351 results about "Flight track" patented technology

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 relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle automatic navigation method and system for power transmission line inspection. Comprising the steps of collecting terrain elevation data and a wind speed fluctuation signal, generating a line corridor boundary elevation distribution diagram, analyzing the influence of a terrain gradient on a conductor sag, and determining a curvature limitation adjustment demand; re-planning the flight path in combination with the obstacle position information, and generating a detection path sequence; calculating a height abrupt change value and an attitude angle deviation value, judging whether an acceleration impact suppression requirement exists or not, and generating an inspection risk score of the vegetation invasion area; determining a cross span distance risk quantitative index, and generating a flight control instruction sequence; and verifying whether the path meets a boundary integrity standard, and generating an automatic navigation path sequence. According to the method, the problems of instability, insufficient coverage and poor safety of an unmanned aerial vehicle routing inspection path under a complex terrain condition are solved, and stable, continuous and efficient automatic generation of the power transmission line routing inspection path is realized.

The invention provides a flight path prediction method and system based on predictive coding. The method comprises the following steps: collecting historical flight path data, and performing flight path prediction on the historical flight path data by using a reference prediction module to obtain a reference prediction point; calculating a high-order motion track residual error between the reference prediction point and the real track point by using a residual error encoder, and carrying out one-hot coding on the high-order motion track residual error to establish a residual error coding index; a high-order probability distribution mapping module is used for learning a deep mapping relation between historical flight path data and residual error coding indexes so as to carry out training; inputting a to-be-predicted historical flight track into the reference prediction module and the trained high-order probability distribution mapping module in parallel to respectively obtain a predicted track point and a predicted residual error coding index; decoding the prediction residual error coding index through a residual error decoder to obtain a residual error at a prediction moment; and adjusting the predicted track point based on the residual error at the prediction moment to obtain a final prediction result.

The invention discloses an unmanned aerial vehicle cluster relay system optimization method, and aims to significantly reduce the overall outage probability of the system by performing adaptive optimization on cooperative position deployment and flight trajectories of multiple unmanned aerial vehicles under multiple actual constraint conditions of flight speed, safety distance, flight boundary and the like. Therefore, the reliability and the service quality of the multi-unmanned aerial vehicle relay communication network are improved. According to the method, a Markov decision process (MDP) model is constructed, a multi-unmanned aerial vehicle trajectory optimization problem is converted into a multi-agent reinforcement learning problem, and efficient optimization of unmanned aerial vehicle trajectories is realized by using a centralized training-distributed execution framework, so that the outage probability of a communication system is minimized.

The invention relates to an unmanned aerial vehicle track optimization method, system and device in an urban high-density airspace and a medium, and the method comprises the steps: dynamically dividing the urban airspace into space-time four-dimensional grid units of different scales according to the vertical height and the time axis based on the real-time airspace traffic density and environmental conditions; establishing a guide vector field pointing to a target position in each four-dimensional grid unit, and when an obstacle and / or a flight conflict is detected, generating a local disturbance vector and dynamically correcting the guide vector field in real time so as to autonomously generate a candidate flight trajectory; and calculating the energy consumption of the candidate flight path based on an unmanned aerial vehicle dynamics model and an environment model, and selecting a path with the lowest total energy consumption as a final flight path under the condition of meeting safety constraints and task time requirements. Compared with the prior art, in a complex dynamic environment of an urban high-density airspace, unification of real-time efficient planning of the flight path of the unmanned aerial vehicle, safe obstacle avoidance and low-energy-consumption optimization is realized.

The invention discloses a multi-unmanned aerial vehicle cooperative task allocation and path planning method based on a genetic algorithm, and relates to an unmanned aerial vehicle path planning method. And operating a Dijkstra algorithm to accurately solve the shortest feasible path between all airports and task points, and finally generating a global path cost matrix for quick query. A complete and safe flight path that each unmanned aerial vehicle starts from an airport, sequentially accesses task points and returns is graphically displayed, a convergence curve of an algorithm, population diversity changes and performance comparison data under different task scales, unmanned aerial vehicle numbers and obstacle densities can be output, and a parameter sensitivity analysis function is supplemented. And the effectiveness, the stability and the practical value of the proposed method in a static urban environment are comprehensively verified.

The invention relates to an unmanned aerial vehicle target tracking method and system based on multi-modal perception and end-to-end, and the method comprises the steps: predicting the future motion track of an unmanned aerial vehicle through the multi-modal fusion of an RGB image, a thermal infrared image and a laser radar point cloud, combining the historical position information of a target, and employing an end-to-end deep learning network. The network structure comprises a trajectory anchor point prediction branch and a target position regression branch, wherein the trajectory anchor point prediction branch outputs a trajectory existence probability, a trajectory offset and a trajectory cost score; and the position regression branch directly predicts the three-dimensional coordinates of the target. And generating the flight path of the unmanned aerial vehicle from the terminal to the terminal. And directly mapping the trajectory points into flight control executable low-level control instructions, including position, speed and attitude instructions, and directly executing the instructions by a flight control system to realize autonomous tracking of the target. The method shows high robustness and accuracy in weak light, shielding and complex environments, and is suitable for various application scenes such as electric power inspection, disaster rescue, security monitoring and automatic driving coordination.

The invention belongs to the field of unmanned aerial vehicle (UAS) traffic management (UTM) and computer science, and particularly relates to a method and system for airspace modeling and path planning of a logistics unmanned aerial vehicle, and the method comprises the steps: S1, receiving multi-source data, and constructing a hierarchical mixed data model; s2, coding the control constraint data and the dynamic environment data into semantic attributes of nodes in a two-dimensional quadtree layer and / or voxels in a three-dimensional octree layer so as to generate a semantic airspace model containing computable rules; s3, based on the semantic airspace model, executing a hierarchical path planning algorithm to determine a flight path from the starting point to the ending point; and S4, combining the refined paths, and outputting a four-dimensional flight path containing a timestamp. The airspace rule is internalized into the model attribute, the real-time performance, safety and compliance of path planning are remarkably improved, the airspace resource utilization efficiency is improved, and key technical support is provided for large-scale urban unmanned aerial vehicle logistics operation.

The invention discloses a smooth processing method and system for flight path data of an aircraft, and the method comprises the steps: obtaining an original flight path data set, each flight path point in the original flight path data set comprising a timestamp, a flight number, latitude and longitude coordinates, height, speed and course angle attributes; grouping according to flight numbers and sorting according to timestamps to obtain a plurality of flight track sequences; the method comprises the following steps of: calculating the time interval of adjacent points for each flight track sequence, dividing the flight track sequence into a plurality of continuous track sections according to an interruption threshold, dynamically calculating an interpolation interval according to a timestamp difference value for each pair of adjacent starting point and ending point in each section, and ensuring that the time interval of any adjacent point in the continuous track sections does not exceed the interruption threshold. According to the method and the system, the problems of wrong interpolation, course angle sudden change, low interpolation interval fixing efficiency and unsmooth track caused by data interruption in the prior art are solved, and the continuity, the accuracy and the visual reality sense of the track data are improved.

The invention provides a public-network-free communication area multi-nest cooperative unmanned aerial vehicle autonomous flight patrol method and system, and relates to the technical field of intelligent control, and the method comprises the steps: building a communication coverage model based on a patrol patrol partitioning scheme and nest resource states; generating a cooperative scheduling plan according to the communication coverage model; according to the collaborative scheduling plan, performing three-dimensional flight path optimization calculation on each aircraft nest to obtain flight path data; processing the flight path data to obtain a three-dimensional environment situation data set; based on the three-dimensional environment situation data set, a cooperative control instruction sequence is generated, and the cooperative control instruction sequence comprises a trajectory optimization strategy and a task redistribution strategy; and executing distributed control according to the cooperative control instruction sequence. According to the method, the communication coverage capability of each nest can be dynamically evaluated, and the optimal cooperative scheduling plan is generated.

The invention relates to the field of power transmission line risk hidden danger monitoring, and provides an unmanned aerial vehicle wire crossing intelligent detection method and system fused with real-time atmospheric compensation, and the method comprises the steps: measuring an atmospheric extinction coefficient in real time, and obtaining a flight path parameter perpendicular to the axial direction of a wire; synchronously collecting multi-source sensing data to generate an observation data set; generating a lead initial point cloud with a credibility label through feature extraction and Bayesian confidence weight; generating a lead complete three-dimensional track based on a catenary equation and a locust optimization algorithm; generating three-dimensional wire space structure data with sub-meter precision by taking the track point as a control point; and calculating an actual vertical distance based on the elevation difference value and generating a crossing identification report. The system comprises a data acquisition module, a multi-source data acquisition module, a point cloud generation module, a catenary parameter optimization module, a three-dimensional structure optimization module and a safety evaluation report module. According to the invention, through real-time atmospheric compensation and dynamic weight fusion, the detection precision and robustness are improved, and the method is suitable for power transmission line inspection in a complex environment.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a civil small unmanned aerial vehicle cat eye detection and countering system which comprises the steps that a cat eye detection module collects cat eye reflection characteristic signals, environment sensing data and unmanned aerial vehicle flight state associated information in an airspace; the threat level evaluation center identifies the type of the unmanned aerial vehicle and the intrusion risk level through an improved random forest model, and measures the real-time airspace position; the dynamic trajectory modeling module constructs a digital twin flight model of the civil small unmanned aerial vehicle, and simulates a flight trajectory and a potential intrusion path by using an intention pre-judgment algorithm; the grading countering unit generates a non-destructive countering strategy according to the intrusion risk grade, the flight path and the potential intrusion path; the man-machine interaction unit constructs a visual airspace monitoring scene, renders a detection result, an intrusion risk level and a countering effect in real time, and carries out man-machine interaction operation. Therefore, the problems of insufficient perception, poor decision collaboration and the like in the prior art are solved.

The invention discloses an unmanned aerial vehicle ISAC system beam and three-dimensional trajectory joint optimization method for covert communication. Aiming at the condition that potential monitors exist in a network architecture in which an unmanned aerial vehicle performs downlink information transmission to a plurality of ground users, a communication system model is firstly constructed, and then an optimization problem conforming to a scene with user scheduling, a communication and sensing integrated waveform and an unmanned aerial vehicle flight path as constraints is constructed by taking maximization of an average covert communication rate of a system as a target; then, based on a BCD method, an original optimization problem is decoupled into sub-problems about user scheduling, a common inductance integrated waveform and a UAV three-dimensional flight path, and convex approximate fitting is carried out on each non-convex sub-problem; and finally, solving by adopting an iterative algorithm to find a global suboptimal solution of the whole optimization problem, an optimal sensing integrated waveform and a UAV flight trajectory. According to the invention, the security of information transmission is improved, the communication quality and concealment are maximized, and concealed transmission of information is realized.

The invention discloses a beam guidance optimization method based on position prediction in unmanned aerial vehicle communication, and relates to the technical field of unmanned aerial vehicle communication optimization, and the method comprises the steps: obtaining the flight path data of an unmanned aerial vehicle, carrying out the adaptive segmentation according to the flight path data, and constructing a state evolution model for each flight path; performing short-term prediction and medium-term residual error calibration prediction according to the state evolution model, and constructing a prediction correction term to generate a prediction point set; mapping the prediction point set to a direction coordinate system of a ground communication array to construct an angle evolution tensor, and screening to obtain a candidate beam direction set; and screening a standby beam direction with the maximum path coverage redundancy from the candidate beam direction set. According to the method, a beam guiding optimization mechanism with state identification, adaptive prediction, space tolerance, historical correction and multi-path dynamic regulation and control capabilities can be realized, and the beam control precision, the failure recovery capability and the communication link stability in a complex airspace motion state during communication of the unmanned aerial vehicle are remarkably improved.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

The invention discloses an unmanned aerial vehicle track prediction method based on HC-TCN-MLP. According to the method, a time convolution network-multi-layer perceptron (TCN-MLP) model with an encoder-decoder structure is adopted, a TCN is adopted by an encoder to extract local time sequence features in flight state data of the unmanned aerial vehicle, and an MLP is adopted by a decoder to enhance the nonlinear representation capability. In order to solve the problem that error accumulation is easy to generate in traditional full autoregressive recursive multi-step prediction, a hybrid correction (HC) mechanism is introduced, and part of real observation data is dynamically fused in the prediction process, so that diffusion of long-time-domain prediction errors is effectively inhibited. And finally, an HC-TCN-MLP unmanned aerial vehicle multi-step trajectory prediction model based on TCN-MLP and HC is constructed to realize a high-precision flight trajectory prediction effect. Experimental results show that compared with a traditional model, the method has remarkable advantages in the aspects of trajectory prediction precision and robustness, and reliable support can be provided for safe and autonomous operation of the unmanned aerial vehicle in a complex dynamic environment through prediction in a scene where effective sensor information is lost in a weak communication environment.

A sprayer adjusting mechanism of the unmanned aerial vehicle spraying device comprises a control steering engine and a sprayer movable support, a sprayer is connected with the sprayer movable support, the sprayer movable support is connected with a spraying rod, the control steering engine comprises a first steering engine, a second steering engine and a third steering engine, the first steering engine and the second steering engine are installed at the tail end of the spraying rod, and the third steering engine is installed at the tail end of the spraying rod. The first steering engine controls the nozzle movable support to move through two pull ropes to enable the nozzle to rotate up and down, the second steering engine controls the nozzle movable support to move through two pull ropes to enable the nozzle to rotate left and right, and the third steering engine is installed at the head end of the spraying rod and used for controlling the nozzle movable support to move to enable the nozzle to rotate axially. The spraying head of the unmanned aerial vehicle spraying device can deflect by + / -45 degrees in four directions and axially rotate by 90 degrees, complex areas such as windowsills and eaves can be accurately covered, the limitation of a traditional flight track is broken through by dynamically adjusting the spraying sector direction, and the unmanned aerial vehicle obtains omnidirectional maneuvering freedom while the optimal spraying quality is kept.

The invention provides an aircraft detection method and system based on eddy current induced optical fiber micro-vibration, and relates to the technical field of aircraft detection.The aircraft detection method comprises the steps that alternating magnetic field data of aircraft eddy current is collected, and time and frequency dimension change features are analyzed and extracted to serve as a magnetic field feature benchmark; the magnetostrictive sensing rod of the magnetoelectric sensing unit is combined with the fiber bragg grating, and magnetic field change generated by the eddy current magnetic field effect of the aircraft is converted into a voltage signal. And decomposing the time-frequency characteristics of the voltage signals according to the magnetic field characteristic reference, generating electric signal components of different frequency band magnetic field energy evolved along with time, screening characteristic magnetic field change components, and matching the characteristic magnetic field change components with a standard magnetic field mode in a preset database to identify types. Motion characteristic parameters are selected according to identification types, motion states are analyzed and flight trajectories are predicted in combination with time sequence characteristics, graded early warning signals are generated according to the position relation between the trajectories and preset safety areas, and detection and safety early warning of the aircraft can be achieved.

The invention discloses an unmanned aerial vehicle millimeter wave ultra-long distance emergency communication method and system. According to the method, prospective channel information is obtained through coupling a rainfall attenuation and atmospheric waveguide opportunity dual-prediction model; a cross-domain collaborative optimization model with the communication frequency point sequence and the flight path sequence as joint decision variables is constructed, and a collaborative control instruction set is solved; and finally, controlling the unmanned aerial vehicle to synchronously execute frequency point switching and track adjustment. According to the method, a traditional mode of isolated optimization of communication and flight control is broken through, the optimal propagation environment is formed by actively and cooperatively changing the spatial position and communication parameters, rainfall attenuation can be effectively avoided, atmospheric waveguides and other opportunities can be reliably utilized, and the reliability, throughput and coverage range of long-distance millimeter wave communication of the unmanned aerial vehicle are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle three-dimensional path planning method based on gradient guidance and adaptive cost. The method comprises the following steps: performing three-dimensional rasterization processing on a flight space of the unmanned aerial vehicle, and performing three-dimensional Euclidean distance transformation on each node in a free space to obtain a three-dimensional distance field; calculating the gradient of each node in the three-dimensional distance field, and generating an obstacle far-away direction vector of each node based on the gradient; constructing a three-dimensional gradient guide item; carrying out dynamic adjustment on an adaptive gradient weight factor in the three-dimensional gradient guide item; self-adaptive balance parameters are constructed, and the self-adaptive balance parameters are dynamically adjusted; constructing a three-dimensional total cost function; and node expansion is carried out by using a three-dimensional total cost function minimum principle, and a continuous flight path is generated. According to the method, intelligent adjustment of the search direction is realized by introducing the obstacle distance field and a gradient guiding mechanism, so that the safety and the calculation efficiency of path planning are improved.

The invention discloses an unknown environment mapping method for a single-beam laser of aircraft, which belongs to the field of three-dimensional map reconstruction technology. Firstly, the data collected by a single-beam LiDAR is aligned with the timestamp of the stepper motor, and the obtained point cloud data is filtered and matched to obtain three-dimensional map information. The pose of the aircraft is estimated by the wheel odometer and IMU, and the loop detection is performed to reduce the cumulative error and update the three-dimensional map information in real time. The frontier search algorithm is used to determine the target point of the aircraft flight, the A* algorithm is used to plan the path trajectory, and the B-spline curve optimization algorithm is used to make the trajectory of the aircraft smoother. The control command is sent to the aircraft, and then the global three-dimensional map of the unknown environment is obtained.

The invention belongs to the technical field of synthetic aperture radars, and relates to a bistatic SAR receiver flight path planning method, electronic equipment and a storage medium. The method comprises the following steps: establishing motion models of a transmitter, a moving target and a receiver; establishing a three-dimensional proportional guidance model, and establishing a motion differential equation of the receiver driven by the enhanced proportional guidance law as a guidance flight path; establishing a mixed integer nonlinear programming model taking the optimal approximation guide flight path as a target function, and taking an imaging constraint, a guide terminal constraint, a flight dynamics constraint and a system communication constraint as constraint conditions; and solving the mixed integer nonlinear programming model to obtain the optimal flight path of the receiver meeting the guidance and imaging dual constraints. According to the method, the small miss distance guidance precision of the maneuvering target can be ensured in the terminal guidance stage; the terminal guidance performance and the SAR imaging quality are both considered, the feasible solution space is greatly expanded, and the feasibility and optimization performance of trajectory planning under complex constraint conditions are improved.

The invention relates to a real-time low-energy-consumption unmanned aerial vehicle multifunctional automatic inspection method and system, and belongs to the field of automatic inspection. The method comprises the following steps: performing semantic field conversion based on a fuzzy geographic instruction, and outputting a geographic semantic field; performing vision-field coupling navigation based on a geographic semantic field, and outputting a real-time flight path and environment preliminary perception data; topological risk identification is carried out based on the real-time flight path and the environment preliminary perception data, and a 3D environment model with risk annotation is obtained; fractal energy consumption optimization is carried out based on the 3D environment model with the risk label, and an optimized waypoint sequence is output; and carrying out multi-modal data acquisition and real-time risk verification based on the optimized waypoint sequence. According to the invention, intelligent inspection of understanding natural language instructions, fusing multi-sensor information, dynamically evaluating risks and optimizing energy consumption is realized.

The invention provides an aerospace craft discrete trajectory precision reconstruction method based on a variable order, and the method comprises the following steps: calculating the order of a reconstruction model based on a given highest-order dynamic parameter of an aerospace craft discrete trajectory; calculating a coefficient of the reconstruction model; based on a given aerospace craft discrete trajectory, constructing a continuous time model of a flight trajectory; and substituting any moment into the continuous time model of the flight trajectory to obtain trajectory data of the aerospace craft at any moment. According to the method, reconstruction can be completed with higher precision based on given discrete trajectory data, and trajectory continuity is considered; order configuration can be flexibly carried out, and the adaptability to discrete trajectories with different dynamic change rules is improved.

The invention discloses an unmanned aerial vehicle cooperative scheduling and target searching method for emergency rescue, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: constructing an unmanned aerial vehicle operation model and an operation time constraint according to an unmanned aerial vehicle communication network system and a preset ground node; decomposing the optimization target of the flight task into a flight path optimization sub-target and a resource scheduling optimization sub-target to respectively obtain sub-optimization problems of a target sensing task and a communication task; and based on the operation time constraint, iteratively solving each sub-optimization problem through the unmanned aerial vehicle operation model to obtain an optimal flight strategy, and regulating and controlling a flight path and communication resources in real time through the strategy. Therefore, by implementing the method and the device, the problem that in the prior art, as efficient resource allocation and scheduling cannot be realized between communication service and perception search, the rescue target information is difficult to transmit to the background in time while the rescue target is quickly and accurately positioned can be solved.

The invention discloses an unmanned aerial vehicle flight path repetition area identification and comprehensive evaluation method and system, and belongs to the technical field of unmanned aerial vehicle intelligent control. The objective of the invention is to accurately find out a repeated flight area of an unmanned aerial vehicle. The method comprises the following steps: completely converting flight track segments of the unmanned aerial vehicle into track segment structure vectors; constructing a multi-scale similarity evaluation method, and performing scoring evaluation on the similarity of the structure vectors of the track segments from two aspects of structure similarity and point location matching similarity; the repetition frequency of the track segments is calculated, the number of total similar segments between each track segment and all other track segments is counted based on a set threshold value, and a repeated track segment set is extracted; introducing a kernel function to construct a trajectory heat function, then calculating an information entropy of a trajectory region based on the heat function, and establishing a heat map of the trajectory region; and the information entropy, the direction consistency and the historical trajectory density are fused to construct a comprehensive scoring model of the flight trajectory repetition region of the unmanned aerial vehicle, and multi-dimensional quantitative evaluation of the flight trajectory repetition region of the unmanned aerial vehicle is realized.

The invention discloses an indoor unmanned aerial vehicle path planning method, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: constructing an indoor Euclidean symbol distance site map of an unmanned aerial vehicle, and generating an initial collision-free discrete path point sequence; constructing a safe flight corridor based on the discrete path point sequence, and generating a control point grid; in combination with a q-calculus theory, calculating a curve tension coefficient q based on a control point grid, and generating a three-dimensional space position path trajectory by using a tensor product q-Bezier curved surface equation; the geometric curvature of the three-dimensional space position path trajectory is calculated, shape parameters are calculated, and a continuous attitude path is generated according to the shape parameters by using a parametric quartic unit quaternion spline interpolation formula; and generating a complete flight path control instruction sequence. According to the method, in a complex indoor environment, a flight path which is highly safe, free of collision, extremely stable, free of oscillation and free of sudden change of angular acceleration is generated, and the flight safety and control quality of the unmanned aerial vehicle are remarkably improved.

The invention provides an unmanned aerial vehicle supervision system based on an electronic license plate. The system comprises a terminal layer, a network layer, a service platform and a supervision platform. Wherein the terminal layer comprises at least one unmanned aerial vehicle provided with an electronic license plate, and the electronic license plate is used for collecting flight dynamic data of the unmanned aerial vehicle in real time and supervising the unmanned aerial vehicle according to the flight dynamic data and electronic fence information issued by the supervision platform; the network layer is used for reporting the flight dynamic data to a service platform and synchronizing the electronic fence information to each electronic license plate; the business platform is used for performing data analysis on the flight dynamic data and reporting the flight dynamic data to the supervision platform; and the supervision platform is used for formulating electronic fence information, generating a flight path according to the flight dynamic data, and supervising the unmanned aerial vehicle according to the flight path. According to the invention, the unmanned aerial vehicle can be better supervised.

The invention relates to a multi-unmanned aerial vehicle cooperative path planning and scheduling method and system. The method comprises the following steps: acquiring task requirements, and determining a plurality of task areas needing aerial survey, coverage requirements and unmanned aerial vehicle parking positions; constructing a coverage path with the minimum turning cost for each task area according to coverage requirements; calculating the flight path cost of the unmanned aerial vehicle according to the parking position and the coverage path of the unmanned aerial vehicle; and according to the flight path cost, planning an access area and an access sequence for each unmanned aerial vehicle by using a genetic algorithm, and generating an unmanned aerial vehicle flight path. According to the method, the task demand is acquired, the unmanned aerial vehicle observation coverage path with turning optimization is generated in the task area, and the optimized unmanned aerial vehicle scheduling scheme and flight path are generated by using the genetic algorithm based on the coverage path cost, so that the surveying and mapping demand can be met while the quality and quantity are guaranteed; the total flight distance is reduced to the maximum extent, the workload distribution among the unmanned aerial vehicles is balanced, and a data acquisition window for large-scale surveying and mapping is shortened.

The invention provides a flight path prediction method based on a fuzzy random configuration integrated network. According to the method, firstly, input features are constructed through phase-space reconstruction and divided into three complementary subsets according to the time scale, each subset corresponds to an independent fuzzy random configuration network (FSCN)-based learner, flight trajectory evolution laws under different time scales are learned respectively, and output targets are kept consistent; and finally, a negative correlation learning strategy and a Bayesian model averaging (BMA) integration strategy are introduced into an integration layer, probability weighted fusion is performed on three prediction results to obtain flight path prediction output at a future moment, and the uncertainty is quantified while the generalization ability of the model is effectively improved. According to the method, the limitation of a traditional trajectory prediction model in processing high-dimensional dynamic data is overcome, the trajectory prediction precision and the result reliability are remarkably improved, and the method can be widely applied to the fields of air traffic management systems, flight path planning and the like.