A method and system for constructing a dynamic topology communication network of a drone cluster

Abstract

The application provides a kind of unmanned aerial vehicle cluster dynamic topology communication network construction method and system, obtain the real-time spatial position information and environmental barrier distribution information of each unmanned aerial vehicle;Predict the change trend of real-time spatial position information and environmental barrier distribution information, generate the spatial trajectory prediction result between each unmanned aerial vehicle, input it into the preset path decision unit to predict the collision risk, generate the optimized flight path instruction;Based on the change event of optimized flight path instruction and the preset communication link quality parameter, from the preset relay node set, stable relay node is screened out, and a relay node update list that meets the preset stability condition is generated;Topology reconstruction is carried out on the inter-node communication connection relationship of unmanned aerial vehicle cluster, and a communication topology reconstruction instruction is generated.The application realizes the cross-domain cooperation of physical path obstacle avoidance and communication topology stability of unmanned aerial vehicle cluster in high-speed motion scene, significantly improves the cluster anti-interference ability and task continuity in dynamic environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication network construction, and in particular to a method and system for constructing a dynamic topology communication network of a UAV cluster. BACKGROUND

[0002] With the increasing application of UAV clusters in complex dynamic environments, how to maintain a stable and efficient communication network during high-speed movement, frequent formation transformation or obstacle avoidance has become a key technical challenge, that is, UAVs need to share data in real time, maintain network connectivity, and quickly adapt to changes in topology structure under changing flight conditions, which requires the communication network to have high adaptability and robustness to deal with problems such as high-speed movement of nodes, unstable links, and environmental interference.

[0003] The current mainstream solution is a communication topology control method based on the combination of distributed self-organizing networks and reinforcement learning, which deploys a lightweight reinforcement learning model on each UAV node to enable it to autonomously decide the optimal connection object and forwarding path; and uses a distributed protocol to achieve fast discovery and topology maintenance between nodes, thereby improving the adaptability and overall stability of the communication network without the need for central node control. The existing solution has some inherent defects, including its reliance on local information for decision-making, which can easily lead to local communication bottlenecks or redundant connections; and the lack of sufficient combination of flight path change trends and communication demand correlations, which leads to communication topology updates lagging behind changes in flight conditions, causing frequent link breaks or high reconstruction costs, and affecting the overall performance and task execution efficiency of the system. SUMMARY

[0004] The present application provides a method and system for constructing a dynamic topology communication network of a UAV cluster to solve the problems of relying on local information for decision-making, which can easily lead to local communication bottlenecks or redundant connections; and the lack of sufficient combination of flight path change trends and communication demand correlations, which leads to communication topology updates lagging behind changes in flight conditions, causing frequent link breaks or high reconstruction costs, and affecting the overall performance and task execution efficiency of the system.

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

[0006] obtaining real-time spatial position information and environmental obstacle distribution information of each UAV in a preset UAV cluster;

[0007] 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;

[0008] input the space trajectory prediction result into a preset path decision unit to predict a collision risk and generate an optimized flight path instruction;

[0009] based on a change event of the optimized flight path instruction and a preset communication link quality parameter, screen stable relay nodes from a preset relay node set to generate a relay node update list meeting a preset stability condition;

[0010] based on the relay node update list, determine an inter-node communication connection relationship of the preset UAV cluster, and perform topology reconstruction on the inter-node communication connection relationship to generate a communication topology reconstruction instruction.

[0011] Optionally, real-time space position information and environmental obstacle distribution information of each UAV in the preset UAV cluster are acquired, including:

[0012] The original space positioning signals of each UAV in the preset UAV cluster are acquired according to a preset positioning signal source, the original space positioning signals are combined, and multi-source fusion positioning data are generated;

[0013] The position coordinates of the multi-source fusion positioning data are converted based on a coordinate system of the preset UAV cluster, and real-time space position information of the each UAV is generated;

[0014] Obstacle detection signals are acquired according to a preset environmental perception signal source, the obstacle detection signals are spatially mapped, and environmental obstacle distribution information of the each UAV is generated.

[0015] Optionally, a change trend of the real-time space position information and the environmental obstacle distribution information is predicted to generate space trajectory prediction results between the each UAV, including:

[0016] A continuous position difference of the real-time space position information is calculated, and a displacement direction of the real-time space position information is synthesized to generate a space displacement vector of the each UAV;

[0017] A motion direction included angle of the environmental obstacle distribution information is analyzed, and an obstacle motion influence parameter is generated based on a preset motion interference risk level;

[0018] The space displacement vector and the obstacle motion influence parameter are input into a preset trajectory prediction unit, vector components of the space displacement vector and the obstacle motion influence parameter are superimposed in the preset trajectory prediction unit, and a superimposition result is generated;

[0019] An offset path of the superimposition result is corrected, and space trajectory prediction results between the each UAV are generated.

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

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

[0022] The safety distance parameter is compared with a preset safety distance threshold, a comparison result is discretized, and a risk quantization parameter is generated;

[0023] An obstacle avoidance direction of the risk quantization parameter is determined, and a required displacement amount of the risk quantization parameter is calculated to generate an original offset vector;

[0024] Motion performance and turning boundaries of the original offset vector are constrained to generate a path adjustment instruction;

[0025] A spatial coordinate range of the path adjustment instruction is checked, and a cluster motion of the path adjustment instruction is collision detected to generate an optimized flight path instruction.

[0026] Optionally, based on a change event of the optimized flight path instruction and a preset communication link quality parameter, a stable relay node is selected from a preset relay node set to generate a relay node update list satisfying a preset stability condition, including:

[0027] A change amplitude parameter is extracted from the optimized flight path instruction;

[0028] The change amplitude parameter is associatedly mapped with a preset communication link quality decay threshold to generate a dynamic quality threshold value;

[0029] The preset relay node set is selected, and a relay node with a preset communication link quality parameter higher than the dynamic quality threshold value is retained to generate a target relay node set;

[0030] A relative motion speed difference between each node in the target relay node set and a UAV in which a path change occurs among the UAVs is calculated, and a node with a relative motion speed difference lower than a preset motion tolerance is retained to generate a relay node update list satisfying a preset stability condition.

[0031] Optionally, the change amplitude parameter is associatedly mapped with a preset communication link quality decay threshold to generate a dynamic quality threshold value, including:

[0032] Based on a preset amplitude interval boundary, continuous values of the change amplitude parameter are divided to generate discrete amplitude interval identifiers;

[0033] The base quality threshold value of the discrete amplitude interval identifier is obtained based on a preset threshold matching rule;

[0034] The amplitude value difference of the time sequence data in the change amplitude parameter is extracted, the rate of the time sequence data is quantized, and a change rate parameter is generated;

[0035] The change rate parameter is input into a preset compensation coefficient conversion function for conversion, and a dynamic compensation coefficient is generated;

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

[0037] Optionally, based on the relay node update list, a node-to-node communication connection relationship of the preset unmanned aerial vehicle cluster is determined, and the node-to-node communication connection relationship is topologically reconstructed to generate a communication topology reconstruction instruction, including:

[0038] Based on a preset signal propagation model, the node-to-node distance of the relay node update list is analyzed for reachability to generate a communication reachability table;

[0039] The links in the communication reachability table are screened, and the links meeting a preset stability condition are retained to generate a stable communication link set;

[0040] The path connectivity of each link in the stable communication link set is verified, and redundant links that do not affect the path connectivity are removed to generate a minimum connected topology structure;

[0041] The change amount of the real-time spatial position information is detected to generate a node position change event;

[0042] The minimum connected topology structure is adjusted for load balancing to respond to the node position change event in real time, and the minimum connected topology structure is reconstructed to generate a communication topology reconstruction instruction.

[0043] In a second aspect, the present application provides an unmanned aerial vehicle cluster dynamic topology communication network construction system, comprising:

[0044] An acquisition module is configured to acquire real-time spatial position information and environmental obstacle distribution information of each unmanned aerial vehicle in a preset unmanned aerial vehicle cluster;

[0045] A prediction module is configured to 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 unmanned aerial vehicles;

[0046] An input module is configured to input the spatial trajectory prediction result into a preset path decision unit to predict a collision risk and generate an optimized flight path instruction.

[0047] The screening module is configured to screen stable relay nodes from the preset relay node set based on the change event of the optimized flight path instruction and a preset communication link quality parameter, to generate a relay node update list meeting a preset stability condition.

[0048] The reconstruction module is configured to determine an inter-node communication connection relationship of the preset UAV cluster based on the relay node update list, and perform topological reconstruction on the inter-node communication connection relationship, to generate a communication topology reconstruction instruction.

[0049] In a third aspect, the present application 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 perform the method for constructing a dynamic topology communication network of a UAV cluster according to any one of the first aspect.

[0050] In a fourth aspect, the present application provides a computer storage medium storing computer program instructions, wherein the computer program instructions are executed by a processor to implement the method for constructing a dynamic topology communication network of a UAV cluster according to any one of the first aspect.

[0051] The present application can obtain real-time spatial position and environmental obstacle distribution information of a UAV cluster, predict a multi-UAV cooperative motion trend to generate a spatial trajectory, and then output an optimized flight path instruction through a path decision unit based on collision risk prediction. The present application can dynamically screen stable relay nodes based on a path change event and a communication quality parameter to generate an update list, and finally reconstruct an inter-node communication connection relationship to generate a topology reconstruction instruction, thereby realizing cross-domain cooperation of physical path obstacle avoidance and communication topology stability of a UAV cluster in a high-speed motion scenario, and significantly improving the anti-interference ability and task continuity of the cluster in a dynamic environment.

[0052] Further, high-precision spatial positioning data can be generated by fusing multiple positioning signals, and position information consistency can be improved by combining cluster coordinate system conversion. Obstacle detection signals can be simultaneously mapped into environmental distribution information to construct a global perception data base, thereby providing a centimeter-level spatial reference and real-time obstacle dynamic modeling capability for motion trajectory prediction, and fundamentally ensuring the accuracy and environmental adaptability of subsequent path decision and topology reconstruction.

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

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only some of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative labor are within the scope of protection of the present application.

[0055] Figure 1 A flow chart of a method for constructing a dynamic topology communication network of a UAV cluster is provided for the embodiments of the present application.

[0056] Figure 2 A structural schematic diagram of a system for constructing a dynamic topology communication network of a UAV cluster is provided for the embodiments of the present application.

[0057] Figure 3 A structural schematic diagram of a computing device is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0058] In order to enable a person skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application.

[0059] In some of the descriptions in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed in the order appearing in this text or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence. Also, "first" and "second" are not different types.

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative labor are within the scope of protection of the present application.

[0061] Figure 1 A flow chart of a method for constructing a dynamic topology communication network of a UAV cluster is provided for the embodiments of the present application, as shown in Figure 1 The method comprises:

[0062] For the high-speed formation flight or emergency obstacle avoidance of unmanned aerial vehicle in dynamic cluster motion scene, the traditional communication network construction method has three defects: the separation of environment obstacle awareness and flight path decision causes obstacle avoidance response delay, the disconnection of physical path change and communication topology adjustment causes link breakage, and the static relay node allocation mechanism cannot adapt to the Doppler shift and signal shielding caused by high-speed motion. These problems cause high communication interruption rate in cluster cooperative task, which seriously restricts the operation reliability in complex environment. In view of these problems, the research and development idea of the application is: a closed-loop coupling mechanism of environment awareness and motion prediction is established, the spatial trajectory prediction is generated through the cooperative processing of real-time spatial position and obstacle distribution information, and the flight instruction considering obstacle avoidance and communication demand is output by the path decision unit; an innovative cross-domain response architecture of physical change event and communication parameters is formed, the path adjustment amount is converted into dynamic communication quality threshold, and the real-time stability screening of relay nodes is realized; finally, based on the topology reconstruction mechanism of the connection relationship between nodes, the adaptive adjustment of network topology under high-speed motion is ensured. This method breaks the decision barrier between physical layer and communication layer from the bottom, forms a three-level cooperative system of environment awareness, path planning and topology reconstruction, and fundamentally solves the problem of communication continuity guarantee in dynamic scene. Based on this, the application provides a dynamic topology communication network construction method for unmanned aerial vehicle cluster, as shown in Figure 1 , comprising:

[0063] Step 101: obtaining real-time spatial position information and environment obstacle distribution information of each unmanned aerial vehicle in a preset unmanned aerial vehicle cluster.

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

[0065] In the embodiment of the application, first, the original spatial positioning signal of each unmanned aerial vehicle in the unmanned aerial vehicle cluster is collected through a preset positioning signal source, second, multi-source signal fusion processing is performed to generate multi-source fusion positioning data, then, the spatial coordinates of the data are converted based on a preset cluster coordinate system, and finally, the obstacle detection signal is obtained by combining a preset environment awareness signal source and the spatial mapping is performed to generate the environment obstacle distribution information, forming a complete data set of real-time spatial position information and environment obstacle distribution information.

[0066] Step 102: predicting the trend of the real-time spatial position information and the environment obstacle distribution information to generate the spatial trajectory prediction result between the unmanned aerial vehicles.

[0067] In this step, the prediction operation refers to the process of calculating and analyzing the trajectory point difference and the motion direction angle, and inferring the motion trend 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 of each UAV and the relative motion relationship.

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

[0069] Step 103: input the spatial trajectory prediction result into the preset path decision unit to predict the collision risk and generate the 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 an obstacle avoidance path instruction; the optimized flight path instruction refers to a three-dimensional track control command output after safety margin calculation, risk level division and feasibility verification.

[0071] In the embodiment of the present application, first, the spatial trajectory prediction result is input into the preset path decision unit, second, the safety margin of each trajectory point is calculated by the collision risk assessment algorithm in the unit, then the path offset instruction is generated according to the preset risk level division rule, and finally the feasibility of the optimized flight path instruction is verified in combination with the motion performance constraint, 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, screen out stable relay nodes from the preset relay node set to generate a relay node update list meeting the preset stability condition.

[0073] In this step, the change event refers to the state that 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 composed of all UAV nodes with data forwarding capability in the cluster; the stable relay node refers to a node that meets the conditions of communication quality higher than the dynamic threshold and relative speed difference lower than the motion tolerance; the preset stability condition refers to a comprehensive screening standard integrating 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 by multi-level screening.

[0074] In the embodiment of the present application, first, the change amplitude parameter of the optimized flight path instruction is extracted, second, the parameter is associated with the preset communication link quality decay threshold to generate a dynamic quality threshold value, then, the preset relay node set is screened for communication quality to retain qualified nodes, finally, the relative motion speed difference between the nodes and the path change unmanned aerial vehicle is calculated and the nodes exceeding the preset motion tolerance are filtered to generate a relay node update list.

[0075] Step 105: Based on the relay node update list, the inter-node communication connection relationship of the preset unmanned aerial vehicle cluster is determined, the inter-node communication connection relationship is topologically reconstructed to generate a communication topology reconstruction instruction.

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

[0077] In the embodiment of the present application, first, the inter-node distance calculation and signal propagation model analysis are performed based on the relay node update list to generate a communication reachability table, second, the preset stability condition is applied to screen links to generate a stable communication link set, then, the full node connectivity is verified and redundant links are eliminated to construct a minimum connected topology structure, and finally, the load balancing adjustment is performed in response to real-time position change events, and a communication topology reconstruction instruction is output.

[0078] For example, first, the real-time three-dimensional coordinates of the unmanned aerial vehicle cluster are obtained by fusing the global navigation satellite system and the visual sensor, and the obstacle space distribution map is constructed by using the laser radar detection signal. Second, the motion vector is synthesized for the position data and the obstacle information to generate a five-second cluster cooperative flight trajectory prediction. Then, the predicted trajectory is input into the path decision unit, the minimum distance between each unmanned aerial vehicle and the obstacle is calculated, and the risk level is divided, and the heading adjustment instruction with obstacle avoidance constraint is output. Next, the communication quality screening threshold is dynamically improved according to the heading change angle, and the relay nodes with qualified signal strength and stable relative speed are retained from the candidate nodes. Finally, the inter-node communication reachability is analyzed, the redundant links are removed to form a tree-shaped topology, the communication load is redistributed in response to the position offset event, and a full-network topology update instruction is generated.

[0079] The embodiment of the present application realizes accurate obstacle avoidance path generation through the closed-loop coupling of environment perception and motion prediction, guarantees the reliability of dynamic screening of relay nodes through cross-domain mapping of physical change events to communication parameters, and finally realizes load optimization and real-time reconstruction mechanism based on the minimum connected topology, which significantly improves the anti-interference ability, communication continuity and task execution robustness of the unmanned aerial vehicle cluster in the high-speed motion scene.

[0080] In order to solve the problem of multi-source positioning signal fusion and environmental obstacle sensing information generation, according to the positioning and environmental sensing signal source, the real-time position and environmental obstacle distribution information of the unmanned aerial vehicle are generated through signal combination, coordinate conversion and space mapping. The present application provides a specific embodiment, step 101, obtaining the real-time spatial position information and environmental obstacle distribution information of each unmanned aerial vehicle in the preset unmanned aerial vehicle cluster, specifically including the following steps:

[0081] Step 111: According to the preset positioning signal source, the original spatial positioning signal of each unmanned aerial vehicle in the preset unmanned aerial vehicle cluster is obtained, and the original spatial positioning signal is combined to generate multi-source fusion positioning data.

[0082] In this step, the preset positioning signal source refers to a hardware module integrating a global navigation satellite receiver and a visual sensor, which is used to synchronously acquire satellite ranging data and image feature point coordinates; the original spatial positioning signal refers to the original measurement data set including satellite pseudo-range observation value and visual feature pixel coordinates, which reflects the initial spatial state of the unmanned aerial vehicle; the combination operation refers to the process of eliminating signal transmission time delay by time stamp alignment and fusing heterogeneous data by weighted least squares method; the multi-source fusion positioning data refers to the structured data set containing fused three-dimensional coordinates, positioning accuracy factor and data source identification.

[0083] In the embodiment of the present application, firstly, the global navigation satellite system signal and visual positioning data are synchronously collected by the preset positioning signal source as the original spatial positioning signal, secondly, time alignment and confidence weighting processing are performed on the two types of signals, then spatial geometric constraint algorithm is used to fuse multi-source data, and finally multi-source fusion positioning data containing three-dimensional coordinates and accuracy index are generated.

[0084] Step 112: Converting the position coordinates of the multi-source fusion positioning data based on the coordinate system of the preset unmanned aerial vehicle cluster, generating the real-time spatial position information of each unmanned aerial vehicle.

[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 the embodiment of the present application, firstly, the preset local coordinate system parameters of the unmanned aerial vehicle cluster are read, secondly, 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 the real-time spatial position information with time stamp is generated.

[0087] Step 113: According to the preset environmental sensing signal source, the obstacle detection signal is obtained, and the spatial mapping of the obstacle detection signal is performed to generate the environmental obstacle distribution information of each unmanned aerial vehicle.

[0088] In this step, the preset environment perception signal source refers to the detection device equipped with laser radar and millimeter wave radar, which is used to emit detection beams and receive obstacle reflection signals; the obstacle detection signal refers to the original detection data set containing echo intensity, time of flight and Doppler shift; the space 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 the embodiment of the application, first, laser detection pulses are emitted by the preset environment perception signal source and reflection signals are received as obstacle detection signals, second, noise filtering and point cloud clustering processing are performed on the original signals, then the clustering results are mapped to the local coordinate system of the UAV cluster, and finally the environmental obstacle distribution information containing the position, size and motion vector of the obstacle is generated.

[0090] The embodiment of the application improves the positioning accuracy by spatiotemporal alignment and confidence fusion of multi-source positioning signals, establishes a unified spatial reference by global-to-local coordinate conversion, and simultaneously realizes accurate mapping of obstacle detection signals to environmental models, providing centimeter-level spatial perception capability for cluster motion decision-making.

[0091] To solve the problem of predicting the spatial trajectory of the UAV based on position changes and obstacle influences, this step calculates the spatial displacement vector, analyzes the obstacle influence parameters, and performs vector superposition and path correction to generate the spatial trajectory prediction result. The application provides a specific embodiment, step 102, predicting the trend of changes in the real-time spatial position information and the environmental obstacle distribution information to generate the spatial trajectory prediction result between the UAVs, specifically including the following steps:

[0092] Step 201: Calculate the continuous position difference 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, the continuous position difference refers to the set of scalar difference values of the three-dimensional coordinates of the UAV at adjacent time points, including east-west difference, north-south difference and vertical difference; the synthesis operation refers to the process of determining the motion vector by vector length calculation and direction cosine, integrating displacement and direction angle; the displacement direction refers to the unit direction vector composed of three coordinate axis displacement components, representing the instantaneous motion direction of the UAV; the spatial displacement vector refers to the motion state vector integrating displacement, direction and velocity information, used for trajectory prediction input.

[0094] In the embodiment of the application, first, the real-time spatial position information at consecutive time points is obtained, the scalar difference values of the coordinates at adjacent time points are calculated, second, the difference values of the three coordinate axes are subjected to vector synthesis operation to determine the motion direction, and finally the displacement and direction are integrated to generate the spatial displacement vector containing the velocity vector.

[0095] Step 202: Analyzing the motion direction included angle of the environmental obstacle distribution information, and generating an obstacle motion influence parameter based on a preset motion interference risk level.

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

[0097] In the embodiment of the present application, first, the geometric included angle between the obstacle motion direction and the UAV motion direction in the environmental obstacle distribution information is analyzed, second, the risk level value is quantized into the obstacle motion influence parameter according to the included angle size and the preset motion interference risk level mapping table.

[0098] Step 203: Inputting the spatial displacement vector and the obstacle motion influence parameter into a preset trajectory prediction unit, superimposing the vector components of the spatial displacement vector and the obstacle motion influence parameter in the preset trajectory prediction unit, and generating a superimposed result.

[0099] In this step, the preset trajectory prediction unit refers to a processor embedded with a vector operation engine, used for performing trajectory simulation calculation; the vector component refers to converting the risk parameter into an offset vector whose direction is perpendicular to the obstacle motion direction; the superimposition operation refers to the vector addition operation of the spatial displacement vector and the offset vector; and the superimposed result refers to the preliminary predicted trajectory containing the original motion trend and the obstacle avoidance offset.

[0100] In the embodiment of the present application, first, the spatial displacement vector is input into the preset trajectory prediction unit, second, the vector component of the obstacle motion influence parameter is extracted, then the vector addition operation is performed to generate the superimposed result, and finally the calculation result is retained in the memory of the prediction unit.

[0101] Step 204: Correcting the offset path of the superimposed result, and generating the spatial trajectory prediction result between the UAVs.

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

[0103] In the embodiment of the present application, firstly, the potential collision path with the obstacle in the superposition result is detected as the deviation path, secondly, the avoidance direction correction calculation is performed based on the obstacle position, and finally, the corrected space trajectory prediction result is output.

[0104] The embodiment of the present application establishes a motion ground state model through accurate synthesis of displacement vectors, generates a deviation vector by combining the quantitative conversion of the obstacle motion interference risk, realizes dynamic obstacle avoidance trajectory prediction through vector superposition and collision path correction, and significantly improves the safety of cluster collaborative flight in complex environments.

[0105] In order to solve the problem of evaluating collision risk and generating safe optimized flight instructions according to the predicted trajectory, this step generates optimized flight path instructions by calculating safety distance, quantifying risk, determining obstacle avoidance vector, constraining performance boundary and performing conflict detection. The present application provides a specific embodiment, step 103, inputting the space trajectory prediction result into a preset path decision unit to predict collision risk and generate optimized flight path instructions, specifically including the following steps:

[0106] Step 301: In the preset path decision unit, the trajectory point distance of the space trajectory prediction result is calculated, the coordinates of the nearest obstacle of the space 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 space trajectory prediction result and the surface of the nearest obstacle, reflecting the collision risk; the coordinates of the nearest obstacle refer to the three-dimensional coordinates of the geometric center of the nearest obstacle to a specific trajectory point determined by a spatial indexing algorithm; the safety distance parameter refers to structured data containing the minimum distance value and the corresponding obstacle number, which is used for risk assessment input.

[0108] In the embodiment of the present application, firstly, the Euclidean distance between each trajectory point in the space trajectory prediction result and the obstacle is calculated in the preset path decision unit, secondly, the coordinates of the nearest obstacle are identified and marked, and finally, the safety distance parameter containing the minimum distance value and the corresponding obstacle identifier is generated.

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

[0110] In this step, the preset safety distance threshold refers to a plurality of warning distance values set according to the size and movement speed of the unmanned aerial vehicle, including a pre-warning threshold and an emergency avoidance threshold; the comparison result refers to the difference between the safety distance parameter and the safety distance threshold and the state identifier of the risk interval to which it belongs; the discretization processing refers to the operation of mapping the continuous distance difference into a preset discrete level, including three levels of low risk, medium risk and high risk; and the risk quantization parameter refers to a numerical risk indicator generated after discretization processing, with a positive value corresponding to high risk and a negative value corresponding to low risk.

[0111] In the embodiment of the present application, first, the safety distance parameter is compared with the preset safety distance threshold, second, the discrete risk level is divided according to the interval to which the comparison result belongs, and finally the risk level is quantized into a risk quantization parameter.

[0112] Step 303: determining the obstacle avoidance direction of the risk quantization parameter, calculating the required displacement amount of the risk quantization parameter, to generate an original offset vector.

[0113] In this step, the obstacle avoidance direction refers to a unit vector pointing from the position of the unmanned aerial vehicle to the opposite direction of the surface normal of the obstacle, which is determined by the highest risk item; the required displacement amount refers to the normal avoidance distance calculated in proportion to the risk quantization parameter value, with a large displacement amount corresponding to high risk; and the original offset vector refers to a three-dimensional motion correction vector integrating the obstacle avoidance direction and the displacement amount.

[0114] In the embodiment of the present application, first, the obstacle avoidance direction is determined according to the highest risk item of the risk quantization parameter, second, the displacement amount perpendicular to the surface of the obstacle is calculated according to the risk value, and finally the original offset vector is generated by integrating the direction and the displacement amount.

[0115] Step 304: constraint the motion performance and steering boundary of the original offset vector to generate path adjustment instructions.

[0116] In this step, the constraint operation refers to the clipping calculation of limiting the direction and size of the original offset vector within the physical motion capability range of the unmanned aerial vehicle; the motion performance refers to the physical motion limit parameters of the unmanned aerial vehicle including the maximum steering angle and the maximum acceleration; the steering boundary refers to a three-dimensional space geometric constraint body composed of a preset airspace range; and the path adjustment instruction refers to a set of heading correction instructions generated after double constraint clipping of the motion performance and the airspace boundary.

[0117] In the embodiment of the present application, first, the maximum steering angle parameter of the unmanned aerial vehicle is read as the motion performance constraint, second, the airspace boundary data is obtained as the steering boundary, and finally the original offset vector is subjected to double constraint clipping to generate the path adjustment instruction.

[0118] Step 305: verifying the airspace coordinate range of the path adjustment instruction, performing conflict detection on the cluster motion of the path adjustment instruction, and generating optimized flight path instructions.

[0119] In this step, the verification operation refers to a verification process of detecting whether the instruction conforms to the airspace rules and the cluster coordination safety; the airspace coordinate range refers to a set of three-dimensional boundary coordinates of a pre-defined flight area of the unmanned aerial vehicle; the cluster motion refers to a set of motion state data of other unmanned aerial vehicles at the current time; and the conflict detection operation refers to an analysis of a spatial intersection between the adjusted path and the cluster motion path.

[0120] In the embodiment of the application, first, it is verified whether the path adjustment instruction exceeds the preset airspace coordinate range, second, it is detected whether the instruction is in spatial conflict with the motion path of other unmanned aerial vehicles in the cluster, and finally, an optimized flight path instruction is generated through conflict avoidance processing.

[0121] The embodiment of the application realizes precise obstacle avoidance decision-making through the trajectory point risk quantization and obstacle avoidance vector generation mechanism, guarantees the feasibility of the instruction in combination with the dual constraints of motion performance and airspace boundary, and finally outputs a safe and reliable optimized flight path through airspace compliance verification and cluster conflict detection, thereby significantly improving the cluster obstacle avoidance success rate in a complex dynamic environment.

[0122] In order to solve the problem of selecting stable relay nodes based on flight path changes and link quality, this step extracts the change amplitude, associates the quality threshold, selects high-quality nodes, and evaluates the relative motion stability to generate a relay node update list. The application provides a specific embodiment, step 104, based on the change event of the optimized flight path instruction and the preset communication link quality parameter, the stable relay nodes are selected from the preset relay node set to generate a relay node update list that meets the preset stability condition, which specifically includes the following steps:

[0123] Step 401: Extract the 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 change in the heading angle and the absolute value of the change in the displacement distance in the optimized flight path instruction, which is used to quantify the path adjustment strength.

[0125] In the embodiment of the application, first, the change in the heading angle and the change in the displacement distance in the optimized flight path instruction are analyzed, second, the absolute values of the two are extracted as the quantitative indicators of the path change, and finally, the change amplitude parameter containing the angle change and the distance change is generated.

[0126] Step 402: Associate the change amplitude parameter with the preset communication link quality decay threshold to generate a dynamic quality threshold value.

[0127] In this step, the preset communication link quality attenuation threshold refers to a reference signal attenuation value table established according to a wireless channel model, containing signal strength loss reference values in different frequency bands; the correlation 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 superimposing it on the basic attenuation value; and the dynamic quality threshold refers to a communication quality screening threshold that is adjusted in real time with the path change amplitude, and the threshold is increased when the change amplitude is increased.

[0128] In the embodiment of the application, first, the basic attenuation value is obtained by querying the preset communication link quality attenuation threshold table, second, the change amplitude parameter is scaled according to the preset proportion coefficient and then superimposed on the basic attenuation value, and finally the dynamic quality threshold that is adjusted dynamically with the path change intensity is generated.

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

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

[0131] In the embodiment of the application, first, the real-time signal strength and time delay parameters of each node in the preset relay node set are obtained, second, the communication link quality parameters are compared with the dynamic quality threshold node by node, and finally all parameter qualified nodes are retained to generate the target relay node set.

[0132] Step 404: calculating the relative motion speed difference between each node in the target relay node set and the unmanned aerial vehicle that has changed the path among the unmanned aerial vehicles, retaining 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 condition.

[0133] In this step, the relative motion speed difference refers to the Euclidean norm of the speed vector difference between the node and the unmanned aerial vehicle that has changed the path, reflecting the relative motion intensity of the two; and the preset motion tolerance refers to the maximum relative speed threshold that guarantees communication reliability, and a Doppler shift will be caused to cause signal distortion when the value is exceeded.

[0134] In the embodiment of the application, first, the speed vector difference norm between each node in the target relay node set and the unmanned aerial vehicle that has changed the path is calculated, second, the norm is compared with the preset motion tolerance threshold, and finally the nodes lower than the threshold are filtered to generate the relay node update list.

[0135] The embodiment of the application realizes cross-domain parameter coordination through dynamic mapping of path change amplitude to communication quality threshold, and combines a dual filtering mechanism of node communication quality preliminary screening and motion compatibility fine screening to ensure signal stability and motion adaptability of the relay node in a high-speed motion scene, and significantly reduces the topology shock probability.

[0136] In order to solve the problem of dynamically calculating the communication link quality threshold value according to the path change amplitude, the step obtains a basic threshold value through amplitude interval division, generates a compensation coefficient by quantizing the change rate, and performs superposition operation to generate a dynamic quality threshold value. The application provides a specific embodiment, step 402, the change amplitude parameter is associated with a preset communication link quality attenuation threshold value to generate a dynamic quality threshold value, specifically including the following steps:

[0137] Step 421: Based on the preset amplitude interval boundary, the continuous value of the change amplitude parameter is divided to generate a discrete amplitude interval identifier.

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

[0139] In the embodiment of the application, first, the preset amplitude interval boundary parameter is read as the division basis, second, the continuous numerical 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 the preset threshold matching rule, the basic quality threshold value of the discrete amplitude interval identifier is obtained.

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

[0142] In the embodiment of the application, first, the preset threshold matching rule table is loaded, second, the corresponding basic quality threshold value is queried 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 to generate a change rate parameter.

[0144] In this step, the time series data refers to a sequence of change amplitude parameter history records collected continuously in a decision period; the amplitude value difference refers to the arithmetic difference value of the change amplitude parameters of adjacent periods, reflecting the short-term change amount; the quantization processing refers to the calculation process of converting the difference value into the change rate by dividing the decision period length; and the change rate parameter refers to the change rate of the change amplitude per unit time, used for evaluating the degree of motion mutation.

[0145] In the embodiment of the application, first, the time series data of the change amplitude parameter is extracted, second, the amplitude value difference of adjacent time points is calculated, then the difference value is divided by the decision period length for rate quantization, and finally the change rate parameter reflecting the change severity is generated.

[0146] Step 424: inputting 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, and the form is that the coefficient is equal to the rate multiplied by the proportion factor; the conversion operation refers to the calculation process of mapping the input parameter to the output value through the mathematical function; and 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 application, first, the preset compensation coefficient conversion function is called, second, the change rate parameter is input into the function to perform linear conversion calculation, and finally the dynamic compensation coefficient positively related to the rate is output.

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

[0150] In this step, the preset weight coefficient refers to the weighting proportion parameter of the basic threshold value and the compensation coefficient, used for balancing the static and dynamic factors; and the superimposition operation refers to the mathematical calculation of performing weighted summation according to the weight coefficient to generate the final threshold value.

[0151] In the embodiment of the application, first, the basic quality threshold value and the dynamic compensation coefficient are obtained, second, the weighted summation operation is performed according to the preset weight coefficient, and finally the dynamic quality threshold value that integrates the static reference and the dynamic compensation is generated.

[0152] The embodiment of the application establishes a static reference threshold through the discretization processing of the amplitude interval, realizes the adaptive adjustment of the quality threshold by combining the dynamic compensation mechanism of the change rate, effectively responds to the communication environment mutation in high-speed motion, and improves the precision and environmental adaptability of the relay node screening.

[0153] To solve the problem of constructing an efficient and stable communication topology based on the updated relay node list, this step generates a communication topology reconstruction instruction by screening stable links through reachability analysis, verifying connectivity to remove redundancy, constructing a minimum topology, and load balancing in response to position changes. The present application provides a specific embodiment, step 105, based on the relay node update list, determines the inter-node communication connection relationship of the preset drone cluster, and reconstructs the topology of the inter-node communication connection relationship to generate a communication topology reconstruction instruction, specifically including the following steps:

[0154] Step 501: Based on the preset signal propagation model, the reachability of the inter-node distance of the relay node update list is analyzed, and a communication reachability table is generated.

[0155] In this step, the preset signal propagation model refers to a radio wave propagation mathematical model containing a free space path loss formula and multipath attenuation parameters, which is used to predict the 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 table refers to a two-dimensional matrix recording the communication reachability state between node pairs, and the matrix elements are Boolean values indicating whether it is reachable or not.

[0156] In the embodiment of the present application, first, the path loss calculation formula in the preset signal propagation model is loaded, second, 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 determined according to the received sensitivity threshold to generate the communication reachability table.

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

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

[0159] In the embodiment of the present application, first, the link data in the communication reachability table is read, second, the preset stability condition is applied to compare the signal strength and time delay parameters of the link, then the link that meets both the lower limit of the strength and the upper limit of the time delay is screened, and finally the stable communication link set is generated.

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

[0161] In this step, the path connectivity refers to the network attribute that there is at least one communication path between any two nodes in the topology structure; the redundant link refers to a non-essential communication link that does not affect the path connectivity of the whole network after being removed; and the minimum connected topology structure refers to an optimized topology form containing the minimum number of links under the premise of maintaining the connectivity of the whole network.

[0162] In the embodiment of the application, firstly, a full connection graph model of a stable communication link set is constructed, secondly, whether there is a communication path between any two nodes is verified, then redundant links that do not affect the connectivity of the whole graph are identified and removed, and finally, the minimum connected topology structure is output.

[0163] Step 504: detecting a change amount of the real-time spatial position information to generate a node position change event.

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

[0165] In the embodiment of the application, firstly, the real-time spatial position information of two continuous frames is acquired, secondly, the Euclidean distance change amount of the coordinates of the same unmanned aerial vehicle is calculated, then the change amount is compared with a 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 connected topology structure to respond to the node position change event in real time, reconstructing the minimum connected topology structure to generate a communication topology reconstruction instruction.

[0167] In this step, the load balancing adjustment operation refers to a calculation process of dynamically optimizing link allocation according to the node data throughput to avoid local congestion; and the reconstruction operation refers to a calculation process of regenerating the topology connection relationship based on the position change event and the load state.

[0168] In the embodiment of the application, firstly, the data load rate of each node in the minimum connected topology structure is analyzed, secondly, the communication link is redistributed according to the load balancing strategy, then the connection relationship of the affected node is adjusted in response to the position change event, and finally, the communication topology reconstruction instruction is generated.

[0169] The embodiment of the application accurately determines the communication feasibility between nodes through a signal propagation model, constructs a lightweight topology by combining stability condition screening and connectivity optimization, triggers dynamic reconstruction in response to position changes in real time, and significantly improves the robustness and resource utilization rate of the unmanned aerial vehicle cluster network in a high-speed motion scene.

[0170] Figure 2A structural schematic diagram of a UAV cluster dynamic topology communication network construction system is provided for an embodiment of the present application, as shown in Figure 2 The system comprises:

[0171] An acquisition module 21 is configured to acquire real-time spatial position information and environmental obstacle distribution information of each UAV in a preset UAV cluster.

[0172] A prediction module 22 is configured to predict a 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.

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

[0174] A screening module 24 is configured to screen stable relay nodes from a preset relay node set based on a change event of the optimized flight path instruction and a preset communication link quality parameter to generate a relay node update list meeting a preset stability condition.

[0175] A reconstruction module 25 is configured to determine an inter-node communication connection relationship of the preset UAV cluster based on the relay node update list, reconstruct the inter-node communication connection relationship to generate a communication topology reconstruction instruction.

[0176] Figure 2 The UAV cluster dynamic topology communication network construction system can perform Figure 1 The UAV cluster dynamic topology communication network construction method of the embodiment shown in the above description. The specific manner in which each module and unit of the UAV cluster dynamic topology communication network construction system performs operations in the above embodiment has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0177] In one possible design, Figure 2 The UAV cluster dynamic topology communication network construction system of the embodiment shown in the above description. The specific manner in which each module and unit of the UAV cluster dynamic topology communication network construction system performs operations in the above embodiment has been described in detail in the embodiment related to the method, and will not be described in detail here. Figure 3 The computing device can comprise 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 configured to: acquire real-time spatial position information of each unmanned aerial vehicle in a preset unmanned aerial vehicle cluster and environmental obstacle distribution information; predict a change trend of the real-time spatial position information and the environmental obstacle distribution information to generate a spatial trajectory prediction result between the unmanned aerial vehicles; input the spatial trajectory prediction result into a preset path decision unit to predict a collision risk and generate an optimized flight path instruction; based on a change event of the optimized flight path instruction and a preset communication link quality parameter, screen out stable relay nodes from a preset relay node set to generate a relay node update list meeting a preset stability condition; and based on the relay node update list, determine an inter-node communication connection relationship of the preset unmanned aerial vehicle cluster, and perform topology reconstruction on the inter-node communication connection relationship to generate a communication topology reconstruction instruction.

[0180] The processing component 32 can 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 can also be 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 elements for executing the above method.

[0181] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage 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 storage, flash memory, magnetic disk or optical disk.

[0182] Of course, the computing device can also include other components, such as an input / output interface, a display component, a communication component, etc.

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

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

[0185] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server. The processing component, the storage component, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0186] The embodiment of the present application further provides a computer storage medium, which stores a computer program, and the computer program can realize the above method when being executed by a computer. Figure 1 The embodiment of the present application further provides a method for constructing a dynamic topology communication network of a UAV cluster.

[0187] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0188] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0189] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and necessary general hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a number of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0190] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for constructing a dynamic topology communication network of a UAV cluster, characterized in that, The method comprises the following steps: acquiring real-time spatial position information and environmental obstacle distribution information of each unmanned aerial vehicle in a preset unmanned aerial vehicle cluster; 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 unmanned aerial vehicles; inputting the spatial trajectory prediction result into a preset path decision unit to predict a collision risk and generate an optimized flight path instruction; based on a change event of the optimized flight path instruction and a preset communication link quality parameter, screening stable relay nodes from a preset relay node set to generate a relay node update list meeting a preset stability condition; based on the relay node update list, determining an inter-node communication connection relationship of the preset unmanned aerial vehicle cluster, and performing topology reconstruction on the inter-node communication connection relationship to generate a communication topology reconstruction instruction; the step of screening stable relay nodes from a preset relay node set based on a change event of the optimized flight path instruction and a preset communication link quality parameter to generate a relay node update list meeting a preset stability condition comprises the following steps: extracting a change amplitude parameter from the optimized flight path instruction; associating and mapping the change amplitude parameter with a preset communication link quality decay threshold to generate a dynamic quality threshold value; screening the preset relay node set to retain relay nodes with a preset communication link quality parameter higher than the dynamic quality threshold value to generate a target relay node set; calculating the relative motion speed difference between each node in the target relay node set and the unmanned aerial vehicle that has changed the path among the unmanned aerial vehicles, and retaining the nodes with a relative motion speed difference lower than a preset motion tolerance to generate a relay node update list meeting a preset stability condition.

2. The method of claim 1, wherein, The method comprises the following steps: acquiring real-time spatial position information and environmental obstacle distribution information of each unmanned aerial vehicle in a preset unmanned aerial vehicle cluster; acquiring original spatial positioning signals of each unmanned aerial vehicle in the preset unmanned aerial vehicle cluster according to a preset positioning signal source, combining the original spatial positioning signals to generate multi-source fusion positioning data; converting the position coordinates of the multi-source fusion positioning data based on the coordinate system of the preset unmanned aerial vehicle cluster to generate real-time spatial position information of the unmanned aerial vehicles; 3. The method of claim 1, wherein, acquiring obstacle detection signals according to a preset environmental perception signal source, and performing spatial mapping on the obstacle detection signals to generate environmental obstacle distribution information of the unmanned aerial vehicles. The method comprises the following steps: calculating the continuous position difference of the real-time spatial position information and synthesizing the displacement direction of the real-time spatial position information to generate a spatial displacement vector of the unmanned aerial vehicles; analyzing the motion direction angle of the environmental obstacle distribution information and generating an obstacle motion influence parameter based on a preset motion interference risk level; inputting the spatial displacement vector and the obstacle motion influence parameter into a preset trajectory prediction unit, superimposing vector components of the spatial displacement vector and the obstacle motion influence parameter in the preset trajectory prediction unit, and generating a superimposition result; correcting an offset path of the superimposition result to generate a spatial trajectory prediction result between the unmanned aerial vehicles.

4. The method of claim 1, wherein, inputting the spatial trajectory prediction result into a preset path decision unit to predict a collision risk, and generating an optimized flight path instruction, including: calculating a trajectory point distance of the spatial trajectory prediction result in the preset path decision unit, marking a nearest obstacle coordinate of the spatial trajectory prediction result, and generating a safety distance parameter; comparing the safety distance parameter with a preset safety distance threshold, discretizing a comparison result, and generating a risk quantization parameter; determining an obstacle avoidance direction of the risk quantization parameter, calculating a required displacement amount of the risk quantization parameter, and generating an original offset vector; constraining motion performance and turning boundaries of the original offset vector to generate a path adjustment instruction; verifying a space coordinate range of the path adjustment instruction, performing conflict detection on cluster motion of the path adjustment instruction, and generating an optimized flight path instruction.

5. The method of claim 1, wherein, associating and mapping the change amplitude parameter with a preset communication link quality decay threshold to generate a dynamic quality threshold value, including: dividing continuous values of the change amplitude parameter based on a preset amplitude interval boundary to generate discrete amplitude interval identifiers; obtaining a basic quality threshold value of the discrete amplitude interval identifier based on a preset threshold matching rule; extracting amplitude value differences of time series data in the change amplitude parameter, quantizing a 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; superimposing the basic quality threshold value, the dynamic compensation coefficient, and a preset weight coefficient to generate a dynamic quality threshold value.

6. The method of claim 1, wherein, based on the relay node update list, determining a node-to-node communication connection relationship of the preset unmanned aerial vehicle cluster, and topologically reconstructing the node-to-node communication connection relationship to generate a communication topology reconstruction instruction, including: performing reachability analysis on node-to-node distances of the relay node update list based on a preset signal propagation model to generate a communication reachability relationship table; filtering links in the communication reachability relationship table, and retaining links that satisfy a preset stability condition to generate a stable communication link set; verifying path connectivity of each link in the stable communication link set, and removing redundant links that do not affect the path connectivity to generate a minimum connected topology structure; detecting a change amount of the real-time spatial position information to generate a node position change event; performing load balancing adjustment on the minimum connected topology structure to respond to the node position change event in real time, and reconstructing the minimum connected topology structure to generate a communication topology reconstruction instruction.

7. A dynamic topology communication network construction system for a UAV cluster, characterized in that, including: An acquisition module is configured to acquire real-time spatial position information and environmental obstacle distribution information of each unmanned aerial vehicle in a preset unmanned aerial vehicle cluster; A prediction module is configured to predict a change trend of the real-time spatial position information and the environmental obstacle distribution information to generate a spatial trajectory prediction result between the unmanned aerial vehicles; An input module is configured to input the spatial trajectory prediction result into a preset path decision unit to predict a collision risk and generate an optimized flight path instruction; A screening module is configured to screen stable relay nodes from a preset relay node set based on a change event of the optimized flight path instruction and a preset communication link quality parameter to generate a relay node update list meeting a preset stability condition; A reconstruction module is configured to determine an inter-node communication connection relationship of the preset unmanned aerial vehicle cluster based on the relay node update list, perform topological reconstruction on the inter-node communication connection relationship, and generate a communication topology reconstruction instruction; The screening of the stable relay nodes from the preset relay node set based on the change event of the optimized flight path instruction and the preset communication link quality parameter to generate the relay node update list meeting the preset stability condition includes: extracting a change amplitude parameter from the optimized flight path instruction; associating and mapping the change amplitude parameter with a preset communication link quality decay threshold to generate a dynamic quality threshold value; screening the preset relay node set to retain relay nodes with a preset communication link quality parameter higher than the dynamic quality threshold value to generate a target relay node set; calculating a relative motion speed difference between each node in the target relay node set and an unmanned aerial vehicle that has a path change among the unmanned aerial vehicles, and retaining nodes with the relative motion speed difference lower than a preset motion tolerance to generate the relay node update list meeting the preset stability condition.

8. A computing device, comprising: The device comprises 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 the method.

9. A computer storage medium, characterized in that, The device stores a computer program, and the computer program is executed by a computer to implement the method.

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