Unmanned aerial vehicle cluster dynamic topology communication network construction method and system

Abstract

The invention provides an unmanned aerial vehicle cluster dynamic topology communication network construction method and system. The method comprises the following steps: acquiring real-time spatial position information and environment obstacle distribution information of each unmanned aerial vehicle; predicting the change trend of the real-time space position information and the environment obstacle distribution information, generating a space trajectory prediction result between the unmanned aerial vehicles, inputting the space trajectory prediction result into a preset path decision unit to predict a collision risk, and generating an optimized flight path instruction; based on the change event of the optimized flight path instruction and a preset communication link quality parameter, screening out stable relay nodes from a preset relay node set, and generating a relay node update list meeting a preset stability condition; and performing topology reconstruction on the communication connection relationship between the nodes of the unmanned aerial vehicle cluster to generate a communication topology reconstruction instruction. According to the invention, cross-domain cooperation of physical path obstacle avoidance and communication topology stability of the unmanned aerial vehicle cluster in a high-speed motion scene is realized, and the cluster anti-interference capability and task continuity in a dynamic environment are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication network construction, and in particular to a method and system for constructing a dynamic topology communication network for an unmanned aerial vehicle (UAV) cluster. Background Art

[0002] With the increasing application of drone swarms in complex dynamic environments, maintaining a stable and efficient communication network during high-speed movement, frequent formation changes, or obstacle avoidance has become a key technical challenge. That is, drones need to share data in real time, maintain network connectivity, and quickly adapt to changes in topology under constantly changing flight conditions. This requires the communication network to be highly adaptable and robust to cope with problems such as high-speed node movement, link instability, and environmental interference.

[0003] The current mainstream approach is a communication topology control method based on a combination of distributed self-organizing networks and reinforcement learning. By deploying a lightweight reinforcement learning model on each drone node, it enables autonomous decision-making on optimal connection targets and forwarding paths. Distributed protocols are used to enable rapid discovery and topology maintenance between nodes, thereby improving the adaptability and overall stability of the communication network without the need for central control. Existing solutions have several inherent flaws, including a reliance on local information for decision-making, which can easily lead to local communication bottlenecks or redundant connections. Furthermore, due to a lack of integration between flight path trends and communication needs, communication topology updates lag behind flight status changes, resulting in frequent link disconnections or costly reconstructions, impacting overall system performance and mission execution efficiency. Summary of the Invention

[0004] The present invention provides a method and system for constructing a dynamic topology communication network for a swarm of unmanned aerial vehicles (UAVs), aiming to address the problems in the prior art of relying on local information for decision-making, which can easily lead to local communication bottlenecks or redundant connections. Furthermore, due to the lack of sufficient integration of the relationship between flight path change trends and communication needs, communication topology updates lag behind flight status changes, resulting in frequent link breakage or excessively high reconstruction costs, affecting the overall system performance and mission execution efficiency.

[0005] In a first aspect, the present invention provides a method for constructing a dynamic topology communication network for a drone cluster, comprising:

[0006] Obtain the real-time spatial location information and environmental obstacle distribution information of each drone in the preset drone cluster;

[0007] Predicting the change trends of the real-time spatial position information and the environmental obstacle distribution information to generate spatial trajectory prediction results between the UAVs;

[0008] Inputting the spatial trajectory prediction result into a preset path decision unit to predict collision risk and generate optimized flight path instructions;

[0009] Based on the change event of the optimized flight path instruction and the preset communication link quality parameter, screening stable relay nodes from a preset set of relay nodes to generate an updated list of relay nodes that meet a preset stability condition;

[0010] Based on the relay node update list, the communication connection relationship between the nodes of the preset drone cluster is determined, and the topology of the communication connection relationship between the nodes is topologically reconstructed to generate a communication topology reconstruction instruction.

[0011] Optionally, obtaining the real-time spatial position information and environmental obstacle distribution information of each drone in a preset drone cluster includes:

[0012] Acquire the original spatial positioning signal of each drone in the preset drone cluster according to the preset positioning signal source, combine the original spatial positioning signals, and generate multi-source fusion positioning data;

[0013] Convert the position coordinates of the multi-source fusion positioning data based on the preset coordinate system of the drone cluster to generate real-time spatial position information of each drone;

[0014] Obstacle detection signals are acquired according to a preset environment perception signal source, and spatial mapping is performed on the obstacle detection signals to generate environmental obstacle distribution information of each UAV.

[0015] Optionally, predicting the change trend of the real-time spatial position information and the environmental obstacle distribution information to generate a spatial trajectory prediction result between the UAVs includes:

[0016] Calculating the continuous position differences of the real-time spatial position information and synthesizing the displacement direction of the real-time spatial position information to generate the spatial displacement vector of each UAV;

[0017] Analyzing the motion direction angle of the environmental obstacle distribution information and generating obstacle motion impact parameters based on a preset motion interference risk level;

[0018] Inputting the spatial displacement vector and the obstacle motion influencing parameter into a preset trajectory prediction unit, and superimposing the vector components of the spatial displacement vector and the obstacle motion influencing parameter in the preset trajectory prediction unit to generate a superposition result;

[0019] The offset path of the superposition result is corrected to generate a spatial trajectory prediction result between the UAVs.

[0020] Optionally, the spatial trajectory prediction result is input into a preset path decision unit to predict the collision risk and generate an optimized flight path instruction, including:

[0021] In the preset path decision unit, the trajectory point distance of the spatial trajectory prediction result is calculated, the coordinates of the nearest obstacle of the spatial trajectory prediction result are marked, and a safety distance parameter is generated;

[0022] Comparing the safety distance parameter with a preset safety distance threshold, performing discretization processing on the comparison result, and generating a risk quantification parameter;

[0023] determining an obstacle avoidance direction of the risk quantification parameter, and calculating a required displacement of the risk quantification parameter to generate an original offset vector;

[0024] constraining the kinematic performance and steering boundaries of the original offset vector to generate a path adjustment instruction;

[0025] Verify the spatial coordinate range of the path adjustment instruction, perform conflict detection on the cluster motion of the path adjustment instruction, and generate an optimized flight path instruction.

[0026] Optionally, based on the change event of the optimized flight path instruction and a preset communication link quality parameter, stable relay nodes are screened from a preset relay node set to generate an updated list of relay nodes that meet a preset stability condition, including:

[0027] extracting a change amplitude parameter from the optimized flight path instruction;

[0028] Associating and mapping the change amplitude parameter with a preset communication link quality attenuation threshold to generate a dynamic quality threshold value;

[0029] Screening a preset set of relay nodes, retaining relay nodes whose preset communication link quality parameters are higher than the dynamic quality threshold value, to generate a target relay node set;

[0030] The relative motion speed difference between each node in the target relay node set and the drone among the drones that has undergone path change is calculated, and the nodes whose relative motion speed difference is lower than the preset motion tolerance are retained to generate an updated relay node list that meets the preset stability condition.

[0031] Optionally, associating and mapping the change amplitude parameter with a preset communication link quality attenuation threshold to generate a dynamic quality threshold value includes:

[0032] Dividing the continuous values ​​of the change amplitude parameter based on preset amplitude interval boundaries to generate discrete amplitude interval identifiers;

[0033] Based on a preset threshold matching rule, obtaining a basic quality threshold value of the discrete amplitude interval identifier;

[0034] Extracting the amplitude value difference of the time series data in the change amplitude parameter, quantizing the rate of the time series data, and generating a change rate parameter;

[0035] Inputting the change rate parameter into a preset compensation coefficient conversion function for conversion to generate a dynamic compensation coefficient;

[0036] The basic quality threshold value, the dynamic compensation coefficient and the preset weight coefficient are superimposed and calculated to generate a dynamic quality threshold value.

[0037] Optionally, based on the relay node update list, determining the communication connection relationship between nodes of the preset drone cluster, and performing topology reconstruction on the communication connection relationship between nodes to generate a communication topology reconstruction instruction, including:

[0038] Performing reachability analysis on the distances between nodes in the relay node update list based on a preset signal propagation model to generate a communication reachability relationship table;

[0039] Screening the links in the communication reachability relationship table and retaining the links that meet the preset stability conditions to generate a stable communication link set;

[0040] Verifying the path connectivity of each link in the stable communication link set, eliminating redundant links that do not affect the path connectivity, and generating a minimum connectivity topology structure;

[0041] Detecting a change in the real-time spatial position information and generating a node position change event;

[0042] The minimum connectivity topology structure is load-balanced adjusted to respond to the node position change event in real time, the minimum connectivity topology structure is reconstructed, and a communication topology reconstructing instruction is generated.

[0043] In a second aspect, the present invention provides a system for constructing a dynamic topology communication network for a drone cluster, comprising:

[0044] The acquisition module is used to obtain the real-time spatial position information and environmental obstacle distribution information of each drone in the preset drone cluster;

[0045] A prediction module, configured to predict the changing trends of the real-time spatial position information and the environmental obstacle distribution information, so as to generate spatial trajectory prediction results between the UAVs;

[0046] An input module, configured to input the spatial trajectory prediction result into a preset path decision unit to predict collision risk and generate optimized flight path instructions;

[0047] a screening module, configured to screen stable relay nodes from a preset set of relay nodes based on the change event of the optimized flight path instruction and a preset communication link quality parameter, so as to generate an updated list of relay nodes that meet a preset stability condition;

[0048] A reconstruction module is used to determine the communication connection relationship between nodes of the preset drone cluster based on the relay node update list, and to topologically reconstruct the communication connection relationship between the nodes to generate a communication topology reconstruction instruction.

[0049] In a third aspect, the present invention provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a method for constructing a dynamic topology communication network for a drone cluster as described in any one of the first aspects.

[0050] In a fourth aspect, the present invention provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a method for constructing a dynamic topology communication network for a drone cluster as described in any one of the first aspects.

[0051] The present invention obtains the real-time spatial position of the UAV cluster and the distribution information of environmental obstacles, predicts the collaborative movement trend of multiple machines to generate a spatial trajectory, and then predicts the collision risk through the path decision unit to output optimized flight path instructions; based on path change events and communication quality parameters, stable relay nodes are dynamically screened to generate an update list, and finally the communication connection relationship between nodes is reconstructed to generate topology reconstruction instructions, so as to realize the cross-domain collaboration of physical path obstacle avoidance and communication topology stability of the UAV cluster in high-speed motion scenarios, and significantly improve the cluster's anti-interference ability and mission continuity in dynamic environments.

[0052] Furthermore, high-precision spatial positioning data is generated by fusing multi-source positioning signals, and the consistency of position information is improved by combining cluster coordinate system conversion. The obstacle detection signal is simultaneously mapped into environmental distribution information to build a global perception data foundation, providing centimeter-level spatial benchmarks and real-time obstacle dynamic modeling capabilities for motion trajectory prediction, fundamentally ensuring the accuracy and environmental adaptability of subsequent path decisions and topology reconstruction.

[0053] These and other aspects of the present invention will become more readily apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A flowchart of a method for constructing a dynamic topology communication network for a drone cluster provided by an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of the structure of a system for constructing a dynamic topology communication network for a drone cluster provided by an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0059] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] Figure 1 The present invention provides a flowchart of a method for constructing a dynamic topology communication network for a drone cluster. Figure 1 As shown, the method includes:

[0062] When drones are flying in formation at high speed or performing emergency obstacle avoidance in dynamic cluster motion scenarios, traditional communication network construction methods have three drawbacks: the disconnect between environmental obstacle perception and flight path decision-making leads to delayed obstacle avoidance response; the disconnect between physical path changes and communication topology adjustments leads to link breakage; and the static relay node allocation mechanism cannot adapt to the Doppler frequency shift and signal obstruction caused by high-speed motion. These problems cause the communication interruption rate in cluster collaborative tasks to remain high, seriously restricting the reliability of operations in complex environments. To address these problems, the research and development ideas of the present invention are as follows: establishing a closed-loop coupling mechanism of environmental perception and motion prediction, generating spatial trajectory predictions through the collaborative processing of real-time spatial position and obstacle distribution information, driving the path decision unit to output flight instructions that take into account both obstacle avoidance and communication needs; innovating the cross-domain response architecture of physical change events and communication parameters, converting the path adjustment amount into a dynamic communication quality threshold, and realizing real-time stability screening of relay nodes; and finally, a topology reconstruction mechanism based on the connection relationship between nodes ensures adaptive adjustment of network topology under high-speed motion. This method breaks the decision-making barriers between the physical layer and the communication layer from the bottom layer, forming a three-level collaborative system of environmental perception, path planning, and topology reconstruction, fundamentally solving the problem of communication continuity in dynamic scenarios. Based on this, the present invention provides a method for constructing a dynamic topology communication network for drone clusters, such as Figure 1 ,include:

[0063] Step 101: Obtain the real-time spatial position information and environmental obstacle distribution information of each drone in a preset drone cluster.

[0064] In this step, real-time spatial position information refers to the three-dimensional coordinate data of the drone generated by multi-source positioning data fusion and coordinate system conversion, including longitude, latitude, altitude and timestamp, which is used to characterize the instantaneous spatial state of the cluster; environmental obstacle distribution information refers to the spatial mapping result based on the obstacle detection signal, including the position coordinates, movement direction and size parameters of the obstacle, which is used to construct a dynamic environment model.

[0065] In an embodiment of the present invention, the original spatial positioning signal of each drone in the drone cluster is first collected through a preset positioning signal source, and then the multi-source signal is fused to generate multi-source fusion positioning data. The spatial coordinates of the data are then converted based on a preset cluster coordinate system. Finally, the obstacle detection signal is obtained in combination with a preset environmental perception signal source and environmental obstacle distribution information is generated through spatial mapping, forming a complete data set of real-time spatial position information and environmental obstacle distribution information.

[0066] Step 102: Predicting the change trends of the real-time spatial position information and the environmental obstacle distribution information to generate spatial trajectory prediction results between the UAVs.

[0067] In this step, the prediction operation refers to the process of processing data through trajectory point difference calculation and motion direction angle analysis, and deducing motion trends based on the mechanical model; the spatial trajectory prediction result refers to the vector set reflecting the future motion path of the cluster, including the predicted position sequence and relative motion relationship of each drone.

[0068] In an embodiment of the present invention, continuous trajectory point difference calculation is first performed on the real-time spatial position information, and then the motion direction angle analysis is performed on the environmental obstacle distribution information. The calculation results are then input into a preset trajectory prediction model, and finally, a spatial trajectory prediction result reflecting the cluster collaborative motion trend is generated through vector superposition correction.

[0069] Step 103: Input the spatial trajectory prediction result into a preset path decision unit to predict the collision risk and generate an optimized flight path instruction.

[0070] In this step, the preset path decision unit refers to a processor embedded with a collision risk assessment algorithm and a motion constraint verification module, which is used to generate obstacle avoidance path instructions; the optimized flight path instruction refers to a three-dimensional trajectory control command output after safety margin calculation, risk level classification and feasibility verification.

[0071] In an embodiment of the present invention, the spatial trajectory prediction result is first input into a preset path decision unit. Secondly, the safety margin of each trajectory point is calculated through the collision risk assessment algorithm in the unit. Then, the path deviation instruction is generated according to the preset risk level classification rules. Finally, the feasibility of the instruction is verified in combination with the UAV motion performance constraints, and the optimized flight path instruction is output.

[0072] Step 104: Based on the change event of the optimized flight path instruction and the preset communication link quality parameter, stable relay nodes are screened out from the preset relay node set to generate an updated list of relay nodes that meet the preset stability conditions.

[0073] In this step, the change event refers to the state where the displacement or angle change of the optimized flight path instruction relative to the previous instruction exceeds the preset threshold; the preset communication link quality parameter refers to a set of communication performance indicators including signal strength, bit error rate and transmission delay; the preset relay node set refers to a candidate list consisting of all drone nodes with data forwarding capabilities in the cluster; a stable relay node refers to a node that simultaneously meets the conditions that the communication quality is higher than the dynamic threshold value and the relative speed difference is lower than the motion tolerance; the preset stability condition refers to a comprehensive screening standard that integrates the communication link quality threshold and the motion compatibility indicator; the relay node update list refers to an ordered set of stable relay nodes generated through multi-level screening.

[0074] In an embodiment of the present invention, a change amplitude parameter of the optimized flight path instruction is first extracted. Secondly, the parameter is associated with a preset communication link quality attenuation threshold to generate a dynamic quality threshold value. Subsequently, a communication quality screening is performed on a preset set of relay nodes to retain qualified nodes. Finally, the relative motion speed difference between the node and the path-changing UAV is calculated and the nodes that exceed the preset motion tolerance are filtered out to generate an updated list of relay nodes.

[0075] Step 105: Based on the relay node update list, determine the communication connection relationship between the nodes of the preset drone cluster, and perform topology reconstruction on the communication connection relationship between the nodes to generate a communication topology reconstruction instruction.

[0076] In this step, the communication connection relationship between nodes refers to the node-to-link logical mapping table established based on communication reachability analysis and stability verification; the topology reconstruction operation refers to the network structure optimization process that performs connectivity verification, redundant link elimination and load balancing adjustment; the communication topology reconstruction instruction refers to the control instruction set that contains the new topology node connection relationship and implementation timestamp.

[0077] In an embodiment of the present invention, first, based on the relay node update list, the node distance calculation and signal propagation model analysis are performed to generate a communication reachability relationship table. Secondly, the preset stability conditions are applied to filter the links to generate a stable communication link set. Then, the connectivity of all nodes is verified and redundant links are eliminated to build a minimum connectivity topology structure. Finally, in response to the real-time location change event, load balancing adjustment is performed and a communication topology reconstruction instruction is output.

[0078] For example, the real-time three-dimensional coordinates of the drone swarm are first acquired through the fusion of the global navigation satellite system and visual sensors, and a spatial distribution map of obstacles is simultaneously constructed using lidar detection signals. Next, motion vectors are synthesized between the position data and obstacle information to generate a prediction of the cluster's coordinated flight trajectory for the next five seconds. The predicted trajectory is then input into the path decision unit, which calculates the minimum distance between each drone and the obstacle, classifies the risk level, and outputs a heading adjustment instruction with obstacle avoidance constraints. The communication quality screening threshold is then dynamically increased based on the heading change angle, retaining relay nodes from the candidate nodes with qualified signal strength and stable relative speed. Finally, the communication accessibility between nodes is analyzed, redundant links are removed to form a tree topology, and communication load is redistributed in response to position offset events, generating instructions for updating the network-wide topology.

[0079] The embodiments of the present invention achieve accurate generation of obstacle avoidance paths through closed-loop coupling of environmental perception and motion prediction, and combine cross-domain mapping of physical change events to communication parameters to ensure the reliability of dynamic screening of relay nodes. Finally, based on the load optimization and real-time reconstruction mechanism of the minimum connectivity topology, the anti-interference capability, communication continuity and mission execution robustness of the drone cluster in high-speed motion scenarios are significantly improved.

[0080] To address the issue of multi-source positioning signal fusion and environmental obstacle perception information generation, this step generates the real-time UAV position and environmental obstacle distribution information through signal combination, coordinate conversion, and spatial mapping based on the positioning and environmental perception signal sources. The present invention provides a specific embodiment, step 101, obtaining the real-time spatial position information and environmental obstacle distribution information of each UAV in a preset UAV cluster, specifically including the following steps:

[0081] Step 111: obtaining the original spatial positioning signal of each UAV in the preset UAV cluster according to the preset positioning signal source, combining the original spatial positioning signals, and generating multi-source fusion positioning data.

[0082] In this step, the preset positioning signal source refers to the hardware module that integrates the global navigation satellite receiver and the visual sensor, which is used to synchronously obtain the satellite ranging data and the coordinates of the image feature points; the original spatial positioning signal refers to the original measurement data set including the satellite pseudorange observation values ​​and the visual feature pixel coordinates, which reflects the initial spatial state of the UAV; the combination operation refers to the process of eliminating the signal transmission delay through timestamp alignment and fusing heterogeneous data using the weighted least squares method; the multi-source fused positioning data refers to the structured data set containing the fused three-dimensional coordinates, positioning precision factor and data source identifier.

[0083] In an embodiment of the present invention, first, a preset positioning signal source is used to synchronously collect global navigation satellite system signals and visual positioning data as original spatial positioning signals. Secondly, time alignment and confidence weighted processing are performed on the two types of signals. Subsequently, a spatial geometric constraint algorithm is used to fuse multi-source data. Finally, multi-source fused positioning data containing three-dimensional coordinates and accuracy indicators are generated.

[0084] Step 112: Convert the position coordinates of the multi-source fusion positioning data based on the preset coordinate system of the drone cluster to generate real-time spatial position information of each drone.

[0085] In this step, the conversion operation refers to the process of mapping the global coordinate system coordinates to the local coordinate system with the cluster center as the origin based on the homogeneous coordinate transformation matrix.

[0086] In an embodiment of the present invention, the preset local coordinate system parameters of the drone cluster are first read, and then the global coordinate system coordinate values ​​in the multi-source fusion positioning data are extracted. Then, the coordinate system rotation and translation calculation is performed to convert the longitude and latitude coordinates into local rectangular coordinates, and finally, real-time spatial position information with a timestamp is generated.

[0087] Step 113: Obtain obstacle detection signals according to a preset environment perception signal source, perform spatial mapping on the obstacle detection signals, and generate environmental obstacle distribution information of each UAV.

[0088] In this step, the preset environmental perception signal source refers to the detection equipment equipped with lidar and millimeter-wave radar, which is used to transmit detection beams and receive obstacle reflection signals; the obstacle detection signal refers to the original detection data set containing echo intensity, flight time and Doppler frequency shift; the spatial mapping operation refers to the process of converting the detection signal into a three-dimensional model of the obstacle in the local coordinate system through coordinate transformation and point cloud registration.

[0089] In an embodiment of the present invention, a laser detection pulse is first emitted by a preset environmental perception signal source and the reflected signal is received as an obstacle detection signal. Secondly, noise filtering and point cloud clustering processing are performed on the original signal. The clustering result is then mapped to the local coordinate system of the drone cluster. Finally, environmental obstacle distribution information including obstacle position, size and motion vector is generated.

[0090] The embodiments of the present invention improve positioning accuracy through spatiotemporal alignment and confidence fusion of multi-source positioning signals, establish a unified spatial reference in combination with global to local coordinate transformation, and simultaneously achieve accurate mapping of obstacle detection signals to environmental models, providing centimeter-level spatial perception capabilities for cluster motion decision-making.

[0091] To solve the problem of predicting the spatial trajectory of drones based on position changes and obstacle impact, this step generates a spatial trajectory prediction result by calculating the spatial displacement vector, analyzing obstacle impact parameters, and performing vector superposition and path correction. The present invention provides a specific embodiment, step 102, predicting the changing trends of the real-time spatial position information and the environmental obstacle distribution information to generate the spatial trajectory prediction results between the drones, specifically including the following steps:

[0092] Step 201: Calculate the continuous position differences of the real-time spatial position information and synthesize the displacement direction of the real-time spatial position information to generate the spatial displacement vector of each UAV.

[0093] In this step, continuous position difference refers to the set of scalar differences in the three-dimensional coordinates of the drone at adjacent moments, including east-west difference, north-south difference, and vertical difference; synthesis operation refers to the process of determining the motion vector through vector modulus calculation and direction cosine, integrating displacement and direction angle; displacement direction refers to the unit direction vector composed of displacement components of the three coordinate axes, which represents the instantaneous motion direction of the drone; spatial displacement vector refers to the motion state vector that integrates displacement, direction, and speed information, and is used for trajectory prediction input.

[0094] In an embodiment of the present invention, first, the real-time spatial position information of continuous moments is obtained, and the scalar difference of the coordinates of adjacent moments is calculated. Then, a vector synthesis operation is performed on the difference of the three coordinate axes to determine the direction of movement. Finally, the displacement amount and direction are integrated to generate a spatial displacement vector containing a velocity vector.

[0095] Step 202: Analyze the motion direction angle of the environmental obstacle distribution information, and generate obstacle motion impact parameters based on a preset motion interference risk level.

[0096] In this step, the motion direction angle refers to the spatial angle difference between the obstacle motion vector and the drone motion vector, reflecting the degree of motion conflict; the analytical operation refers to the geometric processing process of calculating the cosine value through the vector dot product formula and then inferring the angle; the preset motion interference risk level refers to the discrete risk index divided according to the angle range, where 0-30° is a high risk level, 30-60° is a medium risk level, and 60-90° is a low risk level; the obstacle motion impact parameter refers to the quantified risk level value, with the high risk level mapped to a positive value and the low risk level mapped to a negative value.

[0097] In an embodiment of the present invention, the geometric angle between the obstacle movement direction and the drone movement direction in the environmental obstacle distribution information is first analyzed, and then the preset motion interference risk level mapping table is queried according to the size of the angle, and finally the risk level is digitized into an obstacle motion impact parameter.

[0098] Step 203: Input the spatial displacement vector and the obstacle motion influencing parameter into a preset trajectory prediction unit, and superimpose the vector components of the spatial displacement vector and the obstacle motion influencing parameter in the preset trajectory prediction unit to generate a superposition result.

[0099] In this step, the preset trajectory prediction unit refers to a processor embedded in a vector operation engine, which is used to perform trajectory simulation calculations; the vector component refers to the conversion of the risk parameter into an offset vector whose direction is perpendicular to the direction of movement of the obstacle; the superposition operation refers to the vector addition operation of the spatial displacement vector and the offset vector; the superposition result refers to the preliminary predicted trajectory containing the original motion trend and the obstacle avoidance offset.

[0100] In an embodiment of the present invention, the spatial displacement vector is first input into a preset trajectory prediction unit, then the vector components of the obstacle motion influencing parameters are extracted, and then a vector addition operation is performed to generate a superposition result, and finally the calculation result is retained in the prediction unit memory.

[0101] Step 204: Correct the offset path of the superposition result to generate a spatial trajectory prediction result between the UAVs.

[0102] In this step, the offset path refers to the predicted segment in the superposition result that intersects with the spatial position of the obstacle; the correction operation refers to the collision avoidance processing of translating the path point along the normal direction of the obstacle surface.

[0103] In an embodiment of the present invention, a potential collision path with an obstacle in the superposition result is first detected as an offset path, then an avoidance direction correction calculation is performed based on the obstacle position, and finally a corrected spatial trajectory prediction result is output.

[0104] The embodiment of the present invention establishes a motion base state model through the precise synthesis of displacement vectors, generates an offset vector based on the quantitative conversion of obstacle motion interference risks, and realizes dynamic obstacle avoidance trajectory prediction through vector superposition and collision path correction, thereby significantly improving the safety of cluster collaborative flight in complex environments.

[0105] To address the problem of assessing collision risk based on predicted trajectories and generating safe, optimized flight instructions, this step generates optimized flight path instructions by calculating safe distances, quantifying risk, determining obstacle avoidance vectors, constraining performance boundaries, and performing collision detection. The present invention provides a specific embodiment, step 103, in which the spatial trajectory prediction results are input into a pre-set path decision unit to predict collision risk and generate optimized flight path instructions, specifically comprising the following steps:

[0106] Step 301: In the preset path decision unit, the trajectory point distance of the spatial trajectory prediction result is calculated, the coordinates of the nearest obstacle of the spatial trajectory prediction result are marked, and a safety distance parameter is generated.

[0107] In this step, the trajectory point distance refers to the three-dimensional Euclidean distance between each predicted position point in the spatial trajectory prediction result and the nearest obstacle surface, reflecting the collision risk; the nearest obstacle coordinates refer to the three-dimensional coordinates of the geometric center of the obstacle closest to the specific trajectory point determined by the spatial index algorithm; the safety distance parameter refers to structured data containing the minimum distance value and its corresponding obstacle number, which is used for risk assessment input.

[0108] In an embodiment of the present invention, the Euclidean distance between each trajectory point and the obstacle in the spatial trajectory prediction result is first calculated in a preset path decision unit. Then, the coordinates of the nearest obstacle are identified and its spatial position is marked. Finally, a safety distance parameter including the minimum distance value and the corresponding obstacle identifier is generated.

[0109] Step 302: Compare the safety distance parameter with a preset safety distance threshold, discretize the comparison result, and generate a risk quantification parameter.

[0110] In this step, the preset safety distance threshold refers to the multi-level warning distance value set according to the size and movement speed of the drone, including the warning threshold and the emergency avoidance threshold; the comparison result refers to the difference between the safety distance parameter and the safety distance threshold and the status identification of the risk interval to which it belongs; discretization processing refers to the operation of mapping continuous distance differences into preset discrete levels, including low risk, medium risk, and high risk; the risk quantification parameter refers to the numerical risk indicator generated by discretization processing, with high risk corresponding to positive values ​​and low risk corresponding to negative values.

[0111] In an embodiment of the present invention, the safety distance parameter is first compared with a preset safety distance threshold, then discrete risk levels are divided according to the interval to which the comparison result belongs, and finally the risk levels are digitized into risk quantification parameters.

[0112] Step 303: Determine the obstacle avoidance direction of the risk quantification parameter, and calculate the required displacement of the risk quantification parameter to generate an original offset vector.

[0113] In this step, the obstacle avoidance direction refers to the unit vector pointing from the drone's position to the opposite direction of the obstacle surface normal, which is determined by the highest risk item; the required displacement refers to the normal avoidance distance calculated proportionally according to the risk quantification parameter value, with high risk corresponding to large displacement; the original offset vector refers to the three-dimensional motion correction vector that integrates the obstacle avoidance direction and displacement.

[0114] In an embodiment of the present invention, the obstacle avoidance direction is first determined based on the highest risk item of the risk quantification parameter, and then the displacement perpendicular to the obstacle surface is calculated according to the risk value. Finally, the direction and displacement are integrated to generate the original offset vector.

[0115] Step 304: Constrain the motion performance and steering boundaries of the original offset vector to generate a path adjustment instruction.

[0116] In this step, the constraint operation refers to the clipping calculation that limits the direction and size of the original offset vector to the range of the UAV's physical motion capabilities; the motion performance refers to the UAV's physical motion limit parameters including the maximum steering angle and maximum acceleration; the steering boundary refers to the three-dimensional spatial geometric constraint body formed by the preset airspace range; the path adjustment instruction refers to the heading correction instruction set generated by the dual constraints of motion performance and airspace boundaries.

[0117] In an embodiment of the present invention, the maximum steering angle parameter of the UAV is first read as a motion performance constraint, then the airspace boundary data is obtained as a steering boundary, and finally, dual-constraint clipping is performed on the original offset vector to generate a path adjustment instruction.

[0118] Step 305: Verify the spatial coordinate range of the path adjustment instruction, perform conflict detection on the cluster motion of the path adjustment instruction, and generate an optimized flight path instruction.

[0119] In this step, the verification operation refers to the verification process of whether the detection instruction complies with the airspace rules and cluster collaborative safety; the airspace coordinate range refers to the pre-defined three-dimensional boundary coordinate set of the drone's flight area; the cluster motion refers to the motion status data set of other drones at the current moment; the conflict detection operation refers to the calculation of the spatial intersection analysis of the adjusted path and the cluster motion path.

[0120] In an embodiment of the present invention, first, it is verified whether the path adjustment instruction exceeds the preset airspace coordinate range, then the spatial conflict between the instruction and the motion paths of other drones in the cluster is detected, and finally, the optimized flight path instruction is generated through conflict avoidance processing.

[0121] The embodiment of the present invention achieves precise obstacle avoidance decision-making through trajectory point risk quantification and obstacle avoidance vector generation mechanism, combines the dual constraints of motion performance and airspace boundaries to ensure the feasibility of instructions, and finally outputs a safe and reliable optimized flight path through airspace compliance verification and cluster conflict detection, significantly improving the success rate of cluster obstacle avoidance in complex dynamic environments.

[0122] To address the problem of selecting stable relay nodes based on flight path changes and link quality, this step generates an updated relay node list by extracting the change magnitude, associating a mapping quality threshold, selecting high-quality nodes, and evaluating relative motion stability. The present invention provides a specific embodiment, step 104, based on the change event of the optimized flight path instruction and the preset communication link quality parameter, selects stable relay nodes from a preset relay node set to generate an updated relay node list that meets the preset stability conditions, specifically including the following steps:

[0123] Step 401: Extracting a change amplitude parameter from the optimized flight path instruction.

[0124] In this step, the change amplitude parameter refers to the weighted sum of the absolute value of the heading angle change and the absolute value of the displacement distance change in the optimized flight path instruction, which is used to quantify the path adjustment strength.

[0125] In an embodiment of the present invention, the heading angle change and the displacement distance change in the optimized flight path instruction are first analyzed, and then the absolute values ​​of the two are extracted as quantitative indicators of the path change. Finally, a change amplitude parameter including the angle change and the distance change is generated.

[0126] Step 402: Associatively mapping the change amplitude parameter with a preset communication link quality attenuation threshold to generate a dynamic quality threshold.

[0127] In this step, the preset communication link quality attenuation threshold refers to the benchmark signal attenuation value table established based on the wireless channel model, which contains reference values ​​for signal strength loss in different frequency bands; the association mapping operation refers to the process of converting the change amplitude parameter into a quality attenuation compensation amount through a linear scaling function, and then adding it to the basic attenuation value; the dynamic quality threshold value refers to the communication quality screening threshold that is adjusted in real time with the path change amplitude, and the threshold increases when the change amplitude increases.

[0128] In an embodiment of the present invention, a preset communication link quality attenuation threshold table is first queried to obtain a basic attenuation value, then the change amplitude parameter is scaled by a preset proportional coefficient and added to the basic attenuation value, and finally a dynamic quality threshold value is generated that is dynamically adjusted according to the path change intensity.

[0129] Step 403: Screen the preset relay node set, and retain relay nodes whose preset communication link quality parameters are higher than the dynamic quality threshold value to generate a target relay node set.

[0130] In this step, the screening operation refers to the process of comparing the signal strength, delay parameters and dynamic quality threshold value of each node and performing Boolean logic judgment; the target relay node set refers to the subset of candidate nodes generated by quality screening, which is a group of potential relay nodes that meet the current communication quality requirements.

[0131] In an embodiment of the present invention, the real-time signal strength and delay parameters of each node in a preset relay node set are first obtained, and then the communication link quality parameters are compared with the dynamic quality threshold value node by node, and finally all nodes that meet the parameters are retained to generate a target relay node set.

[0132] Step 404: Calculate the relative motion speed difference between each node in the target relay node set and the drone among the drones that have undergone path changes, and retain the nodes whose relative motion speed difference is lower than the preset motion tolerance to generate a relay node update list that meets the preset stability conditions.

[0133] In this step, the relative motion speed difference refers to the Euclidean modulus of the velocity vector difference between the node and the path-changing drone, reflecting the intensity of the relative motion between the two. The preset motion tolerance refers to the maximum relative speed threshold to ensure reliable communication. Exceeding this value will cause Doppler frequency shift and lead to signal distortion.

[0134] In an embodiment of the present invention, the velocity vector difference modulus between each node in the target relay node set and the path-changing drone is first calculated, and then the modulus is compared with a preset motion tolerance threshold. Finally, the nodes below the threshold are filtered and retained to generate a relay node update list.

[0135] The embodiment of the present invention realizes cross-domain parameter coordination through dynamic mapping of path change amplitude to communication quality threshold, and combines the dual filtering mechanism of node communication quality initial screening and motion compatibility fine screening to ensure the signal stability and motion adaptability of relay nodes in high-speed motion scenarios, and significantly reduce the probability of topology oscillation.

[0136] To address the problem of dynamically calculating the communication link quality threshold based on the path change amplitude, this step divides the amplitude into intervals to obtain a basic threshold, quantizes the change rate to generate a compensation coefficient, and performs a superposition operation to generate a dynamic quality threshold. The present invention provides a specific embodiment, step 402, which associates and maps the change amplitude parameter with a preset communication link quality attenuation threshold to generate a dynamic quality threshold, specifically including the following steps:

[0137] Step 421: Divide the continuous values ​​of the change amplitude parameter based on preset amplitude interval boundaries to generate discrete amplitude interval identifiers.

[0138] In this step, the preset amplitude interval boundary refers to a set of thresholds divided according to the intensity of path change, including angle change boundaries and displacement change boundaries, which are used for discrete classification; the continuous value refers to the original continuous numerical value of the heading angle change and displacement change calculated in real time in the optimized flight path instruction; the discrete amplitude interval identifier refers to the discrete classification label generated by boundary division, such as "low change interval", "medium change interval", and "high change interval".

[0139] In an embodiment of the present invention, firstly, a preset amplitude interval boundary parameter is read as a basis for division, then the continuous value of the change amplitude parameter is cut into discrete intervals according to the boundary value, and finally a unique identifier is assigned to each interval to generate a discrete amplitude interval identifier.

[0140] Step 422: Based on a preset threshold matching rule, obtain a basic quality threshold value of the discrete amplitude interval identifier.

[0141] In this step, the preset threshold matching rule refers to a lookup table storing a mapping relationship between discrete identifiers and basic communication quality threshold values; the basic quality threshold value refers to a basic communication signal strength threshold value corresponding to a specific change interval.

[0142] In the embodiment of the present invention, firstly, a preset threshold matching rule table is loaded, then the corresponding basic quality threshold value is searched according to the discrete amplitude interval identifier, and finally the value is output to the dynamic quality calculation process.

[0143] Step 423: extract the amplitude value difference of the time series data in the change amplitude parameter, quantize the rate of the time series data, and generate a change rate parameter.

[0144] In this step, time series data refers to the historical record sequence of change amplitude parameters collected continuously according to the decision cycle; amplitude value difference refers to the arithmetic difference of the change amplitude parameters in adjacent cycles, reflecting the short-term change; quantization processing refers to the calculation process of converting the differential value by the length of the decision cycle into the change rate; the change rate parameter refers to the rate of change of the change amplitude per unit time, which is used to evaluate the degree of motion mutation.

[0145] In an embodiment of the present invention, the historical time series data of the change amplitude parameter is first extracted, and then the difference in amplitude values ​​at adjacent time points is calculated. The difference is then divided by the decision cycle length for rate quantification, and finally a change rate parameter reflecting the severity of the change is generated.

[0146] Step 424: Input the change rate parameter into a preset compensation coefficient conversion function for conversion to generate a dynamic compensation coefficient.

[0147] In this step, the preset compensation coefficient conversion function refers to a linear function that maps the change rate to the compensation coefficient, in the form of a coefficient equal to the rate multiplied by the proportional factor; the conversion operation refers to the calculation process of mapping the input parameter to the output value through a mathematical function; the dynamic compensation coefficient refers to the compensation weight value generated according to the change rate, and the higher the rate, the larger the coefficient.

[0148] In the embodiment of the present invention, firstly, a preset compensation coefficient conversion function is called, then the change rate parameter is input into the function to perform a linear conversion calculation, and finally a dynamic compensation coefficient positively correlated with the rate is output.

[0149] Step 425: Perform a superposition operation on the basic quality threshold value, the dynamic compensation coefficient, and the preset weight coefficient to generate a dynamic quality threshold value.

[0150] In this step, the preset weight coefficient refers to the weighted ratio parameter of the basic threshold value and the compensation coefficient, which is used to balance static and dynamic factors; the superposition operation refers to the mathematical calculation of weighted summation performed according to the weight coefficient to generate the final threshold value.

[0151] In the embodiment of the present invention, a basic quality threshold value and a dynamic compensation coefficient are first obtained, and then a weighted sum operation is performed according to a preset weight coefficient, and finally a dynamic quality threshold value integrating a static reference and dynamic compensation is generated.

[0152] The embodiment of the present invention establishes a static reference threshold through discretization of amplitude intervals, and realizes adaptive adjustment of the quality threshold by combining a dynamic compensation mechanism of the change rate, effectively responding to sudden changes in the communication environment during high-speed movement, and improving the accuracy and environmental adaptability of relay node screening.

[0153] To solve the problem of building an efficient and stable communication topology based on the updated relay node list, this step screens stable links through reachability analysis, verifies connectivity to eliminate redundancy, builds a minimum topology, and load balances responses to location changes to generate communication topology reconstruction instructions. The present invention provides a specific embodiment, step 105, based on the updated relay node list, determines the communication connection relationship between the nodes of the preset drone cluster, and performs topological reconstruction on the communication connection relationship between the nodes to generate a communication topology reconstruction instruction, specifically including the following steps:

[0154] Step 501: Perform reachability analysis on the distances between nodes in the relay node update list based on a preset signal propagation model to generate a communication reachability relationship table.

[0155] In this step, the preset signal propagation model refers to a mathematical model of radio wave propagation that includes a free space path loss formula and multipath attenuation parameters, and is used to predict signal strength at a specific distance; the reachability analysis operation refers to a technical process of determining the feasibility of communication by calculating whether the received signal strength is higher than the receiver sensitivity threshold; the communication reachability relationship table refers to a two-dimensional matrix that records the communication reachability status between node pairs, and the matrix elements are Boolean values ​​indicating whether they are reachable or not.

[0156] In an embodiment of the present invention, the path loss calculation formula in the preset signal propagation model is first loaded, and then the spatial straight-line distance of all node pairs in the relay node update list is calculated. Then, the distance value is substituted into the model to calculate the received signal strength, and finally, the reachability is judged according to the receiving sensitivity threshold to generate a communication reachability relationship table.

[0157] Step 502: Filter the links in the communication reachability relationship table, and retain the links that meet the preset stability conditions to generate a stable communication link set.

[0158] In this step, the stable communication link set refers to a link subset generated by screening through preset stability conditions, including reliable links with signal strength greater than negative eighty dBm and delay less than twenty milliseconds.

[0159] In an embodiment of the present invention, the link data in the communication reachability relationship table is first read, and then the preset stability conditions are applied to compare the signal strength and delay parameters of the link, and then the links that meet both the lower strength limit and the upper delay limit are screened, and finally a stable communication link set is generated.

[0160] Step 503: Verify the path connectivity of each link in the stable communication link set, eliminate redundant links that do not affect the path connectivity, and generate a minimum connectivity topology structure.

[0161] In this step, path connectivity refers to the network property that there is at least one communication path between any two nodes in the topology structure; redundant links refer to non-essential communication links that do not affect the path connectivity of the entire network after removal; and the minimum connectivity topology structure refers to the optimized topology that contains the minimum number of links while maintaining full network connectivity.

[0162] In an embodiment of the present invention, a fully connected graph model of a stable communication link set is first constructed, then it is verified whether a communication path exists between any two nodes, then redundant links that do not affect the connectivity of the entire graph are identified and removed, and finally the minimum connected topology structure is output.

[0163] Step 504: Detect the change in the real-time spatial position information and generate a node position change event.

[0164] In this step, the change in real-time spatial position information refers to the Euclidean distance difference between the three-dimensional coordinates of the same drone at adjacent moments, reflecting the displacement amplitude; the node position change event refers to a structured alarm event triggered when the change exceeds the preset threshold, including the node number and displacement vector.

[0165] In an embodiment of the present invention, first, the real-time spatial position information of two consecutive frames is obtained, then the Euclidean distance change of the same drone coordinate is calculated, and then the change is compared with the preset displacement threshold, and finally a position change event is generated for the node exceeding the threshold.

[0166] Step 505: performing load balancing adjustment on the minimum connectivity topology structure to respond to the node position change event in real time, reconstructing the minimum connectivity topology structure, and generating a communication topology reconstructing instruction.

[0167] In this step, the load balancing adjustment operation refers to the calculation process of dynamically optimizing link allocation based on node data throughput to avoid local congestion; the reconstruction operation refers to the calculation process of regenerating topological connection relationships based on location change events and load status.

[0168] In an embodiment of the present invention, the data load rate of each node in the minimum connectivity topology is first analyzed, and then the communication links are reallocated according to the load balancing strategy. Then, the connection relationship of the affected nodes is adjusted in response to the location change event, and finally a communication topology reconstruction instruction is generated.

[0169] The embodiment of the present invention accurately determines the feasibility of communication between nodes through a signal propagation model, combines stability condition screening and connectivity optimization to build a lightweight topology, responds to position changes in real time to trigger load-aware dynamic reconstruction, and significantly improves the robustness and resource utilization of drone cluster networks in high-speed motion scenarios.

[0170] Figure 2The present invention provides a structural diagram of a system for constructing a dynamic topology communication network for a drone cluster. Figure 2 As shown, the system includes:

[0171] An acquisition module 21 is used to obtain the real-time spatial position information and environmental obstacle distribution information of each drone in a preset drone cluster;

[0172] A prediction module 22 is configured to predict the changing trends of the real-time spatial position information and the environmental obstacle distribution information to generate spatial trajectory prediction results between the UAVs;

[0173] An input module 23 is used to input the spatial trajectory prediction result into a preset path decision unit to predict the collision risk and generate an optimized flight path instruction;

[0174] a screening module 24 configured to screen stable relay nodes from a preset set of relay nodes based on the change event of the optimized flight path instruction and a preset communication link quality parameter, so as to generate an updated list of relay nodes that meet a preset stability condition;

[0175] The reconstruction module 25 is used to determine the communication connection relationship between the nodes of the preset drone cluster based on the relay node update list, and topologically reconstruct the communication connection relationship between the nodes to generate a communication topology reconstruction instruction.

[0176] Figure 2 The UAV cluster dynamic topology communication network construction system can execute Figure 1 The implementation principle and technical effects of the method for constructing a dynamic topology communication network for a drone swarm described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the system for constructing a dynamic topology communication network for a drone swarm in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0177] In one possible design, Figure 2 A system for constructing a dynamic topology communication network for a drone cluster in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0178] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0179] The processing component 32 is used to: obtain the real-time spatial position information and environmental obstacle distribution information of each drone in a preset drone cluster; predict the change trend of the real-time spatial position information and the environmental obstacle distribution information to generate a spatial trajectory prediction result between the drones; input the spatial trajectory prediction result into a preset path decision unit to predict the collision risk and generate an optimized flight path instruction; based on the change event of the optimized flight path instruction and the preset communication link quality parameter, screen out stable relay nodes from a preset relay node set to generate a relay node update list that meets the preset stability conditions; based on the relay node update list, determine the communication connection relationship between the nodes of the preset drone cluster, and perform topological reconstruction on the communication connection relationship between the nodes to generate a communication topology reconstruction instruction.

[0180] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0181] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0182] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0183] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0184] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0185] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0186] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 A method for constructing a dynamic topology communication network for a drone cluster according to the illustrated embodiment.

[0187] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0188] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0189] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for constructing a dynamic topology communication network for a drone cluster, characterized in that: include: Obtain the real-time spatial location information and environmental obstacle distribution information of each drone in the preset drone cluster; Predicting the change trends of the real-time spatial position information and the environmental obstacle distribution information to generate spatial trajectory prediction results between the UAVs; Inputting the spatial trajectory prediction result into a preset path decision unit to predict collision risk and generate optimized flight path instructions; Based on the change event of the optimized flight path instruction and the preset communication link quality parameter, screening stable relay nodes from a preset set of relay nodes to generate an updated list of relay nodes that meet a preset stability condition; Based on the relay node update list, the communication connection relationship between the nodes of the preset drone cluster is determined, and the topology of the communication connection relationship between the nodes is topologically reconstructed to generate a communication topology reconstruction instruction.

2. The method according to claim 1, characterized in that Obtain the real-time spatial location information and environmental obstacle distribution information of each drone in the preset drone cluster, including: Acquire the original spatial positioning signal of each drone in the preset drone cluster according to the preset positioning signal source, combine the original spatial positioning signals, and generate multi-source fusion positioning data; Convert the position coordinates of the multi-source fusion positioning data based on the preset coordinate system of the drone cluster to generate real-time spatial position information of each drone; Obstacle detection signals are acquired according to a preset environment perception signal source, and spatial mapping is performed on the obstacle detection signals to generate environmental obstacle distribution information of each UAV.

3. The method according to claim 1, characterized in that Predicting the change trend of the real-time spatial position information and the environmental obstacle distribution information to generate a spatial trajectory prediction result between the UAVs, including: Calculating the continuous position differences of the real-time spatial position information and synthesizing the displacement direction of the real-time spatial position information to generate the spatial displacement vector of each UAV; Analyzing the motion direction angle of the environmental obstacle distribution information and generating obstacle motion impact parameters based on a preset motion interference risk level; Inputting the spatial displacement vector and the obstacle motion influencing parameter into a preset trajectory prediction unit, and superimposing the vector components of the spatial displacement vector and the obstacle motion influencing parameter in the preset trajectory prediction unit to generate a superposition result; The offset path of the superposition result is corrected to generate a spatial trajectory prediction result between the UAVs.

4. The method according to claim 1, wherein The spatial trajectory prediction result is input into a preset path decision unit to predict the collision risk and generate optimized flight path instructions, including: In the preset path decision unit, the trajectory point distance of the spatial trajectory prediction result is calculated, the coordinates of the nearest obstacle of the spatial trajectory prediction result are marked, and a safety distance parameter is generated; Comparing the safety distance parameter with a preset safety distance threshold, performing discretization processing on the comparison result, and generating a risk quantification parameter; determining an obstacle avoidance direction of the risk quantification parameter, and calculating a required displacement of the risk quantification parameter to generate an original offset vector; constraining the kinematic performance and steering boundaries of the original offset vector to generate a path adjustment instruction; Verify the spatial coordinate range of the path adjustment instruction, perform conflict detection on the cluster motion of the path adjustment instruction, and generate an optimized flight path instruction.

5. The method according to claim 1, wherein Based on the change event of the optimized flight path instruction and the preset communication link quality parameter, stable relay nodes are screened from a preset relay node set to generate an updated list of relay nodes that meet the preset stability condition, including: extracting a change amplitude parameter from the optimized flight path instruction; Associating and mapping the change amplitude parameter with a preset communication link quality attenuation threshold to generate a dynamic quality threshold; Screening a preset relay node set, retaining relay nodes whose preset communication link quality parameters are higher than the dynamic quality threshold value, to generate a target relay node set; The relative motion speed difference between each node in the target relay node set and the drone among the drones that has undergone path change is calculated, and the nodes whose relative motion speed difference is lower than the preset motion tolerance are retained to generate an updated relay node list that meets the preset stability condition.

6. The method according to claim 5, characterized in that Associating and mapping the change amplitude parameter with a preset communication link quality attenuation threshold to generate a dynamic quality threshold value includes: Dividing the continuous values ​​of the change amplitude parameter based on preset amplitude interval boundaries to generate discrete amplitude interval identifiers; Based on a preset threshold matching rule, obtaining a basic quality threshold value of the discrete amplitude interval identifier; Extracting the amplitude value difference of the time series data in the change amplitude parameter, quantizing the rate of the time series data, and generating a change rate parameter; Inputting the change rate parameter into a preset compensation coefficient conversion function for conversion to generate a dynamic compensation coefficient; The basic quality threshold value, the dynamic compensation coefficient and the preset weight coefficient are superimposed and calculated to generate a dynamic quality threshold value.

7. The method according to claim 1, characterized in that Based on the relay node update list, determining the communication connection relationship between nodes of the preset drone cluster, and performing topology reconstruction on the communication connection relationship between nodes to generate a communication topology reconstruction instruction, including: Performing reachability analysis on the distances between nodes in the relay node update list based on a preset signal propagation model to generate a communication reachability relationship table; Screening the links in the communication reachability relationship table and retaining the links that meet the preset stability conditions to generate a stable communication link set; Verifying the path connectivity of each link in the stable communication link set, eliminating redundant links that do not affect the path connectivity, and generating a minimum connectivity topology structure; Detecting a change in the real-time spatial position information and generating a node position change event; The minimum connectivity topology structure is load-balanced adjusted to respond to the node position change event in real time, the minimum connectivity topology structure is reconstructed, and a communication topology reconstructing instruction is generated.

8. A system for constructing a dynamic topology communication network for drone clusters, characterized in that: include: The acquisition module is used to obtain the real-time spatial position information and environmental obstacle distribution information of each drone in the preset drone cluster; A prediction module, configured to predict the changing trends of the real-time spatial position information and the environmental obstacle distribution information, so as to generate spatial trajectory prediction results between the UAVs; An input module, configured to input the spatial trajectory prediction result into a preset path decision unit to predict collision risk and generate optimized flight path instructions; a screening module, configured to screen stable relay nodes from a preset set of relay nodes based on the change event of the optimized flight path instruction and a preset communication link quality parameter, so as to generate an updated list of relay nodes that meet a preset stability condition; A reconstruction module is used to determine the communication connection relationship between nodes of the preset drone cluster based on the relay node update list, and to topologically reconstruct the communication connection relationship between the nodes to generate a communication topology reconstruction instruction.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for constructing a dynamic topology communication network for a drone cluster as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a method for constructing a dynamic topology communication network of a drone cluster is implemented as described in any one of claims 1 to 7.

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