1091 results about "Flight track" patented technology

The invention provides an unmanned aerial vehicle countering plan generation method and system based on an artificial intelligence technology, and the method comprises the steps: analyzing the dynamic monitoring data flow of a target unmanned aerial vehicle, recognizing the flight path characteristics and electromagnetic signal frequency spectrum of the target unmanned aerial vehicle, and constructing a three-dimensional space threat situation model to evaluate the threat level. Decomposing behavior patterns and attack intention parameters of a target unmanned aerial vehicle based on threat levels, generating a dynamic decision factor set in combination with geo-fence constraint conditions and environmental interference factors, generating a plurality of candidate countering plans by matching countering means basic plans in a plan knowledge graph, and constructing a plan logic tree; a multi-dimensional evaluation parameter set is generated based on a three-dimensional space threat situation model, a dynamic decision factor set and a countering efficiency index, collaborative optimization processing is performed on a plan logic tree, an optimal countering plan is generated and converted into an executable instruction set, the executable instruction set is synchronized to a countering device for execution, and the real-time performance and scene adaptability of the unmanned aerial vehicle countering plan are improved.

The invention discloses a self-adaptive control unmanned aerial vehicle flight path correction method and system, and relates to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: collecting flight environment data through a sensor group carried by an unmanned aerial vehicle, constructing a three-dimensional environment model, and dividing the three-dimensional environment model into space units, the real-time pose of the unmanned aerial vehicle is determined based on point cloud matching and pose estimation technologies, trajectory offset is predicted according to a preset flight path, flight trajectory deviation parameters are generated, and a triaxial compensation strategy of horizontal plane, vertical direction and lateral offset is analyzed through trend analysis of the deviation parameters. And finally performing dynamic compensation correction on the flight path of the unmanned aerial vehicle according to the three-axis adjustment amount. The technical problem that the real-time accurate correction efficiency of the flight path of an existing unmanned aerial vehicle in a complex environment is low is solved, and the technical effect of shortening the dynamic compensation response time of the flight path deviation of the unmanned aerial vehicle is achieved through an industrial automatic control system device manufacturing technology and an intelligent monitoring device.

Disclosed herein are a system and a method for controlling multi-UAV cooperative tracking of a ground-moving target. The system includes UAVs, a processor, and a storage device. The UAVs are wirelessly communicatively connected to the processor. The processor is configured to generate a UAV command transmitted to the UAVs. The UAVs are configured to transmit tracking data to the processor. The processor includes a terrain building module, a flight simulation module, a UAV parameter generation module, a flight trajectory prediction module, a flight trajectory determination module, and a flight trajectory control module. The system provided in the embodiments of the present disclosure integrates Model Predictive Control (MPC) with an Improved Whale Optimization Algorithm (IWOA) to enhance the algorithm's convergence speed and precision, thereby improving the accuracy and stability of target tracking.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle inspection control system and method based on data analysis, and the method comprises the steps: receiving environment feature data of a target region, and constructing a static environment model; obtaining kinetic parameters of the unmanned aerial vehicle, and calculating a feasible flight envelope in combination with the static environment model; after taking off, the unmanned aerial vehicle collects environment data in real time, and a multi-level environment topology model is constructed; acquiring state data in real time, and judging an obstacle avoidance level; if yes, local path planning is carried out, and an optimal flight path is generated; acquiring trajectory deviation and energy consumption data, dynamically calibrating the flight envelope, and optimizing the flight envelope; according to the method, environment perception, trajectory optimization and online learning are combined, the method is adaptive to a complex environment, and the intelligent level of unmanned aerial vehicle inspection is improved.

The invention discloses a low-altitude unmanned aerial vehicle flight statistical system, and relates to the technical field of air traffic management. A flight statistical system of a low-altitude unmanned aerial vehicle comprises a multi-source sensing fusion module, a twin airspace modeling module, a behavior portrait modeling module, a non-cooperative target recognition module, a flight statistical analysis module and a self-adaptive scheduling module. According to the invention, a three-dimensional twin airspace model of a monitoring area is constructed based on high-precision map data, a building three-dimensional model and flight information, and space mapping and collision risk analysis of a flight path are carried out, so that real restoration of the flight path and environment collision prediction are realized; the behavior feature vector is generated and the random forest algorithm is adopted to carry out classification modeling, and the behavior portrait of the unmanned aerial vehicle is constructed, so that accurate identification and classification of the behavior pattern of the unmanned aerial vehicle are realized.

The invention discloses an unmanned aerial vehicle inspection method and system based on AI visual control, and belongs to the technical field of unmanned aerial vehicle automatic control, and the method comprises the steps: carrying out the feature component extraction and compression according to discontinuous frame image data collected by an airborne camera and preset inertial navigation data, and obtaining a compressed feature flow; performing multi-mode obstacle safety level parameter analysis, generating an obstacle avoidance decision priority map, constructing a safety corridor, and generating a real-time self-correction route; semantic analysis is carried out on the deviation between the real-time flight trajectory and the real-time self-correction route, and target area abnormal mark data is generated; and dynamically adjusting the bandwidth allocation of the dual-channel communication network, and transmitting the abnormal mark data of the target area to a ground control terminal. According to the method, a non-continuous frame image and inertial navigation fusion modeling mechanism is adopted, and a multi-mode obstacle safety grading and double-channel dynamic bandwidth allocation strategy is combined, so that real-time response of dynamic obstacle avoidance, accurate judgment of anomaly detection and efficient transmission of key data can be realized.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention discloses a multi-aircraft 4D trajectory prediction method based on a graph attention hybrid neural network, and belongs to the technical field of aviation. Historical trajectory implicit information of different aircrafts in an airspace is extracted by utilizing Transform and a long-short-term memory network, the aircrafts in the airspace are regarded as nodes, an aircraft connection network graph is constructed, and modeling is performed on the aircraft implicit information and the aircraft connection network graph through a graph attention network so as to capture interaction information between the aircrafts. The graph attention network integrates the track implicit information of all aircrafts in the airspace and shares and updates the track implicit information, finally decodes the track implicit feature information of the aircrafts through two decoders based on the long and short term memory network, fuses the output of the two decoders, and outputs a prediction track through a full connection layer. Compared with a method which does not consider aircraft space interaction information, the accuracy of flight trajectory prediction is improved, and an accurate trajectory prediction technology provides support for a subsequent risk perception technology and a conflict resolution technology.

The invention discloses a goaf unmanned aerial vehicle inspection system based on monitoring and early warning, and the system comprises an observation data collection module which is used for collecting settlement and fracture main variables to form a time stamp observation set; the data fusion module is used for generating a unified monitoring data set by adopting a fractal small-world network coding consensus algorithm; the initial route planning module is used for constructing a route sequence based on a main variable evolution tensor and a risk map; the flight path execution module is used for collecting a flight path and environment data; the fault-tolerant control module triggers a fractional order sliding mode fault-tolerant and degradation mechanism based on the consistency error; and the closed-loop optimization module is used for dynamically updating the main variable tensor and the path cost function and outputting an optimized track and a structured early warning result. The system has high dynamic responsiveness and multi-source risk adaptability.

The invention provides a low-altitude airspace dynamic decision-making system based on a multi-modal large model, and relates to the technical field of low-altitude airspace traffic management.The low-altitude airspace dynamic decision-making system is characterized in that a first thermal disturbance coverage map, a second shear wind high-risk area map, a third turbulence risk drainage basin map and a fourth sudden obstacle prediction area map are generated by a map construction module; the aircraft can judge whether the aircraft enters the dangerous area in real time and assess the safety of the flight path according to the superposed risk value. When the superposition risk value # imgabs0 # of the flight path section passing through the jth area in the first original flight path of the ith aircraft is in a middle risk section, the system generates a first-level avoidance strategy, and the height, the speed and the course of the aircraft are adjusted; and when the superposed risk value # imgabs1 # is a high-risk section, the system generates a secondary avoidance strategy, and bypasses a high-risk area by remarkably increasing the flight height and reducing the speed.

The present invention provides an unmanned aerial vehicle-based electric power patrol inspection control method and system. The method comprises: creating a flight path on the basis of an electric power patrol inspection area, and collecting images and position information of electric power devices by means of various types of devices carried by an unmanned aerial vehicle, the images and the position information being transmitted to a receiving terminal at a ground station in a wireless transmission mode; the ground station receiving the images and the position information that are collected in real time, processing and analyzing the images to obtain analytic status, such as defects, damage and corrosion, of the electric power devices; and, on the basis of the analytic status from the ground station, using the position information as distinguishing features to generate an electronic patrol inspection report, providing service lives, maintenance costs and maintenance measures of the electric power devices on the basis of the electronic patrol inspection report, and notifying maintenance staff of the position information of the electric power devices. The system comprises an unmanned aerial vehicle control module, an image processing and analysis module and an electronic patrol inspection report generation module. The present invention is of great significance to maintenance and management of electric power devices.

The invention relates to an offshore wind turbine generator data acquisition method and system, and the method comprises the steps: dividing a target offshore wind power plant into a plurality of inspection regions, and obtaining the structure parameters and topographic data of a wind turbine generator of each inspection region; according to the wind turbine generator structure parameters and topographic data, respectively constructing an unmanned aerial vehicle flight path of each inspection area, and presetting a surrounding radius and a vehicle position angle; based on the flight path, the surrounding radius and the camera position angle, controlling the unmanned aerial vehicle to carry out surrounding flight on the wind turbine generator in each inspection area, and collecting a wind turbine generator image and point cloud data in the surrounding flight process; and constructing a wind turbine generator three-dimensional model based on the wind turbine generator image and the point cloud data, constructing a three-dimensional map in combination with the topographic data, and fusing a flight path into the three-dimensional map to complete data acquisition of the target offshore wind plant. According to the invention, autonomous intelligent inspection of the offshore wind turbine generator can be realized.

The invention relates to the technical field of wireless communication, and discloses an unmanned aerial vehicle mobile edge computing resource allocation method based on deep reinforcement learning, which constructs a mixed integer non-convex optimization problem, and the objective of the optimization problem is to collaboratively optimize the flight path of each unmanned aerial vehicle, a mobile user scheduling strategy and a task unloading proportion. Therefore, the time delay-energy consumption minimization is realized; and solving the optimization problem by adopting a multi-agent double-delay depth deterministic strategy gradient algorithm: converting the optimization problem into a Markov game model, and realizing the solution of the optimization problem through an experience playback pool and a double-evaluation network updating strategy. According to the method provided by the invention, the flight path and the unloading proportion of the unmanned aerial vehicle can be adaptively adjusted, so that calculation resource allocation for ground users is efficiently completed. Compared with a global unloading scheme, an average unloading scheme and other deep reinforcement learning methods, the method provided by the invention has excellent performance of lower energy consumption and shorter time delay while completing tasks.

The invention discloses an unmanned aerial vehicle optimal path self-adaptive planning method adopting particle swarm optimization, and relates to the field of unmanned aerial vehicle path planning, and the method comprises the steps: constructing a space constraint model of an unmanned aerial vehicle flight task; initializing a particle swarm based on the spatial constraint model; combining the path smoothness, the path threat probability and the voyage efficiency to establish a multi-target fitness function, and calculating the fitness value of each particle based on a particle swarm; hierarchical particle swarm optimization iteration is executed, in each iteration, control parameters are adaptively adjusted based on the current particle swarm distribution characteristics, and the path node positions of particles are updated; and when a convergence condition is satisfied, outputting a global optimal path as a final flight path of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to execute. According to the unmanned aerial vehicle optimal path self-adaptive planning method based on particle swarm optimization, the problems that the unmanned aerial vehicle route path optimization target is single and difficult to cooperate, and the route planning effect is poor are solved.

The invention discloses an aircraft landing method and system and a vertical take-off and landing aircraft, and relates to the technical field of aircraft control, and the aircraft landing system comprises a landing path planning module which is used for responding to a landing instruction, determining a target landing point of the aircraft, and planning a landing path of the aircraft to obtain the landing path of the aircraft; the trajectory prediction module is used for predicting the flight trajectory of the aircraft based on the flight state information of the aircraft to obtain a predicted flight trajectory; the re-flight decision module is used for evaluating the flight state of the aircraft in the tilting transition stage based on the landing path and the predicted flight path; and according to the first evaluation result, carrying out re-flight decision processing on the aircraft, and determining whether to control the aircraft to carry out re-flight so as to guarantee safe landing of the aircraft. According to the scheme, the automatic landing capability of the aircraft can be effectively improved, and meanwhile, the landing safety of the aircraft is ensured through the re-flight decision module.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved dung beetle algorithm, and relates to the technical field of unmanned aerial vehicle path planning. According to the technical scheme, an unmanned aerial vehicle flight environment is modeled; establishing constraint conditions; establishing a target function; and performing unmanned aerial vehicle global path planning by adopting an improved dung beetle optimization algorithm. The method has the beneficial effects that initialization is carried out by utilizing a good point set strategy, so that the population diversity is improved; an improved sine algorithm is combined with a variation strategy of Levy flight and Brownian motion to be embedded into position updating of dung beetles, so that the search range is expanded, and the global optimization ability of the algorithm is improved; a simplex method and a self-adaptive reverse learning strategy are added for disturbance, and the algorithm is helped to jump out of a local optimal solution; the efficiency and accuracy of unmanned aerial vehicle path planning are remarkably improved, the robustness and adaptability of the algorithm are enhanced, the flight path of the unmanned aerial vehicle is optimized, and further development of the unmanned aerial vehicle technology is promoted.

A method for determining improved flight trajectory. The method includes receiving one or more weather parameters to determine contrail forecast data; receiving one or more flight parameters associated with at least one aircraft 204 to determine flight data thereof; receiving flight schedule including at least one flight plan of at least one aircraft; analyzing at least one flight plan to determine at least one navigational avoidance between at least two aircraft; determining contrail likelihood associated with at least one aircraft; altering one or more flight parameters to determine improved flight trajectory for at least one flight plan; sending at least one flight plan including improved flight trajectory to at least one aircraft; and validating improved flight trajectory using imagery data, when at least one aircraft flies according to at least one flight plan including improved flight trajectory. A system for determining improved flight trajectory.

The invention relates to a hybrid network 4D flight path prediction method based on LSTM and Transform, and belongs to the technical field of flight path prediction. Comprising the following steps: inputting preprocessed flight path data into an embedding layer, mapping the preprocessed flight path data into high-dimensional feature representation, and forming an input sequence; inputting the input sequence into the LSTM network to generate an output representation with a time-dependent feature; performing global feature extraction on the output representation of the LSTM network by using a multi-head attention mechanism so as to capture a dependency relationship and relevance between different trajectory data points; performing normalization processing on global features output by the multi-head attention mechanism, and further enhancing feature expression ability through a feedforward neural network; a mask multi-head attention mechanism is adopted in a decoder, and prediction features are output; and mapping the prediction features output by the decoder to a target space through a linear layer, and generating a final result of flight path prediction. The method is excellent in performance in complex flight path prediction tasks.

The embodiment of the invention provides an unmanned aerial vehicle dynamic deployment method and equipment, and the method comprises the steps: introducing a flight plan, a flight path line and a space-time envelope of flight, predicting the flight path of an unmanned aerial vehicle at a specific time, mastering the full view of the flight process in the whole flight plan, and carrying out the management and control of the capacity of the flight path as a resource, thereby achieving the dynamic deployment of the unmanned aerial vehicle. According to the method, the flight plan is deployed based on the capacity control result, conflict detection is carried out on space-time conflicts possibly existing in the flight process in combination with the flight control area and the flight plans of other unmanned aerial vehicles, and the flight plan is further adjusted in combination with the conflict detection result, so that accurate deployment of the flight of the unmanned aerial vehicles is realized. And the flight efficiency and the airspace utilization rate of the unmanned aerial vehicle are effectively improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle low-altitude complex obstacle detection and avoidance system based on multi-sensor fusion, and the system comprises a multi-mode sensing module which is used for synchronously collecting heterogeneous sensing data of the surrounding environment of an unmanned aerial vehicle; and the sensing fusion processing module is in communication connection with the multi-mode sensing module and is used for carrying out timestamp alignment, space coordinate system unification and deep fusion processing on the heterogeneous sensing data, and all-weather and high-reliability environment sensing is realized through multi-sensor fusion and hardware synchronization. The system has dynamic obstacle prediction and three-dimensional risk map construction capabilities, and realizes crossing from passive obstacle avoidance to active foresight obstacle avoidance. And a safe and smooth flight path is generated in combination with hierarchical planning and model prediction control, so that the autonomous flight safety, reliability and intelligent level of the unmanned aerial vehicle in a low-altitude complex environment are fundamentally improved.

The embodiment of the invention discloses a flight path data analysis method based on a depth auto-encoder and a generative adversarial network, relates to the technical field of air traffic management, and adopts a cubic spline interpolation method to enable each flight path to have the same length; a deep auto-encoder (DAE) and a generative adversarial network (GAN) are used as a basic framework to establish a flight trajectory data analysis framework, and two tasks of trajectory anomaly detection and flow pattern recognition are synchronously executed; in the pre-training stage, on the basis of DAE, a discriminator is integrated to establish an abnormal trajectory detection task, and an abnormal score function is formulated in combination with reconstruction loss and discriminator loss; in the fine tuning stage, a Gaussian mixture model (GMM) is adopted for low-dimensional representation of the trajectory in a potential space output by an encoder to establish a flow pattern recognition task, and in order to enhance clustering, a loss function is constructed in combination with clustering distribution enhancement. The method is suitable for civil aviation air traffic management.

The invention discloses a missile avoidance and rotary enemy locking maneuvering strategy generation method, which comprises the following steps: S1, establishing a kinematics model and a dynamics model of an airplane and a missile based on Newton's law of motion, and simulating flight tracks and states of the airplane and the missile; s2, based on the kinematics model and the dynamics model, establishing a 1v1 beyond-visual-range air combat environment; s3, designing a state space, an action space and a reward function of deep reinforcement learning for the quadruple of deep reinforcement learning, and integrating the three into an over-the-horizon air combat environment to complete the design of a state transfer function; s4, simulating a warplane by using an intelligent agent, and performing repeated simulation confrontation by using the deep reinforcement learning framework to optimize and adjust a strategy and improve the survivability and combat efficiency in an air combat; and through continuous iterative training of the reinforcement learning model, the intelligent agent autonomously learns an effective maneuvering strategy so as to cope with threats of enemy planes and missiles from different directions.

The invention discloses a self-adaptive curvature constraint-based autonomous obstacle avoidance smoothness optimization method and system, and the method comprises the steps: firstly, employing the convex hull characteristics of a B spline to efficiently calculate the third-order and fourth-order derivative information of a trajectory, and enabling the third-order and fourth-order derivative information to be the high-order energy information of the trajectory; and then, curvature information is used as a dynamic regulation factor to be introduced into an autonomous obstacle avoidance algorithm smoothness optimization process, so that constraint conditions of different degrees are applied to different sections. Through the self-adaptive segmented optimization strategy, the unmanned aerial vehicle can generate a flight trajectory which is integrally smooth and meets dynamic constraints in a real-time trajectory planning environment, the flight efficiency, safety and reliability are improved, and the algorithm can effectively reduce violent acceleration change and mechanical structure vibration caused by trajectory abrupt change. The service life of the unmanned aerial vehicle flight platform is prolonged; and meanwhile, the energy consumption in the flight process is reduced, and the inspection duration and the operation range of the unmanned aerial vehicle are prolonged, so that the practicability and the economical efficiency of the autonomous inspection task of the unmanned aerial vehicle are remarkably enhanced.

The invention discloses a target aircraft flight trajectory correction method based on multi-source fusion navigation, and relates to the technical field of trajectory correction. According to the method, multi-source navigation information such as AI visual navigation, inertial measurement and barometric altitude can be fully fused, short-time disturbance and long-time deviation are effectively distinguished through conjoint analysis of short-time envelope and trend envelope, and the sensitivity and reliability of trajectory anomaly detection are remarkably improved; by setting a direction consistency screening and grading weight suppression mechanism, misjudgment caused by air pressure fluctuation or instantaneous noise in low-altitude flight can be suppressed, so that the false alarm rate is reduced; the method has the advantages that the method is simple and easy to implement, an envelope difference accumulated evidence and a threshold linkage triggering mechanism are utilized, early warning of trajectory deviation is achieved, the timeliness of abnormal response in a flight task is improved, stable and accurate trajectory correction can be dynamically generated in a changeable flight environment, and robustness and convergence of the correction process are guaranteed.

The invention provides a method and a system for identifying an abnormal flight overrun event, which are applied to the technical field of data processing. The method comprises the following steps: processing flight path data and flight attitude data based on historical abnormal flight overrun event data and geographical environment information and flight rule information of different airspaces to generate flight state characteristic parameter information; processing the aircraft performance parameter data to generate aircraft performance element associated information; processing the flight state characteristic parameter information and the airplane performance element association information to generate abnormal flight event preliminary identification information; processing the flight trajectory data based on the abnormal flight event preliminary identification information to generate abnormal flight overrun judgment information; and processing the abnormal flight event preliminary identification information and the abnormal flight overrun judgment information based on the target abnormal flight overrun event early warning model, and generating abnormal flight overrun event early warning result information.

The invention provides a manned aircraft landing aid decision-making system and method, and relates to the technical field of aviation. The method specifically comprises the following steps: acquiring current state data of an aircraft in real time, planning a landing trajectory of the aircraft by adopting an index leveling trajectory method, and determining an optimal glide path of the aircraft; based on the current state data of the aircraft and the optimal glide path of the aircraft, calculating an expected attitude angle and an expected thrust variation of the aircraft by adopting an instruction decision algorithm, performing attitude control on the aircraft according to the expected attitude angle of the aircraft, and generating a flight decision suggestion; wherein the attitude control is divided into two parts: one part is used for calculating an expected three-dimensional angle and a three-axis attitude angular velocity by utilizing angle control; according to the other part, the three-axis attitude angular acceleration is calculated through angular velocity control; and the pilot controls the flight path of the aircraft according to the generated flight decision suggestion to ensure that the aircraft lands along the optimal glide path of the aircraft. The invention aims to solve the problem that the landing trajectory is inaccurate when the aircraft lands.

The invention provides a collaborative flight path planning method and system applied to low-altitude aircrafts, relates to the technical field of flight control of the low-altitude aircrafts, and aims to construct a three-dimensional dynamic flight environment model aiming at the problems that a low-altitude flight environment is complicated and multiple aircrafts are difficult to collaborate. Low-altitude area obstacle distribution, real-time weather and multi-aircraft task demand information are covered. Generating an initial cooperative flight path set based on the three-dimensional dynamic flight environment model and the aircraft performance parameters, performing cooperative conflict verification on the initial cooperative flight path set, detecting an overlapping region of preset flight paths of different aircrafts in a space-time dimension, generating a path adjustment instruction according to a verification result in combination with task priority ranking, and performing cooperative conflict verification on the path adjustment instruction. The trajectory with conflicts is corrected, safe and efficient cooperative flight of multiple low-altitude aircrafts is achieved, and the overall performance and task execution efficiency of the low-altitude aircrafts in a complex environment are improved.

The invention provides a dual-unmanned aerial vehicle cooperative obstacle avoidance and communication link reconstruction system and method, and belongs to the technical field of wireless communication and unmanned aerial vehicle cooperative control. The system is composed of two unmanned aerial vehicles, serves as a mobile base station to provide relay communication for users, and cooperatively perceives an aerial target. The method comprises the following steps: establishing an integrated model integrating tracks, NOMA signals and layered continuous interference cancellation; calculating communication and sensing key indexes; constructing an optimization problem which takes maximization of the minimum average interaction rate between the unmanned aerial vehicles as a target and takes power, QoS, perception and security constraints into consideration; a distributed architecture is adopted to decompose problems, and a Dual-TD3 algorithm is proposed to collaboratively optimize beam forming and a flight path. According to the dual-unmanned aerial vehicle cooperative obstacle avoidance and communication link reconstruction system and method, dynamic intelligent distribution of communication and sensing resources is realized, and the interaction rate, the robustness and the resource utilization rate of the system in a dynamic scene are remarkably improved.

The invention relates to the technical field of data processing, and discloses a multi-intelligence interactive cooperative unmanned aerial vehicle formation control method and system, and the method comprises the steps: obtaining real-time data through an unmanned aerial vehicle carrying a multimode sensor, extracting the multi-scale space correlation characteristics of the real-time data, carrying out the dynamic role distribution of the unmanned aerial vehicle, and obtaining an unmanned aerial vehicle formation; establishing a formation topology constraint relation, and planning a task path based on the formation topology constraint relation; predicting a flight path, constructing a state transition covariance matrix based on the predicted flight path and the real-time data, and performing path correction on the unmanned aerial vehicle formation to obtain a nonlinear obstacle avoidance path; establishing an anomaly reconstruction scheme, performing abnormal state confrontation training on the unmanned aerial vehicle formation to obtain an anomaly diagnosis confidence coefficient, and performing role reconstruction on the unmanned aerial vehicle formation when the anomaly diagnosis confidence coefficient exceeds a preset anomaly threshold value; and collecting a task path, a nonlinear obstacle avoidance path and an abnormal reconstruction scheme as a control protocol. The control efficiency of the unmanned aerial vehicle formation can be improved.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a wind disturbance compensation control method for a quad-rotor unmanned aerial vehicle, which comprises the following steps: tracking flight paths of the quad-rotor unmanned aerial vehicle under different wind conditions through a model prediction controller to obtain flight state data under different wind conditions; according to the flight state data and the kinetic equation under different wind conditions, a kinetic model of the four-rotor unmanned aerial vehicle is constructed, and a flight data set is obtained based on the kinetic model; determining the attitude correction of the unmanned aerial vehicle based on the optimized deep network learning model; according to the attitude correction amount of the unmanned aerial vehicle and the flight state data under different wind conditions, the motor basic thrust of the four-rotor unmanned aerial vehicle is obtained; and according to the basic thrust of the motor and the wind disturbance compensation force output by the optimized deep network learning model, obtaining the target thrust of the motor after wind disturbance compensation. The invention further discloses a wind disturbance compensation control device and system for the quad-rotor unmanned aerial vehicle.