differences in the numerical ranges of physical quantities in different control terminal instructions. All task data that have completed field mapping and dimension normalization are structured and arranged according to task type, operation parameters, and spatial coordinates, and finally, a structured cross-terminal task semantic parsing set with unified format and semantic standardization is obtained.

[0022] Step 2.3: Using the center of the work area defined by the cross-end task semantic parsing set as the spatial distribution benchmark, calculate the relative spatial distance and elevation difference between each reference entity in the multi-source environmental perception benchmark anchor point set, and construct a three-dimensional topological adjacency matrix representing the spatial relationship of the micro-environment to obtain the spatial topological relationship matrix. Specifically, this includes: extracting spatial range parameters such as latitude, longitude, and elevation boundaries of the work area from the cross-end task semantic parsing set, determining the geometric center of the work area through mean aggregation calculation, and using this geometric center as the overall spatial distribution benchmark. Subsequently, iterate through the three types of reference entities in the multi-source environmental perception benchmark anchor point set: the meteorological monitoring tower anemometer base, the center of the fire isolation zone calibration pile, and the centroid of the emergency material airdrop target, and calculate the three-dimensional relative spatial distance and elevation difference between any two reference entities in turn. Let the unified spatial benchmark coordinates of the two reference entities be ( , , )and( , , The formula for calculating the three-dimensional relative spatial distance is: The formula for calculating the elevation difference is: The relative spatial distance and elevation difference between each set of reference entities are used as matrix elements. The unique number of each reference entity is used as the row and column index of the matrix. The matrix elements are filled in sequentially to construct a three-dimensional topological adjacency matrix that can completely represent the spatial location and elevation relationship of the micro-environment of the work area. Finally, a standardized spatial topological relationship matrix is ​​obtained.

[0023] Step 2.4 involves performing feature dimensionality reduction and vector serialization encoding on the spatial topology relation matrix to obtain environmental constraint feature vectors. Specifically, this includes: performing feature dimensionality reduction on the constructed spatial topology relation matrix; firstly, calculating the feature correlation within the matrix; extracting the core feature components representing terrain constraints and spatial distribution from the matrix; removing non-critical feature data that are too close or have redundant correlations; retaining spatial distribution features that significantly affect UAV airspace flight and material delivery; sequentially arranging the dimensionality-reduced multidimensional feature data according to the spatial topology association order of the reference entities; converting the two-dimensional feature data in matrix form into a one-dimensional continuous vector form; assigning higher weights to core environmental constraint parameters such as elevation difference and key anchor point spacing during the serialization process; completing the ordered encoding and integration of feature data; and finally obtaining a complete environmental constraint feature vector representing the terrain and spatial constraints of the operating area.

[0024] In this embodiment of the invention, by employing technical means such as reverse parsing the initial task configuration instructions and stripping away redundant encapsulation and signaling, relying on a unified task ontology dictionary to complete field mapping and dimension normalization calculations, calculating the relative spatial distance and elevation difference of anchor points based on the center of the work area and constructing a three-dimensional topological adjacency matrix, and performing feature dimensionality reduction and vector serialization encoding on the spatial topological relationship matrix, the technical problems of inconsistent semantics of multi-terminal control instructions, chaotic physical dimensions that cannot be universally applied, difficulty in quantifying and representing spatial relationships in the work environment, and redundant and complex topological feature dimensions are overcome. Thus, the semantic standardization and regularization of cross-terminal task instructions are achieved, the micro-environmental spatial constraint characteristics of mountainous work areas are quantified, and the dimensions of environmental feature data are simplified.

[0025] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1 involves performing a discrete control point continuous mapping operation on the environmental constraint feature vector to smoothly connect the spatial elevation transition zones between adjacent reference entities. This process generates a virtual spatial fitting surface representing the relationship between terrain undulations and the spatial relationship of the reference entities. Specifically, this includes extracting all spatial control points corresponding to the meteorological monitoring tower anemometer base, the firebreak calibration pile center, and the centroid of the emergency material airdrop target from the environmental constraint feature vector. These discretely distributed points are used as the core control nodes for surface construction, and the three-dimensional coordinates of each control point are defined. For each set of adjacent reference entity control points, a continuous mapping operation is performed. During the operation, two adjacent control points are first obtained and set as control points. and control points Given their respective spatial locations and elevation values, first calculate the horizontal distance between the two control points. The formula for calculating the horizontal distance is: The elevation values ​​of the two are linearly weighted and transitioned according to this horizontal interval. The formula for calculating the elevation of the linearly weighted transition is: ,in Transition nodes between adjacent control points The horizontal distance, with a value range of . At the same time, a smoothing correction factor is introduced based on the natural topographic relief patterns of mountainous terrain. After nonlinear adjustment, the corrected formula for calculating the transition node elevation is as follows: This correction eliminates abrupt changes in terrain elevation, generates elevation transition nodes between adjacent control points segment by segment, fills spatial elevation fault zones between discrete control points, and completes the continuous mapping of all adjacent control points. Based on all the continuously processed spatial nodes, a global surface is fitted according to the spatial distribution order and elevation correlation characteristics of the nodes. During the fitting process, the surface smoothness is optimized using the least squares method. The fitting error is calculated using the following formula: ,in The total number of all continuous nodes. This represents the actual elevation of the node. The node elevations after surface fitting are used to iteratively adjust the fitting parameters to minimize the error. If the error is less than the preset error threshold, the surfaces are gradually pieced together to form a complete spatial surface, and finally a virtual spatial fitting surface is obtained that can represent the undulation of the mountainous terrain in the work area and fully reflect the spatial relationship of various reference entities.

[0026] Step 3.2: Calculate the elevation gradient rate of change on the virtual space fitted surface to obtain the gradient rate of change; project the steeply changing regions with gradient rates exceeding a preset threshold onto the horizontal reference plane and perform recursive subdivision operations to obtain hierarchical terrain mesh units. Specifically, this includes: performing a global traversal on the generated virtual space fitted surface, calculating the elevation gradient rate of change region by region. The calculation method is: selecting two adjacent terrain calculation nodes on the surface and setting them as nodes. and nodes The three-dimensional coordinates are respectively and Using nodes Elevation value minus nodes The elevation values ​​are used to obtain the elevation difference. Calculate the horizontal straight-line distance between two nodes. Ultimately, through the formula The elevation gradient change rate of this local region is obtained. This value visually reflects the steepness and undulation of the terrain, and a threshold for determining the rate of change of elevation gradient is preset. The gradient change rate of each region calculated across the entire domain With the preset threshold Compare them one by one and select the best ones. The terrain abruptly changes direction, and then projects this type of abruptly changing region vertically onto the horizontal reference plane of the working area to obtain the horizontal projection range of the abruptly changing region. A recursive subdivision operation is then performed on the projected abruptly changing region, with the initial coarse-grained mesh size set to... The formula for calculating the mesh size after each level of subdivision is as follows: ,in To further subdivide the levels, After each level of subdivision, the degree of matching between the grid cells and the terrain contour is verified. The matching degree is calculated using the following formula: ,in This refers to the area where the grid cell overlaps with the terrain outline. The total area of ​​the grid cells, when the matching degree The subdivision process continues until all grid cells conform to the boundaries and elevation changes of the steep terrain, ultimately forming a hierarchical terrain grid that balances accuracy and computational efficiency.

[0027] Step 3.3 involves aligning the hierarchical terrain grid cells with the preset mountain wind field model in three-dimensional space and overlaying them in the field. The wind resistance attenuation coefficient and crosswind interference offset at each grid node are extracted and weighted according to the grid topology adjacency relationship to obtain the airspace environment compensation and adjustment parameters. Specifically, this includes aligning the hierarchical terrain grid cells with the preset mountain wind field model in three-dimensional space, calculating the coordinate deviation between the terrain grid nodes and the wind field model nodes, where... , , These represent the x-coordinate, y-coordinate, and elevation coordinates of the hierarchical terrain grid nodes in a spatial rectangular coordinate system, respectively. , , These represent the x-coordinate, y-coordinate, and elevation coordinates of the spatial nodes corresponding to the preset mountain wind field model, respectively. The deviation of the horizontal axis is calculated using the following formula: , The ordinate deviation is calculated using the following formula: , The formula for calculating the elevation coordinate deviation is as follows: The terrain grid positions are adjusted through translation and rotation operations to ensure that the coordinate deviation of all nodes is less than a preset deviation threshold. This ensures that the spatial position of each node in the terrain grid completely coincides with the corresponding wind field data node in the wind field model. Based on this, the terrain data and wind field data are overlaid. At each grid node after overlay, the wind resistance attenuation coefficient and crosswind interference offset corresponding to that spatial position are extracted. Indicates the first The drag attenuation coefficient at each grid node is used to characterize the degree to which mountain airflow reduces the drag on the UAV at that location. Indicates the first The crosswind interference offset at each grid node is used to characterize the lateral offset distance that crosswinds will cause to the UAV's flight trajectory.

[0028] Weighted aggregation calculations are performed based on the topological adjacency relationships of the hierarchical terrain grid. Indicates the first The topological adjacency weight of a grid node is dynamically allocated based on its terrain location; nodes in steeply changing areas have higher weights, while nodes in gently sloping areas have lower weights. This represents the total number of grid nodes within a single adjacent region currently participating in weighted aggregation. Weighted aggregation is calculated as the weighted sum of all nodes within a single adjacent region. The formula for the weighted sum of wind resistance attenuation coefficients is... In the formula The weighted sum of the wind resistance attenuation coefficients of all nodes in the region; the weighted sum formula for crosswind interference offset is... In the formula This is the weighted sum of the crosswind interference offsets of all nodes in the region. The formula for calculating the wind resistance attenuation coefficient for this region is as follows: In the formula This represents the overall wind resistance attenuation coefficient for the region after weighted averaging; the formula for crosswind interference offset is... In the formula The crosswind interference offset of the entire region after weighted averaging is obtained by performing the above weighted aggregation operation on all grid areas of the entire region to smooth the abnormal fluctuations of local wind field data. Finally, the normalized parameters of all regions are integrated to obtain airspace environment compensation adjustment parameters applicable to the entire operating area and directly used for UAV flight path and attitude correction.

[0029] In this embodiment of the invention, by employing the technical means of continuous mapping interpolation of discrete reference entity control points and generating a virtual space fitting surface through least squares fitting, calculating the elevation gradient change rate of the surface, recursively subdividing the steep terrain region into hierarchical terrain grid units according to the grid subdivision formula, and extracting wind field parameters after 3D alignment of the terrain grid and the mountain wind field model through the coordinate deviation formula and performing full-domain fusion according to the weighted aggregation formula, the technical problems of discrete and discontinuous terrain control points in mountain operation areas, insufficient modeling accuracy of steep terrain, and inability to quantify and fully adapt to mountain turbulence interference are overcome. Thus, high-precision digital reconstruction of mountain terrain in the operation area is achieved, the grid modeling granularity of complex terrain is refined, and the full-domain wind field interference characteristics are quantified and fused.

[0030] In a preferred embodiment of the present invention, step 3 above may include: Step 3.4 involves tensor splicing of the airspace environment compensation adjustment parameters with the target waypoint coordinates in the cross-task semantic analysis set, and performing boundary iterative extrapolation along the normal and tangential directions of the flight path to obtain the dynamic safe flight envelope. Specifically, this includes: splicing the previously calculated airspace environment compensation adjustment parameters with the three-dimensional coordinates of the target waypoints planned in the cross-task semantic analysis set in spatial dimensions. Specifically, the two core compensation parameters, wind resistance attenuation coefficient and crosswind interference offset, are combined with the corresponding x-coordinate, y-coordinate, and elevation coordinates of each target waypoint to form a composite waypoint tensor node that integrates spatial location information and airspace environment constraint information. Using each composite waypoint tensor node as the reference center, multiple rounds of boundary iterative extrapolation are performed along the tangential direction of the UAV's preset flight path (the tangential direction being the forward extension direction of flight and the normal direction, and the normal direction being the horizontal extension direction perpendicular to the flight path). The airspace environment compensation adjustment parameters are set as follows: The safety boundary expansion factor is The offset of a single iteration of extrapolation is The three satisfy the calculation formula Each iteration adds the offset to the current boundary node coordinates, continuing the iteration until the extrapolated boundary completely covers dangerous airspace such as steep terrain changes and areas of strong turbulence within the operational area. After all boundary nodes have been iterated, the discrete boundary nodes are sequentially closed and connected through spatial surface fitting, abnormal nodes with protruding boundaries are removed, and the contours are smoothed, ultimately constructing a dynamic safe flight envelope that can dynamically adapt to the mountainous environment.

[0031] Step 3.5: Extract the local curvature extrema and curvature gradient distribution of the dynamic safe flight envelope surface. Analyze these local curvature extrema and curvature gradient distribution as control adjustment coefficients characterizing the flight path maneuver constraint strength and airflow disturbance amplitude. Specifically, this includes: performing a full-domain node-by-node traversal scan of the generated dynamic safe flight envelope surface; sequentially calculating the local curvature of each node on the envelope surface and selecting extrema; and simultaneously solving for the curvature gradient distribution. During the calculation, the difference in the angle between the normal vectors of two adjacent calculation nodes is set to a certain value. The three-dimensional spatial distance between two adjacent computing nodes is Local curvature of a single node Satisfy the calculation formula After calculating through a full-region traversal, the local curvature maxima and minima of the envelope surface are selected, i.e., local curvature extrema. The curvature gradient is then obtained by dividing the difference in local curvature between adjacent nodes by the node spatial spacing. This fully characterizes the spatial variation range of the surface curvature of the envelope. Subsequently, local curvature extrema are transformed into the maneuver constraint intensity of the UAV flight path; a larger curvature extrema indicates stronger terrain constraints and stricter maneuver restrictions. The curvature gradient values ​​are then transformed into airflow disturbance amplitudes; a larger gradient change indicates more severe airflow disturbances. The maximum value of the combined characteristic benchmark of curvature and gradient is set as [value missing]. The control adjustment coefficient is Then the control adjustment coefficient satisfies the calculation formula. Through this linear mapping calculation, the environmental constraints of terrain and airflow are analyzed into standardized control adjustment coefficients that can be directly input into the UAV flight control system.

[0032] Step 3.6: Perform nonlinear position offset compensation on the spatial coordinate mapping matrix based on the control adjustment coefficients, and simultaneously perform dynamic reallocation of tolerance thresholds on the flight control attitude reference base to obtain the task calibration vector set. Specifically, this includes: performing nonlinear position offset compensation on the spatial coordinate mapping matrix of the UAV trajectory based on the analytically obtained control adjustment coefficients. The spatial coordinate mapping matrix is ​​used to convert the geographical coordinates of the operational area into UAV onboard flight control coordinates, with the original target route coordinates set as... The crosswind interference offset is The control adjustment coefficient is The flight path coordinates after nonlinear compensation are: Satisfies the calculation formula This calculation effectively corrects flight path deviation errors caused by turbulent water flow and terrain undulations in mountainous areas. Simultaneously, while performing coordinate compensation, it dynamically reallocates the tolerance threshold of the UAV flight control attitude reference, setting the system's default reference attitude tolerance to [value missing]. The attitude tolerance threshold after dynamic redistribution is Satisfies the calculation formula In areas with extreme curvature and strong airflow disturbance, the control adjustment coefficient is larger and the attitude tolerance threshold is tightened accordingly to improve flight stability. In areas with gentle terrain and stable airflow, the attitude tolerance threshold is moderately relaxed to reduce flight control energy consumption. The route points with completed nonlinear coordinate compensation, dynamically allocated attitude tolerance parameters, and track timing identifiers are integrated and arranged in spatial order and execution timing to finally obtain a mission calibration vector set adapted to complex mountainous airspace environments.

[0033] Step 3.7: Using the 3D envelope nodes represented by the task calibration vector set as the topology mapping source, calculate the shortest path connectivity and state transition cost between adjacent envelope nodes; construct a hierarchical instruction dependency network based on the minimum connected path with the state transition cost to obtain a standardized intermediate task model. Specifically, this includes: using the 3D envelope nodes represented by the task calibration vector set as the topology mapping source nodes, constructing a global trajectory topology network; first calculating the Euclidean distance between adjacent 3D envelope nodes to determine the shortest path connectivity; then comprehensively considering spatial distance, environmental constraints, and flight energy consumption to calculate the state transition cost between nodes, setting the spatial distance between adjacent nodes as... The environmental constraint weighted value corresponding to the control adjustment coefficient is Cost of state transition between nodes Satisfy the calculation formula The system iterates through all feasible path combinations in the topology network, calculates the total state transition cost for each path, and selects the optimal connected path with the minimum total transition cost as the core execution path of the UAV. Based on this optimal path with minimum cost, a hierarchical instruction dependency network is built. The upper-layer network carries general task rules such as task logic, operation sequence, and safety constraints, while the lower-layer network associates execution data such as path coordinates, attitude parameters, and compensation coefficients. This completely decouples the task execution logic from specific control terminal protocols and UAV terminal hardware. The hierarchical instruction dependency network and task calibration vector set are integrated and encapsulated to obtain a standardized task intermediate model that can be adapted to multiple types of terminals.

[0034] In this embodiment of the invention, by employing the technical means of splicing the airspace environment compensation parameters with the target waypoint coordinate tensor, iteratively extrapolating along the tangential and normal directions of the flight path to construct a dynamic safe flight envelope, traversing the surface of the envelope to calculate local curvature and curvature gradient and mapping to generate control adjustment coefficients, performing nonlinear position offset compensation on the flight path coordinates based on the control adjustment coefficients and dynamically allocating flight control attitude tolerance thresholds, calculating the minimum state transition cost path with three-dimensional envelope nodes and building a terminal-independent hierarchical instruction dependency network, the invention overcomes the technical problems of UAV flight safety boundaries not being adaptable to complex mountainous environments, environmental constraints being difficult to convert into executable flight control parameters, flight paths and attitudes being easily affected by turbulence and terrain interference, and strong binding between task models and terminal hardware protocols, thus achieving dynamic adaptive delineation of safe flight airspace for UAVs, completing the quantitative conversion of environmental constraints into flight control parameters, improving the environmental anti-interference accuracy of flight path positioning and attitude control, constructing a generalized and standardized task model, and providing a stable and universal core carrier for multi-terminal task orchestration and protocol adaptation.

[0035] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: Parse the standardized task intermediate model to extract the task calibration vector set. Map the task calibration vector set to the fixed-wing wide-area cruise topology branch and the material delivery pose branch respectively to obtain the multi-terminal adapted task branch flow. Specifically, this includes: performing layered parsing on the standardized task intermediate model, peeling off the hierarchical instruction dependency network and general task rules of the model layer by layer, and extracting the task calibration vector set encapsulated in the model. This vector set contains core parameters such as nonlinearly compensated route coordinates, dynamically allocated flight control attitude tolerance, airspace environment compensation adjustment parameters, and task timing identifiers. A task branch clustering algorithm based on Euclidean distance is adopted. This algorithm uses the reference coordinates of the UAV operation type as the core to calculate the Euclidean distance between each parameter node in the task calibration vector set and the reference points of the two types of operations. Let the coordinates of the fixed-wing wide-area cruise reference center be... The coordinates of the emergency supplies airdrop reference center are: The coordinates of any parameter node in the task calibration vector set are The Euclidean distance from that node to the cruise reference center is... The calculation formula is: Euclidean distance to the airdrop reference center The calculation formula is: .

[0036] By comparison and The numerical value is used to complete branch clustering: if If the parameter node is classified as a fixed-wing wide-area cruise branch, then the cruise path nodes, cruise speed, cruise altitude, inspection interval, and other parameters under this branch are integrated to construct a cruise mission topology adapted to fixed-wing UAVs; if This parameter node is then categorized as the emergency supplies airdrop pose branch. It integrates parameters such as airdrop target coordinates, drop height, drop angle, and payload release sequence to construct a pose control branch structure adapted for airdrop operations. The parameters of both types of branches are validated, and the consistency deviation of parameters within each branch is calculated. A parameter deviation threshold is then set. If the deviation between a parameter node and the branch baseline parameter exceeds Then, through the linear correction formula Calculate the parameter annotations related to the corrected UAV track coordinates. ,in This is a deviation correction factor, dynamically adjusted according to the complexity of the mountainous terrain. The parameters related to the drone's flight path coordinates are labeled, corrected, and then reassigned to the corresponding branches. The task parameters of the two types of branches are sorted according to spatial location order and execution time sequence, respectively, and finally a multi-terminal adaptation task branch flow that can be adapted to different types of drone terminals is obtained.

[0037] Step 4.2, based on the multi-terminal adaptation task branch flow, using the dedicated flight control communication protocol dictionary for each target UAV terminal corresponding to each branch, performs parameter recoding for trajectory coordinate dimension conversion, payload control command bit alignment, and communication timing markers to obtain protocol-compatible command data packets. Specifically, this includes: based on the multi-terminal adaptation task branch flow, for each target UAV terminal corresponding to a branch, retrieving the pre-stored dedicated flight control communication protocol dictionary for that UAV, which contains core content such as the flight control command format, dimension requirements, command bit definitions, and communication timing specifications for the corresponding UAV; performing trajectory coordinate dimension conversion; and setting the original path coordinates in the task calibration vector set as... The dimension conversion coefficient is The coordinate offset correction amount is The converted airborne adaptation coordinates Satisfies the calculation formula: To ensure that the coordinate dimensions are perfectly matched with the target UAV flight control system, eliminate command parsing errors caused by dimensional differences, align the execution load control command bits, and set the standard command bit length to [value missing]. The current instruction bit length is ,like Then, the instruction bits are padded with zeros, and the number of zeros padded is... ;like This involves extracting valid instruction bits to retain core instruction information, eliminating redundant bits, and simultaneously executing communication timing marking to set the instruction sending period. The instruction sequence number is Then the time stamp Each instruction is assigned a unique timing identifier. Through the above parameter recoding operation, the branch task parameters are converted into an instruction format that conforms to the target UAV flight control protocol, and finally a protocol-compatible instruction data packet is obtained.

[0038] Step 4.3 involves reorganizing the instruction fields and arranging the action dependency chains of the protocol-compatible instruction data packets according to the target terminal's flight control underlying execution cycle and task priority scheduling rules to obtain a unified job execution sequence. Specifically, this includes further optimizing and arranging the protocol-compatible instruction data packets, first by retrieving the target UAV terminal's flight control underlying execution cycle. Calculate the instruction sending interval According to the task priority rules for emergency forest fire prevention operations in mountainous areas, the priority weight of patrol tasks is set as follows: The priority weight of the airdrop mission is ,in This aligns with the operational logic of prioritizing emergency supplies delivery over routine patrols, and calculates the overall priority value for each individual instruction. ,in This represents the percentage of patrol mission instructions. This represents the percentage of airdrop mission instructions, and ,according to The numerical value is used to sort all protocol-compatible instruction data packets. The higher the priority value, the higher the order of the instruction. At the same time, the action dependencies between each instruction are sorted out. For example, the cruise positioning must be completed first, the airdrop attitude adjustment must be performed, the payload must be released, and a complete action dependency chain must be built. The sorted instruction data packets are reorganized, and core fields such as instruction identifier, execution parameters, and timing mark are integrated to form a unified instruction format. Finally, a unified operation execution sequence that can be directly issued to each UAV terminal, is ordered in time, and has no execution conflicts is obtained.

[0039] In this embodiment of the invention, by employing techniques such as parsing a standardized intermediate task model to extract the task calibration vector set and branch mapping according to the job type, performing coordinate dimension conversion based on the UAV-specific flight control protocol dictionary, recoding parameters with command bit alignment and timing marks, and reorganizing command fields and arranging action dependency chains according to the flight control execution cycle and task priority, the technical problems of incompatibility between heterogeneous flight control protocols of multiple UAV models, mismatch between task parameters and flight control system dimensions, chaotic command timing, and unclear task priorities leading to delayed emergency response are overcome. This achieves multi-terminal task parameter adaptation, completes unified compatibility of heterogeneous flight control protocols, ensures the consistency of command execution timing and the rationality of priority, obtains a unified job execution sequence that can be directly issued, improves the command issuance efficiency and execution accuracy of collaborative operations of multiple UAV models, and ensures the timeliness and reliability of emergency operations for forest fire prevention in mountainous areas.

[0040] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1: Input the unified job execution sequence into the dynamic routing gateway, distribute it to the corresponding UAV terminal downlink according to the terminal online status and communication link quality, capture and parse the telemetry job status feedback data returned by each terminal in real time, and obtain the multi-machine real-time status time stream. Specifically, this includes: inputting the unified job execution sequence into the dynamic routing gateway, initiating the terminal status awareness and link quality assessment process, determining the online status of each target UAV terminal in real time, and setting the terminal online status identifier as... The standard reception period for terminal heartbeat packets is If continuous If the terminal consistently receives heartbeat feedback data from the corresponding terminal within a standard cycle, the terminal is determined to be online. If continuous If no heartbeat data is received within a certain period, the terminal is considered offline. Command distribution is only performed on terminals with an online status of 1. Simultaneously, the communication link quality of each terminal is quantitatively evaluated, with the link signal-to-noise ratio set to [value missing]. The packet loss rate of the link data is The overall link quality score is The link quality score satisfies the calculation formula. ,in , This is the link quality weight coefficient. and The sum of its values ​​is 1, and it is dynamically adjusted according to the communication environment of the work area. In complex communication environments in mountainous areas, the signal-to-noise ratio weight is appropriately increased. The value of is determined by comparing the overall link quality score with a preset link quality threshold. For terminals with a link quality score greater than the preset threshold, the instruction data of the corresponding branch in the unified job execution sequence is distributed to the dedicated downlink of that terminal to avoid instruction transmission failures caused by poor link quality.

[0041] After the command distribution is completed, the telemetry operation status feedback data transmitted back by each UAV terminal is captured in real time. This data includes two core types of information: the actual pose data of the terminal and the micro-environment perception data collected by the airborne sensors. The actual pose data includes the terminal's real-time spatial coordinates, attitude angle, flight speed, and operation status identifier. The micro-environment perception data includes environmental parameters such as real-time wind speed, wind direction, air pressure, and terrain height around the terminal. All telemetry data transmitted back by the terminals are sorted in timestamp order, duplicate data is removed, and missing telemetry data is filled in using linear interpolation. Finally, a multi-machine real-time status time-series stream is obtained, which is continuously arranged in chronological order and covers the operation status of all online terminals.

[0042] Step 5.2 involves back-mapping the actual pose data and micro-environment perception data from the multi-machine real-time state time-series stream to the original mesh nodes, and performing local elevation correction and iterative update of disturbance field parameters on the virtual space fitted surface to obtain the updated surface state. Specifically, this includes: using hierarchical terrain mesh units as the spatial reference, performing a back-mapping operation on the actual pose data and micro-environment perception data from the multi-machine real-time state time-series stream; and extracting the actual pose coordinates of the corresponding terminal for each telemetry data point in the multi-machine real-time state time-series stream. Traverse the coordinates of all nodes in the hierarchical terrain mesh. Calculate the spatial matching degree between telemetry data and grid nodes. Satisfy the calculation formula ,in The maximum spatial coverage radius of a single grid node, when the matching degree When the value exceeds a preset matching threshold, the telemetry data is mapped to the corresponding grid node, completing the spatial alignment of the telemetry data with the original grid node. Based on the telemetry data after reverse mapping, local elevation correction is performed on the virtual space fitting surface, setting the original elevation of the virtual space fitting surface at the corresponding grid node to be... The actual terrain elevation measured by the terminal in the telemetry data is The corrected grid node elevation is The elevation correction satisfies the calculation formula. ,in This is the elevation correction weighting coefficient, ranging from 0.2 to 0.5. It is dynamically adjusted based on the reliability of the telemetry data; the higher the terminal positioning accuracy and the stronger the sensor reliability, the better. The larger the value, the better it ensures that the corrected elevation matches the actual terrain undulations.

[0043] Iterative updates are performed on the disturbance field parameters of the grid nodes, setting the original disturbance field parameters of the grid nodes, such as the wind resistance attenuation coefficient and the crosswind interference offset, as follows: The actual disturbance field parameters calculated from the micro-environment sensing data in the telemetry data are: The updated perturbation field parameters are The parameter iterative update satisfies the calculation formula ,in To iteratively update the coefficients, the values ​​range from 0.6 to 0.8, ensuring smooth updates of the perturbation field parameters. The corrected elevation data of all grid nodes are then integrated with the updated perturbation field parameters across the entire domain to reconstruct the local morphology and perturbation field distribution of the virtual space fitted surface, ultimately yielding the updated surface state.

[0044] Step 5.3: Using the updated surface state and airspace environment compensation adjustment parameters as spatiotemporal constraint boundaries, perform multi-aircraft trajectory timing conflict detection calculations to complete multi-terminal timing consistency verification and obtain a conflict verification result set. Specifically, this includes: using the updated surface state and airspace environment compensation adjustment parameters as spatiotemporal constraint boundaries; the updated surface state as the spatial constraint boundary, used to limit the terrain safety range and wind disturbance range of the UAV trajectory; the airspace environment compensation adjustment parameters as the environmental constraint boundary, used to limit the environmental safety margin of the UAV trajectory; and the timing requirements of the unified operation execution sequence as the time constraint boundary, used to limit the execution time range of the UAV trajectory. Based on the spatiotemporal constraint boundaries, perform multi-aircraft trajectory timing conflict detection calculations, traversing the trajectory nodes of all online UAV terminals. For any two UAV trajectory nodes, set the three-dimensional coordinates of the first UAV's trajectory node as... The execution time of the instruction for the corresponding track node is The three-dimensional coordinates of the second drone's flight path node are: The execution time of the instruction for the corresponding track node is Calculate the three-dimensional spatial distance between two track nodes. The spatial distance satisfies the calculation formula Calculate the time interval between two track nodes. The time interval satisfies the calculation formula The preset safe distance threshold for drones is The time safety interval threshold is Execute conflict determination: if the spatial distance Less than and time interval Less than If the path node pair is determined to be a path timing conflict, then the path node pair is determined to be a path timing conflict; if only spatial distance is considered... Less than If it is only a time interval, it is determined to be a space track conflict; Less than If a conflict is detected, it is determined to be a time sequence conflict. After completing the conflict detection of all track nodes, a multi-terminal time sequence consistency check is performed to extract the actual execution timestamp of the instructions from each UAV terminal. The standard timestamp for the corresponding instruction in the unified job execution sequence is: Calculate the timing deviation of a single terminal Iterate through the timing deviations of all online terminals and calculate the average timing deviation of all terminals. The mean time series deviation satisfies the calculation formula. ,in The total number of online drone terminals, if the average time deviation is... If the time deviation exceeds the preset time deviation threshold, it is determined that the time of multiple terminals is inconsistent. All track conflict detection results and time consistency verification results are classified and integrated according to terminal number, conflict type, conflict location and conflict time period to finally obtain a conflict verification result set containing complete conflict information.

[0045] Step 5.4 involves identifying trajectory interference nodes and bandwidth contention periods through the conflict verification result set, and performing dynamic trajectory replanning and command transmission window reallocation according to emergency response priority to obtain the final orchestration command set. Specifically, this includes: performing full-domain analysis of the conflict verification result set, focusing on extracting the marked spatiotemporal conflict nodes and spatial trajectory conflict nodes, uniformly marking both types of conflict nodes as trajectory interference nodes, recording information for each trajectory interference node, clearly labeling the three-dimensional spatial coordinates of each interference node, the corresponding UAV terminal number, and the specific time interval of the interference occurrence, while also associating the corresponding terrain features and wind field disturbance parameters to provide basic data support for subsequent trajectory replanning. Simultaneously, while identifying trajectory interference nodes, the identification of communication link bandwidth contention periods is carried out, and the command transmission data volume of each online UAV terminal is extracted in real time, with the command transmission data volume of a single UAV terminal set as... ,in For the terminal number, the corresponding real-time available downlink bandwidth is: The total number of drone terminals sending commands within a single time period is [number missing]. The link bandwidth utilization rate during this period is calculated using a formula. The specific calculation formula is as follows: ,in The total amount of data sent by all terminals during this period is the sum of the data volume of the commands sent. The preset link bandwidth contention threshold is... The calculated bandwidth utilization rate With preset threshold Compare them one by one, if the bandwidth utilization rate within a certain period of time If the specified time period is determined to be a bandwidth contention period, the start and end times of the contention period, the terminal numbers of all participating terminals, the amount of data sent by each terminal during the contention period, and the type of instruction are recorded in detail.

[0046] For all identified trajectory interference nodes, using spatiotemporal constraint boundaries as the core benchmark and considering the needs of emergency forest fire prevention operations in mountainous areas, dynamic trajectory replanning is performed on UAV terminals involved in trajectory interference. This ensures that the replanned trajectory avoids interference nodes, meets environmental constraints such as terrain and wind field, and closely matches the original trajectory to the greatest extent possible, while minimizing temporal deviations. Core evaluation indicators for the replanned trajectory are set, including the spatial deviation between the replanned trajectory and the original trajectory. The time deviation between the replanned trajectory timing and the original trajectory timing At the same time, set terrain constraint weights. With time-series constraint weights Terrain constraint weight The value is greater than the time constraint weight. And satisfy The merits of each feasible replanning path are quantitatively evaluated using a cost function, the formula for which is: The smaller the cost function value, the better the replanning path. All feasible replanning paths that avoid track interference nodes are traversed, and the cost function value of each path is calculated one by one. At the same time, it is verified whether each path meets the spatiotemporal constraint boundary requirements, ensuring that all path nodes are within the safe range of the updated virtual space fitting surface, and that the wind field disturbance parameters corresponding to the path do not exceed the threshold limit of the airspace environment compensation adjustment parameters. The path with the smallest cost function value is selected as the optimal replanning trajectory. The optimal trajectory is smoothed to eliminate abrupt changes in trajectory nodes, and the trajectory update of the corresponding terminal is completed.

[0047] For identified periods of bandwidth contention, and in conjunction with emergency response priority rules, the command sending windows of all online drone terminals are dynamically reallocated to avoid command transmission delays and packet loss caused by bandwidth contention. This ensures that high-priority emergency commands are issued and executed first. Emergency response priority rules are clearly defined; considering the needs of forest fire prevention operations in mountainous areas, emergency supply airdrop terminals have a higher priority than fixed-wing wide-area patrol terminals. The priority weight for emergency supply airdrop terminals is set as follows: The priority weight of the fixed-wing cruise terminal is And satisfy , Calculate the overall priority value for each terminal. The formula for calculating the priority comprehensive value is as follows: ,in Weights for terminal task types. The urgency of the task is weighted accordingly; the higher the urgency, the higher the priority. The larger the value, the better.

[0048] The instruction sending window is allocated based on the priority composite value, and the total instruction sending window duration during the bandwidth contention period is set to [value missing]. Command sending window duration for a single terminal Satisfy the calculation formula ,in For the first The overall priority value of each terminal. The sum of the priority values ​​of all competing terminals ensures that terminals with higher priority are allocated longer window durations and complete instruction transmission first.

[0049] Simultaneously calculate the instruction transmission window offset time for each terminal. The formula for calculating the offset time is: ,in To minimize the window interval, after the reallocation is completed, the link bandwidth utilization rate for each time period is checked to ensure that the bandwidth utilization rate for all time periods is less than the preset threshold. If bandwidth contention persists, the window reallocation operation is repeated until the bandwidth constraint requirements are met. The trajectory parameters of each terminal after dynamic replanning and the parameters of the relocated instruction sending window are integrated with the instruction parameters that do not conflict. They are then arranged according to terminal number and task timing sequence. Core information such as instruction execution identifier, conflict avoidance marker, and bandwidth occupancy control parameters are added. Redundant instructions and duplicate parameters are eliminated, and the instruction format is standardized and unified to ensure that the instructions are fully compatible with the flight control communication protocols of each UAV terminal, have no timing conflicts, and have no bandwidth contention. Finally, a final orchestrated instruction set is formed that can be directly issued to each online UAV terminal and can adapt to complex mountainous environments and meet the needs of emergency operations.

[0050] In this embodiment of the invention, by employing technical means such as identifying track interference nodes and bandwidth competition periods based on conflict verification result sets, constructing a cost function based on spatiotemporal constraint boundaries to complete dynamic trajectory replanning, and redistributing command sending windows according to emergency response priorities, the technical problems of track interference easily causing flight collisions, concentrated competition for communication bandwidth leading to abnormal command transmission, and excessive spatial and temporal deviations in trajectory replanning during multi-UAV collaborative operations are overcome. This achieves safe dynamic optimization of UAV tracks and reasonable scheduling of command sending timing, ensuring priority execution of emergency tasks and improving flight safety and communication link transmission stability in multi-UAV collaborative operations.

[0051] like Figure 2 As shown, embodiments of the present invention also provide a multi-terminal unified orchestration system for unmanned aerial vehicle (UAV) operations, comprising: The acquisition module is used to acquire the initial task configuration instructions issued by the multi-source control terminal, bind the three-dimensional coordinates of the meteorological monitoring tower anemometer base, the center of the fire isolation zone calibration pile, and the centroid of the emergency material airdrop target, and obtain the multi-source environmental perception reference anchor point set; The extraction module is used to standardize the initial task configuration instructions to obtain a cross-end task semantic parsing set; based on the three-dimensional topological distribution features of the cross-end task semantic parsing set and the multi-source environmental perception benchmark anchor point set, the environmental constraint feature vector is obtained. The module is used to construct a virtual space fitting surface through environmental constraint feature vectors; perform mesh subdivision and gradient calculation based on the virtual space fitting surface to obtain compensation adjustment parameters; input the compensation adjustment parameters into the cross-end task semantic parsing set to obtain the task calibration vector set; and perform standardization processing on the task calibration vector set to obtain the standardized task intermediate model. The mapping module is used to process the standardized task intermediate model, and to branch the task calibration vector set according to the fixed-wing wide-area cruise topology and material delivery pose to obtain multi-terminal task branch flow. The multi-terminal task branch flow is combined with the target UAV terminal's exclusive flight control communication protocol to re-encode parameters and reassemble command fields to obtain a unified operation execution sequence. The verification module is used to distribute the unified job execution sequence to the corresponding UAV terminal link, update the virtual space fitted surface state based on the job status feedback data returned by the terminal, and obtain the updated surface state; combine the updated surface state with compensation adjustment parameters to perform time sequence verification and three-dimensional spatial conflict resolution on the unified job execution sequence, and obtain the final orchestration instruction set.

[0052] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0053] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0054] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0055] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-terminal unified orchestration method for unmanned aerial vehicle (UAV) operations, characterized in that, The method includes: The initial task configuration instructions issued by the multi-source control terminal are obtained, and the three-dimensional coordinates of the meteorological monitoring tower anemometer base, the fire isolation zone calibration pile center, and the centroid of the emergency material airdrop target are bound together to obtain the multi-source environmental perception reference anchor point set. The initial task configuration instructions are standardized to obtain a cross-end task semantic parsing set; based on the three-dimensional topological distribution features of the cross-end task semantic parsing set and the multi-source environmental perception benchmark anchor point set, the environmental constraint feature vector is obtained. A virtual space fitting surface is constructed using environmental constraint feature vectors; mesh partitioning and gradient calculation are performed based on the virtual space fitting surface to obtain compensation adjustment parameters; the compensation adjustment parameters are input into the cross-task semantic parsing set to obtain the task calibration vector set; the task calibration vector set is standardized to obtain the standardized task intermediate model. Using the standardized task intermediate model as the processing object, the task calibration vector set is branched and mapped according to the fixed-wing wide-area cruise topology and material delivery pose to obtain multi-terminal task branch flow. The multi-terminal task branch flow is combined with the target UAV terminal's exclusive flight control communication protocol to recode parameters and reorganize command fields to obtain a unified operation execution sequence. The unified operation execution sequence is distributed to the corresponding UAV terminal link. The virtual space fitted surface state is updated based on the operation status feedback data returned by the terminal to obtain the updated surface state. The updated surface state is combined with compensation adjustment parameters to perform time sequence verification and three-dimensional spatial conflict resolution on the unified operation execution sequence to obtain the final orchestration instruction set.

2. The multi-terminal unified orchestration method for UAV operations according to claim 1, characterized in that, The system obtains the initial task configuration instructions issued by the multi-source control terminal, binds the three-dimensional coordinates of the meteorological monitoring tower anemometer base, the center of the fire isolation zone calibration stake, and the centroid of the emergency material airdrop target, and obtains a set of multi-source environmental perception reference anchor points, including: The initial task configuration instructions carrying heterogeneous communication frames issued by the forest area management platform, emergency command center platform and on-site mobile command terminal are captured in real time through the multi-protocol access gateway, and the multi-terminal heterogeneous instruction stream is obtained by parsing. Extract the work area identifier from the multi-terminal heterogeneous instruction stream, and retrieve the measured coordinates of the fixed geographic reference entity that matches the work area identifier through the geographic information database to obtain the original coordinate set of the reference entity; The coordinates of the meteorological monitoring tower anemometer base, the center coordinates of the fire isolation zone calibration piles, and the centroid coordinates of the emergency material airdrop target in the original coordinate set of the reference entity are transformed into a unified spatial reference. This transformation is then combined with the task identifiers in the multi-terminal heterogeneous command stream to obtain a multi-source environmental perception reference anchor point set.

3. The multi-terminal unified orchestration method for UAV operations according to claim 2, characterized in that, The initial task configuration instructions are standardized to obtain a cross-platform task semantic parsing set. Based on the three-dimensional topological distribution features of the cross-end task semantic parsing set and the multi-source environmental perception benchmark anchor point set, the environmental constraint feature vector is obtained, including: The communication protocol is reverse-analyzed on the initial task configuration instructions carried by the multi-source environmental perception reference anchor points. The private link encapsulation header and redundant interactive signaling are stripped off, and the original task payload data is extracted to obtain the decapsulated task data stream. By mapping fields and normalizing units of the unencapsulated task data stream using a unified task ontology dictionary, a structured cross-platform task semantic parsing set is obtained. Using the center of the operation area defined by the cross-end task semantic parsing set as the spatial distribution benchmark, the relative spatial distance and elevation difference between each reference entity in the multi-source environmental perception benchmark anchor point set are calculated, and a three-dimensional topological adjacency matrix representing the spatial relationship of the micro-environment is constructed to obtain the spatial topological relationship matrix. The spatial topological relation matrix is ​​subjected to feature dimensionality reduction and vector serialization encoding to obtain the environmental constraint feature vector.

4. The multi-terminal unified orchestration method for UAV operations according to claim 3, characterized in that, A virtual space fitting surface is constructed using environmental constraint feature vectors; Based on the virtual space fitted surface, mesh generation and gradient calculation are performed to obtain compensation and adjustment parameters, including: Perform discrete control point continuous mapping operation on the environmental constraint feature vector, smoothly connect the spatial elevation transition zone between adjacent reference entities, and fit to generate a virtual space fitting surface that represents the relationship between terrain undulation and spatial association of reference entities. The elevation gradient change rate is calculated on the fitted surface in the virtual space to obtain the gradient change rate; the steeply changing regions with gradient change rates exceeding a preset threshold are projected onto the horizontal reference plane and recursively subdivided to obtain hierarchical terrain grid cells. The hierarchical terrain grid units are aligned with the three-dimensional spatial coordinates and overlaid with the preset mountain wind field model. The wind resistance attenuation coefficient and crosswind interference offset at each grid node are extracted and weighted according to the grid topology adjacency relationship to obtain the airspace environment compensation and adjustment parameters.

5. The multi-terminal unified orchestration method for UAV operations according to claim 4, characterized in that, The compensation adjustment parameters are input into the cross-task semantic parsing set to obtain the task calibration vector set; The task calibration vector set is standardized to obtain a standardized intermediate task model, including: Tensor splicing is performed on the airspace environment compensation adjustment parameters and the target waypoint coordinates in the cross-end task semantic parsing set. Iterative extrapolation is performed along the trajectory normal and tangential to obtain the dynamic safe flight envelope. The local curvature extrema and curvature gradient distribution of the surface of the dynamic safe flight envelope are extracted, and the local curvature extrema and curvature gradient distribution are analyzed as control adjustment coefficients characterizing the strength of flight path maneuver constraints and the amplitude of airflow disturbance. Based on the control adjustment coefficient, nonlinear position offset compensation is performed on the spatial coordinate mapping matrix, and the tolerance threshold is dynamically redistributed simultaneously on the flight control attitude reference to obtain the mission calibration vector set. Using the three-dimensional envelope nodes represented by the task calibration vector set as the source of topology mapping, the shortest path connectivity and state transition cost of adjacent envelope nodes are calculated; a hierarchical instruction dependency network is constructed based on the minimum connected path with the state transition cost to obtain a standardized task intermediate model.

6. The multi-terminal unified orchestration method for UAV operations according to claim 5, characterized in that, Using a standardized intermediate task model as the processing object, the task calibration vector set is branched and mapped according to the fixed-wing wide-area cruise topology and material delivery pose to obtain a multi-terminal task branch flow. This multi-terminal task branch flow is then combined with the target UAV terminal's dedicated flight control communication protocol for parameter recoding and command field reassembly to obtain a unified operation execution sequence, including: The task calibration vector set is extracted by parsing the intermediate model of the standardized task. The task calibration vector set is then mapped to the fixed-wing wide-area cruise topology branch and the material delivery pose branch, respectively, to obtain the multi-terminal adaptation task branch flow. Based on the multi-terminal adaptation task branch flow, the parameters of the target UAV terminal dedicated flight control communication protocol dictionary for each branch are re-encoded to perform track coordinate dimension conversion, payload control command bit alignment and communication timing mark, so as to obtain protocol-compatible command data packets. The protocol-compatible instruction data packets are reorganized into instruction fields and action dependency chains according to the target terminal's flight control underlying execution cycle and task priority scheduling rules to obtain a unified job execution sequence.

7. The multi-terminal unified orchestration method for UAV operations according to claim 6, characterized in that, The unified operation execution sequence is distributed to the corresponding UAV terminal link, and the virtual space fitted surface state is updated based on the operation status feedback data returned by the terminal to obtain the updated surface state. By combining the updated surface state with compensation adjustment parameters, timing verification and three-dimensional spatial conflict resolution are performed on the unified job execution sequence to obtain the final orchestration instruction set, including: The unified operation execution sequence is input into the dynamic routing gateway and distributed to the corresponding UAV terminal downlink according to the terminal online status and communication link quality. The telemetry operation status feedback data returned by each terminal is captured and parsed in real time to obtain the real-time status time stream of multiple UAVs. The actual pose data and micro-environment perception data in the real-time state time stream of the multi-machine are back-mapped to the original mesh nodes. Local elevation correction and disturbance field parameter iterative update are performed on the virtual space fitted surface to obtain the updated surface state. The updated surface state and the airspace environment compensation and adjustment parameters are used as spatiotemporal constraint boundaries. Multi-aircraft trajectory timing conflict detection calculation is performed to complete multi-terminal timing consistency verification and obtain the conflict verification result set. By identifying track interference nodes and bandwidth contention periods through the conflict verification result set, dynamic trajectory replanning and command transmission window reallocation are performed according to emergency response priority to obtain the final orchestration command set.

8. A multi-terminal unified orchestration system for unmanned aerial vehicle (UAV) operations, the system implementing the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire the initial task configuration instructions issued by the multi-source control terminal, bind the three-dimensional coordinates of the meteorological monitoring tower anemometer base, the center of the fire isolation zone calibration pile, and the centroid of the emergency material airdrop target, and obtain the multi-source environmental perception reference anchor point set; The extraction module is used to standardize the initial task configuration instructions to obtain a cross-end task semantic parsing set; based on the three-dimensional topological distribution features of the cross-end task semantic parsing set and the multi-source environmental perception benchmark anchor point set, the environmental constraint feature vector is obtained. The module is used to construct a virtual space fitting surface through environmental constraint feature vectors; perform mesh subdivision and gradient calculation based on the virtual space fitting surface to obtain compensation adjustment parameters; input the compensation adjustment parameters into the cross-end task semantic parsing set to obtain the task calibration vector set; The task calibration vector set is standardized to obtain a standardized intermediate task model. The mapping module is used to process the standardized task intermediate model, and to branch the task calibration vector set according to the fixed-wing wide-area cruise topology and material delivery pose to obtain multi-terminal task branch flow. The multi-terminal task branch flow is combined with the target UAV terminal's exclusive flight control communication protocol to re-encode parameters and reassemble command fields to obtain a unified operation execution sequence. The verification module is used to distribute the unified job execution sequence to the corresponding UAV terminal link, update the virtual space fitted surface state based on the job status feedback data returned by the terminal, and obtain the updated surface state; combine the updated surface state with compensation adjustment parameters to perform time sequence verification and three-dimensional spatial conflict resolution on the unified job execution sequence, and obtain the final orchestration instruction set.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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