582 results about "Drone flies" patented technology

The invention provides an unmanned aerial vehicle flight authority management method and platform based on a block chain technology, and relates to the field of unmanned aerial vehicle flight management, and the method comprises the steps: constructing a block chain network, and encrypting and chaining flight authority; deploying the student model after knowledge distillation to an edge computing device for risk prediction before flight and abnormal flight identification in flight; before the unmanned aerial vehicle takes off, the flight legality is verified, pre-flight risk prediction is executed through a student model, and then a smart contract is called to perform matching verification of on-chain permission records; during execution of the flight task, an abnormal score multi-model combination mechanism is introduced to assist the student model to dynamically identify abnormal flight, and an intelligent contract execution response mechanism is triggered when an abnormal behavior is detected; after the unmanned aerial vehicle lands, the flight record is encrypted and stored in the off-chain storage system, and the flight record hash value, the off-chain storage index and the related access credential are linked. The whole-process authority management of the flight task of the unmanned aerial vehicle is realized, and the supervision efficiency is improved.

The invention relates to the field of unmanned aerial vehicle monitoring, in particular to a low-altitude unmanned aerial vehicle flight monitoring method and system. A low-altitude unmanned aerial vehicle flight monitoring system comprises an obstacle sensing module, a confidence coefficient calculation module, a risk assessment module, an obstacle avoidance distance adjustment module and a flight mode switching module. According to the invention, by quantifying the rainfall interference index, the visibility index, the illumination intensity index, the obstacle complex index and the electromagnetic interference index, the confidence coefficient weight of the millimeter wave radar, the visual sensor and other multi-element sensing equipment is calculated in real time, so that the limitation of traditional fixed priority fusion is broken through; the optimal sensor data can be automatically selected as an obstacle avoidance decision basis according to actual environmental conditions, misjudgment or delayed response caused by sensor conflicts is avoided, the obstacle avoidance decision precision is improved, and the method is particularly suitable for high-reliability flight in complex environments such as urban canyons.

The invention discloses a low-altitude logistics unmanned aerial vehicle path planning method and system, and relates to the technical field of unmanned aerial vehicle logistics transportation, and the method comprises the steps: firstly, carrying out the optimization through calculating a preliminary path and combining the terrain and weather information; a sensor is used for detecting and dynamically avoiding obstacles; secondly, data transmission between the unmanned aerial vehicle and the ground control center is achieved, and the ground control center carries out monitoring, control and data processing. According to the low-altitude logistics unmanned aerial vehicle path planning system, efficient path planning is realized through multi-module cooperation. According to the invention, the flight safety, path planning accuracy and energy utilization efficiency of the unmanned aerial vehicle can be improved, the operation cost is reduced, and the system adapts to complex environmental conditions.

The invention discloses a flight control method and system for an electric power inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection flight control. According to the flight control method of the electric power inspection unmanned aerial vehicle, the current flight position of the unmanned aerial vehicle is obtained in real time, deviation analysis is performed on the current flight position and a target inspection position, and when the deviation exceeds a preset threshold value, disturbance sensing data in a set area is further obtained and a disturbance sensing intensity index is calculated; according to a comparison result of the index and a disturbance attribution judgment threshold value, an offset cause is judged, and a corresponding disturbance response control strategy or a flight error correction strategy is executed respectively, so that the flight stability and the control precision of the electric power inspection unmanned aerial vehicle are dynamically guaranteed; according to the invention, through dual discrimination of the disturbance perception intensity index and the flight error adjustment index, external interference and flight control error causes are accurately distinguished, path offset cause identification and strategy matching are realized, and the flight stability and control precision of the electric power inspection unmanned aerial vehicle in a complex environment are improved.

The invention discloses an airborne infrared-visible double light combined building outer wall hollowing identification and positioning method. The method comprises the following steps: designing flight parameters of an unmanned aerial vehicle; designing a calibration checkerboard; shooting an infrared thermal image and a visible light image of the checkerboard calibration target, recording the images as dual-light images for calibration, and calibrating and calculating internal and external parameters and a distortion coefficient of a camera; collecting images of all external facades of the target building; carrying out distortion removal processing on the dual-light image to form a dual-light image database for detection; the registration between the dual-light images synchronously shot by the unmanned aerial vehicle is realized; realizing registration between the building facade view and the visible light image; determining a correct hollowing area identification result, and marking the infrared thermal image corresponding to the correct hollowing area identification result as a hollowing infrared thermal image; calculating a pixel coordinate range of the hollowing area; and accurate positioning of the hollowing area is realized. According to the method, the visible light image serves as a medium, registration of the infrared thermal image and the macroscopic 3D model is achieved, the problem that calibration and registration are difficult due to the fact that the imaging quality of the infrared thermal image is low is solved, and high readability and user friendliness of an output result are guaranteed.

The invention relates to the technical field of urban three-dimensional modeling, in particular to an urban scene three-dimensional modeling method and system, and the method comprises the steps: recognizing a data missing region in an initial three-dimensional model; according to the space coordinate set of the data missing area and unmanned aerial vehicle flight constraint conditions, generating an initial unmanned aerial vehicle blind compensation path through a multi-objective optimization algorithm; collecting blind compensation data of the data missing region in real time, and calculating the global information entropy of the currently collected blind compensation data; when it is judged that the global information entropy reaches a preset information entropy threshold value, injecting the currently collected blind compensation data into the initial three-dimensional model for incremental updating; when it is judged that the global information entropy does not reach a preset information entropy threshold value, the mutability characteristic of the global information entropy is analyzed, and a subsequent unmanned aerial vehicle blind compensation path and a parameter collection instruction are dynamically adjusted according to the mutability characteristic. The problem that the modeling result is uncertain due to the fact that quality defects exist in collected data in urban three-dimensional modeling is solved.

The invention relates to the technical field of inspection unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle inspection method and system for a tobacco field, and the method comprises the steps: obtaining multi-source remote sensing image data of a preset tobacco planting region; detecting tobacco plants in the remote sensing image data, and performing spatial clustering on detection results to obtain a plurality of tobacco plant cluster targets; and generating an initial inspection path according to the position coordinate and the vegetation index of the tobacco plant cluster target. And in the flight process, the flight pose and environmental parameters of the target unmanned aerial vehicle and the deviation angle between the unmanned aerial vehicle and the current path section are collected in real time. And when the adjustment condition is satisfied, automatically generating an improved path, and adjusting the flight attitude and the camera angle of the target unmanned aerial vehicle. And identifying whether an abnormal condition exists in the image data acquired in the flight process. The tobacco planting scene-oriented inspection method integrating multi-source remote sensing perception, adaptive path planning and intelligent identification is realized.

The invention discloses a state monitoring method and device for a lifting unmanned aerial vehicle and a storage medium. The method comprises the steps that putting information of a put object and flight state information of the lifting unmanned aerial vehicle are acquired; performing unmanned aerial vehicle flight environment modeling according to the launching information, and generating an environment map and an environment matrix; performing path planning by combining the delivery information and the flight state information, and determining a plurality of flight paths; carrying out distribution risk assessment on the plurality of flight paths based on an AHP-fuzzy comprehensive evaluation algorithm to obtain an optimal flight path; the lifting unmanned aerial vehicle is controlled to lift the thrown object according to the optimal flight path, and position information and attitude information are collected; and the hoisting state of the hoisting unmanned aerial vehicle is monitored in real time according to the position information and the attitude information. According to the method, the optimal flight path is determined by evaluating the distribution risk of the flight path, the hoisting state is monitored in real time according to the collected information of hoisting of the unmanned aerial vehicle in the optimal flight path, the monitoring accuracy of hoisting of the unmanned aerial vehicle is effectively improved, and the hoisting efficiency and safety are guaranteed.

The invention provides an unmanned aerial vehicle path planning method and system and a storage medium, and the method comprises the steps: constructing a three-dimensional path planning model of an unmanned aerial vehicle in a target flight region; solving the three-dimensional path planning model by using an improved artificial bee colony algorithm, obtaining the optimal flight path of the unmanned aerial vehicle under each target function, and summarizing the optimal flight path into a Pareto optimal solution set; constructing a plurality of decision intelligent agents in one-to-one correspondence with the plurality of objective functions, performing multi-dimensional scoring under different objective functions on each flight path in the Pareto optimal solution set by using the plurality of decision intelligent agents, obtaining a comprehensive score of each flight path, and determining a global optimal unmanned aerial vehicle flight path based on the comprehensive score; according to the method, through refined multi-dimensional constraint modeling, an improved multi-target artificial bee colony algorithm and multi-agent collaborative decision based on a near-end strategy optimization algorithm, full-process optimization from path generation to intelligent decision is realized.

The invention is suitable for the technical field of unmanned aerial vehicle path planning, and particularly relates to an unmanned aerial vehicle real-time path optimization system based on edge calculation, and the system comprises a data collection unit which is used for collecting the original environment data of a multi-mode sensor, and transmitting the original environment data to an edge processing unit; the edge processing unit is used for performing data fusion on the original environment data input by the data acquisition unit and outputting fusion features; and the path optimization unit is used for performing path search and path optimization according to the fusion features output by the edge processing unit, generating a flight path and controlling the unmanned aerial vehicle through the flight path. According to the method, the deeply-fused environmental spatial-temporal characteristics are deeply coupled with a path optimization algorithm, the splitting of perception and planning is broken, an end-to-end closed-loop optimization process is realized, the adaptability and overall robustness of the system are greatly improved, and through a local smoothing and multi-target evaluation mechanism, the real-time performance of the system is improved. And a continuous and highly feasible flight path of the unmanned aerial vehicle can be output in real time.

The invention belongs to the technical field of unmanned aerial vehicle path adjustment, and particularly relates to an unmanned aerial vehicle flight path adjustment method based on obstacle avoidance in a non-visual state, and the method comprises the steps: generating a field three-dimensional basic model; acquiring three-dimensional space data in a task area of the unmanned aerial vehicle and point cloud data in front of a flight path; acquiring a flight path feature sequence table; respectively obtaining an optimal reference path vector and an instant avoidance path vector; outputting a flight path vector for guiding the unmanned aerial vehicle to execute path adjustment by using the path decision model; simulating a non-visual flight scene on the on-site three-dimensional basic model, and analyzing the flight path vector into a flight control instruction for driving the unmanned aerial vehicle to execute; calculating a path adjustment amplitude based on the optimal reference path vector and the flight path vector; and updating the field three-dimensional basic model, and displaying the flight path of the unmanned aerial vehicle in a three-dimensional form. According to the invention, the flight safety and path adaptive ability of the unmanned aerial vehicle in a complex non-visual environment are significantly improved.

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 relates to the technical field of environment perception and three-dimensional modeling, and discloses an unmanned aerial vehicle flight security early warning method and system for a complex environment, and the method comprises the steps: collecting the environment data of a complex flight scene, and enabling the environment data to comprise a dynamic obstacle, a static topographic feature and a meteorological condition; generating an environment three-dimensional semantic map of the flight complex scene; constructing an initial global path of the flight unmanned aerial vehicle corresponding to the flight complex scene; establishing a dynamic threat identification model of the flight scene of the unmanned aerial vehicle to identify a sudden threat factor of the flying unmanned aerial vehicle, and analyzing a factor potential movement track of the sudden threat factor; calculating a risk coefficient of the sudden threat factor to the flying unmanned aerial vehicle; and generating a smooth obstacle avoidance trajectory of the flying unmanned aerial vehicle to execute security early warning of the flying unmanned aerial vehicle. The safety of autonomous flight of the unmanned aerial vehicle in a complex environment can be improved.

The invention discloses a non-uniform sampling scene self-adaptive frequency spectrum completion method based on an unmanned aerial vehicle platform. The method comprises the following steps: identifying an area shielded by a building; simulating the relationship between the signal intensity and the distance change; training a multi-head graph attention network embedded by building constraint to obtain a frequency spectrum completion intelligent model; in an initial sampling stage, a safety area is generated, candidate waypoints are created, and waypoints around each building are connected to form an unmanned aerial vehicle flight path; adopting an inverse distance weighting algorithm or a Kriging algorithm to finish initial completion so as to estimate the approximate position of the radiation source; and simulating deployment of the unmanned aerial vehicle for secondary sampling, carrying out dense sampling around the identified radiation source, then adaptively perfecting a sampling result by using a spectrum completion intelligent model, completing space completion of spectrum data, and constructing a radio frequency spectrogram. According to the method, the spectrum reconstruction performance is remarkably improved, and the collaborative optimization problem of high-precision requirement and low-cost implementation in urban complex scenes is effectively solved.

The invention discloses a multi-rotor unmanned aerial vehicle broken propeller fault rapid diagnosis and fault-tolerant stable control system and method, belongs to the technical field of unmanned aerial vehicle flight control, and aims to solve the problems that traditional broken propeller detection is high in cost and unreliable, power compensation robustness is insufficient and return flight logic is lacked. According to the system and the method, on the basis of a high-order nonlinear observation theory, rapid and accurate identification of the abnormal state of the unmanned aerial vehicle power system is realized by constructing a multi-modal signal prediction and residual evaluation system; the method effectively prevents the out-of-control, rolling and even crash of the unmanned aerial vehicle caused by power asymmetry, solves the problem that a traditional fixed control strategy is difficult to adapt to fault working conditions, and adapts to dynamic changes under complex working conditions such as hovering and cruising. Finally, closed-loop control from fault detection to safe return can be realized, the autonomous safe return capability of the unmanned aerial vehicle under the condition of the fault of the propulsion system is ensured, and the method is suitable for unmanned aerial vehicle application scenes with high reliability requirements, such as logistics transportation and inspection monitoring.

The invention discloses a warehouse goods accurate inventory method based on RFID and unmanned aerial vehicle cooperation, and the method comprises the steps: S1, pasting an RFID tag on a warehouse-in goods, binding the warehouse-in goods, and putting the warehouse-in goods on a shelf, and storing the goods inventory information in a warehouse management system; s2, establishing a three-dimensional model and a space coordinate system of the warehouse, wherein each storage location corresponds to a unique three-dimensional space coordinate; s3, the unmanned aerial vehicle executes an inventory task according to a preset route; s4, the unmanned aerial vehicle binds the RFID tag with the strongest signal when hovering in front of each storage location, so that the storage locations are in one-to-one correspondence with the goods; s5, comparing the RFID signal value acquired when the unmanned aerial vehicle flies to the next storage location with the RFID signal value acquired when the unmanned aerial vehicle flies to the previous storage location, and binding the tag with the strongest signal to the current storage location; and S6, after the checking task is finished and the unmanned aerial vehicle returns, the system automatically generates a checking report, compares the RFID real-time data with the WMS inventory record and marks a difference item. According to the invention, efficient and accurate unmanned inventory checking can be realized, and the inventory quantity and inventory position accuracy of inventory checking can be ensured.

The invention relates to the technical field of path planning, in particular to a water conservancy unmanned aerial vehicle inspection process control system and method based on big data analysis. Performing path risk assessment based on a water surface anomaly prediction result and a weather anomaly analysis result, performing unmanned aerial vehicle flight anomaly analysis based on unmanned aerial vehicle operation condition data and a historical path risk analysis result, and performing path planning selection. According to the method, the path danger is quantitatively evaluated based on the evolution condition of the weather and the wave condition on the path in the flight process of the unmanned aerial vehicle, the safety of path selection is improved, the influence of the future wind power condition on the wave danger is analyzed in the evaluation process of the wave condition, and the evaluation accuracy is improved. Therefore, the future wave danger is quantitatively predicted and evaluated, and the evaluation accuracy of the path danger is improved.

The invention relates to an unmanned aerial vehicle routing inspection track generation method, which comprises the following steps: firstly obtaining a starting point and an end point in voxel map data, and carrying out unmanned aerial vehicle flight path search by using an optimized path optimization algorithm based on the obtained data, so as to obtain an unmanned aerial vehicle flight path far away from an obstacle. Then, constructing a safe flight corridor of the unmanned aerial vehicle based on the obtained flight path of the unmanned aerial vehicle away from the obstacle, and generating a plurality of initial path points based on the generated safe flight corridor; then, based on the obtained multiple initial path points, a minimum control quantity track is used for track generation, a collision-free unmanned aerial vehicle flight track rotating at the minimum yaw angle is obtained, and track correction is carried out when the unmanned aerial vehicle passes through the inspection target point in the flight process; therefore, the pan-tilt camera on the unmanned aerial vehicle can effectively shoot the inspection target point.

The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a full-task process unmanned aerial vehicle link communication state prediction method which is used for predicting the link communication state of an unmanned aerial vehicle in a full-task process in advance when a task is executed, assisting ground control personnel in mastering the link communication state of the unmanned aerial vehicle in the full-task process, and improving the task performance. Whether link interruption is caused after the unmanned aerial vehicle executes the maneuvering action and the link interruption time are predicted in advance, so that a decision can be made in advance according to the task execution state, for example, the maneuvering action is not executed to ensure link communication, or the maneuvering action is limited, and the unknown state of unmanned aerial vehicle link-free flight is eradicated. Unpredictable and controllable risks are reduced, and the task applicability of the unmanned aerial vehicle is improved.

The invention discloses an unmanned aerial vehicle aerodynamic deceleration control safety assessment method and system, and belongs to the technical field of unmanned aerial vehicle flight control. The problem of improving the flight safety performance of the unmanned aerial vehicle is solved. The method comprises the steps of constructing a hidden layer of an unmanned aerial vehicle aerodynamic deceleration control prediction model, and adopting a three-layer architecture of a dynamic perception layer, an aerodynamic characteristic layer and a control decision layer; constructing an output index of the model; constructing a loss function of an unmanned aerial vehicle aerodynamic deceleration control prediction model, wherein the loss function is a composite loss function of a basic loss function, a speed loss item function, a flight characteristic loss item function and a stability loss item function; a trained unmanned aerial vehicle aerodynamic deceleration control prediction model is obtained; and constructing an unmanned aerial vehicle aerodynamic deceleration control safety evaluation method based on the obtained M groups of input and output data, and comprehensively considering the dimensions of system stability, structural integrity, flight state, energy management, control characteristics and environmental adaptability to establish an evaluation system.

The invention relates to the technical field of unmanned aerial vehicle path planning, belongs to a deep learning-based safe route planning method for an unmanned aerial vehicle in an urban environment, and aims to solve the problems of safety and route optimization when the unmanned aerial vehicle flies in a complex urban environment. According to the method, through a deep reinforcement learning algorithm, an airspace initialization module, an environment perception module, a dynamic risk assessment module, a path planning module and a deep learning module are combined to realize automatic generation of an optimal safe route, obstacle collision is avoided, and flight efficiency is optimized. According to the method, the flight path can be dynamically adjusted, the environment change is responded in real time, and safe and efficient flight of the unmanned aerial vehicle in the urban environment is ensured. The method is suitable for multiple fields of intelligent transportation, logistics distribution, environment monitoring and the like, and has a wide application prospect.

The invention discloses a reusable anti-unmanned aerial vehicle aircraft based on rotary cutting steel blades, and belongs to the technical field of unmanned aerial vehicle countering. The aircraft comprises a seeker cabin, a function cabin, an instrument cabin, a power cabin, a steering engine cabin and a parachute pack cabin. An infrared seeker is arranged in the seeker cabin; the functional cabin comprises a fixed cabin body, a rotating cabin body and a folding and unfolding mechanism, a fixed shaft is arranged at the axis position of the fixed cabin body, N folding steel blades are installed on the rotating cabin body and evenly distributed in the circumferential direction, and the folding and unfolding mechanism drives the folding steel blades to complete unfolding and folding actions. The folding and unfolding mechanism simultaneously drives the rotary cabin to rotate around the axis of the aircraft; a main power supply, an airborne computer, a data chain and an inertial navigation device are arranged in the instrument cabin, and the power cabin provides patrol flight power for the aircraft; a steering engine for controlling the rudder wings to rotate is arranged in the cabin, and a pressurizing device for pressurizing the parachute pack cabin is arranged in the parachute pack cabin. The anti-unmanned aerial vehicle aircraft can be repeatedly used, and the combat time of the aircraft is prolonged to the maximum extent.

The invention relates to a flight path speed-based unmanned aerial vehicle exposure risk assessment method, which is used for assessing a flight exposure risk when an unmanned aerial vehicle flies according to a to-be-assessed flight path in a detector network, and is based on operation parameters of all detectors in the detector network and the to-be-assessed flight path of the unmanned aerial vehicle. And calculating the flight exposure risk value of the current flight path to be evaluated by using the unmanned aerial vehicle flight exposure risk evaluation model. According to the method, quantitative calculation is carried out on the unmanned aerial vehicle flight exposure risk assessment model by using the idea of curve integration and the mode of numerical integration, and the flight exposure risk value of the unmanned aerial vehicle flying according to the to-be-assessed flight path can be clearly calculated, so that the assessment of the flight exposure risk of the unmanned aerial vehicle in the detector network is realized; the flight exposure risk can be used as a flight risk quantitative index to provide support for planning of the flight path of the unmanned aerial vehicle.

The invention relates to the technical field of unmanned aerial vehicle fault detection, in particular to an unmanned aerial vehicle flight control system abnormity tracing method based on a deep matching network. An abnormal knowledge graph containing entities and semantic relations is constructed through the preprocessed multi-source heterogeneous data, and abnormal inducements are mapped into inducement label vectors through a graph encoder; a flight control state anomaly representation vector is generated through the depth time sequence feature extraction network, and anomaly is detected; when an anomaly is detected, calculating a correlation score of an anomaly characterization vector and an inducement label vector through a deep matching network, and screening first K candidate inducements; and then taking the candidate cause nodes as source points in the knowledge graph, performing deep search according to a preset path to construct candidate cause and effect paths, performing sorting through a node correlation scoring mechanism, and outputting interpretable traceability paths. According to the method, based on a deep matching network technology, the accuracy and interpretability of anomaly detection and traceability in a complex environment are improved, and efficient and accurate anomaly inducement matching and causal path reasoning are realized.

The present invention relates to a drone formation control method and system based on multi-agent distributed consistency. The method comprises the following steps: allocating coordinates of drones and target points to obtain a target allocation relationship, an alignment scale, and a translation distance; obtaining a relative state based on the alignment scale and the translation distance; obtaining a first trajectory based on the relative state between the drones using a consistency algorithm when obstacle avoidance is not required; calculating the degree of violation of the formation by the drones' obstacle avoidance when the degree of violation is large, replacing the formation and obtaining a second trajectory based on the new formation when the degree of violation is small, planning an obstacle avoidance trajectory for the drones that need to avoid obstacles to obtain a third trajectory; and obtaining a target trajectory based on the first, second, and third trajectories when reaching the end point coordinates. The method implements a drone flight trajectory optimization strategy to balance safety and trajectory executability, thereby significantly improving the drone system's ability to cope with changing environments.

The invention discloses a large fixed-wing unmanned aerial vehicle fire extinguishing performance evaluation test method, and relates to the field of aviation equipment. The method solves the problem that quantitative evaluation of fire extinguishing agent throwing precision and effective coverage range of a large fixed-wing unmanned aerial vehicle in a complex wind field environment is difficult, scale test parameter design is carried out based on similarity criteria and reference wind tunnel size, and then test model (unmanned aerial vehicle and fire extinguishing agent water tank) and adjustable cabin door structure design are carried out. The unmanned aerial vehicle model is installed on the upper tunnel wall of a wind tunnel through a lifting supporting rod in a back supporting mode, the fire extinguishing agent measuring system is installed on the central axis of a wind tunnel test section and is vertically aligned with a water tank, finally a large fixed-wing unmanned aerial vehicle fire extinguishing performance evaluation wind tunnel test is carried out, the effective range of a fire extinguishing agent is quantitatively treated, and the fire extinguishing performance is evaluated. The test method can effectively research the fire extinguishing performance of the large unmanned aerial vehicle under the influence of the flight speed, the launching angle, the height and the like of the unmanned aerial vehicle.

The invention discloses an unmanned aerial vehicle countering system, and relates to the technical field of unmanned aerial vehicle countering, and the system comprises a plurality of guidance unmanned aerial vehicles which are used for carrying out the rigid interception of black flight unmanned aerial vehicles; the radar is used for discovering a black flight unmanned aerial vehicle target; the medium-wave infrared camera is used for performing image recognition on the black-flight unmanned aerial vehicle target; the control center is used for displaying a signal detected by the radar and a target image and generating a control instruction; the radar transmits the control instruction to the unmanned aerial vehicle, and controls and guides the unmanned aerial vehicle to fly to the black flight unmanned aerial vehicle and collide with the target; guidance of the unmanned aerial vehicle further comprises image terminal guidance, namely, an airborne image recognition and tracking system autonomously finds a target, tracks the target and guides the unmanned aerial vehicle to intercept collision; and guiding the unmanned aerial vehicle to autonomously adjust the flight attitude after the target is hit, and executing a task according to an instruction after stabilization. According to an actual deployment scheme, the radar instructs the guidance unmanned aerial vehicle close to the deployment incoming attack direction to take off and execute a countering task, and the interception probability can be effectively improved.

The invention provides an inspection unmanned aerial vehicle autonomous vision docking inhabitation method based on online learning active disturbance rejection. The method comprises the steps that a video stream of a power transmission line inspection scene is shot based on an unmanned aerial vehicle, an image used for power transmission line detection is acquired to serve as a first image, and the first image carries depth information corresponding to pixel points; based on the trained image segmentation model, identifying the first image; when the identification result shows that the power transmission line exists in the first image, determining an inhabitation point corresponding to the unmanned aerial vehicle and a relative position of the inhabitation point and the unmanned aerial vehicle from a power transmission line graph area in the first image; generating a reference trajectory indicating that the unmanned aerial vehicle flies to the inhabitation point based on the relative position; determining control parameters of the unmanned aerial vehicle based on the relative position, a preset iterative learning linear active-disturbance-rejection control strategy and the reference trajectory; and controlling the unmanned aerial vehicle to fly towards the inhabitation point based on the control parameters. Therefore, the problem that the unmanned aerial vehicle cannot autonomously and accurately fly to the inhabitation point of the power transmission line can be improved.

The invention discloses an unmanned aerial vehicle-based control method and a related device, and is applied to a control platform in an unmanned aerial vehicle control system, and the method comprises the steps: transmitting first request information to a target unmanned aerial vehicle when it is monitored that the target unmanned aerial vehicle enters a preset airspace; detecting whether first response information is received within a preset duration; if yes, verifying the identity information of the target unmanned aerial vehicle through a preset unmanned aerial vehicle database to obtain a first verification result; when the first verification result comprises successful verification, collecting multi-dimensional data; determining a target flight state according to the first response information and the multi-dimensional data; when the target flight state comprises a black flight state, determining a target early warning level according to the multi-dimensional data; determining a target processing scheme corresponding to the target early warning level; and processing the target unmanned aerial vehicle according to the target processing scheme, so that the target unmanned aerial vehicle is out of the black flight state. According to the embodiment of the invention, the recognition accuracy of the flight state of the unmanned aerial vehicle is improved.

The invention provides a transformer abnormal state intelligent identification method, system and device, and the method comprises the steps: an inspection step: obtaining a transformer substation environment image shot by an unmanned plane, and constructing a transformer substation three-dimensional map; a transformer defect screening step: screening an abnormal transformer in the three-dimensional map of the transformer substation through target detection, and obtaining a flight path of an unmanned aerial vehicle; a flight path optimization step: calculating the defect detection value and flight cost of each coordinate point, dynamically adjusting the flight path of the unmanned aerial vehicle based on a dynamic balance strategy, identifying the corner angle of the transformer, generating a virtual coordinate point, and planning an equidistant shooting path surrounding the virtual mark point according to the virtual coordinate point; and a multi-modal identification step: based on the flight path of the unmanned aerial vehicle, synchronously acquiring visual images, infrared thermal imaging and sound data shot by the unmanned aerial vehicle, and identifying the abnormal state of the transformer through a multi-modal data fusion strategy.