629 results about "Flight track" patented technology

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 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 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 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 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 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 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 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.

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 detection countering method, system and equipment and a medium, and the method comprises the steps: obtaining a task instruction and sensing signals of a plurality of sensing devices in a monitoring region in real time, carrying out the preprocessing of the task instruction and the sensing signals, and obtaining the standardized modal data; performing feature extraction and high-level semantic extraction on the task instruction and the standardized modal data corresponding to each sensing signal in parallel to obtain a task context parameter and a high-dimensional feature vector of each sensing signal; fusing the high-dimensional feature vectors based on the task context parameters by using a space-time alignment multi-modal fusion algorithm to obtain multi-modal fusion features; using a neural network model to predict and generate a flight trajectory of the unmanned aerial vehicle in a future time period according to the historical trajectory data and the multi-modal fusion features; and performing threat level judgment and behavior anomaly detection based on the task context parameters and the flight trajectory, generating alarm information, and executing a security countering strategy generated based on the alarm information.

The invention provides a method and a system for acquiring a simulation control point for surveying and mapping, and a medium, and is applied to the technical field of surveying and mapping. The method comprises the following steps: acquiring airborne GNSS positioning data acquired by an airborne GNSS, IMU measurement data acquired by an IMU, original point cloud data, acquired by a three-dimensional laser scanner, of a ground terrain of a measurement area, and reference station GNSS positioning data acquired by a ground reference station GNSS; determining flight path data of the unmanned aerial vehicle according to the airborne GNSS positioning data, the IMU measurement data and the base station GNSS positioning data; performing space-time registration on the flight path data and the original point cloud data to obtain point cloud registration data of the ground topography of the measurement area; and extracting ground points from the point cloud registration data to obtain a simulation control point set of the measurement area. Based on the scheme, the topographic surveying and mapping efficiency and precision can be improved.

The invention relates to the technical field of aircraft trajectory planning, in particular to a dynamic gliding flight trajectory planning method for a fixed-wing aircraft, and the method comprises the steps: building an optimal control problem model with minimum system energy consumption as a target function; obtaining a nonlinear programming problem model; acquiring a corresponding single-time optimal trajectory and corresponding flight state data when the system energy consumption is minimum in the unpowered gliding flight stage and the powered recovery flight stage; and obtaining a multi-section type dynamic gliding flight path. According to the method, the iteration process of solving a nonlinear equation is avoided, the calculation burden of single iteration is remarkably reduced, the solving efficiency and robustness of the whole optimal control problem are greatly improved, and the optimal control effect is improved by designing the multi-section type dynamic gliding flight strategy of the unpowered gliding flight stage and the powered recovery flight stage. The stable periodic energy-obtaining gliding flight is realized by accurately compensating the flight energy deficit.

The invention discloses an unmanned aerial vehicle crossing risk avoidance training system based on AI behavior prediction, and relates to the technical field of unmanned aerial vehicles, and the system comprises an unmanned aerial vehicle behavior prediction module which predicts obstacles, weather changes and other sudden risks possibly encountered by an unmanned aerial vehicle in the flight process based on the flight path, environment information and sensor data of the unmanned aerial vehicle; the risk evasion strategy generation module is used for generating a real-time evasion strategy according to the predicted risk information; the training data acquisition module is used for collecting flight data of the unmanned aerial vehicle in different flight environments as training samples; and the risk avoidance training module is used for training the unmanned aerial vehicle through a reinforcement learning model based on the collected flight data and the generated risk avoidance strategy. According to the method, obstacles, weather changes and sudden risks in flight are monitored in real time through AI behavior prediction, intelligent evaluation and sorting are performed in combination with multiple risk factors, coping strategies are optimized, and it is guaranteed that the unmanned aerial vehicle safely and efficiently executes tasks under various conditions.

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 the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle cluster formation cooperative control method, system and device and a medium. The method comprises the following steps: establishing a dynamic model of each unmanned aerial vehicle in an unmanned aerial vehicle cluster and a dynamic model of an obstacle; establishing an information exchange topology model for describing a data interaction path between the unmanned aerial vehicles in the unmanned aerial vehicle cluster; according to each established model, designing a time-varying formation tracking protocol and a collision avoidance controller based on an artificial potential field method; all the unmanned aerial vehicles in the unmanned aerial vehicle cluster are controlled to form a preset formation which dynamically changes according to task requirements in the flight process through a time-varying formation tracking protocol, and a preset flight path which dynamically changes according to the task requirements is tracked; all the unmanned aerial vehicles are attracted to move towards a preset target point through a gravitational field of the collision avoidance controller, and all the unmanned aerial vehicles are repelled to move towards obstacles and each unmanned aerial vehicle is repelled to move towards other unmanned aerial vehicles through a repulsive force field of the collision avoidance controller, so that control over the unmanned aerial vehicle cluster is achieved.

The invention discloses a method for quickly calculating transient thermal stress of a ceramic radome along a flight path, which comprises the following steps of: determining calculation input which comprises an aerodynamic thermal environment of the ceramic radome and a to-be-evaluated structure scheme; setting a circumferential discrete strategy of the ceramic radome to determine a plurality of meridian planes of the ceramic radome; constructing an aerodynamic thermal environment interpolation model, and determining a meridian plane where aerodynamic heating of the ceramic antenna housing is most strict in the whole flight process; constructing a two-dimensional feature structure model for transient heat transfer and thermal stress analysis of the ceramic radome, and determining an outer contour line of the two-dimensional feature structure model; constructing an axial thermal response calculation interval of the ceramic radome and performing interpolation calculation to obtain aerodynamic thermal environment parameters of the outer contour line at each flight moment; performing heat transfer calculation grid division on the two-dimensional feature structure model, and calculating structure transient temperature distribution of the two-dimensional feature structure model; calculating the temperature gradient of the structure based on the transient temperature distribution of the structure; and calculating transient thermal stress distribution of the ceramic radome by using the structural temperature gradient.

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 provides a multi-unmanned aerial vehicle distributed fault-tolerant control method, an unmanned aerial vehicle control method and an unmanned aerial vehicle control system, and belongs to the technical field of unmanned aerial vehicles. A position ring distributed fault-tolerant controller and an attitude ring distributed fault-tolerant controller are constructed in combination with a multi-unmanned-aerial-vehicle position ring virtual control law and a multi-unmanned-aerial-vehicle attitude ring virtual control law, and fault-tolerant processing is performed on the flight path of the fault unmanned aerial vehicle through the position ring distributed fault-tolerant controller; the fault unmanned aerial vehicle can keep tracking an expected trajectory; fault-tolerant processing is carried out on the attitude of the faulted unmanned aerial vehicle through the attitude ring distributed fault-tolerant controller, so that the faulted unmanned aerial vehicle can maintain a stable attitude angle, thereby ensuring that the whole unmanned aerial vehicle cluster system can operate safely and stably.

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 relates to an unmanned aerial vehicle positioning method and device based on multi-camera joint calibration. The method comprises the following steps: according to a plurality of cameras arranged according to different orientations in an operation environment in which the unmanned aerial vehicle equipment is located, obtaining calibration object images obtained by shooting a preset calibration object by each camera; determining an internal parameter set corresponding to each camera according to a coordinate mapping relationship between the feature points in each calibration object image and the calibration object; determining an external parameter set of each camera relative to the calibration object according to a matching relationship between the feature points in each calibration object image; and in combination with the internal parameter set and the external parameter set corresponding to each camera, generating a visual perception network based on multi-camera joint calibration so as to obtain pose information and three-dimensional coordinates of the unmanned aerial vehicle equipment in real time and plan a flight path. The method can be used for improving the capability of autonomous positioning and navigation operation of unmanned aerial vehicle equipment in a GPS-free environment.

The invention provides an unmanned aerial vehicle laboratory testing method and device, and belongs to the technical field of unmanned aerial vehicle testing. The scheme comprises the following steps: determining a global reference point based on the position of a laboratory, and further determining a conversion relation between a local northeast high coordinate system and a global coordinate system and a plurality of fixed reference points; according to the fixed reference point, sequentially executing stand setting and starting of an automatic measurement tracker in a laboratory, and further obtaining real-time coordinates of the to-be-tested unmanned aerial vehicle by using the automatic measurement tracker; the GNSS signal generator generates a simulation navigation signal according to the real-time coordinates of the to-be-tested unmanned aerial vehicle and records a simulation track; the to-be-tested unmanned aerial vehicle performs indoor flight test based on the simulation navigation signal; and comparing the simulation track with a flight track recorded by a flight control system of the to-be-tested unmanned aerial vehicle so as to output a laboratory test result. The scheme does not depend on hardware modification of the unmanned aerial vehicle, the implementation difficulty is reduced, the implementation cost is saved, and controllable testing of medium and large unmanned aerial vehicles in the indoor environment is facilitated.

The invention relates to the technical field of flight safety of civil aircrafts, in particular to a flight trajectory prediction method under the condition of air high-speed target hood perforation, which comprises the following steps: acquiring calculation coefficient parameter data of a target flight dynamics model, establishing a trajectory prediction framework based on a three-degree-of-freedom target flight dynamics model and target real-time flight data; the method comprises the following steps: performing numerical simulation on a flow field of a hood perforation area through fluid mechanics analysis software to obtain speed field and pressure field data, establishing a hood perforation flow field pneumatic analysis model by adopting a high-order polynomial fitting method, and calculating Mach number and pressure distribution after hood perforation; dynamically updating aerodynamic coefficient data and an aerodynamic parameter database; the speed and pressure distribution data are introduced into the three-degree-of-freedom target flight dynamics model for registration updating, a prediction track is obtained, the prediction precision is improved by 30%-50%, and especially under the condition of a large angle of attack, the prediction error is controlled to be within 5% from 20% or above.

The invention discloses a multi-target distinguishing method based on track spatio-temporal characteristics and dynamic similarity measurement, which comprises the following steps: acquiring original track data, preprocessing the original track data, and generating a plurality of continuous track segments; extracting spatio-temporal features and statistical features, and constructing a similarity matrix; performing hierarchical track clustering analysis on the similarity matrix, wherein the result of the hierarchical track clustering analysis is n clusters divided according to the similarity matrix; loading the relation model, executing conflict resolution and target identification, and calculating track association confidence; and generating a track set based on the track association confidence. According to the technical scheme, multi-target echo signals in a scene that a radar seeker tracks dense targets can be distinguished, and high-accuracy association and high-precision low-delay processing of target tracks are realized.

The invention discloses a tower crane inspection method, system and device and a medium, and is applied to the technical field of image processing, and the method comprises the steps: obtaining RTSP flow, a laser point cloud set and flight path data collected by an unmanned aerial vehicle; calculating a geometric measurement value of each inspection part based on the laser point cloud set and the flight path data; carrying out preprocessing and feature extraction on the RTSP stream and the laser point cloud set to obtain a visible light texture feature, an infrared temperature feature and a laser depth feature; performing cross-modal semantic recognition on the basis of the visible light texture features, the infrared temperature features and the laser depth features to obtain defect categories, confidence coefficients, defect center positions and defect physical quantity regression values of the inspection parts; risk judgment is carried out based on the geometric measurement value, the defect category and the confidence coefficient of each inspection part, the defect center position and the defect physical quantity regression value to obtain the inspection result of each inspection part, so that automatic inspection is realized, and the inspection efficiency and timeliness are improved.

The invention discloses a multi-unmanned-aerial-vehicle intelligent safety communication method and system based on digital twinning. The method comprises the following steps: constructing a multi-unmanned-aerial-vehicle auxiliary communication system; fitting is carried out through Gaussian process regression, and the optimal value of a kernel function hyper-parameter of the prediction model is determined; based on the optimal value of the kernel function hyper-parameter of the prediction model, performing optimization training through digital twinning driving in combination with a deep reinforcement learning algorithm, and constructing a trained digital twinning environment; the unmanned aerial vehicles are guided to generate differentiated flight paths and data acquisition actions in a physical environment through a fusion-elimination updating method, and intelligent safety communication of the multiple unmanned aerial vehicles is achieved. According to the method, the dynamic change rule of the position of the eavesdropper is modeled, and the multi-unmanned aerial vehicle system is guided to adopt a differentiation strategy according to the dynamic change rule, so that active avoidance and secure data transmission under the threat of mobile eavesdropping are effectively realized. The multi-unmanned aerial vehicle intelligent security communication method and system based on digital twinning can be widely applied to the technical field of unmanned aerial vehicle communication.

The invention provides a flight control method and device based on unmanned aerial vehicle flight path prediction, equipment and a medium, and the method comprises the steps: obtaining the task data of an unmanned aerial vehicle, and predicting the flight path data of the unmanned aerial vehicle according to the task data through a preset flight path prediction model; calculating a conflict probability value of the target unmanned aerial vehicle according to the flight path data through a preset conflict early warning model; judging whether the conflict probability value is greater than a preset threshold; if the conflict probability value is larger than a preset threshold value, conflict early warning is carried out, and an avoidance strategy is generated through a preset decision model according to the conflict probability value and the track data; and adjusting flight parameters of the target unmanned aerial vehicle according to the avoidance strategy. According to the method, the flight path of the unmanned aerial vehicle is predicted based on the flight path prediction model, the conflict risk is judged based on the predicted flight path through the conflict early warning model, and finally the avoidance strategy is generated based on the decision model, so that the flight path of the unmanned aerial vehicle is reasonably planned, early warning and conflict avoidance are performed in advance, and the collision risk is reduced.